The MBESS R Package

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Documentation for package ‘MBESS’ version 4.9.0

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A C D E F G H I L M P R S T U V

-- A --

ancova.random.data Generate random data for an ANCOVA model

-- C --

CFA.1 One-factor confirmatory factor analysis model
ci.c Confidence interval for a contrast in a fixed effects ANOVA
ci.c.ancova Confidence interval for an (unstandardized) contrast in ANCOVA with one covariate
ci.cc Confidence interval for the population correlation coefficient
ci.cv Confidence interval for the coefficient of variation
ci.omega2 Confidence Interval for omega-squared (omega^2) for fixed-effects ANOVA and ANCOVA designs (and partial omega-squared omega^2_p for multifactor ANOVA and ANCOVA designs)
ci.pvaf Confidence Interval for the Proportion of Variance Accounted for (in the dependent variable by knowing the levels of the factor)
ci.R Confidence interval for the multiple correlation coefficient
ci.R2 Confidence interval for the population squared multiple correlation coefficient
ci.rc Confidence Interval for a Regression Coefficient
ci.reg.coef Confidence interval for a regression coefficient
ci.reliability Confidence Interval for a Reliability Coefficient
ci.rmsea Confidence interval for the population root mean square error of approximation
ci.sc Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVA
ci.sc.ancova Confidence interval for a standardized contrast in ANCOVA with one covariate
ci.sm Confidence Interval for the Standardized Mean
ci.smd Confidence limits for the standardized mean difference.
ci.smd.c Confidence limits for the standardized mean difference using the control group standard deviation as the divisor.
ci.snr Confidence Interval for the Signal-To-Noise Ratio
ci.src Confidence Interval for a Standardized Regression Coefficient
ci.srsnr Confidence Interval for the Square Root of the Signal-To-Noise Ratio
conf.limits.nc.chisq Confidence limits for noncentral chi square parameters
conf.limits.ncf Confidence limits for noncentral F parameters
conf.limits.nct Confidence limits for a noncentrality parameter from a t-distribution
Cor.Mat.Lomax Correlation matrix for Lomax (1983) data set
Cor.Mat.MM Correlation matrix for Maruyama & McGarvey (1980) data set
cor2cov Correlation Matrix to Covariance Matrix Conversion
covmat.from.cfm Covariance matrix from confirmatory (single) factor model.
cv Function to calculate the regular (which is also biased) estimate of the coefficient of variation or the unbiased estimate of the coefficient of variation.

-- D --

delta2lambda Conversion functions for noncentral t-distribution

-- E --

Expected.R2 Expected value of the squared multiple correlation coefficient

-- F --

F2Rsquare Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square.

-- G --

Gardner.LD The Gardner learning data, which was used by L.R. Tucker

-- H --

HS Complete Data Set of Holzinger and Swineford's (1939) Study

-- I --

intr.plot Regression Surface Containing Interaction
intr.plot.2d Plotting Conditional Regression Lines with Interactions in Two Dimensions

-- L --

lambda2delta Conversion functions for noncentral t-distribution
Lambda2Rsquare Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square.

-- M --

MBES MBESS
mbes MBESS
MBESS MBESS
mbess MBESS
mediation Effect sizes and confidence intervals in a mediation model
mediation.effect.bar.plot Bar plots of mediation effects
mediation.effect.plot Visualizing mediation effects
mr.cv Minimum risk point estimation of the population coefficient of variation
mr.smd Minimum risk point estimation of the population standardized mean difference

-- P --

power.density.equivalence.md Density for power of two one-sided tests procedure (TOST) for equivalence
power.equivalence.md Power of Two One-Sided Tests Procedure (TOST) for Equivalence
power.equivalence.md.plot Plot power of Two One-Sided Tests Procedure (TOST) for Equivalence
prof.salary Cohen et. al. (2003)'s professor salary data set

-- R --

Rsquare2F Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square.
Rsquare2Lambda Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square.

