Description Usage Arguments Details Value Author(s) See Also Examples
Summary of an object of class “piqr
”, after selecting the best tuning parameter.
1 2 |
object |
an object of class “ |
lambda |
a value of lambda in the sequence of the object of class “ |
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 β(p) is reported. Ignored if p is missing. |
... |
for future methods. |
If the best lambda or one value of lambda is chosen a summary of the selected model is printed.
See details in summary.iqr
Gianluca Sottile gianluca.sottile@unipa.it
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
”.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # using simulated data
set.seed(1234)
n <- 1000
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)
best <- gof.piqr(obj, method="AIC", plot=FALSE)
summary(obj, best$minLambda)
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