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MBESS (version 4.4.3)

The MBESS R Package

Description

Implements methods that useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but at this point MBESS contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see for a detailed list of those that have contributed and other details.

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Version

Install

install.packages('MBESS')

Monthly Downloads

8,359

Version

4.4.3

License

GPL-2 | GPL-3

Maintainer

Ken Kelley

Last Published

January 10th, 2018

Functions in MBESS (4.4.3)

HS.data

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

MBESS
F.and.R2.Noncentral.Conversion

Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square.
CFA.1

One-factor confirmatory factor analysis model
Cor.Mat.Lomax

Correlation matrix for Lomax (1983) data set
Gardner.LD

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

Confidence Interval for the Proportion of Variance Accounted for (in the dependent variable by knowing the levels of the factor)
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
Sigma.2.SigmaStar

Construct a covariance matrix with specified error of approximation
Variance.R2

Variance of squared multiple correlation coefficient
aipe.smd

Sample size planning for the standardized mean different from the accuracy in parameter estimation approach
ancova.random.data

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

Confidence interval for a standardized contrast in ANCOVA with one covariate
ci.sm

Confidence Interval for the Standardized Mean
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
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
cor2cov

Correlation Matrix to Covariance Matrix Conversion
covmat.from.cfm

Covariance matrix from confirmatory (single) factor model.
smd

Standardized mean difference
smd.c

Standardized mean difference using the control group as the basis of standardization
Cor.Mat.MM

Correlation matrix for Maruyama & McGarvey (1980) data set
Expected.R2

Expected value of the squared multiple correlation 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
intr.plot

Regression Surface Containing Interaction
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
ci.snr

Confidence Interval for the Signal-To-Noise Ratio
ci.src

Confidence Interval for a Standardized Regression Coefficient
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.
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.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.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.src.sensitivity

Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the standardized regression coefficient
ss.power.R2

Function to plan sample size so that the test of the squared multiple correlation coefficient is sufficiently powerful.
t.and.smd.conversion

Conversion functions for noncentral t-distribution
ss.aipe.crd

Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
ss.aipe.crd.es

Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
theta.2.Sigma.theta

Compute the model-implied covariance matrix of an SEM model
vit

Visualize individual trajectories
vit.fitted

Visualize individual trajectories with fitted curve and quality of fit
ci.R

Confidence interval for the multiple correlation coefficient
ci.R2

Confidence interval for the population squared multiple correlation coefficient
ci.smd

Confidence limits for the standardized mean difference.
intr.plot.2d

Plotting Conditional Regression Lines with Interactions in Two Dimensions
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
ss.aipe.cv.sensitivity

Sensitivity analysis for sample size planning given the Accuracy in Parameter Estimation approach for the coefficient of variation.
ci.smd.c

Confidence limits for the standardized mean difference using the control group standard deviation as the divisor.
conf.limits.ncf

Confidence limits for noncentral F parameters
conf.limits.nct

Confidence limits for a noncentrality parameter from a t-distribution
s.u

Unbiased estimate of the population standard deviation
signal.to.noise.R2

Signal to noise using squared multiple correlation coefficient
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.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.rc

Sample size necessary for the accuracy in parameter estimation approach for an unstandardized regression coefficient of interest
mr.cv

Minimum risk point estimation of the population coefficient of variation
mr.smd

Minimum risk point estimation of the population standardized mean difference
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.reliability

Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients.
ss.aipe.rmsea

Sample size planning for RMSEA in SEM
ss.aipe.smd.sensitivity

Sensitivity analysis for sample size given the Accuracy in Parameter Estimation approach for the standardized mean difference.
ss.aipe.src

sample size necessary for the accuracy in parameter estimation approach for a standardized regression coefficient of interest
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
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.power.pcm

Sample size planning for power for polynomial change models
ss.power.rc

sample size for a targeted regression coefficient
upsilon

This function implements the upsilon effect size statistic as described in Lachowicz, Preacher, & Kelley (in press) for mediation.
verify.ss.aipe.R2

Internal MBESS function for verifying the sample size in ss.aipe.R2
ss.aipe.pcm

Sample size planning for polynomial change models in longitudinal study
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.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.rc.sensitivity

Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient
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
transform_Z.r

Transform Fischer's Z into the scale of a correlation coefficient
transform_r.Z

Transform a correlation coefficient (r) into the scale of Fischer's Z