xy_sim | R Documentation |
xy_sim
Methods of the class xy_sim
## S3 method for class 'xy_sim'
print(x, ...)
## S3 method for class 'xy_sim'
coef(object, ...)
## S3 method for class 'xy_sim'
plot(x, ...)
## S3 method for class 'xy_sim'
transform(`_data`, ...)
## S3 method for class 'xy_sim'
formula(x, ...)
x |
an object of class |
... |
additional parameters |
object |
an object of class |
_data |
an object of class |
With the help of these methods you can further manipulate a cooked simulation.
print.xy_sim()
: Gives you an overview of the simulation
coef.xy_sim()
: Extracts the beta coeffecients of the simulation from
y = X\beta + e
plot.xy_sim()
: Will plot the true effects of the simulation, e.g. X vs y
transform.xy_sim()
: Will return the adjusted simulated data, i.e. it will
apply all nonlinear transformations to the raw simulated
effects and multiply the X by its beta coefficient.
This function is mostly used internally, however,
exposed to the user as it could be needed in edge
cases.
formula.xy_sim()
: Will return a formula object which can be forwarded
to the machine learning algorithm. Note: Uninformative
features are added as well.
# create a simulation
linear_sim <- Xy() %>%
add_linear(p = 5) %>%
simulate(n = 100)
# print the simulation
simulation_info <- linear_sim %>% print()
# get the coefficients of the features
simulation_coefs <- linear_sim %>% coef()
# plot the underlying true effect of X
simulation_plot <- linear_sim %>% plot()
# transform the data of the simulation such that the features are transformed
# e.g. nonlinear features are scaled by their functions.
transformed_simulation <- linear_sim %>% transform()
# fetch the formula
eqn <- linear_sim %>% formula()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.