Introduction
The phytoclass package uses non-negative matrix factorization and simulated annealing to determine the biomass of different phytoplankton groups from pigment concentrations. The methodology is discussed in (Hayward, Pinkerton, and Gutierrez‐Rodriguez 2023) and is similar to the CHEMTAX method of Mackey et al. (1996).
For ease of use, most functions have default options. However the user also has the option to set their own parameters within the program, and instructions on how to do this are listed for each function.
It is important to highlight that naming conventions for phytoplankton groups and their pigments should be adhered to when using the default samples. For example, the user should ensure that pigment names in their sample matrix (S) match the same pigment names in the pigment – Chl a ratio matrix (F).
At present the use of DV Chl a with Prochlorococcus is not supported, however it will be in a future release.
The main function of the package is simulated_annealing() with an associated helper function Cluster()
Other helper functions covered in this document are:
- Matrix_checks
- Bounded_weights
- Steepest_Desc
- NNLS_MF
Main function: simulated_annealing()
This is the main function for the phytoclass package.
It takes in the inputs (listed below) and returns the updated pigment to Chl a ratios, the Chl a biomass of each phytoplankton group, error associated with each group, and a graph displaying the Chl a concentration for each group.
It is important that samples are clustered appropriately before using the function (see the Cluster function).
Arguments:
S = Sample matrix – a matrix of pigment samples. Ensure that Chl a is the final column
F = Pigment to Chl a matrix. If left blank default values will be used. Ensure that pigment columns are in the same order as S and column naming conventions match.
user_defined_min_max = If blank default values are used. To create different min_max values, follow the same structure as the phytoclass::min_max file. See the example below.
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 value is 500.
step = step ratio used. Default value is 0.009.
weight.upper.bound = the upper limit of the weights applied. Default value is 30.
When using the default values, the only argument required is the sample matrix. However, make sure that the pigment names match those in the built-in pigment to Chl matrix Fm.
For the examples that follow the argument niter equals one for processing speed, but should be set much higher to obtain convergence.
Cluster function
Prior to analysis using simulated annealing, pigment samples require clustering.
The Cluster function divides all pigment concentrations by the total Chl a concentration. Following this the data undergoes BoxCox transformation, and the data is hierarchically clustered using the Ward method based on the Manhattan distances between pigment samples. The DynamicTreeCut method of (Langfelder, Zhang, and Horvath 2008) is then used to prune the dendogram into reasonable clusters of specified size(s).
The function returns a list of the clusters and the cluster dendrogram.
An example, using the built-in sample data set Sm:
Cluster.result <- Cluster(Sm, 14)
#> Registered S3 method overwritten by 'generics':
#> method from
#> as.character.dev_topic butcher
#> ..done.
# list of clusters
Cluster.result$cluster.list
#> [[1]]
#> Per X19but Fuco Neox Pra Viol X19hex
#> 1 0.0000 0.03024000 0.0622500 0.005570000 0.01407000 0.007590000 0.08224000
#> 2 0.0000 0.01084000 0.0286400 0.001110000 0.00351000 0.001440000 0.01497000
#> 3 0.0000 0.01560000 0.2172000 0.006400000 0.00920000 0.000000000 0.01740000
#> 4 0.0000 0.01770000 0.2347000 0.007000000 0.01150000 0.000000000 0.01890000
#> 5 0.0000 0.02520000 0.2952000 0.009900000 0.01300000 0.000000000 0.02110000
#> 6 0.0102 0.02220000 0.2275000 0.007600000 0.01070000 0.000000000 0.01900000
#> 7 0.0000 0.01510000 0.1417000 0.004000000 0.00800000 0.002600000 0.02550000
#> 8 0.0000 0.04520000 0.4484000 0.003700000 0.00650000 0.000000000 0.13170000
#> 9 0.0000 0.05180000 0.6228000 0.004900000 0.00750000 0.004000000 0.13210000
#> 10 0.0000 0.02200000 0.1090000 0.004000000 0.00800000 0.003000000 0.04200000
#> 11 0.0000 0.04906269 0.5266418 0.017334346 0.03446357 0.000000000 0.12044287
#> 12 0.0000 0.02052662 0.2714934 0.017692288 0.01994758 0.000000000 0.03011548
#> 13 0.0000 0.02216177 0.1654257 0.012242414 0.01956286 0.000000000 0.02721049
#> 14 0.0000 0.01438545 0.2555594 0.020671983 0.04736366 0.011507712 0.02167651
#> 15 0.0000 0.02619187 0.6025477 0.038948613 0.04929322 0.034893958 0.10082727
#> 16 0.0000 0.02662521 0.2205439 0.024444776 0.06623835 0.027029275 0.04015983
#> 17 0.0000 0.06542364 0.6603459 0.044215705 0.11285620 0.064560275 0.13896777
#> 18 0.0000 0.05315834 0.2584586 0.016123905 0.04701666 0.011387548 0.08065818
#> 19 0.0000 0.06040911 0.4494852 0.027862628 0.07757631 0.025258248 0.10999693
#> 20 0.0000 0.07577285 0.3920489 0.017239170 0.05136883 0.020899111 0.12859331
#> 21 0.0000 0.06067605 0.4112795 0.021768249 0.07620416 0.022592896 0.08973832
#> 22 0.0000 0.04388275 0.6257830 0.012373717 0.03347165 0.013495185 0.12822965
#> 23 0.0000 0.03971444 0.4332551 0.014402749 0.02657611 0.010560487 0.09220387
#> 24 0.0000 0.03092146 0.1639481 0.013383240 0.01262176 0.008793182 0.05881285
#> 25 0.0000 0.02725515 0.1966991 0.009916299 0.01832477 0.009112193 0.05488214
#> 26 0.0000 0.01678833 0.1755048 0.008821737 0.02676049 0.006637964 0.02189435
#> 27 0.0000 0.02444961 0.1273596 0.007049435 0.01323978 0.004447137 0.02218985
#> 28 0.0000 0.01567523 0.1052905 0.006144340 0.01372236 0.006294480 0.02208074
#> 29 0.0000 0.03108003 0.0874373 0.011728700 0.02004430 0.005772257 0.04855466
#> Allo Zea Lut ChlcMGDG18 ChlcMGDG14 Chl_b Tchla Clust
#> 1 0.001880000 0.002010000 0 0 0 0.08661000 0.4585100 1
#> 2 0.001440000 0.001910000 0 0 0 0.01473000 0.1457100 1
#> 3 0.005800000 0.003600000 0 0 0 0.05700000 0.6127000 1
#> 4 0.005400000 0.003800000 0 0 0 0.06190000 0.6207000 1
#> 5 0.001400000 0.007600000 0 0 0 0.05780000 0.5302000 1
#> 6 0.000000000 0.003000000 0 0 0 0.04530000 0.4057000 1
#> 7 0.000800000 0.001000000 0 0 0 0.03120000 0.2117000 1
#> 8 0.004400000 0.002300000 0 0 0 0.00930000 0.6885000 1
#> 9 0.059700000 0.003500000 0 0 0 0.01210000 1.0849000 1
#> 10 0.003000000 0.003000000 0 0 0 0.03200000 0.2060000 1
#> 11 0.029990012 0.039966194 0 0 0 0.15325050 1.0396607 1
#> 12 0.000000000 0.030828916 0 0 0 0.09243326 0.4812043 1
#> 13 0.000000000 0.017661542 0 0 0 0.08720595 0.3409079 1
#> 14 0.009032764 0.018938989 0 0 0 0.14213442 0.4892902 1
#> 15 0.031610292 0.036313122 0 0 0 0.16461333 1.3811642 1
#> 16 0.007170750 0.013507988 0 0 0 0.18532190 0.5711149 1
#> 17 0.016001053 0.028670688 0 0 0 0.41729467 1.7413395 1
#> 18 0.010093841 0.016835362 0 0 0 0.13198111 0.5776707 1
#> 19 0.019249598 0.020581908 0 0 0 0.26015615 1.0739310 1
#> 20 0.024550418 0.020066576 0 0 0 0.22391720 0.9909010 1
#> 21 0.016719215 0.019212773 0 0 0 0.23686078 0.9520484 1
#> 22 0.018189726 0.017601736 0 0 0 0.15579381 1.2494685 1
#> 23 0.014131906 0.012984478 0 0 0 0.12090732 0.9190665 1
#> 24 0.011139374 0.012782671 0 0 0 0.05213182 0.3486558 1
#> 25 0.013767971 0.012392348 0 0 0 0.08734698 0.4556423 1
#> 26 0.008302439 0.010946550 0 0 0 0.07005955 0.3497736 1
#> 27 0.005887899 0.010324368 0 0 0 0.03627212 0.2367185 1
#> 28 0.004908419 0.009914413 0 0 0 0.03735579 0.2117071 1
#> 29 0.009256850 0.009871839 0 0 0 0.09414383 0.3014690 1
# plot of clusters
plot(Cluster.result$cluster.plot)
Example without clustering
The example here uses the built-in sample matrix Sm.
