Package index • psych

All functions
`psych` `psych-package`	A package for personality, psychometric, and psychological research
`AUC()`	Decision Theory measures of specificity, sensitivity, and d prime
`GSBE`	Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010)
`Gleser`	Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.
`Gorsuch`	Example data set from Gorsuch (1997) for an example factor extension.
`Harman` `Harman.5` `Harman.political` `Harman.8`	Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
`ICC()`	Intraclass Correlations (ICC1, ICC2, ICC3 from Shrout and Fleiss)
`iclust()` `ICLUST()`	iclust: Item Cluster Analysis – Hierarchical cluster analysis using psychometric principles
`ICLUST.cluster()`	Function to form hierarchical cluster analysis of items
`ICLUST.graph()`	create control code for ICLUST graphical output
`ICLUST.rgraph()`	Draw an ICLUST graph using the Rgraphviz package
`ICLUST.sort()`	Sort items by absolute size of cluster loadings
`KMO()`	Find the Kaiser, Meyer, Olkin Measure of Sampling Adequacy
`Pinv()`	Compute the Moore-Penrose Pseudo Inverse of a matrix
`faRotate()` `bifactor()` `biquartimin()` `TargetQ()` `TargetT()` `Promax()` `Procrustes()` `target.rot()` `varimin()` `vgQ.bimin()` `vgQ.targetQ()` `vgQ.varimin()` `equamax()`	Perform Procustes,bifactor, promax or targeted rotations and return the inter factor angles.
`RMSEA()`	Root Mean Squared Error of Approximation from chisq, df, and n
`RV()`	Three measures of the correlations between sets of variables
`SD()`	Find the Standard deviation for a vector, matrix, or data.frame - do not return error if there are no cases
`Schmid`	12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
`Tucker`	9 Cognitive variables discussed by Tucker and Lewis (1973)
`vss()` `VSS()` `nfactors()` `vssSelect()` `eigenCi()`	Apply the Very Simple Structure, MAP, and other criteria to determine the appropriate number of factors.
`VSS.parallel()`	Compare real and random VSS solutions
`VSS.plot()`	Plot VSS fits
`scree()` `VSS.scree()`	Plot the successive eigen values for a scree test
`YuleBonett()` `YuleCor()` `Yule()` `Yule.inv()` `Yule2phi()` `Yule2tetra()` `Yule2poly()`	From a two by two table, find the Yule coefficients of association, convert to phi, or tetrachoric, recreate table the table to create the Yule coefficient.
`alpha()` `alpha.ci()` `alpha2r()`	Find two estimates of reliability: Cronbach's alpha and Guttman's Lambda 6.
`anova(<psych>)`	Model comparison for regression, mediation, cluster and factor analysis
`bassAckward()` `bassAckward.diagram()`	The Bass-Ackward factoring algorithm discussed by Goldberg
`bestScales()` `bestItems()`	A bootstrap aggregation function for choosing most predictive unit weighted items
`bfi` `bfi.dictionary`	25 Personality items representing 5 factors
`bi.bars()`	Draw pairs of bargraphs based on two groups
`Thurstone` `Thurstone.33` `Thurstone.33G` `Thurstone.9` `Holzinger` `Holzinger.9` `Bechtoldt` `Bechtoldt.1` `Bechtoldt.2` `Reise`	Seven data sets showing a bifactor solution.
`bigCor()`	Find large correlation matrices by stitching together smaller ones found more rapidly
`biplot(<psych>)`	Draw biplots of factor or component scores by factor or component loadings
`block.random()`	Create a block randomized structure for n independent variables
`bock`	Bock and Liberman (1970) data set of 1000 observations of the LSAT
`cattell`	12 cognitive variables from Cattell (1963)
`circ.tests()`	Apply four tests of circumplex versus simple structure
`scoreOverlap()` `scoreBy()` `cluster.cor()`	Find correlations of composite variables (corrected for overlap) from a larger matrix.
`cluster.fit()`	cluster Fit: fit of the cluster model to a correlation matrix
`cluster.loadings()`	Find item by cluster correlations, corrected for overlap and reliability
`cluster.plot()` `fa.plot()` `factor.plot()`	Plot factor/cluster loadings and assign items to clusters by their highest loading.
