R: Structure learning from missing data

structural.em {bnlearn} | R Documentation

## Structure learning from missing data

### Description

Learn the structure of a Bayesian network from a data set containing missing values using Structural EM.

### Usage

    structural.em(x, maximize = "hc", maximize.args = list(), fit = "mle",
        fit.args = list(), impute, impute.args = list(), return.all = FALSE,
        start = NULL, max.iter = 5, debug = FALSE)

### Arguments

`x` | a data frame containing the variables in the model.
`maximize` | a character string, the score-based algorithm to be used in the “maximization” step. See `structure learning` for details.
`maximize.args` | a list of arguments to be passed to the algorithm specified by `maximize`, such as `restart` for hill-climbing or `tabu` for tabu search.
`fit` | a character string, the parameter learning method to be used in the “maximization” step. See `bn.fit` for details.
`fit.args` | a list of arguments to be passed to the parameter learning method specified by `fit`.
`impute` | a character string, the imputation method to be used in the “expectation” step. See `impute` for details.
`impute.args` | a list of arguments to be passed to the imputation method specified by `impute`.
`return.all` | a boolean value. See below for details.
`start` | a `bn` or `bn.fit` object, the network used to perform the first imputation and as a starting point for the score-based algorithm specified by `maximize`.
`max.iter` | an integer, the maximum number of iterations.
`debug` | a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.

### Value

If `return.all` is `FALSE`, `structural.em()` returns an object of class `bn`. (See `bn-class` for details.)

If `return.all` is `TRUE`, `structural.em()` returns a list with three elements named `dag` (an object of class `bn`), `imputed` (a data frame containing the imputed data from the last iteration) and `fitted` (an object of class `bn.fit`, again from the last iteration; see `bn.fit-class` for details).

### Note

If at least one of the variables in the data `x` does not contain any observed value, the `start` network must be specified and it must be a `bn.fit` object. Otherwise, `structural.em()` is unable to complete the first maximization step because it cannot fit the corresponding local distribution(s).

Note that if `impute` is set to `bayes-lw`, each call to `structural.em` may produce a different model since the imputation is based on a stochastic simulation.

### Author(s)

Marco Scutari

### References

Friedman N (1997). "Learning Belief Networks in the Presence of Missing Values and Hidden Variables". Proceedings of the 14th International Conference on Machine Learning, 125–133.

### See Also

score-based algorithms, bn.fit, impute.

[Package bnlearn version 4.6 Index]