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In rhoneycomb: Analysis of Honeycomb Selection Designs

knitr::opts_chunk$set(echo = TRUE)
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library(rhoneycomb)

A short guide to using the package "rhoneycomb"

The package “rhoneycomb” is a useful statistical tool for the construction and analysis of honeycomb selection designs.

Installation and usage

This section shows the installation of the package and the usage of underlying functions.

Installation

To install the package from CRAN, and then load it, use the following commands:

install.packages("rhoneycomb")
library(rhoneycomb)

Example: Generate available Honeycomb Selection Designs

As a first step in the analysis, the plant breeder should check if any designs are available for the number of under-evaluation entries. We do so by using the following command which returns a data frame containing the available designs. Here we run the function with a vector that contains numbers 1 to 60:

generate(1:60)

Example: Analysis of Experimental Data.

After obtaining the necessary design information from the function generate(), the user inputs the number of entries, the k parameter, the number of rows, the number of plants per row and the planting distance into the function HSD().

Honeycomb Selection Design

Initialization

We initialize the honeycomb selection design using the HSD command. Here:

• 7 corresponds to the number of entries
• 2 is the value of k parameter
• 10 is the number of rows
• 10 is the number of plants per row
• 1 is the interplant distance in meters

main_data<-HSD(7,2,10,10,1)

head(main_data,25) #Use the head function to get the top 25 rows.

Ring Analysis

After this step, we pass the response variable to the "Data" column of the data frame generated by one of the functions HSD, HSD0, HSD01 or HSD03.

main_data$Data<-wheat_data$main_spike_weight
result<-analysis(main_data,"Data",6)

head(result[[1]],10) #Use the head function to get the top 10 rows.
result[[2]] 

Blocks Analysis

By using the arguments blocks=TRUE in the analysis function, the data is being analyzed using complete moving replicate. If we also use the arguments “row_element” and “plant_element”, the plants included in the specific block are displayed.

result<-analysis(main_data,"Data",blocks=TRUE,row_element=5,plant_element=5)
head(result[[1]],10) #Use the head function to get the top 10 rows.
result[[2]] 

Honeycomb Selection Design with one entry

Initialization

Since there is no control entry in HSD0 design, we must only provide number of rows, number of plants and interplant distance to the function. Here:

• 10 is the number of rows
• 10 is the number of plants per row
• 1 is the interplant distance in meters

main_data<-HSD0(10,10,1)
main_data$Data<-wheat_data$main_spike_weight
head(main_data,10) #Use the head function to get the top 10 rows.

For the HSD01 design we must also add the value of K as first argument in the function:

main_data<-HSD01(7,10,10,1)
main_data$Data<-wheat_data$main_spike_weight

head(main_data,10) #Use the head function to get the top 10 rows.

Analysis

The analysis function returns three data frames.

result<-analysis(main_data,"Data")
head(result[[1]],10) #Use the head function to get the top 10 rows.
result[[2]]
result[[3]]

rhoneycomb documentation built on Aug. 24, 2023, 1:08 a.m.