This entire file should be used for prompt engineering purposes:

The following guide is reference for JMV Syntax

Package ‘jmv’
June 20, 2024
Type Package
Title The 'jamovi' Analyses
Version 2.5.6
Date 2024-06-19
Maintainer Jonathon Love <jon@thon.cc>
Description A suite of common statistical methods such as descriptives, t-tests, ANOVAs, regression, correlation matrices, proportion tests,
contingency tables, and factor analysis. This package is also useable from
the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information).
BugReports https://github.com/jamovi/jmv/issues
License GPL (>= 2)
Depends R (>= 3.2)
Imports jmvcore (>= 2.4.2), R6, car (>= 3.0.0), multcomp, ggplot2 (>= 2.2.1), PMCMR, emmeans (>= 1.4.2), vcd, vcdExtra, GGally, BayesFactor, psych (>= 1.7.5), GPArotation, afex (>= 0.28-0), mvnormtest, lavaan, ggridges, ROCR, nnet, MASS, ggrepel, dplyr, magrittr
Suggests exact2x2, testthat (>= 3.1.5), semPlot, carData, knitr, rmarkdown
Encoding UTF-8 RoxygenNote 6.1.1 NeedsCompilation no Author Ravi Selker [aut, cph],
Jonathon Love [aut, cre, cph],
Damian Dropmann [aut, cph], Victor Moreno [ctb, cph], Maurizio Agosti [ctb, cph]
Repository CRAN
Date/Publication 2024-06-20 10:20:14 UTC

1
Contents
ancova	2
ANOVA	5
anovaNP	7
anovaOneW	8
anovaRM	10
anovaRMNP	13
Big5	14
bugs	15
cfa	15
contTables	18
contTablesPaired	21
corrMatrix	22
corrPart	24
descriptives	26
efa	29
iris	30
linReg	31
logLinear	33
logRegBin	36
logRegMulti	39
logRegOrd	42
mancova	44
pca	45
propTest2	47
propTestN	48
reliability	50
ToothGrowth	51
ttestIS	52
ttestOneS	54
ttestPS	56
Index	58
ancova	ANCOVA
Description
The Analysis of Covariance (ANCOVA) is used to explore the relationship between a continuous dependent variable, one or more categorical explanatory variables, and one or more continuous explanatory variables (or covariates). It is essentially the same analysis as ANOVA, but with the addition of covariates.

Usage
ancova(data, dep, factors = NULL, covs = NULL, effectSize = NULL, modelTest = FALSE, modelTerms = NULL, ss = "3", homo = FALSE, norm = FALSE, qq = FALSE, contrasts = NULL, postHoc = NULL, postHocCorr = list("tukey"), postHocES = list(),
postHocEsCi = FALSE, postHocEsCiWidth = 95, emMeans = list(list()), emmPlots = TRUE, emmPlotData = FALSE, emmPlotError = "ci", emmTables = FALSE, emmWeights = TRUE, ciWidthEmm = 95, formula)

Arguments
data	the data as a data frame
dep	the dependent variable from data, variable must be numeric (not necessary when providing a formula, see examples)
factors	the explanatory factors in data (not necessary when providing a formula, see examples)
covs	the explanatory covariates (not necessary when providing a formula, see exam- ples)
effectSize	one or more of 'eta', 'partEta', or 'omega'; use eta², partial eta², and omega² effect sizes, respectively
modelTest	TRUE or FALSE (default); perform an overall model test
modelTerms	a formula describing the terms to go into the model (not necessary when provid- ing a formula, see examples)
ss	'1', '2' or '3' (default), the sum of squares to use
homo	TRUE or FALSE (default), perform homogeneity tests
norm	TRUE or FALSE (default), perform Shapiro-Wilk tests of normality
qq	TRUE or FALSE (default), provide a Q-Q plot of residuals
contrasts	a list of lists specifying the factor and type of contrast to use, one of 'deviation',
'simple', 'difference', 'helmert', 'repeated' or 'polynomial' postHoc	a formula containing the terms to perform post-hoc tests on (see the examples)
postHocCorr	one or more of 'none', 'tukey', 'scheffe', 'bonf', or 'holm'; provide no,
Tukey, Scheffe, Bonferroni, and Holm Post Hoc corrections respectively
postHocES	a possible value of 'd'; provide cohen’s d measure of effect size for the post-hoc tests
postHocEsCi	TRUE or FALSE (default), provide confidence intervals for the post-hoc effect sizes
postHocEsCiWidth
a number between 50 and 99.9 (default: 95), the width of confidence intervals for the post-hoc effect sizes
emMeans	a formula containing the terms to estimate marginal means for (see the exam- ples)
emmPlots	TRUE (default) or FALSE, provide estimated marginal means plots
emmPlotData	TRUE or FALSE (default), plot the data on top of the marginal means

emmPlotError	'none', 'ci' (default), or 'se'. Use no error bars, use confidence intervals, or use standard errors on the marginal mean plots, respectively
emmTables	TRUE or FALSE (default), provide estimated marginal means tables
emmWeights	TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency
ciWidthEmm	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
formula	(optional) the formula to use, see the examples

Value
A results object containing:

results$main	a table of ANCOVA results
results$model	The underlying aov object
results$assump$homo	a table of homogeneity tests
results$assump$norm	a table of normality tests
results$assump$qq	a q-q plot
results$contrasts	an array of contrasts tables
results$postHoc	an array of post-hoc tables
results$emm	an array of the estimated marginal means plots + tables
results$residsOV	an output

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$main$asDF as.data.frame(results$main)
Examples
data('ToothGrowth')
ancova(formula = len ~ supp + dose, data = ToothGrowth) #
# ANCOVA #
# ANCOVA #
#	Sum of Squares	df	Mean Square	F	p #
#	supp	205	1	205.4	11.4	0.001
#	dose	2224	1	2224.3	124.0	< .001
#	Residuals	1023	57	17.9
# #
ancova(
formula = len ~ supp + dose, data = ToothGrowth,
postHoc = ~ supp, emMeans = ~ supp)
ANOVA	ANOVA

Description
The Analysis of Variance (ANOVA) is used to explore the relationship between a continuous de- pendent variable, and one or more categorical explanatory variables.

Usage
ANOVA(data, dep, factors = NULL, effectSize = NULL,
modelTest = FALSE, modelTerms = NULL, ss = "3", homo = FALSE, norm = FALSE, qq = FALSE, contrasts = NULL, postHoc = NULL, postHocCorr = list("tukey"), postHocES = list(),
postHocEsCi = FALSE, postHocEsCiWidth = 95, emMeans = list(list()), emmPlots = TRUE, emmPlotData = FALSE, emmPlotError = "ci", emmTables = FALSE, emmWeights = TRUE, ciWidthEmm = 95, formula)

Arguments
data	the data as a data frame
dep	the dependent variable from data, variable must be numeric (not necessary when providing a formula, see examples)
factors	the explanatory factors in data (not necessary when providing a formula, see examples)
effectSize	one or more of 'eta', 'partEta', or 'omega'; use eta², partial eta², and omega² effect sizes, respectively
modelTest	TRUE or FALSE (default); perform an overall model test
modelTerms	a formula describing the terms to go into the model (not necessary when provid- ing a formula, see examples)
ss	'1', '2' or '3' (default), the sum of squares to use
homo	TRUE or FALSE (default), perform homogeneity tests
norm	TRUE or FALSE (default), perform Shapiro-Wilk tests of normality
qq	TRUE or FALSE (default), provide a Q-Q plot of residuals
contrasts	a list of lists specifying the factor and type of contrast to use, one of 'deviation',
'simple', 'difference', 'helmert', 'repeated' or 'polynomial' postHoc	a formula containing the terms to perform post-hoc tests on (see the examples)

postHocCorr	one or more of 'none', 'tukey', 'scheffe', 'bonf', or 'holm'; provide no,
Tukey, Scheffe, Bonferroni, and Holm Post Hoc corrections respectively
postHocES	a possible value of 'd'; provide cohen’s d measure of effect size for the post-hoc tests
postHocEsCi	TRUE or FALSE (default), provide confidence intervals for the post-hoc effect sizes
postHocEsCiWidth
a number between 50 and 99.9 (default: 95), the width of confidence intervals for the post-hoc effect sizes
emMeans	a formula containing the terms to estimate marginal means for (see the exam- ples)
emmPlots	TRUE (default) or FALSE, provide estimated marginal means plots
emmPlotData	TRUE or FALSE (default), plot the data on top of the marginal means
emmPlotError	'none', 'ci' (default), or 'se'. Use no error bars, use confidence intervals, or use standard errors on the marginal mean plots, respectively
emmTables	TRUE or FALSE (default), provide estimated marginal means tables
emmWeights	TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency
ciWidthEmm	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
formula	(optional) the formula to use, see the examples

Details
ANOVA assumes that the residuals are normally distributed, and that the variances of all groups are equal. If one is unwilling to assume that the variances are equal, then a Welch’s test can be used in- stead (However, the Welch’s test does not support more than one explanatory factor). Alternatively, if one is unwilling to assume that the data is normally distributed, a non-parametric approach (such as Kruskal-Wallis) can be used.

Value
A results object containing:
results$main	a table of ANOVA results
results$model	The underlying aov object
results$assump$homo	a table of homogeneity tests
results$assump$norm	a table of normality tests
results$assump$qq	a q-q plot
results$contrasts	an array of contrasts tables
results$postHoc	an array of post-hoc tables
results$emm	an array of the estimated marginal means plots + tables
results$residsOV	an output

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$main$asDF as.data.frame(results$main)
Examples
data('ToothGrowth')
ANOVA(formula = len ~ dose * supp, data = ToothGrowth) #
# ANOVA #
# ANOVA #
#	Sum of Squares	df	Mean Square	F	p #
#	dose	2426	2	1213.2	92.00	< .001
#	supp	205	1	205.4	15.57	< .001
#	dose:supp	108	2	54.2	4.11	0.022
#	Residuals	712	54	13.2
# #
ANOVA(
formula = len ~ dose * supp, data = ToothGrowth,
emMeans = ~ supp + dose:supp, # est. marginal means for supp and dose:supp emmPlots = TRUE,	# produce plots of those marginal means emmTables = TRUE)	# produce tables of those marginal means
anovaNP	One-Way ANOVA (Non-parametric)

Description
The Kruskal-Wallis test is used to explore the relationship between a continuous dependent variable, and a categorical explanatory variable. It is analagous to ANOVA, but with the advantage of being non-parametric and having fewer assumptions. However, it has the limitation that it can only test a single explanatory variable at a time.

