eChapter Name: Visualization of Output
9789358873404
eBook Name: CROP TRIALS WITH R
by Rumit Patel, Sushil Kumar, Prity Kumari
In this chapter, we will explore multiple comparison tests, with a particular emphasis on three significant tests: least significant difference (LSD), Bonferroni, and the techniques to visualize their outcomes. We aim to facilitate a comprehensive understanding of how to conduct and interpret the results of these comparison tests visualization using the R software.
Treatment Comparison
The “doebioresearch” package is commonly used for testing the significance of treatment groups in various experimental designs such as randomized block design (RBD), completely randomized design (CRD), and least significant difference (LSD). This package excels in conducting multiple comparison tests like LSD, Duncan’s multiple range test (DMRT), and Tukey’s honestly significant difference (HSD). It efficiently calculates all comparisons in the background, resulting in the generation of only group labels, as previously demonstrated in the output of the “doebioresearch” package. Additionally, the “agricolae” package is another useful tool that provides detailed values for multiple comparison tests. These three tests—LSD, DMRT, and HSD—are particularly popular in agricultural experiments and form the focus of our discussion.
I. Least significant difference (LSD) test
• To conduct the least significant difference test for comparing treatment means, the “agricolae” package is essential.
• To install the “agricolae” package, use the following code:
• install.packages (“agricolae”)
• After installation, activate the package with this command:
• library (“agricolae”)
• The data frame required for analysis in this context remains consistent with what is needed for randomized block design (RBD), completely randomized design (CRD), LSD, and variability analysis, among others. As an illustration, consider an example involving variability analysis.