diff --git a/articles/example-analysis.html b/articles/example-analysis.html index 72f916f3..5a58d52f 100644 --- a/articles/example-analysis.html +++ b/articles/example-analysis.html @@ -186,7 +186,7 @@ #> 15 #> 16 #> user system elapsed -#> 47.775 0.008 47.793 +#> 47.791 0.023 47.826 
 # check results in log.lambda.est:
 pander::pander(log.lambda.est)
diff --git a/articles/installation.html b/articles/installation.html index 4e8c55c8..a18802f6 100644 --- a/articles/installation.html +++ b/articles/installation.html @@ -99,12 +99,11 @@

1. Introduction

2. Installation steps

-

R is a free software and can be downloaded from http://cran.r-project.org/. Latest -version as of writing this document is R 4.3.1. Once -the appropriate, operating system dependent, version is downloaded, -install it on your computer following the standard procedure applicable -to the operating system. For Windows the file to be downloaded is the -so-called base distribution: http://cran.r-project.org/bin/windows/base/.

+

R is a free software program and can be downloaded from http://cran.r-project.org/. After +downloading the appropriate version for your computer’s operating +system, install R on your computer following the standard procedure +applicable to the operating system. For Windows the file to be +downloaded is the so-called base distribution: http://cran.r-project.org/bin/windows/base/.

2.1. Installing R

@@ -194,12 +193,12 @@

3. Post-installation## LinkingTo: Rcpp ## Language: en-US ## Roxygen: list(markdown = TRUE) -## Packaged: 2023-10-09 20:26:06 UTC; runner +## Packaged: 2023-10-09 20:42:28 UTC; runner ## Author: Peter Teunis [aut, cph] (Author of the method and original ## code.), Kristina Lai [aut], Kristen Aiemjoy [aut], Douglas Ezra ## Morrison [aut, cre] ## Maintainer: Douglas Ezra Morrison <demorrison@ucdavis.edu> -## Built: R 4.3.1; x86_64-pc-linux-gnu; 2023-10-09 20:26:07 UTC; unix +## Built: R 4.3.1; x86_64-pc-linux-gnu; 2023-10-09 20:42:29 UTC; unix ## RemotePkgRef: local::. ## RemoteType: local ## diff --git a/pkgdown.yml b/pkgdown.yml index 066cc26d..6c16c2b7 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -8,7 +8,7 @@ articles: methodology: methodology.html simulation: simulation.html tutorial: tutorial.html -last_built: 2023-10-09T20:26Z +last_built: 2023-10-09T20:42Z urls: reference: https://ucd-serg.github.io/serocalculator/reference article: https://ucd-serg.github.io/serocalculator/articles diff --git a/search.json b/search.json index 97408f4a..0ae04033 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement . complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to serocalculator","title":"Contributing to serocalculator","text":"outlines propose change serocalculator. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to serocalculator","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to serocalculator","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to serocalculator","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"UCD-SERG/serocalculator\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to serocalculator","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to serocalculator","text":"Please note serocalculator project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"1. Introduction","title":"Serocalculator package installation manual","text":"Package serocalculator written programming language R end user must access working installation R engine. document describes common setup R installed locally user’s computer. screenshots refer classical R interface, package can also opened Graphical User Interfaces R like e.g.  RStudio.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"installation-steps","dir":"Articles","previous_headings":"","what":"2. Installation steps","title":"Serocalculator package installation manual","text":"R free software can downloaded http://cran.r-project.org/. Latest version writing document R 4.3.1. appropriate, operating system dependent, version downloaded, install computer following standard procedure applicable operating system. Windows file downloaded -called base distribution: http://cran.r-project.org/bin/windows/base/.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"installing-r","dir":"Articles","previous_headings":"2. Installation steps","what":"2.1. Installing R","title":"Serocalculator package installation manual","text":"Start R installer follow presented steps: advised R installed folder contain spaces, therefore please adjust destination location accordingly: serocalculator package compatible 32-bit 64-bit version R. Choose preferred platform (). unsure install 32-bit version , however compatible platforms 64-bit version may provide better performance: advised select Registry entries next step best experience: R interpreter, installed Windows, can invoked start menu folder named R. Start preferred version R (32-bit: R i386 64-bit: x64 installed). Graphical user interface R interpreter start new window:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"installing-serocalculator-package","dir":"Articles","previous_headings":"2. Installation steps","what":"2.2. Installing serocalculator package","title":"Serocalculator package installation manual","text":"Since new installation R, serocalculator package must installed first use. 09/20/2023, serocalculator still development. install development version, must install devtools package download serocalculator GitHub.","code":"install.packages(\"devtools\") devtools::install_github(\"ucd-serg/serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"post-installation","dir":"Articles","previous_headings":"","what":"3. Post-installation","title":"Serocalculator package installation manual","text":"Successful installation can confirmed loading package workspace exploring help files manuals distributed package: Additionally, package details can found executing following commands:","code":"# Load package \"seroincidence\". library(serocalculator) # Show R help for the package. ?serocalculator # Show tutorial for the package. vignette(topic = \"tutorial\", package = \"serocalculator\") # Show description. packageDescription(\"serocalculator\") ## Package: serocalculator ## Type: Package ## Title: Estimating Infection Rates from Serological Data ## Version: 0.1.0.9000 ## Date: 2022-03-29 ## Authors@R: c( person(given = \"Peter\", family = \"Teunis\", email = ## \"p.teunis@emory.edu\", role = c(\"aut\", \"cph\"), comment = \"Author ## of the method and original code.\"), person(given = \"Kristina\", ## family = \"Lai\", role = c(\"aut\")), person(given = \"Kristen\", ## family = \"Aiemjoy\", email = \"kaiemjoy@ucdavis.edu\", role = ## c(\"aut\")), person(given = \"Douglas Ezra\", family = \"Morrison\", ## email = \"demorrison@ucdavis.edu\", role = c(\"aut\", \"cre\"))) ## Description: Translates antibody levels measured in a cross-sectional ## population sample into an estimate of the frequency with which ## seroconversions (infections) occur in the sampled population. ## Forked from the \"seroincidence\" package v2.0.0 on CRAN. ## Depends: R (>= 2.10) ## License: GPL-3 ## Imports: dplyr, Rcpp, stats, utils ## Suggests: knitr, rmarkdown, parallel, pander, Hmisc, tidyverse, fs ## VignetteBuilder: knitr ## LazyData: true ## Encoding: UTF-8 ## URL: https://github.com/UCD-SERG/serocalculator, ## https://ucd-serg.github.io/serocalculator/ ## RoxygenNote: 7.2.3 ## NeedsCompilation: yes ## LinkingTo: Rcpp ## Language: en-US ## Roxygen: list(markdown = TRUE) ## Packaged: 2023-10-09 20:26:06 UTC; runner ## Author: Peter Teunis [aut, cph] (Author of the method and original ## code.), Kristina Lai [aut], Kristen Aiemjoy [aut], Douglas Ezra ## Morrison [aut, cre] ## Maintainer: Douglas Ezra Morrison ## Built: R 4.3.1; x86_64-pc-linux-gnu; 2023-10-09 20:26:07 UTC; unix ## RemotePkgRef: local::. ## RemoteType: local ## ## -- File: /home/runner/work/_temp/Library/serocalculator/Meta/package.rds # Show citation. citation(\"serocalculator\") ## To cite package 'serocalculator' in publications use: ## ## Teunis P, Lai K, Aiemjoy K, Morrison D (2022). _serocalculator: ## Estimating Infection Rates from Serological Data_. ## https://github.com/UCD-SERG/serocalculator, ## https://ucd-serg.github.io/serocalculator/. ## ## A BibTeX entry for LaTeX users is ## ## @Manual{, ## title = {serocalculator: Estimating Infection Rates from Serological Data}, ## author = {Peter Teunis and Kristina Lai and Kristen Aiemjoy and Douglas Ezra Morrison}, ## year = {2022}, ## note = {https://github.com/UCD-SERG/serocalculator, ## https://ucd-serg.github.io/serocalculator/}, ## }"},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Serocalculator package methodology","text":"revised seroincidence calculator package provides three refinements method calculating seroincidence published earlier (Teunis et al. 2012) implemented R package seroincidence, versions 1.x: (1) inclusion infection episode rising antibody levels, (2) non–exponential decay serum antibodies infection, (3) age–dependent correction subjects never seroconverted. important note , although implemented methods use specific parametric model, proposed (de Graaf et al. 2014) augmented (Teunis et al. 2016), methods used calculate likelihood function allow seroresponses arbitrary shape.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"a-simple-model-for-the-seroresponse","dir":"Articles","previous_headings":"","what":"1. A simple model for the seroresponse","title":"Serocalculator package methodology","text":"current version serocalculator package uses model (Teunis et al. 2016) shape seroresponse: \\[ \\begin{array}{l@{\\qquad}l} \\text{Infection/colonization episode} & \\text{Waning immunity episode}\\\\ b^{\\prime}(t) = \\mu_{0}b(t) - cy(t) & b(t) = 0 \\\\ y^{\\prime}(t) = \\mu y(t) & y^{\\prime}(t) = -\\nu y(t)^r \\\\ \\end{array} \\] baseline antibody concentration \\(y(0) = y_{0}\\) initial pathogen concentration \\(b(0) = b_{0}\\). serum antibody response \\(y(t)\\) can written \\[ y(t) = y_{+}(t) + y_{-}(t) \\] \\[\\begin{align*} y_{+}(t) & = y_{0}\\text{e}^{\\mu t}[0\\le t 1\\), log concentrations decrease rapidly infection terminated, decay slows low antibody concentrations maintained long period. \\(r\\) approaches 1, exponential decay restored.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"backward-recurrence-time","dir":"Articles","previous_headings":"","what":"2. Backward recurrence time","title":"Serocalculator package methodology","text":"Considering (fundamental) uniform distribution \\(u_{f}\\) sampling within given interval, interval length distribution \\(p(\\Delta t)\\) distribution (cross–sectionally) sampled interval length (Teunis et al. 2012) \\[ q(\\Delta t) = \\frac{p(\\Delta t)\\cdot\\Delta t}{\\overline{\\Delta t_{p}}} \\] joint distribution backward recurrence time cross–sectional interval length product \\(u_{f}\\cdot q\\) probabilities independent. distribution backward recurrence time marginal distribution \\[\\begin{align*} u(\\tau) & = \\int_{\\Delta t=0}^{\\infty} u_{f}(\\tau;\\Delta t)\\cdot q(\\Delta t)\\text{d}\\Delta t\\\\ & = \\int_{0}^{\\infty}\\frac{[0\\le\\tau\\le\\Delta t]}{\\Delta t}\\cdot \\frac{p(\\Delta t)\\cdot \\Delta t}{\\overline{\\Delta t_{p}}}\\text{d}\\Delta t\\\\ & = \\frac{1}{\\overline{\\Delta t_{p}}}\\int_{\\tau}^{\\infty}p(\\Delta t)\\text{d}\\Delta t \\end{align*}\\]","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"incidence-of-seroconversions","dir":"Articles","previous_headings":"","what":"3. Incidence of seroconversions","title":"Serocalculator package methodology","text":"calculate incidence seroconversions, (Teunis et al. 2012), distribution \\(p(\\Delta t)\\) intervals \\(\\Delta t\\) seroconversions, important. Assuming subject sampled completely random intervals seroconversions, accounting interval length bias (Satten et al. 2004; Zelen 2004), distribution backward recurrence times \\(\\tau\\) can written (Teunis et al. 2012) \\[ u(\\tau) = \\frac{1}{\\overline{\\Delta t}} \\int_{\\tau=0}^{\\infty}p(\\Delta t)\\text{d}\\Delta t = \\frac{1-P(\\Delta t)}{\\overline{\\Delta t}} \\] \\(\\overline{\\Delta t}\\) \\(p\\)–distribution expected value \\(\\Delta t\\). density employed estimate seroconversion rates. antibody concentration \\(y\\) observable quantity, need express probability (density) observed \\(y\\) terms density backward recurrence time. First, backward recurrence time can \\(\\tau\\) expressed function serum antibody concentration \\(y\\) \\[ \\tau(y) = \\tau_{+}(y) + \\tau_{-}(y) \\] \\[\\begin{align*} \\tau_{+}(y) & = \\displaystyle{\\frac{1}{\\mu}} \\log\\left(\\displaystyle{\\frac{y_{+}}{y_{0}}}\\right) [0\\le \\tau 0\\)), whether decay exponential (\\(r = 1\\)) proceeds power function (\\(r > 1\\)). Power function decay allows rapid inital decay followed sustained period slow decay (Teunis et al. 2016). Available model variants described Table 1.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"how-to-use-serocalculator-package","dir":"Articles","previous_headings":"","what":"3. How to use serocalculator package","title":"Serocalculator package tutorial","text":"section provides step--step directions usage serocalculator package.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"loading-the-package","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.1. Loading the package","title":"Serocalculator package tutorial","text":"Functionality serocalculator package made available end user library loaded current workspace. Assuming package installed already (covered installation.pdf) loading achieved running following command R console (bear mind text character # comment): moment functions required run seroincidence calculation made available. can checked running: resulting output: briefly describe important objects functions: [DISEASE]Params[MODEL_TYPE]: Monte Carlo sample longitudinal response parameters per antibody various diseases: Campylobacter, Pertussis, Salmonella. Object class: list. [DISEASE]Sim[Low|Medium|High]Data: Example simulated antibody levels data measured cross-sectional population three values lambda (incidence): 0.036 (“Low”), 0.21 (“Medium”) 1.15 (“High”) (1/yr). Object class: data.frame. getAdditionalData: Utility function downloading additional longitudinal response parameters online repository. Files available download coxiellaIFAParams4.zip yersiniaSSIParams4.zip. Object class: function. estimateSeroincidence: Main function package. Estimates seroincidence based supplied cross-section antibody levels data longitudinal response parameters. Object class: function.","code":"# Load package \"seroincidence\" library(serocalculator) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union # List all objects (functions and data) exposed by package \"serocalculator\". ls(\"package:serocalculator\") ## [1] \"campylobacterDelftParams1\" \"campylobacterDelftParams3\" ## [3] \"campylobacterDelftParams4\" \"campylobacterSimHighData\" ## [5] \"campylobacterSimLowData\" \"campylobacterSimMediumData\" ## [7] \"campylobacterSSIParams1\" \"campylobacterSSIParams2\" ## [9] \"campylobacterSSIParams4\" \"estimateSeroincidence\" ## [11] \"fdev\" \"getAdditionalData\" ## [13] \"pertussisIgGPTParams1\" \"pertussisIgGPTParams2\" ## [15] \"pertussisIgGPTParams3\" \"pertussisIgGPTParams4\" ## [17] \"pertussisSimHighData\" \"pertussisSimLowData\" ## [19] \"pertussisSimMediumData\" \"salmonellaSSIParams1\" ## [21] \"salmonellaSSIParams2\" \"salmonellaSSIParams4\""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"specifying-input-data","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.2. Specifying input data","title":"Serocalculator package tutorial","text":"two sets inputs serology calculator must always specified: Simulated antibody levels measured cross-sectional population sample (see campylobacterSimLowData, campylobacterSimMediumData, etc. ). Longitudinal response characteristic set parameters (y1, alpha, yb, r, y0, mu1, t1) (see campylobacterDelftParams1, campylobacterSSIParams2, etc. ). start loading antibody levels measured cross-sectional population sample.