Usage
anovaNP(data, deps, group, es = FALSE, pairs = FALSE, formula)

Arguments
data	the data as a data frame
deps	a string naming the dependent variable in data

group	a string naming the grouping or independent variable in data es	TRUE or FALSE (default), provide effect-sizes
pairs	TRUE or FALSE (default), perform pairwise comparisons
formula	(optional) the formula to use, see the examples

Value
A results object containing:

results$table	a table of the test results
results$comparisons	an array of pairwise comparison tables

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$table$asDF as.data.frame(results$table)

Examples
data('ToothGrowth')
anovaNP(formula = len ~ dose, data=ToothGrowth) #
# ONE-WAY ANOVA (NON-PARAMETRIC) #
# Kruskal-Wallis #
#	X²	df	p
#
#	len	40.7	2	< .001 #
#

anovaOneW	One-Way ANOVA

Description
The Analysis of Variance (ANOVA) is used to explore the relationship between a continuous de- pendent variable, and one or more categorical explanatory variables. This ’One-Way ANOVA’ is a simplified version of the ’normal’ ANOVA, allowing only a single explanatory factor, however also providing a Welch’s ANOVA. The Welch’s ANOVA has the advantage that it need not assume that the variances of all groups are equal.
anovaOneW(data, deps, group, welchs = TRUE, fishers = FALSE, miss = "perAnalysis", desc = FALSE, descPlot = FALSE,
norm = FALSE, qq = FALSE, eqv = FALSE, phMethod = "none", phMeanDif = TRUE, phSig = TRUE, phTest = FALSE, phFlag = FALSE, formula)

Arguments
data	the data as a data frame
deps	a string naming the dependent variables in data
group	a string naming the grouping or independent variable in data
welchs	TRUE (default) or FALSE, perform Welch’s one-way ANOVA which does not assume equal variances
fishers	TRUE or FALSE (default), perform Fisher’s one-way ANOVA which assumes equal variances
miss	'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' ex- cludes a row from all analyses if one of its entries is missing.
desc	TRUE or FALSE (default), provide descriptive statistics
descPlot	TRUE or FALSE (default), provide descriptive plots
norm	TRUE or FALSE (default), perform Shapiro-Wilk test of normality
qq	TRUE or FALSE (default), provide a Q-Q plot of residuals
eqv	TRUE or FALSE (default), perform Levene’s test for homogeneity of variances
phMethod	'none', 'gamesHowell' or 'tukey', which post-hoc tests to provide; 'none' shows no post-hoc tests, 'gamesHowell' shows Games-Howell post-hoc tests where no equivalence of variances is assumed, and 'tukey' shows Tukey post- hoc tests where equivalence of variances is assumed
phMeanDif	TRUE (default) or FALSE, provide mean differences for post-hoc tests
phSig	TRUE (default) or FALSE, provide significance levels for post-hoc tests
phTest	TRUE or FALSE (default), provide test results (t-value and degrees of freedom) for post-hoc tests
phFlag	TRUE or FALSE (default), flag significant post-hoc comparisons
formula	(optional) the formula to use, see the examples

Details
For convenience, this method allows specifying multiple dependent variables, resulting in multiple independent tests.
Note that the Welch’s ANOVA is the same procedure as the Welch’s independent samples t-test.

Value
A results object containing:

results$anova	a table of the test results
results$desc	a table containing the group descriptives results$assump$norm	a table containing the normality tests results$assump$eqv	a table of homogeneity of variances tests
results$plots	an array of groups of plots
results$postHoc	an array of post-hoc tables

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$anova$asDF as.data.frame(results$anova)

Examples
data('ToothGrowth') dat <- ToothGrowth
dat$dose <- factor(dat$dose) anovaOneW(formula = len ~ dose, data = dat)
#
# ONE-WAY ANOVA #
# One-Way ANOVA (Welch's) #
#	F	df1	df2	p #
#	len	68.4	2	37.7	< .001 #
#

anovaRM	Repeated Measures ANOVA

Description
The Repeated Measures ANOVA is used to explore the relationship between a continuous dependent variable and one or more categorical explanatory variables, where one or more of the explanatory variables are ’within subjects’ (where multiple measurements are from the same subject). Addition- ally, this analysis allows the inclusion of covariates, allowing for repeated measures ANCOVAs as well.
anovaRM(data, rm = list(list(label = "RM Factor 1", levels = list("Level 1", "Level 2"))), rmCells = NULL, bs = NULL, cov = NULL, effectSize = NULL, depLabel = "Dependent",
rmTerms = NULL, bsTerms = NULL, ss = "3", spherTests = FALSE, spherCorr = list("none"), leveneTest = FALSE, qq = FALSE, contrasts = NULL, postHoc = NULL, postHocCorr = list("tukey"), emMeans = list(list()), emmPlots = TRUE, emmTables = FALSE, emmWeights = TRUE, ciWidthEmm = 95, emmPlotData = FALSE, emmPlotError = "ci", groupSumm = FALSE)

Arguments
data	the data as a data frame
rm	a list of lists, where each list describes the label (as a string) and the levels
(as vector of strings) of a particular repeated measures factor
rmCells	a list of lists, where each list decribes a repeated measure (as a string) from data defined as measure and the particular combination of levels from rm that it belongs to (as a vector of strings) defined as cell
bs	a vector of strings naming the between subjects factors from data
cov	a vector of strings naming the covariates from data. Variables must be numeric
effectSize	one or more of 'eta', 'partEta', or 'omega'; use eta², partial eta², and omega² effect sizes, respectively
depLabel	a string (default: ’Dependent’) describing the label used for the dependent vari- able throughout the analysis
rmTerms	a list of character vectors describing the repeated measures terms to go into the model
bsTerms	a list of character vectors describing the between subjects terms to go into the model
ss	'2' or '3' (default), the sum of squares to use
spherTests	TRUE or FALSE (default), perform sphericity tests
spherCorr	one or more of 'none' (default), 'GG', or HF; use no p-value correction, the Greenhouse-Geisser p-value correction, and the Huynh-Feldt p-value correction for shericity, respectively
leveneTest	TRUE or FALSE (default), test for homogeneity of variances (i.e., Levene’s test)
qq	TRUE or FALSE (default), provide a Q-Q plot of residuals
contrasts	in development
postHoc	a list of character vectors describing the post-hoc tests that need to be computed
postHocCorr	one or more of 'none', 'tukey' (default), 'scheffe', 'bonf', or 'holm'; use no, Tukey, Scheffe, Bonferroni and Holm posthoc corrections, respectively
emMeans	a list of lists specifying the variables for which the estimated marginal means need to be calculate. Supports up to three variables per term.
emmPlots	TRUE (default) or FALSE, provide estimated marginal means plots

emmTables	TRUE or FALSE (default), provide estimated marginal means tables
emmWeights	TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency
ciWidthEmm	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
emmPlotData	TRUE or FALSE (default), plot the data on top of the marginal means
emmPlotError	'none', 'ci' (default), or 'se'. Use no error bars, use confidence intervals, or use standard errors on the marginal mean plots, respectively
groupSumm	TRUE or FALSE (default), report a summary of the different groups

Details
This analysis requires that the data be in ’wide format’, where each row represents a subject (as opposed to long format, where each measurement of the dependent variable is represented as a row).
A non-parametric equivalent of the repeated measures ANOVA also exists; the Friedman test. How- ever, it has the limitation of only being able to test a single factor.

Value
A results object containing:
results$rmTable	a table
results$bsTable	a table
results$assump$spherTable	a table
results$assump$leveneTable	a table
results$assump$qq	a q-q plot
results$contrasts	an array of tables
results$postHoc	an array of tables
results$emm	an array of the estimated marginal means plots + tables
results$groupSummary	a summary of the groups

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$rmTable$asDF as.data.frame(results$rmTable)
Examples
data('bugs', package = 'jmv') anovaRM(
data = bugs, rm = list(
list(
label = 'Frightening',

levels = c('Low', 'High'))), rmCells = list(
list(
measure = 'LDLF', cell = 'Low'),
list(
measure = 'LDHF', cell = 'High')),
rmTerms = list( 'Frightening'))

#
# REPEATED MEASURES ANOVA #
# Within Subjects Effects #
#	Sum of Squares	df	Mean Square	F	p #
#	Frightening	126	1	126.11	44.2	< .001
#	Residual	257	90	2.85
#
#	Note. Type 3 Sums of Squares #
# #
# Between Subjects Effects #
#	Sum of Squares	df	Mean Square	F	p #
#	Residual	954	90	10.6
#
#	Note. Type 3 Sums of Squares #

anovaRMNP	Repeated Measures ANOVA (Non-parametric)

Description
The Friedman test is used to explore the relationship between a continuous dependent variable and a categorical explanatory variable, where the explanatory variable is ’within subjects’ (where multiple measurements are from the same subject). It is analagous to Repeated Measures ANOVA, but with the advantage of being non-parametric, and not requiring the assumptions of normality or homogeneity of variances. However, it has the limitation that it can only test a single explanatory variable at a time.

