DiffBetaMC {betaMC}

Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Description

Estimate Differences of Standardized Slopes and Generate the Corresponding Sampling Distribution Using the Monte Carlo Method

Usage

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

Arguments

object

Object of class mc, that is, the output of the MC() function.

alpha

Numeric vector. Significance level \alpha.

Details

The vector of differences of standardized regression slopes 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 differences of standardized regression slopes, where \alpha is the significance level.

Value

Returns an object of class betamc 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 ("DiffBetaMC").

Author(s)

Ivan Jacob Agaloos Pesigan

See Also

Other Beta Monte Carlo Functions: BetaMC(), DeltaRSqMC(), MC(), MCMI(), PCorMC(), RSqMC(), SCorMC()

Examples

# 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
)

# DiffBetaMC ---------------------------------------------------------------
out <- DiffBetaMC(mc, alpha = 0.05)

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