Change axis ranges for a ggplot based on a Predict object (rms package)

I am using the rms package to perform Cox regression with age as restricted cubic splines with 4 knots, see reproducible code below from the rms package documentation.
My problem is: How do I change the y axis interval for the ggplot-output? I tried adding scale_y_continuous(limits = c(0,20)) but it didn’t alter the axis

library(ggplot)
library(rms)

# NOT RUN {
# Simulate data from a population model in which the log hazard
# function is linear in age and there is no age x sex interaction

n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n, 
                     rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
S <- Surv(dt,e)

f <- cph(S ~ rcs(age,4) + sex, x=TRUE, y=TRUE)
cox.zph(f, "rank")             # tests of PH
anova(f)
ggplot(Predict(f, age, sex, fun=exp)) + # plot age effect, 2 curves for 2 sexes
  scale_y_continuous(limits = c(0,20))

>Solution :

You can use the ylim argument of coord_cartesian instead:

ggplot(Predict(f, age, sex, fun=exp)) + # plot age effect, 2 curves for 2 sexes
  coord_cartesian(ylim = c(0, 20))