R: Plots empirical variogram faces, including envelopes, as...

variofaces.asreml {asremlPlus}

R Documentation

Plots empirical variogram faces, including envelopes, as described by Stefanova, Smith & Cullis (2009).

Description

A function that produces a plot for each face of an empirical 2D variogram based on residuals produced after the fitting of a model using the function asreml. It also adds envelopes to the plot by simulating data sets in parallel from a multivariate normal distribution with expectation equal to the fitted values obtained from the fixed and spline terms and variance matrix equal to the fitted variance matrix (Stefanova, Smith & Cullis, 2009). The plot is controlled by the residual model, which must consist of two factors corresponding to the two physical dimensions underlying the data. It can also have a third term involving the at or dsum function that defines sections of the data, such as experiments in different environments. In this case, the two variogram faces are produced for each section.

Usage

## S3 method for class 'asreml'
variofaces(asreml.obj, means=NULL, V=NULL, 
           sections = NULL, row.factor = NULL, col.factor = NULL,
           nsim=100, seed = NULL, 
           extra.matrix = NULL, ignore.terms = NULL, fixed.spline.terms = NULL, 
           bound.exclusions = c("F","B","S","C"), tolerance=1E-10, 
           units = "ignore", update = TRUE, trace = FALSE, 
           graphics.device=NULL, ncores = 2, ...)

Arguments

`asreml.obj`	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 appropriate pattern and values given the model fitted to the observed data and the estimates of the parameters obtained. If `V` is `NULL` then `estimateV.asreml` is called to obtain it from `asreml.obj`
`sections`	A single `character` string that specifies the name of the column in the `data.frame` that contains the `factor` that identifies different sections of the data to which separate spatial models have been fitted.
`row.factor`	A single `character` string nominating a `factor` that indexes the rows of a grid that are one dimension of a spatial correlation model. The `factor` must a column in the `data.frame` stored in the `asreml.obj`.
`col.factor`	A single `character` string nominating a `factor` that indexes the columns of a grid that are one dimension of a spatial correlation model. The `factor` must a column in the `data.frame` stored in the `asreml.obj`.
`nsim`	The number of data sets to be simulated in obtaining the envelopes.
`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`.
`extra.matrix`	A `matrix` of order equal to the number of observations that is to be added to the variance `matrix`, the latter based on the information in `asreml.obj`. It is assumed that the sigma-parameterized values of the variance parameter estimates, such as is given in the `varcomp` component of `summary.asreml`, have been used in calculating `extra.matrix`; the values in the `vparameters` component of `G.param` and `R.param` may be either gamma- or sigma-parameterized. The argument `extra.matrix` can be used in conjunction with `ignore.terms` as a workaround to include components of the variance matrix for variance functions that have not been implemented in `estimateV`.
`ignore.terms`	A `character` giving terms from either the `random` or `residual` models that are to be ignored in that their contributions to the variance is not to be included in the estimated matrix. The term names are those given in the `vparameters` component of the `asreml` object or the `varcomp` component produced by `summary.asreml`, but only up to the first exclamation mark (`!`). This can be used in conjunction with `estimateV.asreml` as a workaround to include components of the variance matrix for variance functions that have not been implemented in `estimateV`.
`fixed.spline.terms`	A `character` vector giving one or more spline terms in the `random` model that are regarded as fixed and so are to be ignored because they are not regarded as contributing to the variance. The term names are those given in the `vparameters` component of the `asreml` object or the `varcomp` component produced by `summary.asreml`, but only up to the first exclamation mark (`!`).
`bound.exclusions`	A `character` specifying one or more bound codes that will result in a variance parameter in the `random` model being excluded from contributing to the variance. If set to `NULL` then none will be excluded.
`tolerance`	The value such that eigenvalues less than it are considered to be zero.
`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`. If standardized conditional residuals are plotted and the BLUPs for `units` are to be added then it is the standardized BLUPs that are added.
`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 model is fitted to simulated data and (ii) those specified via `...`, 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.
`graphics.device`	A `character` specifying a graphics device for plotting. The default is `graphics.device = NULL`, which will result in plots being produced on the current graphics device. Setting it to `"windows"`, for example, will result in a windows graphics device being opened.
`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.

Details

The residual model is scanned to ensure that it involves only two factors not included in the at function, and to see if it has a third factor in an at function. If so, the faces of the 2D variogram, each based on one of the two non-at factors, are derived from the residuals in the supplied asreml object using asreml.variogram, this yielding the observed variogram faces. If aom was set to TRUE for the asreml object, the standardized conditional residuals are used. Then nsim data sets are generated by adding the fitted.values, extracted from the asreml object, to a vector of values randomly generated from a normal distribution with expectation zero and variance matrix V. Each data set is analyzed using the model in object and several sets are generated and analyzed in parallel. The variogram values for the faces are obtained using asreml.variogram stored. 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 variofaces.asreml. Plots are produced for each face and include the observed values and the 2.5%, 50% & 97.5% quantiles.

Value

A list with the following components:

face1: a data.frame containing the variogram values on which the plot for the first dimension is based.
face2: a data.frame containing the variogram values on which the plot for the second dimension is based.

Author(s)

Chris Brien

References

Stefanova, K. T., Smith, A. B. & Cullis, B. R. (2009) Enhanced diagnostics for the spatial analysis of field trials. Journal of Agricultural, Biological, and Environmental Statistics, 14, 392–410.

Examples

## 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 variogram faces plot (Stefanaova et al, 2009)
variofaces(current.asr, V=V, ncores = parallel::detectCores())

## End(Not run)

[Package asremlPlus version 4.4.35 Index]