Hi all,
I have just constructed a growth curve model using SPSS (syntax below) and want to do some model checking with a standardised residual x predicted value scatter plot and histogram of standardised residuals. However, SPSS does not appear to recognising the ZRESID command in /SAVE. In addition, when this model is put in manually using the dialogue boxes there is no option to check "standardised residuals". Has anybody encountered this before, and if so how did you circumvent it? Thanks in advance. Steph Syntax: MIXED Weight BY Farm Group WITH Age Baseline_Weight Time /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED =Farm Group Baseline_Weight Age Time Farm*Time Farm*Age Farm*Baseline_Weight  SSTYPE(3) /METHOD = REML /PRINT =G SOLUTION TESTCOV /RANDOM =INTERCEPT Time  SUBJECT(ID) COVTYPE(UN) /SAVE =RESID PRED. 
See page 1104 of the V22 reference manual (or its equivalent for other versions). However, standardized values are not available.
I do not know how to compute standardized values of either coefficients or residuals for mixed models. Perhaps a more knowledgeable person will respond. Gene Maguin Original Message From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Steph Bullen Sent: Monday, July 20, 2015 8:35 AM To: [hidden email] Subject: Standardised/standardized residuals (Mixed) Hi all, I have just constructed a growth curve model using SPSS (syntax below) and want to do some model checking with a standardised residual x predicted value scatter plot and histogram of standardised residuals. However, SPSS does not appear to recognising the ZRESID command in /SAVE. In addition, when this model is put in manually using the dialogue boxes there is no option to check "standardised residuals". Has anybody encountered this before, and if so how did you circumvent it? Thanks in advance. Steph Syntax: MIXED Weight BY Farm Group WITH Age Baseline_Weight Time /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED =Farm Group Baseline_Weight Age Time Farm*Time Farm*Age Farm*Baseline_Weight  SSTYPE(3) /METHOD = REML /PRINT =G SOLUTION TESTCOV /RANDOM =INTERCEPT Time  SUBJECT(ID) COVTYPE(UN) /SAVE =RESID PRED.  View this message in context: http://spssxdiscussion.1045642.n5.nabble.com/StandardisedstandardizedresidualsMixedtp5730188.html Sent from the SPSSX Discussion mailing list archive at Nabble.com. ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD 
It seems one way would be to save RESID and SEPRED and then simply divide the former by the latter. This probably does not have a well defined distribution (like Studentized residuals in linear regression) but is probably reasonable for identifying outlying observations or obvious signs of model misfit. They should at least be symmetric I imagine.

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I was thinking along the same lines as Andy, FWIW.

Bruce Weaver bweaver@lakeheadu.ca http://sites.google.com/a/lakeheadu.ca/bweaver/ "When all else fails, RTFM." NOTE: My Hotmail account is not monitored regularly. To send me an email, please use the address shown above. 
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This same basic question was posted to ResearchGate recently:
https://www.researchgate.net/post/SPSS_Mixed_Model_Linear_standardizing_residuals While looking for info that might help the questioner, I found this old thread, and was reminded that both Andy W and I had speculated that RESID/SEPRED might give the desired standardized residual. I decided to test that idea by estimating a model via Stata's mixed command and saving the standardized residuals via the predict postestimation command. Then I estimated the same model via SPSS MIXED, and saved RESID and SEPRED. Unfortunately, RESID/SEPRED does not match the standardized residuals from Stata. Details can be seen in the uploaded PDF file.* MIXED_standardized_residuals.pdf <http://spssxdiscussion.1045642.n5.nabble.com/file/t7186/MIXED_standardized_residuals.pdf> Cheers, Bruce * I think that even if you do not use Nabble to read the list, you should see a link to the uploaded file. If not, you can visit the Nabble archive and download it from there. Here's the link to this thread: http://spssxdiscussion.1045642.n5.nabble.com/StandardisedstandardizedresidualsMixedtd5730188.html Bruce Weaver wrote > I was thinking along the same lines as Andy, FWIW. > Andy W wrote >> It seems one way would be to save RESID and SEPRED and then simply divide >> the former by the latter. This probably does not have a well defined >> distribution (like Studentized residuals in linear regression) but is >> probably reasonable for identifying outlying observations or obvious >> signs of model misfit. They should at least be symmetric I imagine.   Bruce Weaver [hidden email] http://sites.google.com/a/lakeheadu.ca/bweaver/ "When all else fails, RTFM." NOTE: My Hotmail account is not monitored regularly. To send me an email, please use the address shown above.  Sent from: http://spssxdiscussion.1045642.n5.nabble.com/ ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD

Bruce Weaver bweaver@lakeheadu.ca http://sites.google.com/a/lakeheadu.ca/bweaver/ "When all else fails, RTFM." NOTE: My Hotmail account is not monitored regularly. To send me an email, please use the address shown above. 
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