plot.predCuresurv {curesurv} | R Documentation |
plot method for curesurv prediction objects
Description
Produces figures of (excess) hazard, (net) survival and probability P(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t.
Usage
## S3 method for class 'predCuresurv'
plot(
x,
fun = "all",
conf.int = FALSE,
conf.type = c("log", "log-log", "plain"),
legend.out = TRUE,
xlab = "Time since diagnosis",
ylab.haz = "excess hazard",
ylab.surv = "net survival",
ylab.ptcure = "P(t)",
ylab.cumhaz = "cumulative excess hazard",
ylab.logcumhaz = "logarithm of cumulative excess hazard",
col.haz = "black",
col.surv = "black",
col.ptcure = "black",
col.cumhaz = "black",
col.logcumhaz = "black",
col.tau = "red",
col.ttc = "green4",
col.p95 = "black",
col.pi = "blue",
lty.surv = 1,
lty.haz = 1,
lty.ptcure = 1,
lty.cumhaz = 1,
lty.logcumhaz = 1,
lty.pi = 2,
lty.tau = 2,
lty.ttc = 3,
lty.p95 = 4,
lty.ic = 5,
lwd.main = 1,
lwd.sub = 1,
lwd.ic = 1,
...
)
Arguments
x |
result of the |
fun |
in "haz" or "surv" or "pt_cure", "cumhaz", "logcumhaz", the plot
produced is that of (excess) hazard, or that of (net) survival, or that of
the probability P(t) of being cured at a given time t after diagnosis
knowing that he/she was alive up to time t is provided, or that of
cumulative hazard or that of the logarithm of the cumulative hazard; if
|
conf.int |
an argument expected to be TRUE if the confidence intervals of the
related-indicator specified by the argument "fun" are needed. The default
option is FALSE. Confidence intervals are not available for |
conf.type |
One of "plain", "log", "log-log". The first option causes the standard intervals curve +- k *se(curve), where k is determined from conf.int. The log option calculates intervals based on log(curve). The log-log option bases the intervals on the log(-log(curve)). |
legend.out |
an argument deciding the place of the legend if |
xlab |
label for the x-axis of the plot. |
ylab.haz |
optional label for the y-axis of the plot of excess hazard |
ylab.surv |
optional label for the y-axis of the plot of net survival |
ylab.ptcure |
optional label for the y-axis of the plot of the probability
|
ylab.cumhaz |
optional label for the y-axis of the plot of cumulative excess hazard |
ylab.logcumhaz |
optional label for the y-axis of the plot of logarithm of cumulative excess hazard |
col.haz |
optional argument to specify the color of curve of the excess hazard |
col.surv |
optional argument to specify the color of curve of the net survival |
col.ptcure |
optional argument to specify the color of curve of probability
|
col.cumhaz |
optional argument to specify the color of curve of cumulative excess hazard |
col.logcumhaz |
optional argument to specify the color of curve of the logarithm of cumulative excess hazard |
col.tau |
optional argument to specify the color of curve of time-to-null excess hazard |
col.ttc |
optional argument to specify the color of curve of time-to-cure |
col.p95 |
optional argument to specify the color for the line highlighting |
col.pi |
optional argument to specify the color of cure proportion |
lty.surv |
stands for line types for net survival |
lty.haz |
stands for line types for excess hazard |
lty.ptcure |
stands for line types for probability P(t) of being cured at a given time t after diagnosis knowing that he/she was alive up to time t. |
lty.cumhaz |
stands for line types for cumulative excess hazard |
lty.logcumhaz |
stands for line types for logarithm cumulative excess hazard |
lty.pi |
stands for line types for cure proportion |
lty.tau |
stands for line types for time-to-null excess hazard |
lty.ttc |
stands for line types for time-to-cure |
lty.p95 |
stands for line types for the line highlighting |
lty.ic |
stands for line types for confidence intervals |
lwd.main |
line width for the main line (haz, surv, pt_cure, cumhaz, logcumhaz) |
lwd.sub |
line width for the additionnal lines (ttc, p95, tau...) |
lwd.ic |
line width for the confidence intervals lines |
... |
additional options as in the classical plot method. |
ylab |
optional label for the y-axis of the plot. Depending to the curve
of interest (hazard, survival, probability of being cured at a given time t,
or all),the argument must be named |
Value
No value is returned.
