Hello! I have questions about employing different priors (e.g. lower bounds for some parameters) for cox proportional hazard model. From #86 and Prior Truncation in brms [lb,ub], I get the guidances to employ the nonlinear model syntax to specify different priors (lower bounds for some parameters) for cox proportional hazard model. So I have the following trials:
library(brms)
set.seed(1234)
data(kidney)
str(kidney)
# set different priors
priors <- c(
set_prior("lognormal(0, 1)", class = "b", lb = 0, nlpar = "etax"),
set_prior("normal(0, 1)", class = "b", ub = 1, nlpar = "etay"),
set_prior("normal(0, 1)", class = "b", nlpar = "rest")
)
# both trials have errors
# Trial 1
fit_coxph_brms_1 <- brm(
bf(time | cens(censored) ~ rest + etax + etay, nl = TRUE) +
lf(rest ~ 1 + age) +
lf(etax ~ 0 + sex) +
lf(etay ~ 0 + disease),
data = kidney, family = brmsfamily("cox"), ,
prior = priors
)
# Trial 2
fit_coxph_brms_1 <- brm(bf(time | cens(censored) ~ age + rest + etax + etay,
rest ~ 1 + age,
etax ~ 0 + sex, etay ~ 0 + disease,
nl = TRUE
),
data = kidney, family = brmsfamily("cox"), ,
prior = priors
)
# Compiling Stan program...
# Start sampling
# Error in mod$fit_ptr() :
# Exception: variable does not exist; processing stage=data initialization;
# variable name=Kbhaz; base type=int (in 'model44bc658c9cf5_8367535651e62a616
# ff4442b18259879' at line 54)
My environment:
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] brms_2.13.5 Rcpp_1.0.5.2 nvimcom_0.9-58
loaded via a namespace (and not attached):
[1] Brobdingnag_1.2-6 splines_4.0.2 jsonlite_1.7.0 gtools_3.8.2 StanHeaders_2.21.0-6 RcppParallel_5.0.2 threejs_0.3.3
[8] shiny_1.5.0 assertthat_0.2.1 stats4_4.0.2 backports_1.1.8 pillar_1.4.6 lattice_0.20-41 glue_1.4.1
[15] digest_0.6.25 promises_1.1.1 colorspace_1.4-1 htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18 plyr_1.8.6
[22] dygraphs_1.1.1.6 pkgconfig_2.0.3 rstan_2.21.2 purrr_0.3.4 xtable_1.8-4 mvtnorm_1.1-1 scales_1.1.1
[29] processx_3.4.3 later_1.1.0.1 tibble_3.0.3 splines2_0.3.1 bayesplot_1.7.2 generics_0.0.2 ggplot2_3.3.2
[36] ellipsis_0.3.1 DT_0.14 withr_2.2.0 shinyjs_1.1 cli_2.0.2 survival_3.1-12 magrittr_1.5
[43] crayon_1.3.4 mime_0.9 ps_1.3.3 fansi_0.4.1 nlme_3.1-148 xts_0.12.1 pkgbuild_1.1.0
[50] colourpicker_1.0 rsconnect_0.8.16 tools_4.0.2 loo_2.3.1 prettyunits_1.1.1 lifecycle_0.2.0 matrixStats_0.56.0
[57] stringr_1.4.0 V8_3.2.0 munsell_0.5.0 callr_3.4.3 compiler_4.0.2 rlang_0.4.7 grid_4.0.2
[64] ggridges_0.5.2 htmlwidgets_1.5.1 crosstalk_1.1.0.1 igraph_1.2.5 miniUI_0.1.1.1 base64enc_0.1-3 codetools_0.2-16
[71] gtable_0.3.0 inline_0.3.16 abind_1.4-5 curl_4.3 markdown_1.1 reshape2_1.4.4 R6_2.4.1
[78] gridExtra_2.3 rstantools_2.1.1 zoo_1.8-8 bridgesampling_1.0-0 dplyr_1.0.0 fastmap_1.0.1 shinystan_2.5.0
[85] shinythemes_1.1.2 stringi_1.4.6 parallel_4.0.2 vctrs_0.3.2 tidyselect_1.1.0 coda_0.19-3
Well, could you please help for this problem? By the way, could I get a more detailed instructions about how you implement the Bayesian cox proportional hazard model? For example, about the introduction of the model parameters (e.g. Kbhaz) or data inputs. Thanks so much!