---
title: "Optimized Multi-Location P-rep Design"
output: rmarkdown::html_vignette
bibliography: references.bib
vignette: >
%\VignetteIndexEntry{Optimized Multi-Location P-rep Design}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
options(rmarkdown.html_vignette.check_title = FALSE)
knitr::opts_chunk$set(
collapse = TRUE,
comment = NA,
warning = FALSE,
message = FALSE
)
```
```{r, echo = FALSE}
library(FielDHub)
library(dplyr)
library(knitr)
library(kableExtra)
```
This vignette shows how to generate an **optimized multi-location partially replicated (p-rep) design**
using both the FielDHub Shiny App and the scripting function `multi_location_prep()` from
the `FielDHub` R package.
## Overview
Partially replicated (p-rep) designs are commonly employed in early generation field trials. This type
of design is characterized by replication of a portion of the entries, with the remaining
entries only appearing once in the experiment. Commonly, the part of treatments with reps
is due to an arbitrary decision by the research, also in some cases, it is due to technical
reasons. The replication ratio is typically 1:4 [@Cullis2006-pREP], which means that the portion of treatment
repeated twice is p = 25%. However, the design can be adapted to meet specific needs by adjusting
the values of $p$ and the level of replication. For example, standard varieties (checks) may be
included with higher levels of replication than test lines.
In `FielDHub`, the optimized multi-location p-rep design employs the principles of incomplete
block designs (IBD) to determine the distribution of replicated and non-replicated treatments
across multiple locations.
## Optimization
### Across Location
The function `multi_location_prep()` uses the incomplete blocks design approach [@EdmondsonIBDs]
to optimize the allocation of replicated and un-replicated treatments across the environments.
### Within Location
Each partially replicated (p-rep) design location undergoes an optimization process that involves
the following procedure:
Given a matrix $X$ of integers (p-rep design within location), we want to ensure that the distance between any two occurrences
of the same treatment is at least a distance $d$. More specifically, we want to modify
$X$ to ensure that no treatments appear twice within a distance less than $d$ in the resulting
matrix.
The goal of the optimization process is to find a modified matrix that satisfies this constraint
while maximizing some measure of deviation from the original matrix $X$. In this case, the
measure of deviation is the pairwise Euclidean distance between occurrences of the same treatment.
The process is done by the function `swap_pairs()` that uses a heuristic algorithm that starts
with a distance of $d = 3$ between pairs of occurrences of the same treatment, and
increases this distance by $1$ and repeats the process until either the algorithm no
longer converges or the maximum number of iterations is reached.
The algorithm works by first identifying all pairs of occurrences of the same treatment that
are closer than $d$. For each such pair, the function selects a random occurrence of a different
integer that is at least $d$ away, and swaps the two occurrences. This process is repeated until
no further swaps can be made that increase the pairwise Euclidean distances between occurrences
of the same treatment.
#### Toy Example
Consider a p-rep design where ten treatments are replicated twice and 40 only once. The matrix
(field layout) for this experiment has 6 rows and 10 columns.
$X =$
```{r, echo=FALSE, eval=TRUE}
set.seed(123)
X <- matrix(sample(c(rep(1:10, 2), 11:50), replace = FALSE), ncol = 10)
X[2,2] <- 5
X[2,3] <- 33
print(X)
```
In this initial p-rep design, we notice that the two instances of treatment **5** are positioned
next to each other. Additionally, treatments **7** and **9** are also situated in adjacent cells.
These suboptimal allocations could lead to issues or inaccurate results when analyzing the data
from this experiment due to the short distance between replicated treatments and the likely
spatial correlation between them.
The following table shows the pairwise distances for the replicated treatments
```{r, echo=FALSE, eval=TRUE}
dist_X <- FielDHub:::pairs_distance(X = X)
print(dist_X)
```
#### Swap pairs
We can improve the efficiency of the design by swapping the treatments that are close and
next to each other by using the function `swap_pairs()` from `FielDHub` R package.
```{r}
library(FielDHub)
B <- swap_pairs(X, starting_dist = 3)
```
The new matrix or the optimized p-rep design is,
```{r}
print(B$optim_design)
```
The distances for each pairwise of treatments are,
```{r}
print(B$pairwise_distance)
```
As we can see, the minimum distance that the algorithm reached is `r B$min_distance`. This
means no treatments appear twice within a distance less than `r B$min_distance` in the
resulting prep design. It is a considerable improvement from the first version of the p-rep design
The `FielDHub` function `multi_location_prep()` does internally all the optimization process
and uses the function `swap_pairs()` to maximize the distance between replicated treatments.
## Use case (Multi-Location P-rep Design)
Suppose there is a plant breeding field trial with 150 entries to be tested across five environments,
where up to seven replications of each entry are allowed. Additionally, the project includes three
checks; each replicated six times. We can generate an optimized multi-location partially replicated
design using these parameters. This strategy guarantees that all treatments are present in all
environments but in different amounts of replications.