Usage
anovaRMNP(data, measures, pairs = FALSE, desc = FALSE, plots = FALSE, plotType = "means")

Arguments
data	the data as a data frame
measures	a vector of strings naming the repeated measures variables pairs	TRUE or FALSE (default), perform pairwise comparisons desc	TRUE or FALSE (default), provide descriptive statistics plots	TRUE or FALSE (default), provide a descriptive plot
plotType	'means' (default) or 'medians', the error bars to use in the plot

Value
A results object containing:
results$table	a table of the Friedman test results
results$comp	a table of the pairwise comparisons
results$desc	a table containing the descriptives
results$plot	a descriptives plot

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$table$asDF as.data.frame(results$table)
Examples
data('bugs', package = 'jmv')
anovaRMNP(bugs, measures = vars(LDLF, LDHF, HDLF, HDHF)) #
# REPEATED MEASURES ANOVA (NON-PARAMETRIC) #
# Friedman #
#	X²	df	p
#
#	55.8	3	< .001 #
#

Big5	Big 5

Description
Big 5

bugs	bugs

Description
bugs

Author(s)
Ryan, Wilde & Crist (2013)

References
https://faculty.kutztown.edu/rryan/RESEARCH/PUBS/Ryan,%20Wilde,%20%26%20Crist%202013% 20Web%20exp%20vs%20lab.pdf

cfa	Confirmatory Factor Analysis

Description
Confirmatory Factor Analysis

Usage
cfa(data, factors = list(list(label = "Factor 1", vars = list())), resCov, miss = "fiml", constrain = "facVar", estTest = TRUE,
ci = FALSE, ciWidth = 95, stdEst = FALSE, factCovEst = TRUE, factInterceptEst = FALSE, resCovEst = FALSE,
resInterceptEst = FALSE, fitMeasures = list("cfi", "tli", "rmsea"), modelTest = TRUE, pathDiagram = FALSE, corRes = FALSE,
hlCorRes = 0.1, mi = FALSE, hlMI = 3)

Arguments
data	the data as a data frame
factors	a list containing named lists that define the label of the factor and the vars that belong to that factor
resCov	a list of lists specifying the residual covariances that need to be estimated
miss	'listwise' or 'fiml', how to handle missing values; 'listwise' excludes a row from all analyses if one of its entries is missing, 'fiml' uses a full infor- mation maximum likelihood method to estimate the model.
constrain	'facVar' or 'facInd', how to contrain the model; 'facVar' fixes the factor variances to one, 'facInd' fixes each factor to the scale of its first indicator.

estTest	TRUE (default) or FALSE, provide ’Z’ and ’p’ values for the model estimates
ci	TRUE or FALSE (default), provide a confidence interval for the model estimates
ciWidth	a number between 50 and 99.9 (default: 95) specifying the confidence interval width that is used as 'ci'
stdEst	TRUE or FALSE (default), provide a standardized estimate for the model estimates factCovEst	TRUE (default) or FALSE, provide estimates for the factor (co)variances factInterceptEst
TRUE or FALSE (default), provide estimates for the factor intercepts
resCovEst	TRUE (default) or FALSE, provide estimates for the residual (co)variances
resInterceptEst
TRUE or FALSE (default), provide estimates for the residual intercepts
fitMeasures one or more of 'cfi', 'tli', 'srmr', 'rmsea', 'aic', or 'bic'; use CFI, TLI, SRMR, RMSEA + 90% confidence interval, adjusted AIC, and BIC model fit measures, respectively
modelTest	TRUE (default) or FALSE, provide a chi-square test for exact fit that compares the model with the perfect fitting model
pathDiagram   TRUE or FALSE (default), provide a path diagram of the model
corRes	TRUE or FALSE (default), provide the residuals for the observed correlation ma- trix (i.e., the difference between the expected correlation matrix and the ob- served correlation matrix)
hlCorRes	a number (default: 0.1), highlight values in the 'corRes' table above this value
mi	TRUE or FALSE (default), provide modification indices for the parameters not included in the model
hlMI	a number (default: 3), highlight values in the 'modIndices' tables above this value

Value
A results object containing:
results$factorLoadings	a table containing the factor loadings
results$factorEst$factorCov	a table containing factor covariances estimates results$factorEst$factorIntercept	a table containing factor intercept estimates results$resEst$resCov	a table containing residual covariances estimates
results$resEst$resIntercept	a table containing residual intercept estimates
results$modelFit$test	a table containing the chi-square test for exact fit
results$modelFit$fitMeasures	a table containing fit measures results$modelPerformance$corRes	a table containing residuals for the observed correlation matrix results$modelPerformance$modIndices	a group
results$pathDiagram	an image containing the model path diagram
results$modelSyntax	the lavaan syntax used to fit the model

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$factorLoadings$asDF as.data.frame(results$factorLoadings)

Examples
data <- lavaan::HolzingerSwineford1939 jmv::cfa(
data = data, factors = list(
list(label="Visual", vars=c("x1", "x2", "x3")),
list(label="Textual", vars=c("x4", "x5", "x6")),
list(label="Speed", vars=c("x7", "x8", "x9"))), resCov = NULL)

#

#	85.3	24	< .001 #
# #
# Fit Measures #
#	CFI	TLI	RMSEA	Lower	Upper #
#	0.931	0.896	0.0921	0.0714	0.114 #
#

contTables	Contingency Tables

Description
The X² test of association (not to be confused with the X² goodness of fit) is used to test whether two categorical variables are independent or associated. If the p-value is low, it suggests the variables are not independent, and that there is a relationship between the two variables.

Usage
contTables(data, rows, cols, counts = NULL, layers = NULL,
chiSq = TRUE, chiSqCorr = FALSE, zProp = FALSE, likeRat = FALSE, fisher = FALSE, contCoef = FALSE, phiCra = FALSE,
diffProp = FALSE, logOdds = FALSE, odds = FALSE, relRisk = FALSE, ci = TRUE, ciWidth = 95, compare = "rows",
hypothesis = "different", gamma = FALSE, taub = FALSE, mh = FALSE, obs = TRUE, exp = FALSE, pcRow = FALSE,
pcCol = FALSE, pcTot = FALSE, barplot = FALSE, yaxis = "ycounts", yaxisPc = "total_pc", xaxis = "xrows", bartype = "dodge", formula)

Arguments
data	the data as a data frame
rows	the variable to use as the rows in the contingency table (not necessary when providing a formula, see the examples)
cols	the variable to use as the columns in the contingency table (not necessary when providing a formula, see the examples)
counts	the variable to use as the counts in the contingency table (not necessary when providing a formula, see the examples)
layers	the variables to use to split the contingency table (not necessary when providing a formula, see the examples)
chiSq	TRUE (default) or FALSE, provide X²

chiSqCorr	TRUE or FALSE (default), provide X² with continuity correction
zProp	TRUE or FALSE (default), provide a z test for differences between two proportions
likeRat	TRUE or FALSE (default), provide the likelihood ratio fisher	TRUE or FALSE (default), provide Fisher’s exact test contCoef	TRUE or FALSE (default), provide the contingency coefficient phiCra	TRUE or FALSE (default), provide Phi and Cramer’s V
diffProp	TRUE or FALSE (default), provide the differences in proportions (only available for 2x2 tables)
logOdds	TRUE or FALSE (default), provide the log odds ratio (only available for 2x2 tables) odds	TRUE or FALSE (default), provide the odds ratio (only available for 2x2 tables) relRisk	TRUE or FALSE (default), provide the relative risk (only available for 2x2 tables) ci	TRUE or FALSE (default), provide confidence intervals for the comparative mea-
sures
ciWidth	a number between 50 and 99.9 (default: 95), width of the confidence intervals to provide
compare	columns or rows (default), compare columns/rows in difference of proportions or relative risks (2x2 tables)
hypothesis  'different' (default), 'oneGreater' or 'twoGreater', the alternative hy- pothesis; group 1 different to group 2, group 1 greater than group 2, and group 2 greater than group 1 respectively
gamma	TRUE or FALSE (default), provide gamma
taub	TRUE or FALSE (default), provide Kendall’s tau-b
mh	TRUE or FALSE (default), provide Mantel-Haenszel test for trend
obs	TRUE or FALSE (default), provide the observed counts
exp	TRUE or FALSE (default), provide the expected counts pcRow	TRUE or FALSE (default), provide row percentages pcCol	TRUE or FALSE (default), provide column percentages pcTot	TRUE or FALSE (default), provide total percentages barplot	TRUE or FALSE (default), show barplots
yaxis	ycounts (default) or ypc. Use respectively counts or percentages for the bar plot y-axis
yaxisPc	total_pc (default), column_pc, or row_pc. Use respectively percentages of total, within columns, or within rows for the bar plot y-axis.
xaxis	rows (default), or columns in bar plot X axis bartype	stack or side by side (default), barplot type formula	(optional) the formula to use, see the examples

Value
A results object containing:
results$freqs	a table of proportions
results$chiSq	a table of X² test results
results$odds	a table of comparative measures
results$nom	a table of the ’nominal’ test results
results$gamma	a table of the gamma test results
results$taub	a table of the Kendall’s tau-b test results
results$mh	a table of the Mantel-Haenszel test for trend
results$barplot	an image

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$freqs$asDF as.data.frame(results$freqs)
Examples
data('HairEyeColor')
dat <- as.data.frame(HairEyeColor) contTables(formula = Freq ~ Hair:Eye, dat)
#
# CONTINGENCY TABLES #
# Contingency Tables #
#	Hair	Brown	Blue	Hazel	Green	Total #
#	Black	68	20	15	5	108
#	Brown	119	84	54	29	286
#	Red	26	17	14	14	71
#	Blond	7	94	10	16	127
#	Total	220	215	93	64	592
# # #
# X² Tests
#
#	Value	df	p #
#	X²	138	9	< .001
#	N	592
# #
# Alternatively, omit the left of the formula (`Freq`) if each row # represents a single observation:

contTables(formula = ~ Hair:Eye, dat)

contTablesPaired	Paired Samples Contingency Tables

Description
McNemar test

Usage
contTablesPaired(data, rows, cols, counts = NULL, chiSq = TRUE, chiSqCorr = FALSE, exact = FALSE, pcRow = FALSE, pcCol = FALSE, formula)

Arguments
data	the data as a data frame
rows	the variable to use as the rows in the contingency table (not necessary when providing a formula, see the examples)
cols	the variable to use as the columns in the contingency table (not necessary when providing a formula, see the examples)
counts	the variable to use as the counts in the contingency table (not necessary when providing a formula, see the examples)
chiSq	TRUE (default) or FALSE, provide X²
chiSqCorr	TRUE or FALSE (default), provide X² with continuity correction
exact	TRUE or FALSE (default), provide an exact log odds ratio (requires exact2x2 to be installed)
pcRow	TRUE or FALSE (default), provide row percentages pcCol	TRUE or FALSE (default), provide column percentages formula	(optional) the formula to use, see the examples
Value
A results object containing:
results$freqs	a proportions table
results$test	a table of test results

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$freqs$asDF as.data.frame(results$freqs)
dat <- data.frame(
`1st survey` = c('Approve', 'Approve', 'Disapprove', 'Disapprove'),
`2nd survey` = c('Approve', 'Disapprove', 'Approve', 'Disapprove'),
`Counts` = c(794, 150, 86, 570),
check.names=FALSE)
contTablesPaired(formula = Counts ~ `1st survey`:`2nd survey`, data = dat) #
# PAIRED SAMPLES CONTINGENCY TABLES #
# Contingency Tables #
#	1st survey	Approve	Disapprove	Total #
#	Approve	794	150	944
#	Disapprove	86	570	656
#	Total	880	720	1600
# # #
# McNemar Test #
#	Value	df	p
#
#	X²	17.4	1	< .001
#	X² continuity correction	16.8	1	< .001 #
#

# Alternatively, omit the left of the formula (`Counts`) from the # formula if each row represents a single observation:
contTablesPaired(formula = ~ `1st survey`:`2nd survey`, data = dat)

corrMatrix	Correlation Matrix

Description
Correlation matrices are a way to examine linear relationships between two or more continuous variables.

