Organizes repeated count data along with the covariates. This S4 class is required by the data argument of pcount

unmarkedFramePCount(y, siteCovs=NULL, obsCovs=NULL, mapInfo)

Arguments

y

An RxJ matrix of the repeated count data, where R is the number of sites, J is the maximum number of sampling periods per site.

siteCovs

A data.frame of covariates that vary at the site level. This should have R rows and one column per covariate

obsCovs

Either a named list of data.frames of covariates that vary within sites, or a data.frame with RxJ rows in site-major order.

mapInfo

Currently ignored

Details

unmarkedFramePCount is the S4 class that holds data to be passed to the pcount model-fitting function.

Value

an object of class unmarkedFramePCount

See also

Examples

# Fake data R <- 4 # number of sites J <- 3 # number of visits y <- matrix(c( 1,2,0, 0,0,0, 1,1,1, 2,2,1), nrow=R, ncol=J, byrow=TRUE) y
#> [,1] [,2] [,3] #> [1,] 1 2 0 #> [2,] 0 0 0 #> [3,] 1 1 1 #> [4,] 2 2 1
site.covs <- data.frame(x1=1:4, x2=factor(c('A','B','A','B'))) site.covs
#> x1 x2 #> 1 1 A #> 2 2 B #> 3 3 A #> 4 4 B
obs.covs <- list( x3 = matrix(c( -1,0,1, -2,0,0, -3,1,0, 0,0,0), nrow=R, ncol=J, byrow=TRUE), x4 = matrix(c( 'a','b','c', 'd','b','a', 'a','a','c', 'a','b','a'), nrow=R, ncol=J, byrow=TRUE)) obs.covs
#> $x3 #> [,1] [,2] [,3] #> [1,] -1 0 1 #> [2,] -2 0 0 #> [3,] -3 1 0 #> [4,] 0 0 0 #> #> $x4 #> [,1] [,2] [,3] #> [1,] "a" "b" "c" #> [2,] "d" "b" "a" #> [3,] "a" "a" "c" #> [4,] "a" "b" "a" #>
umf <- unmarkedFramePCount(y=y, siteCovs=site.covs, obsCovs=obs.covs) # organize data
#> Warning: obsCovs contains characters. Converting them to factors.
umf # take a l
#> Data frame representation of unmarkedFrame object. #> y.1 y.2 y.3 x1 x2 x3.1 x3.2 x3.3 x4.1 x4.2 x4.3 #> 1 1 2 0 1 A -1 0 1 a b c #> 2 0 0 0 2 B -2 0 0 d b a #> 3 1 1 1 3 A -3 1 0 a a c #> 4 2 2 1 4 B 0 0 0 a b a
summary(umf) # summarize data
#> unmarkedFrame Object #> #> 4 sites #> Maximum number of observations per site: 3 #> Mean number of observations per site: 3 #> Sites with at least one detection: 3 #> #> Tabulation of y observations: #> 0 1 2 #> 4 5 3 #> #> Site-level covariates: #> x1 x2 #> Min. :1.00 A:2 #> 1st Qu.:1.75 B:2 #> Median :2.50 #> Mean :2.50 #> 3rd Qu.:3.25 #> Max. :4.00 #> #> Observation-level covariates: #> x3 x4 #> Min. :-3.0000 a:6 #> 1st Qu.:-0.2500 b:3 #> Median : 0.0000 c:2 #> Mean :-0.3333 d:1 #> 3rd Qu.: 0.0000 #> Max. : 1.0000
fm <- pcount(~1 ~1, umf, K=10) # fit a model