REML, is unable to fit a model, by simplifying the random model (R.W. Payne).
|Controls what summary output is produced about the simpler random models that are tried (
||Controls the output from the
||Controls the output to present to present from the
||Factor numbering the plots in the design, required if
||Specifies terms that must not be removed from the random model; by default any of the random terms can be removed|
||Terms (fixed or random) for which effects or means are to be printed; default
||Standard errors to be printed with tables of effects and means (
||Whether to include units with missing values in the explanatory factors and variates and/or the y-variates (
||Criterion to choose the best model (
||Whether to exclude models where any estimated variance parameters are held at a bound (
||Model-definition structure for the unsuccessful analysis of each y-variate|
||Saves a model-definition structure for the best model for each y-variate|
||Exit status of the best model for each y-variate|
||Save structure from the analysis of the best model for each y-variate|
VARECOVER can be used to recover after an unsuccessful attempt to fit a
REML model. Usually this can be resolved by omitting non-significant random terms – there may be too little information about these term in the
REML likelihood for the algorithm to find the optimum.
VARECOVER automates the process of finding a simpler model that can be fitted successfully. First it tries random models that omit one term from the random model (and if there is a correlation model defined on the omitted random term, that will be omitted too). Then, if none of these models can be fitted, it tries models that omit two random terms, and so on, until eventually a null random model may have to be fitted. If there is more than one candidate model available from those that omit the same number of random terms,
VARECOVER chooses the one with the smallest Akaike or Schwarz (Bayesian) information coefficient, according to the setting of the
METHOD option. If you set option
VARECOVER excludes models with any estimated variance parameters held at a bound: i.e. the fitting of these models is also regarded as unsuccessful. You can also use the
FORCED option to specify any terms that must not be dropped from the random model (e.g. because you want to estimate their BLUPs).
VRACCUMULATE procedure (which is used to store and then print the information). However, there is also a setting,
best, to print the description of the best model (i.e. the simplified model that has been chosen). By default,
PBEST option specifies the output to be produced from the
REML analysis with the best model. Similarly, the
PTRY option indicates what output should be produced for each candidate random model when it is tried. Their settings are mainly the same as those of the
REML directive. There are also extra settings
sic (with a synonym
bic) to print the Akaike and Schwarz (Bayesian) information coefficients, respectively. The default for both these options is to produce no output.
PTERMS option operates as in
REML, to specify the terms whose means and effects are printed by
PTRY; the default is all the fixed terms. Likewise, the
PSE option controls the type of standard error that is displayed with the means and effects; the default is to give a summary of the standard errors of differences.
PLOTFACTOR option allows you to specify a factor to index the plots, which will used if it is necessary to try the null random model. If this is not set, a local factor called
plots is set up automatically.
MVINCLUDE option controls whether units with missing values in the explanatory factors and variates and/or the y-variate are included in the analysis, as in the
Y parameter specifies the response variate, and the
MODELSTRUCTURE parameter specifies a model-definition structure defining the model used in the unsuccessful
REML analysis. A model-definition structure for the best of the simplified models can be saved, in a pointer, by the
BESTMODEL parameter; the
VMODEL procedure can use this to define the model (using the
VSTRUCTURE directives) so that you can reanalyse it yourself using the
REML directive. Alternatively, you can save the
REML save structure from the analysis with the best model by using the
SAVE parameter. The
EXIT parameter can save a scalar containing the
REML exit status of the best model for each y-variate; see
VKEEP for details.
Model definition structures are defined for the various candidate models. The
VARANDOM procedure is used to fit them, with the
VRACCUMULATE procedure storing the necessary details for the best one to be selected.
Commands for: REML analysis of linear mixed models.
CAPTION 'VARECOVER example'; STYLE=meta SPLOAD '%gendir%/examples/Vaseries.gsh' " analyse data from site G " SUBSET [site.IN.'G'] row,column,entry,yield " try (and fail) to fit a model with row, column and spatial terms " VAOPTIONS [MAXCYCLE=30] VFMODEL [MODEL=RowColumnAndSpatialModel;\ DESCRIPTION='row*column+ar1(x)ar1';\ FIXED=entry] RANDOM=row*column; CONSTRAIN=none VFSTRUCTURE [MODEL=RowColumnAndSpatialModel; TERM=row.column]\ ar,ar; FACTOR=row,column; ORDER=1 VMODEL RowColumnAndSpatialModel " set run interactive, to avoid example stopping after the REML diagnostic " SET [RUN=interactive] REML [PRINT=model] yield " find a simpler random model, that can be fitted successfully " VARECOVER [PRINT=best,deviance,aic,bic,sic,dfrandom]\ yield; MODEL=RowColumnAndSpatialModel; BESTMODEL=SimplerRandomModel VMODEL SimplerRandomModel REML [PRINT=model,components,wald; WORKSPACE=700; FMETH=none] yield