diagL1 (version 1.0.0)

boot_het.regL1: Bootstrapping Linear L1 Models

Description

This function can be used to construct standard errors, confidence intervals and tests of hypotheses regarding linear L1 regression models. The bootstrap method used compute xypairs bootstrap for linear L1 regression.

Usage

boot_het.regL1(x, y, groups, R = 1000)

Value

A list consisting of four elements: a vector of lambda mle estimate for each bootstrap sample and another vector with empirical quantiles. A matrix B of dimension R by p is returned with the R resampled estimates of the vector of L1 linear regression parameters. A matrix U of sampled indices.

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.

R

the number of bootstrap replications.

See Also

boot.rq Bootstrapping Quantile Regression from package quantreg.

Examples

# \donttest{
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))

bt1 = boot_het.regL1(x, y, group_index, 1000)
bt1$lambda_mle_het
# }