set.seed("7683")
Results <- simulated_annealing(Sm, niter = 1)
Results$`condition number`
#> [1] 728.0411
Results$RMSE
#> [1] 0.02819993
Results$MAE
#> Per X19but Fuco Neox Pra Viol
#> 8.532288e-05 1.029630e-05 4.746166e-03 4.346108e-03 3.364625e-03 2.253839e-03
#> X19hex Allo Zea Chl_b Tchla
#> 2.883243e-04 1.750370e-04 7.460491e-05 7.255182e-03 6.520774e-02
Results$Error
#> Per X19but Fuco Neox Pra
#> [1,] -4.522442e-04 2.998121e-05 -0.0138200899 -7.840801e-04 -0.0061179974
#> [2,] -6.216036e-04 4.120877e-05 -0.0189955299 -3.067019e-04 -0.0007864679
#> [3,] -5.332655e-04 3.535246e-05 -0.0162960147 2.472446e-03 -0.0044775819
#> [4,] -4.648612e-04 3.081764e-05 -0.0142056529 2.503969e-03 -0.0035775433
#> [5,] -1.111827e-04 7.370779e-06 -0.0033976227 5.620855e-03 -0.0019343731
#> [6,] -3.887731e-05 2.577344e-06 -0.0011880485 5.380674e-03 -0.0014102286
#> [7,] 0.000000e+00 -1.481838e-05 0.0068306578 3.359748e-03 -0.0003564754
#> [8,] 0.000000e+00 -1.239137e-05 0.0057119070 1.898147e-03 0.0019983654
#> [9,] 0.000000e+00 -6.424739e-06 0.0029615376 1.695727e-03 0.0014498199
#> [10,] 0.000000e+00 -1.403169e-05 0.0064680253 3.314125e-03 -0.0004392935
#> [11,] 0.000000e+00 -5.989623e-06 0.0027609674 2.862882e-03 -0.0018901051
#> [12,] 0.000000e+00 -5.165725e-06 0.0023811845 1.096601e-02 -0.0037115649
#> [13,] 0.000000e+00 -3.440096e-06 0.0015857411 8.055950e-03 -0.0035585943
#> [14,] 0.000000e+00 -1.123305e-05 0.0051779691 7.804847e-03 0.0054590882
#> [15,] -1.010735e-04 6.700599e-06 -0.0030886974 9.669789e-03 0.0025703235
#> [16,] 0.000000e+00 -4.939879e-06 0.0022770788 6.187837e-03 0.0102135493
#> [17,] -7.204535e-05 4.776196e-06 -0.0022016276 2.825672e-03 0.0026713678
#> [18,] 0.000000e+00 -8.860647e-06 0.0040843897 3.472132e-03 0.0062625846
#> [19,] 0.000000e+00 -2.472896e-06 0.0011399021 2.714729e-03 0.0027338804
#> [20,] 0.000000e+00 -1.852945e-06 0.0008541308 -4.167585e-05 -0.0016094239
#> [21,] 0.000000e+00 -3.466027e-06 0.0015976945 6.460336e-04 0.0045334465
#> [22,] -2.188674e-05 1.450966e-06 -0.0006688349 2.342643e-04 -0.0016432033
#> [23,] -5.732325e-05 3.800204e-06 -0.0017517363 2.974590e-03 -0.0018193671
#> [24,] 0.000000e+00 -1.079130e-05 0.0049743407 1.254368e-02 -0.0003315649
#> [25,] 0.000000e+00 -3.197543e-06 0.0014739346 3.631535e-03 -0.0025215078
#> [26,] 0.000000e+00 -5.441175e-06 0.0025081554 3.279447e-03 0.0073943172
#> [27,] 0.000000e+00 -8.340946e-06 0.0038448294 7.443059e-03 0.0043155685
#> [28,] 0.000000e+00 -8.806090e-06 0.0040592414 6.023247e-03 0.0063530778
#> [29,] 0.000000e+00 -2.892364e-06 0.0013332597 7.323287e-03 -0.0054334560
#> Viol X19hex Allo Zea Chl_b
#> [1,] -2.991785e-03 -8.395552e-04 -5.248057e-04 -6.019496e-04 0.0118460542
#> [2,] -6.174530e-04 -1.153957e-03 -7.213385e-04 -2.414874e-04 -0.0002826714
#> [3,] -3.719736e-03 -9.899649e-04 -6.188268e-04 -2.071689e-04 0.0068938258
#> [4,] -4.002116e-03 -8.629777e-04 -5.394471e-04 -1.805944e-04 0.0055741998
#> [5,] -4.044827e-03 -2.064018e-04 -1.290217e-04 -4.319348e-05 0.0020210463
#> [6,] -4.096586e-03 -7.217263e-05 -4.511509e-05 -1.510349e-05 0.0014604394
#> [7,] -1.885126e-06 4.149549e-04 2.593882e-04 -7.434065e-05 -0.0003949694
#> [8,] -7.464511e-04 3.469920e-04 2.169046e-04 7.261464e-05 -0.0037816724
#> [9,] 7.348941e-04 1.799101e-04 1.124618e-04 3.764960e-05 -0.0036888128
#> [10,] -4.075866e-05 3.929254e-04 2.456176e-04 8.222706e-05 -0.0002753353
#> [11,] -4.979837e-03 1.677257e-04 1.048453e-04 3.509978e-05 0.0046831916
#> [12,] -6.385044e-03 1.446543e-04 0.000000e+00 3.027165e-05 0.0041467486
#> [13,] -8.325314e-03 9.633202e-05 0.000000e+00 2.015930e-05 0.0063670271
#> [14,] 5.678401e-04 3.145559e-04 1.966288e-04 6.582677e-05 -0.0143182751
#> [15,] 1.336983e-03 -1.876350e-04 -1.172906e-04 -3.926616e-05 -0.0113128399
#> [16,] 4.803235e-03 1.383300e-04 8.647006e-05 2.894817e-05 -0.0247241794
#> [17,] 1.245298e-03 -1.337464e-04 -8.360487e-05 -2.798897e-05 -0.0074741412
#> [18,] 8.773357e-04 2.481222e-04 1.551011e-04 5.192425e-05 -0.0136479985
#> [19,] 1.315879e-03 6.924779e-05 4.328678e-05 1.449141e-05 -0.0071702762
#> [20,] -7.203085e-04 5.188750e-05 3.243486e-05 1.085844e-05 0.0034916023
#> [21,] 2.093974e-03 9.705818e-05 6.067104e-05 2.031126e-05 -0.0096961674
#> [22,] -7.499585e-04 -4.063098e-05 -2.539842e-05 -8.502801e-06 0.0032340526
#> [23,] -7.845869e-04 -1.064160e-04 -6.652064e-05 -2.226957e-05 0.0019007062
#> [24,] 1.897016e-04 3.021857e-04 1.888962e-04 6.323807e-05 -0.0059962848
#> [25,] -1.046001e-03 8.953990e-05 5.597137e-05 1.873792e-05 0.0033507415
#> [26,] 1.410100e-03 1.523677e-04 9.524499e-05 3.188581e-05 -0.0161049681
#> [27,] 2.173429e-03 2.335691e-04 1.460040e-04 4.887876e-05 -0.0129150333
#> [28,] 3.070484e-03 2.465944e-04 1.541461e-04 5.160454e-05 -0.0163412589
#> [29,] -2.289536e-03 8.099405e-05 5.062936e-05 1.694954e-05 0.0073057578
#> Tchla
#> [1,] 0.189874712
#> [2,] 0.260980269
#> [3,] 0.223891532
#> [4,] 0.195171976
#> [5,] 0.046680061
#> [6,] 0.016322641
#> [7,] -0.093846653
#> [8,] -0.078476095
#> [9,] -0.040688672
#> [10,] -0.088864432
#> [11,] -0.037933031
#> [12,] -0.032715180
#> [13,] -0.021786554
#> [14,] -0.071140303
#> [15,] 0.042435725
#> [16,] -0.031284868
#> [17,] 0.030248241
#> [18,] -0.056115578
#> [19,] -0.015661155
#> [20,] -0.011734934
#> [21,] -0.021950782
#> [22,] 0.009189147
#> [23,] 0.024067168
#> [24,] -0.068342646
#> [25,] -0.020250440
#> [26,] -0.034459637
#> [27,] -0.052824249
#> [28,] -0.055770064
#> [29,] -0.018317703
Results$`F matrix`
#> Per X19but Fuco Neox Pra Viol X19hex Allo Zea
#> Prasinophytes 0.0000 0.0000 0.0000 0.0718 0.2373 0.0621 0.0000 0.000 0.0297
#> Chlorophytes 0.0000 0.0000 0.0000 0.0418 0.0000 0.4047 0.0000 0.000 0.0103
#> Cryptophytes 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.402 0.0000
#> Diatoms-2 0.0000 0.0000 0.7769 0.0000 0.0000 0.0000 0.0000 0.000 0.0000
#> Dinoflagellates-1 0.4665 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 0.0000
#> Haptophytes 0.0000 0.1387 0.0973 0.0000 0.0000 0.0000 0.5726 0.000 0.0000
#> Pelagophytes 0.0000 1.1648 0.9055 0.0000 0.0000 0.0000 0.0000 0.000 0.0000
#> Syn 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 1.2008
#> Chl_b Tchla
#> Prasinophytes 0.8920 1
#> Chlorophytes 0.6174 1
#> Cryptophytes 0.0000 1
#> Diatoms-2 0.0000 1
#> Dinoflagellates-1 0.0000 1
#> Haptophytes 0.0000 1
#> Pelagophytes 0.0000 1
#> Syn 0.0000 1
Results$`Class abundances`
#> Prasinophytes Chlorophytes Cryptophytes Diatoms-2 Dinoflagellates-1
#> 1 0.11415345 0.017760362 8.210281e-03 0.09464098 0.0010566129
#> 2 0.02596671 0.002535096 6.660287e-03 0.05327371 0.0004995775
#> 3 0.08644760 0.000000000 2.425845e-02 0.43415727 0.0016497735
#> 4 0.09150323 0.000000000 2.133523e-02 0.43803319 0.0014148487
#> 5 0.06841045 0.000000000 4.141719e-03 0.39271201 0.0002503224
#> 6 0.05109922 0.000000000 8.693199e-05 0.28070172 0.0226109310
#> 7 0.02874819 0.000963134 1.425962e-03 0.13714426 0.0000000000
#> 8 0.01397285 0.000000000 8.867831e-03 0.45558900 0.0000000000
#> 9 0.01813906 0.003064060 1.377094e-01 0.69375518 0.0000000000
#> 10 0.02909102 0.001821201 6.067940e-03 0.09750384 0.0000000000
#> 11 0.15022669 0.000000000 6.901425e-02 0.58178234 0.0000000000
#> 12 0.09304273 0.000000000 0.000000e+00 0.30656949 0.0000000000
#> 13 0.08888919 0.000000000 0.000000e+00 0.18193124 0.0000000000
#> 14 0.15296582 0.000000000 1.910311e-02 0.26801862 0.0000000000
#> 15 0.19585190 0.054422260 8.585848e-02 0.82403425 0.0005781949
#> 16 0.21437060 0.016654663 1.651401e-02 0.23921925 0.0000000000
#> 17 0.46513088 0.087085634 4.294163e-02 0.84545970 0.0005388426
#> 18 0.14896006 0.000000000 2.206536e-02 0.24626376 0.0000000000
#> 19 0.29334798 0.008823141 4.622372e-02 0.50203773 0.0000000000
#> 20 0.22449402 0.019414622 5.954199e-02 0.42003254 0.0000000000
#> 21 0.27270519 0.002177163 3.956266e-02 0.44719765 0.0000000000
#> 22 0.15966551 0.013751518 4.617374e-02 0.77977476 0.0001096826
#> 23 0.13066475 0.010663096 3.706702e-02 0.54808753 0.0002164486
#> 24 0.04753960 0.011474616 2.401721e-02 0.15520283 0.0000000000
#> 25 0.08334978 0.011075382 3.283349e-02 0.21698830 0.0000000000
#> 26 0.08525731 0.000000000 1.917424e-02 0.19407363 0.0000000000
#> 27 0.04231253 0.001053833 1.304852e-02 0.12406303 0.0000000000
#> 28 0.04149765 0.004643577 1.081055e-02 0.10600086 0.0000000000
#> 29 0.09507180 0.002534130 2.211667e-02 0.07857270 0.0000000000
#> Haptophytes Pelagophytes Syn
#> 1 0.21004894 0.012639373 0.000000000
#> 2 0.04445565 0.010248827 0.002070137
#> 3 0.04890870 0.014588293 0.002689922
#> 4 0.04988337 0.016002411 0.002527718
#> 5 0.04063556 0.018789815 0.005260123
#> 6 0.03431373 0.015565380 0.001322075
#> 7 0.03697534 0.006443106 0.000000000
#> 8 0.19882409 0.010000516 0.001245711
#> 9 0.21414986 0.015902305 0.002180125
#> 10 0.06157777 0.008592416 0.001345809
#> 11 0.19541771 0.015982106 0.027237631
#> 12 0.04912996 0.010692079 0.021770069
#> 13 0.04539578 0.012816440 0.011875242
#> 14 0.03243077 0.006887679 0.009884176
#> 15 0.19139905 0.001523416 0.027496641
#> 16 0.06560214 0.013661036 0.005093240
#> 17 0.25822016 0.028809222 0.013153418
#> 18 0.12565300 0.025906675 0.008821836
#> 19 0.18607885 0.028150523 0.009269014
#> 20 0.21934822 0.037471166 0.010598432
#> 21 0.14981024 0.032063958 0.008531546
#> 22 0.22795899 0.011174393 0.010859876
#> 23 0.16878169 0.015564580 0.008021373
#> 24 0.08978395 0.012605268 0.008032291
#> 25 0.09208545 0.011550302 0.007759552
#> 26 0.03561157 0.009251359 0.006405479
#> 27 0.03476806 0.014792389 0.006680167
#> 28 0.03444581 0.007980578 0.006328068
#> 29 0.08163696 0.015995444 0.005541267
Results$Figure
Example with clustering
Clust1 <- Cluster(Sm, minSamplesPerCluster = 14)$cluster.list[[1]]
#> ..done.
