calculate the brier score for the given model — calculate_brier

The Brier Score is calculated using the formula $$BS=\frac{1}{n}\sum_{t=1}^n{(f_t - o_t)^2}$$ where $n$ is the population size, $f_t$ is the predictions for the row $t$ and $o_t$ is the dichotomous observation for the row $t$.

The function operates according to the chosen model: if the model is Cox, the survival variable is converted into a binary value (1 if the event occurred during follow-up, 0 otherwise), whereas for a logistic regression model, the dependent variable may already be binary or represent survival time; in the latter case, it is transformed into a binary variable using the same rule as in the Cox model.

The confidence interval is calculated by bootstrap resamples.

Usage

calculate_brier_score(
  model,
  data,
  type = c("prediction", "prediction_type_1", "prediction_type_2"),
  n_boot = 1000,
  seed = NULL
)

Arguments

model: Model generated with mv_model_cox() or mv_model_logreg(). Needs the expected prediction parameter already calculated in the model. To generate the predictions you must use the function/s calculate_predictions(), calculate_predictions_recalibrated_type_1() or calculate_predictions_recalibrated_type_2()
data: Data for what the observed predictions will be calculated.
type: Type of the predictions that the calibration plot data should be generated from: "prediction", "prediction_type_1" or "prediction_type_2"
n_boot: number of bootstrap resamples to calculate the Brier Score standar error.
seed: random seed generator

Examples

if (FALSE) { # \dontrun{
model |>
  calculate_brier_score(data, type = "predictions_aggregated")
} # }