We can generate this design using the FielDHub Shiny app and the `FielDHub` `multi_location_prep()` standalone function in R.
## Running the Shiny App
To launch the app you need to run either
```{r, eval=FALSE}
FielDHub::run_app()
```
or
```{r, eval=FALSE}
library(FielDHub)
run_app()
```
## 1. Using the FielDHub Shiny App
Once the app is running, click the tab **Partially Replicated Design** and
select **Optimized Multi-Location p-rep** from the dropdown.
Then, follow the following steps where we will show how to generate an optimized
partially replicated design.
## Inputs
1. **Import entries' list?** Choose whether to import a list with entry numbers and names for genotypes
or treatments.
* If the selection is `No`, that means the app is going to generate synthetic data for entries
and names of the treatment/genotypes based on the user inputs.
* If the selection is `Yes`, the entries list must fulfill a specific format and must be a `.csv` file.
The file must have the columns `ENTRY` and `NAME`. The `ENTRY` column must have a unique entry
integer number for each treatment/genotype. The column `NAME` must have a unique name that
identifies each treatment/genotype. Both `ENTRY` and `NAME` must be unique, duplicates
are not allowed. In the following table, we show an example of the entries list format.
```{r, include=FALSE}
ENTRY <- 1:10
NAME <- c(paste0("Genotype", 1:10))
df <- data.frame(ENTRY,NAME)
```
```{r, echo = FALSE, results='asis'}
df |>
kbl() |>
kable_styling()
```
2. Enter the number of entries in the **Input # of Entries** box as a comma separated list.
In our example we will have 150 entries, so we enter 150 in the box for our sample experiment.
3. Select whether or not the experiment will contain checks under the **Include checks?** option.
The example experiment does, so set this to `Yes`.
4. Once we select `Yes` on the above option, two more boxes appear, the first being **Input # of Checks**
where we set how many checks to include in the experiment. In our case this is 3.
5. Next to this option we have **Input # Check's Reps**, where we set the number of replications for
each check respectively in a comma separated list. We are replicating each of the 3 checks 6 times,
so enter `6,6,6` in this box.
6. Enter the number of locations in **Input # of Locations**. We will run this experiment over 5 locations,
so set **Input # of Locations** to 5.
7. Set the total number of replications of the entries over all locations in the `# of Copies Per Entry`
dropdown box. For this example experiment, set this to 7.
8. Select `serpentine` or `cartesian` in the **Plot Order Layout**. For this example we will use the
default `serpentine` layout.
9. To ensure that randomizations are consistent across sessions, we can set a random seed in the
box labeled **Random Seed**. In this example, we will set it to `2456`.
10. (Optional) Enter the starting plot number in the **Starting Plot Number** box. Since the experiment
has multiple locations, you must enter a comma separated list of numbers the length of the number of
locations for the input to be valid. In this example, we will set it as `1,1001,2001,3001,4001`.
11. (Optional) Enter the location names in the **Input Location Name** box. Since the experiment has six
locations, you must enter a comma separated list of strings for the names of the environments. In this
example, we will set it as `LOC1,LOC2,LOC3,LOC4,LOC5`.
Once we have entered the information for our experiment on the left side panel, click the **Run!**
button to run the design. You will then be prompted to select the dimensions of the field from the list
of options in the dropdown in the middle of the screen with the box labeled **Select dimensions of field**.
In our case, we will select `12 x 19`. Click the **Randomize!** button to randomize the experiment with the
set field dimensions and to see the output plots. If you change the dimensions again, you must re-randomize.
If you change any of the inputs on the left side panel after running an experiment initially,
you have to click the Run and Randomize buttons again, to re-run with the new inputs.
## Outputs
After you run a `Optimized Multi-Location P-rep Design` in FielDHub and set the dimensions of the field, there
are several ways to display the information contained in the field book. The first tab, **Get Random**,
shows the option to change the dimensions of the field and re-randomize, as well as the genotype
allocation matrix generated for the optimized p-rep design, which displays the replications of each
genotype over each location, much like the matrix generated in sparse allocation.
### Randomized Field
The **Randomized Field** tab displays a graphical representation of the randomization of the
entries in a field of the specified dimensions. The replicated entries are the green colored
cells, with the which cells appearing only once in the location. The display includes numbered
labels for the rows and columns. You can copy the field as a table or save it directly as an
Excel file with the _Copy_ and _Excel_ buttons at the top.
### Plot Number Field
On the **Plot Number Field** tab, there is a table display of the field with the plots numbered
according to the Plot Order Layout specified, either _serpentine_ or _cartesian_. You can see
the corresponding entries for each plot number in the field book. Like the Randomized Field tab,
you can copy the table or save it as an Excel file with the _Copy_ and _Excel_ buttons.