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"specifying-cross-sectional-antibody-levels-data","dir":"Articles","previous_headings":"3. How to use serocalculator package > 3.2. Specifying input data","what":"3.2.1. Specifying cross-sectional antibody levels data","title":"Serocalculator package tutorial","text":"user options providing data calculator: ) Using supplied sample data Data set campylobacterSimMediumData provides example cross-sectional antibody levels data Campylobacter. loaded workspace soon package loaded, therefore can referenced right away. small exempt data set: Let us create serology data serologyData based data set: can view data also tabular form: shall pass object later calculator. Object serologyData must contain least one column named IgG, IgM, IgA. Additionally, column Age used calculations present. Otherwise, created --fly initialized NA (available). Column Age, supplied, assumed represent age days. Please, note cross-sectional data set can include extra columns allowing performing seroincidence calculations stratified per factors available data. example column AgeCat can used stratum variable. particular variable created mapping age days (column Age) 15 categories, category 1 grouping youngest subjects category 15 grouping oldest subjects. ii) Loading external files user can load /data R session. Details loading specific file types can found online R manual R Data Import/Export. important input data tabular form column containing levels measured single antibody type. Column name indicate name measured antibody. instance, comma separated file following content: valid cross-sectional data set. Suppose file named c:\\cross-sectional-data.csv. can loaded R like :","code":"# Show three first rows of \"campylobacterSimMediumData\" data.frame. head(campylobacterSimMediumData, 3) ## Age IgG IgM IgA ## 1 4530.743 0.2242790 0.05442598 1.138591e-02 ## 2 5173.256 0.8498734 0.10667042 2.093677e-06 ## 3 24187.164 0.1849355 1.24958865 1.652755e-01 # Assign data.frame \"campylobacterDelftData\" to object named \"serologyData\". serologyData <- campylobacterSimMediumData |> mutate(AgeCat = cut(Age, 15, labels = FALSE)) # Print a few first observations. head(serologyData) ## Age IgG IgM IgA AgeCat ## 1 4530.743 0.2242790 0.0544259797 1.138591e-02 3 ## 2 5173.256 0.8498734 0.1066704172 2.093677e-06 3 ## 3 24187.164 0.1849355 1.2495886495 1.652755e-01 14 ## 4 21427.836 1.1743485 0.0001590331 3.122555e-02 12 ## 5 12560.687 1.0656224 0.0013906805 8.723256e-04 7 ## 6 17083.219 0.1115373 0.4976598547 2.321977e-01 10 View(serologyData) IgG, IgM, IgA 5.337300, 0.4414653, 0.3002395 3.534118, 0.3888226, 0.3543486 2.144549, 0.3320178, 0.3884205 2.957854, 0.4967764, 0.3472340 # Read content of file \"C:\\cross-sectional-data.csv\" into object named # \"serologyData\". serologyData <- read.csv(file = \"C:\\\\cross-sectional-data.csv\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"longParams","dir":"Articles","previous_headings":"3. How to use serocalculator package > 3.2. Specifying input data","what":"3.2.2. Specifying longitudinal response parameters","title":"Serocalculator package tutorial","text":"longitudinal response parameters set consists following items: y1: antibody peak level (ELISA units) alpha: antibody decay rate (1/days current longitudinal parameter sets) yb: baseline antibody level \\(t\\) approaching infinity (\\(y(t->inf)\\)) r: shape factor antibody decay y0: baseline antibody level \\(t=0\\) (\\(y(t=0)\\)) mu1: initial antibody growth rate t1: duration infection Please, refer vignette methodology.pdf details underlying methodology. End user three options loading response parameters R session: ) Using supplied data longitudinal response parameter data sets provided package including Monte-Carlo sample longitudinal response parameters, like campylobacterDelftParams4. particular object list containing three data.frame objects named IgG, IgM IgA. Let’s pick two data sets separately (notice character $ indicating sub-object parent object): Let us assign object campylobacterDelftParams4 variable responseParams: ii) Loading local external files Alternatively, data can loaded external file. Bear mind, likely data organized three files, separately antibody IgG, IgM IgA. Assuming data saved csv files named IgG.csv, IgM.csv IgA.csv end user run following code order create valid input calculator: Note object responseParams list three dataframes named IgG, IgM IgA required. iii) Loading external repository Finally, response parameters can obtained online repository set package. repository hosted ECDC servers. time publishing package two files available: coxiellaIFAParams4.zip: longitudinal response parameters per antibody Coxiella. yersiniaSSIParams4.zip: longitudinal response parameters per antibody Yersinia. Simply use supplied utility function getAdditionalData download data: Internet connection needed function work.","code":"# Show first rows of data.frame \"IgG\" in list \"campylobacterDelftParams4\". head(campylobacterDelftParams4$IgG) ## y1 alpha yb r y0 mu1 t1 ## 1 27.861427 0.0051607511 0 1.019014 0.07791154 0.7296875 8.057456 ## 2 3.306758 0.0011913102 0 1.099056 0.05077868 0.8435221 4.950963 ## 3 18.791581 0.0006869688 0 1.010531 0.09212040 0.6116469 8.694670 ## 4 5.866064 0.0006929407 0 1.019739 0.07694126 0.5232645 8.282421 ## 5 14.364930 0.0035222413 0 1.092096 0.05110497 0.9445758 5.969519 ## 6 5.790477 0.0003697673 0 1.010602 0.09127834 0.7696968 5.391807 # Show first rows of data.frame \"IgM\" in list \"campylobacterDelftParams4\". head(campylobacterDelftParams4$IgM) ## y1 alpha yb r y0 mu1 t1 ## 1 3.0218848 0.0068170529 0 1.073372 0.09900034 0.3980767 8.587573 ## 2 1.4605254 0.0004345318 0 1.042603 0.07462244 0.4741715 6.272224 ## 3 2.1067968 0.0043837064 0 1.068606 0.10507351 0.4748134 6.314615 ## 4 1.1187181 0.0019154929 0 1.071340 0.09971466 0.3124635 7.737306 ## 5 0.4222472 0.0005651563 0 1.000181 0.12935880 0.5040532 2.346977 ## 6 4.5818356 0.0158849155 0 1.071558 0.10262796 0.6777377 5.605037 # Show first rows of data.frame \"IgA\" in list \"campylobacterDelftParams4\". head(campylobacterDelftParams4$IgA) ## y1 alpha yb r y0 mu1 t1 ## 1 2.7471594 0.0235920904 0 2.773443 0.06452524 0.2737697 13.702269 ## 2 0.3361973 0.0001511561 0 1.006182 0.13241137 0.6774068 1.375517 ## 3 1.3402570 0.0072306458 0 1.640734 0.12395023 0.3883815 6.129892 ## 4 7.8335091 0.0028906244 0 2.768401 0.06512216 0.3482461 13.754356 ## 5 0.3069594 0.0029306951 0 1.050954 0.07870162 0.4218624 3.226293 ## 6 1.6315826 0.0044291436 0 1.740029 0.12264517 0.5924527 4.368299 responseParams <- campylobacterDelftParams4 IgGData <- read.csv(file = \"IgG.csv\") IgMData <- read.csv(file = \"IgM.csv\") IgAData <- read.csv(file = \"IgA.csv\") # Create a list named \"responseData\" containing objects named \"IgG\", \"IgM\" and # \"IgA\". responseParams <- list(IgG = IgGData, IgM = IgMData, IgA = IgAData) responseParams <- getAdditionalData(\"coxiellaIFAParams4.zip\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"estimating-seroincidence","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.3. Estimating seroincidence","title":"Serocalculator package tutorial","text":"Main calculation function provided package serocalculator called estimateSeroincidence. takes several arguments: data: data frame cross-sectional data (see variable serologyData ). may whole data set loaded file, can also subset, e.g. data = serologyData[1, ] use first row. tutorial data loaded variable serologyData. required input. antibodies: list antibodies used. can triplet (antibodies = c(\"IgG\", \"IgM\", \"IgA\")) single antibody (antibodies = c(\"IgG\")) combination two antibodies. required input. strata: list categories stratify data. column AgeCat example data set can used example. strata = \"\" (default), whole data set treated single stratum; sample assigned unique identifier (e.g. strata = \"AgeCat\") many strata samples incidence estimate calculated sample. specified, strata initialized \"\". params: list longitudinal parameters antibodies specified. tutorial data loaded variable responseParams. required input. censorLimits: list cutoff levels, one antibody used. antibody levels observations treated censored. required input. par0: List parameters (lognormal) distribution antibody concentrations true seronegatives (.e. never seroconverted), named antibody type (corresponding data). start starting value log(lambda). Value -6 corresponds roughly 1 day (log(1/365.25)), value -4 corresponds roughly 1 week (log(7/365.25)). Users adivised experiment value confirm convergence estimate obtained. Default -6. numCores Number processor cores use calculations computing strata. set 1 R package parallel available (installed default), computations stratum executed parallel. Default 1, ie. execution parallel. example Campylobacter seroincidence calculation three antibodies measured, stratified per AgeCat, measurements 0.1 removed: following text printed. important output subobject Fits containing raw results output incidence. Translation raw results provided custom function summary explained following section. cutoff argument based censoring observed serum antibody measurements (Strid et al. 2001). Cut-levels must always specified calling function estimateSeroincidence (argument censorLimits). Value 0 set antibody measurements censoring needed, instance Using different censoring levels produce different results. Choosing higher cut-causes estimates lambda decrease. Results published together chosen cut-values. cut-level 0.1 set example typical value, users set value reflecting censoring level data.","code":"# Prepare input data. serologyData <- campylobacterSimMediumData |> mutate( AgeCat = cut(Age, 15, labels = FALSE), Age = NA) responseParams <- campylobacterDelftParams4 cutOffs <- list(IgG = 0.1, IgM = 0.1, IgA = 0.1) # Baseline distributions: the distributions of antibody concentrations in # subjects who have never seroconverted. baseLine <- list(IgG = c(log(0.05), 1), IgM = c(log(0.005), 1), IgA = c(log(0.005), 1)) # Assign output of function \"estimateSeroincidence\" to object named # \"seroincidenceData\". Use all available processor cores. seroincidenceData <- estimateSeroincidence( data = serologyData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"AgeCat\", params = responseParams, censorLimits = cutOffs, par0 = baseLine, numCores = parallel::detectCores(), start = -4) # Show content of the output variable... print(seroincidenceData) # ...or simply type in the console: 'seroincidenceData' (without \"'\") and # press ENTER. ## Seroincidence object estimated given the following setup: ## a) Antibodies : IgG, IgM, IgA ## b) Strata : AgeCat ## c) Censor limits: IgG = 0.1, IgM = 0.1, IgA = 0.1 ## ## This object is a list containing the following items: ## Fits - List of outputs of \"optim\" function per stratum. ## Antibodies - Input parameter antibodies of function \"estimateSeroincidence\". ## Strata - Input parameter strata of function \"estimateSeroincidence\". ## CensorLimits - Input parameter censorLimits of function \"estimateSeroincidence\". ## ## Call summary function to obtain output results. censorLimits <- list(IgG = 0, IgM = 0, IgA = 0)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"getting-a-summary-of-seroincidence","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.4. Getting a summary of seroincidence","title":"Serocalculator package tutorial","text":"Output calculator passed function summary calculating output results: summary function returns list objects well. contains sub-objects Antibodies, Strata, CensorLimits, Quantiles Results. important sub-object output summary functions Results, containing estimates Lambda (annual incidence) together lower upper bounds (Lambda.lwr Lambda.upr, respectively). default lower bound 2.5% upper bound 97.5% distribution Lambda. values can changed (see examples). Optionally, summary function returns also Deviance (negative log likelihood estimated Lambda) Convergence (indicator returned function stats::optim; value 0 indicates convergence). may beneficial assign output function variable analysis: one may note dataframe six columns can later post-processed custom calculations.","code":"summary(seroincidenceData) ## Seroincidence estimated given the following setup: ## a) Antibodies : IgG, IgM, IgA ## b) Strata : AgeCat ## c) Censor limits: IgG = 0.1, IgM = 0.1, IgA = 0.1 ## d) Quantiles : 0.025, 0.975 ## ## Seroincidence estimates: ## Lambda.est Lambda.lwr Lambda.upr Deviance Convergence Stratum ## 1 0.09721890 0.06397004 0.14774909 105.29815 0 1 ## 2 0.05497303 0.03573489 0.08456817 181.90627 0 2 ## 3 0.04456096 0.02845903 0.06977327 199.99357 0 3 ## 4 0.02944038 0.01715302 0.05052964 141.32558 0 4 ## 5 0.02569781 0.01436524 0.04597053 123.33213 0 5 ## 6 0.02565297 0.01587275 0.04145941 187.41060 0 6 ## 7 0.03458616 0.02012722 0.05943207 128.84486 0 7 ## 8 0.02776873 0.02640621 0.02920154 133.57166 52 8 ## 9 0.04016261 0.02133209 0.07561541 94.99389 0 9 ## 10 0.01996477 0.01898409 0.02099612 104.30992 52 10 ## 11 0.03356017 0.01917771 0.05872886 141.20168 0 11 ## 12 0.05287648 0.03263923 0.08566140 181.68655 0 12 ## 13 0.05081097 0.02946963 0.08760729 123.66670 52 13 ## 14 0.04813104 0.02945312 0.07865371 186.92218 0 14 ## 15 0.03421042 0.02210895 0.05293569 215.78684 0 15 # Compute seroincidence summary and assign to object \"seroincidenceSummary\". seroincidenceSummary <- summary(seroincidenceData) # Show the results. seroincidenceSummary$Results ## Lambda.est Lambda.lwr Lambda.upr Deviance Convergence Stratum ## 1 0.09721890 0.06397004 0.14774909 105.29815 0 1 ## 2 0.05497303 0.03573489 0.08456817 181.90627 0 2 ## 3 0.04456096 0.02845903 0.06977327 199.99357 0 3 ## 4 0.02944038 0.01715302 0.05052964 141.32558 0 4 ## 5 0.02569781 0.01436524 0.04597053 123.33213 0 5 ## 6 0.02565297 0.01587275 0.04145941 187.41060 0 6 ## 7 0.03458616 0.02012722 0.05943207 128.84486 0 7 ## 8 0.02776873 0.02640621 0.02920154 133.57166 52 8 ## 9 0.04016261 0.02133209 0.07561541 94.99389 0 9 ## 10 0.01996477 0.01898409 0.02099612 104.30992 52 10 ## 11 0.03356017 0.01917771 0.05872886 141.20168 0 11 ## 12 0.05287648 0.03263923 0.08566140 181.68655 0 12 ## 13 0.05081097 0.02946963 0.08760729 123.66670 52 13 ## 14 0.04813104 0.02945312 0.07865371 186.92218 0 14 ## 15 0.03421042 0.02210895 0.05293569 215.78684 0 15"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"other-examples","dir":"Articles","previous_headings":"","what":"4. Other examples","title":"Serocalculator package tutorial","text":"following examples show standard procedure estimating seroincidence.","code":"# 1. Define cross-sectional data. serologyData <- with(campylobacterSimMediumData, data.frame( IgG = IgG.ratio.new, IgM = IgM.ratio.new, IgA = IgA.ratio.new)) # 2. Define longitudinal response data responseParams <- campylobacterDelftParams3 # 3. Define cut-offs cutOffs <- list(IgG = 0.25, IgM = 0.25, IgA = 0.25) # 4. Baseline distributions baseLine <- list(IgG = c(log(0.05), 1), IgM = c(log(0.005), 1), IgA = c(log(0.005), 1)) # 4a. Calculate a single seroincidence rate for all serum samples... seroincidenceData <- estimateSeroincidence( data = serologyData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = responseParams, censorLimits = cutOffs, par0 = baseLine) # 4b. ...or calculate a single seroincidence rate for a single serum sample # (triplet of titres)... seroincidenceData <- estimateSeroincidence( data = serologyData[1, ], antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = responseParams, censorLimits = cutOffs, par0 = baseLine) # 4c. ...or calculate a single seroincidence rate for all serum samples (only IgG) seroincidenceData <- estimateSeroincidence( data = serologyData, antibodies = c(\"IgG\"), strata = \"\", params = responseParams, censorLimits = cutOffs, par0 = baseLine) # 5a. Produce summary of the results with 2.5% and 97.5% bounds... summary(seroincidenceData) # 5b. ...or produce summary of the results with 5% and 95% bounds, do not show # convergence... summary(seroincidenceData, quantiles = c(0.05, 0.95), showConvergence = FALSE) # 5c. ...or produce summary and assign to an object... seroincidenceSummary <- summary(seroincidenceData) # ...and work with the results object from now on (here: display the results). seroincidenceSummary$Results"},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Peter Teunis. Author, copyright holder. Author method original code. Kristina Lai. Author. Kristen Aiemjoy. Author. Douglas Ezra Morrison. Author, maintainer.