# Remove the cluster column/label
Clust1$Clust <- NULL
set.seed("7683")
Results <- simulated_annealing(Clust1, niter = 1)
Results$`condition number`
#> [1] 728.0411
Results$RMSE
#> [1] 0.02819993
Results$MAE
#> Per X19but Fuco Neox Pra Viol
#> 8.532288e-05 1.029630e-05 4.746166e-03 4.346108e-03 3.364625e-03 2.253839e-03
#> X19hex Allo Zea Chl_b Tchla
#> 2.883243e-04 1.750370e-04 7.460491e-05 7.255182e-03 6.520774e-02
Results$Error
#> Per X19but Fuco Neox Pra
#> [1,] -4.522442e-04 2.998121e-05 -0.0138200899 -7.840801e-04 -0.0061179974
#> [2,] -6.216036e-04 4.120877e-05 -0.0189955299 -3.067019e-04 -0.0007864679
#> [3,] -5.332655e-04 3.535246e-05 -0.0162960147 2.472446e-03 -0.0044775819
#> [4,] -4.648612e-04 3.081764e-05 -0.0142056529 2.503969e-03 -0.0035775433
#> [5,] -1.111827e-04 7.370779e-06 -0.0033976227 5.620855e-03 -0.0019343731
#> [6,] -3.887731e-05 2.577344e-06 -0.0011880485 5.380674e-03 -0.0014102286
#> [7,] 0.000000e+00 -1.481838e-05 0.0068306578 3.359748e-03 -0.0003564754
#> [8,] 0.000000e+00 -1.239137e-05 0.0057119070 1.898147e-03 0.0019983654
#> [9,] 0.000000e+00 -6.424739e-06 0.0029615376 1.695727e-03 0.0014498199
#> [10,] 0.000000e+00 -1.403169e-05 0.0064680253 3.314125e-03 -0.0004392935
#> [11,] 0.000000e+00 -5.989623e-06 0.0027609674 2.862882e-03 -0.0018901051
#> [12,] 0.000000e+00 -5.165725e-06 0.0023811845 1.096601e-02 -0.0037115649
#> [13,] 0.000000e+00 -3.440096e-06 0.0015857411 8.055950e-03 -0.0035585943
#> [14,] 0.000000e+00 -1.123305e-05 0.0051779691 7.804847e-03 0.0054590882
#> [15,] -1.010735e-04 6.700599e-06 -0.0030886974 9.669789e-03 0.0025703235
#> [16,] 0.000000e+00 -4.939879e-06 0.0022770788 6.187837e-03 0.0102135493
#> [17,] -7.204535e-05 4.776196e-06 -0.0022016276 2.825672e-03 0.0026713678
#> [18,] 0.000000e+00 -8.860647e-06 0.0040843897 3.472132e-03 0.0062625846
#> [19,] 0.000000e+00 -2.472896e-06 0.0011399021 2.714729e-03 0.0027338804
#> [20,] 0.000000e+00 -1.852945e-06 0.0008541308 -4.167585e-05 -0.0016094239
#> [21,] 0.000000e+00 -3.466027e-06 0.0015976945 6.460336e-04 0.0045334465
#> [22,] -2.188674e-05 1.450966e-06 -0.0006688349 2.342643e-04 -0.0016432033
#> [23,] -5.732325e-05 3.800204e-06 -0.0017517363 2.974590e-03 -0.0018193671
#> [24,] 0.000000e+00 -1.079130e-05 0.0049743407 1.254368e-02 -0.0003315649
#> [25,] 0.000000e+00 -3.197543e-06 0.0014739346 3.631535e-03 -0.0025215078
#> [26,] 0.000000e+00 -5.441175e-06 0.0025081554 3.279447e-03 0.0073943172
#> [27,] 0.000000e+00 -8.340946e-06 0.0038448294 7.443059e-03 0.0043155685
#> [28,] 0.000000e+00 -8.806090e-06 0.0040592414 6.023247e-03 0.0063530778
#> [29,] 0.000000e+00 -2.892364e-06 0.0013332597 7.323287e-03 -0.0054334560
#> Viol X19hex Allo Zea Chl_b
#> [1,] -2.991785e-03 -8.395552e-04 -5.248057e-04 -6.019496e-04 0.0118460542
#> [2,] -6.174530e-04 -1.153957e-03 -7.213385e-04 -2.414874e-04 -0.0002826714
#> [3,] -3.719736e-03 -9.899649e-04 -6.188268e-04 -2.071689e-04 0.0068938258
#> [4,] -4.002116e-03 -8.629777e-04 -5.394471e-04 -1.805944e-04 0.0055741998
#> [5,] -4.044827e-03 -2.064018e-04 -1.290217e-04 -4.319348e-05 0.0020210463
#> [6,] -4.096586e-03 -7.217263e-05 -4.511509e-05 -1.510349e-05 0.0014604394
#> [7,] -1.885126e-06 4.149549e-04 2.593882e-04 -7.434065e-05 -0.0003949694
#> [8,] -7.464511e-04 3.469920e-04 2.169046e-04 7.261464e-05 -0.0037816724
#> [9,] 7.348941e-04 1.799101e-04 1.124618e-04 3.764960e-05 -0.0036888128
#> [10,] -4.075866e-05 3.929254e-04 2.456176e-04 8.222706e-05 -0.0002753353
#> [11,] -4.979837e-03 1.677257e-04 1.048453e-04 3.509978e-05 0.0046831916
#> [12,] -6.385044e-03 1.446543e-04 0.000000e+00 3.027165e-05 0.0041467486
#> [13,] -8.325314e-03 9.633202e-05 0.000000e+00 2.015930e-05 0.0063670271
#> [14,] 5.678401e-04 3.145559e-04 1.966288e-04 6.582677e-05 -0.0143182751
#> [15,] 1.336983e-03 -1.876350e-04 -1.172906e-04 -3.926616e-05 -0.0113128399
#> [16,] 4.803235e-03 1.383300e-04 8.647006e-05 2.894817e-05 -0.0247241794
#> [17,] 1.245298e-03 -1.337464e-04 -8.360487e-05 -2.798897e-05 -0.0074741412
#> [18,] 8.773357e-04 2.481222e-04 1.551011e-04 5.192425e-05 -0.0136479985
#> [19,] 1.315879e-03 6.924779e-05 4.328678e-05 1.449141e-05 -0.0071702762
#> [20,] -7.203085e-04 5.188750e-05 3.243486e-05 1.085844e-05 0.0034916023
#> [21,] 2.093974e-03 9.705818e-05 6.067104e-05 2.031126e-05 -0.0096961674
#> [22,] -7.499585e-04 -4.063098e-05 -2.539842e-05 -8.502801e-06 0.0032340526
#> [23,] -7.845869e-04 -1.064160e-04 -6.652064e-05 -2.226957e-05 0.0019007062
#> [24,] 1.897016e-04 3.021857e-04 1.888962e-04 6.323807e-05 -0.0059962848
#> [25,] -1.046001e-03 8.953990e-05 5.597137e-05 1.873792e-05 0.0033507415
#> [26,] 1.410100e-03 1.523677e-04 9.524499e-05 3.188581e-05 -0.0161049681
#> [27,] 2.173429e-03 2.335691e-04 1.460040e-04 4.887876e-05 -0.0129150333
#> [28,] 3.070484e-03 2.465944e-04 1.541461e-04 5.160454e-05 -0.0163412589
#> [29,] -2.289536e-03 8.099405e-05 5.062936e-05 1.694954e-05 0.0073057578
#> Tchla
#> [1,] 0.189874712
#> [2,] 0.260980269
#> [3,] 0.223891532
#> [4,] 0.195171976
#> [5,] 0.046680061
#> [6,] 0.016322641
#> [7,] -0.093846653
#> [8,] -0.078476095
#> [9,] -0.040688672
#> [10,] -0.088864432
#> [11,] -0.037933031
#> [12,] -0.032715180
#> [13,] -0.021786554
#> [14,] -0.071140303
#> [15,] 0.042435725
#> [16,] -0.031284868
#> [17,] 0.030248241
#> [18,] -0.056115578
#> [19,] -0.015661155
#> [20,] -0.011734934
#> [21,] -0.021950782
#> [22,] 0.009189147
#> [23,] 0.024067168
#> [24,] -0.068342646
#> [25,] -0.020250440
#> [26,] -0.034459637
#> [27,] -0.052824249
#> [28,] -0.055770064
#> [29,] -0.018317703
Results$`F matrix`
#> Per X19but Fuco Neox Pra Viol X19hex Allo Zea
#> Prasinophytes 0.0000 0.0000 0.0000 0.0718 0.2373 0.0621 0.0000 0.000 0.0297
#> Chlorophytes 0.0000 0.0000 0.0000 0.0418 0.0000 0.4047 0.0000 0.000 0.0103
#> Cryptophytes 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.402 0.0000
#> Diatoms-2 0.0000 0.0000 0.7769 0.0000 0.0000 0.0000 0.0000 0.000 0.0000
#> Dinoflagellates-1 0.4665 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 0.0000
#> Haptophytes 0.0000 0.1387 0.0973 0.0000 0.0000 0.0000 0.5726 0.000 0.0000
#> Pelagophytes 0.0000 1.1648 0.9055 0.0000 0.0000 0.0000 0.0000 0.000 0.0000
#> Syn 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 1.2008
#> Chl_b Tchla
#> Prasinophytes 0.8920 1
#> Chlorophytes 0.6174 1
#> Cryptophytes 0.0000 1
#> Diatoms-2 0.0000 1
#> Dinoflagellates-1 0.0000 1
#> Haptophytes 0.0000 1
#> Pelagophytes 0.0000 1
#> Syn 0.0000 1
Results$`Class abundances`
#> Prasinophytes Chlorophytes Cryptophytes Diatoms-2 Dinoflagellates-1
#> 1 0.11415345 0.017760362 8.210281e-03 0.09464098 0.0010566129
#> 2 0.02596671 0.002535096 6.660287e-03 0.05327371 0.0004995775
#> 3 0.08644760 0.000000000 2.425845e-02 0.43415727 0.0016497735
#> 4 0.09150323 0.000000000 2.133523e-02 0.43803319 0.0014148487
#> 5 0.06841045 0.000000000 4.141719e-03 0.39271201 0.0002503224
#> 6 0.05109922 0.000000000 8.693199e-05 0.28070172 0.0226109310
#> 7 0.02874819 0.000963134 1.425962e-03 0.13714426 0.0000000000
#> 8 0.01397285 0.000000000 8.867831e-03 0.45558900 0.0000000000
#> 9 0.01813906 0.003064060 1.377094e-01 0.69375518 0.0000000000
#> 10 0.02909102 0.001821201 6.067940e-03 0.09750384 0.0000000000
#> 11 0.15022669 0.000000000 6.901425e-02 0.58178234 0.0000000000
#> 12 0.09304273 0.000000000 0.000000e+00 0.30656949 0.0000000000
#> 13 0.08888919 0.000000000 0.000000e+00 0.18193124 0.0000000000
#> 14 0.15296582 0.000000000 1.910311e-02 0.26801862 0.0000000000
#> 15 0.19585190 0.054422260 8.585848e-02 0.82403425 0.0005781949
#> 16 0.21437060 0.016654663 1.651401e-02 0.23921925 0.0000000000
#> 17 0.46513088 0.087085634 4.294163e-02 0.84545970 0.0005388426
#> 18 0.14896006 0.000000000 2.206536e-02 0.24626376 0.0000000000
#> 19 0.29334798 0.008823141 4.622372e-02 0.50203773 0.0000000000
#> 20 0.22449402 0.019414622 5.954199e-02 0.42003254 0.0000000000
#> 21 0.27270519 0.002177163 3.956266e-02 0.44719765 0.0000000000
#> 22 0.15966551 0.013751518 4.617374e-02 0.77977476 0.0001096826
#> 23 0.13066475 0.010663096 3.706702e-02 0.54808753 0.0002164486
#> 24 0.04753960 0.011474616 2.401721e-02 0.15520283 0.0000000000
#> 25 0.08334978 0.011075382 3.283349e-02 0.21698830 0.0000000000
#> 26 0.08525731 0.000000000 1.917424e-02 0.19407363 0.0000000000
#> 27 0.04231253 0.001053833 1.304852e-02 0.12406303 0.0000000000
#> 28 0.04149765 0.004643577 1.081055e-02 0.10600086 0.0000000000
#> 29 0.09507180 0.002534130 2.211667e-02 0.07857270 0.0000000000
#> Haptophytes Pelagophytes Syn
#> 1 0.21004894 0.012639373 0.000000000
#> 2 0.04445565 0.010248827 0.002070137
#> 3 0.04890870 0.014588293 0.002689922
#> 4 0.04988337 0.016002411 0.002527718
#> 5 0.04063556 0.018789815 0.005260123
#> 6 0.03431373 0.015565380 0.001322075
#> 7 0.03697534 0.006443106 0.000000000
#> 8 0.19882409 0.010000516 0.001245711
#> 9 0.21414986 0.015902305 0.002180125
#> 10 0.06157777 0.008592416 0.001345809
#> 11 0.19541771 0.015982106 0.027237631
#> 12 0.04912996 0.010692079 0.021770069
#> 13 0.04539578 0.012816440 0.011875242
#> 14 0.03243077 0.006887679 0.009884176
#> 15 0.19139905 0.001523416 0.027496641
#> 16 0.06560214 0.013661036 0.005093240
#> 17 0.25822016 0.028809222 0.013153418
#> 18 0.12565300 0.025906675 0.008821836
#> 19 0.18607885 0.028150523 0.009269014
#> 20 0.21934822 0.037471166 0.010598432
#> 21 0.14981024 0.032063958 0.008531546
#> 22 0.22795899 0.011174393 0.010859876
#> 23 0.16878169 0.015564580 0.008021373
#> 24 0.08978395 0.012605268 0.008032291
#> 25 0.09208545 0.011550302 0.007759552
#> 26 0.03561157 0.009251359 0.006405479
#> 27 0.03476806 0.014792389 0.006680167
#> 28 0.03444581 0.007980578 0.006328068
#> 29 0.08163696 0.015995444 0.005541267
Results$Figure
Example using non-default values
#Create Fm (F matrix). Alternatively, a .csv file can be uploaded.
