From d3cc70ac211b907307b2b4cba35834bb96ee1855 Mon Sep 17 00:00:00 2001 From: athowes Date: Mon, 20 May 2024 16:52:53 +0100 Subject: [PATCH] Change to fig.path --- vignettes/epidist.Rmd | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/vignettes/epidist.Rmd b/vignettes/epidist.Rmd index 14800a603..c00107c63 100644 --- a/vignettes/epidist.Rmd +++ b/vignettes/epidist.Rmd @@ -19,7 +19,7 @@ vignette: > ```{r setup, include=FALSE} # exclude compile warnings from cmdstanr knitr::opts_chunk$set( - fig.path = "figures/getting-started-nowcasting-", + fig.path = "figures/epidist-", cache = TRUE, collapse = TRUE, comment = "#>", @@ -182,13 +182,12 @@ With our censored, truncated, and sampled data, we are now ready to attempt to r # Fit the model {#fit} -If we had access to `obs`, then it would be simple to estimate the delay distribution -However, with only access to `obs_cens_trunc_samp`, we must use a more sophisticated statistical model to make sure that our estimates are not severely biased! +If we had access to `obs`, then it would be simple to estimate the delay distribution. +However, with only access to `obs_cens_trunc_samp`, we must use a statistical model to avoid severely biased estimates! -The primary modelling function in `epidist` is `latent_truncation_censoring_adjusted_delay`. -In a future vignette, we will explain in more detail the structure of the model. - -The parameters of the model are inferred using Bayesian inference. +The main model function in `epidist` is `latent_truncation_censoring_adjusted_delay`. + +The parameters of this model are inferred using Bayesian inference. In particular, we use the the No-U-Turn Sampler (NUTS) Markov chain Monte Carlo (MCMC) algorithm via the `brms` R package. ```{r}