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> ## BayesX MCMC testing
> library("BayesXsrc")
> mcmc <- run.bayesx("mcmc.prg", verbose = FALSE)
> fx1 <- read.table("mcmc_f_x1_pspline.res", header = TRUE)
> fx2 <- read.table("mcmc_f_x2_pspline.res", header = TRUE)
> print(round(head(fx1), digits = 3))
  intnr     x1  pmean pqu2p5  pqu10   pmed  pqu90 pqu97p5 pcat95 pcat80
1     1 -2.999 -0.244 -0.459 -0.386 -0.247 -0.091  -0.003     -1     -1
2     2 -2.985 -0.251 -0.457 -0.388 -0.253 -0.103  -0.021     -1     -1
3     3 -2.981 -0.253 -0.457 -0.388 -0.255 -0.108  -0.025     -1     -1
4     4 -2.974 -0.257 -0.456 -0.389 -0.259 -0.115  -0.033     -1     -1
5     5 -2.963 -0.263 -0.454 -0.391 -0.265 -0.126  -0.045     -1     -1
6     6 -2.962 -0.263 -0.454 -0.391 -0.265 -0.126  -0.046     -1     -1
> print(round(head(fx2), digits = 3))
  intnr    x2  pmean pqu2p5  pqu10   pmed  pqu90 pqu97p5 pcat95 pcat80
1     1 0.001 -0.395 -0.550 -0.496 -0.394 -0.297  -0.241     -1     -1
2     2 0.001 -0.395 -0.549 -0.496 -0.394 -0.297  -0.241     -1     -1
3     3 0.002 -0.394 -0.546 -0.495 -0.393 -0.298  -0.242     -1     -1
4     4 0.003 -0.394 -0.545 -0.494 -0.392 -0.298  -0.243     -1     -1
5     5 0.003 -0.394 -0.544 -0.493 -0.392 -0.297  -0.243     -1     -1
6     6 0.004 -0.393 -0.541 -0.492 -0.391 -0.297  -0.244     -1     -1
> 
> proc.time()
   user  system elapsed 
   0.10    0.06    6.64