predict.glm.bindex: A function to predict CPUE-index from a delta-lognormal GLM...

In rooperc4/GLMGAMRF: Package fits GLM, GAM and Random Forest models to survey data

Description

This function uses a binomial GLM and a log-normal GLM to compute a delta-lognormal index of abundance with associated 95 strapping the data. A dummy variable for year must be included to get the annual abundance index. The function back-transforms the index and the CI's.

Usage

## S3 method for class 'glm.bindex'
predict(pa.model, cpue.model, boot_reps = 500)

Arguments

pa.model	binomial GLM predicting presence or absence
cpue.model	log-normal GLM predicting abundance where catch is positive
boot_reps	number of bootstrap replications (default = 500)

Examples

glm.pa.xvars<-c("inverts","slope","btemp","bdepth")
glm.pa.yvar<-ifelse(PA.data[species.name]>0,1,0)
glm.pa.form <- as.formula(paste("glm.pa.yvar ~", paste(glm.pa.xvars,collapse="+"),"+as.factor(year)",sep=""))
pa.glm <- glm(glm.pa.form, family = binomial, data = PA.data)

CPUE.data<-subset(PA.data,PA.data[species.name]>0)
glm.cpue.yvar<-unlist(log(CPUE.data[species.name]))
glm.cpue.xvars<-c("inverts","slope","btemp","bdepth")
glm.cpue.form <- as.formula(paste("glm.cpue.yvar ~", paste(glm.cpue.xvars,collapse="+"),"+as.factor(year)",sep=""))
cpue.glm <- glm(glm.cpue.form, family = gaussian, data = CPUE.data)

predict.glm.bindex(pa.glm,cpue.glm)

rooperc4/GLMGAMRF documentation built on May 17, 2019, 1:30 p.m.