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metan (version 1.5.0)

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) , Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) , joint Regression Analysis by Eberhart & Russel (1966) (), ecovalence by Wricke (1965), genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method , 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) , geometric adaptability index described by Shahbazi (2019) . 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.packages('metan')

Monthly Downloads

3,307

Version

1.5.0

License

GPL-3

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Repository

https://github.com/TiagoOlivoto/metan

Maintainer

Tiago Olivoto

Last Published

April 12th, 2020

Functions in metan (1.5.0)

Joint analysis of variance

Arrange multiple ggplot2 graphics in a single image window

AMMI-based stability indexes

Resende_indexes

Stability indexes based on a mixed-effect model

Annicchiarico's genotypic confidence index

Clustering analysis

Fox's stability function

Huehn's stability statistics

Data from an alpha lattice design

Schmildt's genotypic confidence index

Multi-trait selection index

Collinearity Diagnostics

Fast way to create bar plots

Coerce to an object of class lpcor

Find possible outliers in a dataset

Graphical analysis of genotype-vs-environment interaction

Genotype-environment winners

Variance-covariance matrices for designed experiments

Genotype-environment means

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

Bind cross-validation objects

Single maize trial

Get data from a model easily

Cross-validation procedure

Two-way table to a 'long' format

Mahalanobis distance from designed experiments

Thennarasu's stability statistics

Mantel test for a set of correlation matrices

Geometric adaptability index

Genotype analysis by fixed-effect models

Genotype-environment analysis by mixed-effect models

Plot the multi-trait genotype-ideotype distance index

Plot the multi-trait stability index

Genotype analysis by mixed-effect models

Path coefficients with minimal multicollinearity

Genotype plus genotype-by-environment model

Within-environment analysis of variance

Pairwise combinations of variables

Genotype by trait biplot

Multi-environment trial of oat

Cluster genotypes or environments

Correlation between stability indexes

Cross-validation procedure

Canonical correlation analysis

Cross-validation procedure

Confidence interval for correlation coefficient

Make a two-way table

Multi-environment trial of maize

Details for genotype-environment trials

Data for examples

Additive Main effects and Multiplicative Interaction

Multi-Environment Trial Analysis

Check for common errors in multi-environment trial data

Genotype-Ideotype Distance Index

impute_missing_val

Missing value imputation

env_dissimilarity

Dissimilarity between environments

Computes Pearson's correlation matrix with p-values

Stability analysis based on Wricke's model

Visualization of a correlation matrix

Eberhart and Russell's regression model

plot.cvalidation

Plot the RMSPD of a cross-validation procedure

Several types of residual plots

Plot the confidence interval for correlation

Multi-trait selection index

plot.env_dissimilarity

Plot an object of class env_dissimilarity

Plot scores in different graphical interpretations

Plot the eigenvalues

Statistics for genotype-vs-environment interaction

Coerce to an object of class lpcor

Data for examples

Mahalanobis Distance

plot.clustering

Plot an object of class clustering

Create a correlation heat map

Linear and Partial Correlation Coefficients

plot.ge_cluster

Plot an object of class ge_cluster

Several types of residual plots

Plot WAASBY values for genotype ranking

Alternative to dplyr::do for doing anything

Descriptive statistics

predict.performs_ammi

Predict the means of a performs_ammi object

Predict the means of a waas object

Plot an object of class ge_reg

Genotype-environment effects

plot.performs_ammi

Several types of residual plots

Print an object of class Shukla

print.AMMI_indexes

Print an object of class AMMI_indexes

Predict method for waasb fits

print.anova_ind

Print an object of class anova_ind

print.anova_joint

Print an object of class anova_joint

print.ge_factanal

Print an object of class ge_factanal

is_balanced_trial

Check if a data set is balanced

Stability analysis and environment stratification

Rescale a variable to have specified minimum and maximum values

Utilities for handling with NA values

Print an object of class ge_reg

Reorder a correlation matrix

Create GGE biplots

Utilities for handling with numbers and strings

Fast way to create line plots

Weighted Average of Absolute Scores

Predict method for gamem fits

Plot heat maps with genotype ranking

Plot the BLUPs for genotypes

Weighting between stability and mean performance

Multi-trait stability index

print.Thennarasu

Print an object ofclass Thennarasu

non_collinear_vars

Select a set of predictors with minimal multicollinearity

plot.anova_joint

Several types of residual plots

Plots an object of class can_cor

plot.ge_effects

Plot an object of class ge_effects

Plot the response surface model

print.superiority

Print an object ofclass superiority

plot.ge_factanal

Plot the ge_factanal model

Print an object of class waas

Several types of residual plots

Several types of residual plots

print.Annicchiarico

Print an object of class Annicchiarico

Print an object ofclass Huehn

Print an object of class Schmildt

Predict a two-way table based on GGE model

Print an object of class Fox

Print an object of class can_cor

print.corr_coef

Print an object of class corr_coef

Pseudoinverse of a square matrix

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.

Combines data.frames by row filling missing values

Print an object of class mtsi

Split a data frame by factors

print.ecovalence

Print an object of class ecovalence

Objects exported from other packages

Generate significance stars from p-values

Encode variables to a factor

Lin e Binns' superiority index

Weighted Average of Absolute Scores

Weighted Average of Absolute Scores

print.colindiag

Print an object of class colindiag

Print an object of class ge_stats

Print an object of class gamem

print.env_dissimilarity

Print an object of class env_dissimilarity

Print the partial correlation coefficients

print.path_coeff

Print an object of class path_coeff

Several types of residual plots

Tukey Honest Significant Differences

print.performs_ammi

Print an object of class performs_ammi

utils_rows_cols

Utilities for handling with rows and columns

print.waas_means

Print an object of class waas_means

Useful functions for computing descriptive statistics

Response surface model

Utilities for handling with classes

Utilities for handling with matrices

Personalized theme for ggplot2-based graphics

Print an object of class waasb

Tidy eval helpers

Shukla's stability variance parameter