Nothing
mplus.run()
according to the lastest version of the
function runModels()
in the MplusAutomation package.mplus.print()
, function did not print result of
a misty.object of type mplus.mplus.print()
for printing a Mplus output file on the R console.mplus()
to create and run a Mplus input to print the output on
the console.update.mplus()
to update specific Mplus input command sections
in the mplus
object, run the updated input file, and print the output on the console.chr.grep()
and chr.grepl()
for multiple pattern matching, i.e.,
grep()
and grepl()
functions for matching a vector of character strings.write.mplus()
is not restricted to variable names with up to 8
characters anymore.run.mplus()
to mplus.run()
.posthoc
in the functions aov.b()
,
aov.w()
and test.welch()
to FALSE
.replace
from modifiedDate
to modified
in the functions mplus.lca()
and mplus.run()
.showOutput
into show.out
and replaceOutfile
into
replace.out
in the function mplus.run()
.message
to the function mplus.run()
.test.welch()
, function did not print post hoc
tests when specifying posthoc = TRUE
.result.lca()
, function excluded all outputs
which involved the word ERROR
even though results were available (thanks to Michael Weber).multilevel.fit()
, function used the
number of observations at the Within level instead of the Between level for
computing RMSEA at the Between Level (thanks to Maurizio Sicorello).descript()
which caused an error message when
specifying a split variable.robust.coef()
which caused an error message in
the presence of missing data on predictor variables.multilevel.icc()
and multilevel.descript()
which caused an error message in when specifying a tibble instead of a data frame
(thanks to Tanja Held).mplus.lca()
, the argument processors
allows to specify the
number of processors and threads separately.item.omega()
, residual covariances can be specified when type = "categ"
..
, +
, -
, ~
, :
, ::
, functions which caused an warning message. center()
, multilevel.icc()
, and multilevel.descript()
which caused an error message in three-level data with ambiguously coded cluster
variables common in longitudinal data. multilevel.descript()
to take into account missing
values, e.g., No. of cases
and No. of clusters
show the number observations
and clusters after excluding missing values. write.sav()
do not require specifying all
three columns label
, values
, and missing
anymore.na
to the function read.mplus()
.freq()
, function did not provide an output. df.subset()
for subsetting data frames using the operators
.
, +
, -
, ~
, :
, ::
, and !
similar to functions from the R package tidyselect
.lagged()
to compute lagged values of variables.df.move()
to move variable(s) in a data frame.read.dta()
and write.dta()
to read and write Stata DTA files.coding()
to code categorical variables, i.e., dummy, simple,
unweighted and weighted effect, repeated, forward Helmert, reverse Helmert, and
orthogonal polynomial coding.effsize()
to compute effect sizes for categorical variables, i.e.,
(adjusted) phi coefficient, (bias-corrected) Cramer's V, (bias-corrected) Tschuprow's T,
(adjusted) Pearson's contingency coefficient, Cohen's w, and Fei.script.copy()
to save a copy of the current script in RStudio
with the current date and time.as.na()
, na.as()``center()
, ci.mean()
, ci.mean.w()
, ci.median()
,
ci.prop()
, ci.var()
, ci.sd()
, cluster.scores()
, cor.matrix()
,
crosstab()
, descript()
, freq()
, item.alpha()
, item.cfa()
, item.invar()
,
item.omega()
, item.reverse()
, item.scores()
, multilevel.cfa()
, multilevel.cor()
,
multilevel.descript()
, multilevel.fit()
, multilevel.icc()
, multilevel.invar()
,
multilevel.omega()
, na.auxiliary()
, na.coverage()
, na.descript()
,
na.indicator()
, na.pattern()
, na.prop()
, na.test()
rec()
, rwg.lindell()
,
skewness()
, and kurtosis()
provide the argument ...
instead of the argument
x
to specify variables from the data frame specified in data
using the operators
.
