This vignette shows how to generate a split-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-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, 4 sub-sub plots and 3 reps. We will run this experiment in just one location.
If the selection is
No, that means the app is going
to generate synthetic data for entries and names of the treatment 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 single column
TREATMENT, containing a list of
unique names that identify each treatment. 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
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
Set the number of sub-sub plots contained with the
Sub-Sub-plots within Sub-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
cartesian in the
Plot Order Layout. For this example we will use the
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-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-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
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 typing,
Then, you can enter the information describing the above design like this:
<- split_split_plot( sspd wp = 3, sp = 2, ssp = 4, reps = 3, type = 2, l = 1, plotNumber = 101, locationNames = "FARGO", seed = 123 )
The description for the inputs that we used to generate the design,
wp = 3is the number of whole-plots.
sp = 2is the number of sub-plots.
ssp = 4is the number of sub-sub-plots.
reps = 3is 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-Split Plot Design Information on the design parameters: List of 6 $ Whole.Plots : int [1:3] 1 2 3 $ Sub.Plots : int [1:2] 1 2 $ Sub.Sub.Plots: int [1:4] 1 2 3 4 $ Locations : num 1 $ typeDesign : chr "RCBD" $ seed : num 123 10 First observations of the data frame with the split_split_plot field book: ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB 1 1 FARGO 101 1 3 1 2 3|1|2 2 2 FARGO 101 1 3 1 3 3|1|3 3 3 FARGO 101 1 3 1 4 3|1|4 4 4 FARGO 101 1 3 1 1 3|1|1 5 5 FARGO 101 1 3 2 2 3|2|2 6 6 FARGO 101 1 3 2 3 3|2|3 7 7 FARGO 101 1 3 2 4 3|2|4 8 8 FARGO 101 1 3 2 1 3|2|1 9 9 FARGO 102 1 1 2 1 1|2|1 10 10 FARGO 102 1 1 2 3 1|2|3
split_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
sspd$fieldBook is a list containing information about
every plot in the field, with information about the location of the plot
and the treatment in each plot. As seen in the output below, the field
book has columns for
<- sspd$fieldBook field_book head(field_book,10)
ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB 1 1 FARGO 101 1 3 1 2 3|1|2 2 2 FARGO 101 1 3 1 3 3|1|3 3 3 FARGO 101 1 3 1 4 3|1|4 4 4 FARGO 101 1 3 1 1 3|1|1 5 5 FARGO 101 1 3 2 2 3|2|2 6 6 FARGO 101 1 3 2 3 3|2|3 7 7 FARGO 101 1 3 2 4 3|2|4 8 8 FARGO 101 1 3 2 1 3|2|1 9 9 FARGO 102 1 1 2 1 1|2|1 10 10 FARGO 102 1 1 2 3 1|2|3
For plotting the layout in function of the coordinates
COLUMN, you can use the the generic
plot() as follows,