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Constructs a table of model selection results for MARK analyses. The table includes the formulas, model name, number of parameters, deviance, AICc, DeltaAICc, model weight and residual deviance. If chat>1 QAICc, QDeltaAICc and QDeviance are used instead.
model.table(
model.list = NULL,
type = NULL,
sort = TRUE,
adjust = TRUE,
ignore = TRUE,
pf = 1,
use.lnl = FALSE,
use.AIC = FALSE,
model.name = TRUE
)result.table - dataframe containing summary of models
name of fitted model
formula for parameter
number of estimated parameters
AICc value or QAICc if chat>1
difference between AICc or QAICc value from model with smallest value
model weight based on exp(-.5*DeltaAICc) or exp(-.5*QDeltaAICc)
residual deviance from saturated model
overdispersion constant if not 1
a vector of model names or a list created by the function
collect.models which has each model object and at the end a
model.table ; If nothing is specified then any mark object in the
workspace is collected for the table. If type is specified all
analyses in parent frame(pf) of that type of model are used.
If specified set of models are of conflicting types or of different data
sets then an error is issued unless ignore=TRUE
type of model (eg "CJS")
if true sorts models by criterion
if TRUE adjusts # of parameters to # of cols in design matrix
if TRUE collects all models and ignores that they are from different models
parent frame value; default=1 so it looks in calling frame of model.table; used in other functions with pf=2 when functions are nested two-deep
display -2lnl instead of deviance
use AIC instead of AICc
if TRUE uses the model.name in each mark object which uses formula notation. If FALSE it uses the R names for the model obtained from collect.model.names or names assigned to marklist elements
Jeff Laake
This function is used by collect.models to construct a table
of model selection results with the models that it collects; however it can
be called directly to construct the table.
collect.model.names, collect.models
# \donttest{
# This example is excluded from testing to reduce package check time
data(dipper)
run.dipper=function()
{
#
# Process data
#
dipper.processed=process.data(dipper,groups=("sex"))
#
# Create default design data
#
dipper.ddl=make.design.data(dipper.processed)
#
# Add Flood covariates for Phi and p that have different values
#
dipper.ddl$Phi$Flood=0
dipper.ddl$Phi$Flood[dipper.ddl$Phi$time==2 | dipper.ddl$Phi$time==3]=1
dipper.ddl$p$Flood=0
dipper.ddl$p$Flood[dipper.ddl$p$time==3]=1
#
# Define range of models for Phi
#
Phi.dot=list(formula=~1)
Phi.time=list(formula=~time)
Phi.sex=list(formula=~sex)
Phi.sextime=list(formula=~sex+time)
Phi.sex.time=list(formula=~sex*time)
Phi.Flood=list(formula=~Flood)
#
# Define range of models for p
#
p.dot=list(formula=~1)
p.time=list(formula=~time)
p.sex=list(formula=~sex)
p.sextime=list(formula=~sex+time)
p.sex.time=list(formula=~sex*time)
p.Flood=list(formula=~Flood)
#
# Return model table and list of models
#
cml=create.model.list("CJS")
return(mark.wrapper(cml,data=dipper.processed,ddl=dipper.ddl,delete=TRUE))
}
dipper.results=run.dipper()
dipper.results
dipper.results$model.table=model.table(dipper.results,model.name=FALSE)
dipper.results
#
# Compute matrices of model weights, number of parameters and Delta AICc values
#
model.weight.matrix=tapply(dipper.results$model.table$weight,
list(dipper.results$model.table$Phi,dipper.results$model.table$p),mean)
model.npar.matrix=tapply(dipper.results$model.table$npar,
list(dipper.results$model.table$Phi,dipper.results$model.table$p),mean)
model.DeltaAICc.matrix=tapply(dipper.results$model.table$DeltaAICc,
list(dipper.results$model.table$p,dipper.results$model.table$Phi),mean)
#
# Output DeltaAICc as a tab-delimited text file that can be read into Excel
# (to do that directly use RODBC or xlsreadwrite package for R)
#
# remove # to use next line
#write.table(model.DeltaAICc.matrix,"DipperDeltaAICc.txt",sep="\t")
# }
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