summary.mark: Summary of MARK model parameters and results
In RMark: R Code for Mark Analysis

summary.mark

R Documentation

Summary of MARK model parameters and results

Description

Creates a summary object of either a MARK model input or model output which includes number of parameters, deviance, AICc, the beta and real parameter estimates and optionally standard errors, confidence intervals and variance-covariance matrices. If there are several groups in the data, the output is structured by group.

Usage

## S3 method for class 'mark'
summary(object,...,se=FALSE,vc=FALSE,showall=TRUE,show.fixed=FALSE,brief=FALSE)

Arguments

`object`	a MARK model object
`...`	additional non-specified argument for S3 generic function
`se`	if FALSE the real parameter estimates are output in PIM format (eg. triangular format); if TRUE, they are displayed as a list with se and confidence interval
`vc`	if TRUE the v-c matrix of the betas is included
`showall`	if FALSE it only returns the values of each unique parameter value
`show.fixed`	if FALSE, each fixed value given NA; otherwise the fixed real value is used. If se=TRUE, default for show.fixed=TRUE
`brief`	if TRUE, does not show real parameter estimates

Details

The structure of the summary of the real parameters depends on the type of model and the value of the argument se and showall. If se=F then only the estimates of the real parameters are shown and they are summarized the result element reals in PIM format. The structure of reals depends on whether the PIMS are upper triangular ("Triang") or a row ("Square" although not really square). For the upper triangular format, the values are passed back as a list of matrices where the list is a list of parameter types (eg Phi and p) and within each type is a list for each group containing the pim as an upper triangular matrix containing the real parameter estimate. For square matrices, reals is a list of matrices with a list element for each parameter type, but there is not a second list layer for groups because in the returned matrix each group is a row in the matrix of real estimates. If se=TRUE then estimates, standard error (se), lower and upper confidence limits (lcl, ucl) and a "Fixed" indicator is passed for each real parameter. If the pims for the model were simplified to represent the unique real parameters (unique rows in the design matrix), then it is possible to restict the summary to only the unique parameters with showall=FALSE. This argument only has an affect if se=TRUE. If showall=FALSE, reals is returned as a dataframe of the unique real parameters specified in the model. This does not mean they will all have unique values and it includes all "Fixed" real parameters and any real parameters that cannot be simplified in the case of parameters such as "pent" in POPAN or "Psi" in "Multistrata" that use the multinomial logit link. Use of showall=FALSE is of limited use but provided for completeness. In most cases the default of showall=TRUE will be satisfactory. In this case, reals is a list of dataframes with a list element for each parameter type. The dataframe contains the estimate, se,lcl, ucl,fixed and the associated default design data for that parameter (eg time,age, cohort etc). The advantage of retrieving the reals in this format is that it is the same regardless of the model, so it enables model averaging the real parameters over different models with differing numbers of unique real parameters.

Value

A list with each of the summarized objects that depends on the argument values. Only the first 4 are given if it is a summary of a model that has not been run.

`model`	type of model (e.g., CJS)
`title`	user define title if any
`model.name`	descriptive name of fitted model
`call`	call to make.mark.model used to construct the model
`npar`	number of fitted parameters
`lnl`	-2xLog Likelihood value
`npar`	Number of parameters (always the number of columns in design matrix)
`chat`	Value of over-dispersion constant if not equal to 1
`npar.unadjusted`	number of estimated parameters from MARK if different than npar
`AICc`	Small sample corrected AIC using npar; named qAICc if chat not equal to 1
`AICc.unadjusted`	Small sample corrected AIC using npar.unadjusted; prefix of q if chat not equal to 1
`beta`	dataframe of beta parameters with estimate, se, lcl, ucl
`vcv`	variance-covariance matrix for beta
`reals`	list of lists, dataframes or matrices depending on value of se and the type of model (triangular versus square PIMS) (see details above)