https://github.com/cran/unmarked
Tip revision: 0e9915b1bbee346e4c283f39772af69032684e39 authored by Ken Kellner on 09 January 2024, 10:20:02 UTC
version 1.4.1
version 1.4.1
Tip revision: 0e9915b
backTransform-methods.rd
\name{backTransform-methods}
\docType{methods}
\alias{backTransform}
\alias{backTransform-methods}
\alias{backTransform,unmarkedEstimate-method}
\alias{backTransform,unmarkedFit-method}
\alias{backTransform,unmarkedLinComb-method}
\alias{show,unmarkedBackTrans-method}
\title{Methods for Function backTransform in Package `unmarked'}
\description{Methods for function \code{backTransform} in Package `unmarked'.
This converts from link-scale to original-scale}
\usage{
\S4method{backTransform}{unmarkedFit}(obj, type)
\S4method{backTransform}{unmarkedEstimate}(obj)
}
\arguments{
\item{obj}{Object of appropriate S4 class}
\item{type}{one of names(obj), eg 'state' or 'det'}
}
\section{Methods}{
\describe{
\item{obj = "unmarkedEstimate"}{Typically done internally}
\item{obj = "unmarkedFit"}{Back-transform a parameter from a fitted model. Only
possible if no covariates are present. Must specify argument type
as one of the values returned by names(obj).}
\item{obj = "unmarkedLinComb"}{Back-transform a predicted value created by
\code{linearComb}. This is done internally by \code{\link{predict}} but
can be done explicitly by user.}
}}
\examples{
\dontrun{
data(mallard)
mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs = mallard.site,
obsCovs = mallard.obs)
(fm <- pcount(~ 1 ~ forest, mallardUMF)) # Fit a model
backTransform(fm, type="det") # This works because there are no detection covariates
#backTransform(fm, type="state") # This doesn't work because covariates are present
lc <- linearComb(fm, c(1, 0), type="state") # Estimate abundance on the log scale when forest=0
backTransform(lc) # Abundance on the original scale
}
}
\keyword{methods}