library(mixAR)
test_that("mixARsim functions work", {
## from simuExperiment.Rd
## explore dist. of the mean of a random sample of length 5.
## (only illustration, such simple cases hardly need simuExperiment)
sim1 <- list(fun="rnorm", args = list(n=5, mean=3, sd = 2))
est1 <- list(fun=mean, args = list())
# a basic report function
fsum1 <- function(x){ wrk <- do.call("c",x)
c(n = length(wrk), mean = mean(wrk), sd = sd(wrk))}
## skip_on_cran()
##
## a1 <- simuExperiment(TRUE, simu = sim1, est = est1, N = 1000, summary_fun = fsum1)
##
## # explore also the dist. of the sample s.d.
## est2 <- est1
## est2$fun <- function(x) c(xbar = mean(x), s = sd(x))
##
## a2 <- simuExperiment(TRUE, simu = sim1, est = est2, N = 1000)
##
## # keep the raw sample means and s.d.'s for further use
## a2a <- simuExperiment(TRUE, simu = sim1, est = est2, N = 1000, raw = TRUE)
## a2a$Summary
##
## # replicate a2a$Summary
## s5 <- sapply(a2a$Raw, identity)
## apply(s5, 1, mean)
## apply(s5, 1, sd)
##
## hist(s5[1,], prob=TRUE)
## lines(density(s5[1,]))
## curve(dnorm(x, mean(s5[1,]), sd(s5[1,])), add = TRUE, col = "red")
##
## mixAR:::.fsummary(a2a$Raw)
## mixAR:::.fsummary(a2a$Raw, merge = TRUE)
##
##
## ## from mixARExperiment.Rd
## set.seed(1234)
## mixARExperiment(exampleModels$WL_II, N = 10)
## mixARExperiment(exampleModels$WL_II, N=10, raw=TRUE)
## mixARExperiment(exampleModels$WL_II, N=10, raw=TRUE, simargs=list(n=500))
##
##
## ## from test_unswitch.Rd
## aII10data <- mixARExperiment(exampleModels$WL_II, N=10, raw=TRUE)
## aII10 <- test_unswitch(aII10data$Raw, exampleModels$WL_II)
## aII10
##
## aII10adata <- mixARExperiment(exampleModels$WL_II, N=10, raw=TRUE, simargs=list(n=500))
## aII10a <- test_unswitch(aII10adata$Raw, exampleModels$WL_II)
## aII10a
##
##
## ## from mixAR_sim.Rd
##
## ## simulate a continuation of BJ ibm data
## ts1 <- mixAR_sim(exampleModels$WL_ibm, n = 30, init = c(346, 352, 357), nskip = 0)
##
## ## ts1 <- mixAR_sim(exampleModels$WL_ibm, n = 400, init = c(346, 352, 357), nskip = 0)
## ## plot(diff(ts(ts1)))
##
## ## plot(diff(mixAR_sim(exampleModels$WL_ibm_gen, n = 100, init = c(346, 352, 357), nskip = 0)),
## ## type = "l")
##
## # a simulation based estimate of the 1-step predictive distribution
## # for the first date after the data.
## s1 <- replicate(1000, mixAR_sim(exampleModels$WL_ibm, n = 1, init = c(346, 352, 357), nskip = 0))
## plot(density(s1))
##
## # load ibm data from BJ
## ## data(ibmclose, package = "fma")
##
## # overlay the 'true' predictive density.
## pdf1 <- mix_pdf(exampleModels$WL_ibm, xcond = as.numeric(fma::ibmclose))
## curve(pdf1, add = TRUE, col = 'blue')
##
## # estimate of 5\% quantile of predictive distribution
## quantile(s1, 0.05)
##
## # Monte Carlo estimate of "expected shortfall"
## # (but the data has not been converted into returns...)
## mean(s1[ s1 <= quantile(s1, 0.05) ])
##
##
## ## from permn.Rd
## m <- matrix(c(11:14,21:24,31:34), ncol=3)
## pm <- permn_cols(m)
## pm[[2]]
})
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