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EFMBC Exmaple

library(impala)
library(BASS)
library(mvBayes)
library(fdasrvf)
library(bayesplot)
#> This is bayesplot version 1.15.0
#> - Online documentation and vignettes at mc-stan.org/bayesplot
#> - bayesplot theme set to bayesplot::theme_default()
#>    * Does _not_ affect other ggplot2 plots
#>    * See ?bayesplot_theme_set for details on theme setting

Generate Example Data

f <- function(x) {
  dnorm(seq(0, 1, length.out = 99),
        sin(2 * pi * x[1] ^ 2) / 4 - x[1] / 10 + 0.5,
        0.05) * x[2]
}

n = 100
nt = 99
p = 3
x_train = matrix(runif(n * p), n)
x_test = matrix(runif(1000 * p), 1000)
e = rnorm(n * 99)
y_train = matrix(0, n, nt)
for (i in 1:n) {
  y_train[i, ] = f(x_train[i, ])
}
y_test = matrix(0, 1000, nt)
for (i in 1:1000) {
  y_test[i, ] = f(x_test[i, ])
}

Generate observation and align using EFDA framework

x_true = c(0.1028, 0.5930)
ftilde_obs = f(x_true)
gam_obs = seq(0, 1, length.out = nt)
vv_obs = gam_to_v(gam_obs)

tt = seq(0, 1, length.out = nt)
out = multiple_align_functions(t(y_train), tt, ftilde_obs, 0.01)
#> ℹ Using lambda = 0.01

#> Aligning 100 functions in SRSF space...

gam_train = out$warping_functions
vv_train = gam_to_v(gam_train)
ftilde_train = out$fn
qtilde_train = out$qn

matplot(tt, t(y_train), type = "l", col = "gray")
lines(tt, ftilde_obs, col = "black")
legend(
  x = 0,
  y = 8,
  legend = c("simulation", "experiment"),
  col = c("gray", "black"),
  lty = 1
)


matplot(tt, ftilde_train, type = "l", col = "gray")
lines(tt, ftilde_obs, col = "black")
legend(
  x = 0,
  y = 8,
  legend = c("simulation", "experiment"),
  col = c("gray", "black"),
  lty = 1
)


matplot(tt, gam_train, type = "l", col = "gray")
lines(tt, gam_obs, col = "black")
legend(
  x = 0,
  y = 8,
  legend = c("simulation", "experiment"),
  col = c("gray", "black"),
  lty = 1
)


matplot(tt, vv_train, type = "l", col = "gray")
lines(tt, vv_obs, col = "black")
legend(
  x = 0,
  y = 8,
  legend = c("simulation", "experiment"),
  col = c("gray", "black"),
  lty = 1
)

Train Emulators

emu_ftilde = mvBayes(bass, x_train, t(ftilde_train), nBasis=2)
#> Starting mvBayes with 2 components, using 1 cores.
#> MCMC Start #-- Jun 12 10:31:11 --# nbasis: 0 
#> MCMC iteration 1000 #-- Jun 12 10:31:11 --# nbasis: 2 
#> MCMC iteration 2000 #-- Jun 12 10:31:11 --# nbasis: 2 
#> MCMC iteration 3000 #-- Jun 12 10:31:11 --# nbasis: 2 
#> MCMC iteration 4000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 5000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 6000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 7000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 8000 #-- Jun 12 10:31:12 --# nbasis: 3 
#> MCMC iteration 9000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 10000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC Start #-- Jun 12 10:31:12 --# nbasis: 0 
#> MCMC iteration 1000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 2000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 3000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 4000 #-- Jun 12 10:31:12 --# nbasis: 1 
#> MCMC iteration 5000 #-- Jun 12 10:31:12 --# nbasis: 2 
#> MCMC iteration 6000 #-- Jun 12 10:31:13 --# nbasis: 2 
#> MCMC iteration 7000 #-- Jun 12 10:31:13 --# nbasis: 3 
#> MCMC iteration 8000 #-- Jun 12 10:31:13 --# nbasis: 1 
#> MCMC iteration 9000 #-- Jun 12 10:31:13 --# nbasis: 3 
#> MCMC iteration 10000 #-- Jun 12 10:31:13 --# nbasis: 2
#> Generating 'samples' attribute, since it was absent in 'bmList[[1]]'
#> Approximating 'residSD', since 'residSDExtract' is None
plot(emu_ftilde)
#> Warning in predict.mvBayes(x, Xtest, returnPostCoefs = TRUE, idxSamples =
#> idxSamples): 'idxSamples' is not an argument of the bayesModel predict
#> function...setting idxSamples='default'


