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.
Arguments
- S
The value of the RESI estimate.
- df
Number of degrees of freedom of the chi-square statistic.
- n
Number of independent samples.
Value
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:
\( chisq = n*S^2 + df\)
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 estimates
S_ests = c(0.2, 0.4, 0.6)
dfs = c(2, 1, 3)
S2chisq(S = S_ests, df = dfs, n = 300)
#> [1] 14 49 111