Usage
corrMatrix(data, vars, pearson = TRUE, spearman = FALSE, kendall = FALSE, sig = TRUE, flag = FALSE, n = FALSE,

ci = FALSE, ciWidth = 95, plots = FALSE, plotDens = FALSE, plotStats = FALSE, hypothesis = "corr")

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables to correlate in data pearson	TRUE (default) or FALSE, provide Pearson’s R
spearman	TRUE or FALSE (default), provide Spearman’s rho kendall	TRUE or FALSE (default), provide Kendall’s tau-b sig	TRUE (default) or FALSE, provide significance levels
flag	TRUE or FALSE (default), flag significant correlations
n	TRUE or FALSE (default), provide the number of cases
ci	TRUE or FALSE (default), provide confidence intervals
ciWidth	a number between 50 and 99.9 (default: 95), the width of confidence intervals to provide
plots	TRUE or FALSE (default), provide a correlation matrix plot
plotDens	TRUE or FALSE (default), provide densities in the correlation matrix plot plotStats	TRUE or FALSE (default), provide statistics in the correlation matrix plot hypothesis	one of 'corr' (default), 'pos', 'neg' specifying the alernative hypothesis; cor-
related, correlated positively, correlated negatively respectively.

Details
For each pair of variables, a Pearson’s r value indicates the strength and direction of the relationship between those two variables. A positive value indicates a positive relationship (higher values of one variable predict higher values of the other variable). A negative Pearson’s r indicates a negative relationship (higher values of one variable predict lower values of the other variable, and vice- versa). A value of zero indicates no relationship (whether a variable is high or low, does not tell us anything about the value of the other variable).
More formally, it is possible to test the null hypothesis that the correlation is zero and calculate a p-value. If the p-value is low, it suggests the correlation co-efficient is not zero, and there is a linear (or more complex) relationship between the two variables.

Value
A results object containing:
results$matrix	a correlation matrix table
results$plot	a correlation matrix plot

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$matrix$asDF as.data.frame(results$matrix)
data('mtcars')
corrMatrix(mtcars, vars = vars(mpg, cyl, disp, hp)) #
# CORRELATION MATRIX #
# Correlation Matrix #
#	mpg	cyl	disp	hp #
#	mpg	Pearson's r	—	-0.852	-0.848	-0.776
#	p-value	—	< .001	< .001	< .001 #
#	cyl	Pearson's r	—	0.902	0.832
#	p-value	—	< .001	< .001 #
#	disp	Pearson's r	—	0.791
#	p-value	—	< .001
#
#	hp	Pearson's r	—
#	p-value	—
# #

corrPart	Partial Correlation

Description
Partial correlation matrices are a way to examine linear relationships between two or more contin- uous variables while controlling for other variables

Usage
corrPart(data, vars, controls, pearson = TRUE, spearman = FALSE, kendall = FALSE, type = "part", sig = TRUE, flag = FALSE,
n = FALSE, hypothesis = "corr")

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables to correlate in data controls	a vector of strings naming the control variables in data pearson	TRUE (default) or FALSE, provide Pearson’s R
spearman	TRUE or FALSE (default), provide Spearman’s rho

kendall	TRUE or FALSE (default), provide Kendall’s tau-b
type	one of 'part' (default) or 'semi' specifying the type of partial correlation to calculate; partial or semipartial correlation.
sig	TRUE (default) or FALSE, provide significance levels
flag	TRUE or FALSE (default), flag significant correlations
n	TRUE or FALSE (default), provide the number of cases
hypothesis	one of 'corr' (default), 'pos', 'neg' specifying the alernative hypothesis; cor- related, correlated positively, correlated negatively respectively.

Details
For each pair of variables, a Pearson’s r value indicates the strength and direction of the relationship between those two variables. A positive value indicates a positive relationship (higher values of one variable predict higher values of the other variable). A negative Pearson’s r indicates a negative relationship (higher values of one variable predict lower values of the other variable, and vice- versa). A value of zero indicates no relationship (whether a variable is high or low, does not tell us anything about the value of the other variable).
More formally, it is possible to test the null hypothesis that the correlation is zero and calculate a p-value. If the p-value is low, it suggests the correlation co-efficient is not zero, and there is a linear (or more complex) relationship between the two variables.

Value
A results object containing:
results$matrix	a (semi)partial correlation matrix table

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$matrix$asDF as.data.frame(results$matrix)
Examples
data('mtcars')
corrPart(mtcars, vars = vars(mpg, cyl, disp), controls = vars(hp)) #
# PARTIAL CORRELATION #
# Partial Correlation #
#	mpg	cyl	disp
#
#	mpg	Pearson's r	—
#	p-value	— #

#
# #	cyl	Pearson's r
p-value	-0.590
< .001	—
—		
#	disp	Pearson's r	-0.606	0.719	—	
#		p-value	< .001	< .001	—	
#						
#	Note. controlling for 'hp' #

descriptives	Descriptives

Description
Descriptives are an assortment of summarising statistics, and visualizations which allow exploring the shape and distribution of data. It is good practice to explore your data with descriptives before proceeding to more formal tests.

Usage
descriptives(data, vars, splitBy = NULL, freq = FALSE,
desc = "columns", hist = FALSE, dens = FALSE, bar = FALSE, barCounts = FALSE, box = FALSE, violin = FALSE, dot = FALSE, dotType = "jitter", boxMean = FALSE, boxLabelOutliers = TRUE, qq = FALSE, n = TRUE, missing = TRUE, mean = TRUE,
median = TRUE, mode = FALSE, sum = FALSE, sd = TRUE, variance = FALSE, range = FALSE, min = TRUE, max = TRUE, se = FALSE, ci = FALSE, ciWidth = 95, iqr = FALSE,
skew = FALSE, kurt = FALSE, sw = FALSE, pcEqGr = FALSE,
pcNEqGr = 4, pc = FALSE, pcValues = "25,50,75", extreme = FALSE, extremeN = 5, formula)

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables of interest in data splitBy	a vector of strings naming the variables used to split vars
freq	TRUE or FALSE (default), provide frequency tables (nominal, ordinal variables only)
desc	'rows' or 'columns' (default), display the variables across the rows or across the columns (default)
hist	TRUE or FALSE (default), provide histograms (continuous variables only) dens	TRUE or FALSE (default), provide density plots (continuous variables only) bar	TRUE or FALSE (default), provide bar plots (nominal, ordinal variables only)

barCounts	TRUE or FALSE (default), add counts to the bar plots
box	TRUE or FALSE (default), provide box plots (continuous variables only) violin	TRUE or FALSE (default), provide violin plots (continuous variables only) dot	TRUE or FALSE (default), provide dot plots (continuous variables only) dotType	.
boxMean	TRUE or FALSE (default), add mean to box plot
boxLabelOutliers
TRUE (default) or FALSE, add labels with the row number to the outliers in the box plot
qq	TRUE or FALSE (default), provide Q-Q plots (continuous variables only)
n	TRUE (default) or FALSE, provide the sample size
missing	TRUE (default) or FALSE, provide the number of missing values
mean	TRUE (default) or FALSE, provide the mean median	TRUE (default) or FALSE, provide the median mode	TRUE or FALSE (default), provide the mode
sum	TRUE or FALSE (default), provide the sum
sd	TRUE (default) or FALSE, provide the standard deviation
variance	TRUE or FALSE (default), provide the variance
range	TRUE or FALSE (default), provide the range
min	TRUE or FALSE (default), provide the minimum
max	TRUE or FALSE (default), provide the maximum
se	TRUE or FALSE (default), provide the standard error
ci	TRUE or FALSE (default), provide confidence intervals for the mean
ciWidth	a number between 50 and 99.9 (default: 95), the width of confidence intervals
iqr	TRUE or FALSE (default), provide the interquartile range
skew	TRUE or FALSE (default), provide the skewness
kurt	TRUE or FALSE (default), provide the kurtosis
sw	TRUE or FALSE (default), provide Shapiro-Wilk p-value
pcEqGr	TRUE or FALSE (default), provide quantiles
pcNEqGr	an integer (default: 4) specifying the number of equal groups
pc	TRUE or FALSE (default), provide percentiles
pcValues	a comma-sepated list (default: 25,50,75) specifying the percentiles
extreme	TRUE or FALSE (default), provide N most extreme (highest and lowest) values
extremeN	an integer (default: 5) specifying the number of extreme values
formula	(optional) the formula to use, see the examples

Value
A results object containing:

results$descriptives	a table of the descriptive statistics
results$descriptivesT	a table of the descriptive statistics
results$frequencies	an array of frequency tables
results$extremeValues	an array of extreme values tables
results$plots	an array of descriptive plots

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$descriptives$asDF as.data.frame(results$descriptives)
Examples
data('mtcars') dat <- mtcars

# frequency tables can be provided for factors dat$gear <- as.factor(dat$gear)
descriptives(dat, vars = vars(mpg, cyl, disp, gear), freq = TRUE) #
#  DESCRIPTIVES
#
# Descriptives #
#	mpg	cyl	disp	gear #
#	N	32	32	32	32
#	Missing	0	0	0	0
#	Mean	20.1	6.19	231	3.69
#	Median	19.2	6.00	196	4.00
#	Minimum	10.4	4.00	71.1	3
#	Maximum	33.9	8.00	472	5
# # #
# FREQUENCIES #
# Frequencies of gear #
#	Levels	Counts #
#	3	15
#	4	12
#	5	5
#