, +
, -
, ~
, :
, ::
. multilevel.icc()
computes intraclass corelation coefficients in
three-level data.multilevel.descript()
computes multilevel descriptive statistics
in three-level data.center()
centers predictor variables in three-level data.na.descript()
provides descriptive statistics for missing data in
two-level and three-lavel data.cor.matrix()
computes tetrachoric and polychoric correlation coefficients.write
and append
to all functions providing a
print function to save the print output into a text file.names
in the function rec()
to .e
.label
and labels
in the read.sav
function to FALSE
.value
in the function na.as()
to na
to make it consistent with the arguments of the function as.na()
.resid.cov
in the function item.omega()
to resocv
to make it consistent with the arguments of the functions item.cfa()
and multilevel.cfa()
.names
in the functions center
, cluster.scores
,
item.reverse
, and rec
to name
to make it consistent with the arguments of the functions
item.scores()
, na.prop()
, and lwg.lindell()
.x
and ...
in the functions df.duplicated()
and df.unique()
to ...
and data
to make it consistent with all other functions using the ...
argument.as.na
and na.as
into one help page.script.open
, script.close
, and script.save
into one help page.skewness
and kurtosis
into one help page.ci.mean
and ci.median
into one help page.ci.var
and ci.sd
into one help page.shift()
and replaced it by the function lagged()
.dummy.c()
and replaced it by the function coding()
cor.phi()
, cor.cont()
, cor.cramer()
, and eta.sq()
and replaced them by the function effsize()
.cor.poly()
and integrated polychoric correlation coefficient into the function cor.matrix()
.multilevel.descript()
, function led to a node stack overflow. shift()
to compute lagged or leading values of a vector.libraries()
, version of the packages were not correctly displayed. test.welch()
, to remove errors for r-devel from a recent change in r-devel.group.ind
to the function result.lca()
to specify.
latent class indicators as grouping variable in the bar charts. mplus.lca()
can be used to conduct latent class analysis with
count, unordered categorical, and ordered categorical indicator variables.result.lca()
can be used to save bar charts with error bars for confidence
intervals for each of the latent class solutions.dominance.manual()
, function provided the wrong rank orderning.mplus.lpa()
and results.lpa()
to mplus.lca()
and results.lca()
.item.invar()
for evaluating configural, metric, scalar, and strict
between-group or longitudinal (partial) measurement invariance.robust.coef()
for computing heteroscedasticity-consistent standard
errors and significance values for linear models estimated by using the lm()
function and generalized linear models estimated by using the glm()
function.dominance()
for linear models estimated by using the lm()
function
and dominance.manual()
to conduct dominance analysis based on a (model-implied)
correlation matrix of the manifest or latent variables.check.resid()
for performing residual diagnostics to detect
nonlinearity (partial residual or component-plus-residual plots), nonconstant
error variance (predicted values vs. residuals plot), and non-normality of residuals
(Q-Q plot and histogram with density plot).mplus.lpa()
for writing Mplus input files for conducting latent
profile analysis based on six different variance-covariance structures.result.lpa()
for creating a summary result table for latent profile
analysis from multiple Mplus output files within subfolders.order
to the function multilevel.cor()
to order variables
in the output table so that variables specified in the argument between
are
shown first.multilevel.cfa()
and multilevel.invar()
.item.cfa()
, multilevel.cfa()
,
and multilevel.invar()
.write.result()
can also write results based on the return object of
the std.coef
function.min.value
in the function item.cfa()
, multilevel.cfa()
,
and multilevel.invar()
to mod.minval
and changed the default setting to 6.63
.r2mlm
from the Imports
field in the DESCRIPTION
due to dependencies issues.multilevel.descript()
can also deal with between-cluster variables by reporting means and standard deviations at the cluster level.print
to the function multilevel.descript()
to request standard deviation of the variance components. multilevel.fit()
for computing simultaneous and level-specific model
fit information for a fitted multilevel model containing no cross-level constraints from the R package lavaan.multilevel.cfa()
