brms
It is available from CRAN, so it can be installed with install.packages(). Here is a documentation example,
library(brms)
# Survival regression modeling the time between the first
# and second recurrence of an infection in kidney patients.
fit3 <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
data = kidney, family = lognormal())
summary(fit3)
plot(fit3, ask = FALSE)
plot(conditional_effects(fit3), ask = FALSE)
fit3cox <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
data = kidney, family = cox)
summary(fit3cox)
plot(fit3cox, ask = FALSE)
In case of compilng error, it is necessary to use the developmental version of rstan (2.21.3 at the time of writing), see below.
2.20.4
It turns out that this version compiles well under module ceuadmin/R/4.3.3 nevertheless problematic with rstan 2.32.6.
We proceed to run the example 3 from brm function under ceuadmin/R/4.3.3-gcc11,
# Survival regression modeling the time between the first
# and second recurrence of an infection in kidney patients.
library(brms)
fit <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
data = kidney, family = lognormal())
summary(fit)
plot(fit, ask = FALSE)
plot(conditional_effects(fit), ask = FALSE)
giving
Compiling Stan program...
Start sampling
SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: Rejecting initial value:
Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
Chain 1: Stan can't start sampling from this initial value.
Chain 1: Rejecting initial value:
Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
Chain 1: Stan can't start sampling from this initial value.
Chain 1:
Chain 1: Gradient evaluation took 0.00047 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.7 seconds.
Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: Rejecting initial value:
Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
Chain 2: Stan can't start sampling from this initial value.
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
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> summary(fit)
Family: lognormal
Links: mu = identity; sigma = identity
Formula: time | cens(censored) ~ age * sex + disease + (1 | patient)
Data: kidney (Number of observations: 76)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Group-Level Effects:
~patient (Number of levels: 38)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.42 0.25 0.03 0.93 1.00 811 1655
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 2.63 0.95 0.71 4.54 1.00 2416 2745
age 0.02 0.02 -0.03 0.06 1.00 2083 2572
sexfemale 2.57 1.12 0.39 4.87 1.00 2136 2373
diseaseGN -0.43 0.51 -1.46 0.55 1.00 2912 3017
diseaseAN -0.55 0.49 -1.53 0.41 1.00 3225 2610
diseasePKD 0.56 0.71 -0.86 1.98 1.00 3377 2972
age:sexfemale -0.03 0.03 -0.08 0.02 1.00 2173 2460
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 1.16 0.13 0.92 1.43 1.00 2140 2025
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
> plot(fit, ask = FALSE)
> plot(conditional_effects(fit), ask = FALSE)
and with family=cox,
> fit <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
+ data = kidney, family = cox)
Compiling Stan program...
Start sampling
SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.008653 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 86.53 seconds.
Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
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> fit
Family: cox
Links: mu = log
Formula: time | cens(censored) ~ age * sex + disease + (1 | patient)
Data: kidney (Number of observations: 76)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Group-Level Effects:
~patient (Number of levels: 38)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.68 0.31 0.08 1.32 1.01 724 945
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 4.13 1.24 1.86 6.65 1.01 1297 1708
age -0.02 0.03 -0.08 0.03 1.01 1408 1867
sexfemale -3.30 1.37 -6.17 -0.71 1.01 1354 1751
diseaseGN 0.30 0.61 -0.85 1.55 1.00 2001 2400
diseaseAN 0.61 0.57 -0.45 1.77 1.00 1958 2376
diseasePKD -0.95 0.84 -2.62 0.73 1.00 1883 2912
age:sexfemale 0.03 0.03 -0.02 0.10 1.01 1439 1938
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).