---
title: "How to Start"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{How to Start}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Identify the Experimental Design

The function `check_design_met` helps us to check the quality of the data and
also to identify the experimental design of the trials. This works as a quality
check or quality control before we fit any model.

```{r setup}
library(agriutilities)
library(agridat)
data(besag.met)
dat <- besag.met
results <- check_design_met(
  data = dat,
  genotype = "gen",
  trial = "county",
  traits = "yield",
  rep = "rep",
  block = "block",
  col = "col",
  row = "row"
)
```

```{r}
print(results)
```

# Single Trial Analysis
The results of the previous function are used in `single_trial_analysis()` to 
fit single trial models.

```{r}
obj <- single_trial_analysis(results, progress = FALSE)
print(obj)
```

# Multi-Environmental Trial Analysis
The results of the previous function are used in `met_analysis()` to 
fit multi-environmental trial models.

```{r, eval=FALSE}
met_results <- met_analysis(obj)
print(met_results)
```


```{r, echo=FALSE}
if (requireNamespace("asreml", quietly = TRUE)) {
  met_results <- met_analysis(obj)
  print(met_results)
}
```
