treg: t-regression model fit

In SMIR: Companion to Statistical Modelling in R

Description

Robust regression by modelling errors as $t$-distributed with known degrees of freedom rather than normal

Usage

treg(lm.object, r, verbose=TRUE)

Arguments

lm.object	An object of class "lm"
r	a vector of degrees of freedom
verbose	TRUE prints estimates for $-2 X $ log likelihood, sigma, and r at each interation.

Details

Fits the $t$ distribution for known degrees of freedom , $r$, and computes the profile likelihood and obtains the joint MLEs of the regression coefficients, sigma and disparity of a robust regression.

Value

an object of class “treg”

weights	working weights
disparity	disparity, i.e. full likelihood
tcoef	robust regression parameter estimates
r	degrees of freedom
sigma	estimate of residual standard deviation

Author(s)

[e-mail]

References

Aitkin, M., Francis, B., Hinde, J. and Darnell, R. (2008). Statistical modelling in R, OUP.

See Also

SMIR::summary.treg

Examples

library(SMIR)
data(stackloss)
stackloss.lm <- lm(y ~ x1 + x2 + x3, data = stackloss)
(stackloss.treg1.1 <- treg(stackloss.lm , r=1.1, verbose = FALSE) )

Example output

Call:
lm(formula = y ~ x1 + x2 + x3, data = stackloss)

Coefficients:
(Intercept)           x1           x2           x3  
   -39.9197       0.7156       1.2953      -0.1521  

SMIR documentation built on May 29, 2017, 10:39 a.m.