mask.check {secr} | R Documentation

## Mask Diagnostics

### Description

`mask.check` evaluates the effect of varying buffer width and mask spacing on either the likelihood or density estimates from secr.fit()

### Usage

    mask.check(object, buffers = NULL, spacings = NULL, poly = NULL,
        LLonly = TRUE, realpar = NULL, session = 1, file = NULL,
        drop = "", tracelevel = 0, ncores = 1, ...)

### Arguments

`object` | object of class ‘capthist’ or ‘secr’
`buffers` | vector of buffer widths
`spacings` | vector of mask spacings
`poly` | matrix of two columns, the x- and y-coordinates of a bounding polygon (optional)
`LLonly` | logical; if TRUE then only the log likelihood is computed
`realpar` | list of parameter values
`session` | vector of session indices (used if `object` spans multiple sessions)
`file` | name of output file (optional)
`drop` | character vector: names of fitted secr object to omit
`tracelevel` | integer for level of detail in reporting (0,1,2)
`ncores` | integer number of cores available for parallel processing
`...` | other arguments passed to secr.fit

### Details

Masks of varying buffer width and spacing are constructed with the ‘trapbuffer’ method in `make.mask`, using the detector locations (‘traps’) from either a capthist object or a previous execution of `secr.fit`. Default values are provided for `buffers` and `spacings` if `object` is of class ‘secr’ (respectively c(1, 1.5, 2) and c(1, 0.75, 0.5) times the values in the existing mask). The default for `buffers` will not work if a detector is on the mask boundary, as the inferred buffer is then 0.

Variation in the mask may be assessed for its effect on –

- the log-likelihood evaluated for given values of the parameters (`LLonly = TRUE`)

- estimates from maximizing the likelihood with each mask (`LLonly = FALSE`)

`realpar` should be a list with one named component for each real parameter (see Examples). It is relevant only if `LLonly = TRUE`. `realpar` may be omitted if `object` is of class ‘secr’; parameter values are then extracted from `object`.

`session` should be an integer or character vector suitable for indexing sessions in `object`, or in `object$capthist` if `object` is a fitted model. Each session is considered separately; a model formula that refers to session or uses session covariates will cause an error.

If `file` is specified and `ncores = 1` then detailed results (including each model fit when `LLonly = FALSE`) are saved to an external .RData file. Loading this file creates or overwrites object(s) in the workspace: `mask.check.output` if `LLonly = TRUE`, otherwise `mask.check.output` and `mask.check.fit`. For multiple sessions these are replaced by lists with one component per session (`mask.check.outputs` and `mask.check.fits`). The `drop` argument is passed to `trim` and applied to each fitted model; use it to save space, at the risk of limiting further computation on the fitted models.

`tracelevel>0` causes more verbose reporting of progress during execution.

If `ncores > 1` the parallel package is used to create processes on multiple cores (see Parallel for more), progress messages are suppressed, and nothing is output to `file`.

The ... argument may be used to override existing settings in `object` - for example, a conditional likelihood fit (`CL = T`) may be selected even if the original model was fitted by maximizing the full likelihood.

### Value

Array of log-likelihoods (`LLonly = TRUE`) or estimates (`LLonly = FALSE`) for each combination of `buffers` and `spacings`. The array has 3 dimensions if `LLonly = FALSE` and both `buffers` and `spacings` have multiple levels; otherwise it collapses to a matrix. Rows generally represent `buffers`, but rows represent `spacings` if a single buffer is specified.

### Warning

`mask.check()` may fail if `object` is a fitted ‘secr’ model and a data object named in the original call of `secr.fit()` (i.e. `object$call`) is no longer in the working environment (`secr.fit` arguments capthist, mask, verify & trace are exempt). Fix by any of (1) applying `mask.check` directly to the ‘capthist’ object, specifying other arguments (`buffers`, `spacings`, `realpar`) as needed, (2) re-fitting the model and running `mask.check` in the same environment, (3) specifying the offending argument(s) in ..., or (4) re-creating the required data objects(s) in the working environment, possibly from saved inputs in `object` (e.g., `mytimecov <- myfit$timecov`).

### Note

When `LLonly = TRUE` the functionality of `mask.check` resembles that of the ‘Tools | ML SECR log likelihood’ menu option in Density 5. The help page in Density 5 for ML SECR 2-D integration (see index) may be helpful.

Warning messages from `secr.fit` are suppressed. ‘capthist’ data provided via the `object` argument are checked with `verify.capthist` if `tracelevel > 0`.

The likelihood-only method is fast, but not definitive. It is reasonable to aim for stability in the third decimal place of the log likelihood. Slight additional variation in the likelihood may cause little change in the estimates; the only way to be sure is to check these by setting `LLonly = FALSE`.

The performance of a mask depends on the detection function; be sure to set the `detectfn` argument appropriately. The hazard rate function has a fat tail that can be problematic.

When provided with an ‘secr’ `object`, `mask.check` constructs a default vector of buffer widths as multiples of the buffer used in `object` even though that value is not saved explicitly. For this trick, detector locations in `traps(object$capthist)` are compared to the bounding box of `object$mask`; the base level of buffer width is the maximum possible within the bounding box.

### References

Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.

Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://www.otago.ac.nz/density.

### See Also

`esa.plot`, `make.mask`, `secr.fit`

### Examples

    ## Not run:

    ## from a capthist object, specifying almost everything
    mask.check (possumCH, spacings = c(20, 30), buffers =c(200, 300),
        realpar = list(g0 = 0.2, sigma = 50), CL = TRUE)

    ## from a fitted model, using defaults
    mask.check (stoat.model.HN)
    ## LL did not change with varying buffer (rows) or spacing (cols):
    ##         78.125  58.59375   39.0625
    ## 1000 -144.0015 -144.0015 -144.0015
    ## 1500 -144.0017 -144.0017 -144.0017
    ## 2000 -144.0017 -144.0017 -144.0017

    ## fit new models for each combination of buffer & spacing,
    ## and save fitted models to a file
    mask.check (stoat.model.HN, buffers = 1500, spacings =
        c(40,60,80), LLonly = FALSE, file = "test", CL = TRUE)

    ## look in more detail at the preceding fits
    ## restores objects `mask.check.output' and `mask.check.fit'
    load("test.RData")
    lapply(mask.check.fit, predict)
    lapply(mask.check.fit, derived)

    ## multi-session data
    mask.check(ovenbird.model.1, session = c("2005","2009"))

    ## clipping mask
    olddir <- setwd(system.file("extdata", package = "secr"))
    possumarea <- read.table("possumarea.txt", header = TRUE)
    setwd(olddir)
    data (possum)
    mask.check (possum.model.0, spacings = c(20, 30), buffers =
        c(200, 300), poly = possumarea, LLonly = FALSE,
        file = "temp", CL = TRUE)

    ## review fitted models
    load ("temp.RData")
    par(mfrow = c(2,2), mar = c(1,4,4,4))
    for (i in 1:4) {
        plot(traps(mask.check.fit[[i]]$capthist), border = 300,
            gridlines = FALSE)
        plot(mask.check.fit[[i]]$mask, add = TRUE)
        lines(possumarea)
        text ( 2698618, 6078427, names(mask.check.fit)[i])
        box()
    }
    par(mfrow = c(1,1), mar = c(5,4,4,2) + 0.1)    ## defaults

    ## End(Not run)

[Package secr version 3.2.1 Index]