This is the main phytoclass algorithm. It performs simulated annealing algorithm for S and F matrices. See the examples (Fm, Sm) for how to set up matrices, and the vignette for more detailed instructions. Different pigments and phytoplankton groups may be used.

Usage

simulated_annealing(
  S,
  Fmat = NULL,
  user_defined_min_max = NULL,
  do_matrix_checks = TRUE,
  niter = 500,
  step = 0.009,
  weight.upper.bound = 30,
  verbose = FALSE
)

Arguments

S: Sample data matrix – a matrix of pigment samples
Fmat: Pigment to Chl a matrix
user_defined_min_max: data frame with some format as min_max built-in data
do_matrix_checks: This should only be set to TRUE when using the default values. This will remove pigment columns that have column sums of 0. Set to FALSE if using customised names for pigments and phytoplankton groups
niter: Number of iterations (default is 500)
step: Step ratio used (default is 0.009)
weight.upper.bound: Upper limit of the weights applied (default value is 30).
verbose: Logical value. Output error and temperature at each iteration. Default value of FALSE

Value

A list containing

Fmat matrix
RMSE (Root Mean Square Error)
condition number
Class abundances
Figure (plot of results)
MAE (Mean Absolute Error)
Error

Examples

# Using the built-in matrices Sm and Fm
set.seed(5326)
sa.example <- simulated_annealing(Sm, Fm, niter = 5)
#> 
#> Condition number = 393
#> 
#> Current error:  0.0283
#> Neighbour's error:  0.0283
#> Temperature (%):  99.1
#>  
#> Current error:  0.0229
#> Neighbour's error:  0.0229
#> Temperature (%):  98.21
#>  
#> Current error:  0.0229
#> Neighbour's error:  0.0237
#> Temperature (%):  97.32
#>  
#> Current error:  0.0203
#> Neighbour's error:  0.0203
#> Temperature (%):  96.45
#>  
#> Current error:  0.0203
#> Neighbour's error:  0.0268
#> Temperature (%):  95.58
#>  
sa.example$Figure