Thanks in advance for your kind attention.
I am using R to fit empirical data to generalized linear models. AIC (Akaike information criterion) is a measure of the goodness of fit returned by calls to glm(). I would also like to calculate the coefficient of determination R2, although there is no consensus about the exact definition for generalized models ([1]http://en.wikipedia.org/wiki/Coefficient_of_determination). I found a package âpsclâ with a pR2 function that computes pseudo-R2 measures for various GLMs. The arguments to the call are a fitted model object of class glm, polr, or mulitnom, and then âadditional arguments to be passed to or from functionsâ. The example from the documentation works well. Browse[1]> data(admit) Browse[1]> require(MASS) Browse[1]> ## ordered probit model Browse[1]> op1 <- polr(score ~ gre.quant + gre.verbal + ap + pt + female, + Hess=TRUE, + data=admit, + method="probit") Browse[1]> pR2(op1) llh llhNull G2 McFadden r2ML r2CU -106.5088203 -151.0299826 89.0423245 0.2947836 0.5682989 0.6032041 Browse[1]> When I try with a glm object rather than polr, I get the following error: Browse[1]> class(fit[[2]]) [1] "glm" "lm" Browse[1]> pR2(fit[[2]]) Error in inherits(x, "data.frame") : object "ds" not found The ds object does exist in the environment, but I do not know how to pass it into pR2: Browse[1]> class(ds) [1] "data.frame" Browse[1]> pR2(fit[[2]], ds) Error in inherits(x, "data.frame") : object "ds" not found Browse[1]> pR2 function (object, ...) { UseMethod("pR2") } <environment: namespace:pscl> Browse[1]> Question 1: How do I find the complete argument signature for pR2 in order to perhaps pass it the ds object? Question 2: If pR2 does not work with glm objects (for some unknown reason), is there another function I can use to calculate R-squared and adjusted R-squared for a generalized linear model? Best regards, \Eric References 1. http://en.wikipedia.org/wiki/Coefficient_of_determination
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