Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping
Source:R/betaNB-s-cor-nb.R
SCorNB.Rd
Estimate Semipartial Correlation Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping
Usage
SCorNB(object, alpha = c(0.05, 0.01, 0.001))
Arguments
- object
Object of class
nb
, that is, the output of theNB()
function.- alpha
Numeric vector. 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 .
- vcov
Sampling variance-covariance matrix of .
- est
Vector of estimated .
- fun
Function used ("SCorNB").
Details
The vector of semipartial correlation coefficients () is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to from the generated sampling distribution of , where is the significance level.
See also
Other Beta Nonparametric Bootstrap Functions:
BetaNB()
,
DeltaRSqNB()
,
DiffBetaNB()
,
NB()
,
PCorNB()
,
RSqNB()
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
)
# SCorNB -------------------------------------------------------------------
out <- SCorNB(nb, alpha = 0.05)
## Methods -----------------------------------------------------------------
print(out)
#> Call:
#> SCorNB(object = nb, alpha = 0.05)
#>
#> Semipartial correlations
#> type = "pc"
#> est se R 2.5% 97.5%
#> NARTIC 0.4312 0.0661 100 0.2822 0.5423
#> PCTGRT 0.3430 0.0770 100 0.1933 0.4708
#> PCTSUPP 0.2385 0.0707 100 0.0940 0.3734
summary(out)
#> Call:
#> SCorNB(object = nb, alpha = 0.05)
#>
#> Semipartial correlations
#> type = "pc"
#> est se R 2.5% 97.5%
#> NARTIC 0.4312 0.0661 100 0.2822 0.5423
#> PCTGRT 0.3430 0.0770 100 0.1933 0.4708
#> PCTSUPP 0.2385 0.0707 100 0.0940 0.3734
coef(out)
#> NARTIC PCTGRT PCTSUPP
#> 0.4311525 0.3430075 0.2384789
vcov(out)
#> NARTIC PCTGRT PCTSUPP
#> NARTIC 0.0043636010 0.0004196016 -0.0011696805
#> PCTGRT 0.0004196016 0.0059260665 -0.0008003066
#> PCTSUPP -0.0011696805 -0.0008003066 0.0049980295
confint(out, level = 0.95)
#> 2.5 % 97.5 %
#> NARTIC 0.28216333 0.5422580
#> PCTGRT 0.19327956 0.4707667
#> PCTSUPP 0.09401164 0.3734456