outlierdummy: Outlier detection and matrix creation

In greybox: Toolbox for Model Building and Forecasting

Outlier detection and matrix creation

Description

Function detects outliers and creates a matrix with dummy variables. Only point outliers are considered (no level shifts).

Usage

outlierdummy(object, ...)

## Default S3 method:
outlierdummy(object, level = 0.999, type = c("rstandard",
  "rstudent"), ...)

## S3 method for class 'alm'
outlierdummy(object, level = 0.999, type = c("rstandard",
  "rstudent"), ...)

Arguments

object	Model estimated using one of the functions of smooth package.
...	Other parameters. Not used yet.
level	Confidence level to use. Everything that is outside the constructed bounds based on that is flagged as outliers.
type	Type of residuals to use: either standardised or studentised.

Details

The detection is done based on the type of distribution used and confidence level specified by user.

Value

The class "outlierdummy", which contains the list:

• outliers - the matrix with the dummy variables, flagging outliers;

• statistic - the value of the statistic for the normalised variable;

• id - the ids of the outliers (which observations have them);

• level - the confidence level used in the process;

• type - the type of the residuals used.

Author(s)

Ivan Svetunkov, [e-mail]

See Also

influence.measures

Examples

# Generate the data with S distribution
xreg <- cbind(rnorm(100,10,3),rnorm(100,50,5))
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rs(100,0,3),xreg)
colnames(xreg) <- c("y","x1","x2")

# Fit the normal distribution model
ourModel <- alm(y~x1+x2, xreg, distribution="dnorm")

# Detect outliers
xregOutlierDummy <- outlierdummy(ourModel)

greybox documentation built on Sept. 16, 2023, 9:07 a.m.