From f63971ea246d63efa994aa26f5e6fa68d8fa8fdd Mon Sep 17 00:00:00 2001 From: kaitejohnson Date: Fri, 30 Aug 2024 14:29:35 -0400 Subject: [PATCH 01/12] update vignette to reflect default NULL seed in mcmcoptions --- R/wwinference.R | 15 ++++++++++++--- man/get_mcmc_options.Rd | 4 ++-- man/wwinference.Rd | 11 ++++++++++- vignettes/wwinference.Rmd | 10 +++++++--- 4 files changed, 31 insertions(+), 9 deletions(-) diff --git a/R/wwinference.R b/R/wwinference.R index a69dc144..cf9c44bb 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -32,12 +32,19 @@ #' example data provided by the package, but this should be specified by the #' user based on the date they are producing a forecast #' @param fit_opts The fit options, which in this case default to the -#' MCMC parameters as defined using `get_mcmc_options()`. +#' MCMC parameters as defined using `get_mcmc_options()`. This includes +#' the arguments passed to [`$sample()`][cmdstanr::model-method-sample]: +#' the number of chains, the number of warmup and sampling iterations, the +#' maximum tree depth, the average acceptance probability, and the stan PRNG +#' seed #' @param generate_initial_values Boolean indicating whether or not to specify #' the initialization of the sampler, default is `TRUE`, meaning that #' initialization lists will be generated and passed as the `init` argument #' to the model object [`$sample()`][cmdstanr::model-method-sample] call. #' function +#' @param initial_values_seed set of integers indicating the random seed of +#' the R sampler of the initial values, default is `NULL`, indicating +#' initialization will be stochastic #' @param compiled_model The pre-compiled model as defined using #' `compile_model()` #' @@ -145,6 +152,7 @@ wwinference <- function(ww_data, model_spec = get_model_spec(), fit_opts = get_mcmc_options(), generate_initial_values = TRUE, + inital_values_seed = NULL, compiled_model = compile_model()) { if (is.null(forecast_date)) { cli::cli_abort( @@ -189,6 +197,7 @@ wwinference <- function(ww_data, ) init_lists <- NULL + set.seed(initial_value_seed) if (generate_initial_values) { init_lists <- c() for (i in 1:fit_opts$n_chains) { @@ -344,7 +353,7 @@ fit_model <- function(compiled_model, #' @param n_chains integer indicating the number of MCMC chains to run, default #' is `4` #' @param seed set of integers indicating the random seed of the stan sampler, -#' default is `123` +#' default is NULL #' @param adapt_delta float between 0 and 1 indicating the average acceptance #' probability, default is `0.95` #' @param max_treedepth integer indicating the maximum tree depth of the @@ -360,7 +369,7 @@ get_mcmc_options <- function( iter_warmup = 750, iter_sampling = 500, n_chains = 4, - seed = 123, + seed = NULL, adapt_delta = 0.95, max_treedepth = 12) { mcmc_settings <- list( diff --git a/man/get_mcmc_options.Rd b/man/get_mcmc_options.Rd index 55dab5ca..454b2c9a 100644 --- a/man/get_mcmc_options.Rd +++ b/man/get_mcmc_options.Rd @@ -8,7 +8,7 @@ get_mcmc_options( iter_warmup = 750, iter_sampling = 500, n_chains = 4, - seed = 123, + seed = NULL, adapt_delta = 0.95, max_treedepth = 12 ) @@ -24,7 +24,7 @@ default is \code{500}} is \code{4}} \item{seed}{set of integers indicating the random seed of the stan sampler, -default is \code{123}} +default is NULL} \item{adapt_delta}{float between 0 and 1 indicating the average acceptance probability, default is \code{0.95}} diff --git a/man/wwinference.Rd b/man/wwinference.Rd index 094202ef..138984d6 100644 --- a/man/wwinference.Rd +++ b/man/wwinference.Rd @@ -17,6 +17,7 @@ wwinference( model_spec = get_model_spec(), fit_opts = get_mcmc_options(), generate_initial_values = TRUE, + inital_values_seed = NULL, compiled_model = compile_model() ) @@ -50,7 +51,11 @@ example data provided by the package, but this should be specified by the user based on the date they are producing a forecast} \item{fit_opts}{The fit options, which in this case default to the -MCMC parameters as defined using \code{get_mcmc_options()}.