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, hcov = NULL, dframe = NULL, naive = FALSE, bygroup = 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

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)

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

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

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)

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.

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, detector and latent class, 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.

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).

Examples

Run this code

# NOT RUN {
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))

# }