Type: Package
Title: Analysis of Selection Index in Plant Breeding
Version: 1.2.0
Maintainer: Zankrut Goyani <zankrut20@gmail.com>
Description: The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) based on the Fisher's (1936 <doi:10.1111/j.1469-1809.1936.tb02137.x>) "discriminant function" Dabholkar (1999 https://books.google.co.in/books?id=mlFtumAXQ0oC&lpg=PA4&ots=Xgxp1qLuxS&dq=elements%20of%20biometrical%20genetics&lr&pg=PP1#v=onepage&q&f=false). In this package selection index is calculated based on the Smith (1937) selection index method.
License: GPL (≥ 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Depends: R (≥ 2.10)
Imports: utils
URL: https://github.com/zankrut20/selection.index
BugReports: https://github.com/zankrut20/selection.index/issues
Suggests: rmarkdown, markdown, knitr, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2023-09-19 11:43:22 UTC; goyani
Author: Zankrut Goyani [aut, cre, cph]
Repository: CRAN
Date/Publication: 2023-09-19 12:40:04 UTC

Construction of selection indices based on number of character grouping

Description

Construction of selection indices based on number of character grouping

Usage

comb.indices(ncomb, pmat, gmat, wmat, wcol = 1, GAY)

Arguments

ncomb

Number of Characters/Traits group

pmat

Phenotypic Variance-Covariance Matrix

gmat

Genotypic Variance-Covariance Matrix

wmat

Weight Matrix

wcol

Weight column number incase more than one weights, by default its 1

GAY

Genetic Advance of comparative Character/Trait i.e. Yield (Optional argument)

Value

Data frame of all possible selection indices

Examples

gmat<- gen.varcov(seldata[,3:9], seldata[,2], seldata[,1])
pmat<- phen.varcov(seldata[,3:9], seldata[,2], seldata[,1])
wmat<- weight.mat(weight)
comb.indices(ncomb = 1, pmat = pmat, gmat = gmat, wmat = wmat, wcol = 1, GAY = 1.075)

Genetic Advance for PRE

Description

Genetic Advance for PRE

Usage

gen.advance(phen_mat, gen_mat, weight_mat)

Arguments

phen_mat

phenotypic matrix value of desired characters

gen_mat

genotypic matrix value of desired characters

weight_mat

weight matrix value of desired characters

Value

Genetic advance of character or character combinations

Examples

gmat<- gen.varcov(seldata[,3:9], seldata[,2], seldata[,1])
pmat<- phen.varcov(seldata[,3:9], seldata[,2], seldata[,1])
gen.advance(phen_mat = pmat[1,1], gen_mat = gmat[1,1], weight_mat = weight[1,2])

Genotypic Variance-Covariance Analysis

Description

Genotypic Variance-Covariance Analysis

Usage

gen.varcov(data, genotypes, replication)

Arguments

data

traits to be analyzed

genotypes

vector containing genotypes/treatments

replication

vector containing replication

Value

A Genotypic Variance-Covariance Matrix

Examples

gen.varcov(data=seldata[,3:9], genotypes=seldata$treat,replication=seldata$rep)

Mean performance of phenotypic data

Description

Mean performance of phenotypic data

Usage

meanPerformance(data, genotypes, replications)

Arguments

data

data for analysis

genotypes

genotypes vector

replications

replication vector

Value

Dataframe of mean performance analysis

Examples

meanPerformance(data = seldata[, 3:9], genotypes = seldata[, 2], replications = seldata[, 1])

Phenotypic Variance-Covariance Analysis

Description

Phenotypic Variance-Covariance Analysis

Usage

phen.varcov(data, genotypes, replication)

Arguments

data

traits to be analyzed

genotypes

vector containing genotypes/treatments

replication

vector containing replication

Value

A Phenotypic Variance-Covariance Matrix

Examples

phen.varcov(data=seldata[,3:9], genotypes=seldata$treat,replication=seldata$rep)

Remove trait or trait combination from possible trait combinations of possible Trait combinations

Description

Remove trait or trait combination from possible trait combinations of possible Trait combinations

Usage

rcomb.indices(ncomb, i, pmat, gmat, wmat, wcol = 1, GAY)

Arguments

ncomb

Number of character combination

i

remove trait or trait combination

pmat

Phenotypic Variance Covariance Matrix

gmat

Genotypic Variance Covariance Matrix

wmat

Weight Matrix

wcol

Respective weight column number of Weight Matrix

GAY

Genetic Advance/Genetic Gain of base selection index

Value

Data frame of possible selection indices with per cent relative efficiency and ranking

Examples

gmat<- gen.varcov(seldata[,3:9], seldata[,2], seldata[,1])
pmat<- phen.varcov(seldata[,3:9], seldata[,2], seldata[,1])
rcomb.indices(ncomb = 2, i = 1, pmat = pmat, gmat = gmat, wmat = weight[,2:3], wcol = 1)

Selection Index DataSet

Description

A dataset containing the data of three replications and 48 progenies with 7 different traits.

Usage

data(seldata)

Format

A data frame with 75 rows and 9 columns

Details

Weight dataset

Description

A dataset containing the data of 2 different weights namely euqal weight and broad sense heritability

Usage

data(weight)

Format

A data frame with 7 rows and 3 columns

Details

Convert dataframe to matrix

Description

Convert dataframe to matrix

Usage

weight.mat(data)

Arguments

data

dataframe of weight

Value

A matrix

Examples

weight.mat(data = weight)