bbn.predict {bbnet} | R Documentation |
Bayesian Belief Network Prediction
Description
bbn.predict
performs predictions using a Bayesian Belief Network (BBN)
model,
accommodating multiple priors
scenarios and allowing for bootstrapping
to assess variability.
Usage
bbn.predict(
bbn.model,
...,
boot_max = 1,
values = 1,
figure = 1,
font.size = 5
)
Arguments
bbn.model |
A matrix or dataframe of interactions between different model |
... |
An X by 2 array of initial changes to the system under investigation.
It requires at least 1 prior scenario (up to 12 priors).
The first column should be a -4 to 4 (including 0) integer value for each |
boot_max |
The number of bootstraps to perform. Suggested range for exploratory analysis 1-1000.
For final analysis recommended size = 1000 - 10000 - note, this can take a long time to run.
Default value is 1, running with no |
values |
This provides a numeric output of |
figure |
Sets the figure options. Default value 1. 0 = no figures produced. 1 = figure is saved in working directory as a PDF file (note, this is overwritten if the name is not changed, and no figure is produced if the existing PDF is open when the new one is generated). 2 = figure is produced in a graphics window. All figures are combined on a single plot where scenario 2 is below scenario 1 (i.e. scenarios work in columns then rows) |
font.size |
Font size for the plot labels. Defaults to 5. |
Details
Supports input of multiple
priors
throughellipsis()
.Allows
bootstrapping
with a specified number of maximum iterations to assess prediction variability.Generates
plots
for visual representation of the predictions.
Value
Plots of the (BBN)
predictions and optionally prints the predicted values.
Examples
data(my_BBN, combined)
bbn.predict(bbn.model = my_BBN, priors1 = combined, boot_max=100, values=1, figure=1, font.size=5)