Basics of simulating sawn timber strength with WoodSimulatR

library(WoodSimulatR)
library(magrittr)
library(ggplot2)
pander::panderOptions('knitr.auto.asis', FALSE);

Introduction

The WoodSimulatR package provides functions for generating artificial datasets of sawn wood properties obtained by destructive and non-destructive testing.

An existing dataset containing some of these properties can be enriched by adding simulated values for the missing properties.

Aim of this document

This document aims to provide an overview of the capabilities of the WoodSimulatR package.

On the one hand, we will simulate a dataset with varying parameters, highlighting both the capabilities of the WoodSimulatR functions and the direction where it should go.

On the other hand, we will illustrate the capabilities of WoodSimulatR with respect to simulating grade determining properties for a dataset with different pre-existing variables.

Simulate a whole dataset

Preliminaries

As a quick summary of each dataset, we will show mean and CoV for all variables, split by country and subsample, and we will show the matrix of correlations.

For this, we define the following function:

summ_fun <- function(ds, grp = c('country', 'subsample', 'loadtype')) {
  grp <- intersect(grp, names(ds));
  v <- setdiff(names(ds), grp);
  
  r <- cor(ds[v]);

  ds <- tibble::add_column(ds, n = 1);
  v <- c('n', v);
  ds <- tidyr::gather(ds, 'property', 'value', !!! rlang::syms(v));
  ds <- dplyr::mutate(
    ds,
    property = factor(
      property,
      levels=v,
      labels=ifelse(v=='n', v, paste0(v, '_mean')),
      ordered = TRUE
    )
  );
  
  grp <- c(grp, 'property');
  ds <- dplyr::group_by(ds, !!! rlang::syms(grp));
  
  summ <- dplyr::summarise(
    ds,
    res = if (property[1] == 'n') sprintf('%.0f', sum(value)) else
      sprintf(
      if(property[1] %in% c('f_mean', 'ip_f_mean')) '%.1f (%.0f)' else '%.0f (%.0f)',
      mean(value), 100*sd(value)/mean(value)),
    .groups = 'drop_last'
  );
  pander::pander(
    tidyr::spread(summ, property, res),
    split.tables = Inf
  );
  
  pander::pander(r)
  
  invisible(summ);
}

compare_with_def <- function(ds, ssd, target = c('mean', 'cov')) {
  target <- match.arg(target);
  
  ds <- dplyr::group_by(ds, country);
  summ <- dplyr::summarise(
    ds,
    f_mean.ach = mean(f),
    f_cov.ach = sd(f) / f_mean.ach,
    E_mean.ach = mean(E),
    E_cov.ach = sd(E) / E_mean.ach,
    rho_mean.ach = mean(rho),
    rho_cov.ach = sd(rho) / rho_mean.ach,
    .groups = 'drop_last'
  );
  
  stopifnot(!anyDuplicated(ssd$country));
  summ <- dplyr::left_join(
    summ,
    dplyr::select(
      dplyr::mutate(ssd, f_cov = f_sd / f_mean, E_cov = E_sd / E_mean, rho_cov = rho_sd / rho_mean), 
      country, f_mean, f_cov, E_mean, E_cov, rho_mean, rho_cov
    ),
    by = 'country'
  );
  
  summ <- tidyr::pivot_longer(
    summ,
    -country,
    names_to = c('gdpname', '.value'),
    names_sep = '_'
  );
  summ <- dplyr::mutate(
    summ,
    gdpname = factor(gdpname, levels = c('f', 'E', 'rho'), ordered = TRUE)
  );

  if (target == 'mean') {
    ggplot(data = summ, aes(mean.ach, mean)) +
      geom_abline(slope = 1, intercept = 0) +
      geom_text(aes(label = country)) +
      geom_point(alpha = 0.5) +
      facet_wrap(vars(gdpname), scales = 'free') +
      theme(axis.text.x = element_text(angle = 90));
  } else {
    ggplot(data = summ, aes(cov.ach, cov)) +
      geom_abline(slope = 1, intercept = 0) +
      geom_text(aes(label = country)) +
      geom_point(alpha = 0.5) +
      facet_wrap(vars(gdpname), scales = 'free') +
      theme(axis.text.x = element_text(angle = 90));
  }
}

Default dataset

The main function for dataset simulation is simulate_dataset(). It can be called without any further arguments to yield a “default” dataset.

