PCorNB {betaNB} R Documentation

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

### Description

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.

### 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.

### 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").

### Author(s)

Ivan Jacob Agaloos Pesigan

Other Beta Nonparametric Bootstrap Functions: BetaNB(), DeltaRSqNB(), DiffBetaNB(), NB(), RSqNB(), SCorNB()

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



[Package betaNB version 1.0.4 Index]