RMark (version 2.1.1)

mark: Interface to MARK for fitting capture-recapture models

Description

Fits user specified models to various types of capture-recapture data by creating input file and running MARK software and retrieving output

Usage

mark(data, ddl = NULL, begin.time = 1, model.name = NULL,
    model = "CJS", title = "", model.parameters = list(),
    initial = NULL, design.parameters = list(),
    right = TRUE, groups = NULL, age.var = NULL,
    initial.ages = 0, age.unit = 1, time.intervals = NULL,
    nocc = NULL, output = TRUE, invisible = TRUE,
    adjust = TRUE, mixtures = 1, se = FALSE,
    filename = NULL, prefix = "mark", default.fixed = TRUE,
    silent = FALSE, retry = 0, options = NULL,
    brief = FALSE, realvcv = FALSE, delete = FALSE,
    external = FALSE, profile.int = FALSE, chat = NULL,
    reverse = FALSE, run = TRUE, input.links = NULL,
    parm.specific = FALSE, mlogit0 = FALSE)

Arguments

data

Either the raw data which is a dataframe with at least one column named ch (a character field containing the capture history) or a processed dataframe

ddl

Design data list which contains a list element for each parameter type; if NULL it is created

begin.time

Time of first capture(release) occasion

model.name

Optional name for the model

model

Type of c-r model (eg CJS, Burnham, Barker)

title

Optional title for the MARK analysis output

model.parameters

List of model parameter specifications

initial

Optional vector of named or unnamed initial values for beta parameters or previously run model object

design.parameters

Specification of any grouping variables for design data for each parameter

right

if TRUE, any intervals created in design.parameters are closed on the right and open on left and vice-versa if FALSE

groups

Vector of names factor variables for creating groups

age.var

Optional index in groups vector of a variable that represents age

initial.ages

Optional vector of initial ages for each age level

age.unit

Increment of age for each increment of time

time.intervals

Intervals of time between the capture occasions

nocc

number of occasions for Nest model; either time.intervals or nocc must be specified for this model

output

If TRUE produces summary of model input and model output

invisible

If TRUE, window for running MARK is hidden

adjust

If TRUE, adjusts npar to number of cols in design matrix, modifies AIC and records both

mixtures

number of mixtures for heterogeneity model

se

if TRUE, se and confidence intervals are shown in summary sent to screen

filename

base filename for files created by MARK.EXE. Files are named filename.*.

prefix

base filename prefix for files created by MARK.EXE; for example if prefix="SpeciesZ" files are named "SpeciesZnnn.*"

default.fixed

if TRUE, real parameters for which the design data have been deleted are fixed to default values

silent

if TRUE, errors that are encountered are suppressed

retry

number of reanalyses to perform with new starting values when one or more parameters are singular

options

character string of options for Proc Estimate statement in MARK .inp file

brief

if TRUE and output=TRUE then a brief summary line is given instead of a full summary for the model

realvcv

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

delete

if TRUE the output files are deleted after the results are extracted

external

if TRUE the mark object is saved externally rather than in the workspace; the filename is kept in its place

profile.int

if TRUE will compute profile intervals for each real parameter; or you can specify a vector of real parameter indices

chat

value of chat used for profile intervals

reverse

if set to TRUE, will reverse timing of transition (Psi) and survival (S) in Multistratum models

run

if FALSE does not run model after creation

input.links

specifies set of link functions for parameters with non-simplified structure

parm.specific

if TRUE, forces a link to be specified for each parameter

mlogit0

if TRUE, any real parameter that is fixed to 0 and has an mlogit link will have its link changed to logit so it can be simplified

Value

• model: a MARK object containing output and extracted results. It is a list with the following elements
• data
  name of the processed data frame
• model
  type of analysis model (see list above)
• title
  title used for analysis
• model.name
  descriptive name of model
• links
  vector of link function(s) used for parameters, one for each row in design matrix or only one if all parameters use the same function
• mixtures
  number of mixtures in Pledger-style closed capture-recapture models
• call
  call to make.mark.model used to construct the model
• parameters
  a list of parameter descriptions including the formula, pim.type, link etc.
• model.parameters
  the list of parameter descriptions used in the call to mark; this is used only by rerun.mark
• time.intervals
  Intervals of time between the capture occasions
• number.of.groups
  number of groups defined in the data
• group.labels
  vector of labels for the groups
• nocc
  number of capture occasions
• begin.time
  single time of vector of times (if different for groups) for the first capture occasion
• covariates
  vector of covariate names (as strings) used in the model
• fixed
  dataframe of parameters set at fixed values; index is the parameter index in the full parameter structure and value is the fixed value for the real parameter
• design.matrix
  design matrix used in the input to MARK.EXE
• pims
  list of pims used for each parameter including any group or strata designations; each parameter in list is denoted by name and within each parameter one or more sub-lists represent groups and strata if any
• design.data
  design data used to construct the design matrix
• strata.labels
  labels for strata if any
• mlogit.list
  structure used to simplify parameters that use mlogit links
• simplify
  list containing pim.translation which translate between all different and simplified pims, real.labels which are labels for real parameters for full (non-simplified) pim structure and links the link function names for the full parameter structure
• output
  base portion of filenames for input,output, vc and residual files output from MARK.EXE
• results
  List of values extracted from MARK ouput
• lll{ lnl -2xLog Likelihood value npar Number of parameters (always the number of columns in design matrix) npar.unadjusted number of estimated parameters from MARK if different than npar n effective sample size AICc Small sample corrected AIC using npar AICc.unadjusted Small sample corrected AIC using npar.unadjusted beta data frame of beta parameters with estimate, se, lcl, ucl real data frame of real parameters with estimate, se, lcl, ucl and fixed beta.vcv variance-covariance matrix for beta derived dataframe of derived parameters if any 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 }
• chat
  over-dispersion constant; if not present assumed to be 1

