Minor adjustments for worked example D

R/main.R

@@ -325,8 +325,8 @@ Model_Run_Many_Reps <- function(FOI_spillover = 0.0, R0 = 1.0, vacc_data = list(
 parameter_setup <- function(FOI_spillover = 0.0, R0 = 1.0, vacc_data = list(), pop_data = list(), year0 = 1940,
                             years_data = c(1941:1942), mode_start = 0, vaccine_efficacy = 1.0, start_SEIRV = list(), dt = 1.0){
 
-  assert_that(FOI_spillover>0.0)
-  assert_that(R0>0.0)
+  assert_that(FOI_spillover>=0.0)
+  assert_that(R0>=0.0)
   assert_that(length(pop_data[, 1])>1, msg = "Need population data for multiple years")
   assert_that(length(pop_data[1, ])>1, msg = "Need population data for multiple age groups")
   n_years = length(pop_data[, 1])-1

vignettes/BWorkedExampleDMCMC.Rmd

@@ -56,9 +56,6 @@ log_params_ini <- c(log(enviro_coeffs_ini), log(c(0.1,0.2)))
 filename_prefix <- "Chain" 
 #Number of iterations to run
 Niter <- 1 
-# Type of parameter set used to calculate spillover FOI and R0 (here both 
-# calculated from environmental covariates)
-type <- "FOI+R0 enviro"  
 # Flag indicating how to set initial population immunity level
 mode_start <- 1 
 # Type of prior likelihood calculation to use
@@ -71,26 +68,23 @@ n_reps <- 5
 p_severe_inf = 0.12
 # Probability of an infection with severe symptoms causing death
 p_death_severe_inf = 0.39
-# Fixed R0 values (here set to NULL because R0 is variable)
-R0_fixed_values = NULL
 # Additional values: severe and fatal case reporting probability are set to NULL due to being estimated
 # as variables; vaccine efficacy and Brazil spillover FOI multiplier set to 1.0 as constants.
-add_values=list(p_rep_severe = NULL, p_rep_death = NULL, vaccine_efficacy = 1.0, m_FOI_Brazil = 1.0)
+add_values=list(p_rep_severe = NA, p_rep_death = NA, vaccine_efficacy = 1.0, m_FOI_Brazil = 1.0)
 # True/false flag indicating whether or not to run model in deterministic mode
 # (so that binomial calculations give average instead of randomized output)
 deterministic = FALSE
 # Variable to set different modes for running on multiple processors simultaneously;
 # here set to "none" so that parallel processing is not used
-mode_parallel="none"
+mode_parallel=FALSE
 ```
 
 We then run the MCMC() function. Output data recorded at each iteration is saved to an output file every 10 iterations. Likelihood is calculated as described in Guide 4 [Link TBA].
 
 ```{r,eval=FALSE}
 MCMC(log_params_ini, input_data, obs_sero_data, obs_case_data, filename_prefix, 
-     Niter, type, mode_start, prior_settings, dt, 
-     n_reps, enviro_data, R0_fixed_values, p_severe_inf, p_death_severe_inf, 
-     add_values, deterministic,mode_parallel,NULL)
+     Niter, mode_start, prior_settings, dt, n_reps, enviro_data, p_severe_inf, 
+     p_death_severe_inf, add_values, deterministic,mode_parallel,NULL)
 ```
 
 The saved output of MCMC() takes the form of a data frame of values of the current likelihood (posterior_current), likelihood calculated from proposed parameter values (posterior_prop), current and proposed parameter values, a flag indicating whether the proposed parameter values were accepted (set to 1 if they were accepted, 0 if they were not), and the chain covariance.