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VAMETA procedure

Performs a REML meta analysis of a series of trials, previously analysed by VASERIES (R.W. Payne).

Options

PRINT = string tokens Controls printed output (model, components, effects, means, stratumvariances, monitoring, vcovariance, deviance, Waldtests, missingvalues, covariancemodels, aic, sic, bic); default mode, comp, Wald
PTRY = string tokens Controls the output to present to present from the REML analysis used to try each model (model, components, effects, means, stratumvariances, monitoring, vcovariance, deviance, Waldtests, missingvalues, covariancemodels, aic, sic, bic); default * i.e. none
PRECOVERY = string tokens Controls what summary output is produced about the models that are tried during recovery (deviance, aic, bic, sic, dffixed, dfrandom, change, exit, best); default devi, aic, sic, dfra, best
FIXED = formula Fixed model terms; if unset, these are taken from the MODELSTRUCTURES
RANDOM = formula Additional random model terms; default * i.e. none
CONSTANT = string token How to treat the constant term (estimate, omit); default esti
FACTORIAL = scalar Limit on the number of factors or covariates in each fixed term; default 3
PTERMS = formula Terms (fixed or random) for which effects or means are to be printed; default * implies all the fixed terms
PSE = string token Standard errors to be printed with tables of effects and means (differences, estimates, alldifferences, allestimates, none); default diff
RECOVER = string token Whether to try to recover with a simpler random model if REML cannot fit the model (yes, no); default no
METHOD = string token How to choose the best model during recovery (aic, sic, bic); default sic

Parameters

Y = variates Response variates
MODELDEFINITIONS = pointers Descriptions of the models for each y-variate, saved from VASERIES
EXIT = scalars Exit status for the fit (zero if successful)
SAVE = vsaves REML save structure from the analysis of each y-variate

Description

VAMETA can perform a REML meta analysis of a series of trials with either incomplete-block or row-and-column designs. The trials must previously have been analysed by the VASERIES procedure, to determine the best random model to use with each trial. Details of the models must be saved using the MODELDEFINITIONS parameter of VASERIES, and then supplied to VAMETA using its own MODELDEFINITIONS parameter. However, you can redefine the fixed model to fit in the meta analysis, and the action to take with the constant term (estimate or omit), by setting the FIXED and CONSTANT options, respectively. The FACTORIAL option sets a limit on the number of factors and variates allowed in each term defined by FIXED (default 3). You can also use the RANDOM option to specify some additional random terms to include in the analysis. Note: these terms are removed, if necessary, from the random terms selected by VASERIES to be fitted independently for any trial.

The PRINT option specifies the output to be produced from the analysis. The settings are mainly the same as those of the PRINT option of the REML directive but with extra settings aic and sic (with a synonym bic) to print the Akaike and Schwarz (Bayesian) information coefficients, respectively. The default is to print model descriptions, estimated variance components and Wald or F tests for fixed effects.

The Y parameter specifies the response variate. The SAVE parameter can save pointer containing a REML save structure from the analysis that can be used e.g. to display further output using the VDISPLAY directive. The EXIT parameter allows you to save a code from REML, giving the “exit status” of the fit (zero if successful).

The random models in meta analysis can become complicated, and REML may be unable to achieve a successful fit if there are more random terms than are actually needed to explain the random variation.(The REML likelihood may be too flat for any clear optimum to be found.) You can guard against this situation by setting option RECOVER=yes. VAMETA then tries models removing first one random term (and any associated spatial model), then two and so on, until successful. Note: it regards a model as successful, if the REML directive returns an exit status of zero (i.e. successful fitting) and there are no bound or aliased variance parameters.

The METHOD option specifies how to choose the random (and spatial) model if there is more than one possible model with the same number of random terms removed:

    aic uses their Akaike information coefficients,
    sic or bic uses their Schwarz (Bayesian) information coefficients (default).

The PRECOVERY option specifies the summary output to be produced about the models that are fitted during recovery. The settings are mainly the same as those of the VRACCUMULATE procedure (which is used to store and then print details of the analyses). There is an extra setting, best, to print the description of the best model. The default is to print the best description, together with the deviance, the Akaike and Schwarz (Bayesian) information coefficients and the number of degrees, for all the models. The PRTRY option, with the same settings as PRINT, controls output from each individual analysis.

The PTERMS option operates as in REML, to specify the terms whose means and effects are printed by PRINT and 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.

Options: PRINT, PTRY, PRECOVERY, FIXED, RANDOM, CONSTANT, FACTORIAL, PTERMS, PSE, RECOVER, METHOD.
Parameters: Y, MODELDEFINITIONS, EXIT, SAVE.

Method

The VRMETA procedure is used to define the random model for the meta analysis, if there are random terms that need to be fitted for only some of the trials. The VRESIDUAL directive is used to define spatial covariance models if required in any of the trials.

See also

Directives: REML, VDISPLAY, VKEEP, VRESIDUAL.
Procedures: VASERIES, VRMETA.
Commands for: REML analysis of linear mixed models.

Example

CAPTION  'VAMETA example'; STYLE=meta
SPLOAD   '%gendir%/examples/Vaseries.gsh'
" find best random model for each trial "
VASERIES [PRINT=best,devi,aic,sic,dfrandom,summary;\
         PBEST=model,components,wald; FIXED=entry;\
         EXPERIMENTS=site; ROWS=row; COLUMNS=column; BLOCKS=block;\
         MVINCLUDE=yvariate; TRYSPATIAL=ifregular; TRYTRENDS=yes;\
         RSTRATEGY=fastoptimal; VCONSTRAINTS=positive]\
         yield; MODELDEFINITIONS=modeldefs; SAVE=save
" meta analysis with these models "
VAMETA   [PRINT=model,components,wald] Y=yield; MODELDEFINITIONS=modeldefs;\
         SAVE=savemeta
Updated on September 5, 2019

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