`cluster2keys()`	Convert a cluster vector (from e.g., kmeans) to a keys matrix suitable for scoring item clusters.
`cohen.d()` `d.robust()` `cohen.d.ci()` `d.ci()` `cohen.d.by()` `d2r()` `r2d()` `d2t()` `t2d()` `m2t()` `m2d()` `d2OVL()` `d2OVL2()` `d2CL()` `d2U3()` `cd.validity()`	Find Cohen d and confidence intervals
`comorbidity()`	Convert base rates of two diagnoses and their comorbidity into phi, Yule, and tetrachorics
`congruence()` `cohen.profile()` `distance()`	Matrix and profile congruences and distances
`corCi()` `cor.ci()`	Bootstrapped and normal confidence intervals for raw and composite correlations
`corPlot()` `corPlotUpperLowerCi()` `cor.plot()` `cor.plot.upperLowerCi()`	Create an image plot for a correlation or factor matrix
`cor.smooth()` `cor.smoother()`	Smooth a non-positive definite correlation matrix to make it positive definite
`cor.wt()`	The sample size weighted correlation may be used in correlating aggregated data
`cor2dist()`	Convert correlations to distances (necessary to do multidimensional scaling of correlation data)
`corFiml()`	Find a Full Information Maximum Likelihood (FIML) correlation or covariance matrix from a data matrix with missing data
`corTest()` `corr.test()` `corr.p()`	Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame.
`correct.cor()`	Find dis-attenuated correlations given correlations and reliabilities
`cortest.bartlett()`	Bartlett's test that a correlation matrix is an identity matrix
`cortest()` `cortest.normal()` `cortest.jennrich()` `cortest.mat()`	Chi square tests of whether a single matrix is an identity matrix, or a pair of matrices are equal.
`cosinor()` `cosinor.plot()` `cosinor.period()` `circadian.phase()` `circadian.mean()` `circadian.sd()` `circadian.stats()` `circadian.F()` `circadian.reliability()` `circular.mean()` `circadian.cor()` `circular.cor()` `circadian.linear.cor()`	Functions for analysis of circadian or diurnal data
`pairwiseCount()` `pairwiseDescribe()` `pairwiseZero()` `pairwiseImpute()` `pairwiseReport()` `pairwiseSample()` `pairwiseCountBig()` `pairwisePlot()` `count.pairwise()`	Count number of pairwise cases for a data set with missing (NA) data and impute values.
`cta()` `cta.15()`	Simulate the C(ues) T(endency) A(ction) model of motivation
`violin()` `violinBy()` `densityBy()`	Create a 'violin plot' or density plot of the distribution of a set of variables
`fa.poly()` `factor.minres()` `factor.wls()`	Deprecated Exploratory Factor analysis functions. Please use fa
`describe()` `describeData()` `describeFast()`	Basic descriptive statistics useful for psychometrics
`describeBy()` `describe.by()`	Basic summary statistics by group
`diagram()` `dia.rect()` `dia.ellipse()` `dia.triangle()` `dia.ellipse1()` `dia.shape()` `dia.arrow()` `dia.curve()` `dia.curved.arrow()` `dia.self()` `dia.cone()` `multi.self()` `multi.arrow()` `multi.curved.arrow()` `multi.rect()`	Helper functions for drawing path model diagrams
`draw.tetra()` `draw.cor()`	Draw a correlation ellipse and two normal curves to demonstrate tetrachoric correlation
`dummy.code()`	Create dummy coded variables
`Dwyer`	8 cognitive variables used by Dwyer for an example.