} +MCMC parameters as defined using \code{get_mcmc_options()}. This includes +the arguments passed to \code{\link[cmdstanr:model-method-sample]{$sample()}}: +the number of chains, the number of warmup and sampling iterations, the +maximum tree depth, the average acceptance probability, and the stan PRNG +seed} \item{generate_initial_values}{Boolean indicating whether or not to specify the initialization of the sampler, default is \code{TRUE}, meaning that @@ -64,6 +69,10 @@ function} \item{x, object}{Object of class \code{wwinference_fit}} \item{...}{Additional parameters passed to the corresponding method} + +\item{initial_values_seed}{set of integers indicating the random seed of +the R sampler of the initial values, default is \code{NULL}, indicating +initialization will be stochastic} } \value{ An object of the \code{ww_inference_fit} class containing the following diff --git a/vignettes/wwinference.Rmd b/vignettes/wwinference.Rmd index 3aa14143..c4b491d5 100644 --- a/vignettes/wwinference.Rmd +++ b/vignettes/wwinference.Rmd @@ -286,8 +286,12 @@ settings (see the documentation for `get_mcmc_options()`), however this vignette will use the default parameter settings which includes running 4 parallel chains with 750 warm up iterations, 500 sampling iterations for each chain, a target average -acceptance probability of 0.95 and a maximum tree depth of 12. The user may wish -to adjust these as they are iterating to reduce model run-time or to achieve +acceptance probability of 0.95 and a maximum tree depth of 12. The default is +not to set a the seed for the random number generator for the MCMC model runs +(which would produce stochastic results each time the model is run), but for +reproducibility we will set the seed of the Stan PRNG to `123`in this vignette. +The user may wish to to adjust these settings as they are iterating to reduce +model run-time or to achieve better convergence on a real-world use case. We also pass our preprocessed datasets (`ww_data_to_fit` and @@ -309,7 +313,7 @@ ww_fit <- wwinference::wwinference( infection_feedback_pmf = infection_feedback_pmf, params = params ), - fit_opts = get_mcmc_options(), + fit_opts = get_mcmc_options(seed = 123), compiled_model = model ) ``` From 20a1ab3cc43f9605ee9509799d63935a3332b631 Mon Sep 17 00:00:00 2001 From: kaitejohnson Date: Fri, 30 Aug 2024 14:50:04 -0400 Subject: [PATCH 02/12] add documentation --- R/wwinference.R | 3 +-- man/wwinference.Rd | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/R/wwinference.R b/R/wwinference.R index cf9c44bb..cb7e23e4 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -43,8 +43,7 @@ #' to the model object [`$sample()`][cmdstanr::model-method-sample] call. #' function #' @param initial_values_seed set of integers indicating the random seed of -#' the R sampler of the initial values, default is `NULL`, indicating -#' initialization will be stochastic +#' the R sampler of the initial values, default is `NULL` #' @param compiled_model The pre-compiled model as defined using #' `compile_model()` #' diff --git a/man/wwinference.Rd b/man/wwinference.Rd index 138984d6..720d3c10 100644 --- a/man/wwinference.Rd +++ b/man/wwinference.Rd @@ -71,8 +71,7 @@ function} \item{...