run multiple models in one session

NOAA-FIMS · Nov 13, 2024 · 325e436 · 325e436
1 parent fbdedb5
commit 325e436
Showing 1 changed file with 94 additions and 59 deletions.
diff --git a/vignettes/fims-demo.Rmd b/vignettes/fims-demo.Rmd
@@ -35,7 +35,8 @@ We will be reading data into the model using the FIMSFrame S4 R class set up in
 
 ```{r prepare-fimsframe-data}
 # use FIMS data frame
-data(package = "FIMS")
+# data(package = "FIMS")
+data("data_mile1")
 fims_frame <- FIMSFrame(data_mile1)
 ```
 
@@ -60,12 +61,12 @@ Here, we define specifications for fleets, recruitment, growth, and maturity mod
 ```{r, max.height='100px', attr.output='.numberLines'}
 # Define fleet specifications for fleet1 and survey1
 fleet1 <- survey1 <- list(
-    selectivity = list(form = "LogisticSelectivity"),
-    data_distribution = c(
-      Index = "TMBDlnormDistribution",
-      AgeComp = "TMBDmultinomDistribution"
-    )
+  selectivity = list(form = "LogisticSelectivity"),
+  data_distribution = c(
+    Index = "TMBDlnormDistribution",
+    AgeComp = "TMBDmultinomDistribution"
   )
+)
 
 # Create default parameters
 default_parameters <- fims_frame |>
@@ -88,85 +89,120 @@ We may update default parameters to experiment with different parameter values.
 ```{r modify-default-parameters}
 parameters <- default_parameters |>
   update_parameters(
-      modified_parameters = list(
-        fleet1 = list(
-          Fleet.log_Fmort.value = log(c(
-            0.009459165, 0.027288858, 0.045063639,
-            0.061017825, 0.048600752, 0.087420554,
-            0.088447204, 0.186607929, 0.109008958,
-            0.132704335, 0.150615473, 0.161242955,
-            0.116640187, 0.169346119, 0.180191913,
-            0.161240483, 0.314573212, 0.257247574,
-            0.254887252, 0.251462108, 0.349101406,
-            0.254107720, 0.418478117, 0.345721184,
-            0.343685540, 0.314171227, 0.308026829,
-            0.431745298, 0.328030899, 0.499675368
-          ))
-        )
+    modified_parameters = list(
+      fleet1 = list(
+        Fleet.log_Fmort.value = log(c(
+          0.009459165, 0.027288858, 0.045063639,
+          0.061017825, 0.048600752, 0.087420554,
+          0.088447204, 0.186607929, 0.109008958,
+          0.132704335, 0.150615473, 0.161242955,
+          0.116640187, 0.169346119, 0.180191913,
+          0.161240483, 0.314573212, 0.257247574,
+          0.254887252, 0.251462108, 0.349101406,
+          0.254107720, 0.418478117, 0.345721184,
+          0.343685540, 0.314171227, 0.308026829,
+          0.431745298, 0.328030899, 0.499675368
+        ))
       )
-    ) |>
-    update_parameters(
-      modified_parameters = list(
-        survey1 = list(
-          LogisticSelectivity.inflection_point.value = 1.5,
-          LogisticSelectivity.slope.value = 2,
-          Fleet.log_q.value = log(3.315143e-07)
-        )
+    )
+  ) |>
+  update_parameters(
+    modified_parameters = list(
+      survey1 = list(
+        LogisticSelectivity.inflection_point.value = 1.5,
+        LogisticSelectivity.slope.value = 2,
+        Fleet.log_q.value = log(3.315143e-07)
       )
-    ) |>
-    update_parameters(
-      modified_parameters = list(
-        recruitment = list(
-          BevertonHoltRecruitment.log_rzero.value = log(1e+06),
-          BevertonHoltRecruitment.log_devs.value = c(
-            0.43787763, -0.13299042, -0.43251973, 0.64861200, 0.50640852,
-            -0.06958319, 0.30246260, -0.08257384, 0.20740372, 0.15289604,
-            -0.21709207, -0.13320626, 0.11225374, -0.10650836, 0.26877132,
-            0.24094126, -0.54480751, -0.23680557, -0.58483386, 0.30122785,
-            0.21930545, -0.22281699, -0.51358369, 0.15740234, -0.53988240,
-            -0.19556523, 0.20094360, 0.37248740, -0.07163145
-          ),
-          BevertonHoltRecruitment.log_devs.estimated = FALSE
-        )
+    )
+  ) |>
+  update_parameters(
+    modified_parameters = list(
+      recruitment = list(
+        BevertonHoltRecruitment.log_rzero.value = log(1e+06),
+        BevertonHoltRecruitment.log_devs.value = c(
+          0.43787763, -0.13299042, -0.43251973, 0.64861200, 0.50640852,
+          -0.06958319, 0.30246260, -0.08257384, 0.20740372, 0.15289604,
+          -0.21709207, -0.13320626, 0.11225374, -0.10650836, 0.26877132,
+          0.24094126, -0.54480751, -0.23680557, -0.58483386, 0.30122785,
+          0.21930545, -0.22281699, -0.51358369, 0.15740234, -0.53988240,
+          -0.19556523, 0.20094360, 0.37248740, -0.07163145
+        ),
+        BevertonHoltRecruitment.log_devs.estimated = FALSE
       )
-    ) |>
-    update_parameters(
-      modified_parameters = list(
-        maturity = list(
-          LogisticMaturity.inflection_point.value = 2.25,
-          LogisticMaturity.inflection_point.estimated = FALSE,
-          LogisticMaturity.slope.value = 3,
-          LogisticMaturity.slope.estimated = FALSE
-        )
+    )
+  ) |>
+  update_parameters(
+    modified_parameters = list(
+      maturity = list(
+        LogisticMaturity.inflection_point.value = 2.25,
+        LogisticMaturity.inflection_point.estimated = FALSE,
+        LogisticMaturity.slope.value = 3,
+        LogisticMaturity.slope.estimated = FALSE
       )
-    ) |>
-    update_parameters(
-      modified_parameters = list(
+    )
+  ) |>
+  update_parameters(
+    modified_parameters = list(
+      population = list(
         Population.log_init_naa.value = c(
           13.80944, 13.60690, 13.40217, 13.19525, 12.98692, 12.77791,
           12.56862, 12.35922, 12.14979, 11.94034, 11.73088, 13.18755
         )
       )
     )
+  )
 ```
 
 ## Initialize modules and fit the model
 With data and parameters in place, we can now initialize modules using `initialize_fims()` and fit the model using `fit_fimt()`.
 ```{r, max.height='100px', attr.output='.numberLines', fit-fims}
 # Run the model without optimization to help ensure a viable model
-test_fit <- parameters|>
+test_fit <- parameters |>
   initialize_fims(data = fims_frame) |>
   (\(parameter_list) list(parameters = parameter_list, version = "FIMS run without optimization"))() |>
   fit_fims(optimize = FALSE)
 