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Teunis P, Lai K, Aiemjoy K, Morrison D (2023). serocalculator: Estimating Infection Rates Serological Data. https://github.com/UCD-SERG/serocalculator, https://ucd-serg.github.io/serocalculator/.","code":"@Manual{, title = {serocalculator: Estimating Infection Rates from Serological Data}, author = {Peter Teunis and Kristina Lai and Kristen Aiemjoy and Douglas Ezra Morrison}, year = {2023}, note = {https://github.com/UCD-SERG/serocalculator, https://ucd-serg.github.io/serocalculator/}, }"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"serocalculator-package","dir":"","previous_headings":"","what":"Estimating Infection Rates from Serological Data","title":"Estimating Infection Rates from Serological Data","text":"Antibody levels measured (cross–sectional) population sample can translated estimate frequency seroconversions (infections) occur sampled population. Formulated simply: presence many high titres indicates many subjects likely experienced infection recently, low titres indicate low frequency infections sampled population. serocalculator script designed use longitudinal response characteristics means set parameters characterizing longitudinal response selected serum antibodies.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Estimating Infection Rates from Serological Data","text":"can install development version GitHub following code: Note Windows Users Windows users need install Rtools, contains collection tools building employing R packages still development. can done either devtools package installation, independently devtools already installed.  devtools installation:    prompted install additional build tools, select “Yes” Rtools installed. | !(Click Yes install Rtools along devtools package)[1.png] |  Independently: Download Rtools https://cran.r-project.org/bin/windows/Rtools/ Run installer Rtools installation may see window asking “Select Additional Tasks”. select box “Edit system PATH”. devtools RStudio put Rtools PATH automatically needed. select box “Save version information registry”. selected default. | ## Getting Help need assistance encounter clear bug, please file issue minimal reproducible example GitHub. Another great resource Epidemiologist R Handbook, includes introductory page asking help R packages via GitHub: https://epirhandbook.com/en/getting-help.html","code":"install.packages(\"devtools\") devtools::install_github(\"ucd-serg/serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"","code":"campylobacterDelftParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"list three dataframes: IgA dataframe containing 4000 rows 7 parameters IgA antibody. IgM dataframe containing 4000 rows 7 parameters IgM antibody. IgG dataframe containing 4000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"","code":"# Show first rows of every dataframe contained in campylobacterDelftParams1 lapply(campylobacterDelftParams1, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.9790816 0.005617736 0 1 NA NA 0 #> 2 0.8561027 0.005686470 0 1 NA NA 0 #> 3 0.8115014 0.005653234 0 1 NA NA 0 #> 4 1.6246947 0.007071109 0 1 NA NA 0 #> 5 1.6912089 0.007544257 0 1 NA NA 0 #> 6 7.4984300 0.011753968 0 1 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 1.4153120 0.004264498 0 1 NA NA 0 #> 2 0.6276850 0.002730359 0 1 NA NA 0 #> 3 3.4163114 0.008804830 0 1 NA NA 0 #> 4 1.8461903 0.005448317 0 1 NA NA 0 #> 5 0.7790359 0.002590766 0 1 NA NA 0 #> 6 0.3165559 0.001623372 0 1 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 12.012193 0.003507050 0 1 NA NA 0 #> 2 11.361843 0.001877301 0 1 NA NA 0 #> 3 6.644749 0.001918942 0 1 NA NA 0 #> 4 12.757673 0.001004286 0 1 NA NA 0 #> 5 10.514934 0.001570312 0 1 NA NA 0 #> 6 8.421454 0.003307627 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"","code":"campylobacterDelftParams3"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"list three dataframes: IgA dataframe containing 4000 rows 7 parameters IgA antibody. IgM dataframe containing 4000 rows 7 parameters IgM antibody. IgG dataframe containing 4000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"","code":"# Show first rows of every dataframe contained in campylobacterDelftParams3 lapply(campylobacterDelftParams3, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.9790816 0.005617736 0 1 0.5968037 0.16152280 3.0647487 #> 2 0.8561027 0.005686470 0 1 0.6490095 0.14953802 1.8519904 #> 3 0.8115014 0.005653234 0 1 0.3162894 0.20908627 4.5064094 #> 4 1.6246947 0.007071109 0 1 0.7882382 0.29381913 2.4616328 #> 5 1.6912089 0.007544257 0 1 0.5962426 0.25043491 4.1629632 #> 6 7.4984300 0.011753968 0 1 7.3277274 0.06639833 0.3468196 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 1.4153120 0.004264498 0 1 0.6213528 0.2079336 3.958986 #> 2 0.6276850 0.002730359 0 1 0.3697257 0.1366566 3.873045 #> 3 3.4163114 0.008804830 0 1 0.2768008 0.2385225 10.535773 #> 4 1.8461903 0.005448317 0 1 0.9193840 0.3195337 2.181853 #> 5 0.7790359 0.002590766 0 1 0.5470029 0.0860775 4.107961 #> 6 0.3165559 0.001623372 0 1 0.2296537 0.0809985 3.962139 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 12.012193 0.003507050 0 1 1.140502 0.3664422 6.425171 #> 2 11.361843 0.001877301 0 1 1.578391 0.3105795 6.355391 #> 3 6.644749 0.001918942 0 1 1.138631 0.3945647 4.470751 #> 4 12.757673 0.001004286 0 1 2.809073 0.2565113 5.899462 #> 5 10.514934 0.001570312 0 1 1.277037 0.6905252 3.053117 #> 6 8.421454 0.003307627 0 1 1.181497 0.3235032 6.071037 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"","code":"campylobacterDelftParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"","code":"# Show first rows of every dataframe contained in campylobacterDelftParams4 lapply(campylobacterDelftParams4, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 2.7471594 0.0235920904 0 2.773443 0.06452524 0.2737697 13.702269 #> 2 0.3361973 0.0001511561 0 1.006182 0.13241137 0.6774068 1.375517 #> 3 1.3402570 0.0072306458 0 1.640734 0.12395023 0.3883815 6.129892 #> 4 7.8335091 0.0028906244 0 2.768401 0.06512216 0.3482461 13.754356 #> 5 0.3069594 0.0029306951 0 1.050954 0.07870162 0.4218624 3.226293 #> 6 1.6315826 0.0044291436 0 1.740029 0.12264517 0.5924527 4.368299 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 3.0218848 0.0068170529 0 1.073372 0.09900034 0.3980767 8.587573 #> 2 1.4605254 0.0004345318 0 1.042603 0.07462244 0.4741715 6.272224 #> 3 2.1067968 0.0043837064 0 1.068606 0.10507351 0.4748134 6.314615 #> 4 1.1187181 0.0019154929 0 1.071340 0.09971466 0.3124635 7.737306 #> 5 0.4222472 0.0005651563 0 1.000181 0.12935880 0.5040532 2.346977 #> 6 4.5818356 0.0158849155 0 1.071558 0.10262796 0.6777377 5.605037 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 27.861427 0.0051607511 0 1.019014 0.07791154 0.7296875 8.057456 #> 2 3.306758 0.0011913102 0 1.099056 0.05077868 0.8435221 4.950963 #> 3 18.791581 0.0006869688 0 1.010531 0.09212040 0.6116469 8.694670 #> 4 5.866064 0.0006929407 0 1.019739 0.07694126 0.5232645 8.282421 #> 5 14.364930 0.0035222413 0 1.092096 0.05110497 0.9445758 5.969519 #> 6 5.790477 0.0003697673 0 1.010602 0.09127834 0.7696968 5.391807 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"","code":"campylobacterSSIParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"list three dataframes: IgA dataframe containing 4000 rows 7 parameters IgA antibody. IgM dataframe containing 4000 rows 7 parameters IgM antibody. IgG dataframe containing 4000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"","code":"# Show first rows of every dataframe contained in campylobacterSSIParams1 lapply(campylobacterSSIParams1, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.7003339 0.022609111 0 1 NA NA 0 #> 2 0.4881885 0.021309170 0 1 NA NA 0 #> 3 0.7607791 0.016486080 0 1 NA NA 0 #> 4 1.5342800 0.014820666 0 1 NA NA 0 #> 5 0.2454033 0.005397104 0 1 NA NA 0 #> 6 0.9043005 0.017098452 0 1 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 0.8079065 0.0012365599 0 1 NA NA 0 #> 2 0.4662612 0.0010868782 0 1 NA NA 0 #> 3 0.9829273 0.0013366391 0 1 NA NA 0 #> 4 0.1463502 0.0009022539 0 1 NA NA 0 #> 5 0.1940006 0.0005063741 0 1 NA NA 0 #> 6 0.7127425 0.0013184843 0 1 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 1.5841532 0.001312580 0 1 NA NA 0 #> 2 1.8657514 0.001438733 0 1 NA NA 0 #> 3 1.3126431 0.001351047 0 1 NA NA 0 #> 4 1.1131536 0.001048270 0 1 NA NA 0 #> 5 1.4794378 0.001274966 0 1 NA NA 0 #> 6 0.9348555 0.001437053 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"","code":"campylobacterSSIParams2"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"","code":"# Show first rows of every dataframe contained in campylobacterSSIParams2 lapply(campylobacterSSIParams2, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.3790329 0.013723334 0 1.180966 NA NA 0 #> 2 1.2994481 0.005642420 0 1.212107 NA NA 0 #> 3 9.4465428 0.011623566 0 1.205368 NA NA 0 #> 4 1.0094504 0.027030054 0 1.114516 NA NA 0 #> 5 0.7431767 0.004296025 0 1.210028 NA NA 0 #> 6 0.5895802 0.005579473 0 1.219622 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 2.0649209 0.011219203 0 1.163448 NA NA 0 #> 2 0.5802861 0.001512339 0 1.140606 NA NA 0 #> 3 1.0067090 0.016613231 0 1.197256 NA NA 0 #> 4 0.6119739 0.013465649 0 1.144594 NA NA 0 #> 5 0.6514105 0.002957626 0 1.142666 NA NA 0 #> 6 0.3944259 0.021869072 0 1.136722 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 1.3482386 0.0025760745 0 1.160319 NA NA 0 #> 2 0.7507566 0.0009904948 0 1.163093 NA NA 0 #> 3 1.5366781 0.0027693686 0 1.169187 NA NA 0 #> 4 1.3857968 0.0060489554 0 1.142833 NA NA 0 #> 5 1.1860646 0.0023857957 0 1.155256 NA NA 0 #> 6 1.6407579 0.0053201985 0 1.148251 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"","code":"campylobacterSSIParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"","code":"# Show first rows of every dataframe contained in campylobacterSSIParams4 lapply(campylobacterSSIParams4, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.3790329 0.013723334 0 1.180966 0.3084470 0.04388666 4.695573 #> 2 1.2994481 0.005642420 0 1.212107 0.8601382 0.12999444 3.173996 #> 3 9.4465428 0.011623566 0 1.205368 1.0658161 0.23740385 9.190702 #> 4 1.0094504 0.027030054 0 1.114516 0.9711994 0.03547790 1.088834 #> 5 0.7431767 0.004296025 0 1.210028 0.6803783 0.04747750 1.859510 #> 6 0.5895802 0.005579473 0 1.219622 0.4211322 0.13267245 2.536050 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 2.0649209 0.011219203 0 1.163448 1.3425809 0.09497971 4.5325273 #> 2 0.5802861 0.001512339 0 1.140606 0.4028595 0.10778318 3.3858110 #> 3 1.0067090 0.016613231 0 1.197256 0.8758258 0.15130671 0.9204797 #> 4 0.6119739 0.013465649 0 1.144594 0.4820747 0.07959144 2.9976911 #> 5 0.6514105 0.002957626 0 1.142666 0.6132294 0.02129935 2.8358109 #> 6 0.3944259 0.021869072 0 1.136722 0.3087485 0.04117147 5.9483978 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 1.3482386 0.0025760745 0 1.160319 0.7311131 0.09686797 6.317734 #> 2 0.7507566 0.0009904948 0 1.163093 0.5792900 0.05599269 4.630574 #> 3 1.5366781 0.0027693686 0 1.169187 0.4715269 0.30069910 3.928852 #> 4 1.3857968 0.0060489554 0 1.142833 0.8390058 0.20711698 2.422848 #> 5 1.1860646 0.0023857957 0 1.155256 0.9270293 0.06708116 3.673325 #> 6 1.6407579 0.0053201985 0 1.148251 1.2162005 0.12496860 2.396015 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated Cross-sectional Data — campylobacterSimLowData","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"Simulated cross-sectional population sample antibody levels data Campylobacter Pertussis lambda 0.036/yr (low), 0.021/yr (medium) 1.15/yr (high).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"","code":"campylobacterSimLowData"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"data frame 500 observations following 2 4 variables: Age IgG IgM IgA","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"","code":"# Show first rows of the data head(campylobacterSimLowData) #> Age IgG IgM IgA #> 1 11871.16 5.532551e-02 2.131281e-03 7.027718e-02 #> 2 7869.00 9.195770e-02 1.303805e-01 1.502870e-01 #> 3 680.00 9.589740e-02 1.992161e-01 1.267946e-01 #> 4 23734.28 2.032342e-01 2.371293e-07 2.956911e-07 #> 5 16968.07 4.604123e-13 1.929238e-05 1.691724e-03 #> 6 769.00 1.311047e-01 1.070782e-01 1.442210e-01 # Summarize the data summary(campylobacterSimLowData) #> Age IgG IgM IgA #> Min. : 42 Min. :0.00000 Min. :0.0000000 Min. :0.0000000 #> 1st Qu.: 6926 1st Qu.:0.02379 1st Qu.:0.0007472 1st Qu.:0.0005475 #> Median :12577 Median :0.12649 Median :0.0445646 Median :0.0195426 #> Mean :13178 Mean :0.22354 Mean :0.0970820 Mean :0.0672308 #> 3rd Qu.:19734 3rd Qu.:0.19127 3rd Qu.:0.1334760 3rd Qu.:0.1295172 #> Max. :27342 Max. :2.59219 Max. :2.1736800 Max. :0.8397106"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log-likelihood — .nll","title":"Calculate log-likelihood — .nll","text":"Calculate log-likelihood","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log-likelihood — .nll","text":"","code":".nll( stratumData, antibodies, params, censorLimits, ivc = FALSE, m = 0, par0, start )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log-likelihood — .nll","text":"stratumData Data frame cross-sectional serology data per antibody age, additional columns, one stratum antibodies Character vector one antibody names. Values must match data. params List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type. censorLimits List cutoffs one named antibody types (corresponding stratumData). ivc ivc = TRUE, biomarker data interval-censored. m parameter's meaning uncertain par0 List parameters (lognormal) distribution antibody concentrations true seronegatives (.e. never seroconverted), named antibody type (corresponding data). start starting value log(lambda). Value -6 corresponds roughly 1 day (log(1/365.25)), -4 corresponds roughly 1 week (log(7 / 365.25)). Default = -6.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log-likelihood — .nll","text":"log-likelihood data current parameter values","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Seroincidence — estimateSeroincidence","title":"Estimate Seroincidence — estimateSeroincidence","text":"Function estimate seroincidences based cross-section serology data longitudinal response model.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Seroincidence — estimateSeroincidence","text":"","code":"estimateSeroincidence( data, antibodies, strata = \"\", params, censorLimits, par0, start = -6, numCores = 1L )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Seroincidence — estimateSeroincidence","text":"data Data frame cross-sectional serology data per antibody age, additional columns identify possible strata. antibodies Character vector one antibody names. Values must match data. strata Character vector strata. Values must match data. Default = \"\". params List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type. censorLimits List cutoffs one named antibody types (corresponding data). par0 List parameters (lognormal) distribution antibody concentrations true seronegatives (.e. never seroconverted), named antibody type (corresponding data). start starting value log(lambda). Value -6 corresponds roughly 1 day (log(1/365.25)), -4 corresponds roughly 1 week (log(7 / 365.25)). Default = -6. numCores Number processor cores use calculations computing strata. set 1 package parallel available, computations executed parallel. Default = 1L.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Seroincidence — estimateSeroincidence","text":"set lambda estimates strata.