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.

1 2 3 4 5 6 | ```
## 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(), ...)
``` |

`asreml.obj` |
An |

`means` |
The |

`V` |
The fitted variance |

`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 |

`extra.matrix` |
A |

`ignore.terms` |
A |

`fixed.spline.terms` |
A |

`bound.exclusions` |
A |

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

`units` |
A |

`update` |
If |

`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 |

`ncores` |
A |

`...` |
Other arguments that are passed down to the function |

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:

**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.

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.

`asremlPlus-package`

, `asreml`

,
`plotVariofaces.data.frame`

, `simulate.asreml`

, `set.seed`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
## 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 = 2)
## End(Not run)
``` |

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