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RE: st: Residuals in Logistic Regression

From   "Nick Cox" <[email protected]>
To   <[email protected]>
Subject   RE: st: Residuals in Logistic Regression
Date   Mon, 12 Apr 2004 17:26:11 +0100

Or, as said, use -glm-. 

[email protected] 

[email protected]
> The -logistic- command was based on a program called 
> -logiodds-that was made available to Stata users in the 
> January 1991 "The Stata News". It was a stimulus to the 
> creation of the Stata Technical Bulletin, which began in May 
> of 1991.  In fact, the first issue had a revised edition of 
> -logiodds-. It was written to implement Hosmer-Lemeshow 
> recomendations regarding covariate patterns and various GOF 
> statistics, which were detailed in their then fairly new 
> text. It was meant to be an alternative to Stata's -logit- 
> command, which kept the observation based residuals. 
> The current post -logistic- commands, lfit, lstat, and lroc, 
> were provided as options to the -logiodds- command (I believe 
> that the 2nd version, the one preceding Stata's official 
> -logistic- command, was called -logiodd2- in STB-1). 
> As it is now, -logistic- still retains the 
> residuals-by-covariate-pattern approach to diagnostics. This 
> underlays the fit statistics as well. Most other commercial 
> software does not do this - hence possible differences in 
> output. In my opinion, the Hosmer-Lemeshow approach of having 
> fit statistics based on covariate pattern is preferable to 
> simply using unadjusted individual observations as the basis 
> of residual and fit statistics.
> The way to get what you want -- observation and not covariate 
> patterns -- is to use -logit-, obtain the linear predictor 
> and fit (mu) statistics using -predict-, and calculate the 
> residuals and fit statistics using the appropriate formulae. 
> You can find them in the manual, or in Hardin & Hilbe 
> (2001-Stata Press). Calculating the residuals is really quiet easy. 
> > At 06:55 PM 4/9/2004 +0100, Nick Cox wrote:
> > >To give this pot another stir, and to use
> > >mutually accessible data, -logit- and -glm-
> > >with logit link and binomial family give quite
> > >different deviance residuals.
> > >
> > >The pattern of -glm-'s makes sense,
> > >but that of -logit-'s is more puzzling.
> > 
> > Ok, after looking in several wrong places, I finally found 
> an explanation 
> > in the Stata Reference Manual G-M, pp. 315-316.  It says 
> that "All the 
> > residual and diagnostic statistics calculated by Stata 
> [NOTE: I think it 
> > really means Stata logistic regression and some related 
> routines] are in 
> > terms of covariate patterns, not observations.  That is, 
> all observations 
> > with the same covariate patterns are given the same 
> residual and diagnostic 
> > statistics."  It says that Hosmer and Lemeshow argue that 
> this is the 
> > better way to do it.
> > 
> > They may be right, but even Stata isn't consistent across 
> routines in the 
> > handling of this.  I'd like for -predict- to offer residual 
> stats that were 
> > based on the individual observations and not the covariate 
> > patterns.

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