extract.mark.output: Extract results from MARK output file (internal use)

View source: R/extract.mark.output.R

extract.mark.outputR Documentation

Extract results from MARK output file (internal use)

Description

Extracts the lnl, AICc, npar, beta and real estimates and returns a list of these results for inclusion in the mark object. The elements beta and real are dataframes with fields estimate,se,lcl,ucl. This function was written for internal use and is called by run.mark.model. It is documented here for more advanced users that might want to modify the code or adapt for their own use.

Usage

extract.mark.output(out, model, adjust, realvcv = FALSE, vcvfile)

Arguments

out

output from MARK analysis (model$output)

model

mark model object

adjust

if TRUE, adjusts number of parameters (npar) to number of columns in design matrix, modifies AIC and records both

realvcv

if TRUE the vcv matrix of the real parameters is extracted and stored in the model results

vcvfile

name of vcv file output

Value

result: list of extracted output elements

lnl

-2xLog-likelihood

deviance

Difference between saturated model and lnl

npar

Number of model parameters

AICc

Small-sample corrected AIC value using npar and n

npar.unadjusted

Number of model parameters as reported by MARK if npar was adjusted

AICc.unadjusted

Small-sample corrected AIC value using npar.unadjusted and n

n

Effective sample size reported by MARK; used in AICc calculation

beta

Dataframe of beta parameters with fields: estimate, se, lcl, ucl

real

Dataframe of real parameters with fields: estimate, se, lcl, ucl

derived.vcv

variance-covariance matrix for derived parameters if any

covariate.values

dataframe with fields Variable and Value which are the covariate names and value used for real parameter estimates in the MARK output

singular

indices of beta parameters that are non-estimable or at a boundary

real.vcv

variance-covariance matrix for real parameters (simplified) if realvcv=TRUE

Author(s)

Jeff Laake

See Also

run.mark.model


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