cal_estimate_multinomial function

Uses a Multinomial calibration model to calculate new probabilities

cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  parameters = NULL,
  ...
)

Arguments

• .data: An ungrouped data.frame object, or tune_results object, that contains predictions and probability columns.
• truth: The column identifier for the true class results (that is a factor). This should be an unquoted column name.
• estimate: A vector of column identifiers, or one of dplyr selector functions to choose which variables contains the class probabilities. It defaults to the prefix used by tidymodels (.pred_). The order of the identifiers will be considered the same as the order of the levels of the truth variable.
• smooth: Applies to the logistic models. It switches between logistic spline when TRUE, and simple logistic regression when FALSE.
• parameters: (Optional) An optional tibble of tuning parameter values that can be used to filter the predicted values before processing. Applies only to tune_results objects.
• ...: Additional arguments passed to the models or routines used to calculate the new probabilities.
• .by: The column identifier for the grouping variable. This should be a single unquoted column name that selects a qualitative variable for grouping. Default to NULL. When .by = NULL no grouping will take place.

Details

When smooth = FALSE, nnet::multinom() function is used to estimate the model, otherwise mgcv::gam() is used.

Examples

library(modeldata)
library(parsnip)
library(dplyr)

f <-
  list(
    ~ -0.5 + 0.6 * abs(A),
    ~ ifelse(A > 0 & B > 0, 1.0 + 0.2 * A / B, -2),
    ~ -0.6 * A + 0.50 * B - A * B
  )

set.seed(1)
tr_dat <- sim_multinomial(500, eqn_1 = f[[1]], eqn_2 = f[[2]], eqn_3 = f[[3]])
cal_dat <- sim_multinomial(500, eqn_1 = f[[1]], eqn_2 = f[[2]], eqn_3 = f[[3]])
te_dat <- sim_multinomial(500, eqn_1 = f[[1]], eqn_2 = f[[2]], eqn_3 = f[[3]])

set.seed(2)
rf_fit <-
  rand_forest() %>%
  set_mode("classification") %>%
  set_engine("randomForest") %>%
  fit(class ~ ., data = tr_dat)

cal_pred <-
  predict(rf_fit, cal_dat, type = "prob") %>%
  bind_cols(cal_dat)
te_pred <-
  predict(rf_fit, te_dat, type = "prob") %>%
  bind_cols(te_dat)

cal_plot_windowed(cal_pred, truth = class, window_size = 0.1, step_size = 0.03)

smoothed_mn <- cal_estimate_multinomial(cal_pred, truth = class)

new_test_pred <- cal_apply(te_pred, smoothed_mn)

cal_plot_windowed(new_test_pred, truth = class, window_size = 0.1, step_size = 0.03)

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_multinomial()