For reproducibility, we will call it with the extra argument random_seed = 12345. This means that we will always generate the same random numbers.

dataset_0 <- simulate_dataset(random_seed = 2345);

summ_fun(dataset_0);
country subsample loadtype n f_mean E_mean rho_mean E_dyn_u_mean ip_f_mean E_dyn_mean ip_rho_mean
C1 C1 t 1250 26.1 (39) 10858 (19) 436 (11) 10334 (19) 25.0 (33) 11704 (18) 452 (11)
C2 C2 t 1250 26.8 (37) 11573 (20) 423 (10) 10829 (19) 28.2 (33) 12160 (19) 442 (9)
C3 C3 t 1250 31.4 (40) 10356 (22) 443 (11) 10044 (20) 24.2 (37) 11367 (20) 454 (10)
C4 C4 t 1250 25.4 (42) 10822 (22) 405 (10) 10122 (20) 26.0 (37) 11357 (20) 425 (10)
  f E rho E_dyn_u ip_f E_dyn ip_rho
f 1 0.6938 0.332 0.6209 0.7269 0.6518 0.3103
E 0.6938 1 0.5475 0.9033 0.9099 0.9487 0.5694
rho 0.332 0.5475 1 0.5953 0.3754 0.6661 0.9417
E_dyn_u 0.6209 0.9033 0.5953 1 0.8512 0.9416 0.6193
ip_f 0.7269 0.9099 0.3754 0.8512 1 0.883 0.3668
E_dyn 0.6518 0.9487 0.6661 0.9416 0.883 1 0.6984
ip_rho 0.3103 0.5694 0.9417 0.6193 0.3668 0.6984 1

The meaning of the properties in dataset_0 is as follows:

  • country, subsample: when analysing properties of sawn wood, the timber typically are grouped by country of origin and possibly by further criteria into subsamples for each country. In a call without further specifications, simulate_dataset() returns generic country names “C1”, “C2” etc., and sets the subsample names equal to the country names. Further options for countries and subsamples are explained below.
  • f: strength of the sawn timber in N/mm² as obtained by destructive testing (tensile strength or bending strength)
  • E: Modulus of elasticity in N/mm² as obtained by destructive testing in tension or in bending
  • rho (as from the Greek symbol ρ): density of a small clear sample cut from the sawn timber, in kg/m³
  • E_dyn_u: dynamic modulus of the green sawn timber in N/mm²
  • ip_f: an “indicating property” (IP) for the strength f in N/mm², obtained non-destructively as a function of E_dyn, ip_rho and the total knot area ratio (tKAR) of a knot cluster of length 150mm (knot_tc – not included in the dataset).
    • IP for tension strength: ipf = 11.98 + 0.003913Edyn − 0.04822ipρ − 34.72 * knottc
    • IP for bending strength: ipf = 21.67 + 0.004302Edyn − 0.05366 * ipρ − 38.43knottc
  • E_dyn: dynamic modulus of the dry sawn timber in N/mm²
  • ip_rho: density of the whole board in kg/m³, calculated by weighing the board and dividing the weight by the product of the measured dimensions.

All properties except E_dyn_u are to be taken as measured on the dry timber and corrected to a moisture content of 12%.

