May 23, 2024 · Jun 19, 2024 · Aug 8, 2024 · Aug 8, 2024 · Aug 8, 2024 · Aug 8, 2024
diff --git a/R/cost_likelihood_biomee.R b/R/cost_likelihood_biomee.R
 #' @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.
  par,
  obs,
  drivers,
  targets
  targets,
  verbose = FALSE
 ){

  # predefine variables for CRAN check compliance
  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)
 }
diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R
 #' \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
    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

  # 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)
 }
diff --git a/R/cost_rmse_biomee.R b/R/cost_rmse_biomee.R
 #' @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
 cost_rmse_biomee <- function(
    par,
    obs,
    drivers
    drivers,
    verbose = FALSE
 ){

  # predefine variables for CRAN check compliance
  ## 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)
 }
diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R
 #' \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
    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
  }else{
    cost <- mean(rmse, na.rm = TRUE)
  }

  if (verbose){
    message(
      paste("Par = ", paste(params_modl, collapse = ", "))
    )
    message(
      paste("RMSE = ", cost)
    )
    message(
      paste("--------------------------")
    )
  }

  return(cost)
 }
diff --git a/man/cost_likelihood_biomee.Rd b/man/cost_likelihood_biomee.Rd
diff --git a/man/cost_likelihood_pmodel.Rd b/man/cost_likelihood_pmodel.Rd
diff --git a/man/cost_rmse_biomee.Rd b/man/cost_rmse_biomee.Rd
diff --git a/man/cost_rmse_pmodel.Rd b/man/cost_rmse_pmodel.Rd
diff --git a/vignettes/new_cost_function.Rmd b/vignettes/new_cost_function.Rmd
    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
 )
 ```
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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.
Expand DownExpand Up		@@ -46,7 +49,8 @@ cost_likelihood_biomee <- function(
		par,
		obs,
		drivers,
		targets
		targets,
		verbose = FALSE
		){

		# predefine variables for CRAN check compliance
Expand DownExpand Up		@@ -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)
		}
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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
Expand DownExpand Up		@@ -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
Expand DownExpand Up		@@ -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)
		}
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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
Expand All		@@ -38,7 +41,8 @@
		cost_rmse_biomee <- function(
		par,
		obs,
		drivers
		drivers,
		verbose = FALSE
		){

		# predefine variables for CRAN check compliance
Expand DownExpand Up		@@ -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)
		}
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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
Expand DownExpand Up		@@ -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
Expand DownExpand Up		@@ -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)
		}
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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
		)
		```

Expand Down