View source: R/mainFunctions.R
qic.select.rq.pen.seq | R Documentation |
Selects tuning parameter \lambda
and a according to information criterion of choice. For a given \hat{\beta}
the information criterion is calculated
as
\log(\sum_{i=1}^n w_i \rho_\tau(y_i-x_i^\top\hat{\beta})) + d*b/(2n),
where d is the number of nonzero coefficients and b depends on the method used. For AIC b=2
,
for BIC b=log(n)
and for PBIC d=log(n)*log(p)
where p is the dimension of \hat{\beta}
.
If septau set to FALSE then calculations are made across the quantiles. Let \hat{\beta}^q
be the coefficient vector for the qth quantile of Q quantiles. In addition let d_q
and b_q
be d and b values from the qth quantile model. Note, for all of these we are assuming eqn and a are the same. Then the summary across all quantiles is
\sum_{q=1}^Q w_q[ \log(\sum_{i=1}^n m_i \rho_\tau(y_i-x_i^\top\hat{\beta}^q)) + d_q*b_q/(2n)],
where w_q
is the weight assigned for the qth quantile model.
## S3 method for class 'rq.pen.seq'
qic.select(
obj,
method = c("BIC", "AIC", "PBIC"),
septau = ifelse(obj$penalty != "gq", TRUE, FALSE),
tauWeights = NULL,
...
)
obj |
A rq.pen.seq or rq.pen.seq.cv object. |
method |
Choice of BIC, AIC or PBIC, a large p BIC. |
septau |
If optimal values of |
tauWeights |
Weights for each quantile. Useful if you set septau to FALSE but want different weights for the different quantiles. If not specified default is to have |
... |
Additional arguments. |
Coefficients of the selected models.
Information criterion values for all considered models.
Model info for the selected models related to the original object obj.
Information criterion summarized across all quantiles. Only returned if septau set to FALSE
Ben Sherwood, ben.sherwood@ku.edu
qrbicrqPen
set.seed(1)
x <- matrix(runif(800),ncol=8)
y <- 1 + x[,1] + x[,8] + (1+.5*x[,3])*rnorm(100)
m1 <- rq.pen(x,y,penalty="ENet",a=c(0,.5,1),tau=c(.25,.75))
qic.select(m1)
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