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 is pseudo R2 in Stata?
A pseudo R-squared only has meaning when compared to another pseudo R-squared of the same type, on the same data, predicting the same outcome. In this situation, the higher pseudo R-squared indicates which model better predicts the outcome.
What is a good pseudo R 2?
McFadden’s pseudo R-squared value between of 0.2 to 0.4 indicates excellent fit.
What is McFadden R2?
McFadden’s R squared measure is defined as. where denotes the (maximized) likelihood value from the current fitted model, and. denotes the corresponding value but for the null model – the model with only an intercept and no covariates.
What is Lroc Stata?
Description. lroc graphs the ROC curve and calculates the area under the curve. lroc requires that the current estimation results be from logistic, logit, probit, or ivprobit; see [R] logistic, [R] logit, [R] probit, or [R] ivprobit.
What is the McFadden’s pseudo R2?
McFadden’s pseudo-R squared denotes the corresponding value but for the null model – the model with only an intercept and no covariates. will be close to zero, as we would hope.
Is a smaller AIC better?
In plain words, AIC is a single number score that can be used to determine which of multiple models is most likely to be the best model for a given dataset. It estimates models relatively, meaning that AIC scores are only useful in comparison with other AIC scores for the same dataset. A lower AIC score is better.
What is a good McFadden score?
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.
What is the McFadden’s pseudo R 2?
How do you calculate pseudo r2?
McFadden’s Pseudo R-Squared. R2 = 1 – [ln LL(Mˆfull)]/[ln LL(Mˆintercept)]. This approach is one minus the ratio of two log likelihoods. The numerator is the log likelihood of the logit model selected and the denominator is the log likelihood if the model just had an intercept.
What value of AIC is good?
The AIC function is 2K – 2(log-likelihood). Lower AIC values indicate a better-fit model, and a model with a delta-AIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to.
Do you want higher or lower AIC?
A lower AIC score is better. AIC is most frequently used in situations where one is not able to easily test the model’s performance on a test set in standard machine learning practice (small data, or time series).
What is 2LL in SPSS?
The Deviance (-2LL) statistic It compares the difference in probability between the predicted outcome and the actual outcome for each case and sums these differences together to provide a measure of the total error in the model.
Can I use fitstat in Stata 11?
Despite the name, many/most commands will work with Stata 11, sometimes with reduced functionality. Also, fitstat isn’t the command you want anyway. You want -brant-, which is also part of the spost13 package. Or better yet, just do
Why is my fitstat not working?
My guess, though, is that you have an outdated version of fitstat. This is the most current version: and install the package, which includes fitstat and many other useful programs. If this doesn’t solve the problem, then show us your code and output using code tags. Also, fitstat after mprobit works fine for me.
What R2 does fitstat report for each model?
For all models except regress, fitstat reports McFadden’s R2, McFadden’s adjusted R2, the maximum likelihood R2, and Cragg & Uhler’s R2. These measures all equal R2 for OLS regression. fitstat reports R2 and the adjusted R2 after regress. fitstat reports the regular and adjusted count R2 for categorical data models.
What are the log-likelihoods in fitstat?
For all models, fitstat reports the log-likelihoods of the full and intercept-only models, the deviance (D), the likelihood ratio chi-square (G2), Akaike’s Information Criterion (AIC), AIC*N, the Bayesian Information Criterion (BIC), and BIC’.