imputation: Impute the missing data with the method selected under the condition.

Description

This function provides eight methods for imputation with categorical varaibles as conditions.

Usage

imputation(origdata, method, vartype, missingpct,
  condition = NULL)

Arguments

origdata

A data frame whose missing values need to be imputed. This data frame should be selected from the missing data GUI.

method

The imputation method selected from the missing data GUI. Must be one of 'Below 10 neighbor','Multiple Imputation','Mode'.

vartype

A vector of the classes of origdata. The length is the same as the number of columns of origdata. The value should be from "integer", "numeric", "factor", and "character".

missingpct

A vector of the percentage of missings of the variables in origdata. The length is the same as the number of columns of origdata. The value should be between 0 and 1.

condition

A vector of categorical variables. The dataset will be partitioned based on those variables, and then the imputation is implemented in each group. There are no missing values in those variables. If it is null, then there is no division. The imputa

Value

The imputed data frame with the last column being the row number from the original dataset. During the procedure of the function, rows may be exchanged, thus a column of row number could keep track of the original row number and then help to find the shadow matrix.

Details

The imputation methods: (1)'Below 10 variable will be replaced by the value which equals to the minimum of the variable minus 10 (2)'Median' means NA's will be replaced by the median of this variable (omit NA's). (3)'Mean' means NA's will be replaced by the mean of this variable (omit NA's). (4)'Random value' means NA's will be replaced by any values of this variable (omit NA's) which are randomly selected. (5)'Regression' uses function aregImpute from package Hmisc. It requires at least two variables to be selected. (6)'Nearest neighbor' replaces NA's by its nearest neighbor. It requires at least two variables to be selected, and no character variables. It returns median for the case if all values in it are NA's. (7)'Multiple Imputation' uses functions from package norm. It requires all selected variables to be numeric (at least integer), and at least two variables to be selected. (8)'Mode' is a method for imputing categorical variables. It requires all selected variables to be character or factor or logical. It will replace NA's by the mode of the variable (omit NA's).