# Tag Info

### Eigenvalues/Eigenvectors of Correlation and Covariance matrices

Expanding on my comment: Since $P = \text{diag}(\Sigma)^{-1/2} \Sigma \text{diag}(\Sigma)^{-1/2}$, where $\text{diag}(\Sigma)$ is the diagonal matrix obtained by considering only the diagonal entries ...
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### What advantages do we find when using a mixed model for nested data instead of multiple regression?

The primary reason why you want to use mixed models for repeated measurement data is that measurements taken on the same patient are correlated. Standard statistical regression models, such as linear ...
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### Eigenvalues/Eigenvectors of Correlation and Covariance matrices

If $\Sigma$ is diagonal (with arbitrary eigenvalues) then $P$ is just the unit matrix (all eigenvalues equal to one), so there cannot be any general relation between the eigenvalues of $\Sigma$ (alone)...
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### Minimum number of observation in multivariate regression

I believe that it is better to think in terms of power to detect effects of interest, rather than rules of thumb. That said, if you want a quick-and-dirty baseline value, the standard rule of thumb ...

### hundreds of linear mixed models

I agree with @rep_ho's "do whatever is commonly done with the data modality you are working with". However, if you are going to quote p-values (for example) you almost certainly need to do ...
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### Univariate approach to a Bivariate logistic regression

This should be possible with bivariate probit regression. The observed bivariate binary response can be represented via thresholding a bivariate normal latent variable. A bivariate normal with ...
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### hundreds of linear mixed models

TO answer your question: yes, from a scientific perspective it is ok to build hundreds of models. For example in fMRI data analysis we fit one model per voxel (3d pixel) per subject in the brain image,...
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### Which variables I should include in the multivariable Cox regression model?

Categorization of continuous covariates is almost never a good idea. The reason is that you are basically throwing away valuable information, which may lead to a loss of statistical power or even ...
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### Multiple group comparisons in a linear mixed-effect model

You did not get gender- or age-specific estimates because you didn't ask for them. The call emmeans(mod, pairwise ~ group asks for marginal means for each group, ...
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