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# R package unmarked
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[](https://cran.r-project.org/package=unmarked)
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`unmarked` is an [R](https://www.r-project.org/) 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.
## Installation
The latest stable version of unmarked can be downloaded from
[CRAN](https://cran.r-project.org/package=unmarked):
``` r
install.packages("unmarked")
```
The latest development version can be installed from Github:
``` r
install.packages("remotes")
remotes::install_github("rbchan/unmarked")
```
## Support
Support is provided through the [unmarked Google
group](http://groups.google.com/group/unmarked). The package
[website](https://rbchan.github.io/unmarked) has more information. You
can report bugs [here](https://github.com/rbchan/unmarked/issues), by
posting to the Google group, or by emailing [the current
maintainer](https://kenkellner.com).
## Example analysis
Below we demonstrate a simple single-season occupancy analysis using
`unmarked`. First, load in a dataset from a CSV file and format:
``` r
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:
``` r
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:
``` r
(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
``` r
(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:
``` r
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:
``` r
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:
``` r
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
|