 # Run the  model with optimization
-fit <-  parameters|>
+fit <- parameters |>
   initialize_fims(data = fims_frame) |>
   (\(parameter_list) list(parameters = parameter_list, version = "FIMS run with optimization"))() |>
   fit_fims(optimize = TRUE)
 
 # Clear memory post-run
 clear()
+
+parameters2 <- parameters |>
+  update_parameters(
+    modified_parameters = list(
+      survey1 = list(
+        LogisticSelectivity.slope.value = 3
+      )
+    )
+  )
+
+parameters3 <- parameters |>
+  update_parameters(
+    modified_parameters = list(
+      survey1 = list(
+        LogisticSelectivity.slope.value = 1
+      )
+    )
+  )
+
+fit2 <- parameters2 |>
+  initialize_fims(data = fims_frame) |>
+  (\(parameter_list) list(parameters = parameter_list, version = "FIMS run with optimization"))() |>
+  fit_fims(optimize = TRUE)
+
+clear()
+
+fit3 <- parameters3 |>
+  initialize_fims(data = fims_frame) |>
+  (\(parameter_list) list(parameters = parameter_list, version = "FIMS run with optimization"))() |>
+  fit_fims(optimize = TRUE)
+
+clear()
+
 ```
 
 ## Plotting Results
@@ -208,5 +244,4 @@ ggplot(catch_results, aes(x = years, y = observed)) +
 # writeLines(json_output, "output.json")
 # recruitment_log <- get_log_module("information")
 # cat(recruitment_log)
-
 ```