R/fssemR-package.R

In fssemR: Fused Sparse Structural Equation Models to Jointly Infer Gene Regulatory Network

##' Solving Sparse Structural Equation Model
##' @docType package
##' @name fssemR
##' @aliases package-fssemR
##' @useDynLib fssemR, .registration=TRUE
##' @import Rcpp methods stats MASS glmnet stringr
##' @examples
##' seed = as.numeric(Sys.time())
##' N = 100                                           # sample size
##' Ng = 5                                            # gene number
##' Nk = 5 * 3                                        # eQTL number
##' Ns = 1                                            # sparse ratio
##' sigma2 = 0.01                                     # sigma2
##' set.seed(seed)
##' library(fssemR)
##' data = randomFSSEMdata(n = N, p = Ng, k = Nk, sparse = Ns, df = 0.3, sigma2 = sigma2,
##'                        u = 5, type = "DG", nhub = 1, dag = TRUE)
##' gamma = cv.multiRegression(data$Data$X, data$Data$Y, data$Data$Sk, ngamma = 20, nfold = 5,
##'                            N, Ng, Nk)
##' fit   = multiRegression(data$Data$X, data$Data$Y, data$Data$Sk, gamma, N, Ng, Nk,
##'                       trans = FALSE)
##' Xs    = data$Data$X
##' Ys    = data$Data$Y
##' Sk    = data$Data$Sk
##'
##' ## cross-validation
##' ## cvfitc <- cv.multiFSSEMiPALM(Xs = Xs, Ys = Ys, Bs = fit$Bs, Fs = fit$Fs, Sk = Sk,
##' ##                              sigma2 = fit$sigma2, nlambda = 10, nrho = 10,
##' ##                              nfold = 5, p = Ng, q = Nk, wt = TRUE)
##'
##' fitm <- opt.multiFSSEMiPALM(Xs = Xs, Ys = Ys, Bs = fit$Bs, Fs = fit$Fs, Sk = Sk,
##'                            sigma2 = fit$sigma2, nlambda = 10, nrho = 10,
##'                            p = Ng, q = Nk, wt = TRUE)
##'
##' fitc0 <- fitm$fit
##'
##' (TPR(fitc0$Bs[[1]], data$Vars$B[[1]]) + TPR(fitc0$Bs[[2]], data$Vars$B[[2]])) / 2
##' (FDR(fitc0$Bs[[1]], data$Vars$B[[1]]) + FDR(fitc0$Bs[[2]], data$Vars$B[[2]])) / 2
##' TPR(fitc0$Bs[[1]] - fitc0$Bs[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
##' FDR(fitc0$Bs[[1]] - fitc0$Bs[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
##' @author Xin Zhou <\email{xxz220@@miami.edu}>
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