Description Usage Arguments Value Examples
This package contains a function called "LPR" to estimate coefficients using "Lasso and Partial Ridge" method and to calculate confidence intervals through bootstrap.
1 |
x |
explanatory variables |
y |
dependent variable |
lambda2 |
tuning parameter for partial ridge, suggested value is 1/n |
B |
the times of bootstrap |
type.boot |
the type of bootstrap, "paired" or "residual" |
alpha |
confidence level |
lambda.opt |
chosen tuning parameter for LASSO |
Beta |
regression coefficients estimated by LASSO |
Beta.LPR |
regression coefficients estimated by LASSO and Partial Ridge(LPR) |
selectset |
coefficients set selected by LASSO |
interval.LPR |
confidence interval through bootstrap |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | #generate dataset
set.seed(2015)
n <- 100
p <- 250
B <- 100
s <- 10
rho <- 0.5
z <- matrix(rnorm(n*p),ncol=p)
x <- matrix(0,n,p)
x[,1] <- z[,1]
for(j in 2:p){
x[,j] <- rho*x[,j-1]+sqrt(1-rho^2)*z[,j]
}
#beta is decaying
beta <- rep(0,p)
ind.s <- sample(1:p,s)
beta[ind.s] <- rnorm(s,1,sqrt(0.001))
for( j in setdiff(1:p,ind.s) ){
beta[j]<-1/(j+3)^2
}
#generate y
epsilon <- rep(0,n)
epsilon <- rnorm(n,0,0.1)
y <- x%*%beta + epsilon
#use LPR
LPR.obj <- LPR(x, y, 1/n, B, type.boot="paired", alpha=0.05)
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