AutoScore (version 1.1.0)

AutoScore_weighting_Ordinal: AutoScore STEP(iii) for ordinal outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)

Description

AutoScore STEP(iii) for ordinal outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)

Usage

AutoScore_weighting_Ordinal(
  train_set,
  validation_set,
  final_variables,
  link = "logit",
  max_score = 100,
  categorize = "quantile",
  quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1),
  max_cluster = 5,
  n_boot = 100
)

Value

Generated cut_vec for downstream fine-tuning process STEP(iv)

AutoScore_fine_tuning_Ordinal.

Arguments

train_set

A processed data.frame that contains data to be analyzed, for training.

validation_set

A processed data.frame that contains data for validation purpose.

final_variables

A vector containing the list of selected variables, selected from Step(ii) AutoScore_parsimony_Ordinal.

link

The link function used to model ordinal outcomes. Default is "logit" for proportional odds model. Other options are "cloglog" (proportional hazards model) and "probit".

max_score

Maximum total score (Default: 100).

categorize

Methods for categorize continuous variables. Options include "quantile" or "kmeans" (Default: "quantile").

quantiles

Predefined quantiles to convert continuous variables to categorical ones. (Default: c(0, 0.05, 0.2, 0.8, 0.95, 1)) Available if categorize = "quantile".

max_cluster

The max number of cluster (Default: 5). Available if categorize = "kmeans".

n_boot

Number of bootstrap cycles to compute 95% CI for performance metrics.

References

• Saffari SE, Ning Y, Feng X, Chakraborty B, Volovici V, Vaughan R, Ong ME, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, arXiv:2202.08407

See Also

AutoScore_rank_Ordinal, AutoScore_parsimony_Ordinal, AutoScore_fine_tuning_Ordinal, AutoScore_testing_Ordinal.

Examples

if (FALSE) {
data("sample_data_ordinal") # Output is named `label`
out_split <- split_data(data = sample_data_ordinal, ratio = c(0.7, 0.1, 0.2))
train_set <- out_split$train_set
validation_set <- out_split$validation_set
ranking <- AutoScore_rank_Ordinal(train_set, ntree=100)
num_var <- 6
final_variables <- names(ranking[1:num_var])
cut_vec <- AutoScore_weighting_Ordinal(
  train_set = train_set, validation_set = validation_set,
  final_variables = final_variables, max_score = 100,
  categorize = "quantile", quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1)
)
}