regL1_het {diagL1}

Fitting Heteroscedastic Linear L1 Models

Description

This function fits an groupwise heteroscedastic L1 regression model using the rq function from the 'quantreg' package.

Usage

regL1_het(
  x,
  y,
  groups,
  na.action = stats::na.omit,
  method = "br",
  model = TRUE,
  tolerance = 0.001,
  max_iteration = 2000,
  ...
)

Arguments

x

the regression design matrix.

y

the regression response vector.

groups

vector with the group index associated with the observation, observations with the same index belong to the same group.

na.action

a function to filter missing data. This is applied to the model.frame after any subset argument has been used. The default (with na.fail) is to create an error if any missing values are found. A possible alternative is na.omit, which deletes observations that contain one or more missing values.

method

the algorithmic method used to compute the fit. There are several options: "br", "fn", "pfn", "sfn", "fnc", "conquer", "pfnb", "qfnb", "ppro" and "lasso". See rq for more details.

model

if TRUE then the model frame is returned. This is essential if one wants to call summary subsequently.

tolerance

threshold that determines when the iterative algorithm should stop.

max_iteration

maximum number of iterations.

...

additional arguments for the fitting routines (see rq.fit.br and rq.fit.fnb, etc. and the functions they call).

Details

L1 regression is an important particular case of quantile regression, so this function inherits from the "rq" class of the quantreg package.

Value

A fitted heteroscedastic L1 linear regression model object.

Examples

set.seed(123)
x1 = matrix(rnorm(20), ncol = 2)
y1 = x1[, 1] + x1[, 2] + rlaplace(10, 0, 5)
x2 = matrix(rnorm(20), ncol = 2)
y2 = x2[, 1] + x2[, 2] + rlaplace(10, 0, 10)
x3 = matrix(rnorm(20), ncol = 2)
y3 = x3[, 1] + x3[, 2] + rlaplace(10, 0, 15)
x4 = matrix(rnorm(20), ncol = 2)
y4 = x4[, 1] + x4[, 2] + rlaplace(10, 0, 20)
x5 = matrix(rnorm(20), ncol = 2)
y5 = x5[, 1] + x5[, 2] + rlaplace(10, 0, 30)

y = c(y1, y2, y3, y4, y5)
x = rbind(x1, x2, x3, x4, x5)
group_index = c(rep(1,10),rep(2,10),rep(3,10),rep(4,10),rep(5,10))
# Fits a heteroscedastic linear regression L1 model
mod1 = regL1_het(x, y, group_index)