Description Usage Arguments Details Value See Also Examples
Internal functions used by secr.fit
.
1 2 3 4 5 6 7 
capthist 

models 
list of formulae for parameters of detection 
timecov 
optional dataframe of values of time (occasionspecific) covariate(s). 
sessioncov 
optional dataframe of values of sessionspecific 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 multisession 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  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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