creditmodel (version 1.3.1)

analysis_nas: missing Analysis

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.