#Create Fm (F matrix). Alternatively, a .csv file can be uploaded.
Fu <- data.frame(
Per = c(0, 0, 0, 0, 1, 0, 0, 0),
X19but = c(0, 0, 0, 0, 0, 1, 1, 0),
Fuco = c(0, 0, 0, 1, 0, 1, 1, 0),
Pra = c(1, 0, 0, 0, 0, 0, 0, 0),
X19hex = c(0, 0, 0, 0, 0, 1, 0, 0),
Allo = c(0, 0, 1, 0, 0, 0, 0, 0),
Zea = c(1, 1, 0, 0, 0, 0, 0, 1),
Chl_b = c(1, 1, 0, 0, 0, 0, 0, 0),
Tchla = c(1, 1, 1, 1, 1, 1, 1, 1)
)
rownames(Fu) <- c(
"Prasinophytes", "Chlorophytes", "Cryptophytes"
, "Diatoms-2", "Dinoflagellates-1",
"Haptophytes", "Pelagophytes", "Syn"
)
Min_max <- data.frame(
Class = c(
"Syn", "Chlorophytes", "Chlorophytes", "Prasinophytes", "Prasinophytes",
"Prasinophytes", "Cryptophytes", "Diatoms-2", "Diatoms-2", "Pelagophytes",
"Pelagophytes", "Pelagophytes", "Dinoflagellates-1", "Haptophytes",
"Haptophytes", "Haptophytes", "Haptophytes", "Diatoms-2", "Cryptophytes",
"Prasinophytes", "Chlorophytes", "Syn", "Dinoflagellates-1", "Pelagophytes"
),
Pig_Abbrev = c(
"Zea", "Zea", "Chl_b", "Pra", "Zea", "Chl_b", "Allo", "Chl_c3",
"Fuco", "Chl_c3", "X19but", "Fuco", "Per", "X19but", "X19hex",
"Fuco", "Tchla", "Tchla", "Tchla", "Tchla", "Tchla", "Tchla", "Tchla",
"Tchla"
),
min = as.numeric(c(
0.0800, 0.0063, 0.1666, 0.0642, 0.0151, 0.4993, 0.2118, 0.0189,
0.3315, 0.1471, 0.2457, 0.3092, 0.3421, 0.0819, 0.2107, 0.0090,
1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000
)),
max = as.numeric(c(
1.2123, 0.0722, 0.9254, 0.4369, 0.1396, 0.9072, 0.5479, 0.1840,
0.9332, 0.2967, 1.0339, 1.2366, 0.8650, 0.2872, 1.3766, 0.4689,
1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000
))
)
set.seed("7683")
Results <- simulated_annealing(
S = Sm,
F = Fu,
user_defined_min_max = Min_max,
do_matrix_checks = TRUE,
niter = 1,
step = 0.01,
weight.upper.bound = 30
)
set.seed("7683")
Results <- simulated_annealing(
S = Sm,
F = Fu,
user_defined_min_max = Min_max,
do_matrix_checks = TRUE,
niter = 1,
step = 0.01,
weight.upper.bound = 30
)
Results$`condition number`
#> [1] 1246.385
Results$RMSE
#> [1] 0.02172976
Results$MAE
#> Per X19but Fuco Pra X19hex Allo
#> 5.760788e-05 2.969032e-05 3.341336e-03 2.608691e-03 1.264442e-04 1.467274e-04
#> Zea Chl_b Tchla
#> 1.377911e-03 4.665555e-03 4.794013e-02
Results$Error
#> Per X19but Fuco Pra X19hex
#> [1,] -1.540353e-04 -4.443987e-05 -5.001243e-03 2.387518e-03 -1.892591e-04
#> [2,] -4.407218e-04 -1.271502e-04 -1.430942e-02 2.905187e-03 -5.415031e-04
#> [3,] -2.789685e-04 -8.048366e-05 -9.057595e-03 2.016773e-03 -3.427612e-04
#> [4,] -2.566387e-04 -7.404139e-05 -8.332585e-03 1.971743e-03 -3.153251e-04
#> [5,] -6.912664e-05 -1.994334e-05 -2.244415e-03 4.556748e-04 -8.493406e-05
#> [6,] -3.495682e-05 -1.008520e-05 -1.134983e-03 6.271547e-04 -4.295051e-05
#> [7,] 0.000000e+00 3.199972e-05 3.601235e-03 4.125569e-04 1.362794e-04
#> [8,] 0.000000e+00 7.954242e-06 8.951668e-04 2.019758e-03 3.387527e-05
#> [9,] 0.000000e+00 1.592528e-06 1.792223e-04 1.446277e-03 6.782205e-06
#> [10,] 0.000000e+00 2.576880e-05 2.900009e-03 -4.195474e-04 1.097433e-04
#> [11,] 0.000000e+00 2.422799e-05 2.726607e-03 -5.535725e-04 1.031814e-04
#> [12,] 0.000000e+00 3.898821e-05 4.387716e-03 -8.908209e-04 1.660417e-04
#> [13,] 0.000000e+00 3.437586e-05 3.868644e-03 -7.854359e-04 1.463988e-04
#> [14,] 0.000000e+00 2.975730e-05 3.348873e-03 6.603140e-03 1.267294e-04
#> [15,] -1.863894e-04 -5.377415e-05 -6.051719e-03 1.505595e-03 -2.290116e-04
#> [16,] -2.192229e-06 -6.324676e-07 -7.117763e-05 1.112948e-02 -2.693533e-06
#> [17,] -1.352676e-04 -3.902530e-05 -4.391890e-03 1.844555e-03 -1.661997e-04
#> [18,] 0.000000e+00 1.660517e-05 1.868739e-03 6.898358e-03 7.071758e-05
#> [19,] -3.749984e-05 -1.081887e-05 -1.217551e-03 3.350522e-03 -4.607506e-05
#> [20,] 0.000000e+00 1.415749e-05 1.593278e-03 -8.877482e-05 6.029347e-05
#> [21,] -9.153627e-06 -2.640862e-06 -2.972014e-04 5.411871e-03 -1.124682e-05
#> [22,] -2.136934e-05 -6.165148e-06 -6.938230e-04 1.408642e-04 -2.625593e-05
#> [23,] -4.430863e-05 -1.278323e-05 -1.438619e-03 2.920773e-04 -5.444082e-05
#> [24,] 0.000000e+00 2.419299e-05 2.722668e-03 -5.527728e-04 1.030323e-04
#> [25,] 0.000000e+00 2.553948e-05 2.874202e-03 -5.835380e-04 1.087667e-04
#> [26,] 0.000000e+00 8.869575e-06 9.981780e-04 8.070696e-03 3.777346e-05
#> [27,] 0.000000e+00 2.948775e-05 3.318538e-03 5.160518e-03 1.255815e-04
#> [28,] 0.000000e+00 2.182335e-05 2.455990e-03 6.128883e-03 9.294058e-05
#> [29,] 0.000000e+00 4.369520e-05 4.917439e-03 -9.983685e-04 1.860877e-04
#> Allo Zea Chl_b Tchla
#> [1,] -2.400743e-04 -6.818570e-03 2.371929e-05 0.071755809
#> [2,] -6.868943e-04 -4.513605e-04 -7.234509e-03 0.205305833
#> [3,] -4.347911e-04 -1.149024e-03 -4.248508e-03 0.129954705
#> [4,] -3.999885e-04 -1.622101e-03 -3.691928e-03 0.119552557
#> [5,] -1.077385e-04 -7.079531e-05 -1.134723e-03 0.032201955
#> [6,] -5.448253e-05 -1.961634e-03 1.641019e-04 0.016284284
#> [7,] 1.728698e-04 -4.950825e-03 3.566148e-03 -0.051669059
#> [8,] 4.297063e-05 2.823614e-05 -3.824066e-03 -0.012843491
#> [9,] 8.603197e-06 5.653189e-06 -2.789617e-03 -0.002571410
#> [10,] 1.392089e-04 -7.300278e-04 1.780952e-03 -0.041608161
#> [11,] 1.308851e-04 8.600506e-05 1.378509e-03 -0.039120259
#> [12,] 0.000000e+00 1.384012e-04 2.218326e-03 -0.062953169
#> [13,] 0.000000e+00 1.220282e-04 1.955896e-03 -0.055505746
#> [14,] 1.607557e-04 1.056331e-04 -1.245496e-02 -0.048048281
#> [15,] -2.905002e-04 -1.908887e-04 -3.597588e-03 0.086827618
#> [16,] -3.416735e-06 -7.188582e-03 -1.599866e-02 0.001021228
#> [17,] -2.108236e-04 -4.764176e-03 -4.480252e-04 0.063013059
#> [18,] 8.970494e-05 5.894541e-05 -1.319301e-02 -0.026811910
#> [19,] -5.844601e-05 -3.814780e-03 -3.685879e-03 0.017468928
#> [20,] 7.648201e-05 -1.089069e-03 1.242080e-03 -0.022859707
#> [21,] -1.426654e-05 -4.000149e-03 -7.419989e-03 0.004264126
#> [22,] -3.330554e-05 -2.188518e-05 -3.507807e-04 0.009954692
#> [23,] -6.905795e-05 -4.537820e-05 -7.273323e-04 0.020640730
#> [24,] 1.306961e-04 8.588080e-05 1.376517e-03 -0.039063743
#> [25,] 1.379701e-04 9.066061e-05 1.453129e-03 -0.041237884
#> [26,] 4.791547e-05 3.148541e-05 -1.556718e-02 -0.014321454
#> [27,] 1.592996e-04 1.046763e-04 -9.655891e-03 -0.047613044
#> [28,] 1.178947e-04 7.746901e-05 -1.163294e-02 -0.035237552
#> [29,] 2.360515e-04 1.551102e-04 2.486142e-03 -0.070553421
Results$`F matrix`
#> Per X19but Fuco Pra X19hex Allo Zea Chl_b Tchla
#> Prasinophytes 0.0000 0.0000 0.0000 0.2279 0.000 0.0000 0.0919 0.8640 1
#> Chlorophytes 0.0000 0.0000 0.0000 0.0000 0.000 0.0000 0.0118 0.2268 1
#> Cryptophytes 0.0000 0.0000 0.0000 0.0000 0.000 0.3321 0.0000 0.0000 1
#> Diatoms-2 0.0000 0.0000 0.8419 0.0000 0.000 0.0000 0.0000 0.0000 1
#> Dinoflagellates-1 0.5176 0.0000 0.0000 0.0000 0.000 0.0000 0.0000 0.0000 1
#> Haptophytes 0.0000 0.1310 0.1053 0.0000 0.894 0.0000 0.0000 0.0000 1
#> Pelagophytes 0.0000 0.6849 0.5205 0.0000 0.000 0.0000 0.0000 0.0000 1
#> Syn 0.0000 0.0000 0.0000 0.0000 0.000 0.0000 0.5054 0.0000 1
Results$`Class abundances`
#> Prasinophytes Chlorophytes Cryptophytes Diatoms-2 Dinoflagellates-1
#> 1 0.06105833 0.1990376885 0.0070029011 0.05694234 2.482309e-04
#> 2 0.01828905 0.0350763698 0.0069735610 0.04052284 2.746169e-04
#> 3 0.04003214 0.1832299487 0.0233391511 0.31687606 6.315298e-04
#> 4 0.05174144 0.1584216877 0.0214629525 0.33514306 5.949039e-04
#> 5 0.05852206 0.0525924650 0.0048017729 0.35050118 1.348690e-04
#> 6 0.04628561 0.0289996797 0.0001257441 0.25875623 2.036438e-02
#> 7 0.03102206 0.0000000000 0.0019730073 0.13798702 0.000000e+00
#> 8 0.01627271 0.0000000000 0.0127580863 0.47748504 0.000000e+00
#> 9 0.02028450 0.0000000000 0.1788768956 0.68834971 0.000000e+00
#> 10 0.03304855 0.0009664837 0.0081552127 0.09931081 0.000000e+00
#> 11 0.14517490 0.0642408514 0.0832722198 0.53387535 0.000000e+00
#> 12 0.08117559 0.0451685318 0.0000000000 0.26589364 0.000000e+00
#> 13 0.07939196 0.0384453911 0.0000000000 0.15590970 0.000000e+00
#> 14 0.16292612 0.0000000000 0.0243302871 0.26087948 0.000000e+00
#> 15 0.23598080 0.0000000000 0.1144974301 0.83377275 1.014099e-03
#> 16 0.23590672 0.0000000000 0.0216475409 0.23819570 4.798564e-06
#> 17 0.53052134 0.0653254799 0.0567307653 0.83706307 9.394163e-04
#> 18 0.16187643 0.0000000000 0.0285198402 0.24256379 0.000000e+00
#> 19 0.32075065 0.0000000000 0.0603616163 0.49828819 1.553036e-04
#> 20 0.21661296 0.1104834137 0.0703885346 0.37615700 0.000000e+00
#> 21 0.29258255 0.0000000000 0.0508481651 0.43735952 3.321923e-05
#> 22 0.14814749 0.1387954008 0.0560138433 0.71755858 9.554773e-05
#> 23 0.11891731 0.1062375285 0.0445581140 0.49948575 1.474983e-04
#> 24 0.05295877 0.0077005651 0.0308795322 0.15235979 0.000000e+00
#> 25 0.07661799 0.0601020386 0.0381094140 0.19072492 0.000000e+00
#> 26 0.09081140 0.0000000000 0.0242271266 0.18713018 0.000000e+00
#> 27 0.04316930 0.0000000000 0.0159703163 0.11605087 0.000000e+00
#> 28 0.04569986 0.0000000000 0.0136732671 0.10235937 0.000000e+00
#> 29 0.07940435 0.0555712748 0.0240573205 0.06305842 0.000000e+00
#> Haptophytes Pelagophytes Syn
#> 1 0.10417599 0.03004452 0.000000000
#> 2 0.02454977 0.01838375 0.001640034
#> 3 0.02474948 0.02384169 0.000000000
#> 4 0.02644976 0.02688619 0.000000000
#> 5 0.02514533 0.03427030 0.004232025
#> 6 0.02194347 0.02922489 0.000000000
#> 7 0.02572588 0.01499203 0.000000000
#> 8 0.14368310 0.03689270 0.001408358
#> 9 0.14705969 0.04714670 0.003182514
#> 10 0.04321779 0.02130115 0.000000000
#> 11 0.12510901 0.04264135 0.045347038
#> 12 0.02981800 0.02091672 0.038231851
#> 13 0.02730204 0.02387892 0.015979875
#> 14 0.02195285 0.01488997 0.004311483
#> 15 0.13347029 0.01970454 0.042724278
#> 16 0.04501592 0.03034426 0.000000000
#> 17 0.17633325 0.07442616 0.000000000
#> 18 0.08546430 0.05722432 0.002022010
#> 19 0.12743977 0.06693543 0.000000000
#> 20 0.13765510 0.07960397 0.000000000
#> 21 0.10124657 0.06997838 0.000000000
#> 22 0.14614628 0.03732076 0.005390572
#> 23 0.10723226 0.03975156 0.002736478
#> 24 0.06098498 0.03021600 0.013556133
#> 25 0.05682837 0.02600165 0.007257886
#> 26 0.02379894 0.01928751 0.004518430
#> 27 0.02258920 0.02823695 0.010701883
#> 28 0.02304180 0.01696960 0.009963192
#> 29 0.04749637 0.03065805 0.001223182
Results$Figure
Helper functions
Matrix_checks
This function will perform checks on the F and S matrices. If the columns sum is 0 or very small it will be eliminated from both the S and F matrix. To use this function, naming for both pigments and phytoplankton classes must follow the same conventions as the default values. The output of this function will be new S and F matrices.
Arguments:
S = S matrix
F = F matrix
MC <- Matrix_checks(Sm, Fm)
Snew <- MC$Snew
Fnew <- MC$Fnew Steep_Desc
