unmarkedFrameOccuMS: Organize data for the multi-state occupancy model fit by...

In unmarked: Models for Data from Unmarked Animals

Organize data for the multi-state occupancy model fit by occuMS

Description

Organizes multi-state occupancy data (currently single-season only) along with covariates. This S4 class is required by the data argument of occuMS

Usage

unmarkedFrameOccuMS(y, siteCovs=NULL, obsCovs=NULL, 
                           numPrimary=1, yearlySiteCovs=NULL)

Arguments

y	An MxR matrix of multi-state occupancy data for a species, where M is the number of sites and R is the maximum number of observations per site (across all primary and secondary periods, if you have multi-season data). Values in y should be integers ranging from 0 (non-detection) to the number of total states - 1. For example, if you have 3 occupancy states, y should contain only values 0, 1, or 2.
siteCovs	A data.frame of covariates that vary at the site level. This should have M 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 MxR rows in the ordered by site-observation (if single-season) or site-primary period-observation (if multi-season).
numPrimary	Number of primary time periods (e.g. seasons) for the dynamic or multi-season version of the model. There should be an equal number of secondary periods in each primary period.
yearlySiteCovs	A data frame with one column per covariate that varies among sites and primary periods (e.g. years). It should have MxT rows where M is the number of sites and T the number of primary periods, ordered by site-primary period. These covariates only used for dynamic (multi-season) models.

Details

unmarkedFrameOccuMS is the S4 class that holds data to be passed to the occuMS model-fitting function.

Value

an object of class unmarkedFrameOccuMS

Author(s)

Ken Kellner [e-mail]

See Also

unmarkedFrame-class, unmarkedFrame, occuMS

Examples

# Fake data
#Parameters
N <- 100; J <- 3; S <- 3
psi <- c(0.5,0.3,0.2)
p11 <- 0.4; p12 <- 0.25; p22 <- 0.3

#Simulate state
z <- sample(0:2, N, replace=TRUE, prob=psi)

#Simulate detection
y <- matrix(0,nrow=N,ncol=J)
for (n in 1:N){
  probs <- switch(z[n]+1,
                  c(0,0,0),
                  c(1-p11,p11,0),
                  c(1-p12-p22,p12,p22))
  
  if(z[n]>0){
    y[n,] <- sample(0:2, J, replace=TRUE, probs)
  }
}

#Covariates
site_covs <- as.data.frame(matrix(rnorm(N*2),ncol=2)) # nrow = # of sites
obs_covs <- as.data.frame(matrix(rnorm(N*J*2),ncol=2)) # nrow = N*J

#Build unmarked frame
umf <- unmarkedFrameOccuMS(y=y,siteCovs=site_covs,obsCovs=obs_covs)

umf                     # look at data
summary(umf)            # summarize      
plot(umf)               # visualize
umf@numStates           # check number of occupancy states detected

unmarked documentation built on Sept. 11, 2024, 8:28 p.m.