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residuals-method.Rd
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residuals-method.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SingleGroup-methods.R
\name{residuals-method}
\alias{residuals-method}
\alias{residuals,SingleGroupClass-method}
\alias{residuals,MixtureClass-method}
\alias{residuals,MultipleGroupClass-method}
\alias{residuals,DiscreteClass-method}
\title{Compute model residuals}
\usage{
\S4method{residuals}{SingleGroupClass}(
object,
type = "LD",
p.adjust = "none",
df.p = FALSE,
approx.z = FALSE,
full.scores = FALSE,
QMC = FALSE,
printvalue = NULL,
tables = FALSE,
verbose = TRUE,
Theta = NULL,
suppress = NA,
theta_lim = c(-6, 6),
quadpts = NULL,
fold = TRUE,
upper = TRUE,
technical = list(),
...
)
}
\arguments{
\item{object}{an object of class \code{SingleGroupClass} or
\code{MultipleGroupClass}. Bifactor models are automatically detected and utilized for
better accuracy}
\item{type}{type of residuals to be displayed.
Can be either \code{'LD'} or \code{'LDG2'} for a local dependence matrix based on the
X2 or G2 statistics (Chen & Thissen, 1997), \code{'Q3'} for the statistic proposed by
Yen (1984), \code{'JSI'} for the jack-knife statistic proposed Edwards et al. (2018),
\code{'exp'} for the expected values for the frequencies of every response pattern,
and \code{'expfull'} for the expected values for every theoretically observable response pattern.
For the 'LD' and 'LDG2' types, the upper diagonal elements represent the standardized
residuals in the form of signed Cramers V coefficients}
\item{p.adjust}{method to use for adjusting all p-values (see \code{\link{p.adjust}}
for available options). Default is \code{'none'}}
\item{df.p}{logical; print the degrees of freedom and p-values?}
\item{approx.z}{logical; transform \eqn{\chi^2(df)} information from LD tests into approximate
z-ratios instead using the transformation \eqn{z=\sqrt{2 * \chi^2} - \sqrt{2 * df - 1}}?}
\item{full.scores}{logical; compute relevant statistics
for each subject in the original data?}
\item{QMC}{logical; use quasi-Monte Carlo integration? If \code{quadpts} is omitted the
default number of nodes is 5000}
\item{printvalue}{a numeric value to be specified when using the \code{res='exp'}
option. Only prints patterns that have standardized residuals greater than
\code{abs(printvalue)}. The default (NULL) prints all response patterns}
\item{tables}{logical; for LD type, return the observed, expected, and standardized residual
tables for each item combination?}
\item{verbose}{logical; allow information to be printed to the console?}
\item{Theta}{a matrix of factor scores used for statistics that require empirical estimates (i.e., Q3).
If supplied, arguments typically passed to \code{fscores()} will be ignored and these values will
be used instead}
\item{suppress}{a numeric value indicating which parameter local dependency combinations
to flag as being too high (for LD, LDG2, and Q3 the standardize correlations are used; for
JSI, the z-ratios are used). Absolute values for the standardized estimates greater than
this value will be returned, while all values less than this value will be set to missing}
\item{theta_lim}{range for the integration grid}
\item{quadpts}{number of quadrature nodes to use. The default is extracted from model (if available)
or generated automatically if not available}
\item{fold}{logical; apply the sum 'folding' described by Edwards et al. (2018) for the JSI statistic?}
\item{upper}{logical; which portion of the matrix (upper versus lower triangle)
should the \code{suppress} argument be applied to?}
\item{technical}{list of technical arguments when models are re-estimated (see \code{\link{mirt}}
for details)}
\item{...}{additional arguments to be passed to \code{fscores()}}
}
\description{
Return model implied residuals for linear dependencies between items or at the person level.
If the latent trait density was approximated (e.g., Davidian curves, Empirical histograms, etc)
then passing \code{use_dentype_estimate = TRUE} will use the internally saved quadrature and
density components (where applicable).
}
\examples{
\dontrun{
x <- mirt(Science, 1)
residuals(x)
residuals(x, tables = TRUE)
residuals(x, type = 'exp')
residuals(x, suppress = .15)
residuals(x, df.p = TRUE)
residuals(x, df.p = TRUE, p.adjust = 'fdr') # apply FWE control
# Pearson's X2 estimate for goodness-of-fit
full_table <- residuals(x, type = 'expfull')
head(full_table)
X2 <- with(full_table, sum((freq - exp)^2 / exp))
df <- nrow(full_table) - extract.mirt(x, 'nest') - 1
p <- pchisq(X2, df = df, lower.tail=FALSE)
data.frame(X2, df, p, row.names='Pearson-X2')
# above FOG test as a function
PearsonX2 <- function(x){
full_table <- residuals(x, type = 'expfull')
X2 <- with(full_table, sum((freq - exp)^2 / exp))
df <- nrow(full_table) - extract.mirt(x, 'nest') - 1
p <- pchisq(X2, df = df, lower.tail=FALSE)
data.frame(X2, df, p, row.names='Pearson-X2')
}
PearsonX2(x)
# extract results manually
out <- residuals(x, df.p = TRUE, verbose=FALSE)
str(out)
out$df.p[1,2]
# with and without supplied factor scores
Theta <- fscores(x)
residuals(x, type = 'Q3', Theta=Theta)
residuals(x, type = 'Q3', method = 'ML')
# Edwards et al. (2018) JSI statistic
N <- 250
a <- rnorm(10, 1.7, 0.3)
d <- rnorm(10)
dat <- simdata(a, d, N=250, itemtype = '2PL')
mod <- mirt(dat, 1)
residuals(mod, type = 'JSI')
residuals(mod, type = 'JSI', fold=FALSE) # unfolded
# LD between items 1-2
aLD <- numeric(10)
aLD[1:2] <- rnorm(2, 2.55, 0.15)
a2 <- cbind(a, aLD)
dat <- simdata(a2, d, N=250, itemtype = '2PL')
mod <- mirt(dat, 1)
# JSI executed in parallel over multiple cores
if(interactive()) mirtCluster()
residuals(mod, type = 'JSI')
}
}
\references{
Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory
Package for the R Environment. \emph{Journal of Statistical Software, 48}(6), 1-29.
\doi{10.18637/jss.v048.i06}
Chen, W. H. & Thissen, D. (1997). Local dependence indices for item pairs using item
response theory. \emph{Journal of Educational and Behavioral Statistics, 22}, 265-289.
Edwards, M. C., Houts, C. R. & Cai, L. (2018). A Diagnostic Procedure to Detect Departures
From Local Independence in Item Response Theory Models.
\emph{Psychological Methods, 23}, 138-149.
Yen, W. (1984). Effects of local item dependence on the fit and equating performance of the three
parameter logistic model. \emph{Applied Psychological Measurement, 8}, 125-145.
}