sharp (version 1.4.6)

SelectionAlgo: Variable selection algorithm

Description

Runs the variable selection algorithm specified in the argument implementation. This function is not using stability.

Usage

SelectionAlgo(
  xdata,
  ydata = NULL,
  Lambda,
  group_x = NULL,
  scale = TRUE,
  family = NULL,
  implementation = PenalisedRegression,
  ...
)

Value

A list with:

selected

matrix of binary selection status. Rows correspond to different model parameters. Columns correspond to predictors.

beta_full

array of model coefficients. Rows correspond to different model parameters. Columns correspond to predictors. Indices along the third dimension correspond to outcome variable(s).

Arguments

xdata

matrix of predictors with observations as rows and variables as columns.

ydata

optional vector or matrix of outcome(s). If family is set to "binomial" or "multinomial", ydata can be a vector with character/numeric values or a factor.

Lambda

matrix of parameters controlling the level of sparsity in the underlying feature selection algorithm specified in implementation. If Lambda=NULL and implementation=PenalisedRegression, LambdaGridRegression is used to define a relevant grid.

group_x

vector encoding the grouping structure among predictors. This argument indicates the number of variables in each group. Only used for models with group penalisation (e.g. implementation=GroupPLS or implementation=SparseGroupPLS).

scale

logical indicating if the predictor data should be scaled.

family

type of regression model. This argument is defined as in glmnet. Possible values include "gaussian" (linear regression), "binomial" (logistic regression), "multinomial" (multinomial regression), and "cox" (survival analysis).

implementation

function to use for variable selection. Possible functions are: PenalisedRegression, SparsePLS, GroupPLS and SparseGroupPLS. Alternatively, a user-defined function can be provided.

...

additional parameters passed to the function provided in implementation.

See Also

VariableSelection, PenalisedRegression, SparsePCA, SparsePLS, GroupPLS, SparseGroupPLS

Other wrapping functions: GraphicalAlgo()

Examples

# Data simulation (univariate outcome)
set.seed(1)
simul <- SimulateRegression(pk = 50)

# Running the LASSO
mylasso <- SelectionAlgo(
  xdata = simul$xdata, ydata = simul$ydata,
  Lambda = c(0.1, 0.2), family = "gaussian",
)

# Data simulation (multivariate outcome)
set.seed(1)
simul <- SimulateRegression(pk = 50, q = 3)

# Running multivariate Gaussian LASSO
mylasso <- SelectionAlgo(
  xdata = simul$xdata, ydata = simul$ydata,
  Lambda = c(0.1, 0.2), family = "mgaussian"
)
str(mylasso)