separate2GroupsCox {glmSparseNet} | R Documentation |
Draws multiple kaplan meyer survival curves (or just 1) and calculates logrank test
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, stop.when.overlap = TRUE, ... )
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 |
stop.when.overlap |
when probs vector allows for overlapping of samples in both groups, then stop. Otherwise it will calculate with duplicate samples, i.e. simply adding them to xdata and ydata (in a different group) |
... |
additional parameters to survminer::ggsurvplot |
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.
survminer::ggsurvplot
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)