Details

This function acts as an interface to the FORTRAN program MARK written by Gary White (http://www.cnr.colostate.edu/~gwhite/mark/mark.htm). It creates the input file for MARK based on user-specified sub-models (model.parameters) for each of the parameters in the capture-recapture model being fitted to the data. It runs MARK.EXE (see note below) and then imports the text output file and binary variance-covariance file that were created. It extracts output values from the text file and creates a list of results that is returned as part of the list (of class mark) which is the return value for this function. The following are the MARK capture-recapture models that are currently supported for argument model: ll{ model Selection in MARK CJS Recaptures only Recovery Recoveries only Burnham Both(Burnham) Barker Both(Barker) Pradel Pradel recruitment only Pradsen Pradel survival and seniority Pradlambda Pradel survival and lambda Pradrec Pradel survival and recruitment LinkBarker Available only in change data type as Link-Barker Closed Closed - no heterogeneity HetClosed Closed with heterogeneity FullHet Closed with full heterogeneity Huggins Huggins with no heterogeneity HugHet Huggins with heterogeneity HugFullHet Huggins with full heterogeneity POPAN POPAN Jolly Burnham formulation for original Jolly-Seber model Known Known - known fate data (e.g, radio-tracking) Multistrata Multistrata - CJS model with strata Robust Robust design with Closed models for secondary periods with no heterogeneity RDHet Robust design with Closed models for secondary periods with heterogeneity RDFHet Robust design with Closed models for secondary periods with full heterogeneity RDHuggins Robust design with Huggins models for secondary periods with no heterogeneity RDHHet Robust design with Huggins models for secondary periods with heterogeneity RDHFHet Robust design with Huggins models for secondary periods with full heterogeneity Nest Nest survival Occupancy Site occupancy modelling OccupHet Site occupancy modelling with mixture model for heterogeneity RDOccupEG Robust design site occupancy modelling; single Psi, espsilon, and gamma RDOccupPE Robust design site occupancy modelling; mutliple Psi and espsilon RDOccupPG Robust design site occupancy modelling; mutliple Psi and gamma RDOccupHetEG Robust design site occupancy modelling with heterogeneity; single Psi, espsilon, and gamma RDOccupHetPE Robust design site occupancy modelling with heterogeneity; mutliple Psi and espsilon RDOccupHetPG Robust design site occupancy modelling with heterogeneity; mutliple Psi and gamma OccupRNPoisson Royle-Nichols Poisson site occupancy modelling OccupRNNegBin Royle-Nichols Negative Binomial site occupancy modelling OccupRPoisson Royle count Poisson site occupancy modelling OccupRNegBin Royle count Negative Binomial site occupancy modelling MSOccupancy Multi-state site occupancy modelling } The function mark is a shell that calls 5 other functions in the following order as needed: 1) process.data, 2) make.design.data, 3) make.mark.model, 4) run.mark.model, and 5) summary.mark. A MARK model can be fitted with this function (mark) or by calling the individual functions that it uses. The calling arguments for mark are a compilation of the calling arguments for each of the functions it calls (with some arguments renamed to avoid conflicts). If data is a processed dataframe (see process.data) then it expects to find a value for ddl. Likewise, if the data have not been processed, then ddl should be NULL. This dual calling structure allows either a single call approach for each model or alternatively for the data to be processed and the design data (ddl) to be created once and then a whole series of models can be analyzed without repeating those steps. For descriptions of the arguments data, begin.time, groups, age.var, initial.ages, age.unit, time.intervals and mixtures see process.data. For descriptions of ddl, design.parameters=parameters, and right, see make.design.data. For descriptions of model.name , model, title,model.parameters=parameters , default.fixed , initial, options, see make.mark.model. And finally, for descriptions of arguments invisible, filename and adjust,see run.mark.model. output,silent, and retry are the only arguments specific to mark. output controls whether a summary of the model input and output are given(if output=TRUE). silent controls whether errors are shown when fitting a model. retry controls the number of times a model will be refitted with new starting values (uses 0) when some parameters are determined to be non-estimable or at a boundary. The latter is the only time it makes sense to retry with new starting values but MARK cannot discern between these two instances. The indices of the beta parameters that are "singular" are stored in results$singular.

See Also

make.mark.model, run.mark.model, make.design.data, process.data, summary.mark

Examples

data(dipper)
dipper.Phidot.pdot=mark(dipper)