This menu provides facilities for the analysis of multiple experiments or meta-analysis data
using the method of residual maximum likelihood (REML), which is also sometimes called
restricted maximum likelihood. The data should be supplied in a single variate with a
factors to specify the different experiments.
Data
Specifies a variate containing the data values.
Fixed Model
The fixed model describes imposed treatment factors and covariates for which the effect of
specified levels or values are of interest. The model is described using a formula, which
can combine main effects and interactions of factors and also covariates.
Random Model
The random model is generally used to describe those factors for which the values present
in an experiment can be considered drawn from some large homogeneous population. The model
is described using a formula, which can combine main effects and interactions of factors
and also covariates.
Experiments
Specifies a factor defining the different experiments to which each data unit belongs.
You can set a different residual term for each level of the factor by clicking on the
Experiment Residual Terms button.
Experiment Residual Terms
Allows you to define residual terms for each of the experiments.
Interactions
Controls the level of interactions to be fitted - you can indicate either All Interactions,
or just main effects (No Interactions), or indicate the level of interaction (that is,
set a limit on the maximum number of factors in the treatment terms that are fitted).
Available Data
This lists data structures appropriate to the current input field. The contents will change
as you move from one field to the next. Double-click on a name to copy it to the current
input field; alternatively, you can type in the name directly.
Operators
This provides a quick way of entering operators in the fixed and random model formulas.
Double-click on the required symbol to copy it to the current input field. You can also
type in operators directly. See model formula for a description of each.
See Also