Project: s41467-024-53046-2 | Function: fit_models (r)

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Complete the masked function based on the algorithm description.
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user: Complete this R function:

**DESCRIPTION:** Pipeline function for fitting multiple TPC models to dataset, implementing the comparative framework for evaluating different mathematical models of thermal performance.

**KEY TERMS:** {
  "model_list": "collection of TPC models to fit",
  "dataset": "thermal performance data",
  "fits": "fitted model objects"
}

**CONTEXT CODE:** 					
					pred_1 <- exp(
						predict(
							fitted_models[[i]], newdata = data.frame(
								temp = temps_for_prediction
							)
						)
					)
					
					pred_2 <- exp(
						predict(
							fitted_models[[j]], newdata = data.frame(
								temp = temps_for_prediction
							)
						)
					)

					# Make sure that there are no infinite heights in 
					# both fits that are being compared.
					if ( any(is.infinite(pred_1)) || any(is.infinite(pred_2)) )
					{
						next
					} else
					{
						
						# Check if the maximum height of the two fits is 
						# higher than what we already have observed.
						max_predicted_height <- max(
							c(
								max_predicted_height,
								pred_1,
								pred_2
							)
						)
						
						# Calculate the Euclidean distance between the 
						# two fits.
						dist_matrix[i,j] <- sqrt(sum((pred_1 - pred_2)^2))
					}
				}
			}
		}
	}
	
	# Return the distance matrix, with maximum normalisation based on 
	# the maximum observed TPC height.
	return(forceSymmetric(dist_matrix / max_predicted_height, uplo = 'U'))
}

# Core function for fitting multiple TPC models to dataset
fit_models <- function(dataset, ID, data_type) {
  stop("Function implementation has been masked")
}

# This function fits each of the 83 models to a thermal performance dataset.
FIT_MODELS <- function(dataset, model_list, temps_for_prediction) {
  # Initialize the matrix to store distances
  n_models <- length(model_list)
  dist_matrix <- matrix(NA, nrow = n_models, ncol = n_models)

  # Initialize max_predicted_height to a very small value
  max_predicted_height <- .Machine$double.eps

  # Fit each model and compute distances
  for (i in seq_along(model_list)) {
    for (j in seq_along(model_list)) {
      # Fit the models
      fitted_model_i <- model_list[[i]](dataset = dataset)
      fitted_model_j <- model_list[[j]](dataset = dataset)

      # Predict values for the given temperatures
      pred_1 <- exp(predict(fitted_model_i, newdata = data.frame(temp = temps_for_prediction)))
      pred_2 <- exp(predict(fitted_model_j, newdata = data.frame(temp = temps_for_prediction)))

      # Check for infinite predictions
      if (any(is.infinite(pred_1)) || any(is.infinite(pred_2))) {
        next
      } else {
        # Update max_predicted_height
        max_predicted_height <- max(max_predicted_height, max(pred_1), max(pred_2))

        # Calculate the Euclidean distance
        dist_matrix[i, j] <- sqrt(sum((pred_1 - pred_2)^2))
      }
    }
  }

  # Normalize the distance matrix by the maximum predicted height
  dist_matrix <- as.dist(dist_matrix / max_predicted_height)

  return(dist_matrix)
}

# This function returns a list of models that have no more than 
# X parameters (the "max_pars" argument).
get_model_list <- function(max_pars)
{
	model_list <- list(
		Johnson_Lewin_4_pars = fit_Johnson_Lewin_4_pars,
		
		extended_Johnson_Lewin_5_pars = fit_extended_Johnson_Lewin_5_pars,
		
		simplified_Johnson_Lewin_4_pars = fit_simplified_Johnson_Lewin_4_pars,
		
		simplified_extended_Johnson_Lewin_5_pars = fit_simplified_extended_Johnson_Lewin_5_pars,
		
		Sharpe_Schoolfield_6_pars = fit_Sharpe_Schoolfield_6_pars,
		
		extended_Sharpe_Schoolfield_7_pars = fit_extended_Sharpe_Schoolfield_7_pars,

		simplified_Sharpe_Schoolfield_6_pars = fit_simplified_Sharpe_Schoolfield_6_pars,
		
		simplified_extended_Sharpe_Schoolfield_7_pars = fit_simplified_extended_Sharpe_Schoolfield_7_pars,
				
		Gaussian_3_pars = fit_Gaussian_3_pars,
		
		double_Gaussian_4_pars = fit_double_Gaussian_4_pars,
		
		modified_Gaussian_4_pars = fit_modified_Gaussian_4_pars,
		
		exponentially_modified_Gaussian_5_pars = fit_exponentially_modified_Gaussian_5_pars,

		Gaussian_Gompertz_5_pars = fit_Gaussian_Gompertz_5_pars,
		
		skew_normal_4_pars = fit_skew_normal_4_pars,
		
		Weibull_4_pars = fit_Weibull_4_pars,

		Logan_I_4_pars = fit_Logan_I_4_pars,
		
		Logan_II_5_pars = fit_Logan_II_5_pars,
		
		Logan_III_4_pars = fit_Logan_III_4_pars,
		
		Lactin_I_3_pars = fit_Lactin_I_3_pars,
		
		Lactin_II_4_pars = fit_Lactin_II_4_pars,
				
		Asbury_Angilletta_6_pars = fit_Asbury_Angilletta_6_pars,
		
		simplified_Asbury_Angilletta_4_pars = fit_simplified_Asbury_Angilletta_4_pars,
		
		Thomas_I_4_pars = fit_Thomas_I_4_pars,
		
		Thomas_II_5_pars = fit_Thomas_II_5_pars,
		
		Ratkowsky_4_pars = fit_Ratkowsky_4_pars,
		
		second_order_polynomial_3_pars = fit_2nd_order_polynomial_3_pars,
				


Complete the function implementation replacing "stop("Not implemented")".