# The MBESS R Package

## Documentation for package ‘MBESS’ version 4.9.42

- DESCRIPTION file.

## Help Pages

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 between-subject fixed-effects ANOVA and ANCOVA designs (and partial omega-squared omega^2_p for between-subject 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 Fisher's Z^\prime
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 (2018) 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