Linear Mixed Models: Subjects and Repeated

This feature requires Custom Tables and Advanced Statistics.

This dialog allows you to select variables that define subjects, repeated observations, Kronecker measures, and to choose a covariance structure for the residuals.

A subject is an observational unit that can be considered independent of other subjects. For example, the blood pressure readings from a patient in a medical study can be considered independent of the readings from other patients. Defining subjects becomes particularly important when there are repeated measurements per subject and you want to model the correlation between these observations. For example, you might expect that blood pressure readings from a single patient during consecutive visits to the doctor are correlated.

Subjects can also be defined by the factor-level combination of multiple variables; for example, you can specify Gender and Age category as subject variables to model the belief that males over the age of 65 are similar to each other but independent of males under 65 and females.

All of the variables specified in the Subjects list are used to define subjects for the residual covariance structure. You can use some or all of the variables to define subjects for the random-effects covariance structure.

The variables specified in this list are used to identify repeated observations. For example, a single variable Week might identify the 10 weeks of observations in a medical study, or Month and Day might be used together to identify daily observations over the course of a year.
Repeated Covariance Type
This specifies the covariance structure for the residuals. The available structures are as follows:
  • Ante-Dependence: First Order
  • AR(1)
  • Direct product AR1 (UN_AR1)
  • Direct product unstructured (UN_UN)
  • Direct product compound symmetry (UN_CS)
  • AR(1): Heterogeneous
  • ARMA(1,1)
  • Compound Symmetry
  • Compound Symmetry: Correlation Metric
  • Compound Symmetry: Heterogeneous
  • Diagonal
  • Factor Analytic: First Order
  • Factor Analytic: First Order, Heterogeneous
  • Huynh-Feldt
  • Scaled Identity
  • Toeplitz
  • Toeplitz: Heterogeneous
  • Unstructured
  • Unstructured: Correlation Metric
  • Spatial: Power
  • Spatial: Exponential
  • Spatial: Gaussian
  • Spatial: Linear
  • Spatial: Linear-log
  • Spatial: Spherical
Kronecker Measures
Select variables that specify the subject structure for Knonecker covariance measurements and determine how the measurement errors are correlated. The field is available only when one of the following Repeated Covariance Type is selected:
  • Direct product AR1 (UN_AR1)
  • Direct product unstructured (UN_UN)
  • Direct product compound symmetry (UN_CS)
Spatial Covariance Coordinates
The variables in this list specify the coordinates of the repeated observations when one of the spatial covariance types is selected for the repeated covariance type.

See the topic Covariance Structures for more information.