summary.piqr {qrcmNP}

Summary After Penalized Quantile Regression Coefficients Modeling

Description

Summary of an object of class “piqr”, after selecting the best tuning parameter.

Usage

## S3 method for class 'piqr'
summary(object, pos.lambda, SE=FALSE, p, cov=FALSE, ...)

Arguments

object

an object of class “piqr”, the result of a call to piqr.

pos.lambda

the position of a lambda in the sequence of the object of class “piqr”. Could be the best after selecting the result of a call to gof.piqr

SE

if TRUE standard errors are printed. Standard errors are computed through sandwich formula only for the regularized parameters.

p

an optional vector of quantiles.

cov

ff TRUE, the covariance matrix of \beta(p) is reported. Ignored if p is missing.

...

for future methods.

Details

If the best lambda or one value of lambda is chosen a summary of the selected model is printed.

Value

See details in summary.iqr

Author(s)

Gianluca Sottile [e-mail]

See Also

piqr, for model fitting; gof.piqr, to find the best lambda value; predict.piqr and plot.piqr, for predicting and plotting objects of class “piqr”.

Examples

# using simulated data

set.seed(1234)
n <- 300
x1 <- rexp(n)
x2 <- runif(n, 0, 5)
x <- cbind(x1,x2)

b <- function(p){matrix(cbind(1, qnorm(p), slp(p, 2)), nrow=4, byrow=TRUE)}
theta <- matrix(0, nrow=3, ncol=4); theta[, 1] <- 1; theta[1,2] <- 1; theta[2:3,3] <- 2
qy <- function(p, theta, b, x){rowSums(x * t(theta %*% b(p)))}

y <- qy(runif(n), theta, b, cbind(1, x))

s <- matrix(1, nrow=3, ncol=4); s[1,3:4] <- 0
obj <- piqr(y ~ x1 + x2, formula.p = ~ I(qnorm(p)) + slp(p, 2), s=s, nlambda=50)

best <- gof.piqr(obj, method="AIC", plot=FALSE)
best2 <- gof.piqr(obj, method="BIC", plot=FALSE)

summary(obj, best$posMinLambda)
summary(obj, best2$posMinLambda)