What is AIC and BIC in Stata?

What is AIC and BIC in Stata?

Note: N=Obs used in calculating BIC; see [R] BIC note. The AIC indicates that the model including the site dummies fits the data better, whereas the BIC indicates the opposite. As is often the case, different model-selection criteria have led to conflicting conclusions.

What is Fitstat Stata?

fitstat is a post-estimation command that computes a variety of measures of fit for many kinds of regression models. It works after the following: clogit, cnreg, cloglog, intreg, logistic, logit, mlogit, nbreg, ocratio, ologit, oprobit, poisson, probit, regress, zinb, and zip.

What measure do we use to evaluate the goodness of fit of a logistic model?

With PROC LOGISTIC, you can get the deviance, the Pearson chi-square, or the Hosmer-Lemeshow test. These are formal tests of the null hypothesis that the fitted model is correct, and their output is a p-value–again a number between 0 and 1 with higher values indicating a better fit.

What is link test in Stata?

The link test looks for a specific type of specification error called a link error wherein< a dependent variable needs to be transformed (linked) to accurately relate to independent variable. The link test adds the squared independent variable to the model and tests for significance versus the nonsquared model.

Should I use AIC or BIC?

AIC is best for prediction as it is asymptotically equivalent to cross-validation. BIC is best for explanation as it is allows consistent estimation of the underlying data generating process.

How is BIC and AIC calculated?

From wiki : AIC=2k−2ln(L) where L is maximum of the likelihood function and k is the number of parameters estimated. The loglike() function is defined here link. You can calculate BIC easily: BIC=ln(n)k−2ln(L) following the same logic.

What is a good McFadden R Squared?

McFadden’s pseudo R-squared value between of 0.2 to 0.4 indicates excellent fit.

What is a good pseudo R2 value?

A rule of thumb that I found to be quite helpful is that a McFadden’s pseudo R2 ranging from 0.2 to 0.4 indicates very good model fit.

How do you know if a logistic model is good?

It examines whether the observed proportions of events are similar to the predicted probabilities of occurence in subgroups of the data set using a pearson chi square test. Small values with large p-values indicate a good fit to the data while large values with p-values below 0.05 indicate a poor fit.

What is AIC in logistic regression?

The Akaike information criterion (AIC) is an estimator of out-of-sample prediction error and thereby relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models.

What is Dfbeta?

The DFBETAS are statistics that indicate the effect that deleting each observation has on the estimates for the regression coefficients. The DFFITS and Cook’s D statistics indicate the effect that deleting each observation has on the predicted values of the model.

How do you know if a logistic regression is good?