R/SparseSEMs-package.R

In Ivis4ml/SparseSEMs: Sparse Structural Equation Models for jointly inferring Gene Regulatory Networks

##' Solving Sparse Structural Equation Model
##' @docType package
##' @name SparseSEMs
##' @aliases package-SparseSEMs
##' @useDynLib SparseSEMs
##' @import Rcpp methods
##' @examples
##' seed = as.numeric(Sys.time())
##' N = 100                                                                     # sample size. 500 sample better than 200 sample, very very
##' Ng = 10                                                                      # gene number
##' Nk = 10 * 3                                                                  # eQTL number
##' Ns = 1                                                                      # sparse ratio
##' sigma2 = 0.01                                                               # sigma2
##' set.seed(seed)
##' library(SparseSEMs)
##' 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 = 50, 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$sigma2, nlambda = 10, nrho = 10,
##'                              nfold = 5, p = Ng, q = Nk, wt = TRUE)
##'
##' system.time(fitc <<- multiFSSEMiPALM(Xs = Xs, Ys = Ys, Bs = fit$Bs, Fs = fit$Fs, Sk = Sk,
##'                                      sigma2 = fit$sigma2, lambda = cvfitc$lambda, rho = cvfitc$rho,
##'                                      Wl = inverseB(fit$Bs), Wf = flinvB(fit$Bs),
##'                                      p = Ng, maxit = 100, threshold = 1e-5, sparse = TRUE,
##'                                      verbose = TRUE, trans = TRUE, strict = TRUE))
##' (TPR(fitc$B[[1]], data$Vars$B[[1]]) + TPR(fitc$B[[2]], data$Vars$B[[2]])) / 2
##' (FDR(fitc$B[[1]], data$Vars$B[[1]]) + FDR(fitc$B[[2]], data$Vars$B[[2]])) / 2
##' TPR(fitc$B[[1]] - fitc$B[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
##' FDR(fitc$B[[1]] - fitc$B[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
##' 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$B[[1]], data$Vars$B[[1]]) + TPR(fitc0$B[[2]], data$Vars$B[[2]])) / 2
##' (FDR(fitc0$B[[1]], data$Vars$B[[1]]) + FDR(fitc0$B[[2]], data$Vars$B[[2]])) / 2
##' TPR(fitc0$B[[1]] - fitc0$B[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
##' FDR(fitc0$B[[1]] - fitc0$B[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
##' @author Xin Zhou <\email{xxz220@@miami.edu}>
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