fit.modelsPlots the results of model fit.
# S3 method for survHE
plot(...)Must include at least one result object saved as
the call to the fit.models function. Nay include other
optional parameters. These include whether the KM curve should be
added add.km and whether the user specifies a profile of covariates
(in the list newdata). Other possibilities are additional
(mainly graphical) options. These are:
xlab = a string with the label for the x-axis (default = "time")
ylab = a string with the label for the y-axis (default = "Survival")
lab.profile = a (vector of) string(s) indicating the labels associated with the strata defining the different
survival curves to plot. Default to the value used by the Kaplan Meier
estimate given in fit.models.
newdata = a list (of lists) providing the values for the relevant covariates If NULL, then will use
the mean values for the covariates if at least one is a continuous variable,
or the combination of the categorical covariates.
xlim = a vector determining the limits for the x-axis
colors = a vector of characters defining the colours in which to plot the different survival curves
what = a string indicating whether the survival, hazard or
cumulative hazard curve should be plotted. Defaults to 'survival', but the
other two options can be specified as 'hazard' or 'cumhazard'
lab.profile = a vector of characters defining the names of the models fitted
add.km = TRUE (whether to also add the Kaplan Meier estimates of the data)
annotate = FALSE (whether to also add text to highlight the observed vs
extrapolated data)
legend.position = a vector of proportions to place the legend. Default
to 'c(.75,.9)', which means 75% across the x-axis and 90% across the y-axis
legend.title = suitable instructions to format the title of the legend;
defaults to 'element_text(size=15,face="bold")' but there may be other
arguments that can be added (using 'ggplot' facilities)
legend.text = suitable instructions to format the text of the legend;
defaults to 'element_text(colour="black", size=14, face="plain")' but there
may be other arguments that can be added (using 'ggplot' facilities)
Gianluca Baio
G Baio (2019). survHE: Survival analysis for health economic evaluation and cost-effectiveness modelling. Journal of Statistical Software (2020). vol 95, 14, 1-47. doi:10.18637/jss.v095.i14
fit.models, write.surv
if (FALSE) {
data(bc)
mle = fit.models(formula=Surv(recyrs,censrec)~group,data=bc,
distr="exp",method="mle")
inla = fit.models(formula=Surv(recyrs,censrec)~group,data=bc,
distr="exp",method="inla")
plot(MLE=mle,INLA=inla)
}
Run the code above in your browser using DataLab