emu_vv = mvBayes(bass, x_train, t(vv_train), nBasis=2)
#> Starting mvBayes with 2 components, using 1 cores.
#> MCMC Start #-- Jun 12 10:31:13 --# nbasis: 0 
#> MCMC iteration 1000 #-- Jun 12 10:31:14 --# nbasis: 8 
#> MCMC iteration 2000 #-- Jun 12 10:31:14 --# nbasis: 10 
#> MCMC iteration 3000 #-- Jun 12 10:31:14 --# nbasis: 10 
#> MCMC iteration 4000 #-- Jun 12 10:31:14 --# nbasis: 9 
#> MCMC iteration 5000 #-- Jun 12 10:31:14 --# nbasis: 9 
#> MCMC iteration 6000 #-- Jun 12 10:31:14 --# nbasis: 9 
#> MCMC iteration 7000 #-- Jun 12 10:31:14 --# nbasis: 9 
#> MCMC iteration 8000 #-- Jun 12 10:31:14 --# nbasis: 9 
#> MCMC iteration 9000 #-- Jun 12 10:31:14 --# nbasis: 9 
#> MCMC iteration 10000 #-- Jun 12 10:31:14 --# nbasis: 9 
#> MCMC Start #-- Jun 12 10:31:14 --# nbasis: 0 
#> MCMC iteration 1000 #-- Jun 12 10:31:14 --# nbasis: 2 
#> MCMC iteration 2000 #-- Jun 12 10:31:14 --# nbasis: 2 
#> MCMC iteration 3000 #-- Jun 12 10:31:14 --# nbasis: 3 
#> MCMC iteration 4000 #-- Jun 12 10:31:14 --# nbasis: 2 
#> MCMC iteration 5000 #-- Jun 12 10:31:15 --# nbasis: 2 
#> MCMC iteration 6000 #-- Jun 12 10:31:15 --# nbasis: 3 
#> MCMC iteration 7000 #-- Jun 12 10:31:15 --# nbasis: 2 
#> MCMC iteration 8000 #-- Jun 12 10:31:15 --# nbasis: 2 
#> MCMC iteration 9000 #-- Jun 12 10:31:15 --# nbasis: 2 
#> MCMC iteration 10000 #-- Jun 12 10:31:15 --# nbasis: 2
#> Generating 'samples' attribute, since it was absent in 'bmList[[1]]'
#> Approximating 'residSD', since 'residSDExtract' is None
plot(emu_vv)
#> Warning in predict.mvBayes(x, Xtest, returnPostCoefs = TRUE, idxSamples =
#> idxSamples): 'idxSamples' is not an argument of the bayesModel predict
#> function...setting idxSamples='default'

Run Calibration using Impala

input_names = c("theta0", "theta1", "theta2")
bounds = list()
bounds[['theta0']] = c(0, 1)
bounds[['theta1']] = c(0, 1)
bounds[['theta2']] = c(0, 1)

setup = CalibSetup(bounds, cf_bounds)

model_ftilde = ModelmvBayes(emu_ftilde, input_names)
model_vv = ModelmvBayes(emu_vv, input_names)

setup = addVecExperiments(setup, t(ftilde_obs), model_ftilde, 0.01, 20, rep(1, nt))
setup = addVecExperiments(setup, t(vv_obs), model_vv, 0.01, 20, rep(1, nt))
setup = setTemperatureLadder(setup, 1.05 ^ (0:3))
setup = setMCMC(setup, 4000, 2000, 1, 10)
out_cal = calibPool(setup)