Author(s)
Juste Goungounga, Judith Breaud, Eugenie Blandin, Olayide Boussari, Valerie Jooste
See Also
predict.curesurv()
, print.curesurv()
, curesurv()
, browseVignettes("curesurv")
Examples
library("curesurv")
library("survival")
testiscancer$age_crmin <- (testiscancer$age- min(testiscancer$age)) /
sd(testiscancer$age)
fit_m1_ad_tneh <- curesurv(Surv(time_obs, event) ~ z_tau(age_crmin) +
z_alpha(age_crmin),
pophaz = "ehazard",
cumpophaz = "cumehazard",
model = "nmixture", dist = "tneh",
link_tau = "linear",
data = testiscancer,
method_opt = "L-BFGS-B")
fit_m1_ad_tneh
#' #mean of age
newdata1 <- with(testiscancer,
expand.grid(event = 0, age_crmin = mean(age_crmin), time_obs = seq(0.001,10,0.1)))
pred_agemean <- predict(object = fit_m1_ad_tneh, newdata = newdata1)
#max of age
newdata2 <- with(testiscancer,
expand.grid(event = 0,
age_crmin = max(age_crmin),
time_obs = seq(0.001,10,0.1)))
pred_agemax <- predict(object = fit_m1_ad_tneh, newdata = newdata2)
# predictions at time 2 years and of age
newdata3 <- with(testiscancer,
expand.grid(event = 0,
age_crmin = seq(min(testiscancer$age_crmin),max(testiscancer$age_crmin), 0.1),
time_obs = 2))
pred_age_val <- predict(object = fit_m1_ad_tneh, newdata = newdata3)
#plot of 3 indicators for mean age
plot(pred_agemean, fun="all")
#plot of net survival for mean and maximum age (comparison)
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(2, 2),
cex = 1.0)
plot(pred_agemax$time,
pred_agemax$ex_haz,
type = "l",
lty = 1,
lwd = 2,
xlab = "Time since diagnosis",
ylab = "excess hazard")
lines(pred_agemean$time,
pred_agemean$ex_haz,
type = "l",
lty = 2,
lwd = 2)
legend("topright",
horiz = FALSE,
legend = c("hE(t) age.max = 79.9", "hE(t) age.mean = 50.8"),
col = c("black", "black"),
lty = c(1, 2, 1, 1, 2, 2))
grid()
plot(pred_agemax$time,
pred_agemax$netsurv,
type = "l",
lty = 1,
lwd = 2,
ylim = c(0, 1),
xlab = "Time since diagnosis",
ylab = "net survival")
lines(pred_agemean$time,
pred_agemean$netsurv,
type = "l",
lty = 2,
lwd = 2)
legend("bottomleft",
horiz = FALSE,
legend = c("Sn(t) age.max = 79.9", "Sn(t) age.mean = 50.8"),
col = c("black", "black"),
lty = c(1, 2, 1, 1, 2, 2))
grid()
plot(pred_agemax$time,
pred_agemax$pt_cure,
type = "l",
lty = 1,
lwd = 2,
ylim = c(0, 1), xlim = c(0,30),
xlab = "Time since diagnosis",
ylab = "probability of being cured P(t)")
lines(pred_agemean$time,
pred_agemean$pt_cure,
type = "l",
lty = 2,
lwd = 2)
abline(v = pred_agemean$tau[1],
lty = 2,
lwd = 2,
col = "blue")
abline(v = pred_agemean$TTC[1],
lty = 2,
lwd = 2,
col = "red")
abline(v = pred_agemax$tau[1],
lty = 1,
lwd = 2,
col = "blue")
abline(v = pred_agemax$TTC[1],
lty = 1,
lwd = 2,
col = "red")
grid()
legend("bottomright",
horiz = FALSE,
legend = c("P(t) age.max = 79.9",
"P(t) age.mean = 50.8",
"TNEH age.max = 79.9",
"TTC age.max = 79.9",
"TNEH age.mean = 50.8",
"TTC age.mean = 50.8"),
col = c("black", "black", "blue", "red", "blue", "red"),
lty = c(1, 2, 1, 1, 2, 2))
val_age <- seq(min(testiscancer$age_crmin),
max(testiscancer$age_crmin), 0.1) * sd(testiscancer$age) +
min(testiscancer$age)
pred_age_val <- predict(object = fit_m1_ad_tneh, newdata = newdata3)
par(mfrow=c(2,2))
plot(val_age,
pred_age_val$ex_haz, type = "l",
lty=1, lwd=2,
xlab = "age",
ylab = "excess hazard")
grid()
plot(val_age,
pred_age_val$netsurv, type = "l", lty=1,
lwd=2, xlab = "age", ylab = "net survival")
grid()
plot(val_age,
pred_age_val$pt_cure, type = "l", lty=1, lwd=2,
xlab = "age",
ylab = "P(t)")
grid()
par(oldpar)