TLE: robust mixture regression based on trimmed likelihood estimation.

Source: R/TLE.short.R

The algorithm fits a mixture regression model after trimming a proportion of the observations, given by tRatio.

TLE(formula, data, nc = 2, tRatio, MaxIt = 200)

# S4 method for formula,ANY,numeric,numeric,numeric
TLE(formula, data,
  nc = 2, tRatio, MaxIt = 200)

Arguments

formula

A symbolic description of the model to be fit.

data

A data frame containing the predictor and response variables, where the last column is the response varible.

nc

Number of mixture components.

tRatio

Trimming proportion.

MaxIt

Maximum iteration.

Value

A S4 object of RobMixReg class.

Examples

library("RobMixReg")
formula01=as.formula("y~x")
x=(gaussData$x);y=as.numeric(gaussData$y);
example_data01=data.frame(x,y)
# \donttest{
res = TLE(formula01,example_data01, nc=2,tRatio=0.05,MaxIt=200)
# }