Nothing
# --------------------------------------------------------------
# Examining the loss functions.
# For a range of smoothing parameters produce plots of loss by
# smoothing parameter.
# Since the loss function is determined somewhat on the way data
# are filled in, multiple imputations are performed.
# --------------------------------------------------------------
options( width=150, max.print=1000000 )
# Retrieve the SGE_TASK_ID and store it as numeric in SGETID
# -----------------------------------------------------------
SGETID <- Sys.getenv(c("SGE_TASK_ID"))
SGENID <- as.numeric(SGETID)
oname1 <- sprintf("RDS/HFResults_1.%05d.rds", SGENID )
oname2 <- sprintf("RDS/HFResults_2.%05d.rds", SGENID )
# -----------------------------------------------------------
library(samon, lib.loc="../../../samlib")
sigmaList <- seq(0.05,5,by=0.01)
# the data
# --------------------------------------
data("DepWork1")
data("DepWork2")
Y1 <- DepWork1
Y2 <- DepWork2
NT <- ncol(Y1)
inmodel <- matrix(1,NT,6)
inmodel[1,] <- 0
inmodel[NT,] <- 0
inmodel[NT-1,4:6] <- 0
seeds <- 3121 + 1:100
seed <- seeds[SGENID]
HF1 <- samonevalIM( mat = Y1, Npart = 10,
sigmaList = sigmaList,
inmodel = inmodel,
seed = seed,
type = "both" )
out1 <- cbind( SGENID, 1, HF1$OutSig )
HF2 <- samonevalIM( mat = Y2, Npart = 10,
sigmaList = sigmaList,
inmodel = inmodel,
seed = seed,
type = "both" )
out2 <- cbind( SGENID, 2, HF2$OutSig )
saveRDS(out1,oname1)
saveRDS(out2,oname2)
# --------------------------------------
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