asremlPlus (version 2.0-2)

variofaces.asreml: plot 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 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 rcov 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 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

variofaces.asreml(object, V, nsim=100, seed = NULL, tolerance=1E-10, 
                  units = "ignore", update = TRUE, trace = FALSE, 
                  graphics.device=NULL, ...)

Arguments

object

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

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.

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.

tolerance

The value such that eigenvalues less than it are consdered 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

update

if TRUEthen 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 mo

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

...

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.

Value

• A list with the following components:
  1. face1:adata.framecontaining 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.

Details

The rcov 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 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.

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.

See Also

asremlPlus-package, asreml, plotvariofaces.asreml, simulate.asreml.

Examples

data(Wheat.dat)
current.asr <- asreml(yield ~ Rep + WithinColPairs + Variety, 
                      random = ~ Row + Column + units,
                      rcov = ~ ar1(Row):ar1(Column), 
                      data=Wheat.dat)
current.asrt <- 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.asreml(current.asr, V=V)