pred.survivalmodel: Apply a multivariate survival model to validation datasets

In SIMMS: Subnetwork Integration for Multi-Modal Signatures

Apply a multivariate survival model to validation datasets

Description

Predicts the risk score for all the training & validation datasets, independently. This function also predicts the risk score for combined training datasets cohort and validation datasets cohort. The risk score estimation is done by multivariate models fit by fit.survivalmodel. The function also predicts risk scores for each of the top.n.features independently. TO BE DEPRECATED AND HAS BEEN REPLACED BY create.classifier.multivariate

Usage

pred.survivalmodel(
  data.directory = ".",
  output.directory = ".",
  feature.selection.datasets = NULL,
  feature.selection.p.threshold = 0.05,
  training.datasets = NULL,
  validation.datasets = NULL,
  top.n.features = 25,
  models = c("1", "2", "3"),
  write.risk.data = TRUE
)

Arguments

data.directory	Path to the directory containing datasets as specified by feature.selection.datasets, training.datasets, validation.datasets
output.directory	Path to the output folder where intermediate and results files will be saved
feature.selection.datasets	A vector containing names of datasets used for feature selection in function derive.network.features()
feature.selection.p.threshold	One of the P values that were used for feature selection in function derive.network.features(). This function does not support vector of P values as used in derive.network.features() for performance reasons
training.datasets	A vector containing names of training datasets
validation.datasets	A vector containing names of validation datasets
top.n.features	A numeric value specifying how many top ranked features will be used for univariate survival modelling
models	A character vector specifying which of the models ('1' = N+E, '2' = N, '3' = E) to run
write.risk.data	A toggle to control whether risk scores and patient risk groups should be written to file

Value

The output files are stored under output.directory/output/

Author(s)

Syed Haider

See Also

create.classifier.multivariate

Examples

# see package's main documentation

SIMMS documentation built on April 24, 2022, 5:06 p.m.