#

# spliting by a variable
descriptives(formula = disp + mpg ~ cyl, dat, median=F, min=F, max=F, n=F, missing=F)

# providing histograms
descriptives(formula = mpg ~ cyl, dat, hist=T, median=F, min=F, max=F, n=F, missing=F)

# splitting by multiple variables descriptives(formula = mpg ~ cyl:gear, dat,
median=F, min=F, max=F, missing=F)

efa	Exploratory Factor Analysis

Description
Exploratory Factor Analysis

Usage
efa(data, vars, nFactorMethod = "parallel", nFactors = 1, minEigen = 0, extraction = "minres", rotation = "oblimin", hideLoadings = 0.3, sortLoadings = FALSE, screePlot = FALSE, eigen = FALSE, factorCor = FALSE, factorSummary = FALSE, modelFit = FALSE, kmo = FALSE, bartlett = FALSE, factorScoreMethod = "Thurstone")

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables of interest in data
nFactorMethod	'parallel' (default), 'eigen' or 'fixed', the way to determine the number of factors
nFactors	an integer (default: 1), the number of factors in the model
minEigen	a number (default: 0), the minimal eigenvalue for a factor to be included in the model
extraction	'minres' (default), 'ml', or 'pa' use respectively ’minimum residual’, ’maxi- mum likelihood’, or ’prinicipal axis’ as the factor extraction method
rotation	'none', 'varimax', 'quartimax', 'promax', 'oblimin' (default), or 'simplimax', the rotation to use in estimation
hideLoadings	a number (default: 0.3), hide factor loadings below this value
sortLoadings	TRUE or FALSE (default), sort the factor loadings by size

screePlot	TRUE or FALSE (default), show scree plot
eigen	TRUE or FALSE (default), show eigenvalue table factorCor	TRUE or FALSE (default), show inter-factor correlations factorSummary	TRUE or FALSE (default), show factor summary
modelFit	TRUE or FALSE (default), show model fit measures and test
kmo	TRUE or FALSE (default), show Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (MSA) results
bartlett	TRUE or FALSE (default), show Bartlett’s test of sphericity results
factorScoreMethod
'Thurstone' (default), 'Bartlett', 'tenBerge', 'Anderson', or 'Harman' use respectively ’Thurstone’, ’Bartlett’, ’ten Berge’, ’Anderson & Rubin’, or ’Harman’ method for estimating factor scores

Value
A results object containing:
results$text	a preformatted

Examples
data('iris')
efa(iris, vars = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)) #
# EXPLORATORY FACTOR ANALYSIS #
# Factor Loadings #
#	1	2	Uniqueness #
#	Sepal.Length	0.993	0.10181
#	Sepal.Width	0.725	0.42199
#	Petal.Length	0.933	0.00483
#	Petal.Width	0.897	0.07088 #
#	Note. 'oblimin' rotation was used #
iris	iris
Description
iris
linReg	Linear Regression

Description
Linear regression is used to explore the relationship between a continuous dependent variable, and one or more continuous and/or categorical explanatory variables. Other statistical methods, such as ANOVA and ANCOVA, are in reality just forms of linear regression.

Usage
linReg(data, dep, covs = NULL, factors = NULL, weights = NULL, blocks = list(list()), refLevels = NULL, intercept = "refLevel", r = TRUE, r2 = TRUE, r2Adj = FALSE, aic = FALSE, bic = FALSE,
rmse = FALSE, modelTest = FALSE, anova = FALSE, ci = FALSE, ciWidth = 95, stdEst = FALSE, ciStdEst = FALSE, ciWidthStdEst = 95, norm = FALSE, qqPlot = FALSE,
resPlots = FALSE, durbin = FALSE, collin = FALSE, cooks = FALSE, emMeans = list(list()), ciEmm = TRUE, ciWidthEmm = 95,
emmPlots = TRUE, emmTables = FALSE, emmWeights = TRUE)

Arguments
data	the data as a data frame
dep	the dependent variable from data, variable must be numeric
covs	the covariates from data
factors	the fixed factors from data
weights	the (optional) weights from data to be used in the fitting process
blocks	a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list
refLevels	a list of lists specifying reference levels of the dependent variable and all the factors
intercept	'refLevel' (default) or 'grandMean', coding of the intercept. Either creates contrast so that the intercept represents the reference level or the grand mean
r	TRUE (default) or FALSE, provide the statistical measure R for the models
r2	TRUE (default) or FALSE, provide the statistical measure R-squared for the mod- els
r2Adj	TRUE or FALSE (default), provide the statistical measure adjusted R-squared
for the models
aic	TRUE or FALSE (default), provide Aikaike’s Information Criterion (AIC) for the models
bic	TRUE or FALSE (default), provide Bayesian Information Criterion (BIC) for the models

rmse	TRUE or FALSE (default), provide RMSE for the models
modelTest	TRUE (default) or FALSE, provide the model comparison between the models and the NULL model
anova	TRUE or FALSE (default), provide the omnibus ANOVA test for the predictors ci	TRUE or FALSE (default), provide a confidence interval for the model coefficients ciWidth	a number between 50 and 99.9 (default: 95) specifying the confidence interval
width
stdEst	TRUE or FALSE (default), provide a standardized estimate for the model coeffi- cients
ciStdEst	TRUE or FALSE (default), provide a confidence interval for the model coefficient standardized estimates
ciWidthStdEst	a number between 50 and 99.9 (default: 95) specifying the confidence interval width
norm	TRUE or FALSE (default), perform a Shapiro-Wilk test on the residuals
qqPlot	TRUE or FALSE (default), provide a Q-Q plot of residuals
resPlots	TRUE or FALSE (default), provide residual plots where the dependent variable and each covariate is plotted against the standardized residuals.
durbin	TRUE or FALSE (default), provide results of the Durbin- Watson test for autocor- relation
collin	TRUE or FALSE (default), provide VIF and tolerence collinearity statistics
cooks	TRUE or FALSE (default), provide summary statistics for the Cook’s distance
emMeans	a formula containing the terms to estimate marginal means for, supports up to three variables per term
ciEmm	TRUE (default) or FALSE, provide a confidence interval for the estimated marginal means
ciWidthEmm	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
emmPlots	TRUE (default) or FALSE, provide estimated marginal means plots
emmTables	TRUE or FALSE (default), provide estimated marginal means tables
emmWeights	TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency

Value
A results object containing:
results$modelFit	a table
results$modelComp	a table
results$models	an array of model specific results
results$predictOV	an output
results$residsOV	an output
results$cooksOV	an output

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelFit$asDF as.data.frame(results$modelFit)
Examples
data('Prestige', package='carData')

linReg(data = Prestige, dep = income,
covs = vars(education, prestige, women),
blocks = list(list('education', 'prestige', 'women')))

#
# LINEAR REGRESSION #
# Model Fit Measures #
#	Model	R	R²
#
#	1	0.802	0.643 #
# #
# MODEL SPECIFIC RESULTS #
# MODEL 1 #
#
# Model Coefficients #
#	Predictor	Estimate	SE	t	p #
#	Intercept	-253.8	1086.16	-0.234	0.816
#	women	-50.9	8.56	-5.948	< .001
#	prestige	141.4	29.91	4.729	< .001
#	education	177.2	187.63	0.944	0.347
# #
logLinear	Log-Linear Regression
Description
Log-Linear Regression

Usage
logLinear(data, factors = NULL, counts = NULL, blocks = list(list()), refLevels = NULL, modelTest = FALSE, dev = TRUE, aic = TRUE,
bic = FALSE, pseudoR2 = list("r2mf"), omni = FALSE, ci = FALSE, ciWidth = 95, RR = FALSE, ciRR = FALSE, ciWidthRR = 95,
emMeans = list(list()), ciEmm = TRUE, ciWidthEmm = 95, emmPlots = TRUE, emmTables = FALSE, emmWeights = TRUE)

Arguments
data	the data as a data frame
factors	a vector of strings naming the factors from data
counts	a string naming a variable in data containing counts, or NULL if each row represents a single observation
blocks	a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list
refLevels	a list of lists specifying reference levels of the dependent variable and all the factors
modelTest	TRUE or FALSE (default), provide the model comparison between the models and the NULL model
dev	TRUE (default) or FALSE, provide the deviance (or -2LogLikelihood) for the mod- els
aic	TRUE (default) or FALSE, provide Aikaike’s Information Criterion (AIC) for the models
bic	TRUE or FALSE (default), provide Bayesian Information Criterion (BIC) for the models
pseudoR2	one or more of 'r2mf', 'r2cs', or 'r2n'; use McFadden’s, Cox & Snell, and Nagelkerke pseudo-R², respectively
omni	TRUE or FALSE (default), provide the omnibus likelihood ratio tests for the pre- dictors
ci	TRUE or FALSE (default), provide a confidence interval for the model coefficient estimates
ciWidth	a number between 50 and 99.9 (default: 95) specifying the confidence interval width
RR	TRUE or FALSE (default), provide the exponential of the log-rate ratio estimate, or the rate ratio estimate
ciRR	TRUE or FALSE (default), provide a confidence interval for the model coefficient rate ratio estimates
ciWidthRR	a number between 50 and 99.9 (default: 95) specifying the confidence interval width
emMeans	a list of lists specifying the variables for which the estimated marginal means need to be calculate. Supports up to three variables per term.

ciEmm	TRUE (default) or FALSE, provide a confidence interval for the estimated marginal means
ciWidthEmm	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
emmPlots	TRUE (default) or FALSE, provide estimated marginal means plots
emmTables	TRUE or FALSE (default), provide estimated marginal means tables
emmWeights	TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency

Value
A results object containing:
results$modelFit	a table
results$modelComp	a table
results$models	an array of model specific results

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelFit$asDF as.data.frame(results$modelFit)
Examples
data('mtcars')

tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl) dat <- as.data.frame(tab)

logLinear(data = dat, factors = vars(gear, cyl), counts = Freq, blocks = list(list("gear", "cyl", c("gear", "cyl"))), refLevels = list(
list(var="gear", ref="3"),
list(var="cyl", ref="4")))

#
# LOG-LINEAR REGRESSION #
# Model Fit Measures #
#	Model	Deviance	AIC	R²-McF
#
#	1	4.12e-10	41.4	1.000
# # #
# MODEL SPECIFIC RESULTS #
# MODEL 1