Customising options

The default dataset created above relies on the following assumptions:

  • It’s a dataset for tensile strength of spruce (Picea abies).
  • The correlations are taken from the “full” sample of Holzforschung Austria’s research project SiOSiP (“SImulation-based Optimisation of Sawn tImber Production”, 2014-2017) on Austrian spruce wood.
  • After log-transforming f, for all variables a normal distribution is assumed.
  • We create data for 5000 boards, split equally between four subsamples.
  • The means and standard deviations of f, E and rho are based on random values chosen from the range of available reference values.
  • The reference values for mean and standard deviation of transformed variables (f in our case) are currently enforced exactly instead of statistically – this has to be improved yet.

All of these assumptions can be modified more or less freely.

Available subsample definitions

For convenience, the WoodSimulatR package contains tables with means and standard deviations for f, E and rho for different countries, obtained in the research projects SiOSiP and GradeWood (Ranta-Maunus, Denzler, and Stapel 2011) or reported in scientific papers (Stapel and van de Kuilen 2014; Rohanová and Nunez 2014). Data from both destructive tension and bending tests are available. Currently, the data is restricted to European spruce (Picea abies, PCAB).

Tensile tests

get_subsample_definitions(loadtype = 't') %>% 
  dplyr::select(-species, -loadtype) %>%
  dplyr::arrange(country) %>%
  pander::pander(split.table = Inf);
project country share f_mean f_sd E_mean E_sd rho_mean rho_sd literature subsample
siosip at 1 29.4 11.3 11912 2232 443 42.1 null at
gradewood at 1 25.1 10.54 10100 2626 435 52.2 Ranta-Maunus et al. (2011) at_1
gradewood ch 1 27.8 12.23 10900 2616 439 52.68 Ranta-Maunus et al. (2011) ch
gradewood de 1 32.6 12.06 12100 2541 451 49.61 Ranta-Maunus et al. (2011) de
gradewood fi 1 33.2 11.29 11800 2242 445 44.5 Ranta-Maunus et al. (2011) fi
null lv 1 30.4 11.55 11700 2808 466 51.26 Stapel et al. (2014) lv
gradewood pl 1 28.2 10.72 11600 2668 452 54.24 Ranta-Maunus et al. (2011) pl
gradewood ro 1 25.6 10.75 10000 2100 390 31.2 Ranta-Maunus et al. (2011) ro
gradewood se 1 27.6 10.49 10200 2346 416 49.92 Ranta-Maunus et al. (2011) se
gradewood si 1 34 14.96 12300 2706 442 39.78 Ranta-Maunus et al. (2011) si
gradewood sk 1 27.2 10.88 10700 2140 408 36.72 Ranta-Maunus et al. (2011) sk
gradewood ua 1 26.7 11.75 10300 2163 392 43.12 Ranta-Maunus et al. (2011) ua

Bending tests

get_subsample_definitions(loadtype = 'be') %>% 
  dplyr::select(-species, -loadtype) %>%
  dplyr::arrange(country) %>%
  pander::pander(split.table = Inf);
project country share f_mean f_sd E_mean E_sd rho_mean rho_sd literature subsample
siosip at 1 41.4 12.7 12294 2636 436 39.9 null at
gradewood de 1 41.5 14.11 12100 3146 441 48.51 Ranta-Maunus et al. (2011) de
gradewood fr 1 42.9 11.15 11900 2023 440 44 Ranta-Maunus et al. (2011) fr
gradewood pl 1 38.5 11.94 11400 2280 440 48.4 Ranta-Maunus et al. (2011) pl
gradewood ro 1 35.5 11.01 9600 1824 387 38.7 Ranta-Maunus et al. (2011) ro
gradewood se 1 42.5 14.87 11300 2486 435 52.2 Ranta-Maunus et al. (2011) se
gradewood se 1 44.8 13.44 12300 2706 435 52.2 Ranta-Maunus et al. (2011) se_1
gradewood si 1 43.7 13.11 12000 2400 445 44.5 Ranta-Maunus et al. (2011) si
gradewood sk 1 34.8 11.48 10200 2040 415 41.5 Ranta-Maunus et al. (2011) sk
null sk 1 41 11.89 12252 2328 434 39.06 Rohanova (2014) sk_1
gradewood ua 1 36.2 10.5 10000 1900 389 38.9 Ranta-Maunus et al. (2011) ua

