# Determines and records the set of significant terms using an `asrtests.object`, taking into account the hierarchy or marginality relations of the terms. 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. ```r ## 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 automatically 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 substitute 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 occurrence 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`. ## Returns 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 significant terms amongst those tested. ## References Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. **Biometrics**, 53 , 983-997. ## Author(s) Chris Brien ## 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 ```r ## 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) ```

chooseModel.asrtests function