SparseSEMs: Solving Sparse Structural Equation Model

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

Description

Solving Sparse Structural Equation Model

Author(s)

Xin Zhou <[e-mail]>

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]])

Ivis4ml/SparseSEMs documentation built on May 23, 2019, 2:43 p.m.