betaNB (version 1.0.6)

DiffBetaNB: Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Description

Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using Nonparametric Bootstrapping

Usage

DiffBetaNB(object, alpha = c(0.05, 0.01, 0.001))

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 differences of standardized regression slopes.

vcov

Sampling variance-covariance matrix of differences of standardized regression slopes.

est

Vector of estimated differences of standardized regression slopes.

fun

Function used ("DiffBetaNB").

Arguments

object

Object of class nb, that is, the output of the NB() function.

alpha

Numeric vector. Significance level \(\alpha\).

Author

Ivan Jacob Agaloos Pesigan

Details

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

See Also

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

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

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