MC.analysis | R Documentation |
Function for running the analysis of the Monte Carlo simulation.
MC.analysis(x, delta, qUpper, p1.det, sim.det, event.ini, event.end,
ntick, summ.data = NULL)
x |
A |
delta |
A |
qUpper |
A |
p1.det |
A |
sim.det |
A |
event.ini |
A time-date string in |
event.end |
A time-date string in |
ntick |
A |
summ.data |
A |
A list
of length 2:
summ |
A |
variance |
A |
J.A. Torres-Matallana
See also setup-class
, MC.setup-methods
, MC.sim-methods
.
## the Monte Carlo simulation: MC.sim
library(EmiStatR)
# library(xts)
# data(Esch_Sure2010)
# P <- IsReg(Esch_Sure2010, format = "%Y-%m-%d %H:%M:%S", tz = "CET")
# P1 <- P[[2]]
# P1 <- P1["2010-08",][1:55]
# P1 <- cbind.data.frame(time=index(P1), P1 = coredata(P1))
data(P1)
P1 <- P1[165:(110*2),]
plot(P1[,2], typ="l")
library(stUPscales)
setting_EmiStatR <- setup(id = "MC_sim1",
nsim = 4, # # use a larger number to have
# a proper confidence band of simulatios
seed = 123,
mcCores = 1,
ts.input = P1,
rng = rng <- list(
qs = 150, # [l/PE/d]
CODs = c(pdf = "nor", mu = 4.378, sigma = 0.751), # log[g/PE/d]
NH4s = c(pdf = "nor", mu = 1.473, sigma = 0.410), # log[g/PE/d]
qf = 0.04, # [l/s/ha]
CODf = 0, # [g/PE/d]
NH4f = 0, # [g/PE/d]
CODr = c(pdf = "nor", mu = 3.60, sigma = 1.45), # 71 log[mg/l]
NH4r = 1, # [mg/l]
nameCSO = "E1", # [-]
id = 1, # [-]
ns = "FBH Goesdorf", # [-]
nm = "Goesdorf", # [-]
nc = "Obersauer", # [-]
numc = 1, # [-]
use = "R/I", # [-]
Atotal = 36, # [ha]
Aimp = c(pdf = "uni", min = 4.5, max = 25), # [ha]
Cimp = c(pdf = "uni", min = 0.25, max = 0.95), # [-]
Cper = c(pdf = "uni", min = 0.05, max = 0.60), # [-]
tfS = 1, # [time steps]
pe = 650, # [PE]
Qd = 5, # [l/s]
Dd = 0.150, # [m]
Cd = 0.18, # [-]
V = 190, # [m3]
lev.ini = 0.10, # [m]
lev2vol = list(lev = c(.06, 1.10, 1.30, 3.30), # [m]
vol = c(0, 31, 45, 190)) # [m3]
),
ar.model = ar.model <- list(
CODs = 0.5,
NH4s = 0.5,
CODr = 0.7),
var.model = var.model <- list(
inp = c("", ""), # c("CODs", "NH4s"), # c("", ""),
w = c(0.04778205, 0.02079010),
A = matrix(c(9.916452e-01, -8.755558e-05,
-0.003189094, 0.994553910), nrow=2, ncol=2),
C = matrix(c(0.009126591, 0.002237936,
0.002237936, 0.001850941), nrow=2, ncol=2)))
MC_setup <- MC.setup(setting_EmiStatR)
sims <- MC.sim(x = MC_setup, EmiStatR.cores = 1)
## Monte Carlo simulation analysis: MC.analysis
# Deterministic simulation
# Definition of structure 1, E1:
E1 <- list(id = 1, ns = "FBH Goesdorf", nm = "Goesdorf", nc = "Obersauer", numc = 1,
use = "R/I", Atotal = 36, Aimp = 25.2, Cimp = 0.80, Cper = 0.30,
tfS = 0, pe = 650, Qd = 5,
Dd = 0.150, Cd = 0.18, V = 190, lev.ini = 0.10,
lev2vol = list(lev = c(.06, 1.10, 1.30, 3.30),
vol = c(0, 31, 45, 190))
)
# Defining deterministic input:
library(EmiStatR)
# data(P1)
input.det <- input(spatial = 0, zero = 1e-5,
folder = system.file("shiny", package = "EmiStatR"),
cores = 1,
ww = list(qs = 150, CODs = 104, NH4s = 4.7),
inf = list(qf= 0.04, CODf = 0, NH4f = 0),
rw = list(CODr = 71, NH4r = 1, stat = "Dahl"),
P1 = P1, st = list(E1=E1), export = 0)
## uncomment to run:
## Invoking `EmiStatR` with the deterministic input:
# sim.det <- EmiStatR(input.det)
## further arguments
# delta <- 10 # minutes
# qUpper <- "q999"
# event.ini <- as.POSIXct("2016-01-02 03:20:00")
# event.end <- as.POSIXct("2016-01-02 12:30:00")
# new_analysis <- MC.analysis(x = sims, delta = delta, qUpper = qUpper, p1.det = P1,
# sim.det = sim.det, event.ini = event.ini, event.end = event.end,
# ntick = 5, summ.data = NULL)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.