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Seroincidence — estimateSeroincidence","text":"","code":"if (FALSE) { estimateSeroincidence(data = csData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = campylobacterDelftParams4, censorLimits = cutOffs, par0 = baseLn, start = -4) estimateSeroincidence(data = csData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = campylobacterDelftParams4, censorLimits = cutOffs, par0 = baseLn, start = -4, numCores = parallel::detectCores()) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate negative log-likelihood (deviance) — fdev","title":"Calculate negative log-likelihood (deviance) — fdev","text":"description added ","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate negative log-likelihood (deviance) — fdev","text":"","code":"fdev(log.lambda, csdata, lnpars, cond)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate negative log-likelihood (deviance) — fdev","text":"log.lambda Initial guess incidence rate csdata cross-sectional sample data lnpars longitudinal antibody decay model parameters cond measurement noise parameters","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Additional Data — getAdditionalData","title":"Get Additional Data — getAdditionalData","text":"Retrieves additional data internet. can file type, purpose function download data longitudinal response parameters online repository.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Additional Data — getAdditionalData","text":"","code":"getAdditionalData( fileName, repoURL = \"http://ecdc.europa.eu/sites/portal/files/documents\", savePath = NULL )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Additional Data — getAdditionalData","text":"fileName Name file download. Required. repoURL Web address remote repository files download . Required. Default = \"http://ecdc.europa.eu/sites/portal/files/documents\" savePath Folder save downloaded unzipped (needed) file. File saved argument NULL. Optional. Default = NULL.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Additional Data — getAdditionalData","text":"Data object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Additional Data — getAdditionalData","text":"","code":"if (FALSE) { getAdditionalData(fileName = \"coxiellaIFAParams4.zip\") getAdditionalData(fileName = \"yersiniaSSIParams4.zip\") getAdditionalData(fileName = \"coxiellaIFAParams4.zip\", savePath = getwd()) getAdditionalData(fileName = \"yersiniaSSIParams4.zip\", savePath = getwd()) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"","code":"pertussisIgGPTParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams1 lapply(pertussisIgGPTParams1, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 309.95227 0.0013582931 0 1 NA NA 0 #> 2 351.07244 0.0014761587 0 1 NA NA 0 #> 3 61.72439 0.0003486033 0 1 NA NA 0 #> 4 1103.35501 0.0023628598 0 1 NA NA 0 #> 5 1402.26889 0.0040798615 0 1 NA NA 0 #> 6 812.85619 0.0012771807 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"","code":"pertussisIgGPTParams2"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams2 lapply(pertussisIgGPTParams2, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 9338.5182 3.908447e-06 0 1.984440 NA NA 0 #> 2 608.0304 1.322816e-05 0 2.380790 NA NA 0 #> 3 926.2819 2.797526e-06 0 2.291821 NA NA 0 #> 4 5023.5881 2.141244e-05 0 1.897520 NA NA 0 #> 5 6357.7694 7.986787e-06 0 1.869652 NA NA 0 #> 6 656075.6374 1.876985e-06 0 2.326428 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"","code":"pertussisIgGPTParams3"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams3 lapply(pertussisIgGPTParams3, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 309.95227 0.0013582931 0 1 0.8858853 0.6482737 9.035669 #> 2 351.07244 0.0014761587 0 1 0.4461476 0.1958226 34.051720 #> 3 61.72439 0.0003486033 0 1 0.7276450 0.2298031 19.323595 #> 4 1103.35501 0.0023628598 0 1 0.4962986 0.1398534 55.105486 #> 5 1402.26889 0.0040798615 0 1 0.5894248 0.6389777 12.167020 #> 6 812.85619 0.0012771807 0 1 1.0949915 1.0617529 6.225373 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"","code":"pertussisIgGPTParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams4 lapply(pertussisIgGPTParams4, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 9338.5182 3.908447e-06 0 1.984440 0.3679380 0.3642614 27.841941 #> 2 608.0304 1.322816e-05 0 2.380790 0.4518361 0.2308821 31.204939 #> 3 926.2819 2.797526e-06 0 2.291821 0.4148741 1.0713510 7.197416 #> 4 5023.5881 2.141244e-05 0 1.897520 0.3258315 0.2198684 43.859306 #> 5 6357.7694 7.986787e-06 0 1.869652 0.6074594 0.3787113 24.440524 #> 6 656075.6374 1.876985e-06 0 2.326428 0.2303021 0.4318404 34.416405 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for Seroincidence Object — print.seroincidence","title":"Print Method for Seroincidence Object — print.seroincidence","text":"Custom print() function show output seroincidence calculator estimateSeroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for Seroincidence Object — print.seroincidence","text":"","code":"# S3 method for seroincidence print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for Seroincidence Object — print.seroincidence","text":"x list containing output function estimateSeroincidence(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for Seroincidence Object — print.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- estimateSeroincidence(...) # print the seroincidence object to the console print(seroincidence) # or simply type (appropriate print method will be invoked automatically) seroincidence }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"Custom print() function show output seroincidence summary summary.seroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"","code":"# S3 method for summary.seroincidence print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"x list containing output function summary.seroincidence(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- estimateSeroincidence(...) # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence) # print the summary of seroincidence object to the console print(seroincidenceSummary) # or simply type (appropriate print method will be invoked automatically) seroincidenceSummary }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"","code":"salmonellaSSIParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"","code":"# Show first rows of every dataframe contained in salmonellaSSIParams1 lapply(salmonellaSSIParams1, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.8846559 0.008526753 0 1 NA NA 0 #> 2 1.0830794 0.012876120 0 1 NA NA 0 #> 3 0.8741444 0.008709623 0 1 NA NA 0 #> 4 1.5351236 0.003502516 0 1 NA NA 0 #> 5 0.3268195 0.002556930 0 1 NA NA 0 #> 6 0.1963830 0.020255418 0 1 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 2.223399974 0.0707174863 0 1 NA NA 0 #> 2 3.496295237 0.0028553885 0 1 NA NA 0 #> 3 0.749391784 0.0008170739 0 1 NA NA 0 #> 4 0.624004098 0.0027294130 0 1 NA NA 0 #> 5 0.008570974 0.0020008248 0 1 NA NA 0 #> 6 1.241508561 0.0009053516 0 1 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 0.6078662 4.586619e-02 0 1 NA NA 0 #> 2 0.8793232 2.602733e-02 0 1 NA NA 0 #> 3 0.2713987 7.542284e-04 0 1 NA NA 0 #> 4 2.1483480 5.438996e+02 0 1 NA NA 0 #> 5 0.1096123 3.154008e-03 0 1 NA NA 0 #> 6 1.1052943 1.190809e-02 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":null,"dir":"Reference","previous_headings":"","what":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"","code":"salmonellaSSIParams2"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"","code":"# Show first rows of every dataframe contained in salmonellaSSIParams2 lapply(salmonellaSSIParams2, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.1645894 0.008301086 0 1.020289 NA NA 0 #> 2 0.4967038 0.007181714 0 1.021778 NA NA 0 #> 3 2.5087166 0.001178859 0 1.017500 NA NA 0 #> 4 1.8760033 0.018867951 0 1.020985 NA NA 0 #> 5 0.2381400 0.003545378 0 1.018420 NA NA 0 #> 6 0.1075755 0.001709320 0 1.017080 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 0.6325777 0.0028957411 0 1.047555 NA NA 0 #> 2 1.4144767 0.0328629331 0 1.039616 NA NA 0 #> 3 1.2642730 0.0008913089 0 1.051353 NA NA 0 #> 4 1.0383240 0.0024629578 0 1.056098 NA NA 0 #> 5 1.9768815 0.0031380379 0 1.052206 NA NA 0 #> 6 1.0602443 0.0140293204 0 1.049931 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 0.5630924 0.003808514 0 1.017216 NA NA 0 #> 2 0.9084731 0.004009900 0 1.021487 NA NA 0 #> 3 1.3531416 0.001163992 0 1.013681 NA NA 0 #> 4 0.7098593 0.004429380 0 1.021024 NA NA 0 #> 5 0.9199583 0.002791685 0 1.020375 NA NA 0 #> 6 8.4187094 0.003258891 0 1.017908 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"","code":"salmonellaSSIParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"","code":"# Show first rows of every dataframe contained in salmonellaSSIParams4 lapply(salmonellaSSIParams4, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.1645894 0.008301086 0 1.020289 0.11973340 0.12222920 2.6031943 #> 2 0.4967038 0.007181714 0 1.021778 0.39052803 0.14802164 1.6247227 #> 3 2.5087166 0.001178859 0 1.017500 2.37886174 0.17727577 0.2998108 #> 4 1.8760033 0.018867951 0 1.020985 1.81306536 0.15521542 0.2198533 #> 5 0.2381400 0.003545378 0 1.018420 0.21870973 0.11699959 0.7274678 #> 6 0.1075755 0.001709320 0 1.017080 0.09313794 0.08910887 1.6172527 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 0.6325777 0.0028957411 0 1.047555 0.5102927 0.07773161 2.7635934 #> 2 1.4144767 0.0328629331 0 1.039616 0.5753705 0.20969035 4.2896625 #> 3 1.2642730 0.0008913089 0 1.051353 1.1804183 0.07957789 0.8624049 #> 4 1.0383240 0.0024629578 0 1.056098 1.0077723 0.03253768 0.9178773 #> 5 1.9768815 0.0031380379 0 1.052206 1.2086867 0.11864577 4.1466813 #> 6 1.0602443 0.0140293204 0 1.049931 0.8761034 0.05988377 3.1856792 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 0.5630924 0.003808514 0 1.017216 0.5040835 0.11686141 0.9472916 #> 2 0.9084731 0.004009900 0 1.021487 0.7985884 0.25997719 0.4958881 #> 3 1.3531416 0.001163992 0 1.013681 0.7592344 0.17166853 3.3662183 #> 4 0.7098593 0.004429380 0 1.021024 0.6843716 0.06179318 0.5917449 #> 5 0.9199583 0.002791685 0 1.020375 0.8494778 0.16093737 0.4952642 #> 6 8.4187094 0.003258891 0 1.017908 3.5463722 1.59327615 0.5426124 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimating Infection Rates from Serological Data — serocalculator","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"package translates antibody levels measured (cross-sectional) population sample estimate frequency seroconversions (infections) occur sampled population.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"detailed documentation type following R console: vignette(\"installation\", package = \"serocalculator\") vignette(\"tutorial\", package = \"serocalculator\") vignette(\"methodology\", package = \"serocalculator\")","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Methods estimating seroincidence Teunis, P. F., van Eijkeren, J. C., Ang, C. W., van Duynhoven, Y. T., Simonsen, J. B., Strid, M. ., van Pelt, W. \"Biomarker dynamics: estimating infection rates serological data\" Statistics Medicine 31, . 20 (September 9, 2012): 2240--48. doi:10.1002/sim.5322. Simonsen, J., Molbak, K., Falkenhorst, G., Krogfelt, K. ., Linneberg, ., Teunis, P. F. \"Estimation incidences infectious diseases based antibody measurements\" Statistics Medicine 28, . 14 (June 30, 2009): 1882--95. doi:10.1002/sim.3592. Applications Monge, S., Teunis, P. F., Friesema, ., Franz, E., Ang, W., van Pelt, W., Mughini-Gras, L. \"Immune response-eliciting exposure Campylobacter vastly exceeds incidence clinically overt campylobacteriosis associated similar risk factors: nationwide serosurvey Netherlands\" Journal Infection, 2018, 1--7, doi:10.1016/j.jinf.2018.04.016 Kretzschmar, M., Teunis, P. F., Pebody, R. G. \"Incidence reproduction numbers pertussis: estimates serological social contact data five European countries\" PLoS Medicine 7, . 6 (June 1, 2010):e1000291. doi:10.1371/journal.pmed.1000291. Simonsen, J., Strid, M. ., Molbak, K., Krogfelt, K. ., Linneberg, ., Teunis, P. \"Sero-epidemiology tool study incidence Salmonella infections humans\" Epidemiology Infection 136, . 7 (July 1, 2008): 895--902. doi:10.1017/S0950268807009314 Simonsen, J., Teunis, P. F., van Pelt, W., van Duynhoven, Y., Krogfelt, K. ., Sadkowska-Todys, M., Molbak K. \"Usefulness seroconversion rates comparing infection pressures countries\" Epidemiology Infection, April 12, 2010, 1--8. doi:10.1017/S0950268810000750. Falkenhorst, G., Simonsen, J., Ceper, T. H., van Pelt, W., de Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Jernberg, C., Rota, M. C., van Duynhoven, Y. T., Teunis, P. F., Krogfelt, K. ., Molbak, K. \"Serological cross-sectional studies salmonella incidence eight European countries: correlation incidence reported cases\" BMC Public Health 12, . 1 (July 15, 2012): 523--23. doi:10.1186/1471-2458-12-523. Teunis, P. F., Falkenhorst, G., Ang, C. W., Strid, M. ., De Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Rota, M. C., Simonsen, J. B., Molbak, K., Van Duynhoven, Y. T., van Pelt, W. \"Campylobacter seroconversion rates selected countries European Union\" Epidemiology Infection 141, . 10 (2013): 2051--57. doi:10.1017/S0950268812002774. de Melker, H. E., Versteegh, F. G., Schellekens, J. F., Teunis, P. F., Kretzschmar, M. \"incidence Bordetella pertussis infections estimated population combination serological surveys\" Journal Infection 53, . 2 (August 1, 2006): 106--13. doi:10.1016/j.jinf.2005.10.020 Quantification seroresponse de Graaf, W. F., Kretzschmar, M. E., Teunis, P. F., Diekmann, O. \"two-phase within-host model immune response application serological profiles pertussis\" Epidemics 9 (2014):1--7. doi:10.1016/j.epidem.2014.08.002. Berbers, G. ., van de Wetering, M. S., van Gageldonk, P. G., Schellekens, J. F., Versteegh, F. G., Teunis, P.F. \"novel method evaluating natural vaccine induced serological responses Bordetella pertussis antigens\" Vaccine 31, . 36 (August 12, 2013): 3732--38. doi:10.1016/j.vaccine.2013.05.073. Versteegh, F. G., Mertens, P. L., de Melker, H. E., Roord, J. J., Schellekens, J. F., Teunis, P. F. \"Age-specific long-term course IgG antibodies pertussis toxin symptomatic infection Bordetella pertussis\" Epidemiology Infection 133, . 4 (August 1, 2005): 737--48. Teunis, P. F., van der Heijden, O. G., de Melker, H. E., Schellekens, J. F., Versteegh, F. G., Kretzschmar, M. E. \"Kinetics IgG antibody response pertussis toxin infection B. pertussis\" Epidemiology Infection 129, . 3 (December 10, 2002):479. doi:10.1017/S0950268802007896.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Author: Peter Teunis p.teunis@emory.edu Author: Doug Ezra Morrison demorrison@ucdavis.edu Author: Kristen Aiemjoy kaiemjoy@ucdavis.edu Author: Kristina Lai kaiemjoy@ucdavis.edu","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Seroincidence Object — summary.seroincidence","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"Calculate seroincidence output seroincidence calculator estimateSeroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"","code":"# S3 method for seroincidence summary( object, ..., quantiles = c(0.025, 0.975), showDeviance = TRUE, showConvergence = TRUE )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"object dataframe containing output function estimateSeroincidence(). ... Additional arguments affecting summary produced. quantiles vector length 2 specifying quantiles lower (first element) upper (second element) bounds lambda. Default = c(0.025, 0.975). showDeviance Logical flag (FALSE/TRUE) reporting deviance (-2*log(likelihood) estimated seroincidence. Default = TRUE. showConvergence Logical flag (FALSE/TRUE) reporting convergence (see help optim() details). Default = TRUE.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"list following items: Results Dataframe maximum likelihood estimate lambda (seroincidence) (column Lambda) corresponding lower (Lambda.lwr) upper (Lambda.upr bounds. Optionally Deviance (Negative log likelihood (NLL) estimated (maximum likelihood) lambda) Covergence (Convergence indicator returned optim(). Value 0 indicates convergence) columns included. Antibodies Character vector names input antibodies used estimateSeroincidence(). Strata Character names strata used estimateSeroincidence(). CensorLimits List cutoffs antibodies used estimateSeroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- estimateSeroincidence(...) # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence) }"}] +[{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement . complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to serocalculator","title":"Contributing to serocalculator","text":"outlines propose change serocalculator. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to serocalculator","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to serocalculator","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to serocalculator","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"UCD-SERG/serocalculator\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to serocalculator","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to serocalculator","text":"Please note serocalculator project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"1. Introduction","title":"Serocalculator package installation manual","text":"Package serocalculator written programming language R end user must access working installation R engine. document describes common setup R installed locally user’s computer. screenshots refer classical R interface, package can also opened Graphical User Interfaces R like e.g.  RStudio.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"installation-steps","dir":"Articles","previous_headings":"","what":"2. Installation steps","title":"Serocalculator package installation manual","text":"R free software program can downloaded http://cran.r-project.org/. downloading appropriate version computer’s operating system, install R computer following standard procedure applicable operating system. Windows file downloaded -called base distribution: http://cran.r-project.org/bin/windows/base/.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"installing-r","dir":"Articles","previous_headings":"2. Installation steps","what":"2.1. Installing R","title":"Serocalculator package installation manual","text":"Start R installer follow presented steps: advised R installed folder contain spaces, therefore please adjust destination location accordingly: serocalculator package compatible 32-bit 64-bit version R. Choose preferred platform (). unsure install 32-bit version , however compatible platforms 64-bit version may provide better performance: advised select Registry entries next step best experience: R interpreter, installed Windows, can invoked start menu folder named R. Start preferred version R (32-bit: R i386 64-bit: x64 installed). Graphical user interface R interpreter start new window:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"installing-serocalculator-package","dir":"Articles","previous_headings":"2. Installation steps","what":"2.2. Installing serocalculator package","title":"Serocalculator package installation manual","text":"Since new installation R, serocalculator package must installed first use. 09/20/2023, serocalculator still development. install development version, must install devtools package download serocalculator GitHub.","code":"install.packages(\"devtools\") devtools::install_github(\"ucd-serg/serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/installation.html","id":"post-installation","dir":"Articles","previous_headings":"","what":"3. Post-installation","title":"Serocalculator package installation manual","text":"Successful installation can confirmed loading package workspace exploring help files manuals distributed package: Additionally, package details can found executing following commands:","code":"# Load package \"seroincidence\". library(serocalculator) # Show R help for the package. ?serocalculator # Show tutorial for the package. vignette(topic = \"tutorial\", package = \"serocalculator\") # Show description. packageDescription(\"serocalculator\") ## Package: serocalculator ## Type: Package ## Title: Estimating Infection Rates from Serological Data ## Version: 0.1.0.9000 ## Date: 2022-03-29 ## Authors@R: c( person(given = \"Peter\", family = \"Teunis\", email = ## \"p.teunis@emory.edu\", role = c(\"aut\", \"cph\"), comment = \"Author ## of the method and original code.\"), person(given = \"Kristina\", ## family = \"Lai\", role = c(\"aut\")), person(given = \"Kristen\", ## family = \"Aiemjoy\", email = \"kaiemjoy@ucdavis.edu\", role = ## c(\"aut\")), person(given = \"Douglas Ezra\", family = \"Morrison\", ## email = \"demorrison@ucdavis.edu\", role = c(\"aut\", \"cre\"))) ## Description: Translates antibody levels measured in a cross-sectional ## population sample into an estimate of the frequency with which ## seroconversions (infections) occur in the sampled population. ## Forked from the \"seroincidence\" package v2.0.0 on CRAN. ## Depends: R (>= 2.10) ## License: GPL-3 ## Imports: dplyr, Rcpp, stats, utils ## Suggests: knitr, rmarkdown, parallel, pander, Hmisc, tidyverse, fs ## VignetteBuilder: knitr ## LazyData: true ## Encoding: UTF-8 ## URL: https://github.com/UCD-SERG/serocalculator, ## https://ucd-serg.github.io/serocalculator/ ## RoxygenNote: 7.2.3 ## NeedsCompilation: yes ## LinkingTo: Rcpp ## Language: en-US ## Roxygen: list(markdown = TRUE) ## Packaged: 2023-10-09 20:42:28 UTC; runner ## Author: Peter Teunis [aut, cph] (Author of the method and original ## code.), Kristina Lai [aut], Kristen Aiemjoy [aut], Douglas Ezra ## Morrison [aut, cre] ## Maintainer: Douglas Ezra Morrison ## Built: R 4.3.1; x86_64-pc-linux-gnu; 2023-10-09 20:42:29 UTC; unix ## RemotePkgRef: local::. ## RemoteType: local ## ## -- File: /home/runner/work/_temp/Library/serocalculator/Meta/package.rds # Show citation. citation(\"serocalculator\") ## To cite package 'serocalculator' in publications use: ## ## Teunis P, Lai K, Aiemjoy K, Morrison D (2022). _serocalculator: ## Estimating Infection Rates from Serological Data_. ## https://github.com/UCD-SERG/serocalculator, ## https://ucd-serg.github.io/serocalculator/. ## ## A BibTeX entry for LaTeX users is ## ## @Manual{, ## title = {serocalculator: Estimating Infection Rates from Serological Data}, ## author = {Peter Teunis and Kristina Lai and Kristen Aiemjoy and Douglas Ezra Morrison}, ## year = {2022}, ## note = {https://github.com/UCD-SERG/serocalculator, ## https://ucd-serg.github.io/serocalculator/}, ## }"},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Serocalculator package methodology","text":"revised seroincidence calculator package provides three refinements method calculating seroincidence published earlier (Teunis et al. 2012) implemented R package seroincidence, versions 1.x: (1) inclusion infection episode rising antibody levels, (2) non–exponential decay serum antibodies infection, (3) age–dependent correction subjects never seroconverted. important note , although implemented methods use specific parametric model, proposed (de Graaf et al. 2014) augmented (Teunis et al. 2016), methods used calculate likelihood function allow seroresponses arbitrary shape.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"a-simple-model-for-the-seroresponse","dir":"Articles","previous_headings":"","what":"1. A simple model for the seroresponse","title":"Serocalculator package methodology","text":"current version serocalculator package uses model (Teunis et al. 2016) shape seroresponse: \\[ \\begin{array}{l@{\\qquad}l} \\text{Infection/colonization episode} & \\text{Waning immunity episode}\\\\ b^{\\prime}(t) = \\mu_{0}b(t) - cy(t) & b(t) = 0 \\\\ y^{\\prime}(t) = \\mu y(t) & y^{\\prime}(t) = -\\nu y(t)^r \\\\ \\end{array} \\] baseline antibody concentration \\(y(0) = y_{0}\\) initial pathogen concentration \\(b(0) = b_{0}\\). serum antibody response \\(y(t)\\) can written \\[ y(t) = y_{+}(t) + y_{-}(t) \\] \\[\\begin{align*} y_{+}(t) & = y_{0}\\text{e}^{\\mu t}[0\\le t 1\\), log concentrations decrease rapidly infection terminated, decay slows low antibody concentrations maintained long period. \\(r\\) approaches 1, exponential decay restored.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"backward-recurrence-time","dir":"Articles","previous_headings":"","what":"2. Backward recurrence time","title":"Serocalculator package methodology","text":"Considering (fundamental) uniform distribution \\(u_{f}\\) sampling within given interval, interval length distribution \\(p(\\Delta t)\\) distribution (cross–sectionally) sampled interval length (Teunis et al. 2012) \\[ q(\\Delta t) = \\frac{p(\\Delta t)\\cdot\\Delta t}{\\overline{\\Delta t_{p}}} \\] joint distribution backward recurrence time cross–sectional interval length product \\(u_{f}\\cdot q\\) probabilities independent. distribution backward recurrence time marginal distribution \\[\\begin{align*} u(\\tau) & = \\int_{\\Delta t=0}^{\\infty} u_{f}(\\tau;\\Delta t)\\cdot q(\\Delta t)\\text{d}\\Delta t\\\\ & = \\int_{0}^{\\infty}\\frac{[0\\le\\tau\\le\\Delta t]}{\\Delta t}\\cdot \\frac{p(\\Delta t)\\cdot \\Delta t}{\\overline{\\Delta t_{p}}}\\text{d}\\Delta t\\\\ & = \\frac{1}{\\overline{\\Delta t_{p}}}\\int_{\\tau}^{\\infty}p(\\Delta t)\\text{d}\\Delta t \\end{align*}\\]","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/methodology.html","id":"incidence-of-seroconversions","dir":"Articles","previous_headings":"","what":"3. Incidence of seroconversions","title":"Serocalculator package methodology","text":"calculate incidence seroconversions, (Teunis et al. 2012), distribution \\(p(\\Delta t)\\) intervals \\(\\Delta t\\) seroconversions, important. Assuming subject sampled completely random intervals seroconversions, accounting interval length bias (Satten et al. 2004; Zelen 2004), distribution backward recurrence times \\(\\tau\\) can written (Teunis et al. 2012) \\[ u(\\tau) = \\frac{1}{\\overline{\\Delta t}} \\int_{\\tau=0}^{\\infty}p(\\Delta t)\\text{d}\\Delta t = \\frac{1-P(\\Delta t)}{\\overline{\\Delta t}} \\] \\(\\overline{\\Delta t}\\) \\(p\\)–distribution expected value \\(\\Delta t\\). density employed estimate seroconversion rates. antibody concentration \\(y\\) observable quantity, need express probability (density) observed \\(y\\) terms density backward recurrence time. First, backward recurrence time can \\(\\tau\\) expressed function serum antibody concentration \\(y\\) \\[ \\tau(y) = \\tau_{+}(y) + \\tau_{-}(y) \\] \\[\\begin{align*} \\tau_{+}(y) & = \\displaystyle{\\frac{1}{\\mu}} \\log\\left(\\displaystyle{\\frac{y_{+}}{y_{0}}}\\right) [0\\le \\tau 0\\)), whether decay exponential (\\(r = 1\\)) proceeds power function (\\(r > 1\\)). Power function decay allows rapid inital decay followed sustained period slow decay (Teunis et al. 2016). Available model variants described Table 1.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"how-to-use-serocalculator-package","dir":"Articles","previous_headings":"","what":"3. How to use serocalculator package","title":"Serocalculator package tutorial","text":"section provides step--step directions usage serocalculator package.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"loading-the-package","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.1. Loading the package","title":"Serocalculator package tutorial","text":"Functionality serocalculator package made available end user library loaded current workspace. Assuming package installed already (covered installation.pdf) loading achieved running following command R console (bear mind text character # comment): moment functions required run seroincidence calculation made available. can checked running: resulting output: briefly describe important objects functions: [DISEASE]Params[MODEL_TYPE]: Monte Carlo sample longitudinal response parameters per antibody various diseases: Campylobacter, Pertussis, Salmonella. Object class: list. [DISEASE]Sim[Low|Medium|High]Data: Example simulated antibody levels data measured cross-sectional population three values lambda (incidence): 0.036 (“Low”), 0.21 (“Medium”) 1.15 (“High”) (1/yr). Object class: data.frame. getAdditionalData: Utility function downloading additional longitudinal response parameters online repository. Files available download coxiellaIFAParams4.zip yersiniaSSIParams4.zip. Object class: function. estimateSeroincidence: Main function package. Estimates seroincidence based supplied cross-section antibody levels data longitudinal response parameters. Object class: function.","code":"# Load package \"seroincidence\" library(serocalculator) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union # List all objects (functions and data) exposed by package \"serocalculator\". ls(\"package:serocalculator\") ## [1] \"campylobacterDelftParams1\" \"campylobacterDelftParams3\" ## [3] \"campylobacterDelftParams4\" \"campylobacterSimHighData\" ## [5] \"campylobacterSimLowData\" \"campylobacterSimMediumData\" ## [7] \"campylobacterSSIParams1\" \"campylobacterSSIParams2\" ## [9] \"campylobacterSSIParams4\" \"estimateSeroincidence\" ## [11] \"fdev\" \"getAdditionalData\" ## [13] \"pertussisIgGPTParams1\" \"pertussisIgGPTParams2\" ## [15] \"pertussisIgGPTParams3\" \"pertussisIgGPTParams4\" ## [17] \"pertussisSimHighData\" \"pertussisSimLowData\" ## [19] \"pertussisSimMediumData\" \"salmonellaSSIParams1\" ## [21] \"salmonellaSSIParams2\" \"salmonellaSSIParams4\""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"specifying-input-data","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.2. Specifying input data","title":"Serocalculator package tutorial","text":"two sets inputs serology calculator must always specified: Simulated antibody levels measured cross-sectional population sample (see campylobacterSimLowData, campylobacterSimMediumData, etc. ). Longitudinal response characteristic set parameters (y1, alpha, yb, r, y0, mu1, t1) (see campylobacterDelftParams1, campylobacterSSIParams2, etc. ). start loading antibody levels measured cross-sectional population sample.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"specifying-cross-sectional-antibody-levels-data","dir":"Articles","previous_headings":"3. How to use serocalculator package > 3.2. Specifying input data","what":"3.2.1. Specifying cross-sectional antibody levels data","title":"Serocalculator package tutorial","text":"user options providing data calculator: ) Using supplied sample data Data set campylobacterSimMediumData provides example cross-sectional antibody levels data Campylobacter. loaded workspace soon package loaded, therefore can referenced right away. small exempt data set: Let us create serology data serologyData based data set: can view data also tabular form: shall pass object later calculator. Object serologyData must contain least one column named IgG, IgM, IgA. Additionally, column Age used calculations present. Otherwise, created --fly initialized NA (available). Column Age, supplied, assumed represent age days. Please, note cross-sectional data set can include extra columns allowing performing seroincidence calculations stratified per factors available data. example column AgeCat can used stratum variable. particular variable created mapping age days (column Age) 15 categories, category 1 grouping youngest subjects category 15 grouping oldest subjects. ii) Loading external files user can load /data R session. Details loading specific file types can found online R manual R Data Import/Export. important input data tabular form column containing levels measured single antibody type. Column name indicate name measured antibody. instance, comma separated file following content: valid cross-sectional data set. Suppose file named c:\\cross-sectional-data.csv. can loaded R like :","code":"# Show three first rows of \"campylobacterSimMediumData\" data.frame. head(campylobacterSimMediumData, 3) ## Age IgG IgM IgA ## 1 4530.743 0.2242790 0.05442598 1.138591e-02 ## 2 5173.256 0.8498734 0.10667042 2.093677e-06 ## 3 24187.164 0.1849355 1.24958865 1.652755e-01 # Assign data.frame \"campylobacterDelftData\" to object named \"serologyData\". serologyData <- campylobacterSimMediumData |> mutate(AgeCat = cut(Age, 15, labels = FALSE)) # Print a few first observations. head(serologyData) ## Age IgG IgM IgA AgeCat ## 1 4530.743 0.2242790 0.0544259797 1.138591e-02 3 ## 2 5173.256 0.8498734 0.1066704172 2.093677e-06 3 ## 3 24187.164 0.1849355 1.2495886495 1.652755e-01 14 ## 4 21427.836 1.1743485 0.0001590331 3.122555e-02 12 ## 5 12560.687 1.0656224 0.0013906805 8.723256e-04 7 ## 6 17083.219 0.1115373 0.4976598547 2.321977e-01 