#
# Model Coefficients #
#	Predictor	Estimate	SE	Z	p #
#	Intercept	-4.71e-16	1.00	-4.71e-16	1.000
#	gear:
#	4 – 3	2.079	1.06	1.961	0.050
#	5 – 3	0.693	1.22	0.566	0.571
#	cyl:
#	6 – 4	0.693	1.22	0.566	0.571
#	8 – 4	2.485	1.04	2.387	0.017
#	gear:cyl:
#	(4 – 3):(6 – 4)	-1.386	1.37	-1.012	0.311
#	(5 – 3):(6 – 4)	-1.386	1.73	-0.800	0.423
#	(4 – 3):(8 – 4)	-26.867	42247.17	-6.36e -4	0.999
#	(5 – 3):(8 – 4)	-2.485	1.44	-1.722	0.085 #
# #

logRegBin	Binomial Logistic Regression

Description
Binomial Logistic Regression

Usage
logRegBin(data, dep, covs = NULL, factors = NULL,
blocks = list(list()), refLevels = NULL, modelTest = FALSE, dev = TRUE, aic = TRUE, bic = FALSE, pseudoR2 = list("r2mf"), omni = FALSE, ci = FALSE, ciWidth = 95, OR = FALSE,
ciOR = FALSE, ciWidthOR = 95, emMeans = list(list()), ciEmm = TRUE, ciWidthEmm = 95, emmPlots = TRUE,
emmTables = FALSE, emmWeights = TRUE, class = FALSE, acc = FALSE, spec = FALSE, sens = FALSE, auc = FALSE, rocPlot = FALSE,
cutOff = 0.5, cutOffPlot = FALSE, collin = FALSE, boxTidwell = FALSE, cooks = FALSE)

Arguments
data	the data as a data frame
dep	a string naming the dependent variable from data, variable must be a factor
covs	a vector of strings naming the covariates from data factors	a vector of strings naming the fixed factors from data

blocks	a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list
refLevels	a list of lists specifying reference levels of the dependent variable and all the factors
modelTest	TRUE or FALSE (default), provide the model comparison between the models and the NULL model
dev	TRUE (default) or FALSE, provide the deviance (or -2LogLikelihood) for the mod- els
aic	TRUE (default) or FALSE, provide Aikaike’s Information Criterion (AIC) for the models
bic	TRUE or FALSE (default), provide Bayesian Information Criterion (BIC) for the models
pseudoR2	one or more of 'r2mf', 'r2cs', or 'r2n'; use McFadden’s, Cox & Snell, and Nagelkerke pseudo-R², respectively
omni	TRUE or FALSE (default), provide the omnibus likelihood ratio tests for the pre- dictors
ci	TRUE or FALSE (default), provide a confidence interval for the model coefficient estimates
ciWidth	a number between 50 and 99.9 (default: 95) specifying the confidence interval width
OR	TRUE or FALSE (default), provide the exponential of the log-odds ratio estimate, or the odds ratio estimate
ciOR	TRUE or FALSE (default), provide a confidence interval for the model coefficient odds ratio estimates
ciWidthOR	a number between 50 and 99.9 (default: 95) specifying the confidence interval width
emMeans	a list of lists specifying the variables for which the estimated marginal means need to be calculate. Supports up to three variables per term.
ciEmm	TRUE (default) or FALSE, provide a confidence interval for the estimated marginal means
ciWidthEmm	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
emmPlots	TRUE (default) or FALSE, provide estimated marginal means plots
emmTables	TRUE or FALSE (default), provide estimated marginal means tables
emmWeights	TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency
class	TRUE or FALSE (default), provide a predicted classification table (or confusion matrix)
acc	TRUE or FALSE (default), provide the predicted accuracy of outcomes grouped by the cut-off value
spec	TRUE or FALSE (default), provide the predicted specificity of outcomes grouped by the cut-off value

sens	TRUE or FALSE (default), provide the predicted sensitivity of outcomes grouped by the cut-off value
auc	TRUE or FALSE (default), provide the rea under the ROC curve (AUC)
rocPlot	TRUE or FALSE (default), provide a ROC curve plot
cutOff	TRUE or FALSE (default), set a cut-off used for the predictions
cutOffPlot	TRUE or FALSE (default), provide a cut-off plot
collin	TRUE or FALSE (default), provide VIF and tolerence collinearity statistics boxTidwell	TRUE or FALSE (default), provide Box-Tidwell test for linearity of the logit cooks	TRUE or FALSE (default), provide summary statistics for the Cook’s distance
Value
A results object containing:

results$modelFit	a table
results$modelComp	a table
results$models	an array of model specific results
results$predictOV	an output
results$residsOV	an output
results$cooksOV	an output

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelFit$asDF as.data.frame(results$modelFit)
Examples
data('birthwt', package='MASS')

dat <- data.frame(
low = factor(birthwt$low), age = birthwt$age,
bwt = birthwt$bwt)

logRegBin(data = dat, dep = low,
covs = vars(age, bwt),
blocks = list(list("age", "bwt")), refLevels = list(list(var="low", ref="0")))

#
# BINOMIAL LOGISTIC REGRESSION #
# Model Fit Measures #
#	Model	Deviance	AIC	R²-McF
#

#	1   4.97e-7   6.00    1.000
# # #
# MODEL SPECIFIC RESULTS #
# MODEL 1 #
# Model Coefficients #
#	Predictor	Estimate	SE	Z	p #
#	Intercept	2974.73225	218237.2	0.0136	0.989
#	age	-0.00653	482.7	-1.35e-5	1.000
#	bwt	-1.18532	87.0	-0.0136	0.989
#
#	Note. Estimates represent the log odds of "low = 1" #	vs. "low = 0"
# #
logRegMulti	Multinomial Logistic Regression

Description
Multinomial Logistic Regression

Usage
logRegMulti(data, dep, covs = NULL, factors = NULL,
blocks = list(list()), refLevels = NULL, modelTest = FALSE, dev = TRUE, aic = TRUE, bic = FALSE, pseudoR2 = list("r2mf"), omni = FALSE, ci = FALSE, ciWidth = 95, OR = FALSE,
ciOR = FALSE, ciWidthOR = 95, emMeans = list(list()), ciEmm = TRUE, ciWidthEmm = 95, emmPlots = TRUE, emmTables = FALSE, emmWeights = TRUE)

Arguments
data	the data as a data frame
dep	a string naming the dependent variable from data, variable must be a factor
covs	a vector of strings naming the covariates from data factors	a vector of strings naming the fixed factors from data
blocks	a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list

refLevels	a list of lists specifying reference levels of the dependent variable and all the factors
modelTest	TRUE or FALSE (default), provide the model comparison between the models and the NULL model
dev	TRUE (default) or FALSE, provide the deviance (or -2LogLikelihood) for the mod- els
aic	TRUE (default) or FALSE, provide Aikaike’s Information Criterion (AIC) for the models
bic	TRUE or FALSE (default), provide Bayesian Information Criterion (BIC) for the models
pseudoR2	one or more of 'r2mf', 'r2cs', or 'r2n'; use McFadden’s, Cox & Snell, and Nagelkerke pseudo-R², respectively
omni	TRUE or FALSE (default), provide the omnibus likelihood ratio tests for the pre- dictors
ci	TRUE or FALSE (default), provide a confidence interval for the model coefficient estimates
ciWidth	a number between 50 and 99.9 (default: 95) specifying the confidence interval width
OR	TRUE or FALSE (default), provide the exponential of the log-odds ratio estimate, or the odds ratio estimate
ciOR	TRUE or FALSE (default), provide a confidence interval for the model coefficient odds ratio estimates
ciWidthOR	a number between 50 and 99.9 (default: 95) specifying the confidence interval width
emMeans	a list of lists specifying the variables for which the estimated marginal means need to be calculate. Supports up to three variables per term.
ciEmm	TRUE (default) or FALSE, provide a confidence interval for the estimated marginal means
ciWidthEmm	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
emmPlots	TRUE (default) or FALSE, provide estimated marginal means plots
emmTables	TRUE or FALSE (default), provide estimated marginal means tables
emmWeights	TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency

Value
A results object containing:
results$modelFit	a table
results$modelComp	a table
results$models	an array of model specific results

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelFit$asDF as.data.frame(results$modelFit)
Examples
data('birthwt', package='MASS')

dat <- data.frame(
race = factor(birthwt$race), age = birthwt$age,
low = factor(birthwt$low))

logRegMulti(data = dat, dep = race,
covs = age, factors = low,
blocks = list(list("age", "low")), refLevels = list(
list(var="race", ref="1"),
list(var="low", ref="0")))

#
# MULTINOMIAL LOGISTIC REGRESSION #
# Model Fit Measures #

# MODEL SPECIFIC RESULTS #
# MODEL 1 #
# Model Coefficients #
#	race	Predictor	Estimate	SE	Z	p #
#	2 - 1	Intercept	0.8155	1.1186	0.729	0.466
#	age	-0.1038	0.0487	-2.131	0.033
#	low:
#	1 – 0	0.7527	0.4700	1.601	0.109
#	3 - 1	Intercept	1.0123	0.7798	1.298	0.194
#	age	-0.0663	0.0324	-2.047	0.041
#	low:
#	1 – 0	0.5677	0.3522	1.612	0.107 #
# #

logRegOrd	Ordinal Logistic Regression

Description
Ordinal Logistic Regression

Usage
logRegOrd(data, dep, covs = NULL, factors = NULL,
blocks = list(list()), refLevels = NULL, modelTest = FALSE, dev = TRUE, aic = TRUE, bic = FALSE, pseudoR2 = list("r2mf"), omni = FALSE, thres = FALSE, ci = FALSE, ciWidth = 95,
OR = FALSE, ciOR = FALSE, ciWidthOR = 95)

Arguments
data	the data as a data frame
dep	a string naming the dependent variable from data, variable must be a factor
covs	a vector of strings naming the covariates from data factors	a vector of strings naming the fixed factors from data
blocks	a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list
refLevels	a list of lists specifying reference levels of the dependent variable and all the factors
modelTest	TRUE or FALSE (default), provide the model comparison between the models and the NULL model
dev	TRUE (default) or FALSE, provide the deviance (or -2LogLikelihood) for the mod- els
aic	TRUE (default) or FALSE, provide Aikaike’s Information Criterion (AIC) for the models
bic	TRUE or FALSE (default), provide Bayesian Information Criterion (BIC) for the models
pseudoR2	one or more of 'r2mf', 'r2cs', or 'r2n'; use McFadden’s, Cox & Snell, and Nagelkerke pseudo-R², respectively
omni	TRUE or FALSE (default), provide the omnibus likelihood ratio tests for the pre- dictors
thres	TRUE or FALSE (default), provide the thresholds that are used as cut-off scores for the levels of the dependent variable
ci	TRUE or FALSE (default), provide a confidence interval for the model coefficient estimates
ciWidth	a number between 50 and 99.9 (default: 95) specifying the confidence interval width

