analysis_nas: missing Analysis

In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

Description

#' analysis_nas is for understanding the reason for missing data and understand distribution of missing data so we can categorise it as:

• missing completely at random(MCAR)

• Mmissing at random(MAR), or

• missing not at random, also known as IM.

Usage

analysis_nas(
  dat,
  class_var = FALSE,
  nas_rate = NULL,
  na_vars = NULL,
  mat_nas_shadow = NULL,
  dt_nas_random = NULL,
  ...
)

Arguments

dat	A data.frame with independent variables and target variable.
class_var	Logical, nas analysis of the nominal variables. Default is TRUE.
nas_rate	A list contains nas rate of each variable.
na_vars	Names of variables which contain nas.
mat_nas_shadow	A shadow matrix of variables which contain nas.
dt_nas_random	A data.frame with random nas imputation.
...	Other parameters.

Value

A data.frame with outliers analysis for each variable.

creditmodel documentation built on Jan. 7, 2022, 5:06 p.m.