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.
The Gardner learning data, which was used by L.R. Tucker
One-factor confirmatory factor analysis model
Correlation matrix for Lomax (1983) data set
Correlation matrix for Maruyama & McGarvey (1980) data set
Expected value of the squared multiple correlation coefficient
Construct a covariance matrix with specified error of approximation
Variance of squared multiple correlation coefficient
Complete Data Set of Holzinger and Swineford's (1939) Study
MBESS
Sample size planning for the standardized mean different from the accuracy
in parameter estimation approach
Generate random data for an ANCOVA model
Confidence interval for the multiple correlation coefficient
Confidence interval for the population squared multiple correlation coefficient
Confidence interval for the population correlation coefficient
Confidence interval for the coefficient of variation
Confidence interval for the population root mean square error of approximation
Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVA
Confidence Interval for the Proportion of Variance Accounted for (in the dependent variable by knowing the levels of the factor)
Confidence Interval for a Regression Coefficient
Confidence interval for a regression coefficient
Confidence Interval for a Reliability Coefficient
Confidence interval for a standardized contrast in ANCOVA with one covariate
Confidence Interval for the Standardized Mean
Regression Surface Containing Interaction
Effect sizes and confidence intervals in a mediation model
Confidence limits for noncentral F parameters
Confidence limits for a noncentrality parameter from a t-distribution
power.equivalence.md.plot
Plot power of Two One-Sided Tests Procedure (TOST) for Equivalence
Cohen et. al. (2003)'s professor salary data set
Minimum risk point estimation of the population coefficient of variation
Minimum risk point estimation of the population standardized mean difference
Sample size planning for an ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) perspective
Sample size planning for a contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) perspective
Sample Size Planning for Accuracy in Parameter Estimation
for the multiple correlation coefficient.
Sensitivity analysis for sample size planning with the goal of Accuracy in Parameter Estimation (i.e., a narrow observed confidence interval)
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
Confidence Interval for the Signal-To-Noise Ratio
Confidence Interval for a Standardized Regression Coefficient
Correlation Matrix to Covariance Matrix Conversion
Covariance matrix from confirmatory (single) factor model.
mediation.effect.bar.plot
Bar plots of mediation effects
Visualizing mediation effects
Sample size necessary for the accuracy in parameter estimation approach
for an unstandardized regression coefficient of interest
Sensitivity analysis for sample size planing from the Accuracy in Parameter
Estimation Perspective for the unstandardized regression coefficient
Sensitivity analysis for sample size given the Accuracy in Parameter Estimation approach for the standardized mean difference.
sample size necessary for the accuracy in parameter estimation approach for a standardized regression coefficient of interest
Transform Fischer's Z into the scale of a correlation coefficient
Transform a correlation coefficient (r) into the scale of Fischer's Z
Function to calculate the regular (which is also biased) estimate of the coefficient of variation or the unbiased estimate of the coefficient of variation.
Plotting Conditional Regression Lines with Interactions in Two Dimensions
Unbiased estimate of the population standard deviation
Signal to noise using squared multiple correlation coefficient
Standardized mean difference
Standardized mean difference using the control group as the basis of
standardization
ss.aipe.rmsea.sensitivity
a priori Monte Carlo simulation for sample size planning for RMSEA in SEM
Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized contrast in ANOVA
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
Sensitivity analysis for sample size planning for the standardized ANOVA contrast from
the Accuracy in Parameter Estimation (AIPE) Perspective
Sample size planning for SEM targeted effects
Sensitivity analysis for sample size planning for the standardized mean from the Accuracy in Parameter Estimation (AIPE)
Perspective
Sample size planning for the standardized mean difference from the
Accuracy in Parameter Estimation (AIPE) perspective
Sample size planning for power for polynomial change models
sample size for a targeted regression coefficient
power.density.equivalence.md
Density for power of two one-sided tests procedure (TOST) for equivalence
Power of Two One-Sided Tests Procedure (TOST) for Equivalence
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
Sample size planning for the coefficient of variation given the goal of Accuracy in Parameter Estimation approach to sample
size planning
Confidence interval for a contrast in a fixed effects ANOVA
Confidence interval for an (unstandardized) contrast in ANCOVA with one covariate
Confidence limits for the standardized mean difference.
Confidence limits for the standardized mean difference using the control
group standard deviation as the divisor.
A function for estimating the mediation effect size as discussed in Lachowicz, Preacher, & Kelley (submitted).
Internal MBESS function for verifying the sample size in ss.aipe.R2
Sensitivity analysis for sample size planing from the Accuracy in Parameter
Estimation Perspective for the standardized regression coefficient
Function to plan sample size so that the test of the squared multiple correlation coefficient is sufficiently powerful.
Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD
Find target sample sizes for the accuracy in standardized conditions means estimation in CRD
Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients.
Sample size planning for RMSEA in SEM
Conversion functions for noncentral t-distribution
Compute the model-implied covariance matrix of an SEM model
Confidence Interval for the Square Root of the Signal-To-Noise Ratio
Confidence limits for noncentral chi square parameters
Sensitivity analysis for sample size planning given the Accuracy in Parameter Estimation approach for the coefficient of variation.
Sample size planning for polynomial change models in longitudinal study
ss.aipe.sem.path.sensitiv
a priori Monte Carlo simulation for sample size planning for SEM targeted effects
Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized mean
Visualize individual trajectories
Visualize individual trajectories with fitted curve and quality of fit
Sample size planning for structural equation modeling from the power analysis perspective
sample size for a targeted regression coefficient