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Class "LavaFit"

Source: R/Regularizers-R6Class.R
LavaFit.Rd

Class "LavaFit"

Class "LavaFit"

Details

Class of object returned by the fitting function lava(). Inherits fields and methods of QuadrupenFit

See also

QuadrupenFit, lava()

Super class

quadrupen::QuadrupenFit -> LavaFit

Active bindings

penalty

character describing the regularizer/penalty

sparse_coef

sparse part of the decomposition of the coefficients

dense_coef

dense part of the decomposition of the coefficients

debias

logical, should we rely on the debias coefficient of the regularizer (if available) or not

Methods

Public methods

Inherited methods

Method new()

Initialize a LavaFit model

Usage

LavaFit$new(data, intercept, regParam)

Arguments

data

a DataModel object

intercept

a logical; should an intercept be included in the mode?

regParam

a list with two elements, a vector and a scalar, for the regularization

Method fit()

function performing the optimization

Usage

LavaFit$fit(control)

Arguments

control

list controlling the optimization process Plot method for lava regularization path

Method plot_path()

Produce a plot of the solution path of a LavaFit object.

Usage

LavaFit$plot_path(
  xvar = c("lambda", "fraction", "df"),
  log_scale = TRUE,
  component = "both",
  title = paste("Lava path:", component, "component(s)"),
  standardize = TRUE,
  labels = NULL
)

Arguments

xvar

variable to plot on the X-axis: either "lambda" (\(\ell_1\) penalty level, or \(\ell_2\) for ridge and \(\ell_\infty\)) or "fraction" (\(\ell_1\)-norm of the coefficients) or df for estimated degrees of freedom. Default is set to "lambda".

log_scale

logical; indicates if a log-scale should be used when xvar="lambda". Default is TRUE.

component

a character indicating the component to plot: both (sum of sparse and dense), sparse or dense. Default to both.

title

the title. Default is set to the model name followed by what is on the Y-axis.

standardize

logical; standardize the coefficients before plotting (with the norm of the predictor). Default is TRUE.

labels

vector indicating the names associated to the plotted variables. When specified, a legend is drawn in order to identify each variable. Only relevant when the number of predictor is small. Remind that the intercept does not count. Default is NULL.

Returns

a ggplot2 object .

Method clone()

The objects of this class are cloneable with this method.

Usage

LavaFit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.