Computes the predicted survivor function for a phpe model.
Usage
# S3 method for phpe
survfit(formula, newdata, ...)Arguments
- formula
an object of the class phpe
- newdata
a data frame containing the set of explanatory variables.
- ...
further arguments passed to or from other methods.
Examples
# \donttest{
# ML approach:
library(YPPE)
mle <- phpe(Surv(time, status)~arm, data=ipass, n_int=10, approach="mle", init = 0)
summary(mle)
#> Call:
#> phpe(formula = Surv(time, status) ~ arm, data = ipass, n_int = 10,
#> approach = "mle", init = 0)
#>
#> Proportional hazards coefficients:
#> Estimate StdErr z.value p.value
#> arm -0.310898 0.066011 -4.7098 2.479e-06 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> ---
#> loglik = -2810.835 AIC = 5643.671
ekm <- survival::survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(mle, newdata)
plot(ekm, col=1:2)
with(St, lines(time, surv[[1]]))
with(St, lines(time, surv[[2]], col=2))
# Bayesian approach:
bayes <- phpe(Surv(time, status)~arm, data=ipass, n_int=10, approach="bayes")
#>
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000327 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.27 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 2.563 seconds (Warm-up)
#> Chain 1: 2.214 seconds (Sampling)
#> Chain 1: 4.777 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.00033 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.3 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 2.452 seconds (Warm-up)
#> Chain 2: 2.25 seconds (Sampling)
#> Chain 2: 4.702 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.00031 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 3.1 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
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#> Chain 3:
#> Chain 3: Elapsed Time: 2.443 seconds (Warm-up)
#> Chain 3: 2.28 seconds (Sampling)
#> Chain 3: 4.723 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000309 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 3.09 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 2.457 seconds (Warm-up)
#> Chain 4: 2.362 seconds (Sampling)
#> Chain 4: 4.819 seconds (Total)
#> Chain 4:
summary(bayes)
#> Call:
#> phpe(formula = Surv(time, status) ~ arm, data = ipass, n_int = 10,
#> approach = "bayes")
#>
#> Proportional hazards coefficients:
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> arm -0.31 0.001 0.065 -0.439 -0.354 -0.311 -0.267 -0.183 3169.847 1
#> ---
#> Inference for Stan model: yppe.
#> 4 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#>
ekm <- survival::survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(bayes, newdata)
plot(ekm, col=1:2)
with(St, lines(time, surv[[1]]))
with(St, lines(time, surv[[2]], col=2))
# }