RMark (version 3.0.0)

create.model.list: Creates a dataframe of all combinations of parameter specifications

Description

Creates a dataframe of all combinations of parameter specifications for each parameter in a particular type of MARK model. It is used together with mark.wrapper to run a series of models from sets of parameter specifications.

Usage

create.model.list(model)

Value

dataframe of all combinations of parameter specifications for a model. Each field (column) is the name of a type of parameter (e.g., p and Phi for CJS). The values are character strings identifying particular parameter specifications.

Arguments

model

character string identifying the type of model (e.g., "CJS")

Author

Jeff Laake

Details

This function scans the frame of the calling enviroment and collects all list objects that contain a formula and have names that match parameter. where parameter is the name of a type of parameter in the model type. For example, it looks for Phi. and p. for model="CJS". Any number of characters can follow the period. Each of the named objects should specify a list that matches the structure of a parameter specification as described in make.mark.model. It only collects list objects that contain an element named formula, thus it will not collect one like Phi.fixed=list(fixed=1). If you want to do something like that, specify it as Phi.fixed=list(formula=~1,fixed=1). It is safest to use this inside a function that defines all of the parameter specifications as shown in the example below. The primary use for this function is to create a dataframe which is passed to mark.wrapper to construct and run each of the models. It was written as a separate function to provide flexibility to add/delete/modify the list prior to passing to mark.wrapper. For example, only certain combinations may make sense for some parameter specifications. Thus you could define a set to create all the combinations and then delete the ones from the dataframe that do not make sense. you want, add others and re-run the function and merge the resulting dataframes. If there are no specifications found for a particular model parameter, it is not included in the list and when it is passed to make.mark.model, the default specification will be used for that parameter.

See Also

mark.wrapper

Examples

# \donttest{
# This example is excluded from testing to reduce package check time
#
# Compare this to the run.dipper shown under ?dipper
# It is only necessary to create each parameter specification and 
# create.model.list and mark.wrapper will create and run models for 
# each combination. Notice that the naming of the parameter 
# specifications has been changed to accommodate format for 
# create.model.list. Only a subset of the parameter specifications 
# are used here in comparison to other run.dipper
#
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.Flood=list(formula=~Flood)
     #
     #  Define range of models for p
     #
     p.dot=list(formula=~1)
     p.time=list(formula=~time)
     p.Flood=list(formula=~Flood)
     #
     # Run all pairings of models
     #
     dipper.model.list=create.model.list("CJS")
     dipper.results=mark.wrapper(dipper.model.list,
              data=dipper.processed,ddl=dipper.ddl,delete=TRUE)
     #
     # Return model table and list of models
     #
     return(dipper.results)
}
dipper.results=run.dipper()
# }