bic.BLMCP | R Documentation |
Selects a point along the regularization path of a fitted BLMCP
object according to
the BIC.
bic.BLMCP( G, E, Y, weight = NULL, lambda1_set = NULL, lambda2_set = NULL, nlambda1 = 20, nlambda2 = 20, gamma1 = 6, gamma2 = 6, max_iter = 200 )
G |
Input matrix of |
E |
Input matrix of |
Y |
Response variable. A quantitative vector for continuous response. For survival response, |
weight |
Observation weights. |
lambda1_set |
A user supplied lambda sequence for group minimax concave penalty (MCP), where each main G effect and its corresponding interactions are regarded as a group. |
lambda2_set |
A user supplied lambda sequence for MCP accommodating interaction selection. |
nlambda1 |
The number of lambda1 values. |
nlambda2 |
The number of lambda2 values. |
gamma1 |
The regularization parameter of the group MCP penalty. |
gamma2 |
The regularization parameter of the MCP penalty. |
max_iter |
Maximum number of iterations. |
An object with S3 class "bic.BLMCP"
is returned, which is a list with the ingredients of the BIC fit.
call |
The call that produced this object. |
alpha |
The matrix of the coefficients for main E effects, each column corresponds to one combination of (lambda1,lambda2). |
beta |
The coefficients for main G effects and G-E interactions, each column corresponds to
one combination of (lambda1,lambda2). For each column, the first element is the first G effect and
the second to ( |
df |
The number of nonzeros for each value of (lambda1,lambda2). |
BIC |
Bayesian Information Criterion for each value of (lambda1,lambda2). |
alpha_estimate |
Final alpha estimate using Bayesian Information Criterion. |
beta_estimate |
Final beta estimate using Bayesian Information Criterion. |
lambda_combine |
The matrix of (lambda1, lambda2), with the first column being the values of lambda1, the second being the values of lambda2. |
Mengyun Wu, Yangguang Zang, Sanguo Zhang, Jian Huang, and Shuangge Ma.
Accommodating missingness in environmental measurements in gene-environment interaction
analysis. Genetic Epidemiology, 41(6):523-554, 2017.
Jin Liu, Jian Huang, Yawei Zhang, Qing
Lan, Nathaniel Rothman, Tongzhang Zheng, and Shuangge Ma.
Identification of gene-environment interactions in cancer studies using penalization.
Genomics, 102(4):189-194, 2013.
predict
, coef
and plot
methods,
and the BLMCP
function.
set.seed(100) sigmaG=AR(0.3,50) G=MASS::mvrnorm(150,rep(0,50),sigmaG) E=matrix(rnorm(150*5),150,5) E[,2]=E[,2]>0;E[,3]=E[,3]>0 alpha=runif(5,2,3) beta=matrix(0,5+1,50);beta[1,1:8]=runif(8,2,3) beta[2:4,1]=runif(3,2,3) beta[2:3,2]=runif(2,2,3) beta[5,3]=runif(1,2,3) # continuous with Normal error y1=simulated_data(G=G,E=E,alpha=alpha,beta=beta,error=rnorm(150),family="continuous") # survival with Normal error y2=simulated_data(G,E,alpha,beta,rnorm(150,0,1),family="survival",0.8,1) # continuous fit1<-bic.BLMCP(G,E,y1,weight=NULL,lambda1_set=NULL,lambda2_set=NULL, nlambda1=10,nlambda2=10,gamma1=6,gamma2=6,max_iter=200) coef1=coef(fit1) y1_hat=predict(fit1,E,G) plot(fit1) ## survival fit2<-bic.BLMCP(G,E,y2,weight=NULL,lambda1_set=NULL,lambda2_set=NULL, nlambda1=20,nlambda2=20,gamma1=6,gamma2=6,max_iter=200) coef2=coef(fit2) y2_hat=predict(fit2,E,G) plot(fit2)
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