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]