SIR_threshold_opt: SIR optimally thresholded

In SIRthresholded: Sliced Inverse Regression with Thresholding

SIR optimally thresholded

Description

Apply a single-index SIR on (X,Y) with H slices, with a soft/hard thresholding of the interest matrix \widehat{\Sigma}_n^{-1}\widehat{\Gamma}_n by an optimal parameter \lambda_{opt}. The \lambda_{opt} is found automatically among a vector of n_lambda \lambda, starting from 0 to the maximum value of \widehat{\Sigma}_n^{-1}\widehat{\Gamma}_n. For each feature of X, the number of \lambda associated with a selection of this feature is stored (in a vector of size p). This vector is sorted in a decreasing way. Then, thanks to strucchange::breakpoints, a breakpoint is found in this sorted vector. The coefficients of the variables at the left of the breakpoint, tend to be automatically toggled to 0 due to the thresholding operation based on \lambda_{opt}, and so should be removed (useless variables). Finally, \lambda_{opt} corresponds to the first \lambda such that the associated \hat{b} provides the same number of zeros as the breakpoint's value.

For example, for X \in R^{10} and n_lambda=100, this sorted vector can look like this :

X10	X3	X8	X5	X7	X9	X4	X6	X2	X1
2	3	3	4	4	4	6	10	95	100

Here, the breakpoint would be 8.

Usage

SIR_threshold_opt(
  Y,
  X,
  H = 10,
  n_lambda = 100,
  thresholding = "hard",
  graph = TRUE,
  output = TRUE,
  choice = ""
)

Arguments

Y	A numeric vector representing the dependent variable (a response vector).
X	A matrix representing the quantitative explanatory variables (bind by column).
H	The chosen number of slices (default is 10).
n_lambda	The number of lambda to test. The n_lambda tested lambdas are uniformally distributed between 0 and the maximum value of the interest matrix. (default is 100).
thresholding	The thresholding method to choose between hard and soft (default is hard).
graph	A boolean, set to TRUE to plot graphs (default is TRUE).
output	A boolean, set to TRUE to print informations (default is TRUE).
choice	the graph to plot: "estim_ind" Plot the estimated index by the SIR model versus Y. "opt_lambda" Plot the choice of the optimal lambda. "cos2_selec" Plot the evolution of cos^2 and variable selection according to lambda. "regul_path" Plot the regularization path of b. "" Plot every graphs (default).

Value

An object of class SIR_threshold_opt, with attributes:

b	This is the optimal estimated EDR direction, which is the principal eigenvector of the interest matrix.
lambdas	A vector that contains the tested lambdas.
lambda_opt	The optimal lambda.
mat_b	A matrix of size p*n_lambda that contains an estimation of beta in the columns for each lambda.
n_lambda	The number of lambda tested.
vect_nb_zeros	The number of 0 in b for each lambda.
list_relevant_variables	A list that contains the variables selected by the model.
fit_bp	An object of class breakpoints from the strucchange package, that contains informations about the breakpoint which allows to deduce the optimal lambda.
indices_useless_var	A vector that contains p items: each variable is associated with the number of lambda that selects this variable.
vect_cos_squared	A vector that contains for each lambda, the cosine squared between vanilla SIR and SIR thresholded.
Y	The response vector.
n	Sample size.
p	The number of variables in X.
H	The chosen number of slices.
M1	The interest matrix thresholded with the optimal lambda.
thresholding	The thresholding method used.
call	Unevaluated call to the function.
X_reduced	The X data restricted to the variables selected by the model. It can be used to estimate a new SIR model on the relevant variables to improve the estimation of b.
index_pred	The index Xb' estimated by SIR.

Examples

# Generate Data
set.seed(2)
n <- 200
beta <- c(1,1,rep(0,8))
X <- mvtnorm::rmvnorm(n,sigma=diag(1,10))
eps <- rnorm(n)
Y <- (X%*%beta)**3+eps

# Apply SIR with soft thresholding
SIR_threshold_opt(Y,X,H=10,n_lambda=300,thresholding="soft")

SIRthresholded documentation built on July 10, 2023, 2:03 a.m.