metan

Package for multi-environment trial analysis

https://github.com/tiagoolivoto/metan

Keywords

Repository

Package for multi-environment trial analysis

README.Rmd

---
always_allow_html: yes
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#",
  fig.path = "man/figures/README-"
)

```

# metan 



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[](https://zenodo.org/badge/latestdoi/130062661)




`metan` (**m**ulti-**e**nvironment **t**rials **an**alysis) provides useful functions for analyzing multi-environment trial data using parametric and non-parametric methods. The package will help you to:

-   [Inspect](https://tiagoolivoto.github.io/metan/reference/inspect.html) data for possible common errors;
-   Manipulate [rows and columns](https://tiagoolivoto.github.io/metan/reference/utils_rows_cols.html);
-   Manipulate [numbers and strings](https://tiagoolivoto.github.io/metan/reference/utils_num_str.html);
-   Manipulate [`NA`s and `0`s](https://tiagoolivoto.github.io/metan/reference/utils_na_zero.html);
-   Compute [descriptive statistics](https://tiagoolivoto.github.io/metan/reference/desc_stat.html);
-   Compute [within-environment](https://tiagoolivoto.github.io/metan/reference/anova_ind.html) and [joint-analysis of variance](https://tiagoolivoto.github.io/metan/reference/anova_joint.html);
-   Compute [AMMI analysis](https://tiagoolivoto.github.io/metan/reference/performs_ammi.html) with prediction considering different numbers of interaction principal component axes;
-   Compute [AMMI-based stability indexes](https://tiagoolivoto.github.io/metan/reference/ammi_indexes.html);
-   Compute [GGE biplot analysis](https://tiagoolivoto.github.io/metan/reference/gge.html);
-   Compute [GT](https://tiagoolivoto.github.io/metan/reference/gtb.html) and [GYT](https://tiagoolivoto.github.io/metan/reference/gytb.html) biplot analysis;
-   Compute [BLUP-based stability indexes](https://tiagoolivoto.github.io/metan/reference/blup_indexes.html);
-   Compute variance components and genetic parameters in [single environment](https://tiagoolivoto.github.io/metan/reference/gamem.html) and [multi-environment](https://tiagoolivoto.github.io/metan/reference/gamem_met.html) trials using mixed-effect models;
-   Perform cross-validation procedures for [AMMI-family](https://tiagoolivoto.github.io/metan/reference/cv_ammif.html) and [BLUP](https://tiagoolivoto.github.io/metan/reference/cv_blup.html) models;
-   Compute parametric and nonparametric [stability statistics](https://tiagoolivoto.github.io/metan/reference/ge_stats.html);
-   Implement [biometrical models](https://tiagoolivoto.github.io/metan/reference/index.html#section-biometry).

# Installation

Install the released version of `metan` from [CRAN](https://CRAN.R-project.org/package=metan) with:

```{r, eval=FALSE}
install.packages("metan")

```

Or install the development version from [GitHub](https://github.com/TiagoOlivoto/metan) with:

```{r, eval=FALSE}
devtools::install_github("TiagoOlivoto/metan")

# To build the HTML vignette use
devtools::install_github("TiagoOlivoto/metan", build_vignettes = TRUE)

```

*Note*: If you are a Windows user, you should also first download and install the latest version of [Rtools](https://cran.r-project.org/bin/windows/Rtools/).

For the latest release notes on this development version, see the [NEWS file](https://tiagoolivoto.github.io/metan/news/index.html).

## Cheatsheet



# Getting started

`metan` offers a set of functions that can be used to manipulate, summarize, analyze and plot typical multi-environment trial data. Maybe, one of the first functions users should use would be [`inspect()`](https://tiagoolivoto.github.io/metan/reference/inspect.html). Here, we will inspect the example dataset `data_ge` that contains data on two variables assessed in 10 genotypes growing in 14 environments.

```{r INSPECT, fig.width=7, fig.height=7, fig.align="center", message=FALSE, warning=FALSE}
library(metan)
inspect(data_ge, plot = TRUE)
```

No issues while inspecting the data. If any issue is given here (like outliers, missing values, etc.) consider using [`find_outliers()`](https://tiagoolivoto.github.io/metan/reference/find_outliers.html) to find possible outliers in the data set or any `metan`'s data manipulation tool such as [remove_rows_na()](https://tiagoolivoto.github.io/metan/reference/utils_na_zero.html) to remove rows with `NA` values, [replace_zero()](https://tiagoolivoto.github.io/metan/reference/utils_na_zero.html) to replace 0's with `NA`, [as_factor()](https://tiagoolivoto.github.io/metan/reference/utils_as.html) to convert desired columns to factor, [`find_text_in_num()`](https://tiagoolivoto.github.io/metan/reference/utils_num_str.html) to find text fragments in columns assumed to be numeric, or even [`tidy_strings()`](https://tiagoolivoto.github.io/metan/reference/utils_num_str.html) to tidy up strings.

# Descriptive statistics

`metan` provides [a set of functions](https://tiagoolivoto.github.io/metan/reference/utils_stats.html) to compute descriptive statistics. The easiest way to do that is by using [`desc_stat()`](https://tiagoolivoto.github.io/metan/reference/desc_stat.html).

