SCorMC {betaMC} R Documentation

## Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

### Description

Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

### Usage

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


### Arguments

 object Object of class mc, that is, the output of the MC() function. alpha Numeric vector. Significance level \alpha.

### Details

The vector of semipartial correlation coefficients (r_{s}) is 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_{s}, where \alpha is the significance level.

### 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_{s}.

vcov

Sampling variance-covariance matrix of r_{s}.

est

Vector of estimated r_{s}.

fun

Function used ("SCorMC").

### Author(s)

Ivan Jacob Agaloos Pesigan

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

### 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
)

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

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



[Package betaMC version 1.3.1 Index]