bic.PTReg | R Documentation |
Selects a point along the regularization path of a fitted PTReg
object according to
the BIC.
bic.PTReg( G, E, Y, lambda1_set, lambda2_set, gamma1, gamma2, max_init, h = NULL, tau = 0.4, mu = 2.5, family = c("continuous", "survival") )
G |
Input matrix of |
E |
Input matrix of |
Y |
Response variable. A quantitative vector for |
lambda1_set |
A user supplied lambda sequence for minimax concave penalty (MCP) accommodating main G effect selection. |
lambda2_set |
A user supplied lambda sequence for MCP accommodating interaction selection. |
gamma1 |
The regularization parameter of the MCP penalty corresponding to G effects. |
gamma2 |
The regularization parameter of the MCP penalty corresponding to G-E interactions. |
max_init |
The number of initializations. |
h |
The number of the trimmed samples if the parameter |
tau |
The threshold value used in stability selection. |
mu |
The parameter for screening outliers with extreme absolute residuals if the number of
the trimmed samples |
family |
Response type of |
An object with S3 class "bic.PTReg"
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 ( |
intercept |
Matrix of the intercept estimate, each column corresponds to one combination of (lambda1,lambda2). |
df |
The number of nonzeros for each value of (lambda1,lambda2). |
BIC |
Bayesian Information Criterion for each value of (lambda1,lambda2). |
family |
The same as input |
intercept_estimate |
Final intercept estimate using Bayesian Information Criterion. |
alpha_estimate |
Final alpha estimate using Bayesian Information Criterion. |
beta_estimate |
Final beta estimate using Bayesian Information Criterion. |
lambda_combine |
Matrix of (lambda1, lambda2), with the first column being the values of lambda1, the second being the values of lambda2. |
Yaqing Xu, Mengyun Wu, Shuangge Ma, and Syed Ejaz Ahmed. Robust gene-environment interaction analysis using penalized trimmed regression. Journal of Statistical Computation and Simulation, 88(18):3502-3528, 2018.
sigmaG<-AR(rho=0.3,p=30) sigmaE<-AR(rho=0.3,p=3) set.seed(300) G=MASS::mvrnorm(150,rep(0,30),sigmaG) EC=MASS::mvrnorm(150,rep(0,2),sigmaE[1:2,1:2]) ED = matrix(rbinom((150),1,0.6),150,1) E=cbind(EC,ED) alpha=runif(3,0.8,1.5) beta=matrix(0,4,30) beta[1,1:4]=runif(4,1,1.5) beta[2,c(1,2)]=runif(2,1,1.5) lambda1_set=lambda2_set=c(0.2,0.25,0.3,0.35,0.4,0.5) #continuous response with outliers/contaminations in response variable y1=simulated_data(G,E,alpha,beta,error=c(rnorm(140),rcauchy(10,0,5)),family="continuous") fit1<-bic.PTReg(G,E,y1,lambda1_set,lambda2_set,gamma1=6,gamma2=6, max_init=50,tau=0.6,mu=2.5,family="continuous") coefficients1=coefficients(fit1) y_predict=predict(fit1,E,G) plot(fit1) # survival with Normal error y2=simulated_data(G,E,alpha,beta,rnorm(150,0,1),family="survival",0.7,0.9) fit2<-bic.PTReg(G,E,y2,lambda1_set,lambda2_set,gamma1=6,gamma2=6, max_init=50,tau=0.6,mu=2.5,family="survival") coefficients2=coefficients(fit2) y_predict=predict(fit2,E,G) plot(fit2)
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