dmcp | R Documentation |
This function obtains the first derivative function of MCP (Minimax Concave Penalty)
dmcp(theta, lambda, gamma = 3)
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. |
Rigorously speaking, the regularization parametre \gamma
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 \gamma
,
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.
the first derivative of MCP function.
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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s12863-017-0495-5")}
theta=runif(20,-5,5)
lambda=1
dmcp(theta,lambda,gamma=3)
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