rq.fit.sfnc: Sparse Constrained Regression Quantile Fitting

In quantreg: Quantile Regression

Sparse Constrained Regression Quantile Fitting

Description

Fit constrained regression quantiles using a sparse implementation of the Frisch-Newton Interior-point algorithm.

Usage

rq.fit.sfnc(x, y, R, r, tau = 0.5,
            rhs = (1-tau)*c(t(x) %*% rep(1,length(y))),control)

Arguments

x	structure of the design matrix X stored in csr format
y	response vector
R	constraint matrix stored in csr format
r	right-hand-side of the constraint
tau	desired quantile
rhs	the right-hand-side of the dual problem; regular users shouldn't need to specify this.
control	control paramters for fitting see sfn.control

Details

This is a sparse implementation of the Frisch-Newton algorithm for constrained quantile regression described in Koenker and Portnoy (1996). The sparse matrix linear algebra is implemented through the functions available in the R package SparseM.

Value

coef	Regression quantile coefficients
ierr	Error code for the internal Fortran routine srqfn: 1: insufficient work space in call to extract 3: insufficient storage in iwork when calling ordmmd 4: insufficient storage in iwork when calling sfinit 5: nnzl > nnzlmax when calling sfinit 6: nsub > nsubmax when calling sfinit 7: insufficient work space in iwork when calling symfct 8: inconsistancy in input when calling symfct 9: tmpsiz > tmpmax when calling symfct; increase tmpmax 10: nonpositive diagonal encountered when calling blkfct 11: insufficient work storage in tmpvec when calling blkfct 12: insufficient work storage in iwork when calling blkfct 13: nnzd > nnzdmax in e,je when calling amub 14: nnzd > nnzdmax in g,jg when calling amub 15: nnzd > nnzdmax in h,jh when calling aplb 15: tiny diagonals replaced with Inf when calling blkfct
it	Iteration count
time	Amount of time used in the computation

Author(s)

Pin Ng

References

Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R; https://CRAN.R-project.org/package=SparseM

Koenker, R. and P. Ng(2005). Inequality Constrained Quantile Regression, Sankya, 418-440.

See Also

rq.fit.sfn for the unconstrained version, SparseM for the underlying sparse matrix R package.

Examples

## An artificial example :
n <- 200
p <- 50
set.seed(17)
X <- rnorm(n*p)
X[abs(X) < 2.0] <- 0
X <- cbind(1,matrix(X,n,p))
y <- 0.5 * apply(X,1,sum) + rnorm(n) ## true beta = (0.5, 0.5, ...)
R <- rbind(diag(p+1), -diag(p+1))
r <- c(rep( 0, p+1), rep(-1, p+1))

sX <- as.matrix.csr(X)
sR <- as.matrix.csr(R)
try(rq.o <- rq.fit.sfnc(sX, y, sR, r)) #-> not enough tmp memory

(tmpmax <- floor(1e5 + exp(-12.1)*(sX@ia[p+1]-1)^2.35))
## now ok:
rq.o <- rq.fit.sfnc(sX, y, sR, r, control = list(tmpmax = tmpmax))

quantreg documentation built on Oct. 22, 2024, 5:07 p.m.