predict.and.impute: Predict or impute missing data from a Bayesian network

In bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Predict or impute missing data from a Bayesian network

Description

Impute missing values in a data set or predict a variable from a Bayesian network.

Usage

## S3 method for class 'bn.fit'
predict(object, node, data, cluster, method = "parents", ...,
  prob = FALSE, debug = FALSE)

impute(object, data, cluster, method, ..., strict = TRUE, debug = FALSE)

Arguments

object	an object of class bn.fit for impute; or an object of class bn or bn.fit for predict.
data	a data frame containing the data to be imputed. Complete observations will be ignored.
node	a character string, the label of a node.
cluster	an optional cluster object from package parallel.
method	a character string, the method used to impute the missing values or predict new ones. The default value is "parents" for predict() and "bayes-lw" for impute().
...	additional arguments for the imputation method. See below.
prob	a boolean value, used only by predict(). If TRUE and the node being predicted is discrete (including a discrete node in a conditional Gaussian network), the probabilities used for prediction are attached to the predicted values as an attribute called prob, a matrix with one row per level of the target variable and one column per observation. This includes the observations predicted from incomplete predictors with the "bayes-lw" and "exact" methods, which are handled by imputing the target variable.
strict	a boolean value. If TRUE, impute() will produce an error if the data were not imputed successfully, that is, if they still contain missing values. If FALSE, it will return the partially imputed data with a warning.
debug	a boolean value. If TRUE, a lot of debugging output is printed. Otherwise, the function is completely silent.

Details

predict() returns the predicted values for node given the data specified by data and the fitted network. Depending on the value of method, the predicted values are computed as follows.

• "parents": the predicted values are computed by plugging in the new values for the parents of node in the local probability distribution of node extracted from fitted.

• "bayes-lw": the predicted values are computed by averaging likelihood weighting simulations performed using all the available nodes as evidence (obviously, with the exception of the node whose values we are predicting). The number of random samples which are averaged for each new observation is controlled by the n optional argument; the default is 500. If the variable being predicted is discrete, the predicted level is that with the highest conditional probability. If the variable is continuous, the predicted value is the expected value of the conditional distribution. The variables used to compute the predicted values can be specified with the from optional argument; the default is to use all relevant variables from the data. Note that the predicted values will differ in each call to predict() since this method is based on a stochastic simulation.

• "exact": the predicted values are computed using exact inference. They are maximum a posteriori estimates obtained using junction trees and belief propagation for discrete networks, or posterior expectations computed using closed-form expressions for the multivariate normal distribution for Gaussian networks. Other networks are not supported. The variables that are used to compute the predicted values can be specified with the from optional argument; the default is to use those in the Markov blanket of node.

impute() is based on predict(), and can impute missing values with the same methods ("parents", "bayes-lw" and "exact"). The method "bayes-lw" can take an additional argument n, the number of random samples averaged for each observation. As in predict(), imputed values will differ in each call to impute() when method is set to "bayes-lw".

If object contains NA parameter estimates (because parameters were learned from data with unobserved discrete parent configurations), predict will predict NAs when those parent configurations appear in data. See bn.fit for details on how to make sure bn.fit objects contain no NA parameter estimates.

Value

predict() returns a numeric vector (for Gaussian, conditional Gaussian and zero-inflated nodes), a factor (for categorical nodes) or an ordered factor (for ordinal nodes). If prob = TRUE and the predicted node is discrete, the probabilities used for prediction are attached to the predicted values as an attribute called prob.

impute() returns a data frame with the same structure as data.

Note

Ties in prediction are broken using Bayesian tie breaking, that is, sampling at random from the tied values. Therefore, setting the random seed is required to get reproducible results.

Classifiers have a separate predict() method, see naive.bayes.

Author(s)

Marco Scutari

Examples

# missing data imputation.
with.missing.data = gaussian.test
with.missing.data[sample(nrow(with.missing.data), 500), "F"] = NA
fitted = bn.fit(model2network("[A][B][E][G][C|A:B][D|B][F|A:D:E:G]"),
           gaussian.test)
imputed = impute(fitted, with.missing.data)

# predicting a variable in the test set.
training = bn.fit(model2network("[A][B][E][G][C|A:B][D|B][F|A:D:E:G]"),
           gaussian.test[1:2000, ])
test = gaussian.test[2001:nrow(gaussian.test), ]
predicted = predict(training, node = "F", data = test)

# obtain the conditional probabilities for the values of a single variable
# given a subset of the rest, they are computed to determine the predicted
# values.
fitted = bn.fit(model2network("[A][C][F][B|A][D|A:C][E|B:F]"), learning.test)
evidence = data.frame(A = factor("a", levels = levels(learning.test$A)),
                      F = factor("b", levels = levels(learning.test$F)))
predicted = predict(fitted, "C", evidence,
              method = "bayes-lw", prob = TRUE)
attr(predicted, "prob")

bnlearn documentation built on July 17, 2026, 5:08 p.m.