Estimate Standardized Regression Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Estimate Standardized Regression Coefficients and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

BetaNB(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 \(\boldsymbol{\hat{\beta}}\).

jackknife

Jackknife estimates.

est

Vector of estimated \(\boldsymbol{\hat{\beta}}\).

fun

Function used ("BetaNB").

Details

The vector of standardized regression coefficients (\(\boldsymbol{\hat{\beta}}\)) is estimated from bootstrap samples. Confidence intervals are generated by obtaining percentiles corresponding to \(100(1 - \alpha)\%\) from the generated sampling distribution of \(\boldsymbol{\hat{\beta}}\), where \(\alpha\) is the significance level.

See also

Other Beta Nonparametric Bootstrap Functions: DeltaRSqNB(), DiffBetaNB(), NB(), PCorNB(), 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
)

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

## Methods -----------------------------------------------------------------
print(out)
#> Call:
#> BetaNB(object = nb, alpha = 0.05)
#> 
#> Standardized regression slopes
#> type = "pc"
#>            est     se   R   2.5%  97.5%
#> NARTIC  0.4951 0.0708 100 0.3498 0.6141
#> PCTGRT  0.3915 0.0797 100 0.2440 0.5192
#> PCTSUPP 0.2632 0.0813 100 0.1033 0.4193
summary(out)
#> Call:
#> BetaNB(object = nb, alpha = 0.05)
#> 
#> Standardized regression slopes
#> type = "pc"
#>            est     se   R   2.5%  97.5%
#> NARTIC  0.4951 0.0708 100 0.3498 0.6141
#> PCTGRT  0.3915 0.0797 100 0.2440 0.5192
#> PCTSUPP 0.2632 0.0813 100 0.1033 0.4193
coef(out)
#>    NARTIC    PCTGRT   PCTSUPP 
#> 0.4951451 0.3914887 0.2632477 
vcov(out)
#>               NARTIC       PCTGRT      PCTSUPP
#> NARTIC   0.005012960 -0.002549951 -0.002273111
#> PCTGRT  -0.002549951  0.006346647 -0.002320642
#> PCTSUPP -0.002273111 -0.002320642  0.006606168
confint(out, level = 0.95)
#>             2.5 %    97.5 %
#> NARTIC  0.3497796 0.6141490
#> PCTGRT  0.2440254 0.5192221
#> PCTSUPP 0.1032870 0.4193349