# Function to Fit the Penalized Parametric Bayesian Accelerated Failure Time Model with Group Lasso Prior Penalized parametric Bayesian accelerated failure time model with group lasso prior is implemented to analyze survival data with high-dimensional covariates. ```r aftGL(Y, data, grpInx, hyperParams, startValues, mcmc) ``` ## Arguments - `Y`: a data.frame containing univariate time-to-event outcomes from `n` subjects. It is of dimension $n\times 2$: the columns correspond to $y$, $\delta$. - `data`: a data.frame containing $p$ covariate vectors from `n` subjects. It is of dimension $n\times p$. - `grpInx`: a vector of $p$ group indicator for each variable - `hyperParams`: a list containing hyperparameter values in hierarchical models: (`nu0`, `sigSq0`): hyperparameters for the prior of $\sigma^2$; (`alpha0`, `h0`): hyperparameters for the prior of $\alpha$; (`rLam`, `deltaLam`): hyperparameters for the prior of $\lambda^2$. - `startValues`: a list containing starting values for model parameters. See Examples below. - `mcmc`: a list containing variables required for MCMC sampling. Components include, `numReps`, total number of scans; `thin`, extent of thinning; `burninPerc`, the proportion of burn-in. See Examples below. ## Returns `aftGL` returns an object of class `aftGL`. ## References Lee, K. H., Chakraborty, S., and Sun, J. (2017). Variable Selection for High-Dimensional Genomic Data with Censored Outcomes Using Group Lasso Prior. **Computational Statistics and Data Analysis**, Volume 112, pages 1-13. ## Author(s) Kyu Ha Lee, Sounak Chakraborty, (Tony) Jianguo Sun ## See Also `VS` ## Examples ```r # generate some survival data set.seed(204542) p = 20 n = 200 logHR.true <- c(rep(4, 10), rep(0, (p-10))) CovX<-matrix(0,p,p) for(i in 1:10){ for(j in 1:10){ CovX[i,j] <- 0.3^abs(i-j) } } diag(CovX) <- 1 data <- apply(rmvnorm(n, sigma=CovX, method="chol"), 2, scale) pred <- as.vector(exp(rowSums(scale(data, center = FALSE, scale = 1/logHR.true)))) t <- rexp(n, rate = pred) cen <- runif(n, 0, 8) tcen <- pmin(t, cen) di <- as.numeric(t <= cen) n <- dim(data)[1] p <- dim(data)[2] Y <- data.frame(cbind(tcen, di)) colnames(Y) <- c("time", "event") grpInx <- 1:p K <- length(unique(grpInx)) ############################ hyperParams <- list(nu0=3, sigSq0=1, alpha0=0, h0=10^6, rLam=0.5, deltaLam=2) ############################ startValues <- list(alpha=0.1, beta=rep(1,p), sigSq=1, tauSq=rep(0.4,p), lambdaSq=5, w=log(tcen)) ############################ mcmc <- list(numReps=100, thin=1, burninPerc=0.5) ############################ fit <- aftGL(Y, data, grpInx, hyperParams, startValues, mcmc) ## Not run: vs <- VS(fit, X=data) ## End(Not run) ```

aftGL function