What are covariance structures?

Covariance Structures are just patterns in covariance matrices. Some of these patterns occur often enough in some statistical procedures that they have names. For example, the Compound Symmetry structure just means that all the variances are equal to each other and all the covariances are equal to each other.

What is autoregressive covariance structure?

The autoregressive (Lag 1) structure considers correlations to be highest for time adjacent times, and a systematically decreasing correlation with increasing distance between time points. Between time 1 and time 3 the correlation would be less, and equal to ρ t 1 − t 3 .

When would you use unstructured covariance?

When They Work Well A different approach to fitting repeated measures as well as other clustered data is to fit a mixed model, a.k.a. a random effects model. These models often use unstructured covariance matrices for the random effects. In these models, one or more random effects are included in the model.

What is exchangeable covariance structure?

Compound Symmetric or Exchangeable ▶ Exchangeable structure specifies that observations on the. same subject have homogeneous covariance σ1 and. homogeneous variance σ2. ▶ The correlation does not depend on the value of the lag, i.e. the correlations between two observations are equal for all.

What are the names of the covariance structures?

Covariance Structures are just patterns in covariance matrices. Some of these patterns occur often enough in some statistical procedures that they have names. You may have heard of some of these names–Compound Symmetry, Variance Components, Unstructured, for example.

How to specify the covariance structure in proc mixed?

SPECIFYING THE COVARIANCE STRUCTURE PROC MIXED NOTATION A lot of the notation for MIXED is similar to what is in GLM, but often the meaning is different. There are two ways to specify a covariance structure in PROC MIXED, the RANDOM statement and the REPEATED statement.

Are there any concepts inherent in a covariance matrix?

There are two concepts inherent in a covariance matrix–covariance and matrix. Either one can throw you off. Let’s start with matrix. If you never took linear algebra, the idea of matrices can be frightening. (And if you still are in school, I highly recommend you take it. Highly ).

Which is the covariance of a variable with itself?

Second, the diagonal cells of the matrix contain the variances of each variable. A covariance of a variable with itself is simply the variance. So you have a context for interpreting these covariance values. Once again, a covariance matrix is just the table without the row and column headings.