covariates_plot {TwoTimeScales} | R Documentation |
Plot of the covariates' effects
Description
covariates_plot()
produces a plot of the covariates' effects (\hat\beta
)
with confidence intervals, or of the Hazard Ratios (\exp(\hat\beta)
) with confidence intervals.
Usage
covariates_plot(
fitted_model,
confidence_lev = 0.95,
plot_options = list(),
...
)
Arguments
fitted_model |
A list returned by the function |
confidence_lev |
The level of confidence for the CIs. Default is 0.95 ( |
plot_options |
A list of options for the plot:
|
... |
further arguments passed to plot() |
Value
A plot of the covariates' effects. The different covariates are plotted on the x-axis, and on the y-axis the effects on the coefficient- or on the HR-scale are plotted. The main estimate is represented by a point and the CIs are added as vertical bars.
Examples
# Create some fake data - the bare minimum
id <- 1:20
u <- c(5.43, 3.25, 8.15, 5.53, 7.28, 6.61, 5.91, 4.94, 4.25, 3.86, 4.05, 6.86,
4.94, 4.46, 2.14, 7.56, 5.55, 7.60, 6.46, 4.96)
s <- c(0.44, 4.89, 0.92, 1.81, 2.02, 1.55, 3.16, 6.36, 0.66, 2.02, 1.22, 3.96,
7.07, 2.91, 3.38, 2.36, 1.74, 0.06, 5.76, 3.00)
ev <- c(1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1)
x1 <- c(0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0)
fakedata <- as.data.frame(cbind(id, u, s, ev, x1))
covs <- subset(fakedata, select = c("x1"))
fakedata2ts <- prepare_data(u = fakedata$u,
s_out = fakedata$s,
ev = fakedata$ev,
ds = .5,
individual = TRUE,
covs = covs)
# Fit a fake model - not optimal smoothing
fakemod <- fit2ts(fakedata2ts,
optim_method = "grid_search",
lrho = list(seq(1 ,1.5 ,.5),
seq(1 ,1.5 ,.5)))
# Covariates plot with default options
covariates_plot(fakemod)
# Plot the hazard ratios instead
covariates_plot(fakemod,
plot_options = list(
HR = TRUE))
# Change confidence level
covariates_plot(fakemod,
confidence_lev = .99)