carolina2: Analysis of North Carolina design II

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

Description

The function performs analysis of North Carolina II design (Comstock and Rosbinson 1952).

Usage

carolina2(dataframe, set, male, female, replication, yvar)

Arguments

dataframe	Dataframe with the variablesevariables set, male, female, replication and other response variables
set	Name of column with set variables
male	Name of column with male parent information
female	Name of column with female parent information
replication	Name of column with replication column
yvar	Name of response variable to be used for the analysis

Value

The following values as list are returned

model	model - use anova (model) to see analysis of variables
var.m	Male variance
var.f	Female variance
var.mf	Male*Female variance
var.AM	Additive male variance
var.Af	Additive female variance
var.D	Dominance variance

Author(s)

Umesh Rosyara

References

Comstock R.F., Rosbinson F.F (1952). Estimation of average dominance of genes. In Heterosis, Iowa State College Press, Iowa City, Iowa, chapter 30.

Singh R.K., Chaudhary B.D.(1985) Biometrical Methods in Quantitative Genetics Analysis, Kalyani Publishers.

Mather K., Jinks J.L. (1971). Biometrical Genetics. Chapman & Hall, London.

Saxton A. (2004) Genetic Analysis of Complex Traits Using SAS. SAS Institute, Inc.

Examples

 data(northcaro2)
 # for trait yield 
myo <- carolina2(dataframe = northcaro2, set = "set", male = "male", female = "female",
 replication = "rep", yvar = "yield")
anova(myo$model) # anova 
myo$var.m
myo$var.f
myo$var.mf
myo$var.Af
myo$var.D

# for trait tuber  
tum <- carolina2(dataframe = northcaro2, set = "set", male = "male", female = "female", 
replication = "rep", yvar = "tuber")
anova(tum$model) 
anova(tum$model) # anova 
tum$var.m
tum$var.f
tum$var.mf
tum$var.Af
tum$var.D

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