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)
desplot(block ~ col+row, yates.oats, 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(res ~ col + row, gnut, 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.

Session info

sessionInfo()
## R version 3.2.5 (2016-04-14)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
## 
## locale:
## [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] mapproj_1.2-4        maps_3.1.0           HH_3.1-31            gridExtra_2.2.1     
##  [5] multcomp_1.4-4       TH.data_1.0-7        MASS_7.3-45          survival_2.39-2     
##  [9] mvtnorm_1.0-5        latticeExtra_0.6-28  RColorBrewer_1.1-2   gge_1.0             
## [13] agridat_1.13         lme4_1.1-12          Matrix_1.2-5         nlme_3.1-127        
## [17] knitr_1.12.3         rmarkdown_0.9.5      BlandAltmanLeh_0.3.1 dplyr_0.4.3         
## [21] LaunchR_1.0.8.0      kw_1.64              desplot_1.00         lattice_0.20-33     
## [25] reshape2_1.4.1       rio_0.3.10           lucid_1.4            R2jags_0.5-7        
## [29] rjags_4-6            R2WinBUGS_2.1-21     boot_1.3-18          coda_0.18-1         
## [33] devtools_1.11.0     
## 
## loaded via a namespace (and not attached):
##  [1] Biobase_2.28.0      jsonlite_0.9.19     splines_3.2.5       Formula_1.2-1      
##  [5] shiny_0.13.2        assertthat_0.1      urltools_1.4.0      cellranger_1.0.0   
##  [9] yaml_2.1.13         readODS_1.6.2       chron_2.3-47        digest_0.6.9       
## [13] minqa_1.2.4         colorspace_1.2-6    sandwich_2.3-4      htmltools_0.3.5    
## [17] httpuv_1.3.3        plyr_1.8.3          XML_3.98-1.4        haven_0.2.0        
## [21] xtable_1.8-2        scales_0.4.0        openxlsx_3.0.0      gmp_0.5-12         
## [25] ggplot2_2.1.0       withr_1.0.1         nnet_7.3-12         BiocGenerics_0.14.0
## [29] lazyeval_0.1.10     Rmpfr_0.6-0         mime_0.4            magrittr_1.5       
## [33] readxl_0.1.1        memoise_1.0.0       evaluate_0.8.3      xml2_0.1.2         
## [37] foreign_0.8-66      tools_3.2.5         data.table_1.9.6    formatR_1.3        
## [41] stringr_1.0.0       munsell_0.4.3       cluster_2.0.4       pcaMethods_1.58.0  
## [45] compiler_3.2.5      vcd_1.4-1           nloptr_1.0.4        leaps_2.9          
## [49] tcltk_3.2.5         gtable_0.2.0        codetools_0.2-14    abind_1.4-3        
## [53] DBI_0.3.1           curl_0.9.7          R6_2.1.2            zoo_1.7-12         
## [57] Hmisc_3.17-3        readr_0.2.2         stringi_1.0-1       parallel_3.2.5     
## [61] Rcpp_0.12.4         rpart_4.1-10        acepack_1.3-3.3     gclus_1.3.1        
## [65] lmtest_0.9-34

References

Ryder, K. 1981. “Field Plans: Why the Biometrician Finds Them Useful.” Experimental Agriculture 17: 243–56. doi:10.1017/S0014479700011601.

Yates, Frank. 1935. “Complex Experiments.” Journal of the Royal Statistical Society Suppl 2: 181–247. doi:10.2307/2983638.