`eigen.loadings()`	Convert eigen vectors and eigen values to the more normal (for psychologists) component loadings
`ellipses()` `minkowski()`	Plot data and 1 and 2 sigma correlation ellipses
`error.bars()` `error.bars.tab()`	Plot means and confidence intervals
`error.bars.by()`	Plot means and confidence intervals for multiple groups
`errorCircles()`	Two way plots of means, error bars, and sample sizes
`error.crosses()`	Plot x and y error bars
`error.dots()`	Show a dot.chart with error bars for different groups or variables
`esem()` `esemDiagram()` `esem.diagram()` `cancorDiagram()` `interbattery()`	Perform and Exploratory Structural Equation Model (ESEM) by using factor extension techniques
`fa()` `fac()` `fa.pooled()` `fa.sapa()`	Exploratory Factor analysis using MinRes (minimum residual) as well as EFA by Principal Axis, Weighted Least Squares or Maximum Likelihood
`fa.diagram()` `het.diagram()` `extension.diagram()` `fa.graph()` `fa.rgraph()`	Graph factor loading matrices
`fa.extension()` `fa.extend()` `faRegression()` `faReg()`	Apply Dwyer's factor extension to find factor loadings for extended variables
`lookup()` `lookupItems()` `fa.lookup()` `item.lookup()` `itemSort()` `keys.lookup()` `lookupFromKeys()` `lmCorLookup()`	A set of functions for factorial and empirical scale construction
`fa.parallel()` `paSelect()` `fa.parallel.poly()` `plot(<poly.parallel>)`	Scree plots of data or correlation matrix compared to random “parallel" matrices
`fa.random()`	A first approximation to Random Effects Exploratory Factor Analysis
`fa.sort()` `fa.organize()`	Sort factor analysis or principal components analysis loadings
`faCor()`	Correlations between two factor analysis solutions
`fa.multi()` `fa.multi.diagram()`	Multi level (hierarchical) factor analysis
`faRotations()`	Multiple rotations of factor loadings to find local minima
`factor.congruence()` `fa.congruence()`	Coefficient of factor congruence
`factor.fit()`	How well does the factor model fit a correlation matrix. Part of the VSS package
`factor.model()`	Find R = F F' + U2 is the basic factor model
`factor.residuals()`	R* = R- F F'
`factor.rotate()`	“Hand" rotate a factor loading matrix
`factor.scores()`	Various ways to estimate factor scores for the factor analysis model
`fa.stats()` `factor.stats()`	Find various goodness of fit statistics for factor analysis and principal components
`factor2cluster()`	Extract cluster definitions from factor loadings
`fisherz()` `fisherz2r()` `r.con()` `r2t()` `t2r()` `g2r()` `chi2r()` `r2chi()` `r2c()` `cor2cov()` `r2p()`	Transformations of r, d, and t including Fisher r to z and z to r and confidence intervals
`fparse()`	Parse and extend formula input from a model and return the DV(s), IV(s), and associated terms.
`geometric.mean()`	Find the geometric mean of a vector or columns of a data.frame.
`glb.algebraic()`	Find the greatest lower bound to reliability.
`splitHalf()` `guttman()` `tenberge()` `glb()` `glb.fa()`	Alternative estimates of test reliabiity
`harmonic.mean()`	Find the harmonic mean of a vector, matrix, or columns of a data.frame
`headTail()` `headtail()` `topBottom()` `quickView()`	Combine calls to head and tail
`iclust.diagram()`	Draw an ICLUST hierarchical cluster structure diagram
`interp.median()` `interp.quantiles()` `interp.quartiles()` `interp.boxplot()` `interp.values()` `interp.qplot.by()`	Find the interpolated sample median, quartiles, or specific quantiles for a vector, matrix, or data frame
`irt.fa()` `irt.select()` `fa2irt()`	Item Response Analysis by Exploratory Factor Analysis of tetrachoric/polychoric correlations
`irt.item.diff.rasch()` `irt.discrim()`	Simple function to estimate item difficulties using IRT concepts
`irt.person.rasch()` `irt.0p()` `irt.1p()` `irt.2p()`	Item Response Theory estimate of theta (ability) using a Rasch (like) model
`irt.responses()`	Plot probability of multiple choice responses as a function of a latent trait
`kaiser()`	Apply the Kaiser normalization when rotating factors
`cohen.kappa()` `wkappa()`	Find Cohen's kappa and weighted kappa coefficients for correlation of two raters
`lmCor()` `setCor()` `lmDiagram()` `lmCor.diagram()` `set.cor()` `mat.regress()` `matReg()` `crossValidation()` `crossValidationBoot()` `matPlot()`	Multiple Regression, Canonical and Set Correlation from matrix or raw input
`logistic()` `logit()` `logistic.grm()`	Logistic transform from x to p and logit transform from p to x
`lowerUpper()`	Combine two square matrices to have a lower off diagonal for one, upper off diagonal for the other
`make.keys()` `keys2list()` `selectFromKeys()` `makePositiveKeys()`	Create a keys matrix for use by score.items or cluster.cor
`manhattan()`	"Manhattan" plots of correlations with a set of criteria.
