impute_missing_data: Impute missing data in data frame

In bayes-modeling/wrmbn: Water Resource Management using Bayesian Network

Description

Impute missing data in data frame

Usage

impute_missing_data(
  fitted,
  data,
  continuous_variables,
  discrete_variables,
  time_column = NULL,
  normalize_type = NULL,
  normalizers = NULL,
  debug = FALSE
)

Arguments

fitted	A fitted model
data	A data frame, contains missing data, each row is a time value and observations, structure is the same as learning data before process
continuous_variables	Column names of continuous variables
discrete_variables	Column names of discrete variables
time_column	Column name of "data", which values is time stamp, default is NULL
normalize_type	Normalization type for continuous variables, "mean_normalization", "min_max" or "standardisation", default is NULL
normalizers	Normalize parameters, default is NULL

Value

A full filled data frame

Examples

library(wrmbn)
data("data")
data("preprocessed")
data("trained_models")
head(data)
continuous_variables <- preprocessed$continuous_variables
discrete_variables <- preprocessed$discrete_variables
desire_layers <- preprocessed$desire_layers
time_column <- "date"
normalize_type <- preprocessed$normalize_tye
normalizers <- preprocessed$normalizers
fitted <- trained_models$hc$fitted

imputed_data <- impute_missing_data(fitted, data, continuous_variables, discrete_variables,
                                    time_column, normalize_type, normalizers, debug = FALSE)
head(imputed_data)

bayes-modeling/wrmbn documentation built on Dec. 19, 2021, 6:45 a.m.