destrunc: Calculate skewness and kurtosis based on truncated normal...

In detectnorm: Detect Nonnormality in Meta-Analysis without Raw Data

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,
  ...
)

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

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.

References

\insertRef

shah1966estimationdetectnorm

\insertRef

robert1995simulationdetectnorm

\insertRef

barr1999meandetectnorm

See Also

desbeta

Examples

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)

detectnorm documentation built on July 16, 2022, 5:05 p.m.