我用STATA算出来的pseudo R^2很小，只有0.002，UCLA网站上说pseudo R^2和最小二乘法的回归的R^2不同，具体说法如下：“Pseudo R2 - This is McFadden's pseudo R-squared. It is calculated as 1 - ll(model)/ll(null) . Negative binomial regression does not have an equivalent to the R-squared measure found in OLS regression; however, many people have attempted to create one. Because this statistic does not mean what R-square means in OLS regression (the proportion of variance for the response variable explained by the predictors), we suggest interpreting this statistic with caution.”
那么应该如何解释pseudo R^2呢？谢谢
已解决，见这个链接中，有解释，和大家分享http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm
McFadden's mirrors approaches 1 and 2 from the list above. The log likelihood of the intercept model is treated as a total sum of squares, and the log likelihood of the full model is treated as the sum of squared errors (like in approach 1).
The ratio of the likelihoods suggests the level of improvement over the intercept model offered by the full model (like in approach 2).
A likelihood falls between 0 and 1, so the log of a likelihood is less than or equal to zero. If a model has a very low likelihood, then the log of the likelihood will have a larger magnitude than the log of a more likely model. Thus, a small ratio of log likelihoods indicates that the full model is a far better fit than the intercept model.
If comparing two models on the same data, McFadden's would be higher for the model with the greater likelihood.
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