`mat.sort()` `matSort()`	Sort the elements of a correlation matrix to reflect factor loadings
`%+%`	A function to add two vectors or matrices
`mediate()` `mediate.diagram()` `moderate.diagram()`	Estimate and display direct and indirect effects of mediators and moderator in path models
`psych.misc()` `lowerCor()` `cor2()` `lowerMat()` `matMult()` `tableF()` `reflect()` `progressBar()` `shannon()` `test.all()` `levels2numeric()` `char2numeric()` `nchar2numeric()` `isCorrelation()` `isCovariance()` `fromTo()` `cs()` `acs()` `SAPAfy()`	Miscellaneous helper functions for the psych package
`mixedCor()` `mixed.cor()`	Find correlations for mixtures of continuous, polytomous, and dichotomous variables
`mssd()` `rmssd()` `autoR()`	Find von Neuman's Mean Square of Successive Differences
`multi.hist()` `histBy()`	Multiple histograms with density and normal fits on one page
`mlr()` `mlArrange()` `mlPlot()` `multilevel.reliability()`	Find and plot various reliability/gneralizability coefficients for multilevel data
`omega()` `omegaSem()` `omegah()` `omegaFromSem()` `omegaDirect()` `directSl()`	Calculate McDonald's omega estimates of general and total factor saturation
`omega.diagram()` `omega.graph()`	Graph hierarchical factor structures
`outlier()`	Find and graph Mahalanobis squared distances to detect outliers
`p.rep()` `p.rep.f()` `p.rep.r()` `p.rep.t()`	Find the probability of replication for an F, t, or r and estimate effect size
`paired.r()`	Test the difference between (un)paired correlations
`pairs(<panels>)`	SPLOM, histograms and correlations for a data matrix
`parcels()` `keysort()`	Find miniscales (parcels) of size 2 or 3 from a set of items
`partial.r()`	Find the partial correlations for a set (x) of variables with set (y) removed.
`phi()`	Find the phi coefficient of correlation between two dichotomous variables
`phi.demo()`	A simple demonstration of the Pearson, phi, and polychoric corelation
`phi2tetra()` `phi2poly()`	Convert a phi coefficient to a tetrachoric correlation
`plot(<psych>)` `plot(<irt>)` `plot(<poly>)` `plot(<residuals>)`	Plotting functions for the psych package of class “psych"
`polar()`	Convert Cartesian factor loadings into polar coordinates
`Yule2poly.matrix()` `phi2poly.matrix()` `Yule2phi.matrix()`	Phi or Yule coefficient matrix to polychoric coefficient matrix
`predict(<psych>)`	Prediction function for factor analysis, principal components (pca), bestScales
`predicted.validity()` `item.validity()` `validityItem()`	Find the predicted validities of a set of scales based on item statistics
`principal()`	Principal components analysis (PCA)
`print(<psych>)` `summary(<psych>)`	Print and summary functions for the psych class
`r.test()`	Tests of significance for correlations
`rangeCorrection()`	Correct correlations for restriction of range. (Thorndike Case 2)
`reliability()` `plot(<reliability>)`	Reports 7 different estimates of scale reliabity including alpha, omega, split half
`rescale()`	Function to convert scores to “conventional " metrics
`residuals(<psych>)` `resid(<psych>)`	Extract residuals from various psych objects
`reverse.code()`	Reverse the coding of selected items prior to scale analysis
`sat.act`	3 Measures of ability: SATV, SATQ, ACT
`scaling.fits()`	Test the adequacy of simple choice, logistic, or Thurstonian scaling.
