RMark (version 3.0.0)

model.table: 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
)

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

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

Author

Jeff Laake

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.

See Also

collect.model.names, collect.models

Examples

# \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")
# }