variofaces.asreml: Plots empirical variogram faces, including envelopes, as...

Description Usage Arguments Details Value Author(s) References See Also Examples


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 constrolled 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.


## S3 method for class 'asreml'
variofaces(asreml.obj, means=NULL, V=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 = detectCores(), ...)



An asreml object from a call to asreml in which the data argument has been set.


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.


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


The number of data sets to be simulated in obtaining the envelopes.


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.


A matrix of order equal to the number of observations that is to be added to the varaince 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.


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.


A character vector giving one or mor 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 (!).


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.


The value such that eigenvalues less than it are considered to be zero.


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.


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 calls are made to asreml in which the only changes from the previous call are (i) the model is fitted to simulated data and (ii) modifications specified via ... are made, except that changes cannot be made to any of the models.


If TRUE then partial iteration details are displayed when ASReml-R functions are invoked; if FALSE then no output is displayed.


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.


A numeric specifying the number of cores to use in doing the simulations.


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.


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 consitional 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.


A list with the following components:

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

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


Chris Brien


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.

See Also

asremlPlus-package, asreml,, simulate.asreml, set.seed.


## Not run: 
current.asr <- asreml(yield ~ Rep + WithinColPairs + Variety, 
                      random = ~ Row + Column + units,
                      residual = ~ ar1(Row):ar1(Column), 
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 = 2)

## End(Not run)

asremlPlus documentation built on Sept. 23, 2021, 5:07 p.m.