check.outliers

This is a new function coming with version 0.3.0 !

# Clean environment

closeAllConnections()

rm(list=ls())

# Set enviroment

setwd("~/your working directory")

# Load packages

library(bnpa)

# Use working datasets from package

data.to.work <- dataQuantC

head(data.to.work)

# IMPORTANT: use always data.to.work this function will do update only on data.to.work

# and keep your original data

####################################################################################

# check.outliers.R - Indentifies and gives an option to remove outliers.

####################################################################################

# scan all variables from the data set and check and remove outliers for each one

for (x in 1:length(names(data.to.work)))

{

# Mount a variable to load each variable and pass to the check.outlier function

commandAssign <- paste("variable.content <- data.to.work$", names(data.to.work)[x], sep = "")

eval(parse(text=commandAssign))

# Call the function

outlier <- check.outliers(data.to.work, variable.content, names(data.to.work)[x])

# The 'check.outlier' function ask for each variable if remove or not the outliers,

# if you wish to stop watching the outliers just type <ENTER> and it will return

# an empty variable (outlier == ""), so the next command will exit from the loop

if (outlier == "")

{

break

} # if (outlier == "")

} # for (x in 1:length(names(data.to.work)))

###################################################################################

RESULTS:

###################################################################################

After analyzing the variable the function shows the results and then ask if you wish to remove the outliers. Then you can choose between 3 options:

• Type <ENTER> and the function just ends:

• Type <no>, then the function analyzes the next variable and ask again:

• Type <yes>, then the function will move 'NAs' to the outliers and process the next variable:

Considering the variiable 'A' obseve its content before remove the outliers:

The content of variiable 'A' after remove the outliers: