read.dataset: Read a dataset from file.

In bnstruct: Bayesian Network Structure Learning from Data with Missing Values

Read a dataset from file.

Description

There are two ways to build a BNDataset: using two files containing respectively header informations and data, and manually providing the data table and the related header informations (variable names, cardinality and discreteness).

Usage

read.dataset(
  object,
  data.file,
  header.file,
  data.with.header = FALSE,
  na.string.symbol = "?",
  sep.symbol = "",
  starts.from = 1,
  num.time.steps = 1
)

## S4 method for signature 'BNDataset,character,character'
read.dataset(
  object,
  data.file,
  header.file,
  data.with.header = FALSE,
  na.string.symbol = "?",
  sep.symbol = "",
  starts.from = 1,
  num.time.steps = 1
)

Arguments

object	the BNDataset object.
data.file	the data file.
header.file	the header file.
data.with.header	TRUE if the first row of dataset file is an header (e.g. it contains the variable names).
na.string.symbol	character that denotes NA in the dataset.
sep.symbol	separator among values in the dataset.
starts.from	starting value for entries in the dataset (observed values, default is 1).
num.time.steps	number of instants composing the observations (1, unless it is a dynamic system).

Details

The key informations needed are: 1. the data; 2. the state of variables (discrete or continuous); 3. the names of the variables; 4. the cardinalities of the variables (if discrete), or the number of levels they have to be quantized into (if continuous). Names and cardinalities/leves can be guessed by looking at the data, but it is strongly advised to provide _all_ of the informations, in order to avoid problems later on during the execution.

Data can be provided in form of data.frame or matrix. It can contain NAs. By default, NAs are indicated with '?'; to specify a different character for NAs, it is possible to provide also the na.string.symbol parameter. The values contained in the data have to be numeric (real for continuous variables, integer for discrete ones). The default range of values for a discrete variable X is [1,|X|], with |X| being the cardinality of X. The same applies for the levels of quantization for continuous variables. If the value ranges for the data are different from the expected ones, it is possible to specify a different starting value (for the whole dataset) with the starts.from parameter. E.g. by starts.from=0 we assume that the values of the variables in the dataset have range [0,|X|-1]. Please keep in mind that the internal representation of bnstruct starts from 1, and the original starting values are then lost.

It is possible to use two files, one for the data and one for the metadata, instead of providing manually all of the info. bnstruct requires the data files to be in a format subsequently described. The actual data has to be in (a text file containing data in) tabular format, one tuple per row, with the values for each variable separated by a space or a tab. Values for each variable have to be numbers, starting from 1 in case of discrete variables. Data files can have a first row containing the names of the corresponding variables.

In addition to the data file, a header file containing additional informations can also be provided. An header file has to be composed by three rows of tab-delimited values: 1. list of names of the variables, in the same order of the data file; 2. a list of integers representing the cardinality of the variables, in case of discrete variables, or the number of levels each variable has to be quantized in, in case of continuous variables; 3. a list that indicates, for each variable, if the variable is continuous (c or C), and thus has to be quantized before learning, or discrete (d or D).

See Also

BNDataset

Examples

## Not run: 
dataset <- BNDataset()
dataset <- read.dataset(dataset, "file.data", "file.header")

## End(Not run)

bnstruct documentation built on Dec. 1, 2022, 1:22 a.m.