diff --git a/articles/development_patterns.html b/articles/development_patterns.html index 954c1c2d..7bfa1c81 100644 --- a/articles/development_patterns.html +++ b/articles/development_patterns.html @@ -122,7 +122,7 @@

Constructing and Using Objects
 sir_flows_reader = CSVReader(sir_path, "flows.csv")
 sir_flows_reader
-#> Classes 'CSVReader', 'Reader', 'Base' <environment: 0x55a2239e9a90> 
+#> Classes 'CSVReader', 'Reader', 'Base' <environment: 0x55c7c6d47b60> 
 #> read : function ()  
 #> read_base : function ()

We get a print out of the S3 class of the object, which @@ -241,7 +241,7 @@

Defining Classes} does_nothing = DoesNothing() does_nothing -#> Classes 'DoesNothing', 'Base' <environment: 0x55a2247a11f0> +#> Classes 'DoesNothing', 'Base' <environment: 0x55c7c7afbdf0>

The first line in this constructor uses the Base function to create an environment called self. The second line sets selfs S3 class to @@ -256,7 +256,7 @@

Defining Classes} does_nothing = DoesNothing(10) does_nothing -#> Classes 'DoesNothing', 'Base' <environment: 0x55a2252135a0> +#> Classes 'DoesNothing', 'Base' <environment: 0x55c7c856a980>

This looks identical to the first version, but now we have stored a value for n.

diff --git a/articles/likelihood_prior_specs.html b/articles/likelihood_prior_specs.html
index b25f1269..58d3ed1d 100644
--- a/articles/likelihood_prior_specs.html
+++ b/articles/likelihood_prior_specs.html
@@ -185,8 +185,74 @@ 
Distributional Parameter Trans
 

 
Priors on distributional parameters
 

-

-

+
We can specify priors on distributional parameters by:

+
    
+
  1. creating a variable in the model for this distributional
+parameter
    2. 
+
  2. referring to the distributional parameter name when specifying the
+prior on the focal parameter
    3. 
+
  3. using the distributional parameter name to specify its own prior in
+the usual way
    4. 
+

+
+spec = (
+  mp_tmb_library("starter_models", "sir", package = "macpan2")
+  
+  ## 1. update the spec to include a new variable named 'my_var' to serve as
+  ## the standard deviation parameter for the Normal prior on 'beta'.
+  ## A numeric value of 0.1 is specified as the default for 'my_var', the 
+  ## starting value in optimization.
+  |> mp_tmb_insert(default = list(my_var = 0.1))
+)
+## generate data for calibration
+data = mp_simulator(spec, 50, "infection") |> mp_trajectory()
+
+## set-up calibrator
+cal = mp_tmb_calibrator(
+    spec
+  , data
+  , traj = "infection"
+    ## 2. We set a Normal prior on beta, and set the `sd` argument to 'my_var'
+    ## (would it ever make sense to use mp_fit for `sd` here?)
+  , par = list(beta = mp_normal(location = 0.35, sd = mp_nofit("my_var"))
+      ## 3. setting a log-normal prior on 'my_var'
+    , my_var = mp_log_normal(1,1) 
+  )
+  , default = list(beta = 0.25)
+)
+
+## we can see the prior density for both 'beta' and 'my_var' in the calibration
+## objective function
+cal$simulator$tmb_model$obj_fn$obj_fn_expr
+#> ~-sum(dpois(obs_infection, clamp(sim_infection))) - sum(dnorm(beta, 
+#>     0.35, exp(my_var))) - sum(dnorm(log(my_var), 1, 1))
+#> <environment: 0x556242881b58>
+
+
+mp_optimize(cal)
+#> $par
+#>    params    params 
+#> 0.2000017 1.0015207 
+#> 
+#> $objective
+#> [1] 53.08736
+#> 
+#> $convergence
+#> [1] 0
+#> 
+#> $iterations
+#> [1] 9
+#> 
+#> $evaluations
+#> function gradient 
+#>       10       10 
+#> 
+#> $message
+#> [1] "relative convergence (4)"
+mp_tmb_coef(cal)
+#>       term    mat row col default  type  estimate   std.error
+#> 1   params   beta   0   0    0.25 fixed 0.2000017 0.009166585
+#> 2 params.1 my_var   0   0    0.10 fixed 1.0015207 0.707373865