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

cv_ammi()

Cross-validation procedure

cv_ammif()

Cross-validation procedure

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(<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(<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

gamem_met()

Genotype-environment analysis by mixed-effect models

Resende_indexes()

Stability indexes based on a mixed-effect model

waasb()

Weighted Average of Absolute Scores

wsmp()

Weighting between stability and mean performance

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(<waasb>)

Predict method for waasb fits

print(<waasb>)

Print an object of class waasb

GGE

Functions for GGE analysis

gge()

Genotype plus genotype-by-environment model

gtb()

Genotype by trait biplot

plot(<gge>)

Create GGE biplots

predict(<gge>)

Predict a two-way table based on GGE model

Selection indexes

Selection indexes and simultaneous selection for mean performance and stability

fai_blup()

Multi-trait selection index

mtsi()

Multi-trait stability index

mgidi()

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

print(<mtsi>)

Print an object of class mtsi

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

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_winners()

Genotype-environment winners

is_balanced_trial()

Check if a data set is balanced

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

ge_factanal()

Stability analysis and environment stratification

ge_reg()

Eberhart and Russell's regression model

ge_stats()

Statistics for genotype-vs-environment interaction

gai()

Geometric adaptability index

plot(<anova_joint>)

Several types of residual plots

plot(<env_dissimilarity>)

Plot an object of class env_dissimilarity

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(<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

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

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

covcor_design()

Variance-covariance matrices for designed experiments

is.lpcor()

Coerce to an object of class lpcor

lpcor()

Linear and Partial Correlation Coefficients

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

print(<corr_coef>)

Print an object of class corr_coef

print(<lpcor>)

Print the partial correlation coefficients

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()

Clustering analysis

mahala()

Mahalanobis Distance

mahala_design()

Mahalanobis distance from designed experiments

plot(<clustering>)

Plot an object of class clustering

colindiag()

Collinearity Diagnostics

non_collinear_vars()

Select a set of predictors with minimal multicollinearity

path_coeff()

Path coefficients with minimal multicollinearity

print(<colindiag>)

Print an object of class colindiag

print(<path_coeff>)

Print an object of class path_coeff

Plot two-way data

Create bar or line plots for two-way data quickly

plot_factbars()

Fast way to create a bar plot

plot_factlines()

Fast way to create a line plot

plot_lines()

Fast way to create a line plot

plot(<resp_surf>)

Plot the response surface model

resp_surf()

Response surface model

Descriptive

Useful functions for computing descriptive statistics

desc_stat() desc_wider()

Descriptive statistics

find_outliers()

Find possible outliers in a dataset

inspect()

Check for common erros in multi-environment trial data

remove_rows_na() remove_cols_na() select_cols_na() select_rows_na() has_na() replace_na() random_na()

Utilities for handling with NA values

av_dev() ci_mean() cv() freq_table() hmean() hm_mean() gmean() gm_mean() kurt() range_data() sd_amo() sd_pop() sem() skew() sum_dev() sum_sq_dev() var_pop() var_amo() valid_n() cv_by() max_by() means_by() min_by() n_by() sd_by() sem_by()

Useful functions for computing descriptive statistics

Data manipulation

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

all_upper_case() all_lower_case() all_title_case() extract_number() extract_string() find_text_in_num() has_text_in_num() remove_space() remove_strings() replace_number() replace_string() round_cols() tidy_strings()

Utilities for handling with numbers and strings

add_cols() add_rows() all_pairs() colnames_to_lower() colnames_to_upper() colnames_to_title() column_to_first() column_to_last() column_exists() concatenate() get_levels() get_level_size() reorder_cols() remove_cols() remove_rows() select_first_col() select_last_col() select_numeric_cols() select_non_numeric_cols() select_cols() select_rows()

Utilities for handling with rows and columns

make_upper_tri() make_lower_tri() 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

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

add_class() has_class() remove_class() set_class()

Utilities for handling with classes

arrange_ggplot()

Arrange multiple ggplot2 graphics in a single image window

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()

Get data from a model easily

metan-package

Multi-Environment Trial Analysis

rbind_fill()

Combines data.frames by row filling missing values

resca()

Rescale a variable to have specified minimum and maximum values

residual_plots()

Several types of residual plots

stars_pval()

Generate significance stars from p-values

to_factor()

Encode variables to a factor

theme_metan() theme_metan_minimal() transparent_color() alpha_color()

Personalized theme for ggplot2-based graphics

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