I would advice setting up the FSGD files with 8 classes based on your 3 discrete, 2-level variables, eg, MaleControlWhite then use two continuous variables Age and eTIV.
You should definitely normalize the eTIV (and probably age too)

On 11/10/2021 7:07 PM, Zeng, Qi wrote:

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Hi Douglas,

Just to be clear, should I recode discrete variables as numeric as sample1) or change discrete variables into strings 2) or even change the comparison groups into strings too 3)?
1)
Variables AgeGenderRaceeTIV
InputSub001_baseControl85111350519.71
InputSub002_baseControl81.3  011558515.86
InputSub003_baseControl81.3   001558515.86

or 2)
Variables AgeGenderRaceeTIV
InputSub001_baseControl85"Female""White"1350519.71
InputSub002_baseControl81.3 "Male""White"1558515.86
InputSub003_baseControl72.7 "Male""White"1187649.47

or 3)
Variables AgeGenderRaceeTIV
InputSub001_base"Control"85"Female""White"1350519.71
InputSub002_base"Control"81.3 "Male""White"1558515.86
InputSub003_base"Control"72.7 "Male""White"1187649.47


Best,
Zeng



On Wed, Nov 10, 2021 at 6:38 PM Douglas N. Greve <dgr...@mgh.harvard.edu> wrote:

    Did you read the error message? It says:
    Variable 2 has character string Female
    Variables should be continuous numbers
    You have listed Gender and Race as strings, they have to be
    numbers (eg, 1, 2)
    You probably want to have these be discrete variables instead of
    continuous

    On 11/10/2021 6:20 PM, Zeng, Qi wrote:

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    Hi Douglas,

    Here is the command line:
    mri_glmfit \
      --y rh.thickness-rate.MyStudy.10.mgh \
      --fsgd FSGD/MyStudy.fsgd dods\
      --C Contrasts/Case-Control_age_gender_race_etiv.mtx\
      --surf fsaverage rh \
      --cortex \
      --glmdir
    rh.thickness-rate.MyStudy.10.mgh_age_gender_race_etiv.glmdir \
      --eres-save

    Also error message in terminal:
    gdfRead(): reading FSGD/MyStudy.fsgd
    WARNING: variable 1 is "Gender" which is often a discrete factor
      The proper way to handle discrete factors is to create classes.
      See *MailScanner has detected a possible fraud attempt from
    "secure-web.cisco.com" claiming to be*
    https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples
    
<https://secure-web.cisco.com/1NHVxBIRnha6EjEdEho81-VE9d-vjJ6ivzt10z28vhX9c94pCwosQpkLG4Hfu3nLYp7KMDne5wAk9mK45YIQJEZBe6UlteF2wwLohmZJzp070UjHvv6fqugRAkgBk0uNO3Fsgo5BzblW95J5iO5sXWwZLK-LGAZJJTBaU7mLTkO5L70JRSscCW3pdc-G5KchZMbVyeY65gGH4w5MMIYBSVHvX03spMjZ85XkHbkgYYmWXTFnzqUGhqGgHygP5sLeR7OWPKIzEb7X3pHi7POf0UyEHJAZpQLU9bgT2BnOcClt0kW4mll1ODQDC2Y0U7-0f/https%3A%2F%2Fsurfer.nmr.mgh.harvard.edu%2Ffswiki%2FFsgdExamples>
    ERROR: gdfReadV1: Format Error: Input line 1, subjid = sub001_base
     Variable 2 has character string Female
     Variables should be continuous numbers
    FSGDF Format Error: file = FSGD/MyStudy.fsgd, tag=Input

    Also attached FSGD file below.
    Thank you so much for the help!

    Best,
    Zeng




    On Wed, Nov 10, 2021 at 5:41 PM Douglas N. Greve
    <dgr...@mgh.harvard.edu> wrote:

        please send your command line and full terminal output as
        well as the fsgd file

        On 11/10/2021 5:12 PM, Zeng, Qi wrote:

                External Email - Use Caution

        Hi Freesurfer experts,

        Here, I want to design a simple group comparison (two
        levels: Case vs. Control) and also count for 4
        covariates (two continuous and two categoricals). However,
        the GLM model cannot recognize my discrete variables. What
        do you suggest to change?
        >> FSDG file:
        GroupDescriptorFile 1
        Title MyStudy
        Class Control
        Class Case
        Variables agegenderrace eTIV
        Inputsub001_baseControl85FemaleWhite1350519
        Contrast:
        >> Design matrix for Case-Control and regress out the effect
        of age, gender, race, and eTIV.
        -1 1 0 0 0 0 0 0 0 0

        Best,
        Zeng




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