RMark (version 3.0.0)

extract.indices: Various utility functions

Description

Miscellaneous set of functions that can be used with results from the package.

Usage

extract.indices(model,parameter,df)
 
        nat.surv(model,df)
         pop.est(ns,ps,design,p.vcv)
         compute.Sn(x,df,criterion)
         logitCI(x,se)
         search.output.files(x,string)

Arguments

model

a mark model object

parameter

character string for a type of parameter for that model (eg, "Phi","p")

df

dataframe containing the columns group, row, column which specify the group number, the row number and column number of the PIM

ns

vector of counts of animals captured

ps

vector of capture probability estimates which match counts

design

design matrix that specifies how counts will be aggregate

p.vcv

variance-covariance matrix for capture probability estimates

x

marklist of models for compute.Sn and a vector of real estimates for logitCI

se

vector of std errors for real estimates

criterion

vector of model selection criterion values (eg AICc)

string

string to be found in output files contained in models in x

Author

Jeff Laake

Details

Function extract.indices extracts the parameter indices from the parameter index matrices (PIMS) for a particular type of parameter that match a set of group numbers and rows and columns that are defined in the dataframe df. It returns a vector of indices which can be used to specify the set of real parameters to be extracted by covariate.predictions using the index column in data or the indices argument. If df is NULL, it returns a dataframe with all of the indices with model.index being the unique index across all parameters and the par.index which is an index to the row in the design data. If parameter is NULL then the the dataframe is given for all of the parameters.

Function nat.surv produces estimates of natural survival (Sn) from total survival (S) and recovery rate (r) from a joint live-dead model in which all harvest recoveries are reported. In that case, Taylor et al 2005 suggest the following estimator of natural survival Sn=S + (1-S)*r. The arguments for the function are a mark model object and a dataframe df that defines the set of groups and times (row,col) for the natural survival computations. It returns a list with elements: 1) Sn - a vector of estimates for natural survival; one for each entry in df and 2) vcv - a variance-covariance matrix for the estimates of natural survival.

Function pop.est produces estimates of abundance using a vector of counts of animals captured (ns) and estimates of capture probabilities (ps). The estimates can be aggregated or averaged using the design matrix argument. If individual estimates are needed, use an nxn identity matrix for design where n is the length of ns. To get a total of all the estimates use a nx1 column matrix of 1s. Any other design matrix can be specified to subset, aggregate and/or average the estimates. The argument p.vcv is needed to compute the variance-covariance matrix for the abundance estimates using the formula described in Taylor et al. (2002). The function returns a list with elements: 1) Nhat - a vector of abundance estimates and 2) vcv - variance-covariance matrix for the abundance estimates.

Function Compute.Sn creates list structure for natural survival using nat.surv to be used for model averaging natural survival estimates (e.g., model.average(compute.Sn(x,df,criterion))). It returns a list with elements estimates, vcv, weight: 1) estimates - matrix of estimates of natural survival, 2)vcv - list of var-cov matrix for the estimates, and 3) weight - vector of model weights.

Function search.output.filessearches for occurrence of a specific string in output files associated with models in a marklist x. It returns a vector of model numbers in the marklist which have an output file containing the string.

References

TAYLOR, M. K., J. LAAKE, H. D. CLUFF, M. RAMSAY and F. MESSIER. 2002. Managing the risk from hunting for the Viscount Melville Sound polar bear population. Ursus 13: 185-202.

TAYLOR, M. K., J. LAAKE, P. D. MCLOUGHLIN, E. W. BORN, H. D. CLUFF, S. H. FERGUSON, A. ROSING-ASVID, R. SCHWEINSBURG and F. MESSIER. 2005. Demography and viability of a hunted population of polar bears. Arctic 58: 203-214.

Examples

# \donttest{
# This example is excluded from testing to reduce package check time
# Example of computing N-hat for occasions 2 to 7 for the p=~time model
data(dipper)
md=mark(dipper,model.parameters=list(p=list(formula=~time),
       Phi=list(formula=~1)),delete=TRUE)
# Create a matrix from the capture history strings 
xmat=matrix(as.numeric(unlist(strsplit(dipper$ch,""))),
      ncol=nchar(dipper$ch[1]),byrow=TRUE)
# sum number of captures in each column but don't use the first 
# column because p[1] can't be estimated
ns=colSums(xmat)[-1]
# extract the indices and then get covariate predictions for p(2),...,p(7)
# which are row-colums 1-6 in PIM for p 
p.indices=extract.indices(md,"p",df=data.frame(group=rep(1,6),
   row=1:6,col=1:6))
p.list=covariate.predictions(md,data=data.frame(index=p.indices))
# call pop.est using diagonal design matrix to get 
# separate estimate for each occasion
pop.est(ns,p.list$estimates$estimate,
  design=diag(1,ncol=6,nrow=6),p.list$vcv)
# }