for conducting multilevel confirmatory factor analysis using the R package lavaan to investigate four types
of constructs, i.e., within-cluster, shared, configural, and simultaneous shared and configural cluster constructs.multilevel.invar()
for evaluating configural, metric, and scalar cross-level measurement invariance using multilevel confirmatory factor
analysis.multilevel.omega()
for computing point estimate and Monte Carlo confidence interval for the multilevel composite reliability defined by Lai (2021) for a within-cluster construct, shared cluster-level construct, and configural cluster construct.multilevel.cor()
, e.g., warning message is printed when absolute correlations are greater than 1.cluster
in the function multilevel.cor()
, multilevel.descript()
, and multilevel.icc()
can also be specified using the variable name of the cluster variable in x
.item.cfa()
function, e.g., loglikelihood and information criteria are shown above chi-square test of model fit and label Ad Hoc
changed to Scaled
. multilevel.cor()
, which caused an error message (thanks to Richard Janzen).libraries()
to load and attach multiple add-on packages at once. check.outlier()
computes statistical measures for leverage, distance,
and influence for linear models estimated by using the lm()
functionwrite.result()
, result tables are in line with the arguments
print
, tri
, digits
, p.digits
, and icc.digits
specified in the object x
(thanks to Stefan Kulakow).crosstab()
displays marginal row-wise, column-wise, and total percentages in the output (thanks to Joachim Fritz Punter and Lisa Bucher). Note that the function now also returns the crosstable in the list element result$crosstab
of the return object .Value
sections in the documentation of the functions.weighted
in the test.t
and the na.auxiliary
function
to FALSE
in line with the recommendation by Delacre et al. (2021).collin.diag()
to check.collin()
.read.mplus()
, an error message was printed if comments in the Mplus input file contains special characters (e.g., ä, ü, ö).std.coef()
, the function was not applicable to predictors specified as character vector or factor.script.close()
, script.new()
, script.open()
, and script.save()
to close, open, and save R scripts in RStudio. setsource()
to set the working directory to the source file location in RStudio equivalent to using the menu item Session - Set Working Directory - To Source File Location
.restart()
to restart the RStudio session equivalent to using the menu item Session - Restart R
.multilevel.r2.manual()
to compute R-squared measures by Rights and Sterba (2019) for
multilevel and linear mixed effects models by manually inputting parameter estimates. center()
, cluster.scores()
, rec()
, and item.reverse()
can be applied to more than one variable at once.aov.w()
for performing repeated measures analysis of variance (within-subject ANOVA) including paired-samples t-tests for multiple comparison, descriptive statistics, effect size measures, and a plot showing error bars for within-subject confidence intervals.ci.mean.w()
for computing difference-adjusted Cousineau-Morey within-subject confidence intervals.ci.mean.diff()
computes the confidence interval for the difference for an arithmetic mean in a one-sample design.aov.b()
, test.t()
, test.welch()
, and test.z()
plot difference-adjusted confidence intervals in two-sample design by default.jitter.height
to the functions aov.b()
, test.levene()
, test.t()
, aov.welch()
, and test.z()
.adjust
to the function ci.mean()
, to apply difference-adjustment for the confidence interval.test.t()
displays the confidence interval for the mean difference in the one-sample t-test.test.t()
, result table provided by the function did not display the confidence interval correctly.aov.b()
for performing between-subject analysis of variance including Tukey HSD post hoc test for multiple comparison.as.na()
is also applicable to arraysplot
and arguments for various graphical parameters for plotting results to the functions test.levene()
, test.t()
, test.welch()
, and test.z()
.write
for writing results into an Excel file to the functions cor.matrix()
, crosstab()
,
descript()
, freq()
, item.alpha()
, item.cfa()
, item.omega()
, multilevel.cor()
, multilevel.descript()
,
na.coverage()
, na.descript()
, and na.pattern()
posthoc
for conducting Games-Howell post hoc test for multiple comparison
to the functions test.welch()
.item.cfa()
for conducting confirmatory factor analysis using the R package lavaan.write.result()
