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 |
alpha |
Numeric vector.
Significance level |
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
See Also
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)