README.md

In rakheon/svreg: Structural Varying-coefficient Regression

svreg: the structural varying-coefficient regression

The R package svreg implements the structural varying-coefficient regression (svReg) for the model selection of a varying-coefficient model with structured main predictors or modifying variables. It also provides functions for performing the pliable lasso (plasso) by Tibshirani and Friedman (2019).

Relevant references can be found at: svReg: Structural Varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups by Kim et al. (2021). A Pliable Lasso by Tibshirani and Friedman (2019).

Installation

devtools::install_github("rakheon/svreg", force = TRUE)

Example

library(svreg)

# data generation
x=matrix(rnorm(100*5, 0, 1),100,5)
z1=matrix(rnorm(100*3, 0, 1),100,3)
z2=matrix(as.factor(sample(0:3, 100*2, prob=c(1/4,1/4,1/4,1/4), replace = TRUE)),100,2)
z2=as.data.frame(model.matrix(~., data=as.data.frame(z2))[,-1])
z=cbind(z1, z2)
z=as.matrix(z)
y=2*x[,1] - (2+2*z[,1])*x[,2] + (2+3*z[,4]+2*z[,5]-2*z[,6])*x[,3] + rnorm(100, 0, 1)

# fitting the plasso and the svReg for a sequence of tuning parameters
plasso_res = plasso(X = x, Z = z, Y = y, lambda_seq = c(1, 0.5), alpha = 0.5)
svReg_res = svReg(X = x, Z = z, Y = y, df_X = rep(1,5), df_Z = c(1,1,1,3,3), lambda_seq = c(1, 0.5), alpha = 0.5)

# Perform k-fold cross validation
cv.plasso_res = cv.plasso(X = x, Z = z, Y = y, lambda_seq = c(1, 0.5), alpha = 0.5)
cv.svReg_res = cv.svReg(X = x, Z = z, Y = y, df_X = rep(1,5), df_Z = c(1,1,1,3,3), lambda_seq = c(1, 0.5), alpha = 0.5)

# Correlated design for grouped main predictors
x=matrix(rnorm(100*5, 0, 1),100,5)
z1=matrix(rnorm(100*3, 0, 1),100,3)
z2=matrix(as.factor(sample(0:3, 100*2, prob=c(1/4,1/4,1/4,1/4), replace = TRUE)),100,2)
z2=as.data.frame(model.matrix(~., data=as.data.frame(z2))[,-1])
z=cbind(z1, z2)
z=as.matrix(z)
x[,3] = 2/3*x[,1] + 2/3*x[,2] + 1/3*rnorm(100, 0, 1)
y = x[,1] + x[,2] + (2+3*z[,4]+2*z[,5]-2*z[,6])*x[,4] + rnorm(100, 0, 1)

# fitting the svReg for a sequence of tuning parameters and perform k-fold cross validation
svReg_res = svReg(X = x, Z = z, Y = y, df_X = c(3,1,1), df_Z = c(1,1,1,3,3), lambda_seq = c(1, 0.5), alpha = 0.5)
cv.svReg_res = cv.svReg(X = x, Z = z, Y = y, df_X = c(3,1,1), df_Z = c(1,1,1,3,3), lambda_seq = c(1, 0.5), alpha = 0.5)

rakheon/svreg documentation built on Feb. 10, 2021, 5:25 p.m.