can also write results based on the return object of the item.cfa()
function.exclude
of the function freq()
can also be set to FALSE
.multilevel.cor()
to make it consistent with the output of the function item.cfa()
.na.omit
in the function multilevel.cor()
to missing
to make it consistent with the arguments of the function item.cfa()
.estimator
in the function multilevel.cor()
to ML
, so that full information maximum likelihood method is used for dealing with missing data.multilevel.cor()
, function did not use Huber-White
robust standard errors, but conventional standard errors when specifying estimator = "MLR"
. multilevel.r2()
for computing R-squared measures for multilevel and linear mixed effects models.write.xlsx()
for writing Excel files (.xlsx).write.result()
for writing results of a misty object into an Excel file.multilevel.descript()
.round
to the function freq()
for rounding numeric variables.na.test()
function when running into numerical problems.sig
in the functions cor.matrix()
and multilevel.cor()
to FALSE
.collin.diag()
function. print.misty.object()
, function did not print the result object of the the function crosstab()
correctly when requesting percentages.multilevel.cor()
for computing the within-group and between-group correlation matrix using the lavaan package.na.test()
for performing Little's missing completely at random (MCAR) test.indirect()
for computing confidence intervals for the indirect effect using the asymptotic normal method, the distribution of the product method, and the Monte Carlo method.multilevel.indirect()
for computing confidence intervals for the indirect effect in a 1-1-1 multilevel mediation model using the Monte Carlo method.cor.matrix()
highlights statistically significant correlation coefficients in boldface.cor.matrix()
shows the results in a table when computing a correlation coefficient for two variables.stat
) and degrees of freedom (df
) to the argument print
in the function cor.matrix()
.continuity
for continuity correction to the function cor.matrix()
for testing Spearman's rank-order correlation coefficient and Kendall's Tau-b correlation.cor.matrix()
when computing Spearman's rank-order correlation coefficient or Kendall's Tau-b correlation.group
in the functions center()
, group.scores()
, multilevel.descript()
, multilevel.icc()
, and rwg.lindell()
to cluster
.group.scores()
to cluster.scores()
.cor.matrix()
, function did not print sample sizes when specifying a grouping variable and using listwise deletion.write.mplus()
writes a Mplus input template with variables names specified in the DATA command along with the tab-delimited data file by default.print()
in the write.mplus()
function.weighted
in the test.welch()
function into FALSE
following the recommendation by Delacre et al. (2021).cohens.d()
, function printed warning messages of the pt()
function.cohens.d()
, function could not deal with more than one variable in a one-sample design.test.t()
for performing one-sample, two-sample, and paired-sample t-tests including Cohen's d effect size measure.test.welch()
for performing Welch's t-test including Cohen's d effect size measure and Welch's ANOVA including $\eta^2$ and $\omega^2$ effect size measures.print
in the function descript()
.format
, label
, labels
, missing
to the function read.sav()
to remove variable formats, variable labels, value labels, value labels for user-defined missings, and widths from attributes of the variable.item.reverse()
can also be applied to to items with non-integer values.cor.matrix()
when specifying a grouping variable comprises the combined results of both groups in the matrices. read.mplus()
can also deal with consecutive variables (e.g., x1-x5
).group
and split
arguments to the function cohens.d()
.test.z
function.cohens.d()
computes various kinds of Cohen's d, Hedges' d, and Glass's $\Delta$ including confidence intervals, e.g., weighted and unweighted pooled standard deviation in a two-sample design, with and without controlling for the correlation between the two sets of measurement in a paired-sample design, or with and without the small-sample correction factor. alpha.coef()
to item.alpha()
, cont.coef()
to cor.cont()
, cramers.v()
to cor.cramer()
, levenes.test()
to test.levene()
, mgsub()
to chr.gsub()
, omega.coef()
to item.omega()
, reverse.item()
to item.reverse()
, phi.coef()
to cor.phi()
, poly.cor()
to cor.poly()
, scores()
to item.scores()
, stromit()
to chr.omit()
, trim()
