NOTE: Active unmarked development is proceeding at https://github.com/hmecology/unmarked.
Please send all issues and pull requests to that repository.
unmarked is an R package for analyzing
ecological data arising from several popular sampling techniques. The
sampling methods include point counts, occurrence sampling, distance
sampling, removal, double observer, and many others. unmarked uses
hierarchical models to incorporate covariates of the latent abundance
(or occupancy) and imperfect detection processes.
The latest stable version of unmarked can be downloaded from CRAN:
install.packages("unmarked")The latest development version can be installed from Github:
install.packages("remotes")
remotes::install_github("rbchan/unmarked")Support is provided through the unmarked Google group. The package website has more information. You can report bugs here, by posting to the Google group, or by emailing the current maintainer.
Below we demonstrate a simple single-season occupancy analysis using
unmarked. First, load in a dataset from a CSV file and format:
library(unmarked)
wt <- read.csv(system.file("csv","widewt.csv", package="unmarked"))
# Presence/absence matrix
y <- wt[,2:4]
# Site and observation covariates
siteCovs <- wt[,c("elev", "forest", "length")]
obsCovs <- list(date=wt[,c("date.1", "date.2", "date.3")]) Create an unmarkedFrame, a special type of data.frame for unmarked
analyses:
umf <- unmarkedFrameOccu(y = y, siteCovs = siteCovs, obsCovs = obsCovs)
summary(umf)## unmarkedFrame Object
##
## 237 sites
## Maximum number of observations per site: 3
## Mean number of observations per site: 2.81
## Sites with at least one detection: 79
##
## Tabulation of y observations:
## 0 1 <NA>
## 483 182 46
##
## Site-level covariates:
## elev forest length
## Min. :-1.436125 Min. :-1.265352 Min. :0.1823
## 1st Qu.:-0.940726 1st Qu.:-0.974355 1st Qu.:1.4351
## Median :-0.166666 Median :-0.064987 Median :1.6094
## Mean : 0.007612 Mean : 0.000088 Mean :1.5924
## 3rd Qu.: 0.994425 3rd Qu.: 0.808005 3rd Qu.:1.7750
## Max. : 2.434177 Max. : 2.299367 Max. :2.2407
##
## Observation-level covariates:
## date
## Min. :-2.90434
## 1st Qu.:-1.11862
## Median :-0.11862
## Mean :-0.00022
## 3rd Qu.: 1.30995
## Max. : 3.80995
## NA's :42
Fit a null occupancy model and a model with covariates, using the occu
function:
(mod_null <- occu(~1~1, data=umf))##
## Call:
## occu(formula = ~1 ~ 1, data = umf)
##
## Occupancy:
## Estimate SE z P(>|z|)
## -0.665 0.139 -4.77 1.82e-06
##
## Detection:
## Estimate SE z P(>|z|)
## 1.32 0.174 7.61 2.82e-14
##
## AIC: 528.987
(mod_covs <- occu(~date~elev, data=umf))##
## Call:
## occu(formula = ~date ~ elev, data = umf)
##
## Occupancy:
## Estimate SE z P(>|z|)
## (Intercept) -0.738 0.157 -4.71 2.45e-06
## elev 0.885 0.174 5.10 3.49e-07
##
## Detection:
## Estimate SE z P(>|z|)
## (Intercept) 1.2380 0.180 6.869 6.47e-12
## date 0.0603 0.121 0.497 6.19e-01
##
## AIC: 498.158
Rank them using AIC:
fl <- fitList(null=mod_null, covs=mod_covs)
modSel(fl)## nPars AIC delta AICwt cumltvWt
## covs 4 498.16 0.00 1e+00 1.00
## null 2 528.99 30.83 2e-07 1.00
Estimate occupancy probability using the top-ranked model at the first six sites:
head(predict(mod_covs, type='state'))## Predicted SE lower upper
## 1 0.1448314 0.03337079 0.09080802 0.2231076
## 2 0.1499962 0.03351815 0.09535878 0.2280473
## 3 0.2864494 0.03346270 0.22555773 0.3562182
## 4 0.3035399 0.03371489 0.24175619 0.3733387
## 5 0.1607798 0.03374307 0.10502635 0.2382512
## 6 0.1842147 0.03392277 0.12669813 0.2600662
Predict occupancy probability at a new site with given covariate values:
nd <- data.frame(elev = 1.2)
predict(mod_covs, type="state", newdata=nd)## Predicted SE lower upper
## 1 0.5803085 0.06026002 0.4598615 0.6918922