Update

package_check_build.R

@@ -45,7 +45,5 @@ install.packages(paste0(package_name, "_", ver, ".zip"), repos = NULL)
 install.packages(paste0(package_name, "_", ver, ".tar.gz"), repos = NULL)
 
 
-
 require(surveyplanning)
-export.genkopa("2018C4")
 

surveyplanning/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: surveyplanning
 Type: Package
 Title: Survey Planning Tools
-Version: 3.0
-Date: 2019-03-25
+Version: 4.0
+Date: 2020-05-20
 Depends: R (>= 3.0.0)
 Imports: data.table (>= 1.11.4), laeken, stats
 Authors@R: c(person("Juris", "Breidaks", email = "rcsb@csb.gov.lv",  role = c("aut", "cre")), person("Martins", "Liberts", role = "aut"), person("Janis", "Jukams", role = "aut"))
@@ -15,5 +15,4 @@ URL: https://csblatvia.github.io/surveyplanning/
 BugReports: https://github.com/CSBLatvia/surveyplanning/issues/
 NeedsCompilation: yes
 LazyData: true
-RoxygenNote: 6.1.1
-
+RoxygenNote: 7.1.0

surveyplanning/R/MoE_P.R

@@ -1,6 +1,6 @@
 #' Margin of error for proportion
 #'
-#' The function computes margin of error for proportion. The calculation takes into proportion, expected response rate and design effect.
+#' @description The function computes margin of error for proportion. The calculation takes into proportion, expected response rate and design effect.
 #'
 #' @param P The expected proportion for variable of interest.
 #' @param n The expected sample size.
@@ -17,7 +17,7 @@
 #' @keywords surveysampling
 #'
 #' @examples
-#' library(data.table)
+#' library("data.table")
 #' n <- 100
 #' pop <- 1000
 #'

surveyplanning/R/MoE_Y.R

@@ -1,6 +1,6 @@
 #' Margin of error for count
 #'
-#' The function computes margin of error for count. The calculation takes into proportion, expected response rate and design effect.
+#' @description The function computes margin of error for count. The calculation takes into proportion, expected response rate and design effect.
 #'
 #' @param P The expected proportion for variable of interest.
 #' @param n The expected sample size.
@@ -17,7 +17,7 @@
 #' @keywords surveysampling
 #'
 #' @examples
-#' library(data.table)
+#' library("data.table")
 #' n <- 100
 #' pop <- 1000
 #'

surveyplanning/R/dom_optimal_allocation.R

@@ -1,6 +1,6 @@
 #' Optimal sample size allocation
 #'
-#' The function computes optimal sample size allocation over strata and domain for population.
+#' @description The function computes optimal sample size allocation over strata and domain for population.
 #'
 #' @param id Variable for unit ID codes. One dimensional object convertible to one-column \code{data.table} or variable name as character, column number.
 #' @param Dom Optional variables used to define population domains. If supplied, values are calculated for each domain. An object convertible to \code{data.table} or variable names as character vector, column numbers.
@@ -95,9 +95,9 @@
 #' @keywords surveysampling
 #'
 #' @examples
-#' library(laeken)
-#' library(data.table)
-#' data(ses)
+#' library("laeken")
+#' library("data.table")
+#' data("ses")
 #' data <- data.table(ses)
 #' data[, H := paste(location, NACE1, size, sep = "_")]
 #' data[, id := .I]

surveyplanning/R/expsize.R

@@ -1,6 +1,6 @@
 #' Sample size calculation
 #'
-#' The function computes minimum sample size for each stratum to achieve defined precision (CV) for the estimates of totals in each stratum. The calculation takes into account expected totals, population variance, expected response rate and design effect in each stratum.
+#' @description The function computes minimum sample size for each stratum to achieve defined precision (CV) for the estimates of totals in each stratum. The calculation takes into account expected totals, population variance, expected response rate and design effect in each stratum.
 #'
 #' @param Yh The expected totals for variables of interest in each stratum. Object convertible to \code{data.table}, variable names as character vector, or column numbers.
 #' @param H The stratum variable. One dimensional object convertible to one-column \code{data.table}, variable name as character, or column number.
@@ -27,7 +27,7 @@
 #'
 #' @keywords surveysampling
 #' @examples
-#' library(data.table)
+#' library("data.table")
 #' data <- data.table(H = 1:3, Yh = 10 * 1:3,
 #'                    Yh1 = 10 * 4:6, s2h = 10 * runif(3),
 #'                    s2h2 = 10 * runif(3), CVh = rep(4.9,3),

