secr.design.MS {secr}

R Documentation

Construct Detection Model Design Matrices and Lookups

Description

Internal functions used by secr.fit.

Usage

secr.design.MS (capthist, models, timecov = NULL, sessioncov = NULL, 
    groups = NULL, hcov = NULL, dframe = NULL, naive = FALSE, CL = FALSE,
    keep.dframe = FALSE, full.dframe = FALSE, ignoreusage = FALSE, contrasts = NULL, ...)

make.lookup (tempmat)

insertdim (x, dimx, dims) 

Arguments

capthist	capthist object
models	list of formulae for parameters of detection
timecov	optional dataframe of values of time (occasion-specific) covariate(s).
sessioncov	optional dataframe of values of session-specific covariate(s).
groups	optional vector of one or more variables with which to form groups. Each element should be the name of a factor variable in the covariates attribute of capthist.
hcov	character name of an individual (capthist) covariate for known class membership in h2 models
dframe	optional data frame of design data for detection parameters
naive	logical if TRUE then modelled detection probability is for a naive animal (not caught previously); if FALSE then detection probability is contingent on individual's history of detection
CL	logical; TRUE for model to be fitted by maximizing the conditional likelihood
keep.dframe	logical; if TRUE the dataframe of design data is included in the output
full.dframe	logical; if FALSE then padding rows are purged from output dframe (ignored if keep.dframe = FALSE)
ignoreusage	logical; if TRUE any usage attribute of traps(capthist) is ignored
contrasts	contrast specification as for model.matrix
...	other arguments passed to the R function model.matrix
tempmat	matrix for which row lookup required
x	vector of character, numeric or factor values
dimx	vector of notional dimensions for x to fill in target array
dims	vector of notional dimensions of target array

Details

These are internal secr functions that you are unlikely ever to use.

Each real parameter is notionally different for each unique combination of session, individual, occasion, detector and latent class, i.e., for R sessions, n individuals, S occasions, K detectors and M latent classes there are potentially R \times n \times S \times K \times M different values. Actual models always predict a much reduced set of distinct values, and the number of rows in the design matrix is reduced correspondingly; a parameter index array allows these to retrieved for any combination of session, individual, occasion and detector.

The keep.dframe option is provided for the rare occasions that a user may want to check the data frame that is an intermediate step in computing each design matrix with model.matrix (i.e. the data argument of model.matrix).

... may be used to pass contrasts.arg to model.matrix.

Value

For secr.design.MS, a list with the components

designMatrices	list of reduced design matrices, one for each real detection parameter
parameterTable	index to row of the reduced design matrix for each real detection parameter; dim(parameterTable) = c(uniquepar, np), where uniquepar is the number of unique combinations of paramater values (uniquepar < RnSKM) and np is the number of parameters in the detection model.
PIA	Parameter Index Array - index to row of parameterTable for a given session, animal, occasion and detector; dim(PIA) = c(R,n,S,K,M)
R	number of sessions

If models is empty then all components are NULL except for PIA which is an array of 1's (M set to 1).

Optionally (keep.dframe = TRUE) -

dframe	dataframe of design data, one column per covariate, one row for each c(R,n,S,K,M). For multi-session models n, S, and K refer to the maximum across sessions
validdim	list giving the valid dimensions (n, S, K, M) before padding

For make.lookup, a list with components

lookup	matrix of unique rows
index	indices in lookup of the original rows

For insertdim, a vector with length prod(dims) containing the values replicated according to dimx.

See Also

D.designdata, model.matrix

Examples

secr.design.MS (captdata, models = list(g0 = ~b))$designMatrices
secr.design.MS (captdata, models = list(g0 = ~b))$parameterTable

## peek at design data constructed for learned response model
head(captdata)
temp <- secr.design.MS (captdata, models = list(g0 = ~b),
    keep.dframe = TRUE)
a1 <- temp$dframe$animal == 1 &  temp$dframe$detector %in% 8:10
temp$dframe[a1,]

## ... and trap specific learned response model
temp <- secr.design.MS (captdata, models = list(g0 = ~bk),
    keep.dframe = TRUE)
a1 <- temp$dframe$animal == 1 &  temp$dframe$detector %in% 8:10
temp$dframe[a1,]

## place values 1:6 in different dimensions
insertdim(1:6, 1:2, c(2,3,6))
insertdim(1:6, 3, c(2,3,6))

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