Add a spline-generated variable

Usage

simulate_spline(
  data,
  predictor,
  variable,
  coefficients,
  knots = c(0.25, 0.5, 0.75),
  degree = 3L,
  output_range = NULL,
  noise_variance = 0,
  seed = NULL,
  batch = NULL
)

Arguments

data: Base data.frame.
predictor: Predictor variable.
variable: Name of the generated variable.
coefficients: Spline coefficients.
knots: Quantile probabilities for interior knots.
degree: Polynomial degree.
output_range: Optional numeric pair. The spline value v is rescaled to output_range[1] + v * diff(output_range), which maps [0, 1] onto the pair. Values outside [0, 1] are rescaled the same way, not clipped.
noise_variance: Non-negative Gaussian noise variance; the noise added to each value has standard deviation sqrt(noise_variance).
seed: Optional random seed.
batch: Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.

Value

A simulab_sim base data.frame holding data with the spline variable added. as.data.frame(x, what = "basis") gives the tidy basis table, one row per observation/basis combination, and as.data.frame(x, what = "parameters") gives one row per basis coefficient.

Examples

data <- data.frame(id = 1:100, x = stats::runif(100))
result <- simulate_spline(
  data, predictor = "x", variable = "y",
  coefficients = c(0.1, 0.2, 0.5, 0.4, 0.7, 0.6, 0.9),
  knots = c(0.25, 0.5, 0.75), seed = 1
)
head(result)
#> <simulab_sim:spline> 6 rows x 3 columns
#>   id         x         y
#> 1  1 0.9390541 0.7459875
#> 2  2 0.5585891 0.4881785
#> 3  3 0.5710001 0.4964202
#> 4  4 0.1073091 0.2323486
#> 5  5 0.3887622 0.4466184
#> 6  6 0.9774780 0.8660810
#> 
#> Truth (parameters):
#>     basis coefficient
#> 1 basis_1         0.1
#> 2 basis_2         0.2
#> 3 basis_3         0.5
#> 4 basis_4         0.4
#> 5 basis_5         0.7
#> 6 basis_6         0.6
#> 7 basis_7         0.9
#> 
#> Other tables: basis. Read one with as.data.frame(x, what = "basis").