# Reparamterizes each random (deviations) term involving `devn.fac` to a fixed term and ensures that the same term, with `trend.num` replacing `devn.fac`, is included if any other term with `trend.num` is included in `terms`. This function reparamterizes each random (deviations) term involving `devn.fac` to a fixed term and ensures that the same term with `trend.num` replacing `devn.fac` is included if any other term with `trend.num` is included in `terms`. It also ansures that any term with `spl{trend.num}` replacing `devn.fac` in a term being reparameterized is removed from the model. ```r ## S3 method for class 'asrtests' reparamSigDevn(asrtests.obj,terms = NULL, trend.num = NULL, devn.fac = NULL, allow.unconverged = TRUE, allow.fixedcorrelation = TRUE, checkboundaryonly = FALSE, denDF = "numeric", IClikelihood = "none", trace = FALSE, update = TRUE, set.terms = NULL, ignore.suffices = TRUE, bounds = "P", initial.values = NA,...) ``` ## Arguments - `asrtests.obj`: an `asrtests.object` containing the components (i) `asreml.obj`, (ii) `wald.tab`, and (iii) `test.summary`. - `terms`: A character string vector giving the random terms that are to be reparameterized. - `trend.num`: A character string giving the name of the numeric covariate that corresponds to `devn.fac` and is potentially included in terms in the fitted model. - `devn.fac`: A character string giving the name of the factor that corresponds to `trend.num` and is included in terms in the fitted model. The name must match those in the `vparameters` component of the `asreml.obj` component in the `asrtests.obj`. - `allow.unconverged`: A `logical` indicating whether to accept a new model even when it does not converge. Initially all changes are made with `allow.unconverged` set to `TRUE`. If `allow.unconverged` has been set to `FALSE` in the call and the final fit does not converge, an attempt is made to achieve convergence by removing any boundary terms. If this is unsuccessful, the supplied `asrtests.obj` is returned. - `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. - `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. - `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`.) - `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) the models are updated 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. - `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. - ``...``: further arguments passed to `asreml` via `changeTerms.asrtests` and `as.asrtests`. ## Returns An `asrtests.object` containing the components (i) `asreml.obj`, (ii) `wald.tab`, and (iii) `test.summary`. ## 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 `as.asrtests`, `changeTerms.asrtests`, `testranfix.asrtests`, `testresidual.asrtests`, `newfit.asreml`, `chooseModel.asrtests` ## Examples ```r ## Not run: data(WaterRunoff.dat) asreml.options(keep.order = TRUE) #required for asreml-R4 only current.asr <- asreml(fixed = log.Turbidity ~ Benches + Sources + Type + Species + Sources:Type + Sources:Species + Sources:Species:xDay + Sources:Species:Date, data = WaterRunoff.dat, keep.order = TRUE) current.asrt <- as.asrtests(current.asr, NULL, NULL) #Examine terms that describe just the interactions of Date and the treatment factors terms.treat <- c("Sources", "Type", "Species", "Sources:Type", "Sources:Species") date.terms <- sapply(terms.treat, FUN=function(term){paste("Date:",term,sep="")}, simplify=TRUE) date.terms <- c("Date", date.terms) date.terms <- unname(date.terms) treat.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(treat.marginality) <- date.terms colnames(treat.marginality) <- date.terms choose <- chooseModel(current.asrt, treat.marginality, denDF="algebraic") current.asrt <- choose$asrtests.obj current.asr <- current.asrt$asreml.obj sig.date.terms <- choose$sig.terms #Remove all Date terms left in the fixed model terms <- "(Date/(Sources * (Type + Species)))" current.asrt <- changeTerms(current.asrt, dropFixed = terms) #if there are significant date terms, reparameterize to xDays + spl(xDays) + Date if (length(sig.date.terms) != 0) { #add lin + spl + devn for each to fixed and random models trend.date.terms <- sapply(sig.date.terms, FUN=function(term){sub("Date","xDay",term)}, simplify=TRUE) trend.date.terms <- paste(trend.date.terms, collapse=" + ") current.asrt <- changeTerms(current.asrt, addFixed=trend.date.terms) trend.date.terms <- sapply(sig.date.terms, FUN=function(term){sub("Date","spl(xDay)",term)}, simplify=TRUE) trend.date.terms <- c(trend.date.terms, sig.date.terms) trend.date.terms <- paste(trend.date.terms, collapse=" + ") current.asrt <- changeTerms(current.asrt, addRandom = trend.date.terms) current.asrt <- rmboundary(current.asrt) } #Now test terms for sig date terms spl.terms <- sapply(terms.treat, FUN=function(term){paste("spl(xDay):",term,sep="")}, simplify=TRUE) spl.terms <- c("spl(xDay)",spl.terms) lin.terms <- sapply(terms.treat, FUN=function(term){paste(term,":xDay",sep="")}, simplify=TRUE) lin.terms <- c("xDay",lin.terms) systematic.terms <- c(terms.treat, lin.terms, spl.terms, date.terms) systematic.terms <- unname(systematic.terms) treat.marginality <- matrix(c(1,0,0,0,0,0, 1,1,0,0,0,0, 1,0,1,0,0,0, 1,0,1,1,0,0, 1,1,1,1,1,0, 1,1,1,1,1,1), nrow=6) systematic.marginality <- kronecker(matrix(c(1,0,0,0, 1,1,0,0, 1,1,1,0, 1,1,1,1), nrow=4), treat.marginality) systematic.marginality <- systematic.marginality[-1, -1] rownames(systematic.marginality) <- systematic.terms colnames(systematic.marginality) <- systematic.terms choose <- chooseModel(current.asrt, systematic.marginality, denDF="algebraic", pos=TRUE) current.asrt <- choose$asrtests.obj #Check if any deviations are significant and, for those that are, go back to #fixed dates current.asrt <- reparamSigDevn(current.asrt, choose$sig.terms, trend.num = "xDay", devn.fac = "Date", denDF = "algebraic") ## End(Not run) ```

reparamSigDevn.asrtests function