| adjust.value | R Documentation |
Adjust value of over-dispersion constant or another result value for a collection of models which modifies model selection criterion and estimated standard errors.
adjust.value(field="n",value,model.list)
adjust.chat(chat=1,model.list)
field |
Character string containing name of the field; either
|
value |
new value for field |
model.list |
marklist created by the function
|
chat |
Over-dispersion scale |
The value of chat is stored with the model object except when there
is no over-dispersion (chat=1). This function assigns a new value of
chat for the collection of models specified by model.list
and/or type. The value of chat is used by
model.table for model selection in computing QAICc unless
chat=1. It is also used in summary.mark,
get.real and compute.real to adjust standard
errors and confidence intervals. Note that the standard errors and
confidence intervals in results$beta,results$beta.vcv
results$real, results$derived and results$derived.vcv
are not modified and always assume chat=1.
It can also be used to modify a field in model$results such as
n which is ESS (effective sample size) from MARK output that is used
in AICc/QAICc calculations.
model.list with all models given the new chat value and model.table adjusted for chat values
See note in collect.models
Jeff Laake
model.table, summary.mark,
get.real ,compute.real
#
# The following are examples only to demonstrate selecting different
# model sets for adjusting chat and showing model selection table.
# It is not a realistic analysis.
#
# This example is excluded from testing to reduce package check time
data(dipper)
do_example=function()
{
mod1=mark(dipper,delete=TRUE)
mod2=mark(dipper,model.parameters=list(Phi=list(formula=~time)),delete=TRUE)
mod3=mark(dipper,model="POPAN",initial=1,delete=TRUE)
cjs.results=collect.models(type="CJS")
cjs.results # show model selection results for "CJS" models
}
cjs.results=do_example()
cjs.results
# adjust chat for all models to 2
cjs.results=adjust.chat(2,cjs.results)
cjs.results
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