Time-varying Detector Covariates

Description

Extract or replace time varying trap covariates

Usage

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

Arguments

object

an object of class traps

value

a list of named vectors

...

other arguments (not used)

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).

Value

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

Note

It is usually better to model varying effort directly, via the usage attribute (see secr-varyingeffort.pdf).

Models for data from detectors of type ‘multi’, ‘polygonX’ or ‘transectX’ take much longer to fit when detector covariates of any sort are used.

See secr-varyingeffort.pdf for input of detector covariates from a file.

Examples

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# 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


## End(Not run)

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