timevaryingcov {secr} | R Documentation

## Time-varying Covariates

### Description

Extract or replace time varying covariates

### Usage

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

### Arguments

`object` | an object of class `traps` or `capthist`
`value` | a list of named vectors
`...` | other arguments (not used)

### Details

The timevaryingcov attribute 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.

In secr models, time-varying covariates are restricted to traps objects. Time-varying (session-specific) individual covariates may be used in openCR. The following remarks apply to time-varying traps covariates.

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)`.

When converting a multi-session capthist object into a robust-design “single-session” object with function `join` the argument ‘timevaryingcov’ is used to collate covariate values across sessions in a form suitable for inclusion in openCR models (see `join`).

### 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.

### See Also

`join`

### 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

    ## End(Not run)

[Package secr version 3.2.1 Index]