chooseModel.asrtests: Determines and records the set of significant terms using an...

In asremlPlus: Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Determines and records the set of significant terms using an asrtests.object, taking into account the hierarchy or marginality relations of the terms.

Description

Performs a series of hypothesis tests on a set of fixed and/or random terms taking into account the marginality of terms. In particular, a term will not be tested if it is marginal to (or nested in) one that is significant. For example, if A:B is significant, then neither A nor B will be tested. For a random term, the term is removed from the model fit, any boundary terms are removed using rmboundary.asrtests and a REML likelihood ratio test is performed using REMLRT.asreml. If it is not significant and drop.ran.ns is TRUE, the term is permanently removed from the model. Note that if boundary terms are removed, the reduced model may not be nested in the full model in which case the test is not valid. For fixed terms, the Wald tests are performed and the p-value for the term obtained. If it is not significant and drop.fix.ns is TRUE, the term is permanently removed from the model. A row that records the outcome of a test is added to test.summary for each term that is tested.

Usage

## S3 method for class 'asrtests'
chooseModel(object, terms.marginality=NULL, 
            alpha = 0.05, allow.unconverged = TRUE, 
            allow.fixedcorrelation = TRUE,  
            checkboundaryonly = FALSE, drop.ran.ns=TRUE, 
            positive.zero = FALSE, bound.test.parameters = "none", 
            drop.fix.ns=FALSE, denDF = "numeric",  dDF.na = "none", 
            dDF.values = NULL, trace = FALSE, update = TRUE, 
            set.terms = NULL, ignore.suffices = TRUE, 
            bounds = "P", initial.values = NA, 
            IClikelihood = "none", ...)

Arguments

object	an asrtests.object containing the components (i) asreml.obj, (ii) wald.tab, and (iii) test.summary.
terms.marginality	A square matrix of ones and zeros with row and column names being the names of the terms to be tested. The names of fixed terms must match those in the wald.tab component of the object, while the names of random terms must match those in the vparameters component of the asreml.obj component in the object. The diagonal elements of the matrix should be one, indicating that a term is marginal to itself. Elements should be one if the row term is marginal to the column term. All other elements should be zero.
alpha	The significance level for the test.
allow.unconverged	A logical indicating whether to accept a new model even when it does not converge. If FALSE and a fit when a term is removed does not converge, the term will not be removed.
allow.fixedcorrelation	A logical indicating whether to accept a new model even when it contains correlations in the model whose values have been designated as fixed, bound or singular. If FALSE and the new model contains correlations whose values have not been able to be estimated, the supplied asrtests.obj is returned. The fit in the asreml.obj component of the supplied asrtests.obj will also be tested and a warning issued if both fixed correlations are found in it and allow.fixedcorrelation is FALSE.
checkboundaryonly	If TRUE then boundary and singular terms are not removed by rmboundary.asrtests; a warning is issued instead.
drop.ran.ns	A logical indicating whether to drop nonsignificant random terms from the model.
positive.zero	Indicates whether the hypothesized values for the variance components being tested are on the boundary of the parameter space. For example, this is true for positively-constrained variance components that, under the reduced model, are zero. This argument does not need to be set if bound.test.parameters is set.
bound.test.parameters	Indicates whether for the variance components being tested, at least some of the hypothesized values are on the boundary of the parameter space. The possibilities are "none", "onlybound" and "one-and-one". The default is "none", although if it is set to "none" and positive.zero is TRUE then bound.test.parameters is taken to be "onlybound". When bound.test.parameters is set to "one-and-one", it signifies that there are two parameters being tested, one of which is bound and the other is not. For example, the latter is true for testing a covariance and a positively-constrained variance component that, under the reduced model, are zero.
drop.fix.ns	A logical indicating whether to drop a fixed term from the model when it is nonsignificant
denDF	Specifies the method to use in computing approximate denominator degrees of freedom when wald.asreml is called. Can be none to suppress the computations, numeric for numerical methods, algebraic for algebraic methods or default, the default, to autommatically choose numeric or algebraic computations depending on problem size. The denominator degrees of freedom are calculated according to Kenward and Roger (1997) for fixed terms in the dense part of the model.
dDF.na	The method to use to obtain substitute denominator degrees of freedom. when the numeric or algebraic methods produce an NA. If dDF.na = "none", no subtitute denominator degrees of freedom are employed; if dDF.na = "residual", the residual degrees of freedom from asreml.obj$nedf are used; if dDF.na = "maximum", the maximum of those denDF that are available, excluding that for the Intercept, is used; if all denDF are NA, asreml.obj$nedf is used. If dDF.na = "supplied", a vector of values for the denominator degrees of freedom is to be supplied in dDF.values. Any other setting is ignored and a warning message produced. Generally, substituting these degrees of freedom is anticonservative in that it is likely that the degrees of freedom used will be too large.
dDF.values	A vector of values to be used when dDF.na = "supplied". Its values will be used when denDF in a test for a fixed effect is NA. This vector must be the same length as the number of fixed terms, including (Intercept) whose value could be NA.
trace	If TRUE then partial iteration details are displayed when ASReml-R functions are invoked; if FALSE then no output is displayed.
update	If TRUE, and set.terms is NULL, then newfit.asreml is called to fit the model to be tested, using the values of the variance parameters stored in the asreml.object, that is stored in asrtests.obj, as starting values. If FALSE or set.terms is not NULL, then newfit.asreml will not use the stored variance parameter values as starting values when fitting the new model, the only modifications being (i) to the terms in the fixed and random models corresponding to terms in terms.marginality and (ii) those specified via ....
set.terms	A character vector specifying the terms that are to have bounds and/or initial values set prior to fitting. The names must match those in the vparameters component of the asreml.obj component in the new asrtests.object.
ignore.suffices	A logical vector specifying whether the suffices of the asreml-assigned names of the variance terms (i.e. the information to the right of an "!", other than "R!") is to be ignored in matching elements of terms. If TRUE for an element of terms, the suffices are stripped from the asreml-assigned names. If FALSE for an element of terms, the element must exactly match an asreml-assigned name for a variance term. This vector must be of length one or the same length as terms. If it is of length one then the same action is applied to the asreml-assigned suffices for all the terms in terms.
bounds	A character vector specifying the bounds to be applied to the terms specified in set.terms. This vector must be of length one or the same length as set.terms. If it is of length one then the same constraint is applied to all the terms in set.terms. If any of the bounds are equal to NA then they are left unchanged for those terms.
initial.values	A character vector specifying the initial values for the terms specified in terms. This vector must be of length one or the same length as terms. If it is of length one then the same initial value is applied to all the terms in terms. If any of the initial.values are equal to NA then they are left unchanged for those terms.
IClikelihood	A character that controls both the occurence and the type of likelihood for information criterion in the test.summary of the new asrtests.object. If none, none are included. Otherwise, if REML, then the AIC and BIC based on the Restricted Maximum Likelihood are included; if full, then the AIC and BIC based on the full likelihood, evaluated using REML estimates, are included. (See also infoCriteria.asreml.)
...	further arguments passed to asreml, wald.asreml and as.asrtests via testranfix.asrtests.

