s_soda: S-SODA algorithm for general index model variable selection

In sodavis: SODA: Main and Interaction Effects Selection for Logistic Regression, Quadratic Discriminant and General Index Models

Description

S-SODA is an extension of SODA to conduct variable selection for general index models with continuous response. S-SODA first evenly discretizes the continuous response into H slices, and then apply SODA on the discretized response. Compared with existing variable selection methods based on the Sliced Inverse Regression (SIR), SODA requires neither the linearity nor the constant variance condition and is much more robust.

Usage

s_soda(x, y, H = 5, gam = 0, minF = 3, norm = F, debug = F)

Arguments

x	The design matrix, of dimensions n * p, without an intercept. Each row is an observation vector.
y	The response vector of dimension n * 1.
H	The number of slices.
gam	EBIC penalization coefficient parameter for SODA.
minF	Minimum number of steps in forward interaction screening. Default is minF=3.
norm	If set as True, S-SODA first marginally quantile-normalize each predictor to the standard normal distribution.
debug	If print debug information.

Value

BIC	Trace of extended Bayesian information criterion (EBIC) score.
Var	Trace of selected variables.
Term	Trace of selected main and interaction terms.
best_BIC	Final selected term set EBIC score.
best_Var	Final selected variables.
best_Term	Final selected main and interaction terms.

Examples

# # (uncomment the code to run)
# # Simulation:  x1 / (1 + x2^2) example 
# N = 500
# x1 = runif(N, -3, +3)
# x2 = runif(N, -3, +3)
# x3 = x1 / exp(x2^2) + rnorm(N, 0, 0.2)
# ss = s_soda_model(cbind(x1,x2), x3, H=25)
# 
# # true surface in grid
# MM = 50
# xx1 = seq(-3, +3, length.out = MM)
# xx2 = seq(-3, +3, length.out = MM)
# yyy = matrix(0, MM, MM)
# for(i in 1:MM)
#   for(j in 1:MM)
#     yyy[i,j] = xx1[i] / exp(xx2[j]^2)
# 
# # predicted surface
# ppp = s_soda_pred_grid(xx1, xx2, ss, po=1)
# 
# par(mfrow=c(1, 2), mar=c(1.75, 3, 1.25, 1.5))
# persp(xx1, xx2, yyy, theta=-45, xlab="X1", ylab="X2", zlab="Y")
# persp(xx1, xx2, ppp, theta=-45, xlab="X1", ylab="X2", zlab="Pred")
# 
# # Pumadyn dataset
# #data(pumadyn);
# #s_soda(pumadyn_isample_x, pumadyn_isample_y, H=25, gam=0)

sodavis documentation built on May 2, 2019, 12:38 p.m.