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Repeated Measures (Data in Single Variate) Options

Selects information to be printed by the analysis and controls certain aspects of the method used.


This specifies which items of output are to be produced by the analysis.

Model Description of the model fitted by the analysis
Variance components Estimates of variance parameters
Estimated effects Estimates of regression coefficients
Predicted means Predicted means
Residual checks Uses the VCHECK procedure to check the residuals for outliers and variance stability
Stratum variances Estimates of approximate stratum variances
Covariance model Estimated covariance models in matrix format
Variance-covariance matrix Variance-covariance matrix for the variance parameters
Deviance The residual deviance
Wald tests Wald Tests for fixed model terms and accompanying F-statistics (if selected)
Missing value estimates Estimates of values missing from the input
Monitoring Monitoring information at each iteration
Akaike information coefficient (AIC) Akaike information coefficient to assess the random model
Schwarz information coefficient (SIC) Schwarz information coefficient to assess the random model

Standard errors

Tables of means and effects are accompanied by estimates of standard errors. You can choose whether Genstat computes standard errors or standard errors of differences (SEDs) for the tables.

Method for calculating F-statistics

This controls whether Wald tests for fixed effects are accompanied with approximate F statistics and corresponding numbers of residual degrees of freedom. The computations, using the method devised by Kenward & Roger (1997), can be time consuming with large or complicated models. So, the default setting automatic, can be used to allow Genstat to assess the model itself and decide automatically whether to do the computations and which method to use. The other settings allow you to control what to do yourself:

none No F statistics are produced
algebraic F statistics are calculated using algebraic derivatives (which may involve large matrix calculations)
numerical F statistics are calculated using numerical derivatives (which require an extra evaluation of the mixed model equations for every variance parameter).

Model terms for effects and means

This specifies the model terms, as a formula, for which tables of means and/or effects are displayed. For covariates, the associated linear regression parameter can be printed as an effect, but predicted means are not available. Predicted means for other terms are adjusted to the mean of the covariate (but see note below). The formula can include the string ‘Constant‘ to include entries for the constant term.

If no formula is specified, means or effects are produced for all the fixed model terms and none of the random terms.

Estimate constant term

Specifies whether a constant term is included in the fixed model.

Covariates centred to zero mean

Specifies whether covariates are centred to zero mean during the analysis. This applies to all covariates in the model. If covariates are centred, tables of predicted means are based on the mean covariate value, otherwise zero for each covariate.

Optimization method

The AI (Average Information) method is the standard optimization method for REML in Genstat. It uses sparse matrices and is particularly recommended for large datasets and/or complex models. An alternative method is Fisher scoring which, if selected, also allows an absorbing factor to be specified in the model. This can be used to reduce the time or space requirements when fitting large models with many parameters. A more detailed discussion of the use and choice of absorbing factors can be found in the Genstat  Reference Manual which is available from the menu by selecting Help | Reference Manual | Directives.l.

Maximum iterations

This specifies the maximum number of iterations to use with maximum likelihood and maximum REML likelihood.

See also

Updated on April 2, 2019

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