Converts the robust effect size index (S) to Chi-square statistic, given that S is greater than 0. For an S value of 0, only an upper bound on the Chi-square statistic can be computed. Vector arguments are accepted. If different length arguments are passed they are dealt with in the usual way of R.
S2chisq(S, df, n)
Arguments
S: The value of the RESI estimate.
df: Number of degrees of freedom of the chi-square statistic.
n: Number of independent samples.
Returns
Returns a scalar or vector argument of the Chi-square statistic.
Details
The formula for converting a RESI estimate above 0 to Chi-square statistic is:
chisq=n∗S2+df
If the RESI estimate is 0, all that is known is that the Chi-square statistic is less than or equal to the degrees of freedom.
Examples
# convert S estimates with corresponding degrees of freedom to Chi-square estimatesS_ests = c(0.2,0.4,0.6)dfs = c(2,1,3)S2chisq(S = S_ests, df = dfs, n =300)