separate2GroupsCox {glmSparseNet} | R Documentation

## Separate data in High and Low risk groups (based on Cox model)

### Description

Draws multiple kaplan meyer survival curves (or just 1) and calculates logrank test

### Usage

    separate2GroupsCox(
      chosen.btas,
      xdata,
      ydata,
      probs = c(0.5, 0.5),
      no.plot = FALSE,
      plot.title = "SurvivalCurves",
      xlim = NULL,
      ylim = NULL,
      expand.yzero = FALSE,
      legend.outside = FALSE,
      ...
    )

### Arguments

`chosen.btas` | list of testing coefficients to calculate prognostic indexes, for example “list(Age = some_vector)“
`xdata` | n x m matrix with n observations and m variables
`ydata` | Survival object
`probs` | How to separate high and low risk patients 50%-50% is the default, but for top and bottom 40% -> c(.4,.6)
`no.plot` | Only calculate p-value and do not generate survival curve plot
`plot.title` | Name of file if
`xlim` | Optional argument to limit the x-axis view
`ylim` | Optional argument to limit the y-axis view
`expand.yzero` | expand to y = 0
`legend.outside` | If TRUE legend will be outside plot, otherwise inside
`...` | additional parameters to survminer::ggsurvplot

### Value

object with logrank test and kaplan-meier survival plot

A list with plot, p-value and kaplan-meier object. The plot was drawn from survminer::ggsurvplot with only the palette, data and fit arguments being defined and keeping all other defaults that can be customized as additional parameters to this function.

### See Also

survminer::ggsurvplot

### Examples

    data('cancer', package = 'survival')
    xdata <- ovarian[,c('age', 'resid.ds')]
    ydata <- data.frame(time = ovarian$futime, status = ovarian$fustat)
    separate2GroupsCox(c(age = 1, 0), xdata, ydata)
    separate2GroupsCox(c(age = 1, 0.5), xdata, ydata)
    separate2GroupsCox(c(age = 1), c(1,0,1,0,1,0),
                       data.frame(time = runif(6), status = rbinom(6, 1, .5)))
    separate2GroupsCox(list(aa = c(age = 1, 0.5),
                            bb = c(age = 0, 1.5)), xdata, ydata)

[Package glmSparseNet version 1.8.1 Index]