plot.proprate2.fit {surv2sample} | R Documentation |
This function plots estimates of cumulative rates (cumulative hazards or odds functions) for two samples of censored data. It may plot both separate estimates from the two samples and estimates based on the proportional rate model.
## S3 method for class 'proprate2.fit': plot(x, log.transform = FALSE, diff = FALSE, lwds = 1, cols = 1, ltys, ...)
x |
a "proprate2.fit" object, as returned by proprate2 . |
log.transform |
logical. Should the logarithms of cumulative rates be plotted? |
diff |
logical. Instead of two curves, should the difference of their logarithms be plotted? |
lwds, cols, ltys |
vectors of length equal to the number of curves in plots
(4 if diff is FALSE , 2 if TRUE ). These give line widths,
colours and line types for each curve. If of length 1, the value is replicated. |
... |
further plotting parameters. |
If diff
is FALSE
, four curves are plotted (two individual sample
estimates and two model based estimates). In this case, ltys
defaults
to c(1,1,2,2)
. If diff
is FALSE
,
the function plots their differences. Then ltys
defaults to c(1,1)
.
To omit a curve, set the corresponding component of lty
to 0
.
Using these plots one may visually assess the validity of the proportional rate assumption.
David Kraus (http://www.davidkraus.net/)
proprate2
for estimation
proprate2.neyman
, proprate2.ks
,
proprate2.gs
for tests of the proportional rate
assumption
## chronic active hepatitis data data(hepatitis) ## fit the proportional odds model fit = with(hepatitis, proprate2(Surv(time, status), treatment, model = 1)) ## plot model-based and model-free estimates of odds functions plot(fit) ## their logarithms plot(fit, log.transform = TRUE) ## differences of log-functions plot(fit, diff = TRUE)