This algorithm will perform the steepest descent algorithm without being bound by any limits. The results may show unrealistic pig:Chl a ratios for certain groups, although the error can often be very low.
Arguments:
F = F matrix
S = S matrix
num.loops = the number of loops/iterations to perform
MC <- Matrix_checks(Sm, Fm)
Snew <- MC$Snew
Fnew <- MC$Fnew
SDRes <- Steepest_Desc(Fnew, Snew, num.loops = 10)Bounded_weights
This function determines the weights to use for the S matrix, for which the default upper bound is 30 in the simulated_annealing() function call.
Arguments:
S = sample matrix
weight.upper.bound = maximum weights upper bound
Bounded_weights(Sm, weight.upper.bound = 30)
#> Per X19but Fuco Neox Pra Viol X19hex
#> 30.000000 30.000000 3.405156 30.000000 30.000000 30.000000 15.742517
#> Allo Zea Lut ChlcMGDG18 ChlcMGDG14 Chl_b Tchla
#> 30.000000 30.000000 30.000000 30.000000 30.000000 9.185585 1.000000NNLS_MF
This performs non-negative least least squares matrix factorization between the S and F matrices. Ensure that Chl a is the final column and that the columns in S and F are in the same order. If the weights are left blank then no weights will be used.
Arguments:
Fn = F matrix
S = sample matrix
cm = weights for each column
MC <- Matrix_checks(Sm, Fm)
Snew <- MC$Snew
Fnew <- MC$Fnew
cm <- Bounded_weights(Snew, weight.upper.bound = 30)
Results <- NNLS_MF(Fnew, Snew, cm)
Results$`F matrix`
#> Per X19but Fuco Neox Pra Viol X19hex Allo Zea Chl_b Tchla
#> Prasinophytes 0 0 0 1 1 1 0 0 1 1 1
#> Chlorophytes 0 0 0 1 0 1 0 0 1 1 1
#> Cryptophytes 0 0 0 0 0 0 0 1 0 0 1
#> Diatoms-2 0 0 1 0 0 0 0 0 0 0 1
#> Dinoflagellates-1 1 0 0 0 0 0 0 0 0 0 1
#> Haptophytes 0 1 1 0 0 0 1 0 0 0 1
#> Pelagophytes 0 1 1 0 0 0 0 0 0 0 1
#> Syn 0 0 0 0 0 0 0 0 1 0 1
Results$RMSE
#> [1] 0.1127568
Results$`C matrix`
#> Prasinophytes Chlorophytes Cryptophytes Diatoms-2 Dinoflagellates-1
#> [1,] 0.088117948 0.00000000 0.0217204089 0.4901801 0.0037567947
#> [2,] 0.055584649 0.00000000 0.0373938768 0.6222451 0.0027804911
#> [3,] 0.023402989 0.00000000 0.0237891848 0.8898721 0.0015045577
#> [4,] 0.025080760 0.00000000 0.0208394859 0.8879702 0.0013748370
#> [5,] 0.027278768 0.00000000 0.0050338409 0.8891283 0.0007088272
#> [6,] 0.024427004 0.00000000 0.0006420380 0.8482800 0.0403115206
#> [7,] 0.029818901 0.00000000 0.0056863026 0.8501687 0.0004333909
#> [8,] 0.006998486 0.00000000 0.0097623255 0.8482667 0.0004993785
#> [9,] 0.007273723 0.00000000 0.0834778062 0.8126633 0.0005630781
#> [10,] 0.041423150 0.00000000 0.0248502005 0.7213906 0.0007292296
#> [11,] 0.033785036 0.00000000 0.0480895900 0.7854960 0.0007137484
#> [12,] 0.047211598 0.00000000 0.0005753971 0.8297532 0.0005753971
#> [13,] 0.065913285 0.00000000 0.0008208615 0.7888618 0.0008208615
#> [14,] 0.088836617 0.00000000 0.0299819886 0.8287089 0.0006540520
#> [15,] 0.058417376 0.00000000 0.0440282484 0.8389594 0.0009788646
#> [16,] 0.125844391 0.00000000 0.0258929304 0.7449531 0.0010843156
#> [17,] 0.085675844 0.00000000 0.0205483370 0.7943691 0.0012253888
#> [18,] 0.080155362 0.00000000 0.0327531006 0.6997688 0.0009135323
#> [19,] 0.077226096 0.00000000 0.0355593296 0.7586179 0.0010293554
#> [20,] 0.065007214 0.00000000 0.0509516342 0.7059805 0.0011235075
#> [21,] 0.078274256 0.00000000 0.0339483113 0.7547636 0.0009751164
#> [22,] 0.031530158 0.00000000 0.0263143309 0.8543388 0.0008336558
#> [23,] 0.037075351 0.00000000 0.0290232371 0.8314658 0.0009196788
#> [24,] 0.062767108 0.00151289 0.0562106906 0.6948740 0.0008153781
#> [25,] 0.058367609 0.00000000 0.0575224093 0.7466852 0.0009684633
#> [26,] 0.069420509 0.00000000 0.0401302883 0.8048198 0.0007330733
#> [27,] 0.060138593 0.00000000 0.0395615803 0.7292408 0.0006281012
#> [28,] 0.075116512 0.00000000 0.0391858968 0.7480381 0.0007317574
#> [29,] 0.109413008 0.00000000 0.0750124557 0.5374235 0.0015472772
#> Haptophytes Pelagophytes Syn
#> [1,] 0.39622477 0.00000000 0.0000000000
#> [2,] 0.28199589 0.00000000 0.0000000000
#> [3,] 0.06143118 0.00000000 0.0000000000
#> [4,] 0.06473471 0.00000000 0.0000000000
#> [5,] 0.06518414 0.01266611 0.0000000000
#> [6,] 0.07389414 0.01244533 0.0000000000
#> [7,] 0.11389271 0.00000000 0.0000000000
#> [8,] 0.13447311 0.00000000 0.0000000000
#> [9,] 0.09602212 0.00000000 0.0000000000
#> [10,] 0.21160678 0.00000000 0.0000000000
#> [11,] 0.10185148 0.00000000 0.0300641351
#> [12,] 0.07127730 0.00000000 0.0506071021
#> [13,] 0.11859426 0.00000000 0.0249889572
#> [14,] 0.05181846 0.00000000 0.0000000000
#> [15,] 0.05761614 0.00000000 0.0000000000
#> [16,] 0.10222524 0.00000000 0.0000000000
#> [17,] 0.09818131 0.00000000 0.0000000000
#> [18,] 0.18640925 0.00000000 0.0000000000
#> [19,] 0.12756732 0.00000000 0.0000000000
#> [20,] 0.17693716 0.00000000 0.0000000000
#> [21,] 0.13203875 0.00000000 0.0000000000
#> [22,] 0.08698308 0.00000000 0.0000000000
#> [23,] 0.10151595 0.00000000 0.0000000000
#> [24,] 0.18371728 0.00000000 0.0001026917
#> [25,] 0.13645632 0.00000000 0.0000000000
#> [26,] 0.08489631 0.00000000 0.0000000000
#> [27,] 0.14672943 0.01494256 0.0087589477
#> [28,] 0.13363978 0.00000000 0.0032879586
#> [29,] 0.27660378 0.00000000 0.0000000000References
samples <- phytoclass::Sm
# samples <- read.csv()
F_matrix <- phytoclass::Fm
# F_matrix <- read.csv("custom_F_matrix.csv")
min_max_matrix <- phytoclass::min_max
write.csv(min_max_matrix, file="eg_minMax.csv")
phytoclass::simulated_annealing(
samples,
Fmat = F_matrix,
user_defined_min_max = min_max_matrix
)
#>
#> Condition number = 393
#> Iterations: 001 of 500
#> Current error: 0.0285
#> Neighbour's error: 0.0285
#> Temperature (%): 99.1
#> Iterations: 002 of 500
#> Current error: 0.0263
#> Neighbour's error: 0.0263
#> Temperature (%): 98.21
#> Iterations: 003 of 500
#> Current error: 0.0257
#> Neighbour's error: 0.0257
#> Temperature (%): 97.32
#> Iterations: 004 of 500
#> Current error: 0.0257
#> Neighbour's error: 0.0275
#> Temperature (%): 96.45
#> Iterations: 005 of 500
#> Current error: 0.0257
#> Neighbour's error: 0.026
#> Temperature (%): 95.58
#> Iterations: 006 of 500
#> Current error: 0.0255
#> Neighbour's error: 0.0255
#> Temperature (%): 94.72
#> Iterations: 007 of 500
#> Current error: 0.0255
#> Neighbour's error: 0.0268
#> Temperature (%): 93.87
#> Iterations: 008 of 500
#> Current error: 0.0255
#> Neighbour's error: 0.0261
#> Temperature (%): 93.02
#> Iterations: 009 of 500
#> Current error: 0.0255
#> Neighbour's error: 0.0255
#> Temperature (%): 92.19
#> Iterations: 010 of 500
#> Current error: 0.0255
#> Neighbour's error: 0.0264
#> Temperature (%): 91.36
#> Iterations: 011 of 500
#> Current error: 0.0255
#> Neighbour's error: 0.0284
#> Temperature (%): 90.53
#> Iterations: 012 of 500
#> Current error: 0.0255
#> Neighbour's error: 0.0255
#> Temperature (%): 89.72
#> Iterations: 013 of 500
#> Current error: 0.0245
#> Neighbour's error: 0.0245
#> Temperature (%): 88.91
#> Iterations: 014 of 500
#> Current error: 0.0245
#> Neighbour's error: 0.0256
#> Temperature (%): 88.11
#> Iterations: 015 of 500
#> Current error: 0.0237
#> Neighbour's error: 0.0237
#> Temperature (%): 87.32
#> Iterations: 016 of 500
#> Current error: 0.0237
#> Neighbour's error: 0.0269
#> Temperature (%): 86.53
#> Iterations: 017 of 500
#> Current error: 0.0237
#> Neighbour's error: 0.0257
#> Temperature (%): 85.75
#> Iterations: 018 of 500
#> Current error: 0.0237
#> Neighbour's error: 0.0272
#> Temperature (%): 84.98
#> Iterations: 019 of 500
#> Current error: 0.0237
#> Neighbour's error: 0.0263
#> Temperature (%): 84.22
#> Iterations: 020 of 500
#> Current error: 0.0237
#> Neighbour's error: 0.028
#> Temperature (%): 83.46
#> Iterations: 021 of 500
#> Current error: 0.0237
#> Neighbour's error: 0.0274
#> Temperature (%): 82.71
#> Iterations: 022 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0198
#> Temperature (%): 81.96
#> Iterations: 023 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0234
#> Temperature (%): 81.23
#> Iterations: 024 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0269
#> Temperature (%): 80.49
#> Iterations: 025 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0247
#> Temperature (%): 79.77
#> Iterations: 026 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0265
#> Temperature (%): 79.05
#> Iterations: 027 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0242
#> Temperature (%): 78.34
#> Iterations: 028 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0238
#> Temperature (%): 77.64
#> Iterations: 029 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0237
#> Temperature (%): 76.94
#> Iterations: 030 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0262
#> Temperature (%): 76.24
#> Iterations: 031 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0249
#> Temperature (%): 75.56
#> Iterations: 032 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0254
#> Temperature (%): 74.88
#> Iterations: 033 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0276
#> Temperature (%): 74.2
#> Iterations: 034 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0252
#> Temperature (%): 73.54
#> Iterations: 035 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0257
#> Temperature (%): 72.87
#> Iterations: 036 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0231
#> Temperature (%): 72.22
#> Iterations: 037 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0238
#> Temperature (%): 71.57
#> Iterations: 038 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0242
#> Temperature (%): 70.92
#> Iterations: 039 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0249
#> Temperature (%): 70.29
#> Iterations: 040 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0242
#> Temperature (%): 69.65
#> Iterations: 041 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0281
#> Temperature (%): 69.03
#> Iterations: 042 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0263
#> Temperature (%): 68.41
#> Iterations: 043 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0224
#> Temperature (%): 67.79
#> Iterations: 044 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0259
#> Temperature (%): 67.18
#> Iterations: 045 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0231
#> Temperature (%): 66.58
#> Iterations: 046 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0251
#> Temperature (%): 65.98
#> Iterations: 047 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0229
#> Temperature (%): 65.38
#> Iterations: 048 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0267
#> Temperature (%): 64.79
#> Iterations: 049 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0251
#> Temperature (%): 64.21
#> Iterations: 050 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0252
#> Temperature (%): 63.63
