impute_variables: Impute missing observations for a given Dataset

In oislen/BuenaVista: Functions for Everyday Data Science Tasks

Description

Impute missing observations for a given dataset. Missing observations can be imputed using the mean, the mode, multiple linear regression or binominal logistic regression. When using mice package, the NA values must only be in one column.

Usage

impute_variables(dataset, percentage = NULL, y_index = NULL,
  type = c("mean", "mode", "mice"), method = c("cart", "rf", "norm.predict",
  "norm.boot", "logreg", "logreg.boot"), file_name = NULL, directory = NULL)

Arguments

dataset	The dataset that the power terms are derived from
percentage	The percentage specifies identifies the attributes with a missing number of observations
y_index	A natural number specifying the column in the data frame to be imputed
type	The type of imputation to be used. Either "mean", "mode" or "mice".
method	The method of imputation to be used in conjungtion with type equal to mice.
file_name	A character object indicating the file name when saving the data frame. The default is NULL. The name must include the .csv suffixs.
directory	A character object specifying the directory where the data frame is to be saved as a .csv file.

Value

Outputs the imputed data as a data frame

See Also

remove_variables, derive_variables, extract_variables, standardise_variables, transform_variables

oislen/BuenaVista documentation built on May 16, 2019, 8:12 p.m.