marked (version 1.2.6)

predict.crm: Compute estimates of real parameters

Description

Computes real estimates and their var-cov for a particular subset of parameters. The argument newdata may not work with all models. A better approach to compute real estimates for a subset of values or a new set of values is to specify a limited range of the values in ddl for each parameter. Make sure to include a complete set of values that spans the factor levels and individual covariates used in the formulas for the model object or you will receive an error that the number of columns in the design matrix does not match the number of beta parameters. You cannot change the levels of any factor variable or modify the design data in anyway that changes the design matrix.

Usage

# S3 method for crm
predict(object,newdata=NULL,ddl=NULL,parameter=NULL,unique=TRUE,
                   vcv=FALSE,se=FALSE,chat=1,subset=NULL,select=NULL,
                   real.ids=NULL,merge=FALSE,...)

Value

A data frame (real) is returned if vcv=FALSE; otherwise, a list is returned also containing vcv.real:

real

data frame containing estimates, and if vcv=TRUE it also contains standard errors and confidence intervals

vcv.real

variance-covariance matrix of real estimates

Arguments

object

model object;

newdata

a dataframe for crm

ddl

list of dataframes for design data

parameter

name of real parameter to be computed (eg "Phi")

unique

TRUE if only unique values should be returned

vcv

logical; if TRUE, computes and returns v-c matrix of real estimates

se

logical; if TRUE, computes std errors and conf itervals of real estimates

chat

over-dispersion value

subset

logical expression using fields in real dataframe

select

character vector of field names in real that you want to include

real.ids

animal ids passed to TMB code for computation of real parameter values

merge

default FALSE but if TRUE, the ddl for the parameter is merged (cbind) to the estimates

...

generic arguments not used here

Author

Jeff Laake

Examples

data(dipper)
dipper.proc=process.data(dipper,model="cjs",begin.time=1)
dipper.ddl=make.design.data(dipper.proc)
mod.Phisex.pdot=crm(dipper.proc,dipper.ddl,
   model.parameters=list(Phi=list(formula=~sex+time),p=list(formula=~1)),hessian=TRUE)
xx=predict(mod.Phisex.pdot,ddl=dipper.ddl)
xx
xx=predict(mod.Phisex.pdot,newdata=dipper[c(1,23),],vcv=TRUE)
xx