| grain_predict | R Documentation |
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.
## S3 method for class 'grain' predict( object, response, predictors = setdiff(names(newdata), response), newdata, type = "class", ... )
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 |
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 |
Søren Højsgaard, sorenh@math.aau.dk
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/.
grain
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")