#ASREML R FA1 PLOT CODE#

Orange.pvals$Tree <- factor(orange.pvals$Tree) Orange.pvals <- orange.pv$predictions$pvals 1.7): Fitting a Model in ASReml-R Now that we are assured that the data are formatted correctly, we can fit a linear mixed model using ASReml-R. After creating a TP object, a model can be fitted on the data. 1.7 Scatter plot of height and volume Make a scatterplot of height and volume to check for outliers (Fig. rcovat(LOCATION):ar1(ROW):ar1(RANGE)) Unfortunately, asreml-r is expensive and difficult to learn. It is relatively easy to use the asreml package to specify a spatial model (i.e. (see statgenHTP tutorial: 2.Outlier detection for single observations). In the past, I have used asreml-r to account for spatial auto-correlation in agricultural field trials that were laid out in a row and range design. We will use the TP object phenoTPOut in which annotated time points were replaced by NA because they were considered to be outliers. Orange.pv <- predict(orange.asr, classify='Tree:x', predictpoints=list(x=seq(150,1500,50))) Modeling with spatial terms using Example 1. The package asreml and latticeExtra are required. It is easily modified to suit other cases.

There are a number of ways to go about adding predicted values and confidence bands (or intervals) to a trellis plot but IвЂ™ve found the following the simplest. The example comes from the ASReml-R reference manual.

#ASREML R FA1 PLOT CODE#

The following R code adds a 95% confidence band to a fitted cubic smoothing spline.

Asreml-r fa1 plot