Simulated dataset with data from specific countries

ssd_c <- get_subsample_definitions(
  country = c('at', 'de', 'fi', 'pl', 'se', 'si', 'sk'),
  loadtype = 't'
);

dataset_c <- simulate_dataset(
  random_seed = 12345,
  n = 5000,
  subsets = ssd_c
);

summ_fun(dataset_c);
country subsample loadtype n f_mean E_mean rho_mean E_dyn_u_mean ip_f_mean E_dyn_mean ip_rho_mean
at at t 714 29.4 (38) 11982 (19) 443 (10) 11184 (18) 29.4 (31) 12678 (17) 459 (9)
de de t 715 32.6 (37) 12021 (21) 450 (12) 11266 (20) 29.9 (33) 12811 (19) 464 (11)
fi fi t 714 33.2 (34) 11755 (19) 445 (9) 11062 (18) 29.3 (30) 12541 (17) 458 (9)
pl pl t 714 28.2 (38) 11573 (23) 451 (12) 10943 (21) 27.6 (37) 12417 (21) 465 (11)
se se t 714 27.6 (38) 10239 (23) 415 (12) 9814 (22) 24.0 (38) 11004 (21) 432 (11)
si si t 715 34.0 (44) 12352 (22) 442 (9) 11510 (20) 31.1 (35) 13022 (19) 459 (8)
sk sk t 714 27.2 (40) 10587 (19) 406 (9) 9946 (18) 25.3 (33) 11194 (18) 425 (9)
  f E rho E_dyn_u ip_f E_dyn ip_rho
f 1 0.7486 0.3253 0.6655 0.7819 0.6867 0.31
E 0.7486 1 0.6452 0.919 0.9217 0.9593 0.6508
rho 0.3253 0.6452 1 0.6738 0.4833 0.7334 0.9474
E_dyn_u 0.6655 0.919 0.6738 1 0.8739 0.9526 0.6865
ip_f 0.7819 0.9217 0.4833 0.8739 1 0.9006 0.4658
E_dyn 0.6867 0.9593 0.7334 0.9526 0.9006 1 0.7525
ip_rho 0.31 0.6508 0.9474 0.6865 0.4658 0.7525 1

Compare achieved means with the defined values. It can be seen that the means of f are met exactly, while the means of E and rho are only met approximately, which is the desideratum when we are dealing with simulation.

compare_with_def(dataset_c, ssd_c, 'm')

Compare achieved coefficients of variation with the defined values. Again, we have undesirable exact values for f.

compare_with_def(dataset_c, ssd_c, 'cov')

Different subsample sizes

ssd_cn <- get_subsample_definitions(
  country = c(at = 1, de = 3, fi = 1.5, pl = 2, se = 3, si = 1, sk = 1),
  loadtype = 't'
);

dataset_cn <- simulate_dataset(
  random_seed = 12345,
  n = 5000,
  subsets = ssd_cn
);