```{r}
desc_stat(data_ge2)
```

# AMMI model

## Fitting the model

The AMMI model is fitted with the function [`performs_ammi()`](https://tiagoolivoto.github.io/metan/reference/performs_ammi.html). To analyze multiple variables at once we can use a comma-separated vector of unquoted variable names, or use any select helper in the argument `resp`. Here, using `everything()` we apply the function to all numeric variables in the data. For more details, see the [complete vignette](https://tiagoolivoto.github.io/metan/articles/vignettes_ammi.html).

```{r}
model <- performs_ammi(data_ge,
                       env = ENV,
                       gen = GEN,
                       rep = REP,
                       resp = everything(),
                       verbose = FALSE)
# Significance of IPCAs
get_model_data(model, "ipca_pval")
```

## Biplots

The well-known AMMI1 and AMMI2 biplots can be created with [`plot_scores()`](file:///D:/Desktop/metan/docs/reference/plot_scores.html). Note that since [`performs_ammi`](https://tiagoolivoto.github.io/metan/reference/performs_ammi.html) allows analyzing multiple variables at once, e.g., `resp = c(v1, v2, ...)`, the output `model` is a list, in this case with two elements (GY and HM). By default, the biplots are created for the first variable of the model. To choose another variable use the argument `var` (e.g., `var = "HM"`).


```{r AMMI, fig.width=10, fig.height=5, message=FALSE, warning=FALSE}
a <- plot_scores(model)
b <- plot_scores(model,
                 type = 2, # AMMI 2 biplot
                 polygon = TRUE, # show a polygon
                 highlight = c("G4", "G5", "G6"), #highlight genotypes
                 col.alpha.env = 0.5, # alpha for environments
                 col.alpha.gen = 0, # remove the other genotypes
                 col.env = "gray", # color for environment point
                 col.segm.env = "gray", # color for environment segment
                 plot_theme = theme_metan_minimal()) # theme
arrange_ggplot(a, b, tag_levels = "a")

```

# GGE model

The GGE model is fitted with the function [`gge()`](https://tiagoolivoto.github.io/metan/reference/gge.html). For more details, see the [complete vignette](https://tiagoolivoto.github.io/metan/articles/vignettes_gge.html).

```{r GGE, fig.width=15, fig.height=5, message=FALSE, warning=FALSE}
model <- gge(data_ge, ENV, GEN, GY)
model2 <- gge(data_ge, ENV, GEN, GY, svp = "genotype")
model3 <- gge(data_ge, ENV, GEN, GY, svp = "symmetrical")
a <- plot(model)
b <- plot(model2, type = 8)
c <- plot(model2,
          type = 2,
          col.gen = "black",
          col.env = "gray70",
          axis.expand = 1.5,
          plot_theme = theme_metan_minimal())
arrange_ggplot(a, b, c, tag_levels = "a")

```

# BLUP model

Linear-mixed effect models to predict the response variable in METs are fitted using the function [gamem_met()](https://tiagoolivoto.github.io/metan/reference/gamem_met.html). Here we will obtain the predicted means for genotypes in the variables `GY` and `HM`. For more details, see the [complete vignette](https://tiagoolivoto.github.io/metan/articles/vignettes_blup.html).

```{r }
model2 <- 
  gamem_met(data_ge,
            env = ENV,
            gen = GEN,
            rep = REP,
            resp = everything())
# Get the variance components
get_model_data(model2, what = "vcomp")

```

## Plotting the BLUPs for genotypes

To produce a plot with the predicted means, use the function [`plot_blup()`](https://tiagoolivoto.github.io/metan/reference/plot_blup.html).

```{r BLUP, fig.width=10, fig.height=5, message=FALSE, warning=FALSE}
a <- plot_blup(model2)
b <- plot_blup(model2,
               prob = 0.2,
               col.shape = c("gray20", "gray80"),
               invert = TRUE)
arrange_ggplot(a, b, tag_levels = "a")

```

# Computing parametric and non-parametric stability indexes

The easiest way to compute parametric and non-parametric stability indexes in `metan` is by using the function [`ge_stats()`](https://tiagoolivoto.github.io/metan/reference/ge_stats.html). It is a wrapper function around a lot of specific functions for stability indexes. To get the results into a *"ready-to-read"* file, use [get_model_data()](https://tiagoolivoto.github.io/metan/reference/get_model_data.html) or its shortcut [`gmd()`](https://tiagoolivoto.github.io/metan/reference/get_model_data.html).

```{r}
stats <- ge_stats(data_ge, ENV, GEN, REP, GY)
get_model_data(stats)
```

# Citation

```{r, comment=""}
citation("metan")
```

# Getting help


-   If you encounter a clear bug, please file a minimal reproducible example on [github](https://github.com/TiagoOlivoto/metan/issues)

-   Suggestions and criticisms to improve the quality and usability of the package are welcome!




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