View source: R/SIR_threshold.R
SIR_threshold  R Documentation 
Apply a singleindex SIR
on (X,Y)
with H
slices, with a parameter \lambda
which
apply a soft/hard thresholding to the interest matrix \widehat{\Sigma}_n^{1}\widehat{\Gamma}_n
.
SIR_threshold(
Y,
X,
H = 10,
lambda = 0,
thresholding = "hard",
graph = TRUE,
choice = ""
)
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). 
lambda 
The thresholding parameter (default is 0). 
thresholding 
The thresholding method to choose between hard and soft (default is hard). 
graph 
A boolean that must be set to true to display graphics (default is TRUE). 
choice 
the graph to plot:

An object of class SIR_threshold, with attributes:
b 
This is an estimated EDR direction, which is the principal eigenvector of the interest matrix. 
M1 
The interest matrix thresholded. 
eig_val 
The eigenvalues of the interest matrix thresholded. 
eig_vect 
A matrix corresponding to the eigenvectors of the interest matrix. 
Y 
The response vector. 
n 
Sample size. 
p 
The number of variables in X. 
H 
The chosen number of slices. 
nb.zeros 
The number of 0 in the estimation of the vector beta. 
index_pred 
The index Xb' estimated by SIR. 
list.relevant.variables 
A list that contains the variables selected by the model. 
cos_squared 
The cosine squared between vanilla SIR and SIR thresholded. 
lambda 
The thresholding parameter used. 
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. 
# Generate Data
set.seed(10)
n < 500
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 hard thresholding
SIR_threshold(Y, X, H = 10, lambda = 0.2, thresholding = "hard")
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