secr (version 3.0.1)

timevaryingcov: Time-varying Detector Covariates

Description

Extract or replace time varying trap covariates

Usage

timevaryingcov(object, ...)
timevaryingcov(object) <- value

Arguments

object

an object of class traps

value

a list of named vectors

…

other arguments (not used)

Value

timevaryingcov(object) returns the timevaryingcov attribute of object (may be NULL).

Details

The timevaryingcov attribute of a traps object is a list of one or more named vectors. Each vector identifies a subset of columns of covariates(object), one for each occasion. If character values are used they should correspond to covariate names.

The name of the vector may be used in a model formula; when the model is fitted, the value of the trap covariate on a particular occasion is retrieved from the column indexed by the vector.

For replacement, if object already has a usage attribute, the length of each vector in value must match exactly the number of columns in usage(object).

Examples

# make a trapping grid with simple covariates
temptrap <- make.grid(nx = 6, ny = 8, detector = "proximity")
covariates (temptrap) <- data.frame(matrix(
    c(rep(1,48*3),rep(2,48*2)), ncol = 5))
head(covariates (temptrap))

# identify columns 1-5 as daily covariates
timevaryingcov(temptrap) <- list(blockt = 1:5)
timevaryingcov(temptrap)

## Not run: ------------------------------------
# 
# # default density = 5/ha, noccasions = 5
# CH <- sim.capthist(temptrap, detectpar = list(g0 = c(0.15, 0.15,
#     0.15, 0.3, 0.3), sigma = 25))
# 
# fit.1 <- secr.fit(CH, trace = FALSE) 
# fit.tvc2 <- secr.fit(CH, model = g0 ~ blockt, trace = FALSE) 
# 
# # because variation aligns with occasions, we get the same with:
# fit.t2 <- secr.fit(CH, model = g0 ~ tcov, timecov = c(1,1,1,2,2),
#     trace = FALSE) 
# 
# predict(fit.t2, newdata = data.frame(tcov = 1:2))
# predict(fit.tvc2, newdata = data.frame(blockt = 1:2))
# 
# # now model some more messy variation
# covariates (traps(CH))[1:10,] <- 3
# fit.tvc3 <- secr.fit(CH, model = g0 ~ blockt, trace = FALSE) 
# 
# AIC(fit.tvc2, fit.t2, fit.tvc3)
# # fit.tvc3 is the 'wrong' model
# 
## ---------------------------------------------