10 View(serologyData) IgG, IgM, IgA 5.337300, 0.4414653, 0.3002395 3.534118, 0.3888226, 0.3543486 2.144549, 0.3320178, 0.3884205 2.957854, 0.4967764, 0.3472340 # Read content of file \"C:\\cross-sectional-data.csv\" into object named # \"serologyData\". serologyData <- read.csv(file = \"C:\\\\cross-sectional-data.csv\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"longParams","dir":"Articles","previous_headings":"3. How to use serocalculator package > 3.2. Specifying input data","what":"3.2.2. Specifying longitudinal response parameters","title":"Serocalculator package tutorial","text":"longitudinal response parameters set consists following items: y1: antibody peak level (ELISA units) alpha: antibody decay rate (1/days current longitudinal parameter sets) yb: baseline antibody level \\(t\\) approaching infinity (\\(y(t->inf)\\)) r: shape factor antibody decay y0: baseline antibody level \\(t=0\\) (\\(y(t=0)\\)) mu1: initial antibody growth rate t1: duration infection Please, refer vignette methodology.pdf details underlying methodology. End user three options loading response parameters R session: ) Using supplied data longitudinal response parameter data sets provided package including Monte-Carlo sample longitudinal response parameters, like campylobacterDelftParams4. particular object list containing three data.frame objects named IgG, IgM IgA. Let’s pick two data sets separately (notice character $ indicating sub-object parent object): Let us assign object campylobacterDelftParams4 variable responseParams: ii) Loading local external files Alternatively, data can loaded external file. Bear mind, likely data organized three files, separately antibody IgG, IgM IgA. Assuming data saved csv files named IgG.csv, IgM.csv IgA.csv end user run following code order create valid input calculator: Note object responseParams list three dataframes named IgG, IgM IgA required. iii) Loading external repository Finally, response parameters can obtained online repository set package. repository hosted ECDC servers. time publishing package two files available: coxiellaIFAParams4.zip: longitudinal response parameters per antibody Coxiella. yersiniaSSIParams4.zip: longitudinal response parameters per antibody Yersinia. Simply use supplied utility function getAdditionalData download data: Internet connection needed function work.","code":"# Show first rows of data.frame \"IgG\" in list \"campylobacterDelftParams4\". head(campylobacterDelftParams4$IgG) ## y1 alpha yb r y0 mu1 t1 ## 1 27.861427 0.0051607511 0 1.019014 0.07791154 0.7296875 8.057456 ## 2 3.306758 0.0011913102 0 1.099056 0.05077868 0.8435221 4.950963 ## 3 18.791581 0.0006869688 0 1.010531 0.09212040 0.6116469 8.694670 ## 4 5.866064 0.0006929407 0 1.019739 0.07694126 0.5232645 8.282421 ## 5 14.364930 0.0035222413 0 1.092096 0.05110497 0.9445758 5.969519 ## 6 5.790477 0.0003697673 0 1.010602 0.09127834 0.7696968 5.391807 # Show first rows of data.frame \"IgM\" in list \"campylobacterDelftParams4\". head(campylobacterDelftParams4$IgM) ## y1 alpha yb r y0 mu1 t1 ## 1 3.0218848 0.0068170529 0 1.073372 0.09900034 0.3980767 8.587573 ## 2 1.4605254 0.0004345318 0 1.042603 0.07462244 0.4741715 6.272224 ## 3 2.1067968 0.0043837064 0 1.068606 0.10507351 0.4748134 6.314615 ## 4 1.1187181 0.0019154929 0 1.071340 0.09971466 0.3124635 7.737306 ## 5 0.4222472 0.0005651563 0 1.000181 0.12935880 0.5040532 2.346977 ## 6 4.5818356 0.0158849155 0 1.071558 0.10262796 0.6777377 5.605037 # Show first rows of data.frame \"IgA\" in list \"campylobacterDelftParams4\". head(campylobacterDelftParams4$IgA) ## y1 alpha yb r y0 mu1 t1 ## 1 2.7471594 0.0235920904 0 2.773443 0.06452524 0.2737697 13.702269 ## 2 0.3361973 0.0001511561 0 1.006182 0.13241137 0.6774068 1.375517 ## 3 1.3402570 0.0072306458 0 1.640734 0.12395023 0.3883815 6.129892 ## 4 7.8335091 0.0028906244 0 2.768401 0.06512216 0.3482461 13.754356 ## 5 0.3069594 0.0029306951 0 1.050954 0.07870162 0.4218624 3.226293 ## 6 1.6315826 0.0044291436 0 1.740029 0.12264517 0.5924527 4.368299 responseParams <- campylobacterDelftParams4 IgGData <- read.csv(file = \"IgG.csv\") IgMData <- read.csv(file = \"IgM.csv\") IgAData <- read.csv(file = \"IgA.csv\") # Create a list named \"responseData\" containing objects named \"IgG\", \"IgM\" and # \"IgA\". responseParams <- list(IgG = IgGData, IgM = IgMData, IgA = IgAData) responseParams <- getAdditionalData(\"coxiellaIFAParams4.zip\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"estimating-seroincidence","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.3. Estimating seroincidence","title":"Serocalculator package tutorial","text":"Main calculation function provided package serocalculator called estimateSeroincidence. takes several arguments: data: data frame cross-sectional data (see variable serologyData ). may whole data set loaded file, can also subset, e.g. data = serologyData[1, ] use first row. tutorial data loaded variable serologyData. required input. antibodies: list antibodies used. can triplet (antibodies = c(\"IgG\", \"IgM\", \"IgA\")) single antibody (antibodies = c(\"IgG\")) combination two antibodies. required input. strata: list categories stratify data. column AgeCat example data set can used example. strata = \"\" (default), whole data set treated single stratum; sample assigned unique identifier (e.g. strata = \"AgeCat\") many strata samples incidence estimate calculated sample. specified, strata initialized \"\". params: list longitudinal parameters antibodies specified. tutorial data loaded variable responseParams. required input. censorLimits: list cutoff levels, one antibody used. antibody levels observations treated censored. required input. par0: List parameters (lognormal) distribution antibody concentrations true seronegatives (.e. never seroconverted), named antibody type (corresponding data). start starting value log(lambda). Value -6 corresponds roughly 1 day (log(1/365.25)), value -4 corresponds roughly 1 week (log(7/365.25)). Users adivised experiment value confirm convergence estimate obtained. Default -6. numCores Number processor cores use calculations computing strata. set 1 R package parallel available (installed default), computations stratum executed parallel. Default 1, ie. execution parallel. example Campylobacter seroincidence calculation three antibodies measured, stratified per AgeCat, measurements 0.1 removed: following text printed. important output subobject Fits containing raw results output incidence. Translation raw results provided custom function summary explained following section. cutoff argument based censoring observed serum antibody measurements (Strid et al. 2001). Cut-levels must always specified calling function estimateSeroincidence (argument censorLimits). Value 0 set antibody measurements censoring needed, instance Using different censoring levels produce different results. Choosing higher cut-causes estimates lambda decrease. Results published together chosen cut-values. cut-level 0.1 set example typical value, users set value reflecting censoring level data.","code":"# Prepare input data. serologyData <- campylobacterSimMediumData |> mutate( AgeCat = cut(Age, 15, labels = FALSE), Age = NA) responseParams <- campylobacterDelftParams4 cutOffs <- list(IgG = 0.1, IgM = 0.1, IgA = 0.1) # Baseline distributions: the distributions of antibody concentrations in # subjects who have never seroconverted. baseLine <- list(IgG = c(log(0.05), 1), IgM = c(log(0.005), 1), IgA = c(log(0.005), 1)) # Assign output of function \"estimateSeroincidence\" to object named # \"seroincidenceData\". Use all available processor cores. seroincidenceData <- estimateSeroincidence( data = serologyData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"AgeCat\", params = responseParams, censorLimits = cutOffs, par0 = baseLine, numCores = parallel::detectCores(), start = -4) # Show content of the output variable... print(seroincidenceData) # ...or simply type in the console: 'seroincidenceData' (without \"'\") and # press ENTER. ## Seroincidence object estimated given the following setup: ## a) Antibodies : IgG, IgM, IgA ## b) Strata : AgeCat ## c) Censor limits: IgG = 0.1, IgM = 0.1, IgA = 0.1 ## ## This object is a list containing the following items: ## Fits - List of outputs of \"optim\" function per stratum. ## Antibodies - Input parameter antibodies of function \"estimateSeroincidence\". ## Strata - Input parameter strata of function \"estimateSeroincidence\". ## CensorLimits - Input parameter censorLimits of function \"estimateSeroincidence\". ## ## Call summary function to obtain output results. censorLimits <- list(IgG = 0, IgM = 0, IgA = 0)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"getting-a-summary-of-seroincidence","dir":"Articles","previous_headings":"3. How to use serocalculator package","what":"3.4. Getting a summary of seroincidence","title":"Serocalculator package tutorial","text":"Output calculator passed function summary calculating output results: summary function returns list objects well. contains sub-objects Antibodies, Strata, CensorLimits, Quantiles Results. important sub-object output summary functions Results, containing estimates Lambda (annual incidence) together lower upper bounds (Lambda.lwr Lambda.upr, respectively). default lower bound 2.5% upper bound 97.5% distribution Lambda. values can changed (see examples). Optionally, summary function returns also Deviance (negative log likelihood estimated Lambda) Convergence (indicator returned function stats::optim; value 0 indicates convergence). may beneficial assign output function variable analysis: one may note dataframe six columns can later post-processed custom calculations.","code":"summary(seroincidenceData) ## Seroincidence estimated given the following setup: ## a) Antibodies : IgG, IgM, IgA ## b) Strata : AgeCat ## c) Censor limits: IgG = 0.1, IgM = 0.1, IgA = 0.1 ## d) Quantiles : 0.025, 0.975 ## ## Seroincidence estimates: ## Lambda.est Lambda.lwr Lambda.upr Deviance Convergence Stratum ## 1 0.09721890 0.06397004 0.14774909 105.29815 0 1 ## 2 0.05497303 0.03573489 0.08456817 181.90627 0 2 ## 3 0.04456096 0.02845903 0.06977327 199.99357 0 3 ## 4 0.02944038 0.01715302 0.05052964 141.32558 0 4 ## 5 0.02569781 0.01436524 0.04597053 123.33213 0 5 ## 6 0.02565297 0.01587275 0.04145941 187.41060 0 6 ## 7 0.03458616 0.02012722 0.05943207 128.84486 0 7 ## 8 0.02776873 0.02640621 0.02920154 133.57166 52 8 ## 9 0.04016261 0.02133209 0.07561541 94.99389 0 9 ## 10 0.01996477 0.01898409 0.02099612 104.30992 52 10 ## 11 0.03356017 0.01917771 0.05872886 141.20168 0 11 ## 12 0.05287648 0.03263923 0.08566140 181.68655 0 12 ## 13 0.05081097 0.02946963 0.08760729 123.66670 52 13 ## 14 0.04813104 0.02945312 0.07865371 186.92218 0 14 ## 15 0.03421042 0.02210895 0.05293569 215.78684 0 15 # Compute seroincidence summary and assign to object \"seroincidenceSummary\". seroincidenceSummary <- summary(seroincidenceData) # Show the results. seroincidenceSummary$Results ## Lambda.est Lambda.lwr Lambda.upr Deviance Convergence Stratum ## 1 0.09721890 0.06397004 0.14774909 105.29815 0 1 ## 2 0.05497303 0.03573489 0.08456817 181.90627 0 2 ## 3 0.04456096 0.02845903 0.06977327 199.99357 0 3 ## 4 0.02944038 0.01715302 0.05052964 141.32558 0 4 ## 5 0.02569781 0.01436524 0.04597053 123.33213 0 5 ## 6 0.02565297 0.01587275 0.04145941 187.41060 0 6 ## 7 0.03458616 0.02012722 0.05943207 128.84486 0 7 ## 8 0.02776873 0.02640621 0.02920154 133.57166 52 8 ## 9 0.04016261 0.02133209 0.07561541 94.99389 0 9 ## 10 0.01996477 0.01898409 0.02099612 104.30992 52 10 ## 11 0.03356017 0.01917771 0.05872886 141.20168 0 11 ## 12 0.05287648 0.03263923 0.08566140 181.68655 0 12 ## 13 0.05081097 0.02946963 0.08760729 123.66670 52 13 ## 14 0.04813104 0.02945312 0.07865371 186.92218 0 14 ## 15 0.03421042 0.02210895 0.05293569 215.78684 0 15"},{"path":"https://ucd-serg.github.io/serocalculator/articles/tutorial.html","id":"other-examples","dir":"Articles","previous_headings":"","what":"4. Other examples","title":"Serocalculator package tutorial","text":"following examples show standard procedure estimating seroincidence.","code":"# 1. Define cross-sectional data. serologyData <- with(campylobacterSimMediumData, data.frame( IgG = IgG.ratio.new, IgM = IgM.ratio.new, IgA = IgA.ratio.new)) # 2. Define longitudinal response data responseParams <- campylobacterDelftParams3 # 3. Define cut-offs cutOffs <- list(IgG = 0.25, IgM = 0.25, IgA = 0.25) # 4. Baseline distributions baseLine <- list(IgG = c(log(0.05), 1), IgM = c(log(0.005), 1), IgA = c(log(0.005), 1)) # 4a. Calculate a single seroincidence rate for all serum samples... seroincidenceData <- estimateSeroincidence( data = serologyData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = responseParams, censorLimits = cutOffs, par0 = baseLine) # 4b. ...or calculate a single seroincidence rate for a single serum sample # (triplet of titres)... seroincidenceData <- estimateSeroincidence( data = serologyData[1, ], antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = responseParams, censorLimits = cutOffs, par0 = baseLine) # 4c. ...or calculate a single seroincidence rate for all serum samples (only IgG) seroincidenceData <- estimateSeroincidence( data = serologyData, antibodies = c(\"IgG\"), strata = \"\", params = responseParams, censorLimits = cutOffs, par0 = baseLine) # 5a. Produce summary of the results with 2.5% and 97.5% bounds... summary(seroincidenceData) # 5b. ...or produce summary of the results with 5% and 95% bounds, do not show # convergence... summary(seroincidenceData, quantiles = c(0.05, 0.95), showConvergence = FALSE) # 5c. ...or produce summary and assign to an object... seroincidenceSummary <- summary(seroincidenceData) # ...and work with the results object from now on (here: display the results). seroincidenceSummary$Results"},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Peter Teunis. Author, copyright holder. Author method original code. Kristina Lai. Author. Kristen Aiemjoy. Author. Douglas Ezra Morrison. Author, maintainer.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Teunis P, Lai K, Aiemjoy K, Morrison D (2023). serocalculator: Estimating Infection Rates Serological Data. https://github.com/UCD-SERG/serocalculator, https://ucd-serg.github.io/serocalculator/.","code":"@Manual{, title = {serocalculator: Estimating Infection Rates from Serological Data}, author = {Peter Teunis and Kristina Lai and Kristen Aiemjoy and Douglas Ezra Morrison}, year = {2023}, note = {https://github.com/UCD-SERG/serocalculator, https://ucd-serg.github.io/serocalculator/}, }"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"serocalculator-package","dir":"","previous_headings":"","what":"Estimating Infection Rates from Serological Data","title":"Estimating Infection Rates from Serological Data","text":"Antibody levels measured (cross–sectional) population sample can translated estimate frequency seroconversions (infections) occur sampled population. Formulated simply: presence many high titres indicates many subjects likely experienced infection recently, low titres indicate low frequency infections sampled population. serocalculator script designed use longitudinal response characteristics means set parameters characterizing longitudinal response selected serum antibodies.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Estimating Infection Rates from Serological Data","text":"can install development version GitHub following code: Note Windows Users Windows users need install Rtools, contains collection tools building employing R packages still development. can done either devtools package installation, independently devtools already installed.  devtools installation:    prompted install additional build tools, select “Yes” Rtools installed. | !