| Type: | Package |
| Title: | Sure Independence Screening via Quantile Correlation and Composite Quantile Correlation |
| Version: | 0.1 |
| Date: | 2015-12-02 |
| Author: | Xuejun Ma, Jingxiao Zhang, Jingke Zhou |
| Maintainer: | Xuejun Ma <yinuoyumi@163.com> |
| Description: | Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data. |
| License: | GPL-2 |
| URL: | http://www.r-project.org |
| NeedsCompilation: | no |
| Packaged: | 2015-12-02 11:38:00 UTC; yinuo |
| Repository: | CRAN |
| Date/Publication: | 2015-12-02 14:22:26 |
Sure Independence Screening via Quantile Correlation and Composite Quantile Correlation
Description
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Details
| Package: | QCSIS |
| Type: | Package |
| Title: | Sure Independence Screening via Quantile Correlation and Composite Quantile Correlation |
| Version: | 0.1 |
| Date: | 2015-12-02 |
| Author: | Xuejun Ma, Jingxiao Zhang, Jingke Zhou |
| Maintainer: | Xuejun Ma <yinuoyumi@163.com> |
| Description: | Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data. |
| License: | GPL-2 |
| URL: | http://www.r-project.org |
Index of help topics:
CQCSIS Compsote Quantile Correlation-Sure Independence
Screening (CQC-SIS)
QCSIS Quantile Correlation-Sure Independence
Screening (QC-SIS)
QCSIS-package Sure Independence Screening via Quantile
Correlation and Composite Quantile Correlation
cqc Composite Quantile Correlation
qc Quantile Correlation
Author(s)
Xuejun Ma, Jingxiao Zhang, Jingke Zhou
Maintainer: Xuejun Ma <yinuoyumi@163.com>
References
Xuejun Ma and Jingxiao Zhang. Robust model-free feature screening via quantile correlation. Journal of Multivariate Analysis. Online, 2015.
Xuejun Ma et al.. Robust feature screening via composite quantile correlation learning. In submission.
Compsote Quantile Correlation-Sure Independence Screening (CQC-SIS)
Description
The function implemrnts the composite quantile correlation-sure independence screening (CQC-SIS).
Usage
CQCSIS(x, y, d)
Arguments
x |
The design matrix, of dimensions n * p, without an intercept. |
y |
The response vector of dimension n * 1. |
d |
The tuning parameter used to covarites had significant effect on the response variable, such as [n/log(n)], or n-1. |
Value
w |
The estimate of w. |
M |
The subscript of x recuited by CQC-SIS. |
Author(s)
Xuejun Ma, Jingxiao Zhang, Jingke Zhou
References
Xuejun Ma et al.. Robust feature screening via composite quantile correlation learning. In submission.
Examples
n <- 20
p <- 200
x <- matrix(rnorm(n * p), n, p)
e <- rnorm(n, 0, 1)
beta1 <- 3 - runif(1)
beta2 <- 3 - runif(1)
beta3 <- 3 - runif(1)
y <- beta1 * x[, 1] + beta2 * x[, 2] + beta3 * x[, 3] + e
d <- 19
fit.CQCSIS <- CQCSIS(x = x, y = y, d = d)
fit.CQCSIS$M
Quantile Correlation-Sure Independence Screening (QC-SIS)
Description
The function implemrnts the quantile correlation-sure independence screening (QC-SIS).
Usage
QCSIS(x, y, tau, d)
Arguments
x |
The design matrix, of dimensions n * p, without an intercept. |
y |
The response vector of dimension n * 1. |
tau |
The quantile(s) to be estimated. By default, tau=1:(n-1)/n. |
d |
The tuning parameter used to covarites had significant effect on the response variable, such as [n/log(n)],or n-1 |
Value
w |
The estimate of w. |
M |
The subscript of x recuited by QC-SIS. |
Author(s)
Xuejun Ma, Jingxiao Zhang, Jingke Zhou
References
Xuejun Ma and Jingxiao Zhang. Robust model-free feature screening via quantile correlation. Journal of Multivariate Analysis. Online, 2015.
Examples
n <- 20
p <- 200
r <- 0.05
x <- matrix(rnorm(n * p), n, p)
e <- rnorm(n, 0, 1)
inde <- sample(n, r * n)
x[inde, 1] <- 2 * sqrt(rchisq(r * n, df = p))
y <- 5 * x[, 1] + 5 * x[, 2] + 5 * x[, 3] + e
d <- 19
fit.QCSIS <- QCSIS(x = x, y = y, d = d)
fit.QCSIS$M
Composite Quantile Correlation
Description
cqc is used to compute the composite quantile correlation.
Usage
cqc(x, y)
Arguments
x |
The covariate variable. |
y |
The response variable. |
Value
cqc |
The value of composite quantile correlation. |
Author(s)
Xuejun Ma, Jingxiao Zhang, Jingke Zhou
References
Xuejun Ma et al.. Robust feature screening via composite quantile correlation learning. In submission.
Examples
x <- rnorm(100)
y <- rnorm(100)
cqc(x = x, y = y)
Quantile Correlation
Description
qc is used to compute the quantile correlation with given quantiles.
Usage
qc(x, y, tau)
Arguments
x |
The covariate variable. |
y |
The response variable. |
tau |
The quantile(s) to be estimated. |
Value
tau |
The quantile(s). |
rho |
The value of quantile correlation. |
Author(s)
Xuejun Ma, Jingxiao Zhang, Jingke Zhou
References
Li et al.. Quantile correlations and quantile autoregressive modeling. Journal of the American Statistical Association,2015,110(509):246–261.
Examples
n <- 1000
x <- rnorm(n)
y <- 2 * x + rt(n,df = 1)
tau <- 1:9 / 10
qc(x = x, y = y, tau = tau)