PCorMC {betaMC} | R Documentation |
Estimate Squared Partial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method
PCorMC(object, alpha = c(0.05, 0.01, 0.001))
object |
Object of class |
alpha |
Numeric vector.
Significance level |
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.
Returns an object
of class betamc
which is a list with the following elements:
Function call.
Function arguments.
Sampling distribution of
r^{2}_{p}
.
Sampling variance-covariance matrix of
r^{2}_{p}
.
Vector of estimated
r^{2}_{p}
.
Function used ("PCorMC").
Ivan Jacob Agaloos Pesigan
Other Beta Monte Carlo Functions:
BetaMC()
,
DeltaRSqMC()
,
DiffBetaMC()
,
MC()
,
MCMI()
,
RSqMC()
,
SCorMC()
# 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)
summary(out)
coef(out)
vcov(out)
confint(out, level = 0.95)