Package 'stability'

Title: Stability Analysis of Genotype by Environment Interaction (GEI)
Description: Functionalities to perform Stability Analysis of Genotype by Environment Interaction (GEI) to identify superior and stable genotypes under diverse environments. It performs Eberhart & Russel's ANOVA (1966) (<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson (1963) Joint Linear Regression (<doi:10.1071/AR9630742>), Wricke (1962, 1964) Ecovalence, Shukla's stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>) and Kang's (1991) (<doi:10.2134/agronj1991.00021962008300010037x>) simultaneous selection for high yielding and stable parameter.
Authors: Muhammad Yaseen [aut, cre], Kent M. Eskridge [aut, ctb], Ghulam Murtaza [aut, ctb]
Maintainer: Muhammad Yaseen <[email protected]>
License: GPL-2
Version: 0.5.0
Built: 2025-03-08 02:29:45 UTC
Source: https://github.com/myaseen208/stability

Help Index

Additive ANOVA for Genotypes by Environment Interaction (GEI) model

Description

Additive ANOVA for Genotypes by Environment Interaction (GEI) model

Usage

add_anova(.data, .y, .rep, .gen, .env)

## Default S3 method:
add_anova(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Additive ANOVA

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
YieldANOVA <-
     add_anova(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
      )
YieldANOVA

Additive Main Effects and Multiplicative Interaction (AMMI)

Description

Performs Additive Main Effects and Multiplicative Interaction (AMMI) Analysis for Genotypes by Environment Interaction (GEI)

Usage

ammi(.data, .y, .rep, .gen, .env)

## Default S3 method:
ammi(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Stability Measures

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
Yield.ammi <-
     ammi(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
      )
Yield.ammi

Additive Main Effects and Multiplicative Interaction (AMMI) Biplot

Description

Plots Additive Main Effects and Multiplicative Interaction (AMMI) for Genotypes by Environment Interaction (GEI)

Usage

ammi_biplot(.data, .y, .rep, .gen, .env)

## Default S3 method:
ammi_biplot(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Stability Measures

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
     ammi_biplot(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
      )

Eberhart & Russel’s Model ANOVA

Description

ANOVA of Eberhart & Russel’s Model

Usage

er_anova(.data, .y, .rep, .gen, .env)

## Default S3 method:
er_anova(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Additive ANOVA

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
Yield.er_anova <-
         er_anova(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
          )
Yield.er_anova

Data for Genotypes by Environment Interaction (GEI)

Description

ge_data is used for performing Genotypes by Environment Interaction (GEI) Analysis.

Usage

data(ge_data)

Format

A data.frame 1320 obs. of 6 variables.

Details

  • Gen Genotype

  • Institute Institute

  • Rep Replicate

  • Block Block

  • Env Environment

  • Yield Yield Response

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)

Genotype by Environment Interaction Effects

Description

Calcuates Genotype by Environment Interaction Effects

Usage

ge_effects(.data, .y, .gen, .env)

## Default S3 method:
ge_effects(.data, .y, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.gen

Genotypes Factor
.env

Environment Factor

Value

Effects

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
Yield.Effects <-
              ge_effects(
                .data  = ge_data
               , .y    = Yield
               , .gen  = Gen
               , .env  = Env
               )
names(Yield.Effects)

Yield.Effects$ge_means
Yield.Effects$ge_effects
Yield.Effects$gge_effects

Genotype by Environment Interaction Means and Ranks

Description

Calcuates Genotype by Environment Interaction Means along with their Ranks

Usage

ge_means(.data, .y, .gen, .env)

## Default S3 method:
ge_means(.data, .y, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.gen

Genotypes Factor
.env

Environment Factor

Value

Means and Ranks

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)

Yield.ge_means <-
          ge_means(
                .data  = ge_data
               , .y    = Yield
               , .gen  = Gen
               , .env  = Env
               )

Yield.ge_means$ge_means
Yield.ge_means$ge_ranks
Yield.ge_means$g_means
Yield.ge_means$e_means

Genotype plus Genotypes by Environment (GGE) Interaction Biplot

Description

Plots Genotype plus Genotypes by Environment (GGE) Interaction Biplot for Genotypes by Environment Interaction (GEI)

Usage

gge_biplot(.data, .y, .rep, .gen, .env)

## Default S3 method:
gge_biplot(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Stability Measures

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
     gge_biplot(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
      )

Individual ANOVA for Each Environment

Description

Individual ANOVA for Each Environment

Usage

## Default S3 method:
indiv_anova(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Additive ANOVA

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

  3. Ghulam Murtaza ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
Yield.indiv_anova <-
         indiv_anova(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
          )
Yield.indiv_anova

Additive Main Effects and Multiplicative Interacion Stability Value

Description

Additive ANOVA for Genotypes by Environment Interaction (GEI) model

Usage

stab_asv(.data, .y, .rep, .gen, .env)

