# Produce sets of simulated data from a multivariate normal distribution and save quantities related to the simulated data Produce in parallel sets of simulated data corresponding to an `asreml` model, along with its fitted values and residuals. A variance matrix `V`, corresponding to the `random` and `residual` models must be supplied. What to save is specified by the `which` argument. ```r ## S3 method for class 'asreml' simulate(object, nsim=100, seed = NULL, means=NULL, V, tolerance = 1E-10, update = TRUE, trace = FALSE, which="data", units = "ignore", ncores = 2, ...) ``` ## Arguments - `object`: An `asreml` object from a call to `asreml` in which the `data` argument has been set. - `means`: The `vector` of means to be used in generating simulated data sets. If it is `NULL`, the fitted values based on `object` are used. It must be the same length as the response variable for `object`. - `V`: The fitted variance `matrix`, i.e. having the pattern and values that conform to the model fit stored in the supplied `object`. - `nsim`: The number of data sets to be simulated. - `seed`: A single value, interpreted as an integer, that specifies the starting value of the random number generator. The "L'Ecuyer-CMRG" random generator is used and `nextRNGStream` is used to seed each core from the original `seed`. - `tolerance`: The value such that eigenvalues less than it are considered to be zero. - `update`: If `TRUE`then the arguments `R.param` and `G.param` are set to those in the `asreml` object supplied in `object` so that the values from the original model are used as starting values. If `FALSE` then `asreml` `calls` are evaluated, the only changes from the previous call being that (i) the model is fitted to simulated data and (ii) modifications specified via `...` are mode, except that changes cannot be made to any of the models. - `trace`: If `TRUE` then partial iteration details are displayed when ASReml-R functions are invoked; if `FALSE` then no output is displayed. - `which`: The quantities from the simulated data set to be stored. Any combination of `"response"`, `"residuals"` and `"fitted"`, or `"all"`. If `residuals` and/or `fitted` is specified, those for the analysis stored in `object` will be added to the `data.frame` nominated in the `data` argument of `object` and the modified `data.frame` added as a component named `data` in the `list` that is the `value` returned by the `function`. - `units`: A `character` indicating whether the BLUPs for `units` are added to the residuals when this reserved factor is included in the `random` model. Possible values are `addtoresiduals` and `ignore`. - `ncores`: A `numeric` specifying the number of cores to use in doing the simulations. In choosing a value for `ncores`, it is necessary to take into account other processes that are using parallel processing at the same time. - ``...``: Other arguments that are passed down to the function asreml. Changes to the models are not allowed. Other changes are dangerous and generally should be avoided. ## Returns A `list` with the following components whose presence depends on the setting of `which`: 1. observed: present if `which` includes `residuals` or `fitted`, in which case it will be the `data.frame` on which the fit in `object` is based, with `residuals` and/or `fitted`. 2. data: present if `which` includes `data`, a `data.frame` containing the simulated data sets. 3. fitted: present if `which` includes `fitted`, a `data.frame` containing the fitted values from the analyses of the simulated data sets. 4. residuals: present if `which` includes `residuals`, a `data.frame` containing the residuals from the analyses of the simulated data sets. ## Details Generate `nsim` sets of data and analyse them using `asreml` using the model in `object`, performing the generation and analysis of several sets in parallel. Note, if the analysis for a data set does not converge in `maxiter` iterations, it is discarded and a replacement data set generated. The value of `maxiter` can be specified in the call to `simulate.asreml`. The fitted values and residuals are extracted as required. If `aom = TRUE` when the simulated data are analysed, standardised conditional residuals are stored. If `which` includes `residuals` or `fitted`, the specified quantities for the observed data are added to the `data.frame` on which the fit in `object` is based. ## Author(s) Chris Brien ## See Also `asreml`, `newfit.asreml`, `variofaces.asreml`, `plotVariofaces.data.frame`, `set.seed`. ## Examples ```r ## Not run: data(Wheat.dat) current.asr <- asreml(yield ~ Rep + WithinColPairs + Variety, random = ~ Row + Column + units, residual = ~ ar1(Row):ar1(Column), data=Wheat.dat) current.asrt <- as.asrtests(current.asr, NULL, NULL) current.asrt <- rmboundary.asrtests(current.asrt) # Form variance matrix based on estimated variance parameters s2 <- current.asr$sigma2 gamma.Row <- current.asr$gammas[1] gamma.unit <- current.asr$gammas[2] rho.r <- current.asr$gammas[4] rho.c <- current.asr$gammas[5] row.ar1 <- mat.ar1(order=10, rho=rho.r) col.ar1 <- mat.ar1(order=15, rho=rho.c) V <- gamma.Row * fac.sumop(Wheat.dat$Row) + gamma.unit * diag(1, nrow=150, ncol=150) + mat.dirprod(col.ar1, row.ar1) V <- s2*V #Produce residuals from 100 simulated data sets resid <- simulate(current.asr, V=V, which="residuals", ncores = parallel::detectCores()) ## End(Not run) ```

simulate.asreml function