Parametric resampling scheme for a parametric regression model

Generate a new, resampled dataset of the same shape as data following the given model. The covariates are kept the same and the response variables are drawn according to model$sample_yx().

Usage

resample_param(data, model)

Arguments

data

data.frame() with columns x and y containing the original data

model

ParamRegrModel to use for the resampling

Value

data.frame() with columns x and y containing the resampled data

Examples

# Create an example dataset
n <- 10
x <- cbind(runif(n), rbinom(n, 1, 0.5))
model <- NormalGLM$new()
params <- list(beta = c(2, 3), sd = 1)
y <- model$sample_yx(x, params = params)
data <- dplyr::tibble(x = x, y = y)

# Fit the model to the data
model$fit(data, params_init = params, inplace = TRUE)

# Resample from the model given data
resample_param(data, model)
#> # A tibble: 10 × 2
#>     x[,1]  [,2]       y
#>     <dbl> <dbl>   <dbl>
#>  1 0.387      1  3.50  
#>  2 0.0119     0  1.36  
#>  3 0.233      0 -0.314 
#>  4 0.0979     0  0.0419
#>  5 0.700      0  1.40  
#>  6 0.214      1  2.41  
#>  7 0.277      1  2.97  
#>  8 0.747      1  4.72  
#>  9 0.118      1  3.15  
#> 10 0.296      0 -0.335