}{Additional parameters passed to the corresponding method} \item{initial_values_seed}{set of integers indicating the random seed of -the R sampler of the initial values, default is \code{NULL}, indicating -initialization will be stochastic} +the R sampler of the initial values, default is \code{NULL}} } \value{ An object of the \code{ww_inference_fit} class containing the following From 6ef15313be7c64cb4e367bec4996bbfc0f1dc7bb Mon Sep 17 00:00:00 2001 From: kaitejohnson Date: Fri, 30 Aug 2024 15:00:38 -0400 Subject: [PATCH 03/12] fix typo --- R/wwinference.R | 2 +- man/wwinference.Rd | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/R/wwinference.R b/R/wwinference.R index cb7e23e4..b597bc45 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -151,7 +151,7 @@ wwinference <- function(ww_data, model_spec = get_model_spec(), fit_opts = get_mcmc_options(), generate_initial_values = TRUE, - inital_values_seed = NULL, + initial_values_seed = NULL, compiled_model = compile_model()) { if (is.null(forecast_date)) { cli::cli_abort( diff --git a/man/wwinference.Rd b/man/wwinference.Rd index 720d3c10..d9794b63 100644 --- a/man/wwinference.Rd +++ b/man/wwinference.Rd @@ -17,7 +17,7 @@ wwinference( model_spec = get_model_spec(), fit_opts = get_mcmc_options(), generate_initial_values = TRUE, - inital_values_seed = NULL, + initial_values_seed = NULL, compiled_model = compile_model() ) @@ -63,15 +63,15 @@ initialization lists will be generated and passed as the \code{init} argument to the model object \code{\link[cmdstanr:model-method-sample]{$sample()}} call. function} +\item{initial_values_seed}{set of integers indicating the random seed of +the R sampler of the initial values, default is \code{NULL}} + \item{compiled_model}{The pre-compiled model as defined using \code{compile_model()}} \item{x, object}{Object of class \code{wwinference_fit}} \item{...}{Additional parameters passed to the corresponding method} - -\item{initial_values_seed}{set of integers indicating the random seed of -the R sampler of the initial values, default is \code{NULL}} } \value{ An object of the \code{ww_inference_fit} class containing the following From 051525e99a8416dd9097f5076121ca0ebcca1d17 Mon Sep 17 00:00:00 2001 From: kaitejohnson Date: Fri, 30 Aug 2024 15:19:19 -0400 Subject: [PATCH 04/12] fix typo --- R/wwinference.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/wwinference.R b/R/wwinference.R index b597bc45..7946a7a8 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -196,7 +196,7 @@ wwinference <- function(ww_data, ) init_lists <- NULL - set.seed(initial_value_seed) + set.seed(initial_values_seed) if (generate_initial_values) { init_lists <- c() for (i in 1:fit_opts$n_chains) { From beffd1394ff5ee98a267551f88ed40b861700cd1 Mon Sep 17 00:00:00 2001 From: Kaitlyn Johnson <94390107+kaitejohnson@users.noreply.github.com> Date: Fri, 6 Sep 2024 16:14:31 -0400 Subject: [PATCH 05/12] Update R/wwinference.R Co-authored-by: Dylan H. Morris --- R/wwinference.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/wwinference.R b/R/wwinference.R index 7946a7a8..ea73e3cc 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -33,7 +33,7 @@ #' user based on the date they are producing a forecast #' @param fit_opts The fit options, which in this case default to the #' MCMC parameters as defined using `get_mcmc_options()`. This includes -#' the arguments passed to [`$sample()`][cmdstanr::model-method-sample]: +#' the following arguments, which are passed to [`$sample()`][cmdstanr::model-method-sample]: #' the number of chains, the number of warmup and sampling iterations, the #' maximum tree depth, the average acceptance probability, and the stan PRNG #' seed From a997170ca9704cfc1471fa26c13bfca2734d0940 Mon Sep 17 00:00:00 2001 From: Kaitlyn Johnson Date: Fri, 6 Sep 2024 20:22:01 +0000 Subject: [PATCH 06/12] use withr(seed) not set.seed --- R/wwinference.R | 26 +++++++++++++++----------- man/wwinference.Rd | 8 ++++---- 2 files changed, 19 insertions(+), 15 deletions(-) diff --git a/R/wwinference.R b/R/wwinference.R index ea73e3cc..6dc9f276 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -33,10 +33,10 @@ #' user based on the date they are producing a forecast #' @param fit_opts The fit options, which in this case default to the #' MCMC parameters as defined using `get_mcmc_options()`. This includes -#' the following arguments, which are passed to [`$sample()`][cmdstanr::model-method-sample]: -#' the number of chains, the number of warmup and sampling iterations, the -#' maximum tree depth, the average acceptance probability, and the stan PRNG -#' seed +#' the following arguments, which are passed to [`$sample()`] +#' [cmdstanr::model-method-sample]:the number of chains, the number of warmup +#' and sampling iterations, the maximum tree depth, the average acceptance +#' probability, and the stan PRNG seed #' @param generate_initial_values Boolean indicating whether or not to specify #' the initialization of the sampler, default is `TRUE`, meaning that #' initialization lists will be generated and passed as the `init` argument @@ -195,14 +195,18 @@ wwinference <- function(ww_data, compute_likelihood = as.integer(model_spec$compute_likelihood) ) - init_lists <- NULL - set.seed(initial_values_seed) - if (generate_initial_values) { - init_lists <- c() - for (i in 1:fit_opts$n_chains) { - init_lists[[i]] <- get_inits_for_one_chain(stan_data_list, params) - } + init_lists <- c() + if (is.null(initial_values_seed)) { + initial_values_seed <- sample.int(.Machine$integer.max, 1L) } + withr::with_seed(initial_values_seed, { + init_lists <- lapply( + 1:fit_opts$n_chains, + \(x) { + get_inits_for_one_chain(stan_data_list, params) + } + ) + }) # This returns the cmdstan object if the model runs, and result = NULL if # the model errors diff --git a/man/wwinference.Rd b/man/wwinference.Rd index d9794b63..90a13856 100644 --- a/man/wwinference.Rd +++ b/man/wwinference.Rd @@ -52,10 +52,10 @@ user based on the date they are producing a forecast} \item{fit_opts}{The fit options, which in this case default to the MCMC parameters as defined using \code{get_mcmc_options()}. This includes -the arguments passed to \code{\link[cmdstanr:model-method-sample]{$sample()}}: -the number of chains, the number of warmup and sampling iterations, the -maximum tree depth, the average acceptance probability, and the stan PRNG -seed} +the following arguments, which are passed to \code{\link[=$sample]{$sample()}} +\link[cmdstanr:model-method-sample]{cmdstanr::model-method-sample}:the number of chains, the number of warmup +and sampling iterations, the maximum tree depth, the average acceptance +probability, and the stan PRNG seed} \item{generate_initial_values}{Boolean indicating whether or not to specify the initialization of the sampler, default is \code{TRUE}, meaning that From f12783e04df44e2c796c35ec72e74a7b0385bdca Mon Sep 17 00:00:00 2001 From: Kaitlyn Johnson Date: Fri, 6 Sep 2024 20:57:11 +0000 Subject: [PATCH 07/12] remove dependence on params for initialization --- R/initialization.R | 37 +++++++++++++++++++--------------- R/wwinference.R | 2 +- man/get_inits_for_one_chain.Rd | 5 +---- 3 files changed, 23 insertions(+), 21 deletions(-) diff --git a/R/initialization.R b/R/initialization.R index c420d5eb..04cf8172 100644 --- a/R/initialization.R +++ b/R/initialization.R @@ -2,8 +2,6 @@ #' near the center of the prior distribution #' #' @param stan_data a list of data elements that will be passed to stan -#' @param params a dataframe of parameter values that are passed to stan -#' to specify the priors in the model #' @param stdev a numeric value indicating the standard deviation to sample #' from when initializing, particularly from a standard normal. Also acts as #' a multiplier on the prior standard deviation, to restrict the initial value @@ -11,7 +9,7 @@ #' #' @return a list of initial values for each of the parameters in the #' `wwinference` model -get_inits_for_one_chain <- function(stan_data, params, stdev = 0.01) { +get_inits_for_one_chain <- function(stan_data, stdev = 0.01) { # Define some variables pop <- stan_data$state_pop n_weeks <- as.numeric(stan_data$n_weeks) @@ -22,7 +20,7 @@ get_inits_for_one_chain <- function(stan_data, params, stdev = 0.01) { n_ww_lab_sites <- as.numeric(stan_data$n_ww_lab_sites) # Estimate of number of initial infections i_first_obs_est <- ( - mean(stan_data$hosp[1:7], na.rm = TRUE) / params$p_hosp_mean + mean(stan_data$hosp[1:7], na.rm = TRUE) / stan_data$p_hosp_prior_mean ) logit_i_frac_est <- stats::qlogis(i_first_obs_est / pop) @@ -39,7 +37,8 @@ get_inits_for_one_chain <- function(stan_data, params, stdev = 0.01) { sigma_initial_exp_growth_rate = abs(stats::rnorm(1, 0, stdev)), autoreg_rt = abs(stats::rnorm( 1, - params$autoreg_rt_a / (params$autoreg_rt_a + params$autoreg_rt_b), + stan_data$autoreg_rt_a / + (stan_data$autoreg_rt_a + stan_data$autoreg_rt_b), 0.05 )), log_r_mu_intercept = stats::rnorm( @@ -63,27 +62,33 @@ get_inits_for_one_chain <- function(stan_data, params, stdev = 0.01) { mean_initial_exp_growth_rate = stats::rnorm(1, 0, stdev), inv_sqrt_phi_h = 1 / sqrt(200) + stats::rnorm(1, 1 / 10000, 1 / 10000), mode_sigma_ww_site = abs(stats::rnorm( - 1, params$mode_sigma_ww_site_prior_mode, - stdev * params$mode_sigma_ww_site_prior_sd + 1, stan_data$mode_sigma_ww_site_prior_mode, + stdev * stan_data$mode_sigma_ww_site_prior_sd )), sd_log_sigma_ww_site = abs(stats::rnorm( - 1, params$sd_log_sigma_ww_site_prior_mode, - stdev * params$sd_log_sigma_ww_site_prior_sd + 1, stan_data$sd_log_sigma_ww_site_prior_mode, + stdev * stan_data$sd_log_sigma_ww_site_prior_sd )), eta_log_sigma_ww_site = abs(stats::rnorm(n_ww_lab_sites, 0, stdev)), - p_hosp_mean = stats::rnorm(1, stats::qlogis(params$p_hosp_mean), stdev), + p_hosp_mean = stats::rnorm( + 1, stats::qlogis(stan_data$p_hosp_prior_mean), + stdev + ), p_hosp_w = stats::rnorm(tot_weeks, 0, stdev), p_hosp_w_sd = abs(stats::rnorm(1, 0.01, 0.001)), - t_peak = stats::rnorm(1, params$t_peak_mean, stdev * params$t_peak_sd), + t_peak = stats::rnorm( + 1, stan_data$viral_shedding_pars[1], + stdev * stan_data$viral_shedding_pars[2] + ), viral_peak = stats::rnorm( - 1, params$viral_peak_mean, - stdev * params$viral_peak_sd + 1, stan_data$viral_shedding_pars[3], + stdev * stan_data$viral_shedding_pars[4] ), dur_shed = stats::rnorm( - 1, params$duration_shedding_mean, - stdev * params$duration_shedding_sd + 1, stan_data$viral_shedding_pars[5], + stdev * stan_data$viral_shedding_pars[6] ), - log10_g = stats::rnorm(1, params$log10_g_prior_mean, 0.5), + log10_g = stats::rnorm(1, stan_data$log10_g_prior_mean, 0.5), ww_site_mod_raw = abs(stats::rnorm(n_ww_lab_sites, 0, stdev)), ww_site_mod_sd = abs(stats::rnorm(1, 0, stdev)), hosp_wday_effect = to_simplex(abs( diff --git a/R/wwinference.R b/R/wwinference.R index e72fe377..78b6eb48 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -205,7 +205,7 @@ wwinference <- function(ww_data, init_lists <- lapply( 1:fit_opts$n_chains, \(x) { - get_inits_for_one_chain(stan_data_list, params) + get_inits_for_one_chain(stan_data_list) } ) }) diff --git a/man/get_inits_for_one_chain.Rd b/man/get_inits_for_one_chain.Rd index 4e3fda58..ecf56b29 100644 --- a/man/get_inits_for_one_chain.Rd +++ b/man/get_inits_for_one_chain.Rd @@ -5,14 +5,11 @@ \title{Given a set of prior parameters and stan data, initialize the model near the center