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Uses a Multinomial calibration model to calculate new probabilities

Source: R/cal-estimate-multinom.R
cal_estimate_multinomial.Rd

Uses a Multinomial calibration model to calculate new probabilities

Usage

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

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

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

# S3 method for 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.

See also

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

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)