#> Iterations: 051 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0266
#> Temperature (%): 63.06
#> Iterations: 052 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0254
#> Temperature (%): 62.49
#> Iterations: 053 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0242
#> Temperature (%): 61.93
#> Iterations: 054 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.0263
#> Temperature (%): 61.37
#> Iterations: 055 of 500
#> Current error: 0.0198
#> Neighbour's error: 0.023
#> Temperature (%): 60.82
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#> Neighbour's error: 0.0208
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#> Neighbour's error: 0.0223
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#> Neighbour's error: 0.0193
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#> Neighbour's error: 0.017
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#> Neighbour's error: 0.0188
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#> Neighbour's error: 0.0185
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#> Neighbour's error: 0.0192
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#> Neighbour's error: 0.0184
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#> Neighbour's error: 0.0189
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#> Neighbour's error: 0.0231
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#> Neighbour's error: 0.0194
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#> Neighbour's error: 0.0219
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#> Neighbour's error: 0.0213
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#> Neighbour's error: 0.02
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#> Neighbour's error: 0.017
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#> Neighbour's error: 0.0175
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#> Neighbour's error: 0.0208
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#> Neighbour's error: 0.0186
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#> Neighbour's error: 0.0212
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#> Neighbour's error: 0.0237
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#> Neighbour's error: 0.0226
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#> Neighbour's error: 0.0195
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#> Neighbour's error: 0.0209
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#> Neighbour's error: 0.0208
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#> Neighbour's error: 0.0222
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#> Neighbour's error: 0.0186
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#> Current error: 0.0186
#> Neighbour's error: 0.0201
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#> Current error: 0.0186
#> Neighbour's error: 0.0193
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#> Neighbour's error: 0.0206
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#> Neighbour's error: 0.0181
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#> Neighbour's error: 0.0185
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#> Neighbour's error: 0.0186
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#> Current error: 0.0181
#> Neighbour's error: 0.0189
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#> Current error: 0.0181
#> Neighbour's error: 0.0183
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#> Current error: 0.0154
#> Neighbour's error: 0.0154
#> Temperature (%): 32.01
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#> Current error: 0.0154
#> Neighbour's error: 0.0188
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#> Current error: 0.0154
#> Neighbour's error: 0.02
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#> Neighbour's error: 0.0199
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#> Current error: 0.0154
#> Neighbour's error: 0.016
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#> Current error: 0.0154
#> Neighbour's error: 0.0188
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#> Current error: 0.0154
#> Neighbour's error: 0.0194
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#> Current error: 0.0154
#> Neighbour's error: 0.0162
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#> Current error: 0.0154
#> Neighbour's error: 0.0211
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#> Neighbour's error: 0.0188
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#> Neighbour's error: 0.0194
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#> Current error: 0.0154
#> Neighbour's error: 0.019
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#> Neighbour's error: 0.0211
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#> Neighbour's error: 0.0199
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#> Neighbour's error: 0.023
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#> Neighbour's error: 0.0188
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#> Current error: 0.0154
#> Neighbour's error: 0.0211
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#> Current error: 0.0154
#> Neighbour's error: 0.0191
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#> Current error: 0.0154
#> Neighbour's error: 0.0198
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#> Current error: 0.0154
#> Neighbour's error: 0.0169
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#> Current error: 0.0154
#> Neighbour's error: 0.0169
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#> Current error: 0.0154
#> Neighbour's error: 0.0168
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#> Current error: 0.0154
#> Neighbour's error: 0.0162
#> Temperature (%): 26.24
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#> Current error: 0.0154
#> Neighbour's error: 0.0187
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#> Neighbour's error: 0.0194
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#> Neighbour's error: 0.0186
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#> Neighbour's error: 0.0182
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#> Current error: 0.0154
#> Neighbour's error: 0.0204
#> Temperature (%): 25.08
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#> Current error: 0.0154
#> Neighbour's error: 0.0187
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#> Current error: 0.0154
#> Neighbour's error: 0.0192
#> Temperature (%): 24.63
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#> Current error: 0.0154
#> Neighbour's error: 0.0159
#> Temperature (%): 24.41
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#> Current error: 0.0154
#> Neighbour's error: 0.0178
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#> Current error: 0.0154
#> Neighbour's error: 0.0178
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#> Neighbour's error: 0.0184
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#> Neighbour's error: 0.0185
#> Temperature (%): 23.54
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#> Current error: 0.0154
#> Neighbour's error: 0.0178
#> Temperature (%): 23.33
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#> Current error: 0.0154
#> Neighbour's error: 0.0192
#> Temperature (%): 23.12
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#> Current error: 0.0154
#> Neighbour's error: 0.0185
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#> Neighbour's error: 0.0174
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#> Current error: 0.0154
#> Neighbour's error: 0.0204
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#> Current error: 0.0154
#> Neighbour's error: 0.0184
#> Temperature (%): 22.3
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#> Current error: 0.0154
#> Neighbour's error: 0.0182
#> Temperature (%): 22.1
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#> Neighbour's error: 0.0192
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#> Neighbour's error: 0.0177
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#> Neighbour's error: 0.0185
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#> Neighbour's error: 0.0168
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#> Neighbour's error: 0.0169
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#> Neighbour's error: 0.0166
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#> Neighbour's error: 0.0184
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#> Neighbour's error: 0.0182
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#> Neighbour's error: 0.0181
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#> Current error: 0.0154
#> Neighbour's error: 0.0194
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#> Neighbour's error: 0.0192
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#> Neighbour's error: 0.019
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#> Neighbour's error: 0.0181
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#> Neighbour's error: 0.0187
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#> Neighbour's error: 0.0184
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#> Neighbour's error: 0.0168
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#> Neighbour's error: 0.0202
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#> Neighbour's error: 0.0192
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#> Current error: 0.0154
#> Neighbour's error: 0.0178
#> Temperature (%): 18.44
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#> Current error: 0.0182
#> Neighbour's error: 0.0182
#> Temperature (%): 18.27
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#> Current error: 0.0178
#> Neighbour's error: 0.0178
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#> Neighbour's error: 0.0167
#> Temperature (%): 17.95
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#> Current error: 0.0167
#> Neighbour's error: 0.0173
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#> Neighbour's error: 0.0192
#> Temperature (%): 17.63
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#> Neighbour's error: 0.0172
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#> Neighbour's error: 0.0182
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#> Neighbour's error: 0.0173
#> Temperature (%): 17.15
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#> Neighbour's error: 0.0191
#> Temperature (%): 17
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#> Neighbour's error: 0.0183
#> Temperature (%): 16.85
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#> Current error: 0.0167
#> Neighbour's error: 0.0184
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#> Neighbour's error: 0.0173
