From 543f81b6e4912c37a6a25196f56864032c2238a9 Mon Sep 17 00:00:00 2001
From: andrewhooker ODE solution of PK model, mult
of describing the same model.
tic(); eval <- evaluate_design(poped.db); toc()
-#> Elapsed time: 2.773 seconds.
+#> Elapsed time: 2.704 seconds.
tic(); eval <- evaluate_design(poped.db.Rcpp); toc()
-#> Elapsed time: 1.179 seconds.
The difference is noticeable and gets larger for more complex ODE models.
@@ -1908,7 +1908,7 @@We can see that the result, based on MC sampling, is somewhat variable with so few samples.
@@ -1941,7 +1941,7 @@#> 5.021700 2.980981 14.068646 29.765030 36.691675 26.754137 31.469425 #> SIGMA[2,2] #> 25.311870 -#> Elapsed time: 0.13 seconds.
The D2 method is the same as removing the last design point, as you can se below.
diff --git a/articles/intro-poped.html b/articles/intro-poped.html index 897fb2b..4db39fd 100644 --- a/articles/intro-poped.html +++ b/articles/intro-poped.html @@ -432,7 +432,7 @@We see that there are four distinct sample times for this design. @@ -493,7 +493,7 @@
Here we see that the optimization ran somewhat quicker, but gave a @@ -558,7 +558,7 @@
We see that the optimal doses are 31.6 and 55.2 for the two groups. This leads to population trough concentrations of 0.2 and 0.35 for the two groups of patients at 240 hours:
diff --git a/articles/model_def_other_pkgs.html b/articles/model_def_other_pkgs.html index b9ccf7f..00386b6 100644 --- a/articles/model_def_other_pkgs.html +++ b/articles/model_def_other_pkgs.html @@ -781,7 +781,7 @@