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Hi Dr. Greve,
Sorry to bring this back up, but I didn't fully understand how to calculate
the variance in order to get the confidence intervals for each covariate,
so I wanted to double check the steps.
A colleague in the lab is helping me to derive the variance using Matlab,
and just wanted to make sure of the formula I would use:

variance of B = rvar.mgh * inverse(transpose(Xg.dat)*(Xg.dat))

Is this the correct formula to derive variance in Matlab?

Thanks again for your help,
Batool


On Fri, Oct 9, 2020 at 5:46 AM Douglas N. Greve <dgr...@mgh.harvard.edu>
wrote:

> You can standardize them yourself. rvar.mgh is the residual variance and
> Xg is the design matrix. Between those and the beta values, you can
> standarize the betas
>
> On 10/7/2020 3:39 PM, Batool Rizvi wrote:
>
>         External Email - Use Caution
> Hi Dr. Greve,
>
> Thank you for your response! Not sure if you already addressed this, but
> my second question was whether there is a way to derive the (95%)
> confidence intervals for my covariates that were included in the mri_glmfit
> analyses?
>
> Thanks again!
> Batool
>
> On Wed, Oct 7, 2020 at 7:31 AM Douglas N. Greve <dgr...@mgh.harvard.edu>
> wrote:
>
>>
>>
>> On 10/6/2020 4:26 PM, Batool Rizvi wrote:
>>
>>         External Email - Use Caution
>> Hi,
>>
>> I had a couple additional questions related to this topic.
>>
>> 1. The beta-coefficients I derived from using the mri_segstats command
>> with the input beta.mgh, are these values unstandardized?
>>
>> Yes, that have not been standarized
>>
>>
>> 2. Just wondering, is there a possible way to also derive 95% confidence
>> intervals for my covariates that were included in the mri_glmfit analyses?
>>
>> You can standardize them yourself. rvar.mgh is the residual variance and
>> Xg is the design matrix. Between those and the beta values, you can
>> standarize the betas
>>
>>
>> Thanks so much for your help!
>> Batool
>>
>>
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