Class "LavaFit"

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

See also

QuadrupenFit, lava()

Super class

QuadrupenFit -> LavaFit

Active bindings

penalty

character describing the regularizer/penalty

lambda1

vector of tuning parameters for the l1 penalty (sparse component)

lambda2

vector of tuning parameters for the l2 penalty (dense component)

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

• LavaFit$new()

• LavaFit$fit()

• LavaFit$plot_path()

• LavaFit$clone()

Inherited methods

• QuadrupenFit$criteria()
• QuadrupenFit$cross_validate()
• QuadrupenFit$get_model()
• QuadrupenFit$plot()
• QuadrupenFit$predict()
• QuadrupenFit$print()
• QuadrupenFit$show()
• QuadrupenFit$stability()

---

LavaFit$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

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LavaFit$fit()

function performing the optimization

Usage

LavaFit$fit(control)

Arguments

control

list controlling the optimization process Plot method for lava regularization path

---

LavaFit$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 .

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LavaFit$clone()

The objects of this class are cloneable with this method.

Usage

LavaFit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.