This function computes an MM-regression estimators for linear models using deterministic starting points.
lmrobdetMM(
formula,
data,
subset,
weights,
na.action,
model = TRUE,
x = !control$compute.rd,
y = FALSE,
singular.ok = TRUE,
contrasts = NULL,
offset = NULL,
control = lmrobdet.control()
)A list with the following components:
The estimated vector of regression coefficients
The robust residual M-scale estimate using the final residuals from the converged iterated weighted least square (IRWLS) algorithm final estimate
The vector of residuals associated with the robust fit
Value of the objective function at the final MM-estimator
Logical value indicating whether IRWLS iterations for the MM-estimator have converged
Number of IRWLS iterations for the MM-estimator
Robustness weights for the MM-estimator
Fitted values associated with the robust fit
Numeric rank of the fitted linear model
The estimated covariance matrix of the regression estimates
The residual degrees of freedom
The residual degrees of freedom
Minimum robust scale associated with the preliminary highly robust but inefficient S-estimator.
The robust multiple correlation coefficient
The adjusted robust multiple correlation coefficient taking into account the degrees of freedom of each term
(only where relevant) the contrasts used
(only where relevant) a record of the levels of the factors used in fitting
the matched call
if requested, the model frame used
if requested, the model matrix used
if requested, the response vector used
The terms object used.
Used to separate continuous from categorical columns in the design matrix
(where relevant) information returned by model.frame on the special handling of NAs
a symbolic description of the model to be fit.
an optional data frame, list or environment containing
the variables in the model. If not found in data, model variables
are taken from environment(formula), which usually is the
root environment of the current R session.
an optional vector specifying a subset of observations to be used.
an optional vector of weights to be used in the fitting process.
a function to indicates what should happen when the data contain NAs.
The default is set by the na.action setting of options, and is
na.fail if that is unset.
logical value indicating whether to return the model frame
logical value indicating whether to return the model matrix
logical value indicating whether to return the vector of responses
logical value. If FALSE a singular fit produces an error.
an optional list. See the contrasts.arg of model.matrix.default.
this can be used to specify an a priori known component to be included in the linear predictor during fitting. An offset term can be included in the formula instead or as well, and if both are specified their sum is used.
a list specifying control parameters as returned by the function lmrobdet.control.
Matias Salibian-Barrera, matias@stat.ubc.ca, based on lmrob from package robustbase
This function computes MM-regression estimators
computed using Pen~a-Yohai candidates (instead of subsampling ones).
This function makes use of the functions lmrob.fit,
lmrob..M..fit, .vcov.avar1, lmrob.S and
lmrob.lar, from robustbase,
along with utility functions used by these functions,
modified so as to include use of the analytic form of the
optimal psi and rho functions (for the optimal psi function , see
Section 5.8.1 of Maronna, Martin, Yohai and Salibian Barrera, 2019)
DCML, MMPY, SMPY
data(coleman, package='robustbase')
m2 <- lmrobdetMM(Y ~ ., data=coleman)
m2
summary(m2)
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