#> Temperature (%): 16.54
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#> Neighbour's error: 0.0187
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#> Neighbour's error: 0.0166
#> Temperature (%): 16.25
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#> Neighbour's error: 0.0178
#> Temperature (%): 16.1
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#> Neighbour's error: 0.0166
#> Temperature (%): 15.96
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#> Neighbour's error: 0.0162
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#> Neighbour's error: 0.0166
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#> Current error: 0.0162
#> Neighbour's error: 0.0162
#> Temperature (%): 15.53
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#> Neighbour's error: 0.0176
#> Temperature (%): 15.39
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#> Current error: 0.0159
#> Neighbour's error: 0.0159
#> Temperature (%): 15.25
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#> Neighbour's error: 0.0178
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#> Neighbour's error: 0.0161
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#> Neighbour's error: 0.019
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#> Neighbour's error: 0.0169
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#> Temperature (%): 14.58
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#> Current error: 0.0159
#> Neighbour's error: 0.0172
#> Temperature (%): 14.45
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#> Neighbour's error: 0.0178
#> Temperature (%): 14.32
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#> Neighbour's error: 0.017
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#> Neighbour's error: 0.0179
#> Temperature (%): 14.06
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#> Neighbour's error: 0.0172
#> Temperature (%): 13.93
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#> Current error: 0.0159
#> Neighbour's error: 0.0166
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#> Current error: 0.0159
#> Neighbour's error: 0.0174
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#> Neighbour's error: 0.0172
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#> Neighbour's error: 0.0167
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#> Current error: 0.0159
#> Neighbour's error: 0.0163
#> Temperature (%): 13.32
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#> Current error: 0.0159
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#> Temperature (%): 13.2
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#> Neighbour's error: 0.0172
#> Temperature (%): 12.96
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#> Current error: 0.0159
#> Neighbour's error: 0.0161
#> Temperature (%): 12.84
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#> Neighbour's error: 0.017
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#> Neighbour's error: 0.0159
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#> Current error: 0.0159
#> Neighbour's error: 0.0161
#> Temperature (%): 12.39
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#> Neighbour's error: 0.0174
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#> Neighbour's error: 0.017
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#> Temperature (%): 12.06
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#> Neighbour's error: 0.0177
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#> Temperature (%): 2.13
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#> Temperature (%): 2.11
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#> Temperature (%): 2.09
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#> Temperature (%): 2.07
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#> Temperature (%): 2.05
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#> Temperature (%): 2.03
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#> Current error: 0.0149
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#> Temperature (%): 2.01
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#> Current error: 0.0149
#> Neighbour's error: 0.015
#> Temperature (%): 1.99
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#> Temperature (%): 1.98
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#> Temperature (%): 1.96
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#> Temperature (%): 1.94
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#> Temperature (%): 1.92
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#> Temperature (%): 1.91
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#> Temperature (%): 1.89
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#> Temperature (%): 1.87
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#> Temperature (%): 1.86
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#> Temperature (%): 1.84
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#> Temperature (%): 1.82
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#> Neighbour's error: 0.015
#> Temperature (%): 1.81
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#> Neighbour's error: 0.015
#> Temperature (%): 1.79
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#> Neighbour's error: 0.015
#> Temperature (%): 1.77
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#> Temperature (%): 1.76
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#> Temperature (%): 1.74
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#> Temperature (%): 1.73
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#> Neighbour's error: 0.0152
#> Temperature (%): 1.71
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#> Neighbour's error: 0.015
#> Temperature (%): 1.7
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#> Neighbour's error: 0.0151
#> Temperature (%): 1.68
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#> Current error: 0.0149
#> Neighbour's error: 0.015
#> Temperature (%): 1.66
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#> Current error: 0.0149
#> Neighbour's error: 0.015
#> Temperature (%): 1.65
#> Iterations: 455 of 500
#> Current error: 0.0149
#> Neighbour's error: 0.0149
#> Temperature (%): 1.63
#> Iterations: 456 of 500
#> Current error: 0.0149
#> Neighbour's error: 0.0151
#> Temperature (%): 1.62
#> Iterations: 457 of 500
#> Current error: 0.0149
#> Neighbour's error: 0.015
#> Temperature (%): 1.61
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#> Current error: 0.0149
#> Neighbour's error: 0.0151
#> Temperature (%): 1.59
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#> Temperature (%): 1.58
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#> Neighbour's error: 0.015
#> Temperature (%): 1.56
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#> Temperature (%): 1.55
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#> Neighbour's error: 0.015
#> Temperature (%): 1.53
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#> Temperature (%): 1.52
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#> Neighbour's error: 0.015
#> Temperature (%): 1.51
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#> Neighbour's error: 0.015
#> Temperature (%): 1.49
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#> Neighbour's error: 0.0151
#> Temperature (%): 1.48
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#> Neighbour's error: 0.0151
#> Temperature (%): 1.47
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#> Neighbour's error: 0.015
#> Temperature (%): 1.45
#> Iterations: 469 of 500
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#> Neighbour's error: 0.015
#> Temperature (%): 1.44
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#> Neighbour's error: 0.015
#> Temperature (%): 1.43
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#> Neighbour's error: 0.015
#> Temperature (%): 1.41
#> Iterations: 472 of 500
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#> Neighbour's error: 0.0151
#> Temperature (%): 1.4
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#> Neighbour's error: 0.015
#> Temperature (%): 1.39
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#> Neighbour's error: 0.015
#> Temperature (%): 1.38
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#> Temperature (%): 1.36
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#> Temperature (%): 1.35
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#> Temperature (%): 1.34
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#> Neighbour's error: 0.015
#> Temperature (%): 1.33
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#> Neighbour's error: 0.015
#> Temperature (%): 1.32
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#> Neighbour's error: 0.015
#> Temperature (%): 1.3
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#> Neighbour's error: 0.0149
#> Temperature (%): 1.29
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#> Neighbour's error: 0.015
#> Temperature (%): 1.28
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#> Temperature (%): 1.27
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#> Temperature (%): 1.26
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#> Neighbour's error: 0.015
#> Temperature (%): 1.25
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#> Neighbour's error: 0.015
#> Temperature (%): 1.24
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#> Temperature (%): 1.22
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#> Temperature (%): 1.21
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#> Neighbour's error: 0.015
#> Temperature (%): 1.2
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#> Neighbour's error: 0.0149
#> Temperature (%): 1.19
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#> Neighbour's error: 0.0149
#> Temperature (%): 1.18
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#> Neighbour's error: 0.015
#> Temperature (%): 1.17
#> Iterations: 493 of 500
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#> Neighbour's error: 0.015
#> Temperature (%): 1.16
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#> Neighbour's error: 0.015
#> Temperature (%): 1.15
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#> Neighbour's error: 0.015
#> Temperature (%): 1.14
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#> Current error: 0.0149
#> Neighbour's error: 0.015
#> Temperature (%): 1.13
#> Iterations: 497 of 500
#> Current error: 0.0149
#> Neighbour's error: 0.0149
#> Temperature (%): 1.12
#> Iterations: 498 of 500
#> Current error: 0.0149
#> Neighbour's error: 0.0149
#> Temperature (%): 1.11
#> Iterations: 499 of 500
#> Current error: 0.0149