-- S --

s.u Unbiased estimate of the population standard deviation
Sigma.2.SigmaStar Construct a covariance matrix with specified error of approximation
signal.to.noise.R2 Signal to noise using squared multiple correlation coefficient
smd Standardized mean difference
smd.c Standardized mean difference using the control group as the basis of standardization
ss.aipe.c Sample size planning for an ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) perspective
ss.aipe.c.ancova Sample size planning for a contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) perspective
ss.aipe.c.ancova.sensitivity Sensitivity analysis for sample size planning for the (unstandardized) contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) Perspective
ss.aipe.crd.both.fixedbudget Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.crd.both.fixedwidth Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.crd.es.both.fixedbudget Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ss.aipe.crd.es.both.fixedwidth Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ss.aipe.crd.es.nclus.fixedbudget Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ss.aipe.crd.es.nclus.fixedwidth Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ss.aipe.crd.es.nindiv.fixedbudget Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ss.aipe.crd.es.nindiv.fixedwidth Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
ss.aipe.crd.nclus.fixedbudget Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.crd.nclus.fixedwidth Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.crd.nindiv.fixedbudget Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.crd.nindiv.fixedwidth Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.cv Sample size planning for the coefficient of variation given the goal of Accuracy in Parameter Estimation approach to sample size planning
ss.aipe.cv.sensitivity Sensitivity analysis for sample size planning given the Accuracy in Parameter Estimation approach for the coefficient of variation.
ss.aipe.pcm Sample size planning for polynomial change models in longitudinal study
ss.aipe.R2 Sample Size Planning for Accuracy in Parameter Estimation for the multiple correlation coefficient.
ss.aipe.R2.sensitivity Sensitivity analysis for sample size planning with the goal of Accuracy in Parameter Estimation (i.e., a narrow observed confidence interval)
ss.aipe.rc Sample size necessary for the accuracy in parameter estimation approach for an unstandardized regression coefficient of interest
ss.aipe.rc.sensitivity Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient
ss.aipe.reg.coef Sample size necessary for the accuracy in parameter estimation approach for a regression coefficient of interest
ss.aipe.reg.coef.sensitivity Sensitivity analysis for sample size planning from the Accuracy in Parameter Estimation Perspective for the (standardized and unstandardized) regression coefficient
ss.aipe.reliability Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients.
ss.aipe.rmsea Sample size planning for RMSEA in SEM
ss.aipe.rmsea.sensitivity a priori Monte Carlo simulation for sample size planning for RMSEA in SEM
ss.aipe.sc Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized contrast in ANOVA
ss.aipe.sc.ancova Sample size planning from the AIPE perspective for standardized ANCOVA contrasts
ss.aipe.sc.ancova.sensitivity Sensitivity analysis for the sample size planning method for standardized ANCOVA contrast
ss.aipe.sc.sensitivity Sensitivity analysis for sample size planning for the standardized ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) Perspective
ss.aipe.sem.path Sample size planning for SEM targeted effects
ss.aipe.sem.path.sensitiv a priori Monte Carlo simulation for sample size planning for SEM targeted effects
ss.aipe.sm Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized mean
ss.aipe.sm.sensitivity Sensitivity analysis for sample size planning for the standardized mean from the Accuracy in Parameter Estimation (AIPE) Perspective
ss.aipe.smd Sample size planning for the standardized mean difference from the Accuracy in Parameter Estimation (AIPE) perspective
ss.aipe.smd.full Sample size planning for the standardized mean different from the accuracy in parameter estimation approach
ss.aipe.smd.lower Sample size planning for the standardized mean different from the accuracy in parameter estimation approach
ss.aipe.smd.sensitivity Sensitivity analysis for sample size given the Accuracy in Parameter Estimation approach for the standardized mean difference.
ss.aipe.smd.upper Sample size planning for the standardized mean different from the accuracy in parameter estimation approach
ss.aipe.src sample size necessary for the accuracy in parameter estimation approach for a standardized regression coefficient of interest
ss.aipe.src.sensitivity Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the standardized regression coefficient
ss.power.pcm Sample size planning for power for polynomial change models
ss.power.R2 Function to plan sample size so that the test of the squared multiple correlation coefficient is sufficiently powerful.
ss.power.rc sample size for a targeted regression coefficient
ss.power.reg.coef sample size for a targeted regression coefficient
ss.power.sem Sample size planning for structural equation modeling from the power analysis perspective

-- T --

theta.2.Sigma.theta Compute the model-implied covariance matrix of an SEM model
transform_r.Z Transform a correlation coefficient (r) into the scale of Fischer's _Z_
transform_Z.r Transform Fischer's _Z_ into the scale of a correlation coefficient

-- U --

upsilon This function implements the upsilon effect size statistic as described in Lachowicz, Preacher, & Kelley (in press) for mediation.

-- V --

var.ete The Variance of the Estimated Treatment Effect at Selected Covariate Values in a Two-group ANCOVA.
Variance.R2 Variance of squared multiple correlation coefficient
verify.ss.aipe.R2 Internal MBESS function for verifying the sample size in ss.aipe.R2
vit Visualize individual trajectories
vit.fitted Visualize individual trajectories with fitted curve and quality of fit