destrunc: Calculate skewness and kurtosis based on truncated normal distribution in one group

Description

This function can be used to calculate the skewness and kurtosis based on the truncated normal distribution. Also, it can be used to estimate the mean and variance of the parent distribution (the distribution before truncated).

Usage

destrunc(
  vmean,
  vsd,
  lo,
  hi,
  rawdata = NULL,
  showFigure = FALSE,
  xstart,
  btol,
  ftol,
  ...
)

Value

If `showFigure = TRUE`, the output will be a list with two objects: one is the data frame of parent mean and standard deviation (pmean and psd), mean and standard deviation of truncated normal distribution (mean and sd), and skewness and kurtosis; the other is the theoretical figures of beta and normal distributions. If `showFigure = FALSE`, the output will be only the data frame.

Arguments

vmean: sample mean of the truncated data
vsd: sample standard deviation of the truncated data
lo: minimum possible value
hi: maximum possible value
rawdata: when raw data is available, we could still use it to check it figuratively, if the data was closed to the normal distribution, or truncated normal distribution.
showFigure: when showFigure = TRUE, it will display the plots with theoretical normal curve and the truncated normal curve.
xstart: see the package nleqslv
btol: see the package nleqslv
ftol: see the package nleqslv
...: other arguments

References

shah1966estimationdetectnorm

robert1995simulationdetectnorm

barr1999meandetectnorm

Examples

Run this code

data("trun_mdat")
destrunc(vmean=trun_mdat$m2[6], vsd=trun_mdat$sd2[6],
hi = 4, lo = 0, showFigure = TRUE)
#example2
destrunc(vmean=trun_mdat$m1[17], vsd=trun_mdat$sd1[17],
hi = 4, lo = 0, showFigure = TRUE)