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metan (version 1.15.0)

Multi Environment Trials Analysis

Description

Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) , Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) , geometric adaptability index by Mohammadi & Amri (2008) , joint regression analysis by Eberhart & Russel (1966) , genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) , scale-adjusted coefficient of variation by Doring & Reckling (2018) , stability variance by Shukla (1972) , weighted average of absolute scores by Olivoto et al. (2019a) , and multi-trait stability index by Olivoto et al. (2019b) . Non-parametric methods includes superiority index by Lin & Binns (1988) , nonparametric measures of phenotypic stability by Huehn (1990) , TOP third statistic by Fox et al. (1990) . Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.

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Install

install.packages('metan')

Monthly Downloads

3,307

Version

1.15.0

License

GPL-3

Issues

Pull Requests

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Repository

https://github.com/TiagoOlivoto/metan

Maintainer

Tiago Olivoto

Last Published

July 15th, 2021

Functions in metan (1.15.0)

Huehn's stability statistics

Adjusted Coefficient of Variation

AMMI-based stability indexes

Shukla's stability variance parameter

Thennarasu's stability statistics

Schmildt's genotypic confidence index

Fox's stability function

Smith-Hazel index

Annicchiarico's genotypic confidence index

Stability indexes based on a mixed-effect model

Data from an alpha lattice design

Collinearity Diagnostics

Pairwise combinations of variables

Canonical correlation analysis

Single maize trial

Bind cross-validation objects

Fast way to create bar plots

Multi-environment trial of oat

Multi-environment trial of maize

Arrange separate ggplots into the same graphic

Genotype analysis by fixed-effect models

Within-environment analysis of variance

Geometric adaptability index

Clustering analysis

Cluster genotypes or environments

Adjusted Coefficient of Variation as yield stability index

Coerce to an object of class lpcor

Computes Pearson's correlation matrix with p-values

Confidence interval for correlation coefficient

Correlation between stability indexes

Joint analysis of variance

Alternative to dplyr::do for doing anything

Genotype-environment winners

Generate normal, correlated variables

Variance-covariance matrices for designed experiments

correlated_vars

Generate correlated variables

Stability analysis based on Wricke's model

Genotype-environment analysis by mixed-effect models

Eberhart and Russell's regression model

Cross-validation procedure

Genotype analysis by mixed-effect models

Cross-validation procedure

Cross-validation procedure

Visualization of a correlation matrix

coincidence_index

Computes the coincidence index of genotype selection

Statistics for genotype-vs-environment interaction

Data for examples

Sample size planning for a desired Pearson's correlation confidence interval

Genotype-environment means

Generate a covariance matrix

Simulate genotype and genotype-environment data

Stability analysis and environment stratification

Get data from a model easily

Find possible outliers in a dataset

Details for genotype-environment trials

Multi-trait selection index

Genotype plus genotype-by-environment model

non_collinear_vars

Select a set of predictors with minimal multicollinearity

Coerce to an object of class lpcor

env_dissimilarity

Dissimilarity between environments

Descriptive statistics

env_stratification

Environment stratification

Genotype by trait biplot

Power Law Residuals as yield stability index

Genotype-environment effects

Graphical analysis of genotype-vs-environment interaction

Data for examples

Mantel test for a set of correlation matrices

Get a distance matrix

Multi-Environment Trial Analysis

is_balanced_trial

Check if a data set is balanced

Path coefficients with minimal multicollinearity

Genotype by yield*trait biplot

Plots an object of class can_cor

plot.clustering

Plot an object of class clustering

Additive Main effects and Multiplicative Interaction

Plot the multi-trait stability index

impute_missing_val

Missing value imputation

Several types of residual plots

Make a two-way table

Check for common errors in multi-environment trial data

Plot heat maps with genotype ranking

print.Thennarasu

Print an object ofclass Thennarasu

Multi-trait mean performance and stability index

Two-way table to a 'long' format

Multi-trait stability index

Mahalanobis distance from designed experiments

print.anova_ind

Print an object of class anova_ind

Create a correlation heat map

Fast way to create line plots

plot.correlated_vars

Plot an object of class correlated_vars

Print an object of class waas

plot.performs_ammi

Several types of residual plots

Plot the response surface model

Plot the Smith-Hazel index

print.waas_means

Print an object of class waas_means

plot.anova_joint

Several types of residual plots

plot.cvalidation

Plot the RMSPD of a cross-validation procedure

Several types of residual plots

plot.env_dissimilarity

Plot an object of class env_dissimilarity

Print an object of class Schmildt

Generate significance stars from p-values

Split a data frame by factors

Plot the multi-trait stability index

plot.ge_factanal

Plot the ge_factanal model

Predict method for waasb fits

Predict the means of a waas object

Linear and Partial Correlation Coefficients

Multitrait Genotype-Ideotype Distance Index

Print an object of class Shukla

Print an object of class can_cor

print.anova_joint

Print an object of class anova_joint

print.path_coeff

Print an object of class path_coeff

Mahalanobis Distance

Weighted Average of Absolute Scores

Weighted Average of Absolute Scores

Mean performance and stability in multi-environment trials

Plot an object of class ge_reg

Plot WAASBY values for genotype ranking

print.performs_ammi

Print an object of class performs_ammi

print.corr_coef

Print an object of class corr_coef

Several types of residual plots

Several types of residual plots

Several types of residual plots

Predict method for gamem fits

print.AMMI_indexes

Print an object of class AMMI_indexes

print.Annicchiarico

Print an object of class Annicchiarico

Print the partial correlation coefficients

Create GGE, GT or GYT biplots

Plot the multi-trait genotype-ideotype distance index

plot.env_stratification

Plot the env_stratification model

Response surface model

Personalized theme for ggplot2-based graphics

Lin e Binns' superiority index

print.ecovalence

Print an object of class ecovalence

Utilities for data Copy-Pasta

Print an object of class mgidi Print a mgidi object in two ways. By default, the results are shown in the R console. The results can also be exported to the directory.

Plot the eigenvalues

Plot scores in different graphical interpretations

Print an object of class sh

Predict a two-way table based on GGE model

Print an object of class gamem

print.ge_factanal

Print an object of class ge_factanal

plot.ge_cluster

Plot an object of class ge_cluster

Multi-trait selection index

plot.ge_effects

Plot an object of class ge_effects

Plot the BLUPs for genotypes

Print an object of class waasb

predict.performs_ammi

Predict the means of a performs_ammi object

Objects exported from other packages

print.superiority

Print an object ofclass superiority

Tukey Honest Significant Differences

Utilities for data organization

Transpose a data frame

print.env_dissimilarity

Print an object of class env_dissimilarity

Print an object of class Fox

Tidy eval helpers

Plot the confidence interval for correlation

Print an object ofclass Huehn

print.env_stratification

Print the env_stratification model

Useful functions for computing descriptive statistics

Utilities for handling with numbers and strings

Selects a best subset of predictor variables.

print.colindiag

Print an object of class colindiag

Print an object of class ge_stats

Pseudoinverse of a square matrix

print.coincidence

Print an object of class coincidence

Print an object of class ge_reg

utils_rows_cols

Utilities for handling with rows and columns

Utilities for text progress bar in the terminal

Print an object of class mtsi

Rescale a variable to have specified minimum and maximum values

Reorder a correlation matrix

Print an object of class mtmps

Utilities for set operations for many sets

Encode variables to a specific format

Weighting between stability and mean performance

Weighted Average of Absolute Scores

Helper function for binding data

Utilities for handling with classes

Utilities for handling with matrices

Draw Venn diagrams

Utilities for handling with NA and zero values