grain_predict: Make predictions from a probabilistic network

In gRain: Graphical Independence Networks

Make predictions from a probabilistic network

Description

Makes predictions (either as the most likely state or as the conditional distributions) of variables conditional on finding (evidence) on other variables in an independence network.

Usage

## S3 method for class 'grain'
predict(
  object,
  response,
  predictors = setdiff(names(newdata), response),
  newdata,
  type = "class",
  ...
)

Arguments

object	A grain object
response	A vector of response variables to make predictions on
predictors	A vector of predictor variables to make predictions from. Defaults to all variables that are note responses.
newdata	A data frame
type	If "class", the most probable class is returned; if "distribution" the conditional distribution is returned.
...	Not used

Value

A list with components

pred	A list with the predictions
pFinding	A vector with the probability of the finding (evidence) on which the prediction is based

Author(s)

Søren Højsgaard, [e-mail]

References

Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. https://www.jstatsoft.org/v46/i10/.

See Also

grain

Examples

data(chest_cpt)
data(chestSim500)

chest.bn <- grain(compileCPT(chest_cpt))
nd <- chestSim500[1:4]

predict(chest.bn, response="bronc", newdata=nd)
predict(chest.bn, response="bronc", newdata=nd, type="distribution")

gRain documentation built on Nov. 21, 2023, 5:07 p.m.