to chr.trim()
, z.test()
to test.z()
,use
in the cor.matrix()
function into na.omit
.method
in the functions multilevel.descript()
and multilevel.icc()
to "lme4"
; if the lme4 package is not installed, "aov"
will be used.ci.mean.diff()
and ci.mean.prop()
when computing confidence intervals in two-sample designs, i.e., results are divided in two rows according to the grouping variable.ci.mean.diff()
and ci.mean.prop()
when computing confidence intervals in paired-sample designs, i.e., output reports the number of missing data pairs (nNA
), instead of number of missing values for each variable separately (nNA1
and nNA2
).descript()
when specifying the argument levenes.test()
, i.e., duplicated labels in the column group
or variable
are not shown.cohens.d()
into a generic function with the methods cohens.d.default()
and cohens.d.formula()
.hypo
and descript
to the functions test.levene()
and test.z()
.freq
, descript
, and crosstab
function.as.na
in the as.na()
function into na
.center()
which caused an error message in case of groups with only one observation when trying to apply group mean centering. center()
which caused an error message when trying to apply grand mean centering of a Level 1 predictor.cohens.d()
, an error message was printed in the between subject design whenever specifying a grouping variable with missing values.cor.matrix()
, which caused an error when using listwise deletion for missing data while specifying a grouping variable.descript()
, which caused an error message when selection only one or two argument statistical measures using the argument print
.freq()
, where the argument split
was broken.test.zz()
, where the alternative hypothesis was displayed wrong when specifying alternative = "greater"
or alternative = "less"
.collin.diag()
for collinearity diagnostics including tolerance, (generalized) standard error inflation factor, (generalized) variance inflation factor, eigenvalues, conditional indices, and variance proportions for linear, generalized linear, and mixed-effects models.std.coef()
for computing standardized coefficients (StdX, StdY, and StdYX) for linear models estimated by using the lm()
function.mgsub()
for multiple pattern matching and replacements, i.e., gsub()
function for matching and replacing a vector of character strings.df.duplicated()
and df.unique()
extracting duplicated or unique rows of a matrix or data frame.read.xlsx()
, default setting of the argument progress
was wrong.print.misty.object()
.z.test()
for performing one sample, two sample, and paired sample z-test.omega.coef()
does not access internal slots of a fitted lavaan object anymore (requested by Yves Rosseel).levenes.test()
.size.mean()
, size.prop()
, and size.cor()
to include greek letters.theta
in the size.mean()
function into delta
.ci.mean()
, ci.mean.diff()
, ci.median()
, ci.prop()
, ci.prop.diff()
, ci.sd()
, ci.var()
for computing confidence interval for the arithmetic mean, the difference in arithmetic means, the median, the proportion, the difference in proportions, the variance, and the standard deviation.levenes.test()
for conducting Levene's test for homogeneity of variance. omega.coef()
for computing coefficient omega (McDonald, 1978), hierarchical omega (Kelley & Pornprasertmanit, 2016), and categorical omega (Green & Yang, 2009).read.xlsx()
for reading Excel files (.xlsx).coef.alpha()
.cor.matrix()
.as.na()
can also replace user-specified values with missing values in lists.use
in the alpha.coef()
function into a logical argument na.omit
.pval.digits
in the cor.matrix()
function into p.digits
.print.cont.coef()
, print.cramers.v()
, print.na.auxiliary()
, print.na.coverage()
, print.phi.coef()
, and print.poly.cor()
into print.square.matrix()
is.vector()
function was used to test if an object is a vector. Instead is.atomic()
function is used to test if an object is a vector.as.na()
, function converted strings in data frames to factors.trim()
for removing whitespace from start and/or end of a string. Note that this function is equivalent to the function trimws()
in the base
package. However, the trimws()
function fails to remove whitespace in some instances.cohens.d()
, function returned NA
for Cohen's d in within-subject design in the presence of missing valuesalpha.coef()
, function did not provide any item statistics irrespective of the argument print
as.na()
, function always generated a warning message irrespective of the argument as.na
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