Create a new Method — create

Create a Method which can fit() data in an Experiment.

Usage

create_method(.method_fun, .name = NULL, ...)

Arguments

.method_fun: The user-defined method function.
.name: (Optional) The name of the Method, helpful for later identification.
...: User-defined default arguments to pass into .method_fun().

Value

A new Method object.

Examples

# generate some data
dgp_fun <- function(n, beta, rho, sigma) {
  cov_mat <- matrix(c(1, rho, rho, 1), byrow = TRUE, nrow = 2, ncol = 2)
  X <- MASS::mvrnorm(n = n, mu = rep(0, 2), Sigma = cov_mat)
  y <- X %*% beta + rnorm(n, sd = sigma)
  return(list(X = X, y = y))
}

dgp <- create_dgp(.dgp_fun = dgp_fun,
                  .name = "Linear Gaussian DGP",
                  n = 50, beta = c(1, 0), rho = 0, sigma = 1)
data_corr <- dgp$generate(rho = 0.7)

# create an example Method function
lm_fun <- function(X, y, cols) {
  X <- X[, cols]
  lm_fit <- lm(y ~ X)
  pvals <- summary(lm_fit)$coefficients[-1, "Pr(>|t|)"] %>%
    setNames(paste(paste0("X", cols), "p-value"))
  return(pvals)
}

# create Method with default argument `cols`
lm_method <- create_method(
  .method_fun = lm_fun,
  .name = "OLS",
  cols = c(1, 2)
)

print(lm_method)
#> Method Name: OLS 
#>    Function: function (X, y, cols)  
#>    Parameters: List of 1
#>      $ cols: num [1:2] 1 2

# fit the Method on data with non-default arguments
lm_method$fit(data_corr, cols = 2)
#> # A tibble: 1 × 1
#>   `X2 p-value`
#>          <dbl>
#> 1 0.0000000208

# fit the Method on data with default arguments
lm_method$fit(data_corr)
#> # A tibble: 1 × 2
#>   `X1 p-value` `X2 p-value`
#>          <dbl>        <dbl>
#> 1      0.00394       0.0753