AutoScore_weighting_Survival {AutoScore} | R Documentation |
AutoScore STEP(iii) for survival outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)
Description
AutoScore STEP(iii) for survival outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)
Usage
AutoScore_weighting_Survival(
train_set,
validation_set,
final_variables,
max_score = 100,
categorize = "quantile",
max_cluster = 5,
quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1),
time_point = c(1, 3, 7, 14, 30, 60, 90)
)
Arguments
train_set |
A processed |
validation_set |
A processed |
final_variables |
A vector containing the list of selected variables, selected from Step(ii) |
max_score |
Maximum total score (Default: 100). |
categorize |
Methods for categorize continuous variables. Options include "quantile" or "kmeans" (Default: "quantile"). |
max_cluster |
The max number of cluster (Default: 5). Available if |
quantiles |
Predefined quantiles to convert continuous variables to categorical ones. (Default: c(0, 0.05, 0.2, 0.8, 0.95, 1)) Available if |
time_point |
The time points to be evaluated using time-dependent AUC(t). |
Value
Generated cut_vec
for downstream fine-tuning process STEP(iv) AutoScore_fine_tuning
.
References
Xie F, Ning Y, Yuan H, et al. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J Biomed Inform. 2022;125:103959. doi:10.1016/j.jbi.2021.103959
See Also
AutoScore_rank_Survival
,
AutoScore_parsimony_Survival
,
AutoScore_fine_tuning_Survival
,
AutoScore_testing_Survival
.
Examples
## Not run:
data("sample_data_survival") #
out_split <- split_data(data = sample_data_survival, ratio = c(0.7, 0.1, 0.2))
train_set <- out_split$train_set
validation_set <- out_split$validation_set
ranking <- AutoScore_rank_Survival(train_set, ntree=5)
num_var <- 6
final_variables <- names(ranking[1:num_var])
cut_vec <- AutoScore_weighting_Survival(
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),
time_point = c(1,3,7,14,30,60,90)
)
## End(Not run)