add more documentation and tests on prop_female and na value changes

inst/include/population_dynamics/population/population.hpp

@@ -45,7 +45,7 @@ struct Population : public fims_model_object::FIMSObject<Type> {
   fims::Vector<Type>
       log_init_naa;         /*!< estimated parameter: log numbers at age*/
   fims::Vector<Type> log_M; /*!< estimated parameter: log Natural Mortality*/
-  fims::Vector<Type> proportion_female; /*!< estimated parameter: proportion female by year or age */
+  fims::Vector<Type> proportion_female; /*!< estimated parameter: proportion female by age */
 
   // Transformed values
   fims::Vector<Type> M; /*!< transformed parameter: Natural Mortality*/
@@ -74,7 +74,7 @@ struct Population : public fims_model_object::FIMSObject<Type> {
   fims::Vector<Type> unfished_spawning_biomass; /*!< Derived quanity Spawning
                                                  biomass assuming unfished*/
   fims::Vector<Type> proportion_mature_at_age; /*!< Derived quantity: Proportion
-                                                matura at age */
+                                                mature at age */
   fims::Vector<Type> expected_numbers_at_age;  /*!< Expected values: Numbers at
                                                 age (thousands?? millions??) */
   fims::Vector<Type> expected_catch;           /*!< Expected values: Catch*/

tests/testthat/test-population-interface.R

@@ -0,0 +1,28 @@
+library(testthat)
+test_that("Population input settings work as expected", {
+  population <- new(Population)
+  nyears <- 10
+  nages <- 10
+  population$log_M <- rep(-1, nyears*nages)
+  population$estimate_M <- FALSE
+  population$log_init_naa <- log(rep(1, nages))
+  population$estimate_init_naa <- TRUE
+  population$nages <- nages
+  population$ages <- 1:nages
+  population$nfleets <- 2
+  population$nseasons <- 1
+  population$nyears <- nyears
+  population$proportion_female <- rep(0.5, nages)
+  population$estimate_prop_female <- FALSE
+
+  expect_equal(population$get_id(), 1)
+  expect_equal(population$log_M, rep(-1, nyears*nages))
+  expect_false(population$estimate_M)
+  expect_equal(population$log_init_naa, rep(0, nages))
+  expect_true(population$estimate_init_naa)
+  expect_false(population$estimate_prop_female)
+  expect_equal(population$proportion_female, rep(0.5, nages))
+
+  clear()
+
+})

vignettes/fims-demo.Rmd

@@ -99,7 +99,7 @@ Now that we've prepared the data, let's pass it into FIMS. Each module in the FI
 
 #### Fleet Data
 
-Each fleet is required to have data in order to evaluate the objective function. Currently FIMS only has a fleet module that is used to set up both fleets and surveys. FIMS contains an Index module and AgeComp module to pass data objects into the fleet module. Each of these data modules require a dimension be added to indicate the dimensions of the raw data (e.g. nyears x nages matrix). Given this information, FIMS is able to correctly apply dimension folding for model output.
+Each fleet is required to have data in order to evaluate the objective function. Currently FIMS only has a fleet module that is used to set up both fleets and surveys. FIMS contains an Index module and AgeComp module to pass data objects into the fleet module. Each of these data modules require a dimension be added to indicate the dimensions of the raw data (e.g. nyears x nages matrix). Any years with missing data should be specified with a value set to -999. Given this information, FIMS is able to correctly apply dimension folding for model output.
 
 Using the `methods::show()` function, we can see that the Index module has a vector field named *index_data* and the AgeComp module has a vector field names *age_comp_data*.