(Click Yes install Rtools along devtools package)[1.png] |  Independently: Download Rtools https://cran.r-project.org/bin/windows/Rtools/ Run installer Rtools installation may see window asking “Select Additional Tasks”. select box “Edit system PATH”. devtools RStudio put Rtools PATH automatically needed. select box “Save version information registry”. selected default. | ## Getting Help need assistance encounter clear bug, please file issue minimal reproducible example GitHub. Another great resource Epidemiologist R Handbook, includes introductory page asking help R packages via GitHub: https://epirhandbook.com/en/getting-help.html","code":"install.packages(\"devtools\") devtools::install_github(\"ucd-serg/serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"","code":"campylobacterDelftParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"list three dataframes: IgA dataframe containing 4000 rows 7 parameters IgA antibody. IgM dataframe containing 4000 rows 7 parameters IgM antibody. IgG dataframe containing 4000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter Delft Response Parameters Data for Model 1 — campylobacterDelftParams1","text":"","code":"# Show first rows of every dataframe contained in campylobacterDelftParams1 lapply(campylobacterDelftParams1, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.9790816 0.005617736 0 1 NA NA 0 #> 2 0.8561027 0.005686470 0 1 NA NA 0 #> 3 0.8115014 0.005653234 0 1 NA NA 0 #> 4 1.6246947 0.007071109 0 1 NA NA 0 #> 5 1.6912089 0.007544257 0 1 NA NA 0 #> 6 7.4984300 0.011753968 0 1 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 1.4153120 0.004264498 0 1 NA NA 0 #> 2 0.6276850 0.002730359 0 1 NA NA 0 #> 3 3.4163114 0.008804830 0 1 NA NA 0 #> 4 1.8461903 0.005448317 0 1 NA NA 0 #> 5 0.7790359 0.002590766 0 1 NA NA 0 #> 6 0.3165559 0.001623372 0 1 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 12.012193 0.003507050 0 1 NA NA 0 #> 2 11.361843 0.001877301 0 1 NA NA 0 #> 3 6.644749 0.001918942 0 1 NA NA 0 #> 4 12.757673 0.001004286 0 1 NA NA 0 #> 5 10.514934 0.001570312 0 1 NA NA 0 #> 6 8.421454 0.003307627 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"","code":"campylobacterDelftParams3"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"list three dataframes: IgA dataframe containing 4000 rows 7 parameters IgA antibody. IgM dataframe containing 4000 rows 7 parameters IgM antibody. IgG dataframe containing 4000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter Delft Response Parameters Data for Model 3 — campylobacterDelftParams3","text":"","code":"# Show first rows of every dataframe contained in campylobacterDelftParams3 lapply(campylobacterDelftParams3, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.9790816 0.005617736 0 1 0.5968037 0.16152280 3.0647487 #> 2 0.8561027 0.005686470 0 1 0.6490095 0.14953802 1.8519904 #> 3 0.8115014 0.005653234 0 1 0.3162894 0.20908627 4.5064094 #> 4 1.6246947 0.007071109 0 1 0.7882382 0.29381913 2.4616328 #> 5 1.6912089 0.007544257 0 1 0.5962426 0.25043491 4.1629632 #> 6 7.4984300 0.011753968 0 1 7.3277274 0.06639833 0.3468196 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 1.4153120 0.004264498 0 1 0.6213528 0.2079336 3.958986 #> 2 0.6276850 0.002730359 0 1 0.3697257 0.1366566 3.873045 #> 3 3.4163114 0.008804830 0 1 0.2768008 0.2385225 10.535773 #> 4 1.8461903 0.005448317 0 1 0.9193840 0.3195337 2.181853 #> 5 0.7790359 0.002590766 0 1 0.5470029 0.0860775 4.107961 #> 6 0.3165559 0.001623372 0 1 0.2296537 0.0809985 3.962139 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 12.012193 0.003507050 0 1 1.140502 0.3664422 6.425171 #> 2 11.361843 0.001877301 0 1 1.578391 0.3105795 6.355391 #> 3 6.644749 0.001918942 0 1 1.138631 0.3945647 4.470751 #> 4 12.757673 0.001004286 0 1 2.809073 0.2565113 5.899462 #> 5 10.514934 0.001570312 0 1 1.277037 0.6905252 3.053117 #> 6 8.421454 0.003307627 0 1 1.181497 0.3235032 6.071037 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"","code":"campylobacterDelftParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterDelftParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter Delft Response Parameters Data for Model 4 — campylobacterDelftParams4","text":"","code":"# Show first rows of every dataframe contained in campylobacterDelftParams4 lapply(campylobacterDelftParams4, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 2.7471594 0.0235920904 0 2.773443 0.06452524 0.2737697 13.702269 #> 2 0.3361973 0.0001511561 0 1.006182 0.13241137 0.6774068 1.375517 #> 3 1.3402570 0.0072306458 0 1.640734 0.12395023 0.3883815 6.129892 #> 4 7.8335091 0.0028906244 0 2.768401 0.06512216 0.3482461 13.754356 #> 5 0.3069594 0.0029306951 0 1.050954 0.07870162 0.4218624 3.226293 #> 6 1.6315826 0.0044291436 0 1.740029 0.12264517 0.5924527 4.368299 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 3.0218848 0.0068170529 0 1.073372 0.09900034 0.3980767 8.587573 #> 2 1.4605254 0.0004345318 0 1.042603 0.07462244 0.4741715 6.272224 #> 3 2.1067968 0.0043837064 0 1.068606 0.10507351 0.4748134 6.314615 #> 4 1.1187181 0.0019154929 0 1.071340 0.09971466 0.3124635 7.737306 #> 5 0.4222472 0.0005651563 0 1.000181 0.12935880 0.5040532 2.346977 #> 6 4.5818356 0.0158849155 0 1.071558 0.10262796 0.6777377 5.605037 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 27.861427 0.0051607511 0 1.019014 0.07791154 0.7296875 8.057456 #> 2 3.306758 0.0011913102 0 1.099056 0.05077868 0.8435221 4.950963 #> 3 18.791581 0.0006869688 0 1.010531 0.09212040 0.6116469 8.694670 #> 4 5.866064 0.0006929407 0 1.019739 0.07694126 0.5232645 8.282421 #> 5 14.364930 0.0035222413 0 1.092096 0.05110497 0.9445758 5.969519 #> 6 5.790477 0.0003697673 0 1.010602 0.09127834 0.7696968 5.391807 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"","code":"campylobacterSSIParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"list three dataframes: IgA dataframe containing 4000 rows 7 parameters IgA antibody. IgM dataframe containing 4000 rows 7 parameters IgM antibody. IgG dataframe containing 4000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter SSI Response Parameters Data for Model 1 — campylobacterSSIParams1","text":"","code":"# Show first rows of every dataframe contained in campylobacterSSIParams1 lapply(campylobacterSSIParams1, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.7003339 0.022609111 0 1 NA NA 0 #> 2 0.4881885 0.021309170 0 1 NA NA 0 #> 3 0.7607791 0.016486080 0 1 NA NA 0 #> 4 1.5342800 0.014820666 0 1 NA NA 0 #> 5 0.2454033 0.005397104 0 1 NA NA 0 #> 6 0.9043005 0.017098452 0 1 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 0.8079065 0.0012365599 0 1 NA NA 0 #> 2 0.4662612 0.0010868782 0 1 NA NA 0 #> 3 0.9829273 0.0013366391 0 1 NA NA 0 #> 4 0.1463502 0.0009022539 0 1 NA NA 0 #> 5 0.1940006 0.0005063741 0 1 NA NA 0 #> 6 0.7127425 0.0013184843 0 1 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 1.5841532 0.001312580 0 1 NA NA 0 #> 2 1.8657514 0.001438733 0 1 NA NA 0 #> 3 1.3126431 0.001351047 0 1 NA NA 0 #> 4 1.1131536 0.001048270 0 1 NA NA 0 #> 5 1.4794378 0.001274966 0 1 NA NA 0 #> 6 0.9348555 0.001437053 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"","code":"campylobacterSSIParams2"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter SSI Response Parameters Data for Model 2 — campylobacterSSIParams2","text":"","code":"# Show first rows of every dataframe contained in campylobacterSSIParams2 lapply(campylobacterSSIParams2, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.3790329 0.013723334 0 1.180966 NA NA 0 #> 2 1.2994481 0.005642420 0 1.212107 NA NA 0 #> 3 9.4465428 0.011623566 0 1.205368 NA NA 0 #> 4 1.0094504 0.027030054 0 1.114516 NA NA 0 #> 5 0.7431767 0.004296025 0 1.210028 NA NA 0 #> 6 0.5895802 0.005579473 0 1.219622 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 2.0649209 0.011219203 0 1.163448 NA NA 0 #> 2 0.5802861 0.001512339 0 1.140606 NA NA 0 #> 3 1.0067090 0.016613231 0 1.197256 NA NA 0 #> 4 0.6119739 0.013465649 0 1.144594 NA NA 0 #> 5 0.6514105 0.002957626 0 1.142666 NA NA 0 #> 6 0.3944259 0.021869072 0 1.136722 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 1.3482386 0.0025760745 0 1.160319 NA NA 0 #> 2 0.7507566 0.0009904948 0 1.163093 NA NA 0 #> 3 1.5366781 0.0027693686 0 1.169187 NA NA 0 #> 4 1.3857968 0.0060489554 0 1.142833 NA NA 0 #> 5 1.1860646 0.0023857957 0 1.155256 NA NA 0 #> 6 1.6407579 0.0053201985 0 1.148251 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"","code":"campylobacterSSIParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSSIParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Campylobacter SSI Response Parameters Data for Model 4 — campylobacterSSIParams4","text":"","code":"# Show first rows of every dataframe contained in campylobacterSSIParams4 lapply(campylobacterSSIParams4, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.3790329 0.013723334 0 1.180966 0.3084470 0.04388666 4.695573 #> 2 1.2994481 0.005642420 0 1.212107 0.8601382 0.12999444 3.173996 #> 3 9.4465428 0.011623566 0 1.205368 1.0658161 0.23740385 9.190702 #> 4 1.0094504 0.027030054 0 1.114516 0.9711994 0.03547790 1.088834 #> 5 0.7431767 0.004296025 0 1.210028 0.6803783 0.04747750 1.859510 #> 6 0.5895802 0.005579473 0 1.219622 0.4211322 0.13267245 2.536050 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 2.0649209 0.011219203 0 1.163448 1.3425809 0.09497971 4.5325273 #> 2 0.5802861 0.001512339 0 1.140606 0.4028595 0.10778318 3.3858110 #> 3 1.0067090 0.016613231 0 1.197256 0.8758258 0.15130671 0.9204797 #> 4 0.6119739 0.013465649 0 1.144594 0.4820747 0.07959144 2.9976911 #> 5 0.6514105 0.002957626 0 1.142666 0.6132294 0.02129935 2.8358109 #> 6 0.3944259 0.021869072 0 1.136722 0.3087485 0.04117147 5.9483978 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 1.3482386 0.0025760745 0 1.160319 0.7311131 0.09686797 6.317734 #> 2 0.7507566 0.0009904948 0 1.163093 0.5792900 0.05599269 4.630574 #> 3 1.5366781 0.0027693686 0 1.169187 0.4715269 0.30069910 3.928852 #> 4 1.3857968 0.0060489554 0 1.142833 0.8390058 0.20711698 2.422848 #> 5 1.1860646 0.0023857957 0 1.155256 0.9270293 0.06708116 3.673325 #> 6 1.6407579 0.0053201985 0 1.148251 1.2162005 0.12496860 2.396015 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated Cross-sectional Data — campylobacterSimLowData","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"Simulated cross-sectional population sample antibody levels data Campylobacter Pertussis lambda 0.036/yr (low), 0.021/yr (medium) 1.15/yr (high).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"","code":"campylobacterSimLowData"},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"data frame 500 observations following 2 4 variables: Age IgG IgM IgA","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/campylobacterSimLowData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulated Cross-sectional Data — campylobacterSimLowData","text":"","code":"# Show first rows of the data head(campylobacterSimLowData) #> Age IgG IgM IgA #> 1 11871.16 5.532551e-02 2.131281e-03 7.027718e-02 #> 2 7869.00 9.195770e-02 1.303805e-01 1.502870e-01 #> 3 680.00 9.589740e-02 1.992161e-01 1.267946e-01 #> 4 23734.28 2.032342e-01 2.371293e-07 2.956911e-07 #> 5 16968.07 4.604123e-13 1.929238e-05 1.691724e-03 #> 6 769.00 1.311047e-01 1.070782e-01 1.442210e-01 # Summarize the data summary(campylobacterSimLowData) #> Age IgG IgM IgA #> Min. : 42 Min. :0.00000 Min. :0.0000000 Min. :0.0000000 #> 1st Qu.: 6926 1st Qu.:0.02379 1st Qu.:0.0007472 1st Qu.:0.0005475 #> Median :12577 Median :0.12649 Median :0.0445646 Median :0.0195426 #> Mean :13178 Mean :0.22354 Mean :0.0970820 Mean :0.0672308 #> 3rd Qu.:19734 3rd Qu.:0.19127 3rd Qu.:0.1334760 3rd Qu.:0.1295172 #> Max. :27342 Max. :2.59219 Max. :2.1736800 Max. :0.8397106"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log-likelihood — .nll","title":"Calculate log-likelihood — .nll","text":"Calculate log-likelihood","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log-likelihood — .nll","text":"","code":".nll( stratumData, antibodies, params, censorLimits, ivc = FALSE, m = 0, par0, start )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log-likelihood — .nll","text":"stratumData Data frame cross-sectional serology data per antibody age, additional columns, one stratum antibodies Character vector one antibody names. Values must match data. params List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type. censorLimits List cutoffs one named antibody types (corresponding stratumData). ivc ivc = TRUE, biomarker data interval-censored. m parameter's meaning uncertain par0 List parameters (lognormal) distribution antibody concentrations true seronegatives (.e. never seroconverted), named antibody type (corresponding data). start starting value log(lambda). Value -6 corresponds roughly 1 day (log(1/365.25)), -4 corresponds roughly 1 week (log(7 / 365.25)). Default = -6.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log-likelihood — .nll","text":"log-likelihood data current parameter values","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Seroincidence — estimateSeroincidence","title":"Estimate Seroincidence — estimateSeroincidence","text":"Function estimate seroincidences based cross-section serology data longitudinal response model.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Seroincidence — estimateSeroincidence","text":"","code":"estimateSeroincidence( data, antibodies, strata = \"\", params, censorLimits, par0, start = -6, numCores = 1L )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Seroincidence — estimateSeroincidence","text":"data Data frame cross-sectional serology data per antibody age, additional columns identify possible strata. antibodies Character vector one antibody names. Values must match data. strata Character vector strata. Values must match data. Default = \"\". params List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type. censorLimits List cutoffs one named antibody types (corresponding data). par0 List parameters (lognormal) distribution antibody concentrations true seronegatives (.e. never seroconverted), named antibody type (corresponding data). start starting value log(lambda). Value -6 corresponds roughly 1 day (log(1/365.25)), -4 corresponds roughly 1 week (log(7 / 365.25)). Default = -6. numCores Number processor cores use calculations computing strata. set 1 package parallel available, computations executed parallel. Default = 1L.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Seroincidence — estimateSeroincidence","text":"set lambda estimates strata.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/estimateSeroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Seroincidence — estimateSeroincidence","text":"","code":"if (FALSE) { estimateSeroincidence(data = csData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = campylobacterDelftParams4, censorLimits = cutOffs, par0 = baseLn, start = -4) estimateSeroincidence(data = csData, antibodies = c(\"IgG\", \"IgM\", \"IgA\"), strata = \"\", params = campylobacterDelftParams4, censorLimits = cutOffs, par0 = baseLn, start = -4, numCores = parallel::detectCores()) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate negative log-likelihood (deviance) — fdev","title":"Calculate negative log-likelihood (deviance) — fdev","text":"description added ","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate negative log-likelihood (deviance) — fdev","text":"","code":"fdev(log.lambda, csdata, lnpars, cond)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate negative log-likelihood (deviance) — fdev","text":"log.lambda Initial guess incidence rate csdata cross-sectional sample data lnpars longitudinal antibody decay model parameters cond measurement noise parameters","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Additional Data — getAdditionalData","title":"Get Additional Data — getAdditionalData","text":"Retrieves additional data internet. can file type, purpose function download data longitudinal response parameters online repository.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Additional Data — getAdditionalData","text":"","code":"getAdditionalData( fileName, repoURL = \"http://ecdc.europa.eu/sites/portal/files/documents\", savePath = NULL )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Additional Data — getAdditionalData","text":"fileName Name file download. Required. repoURL Web address remote repository files download . Required. Default = \"http://ecdc.europa.eu/sites/portal/files/documents\" savePath Folder save downloaded unzipped (needed) file. File saved argument NULL. Optional. Default = NULL.