model.table: Create table of MARK model selection results

In RMark: R Code for Mark Analysis

Create table of MARK model selection results

Description

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.

Usage

model.table(
  model.list = NULL,
  type = NULL,
  sort = TRUE,
  adjust = TRUE,
  ignore = TRUE,
  pf = 1,
  use.lnl = FALSE,
  use.AIC = FALSE,
  model.name = TRUE
)

Arguments

model.list	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	type of model (eg "CJS")
sort	if true sorts models by criterion
adjust	if TRUE adjusts # of parameters to # of cols in design matrix
ignore	if TRUE collects all models and ignores that they are from different models
pf	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
use.lnl	display -2lnl instead of deviance
use.AIC	use AIC instead of AICc
model.name	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

Details

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.

Value

result.table - dataframe containing summary of models

model.name	name of fitted model
parameter.name - an entry for each parameter	formula for parameter
npar	number of estimated parameters
AICc or QAICc	AICc value or QAICc if chat>1
DeltaAICc or DeltaQAICc	difference between AICc or QAICc value from model with smallest value
weight	model weight based on exp(-.5*DeltaAICc) or exp(-.5*QDeltaAICc)
Deviance or QDeviance	residual deviance from saturated model
chat	overdispersion constant if not 1

Author(s)

Jeff Laake

See Also

collect.model.names, collect.models

Examples

# 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")

RMark documentation built on Aug. 14, 2022, 1:05 a.m.