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| bio | website | adamscorner.com |
|---|---|---|
| location | ||
| age | ||
| visits | member for | 2 years, 6 months |
| seen | Jun 2 '11 at 16:29 | |
| stats | profile views | 15 |
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Feb 8 |
comment |
Consequence of ignoring the order of a categorical variable with different levels in logistic regression Yes, nested models are hierarchical in nature. In the case of a variable representing degree (i.e. degree of risk), you do not need a hierarchical model. Depending on how the risk index you're using is divided, you may even want to consider grouping the levels on the index (i.e. if it's 1-9 consider grouping as high [7-9], medium [4-6], and low [1-3]) as this may allow you to better represent the dominance effect where those in the reference group represent a substantially different outcome than those in the comparisons. |
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Jan 4 |
comment |
Does rpart use multivariate splits by default? If you use "rsq.rpart(fit)", where fit is your rpart model, you can see that cp is calculated using the decrease in relative error. |
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Dec 30 |
comment |
Does rpart use multivariate splits by default? So, I checked the rpart reference manual, and apparently I was incorrect. While one of the criterion for stopping in rpart is called "cp" this is short for "complexity parameter" and is merely the minimum amount r^2 must increase by in order to pursue a particular split. I have corrected my post above to reflect this. The lack of statistical tests in splitting rules is one of the reasons why I use the party package over the rpart package. With the party package the default method implements a Bonferroni corrected p-value as a stopping criterion (default p=0.05). For details see the vignette. |