surveyplanning/R/expvar.R

@@ -1,6 +1,6 @@
 #' Expected precision for the estimates of totals
 #'
-#' The function computes expected precision as variance, standard error, and coefficient of variation for the estimates.
+#' @description The function computes expected precision as variance, standard error, and coefficient of variation for the estimates.
 #'
 #' @param Yh The expected totals for variables of interest in each stratum. Object convertible to \code{data.table}, variable names as character vector, or column numbers.
 #' @param Zh Optional variables of denominator for the expected ratio estimation in each stratum. Object convertible to \code{data.table}, variable names as character vector, or column numbers.
@@ -58,7 +58,7 @@
 #' @keywords surveysampling
 #'
 #' @examples
-#' library(data.table)
+#' library("data.table")
 #' data <- data.table(H = 1:3, Yh = 10 * 1:3,
 #'                    Yh1 = 10 * 4:6, s2h = 10 * runif(3),
 #'                    s2h2 = 10 * runif(3), nh = rep(4 * 1:3),

surveyplanning/R/min_count.R

@@ -1,7 +1,6 @@
 #' Minimal count of respondents for the given relative margin of error
 #'
-#' The function computes minimal proportion for the given relative margin of error. The calculation takes into sample size, population size,
-#' margin of error, expected response rate and design effect.
+#' @description The function computes minimal proportion for the given relative margin of error. The calculation takes into sample size, population size, margin of error, expected response rate and design effect.
 #'
 #' @param n The expected sample size.
 #' @param pop Population size.
@@ -21,7 +20,7 @@
 #' min_count(n = 15e3, pop = 2e6, RMoE = 0.1)
 #'
 #' \dontrun{
-#' library(data.table)
+#' library("data.table")
 #' min_count(n = c(10e3, 15e3, 20e3), pop = 2e6, 0.1)
 #'
 #' n <- seq(10e3, 30e3, length.out = 11)

surveyplanning/R/min_prop.R

@@ -1,7 +1,6 @@
 #' Minimal proportion for the given relative margin of error
 #'
-#' The function computes minimal proportion for the given relative margin of error. The calculation takes into sample size, population size,
-#' margin of error, expected response rate and design effect.
+#' @description The function computes minimal proportion for the given relative margin of error. The calculation takes into sample size, population size, margin of error, expected response rate and design effect.
 #'
 #' @param n The expected sample size.
 #' @param pop Population size.

surveyplanning/R/optsize.R

@@ -1,6 +1,6 @@
 #' Optimal sample size allocation
 #'
-#' The function computes optimal sample size allocation over strata.
+#' @description The function computes optimal sample size allocation over strata.
 #'
 #' @section Details:
 #' If \code{s2h} and \code{Rh} is not defined, the sample allocation will be calculated as proportional allocation (proportional to the population size).
@@ -32,7 +32,7 @@
 #' @keywords surveysampling
 #'
 #' @examples
-#' library(data.table)
+#' library("data.table")
 #' data <- data.table(H = 1 : 3,
 #'                    s2h=10 * runif(3),
 #'                    s2h2 = 10 * runif(3),

surveyplanning/R/prop_dom_optimal_allocation.R

@@ -1,6 +1,6 @@
 #' Optimal sample size allocation for proportion
 #'
-#' The function computes optimal sample size allocation over strata and domain for proportion.
+#' @description The function computes optimal sample size allocation over strata and domain for proportion.
 #'
 #' @param H The stratum variable. One dimensional object convertible to one-column \code{data.table} or variable name as character, column number.
 #' @param Dom}{Variables used to define population domains. An object convertible to \code{data.table} or variable names as character vector, column numbers.
@@ -39,9 +39,9 @@
 #' @keywords surveysampling
 
 #' @examples
-#' library(data.table)
-#' library(laeken)
-#' data(eusilc)
+#' library("data.table")
+#' library("laeken")
+#' data("eusilc")
 #' eusilc <- data.table(eusilc)
 #' dataset <- eusilc[, .(poph = sum(db090)), by = c("db040")]
 #' dataset[, dom := "1"]

surveyplanning/R/round2.R

@@ -1,6 +1,6 @@
 #' Rounding numbers
 #'
-#' The function rounds the values in its first argument to the specified number of decimal places (default 0).
+#' @description The function rounds the values in its first argument to the specified number of decimal places (default 0).
 #'
 #' @param x a numeric vector.
 #' @param n integer indicating the number of decimal places.

surveyplanning/R/s2.R

@@ -1,6 +1,6 @@
 #' Population variance
 #'
-#' The function to estimate population variance \eqn{S^2}.
+#' @description The function to estimate population variance \eqn{S^2}.
 #'
 #' @section Details:
 #' If \code{w} is not defined, the result is equal to the result of the function \code{var}.
@@ -16,7 +16,7 @@
 #' all.equal(s2(1:10), var(1:10))
 #'
 #' @export s2
-#'
+
 
 # S^2 estimation
 s2 <- function(y, w = NULL) {