secr.design.MS: 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, dframe = NULL, naive = FALSE, bygroup = FALSE,
    ...)

make.lookup (tempmat)

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.

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

bygroup

logical if TRUE then the individual dimension of the parameter matrix is automatically collapsed to one row per group; if FALSE then the full dimensionality is retained (one row per individual)

...

other arguments passed to the Rfunction model.matrix

tempmat

matrix for which row lookup required

Value

For secr.design.MS, a list with the components
designMatriceslist of reduced design matrices, one for each real detection parameter
parameterTableindex 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 < $RnSK$) and np is the number of parameters in the detection model.
PIAParameter Index Array - index to row of parameterTable for a given session, animal, occasion and detector; dim(PIA) = c(R,n,S,K)
For make.lookup, a list with components
lookupmatrix of unique rows
indexindices in lookup of the original rows

Details

This is an internal secr function that you are unlikely ever to use. ...may be used to pass contrasts.arg to model.matrix. Each real parameter is notionally different for each unique combination of session, individual, occasion and detector, i.e., for $R$ sessions, $n$ individuals, $S$ occasions and $K$ detectors there are potentially $R \times n \times S \times K$ 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.

Examples

Run this code

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