Look at posteriors and chain diagnostics

uu = seq(2500, 4000, 2)
theta = tran_unif(out_cal$theta[uu, 1, ], setup$bounds_mat, names(setup$bounds))
expnums = 1

cnt = 1
ftilde_pred_obs = vector(mode = "list", length = setup$nexp)
gam_pred_obs = vector(mode = "list", length = setup$nexp)
for (idx in expnums) {
  time_new = seq(0, 1, length.out = nt)

  fn = setup$models[[1]]$input_names
  parmat_array = matrix(0, length(theta[[fn[1]]]), length(fn))
  for (i in 1:length(fn)) {
    parmat_array[, i] = theta[[fn[i]]]
  }

  ftilde_pred_obs[[idx]] = evalm(setup$models[[cnt]], theta)
  vv_pred_obs = evalm(setup$models[[cnt + 1]], theta)
  cnt = cnt + 2
  gam_pred_obs[[idx]] = v_to_gam(t(vv_pred_obs))

  matplot(
    time_new,
    ftilde_train,
    type = "l",
    col = "gray",
    lty = 1
  )
  matplot(
    time_new,
    t(ftilde_pred_obs[[idx]]),
    type = "l",
    col = "lightblue",
    lty = 1,
    add = TRUE
  )
  lines(time_new, ftilde_obs, col = "black")
  legend(
    x = 0,
    y = 8,
    legend = c("simulation", "prediction", "experiment"),
    col = c("gray", "lightblue", "black"),
    lty = 1
  )

  matplot(
    time_new,
    vv_train,
    type = "l",
    col = "gray",
    lty = 1
  )
  matplot(
    time_new,
    t(vv_pred_obs),
    type = "l",
    col = "lightblue",
    lty = 1,
    add = TRUE
  )
  lines(time_new, vv_obs, col = "black")
  legend(
    x = 0,
    y = 8,
    legend = c("simulation", "prediction", "experiment"),
    col = c("gray", "lightblue", "black"),
    lty = 1
  )

  matplot(
    time_new,
    gam_train,
    type = "l",
    col = "gray",
    lty = 1
  )
  matplot(
    time_new,
    gam_pred_obs[[idx]],
    type = "l",
    col = "lightblue",
    lty = 1,
    add = TRUE
  )
  lines(time_new, gam_obs, col = "black")
  legend(
    x = 0,
    y = 8,
    legend = c("simulation", "prediction", "experiment"),
    col = c("gray", "lightblue", "black"),
    lty = 1
  )
}


samps = data.frame(theta)
mcmc_pairs(samps, off_diag_fun="hex", diag_fun = "dens")
#> Warning: Only one chain in 'x'. This plot is more useful with multiple chains.


mcmc_hist(samps)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.


mcmc_trace(samps)


plot(out_cal$llik, type="l", main="Log-Likelihood")


# misaligned prediction
for (idx in expnums){
  obspred = matrix(0, nt, length(uu))
  for (j in 1:length(uu)){
    obspred[,j] = warp_f_gamma(ftilde_pred_obs[[idx]][j,], time_new, invertGamma(gam_pred_obs[[idx]][,j]))
  }

  matplot(
    time_new,
    t(y_train),
    type = "l",
    col = "gray",
    lty = 1
  )
  matplot(
    time_new,
    obspred,
    type = "l",
    col = "lightblue",
    lty = 1,
    add = TRUE
  )
  lines(time_new, ftilde_obs, col = "black")
  legend(
    x = 0,
    y = 8,
    legend = c("simulation", "prediction", "experiment"),
    col = c("gray", "lightblue", "black"),
    lty = 1
  )

}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.