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Additional Data — getAdditionalData","text":"Data object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Additional Data — getAdditionalData","text":"","code":"if (FALSE) { getAdditionalData(fileName = \"coxiellaIFAParams4.zip\") getAdditionalData(fileName = \"yersiniaSSIParams4.zip\") getAdditionalData(fileName = \"coxiellaIFAParams4.zip\", savePath = getwd()) getAdditionalData(fileName = \"yersiniaSSIParams4.zip\", savePath = getwd()) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"","code":"pertussisIgGPTParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 1 — pertussisIgGPTParams1","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams1 lapply(pertussisIgGPTParams1, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 309.95227 0.0013582931 0 1 NA NA 0 #> 2 351.07244 0.0014761587 0 1 NA NA 0 #> 3 61.72439 0.0003486033 0 1 NA NA 0 #> 4 1103.35501 0.0023628598 0 1 NA NA 0 #> 5 1402.26889 0.0040798615 0 1 NA NA 0 #> 6 812.85619 0.0012771807 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"","code":"pertussisIgGPTParams2"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 2 — pertussisIgGPTParams2","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams2 lapply(pertussisIgGPTParams2, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 9338.5182 3.908447e-06 0 1.984440 NA NA 0 #> 2 608.0304 1.322816e-05 0 2.380790 NA NA 0 #> 3 926.2819 2.797526e-06 0 2.291821 NA NA 0 #> 4 5023.5881 2.141244e-05 0 1.897520 NA NA 0 #> 5 6357.7694 7.986787e-06 0 1.869652 NA NA 0 #> 6 656075.6374 1.876985e-06 0 2.326428 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"","code":"pertussisIgGPTParams3"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 3 — pertussisIgGPTParams3","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams3 lapply(pertussisIgGPTParams3, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 309.95227 0.0013582931 0 1 0.8858853 0.6482737 9.035669 #> 2 351.07244 0.0014761587 0 1 0.4461476 0.1958226 34.051720 #> 3 61.72439 0.0003486033 0 1 0.7276450 0.2298031 19.323595 #> 4 1103.35501 0.0023628598 0 1 0.4962986 0.1398534 55.105486 #> 5 1402.26889 0.0040798615 0 1 0.5894248 0.6389777 12.167020 #> 6 812.85619 0.0012771807 0 1 1.0949915 1.0617529 6.225373 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"","code":"pertussisIgGPTParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"dataframe IgG containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/pertussisIgGPTParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pertussis IgG-PT Response Parameters Data for Model 4 — pertussisIgGPTParams4","text":"","code":"# Show first rows of every dataframe contained in pertussisIgGPTParams4 lapply(pertussisIgGPTParams4, head) #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 9338.5182 3.908447e-06 0 1.984440 0.3679380 0.3642614 27.841941 #> 2 608.0304 1.322816e-05 0 2.380790 0.4518361 0.2308821 31.204939 #> 3 926.2819 2.797526e-06 0 2.291821 0.4148741 1.0713510 7.197416 #> 4 5023.5881 2.141244e-05 0 1.897520 0.3258315 0.2198684 43.859306 #> 5 6357.7694 7.986787e-06 0 1.869652 0.6074594 0.3787113 24.440524 #> 6 656075.6374 1.876985e-06 0 2.326428 0.2303021 0.4318404 34.416405 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for Seroincidence Object — print.seroincidence","title":"Print Method for Seroincidence Object — print.seroincidence","text":"Custom print() function show output seroincidence calculator estimateSeroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for Seroincidence Object — print.seroincidence","text":"","code":"# S3 method for seroincidence print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for Seroincidence Object — print.seroincidence","text":"x list containing output function estimateSeroincidence(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for Seroincidence Object — print.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- estimateSeroincidence(...) # print the seroincidence object to the console print(seroincidence) # or simply type (appropriate print method will be invoked automatically) seroincidence }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"Custom print() function show output seroincidence summary summary.seroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"","code":"# S3 method for summary.seroincidence print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"x list containing output function summary.seroincidence(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- estimateSeroincidence(...) # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence) # print the summary of seroincidence object to the console print(seroincidenceSummary) # or simply type (appropriate print method will be invoked automatically) seroincidenceSummary }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":null,"dir":"Reference","previous_headings":"","what":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"","code":"salmonellaSSIParams1"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Salmonella SSI Response Parameters Data for Model 1 — salmonellaSSIParams1","text":"","code":"# Show first rows of every dataframe contained in salmonellaSSIParams1 lapply(salmonellaSSIParams1, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.8846559 0.008526753 0 1 NA NA 0 #> 2 1.0830794 0.012876120 0 1 NA NA 0 #> 3 0.8741444 0.008709623 0 1 NA NA 0 #> 4 1.5351236 0.003502516 0 1 NA NA 0 #> 5 0.3268195 0.002556930 0 1 NA NA 0 #> 6 0.1963830 0.020255418 0 1 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 2.223399974 0.0707174863 0 1 NA NA 0 #> 2 3.496295237 0.0028553885 0 1 NA NA 0 #> 3 0.749391784 0.0008170739 0 1 NA NA 0 #> 4 0.624004098 0.0027294130 0 1 NA NA 0 #> 5 0.008570974 0.0020008248 0 1 NA NA 0 #> 6 1.241508561 0.0009053516 0 1 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 0.6078662 4.586619e-02 0 1 NA NA 0 #> 2 0.8793232 2.602733e-02 0 1 NA NA 0 #> 3 0.2713987 7.542284e-04 0 1 NA NA 0 #> 4 2.1483480 5.438996e+02 0 1 NA NA 0 #> 5 0.1096123 3.154008e-03 0 1 NA NA 0 #> 6 1.1052943 1.190809e-02 0 1 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":null,"dir":"Reference","previous_headings":"","what":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"","code":"salmonellaSSIParams2"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Salmonella SSI Response Parameters Data for Model 2 — salmonellaSSIParams2","text":"","code":"# Show first rows of every dataframe contained in salmonellaSSIParams2 lapply(salmonellaSSIParams2, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.1645894 0.008301086 0 1.020289 NA NA 0 #> 2 0.4967038 0.007181714 0 1.021778 NA NA 0 #> 3 2.5087166 0.001178859 0 1.017500 NA NA 0 #> 4 1.8760033 0.018867951 0 1.020985 NA NA 0 #> 5 0.2381400 0.003545378 0 1.018420 NA NA 0 #> 6 0.1075755 0.001709320 0 1.017080 NA NA 0 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 0.6325777 0.0028957411 0 1.047555 NA NA 0 #> 2 1.4144767 0.0328629331 0 1.039616 NA NA 0 #> 3 1.2642730 0.0008913089 0 1.051353 NA NA 0 #> 4 1.0383240 0.0024629578 0 1.056098 NA NA 0 #> 5 1.9768815 0.0031380379 0 1.052206 NA NA 0 #> 6 1.0602443 0.0140293204 0 1.049931 NA NA 0 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 0.5630924 0.003808514 0 1.017216 NA NA 0 #> 2 0.9084731 0.004009900 0 1.021487 NA NA 0 #> 3 1.3531416 0.001163992 0 1.013681 NA NA 0 #> 4 0.7098593 0.004429380 0 1.021024 NA NA 0 #> 5 0.9199583 0.002791685 0 1.020375 NA NA 0 #> 6 8.4187094 0.003258891 0 1.017908 NA NA 0 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":null,"dir":"Reference","previous_headings":"","what":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"List data frames longitudinal parameters. data frame contains Monte Carlo samples antibody type.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"","code":"salmonellaSSIParams4"},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"list three dataframes: IgA dataframe containing 3000 rows 7 parameters IgA antibody. IgM dataframe containing 3000 rows 7 parameters IgM antibody. IgG dataframe containing 3000 rows 7 parameters IgG antibody.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/salmonellaSSIParams4.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Salmonella SSI Response Parameters Data for Model 4 — salmonellaSSIParams4","text":"","code":"# Show first rows of every dataframe contained in salmonellaSSIParams4 lapply(salmonellaSSIParams4, head) #> $IgA #> y1 alpha yb r y0 mu1 t1 #> 1 0.1645894 0.008301086 0 1.020289 0.11973340 0.12222920 2.6031943 #> 2 0.4967038 0.007181714 0 1.021778 0.39052803 0.14802164 1.6247227 #> 3 2.5087166 0.001178859 0 1.017500 2.37886174 0.17727577 0.2998108 #> 4 1.8760033 0.018867951 0 1.020985 1.81306536 0.15521542 0.2198533 #> 5 0.2381400 0.003545378 0 1.018420 0.21870973 0.11699959 0.7274678 #> 6 0.1075755 0.001709320 0 1.017080 0.09313794 0.08910887 1.6172527 #> #> $IgM #> y1 alpha yb r y0 mu1 t1 #> 1 0.6325777 0.0028957411 0 1.047555 0.5102927 0.07773161 2.7635934 #> 2 1.4144767 0.0328629331 0 1.039616 0.5753705 0.20969035 4.2896625 #> 3 1.2642730 0.0008913089 0 1.051353 1.1804183 0.07957789 0.8624049 #> 4 1.0383240 0.0024629578 0 1.056098 1.0077723 0.03253768 0.9178773 #> 5 1.9768815 0.0031380379 0 1.052206 1.2086867 0.11864577 4.1466813 #> 6 1.0602443 0.0140293204 0 1.049931 0.8761034 0.05988377 3.1856792 #> #> $IgG #> y1 alpha yb r y0 mu1 t1 #> 1 0.5630924 0.003808514 0 1.017216 0.5040835 0.11686141 0.9472916 #> 2 0.9084731 0.004009900 0 1.021487 0.7985884 0.25997719 0.4958881 #> 3 1.3531416 0.001163992 0 1.013681 0.7592344 0.17166853 3.3662183 #> 4 0.7098593 0.004429380 0 1.021024 0.6843716 0.06179318 0.5917449 #> 5 0.9199583 0.002791685 0 1.020375 0.8494778 0.16093737 0.4952642 #> 6 8.4187094 0.003258891 0 1.017908 3.5463722 1.59327615 0.5426124 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimating Infection Rates from Serological Data — serocalculator","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"package translates antibody levels measured (cross-sectional) population sample estimate frequency seroconversions (infections) occur sampled population.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"detailed documentation type following R console: vignette(\"installation\", package = \"serocalculator\") vignette(\"tutorial\", package = \"serocalculator\") vignette(\"methodology\", package = \"serocalculator\")","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Methods estimating seroincidence Teunis, P. F., van Eijkeren, J. C., Ang, C. W., van Duynhoven, Y. T., Simonsen, J. B., Strid, M. ., van Pelt, W. \"Biomarker dynamics: estimating infection rates serological data\" Statistics Medicine 31, . 20 (September 9, 2012): 2240--48. doi:10.1002/sim.5322. Simonsen, J., Molbak, K., Falkenhorst, G., Krogfelt, K. ., Linneberg, ., Teunis, P. F. \"Estimation incidences infectious diseases based antibody measurements\" Statistics Medicine 28, . 14 (June 30, 2009): 1882--95. doi:10.1002/sim.3592. Applications Monge, S., Teunis, P. F., Friesema, ., Franz, E., Ang, W., van Pelt, W., Mughini-Gras, L. \"Immune response-eliciting exposure Campylobacter vastly exceeds incidence clinically overt campylobacteriosis associated similar risk factors: nationwide serosurvey Netherlands\" Journal Infection, 2018, 1--7, doi:10.1016/j.jinf.2018.04.016 Kretzschmar, M., Teunis, P. F., Pebody, R. G. \"Incidence reproduction numbers pertussis: estimates serological social contact data five European countries\" PLoS Medicine 7, . 6 (June 1, 2010):e1000291. doi:10.1371/journal.pmed.1000291. Simonsen, J., Strid, M. ., Molbak, K., Krogfelt, K. ., Linneberg, ., Teunis, P. \"Sero-epidemiology tool study incidence Salmonella infections humans\" Epidemiology Infection 136, . 7 (July 1, 2008): 895--902. doi:10.1017/S0950268807009314 Simonsen, J., Teunis, P. F., van Pelt, W., van Duynhoven, Y., Krogfelt, K. ., Sadkowska-Todys, M., Molbak K. \"Usefulness seroconversion rates comparing infection pressures countries\" Epidemiology Infection, April 12, 2010, 1--8. doi:10.1017/S0950268810000750. Falkenhorst, G., Simonsen, J., Ceper, T. H., van Pelt, W., de Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Jernberg, C., Rota, M. C., van Duynhoven, Y. T., Teunis, P. F., Krogfelt, K. ., Molbak, K. \"Serological cross-sectional studies salmonella incidence eight European countries: correlation incidence reported cases\" BMC Public Health 12, . 1 (July 15, 2012): 523--23. doi:10.1186/1471-2458-12-523. Teunis, P. F., Falkenhorst, G., Ang, C. W., Strid, M. ., De Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Rota, M. C., Simonsen, J. B., Molbak, K., Van Duynhoven, Y. T., van Pelt, W. \"Campylobacter seroconversion rates selected countries European Union\" Epidemiology Infection 141, . 10 (2013): 2051--57. doi:10.1017/S0950268812002774. de Melker, H. E., Versteegh, F. G., Schellekens, J. F., Teunis, P. F., Kretzschmar, M. \"incidence Bordetella pertussis infections estimated population combination serological surveys\" Journal Infection 53, . 2 (August 1, 2006): 106--13. doi:10.1016/j.jinf.2005.10.020 Quantification seroresponse de Graaf, W. F., Kretzschmar, M. E., Teunis, P. F., Diekmann, O. \"two-phase within-host model immune response application serological profiles pertussis\" Epidemics 9 (2014):1--7. doi:10.1016/j.epidem.2014.08.002. Berbers, G. ., van de Wetering, M. S., van Gageldonk, P. G., Schellekens, J. F., Versteegh, F. G., Teunis, P.F. \"novel method evaluating natural vaccine induced serological responses Bordetella pertussis antigens\" Vaccine 31, . 36 (August 12, 2013): 3732--38. doi:10.1016/j.vaccine.2013.05.073. Versteegh, F. G., Mertens, P. L., de Melker, H. E., Roord, J. J., Schellekens, J. F., Teunis, P. F. \"Age-specific long-term course IgG antibodies pertussis toxin symptomatic infection Bordetella pertussis\" Epidemiology Infection 133, . 4 (August 1, 2005): 737--48. Teunis, P. F., van der Heijden, O. G., de Melker, H. E., Schellekens, J. F., Versteegh, F. G., Kretzschmar, M. E. \"Kinetics IgG antibody response pertussis toxin infection B. pertussis\" Epidemiology Infection 129, . 3 (December 10, 2002):479. doi:10.1017/S0950268802007896.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Author: Peter Teunis p.teunis@emory.edu Author: Doug Ezra Morrison demorrison@ucdavis.edu Author: Kristen Aiemjoy kaiemjoy@ucdavis.edu Author: Kristina Lai kaiemjoy@ucdavis.edu","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Seroincidence Object — summary.seroincidence","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"Calculate seroincidence output seroincidence calculator estimateSeroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"","code":"# S3 method for seroincidence summary( object, ..., quantiles = c(0.025, 0.975), showDeviance = TRUE, showConvergence = TRUE )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"object dataframe containing output function estimateSeroincidence(). ... Additional arguments affecting summary produced. quantiles vector length 2 specifying quantiles lower (first element) upper (second element) bounds lambda. Default = c(0.025, 0.975). showDeviance Logical flag (FALSE/TRUE) reporting deviance (-2*log(likelihood) estimated seroincidence. Default = TRUE. showConvergence Logical flag (FALSE/TRUE) reporting convergence (see help optim() details). Default = TRUE.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"list following items: Results Dataframe maximum likelihood estimate lambda (seroincidence) (column Lambda) corresponding lower (Lambda.lwr) upper (Lambda.upr bounds. Optionally Deviance (Negative log likelihood (NLL) estimated (maximum likelihood) lambda) Covergence (Convergence indicator returned optim(). Value 0 indicates convergence) columns included. Antibodies Character vector names input antibodies used estimateSeroincidence(). Strata Character names strata used estimateSeroincidence(). CensorLimits List cutoffs antibodies used estimateSeroincidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Seroincidence Object — summary.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- estimateSeroincidence(...) # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence) }"}]