unmarked (version 1.4.3)

unmarkedFrameOccuTTD: Create an unmarkedFrameOccuTTD object for the time-to-detection model fit by occuTTD

Description

Organizes time-to-detection occupancy data along with covariates. This S4 class is required by the data argument of occuTTD

Usage

unmarkedFrameOccuTTD(y, surveyLength, siteCovs=NULL, obsCovs=NULL, 
                           numPrimary=1, yearlySiteCovs=NULL)

Value

an object of class unmarkedFrameOccuTTD

Arguments

y

An MxR matrix of time-to-detection data for a species, where M is the number of sites and R is the maximum number of observations per site (across all primary periods and observations, if you have multi-season data). Values in y should be positive.

surveyLength

The maximum length of a survey, in the same units as y. You can provide either a single value (if all surveys had the same max length), or a matrix matching the dimensions of y (if surveys had different max lengths).

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.

Author

Ken Kellner

Details

unmarkedFrameOccuTTD is the S4 class that holds data to be passed to the occuTTD model-fitting function.

Examples

  
  # For a single-season model
  N <- 100 #Number of sites
  psi <- 0.4 #Occupancy probability
  lam <- 7 #Parameter for exponential distribution of time to detection
  Tmax <- 10 #Maximum survey length

  z <- rbinom(N, 1, psi) #Simulate occupancy
  y <- rexp(N, 1/lam) #Simulate time to detection
  y[z==0] <- Tmax
  y[y>Tmax] <- Tmax
  
  sc <- as.data.frame(matrix(rnorm(N*2),ncol=2)) #Site covs
  oc <- as.data.frame(matrix(rnorm(N*2),ncol=2)) #obs covs

  umf <- unmarkedFrameOccuTTD(y=y, surveyLength=Tmax, siteCovs=sc, obsCovs=oc)