Provided that my model is well-fit and correctly classifies 77% of the cases with sensitivity, specificity, positive predictive value, and negative predictive value of 65%, 58%, 61%, and 62% respectively. Would this amount of variance weaken my conclusion?
To rephrase my question: I used a binomial regression model for my dependent dichotomous variable. The model meets all the statistical assumptions and is well fit. However, it explains only 41% of the variance in my dependent variable, i.e. r-square=0.41. This piece of info is not a stand-alone while interpreting my results, yet I’ve seen models explaining >85% of the variance using the same dependent variable I’ve used. Could this r square result weaken my model, in spite of having all the assumptions met? Thank you in advance!