Function reference

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 models

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 models

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(): Linear and partial correlation coefficients

corr_plot(): Visualization of a correlation matrix

corr_focus(): Focus on section 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

network_plot(): Network plot of a correlation matrix

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

acv(): Adjusted Coefficient of Variation

desc_stat() desc_wider(): Descriptive statistics

find_outliers(): Find possible outliers in a dataset

inspect(): Check for common errors in multi-environment trial data

fill_na() has_na() prop_na() remove_rows_na() remove_rows_all_na() remove_cols_na() remove_cols_all_na() select_cols_na() select_rows_na() replace_na() random_na() has_zero() remove_rows_zero() remove_cols_zero() select_cols_zero() select_rows_zero() replace_zero(): Utilities for handling with NA and zero values

av_dev() ci_mean_t() ci_mean_z() cv() freq_table() freq_hist() hmean() gmean() kurt() n_missing() n_unique() n_valid() pseudo_sigma() range_data() row_col_mean() row_col_sum() sd_amo() sd_pop() sem() skew() sum_dev() ave_dev() sum_sq_dev() sum_sq() var_pop() var_amo() cv_by() max_by() min_by() means_by() mean_by() n_by() sd_by() var_by() sem_by() sum_by(): 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

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

difference_var() intersect_var() union_var() width_of() width_greater_than() width_less_than() lower_case_only() upper_case_only() title_case_only(): Select helper

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() get_wd_here() open_wd_here() open_wd(): Set and get 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

sample_random() sample_systematic(): Random Sampling

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

One-way analysis

AMMI

Cross-validation

Fit models

Plot models

Predict models

Print models

BLUP

Fit models

Plot models

Predict models

Print models

GGE

Selection indexes

Genotype-environment interaction

Initial approaches

Parametric methods

Non-parametric methods

Biometry

Correlation coefficient

Canonical correlation coefficient

Clustering analysis

Path analysis

Plot two-way data

Descriptive

Data manipulation

Copy-Paste

Data organization

Coerce variables to a specific type

Numbers and strings

Columns and rows

Matrices

Sets

Progress bar

Select helpers

Other useful functions

Datasets