summ_fun(dataset_cn);
country subsample loadtype n f_mean E_mean rho_mean E_dyn_u_mean ip_f_mean E_dyn_mean ip_rho_mean
at at t 400 29.4 (38) 12189 (19) 444 (10) 11320 (18) 30.1 (30) 12815 (17) 460 (9)
de de t 1200 32.6 (37) 12102 (21) 451 (11) 11302 (19) 30.2 (33) 12861 (19) 464 (11)
fi fi t 600 33.2 (34) 11838 (18) 445 (10) 11116 (18) 29.7 (30) 12600 (17) 459 (9)
pl pl t 800 28.2 (38) 11501 (23) 451 (12) 10885 (21) 27.4 (37) 12378 (21) 465 (11)
se se t 1200 27.6 (38) 10206 (23) 417 (12) 9769 (22) 23.9 (39) 10989 (22) 432 (11)
si si t 400 34.0 (44) 12218 (21) 441 (9) 11326 (19) 30.7 (34) 12801 (19) 457 (9)
sk sk t 400 27.2 (40) 10716 (19) 407 (9) 10042 (18) 26.0 (32) 11301 (18) 426 (9)
  f E rho E_dyn_u ip_f E_dyn ip_rho
f 1 0.7386 0.3347 0.6617 0.7733 0.68 0.3249
E 0.7386 1 0.6581 0.9214 0.9231 0.9601 0.6675
rho 0.3347 0.6581 1 0.6852 0.4949 0.7485 0.9523
E_dyn_u 0.6617 0.9214 0.6852 1 0.8715 0.9547 0.6991
ip_f 0.7733 0.9231 0.4949 0.8715 1 0.8985 0.4798
E_dyn 0.68 0.9601 0.7485 0.9547 0.8985 1 0.7673
ip_rho 0.3249 0.6675 0.9523 0.6991 0.4798 0.7673 1

Own specification of means and standard deviations

In a similar manner to the predefined country specifications, we can also define our own. Since Version 0.6.0, we can also used different sample identifier columns (instead of the standard “country” and “subsample”) – for details, check the help on simulate_dataset().

As an example, we define different properties for boards with different cross sections (width and thickness, given in mm).

ssd_custom <- tibble::tribble(
  ~width, ~thickness, ~f_mean, ~f_sd,
      80,     40,      27.5,    9.0,
     140,     40,      29.4,    9.7,
     160,     60,      31.6,    9.3,
     200,     50,      30.2,   11.4, 
     240,     95,      25.5,    4.8,
     250,     40,      25.3,   11.2
);

dataset_custom <- simulate_dataset(
  random_seed = 12345,
  n = 5000,
  subsets = ssd_custom
);

summ_fun(dataset_custom, grp = c('width', 'thickness', 'loadtype'));
#> 
#> ------------------------------------------------------------------------------------------------------------------------------
#>  width   thickness   loadtype    n     f_mean       E_mean     rho_mean   E_dyn_u_mean   ip_f_mean   E_dyn_mean   ip_rho_mean 
#> ------- ----------- ---------- ----- ----------- ------------ ---------- -------------- ----------- ------------ -------------
#>   80        40          t       833   27.5 (33)   11647 (16)   439 (9)     10917 (16)    28.4 (27)   12372 (16)     454 (9)   
#> 
#>   140       40          t       834   29.4 (33)   11939 (17)   443 (9)     11208 (16)    29.6 (27)   12671 (16)     458 (9)   
#> 
#>   160       60          t       833   31.6 (29)   12329 (15)   447 (9)     11466 (15)    31.0 (23)   13003 (14)     462 (9)   
#> 
#>   200       50          t       833   30.2 (38)   12052 (17)   444 (9)     11275 (17)    29.9 (28)   12770 (16)     460 (9)   
#> 
#>   240       95          t       834   25.5 (19)   11601 (14)   442 (9)     10904 (15)    27.8 (23)   12347 (14)     458 (8)   
#> 
#>   250       40          t       833   25.3 (44)   11155 (20)   434 (9)     10538 (19)    26.1 (36)   11933 (19)     451 (9)   
#> ------------------------------------------------------------------------------------------------------------------------------
#> 
#> 
#> -----------------------------------------------------------------------------
#>    &nbsp;        f        E       rho     E_dyn_u    ip_f    E_dyn    ip_rho 
#> ------------- -------- -------- -------- --------- -------- -------- --------
#>     **f**        1      0.6879   0.2889   0.5814    0.7254   0.617    0.2602 
#> 
#>     **E**      0.6879     1      0.6334    0.885    0.8914   0.9435   0.6329 
#> 
#>    **rho**     0.2889   0.6334     1       0.639    0.4458   0.7156   0.9301 
#> 
#>  **E_dyn_u**   0.5814   0.885    0.639       1      0.825    0.931    0.6527 
#> 
#>   **ip_f**     0.7254   0.8914   0.4458    0.825      1      0.8701   0.4123 
#> 
#>   **E_dyn**    0.617    0.9435   0.7156    0.931    0.8701     1      0.7361 
#> 
#>  **ip_rho**    0.2602   0.6329   0.9301   0.6527    0.4123   0.7361     1    
#> -----------------------------------------------------------------------------

