This vignette shows how to generate a Split-Plot
Design using both the FielDHub Shiny App and the scripting
split_plot() from the
To launch the app you need to run either
Once the app is running, go to Other Designs > Split-Plot Design
Then, follow the following steps where we show how to generate this kind of design by an example with 3 whole plots, 2 sub-plots and 3 reps. We will run this experiment in just one location.
Import entries’ list? Choose whether to import a
list with entry numbers and names for 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
.csvfile. The file must have the single column
TREATMENT, containing a list of unique names that identify each treatment/genotype. Duplicate values are not allowed, all entries must be unique. In the following, we show an example of the entries list format. This example has an entry list with 5 whole-plots and 3 sub-plots.
Choose whether to use the split-plot design in a RCBD or CRD with the Select SPD Type box.
Set the number of whole-plots in the design with the Whole-plots box. Set it to
Set the number of sub-plots contained with the Sub-plots Within Whole-plots box. Set it to
Select the number of replications of these treatments with the Input # of Full Reps box. Set it to
Enter the number of locations in Input # of Locations. We will run this experiment over a single location, so set it to
cartesianin the Plot Order Layout. For this example we will use the default
Enter the starting plot number in the Starting Plot Number box. If 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. For this case, set it to
Enter a name for the location of the experiment in the Input Location box. If there are multiple locations, each name must be in a comma separated list. Set it to
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
Once we have entered the information for our experiment on the left side panel, click the Run! button to run the design.
After you run a split-plot design in FielDHub, there are several ways to display the information contained in the field book.
When you first click the run button on a split-plot design, FielDHub
displays the Field Layout tab, which shows the entries and their
arrangement in the field. In the box below the display, you can change
the layout of the field. You can also display a heatmap over the field
by changing Type of Plot to
view a heatmap, you must first simulate an experiment over the described
field with the Simulate! button. A pop-up window will
appear where you can enter what variable you want to simulate along with
minimum and maximum values.
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 on that plot. This table is searchable, and we can filter the data in relevant columns. If we have simulated data for a heatmap, an additional column for that variable appears in the field book.
You can run the same design with a function in the FielDHub package,
First, you need to load the
FielDHub package by
Then, you can enter the information describing the above design like this:
spd <- split_plot( wp = 5, sp = 3, reps = 4, type = 2, plotNumber = 101, locationNames = "FARGO", l = 1, seed = 1240 )
The description for the inputs that we used to generate the design,
wp = 5is the number of whole-plots.
sp = 3is the number of sub-plots.
reps = 4is the number of reps
type = 2CRD or RCBD, 1 or 2 respectively
l = 1is the number of locations.
plotNumber = 101is the starting plot number.
locationNames = "FARGO"is an optional name for each location.
seed = 1240is the random seed to replicate identical randomizations.
Split Plot Design Information on the design parameters: List of 7 $ WholePlots : int [1:5] 1 2 3 4 5 $ SubPlots : int [1:3] 1 2 3 $ locationNumber: num 1 $ locationNames : chr "FARGO" $ plotNumbers : num 101 $ typeDesign : chr "RCBD" $ seed : num 1240 10 First observations of the data frame with the split_plot field book: ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB 1 1 FARGO 101 1 2 2 2|2 2 2 FARGO 101 1 2 1 2|1 3 3 FARGO 101 1 2 3 2|3 4 4 FARGO 102 1 4 3 4|3 5 5 FARGO 102 1 4 2 4|2 6 6 FARGO 102 1 4 1 4|1 7 7 FARGO 103 1 1 1 1|1 8 8 FARGO 103 1 1 3 1|3 9 9 FARGO 103 1 1 2 1|2 10 10 FARGO 104 1 3 3 3|3
split_plot() function returns a list consisting of
all the information displayed in the output tabs in the FielDHub app:
design information, plot layout, plot numbering, entries list, and field
book. These are accessible by the
spd$fieldBook is a list containing information about
every plot in the field, with information about the location of the plot
and the treatment in each plot. As seen in the output below, the field
book has columns for
field_book <- spd$fieldBook head(spd$fieldBook, 10)
ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB 1 1 FARGO 101 1 2 2 2|2 2 2 FARGO 101 1 2 1 2|1 3 3 FARGO 101 1 2 3 2|3 4 4 FARGO 102 1 4 3 4|3 5 5 FARGO 102 1 4 2 4|2 6 6 FARGO 102 1 4 1 4|1 7 7 FARGO 103 1 1 1 1|1 8 8 FARGO 103 1 1 3 1|3 9 9 FARGO 103 1 1 2 1|2 10 10 FARGO 104 1 3 3 3|3