Hello,
I’m new to categorical models, and I wanted to check if I should be using a slightly different syntax for group-level effects. I have samples (e.g. a box containing items) from which I exhaustively sample all items, each being a different outcome (12 categories). The total number of items can differ among samples, and I have 5 independent samples per treatment (control vs impact).
When I fit the model structure
model <- brms::brm(response ~ treatment + (1 | sample_id), data = data,
family = "categorical", chains = 4, cores = 4, iter = 1e4,
sample_prior = "yes")
It seems to fit fine, but then I get the following error message:
> summary(model)
Error: Duplicate variable names are not allowed in draws objects.
The following variable names are duplicates:
{'prior_sd_sample_id'}
Looking at model$fit and stancode(model) I can see that there are separate group-level standard deviations being estimated for each item (makes sense), however they all contain the same parameter name which I think is what’s causing the issue?
Did I code this incorrectly? Thanks for your help! Full reprex below.
library(brms)
rstan::rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
data_as_string <- "response,treatment,sample_id\n A,control,ID_1\n C,control,ID_1\n C,control,ID_1\n D,control,ID_1\n E,control,ID_1\n B,control,ID_1\n C,control,ID_1\n D,control,ID_1\n E,control,ID_1\n D,control,ID_1\n C,control,ID_2\n C,control,ID_2\n C,control,ID_2\n E,control,ID_2\n E,control,ID_2\n C,control,ID_2\n C,control,ID_3\n C,control,ID_3\n C,control,ID_3\n C,control,ID_3\n C,control,ID_4\n C,control,ID_4\n C,control,ID_4\n D,control,ID_4\n D,control,ID_4\n F,control,ID_4\n C,control,ID_4\n G,control,ID_4\n C,control,ID_4\n D,control,ID_4\n F,control,ID_4\n F,control,ID_4\n C,control,ID_4\n D,control,ID_4\n C,control,ID_4\n F,control,ID_4\n H,control,ID_4\n D,control,ID_4\n D,control,ID_4\n D,control,ID_4\n C,control,ID_5\n C,control,ID_5\n E,control,ID_5\n E,control,ID_5\n E,control,ID_5\n C,control,ID_5\n I,control,ID_5\n D,control,ID_5\n C,control,ID_5\n G,impact,ID_6\n C,impact,ID_6\n E,impact,ID_6\n C,impact,ID_6\n F,impact,ID_6\n G,impact,ID_6\n C,impact,ID_6\n C,impact,ID_6\n D,impact,ID_6\n D,impact,ID_6\n C,impact,ID_6\n C,impact,ID_6\n D,impact,ID_6\n D,impact,ID_6\n D,impact,ID_6\n G,impact,ID_6\n C,impact,ID_6\n D,impact,ID_6\n C,impact,ID_6\n G,impact,ID_7\n C,impact,ID_7\n D,impact,ID_7\n C,impact,ID_7\n J,impact,ID_8\n D,impact,ID_8\n C,impact,ID_8\n C,impact,ID_8\n D,impact,ID_8\n D,impact,ID_8\n C,impact,ID_8\n C,impact,ID_8\n C,impact,ID_8\n E,impact,ID_8\n D,impact,ID_8\n C,impact,ID_9\n E,impact,ID_9\n G,impact,ID_9\n C,impact,ID_9\n E,impact,ID_9\n C,impact,ID_9\n C,impact,ID_9\n E,impact,ID_9\n C,impact,ID_9\n E,impact,ID_9\n L,impact,ID_9\n D,impact,ID_10\n M,impact,ID_10\n D,impact,ID_10\n D,impact,ID_10\n C,impact,ID_10\n C,impact,ID_10\n D,impact,ID_10\n D,impact,ID_10\n D,impact,ID_10\n E,impact,ID_10\n C,impact,ID_10\n C,impact,ID_10\n E,impact,ID_10\n D,impact,ID_10\n C,impact,ID_10\n D,impact,ID_10\n D,impact,ID_10\n D,impact,ID_10\n D,impact,ID_10\n"
data <- read.csv(text = data_as_string, sep = ",", header = TRUE,
stringsAsFactors = FALSE)
model <- brms::brm(response ~ treatment + (1 | sample_id), data = data,
family = "categorical", chains = 4, cores = 4, iter = 1e4,
sample_prior = "yes")
summary(model)
sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] brms_2.16.1 Rcpp_1.0.7
loaded via a namespace (and not attached):
[1] nlme_3.1-152 matrixStats_0.60.1 xts_0.12.1 threejs_0.3.3 rstan_2.21.3
[6] tensorA_0.36.2 tools_4.1.0 backports_1.2.1 utf8_1.2.2 R6_2.5.1
[11] DT_0.19 DBI_1.1.1 mgcv_1.8-35 projpred_2.0.2 colorspace_2.0-2
[16] tidyselect_1.1.1 gridExtra_2.3 prettyunits_1.1.1 processx_3.5.2 Brobdingnag_1.2-6
[21] emmeans_1.6.0 curl_4.3.2 compiler_4.1.0 cli_3.0.1 shinyjs_2.0.0
[26] sandwich_3.0-1 colourpicker_1.1.0 posterior_1.1.0 scales_1.1.1 dygraphs_1.1.1.6
[31] checkmate_2.0.0 mvtnorm_1.1-2 ggridges_0.5.3 callr_3.7.0 StanHeaders_2.21.0-7
[36] stringr_1.4.0 digest_0.6.27 minqa_1.2.4 base64enc_0.1-3 pkgconfig_2.0.3
[41] htmltools_0.5.2 lme4_1.1-27.1 fastmap_1.1.0 htmlwidgets_1.5.4 rlang_0.4.11
[46] shiny_1.6.0 farver_2.1.0 generics_0.1.0 jsonlite_1.7.2 zoo_1.8-9
[51] crosstalk_1.1.1 gtools_3.9.2 dplyr_1.0.7 distributional_0.2.2 inline_0.3.19
[56] magrittr_2.0.1 loo_2.4.1 bayesplot_1.8.1 Matrix_1.3-3 munsell_0.5.0
[61] fansi_0.5.0 abind_1.4-5 lifecycle_1.0.0 multcomp_1.4-17 stringi_1.7.4
[66] MASS_7.3-54 pkgbuild_1.2.0 plyr_1.8.6 grid_4.1.0 parallel_4.1.0
[71] promises_1.2.0.1 crayon_1.4.1 miniUI_0.1.1.1 lattice_0.20-44 splines_4.1.0
[76] ps_1.6.0 pillar_1.6.2 igraph_1.2.6 boot_1.3-28 estimability_1.3
[81] markdown_1.1 shinystan_2.5.0 codetools_0.2-18 reshape2_1.4.4 stats4_4.1.0
[86] rstantools_2.1.1 glue_1.4.2 V8_3.4.2 RcppParallel_5.1.4 vctrs_0.3.8
[91] nloptr_1.2.2.2 httpuv_1.6.2 gtable_0.3.0 purrr_0.3.4 assertthat_0.2.1
[96] ggplot2_3.3.5 mime_0.11 xtable_1.8-4 coda_0.19-4 later_1.3.0
[101] survival_3.2-11 rsconnect_0.8.24 tibble_3.1.4 shinythemes_1.2.0 gamm4_0.2-6
[106] TH.data_1.0-10 ellipsis_0.3.2 bridgesampling_1.1-2