of the prior distribution} \usage{ -get_inits_for_one_chain(stan_data, params, stdev = 0.01) +get_inits_for_one_chain(stan_data, stdev = 0.01) } \arguments{ \item{stan_data}{a list of data elements that will be passed to stan} -\item{params}{a dataframe of parameter values that are passed to stan -to specify the priors in the model} - \item{stdev}{a numeric value indicating the standard deviation to sample from when initializing, particularly from a standard normal. Also acts as a multiplier on the prior standard deviation, to restrict the initial value From ce35072d3f6bfbb01de9665b2da9f7151484850d Mon Sep 17 00:00:00 2001 From: Kaitlyn Johnson <94390107+kaitejohnson@users.noreply.github.com> Date: Fri, 6 Sep 2024 17:07:31 -0400 Subject: [PATCH 08/12] Update vignettes/wwinference.Rmd Co-authored-by: Dylan H. Morris --- vignettes/wwinference.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/wwinference.Rmd b/vignettes/wwinference.Rmd index 43887173..6632a80c 100644 --- a/vignettes/wwinference.Rmd +++ b/vignettes/wwinference.Rmd @@ -310,7 +310,7 @@ the default parameter settings which includes running 4 parallel chains with acceptance probability of 0.95 and a maximum tree depth of 12. The default is not to set a the seed for the random number generator for the MCMC model runs (which would produce stochastic results each time the model is run), but for -reproducibility we will set the seed of the Stan PRNG to `123`in this vignette. +reproducibility we will set the seed of the Stan PRNG to `123` in this vignette. The user may wish to to adjust these settings as they are iterating to reduce model run-time or to achieve better convergence on a real-world use case. From 8649980e0bbd7b8d818de01b7cad284ee1ca5465 Mon Sep 17 00:00:00 2001 From: Kaitlyn Johnson <94390107+kaitejohnson@users.noreply.github.com> Date: Fri, 6 Sep 2024 17:36:43 -0400 Subject: [PATCH 09/12] Update vignettes/wwinference.Rmd Co-authored-by: Dylan H. Morris --- vignettes/wwinference.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vignettes/wwinference.Rmd b/vignettes/wwinference.Rmd index 6632a80c..236d2d97 100644 --- a/vignettes/wwinference.Rmd +++ b/vignettes/wwinference.Rmd @@ -311,9 +311,9 @@ acceptance probability of 0.95 and a maximum tree depth of 12. The default is not to set a the seed for the random number generator for the MCMC model runs (which would produce stochastic results each time the model is run), but for reproducibility we will set the seed of the Stan PRNG to `123` in this vignette. -The user may wish to to adjust these settings as they are iterating to reduce -model run-time or to achieve -better convergence on a real-world use case. + +When applying the model to real data, experimenting with these MCMC settings may make it possible +to achieve improved model convergence and/or faster model fitting times. See the [Stan User's Guide](https://mc-stan.org/docs/cmdstan-guide/diagnose_utility.html#building-the-diagnose-command) for an introduction to No-U-Turn sampler convergence diagnostics and configuration parameters. We also pass our preprocessed datasets (`ww_data_to_fit` and `hosp_data_preprocessed`), specify our model using `get_model_spec()`, From 5fb022c39b0771ec8514aed7b3adfb2546c77ac7 Mon Sep 17 00:00:00 2001 From: kaitejohnson Date: Fri, 6 Sep 2024 17:46:45 -0400 Subject: [PATCH 10/12] run pre-commit --- vignettes/wwinference.