Construct Detection Model Design Matrices and Lookups
Internal functions used by secr.fit.
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)capthist |
|
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 |
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 |
ignoreusage |
logical; if TRUE any usage attribute of traps(capthist) is ignored |
contrasts |
contrast specification as for |
... |
other arguments passed to the R function
|
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 |
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 x n x S x K x 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).
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.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.