aug.rcb: Analysis of augmented random block design

Description Usage Arguments Value Author(s) Examples

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

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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

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# 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.