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

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

Usage

PCorMC(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 \(\alpha\).

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 \(r^{2}_{p}\).

vcov

Sampling variance-covariance matrix of \(r^{2}_{p}\).

est

Vector of estimated \(r^{2}_{p}\).

fun

Function used ("PCorMC").

Details

The vector of squared partial correlation coefficients (\(r^{2}_{p}\)) 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 \(r^{2}_{p}\), where \(\alpha\) is the significance level.

See also

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

Author

Ivan Jacob Agaloos Pesigan

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
)

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

## Methods -----------------------------------------------------------------
print(out)
#> Call:
#> PCorMC(object = mc, alpha = 0.05)
#> 
#> Squared partial correlations
#> type = "hc3"
#>            est     se   R   2.5%  97.5%
#> NARTIC  0.4874 0.1228 100 0.1690 0.6040
#> PCTGRT  0.3757 0.1085 100 0.1102 0.5049
#> PCTSUPP 0.2254 0.1035 100 0.0274 0.4129
summary(out)
#> Call:
#> PCorMC(object = mc, alpha = 0.05)
#> 
#> Squared partial correlations
#> type = "hc3"
#>            est     se   R   2.5%  97.5%
#> NARTIC  0.4874 0.1228 100 0.1690 0.6040
#> PCTGRT  0.3757 0.1085 100 0.1102 0.5049
#> PCTSUPP 0.2254 0.1035 100 0.0274 0.4129
coef(out)
#>    NARTIC    PCTGRT   PCTSUPP 
#> 0.4874382 0.3757383 0.2253739 
vcov(out)
#>              NARTIC      PCTGRT     PCTSUPP
#> NARTIC  0.015070964 0.005353598 0.002240937
#> PCTGRT  0.005353598 0.011763713 0.001066046
#> PCTSUPP 0.002240937 0.001066046 0.010704482
confint(out, level = 0.95)
#>             2.5 %    97.5 %
#> NARTIC  0.1689777 0.6039797
#> PCTGRT  0.1102259 0.5049136
#> PCTSUPP 0.0273768 0.4128501