OR	TRUE or FALSE (default), provide the exponential of the log-odds ratio estimate, or the odds ratio estimate
ciOR	TRUE or FALSE (default), provide a confidence interval for the model coefficient odds ratio estimates
ciWidthOR	a number between 50 and 99.9 (default: 95) specifying the confidence interval width

Value
A results object containing:
results$modelFit	a table
results$modelComp	a table
results$models	an array of model specific results

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelFit$asDF as.data.frame(results$modelFit)
Examples
set.seed(1337)

y <- factor(sample(1:3, 100, replace = TRUE)) x1 <- rnorm(100)
x2 <- rnorm(100)
df <- data.frame(y=y, x1=x1, x2=x2) logRegOrd(data = df, dep = y,
covs = vars(x1, x2),
blocks = list(list("x1", "x2")))

#
# ORDINAL LOGISTIC REGRESSION #
# Model Fit Measures #
#	Model	Deviance	AIC	R²-McF
#
#	1	218	226	5.68e-4 #
# #
# MODEL SPECIFIC RESULTS #
# MODEL 1 #
# Model Coefficients

#
#	Predictor	Estimate	SE	Z	p #
#	x1	0.0579	0.193	0.300	0.764
#	x2	0.0330	0.172	0.192	0.848 #
# #

mancova	MANCOVA

Description
Multivariate Analysis of (Co)Variance (MANCOVA) is used to explore the relationship between multiple dependent variables, and one or more categorical and/or continuous explanatory variables.

Usage
mancova(data, deps, factors = NULL, covs = NULL,
multivar = list("pillai", "wilks", "hotel", "roy"), boxM = FALSE, shapiro = FALSE, qqPlot = FALSE)

Arguments
data	the data as a data frame
deps	a string naming the dependent variable from data, variable must be numeric
factors	a vector of strings naming the factors from data covs	a vector of strings naming the covariates from data
multivar	one or more of 'pillai', 'wilks', 'hotel', or 'roy'; use Pillai’s Trace, Wilks’ Lambda, Hotelling’s Trace, and Roy’s Largest Root multivariate statis- tics, respectively
boxM	TRUE or FALSE (default), provide Box’s M test
shapiro	TRUE or FALSE (default), provide Shapiro-Wilk test
qqPlot	TRUE or FALSE (default), provide a Q-Q plot of multivariate normality

Value
A results object containing:
results$multivar	a table
results$univar	a table
results$assump$boxM	a table
results$assump$shapiro	a table
results$assump$qqPlot	an image

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$multivar$asDF as.data.frame(results$multivar)
Examples
data('iris')

mancova(data = iris,
deps = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width), factors = Species)

#
# MANCOVA #
#  Multivariate Tests #
#	value	F	df1	df2	p
#
#	Species	Pillai's Trace	1.19	53.5	8	290	< .001
#	Wilks' Lambda	0.0234	199	8	288	< .001
#	Hotelling's  Trace	32.5	581	8	286	< .001
#	Roy's Largest Root	32.2	1167	4	145	< .001 #
# #

#

pca	Principal Component Analysis

Description
Principal Component Analysis

Usage
pca(data, vars, nFactorMethod = "parallel", nFactors = 1, minEigen = 1, rotation = "varimax", hideLoadings = 0.3, sortLoadings = FALSE, screePlot = FALSE, eigen = FALSE, factorCor = FALSE, factorSummary = FALSE, kmo = FALSE, bartlett = FALSE)

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables of interest in data
nFactorMethod	'parallel' (default), 'eigen' or 'fixed', the way to determine the number of factors
nFactors	an integer (default: 1), the number of components in the model
minEigen	a number (default: 1), the minimal eigenvalue for a component to be included in the model
rotation	'none', 'varimax' (default), 'quartimax', 'promax', 'oblimin', or 'simplimax', the rotation to use in estimation
hideLoadings	a number (default: 0.3), hide loadings below this value sortLoadings	TRUE or FALSE (default), sort the factor loadings by size screePlot	TRUE or FALSE (default), show scree plot
eigen	TRUE or FALSE (default), show eigenvalue table factorCor	TRUE or FALSE (default), show inter-factor correlations factorSummary	TRUE or FALSE (default), show factor summary
kmo	TRUE or FALSE (default), show Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (MSA) results
bartlett	TRUE or FALSE (default), show Bartlett’s test of sphericity results

Value
A results object containing:
results$loadings	a table
results$factorStats$factorSummary	a table
results$factorStats$factorCor	a table
results$modelFit$fit	a table
results$assump$bartlett	a table
results$assump$kmo	a table
results$eigen$initEigen	a table
results$eigen$screePlot	an image
results$factorScoresOV	an output

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$loadings$asDF as.data.frame(results$loadings)

Examples
data('iris')
pca(iris, vars = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)) #
# PRINCIPAL COMPONENT ANALYSIS #
# Component Loadings #
#	1	Uniqueness #
#	Sepal.Length	0.890	0.2076
#	Sepal.Width	-0.460	0.7883
#	Petal.Length	0.992	0.0168
#	Petal.Width	0.965	0.0688
#
#	Note. 'varimax' rotation was used #
propTest2	Proportion Test (2 Outcomes)

Description
The Binomial test is used to test the Null hypothesis that the proportion of observations match some expected value. If the p-value is low, this suggests that the Null hypothesis is false, and that the true proportion must be some other value.

Usage
propTest2(data, vars, areCounts = FALSE, testValue = 0.5, hypothesis = "notequal", ci = FALSE, ciWidth = 95, bf = FALSE, priorA = 1, priorB = 1, ciBayes = FALSE, ciBayesWidth = 95, postPlots = FALSE)

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables of interest in data areCounts	TRUE or FALSE (default), the variables are counts testValue	a number (default: 0.5), the value for the null hypothesis
hypothesis	'notequal' (default), 'greater' or 'less', the alternative hypothesis
ci	TRUE or FALSE (default), provide confidence intervals
ciWidth	a number between 50 and 99.9 (default: 95), the confidence interval width

bf	TRUE or FALSE (default), provide Bayes factors priorA	a number (default: 1), the beta prior ’a’ parameter priorB	a number (default: 1), the beta prior ’b’ parameter
ciBayes	TRUE or FALSE (default), provide Bayesian credible intervals ciBayesWidth	a number between 50 and 99.9 (default: 95), the credible interval width postPlots	TRUE or FALSE (default), provide posterior plots
Value
A results object containing:
results$table	a table of the proportions and test results
results$postPlots	an array of the posterior plots

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$table$asDF as.data.frame(results$table)
Examples
dat <- data.frame(x=c(8, 15))
propTest2(dat, vars = x, areCounts = TRUE) #
# PROPORTION TEST (2 OUTCOMES) #
# Binomial Test #
#	Level	Count	Total	Proportion	p #
#	x	1	8	23	0.348	0.210
#	2	15	23	0.652	0.210 #
#	Note. Ha is proportion != 0.5 #

propTestN	Proportion Test (N Outcomes)

Description
The X² Goodness of fit test (not to be confused with the X² test of independence), tests the Null hypothesis that the proportions of observations match some expected proportions. If the p-value is low, this suggests that the Null hypothesis is false, and that the true proportions are different to those tested.

Usage
propTestN(data, var, counts = NULL, expected = FALSE, ratio = NULL, formula)

Arguments
data	the data as a data frame
var	the variable of interest in data (not necessary when using a formula, see the examples)
counts	the counts in data
expected	TRUE or FALSE (default), whether expected counts should be displayed
ratio	a vector of numbers: the expected proportions
formula	(optional) the formula to use, see the examples

Value
A results object containing:
results$props	a table of the proportions
results$tests	a table of the test results

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$props$asDF as.data.frame(results$props)
Examples
data('HairEyeColor')
dat <- as.data.frame(HairEyeColor)
propTestN(formula = Freq ~ Eye, data = dat, ratio = c(1,1,1,1)) #
# PROPORTION TEST (N OUTCOMES) #
# Proportions #

#
#	X²	df	p #
#	133	3	< .001 #
#

reliability	Reliability Analysis

Description
Reliability Analysis

Usage
reliability(data, vars, alphaScale = TRUE, omegaScale = FALSE, meanScale = FALSE, sdScale = FALSE, corPlot = FALSE, alphaItems = FALSE, omegaItems = FALSE, meanItems = FALSE, sdItems = FALSE, itemRestCor = FALSE, revItems = NULL)

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables of interest in data alphaScale	TRUE (default) or FALSE, provide Cronbach’s alpha omegaScale	TRUE or FALSE (default), provide McDonald’s omega meanScale	TRUE or FALSE (default), provide the mean
sdScale	TRUE or FALSE (default), provide the standard deviation
corPlot	TRUE or FALSE (default), provide a correlation plot
alphaItems	TRUE or FALSE (default), provide what the Cronbach’s alpha would be if the item was dropped
omegaItems	TRUE or FALSE (default), provide what the McDonald’s omega would be if the item was dropped
meanItems	TRUE or FALSE (default), provide item means
sdItems	TRUE or FALSE (default), provide item standard deviations
itemRestCor	TRUE or FALSE (default), provide item-rest correlations
revItems	a vector containing strings naming the varibales that are reverse scaled

Value
A results object containing:

results$scale	a table
results$items	a table
results$corPlot	an image
results$meanScoreOV	an output
results$sumScoreOV	an output

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$scale$asDF as.data.frame(results$scale)

Examples
data('iris')

reliability(iris, vars = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'), omegaScale = TRUE)

#
# RELIABILITY ANALYSIS #
#  Scale Reliability Statistics #
#	Cronbach's alpha	McDonald's omega #
#	scale	0.708	0.848
# #

ToothGrowth	Tooth Growth

Description
Tooth Growth

ttestIS	Independent Samples T-Test

Description
The Student’s Independent samples t-test (sometimes called a two-samples t-test) is used to test the null hypothesis that two groups have the same mean. A low p-value suggests that the null hypothesis is not true, and therefore the group means are different.

