Yes, that is correct
On 11/19/2020 11:43 AM, Batool Rizvi wrote:
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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 <dgreve@mgh.harvard.edumailto:dgreve@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:
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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 <dgreve@mgh.harvard.edumailto:dgreve@mgh.harvard.edu> wrote:
On 10/6/2020 4:26 PM, Batool Rizvi wrote:
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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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