sommer (version 3.4)

spatPlots: Spatial plots

Description

Plot fitted values for all terms in a model to assess spatial fit.

Usage

spatPlots(object, by=NULL, colfunc=NULL,
         row="ROW",range="RANGE", wire=FALSE)

Arguments

object

a mixed model fitted using mmer2.

by

A character argument specifying the column name in the data frame in case the asreml model will specify a diagonal structure, meaning we will fit a different 2d spline at each FIELDINST

colfunc

an optional function to creat colors for the spatial plots.

row

name of the column in the dataset that identifies the spatial position in the x coordinate.

range

name of the column in the dataset that identifies the spatial position in the y coordinate.

wire

a TRUE/FALSE statement indicating if the function should return a wire plot or the default levelplot.

Value

plots: spatial plots for all fitted values from the mmer2 model terms in the random part.

fits

A new dataset with the fitted values for all terms in the model.

Details

The function only takes the asreml model and builds the fitted values for the terms required by building the incidence matrix and getting the BLUPs from the asreml model to build fitted = Zu. Then it makes a levelplot or wireplot to asses the spatial fit.

References

Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744

Lee, D.-J., Durban, M., and Eilers, P.H.C. (2013). Efficient two-dimensional smoothing with P-spline ANOVA mixed models and nested bases. Computational Statistics and Data Analysis, 61, 22 - 37.

Rodriguez-Alvarez, M.X, Boer, M.P., van Eeuwijk, F.A., and Eilers, P.H.C. (2017). Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics (to appear). https://doi.org/10.1016/j.spasta.2017.10.003.

See Also

mmer and mmer2-- the core functions of the package

Examples

# NOT RUN {
data(CPdata)
head(CPpheno)
CPgeno[1:4,1:4]
#### create the variance-covariance matrix 
A <- A.mat(CPgeno) # additive relationship matrix
#### look at the data and fit the model
head(CPpheno)

# mix1 <- mmer2(Yield~1,
#               random=~g(id)
#                       + Rowf + Colf
#                       + spl2D(Row,Col),
#               rcov=~units,
#               G=list(id=A),
#               data=CPpheno)
# summary(mix1)
# #### get the spatial plots
# spatPlots(mix1,row = "Row", range = "Col")

# }