| Method | R Documentation |
R6 class representing a methodMethod which can fit() data in an Experiment.
Generally speaking, users won't directly interact with the Method R6
class, but instead indirectly through create_method() and the
following Experiment helpers:
add_method()
update_method()
remove_method()
get_methods()
fit_experiment()
nameThe name of the Method.
method_funThe user-defined method function.
method_paramsA (named) list of default parameters to input into the method function.
new()Initialize a new Method object.
Method$new(.method_fun, .name = NULL, ...)
.method_funThe user-defined method function.
.name(Optional) The name of the Method, helpful for later
identification.
...User-defined default arguments to pass into .method_fun().
A new instance of Method.
fit()Fit a Method on data using the provided Method
parameters.
Method$fit(data_list, ..., .simplify = TRUE)
data_listList of data to pass into Method$method_fun().
If named, should match arguments in Method$method_fun().
...User-defined arguments to pass into Method$method_fun()
that will overwrite the initialized Method parameters. If no
additional arguments are provided, the Method will be fit using
Method$method_fun() and the parameters that were set when
Method$new() was called.
.simplifyIf TRUE, remove list wrapping from any column that has
scalar values.
methodA Method object.
Result of Method$method_fun(), coerced into a single
tibble row.
print()Print a Method in a nice format, showing the
Method's name, function, and parameters.
Method$print()
The original Method object, invisibly.
clone()The objects of this class are cloneable with this method.
Method$clone(deep = FALSE)
deepWhether to make a deep clone.
create_method
# 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|)"]
names(pvals) <- paste(names(pvals), "p-value")
return(pvals)
}
# create Method with default argument `cols`
lm_method <- Method$new(
.method_fun = lm_fun,
.name = "OLS",
cols = c(1, 2)
)
print(lm_method)
# fit the Method on data with non-default arguments
lm_method$fit(data_corr, cols = 2)
# fit the Method on data with default arguments
lm_method$fit(data_corr)
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