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/wwinference.Rmd b/vignettes/wwinference.Rmd index 236d2d97..347f9838 100644 --- a/vignettes/wwinference.Rmd +++ b/vignettes/wwinference.Rmd @@ -313,7 +313,7 @@ not to set a the seed for the random number generator for the MCMC model runs reproducibility we will set the seed of the Stan PRNG to `123` in this vignette. When applying the model to real data, experimenting with these MCMC settings may make it possible -to achieve improved model convergence and/or faster model fitting times. See the [Stan User's Guide](https://mc-stan.org/docs/cmdstan-guide/diagnose_utility.html#building-the-diagnose-command) for an introduction to No-U-Turn sampler convergence diagnostics and configuration parameters. +to achieve improved model convergence and/or faster model fitting times. See the [Stan User's Guide](https://mc-stan.org/docs/cmdstan-guide/diagnose_utility.html#building-the-diagnose-command) for an introduction to No-U-Turn sampler convergence diagnostics and configuration parameters. We also pass our preprocessed datasets (`ww_data_to_fit` and `hosp_data_preprocessed`), specify our model using `get_model_spec()`, From c0e8aeec631a00173a7b98b3d85dd2be4995180b Mon Sep 17 00:00:00 2001 From: kaitejohnson Date: Fri, 6 Sep 2024 18:05:49 -0400 Subject: [PATCH 11/12] update test data, fix bug in test data generation --- R/sysdata.rda | Bin 37855 -> 38039 bytes data-raw/test_data.R | 4 ++-- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/sysdata.rda b/R/sysdata.rda index 8011a7ee48e0683bee366bd7fa21a3aa5533dc7e..69b428117c2b7062b5b92c766ab6d995d2a310b0 100644 GIT binary patch literal 38039 zcmb@t1ymjDvMszIxDzx;a0u@18r%s4cXxMpcXxM!yAv$9ySux~zq0o^`<#959rwR) zyw{^gudnI)s+&2hW>xp#opiDRar)kkU>Fn zG1D}W6cbsYpa_4o{wmk4+$aIL<$yk%mG-BsjEqd!X-;8bZSI)+$=9ysdG?CDkXDxgk*R`jtsP~jAV)o@)12eJ^6zOsD2-GJ&V*=e>%mdcfWVU{N-wd%&0vF%37YXwjat?I`d9E+fu+3j2mU> zcQa-jL~xTn)8CC;{nPAm5$P!MONJHI=7`{Y~)aW=`?CBXnASU0tDO 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zcPx^$f8fut9hNcB1B1{a<*5-6)cY>&-(qa_hY40S>icF`vCSgCQjq@x6!zGNOJTfP zJD>$~*ZU_W=F&~_>X<3bdpPBYtK4QNe>@AjbrAw#exZ?89r24<{n_tjpV)o=oYeOa z!^Qj8Usu2yT56b1nRh~;O@Ugnc9sCZMXHBcu7I*IF^mUPlIeL~(DRPAht1@>xImvg zg<!c=!h1XYi*@e@rUTHbaInG_x+m>rKx Date: Fri, 6 Sep 2024 18:27:09 -0400 Subject: [PATCH 12/12] fix spacing in cmdstanr::sample reference --- R/wwinference.R | 5 +++-- man/wwinference.Rd | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/R/wwinference.R b/R/wwinference.R index 78b6eb48..1e10e6ea 100644 --- a/R/wwinference.R +++ b/R/wwinference.R @@ -33,8 +33,9 @@ #' user based on the date they are producing a forecast #' @param fit_opts The fit options, which in this case default to the #' MCMC parameters as defined using `get_mcmc_options()`. This includes -#' the following arguments, which are passed to [`$sample()`] -#' [cmdstanr::model-method-sample]:the number of chains, the number of warmup +#' the following arguments, which are passed to +#' [`$sample()`][cmdstanr::model-method-sample]: +#' the number of chains, the number of warmup #' and sampling iterations, the maximum tree depth, the average acceptance #' probability, and the stan PRNG seed #' @param generate_initial_values Boolean indicating whether or not to specify diff --git a/man/wwinference.Rd b/man/wwinference.Rd index e17ed523..41306ea9 100644 --- a/man/wwinference.Rd +++ b/man/wwinference.Rd @@ -52,8 +52,9 @@ user based on the date they are producing a forecast} \item{fit_opts}{The fit options, which in this case default to the MCMC parameters as defined using \code{get_mcmc_options()}. This includes -the following arguments, which are passed to \code{\link[=$sample]{$sample()}} -\link[cmdstanr:model-method-sample]{cmdstanr::model-method-sample}:the number of chains, the number of warmup +the following arguments, which are passed to +\code{\link[cmdstanr:model-method-sample]{$sample()}}: +the number of chains, the number of warmup and sampling iterations, the maximum tree depth, the average acceptance probability, and the stan PRNG seed}