betaMC (version 1.3.3)

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

Description

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

Usage

RSqMC(object, alpha = c(0.05, 0.01, 0.001))

Value

Returns an object of class betamc which is a list with the following elements:

call

Function call.

args

Function arguments.

thetahatstar

Sampling distribution of \(R^{2}\) and \(\bar{R}^{2}\).

vcov

Sampling variance-covariance matrix of \(R^{2}\) and \(\bar{R}^{2}\).

est

Vector of estimated \(R^{2}\) and \(\bar{R}^{2}\).

fun

Function used ("RSqMC").

Arguments

object

Object of class mc, that is, the output of the MC() function.

alpha

Numeric vector. Significance level \(\alpha\).

Author

Ivan Jacob Agaloos Pesigan

Details

R-squared (\(R^{2}\)) and adjusted R-squared (\(\bar{R}^{2}\)) are derived from each randomly generated vector of parameter estimates. Confidence intervals are generated by obtaining percentiles corresponding to \(100(1 - \alpha)\%\) from the generated sampling distribution of \(R^{2}\) and \(\bar{R}^{2}\), where \(\alpha\) is the significance level.

See Also

Other Beta Monte Carlo Functions: BetaMC(), DeltaRSqMC(), DiffBetaMC(), MC(), MCMI(), PCorMC(), SCorMC()

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaMC")

# Fit Model in lm ----------------------------------------------------------
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)

# MC -----------------------------------------------------------------------
mc <- MC(
  object,
  R = 100, # use a large value e.g., 20000L for actual research
  seed = 0508
)

# RSqMC --------------------------------------------------------------------
out <- RSqMC(mc, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)