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#> Temperature (%): 1.1
#> Iterations: 500 of 500
#> Current error: 0.0149
#> Neighbour's error: 0.015
#> Temperature (%): 1.09
#> $`F matrix`
#> Per X19but Fuco Neox Pra Viol
#> Prasinophytes 0.0000000 0.0000000 0.0000000 0.10182858 0.2218301 0.04514280
#> Chlorophytes 0.0000000 0.0000000 0.0000000 0.01059029 0.0000000 0.01506751
#> Cryptophytes 0.0000000 0.0000000 0.0000000 0.00000000 0.0000000 0.00000000
#> Diatoms-2 0.0000000 0.0000000 0.7272645 0.00000000 0.0000000 0.00000000
#> Dinoflagellates-1 0.3437968 0.0000000 0.0000000 0.00000000 0.0000000 0.00000000
#> Haptophytes 0.0000000 0.1290161 0.3125049 0.00000000 0.0000000 0.00000000
#> Pelagophytes 0.0000000 1.0920202 0.7309849 0.00000000 0.0000000 0.00000000
#> Syn 0.0000000 0.0000000 0.0000000 0.00000000 0.0000000 0.00000000
#> X19hex Allo Zea Chl_b Tchla
#> Prasinophytes 0.000000 0.0000000 0.132330631 0.9051608 1
#> Chlorophytes 0.000000 0.0000000 0.008485678 0.1666440 1
#> Cryptophytes 0.000000 0.4646961 0.000000000 0.0000000 1
#> Diatoms-2 0.000000 0.0000000 0.000000000 0.0000000 1
#> Dinoflagellates-1 0.000000 0.0000000 0.000000000 0.0000000 1
#> Haptophytes 1.077477 0.0000000 0.000000000 0.0000000 1
#> Pelagophytes 0.000000 0.0000000 0.000000000 0.0000000 1
#> Syn 0.000000 0.0000000 1.204566937 0.0000000 1
#>
#> $RMSE
#> [1] 0.01491279
#>
#> $`condition number`
#> [1] 1667.503
#>
#> $`Class abundances`
#> Prasinophytes Chlorophytes Cryptophytes Diatoms-2 Dinoflagellates-1
#> 1 0.04939037 0.2674506283 0.004336731 0.03779280 1.958845e-04
#> 2 0.01530428 0.0541707929 0.004550274 0.04039466 5.166035e-04
#> 3 0.03861085 0.1925752392 0.014903701 0.33265519 8.924546e-04
#> 4 0.04886145 0.1652013847 0.013819600 0.35545934 8.661670e-04
#> 5 0.06349946 0.0227570950 0.003296498 0.39802849 2.463754e-04
#> 6 0.04661294 0.0085694556 0.000000000 0.28530634 2.950709e-02
#> 7 0.02894467 0.0001189124 0.001392688 0.15063574 0.000000e+00
#> 8 0.01630621 0.0000000000 0.009074061 0.51844382 0.000000e+00
#> 9 0.02208744 0.0000000000 0.129343573 0.77632649 6.638112e-05
#> 10 0.03206256 0.0067671950 0.006034346 0.10968673 0.000000e+00
#> 11 0.16146478 0.0130018934 0.064368301 0.64240339 0.000000e+00
#> 12 0.09724987 0.0000000000 0.000000000 0.32861638 0.000000e+00
#> 13 0.09342096 0.0030390955 0.000000000 0.19784230 0.000000e+00
#> 14 0.15438398 0.0000000000 0.017061724 0.29034083 0.000000e+00
#> 15 0.22868486 0.0640121013 0.076782468 0.88284069 1.513680e-03
#> 16 0.22734357 0.0000000000 0.015607501 0.27030547 5.156988e-05
#> 17 0.42512005 0.2952389173 0.034618265 0.81189544 6.975029e-05
#> 18 0.16142251 0.0000000000 0.021550951 0.28080052 0.000000e+00
#> 19 0.30983739 0.0000000000 0.044002929 0.56613534 5.545317e-04
#> 20 0.19769743 0.1764933517 0.049604298 0.40226666 0.000000e+00
#> 21 0.28016993 0.0000000000 0.037213977 0.50145175 2.704295e-04
#> 22 0.13939336 0.1599654858 0.038434719 0.76882138 0.000000e+00
#> 23 0.11341284 0.1264773637 0.030605891 0.53606462 4.790042e-05
#> 24 0.05793571 0.0200318027 0.022893016 0.17091093 0.000000e+00
#> 25 0.07776052 0.0740261180 0.027468711 0.21076162 0.000000e+00
#> 26 0.08747986 0.0000000000 0.017332751 0.21258301 0.000000e+00
#> 27 0.04541103 0.0000000000 0.011978105 0.13766871 0.000000e+00
#> 28 0.04769479 0.0000000000 0.010215066 0.11960531 0.000000e+00
#> 29 0.08270267 0.0681509675 0.018295620 0.06930753 0.000000e+00
#> Haptophytes Pelagophytes Syn
#> 1 0.07982418 0.019519402 0.000000e+00
#> 2 0.01921001 0.011400539 1.628353e-04
#> 3 0.01878320 0.014279361 0.000000e+00
#> 4 0.02027310 0.016218971 0.000000e+00
#> 5 0.02058727 0.021784807 0.000000e+00
#> 6 0.01754595 0.018158231 0.000000e+00
#> 7 0.02086295 0.009745035 0.000000e+00
#> 8 0.11849494 0.026136800 4.416415e-05
#> 9 0.12340901 0.033163648 5.034586e-04
#> 10 0.03677786 0.014671307 0.000000e+00
#> 11 0.11150925 0.031645856 1.526727e-02
#> 12 0.02671662 0.014830549 1.379091e-02
#> 13 0.02510424 0.017210330 4.290956e-03
#> 14 0.01786675 0.009636891 0.000000e+00
#> 15 0.10471079 0.014409234 8.210369e-03
#> 16 0.03763774 0.020169093 0.000000e+00
#> 17 0.12953501 0.044862054 0.000000e+00
#> 18 0.07433616 0.039560550 0.000000e+00
#> 19 0.10777707 0.045623692 0.000000e+00
#> 20 0.11262193 0.052217323 0.000000e+00
#> 21 0.08584220 0.047100119 0.000000e+00
#> 22 0.11713177 0.025721767 0.000000e+00
#> 23 0.08605538 0.026402504 0.000000e+00
#> 24 0.05227917 0.020954375 3.650770e-03
#> 25 0.04745310 0.017666775 5.054191e-04
#> 26 0.01975543 0.012622539 0.000000e+00
#> 27 0.01955124 0.018963443 3.145990e-03
#> 28 0.01987291 0.011578312 2.740704e-03
#> 29 0.04162188 0.021390295 0.000000e+00
#>
#> $Figure
#>
#> $MAE
#> Per X19but Fuco Neox Pra Viol
#> 5.734827e-05 1.618389e-07 2.417536e-03 2.571818e-03 3.669760e-03 4.677463e-03
#> X19hex Allo Zea Chl_b Tchla
#> 4.663162e-05 7.203730e-05 3.161054e-03 5.510024e-03 3.109256e-02
#>
#> $Error
#> Per X19but Fuco Neox Pra
#> [1,] -8.577831e-05 1.381492e-07 -2.063661e-03 -0.0025966114 0.0047804940
#> [2,] -5.749745e-04 9.260178e-07 -1.383278e-02 -0.0019536153 0.0046580476
#> [3,] -2.811511e-04 4.528043e-07 -6.763953e-03 0.0013016896 0.0018880688
#> [4,] -2.641549e-04 4.254312e-07 -6.355056e-03 0.0011656854 0.0021007296
#> [5,] -8.395392e-05 1.352109e-07 -2.019769e-03 0.0036497393 -0.0004395582
#> [6,] 8.465033e-06 -1.363325e-08 2.036524e-04 0.0036465100 0.0004122764
#> [7,] 0.000000e+00 -3.306304e-07 4.938929e-03 0.0014960184 0.0016495230
#> [8,] 0.000000e+00 -8.362179e-08 1.249135e-03 0.0014833165 0.0020669338
#> [9,] -1.143248e-05 1.841243e-08 -2.750433e-04 0.0013439278 0.0013270945
#> [10,] 0.000000e+00 -1.474631e-07 2.202792e-03 0.0010768938 0.0010763834
#> [11,] 0.000000e+00 -6.505119e-09 9.717292e-05 0.0003555022 -0.0007165001
#> [12,] 0.000000e+00 -1.169847e-07 1.747508e-03 0.0076158168 -0.0026932229
#> [13,] 0.000000e+00 -1.499343e-08 2.239706e-04 0.0038150560 -0.0018515353
#> [14,] 0.000000e+00 -2.999291e-07 4.480316e-03 0.0029530645 0.0086944960
#> [15,] -1.889647e-04 3.043346e-07 -4.546125e-03 0.0070897568 0.0015653142
#> [16,] -1.485495e-05 2.392444e-08 -3.573813e-04 0.0012815187 0.0137769592
#> [17,] -7.258521e-06 1.169012e-08 -1.746260e-04 -0.0006089940 0.0057610304
#> [18,] 0.000000e+00 -1.736456e-08 2.593903e-04 -0.0003554585 0.0091071800
#> [19,] -8.506424e-05 1.369991e-07 -2.046481e-03 -0.0009625304 0.0058478397
#> [20,] 0.000000e+00 -1.563948e-07 2.336212e-03 -0.0031145718 0.0025325210
#> [21,] -4.732099e-05 7.621222e-08 -1.138452e-03 -0.0031064384 0.0083251579
#> [22,] 0.000000e+00 -4.469120e-08 6.675934e-04 -0.0016384017 0.0008970395
#> [23,] -9.726323e-06 1.566461e-08 -2.339966e-04 0.0009417425 0.0009243646
#> [24,] 0.000000e+00 -1.111440e-07 1.660259e-03 0.0098215567 -0.0011097659
#> [25,] 0.000000e+00 -1.940096e-07 2.898100e-03 0.0006594636 -0.0001966912
#> [26,] 0.000000e+00 -7.301175e-08 1.090643e-03 -0.0004811976 0.0097967983
#> [27,] 0.000000e+00 -1.324818e-07 1.979001e-03 0.0044732965 0.0054059004
#> [28,] 0.000000e+00 -7.823179e-08 1.168620e-03 0.0026283454 0.0065041628
#> [29,] 0.000000e+00 -2.073877e-07 3.097941e-03 0.0029660154 0.0003174594
#> Viol X19hex Allo Zea Chl_b
#> [1,] 0.0021341282 -3.980574e-05 -6.346148e-05 -8.539068e-03 0.0016187021
#> [2,] 0.0015409736 -2.668191e-04 -4.253842e-04 -1.641041e-04 -0.0083997959
#> [3,] -0.0042560160 -1.304692e-04 -2.080044e-04 -2.369373e-03 -0.0011074103
#> [4,] -0.0041646937 -1.225820e-04 -1.954300e-04 -3.107937e-03 -0.0005930229
#> [5,] -0.0031810505 -3.895913e-05 -6.211174e-05 -6.148674e-04 -0.0006069997
#> [6,] -0.0029874735 3.928230e-06 0.000000e+00 -4.354772e-03 0.0019544752
#> [7,] 0.0025321416 9.526650e-05 1.518814e-04 -7.560982e-03 0.0034113781
#> [8,] -0.0005665106 2.409445e-05 3.841328e-05 1.481902e-05 -0.0044188375
#> [9,] 0.0015173494 -5.305283e-06 -8.458105e-06 -3.262956e-06 -0.0039143837
#> [10,] 0.0031448873 4.248943e-05 6.774003e-05 -3.601321e-03 0.0001372254
#> [11,] -0.0037313124 1.874359e-06 2.988252e-06 1.152804e-06 0.0022745599
#> [12,] -0.0047579814 3.370750e-05 0.000000e+00 2.073143e-05 0.0004585198
#> [13,] -0.0061931664 4.320146e-06 0.000000e+00 2.657058e-06 0.0023698565
#> [14,] 0.0035830416 8.642036e-05 1.377782e-04 -3.852492e-03 -0.0141359545
#> [15,] 0.0100488672 -8.768975e-05 -1.398020e-04 -5.393261e-05 -0.0122948654
#> [16,] 0.0142654434 -6.893492e-06 -1.099015e-05 -1.378015e-02 -0.0156516900
#> [17,] 0.0124696043 -3.368344e-06 -5.370081e-06 -9.071320e-03 -0.0045188899
#> [18,] 0.0033653770 5.003353e-06 7.976744e-06 -3.884239e-03 -0.0125875390
#> [19,] 0.0056481790 -3.947438e-05 -6.293319e-05 -8.606335e-03 -0.0026800671
#> [20,] 0.0044371616 4.506296e-05 7.184296e-05 -4.741583e-03 0.0016793558
#> [21,] 0.0054093625 -2.195948e-05 -3.500955e-05 -8.796012e-03 -0.0048765063
#> [22,] 0.0020253533 1.287714e-05 2.052976e-05 -1.094054e-03 0.0002390519
#> [23,] 0.0021224280 -4.513536e-06 -7.195838e-06 -1.786489e-03 -0.0012733070
#> [24,] 0.0080603358 3.202457e-05 5.105612e-05 1.969636e-05 -0.0085304680
#> [25,] 0.0046891211 5.590116e-05 8.912207e-05 3.438139e-05 -0.0015427371
#> [26,] 0.0037077934 2.103731e-05 3.353935e-05 -1.369673e-03 -0.0162940160
#> [27,] 0.0046924073 3.817276e-05 6.085805e-05 2.347773e-05 -0.0143228516
#> [28,] 0.0094095264 2.254139e-05 3.593728e-05 1.386383e-05 -0.0164931204
#> [29,] 0.0010047447 5.975587e-05 9.526755e-05 -4.187808e-03 0.0014051073
#> Tchla
#> [1,] 0.026541275
#> [2,] 0.177906940
#> [3,] 0.086992962
#> [4,] 0.081734024
#> [5,] 0.025976778
#> [6,] -0.002619226
#> [7,] -0.063520845
#> [8,] -0.016065453
#> [9,] 0.003537404
#> [10,] -0.028330677
#> [11,] -0.001249766
#> [12,] -0.022475152
#> [13,] -0.002880544
#> [14,] -0.057622505
#> [15,] 0.058468896
#> [16,] 0.004596374
#> [17,] 0.002245910
#> [18,] -0.003336086
#> [19,] 0.026320329
#> [20,] -0.030046631
#> [21,] 0.014641923
#> [22,] -0.008586092
#> [23,] 0.003009491
#> [24,] -0.021353023
#> [25,] -0.037273213
#> [26,] -0.014027048
#> [27,] -0.025452451
#> [28,] -0.015029924
#> [29,] -0.039843418
Hayward, A., M. H. Pinkerton, and A. Gutierrez‐Rodriguez. 2023.
“phytoclass: A pigment‐based
chemotaxonomic method to determine the biomass of phytoplankton
classes.” Limnology and Oceanography: Methods 21
(4): 220–41. https://doi.org/10.1002/lom3.10541.
Langfelder, p., B. Zhang, and S. Horvath. 2008. “Defining clusters from a hierarchical cluster tree: the
Dynamic Tree Cut package for R.” Bioinformatics
24 (5): 719–20.
Mackey, M. D., D. J. Mackey, H. W. Higgins, and S. W. Wright. 1996.
“CHEMTAX-a Program for Estimating Class Abundances from Chemical
Markers: Application to HPLC Measurements of Phytoplankton.”
Marine Ecology Progress Series 144: 265–83. https://doi.org/10.3354/meps144265.