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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fitIndices.R
\name{nullRMSEA}
\alias{nullRMSEA}
\title{Calculate the RMSEA of the null model}
\usage{
nullRMSEA(object, scaled = FALSE, silent = FALSE)
}
\arguments{
\item{object}{The lavaan model object provided after running the \code{cfa},
\code{sem}, \code{growth}, or \code{lavaan} functions.}
\item{scaled}{If \code{TRUE}, the scaled (or robust, if available) RMSEA
is returned. Ignored if a robust test statistic was not requested.}
\item{silent}{If \code{TRUE}, do not print anything on the screen.}
}
\value{
A value of RMSEA of the null model (a \code{numeric} vector)
returned invisibly.
}
\description{
Calculate the RMSEA of the null (baseline) model
}
\details{
RMSEA of the null model is calculated similar to the formula provided in the
\code{lavaan} package. The standard formula of RMSEA is
\deqn{ RMSEA =\sqrt{\frac{\chi^2}{N \times df} - \frac{1}{N}} \times
\sqrt{G} }
where \eqn{\chi^2} is the chi-square test statistic value of the target
model, \eqn{N} is the total sample size, \eqn{df} is the degree of freedom
of the hypothesized model, \eqn{G} is the number of groups. Kenny proposed
in his website that
"A reasonable rule of thumb is to examine the RMSEA for the null model and
make sure that is no smaller than 0.158. An RMSEA for the model of 0.05 and
a TLI of .90, implies that the RMSEA of the null model is 0.158. If the
RMSEA for the null model is less than 0.158, an incremental measure of fit
may not be that informative."
See also \url{http://davidakenny.net/cm/fit.htm}
}
\examples{
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939)
nullRMSEA(fit)
}
\references{
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The
performance of RMSEA in models with small degrees of freedom.
\emph{Sociological Methods Research, 44}(3), 486--507.
\doi{10.1177/0049124114543236}
}
\seealso{
\itemize{
\item \code{\link[=miPowerFit]{miPowerFit()}} For the modification indices and their
power approach for model fit evaluation
\item \code{\link[=moreFitIndices]{moreFitIndices()}} For other fit indices
}
}
\author{
Ruben Arslan (Humboldt-University of Berlin, \email{rubenarslan@gmail.com})
Terrence D. Jorgensen (University of Amsterdam; \email{TJorgensen314@gmail.com})
}
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