Description Usage Arguments Details Value Author(s) References See Also Examples
Coxnet
fits a Cox model regularized with net, elastic-net or lasso penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients.
Moreover, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter lambda
.
loCoxnet
fits a varying coefficient Cox model by kernel smoothing, incorporated with the aforementioned penalties.
The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.
1 2 3 4 5 6 7 8 9 10 | Coxnet(x, y, Omega = NULL, penalty = c("Lasso", "Enet", "Net"),
alpha = 1, lambda = NULL, nlambda = 50, rlambda = NULL, nfolds = 1, foldid = NULL,
inzero = TRUE, adaptive = c(FALSE, TRUE), aini = NULL, isd = FALSE,
ifast = TRUE, keep.beta = FALSE, thresh = 1e-6, maxit = 1e+5)
loCoxnet(x, y, w, w0 = NULL, h = NULL, hnext = NULL, Omega = NULL,
penalty = c("Lasso", "Enet", "Net"), alpha = 1, lambda = NULL,
nlambda = 50, rlambda = NULL, nfolds = 1, foldid = NULL,
adaptive = c(FALSE, TRUE), aini = NULL, isd = FALSE, keep.beta = FALSE,
thresh = 1e-6, thresh2 = 1e-10, maxit = 1e+5)
|
x |
input matrix. Each row is an observation vector. |
y |
response variable. |
w |
input vector, same length as |
w0 |
evaluation local points. The output of estimates are evaludated at these local value |
h |
bandwidth. |
hnext |
an increase in bandwidth |
Omega |
correlation/adjancy matrix with zero diagonal, used for |
penalty |
penalty type. Can choose |
alpha |
ratio between L1 and Laplacian for λ*{α*||β||_1+(1-α)/2*(β^{T}Lβ)}, where L is a Laplacian matrix calculated from λ*{α*||β||_1+(1-α)/2*||β||_2} . |
lambda |
a user supplied decreasing sequence. If |
nlambda |
number of |
rlambda |
fraction of |
nfolds |
number of folds. Default is |
foldid |
an optional vector of values between 1 and |
inzero |
logical flag for simultaneously tuning the number of non-zero coefficients with |
adaptive |
logical flags for adaptive version. Default is |
aini |
a user supplied initial estimate of β. It is a list including |
isd |
logical flag for outputing standardized coefficients. |
ifast |
logical flag for efficient calculation of risk set updates. Default is |
keep.beta |
logical flag for returning estimates for all |
thresh |
convergence threshold for coordinate descent. Default value is |
thresh2 |
threshold for removing very small |
maxit |
Maximum number of iterations for coordinate descent. Default is |
One-step coordinate descent algorithm is applied for each lambda
. ifast = TRUE
adopts an efficient way to update risk set and sometimes the algorithm ends before all nlambda
values of lambda
have been evaluated. To evaluate small values of lambda
, use ifast = FALSE
. The two methods only affect the efficiency of algorithm, not the estimates.
Cross-validation partial likelihood is used for tuning parameters. For inzero = TRUE
, we further select the number of non-zero coefficients obtained from regularized Cox model at each lambda
. This is motivated by formulating L0 variable selection in ADMM form.
For vayring coefficients methods, the bandwidth is selected by cross-validation. We recommend to check whether a small increase of h
, say h+hnext
, will improve the current cvm
.
Coxnet
outputs an object with S3 class "Coxnet"
.
Beta |
estimated coefficients. |
Beta0 |
coefficients after tuning the number of non-zeros, for |
fit |
a data.frame containing |
fit0 |
a data.frame containing |
lambda.max |
value of |
lambda.opt |
value of |
cv.nzero |
|
penalty |
penalty type. |
adaptive |
logical flags for adaptive version (see above). |
flag |
convergence flag (for internal debugging). |
loCoxnet
outputs an object with S3 class "Coxnet"
and "loCoxnet"
.
Beta |
a list of estimated coefficients with length of |
fit |
a data.frame containing |
lambda.max |
value of |
cvh |
a data.frame containing bandwidth, |
penalty |
penalty type. |
adaptive |
logical flags for adaptive version (see above). |
flag |
convergence flag (for internal debugging). |
Xiang Li, Donglin Zeng and Yuanjia Wang
Maintainer: Xiang Li <xl2473@columbia.edu>
Friedman, J., Hastie, T. and Tibshirani, R. (2008)
Regularization Paths for Generalized Linear Models via Coordinate
Descent, Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
http://www.jstatsoft.org/v33/i01/
Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011)
Regularization Paths for Cox's Proportional Hazards Model via
Coordinate Descent, Journal of Statistical Software, Vol. 39(5)
1-13
http://www.jstatsoft.org/v39/i05/
Sun, H., Lin, W., Feng, R., and Li, H. (2014)
Network-regularized high-dimensional cox regression for analysis of genomic data, Statistica Sinica.
http://www3.stat.sinica.edu.tw/statistica/j24n3/j24n319/j24n319.html
van Houwelingen, H. C., Bruinsma, T., Hart, A. A., van't Veer, L. J., & Wessels, L. F. (2006)
Cross-validated Cox regression on microarray gene expression data. Statistics in medicine, 25(18), 3201-3216.
http://onlinelibrary.wiley.com/doi/10.1002/sim.2353/full
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