Value

A list containing:

1. asrtests.obj: an asrtests.object containing the components (i) asreml.obj, (ii) wald.tab, and (iii) test.summary.;

2. sig.tests: a character vector whose elements are the the significant terms amongst those tested.

Author(s)

Chris Brien

References

Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983-997.

See Also

chooseModel, chooseModel.data.frame, as.asrtests, testranfix.asrtests,
testresidual.asrtests, REMLRT.asreml, rmboundary.asrtests, newfit.asreml,
changeModelOnIC.asrtests, changeTerms.asrtests, reparamSigDevn.asrtests

Examples

## Not run: 
data(WaterRunoff.dat)
asreml.options(keep.order = TRUE) #required for asreml-R4 only
current.asr <- asreml(log.Turbidity ~ Benches + (Sources * (Type + Species)) * Date, 
                      random = ~Benches:MainPlots:SubPlots:spl(xDay), 
                      data = WaterRunoff.dat, keep.order = TRUE)
current.asrt <- as.asrtests(current.asr, NULL, NULL)
terms.treat <- c("Sources", "Type", "Species", 
                 "Sources:Type", "Sources:Species")
terms <- sapply(terms.treat, 
                FUN=function(term){paste("Date:",term,sep="")}, 
                simplify=TRUE)
terms <- c("Date", terms)
terms <- unname(terms)
marginality <-  matrix(c(1,0,0,0,0,0, 1,1,0,0,0,0,  1,0,1,0,0,0, 
                         1,0,1,1,0,0, 1,1,1,0,1,0, 1,1,1,1,1,1), nrow=6)
rownames(marginality) <- terms
colnames(marginality) <- terms
choose <- chooseModel(current.asrt, marginality) 
current.asrt <- choose$asrtests.obj
sig.terms <- choose$sig.terms

## End(Not run)

asremlPlus documentation built on Nov. 5, 2023, 5:07 p.m.