Further available options

  • bending strength simulation
  • without log-transform
  • from own data using simbase_covar()

Add simulated values to a dataset

For adding simulated values to a dataset, we first need to establish the relationship between these values and some variables in the dataset.

In WoodSimulatR, relationships are established in the following way:

  1. We determine the covariance matrix and the means of a set of variables, based on some kind of learning dataset. This is done using the function simbase_covar(); the resulting simbase has class “simbase_covar”.
  2. We also have the option to establish different relationships for different subsets of the data, e.g. for different countries of origin. This is done by grouping the dataset accordingly before calling simbase_covar(); the resulting simbase has class “simbase_list”.

For both these options, it is possible to transform the involved variables.

To visualise the result of the simulation, we use scatterplots and define them in the following function:

plot_sim_gdp <- function(ds, simb, simulated_vars, ...) {
  extra_aes <- rlang::enexprs(...);
  ds <- dplyr::rename(ds, f_ref = f, E_ref = E, rho_ref = rho);
  if (!any(simulated_vars %in% names(ds))) ds <- simulate_conditionally(data = ds, simbase = simb);
  ds <- tidyr::pivot_longer(
    data = ds,
    cols = tidyselect::any_of(c('f_ref', 'E_ref', 'rho_ref', simulated_vars)),
    names_to = c('name', '.value'),
    names_sep = '_'
  );
  ds <- dplyr::mutate(
    ds,
    name = factor(name, levels = c('f', 'E', 'rho'), ordered = TRUE)
  );
  simname <- names(ds);
  simname <- simname[dplyr::cumany(simname == 'name')];
  simname <- setdiff(simname, c('name', 'ref'));
  stopifnot(length(simname) == 1);
  ggplot(data = ds, mapping = aes(.data[[simname]], ref, !!!extra_aes)) +
    geom_point(alpha = .2, shape = 20) +
    geom_abline(slope = 1, intercept = 0, alpha = .5, linetype = 'twodash') +
    facet_wrap(vars(name), scales = 'free') +
    theme(axis.text.x = element_text(angle = 90));
} # undebug(plot_sim_gdp)

simbase_covar without transformation

The main approach in WoodSimulatR is to conditionally simulate based on the means and the covariance matrix. As a start, we create basis data for the simulation without applying any transformation.

As we later want to add simulated GDP values to a dataset which already contains GDP values, we rename the GDP values for the simbase_covar to some other names not yet present in the target dataset, by suffixing with _siml (for SIMulation with Linear relationships)

sb_untransf <- dataset_0 %>%
  dplyr::rename(f_siml = f, E_siml = E, rho_siml = rho) %>%
  simbase_covar(
    variables = c('f_siml', 'E_siml', 'rho_siml', 'ip_f', 'E_dyn', 'ip_rho')
  );

sb_untransf;
#> $label
#> [1] "n5000_t_cov"
#> 
#> $variables
#> [1] "f_siml"   "E_siml"   "rho_siml" "ip_f"     "E_dyn"    "ip_rho"  
#> 
#> $transforms
#> list()
#> 
#> $covar
#>               f_siml     E_siml   rho_siml        ip_f      E_dyn     ip_rho
#> f_siml     123.48879   17833.53   176.0599    74.39333   16294.87   157.3809
#> E_siml   17833.52632 5350561.53 60442.9207 19384.22862 4936929.13 60112.9581
#> rho_siml   176.05989   60442.92  2277.8973   165.03216   71523.40  2051.2843
#> ip_f        74.39333   19384.23   165.0322    84.82026   18296.48   154.1760
#> E_dyn    16294.87244 4936929.13 71523.3978 18296.48185 5061578.82 71714.5604
#> ip_rho     157.38089   60112.96  2051.2843   154.17601   71714.56  2083.1286
#> 
#> $means
#>      f_siml      E_siml    rho_siml        ip_f       E_dyn      ip_rho 
#>    27.44698 10902.33238   426.68168    25.84706 11646.95452   443.01789 
#> 
#> $loadtype
#> [1] "t"
#> 
#> attr(,"class")
#> [1] "simbase_covar"

Adding the simulated GDP values to a dataset is done by calling simulate_conditionally().

dataset_c_sim <- simulate_conditionally(dataset_c, sb_untransf);
names(dataset_c_sim) %>% pander::pander();

subsample, country, f, E, rho, E_dyn_u, ip_f, E_dyn, ip_rho, loadtype, f_siml, E_siml and rho_siml

For a visual comparison:

plot_sim_gdp(dataset_c_sim, sb_untransf, c('f_siml', 'E_siml', 'rho_siml'));

This looks good for E and ρ, but wrong in the f simulation.

simbase_covar with log-transformed f

We might try using transforms to improve the result. For this, we have to pass a list with named entries corresponding to the GDP we want to transform.

The entry itself must be an object of class "trans" (from the package scales, version < 1.3.0) or class "transform" (package scales, version ≥ 1.3.0). As we want to use a log-transform, the required entry is scales::log_trans().

sb_transf <- dataset_0 %>%
  dplyr::rename(f_simt = f, E_simt = E, rho_simt = rho) %>%
  simbase_covar(
    variables = c('f_simt', 'E_simt', 'rho_simt', 'ip_f', 'E_dyn', 'ip_rho'),
    transforms = list(f_simt = scales::log_trans())
  );
dataset_c_sim <- simulate_conditionally(dataset_c_sim, sb_transf);
plot_sim_gdp(dataset_c_sim, sb_transf, c('f_simt', 'E_simt', 'rho_simt'));

Now, this looks much better (which is no surprise, as dataset_c itself has been simulated with lognormal f).

simbase_covar with log-transformed f and derived on a grouped dataset

If we group the reference dataset (dataset_0), e.g. by country, we get an object of class “simbase_list” with separate simbases for each group (technically, this is a tibble with the grouping variables and an extra column .simbase which contains several objects of class “simbase_covar”).

sb_group <- dataset_0 %>%
  dplyr::group_by(country) %>%
  dplyr::rename(f_simg = f, E_simg = E, rho_simg = rho) %>%
  simbase_covar(
    variables = c('f_simg', 'E_simg', 'rho_simg', 'ip_f', 'E_dyn', 'ip_rho'),
    transforms = list(f_simg = scales::log_trans())
  );

sb_group
#> # A tibble: 4 × 2
#>   country .simbase  
#>   <chr>   <list>    
#> 1 C1      <smbs_cvr>
#> 2 C2      <smbs_cvr>
#> 3 C3      <smbs_cvr>
#> 4 C4      <smbs_cvr>

If we add variables to a dataset using such a “simbase_list”, it is required that all grouping variables stored in the “simbase_list” object are also available in this dataset.

In our case: the dataset must contain the variable “country”. Values of “country” which do not also exist in our “simbase” object will result in NA values for the variables to be simulated.

Therefore, we add the variables in this case not to the dataset dataset_c (which has different values for “country”) but to the dataset_0 itself.

dataset_0_sim <- simulate_conditionally(dataset_0, sb_group);
plot_sim_gdp(dataset_0_sim, sb_group, c('f_simg', 'E_simg', 'rho_simg'), colour=country);

Simulate a whole dataset, based on a simbase_list object

Simbase objects of class “simbase_list” can also be used for simulating an entire dataset, as long as the “simbase_list” only has the grouping variable(s) “country” and/or “subsample”, and as long as the value combinations in “country”/“subsample” match those given in the “subsets” argument to the function simulate_dataset.

