Package index • betaMC

Monte Carlo

MC(): Generate the Sampling Distribution of Regression Parameters Using the Monte Carlo Method

MCMI(): Generate the Sampling Distribution of Regression Parameters Using the Monte Carlo Method for Data with Missing Values

Standardized Regression Coefficients

BetaMC(): Estimate Standardized Regression Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Multiple Correlation

RSqMC(): Estimate Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared) and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Semipartial Correlation

SCorMC(): Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Improvement in R-Squared

DeltaRSqMC(): Estimate Improvement in R-Squared and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Squared Partial Correlation

PCorMC(): Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Differences of Standardized Regression Coefficients

DiffBetaMC(): Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Methods

coef(<betamc>): Estimated Parameter Method for an Object of Class betamc

confint(<betamc>): Confidence Intervals Method for an Object of Class betamc

print(<betamc>): Print Method for an Object of Class betamc

print(<mc>): Print Method for an Object of Class mc

summary(<betamc>): Summary Method for an Object of Class betamc

summary(<mc>): Summary Method for an Object of Class mc

vcov(<betamc>): Sampling Variance-Covariance Matrix Method for an Object of Class betamc

Data

nas1982: 1982 National Academy of Sciences Doctoral Programs Data