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One-way analysis

Analyze genotypes in single environment trials using fixed- or mixed-effect models

gafem()
Genotype analysis by fixed-effect models
gamem()
Genotype analysis by mixed-effect models
plot(<gafem>)
Several types of residual plots
plot(<gamem>)
Several types of residual plots
predict(<gamem>)
Predict method for gamem fits
print(<gamem>)
Print an object of class gamem

AMMI

Functions for AMMI analysis

Cross-validation

cv_ammi()
Cross-validation procedure
cv_ammif()
Cross-validation procedure

Fit models

ammi_indexes() AMMI_indexes()
AMMI-based stability indexes
impute_missing_val()
Missing value imputation
performs_ammi()
Additive Main effects and Multiplicative Interaction
waas()
Weighted Average of Absolute Scores
waas_means()
Weighted Average of Absolute Scores

Plot models

plot(<cvalidation>)
Plot the RMSPD of a cross-validation procedure
plot(<performs_ammi>)
Several types of residual plots
plot(<waas>)
Several types of residual plots

Predict models

predict(<waas>)
Predict the means of a waas object
predict(<performs_ammi>)
Predict the means of a performs_ammi object
print(<ammi_indexes>)
Print an object of class ammi_indexes
print(<performs_ammi>)
Print an object of class performs_ammi
print(<waas>)
Print an object of class waas
print(<waas_means>)
Print an object of class waas_means

BLUP

Analyze genotypes in single- or multi-environment trials using mixed-effect models with variance components and genetic parameter estimation.

cv_blup()
Cross-validation procedure

Fit models

gamem_met()
Genotype-environment analysis by mixed-effect models
hmgv() rpgv() hmrpgv() blup_indexes()
Stability indexes based on a mixed-effect model
waasb()
Weighted Average of Absolute Scores
wsmp()
Weighting between stability and mean performance

Plot models

plot_blup()
Plot the BLUPs for genotypes
plot_eigen()
Plot the eigenvalues
plot_scores()
Plot scores in different graphical interpretations
plot_waasby()
Plot WAASBY values for genotype ranking
plot(<wsmp>)
Plot heat maps with genotype ranking
plot(<waasb>)
Several types of residual plots

Predict models

predict(<waasb>)
Predict method for waasb fits
print(<waasb>)
Print an object of class waasb

GGE

Functions for GGE, GT, and GYT biplot analysis

gge()
Genotype plus genotype-by-environment model
gtb()
Genotype by trait biplot
gytb()
Genotype by yield*trait biplot
plot(<gge>)
Create GGE, GT or GYT biplots
predict(<gge>)
Predict a two-way table based on GGE model

Selection indexes

Indexes for simultaneous selection for mean performance and stability

coincidence_index()
Computes the coincidence index of genotype selection
fai_blup()
Multi-trait selection index
mps()
Mean performance and stability in multi-environment trials
mtmps()
Multi-trait mean performance and stability index
mtsi()
Multi-trait stability index
mgidi()
Multitrait Genotype-Ideotype Distance Index
plot(<fai_blup>)
Multi-trait selection index
plot(<mgidi>)
Plot the multi-trait genotype-ideotype distance index
print(<mgidi>)
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(<mtsi>)
Plot the multi-trait stability index
plot(<mtmps>)
Plot the multi-trait stability index
plot(<sh>)
Plot the Smith-Hazel index
print(<coincidence>)
Print an object of class coincidence
print(<mtsi>)
Print an object of class mtsi
print(<mtmps>)
Print an object of class mtmps
print(<sh>)
Print an object of class sh
Smith_Hazel()
Smith-Hazel index

Genotype-environment interaction

Visualize genotype-environment interaction patterns, rank genotypes within environments, compute genotype, environment, and genotype-environment effects; cluster environments, and compute parametric and non-parametric stability indexes

Initial approaches

anova_ind()
Within-environment analysis of variance
anova_joint()
Joint analysis of variance
ge_cluster()
Cluster genotypes or environments
ge_details()
Details for genotype-environment trials
ge_effects()
Genotype-environment effects
ge_means()
Genotype-environment means
ge_plot()
Graphical analysis of genotype-vs-environment interaction
ge_simula() g_simula()
Simulate genotype and genotype-environment data
ge_winners()
Genotype-environment winners
is_balanced_trial()
Check if a data set is balanced

Parametric methods

Annicchiarico()
Annicchiarico's genotypic confidence index
corr_stab_ind()
Correlation between stability indexes
ecovalence()
Stability analysis based on Wricke's model
env_dissimilarity()
Dissimilarity between environments
env_stratification()
Environment stratification
ge_acv()
Adjusted Coefficient of Variation as yield stability index
ge_factanal()
Stability analysis and environment stratification
ge_polar()
Power Law Residuals as yield stability index
ge_reg()
Eberhart and Russell's regression model
ge_stats()
Parametric and non-parametric stability statistics
gai()
Geometric adaptability index
plot(<anova_joint>)
Several types of residual plots
plot(<env_dissimilarity>)
Plot an object of class env_dissimilarity
plot(<env_stratification>)
Plot the env_stratification model
plot(<ge_cluster>)
Plot an object of class ge_cluster
plot(<ge_effects>)
Plot an object of class ge_effects
plot(<ge_factanal>)
Plot the ge_factanal model
plot(<ge_reg>)
Plot an object of class ge_reg
print(<Annicchiarico>)
Print an object of class Annicchiarico
print(<anova_ind>)
Print an object of class anova_ind
print(<anova_joint>)
Print an object of class anova_joint
print(<ecovalence>)
Print an object of class ecovalence
print(<env_dissimilarity>)
Print an object of class env_dissimilarity
print(<env_stratification>)
Print the env_stratification model
print(<ge_factanal>)
Print an object of class ge_factanal
print(<ge_reg>)
Print an object of class ge_reg
print(<ge_stats>)
Print an object of class ge_stats
print(<Shukla>)
Print an object of class Shukla
print(<Schmildt>)
Print an object of class Schmildt
Schmildt()
Schmildt's genotypic confidence index

Non-parametric methods

Fox()
Fox's stability function
Huehn()
Huehn's stability statistics
print(<Fox>)
Print an object of class Fox
print(<Huehn>)
Print an object ofclass Huehn
print(<superiority>)
Print an object ofclass superiority
print(<Thennarasu>)
Print an object ofclass Thennarasu
Shukla()
Shukla's stability variance parameter
superiority()
Lin e Binns' superiority index
Thennarasu()
Thennarasu's stability statistics

Biometry

Useful functions for biometric models

Correlation coefficient

as.lpcor()
Coerce to an object of class lpcor
corr_coef()
Computes Pearson's correlation matrix with p-values
corr_plot()
Visualization of a correlation matrix
corr_ci()
Confidence interval for correlation coefficient
corr_ss()
Sample size planning for a desired Pearson's correlation confidence interval
correlated_vars()
Generate correlated variables
covcor_design()
Variance-covariance matrices for designed experiments
get_corvars()
Generate normal, correlated variables
get_covmat()
Generate a covariance matrix
is.lpcor()
Coerce to an object of class lpcor
lpcor()
Linear and Partial Correlation Coefficients
mantel_test()
Mantel test
pairs_mantel()
Mantel test for a set of correlation matrices
plot_ci()
Plot the confidence interval for correlation
plot(<corr_coef>)
Create a correlation heat map
plot(<correlated_vars>)
Plot an object of class correlated_vars
print(<corr_coef>)
Print an object of class corr_coef
print(<lpcor>)
Print the partial correlation coefficients

Canonical correlation coefficient

can_corr()
Canonical correlation analysis
plot(<can_cor>)
Plots an object of class can_cor
print(<can_cor>)
Print an object of class can_cor

Clustering analysis

clustering()
Clustering analysis
get_dist()
Get a distance matrix
mahala()
Mahalanobis Distance
mahala_design()
Mahalanobis distance from designed experiments
plot(<clustering>)
Plot an object of class clustering

Path analysis

colindiag()
Collinearity Diagnostics
non_collinear_vars()
Select a set of predictors with minimal multicollinearity
path_coeff() path_coeff_mat() path_coeff_seq()
Path coefficients with minimal multicollinearity
print(<colindiag>)
Print an object of class colindiag
print(<path_coeff>)
Print an object of class path_coeff
plot(<path_coeff>)
Plots an object of class path_coeff
select_pred()
Selects a best subset of predictor variables.

Plot two-way data

Create bar or line plots for two-way data quickly

plot_bars() plot_factbars()
Fast way to create bar plots
plot_lines() plot_factlines()
Fast way to create line plots
plot(<resp_surf>)
Plot the response surface model
resp_surf()
Response surface model

Descriptive

Useful functions for computing descriptive statistics

Data manipulation

Utilities for handling with columns, rows, numbers, strings, and matrices.

Copy-Paste

clip_read() clip_write()
Utilities for data Copy-Pasta

Data organization

add_seq_block() recode_factor() df_to_selegen_54()
Utilities for data organization

Coerce variables to a specific type

as_numeric() as_integer() as_logical() as_character() as_factor()
Encode variables to a specific format

Numbers and strings

Columns and rows

Matrices

make_upper_tri() make_lower_tri() make_lower_upper() make_sym() tidy_sym()
Utilities for handling with matrices
make_long()
Two-way table to a 'long' format
make_mat()
Make a two-way table
reorder_cormat()
Reorder a correlation matrix
solve_svd()
Pseudoinverse of a square matrix

Sets

set_intersect() set_union() set_difference()
Utilities for set operations for many sets
venn_plot()
Draw Venn diagrams

Progress bar

progress() run_progress()
Utilities for text progress bar in the terminal

Select helpers

Other useful functions

add_class() has_class() remove_class() set_class()
Utilities for handling with classes
arrange_ggplot()
Arrange separate ggplots into the same graphic
split_factors() as.split_factors() is.split_factors()
Split a data frame by factors
bind_cv()
Bind cross-validation objects
comb_vars()
Pairwise combinations of variables
doo()
Alternative to dplyr::do for doing anything
get_model_data() gmd() sel_gen()
Get data from a model easily
metan-package
Multi-Environment Trial Analysis
rbind_fill_id()
Helper function for binding rows
resca()
Rescale a variable to have specified minimum and maximum values
residual_plots()
Several types of residual plots
set_wd_here()
Set the Working Directory quicky
stars_pval()
Generate significance stars from p-values
theme_metan() theme_metan_minimal() transparent_color() ggplot_color() alpha_color()
Personalized theme for ggplot2-based graphics
transpose_df()
Transpose a data frame
tukey_hsd()
Tukey Honest Significant Differences

Datasets

Data for reproducible examples

data_alpha
Data from an alpha lattice design
data_g
Single maize trial
data_ge
Multi-environment trial of oat
data_ge2
Multi-environment trial of maize
int.effects
Data for examples
meansGxE
Data for examples