rstatisticslogistics

Logistic Regression Using R

I am running logistic regressions using R right now, but I cannot seem to get many useful model fit statistics. I am looking for metrics similar to SAS:

http://www.ats.ucla.edu/stat/sas/output/sas_logit_output.htm

Does anyone know how (or what packages) I can use to extract these stats?

Thanks

Solution

  • Here's a Poisson regression example:

    ## from ?glm:
d.AD <- data.frame(counts=c(18,17,15,20,10,20,25,13,12),
      outcome=gl(3,1,9),
      treatment=gl(3,3))
glm.D93 <- glm(counts ~ outcome + treatment,data = d.AD, family=poisson())

    Now define a function to fit an intercept-only model with the same response, family, etc., compute summary statistics, and combine them into a table (matrix). The formula .~1 in the update command below means "refit the model with the same response variable [denoted by the dot on the LHS of the tilde] but with only an intercept term [denoted by the 1 on the RHS of the tilde]"

    glmsumfun <- function(model) {
   glm0 <- update(model,.~1)  ## refit with intercept only
   ## apply built-in logLik (log-likelihood), AIC,
   ##  BIC (Bayesian/Schwarz Information Criterion) functions
   ## to models with and without intercept ('model' and 'glm0');
   ## combine the results in a two-column matrix with appropriate
   ## row and column names
   matrix(c(logLik(glm.D93),BIC(glm.D93),AIC(glm.D93),
           logLik(glm0),BIC(glm0),AIC(glm0)),ncol=2,
     dimnames=list(c("logLik","SC","AIC"),c("full","intercept_only")))
}

    Now apply the function:

    glmsumfun(glm.D93)

    The results:

                full intercept_only
logLik -23.38066      -26.10681
SC      57.74744       54.41085
AIC     56.76132       54.21362

    EDIT: