Simulate Values

Draw values, most likely model parameters.

Usage

simulate_values(
  lower,
  upper,
  k,
  distr = NULL,
  cast_to_data_frame = TRUE,
  add_id_column = "numeric",
  seed = NULL,
  ...
)

Arguments

lower, upper

Numeric vectors, indicating the lower/upper boundary of the drawn values.

k

Numeric, the number of values to be drawn for each value pair of lower/upper. If named numeric, the labels are used for the column names of the returned object

distr

Character, indicating which distribution to draw from. Currently available are: "unif" for a uniform distribution or "tnorm" for a truncated normal distribution. NUll will lead to "unif" (default).

cast_to_data_frame

Logical, controls whether the returned object is of type data.frame (TRUE) or matrix (FALSE). Default is TRUE

add_id_column

Character, controls whether an ID column should be added. Options are "numeric", "character", or "none". If "numeric" or "character" the column ID provides values from 1 to k of the respective type. If none, no column is added. Note that "character" casts all simulated values to character if the argument cast_to_data_frame is set to FALSE.

seed

Numeric, optional seed for making the simulation reproducable (see details)

...

Further arguments relevant for the distribution to draw from

Value

If cast_to_data_frame is TRUE, a data.frame with k rows and at least length(lower);length(upper) columns. Otherwise a matrix with the same number of rows and columns. Columns are labeled either from V1 to Vk or in case lower and upper are named numeric vectors using the labels of both vectors.

If add_id_column is not "none", an ID column is provided of the respective data type.

The data type of the parameters will be numeric, unless add_id_column is "character" and cast_to_data_frame is FALSE. In this case the returned matrix will be of type character.

Details

When drawing from a truncated normal distribution, users must provide values for the arguments means and sds. These are numeric vectors of the same size as lower and upper, and indicate the mean and the standard deviation of the normal distributions.

Examples

# Example 1: Draw from uniform distributions ------------------------------
lower <- c(a = 1, b = 1, c = 1)
upper <- c(a = 3, b = 4, c = 5)
values <- simulate_values(
  lower = lower,
  upper = upper,
  k = 50,
  add_id_column = "none"
)
summary(values)
#>        a               b               c        
#>  Min.   :1.010   Min.   :1.336   Min.   :1.073  
#>  1st Qu.:1.310   1st Qu.:2.063   1st Qu.:2.196  
#>  Median :1.890   Median :2.673   Median :2.688  
#>  Mean   :1.907   Mean   :2.706   Mean   :2.832  
#>  3rd Qu.:2.555   3rd Qu.:3.287   3rd Qu.:3.543  
#>  Max.   :2.985   Max.   :3.982   Max.   :4.977  

# Example 2: Draw from truncated normal distributions ---------------------
lower <- c(a = 1, b = 1, c = 1)
upper <- c(a = 3, b = 4, c = 5)
means <- c(a = 2, b = 2.5, c = 3)
sds <- c(a = 0.5, b = 0.5, c = 0.5)
values <- simulate_values(
  lower = lower,
  upper = upper,
  distr = "tnorm",
  k = 5000,
  add_id_column = "none",
  means = means,
  sds = sds
)
quantile(values$a, probs = c(0.025, 0.5, 0.975))
#>     2.5%      50%    97.5% 
#> 1.165065 1.999474 2.847122 
quantile(values$b, probs = c(0.025, 0.5, 0.975))
#>     2.5%      50%    97.5% 
#> 1.554678 2.487269 3.451515 
quantile(values$c, probs = c(0.025, 0.5, 0.975))
#>     2.5%      50%    97.5% 
#> 2.018759 3.006145 3.966381