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##' FSSEMsolver
seed = as.numeric(Sys.time())
N = c(100, 120) # sample size. 500 sample better than 200 sample, very very
Ng = 30 # gene number
Nk = 30 * 3 # eQTL number
Ns = 30 / Ng # sparse ratio
sigma2 = 0.10 # sigma2
set.seed(seed)
library(fssemR)
data = randomFSSEMdata2(n = N, p = Ng, k = Nk, sparse = Ns, df = 0.3, sigma2 = sigma2, u = 5, type = "DG", dag = T)
## data = randomFSSEMdata(n = N, p = Ng, k = Nk, sparse = Ns, df = 0.3, sigma2 = sigma2, u = 5, type = "ER", nhub = 1)
## data$Data$X = list(data$Data$X, data$Data$X)
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.multiFSSEMiPALM2(Xs = Xs, Ys = Ys, Bs = fit$Bs, Fs = fit$Fs, Sk = Sk,
sigma2 = fit$sigma2, nlambda = 10, nrho = 10,
nfold = 5, p = Ng, q = Nk, wt = T)
system.time(fitc <<- multiFSSEMiPALM2(Xs = Xs, Ys = Ys, Bs = fit$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 = T, verbose = T, trans = T, strict = T))
(TPR(fitc$Bs[[1]], data$Vars$B[[1]]) + TPR(fitc$Bs[[2]], data$Vars$B[[2]])) / 2
(FDR(fitc$Bs[[1]], data$Vars$B[[1]]) + FDR(fitc$Bs[[2]], data$Vars$B[[2]])) / 2
TPR(fitc$Bs[[1]] - fitc$Bs[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
FDR(fitc$Bs[[1]] - fitc$Bs[[2]], data$Vars$B[[1]] - data$Vars$B[[2]])
fitm <- opt.multiFSSEMiPALM2(Xs = Xs, Ys = Ys, Bs = fit$Bs, Fs = fit$Fs, Sk = Sk,
sigma2 = fit$sigma2, nlambda = 10, nrho = 10,
p = Ng, q = Nk, wt = T)
fitc0 <- fitm$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]])
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