separate2GroupsCox: Separate data in High and Low risk groups (based on Cox...

In sysbiomed/glmSparseNet: Network Centrality Metrics for Elastic-Net Regularized Models

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(
  chosenBetas,
  xdata,
  ydata,
  probs = c(0.5, 0.5),
  noPlot = FALSE,
  plotTitle = "SurvivalCurves",
  xlim = NULL,
  ylim = NULL,
  expandYZero = FALSE,
  legendOutside = FALSE,
  stopWhenOverlap = TRUE,
  ...,
  chosen.btas = deprecated(),
  no.plot = deprecated(),
  plot.title = deprecated(),
  expand.yzero = deprecated(),
  legend.outside = deprecated(),
  stop.when.overlap = deprecated()
)

Arguments

chosenBetas	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).
noPlot	Only calculate p-value and do not generate survival curve plot.
plotTitle	Name of file if.
xlim	Optional argument to limit the x-axis view.
ylim	Optional argument to limit the y-axis view.
expandYZero	expand to y = 0.
legendOutside	If TRUE legend will be outside plot, otherwise inside.
stopWhenOverlap	when probs vector allows for overlapping of samples in both groups, then stop.
...	additional parameters to survminer::ggsurvplot
chosen.btas
no.plot
plot.title
expand.yzero
legend.outside
stop.when.overlap	Otherwise it will calculate with duplicate samples, i.e. simply adding them to xdata and ydata (in a different group).

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

xdata <- survival::ovarian[, c("age", "resid.ds")]
ydata <- data.frame(
    time = survival::ovarian$futime,
    status = survival::ovarian$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)

sysbiomed/glmSparseNet documentation built on Feb. 17, 2024, 1:38 p.m.