selection: Create selection (non-outlying) vector from model

In robust2sls: Outlier Robust Two-Stage Least Squares Inference and Testing

Create selection (non-outlying) vector from model

Description

selection uses the data and model objects to create a list with five elements that are used to determine whether the observations are judged as outliers or not.

Usage

selection(data, yvar, model, cutoff, bias_correction = NULL)

Arguments

data	A dataframe.
yvar	A character vector of length 1 that refers to the name of the dependent variable in the data set.
model	A model object of class ivreg whose parameters are used to calculate the residuals.
cutoff	A numeric cutoff value used to judge whether an observation is an outlier or not. If its absolute value is larger than the cutoff value, the observations is classified as being an outlier.
bias_correction	A numeric factor used to correct the estimate of sigma under the null hypothesis of no outliers or NULL if no correction should be done.

Value

A list with five elements. The first four are vectors whose length equals the number of observations in the data set. Unlike the residuals stored in a model object (usually accessible via model$residuals), it does not ignore observations where any of y, x or z are missing. It instead sets their values to NA.

The first element is a double vector containing the residuals for each observation based on the model estimates. The second element contains the standardised residuals, the third one a logical vector with TRUE if the observation is judged as not outlying, FALSE if it is an outlier, and NA if any of y, x, or z are missing. The fourth element of the list is an integer vector with three values: 0 if the observations is judged to be an outlier, 1 if not, and -1 if missing. The fifth and last element stores the ivreg model object based on which the four vectors were calculated.

Warning

Unlike the residuals stored in a model object (usually accessible via model$residuals), this function returns vectors of the same length as the original data set even if any of the y, x, or z variables are missing. The residuals for those observations are set to NA.

robust2sls documentation built on Jan. 11, 2023, 5:13 p.m.