iclust: Item Cluster Analysis -- Hierarchical cluster analysis using psychometric principles
Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt
Decision Theory measures of specificity, sensitivity, and d prime
Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.
Example data set from Gorsuch (1997) for an example factor extension.
Intraclass Correlations (ICC1, ICC2, ICC3 from Shrout and Fleiss)
Function to form hierarchical cluster analysis of items
Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010)
Draw an ICLUST graph using the Rgraphviz package
A package for personality, psychometric, and psychological research
create control code for ICLUST graphical output
Find the Kaiser, Meyer, Olkin Measure of Sampling Adequacy
Compute the Moore-Penrose Pseudo Inverse of a matrix
Apply the Very Simple Structure, MAP, and other criteria to determine the appropriate number of factors.
Compare real and random VSS solutions
9 Cognitive variables discussed by Tucker and Lewis (1973)
Find the Standard deviation for a vector, matrix, or data.frame - do not return error if there are no cases
12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation
Perform Procustes,bifactor, promax or targeted rotations and return the inter factor angles.
Find two estimates of reliability: Cronbach's alpha and Guttman's Lambda 6.
Plot the successive eigen values for a scree test
Plot VSS fits
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.
Sort items by absolute size of cluster loadings
25 Personality items representing 5 factors
The Bass-Ackward factoring algorithm discussed by Goldberg
Model comparison for regression, mediation, and factor analysis
A bootstrap aggregation function for choosing most predictive unit weighted items
Draw pairs of bargraphs based on two groups
Convert a cluster vector (from e.g., kmeans) to a keys matrix suitable for scoring item clusters.
Seven data sets showing a bifactor solution.
Plot factor/cluster loadings and assign items to clusters by their highest loading.
Find correlations of composite variables (corrected for overlap) from a larger matrix.
Apply four tests of circumplex versus simple structure
Find dis-attenuated correlations given correlations and reliabilities
Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame.
Draw biplots of factor or component scores by factor or component loadings
Create a block randomized structure for n independent variables
Bootstrapped and normal confidence intervals for raw and composite correlations
Create an image plot for a correlation or factor matrix
The sample size weighted correlation may be used in correlating aggregated data
Smooth a non-positive definite correlation matrix to make it positive definite
12 cognitive variables from Cattell (1963)
Find a Full Information Maximum Likelihood (FIML) correlation or covariance matrix from a data matrix with missing data
cluster Fit: fit of the cluster model to a correlation matrix
Find item by cluster correlations, corrected for overlap and reliability
Convert correlations to distances (necessary to do multidimensional scaling of correlation data)
Bock and Liberman (1970) data set of 1000 observations of the LSAT
Chi square tests of whether a single matrix is an identity matrix, or a pair of matrices are equal.
Bartlett's test that a correlation matrix is an identity matrix
Functions for analysis of circadian or diurnal data
Count number of pairwise cases for a data set with missing (NA) data and impute values.
Find Cohen d and confidence intervals
Simulate the C(ues) T(endency) A(ction) model of motivation
Create a 'violin plot' or density plot of the distribution of a set of variables
Convert base rates of two diagnoses and their comorbidity into phi, Yule, and tetrachorics
Basic descriptive statistics useful for psychometrics
Deprecated Exploratory Factor analysis functions. Please use fa
8 cognitive variables used by Dwyer for an example.
Convert eigen vectors and eigen values to the more normal (for psychologists) component loadings
Basic summary statistics by group
Plot data and 1 and 2 sigma correlation ellipses
Plot means and confidence intervals
Helper functions for drawing path model diagrams
Plot x and y error bars
Show a dot.chart with error bars for different groups or variables
Plot means and confidence intervals for multiple groups
Two way plots of means, error bars, and sample sizes
Create dummy coded variables
Draw a correlation ellipse and two normal curves to demonstrate tetrachoric correlation
Correlations between two factor analysis solutions
Sort factor analysis or principal components analysis loadings
Scree plots of data or correlation matrix compared to random ``parallel" matrices
Apply Dwyer's factor extension to find factor loadings for extended variables
A set of functions for factorial and empirical scale construction
Multi level (hierarchical) factor analysis
A first approximation to Random Effects Exploratory Factor Analysis
Graph factor loading matrices
Exploratory Factor analysis using MinRes (minimum residual) as well as EFA by Principal Axis, Weighted Least Squares or Maximum Likelihood
Find R = F F' + U2 is the basic factor model
Perform and Exploratory Structural Equation Model (ESEM) by using factor extension techniques
R* = R- F F'
How well does the factor model fit a correlation matrix. Part of the VSS package
Coefficient of factor congruence
``Hand" rotate a factor loading matrix
Find the interpolated sample median, quartiles, or specific quantiles for a vector, matrix, or data frame
Item Response Analysis by Exploratory Factor Analysis of tetrachoric/polychoric correlations
Various ways to estimate factor scores for the factor analysis model
Find the greatest lower bound to reliability.
Find the geometric mean of a vector or columns of a data.frame.
Find various goodness of fit statistics for factor analysis and principal components
Extract cluster definitions from factor loadings
Find the harmonic mean of a vector, matrix, or columns of a data.frame
Alternative estimates of test reliabiity
Item Response Theory estimate of theta (ability) using a Rasch (like) model
Simple function to estimate item difficulties using IRT concepts
Transformations of r, d, and t including Fisher r to z and z to r and confidence intervals
Combine calls to head and tail
Parse and exten formula input from a model and return the DV, IV, and associated terms.
Create a keys matrix for use by score.items or cluster.cor
Draw an ICLUST hierarchical cluster structure diagram
Combine two square matrices to have a lower off diagonal for one, upper off diagonal for the other
Sort the elements of a correlation matrix to reflect factor loadings
"Manhattan" plots of correlations with a set of criteria.
Logistic transform from x to p and logit transform from p to x
Estimate and display direct and indirect effects of mediators and moderator in path models
Find Cohen's kappa and weighted kappa coefficients for correlation of two raters
Apply the Kaiser normalization when rotating factors
Plot probability of multiple choice responses as a function of a latent trait
A function to add two vectors or matrices
Find the probability of replication for an F, t, or r and estimate effect size
Find von Neuman's Mean Square of Successive Differences
Graph hierarchical factor structures
Find and graph Mahalanobis squared distances to detect outliers
Find and plot various reliability/gneralizability coefficients for multilevel data
Calculate McDonald's omega estimates of general and total factor saturation
Find correlations for mixtures of continuous, polytomous, and dichotomous variables
Miscellaneous helper functions for the psych package
Plotting functions for the psych package of class ``psych"
Convert a phi coefficient to a tetrachoric correlation
A simple demonstration of the Pearson, phi, and polychoric corelation
Convert Cartesian factor loadings into polar coordinates
Find miniscales (parcels) of size 2 or 3 from a set of items
SPLOM, histograms and correlations for a data matrix
Test the difference between (un)paired correlations
Find the partial correlations for a set (x) of variables with set (y) removed.
Phi or Yule coefficient matrix to polychoric coefficient matrix
Prediction function for factor analysis, principal components (pca), bestScales
Find the phi coefficient of correlation between two dichotomous variables
Extract residuals from various psych objects
Function to convert scores to ``conventional
" metrics
Print and summary functions for the psych class
Principal components analysis (PCA)
Multiple histograms with density and normal fits on one page
Test the adequacy of simple choice, logistic, or Thurstonian scaling.
Draw a scatter plot with associated X and Y histograms, densities and correlation
Reverse the coding of selected items prior to scale analysis
3 Measures of ability: SATV, SATQ, ACT
Tests of significance for correlations
Correct correlations for restriction of range. (Thorndike Case 2)
A utility for basic data cleaning and recoding. Changes values outside of minimum and maximum limits to NA.
Apply the Schmid Leiman transformation to a correlation matrix
Functions to simulate psychological/psychometric data.
Score scales and find Cronbach's alpha as well as associated statistics
create VSS like data
Multiple Regression and Set Correlation from matrix or raw input
Score item composite scales and find Cronbach's alpha, Guttman lambda 6 and item whole correlations
Find Item Response Theory (IRT) based scores for dichotomous or polytomous items
Score multiple choice items and provide basic test statistics
Score items using regression or correlation based weights
Simulate a congeneric data set
Further functions to simulate psychological/psychometric data.
Generate simulated data structures for circumplex, spherical, or simple structure
Create a population or sample correlation matrix, perhaps with hierarchical structure.
Simulate a 3 way balanced ANOVA or linear model, with or without repeated measures.
Simulations of circumplex and simple structure
Simulate multilevel data with specified within group and between group correlations
Functions to simulate psychological/psychometric data.
Calculate univariate or multivariate (Mardia's test) skew and kurtosis for a vector, matrix, or data.frame
Create correlation matrices or data matrices with a particular measurement and structural model
Create factor model matrices from an input list
Draw a structural equation model specified by two measurement models and a structural model
Make "radar" or "spider" plots.
Find statistics (including correlations) within and between groups for basic multilevel analyses
Convert a table with counts to a matrix or data.frame representing those counts.
Form a super matrix from two sub matrices.
Find the Squared Multiple Correlation (SMC) of each variable with the remaining variables in a matrix
Project Talent data set from Marion Spengler and Rodica Damian
An example of the distinction between within group and between group correlations
Find the Winsorized scores, means, sds or variances for a vector, matrix, or data.frame
Thurstone Case V scaling
Tetrachoric, polychoric, biserial and polyserial correlations from various types of input
Testing of functions in the psych package
Find various test-retest statistics, including test, person and item reliability
Find the trace of a square matrix
Several indices of the unidimensionality of a set of variables.
Data set testing causal direction in presumed media influence
A simple demonstration (and test) of various IRT scoring algorthims.