detectnorm: Calculate skewness and kurtosis based on Beta or truncated...

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

Calculate skewness and kurtosis based on Beta or truncated normal distribution in a meta-analysis for SMD (Two independent groups)

Description

This function can be used to calculate the skewness and kurtosis based on the Beta distribution with the dataset used to conduct meta-analysis.

Usage

detectnorm(
  m1i,
  sd1i,
  n1i,
  lo1i,
  hi1i,
  m2i,
  sd2i,
  n2i,
  lo2i,
  hi2i,
  data,
  showFigure = FALSE,
  distri = "beta",
  ...
)

Arguments

m1i	vector to the means of first group
sd1i	vector to specifiy the standard deviation of first group
n1i	vector to specify the sample size of first group
lo1i	vector to specify the possible minimum of the first group
hi1i	vector to specify the possible maximum of the first group
m2i	vector to the means of second group
sd2i	vector to specifiy the standard deviation of second group
n2i	vector to specify the sample size of second group
lo2i	vector to specify the possible minimum of the second group
hi2i	vector to specify the possible maximum of the second group
data	the opitional original data frame containing the data for the function
showFigure	when showFigure = TRUE, it will display all the plots (within the result as a list, result$fig) with theoretical normal curve and the truncated normal curve.
distri	Beta distribution is used when using 'distri = "beta"'; Truncated normal distribution is used when using 'distri = "truncnorm"'
...	other arguments

Value

The output of the data frame adding some columns of the possible skewness and kurtosis for each groups.

References

\insertRef

barr1999meandetectnorm

\insertRef

johnson1995continuousdetectnorm

\insertRef

robert1995simulationdetectnorm

\insertRef

shah1966estimationdetectnorm

\insertRef

smithson2006betterdetectnorm

\insertRef

sun2020influencedetectnorm

Examples

#truncated normal data
data("trun_mdat")
ex <- detectnorm(m1i = m1,sd1i = sd1,n1i = n1,
hi1i = 4,lo1i = 0,m2i = m2,sd2i = sd2,n2i = n2,
hi2i = 4,lo2i=0,distri = "truncnorm", data = trun_mdat)
head(ex)
#extremely non-normal data
data("beta_mdat")
ex2 <- detectnorm(m1i = m1,sd1i = sd1,n1i = n1,
hi1i = hi1,lo1i = lo1,m2i = m2,sd2i = sd2,n2i = n2,
hi2i = hi2,lo2i=lo2,distri = "beta", data = beta_mdat)
head(ex2)
mean(ex2$skew1)#sample skewness calculated from the sample
mean(ex2$g1_skewness) #estimated using beta

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