thanks for the clarification - I'll look into using other methods for model fitting.
LT On Fri, Mar 22, 2013 at 8:46 AM, MCLAREN, Donald <mclaren.don...@gmail.com>wrote: > You'd want an F test with 2 rows. One for the F-test of var 1 and one for > the F-test of var2. A significant F-test won't tell you if your > significantly better though than the F-test of var 1 only in the model. > > However, this sounds more like a model fitting question, which would be > best addressed using AIC, BIC, etc. metrics of the overall model fit. > > Best Regards, Donald McLaren > ================= > D.G. McLaren, Ph.D. > Research Fellow, Department of Neurology, Massachusetts General Hospital > and > Harvard Medical School > Postdoctoral Research Fellow, GRECC, Bedford VA > Website: http://www.martinos.org/~mclaren > Office: (773) 406-2464 > ===================== > This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED > HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is > intended only for the use of the individual or entity named above. If the > reader of the e-mail is not the intended recipient or the employee or agent > responsible for delivering it to the intended recipient, you are hereby > notified that you are in possession of confidential and privileged > information. Any unauthorized use, disclosure, copying or the taking of any > action in reliance on the contents of this information is strictly > prohibited and may be unlawful. If you have received this e-mail > unintentionally, please immediately notify the sender via telephone at > (773) > 406-2464 or email. > > > On Fri, Mar 22, 2013 at 11:38 AM, Laura M. Tully < > tully.la...@googlemail.com> wrote: > >> Thanks Donald for your help. One final question - putting the "does the >> amount of variance explained vary by group?" question aside, if I wanted to >> run a multiple regression style model within one group looking at the >> contribution of the two behavioral variables together, does it make sense >> to weight the two variables as part of the same chunk of variance e.g. 0 0 >> 0 0 .125 .125 .125 .125 .125 .125 .125 .125 (which I think will run a >> model looking at the contribution of variables 1 & 2 together regardless of >> group or gender). If so, I'm assuming I could adjust the model and run >> separate GLMs within each group, as you suggested - I just want to make >> sure I am understanding the weights correctly first... >> >> LT >> >> >> On Fri, Mar 22, 2013 at 8:23 AM, MCLAREN, Donald < >> mclaren.don...@gmail.com> wrote: >> >>> >>> On Thu, Mar 21, 2013 at 8:59 PM, Laura M. Tully < >>> tully.la...@googlemail.com> wrote: >>> >>>> oops sorry! (I also miscalculated the # of regressors - there's >>>> actually 12 (not 10 as previously noted). Here is the list of column >>>> labels: >>>> Grp1male Grp1female Grp2male Grp2female Grp1maleVar1 Grp1femaleVar1 >>>> Grp1maleVar2 Grp1femaleVar2 Grp2maleVar1 Grp2femaleVar1 Grp2maleVar2 >>>> Grp2femaleVar2 >>>> >>>> And what I think is actually an F test looking for group x var 1 >>>> interaction OR group x variable 2 interaction whilst accounting for gender. >>>> .5 .5 -.5 -.5 0 0 0 0 0 0 >>>> 0 0 0 0 0 0 .5 .5 -.5 -.5 >>>> >>> >>> The Contrast for group*var1 would be: 0 0 0 0 .5 .5 0 0 -.5 -.5 0 0 >>> The Contrast for group*var2 would be: 0 0 0 0 0 0 .5 .5 0 0 -.5 -.5 >>> >>> >>> >>>> >>>> But what I actually WANT to test is a multiple regression style model >>>> - i.e. if I put var 1 AND 2 into the model together do they explain more >>>> variance than either variable alone, AND does this vary by group (is this >>>> even a sensible contrast to make?). Which I *think* would look something >>>> like this... >>>> >>>> 0 0 0 0 .125 .125 .125 .125 .125 .125 .125 .125 >>>> >>> >>> People generally don't ask that question. The answer is when you add >>> more variables, you will explain more variance. Tests about overall model >>> fits are generally assessed with the AIC, BIC, etc. metrics. I'm not sure >>> if there is anyway in regression to say that the amount of variance >>> explained is different by group unless you run 2 separate models. If you >>> think this might be a valid question, I'd consult a statistician - which I >>> am not. >>> >>> >>>> >>>> Laura. >>>> >>>> >>>> >>>> On Thu, Mar 21, 2013 at 5:51 PM, MCLAREN, Donald < >>>> mclaren.don...