getAutoKM.list {Coxmos}

getAutoKM.list

Description

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

Usage

getAutoKM.list(
  type = "LP",
  lst_models,
  comp = 1:2,
  top = NULL,
  ori_data = TRUE,
  BREAKTIME = NULL,
  n.breaks = 20,
  only_sig = FALSE,
  alpha = 0.05,
  title = NULL,
  verbose = FALSE
)

Arguments

type

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

lst_models

List of Coxmos models.

comp

Numeric vector. Vector of length two. Select which components to plot (default: c(1,2)).

top

Numeric. Show "top" first variables. If top = NULL, all variables are shown (default: 10).

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).

only_sig

Logical. If "only_sig" = TRUE, then only significant log-rank test variables are returned (default: FALSE).

alpha

Numeric. Numerical values are regarded as significant if they fall below the threshold (default: 0.05).

title

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

verbose

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

Value

A list of two elements per each model in the list: info_logrank_num: A list of two data.frames with the numerical variables categorize as qualitative and the cutpoint to divide the data into two groups. LST_PLOTS: A list with the Kaplan-Meier Plots.

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")
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)
getAutoKM.list(type = "LP", lst_models)