P-value for polyserial correlation
correlation, r
Solution
If you form the returned object with:
polS <- polyserial(x, y, ML=TRUE, std.err=TRUE) # ML estimate
... You should have no difficulty forming a p-value for the hypothesis: `rho == 0` using a z-statistic formed by the ratio of a parameter divided by its standard error. But that is not the same as testing the assumption of bivariate normality. For that you need to examine "chisq" component of `polS`. The print method for objects of class 'polycor' hands that to you in a nice little sentence. You interpret that result in the usual manner: Low p-values are stronger evidence against the null hypothesis (in this case H0: bivariate normality). As a scientist, you do not "want" either result. You want to understand what the data is telling you.
Problem
I have some basic questions concerning the `polyserial()` {polycor} function. - Does a p-value exist for rho, or can it be calculated? - For the assumption of a bivariate normal, is the tested null hypothesis "Yes, bivariate normal"? That is, do I want a high or low p-value. Thanks.