@gmail.com> wrote: >>>> >>>>> Please include the list of the column labels. >>>>> >>>>> Best Regards, Donald McLaren >>>>> ================= >>>>> D.G. McLaren, Ph.D. >>>>> Research Fellow, Department of Neurology, Massachusetts General >>>>> Hospital and >>>>> Harvard Medical School >>>>> Postdoctoral Research Fellow, GRECC, Bedford VA >>>>> Website: http://www.martinos.org/~mclaren >>>>> Office: (773) 406-2464 >>>>> ===================== >>>>> This e-mail contains CONFIDENTIAL INFORMATION which may contain >>>>> PROTECTED >>>>> HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is >>>>> intended only for the use of the individual or entity named above. If >>>>> the >>>>> reader of the e-mail is not the intended recipient or the employee or >>>>> agent >>>>> responsible for delivering it to the intended recipient, you are hereby >>>>> notified that you are in possession of confidential and privileged >>>>> information. Any unauthorized use, disclosure, copying or the taking >>>>> of any >>>>> action in reliance on the contents of this information is strictly >>>>> prohibited and may be unlawful. If you have received this e-mail >>>>> unintentionally, please immediately notify the sender via telephone at >>>>> (773) >>>>> 406-2464 or email. >>>>> >>>>> >>>>> On Thu, Mar 21, 2013 at 6:43 PM, Laura M. Tully < >>>>> tully.la...@googlemail.com> wrote: >>>>> >>>>>> hi Experts, >>>>>> >>>>>> I'm struggling to conceptualize the appropriate contrasts for my >>>>>> cortical thickness analysis. I have four classes [two groups; two levels >>>>>> (patients,controls; male,female) and two behavioral variables. I want to >>>>>> see if together the two variables account significant proportion of the >>>>>> variance in y (thickness) and if this differs by group whilst regressing >>>>>> out gender. - i.e. if I enter both behavioral variables into the model >>>>>> does >>>>>> it account for more variance than either variable on their own (after >>>>>> controlling for gender)? What I have is this: >>>>>> >>>>>> .5 .5 -.5 -.5 0 0 0 0 >>>>>> 0 0 0 0 .5 .5 -.5 -.5 >>>>>> >>>>>> Does this look right? >>>>>> >>>>>> Thanks! >>>>>> >>>>>> Laura. >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> -- >>>>>> Laura M. Tully, MA >>>>>> Social Neuroscience & Psychopathology, Harvard University >>>>>> Center for the Assessment and Prevention of Prodromal States, UCLA >>>>>> Semel Institute of Neuroscience >>>>>> ltu...@mednet.ucla.edu >>>>>> ltu...@fas.harvard.edu >>>>>> 310-267-0170 >>>>>> -- >>>>>> My musings as a young clinical scientist: >>>>>> http://theclinicalbrain.blogspot.com/ >>>>>> Follow me on Twitter: @tully_laura >>>>>> >>>>>> _______________________________________________ >>>>>> Freesurfer mailing list >>>>>> Freesurfer@nmr.mgh.harvard.edu >>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>>>> >>>>>> >>>>>> The information in this e-mail is intended only for the person to >>>>>> whom it is >>>>>> addressed. If you believe this e-mail was sent to you in error and >>>>>> the e-mail >>>>>> contains patient information, please contact the Partners Compliance >>>>>> HelpLine at >>>>>> http://www.partners.org/complianceline . If the e-mail was sent to >>>>>> you in error >>>>>> but does not contain patient information, please contact the sender >>>>>> and properly >>>>>> dispose of the e-mail. >>>>>> >>>>>> >>>>> >>>> >>>> >>>> -- >>>> -- >>>> Laura M. Tully, MA >>>> Social Neuroscience & Psychopathology, Harvard University >>>> Center for the Assessment and Prevention of Prodromal States, UCLA >>>> Semel Institute of Neuroscience >>>> ltu...@mednet.ucla.edu >>>> ltu...@fas.harvard.edu >>>> 310-267-0170 >>>> -- >>>> My musings as a young clinical scientist: >>>> http://theclinicalbrain.blogspot.com/ >>>> Follow me on Twitter: @tully_laura >>>> >>> >>> >> >> >> -- >> -- >> Laura M. Tully, MA >> Social Neuroscience & Psychopathology, Harvard University >> Center for the Assessment and Prevention of Prodromal States, UCLA Semel >> Institute of Neuroscience >> ltu...@mednet.ucla.edu >> ltu...@fas.harvard.edu >> 310-267-0170 >> -- >> My musings as a young clinical scientist: >> http://theclinicalbrain.blogspot.com/ >> Follow me on Twitter: @tully_laura >> > > -- -- Laura M. Tully, MA Social Neuroscience & Psychopathology, Harvard University Center for the Assessment and Prevention of Prodromal States, UCLA Semel Institute of Neuroscience ltu...@mednet.ucla.edu ltu...@fas.harvard.edu 310-267-0170 -- My musings as a young clinical scientist: http://theclinicalbrain.blogspot.com/ Follow me on Twitter: @tully_laura
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