dmcp {interep} | R Documentation |
This function obtains the first derivative function of MCP (Minimax Concave Penalty)
Description
This function obtains the first derivative function of MCP (Minimax Concave Penalty)
Usage
dmcp(theta, lambda, gamma = 3)
Arguments
theta |
a coefficient vector. |
lambda |
the tuning parameter. |
gamma |
the regularization parameter in MCP (Minimax Concave Penalty). It balances between the unbiasedness and concavity of MCP. |
Details
Rigorously speaking, the regularization parametre needs to be obtained via a data-driven approach.
Published studies suggest experimenting with a few values, such as 1.8, 3, 4.5, 6, and 10, then fixing its value. In our numerical
study, we have examined this sequence and found that the results are not sensitive to the choice of value of
,
and set the value at 3. In practice, to be prudent, values other than 3 should also be investigated. Similar discussions can be found
in the references below.
Value
the first derivative of MCP function.
References
Ren, J., Du, Y., Li, S., Ma, S., Jiang, Y. and Wu, C. (2019). Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis. Genetic epidemiology, 43(3), 276-291 doi:10.1002/gepi.22194
Ren, J., Jung, L., Du, Y., Wu, C., Jiang, Y. and Liu, J. (2019). regnet: Network-Based Regularization for Generalized Linear Models. R package, version 0.4.0
Wu, C., Zhang, Q., Jiang, Y. and Ma, S. (2018). Robust network-based analysis of the associations between (epi) genetic measurements. Journal of multivariate analysis, 168, 119-130 doi:10.1016/j.jmva.2018.06.009
Ren, J., He, T., Li, Y., Liu, S., Du, Y., Jiang, Y. and Wu, C. (2017). Network-based regularization for high dimensional SNP data in the case–control study of Type 2 diabetes. BMC genetics, 18(1), 44 doi:10.1186/s12863-017-0495-5
Examples
theta=runif(20,-5,5)
lambda=1
dmcp(theta,lambda,gamma=3)