predict.Coxmos: predict.Coxmos

In Coxmos: Cox MultiBlock Survival

predict.Coxmos

Description

Generates the prediction score matrix for Partial Least Squares (PLS) Survival models, facilitating the transformation of high-dimensional data into a reduced space while preserving the most relevant information for survival analysis.

Usage

## S3 method for class 'Coxmos'
predict(object, ..., newdata = NULL)

Arguments

object	Coxmos model
...	additional arguments affecting the predictions produced.
newdata	Numeric matrix or data.frame. New data for explanatory variables (raw data). Qualitative variables must be transform into binary variables.

Details

The predict.Coxmos function is designed to compute the prediction scores for new data based on a previously trained PLS Survival model. The function leverages the dimensional reduction capabilities of PLS to project the new data into a lower-dimensional space, which is particularly beneficial when dealing with high-dimensional datasets in survival analysis. The score matrix obtained serves as a compact representation of the original data, capturing the most salient features that influence survival outcomes.

Value

Score values data.frame for new data using the Coxmos model selected.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

Examples

data("X_proteomic")
data("Y_proteomic")
set.seed(123)
index_train <- caret::createDataPartition(Y_proteomic$event, p = .5, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:50]
Y_train <- Y_proteomic[index_train,]

X_test <- X_proteomic[-index_train,1:50]
Y_test <- Y_proteomic[-index_train,]
model <- splsicox(X_train, Y_train, n.comp = 2) #after CV
predict(object = model, newdata = X_test)

Coxmos documentation built on April 4, 2025, 12:20 a.m.