Author: Kevin Wright
Date: 2023-03-09

# Plotting field maps with the desplot package

#### Kevin Wright

#### 2023-03-09

# Abstract

This short note shows how to plot a field map from an agricultural experiment and why that may be useful.

# R setup

    library("knitr")
    knitr::opts_chunk$set(fig.align="center", fig.width=6, fig.height=6)
    options(width=90)

## Example 1

First, a plot of the experimental design of the oats data from Yates (1935).

    library(agridat)
    library(desplot)
    data(yates.oats)
    # Older versions of agridat used x/y here instead of col/row
    if(is.element("x",names(yates.oats)))
       yates.oats <- transform(yates.oats, col=x, row=y)
    desplot(yates.oats, block ~ col+row,
            col=nitro, text=gen, cex=1, out1=block,
            out2=gen, out2.gpar=list(col = "gray50", lwd = 1, lty = 1))

## Example 2

This next example is from Ryder (1981). Fit an ordinary RCB model with fixed effects for `block` and `genotype`. Plot a heatmap of the residuals.

    library(agridat)
    library(desplot)
    data(ryder.groundnut)
    gnut <- ryder.groundnut
    m1 <- lm(dry ~ block + gen, gnut) # Standard RCB model
    gnut$res <- resid(m1)
    desplot(gnut, res ~ col + row, text=gen, cex=1,
            main="ryder.groundnut residuals from RCB model")

Note the largest positive/negative residuals are adjacent to each other, perhaps caused by the original data values being swapped. Checking with experiment investigators (managers, data collectors, etc.) is recommended.

## References

Ryder, K. 1981. “Field Plans: Why the Biometrician Finds Them Useful.” Experimental Agriculture 17: 243–56. https://doi.org/10.1017/S0014479700011601.
Yates, Frank. 1935. “Complex Experiments.” Journal of the Royal Statistical Society Suppl 2: 181–247. https://doi.org/10.2307/2983638.