```Date: Mon, 28 Sep 1998 06:14:33 -0400 Reply-To: Tra Sender: "SAS(r) Discussion" Comments: RFC822 error: More than one sender was specified. Second and following senders discarded. From: Tra Subject: Re: Computation of Pearson residual in Logit Regression Comments: To: Tae-Sung Shin FREQ and WEIGHT are not the same, in my view. FREQ = 10, means there were 10 independent experimental units which had these values of var1-var2. FREQ must be an integer (if not then LOGISTIC will truncate it). WEIGHT = 10, means that the variance (in some sense) is 10-fold smaller for this unit than for another with WEIGHT = 1. This could be useful for modelling over-dispersion. Unlike FREQ, WEIGHT can take non-integer values. In your problem, I guess you have frequencies. However, you have separated the negative and positive responses. You will obtain more informative residuals if you combine the negative and positive data and use the model y/n = VAR1 form of the model statement. You could use this progarm fragment: data a2; merge aaa(where=(var2=0) rename=(var3=nneg)) aaa(where=(var2=1) rename=(var3=npos)); by var1; drop var2; ntot = nneg+npos; run; proc logistic data=a2; model npos/ntot = var1/influence; run; Personally, I prefer to use GENMOD, although it does not produce the residual plots within the procedure, you can capture the residual info into datasets and use proc plot. proc genmod data=a2; model npos/ntot = var1/d=binomial residuals obstats; run; An advantage of genmod is that it gives you the deviance/DF, which can be used to decide if over-dispersion is a problem. In your case, the value is very close to 1, so the data are well modelled by the assumed binomial/logistic model. The residuals appear to be random, with no outliers. Hope this helps. Tim Auton Proteus Molecular Design Ltd ______________________________ Reply Separator _________________________________ Subject: Computation of Pearson residual in Logit Regression Author: Tae-Sung Shin Sender: "SAS(r) Discussion" at interlink Date: 24/09/1998 08:41 Received: by ccmail Received: from Icthus by proteus.co.uk (UUPC/extended 1.11) with UUCP; Thu, 24 Sep 1998 08:24:25 BST Return-Path: Received: from VM.AKH-WIEN.AC.AT (VM121.AKH-Wien.ac.at [149.148.150.2]) by peters gate.proteus.co.uk (8.6.12/8.6.6) with SMTP id WAA04326 for ; Fri, 25 Sep 1998 22:37:33 GMT Message-Id: <199809252237.WAA04326@petersgate.proteus.co.uk> Received: from AKH-WIEN.AC.AT by VM.AKH-WIEN.AC.AT (IBM VM SMTP V2R3) with BSMTP id 7477; Sat, 26 Sep 98 02:04:15 CED Received: from AKH-WIEN.AC.AT (NJE origin LISTSERV@AWIIMC12) by AKH-WIEN.AC.AT (L Mail V1.2c/1.8c) with BSMTP id 1481; Sat, 26 Sep 1998 02:04:15 +0200 Date: Fri, 25 Sep 1998 18:41:56 -0500 Reply-To: Tae-Sung Shin Sender: "SAS(r) Discussion" From: Tae-Sung Shin X-ccAdmin: postmaster@Icthus Subject: Computation of Pearson residual in Logit Regression To: AKH-WIEN.AC.AT!SAS-L Hello SAS users, This is simple question for logit regression. Say we have the following data & program. data aaa; input var1 var2 var3; cards; 23.840 1.000 4.000 22.690 1.000 5.000 24.770 1.000 17.000 25.840 1.000 21.000 26.790 1.000 15.000 27.740 1.000 20.000 28.670 1.000 15.000 30.410 1.000 14.000 22.690 0.000 9.000 23.840 0.000 10.000 24.770 0.000 11.000 25.840 0.000 18.000 26.790 0.000 7.000 27.740 0.000 4.000 28.670 0.000 3.000 30.410 0.000 0.000 proc logistic; model var2=var1/influence; freq var3;run; proc logistic; model var2=var1/influence; weight var3;run; As far as I know and as in SAS manual, above two procedure should give us the same pearson & deviance residuals but it's not true. It seems to me that's because weights are included in the computation of the residuals, but frequencies are not... Could anybody explain why? or is it a bug? Thanks in advance. Tae-Sung Shin ```

Back to: Top of message | Previous page | Main SAS-L page