`scatterHist()` `scatter.hist()`	Draw a scatter plot with associated X and Y histograms, densities and correlation
`schmid()`	Apply the Schmid Leiman transformation to a correlation matrix
`score.alpha()`	Score scales and find Cronbach's alpha as well as associated statistics
`scoreIrt()` `scoreIrt.1pl()` `scoreIrt.2pl()` `score.irt()` `score.irt.2()` `score.irt.poly()` `irt.stats.like()` `make.irt.stats()` `irt.tau()` `irt.se()`	Find Item Response Theory (IRT) based scores for dichotomous or polytomous items
`scoreItems()` `score.items()` `scoreFast()` `scoreVeryFast()` `response.frequencies()` `responseFrequency()` `removeMissing()`	Score item composite scales and find Cronbach's alpha, Guttman lambda 6 and item whole correlations
`score.multiple.choice()`	Score multiple choice items and provide basic test statistics
`scoreWtd()`	Score items using regression or correlation based weights
`scrub()`	A utility for basic data cleaning and recoding. Changes values outside of minimum and maximum limits to NA.
`sim()` `sim.simplex()` `sim.minor()`	Functions to simulate psychological/psychometric data.
`sim.VSS()`	create VSS like data
`sim.anova()`	Simulate a 3 way balanced ANOVA or linear model, with or without repeated measures.
`sim.congeneric()`	Simulate a congeneric data set with or without minor factors
`sim.hierarchical()` `sim.bonds()` `make.hierarchical()`	Create a population or sample correlation matrix, perhaps with hierarchical structure.
`sim.rasch()` `sim.irt()` `sim.npl()` `sim.npn()` `sim.poly()` `sim.poly.npn()` `sim.poly.npl()` `sim.poly.ideal()` `sim.poly.ideal.npn()` `sim.poly.ideal.npl()` `sim.poly.mat()`	Functions to simulate psychological/psychometric data.
`sim.item()` `sim.circ()` `sim.dichot()` `item.dichot()` `sim.spherical()` `con2cat()`	Generate simulated data structures for circumplex, spherical, or simple structure
`sim.multi()` `sim.multilevel()`	Simulate multilevel data with specified within group and between group correlations
`sim.omega()` `sim.parallel()`	Further functions to simulate psychological/psychometric data.
`sim.structure()` `sim.structural()` `simCor()` `sim.correlation()`	Create correlation matrices or data matrices with a particular measurement and structural model
`simulation.circ()` `circ.sim.plot()`	Simulations of circumplex and simple structure
`skew()` `kurtosi()` `mardia()`	Calculate univariate or multivariate (Mardia's test) skew and kurtosis for a vector, matrix, or data.frame
`small.msq`	A small example data set taken from a larger data set
`smc()`	Find the Squared Multiple Correlation (SMC) of each variable with the remaining variables in a matrix
`spider()` `radar()`	Make "radar" or "spider" plots.
`statsBy()` `statsBy.boot()` `statsBy.boot.summary()` `faBy()`	Find statistics (including correlations) within and between groups for basic multilevel analyses
`structure.diagram()` `structure.graph()` `structure.sem()` `lavaan.diagram()` `sem.diagram()` `sem.graph()`	Draw a structural equation model specified by two measurement models and a structural model
`structure.list()` `phi.list()`	Create factor model matrices from an input list
`superMatrix()` `superCor()` `super.matrix()`	Form a super matrix from two sub matrices.
`table2matrix()` `table2df()`	Convert a table with counts to a matrix or data.frame representing those counts.
`Tal.Or`	Data set testing causal direction in presumed media influence
`test.irt()`	A simple demonstration (and test) of various IRT scoring algorthims.
`test.psych()`	Testing of functions in the psych package
`testRetest()`	Find various test-retest statistics, including test, person and item reliability
`tetrachoric()` `polychoric()` `biserial()` `polyserial()` `polydi()` `poly.mat()`	Tetrachoric, polychoric, biserial and polyserial correlations from various types of input
`thurstone()`	Thurstone Case V scaling
`tr()`	Find the trace of a square matrix
`unidim()`	Several indices of the unidimensionality of a set of variables.
`winsor()` `winsor.mean()` `winsor.means()` `winsor.sd()` `winsor.var()`	Find the Winsorized scores, means, sds or variances for a vector, matrix, or data.frame
`withinBetween`	An example of the distinction between within group and between group correlations

Reference

All functions