desbeta: Calculate skewness and kurtosis based on Beta distribution in...

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

Calculate skewness and kurtosis based on Beta distribution in one group

Description

This function can be used to calculate the skewness and kurtosis based on the Beta distribution. Also, this function estimate the shape parameters alpha and beta.

Usage

desbeta(
  vmean,
  vsd,
  lo,
  hi,
  method = "MM",
  rawdata = NULL,
  showFigure = FALSE,
  ...
)

Arguments

vmean	sample mean of the truncated data
vsd	sample standard deviation of the truncated data
lo	minimum possible value
hi	maximum possible value
method	when method = 'MM', the method used is the method of moments, when method = "ML', the method used to estimate the distribution is maximum likelihood
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.
...	other arguments

Value

If 'showFigure = TRUE', the output will be a list with two objects: one is the data frame of shape parameters (alpha and beta), mean and standard deviation of standard beta 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

johnson1995continuousdetectnorm

\insertRef

smithson2006betterdetectnorm

See Also

destrunc

Examples

data('beta_mdat')
desbeta(vmean=beta_mdat$m2[6], vsd=beta_mdat$sd2[6],
hi = beta_mdat$hi2[6], lo = beta_mdat$lo2[6], showFigure = TRUE)

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