grain_predict: Make predictions from a probabilistic network

In DataSciBurgoon/gRain: Graphical Independence Networks

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 distrubtion 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. http://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")

DataSciBurgoon/gRain documentation built on March 25, 2020, 12:02 a.m.