getTestKM.list {Coxmos}

getTestKM.list

Description

Run the function "getTestKM" for a list of models. More information in "?getTestKM".

Usage

getTestKM.list(
  lst_models,
  X_test,
  Y_test,
  lst_cutoff,
  type = "LP",
  ori_data = TRUE,
  BREAKTIME = NULL,
  n.breaks = 20,
  title = NULL,
  verbose = FALSE
)

Arguments

lst_models

List of Coxmos model

X_test

Numeric matrix or data.frame. Explanatory variables for test data (raw format). Qualitative variables must be transform into binary variables.

Y_test

Numeric matrix or data.frame. Response variables for test data. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.

lst_cutoff

Numeric vector. Cutoff vector to split the observations into two groups for each model. Recommended to compute optimal cutoff value with getAutoKM() or getAutoKM.list() functions.

type

Character. Kaplan Meier for complete model linear predictor ("LP"), for PLS components ("COMP") or for original variables ("VAR") (default: LP).

ori_data

Logical. Compute the Kaplan-Meier plot with the raw-data or the normalize-data to compute the best cut-point for splitting the data into two groups. Only used when type = "VAR" (default: TRUE).

BREAKTIME

Numeric. Size of time to split the data into "total_time / BREAKTIME + 1" points. If BREAKTIME = NULL, "n.breaks" is used (default: NULL).

n.breaks

Numeric. If BREAKTIME is NULL, "n.breaks" is the number of time-break points to compute (default: 20).

title

Character. Kaplan-Meier plot title (default: NULL).

verbose

Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).

Value

A list where each element corresponds to a Kaplan-Meier plot generated for each model in the input list. Each plot visualizes the survival probabilities based on the specified cutoff values for the respective model. The list's names correspond to the names of the models provided in the input list.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

References

Kaplan EL, Kaplan EL, Meier P (1958). “Nonparametric Estimation from Incomplete Observations.” Journal of the American Statistical Association. doi:10.1007/978-1-4612-4380-9_25, https://link.springer.com/chapter/10.1007/978-1-4612-4380-9_25.

Examples

data("X_proteomic")
data("Y_proteomic")
X_proteomic <- X_proteomic[1:50,]
Y_proteomic <- Y_proteomic[1:50,]
set.seed(123)
index_train <- caret::createDataPartition(Y_proteomic$event, p = .5, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:20]
Y_train <- Y_proteomic[index_train,]
X_test <- X_proteomic[-index_train,1:20]
Y_test <- Y_proteomic[-index_train,]
splsicox.model <- splsicox(X_train, Y_train, n.comp = 1, penalty = 0.5, x.center = TRUE,
x.scale = TRUE)
splsdrcox.model <- splsdrcox(X_train, Y_train, n.comp = 1, penalty = 0.5, x.center = TRUE,
x.scale = TRUE)
lst_models = list("sPLSICOX" = splsicox.model, "sPLSDRCOX" = splsdrcox.model)
lst_results = getAutoKM.list(type = "LP", lst_models)
lst_cutoff <- getCutoffAutoKM.list(lst_results)
getTestKM.list(lst_models, X_test, Y_test, lst_cutoff)