lmrob..M..fit {robustbase}

Compute M-estimators of regression

Description

This function performs RWLS iterations to find an M-estimator of regression. When started from an S-estimated beta.initial, this results in an MM-estimator.

Usage

lmrob..M..fit(x = obj$x, y = obj$y,
              beta.initial = obj$coef, scale = obj$scale, control = obj$control,
              obj,
              mf,
              method = obj$control$method)

Arguments

x

design matrix (n \times p) typically including a column of 1s for the intercept.

y

numeric response vector (of length n).

beta.initial

numeric vector (of length p) of initial estimate. Usually the result of an S-regression estimator.

scale

robust residual scale estimate. Usually an S-scale estimator.

control

list of control parameters, as returned by lmrob.control. Currently, the components c("max.it", "rel.tol","trace.lev", "psi", "tuning.psi", "mts", "subsampling") are accessed.

obj

an optional lmrob-object. If specified, this is typically used to set values for the other arguments.

mf

defunct.

method

optional; the method used for obj computation.

Details

This function is used by lmrob.fit (and anova(<lmrob>, type = "Deviance")) and typically not to be used on its own.

Value

A list with the following elements:

coef

the M-estimator (or MM-estim.) of regression

control

the control list input used

scale

The residual scale estimate

seed

The random number generator seed

converged

TRUE if the RWLS iterations converged, FALSE otherwise

Author(s)

Matias Salibian-Barrera and Martin Maechler

References

Yohai, 1987

See Also

lmrob.fit, lmrob; rlm from package MASS.

Examples

data(stackloss)
X <- model.matrix(stack.loss ~ . , data = stackloss)
y <- stack.loss
## Compute manual MM-estimate:
## 1) initial LTS:
m0 <- ltsReg(X[,-1], y)
## 2) M-estimate started from LTS:
m1 <- lmrob..M..fit(X, y, beta.initial = coef(m0), scale = m0$scale, method = "SM",
                    control = lmrob.control(tuning.psi = 1.6, psi = 'bisquare'))
## no 'method' (nor 'obj'):
m1. <- lmrob..M..fit(X, y, beta.initial = coef(m0), scale = m0$scale,
                     control = m1$control)
stopifnot(all.equal(m1, m1., tol = 1e-15)) # identical {call *not* stored!}

cbind(m0$coef, m1$coef)
## the scale is kept fixed:
stopifnot(identical(unname(m0$scale), m1$scale))

##  robustness weights: are
r.s <- with(m1, residuals/scale) # scaled residuals
m1.wts <- Mpsi(r.s, cc = 1.6, psi="tukey") / r.s
summarizeRobWeights(m1.wts)
##--> outliers 1,3,4,13,21
which(m0$lts.wt == 0) # 1,3,4,21 but not 13

## Manually add M-step to SMD-estimate (=> equivalent to "SMDM"):
m2 <- lmrob(stack.loss ~ ., data = stackloss, method = 'SMD')
m3 <- lmrob..M..fit(obj = m2)

## Simple function that allows custom initial estimates
## (Deprecated; use init argument to lmrob() instead.) %% MM: why deprecated?
lmrob.custom <- function(x, y, beta.initial, scale, terms) {
  ## initialize object
  obj <- list(control = lmrob.control("KS2011"),
              terms = terms) ## terms is needed for summary()
  ## M-step
  obj <- lmrob..M..fit(x, y, beta.initial, scale, obj = obj)
  ## D-step
  obj <- lmrob..D..fit(obj, x)
  ## Add some missing elements
  obj$cov <- TRUE ## enables calculation of cov matrix
  obj$p <- obj$qr$rank
  obj$degree.freedom <- length(y) - obj$p
  ## M-step
  obj <- lmrob..M..fit(x, y, obj=obj)
  obj$control$method <- ".MDM"
  obj
}

m4 <- lmrob.custom(X, y, m2$init$init.S$coef,
                   m2$init$scale, m2$terms)
stopifnot(all.equal(m4$coef, m3$coef))

## Start from ltsReg:
m5 <- ltsReg(stack.loss ~ ., data = stackloss)
m6 <- lmrob.custom(m5$X, m5$Y, coef(m5), m5$scale, m5$terms)