### Field Book
The **Field Book** displays all the information on the experimental design in a table format.
It contains the specific plot number and the row and column address of each entry, as well as
the corresponding treatment/genotype on that plot. This table is searchable, and we can filter the data
in relevant columns.
## 2. Using the `FielDHub` function: `multi_location_prep()`.
You can run the same design with the function `multi_location_prep()` in the `FielDHub` package.
First, you need to load the `FielDHub` package typing,
```{r, echo = TRUE}
library(FielDHub)
```
Then, you can enter the information describing the above design like this:
```{r, echo = TRUE}
optim_multi_prep <- multi_location_prep(
lines = 150,
l = 5,
copies_per_entry = 7,
checks = 3,
rep_checks = c(6,6,6),
plotNumber = c(1,1001,2001,3001,4001),
locationNames = c("LOC1", "LOC2", "LOC3", "LOC4", "LOC5"),
seed = 2456
)
```
#### Details on the inputs entered in `multi_location_prep()` above
The description for the inputs that we used to generate the design,
* `lines = 150` is the number of entries in the field.
* `l = 5` is the number of locations.
* `copies_per_entry = 7` is the number of copies of each entry.
* `checks = 3` is the (optional) number of checks.
* `rep_checks = c(6,6,6)` is the (optional) number of replications of each check,
in a vector the length of the number of checks.
* `locationNames = c("LOC1", "LOC2", "LOC3", "LOC4", "LOC5")` are optional names for the locations.
* `seed = 2456` is the random seed to replicate identical randomizations.
### Print `optim_multi_prep` object
To print a summary of the information that is in the object `optim_multi_prep`, we can use the generic
function `print()`.
The `multi_location_prep()` function returns all the same objects as in `partially_replicated()` and in addition
`list_locs`, `allocation`, and `size_locations`. The object `list_locs` is a list of data frames. Each data frame
has three columns; `ENTRY`, `NAME` and `REPS` with the information to randomize to each environment. The object
`allocation` is the binary allocation matrix of genotypes to locations, and `size_locations` is a data frame with
a column for each location and a row indicating the size of the location (number of field plots).
For example, we can display the `allocation` object. Let us print the first ten genotypes allocation.
```{r, echo=TRUE, eval=FALSE}
print(head(optim_multi_prep$allocation, 10))
```
```{r, echo=FALSE, eval=TRUE}
print(head(optim_multi_prep$allocation, 10))
```
Let us add two new columns to the allocation table. We can add the number of copies by genotype;
it should be 7 for all of them. We can also add the average allocation by genotype. Each treatment will appear 1.4 times in average.
```{r, echo=FALSE, eval=TRUE}
optim_multi_prep$allocation |>
dplyr::mutate(
Copies = rowSums(across(everything())),
Avg = Copies / 5
) |>
head(10) |>
`rownames<-`(paste0("Gen-", 1:10)) |>
kbl() |>
kable_styling()
```
We can manipulate the `optim_multi_prep` object as any other list in R. We can first display the design parameters
for the randomizations with the following code:
```{r, echo=TRUE, eval=FALSE}
print(optim_multi_prep)
```
which outputs:
```{r, echo=FALSE, eval=TRUE}
print(optim_multi_prep)
```
### Access to `optim_multi_prep` output
All objects are accessible by the `$` operator, i.e. `optim_multi_prep$layoutRandom[[1]]` for `LOC1`,
`optim_multi_prep$fieldBook` for the `fieldBook` with all locations.
`optim_multi_prep$fieldBook` is a data frame containing information about every plot in the field,
with information about the location of the plot and the treatment in each plot. As seen in the
output below, the field book has columns for `ID`, `EXPT`, `LOCATION`, `YEAR`, `PLOT`, `ROW`,
`COLUMN`, `CHECKS`, `ENTRY`, and `TREATMENT`.
Let us see the first 10 rows of the field book for this experiment.
```{r, echo=TRUE, eval=FALSE}
field_book <- optim_multi_prep$fieldBook
head(field_book, 10)
```
```{r, echo=FALSE, eval=TRUE}
field_book <- optim_multi_prep$fieldBook
head(field_book, 10)
```
### Plot field layout
For plotting the layout in function of the coordinates `ROW` and `COLUMN` in the field book object
we can use the generic function `plot()` as follows. This plots only the first location, but this
is indexable by location using the dollar sign operator as well.
#### Field Layout for Location 1
```{r, fig.align='center', fig.width=7.2, fig.height=5.5}
plot(optim_multi_prep, l = 1)
```
In the figure above, green plots contain replicated entries, and gray plots contain entries
that only appear once.
#### Field Layout for Location 5
Also, for example the location five:
```{r, fig.align='center', fig.width=7.2, fig.height=5.5}
plot(optim_multi_prep, l = 5)
```
# References