grplars {robustHD} | R Documentation |
(Robust) groupwise least angle regression
Description
(Robustly) sequence groups of candidate predictors according to their predictive content and find the optimal model along the sequence.
Usage
grplars(x, ...)
## S3 method for class 'formula'
grplars(formula, data, ...)
## S3 method for class 'data.frame'
grplars(x, y, ...)
## Default S3 method:
grplars(
x,
y,
sMax = NA,
assign,
fit = TRUE,
s = c(0, sMax),
crit = c("BIC", "PE"),
splits = foldControl(),
cost = rmspe,
costArgs = list(),
selectBest = c("hastie", "min"),
seFactor = 1,
ncores = 1,
cl = NULL,
seed = NULL,
model = TRUE,
...
)
rgrplars(x, ...)
## S3 method for class 'formula'
rgrplars(formula, data, ...)
## S3 method for class 'data.frame'
rgrplars(x, y, ...)
## Default S3 method:
rgrplars(
x,
y,
sMax = NA,
assign,
centerFun = median,
scaleFun = mad,
regFun = lmrob,
regArgs = list(),
combine = c("min", "euclidean", "mahalanobis"),
const = 2,
prob = 0.95,
fit = TRUE,
s = c(0, sMax),
crit = c("BIC", "PE"),
splits = foldControl(),
cost = rtmspe,
costArgs = list(),
selectBest = c("hastie", "min"),
seFactor = 1,
ncores = 1,
cl = NULL,
seed = NULL,
model = TRUE,
...
)
Arguments
x |
a matrix or data frame containing the candidate predictors. |
... |
additional arguments to be passed down. |
formula |
a formula describing the full model. |
data |
an optional data frame, list or environment (or object coercible
to a data frame by |
y |
a numeric vector containing the response. |
sMax |
an integer giving the number of predictor groups to be
sequenced. If it is |
assign |
an integer vector giving the predictor group to which each predictor variable belongs. |
fit |
a logical indicating whether to fit submodels along the sequence
( |
s |
an integer vector of length two giving the first and last
step along the sequence for which to compute submodels. The default
is to start with a model containing only an intercept (step 0) and
iteratively add all groups along the sequence (step |
crit |
a character string specifying the optimality criterion to be
used for selecting the final model. Possible values are |
splits |
an object giving data splits to be used for prediction error
estimation (see |
cost |
a cost function measuring prediction loss (see
|
costArgs |
a list of additional arguments to be passed to the
prediction loss function |
selectBest , seFactor |
arguments specifying a criterion for selecting
the best model (see |
ncores |
a positive integer giving the number of processor cores to be
used for parallel computing (the default is 1 for no parallelization). If
this is set to |
cl |
a parallel cluster for parallel computing as generated by
|
seed |
optional initial seed for the random number generator (see
|
model |
a logical indicating whether the model data should be included in the returned object. |
centerFun |
a function to compute a robust estimate for the center
(defaults to |
scaleFun |
a function to compute a robust estimate for the scale
(defaults to |
regFun |
a function to compute robust linear regressions that can be
interpreted as weighted least squares (defaults to
|
regArgs |
a list of arguments to be passed to |
combine |
a character string specifying how to combine the data
cleaning weights from the robust regressions with each predictor group.
Possible values are |
const |
numeric; tuning constant for multivariate winsorization to be used in the initial corralation estimates based on adjusted univariate winsorization (defaults to 2). |
prob |
numeric; probability for the quantile of the
|
Value
If fit
is FALSE
, an integer vector containing the indices of
the sequenced predictor groups.
Else if crit
is "PE"
, an object of class
"perrySeqModel"
(inheriting from classes "perryTuning"
,
see perryTuning
). It contains information on the
prediction error criterion, and includes the final model as component
finalModel
.
Otherwise an object of class "grplars"
(inheriting from class
"seqModel"
) with the following components:
active
an integer vector containing the sequence of predictor groups.
s
an integer vector containing the steps for which submodels along the sequence have been computed.
coefficients
a numeric matrix in which each column contains the regression coefficients of the corresponding submodel along the sequence.
fitted.values
a numeric matrix in which each column contains the fitted values of the corresponding submodel along the sequence.
residuals
a numeric matrix in which each column contains the residuals of the corresponding submodel along the sequence.
df
an integer vector containing the degrees of freedom of the submodels along the sequence (i.e., the number of estimated coefficients).
robust
a logical indicating whether a robust fit was computed.
scale
a numeric vector giving the robust residual scale estimates for the submodels along the sequence (only returned for a robust fit).
crit
an object of class
"bicSelect"
containing the BIC values and indicating the final model (only returned if argumentcrit
is"BIC"
and arguments
indicates more than one step along the sequence).muX
a numeric vector containing the center estimates of the predictor variables.
sigmaX
a numeric vector containing the scale estimates of the predictor variables.
muY
numeric; the center estimate of the response.
sigmaY
numeric; the scale estimate of the response.
x
the matrix of candidate predictors (if
model
isTRUE
).y
the response (if
model
isTRUE
).assign
an integer vector giving the predictor group to which each predictor variable belongs.
w
a numeric vector giving the data cleaning weights (only returned for a robust fit).
call
the matched function call.
Author(s)
Andreas Alfons
References
Alfons, A., Croux, C. and Gelper, S. (2016) Robust groupwise least angle regression. Computational Statistics & Data Analysis, 93, 421–435. doi:10.1016/j.csda.2015.02.007
See Also
coef
,
fitted
,
plot
,
predict
,
residuals
,
rstandard
,
lmrob
Examples
data("TopGear")
# keep complete observations
keep <- complete.cases(TopGear)
TopGear <- TopGear[keep, ]
# remove information on car model
info <- TopGear[, 1:3]
TopGear <- TopGear[, -(1:3)]
# log-transform price
TopGear$Price <- log(TopGear$Price)
# robust groupwise LARS
rgrplars(MPG ~ ., data = TopGear, sMax = 15)