Usage
ttestIS(data, vars, group, students = TRUE, bf = FALSE, bfPrior = 0.707, welchs = FALSE, mann = FALSE,
hypothesis = "different", norm = FALSE, qq = FALSE, eqv = FALSE, meanDiff = FALSE, ci = FALSE, ciWidth = 95, effectSize = FALSE, ciES = FALSE, ciWidthES = 95, desc = FALSE, plots = FALSE,
miss = "perAnalysis", formula)

Arguments
data	the data as a data frame
vars	the dependent variables (not necessary when using a formula, see the examples)
group	the grouping variable with two levels (not necessary when using a formula, see the examples)
students	TRUE (default) or FALSE, perform Student’s t-tests
bf	TRUE or FALSE (default), provide Bayes factors
bfPrior	a number between 0.01 and 2 (default 0.707), the prior width to use in calculat- ing Bayes factors
welchs	TRUE or FALSE (default), perform Welch’s t-tests
mann	TRUE or FALSE (default), perform Mann-Whitney U tests
hypothesis  'different' (default), 'oneGreater' or 'twoGreater', the alternative hy- pothesis; group 1 different to group 2, group 1 greater than group 2, and group 2 greater than group 1 respectively
norm	TRUE or FALSE (default), perform Shapiro-Wilk tests of normality
qq	TRUE or FALSE (default), provide Q-Q plots of residuals
eqv	TRUE or FALSE (default), perform Levene’s tests for homogeneity of variances
meanDiff	TRUE or FALSE (default), provide means and standard errors
ci	TRUE or FALSE (default), provide confidence intervals
ciWidth	a number between 50 and 99.9 (default: 95), the width of confidence intervals
effectSize	TRUE or FALSE (default), provide effect sizes
ciES	TRUE or FALSE (default), provide confidence intervals for the effect-sizes

ciWidthES	a number between 50 and 99.9 (default: 95), the width of confidence intervals for the effect sizes
desc	TRUE or FALSE (default), provide descriptive statistics
plots	TRUE or FALSE (default), provide descriptive plots
miss	'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' ex- cludes a row from all analyses if one of its entries is missing.
formula	(optional) the formula to use, see the examples

Details
The Student’s independent t-test assumes that the data from each group are from a normal distri- bution, and that the variances of these groups are equal. If unwilling to assume the groups have equal variances, the Welch’s t-test can be used in it’s place. If one is additionally unwilling to as- sume the data from each group are from a normal distribution, the non-parametric Mann-Whitney U test can be used instead (However, note that the Mann-Whitney U test has a slightly different null hypothesis; that the distributions of each group is equal).

Value
A results object containing:
results$ttest	a table containing the t-test results
results$assum$norm	a table containing the normality tests results$assum$eqv	a table containing the homogeneity of variances tests results$desc	a table containing the group descriptives
results$plots	an array of groups of plots

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$ttest$asDF as.data.frame(results$ttest)
Examples
data('ToothGrowth')
ttestIS(formula = len ~ supp, data = ToothGrowth) #
# INDEPENDENT SAMPLES T-TEST #
# Independent Samples T-Test #
#	statistic	df	p
#
#	len	Student's t	1.92	58.0	0.060 #

#

ttestOneS	One Sample T-Test

Description
The Student’s One-sample t-test is used to test the null hypothesis that the true mean is equal to a particular value (typically zero). A low p-value suggests that the null hypothesis is not true, and therefore the true mean must be different from the test value.

Usage
ttestOneS(data, vars, students = TRUE, bf = FALSE, bfPrior = 0.707, wilcoxon = FALSE, testValue = 0, hypothesis = "dt", norm = FALSE, qq = FALSE, meanDiff = FALSE, ci = FALSE, ciWidth = 95, effectSize = FALSE, ciES = FALSE, ciWidthES = 95, desc = FALSE, plots = FALSE, miss = "perAnalysis", mann = FALSE)

Arguments
data	the data as a data frame
vars	a vector of strings naming the variables of interest in data students	TRUE (default) or FALSE, perform Student’s t-tests
bf	TRUE or FALSE (default), provide Bayes factors
bfPrior	a number between 0.5 and 2.0 (default 0.707), the prior width to use in calculat- ing Bayes factors
wilcoxon	TRUE or FALSE (default), perform Wilcoxon signed rank tests
testValue	a number specifying the value of the null hypothesis
hypothesis	'dt' (default), 'gt' or 'lt', the alternative hypothesis; different to testValue, greater than testValue, and less than testValue respectively
norm	TRUE or FALSE (default), perform Shapiro-wilk tests of normality qq	TRUE or FALSE (default), provide a Q-Q plot of residuals meanDiff	TRUE or FALSE (default), provide means and standard deviations
ci	TRUE or FALSE (default), provide confidence intervals for the mean difference ciWidth	a number between 50 and 99.9 (default: 95), the width of confidence intervals effectSize   TRUE or FALSE (default), provide Cohen’s d effect sizes
ciES	TRUE or FALSE (default), provide confidence intervals for the effect-sizes
ciWidthES	a number between 50 and 99.9 (default: 95), the width of confidence intervals for the effect sizes
desc	TRUE or FALSE (default), provide descriptive statistics

plots	TRUE or FALSE (default), provide descriptive plots
miss	'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' ex- cludes a row from all analyses if one of its entries is missing.
mann	deprecated

Details
The Student’s One-sample t-test assumes that the data are from a normal distribution – in the case that one is unwilling to assume this, the non-parametric Wilcoxon signed-rank can be used in it’s place (However, note that the Wilcoxon signed-rank has a slightly different null hypothesis; that the
*median* is equal to the test value).

Value
A results object containing:

results$ttest	a table containing the t-test results
results$normality	a table containing the normality test results
results$descriptives	a table containing the descriptives
results$plots	an image of the descriptive plots
results$qq	an array of Q-Q plots

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$ttest$asDF as.data.frame(results$ttest)

Examples
data('ToothGrowth')
ttestOneS(ToothGrowth, vars = vars(len, dose)) #
# ONE SAMPLE T-TEST #
# One Sample T-Test #
#	statistic	df	p
#
#	len	Student's t	19.1	59.0	< .001
#	dose	Student's t	14.4	59.0	< .001 #
#

ttestPS	Paired Samples T-Test

Description
The Student’s paired samples t-test (sometimes called a dependent-samples t-test) is used to test the null hypothesis that the difference between pairs of measurements is equal to zero. A low p-value suggests that the null hypothesis is not true, and that the difference between the measurement pairs is not zero.

Usage
ttestPS(data, pairs, students = TRUE, bf = FALSE, bfPrior = 0.707, wilcoxon = FALSE, hypothesis = "different", norm = FALSE,
qq = FALSE, meanDiff = FALSE, ci = FALSE, ciWidth = 95, effectSize = FALSE, ciES = FALSE, ciWidthES = 95, desc = FALSE, plots = FALSE, miss = "perAnalysis")

Arguments
data	the data as a data frame
pairs	a list of lists specifying the pairs of measurement in data students	TRUE (default) or FALSE, perform Student’s t-tests
bf	TRUE or FALSE (default), provide Bayes factors
bfPrior	a number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors
wilcoxon	TRUE or FALSE (default), perform Wilcoxon signed rank tests
hypothesis  'different' (default), 'oneGreater' or 'twoGreater', the alternative hy- pothesis; measure 1 different to measure 2, measure 1 greater than measure 2, and measure 2 greater than measure 1 respectively
norm	TRUE or FALSE (default), perform Shapiro-wilk normality tests qq	TRUE or FALSE (default), provide a Q-Q plot of residuals meanDiff	TRUE or FALSE (default), provide means and standard errors ci	TRUE or FALSE (default), provide confidence intervals
ciWidth	a number between 50 and 99.9 (default: 95), the width of confidence intervals
effectSize	TRUE or FALSE (default), provide effect sizes
ciES	TRUE or FALSE (default), provide confidence intervals for the effect-sizes
ciWidthES	a number between 50 and 99.9 (default: 95), the width of confidence intervals for the effect sizes
desc	TRUE or FALSE (default), provide descriptive statistics
plots	TRUE or FALSE (default), provide descriptive plots
miss	'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' ex- cludes a row from all analyses if one of its entries is missing

Details
The Student’s paired samples t-test assumes that pair differences follow a normal distribution – in the case that one is unwilling to assume this, the non-parametric Wilcoxon signed-rank can be used in it’s place (However, note that the Wilcoxon signed-rank has a slightly different null hypothesis; that the two groups of measurements follow the same distribution).

Value
A results object containing:
results$ttest	a table containing the t-test results
results$norm	a table containing the normality test results
results$desc	a table containing the descriptives
results$plots	an array of the descriptive plots

Tables can be converted to data frames with asDF or as.data.frame. For example:
results$ttest$asDF as.data.frame(results$ttest)
Examples
data('bugs', package = 'jmv')

ttestPS(bugs, pairs = list(
list(i1 = 'LDLF', i2 = 'LDHF')))

#
# PAIRED SAMPLES T-TEST #
# Paired Samples T-Test #
#	statistic	df	p
#
#	LDLF	LDHF	Student's t	-6.65	90.0	< .001 #
#

Index

∗ data
Big5, 14
iris, 30
ToothGrowth, 51
ancova, 2
ANOVA, 5
anovaNP, 7
anovaOneW, 8
anovaRM, 10
anovaRMNP, 13
as.data.frame, 4, 6, 8, 10, 12, 14, 16, 20, 21,
23, 25, 28, 33, 35, 38, 41, 43, 45, 46,
48, 49, 51, 53, 55, 57
reliability, 50
ToothGrowth, 51
ttestIS, 52
ttestOneS, 54
ttestPS, 56
Big5, 14
bugs, 15
cfa, 15
contTables, 18
contTablesPaired, 21
corrMatrix, 22
corrPart, 24
descriptives, 26
efa, 29
iris, 30
linReg, 31
logLinear, 33
logRegBin, 36
logRegMulti, 39
logRegOrd, 42
mancova, 44
pca, 45
propTest2, 47
propTestN, 48

58