A collection of miscellaneous tools and functions, such as tools to generate descriptive statistics tables, format output, visualize relations among variables or check distributions.
R package with general utility and convenience functions.
Some of these are general functions that help using and
exploring SEM style data. Others are more specific.
This package has grown out of my own work, and is often to automate
To get the latest development version, use:
To get the version on cran:
I do not have any vignettes or demos for this package. However, the functions are documented and I have included examples in the function documentation that are relatively basic. Below are just a few examples that I tend to use it for:
testdistr(c(mtcars$mpg, 60), "normal", extremevalues = "theoretical", robust = TRUE)
testdistr(winsorizor(c(mtcars$mpg, 60), .01), "normal", extremevalues = "theoretical", robust = TRUE)
testdistr(resid(lm(Petal.Length ~ Species, data = iris)), "normal")
plot(SEMSummary(~ Petal.Length + Sepal.Length + Petal.Width + Sepal.Width, data = iris))
This package also has some utility functions used for other packages, such as calculating empirical p-values from bootstrapping or MCMC samples as from a Bayesian analysis, etc. These are probably less interesting to most users.
detailedTests()is now more generic and dispatches to
.detailedTestsVGLM()to provide detailed tests for both linear mixed effects models and multinomial logistic regression models fit by
ezMULTINOM()is now deprecated in favor of the new, more generic
testdistr() now creates more appropriate plots for discrete
distributions including the Poisson and Negative Binomial.
moments() now updated to accomodate changes in the lavaan
package (thanks to Yves Rosseel)
TukeyHSDgg() updated to use the emmeans package instead of
the now defunct lsmeans package.
formatLMER() returned the lower confidence interval twice
instead of the lower and upper confidence interval.
This is now fixed.
R2LMER() A simple function to calculate the marginal and
conditional variance accounted for by a model estimated by
compareLMER() A function to compare two models estimated by
lmer() include significance tests and effect sizes
for estimates of the variance explained.
detailedTests() A function to compute detailed tests on a
model estimated from
lmer() including confidence intervals
for parameters, significance tests, where possible,
overall model fit, and effect sizes for the model and each variable.
formatLMER() A function to nicely format detailed model results,
possibly from multiple models. Requires results from
detailedTests() based on
lmer() models, at the moment.
iccMixed() A function to calculate the intraclass correlation
coefficient using mixed effects models. Works with either
normally distributed outcomes or binary outcomes, in which case
the latent variable estimate of the ICC is computed.
nEffective() Calculates the effective sample size based on
the number of independent units, number of observations per
unit, and the intraclass correlation coefficient.
acfByID() Calculates the lagged autocorrelation of a variable
by an ID variable and returns a data.table for further use,
such as examination, summary, or plotting
meanDeviations() A simple function to calculate means and mean
deviations, useful for creating between and within versions of
a variable in a data.table
as.na() function added to convert data to missing (NA) while
preserving the class/type of the data (useful for data.table).
meanDecompose() function added to decompose multilevel or
repeated measures data into means and residuals.
timeshift() function added to center a time variable at a new
zero point. Useful when times may start and end off the standard
24 hour period (e.g., 11am to 2am, which technically fall on
intSigRegGraph() function added to graph regions of significance
from interactions with linear models as well as the mostly helper
ezMULTINOM() new function added to make running multinomial
logistic regression easy in R, along with all pairwise contrasts
and omnibus tests of statistical significance.
testdistr() function expanded to cover multivariate
normal data, and the old
mvqq() function is now deprecated.
testdistr() includes optional robust estimates for
univariate and multivariate normal data
formatHtest() gains support for pearson, kendal, and spearman
correlations from the
logicals A series of support functions for findings values in
a particular range, such as
%gele% for values greater than or
equal to the min and less than or equal to the max as well as
to automatically subset the data when prefixed with an s,
winsorizor() now properly handles atomic data. Fixes
an issue where variables in a data table would be
atomic after calling the
scale() function and
winsorizor() would fail.
egltable() now works with data.tables
testdistr() function to plot data against different theoretical
ggplot2. A sort of generalized
allowing other distributions besides the normal distribution.
winsorizor() Function moved from
pscore package. Sets any values
beyond specific quantiles of the empirical data to the specified
quantiles. Can work on vectors, data frames, or matrices.
Initial release to CRAN.