orlm {MASS} | R Documentation |
Fits a robust linear regression model, using an M-estimator with Huber's psi function.
orlm(formula, data, weights, subset, na.action, model=FALSE, k=1.345, sw=1000, ...)
formula |
a formula object, with the response on the left of a ~ operator,
and the terms, separated by + operators, on the right.
|
data |
an optional data.frame in which to interpret the variables named in the
formula, or in the subset and the weights argument.
|
weights |
optional weights; if supplied, the algorithm fits to minimize the sum of the weights multiplied into the squared residuals. The weights must be strictly positive. |
subset |
optional expression saying that only a subset of the rows of the data should be used in the fit. |
na.action |
a missing-data filter function, applied to the model.frame , after
any subset argument has been used.
|
model |
flag to control what is returned. If this is TRUE , then the model frame
is returned. X and y are always returned.
|
k |
The control value for Winsorizing. The default gives 95% efficiency at the normal. |
sw |
switch to Huber proposal 2 scale at iteration sw and beyond.
|
... |
additional arguments for the fitting routines.
The most likely one is maxit , which sets the iteration limit, by default
20.
|
The fit uses Huber's M-estimator, and initially uses the median
absolute deviation scale estimate based on the residuals. This can be
changed to Huber's proposal 2 after sw
iterations.
Generic functions such as print
and summary
have methods to
show the results of the fit.
an object of class rlm
representing the fit, inheriting from lm
.
This has all the components of an lm
object, plus k,
the scale s
and
conv
which is a vector monitoring the convergence.
data(phones) attach(phones) res <- orlm(calls ~ year) print(res) data(stackloss) rlm(stack.loss ~ stack.x)