Package 'variability'

Title: Genetic Variability Analysis for Plant Breeding Research
Description: Performs analysis of various genetic parameters like genotypic and phenotypic coefficient of variance, heritability, genetic advance, genetic advance as a percentage of mean. The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079).
Authors: Raj Popat [aut, cre], Rumit Patel [aut], Dinesh Parmar [aut]
Maintainer: Raj Popat <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2025-01-27 04:54:45 UTC
Source: https://github.com/cran/variability

Help Index

Analysis of Covariance

Description

Analysis of Covariance

Usage

ancova(data, genotypes, replication)

Arguments

data

traits to be analyzed
genotypes

vector containing genotypes
replication

vector containing replications

Value

ANCOVA, genotypic and phenotypic covariance

Examples

data(vardata)
ancova(vardata[3:11],vardata$Genotypes,vardata$Replication)

Estimation of Genetic Parameters

Description

Estimation of Genetic Parameters

Usage

gen.var(data, genotypevector, replicationvector)

Arguments

data

traits to be analyzed
genotypevector

vector containing genotypes
replicationvector

vector containig replications

Value

ANOVA, genotypic and phenotypic coefficient of variance, heritability, genetic advance and genetic advance as percentage of mean.

Examples

data(vardata)
gen.var(vardata[3:11],vardata$Genotypes,vardata$Replication)

Genotypic Correlation Analysis

Description

Genotypic Correlation Analysis

Usage

geno.corr(data, genotypes, replication)

Arguments

data

traits to be analyzed
genotypes

vector containing genotypes
replication

vector containing replications

Value

Genotypic correlation matrix

Examples

data(vardata)
geno.corr(vardata[3:11],vardata$Genotypes,vardata$Replication)

Genotypic Path Analysis

Description

Genotypic Path Analysis

Usage

geno.path(dependent.var, independent.var, genotypes, replication)

Arguments

dependent.var

trait to be used a dependent variable
independent.var

traits to be used as an indpendent variables
genotypes

vector containing genotpes
replication

vector containing replications

Value

Direct effects, indirect effects and residual

Examples

data(vardata)
# Grain yield is considered as a dependent variable
geno.path(vardata[11],vardata[3:10],vardata$Genotypes,vardata$Replication)

Phenotypic Correlation Analysis

Description

Phenotypic Correlation Analysis

Usage

pheno.corr(data, genotypes, replication)

Arguments

data

traits to be analyzed
genotypes

vector containing genotypes
replication

vector containing replications

Value

Phenotypic correlation

Examples

data(vardata)
pheno.corr(vardata[3:11],vardata$Genotypes,vardata$Replication)

Phenotypic Path Analysis

Description

Phenotypic Path Analysis

Usage

pheno.path(dependent.var, independent.var, genotypes, replication)

Arguments

dependent.var

trait to be considered as a dependent variable
independent.var

traits to be connsidered as an independent variables
genotypes

vector containing genotypes
replication

vector containing replicatons

Value

Direct effects, indirect effects and residual

Examples

data(vardata)
pheno.path(vardata[11],vardata[3:10],vardata$Genotypes,vardata$Replication)

Variability Data

Description

The data consists of genotypes, replications and nine traits

Usage

vardata

Format

The data has 11 columns and 120 rows

Genotypes

40 genotypes

Replication

3 replications

DFF

Days to 50 per cent flowering

PH

Plant height

PL

Panicle length

PW

Panicle weight

HI

Harvest index

TW

Test weight

MILL

Milling percentage

HRR

Head rice recovery

GY

Grain Yield