Merge pull request #50 from generable/develop

v0.3.3

Author: jtimonen

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: bmstate
 Type: Package
 Title: Bayesian multistate modeling
-Version: 0.3.1
+Version: 0.3.3
 Authors@R:
     c(person(given = "Juho",
            family = "Timonen",

R/MultistateModel.R

@@ -47,7 +47,7 @@ MultistateModel <- R6::R6Class("MultistateModel",
     categorical = NULL,
     normalizer_locations = NULL,
     normalizer_scales = NULL,
-    xpsr_normalizer_loc = 7.5,
+    xpsr_normalizer_loc = 5.5, # corresponds to about dose=30
     xpsr_normalizer_scale = 0.5,
     n_grid = NULL,
     simulate_log_hazard_multipliers = function(df_subjects, beta) {

R/MultistateModelFit.R

@@ -414,7 +414,7 @@ msmfit_exposure <- function(fit, oos = FALSE, data = NULL) {
   out <- list()
   for (s in seq_len(S)) {
     if (sd$do_pk == 1) {
-      x_xpsr <- log_ss_area_under_conc(sd$dose_ss, log_pkpar[[s]]) # log D/(CL*V2)
+      x_xpsr <- log_ss_area_under_conc(sd$dose_ss, log_pkpar[[s]]) # log D/CL
     } else {
       x_xpsr <- NULL
     }

R/PKModel.R

@@ -110,11 +110,9 @@ PKModel <- R6::R6Class("PKModel",
     #' @return A numeric value
     compute_xpsr = function(theta, dose) {
       CL <- theta[2]
-      V2 <- theta[3]
       checkmate::assert_number(CL, lower = 0)
-      checkmate::assert_number(V2, lower = 0)
       checkmate::assert_number(dose, lower = 0)
-      log(dose) - log(CL) - log(V2)
+      log(dose) - log(CL)
     },
 
     #' @description Simulate data with many subjects

R/stan.R

@@ -102,8 +102,7 @@ fit_stan <- function(model, data,
     model$set_normalizers(data)
     if (!is.null(data$dosing)) {
       log_mu_CL <- -2 # should match msm.stan
-      log_mu_V2 <- -2 # should match msm.stan
-      aaa <- log(data$dosing$dose_ss) - log_mu_CL - log_mu_V2
+      aaa <- log(data$dosing$dose_ss) - log_mu_CL
       loc <- mean(aaa)
       sca <- stats::sd(aaa)
       model$set_xpsr_normalizers(loc, sca)

inst/stan/msm.stan

@@ -2,7 +2,7 @@ functions {
 
   // log SS AUC
   vector log_ss_area_under_conc(vector dose_ss, matrix log_theta_pk){
-    return(log(dose_ss) - log_theta_pk[:,2] - log_theta_pk[:,3]) ; // log D/(CL*V2)
+    return(log(dose_ss) - log_theta_pk[:,2]) ; // log D/CL
   }
 
   // log of hazard multiplier
@@ -448,7 +448,7 @@ model {
   if(do_haz == 1){
     if(nc_haz > 0){
       for(k in 1:nc_haz){
-        beta_oth[1, k] ~ normal(0, 2);
+        beta_oth[1, k] ~ normal(0, 1);
       }
     }
     if(I_xpsr==1){

vignettes/analysis.Rmd

@@ -142,7 +142,7 @@ mod$get_knots()
 We fit the model by optimizing.
 
 ```{r}
-fit <- fit_stan(mod, data = pd, method = "optimize")
+fit <- fit_stan(mod, data = pd, method = "optimize", init = 0.1)
 ```
 
 ## Inferred baseline hazards