| Type: | Package |
| Title: | Stability Analysis of Genotype by Environment Interaction (GEI) |
| Version: | 0.6.0 |
| Maintainer: | Muhammad Yaseen <myaseen208@gmail.com> |
| Description: | Provides functionalities for performing stability analysis of genotype by environment interaction (GEI) to identify superior and stable genotypes across diverse environments. It implements Eberhart and Russell’s ANOVA method (1966)(<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson’s Joint Linear Regression method (1963) (<doi:10.1071/AR9630742>), Wricke’s Ecovalence (1962, 1964), Shukla’s stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>), Kang’s simultaneous selection for high yield and stability (1991) (<doi:10.2134/agronj1991.00021962008300010037x>), Additive Main Effects and Multiplicative Interaction (AMMI) method and Genotype plus Genotypes by Environment (GGE) Interaction methods. |
| URL: | https://myaseen208.com/stability/ https://CRAN.R-project.org/package=stability |
| BugReports: | https://github.com/myaseen208/stability/issues |
| Depends: | R (≥ 3.1) |
| Imports: | dplyr, ggplot2, ggfortify, lme4, magrittr, matrixStats, reshape2, rlang, scales, stats, tibble, tidyr |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Note: | 1. School of Mathematical & Statistical Sciences, Clemson University, Clemson, South Carolina, USA 2. Department of Mathematics and Statistics, University of Agriculture Faisalabad, Faisalabad, Pakistan |
| NeedsCompilation: | no |
| Packaged: | 2024-09-28 23:40:31 UTC; myaseen208 |
| Author: | Muhammad Yaseen 
[aut, cre, cph],
Kent M. Eskridge [aut, ctb] |
| Repository: | CRAN |
| Date/Publication: | 2024-09-29 06:30:02 UTC |
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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$gge_effects
Genotype by Environment Interaction Means and Ranks
Description
Calcuates Genotype by Environment Interaction Means along with their Ranks
Usage
ge_means(.data, .y, .gen, .env)
## Default S3 method:
ge_means(.data, .y, .gen, .env)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Value
Means and Ranks
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
References
Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.
Examples
data(ge_data)
Yield.ge_means <-
ge_means(
.data = ge_data
, .y = Yield
, .gen = Gen
, .env = Env
)
Yield.ge_means$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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
Ghulam Murtaza (gmurtaza208@gmail.com)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
References
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
References
Kang, M.S. (1988). A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications, 16, 1-2.
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
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)
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)