Here’s a reprex and some revised Stan code. Also I think we’re getting off topic here and I should probably post this information somewhere else.
library(rstan)
library(cmdstanr)
prefit_psi_step <- stan_model(
"submodel-two-psi-step.stan"
)
cmd_prefit_psi_step <- cmdstan_model(
"submodel-two-psi-step.stan"
)
psi_step_data <- list(n_patients = 16L, baseline_measurement = c(-0.935695521120025,
-2.12761603089136, -0.876325387003469, 0.931350429946891, 0.422731897638725,
-1.30375210258178, 1.85158267797611, -0.289448169455647, 0.325903831278964,
-1.2757407765375, 1.58794213975781, 1.59307842403321, -0.0584097720952887,
1.80357260097634, -2.30428952138529, 0.655115279462301), log_crude_event_rate = 3.17644992456411,
event_time = c(3.88219330975027, 17.5607555174655, 8.14097264393642,
0.874471581905152, 0.875182734259227, 1.39419961264823, 1.13884032148061,
43.2464501785995, 1.01706489078387, 19.9898187874785, 1.1633097689907,
0.769646147838224, 9.44064603011716, 1.49631432330604, 22.3176589776014,
1.04117377075568), long_beta = structure(c(0.931386697031433,
0.865137767714317, 0.973505472094455, 0.880036367203131,
0.935119573179882, 0.910063465843014, 1.34798084760589, 0.919172808940375,
1.01141192543184, 0.890762064216965, 1.05317362916398, 1.22448828904961,
0.843895992304378, 0.872039892731229, 0.854977513431319,
0.958580645647191, -0.511886090506962, 0.660548718396229,
1.21418574954005, 1.57400898285864, 1.48149041414514, 0.176628427838126,
1.46367493523126, 0.437751542691176, 1.21862018076306, 0.117778624770988,
0.988969750444339, 0.940177413736985, 0.142150803498799,
1.96856722070831, 0.366973246331529, 1.02998350811324), .Dim = c(16L,
2L)))
res <- sampling(
prefit_psi_step,
data = psi_step_data,
chains = 1,
warmup = 100,
iter = 110,
control = list(adapt_delta = 0.9)
)
res <- cmd_prefit_psi_step$sample(
data = psi_step_data,
chains = 1,
iter_warmup = 100,
iter_sampling = 110,
adapt_delta = 0.9
)
submodel-two-psi-step.stan (2.7 KB)