Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

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

Arguments

object

Object of class nb, that is, the output of the NB() function.

alpha

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

Value

Returns an object of class betanb 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 ("PCorNB").

Details

The vector of squared partial correlation coefficients (\(r^{2}_{p}\)) is estimated from bootstrap samples. 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 Nonparametric Bootstrap Functions: BetaNB(), DeltaRSqNB(), DiffBetaNB(), NB(), RSqNB(), SCorNB()

Author

Ivan Jacob Agaloos Pesigan

Examples

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

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

# NB -----------------------------------------------------------------------
nb <- NB(
  object,
  R = 100, # use a large value e.g., 5000L for actual research
  seed = 0508
)

# PCorNB -------------------------------------------------------------------
out <- PCorNB(nb, alpha = 0.05)

## Methods -----------------------------------------------------------------
print(out)
#> Call:
#> PCorNB(object = nb, alpha = 0.05)
#> 
#> Squared partial correlations
#> type = "pc"
#>            est     se   R   2.5%  97.5%
#> NARTIC  0.4874 0.1048 100 0.2750 0.6471
#> PCTGRT  0.3757 0.1104 100 0.1821 0.5579
#> PCTSUPP 0.2254 0.1115 100 0.0434 0.4506
summary(out)
#> Call:
#> PCorNB(object = nb, alpha = 0.05)
#> 
#> Squared partial correlations
#> type = "pc"
#>            est     se   R   2.5%  97.5%
#> NARTIC  0.4874 0.1048 100 0.2750 0.6471
#> PCTGRT  0.3757 0.1104 100 0.1821 0.5579
#> PCTSUPP 0.2254 0.1115 100 0.0434 0.4506
coef(out)
#>    NARTIC    PCTGRT   PCTSUPP 
#> 0.4874382 0.3757383 0.2253739 
vcov(out)
#>              NARTIC      PCTGRT     PCTSUPP
#> NARTIC  0.010979538 0.003168814 0.002238300
#> PCTGRT  0.003168814 0.012181158 0.001360438
#> PCTSUPP 0.002238300 0.001360438 0.012442963
confint(out, level = 0.95)
#>              2.5 %    97.5 %
#> NARTIC  0.27502096 0.6471038
#> PCTGRT  0.18213410 0.5578763
#> PCTSUPP 0.04343062 0.4506086