tof_find_cv_predictions: Calculate and store the predicted outcomes for each...

In keyes-timothy/tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles

Calculate and store the predicted outcomes for each validation set observation during model tuning

Description

Calculate and store the predicted outcomes for each validation set observation during model tuning

Usage

tof_find_cv_predictions(
  split_data,
  prepped_recipe,
  lambda,
  alpha,
  model_type,
  outcome_colnames
)

Arguments

split_data	An 'rsplit' object from the rsample package. Alternatively, an unsplit tbl_df can be provided, though this is not recommended.
prepped_recipe	A trained recipe
lambda	A single numeric value indicating which penalty (lambda) value should be used to make the predictions
alpha	A single numeric value indicating which mixture (alpha) value should be used to make the predictions
model_type	A string indicating which kind of elastic net model to build. If a continuous response is being predicted, use "linear" for linear regression; if a categorical response with only 2 classes is being predicted, use "two-class" for logistic regression; if a categorical response with more than 2 levels is being predicted, use "multiclass" for multinomial regression; and if a time-to-event outcome is being predicted, use "survival" for Cox regression.
outcome_colnames	Quoted column names indicating which columns in the data being fit represent the outcome variables (with all others assumed to be predictors).

Value

A tibble containing the predicted and true values for the outcome for each of the validation observations in 'split_data'.

keyes-timothy/tidytof documentation built on May 7, 2024, 12:33 p.m.