Description Usage Arguments Details Value Author(s) References
This function performs the actual fitting of multinomial response models with structured penalties.
Function MRSP
is actually just a user-friendly wrapper that prepares calls to this function.
MRSP.fit
is designed mostly for internal use and therefore not user-friendly, so that it is
highly recommended to use MRSP
instead of calling MRSP.fit
directly.
1 2 3 4 5 6 7 | MRSP.fit(dat, coef.init = NULL, coef.stand.init = NULL, coef.pretres.init = NULL,
offset = NULL, weights = NULL, grpindex = NULL, penindex = NULL, lambda,
lambdaR = lambda, lambdaF = lambda, gamma = 1, psi = 1, indg = NULL,
indcs = NULL, model = NULL, constr = NULL, control = NULL,
fista.control = NULL, Proximal.control = NULL, Proximal.args = NULL,
penweights = NULL, mlfit = NULL, adaptive = FALSE, threshold = FALSE,
refit = FALSE, fusion = FALSE, nonneg = FALSE, ...)
|
dat |
A list that contains the data in the format required by
If available, covariates whose value varies from class to class can be included in an entry
|
coef.init |
An optional coefficient object supplying initial coefficient values to be used. A list whose first entry is
a matrix of dimension |
coef.stand.init |
Optional initial coefficient values for the standardized predictors. Same structure as |
coef.pretres.init |
Optional initial coefficient values, prior to potential thresholding, for the standardized predictors. Same structure as |
offset |
An optional vector or matrix of offset values to be used. Either length |
weights |
An optional vector of observation weights of length |
grpindex |
A list of one or two integer vectors that indicate which columns of the design matrix form a group that has to be penalized jointly, e.g. the different dummies of a categorical predictor. The first element is the grouping vector for x, the optional second one for V. Those columns with the same number belong to one group. The numbers must begin with 1 and increase with every group. An example: grpindex = list(c(1,2,3,3,4,4,4)) means that variables 1 and 2 form their own, 'scalar' group; variables 3 and 4 as well as variables 5, 6 and 7 form multi-parameter-groups. |
penindex |
A list of one or two vectors which specifies the exact penalty type to use for each covariate. The first entry
specifies the penalty type for the variables in The '4-series' only makes sense for ordinal models! |
lambda |
Optional object specifying the lambda values to be used as tuning parameter(s) for
the main variable selection penalty. Either a vector or a single numeric. If missing,
a suitable grid of lambda values is computed. See arguments |
lambdaR |
Lambda(s) to be used for ridge penalties. Typically, if only a ridge penalty and no other
penalty is used, one can specify the Ridge lambda via argument |
lambdaF |
Lambda(s) to be used for fusion penalties. Not available yet for end-users, but included for
compatibility with future releases of |
gamma |
See argument |
psi |
See argument |
indg |
A vector of the column indices of the category-specific variables that are equipped with global coefficients. |
indcs |
A vector of the column indices of the category-specific variables that are equipped with category-specific coefficients |
model |
An object of class |
constr |
The identifiability constraint to be used. The coefficients of
predictors which do not vary over categories (i.e. global/individual-specific
predictors) are not identifiable in (unpenalized) multinomial logit models.
If |
control |
An object of class |
fista.control |
An object of class |
Proximal.control, Proximal.args |
Arguments to be passed to the proximal gradient algorithm. Not intended for end-users! |
penweights |
An optional list containing weights for the various penalty terms of different
coefficients or coefficient groups. Assuming that category-specific covariates
are present, the first element of penweights is a list of length two, with the
first element of |
mlfit |
A list that contains information about the ML or 'pseudo-ML' coefficients of the
specified model. It must contain at least one entry called 'coef.stand' that has
the same structure as the coefficient object (see |
adaptive |
Should adaptive weights be used? Use |
threshold |
If |
refit |
Should refitting be performed? If |
fusion |
If fusion penalties are used, this specifies the type of fusion.
Not yet supported for end-users of |
nonneg |
If |
... |
Further arguments or objects to be passed to |
This function does the actual work of fitting multinomial response models with
structured penalties. It is intended mainly for internal use. The main purpose
of function MRSP
is to provide a user-friendly wrapper that prepares
and evaluates a call to MRSP.fit
.
Depending on nrlambda
, either an object of class MRSP
or of
class MRSP.list
, which are lists of length
nrlambda
whose elements are MRSP
objects.
Wolfgang Poessnecker
Tutz, G., Poessnecker, W., Uhlmann, L. (2015)
Variable Selection in General Multinomial Logit Models
Computational Statistics and Data Analysis, Vol. 82, 207-222.
http://www.sciencedirect.com/science/article/pii/S0167947314002709
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