aug.rcb: Analysis of augmented random block design

In plantbreeding: Analysis and visualization of data from plant breeding and genetics experiments

Description

The function implements analysis of augmented random block design. The function assumes that checks (controls) are replicated r times making complete blocks while other treatments (new treatments) are unreplicated. Once the desired block size is determined, the checks are completely randomized making complete blocks and remaining plots / experimental units are also completely randomized however new treatments are unreplicated.

Usage

aug.rcb(dataframe, genotypes, block, yvar)

Arguments

dataframe	Dataframe object with at least variable containing genotypes, blocks and one response variable to be analyzed
genotypes	Name of column consisting of genotype or treatments (use "nameofcolumn" format)
block	Name of column consisting of block (use "nameofblock" format)
yvar	Name of response variable column (use "yvar" format)

Value

A list consisting of the following items :

anova	Analysis of variance object
adjusted_values	dataframe Table with raw and adjusted values
se_check	Difference between check means
se_within	Difference adjusted yield of two varitiesvarieties / entries in same block
SE_siff	Difference between two varieties / entries in different blocks
se_geno	Difference between two varieties /entries and a check mean

Author(s)

Umesh R. Rosyara

Examples

# Example 
data(augblock)
out <- aug.rcb(dataframe = augblock, genotypes = "var", block = "blk", yvar = "gw")
out$anova  # analysis of variance 
out$adjusted_values # yield observed and expected value table  

# calculation of means
stab <- aggregate( gw ~ var, data=augblock, FUN= mean)

hist(stab$gw, col = "cadetblue", xlab = "Grain Yield", 
main = "Mean yields from Augmented Yield Trial")

plantbreeding documentation built on May 2, 2019, 4:54 p.m.