To demonstrate, we calculate a “simbase_list” based on the dataset_c created above. Here, we do not rename any of the variables.

sb_group_c <- dataset_c %>%
  dplyr::group_by(country) %>%
  simbase_covar(
    variables = c('f', 'E', 'rho', 'ip_f', 'E_dyn', 'ip_rho'),
    transforms = list(f = scales::log_trans())
  );

sb_group_c
#> # A tibble: 7 × 2
#>   country .simbase  
#>   <chr>   <list>    
#> 1 at      <smbs_cvr>
#> 2 de      <smbs_cvr>
#> 3 fi      <smbs_cvr>
#> 4 pl      <smbs_cvr>
#> 5 se      <smbs_cvr>
#> 6 si      <smbs_cvr>
#> 7 sk      <smbs_cvr>

This “simbase_list” is now used as input to simulate_dataset with the subset definitions used previously (ssd_cn).

dataset_cn2 <- simulate_dataset(
  random_seed = 12345,
  n = 5000,
  subsets = ssd_cn,
  simbase = sb_group_c
);

summ_fun(dataset_cn2);
country subsample loadtype n f_mean E_mean rho_mean ip_f_mean E_dyn_mean ip_rho_mean
at at t 400 29.4 (38) 12190 (19) 444 (10) 29.9 (30) 12844 (17) 460 (9)
de de t 1200 32.6 (37) 12122 (20) 450 (11) 30.2 (31) 12891 (19) 465 (11)
fi fi t 600 33.2 (34) 11903 (19) 445 (10) 29.7 (29) 12698 (18) 459 (10)
pl pl t 800 28.2 (38) 11620 (24) 451 (12) 27.8 (39) 12459 (22) 465 (11)
se se t 1200 27.6 (38) 10196 (22) 417 (12) 23.9 (37) 10983 (20) 433 (11)
si si t 400 34.0 (44) 12466 (22) 442 (9) 31.6 (34) 13100 (19) 460 (8)
sk sk t 400 27.2 (40) 10563 (21) 406 (9) 25.5 (36) 11245 (19) 426 (9)
  f E rho ip_f E_dyn ip_rho
f 1 0.7467 0.3238 0.7827 0.6814 0.3103
E 0.7467 1 0.6582 0.9221 0.9601 0.6651
rho 0.3238 0.6582 1 0.4939 0.7474 0.9515
ip_f 0.7827 0.9221 0.4939 1 0.8992 0.4804
E_dyn 0.6814 0.9601 0.7474 0.8992 1 0.767
ip_rho 0.3103 0.6651 0.9515 0.4804 0.767 1

Conclusions

The package WoodSimulatR has functions for simulating entire datasets of sawn timber properties, both based on internal definitions and on externally supplied base data.

WoodSimulatR also has functions for adding simulated grade determining properties (or other properties) to a given dataset, based on a covariance matrix approach.

The functions for adding simulated variables are suitable for all kinds of datasets, if one calculates an appropriate simbase_covar object oneself, by a call to simbase_covar using reference data.

The simulation methods also support variable transformations to accommodate non-normally distributed variables.

References

Ranta-Maunus, Alpo, Julia K. Denzler, and Peter Stapel. 2011. Strength of European Timber. Part 2. Properties of Spruce and Pine Tested in Gradewood Project. VTT.
Rohanová, Alena, and Erika Nunez. 2014. “Prediction Models of Slovakian Structural Timber.” Wood Research 59 (5): 757–69.
Stapel, Peter, and Jan-Willem G. van de Kuilen. 2014. “Efficiency of Visual Strength Grading of Timber with Respect to Origin, Species, Cross Section, and Grading Rules: A Critical Evaluation of the Common Standards.” Holzforschung 68 (2): 203–16.