\name{sim}
\docType{data}
\alias{sim}
\alias{beta.true}
\alias{mu.true}
\alias{phi.true}
\alias{theta.true}
\alias{fam}
\alias{pred}
\alias{vars}
\alias{ladata}
\alias{redata}
\title{Simulated Life History Data}
\description{
Data on life history traits for four years and five fitness components
}
\usage{data(sim)}
\format{
Loads nine objects.
The objects \code{beta.true}, \code{mu.true}, \code{phi.true}, and
\code{theta.true} are the simulation truth parameter values in
different parametrizations.
\describe{
\item{beta.true}{Regression coefficient vector for model
\code{resp ~ varb + 0 + z1 + z2 + I(z1^2) + I(z1*z2) + I(z2^2)}.}
\item{mu.true}{Unconditional mean value parameter vector for same
model.}
\item{phi.true}{Unconditional canonical value parameter vector for
same model.}
\item{theta.true}{Conditional canonical value parameter vector for
same model.}
}
The objects \code{fam}, \code{pred}, and \code{vars}
specify the aster model graphical and probabilistic structure.
\describe{
\item{fam}{Integer vector giving the families of the variables in
the graph.}
\item{pred}{Integer vector giving the predecessors of the variables in
the graph.}
\item{vars}{Character vector giving the names of the variables in
the graph.}
}
The objects \code{ladata} and \code{redata} are the simulated data
in two forms \code{"wide"} and \code{"long"} in the terminology
of the \code{reshape} function.
\describe{
\item{ladata}{Data frame with variables \code{y}, \code{z1},
\code{z2} used for Lande-Arnold type estimation of fitness landscape.
\code{y} is the response, fitness, and \code{z1} and \code{z1} are
predictor variables, phenotypes.}
\item{redata}{Data frame with variables \code{resp}, \code{z1},
\code{z2}, \code{varb}, \code{id}, \code{root}
used for aster type estimation of fitness landscape.
\code{resp} is the response, containing all components of fitness,
and \code{z1} and \code{z1} are predictor variables, phenotypes.
\code{varb} is a factor whose levels are are elements of \code{vars}
indicating which elements of \code{resp} go with which nodes of the
aster model graphical structure. The variables \code{z1} and \code{z2}
have been set equal to zero except when \code{grep("nseed", varb)} is
\code{TRUE}. For the rationale see Section 3.2 of TR 669 referenced
below.
}
}
}
\source{
Geyer, C. J and Shaw, R. G. (2008)
Supporting Data Analysis for a talk to be given at Evolution 2008.
Technical Report No. 669. School of Statistics, University of Minnesota.
\url{http://hdl.handle.net/11299/56204}.
}
\references{
Geyer, C. J and Shaw, R. G. (2009)
Hypothesis Tests and Confidence Intervals
Involving Fitness Landscapes fit by Aster Models.
Technical Report No. 671. School of Statistics, University of Minnesota.
\url{http://hdl.handle.net/11299/56219}.
}
\examples{
data(sim)
\dontrun{
### CRAN policy says examples must take < 5 sec. This doesn't.
out6 <- aster(resp ~ varb + 0 + z1 + z2 + I(z1^2) + I(z1*z2) + I(z2^2),
pred, fam, varb, id, root, data = redata)
summary(out6)
}
lout <- lm(y ~ z1 + z2 + I(z1^2) + I(z1*z2) + I(z2^2), data = ladata)
summary(lout)
}
\keyword{datasets}