Hi there,
we would like to test the effect of a binary variable (genotype = carrier vs. homozygous) on cortical thicknes in mri-glmfit. Since we are also controlling for gender and aquisition site (4 sites) we already have 16 groups. In order to control for age as a covariate we need at least 2 subjects per group to be able to estimate an age slope.
If we include the aforementioned binary variable (genotype) as a factor (two different "groups"), we would have 32 groups and unfortunately not enough subjects per group.
Is it possible to include binary variables ("dummy variable" coded as 0 and 1) such as genotype or gender as covariates (slope), in order to reduce the number of groups and examine the effect on thickness? In simple regression this would not affect the results - what would we expect here?
Many thanks,
Stefan
you could try coding genotype as 0,1,2 (missing, heterozygous, homozygous) instead of coding as discrete factors.
n.
On Wed, 2009-12-02 at 12:54 -0500, Stefan Brauns wrote:
Hi there,
we would like to test the effect of a binary variable (genotype = carrier vs. homozygous) on cortical thicknes in mri-glmfit. Since we are also controlling for gender and aquisition site (4 sites) we already have 16 groups. In order to control for age as a covariate we need at least 2 subjects per group to be able to estimate an age slope.
If we include the aforementioned binary variable (genotype) as a factor (two different "groups"), we would have 32 groups and unfortunately not enough subjects per group.
Is it possible to include binary variables ("dummy variable" coded as 0 and 1) such as genotype or gender as covariates (slope), in order to reduce the number of groups and examine the effect on thickness? In simple regression this would not affect the results - what would we expect here?
Many thanks,
Stefan
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
This might not be the best model as it assumes a certain relationship between the groups (eg, homozygous will have a slope that is twice that of heterozygous).
doug
Nick Schmansky wrote:
you could try coding genotype as 0,1,2 (missing, heterozygous, homozygous) instead of coding as discrete factors.
n.
On Wed, 2009-12-02 at 12:54 -0500, Stefan Brauns wrote:
Hi there,
we would like to test the effect of a binary variable (genotype = carrier vs. homozygous) on cortical thicknes in mri-glmfit. Since we are also controlling for gender and aquisition site (4 sites) we already have 16 groups. In order to control for age as a covariate we need at least 2 subjects per group to be able to estimate an age slope.
If we include the aforementioned binary variable (genotype) as a factor (two different "groups"), we would have 32 groups and unfortunately not enough subjects per group.
Is it possible to include binary variables ("dummy variable" coded as 0 and 1) such as genotype or gender as covariates (slope), in order to reduce the number of groups and examine the effect on thickness? In simple regression this would not affect the results - what would we expect here?
Many thanks,
Stefan
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Do you mean having just another column in your design matrix with 0s and 1s? You can do this, but not with an FSGD. You'll have to supply your own matrix. An easy way to do this would be to run mri_glmfit with and FSGD without the genotype. This will create a matrix Xg.dat in the output dir, then just modify that matrix and pass it to a new call to mri_glmfit
doug
Stefan Brauns wrote:
Hi there,
we would like to test the effect of a binary variable (genotype = carrier vs. homozygous) on cortical thicknes in mri-glmfit. Since we are also controlling for gender and aquisition site (4 sites) we already have 16 groups. In order to control for age as a covariate we need at least 2 subjects per group to be able to estimate an age slope.
If we include the aforementioned binary variable (genotype) as a factor (two different "groups"), we would have 32 groups and unfortunately not enough subjects per group.
Is it possible to include binary variables ("dummy variable" coded as 0 and 1) such as genotype or gender as covariates (slope), in order to reduce the number of groups and examine the effect on thickness? In simple regression this would not affect the results - what would we expect here?
Many thanks,
Stefan
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
freesurfer@nmr.mgh.harvard.edu
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Douglas N Greve -
Nick Schmansky -
Stefan Brauns