## Default S3 method:
stab_asv(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Additive ANOVA

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
YieldASV <-
     stab_asv(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
      )
YieldASV

Stability Distance in AMMI

Description

Stability Distance of Genotypes in Additive ANOVA for Genotypes by Environment Interaction (GEI) model

Usage

stab_dist(.data, .y, .rep, .gen, .env, .m = 2)

## Default S3 method:
stab_dist(.data, .y, .rep, .gen, .env, .m = 2)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor
.m

No of PCs retained

Value

Stability Distance

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

Examples

data(ge_data)
YieldDist <-
     stab_dist(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
          , .m    = 2
      )
YieldDist

Stability Fox Function

Description

Performs a stability analysis based on the criteria of Fox et al. (1990), using the statistical "TOP third" only. In Fox function, a stratified ranking of the genotypes at each environment separately is done. The proportion of locations at which the genotype occurred in the top third are expressed in TOP output.

Usage

stab_fox(.data, .y, .rep, .gen, .env)

## Default S3 method:
stab_fox(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

  1. Fox, P.N. and Skovmand, B. and Thompson, B.K. and Braun, H.J. and Cormier, R. (1990). Yield and adaptation of hexaploid spring triticale. Euphytica, 47, 57-64.

Examples

data(ge_data)
YieldFox <-
     stab_fox(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
      )
YieldFox

Stability Kang Function

Description

Performs a stability analysis based on the Kang (1988) criteria. Kang nonparametric stability (ranksum) uses both "trait single value" and stability variance (Shukla, 1972), and the genotype with the lowest ranksum is commonly the most favorable one.

Usage

stab_kang(.data, .y, .rep, .gen, .env)

## Default S3 method:
stab_kang(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

  1. Kang, M.S. (1988). A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications, 16, 1-2.

  2. Shukla, G.K. (1972). Some aspects of partitioning genotype environmental components of variability. Heredity, 29, 237-245.

Examples

data(ge_data)
YieldKang <-
     stab_kang(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
      )
YieldKang

Modified Additive Main Effects and Multiplicative Interacion Stability Value

Description

Additive ANOVA for Genotypes by Environment Interaction (GEI) model

Usage

stab_masv(.data, .y, .rep, .gen, .env, .m = 2)

## Default S3 method:
stab_masv(.data, .y, .rep, .gen, .env, .m = 2)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor
.m

No of PCs retained

Value

Additive ANOVA

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
YieldMASV <-
     stab_masv(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
          , .m    = 2
      )
YieldMASV

Stability Measures for Genotypes by Environment Interaction (GEI)

Description

Stability Measures for Genotypes by Environment Interaction (GEI)

Usage

stab_measures(.data, .y, .gen, .env)

## Default S3 method:
stab_measures(.data, .y, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.gen

Genotypes Factor
.env

Environment Factor

Value

Stability Measures

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
Yield.StabMeasures <- stab_measures(
                .data  = ge_data
               , .y    = Yield
               , .gen  = Gen
               , .env  = Env
               )
Yield.StabMeasures

Stability Parameters for Genotypes by Environment Interaction (GEI)

Description

Stability Parameters for Genotypes by Environment Interaction (GEI)

Usage

stab_par(.data, .y, .rep, .gen, .env, alpha = 0.1, .envCov = NULL)

## Default S3 method:
stab_par(.data, .y, .rep, .gen, .env, alpha = 0.1,
  .envCov = NULL)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor
alpha

Level of Significance, default is 0.1
.envCov

Environmental Covariate, default is NULL

Value

Stability Parameters

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
Yield.StabPar <-
   stab_par(
            .data   = ge_data
          , .y      = Yield
          , .rep    = Rep
          , .gen    = Gen
          , .env    = Env
          , alpha   = 0.1
          , .envCov = NULL
)

Yield.StabPar

Individual Regression for each Genotype

Description

Individual Regression for each Genotype in Genotypes by Environment Interaction (GEI)

Usage

stab_reg(.data, .y, .rep, .gen, .env)

## Default S3 method:
stab_reg(.data, .y, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor

Value

Additive ANOVA

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples

data(ge_data)
Yield.StabReg <-
        stab_reg(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
          )

Yield.StabReg

Stability Analysis of Genotype by Environment Interaction (GEI)

Description

The stability package provides functionalities to perform Stability Analysis of Genotype by Environment Interaction (GEI) to identify superior and stable genotypes under diverse environments. It performs Eberhart & Russel's ANOVA (1966), Finlay and Wilkinson (1963) Joint Linear Regression, Wricke (1962, 1964) Ecovalence, Shukla's stability variance parameter (1972) and Kang's (1991) simultaneous selection for high yielding and stable parameter.

Author(s)

  1. Muhammad Yaseen ([email protected])

  2. Kent M. Edkridge ([email protected])

  3. Ghulam Murtaza ([email protected])