added verbose option for calibration

R/cost_likelihood_biomee.R

@@ -16,6 +16,9 @@
 #' @param targets A character vector indicating the target variables for which the
 #' optimization will be done. This should be a subset of \code{c("GPP", "LAI",
 #' "Density", "Biomass")}.
+#' @param verbose A logical specifying whether to print parameter and log-likelihood 
+#' values during each iteration of the calibration as message. Defaults to 
+#' \code{FALSE}
 #' 
 #' @return The log-likelihood of the simulated 
 #' targets by the biomee model versus the observed targets. 
@@ -46,7 +49,8 @@ cost_likelihood_biomee <- function(
   par,
   obs,
   drivers,
-  targets
+  targets,
+  verbose = FALSE
 ){
 
   # predefine variables for CRAN check compliance
@@ -108,6 +112,18 @@ cost_likelihood_biomee <- function(
   if(is.nan(ll) || is.na(ll) | ll == 0){
     ll <- -Inf
   }
+
+  if (verbose){
+    message(
+      paste("Par = ", paste(drivers$params_species, collapse = ", "))
+    )
+    message(
+      paste("Log-likelihood = ", ll)
+    )
+    message(
+      paste("--------------------------")
+    )
+  }
 
   return(ll)
 }

R/cost_likelihood_pmodel.R

@@ -28,6 +28,9 @@
 #' \code{FALSE}.
 #' @param ncores An integer specifying the number of cores used for parallel
 #' computing. Defaults to 2.
+#' @param verbose A logical specifying whether to print parameter and log-likelihood 
+#' values during each iteration of the calibration as message. Defaults to 
+#' \code{FALSE}
 #' 
 #' @return The log-likelihood of the observed target values, assuming that they
 #' are independent, normally distributed and centered on the predictions
@@ -78,7 +81,8 @@ cost_likelihood_pmodel <- function(
     targets,
     par_fixed = NULL,   # non-calibrated model parameters
     parallel = FALSE,
-    ncores = 2
+    ncores = 2,
+    verbose = FALSE
 ){
   # predefine variables for CRAN check compliance
   sitename <- data <- gpp_mod <- NULL
@@ -215,6 +219,18 @@ cost_likelihood_pmodel <- function(
 
   # trap boundary conditions
   if(is.nan(ll) | is.na(ll) | ll == 0){ll <- -Inf}
+
+  if (verbose){
+    message(
+      paste("Par = ", paste(params_modl, collapse = ", "))
+    )
+    message(
+      paste("Log-likelihood = ", ll)
+    )
+    message(
+      paste("--------------------------")
+    )
+  }
 
   return(ll)
 }

R/cost_rmse_biomee.R

@@ -12,6 +12,9 @@
 #' @param obs A nested data frame of observations, following the structure of \code{biomee_validation},
 #' for example.
 #' @param drivers A nested data frame of driver data, for example \code{biomee_gs_leuning_drivers}.
+#' @param verbose A logical specifying whether to print parameter and cost (RMSE) 
+#' values during each iteration of the calibration as message. Defaults to 
+#' \code{FALSE}
 #' 
 #' @return The root mean squared error (RMSE) between the observed and simulated
 #' values of \code{'GPP','LAI','Density'} and \code{'Biomass'} (all variables
@@ -38,7 +41,8 @@
 cost_rmse_biomee <- function(
     par,
     obs,
-    drivers
+    drivers,
+    verbose = FALSE
 ){
 
   # predefine variables for CRAN check compliance
@@ -85,5 +89,17 @@ cost_rmse_biomee <- function(
   ## Calculate cost (RMSE) across the N targets
   cost <- mean(dff$error_rel^2, na.rm = TRUE)
 
+  if (verbose){
+    message(
+      paste("Par = ", paste(drivers$params_species, collapse = ", "))
+    )
+    message(
+      paste("RMSE = ", cost)
+    )
+    message(
+      paste("--------------------------")
+    )
+  }
+
   return(cost)
 }

R/cost_rmse_pmodel.R

@@ -27,6 +27,9 @@
 #' \code{FALSE}.
 #' @param ncores An integer specifying the number of cores used for parallel
 #' computing. Defaults to 2.
+#' @param verbose A logical specifying whether to print parameter and cost (RMSE) 
+#' values during each iteration of the calibration as message. Defaults to 
+#' \code{FALSE}
 #' 
 #' @return The root mean squared error (RMSE) between observed values and P-model
 #' predictions. The RMSE is computed for each target separately and then aggregated
@@ -75,7 +78,8 @@ cost_rmse_pmodel <- function(
     target_weights = NULL, # if using several targets, how are the individual 
                            # RMSE weighted? named vector
     parallel = FALSE,
-    ncores = 2
+    ncores = 2,
+    verbose = FALSE
 ){
 
   # predefine variables for CRAN check compliance
@@ -204,6 +208,18 @@ cost_rmse_pmodel <- function(
   }else{
     cost <- mean(rmse, na.rm = TRUE)
   }
+
+  if (verbose){
+    message(
+      paste("Par = ", paste(params_modl, collapse = ", "))
+    )
+    message(
+      paste("RMSE = ", cost)
+    )
+    message(
+      paste("--------------------------")
+    )
+  }
 
   return(cost)
 }

vignettes/new_cost_function.Rmd

@@ -61,7 +61,8 @@ pars_calib_rmse <- calib_sofun(
     tau_acclim         = 30.0,
     kc_jmax            = 0.41
   ),
-  targets = "gpp"           # define target variable GPP
+  targets = "gpp",           # define target variable GPP
+  verbose = FALSE            # set to TRUE to print parameter and cost values during each iteration of the calibration
 )
 ```