Fitting data and finding area under curve

Question

If I have the following data:

data={{63.0359,-0.377468},{63.1371,-0.36889},{63.236,-0.340264},{63.3372,-0.311102},{63.4386,-0.317515},{63.5377,-0.302648},{63.6382,-0.285227},{63.7386,-0.309458},{63.8393,-0.271844},{63.9399,-0.274895},{64.0393,-0.286301},{64.1397,-0.245381},{64.2408,-0.284001},{64.3407,-0.237523},{64.4422,-0.197306},{64.54,-0.186697},{64.6404,-0.133621},{64.7412,-0.262931},{64.8425,-0.134602},{64.9444,-0.180465},{65.0425,-0.141258},{65.1429,-0.124103},{65.2432,-0.14909},{65.3423,-0.165316},{65.443,-0.119178},{65.5434,-0.154446},{65.6434,-0.142109},{65.7436,-0.184858},{65.844,-0.161701},{65.945,-0.109441},{66.0458,-0.198008},{66.1447,-0.0930774},{66.2465,-0.11443},{66.3454,-0.0846786},{66.4464,-0.0878157},{66.5471,-0.148279},{66.6477,-0.122703},{66.7478,-0.129607},{66.846,-0.130622},{66.9465,-0.136439},{67.048,-0.237768},{67.1481,-0.124143},{67.2486,-0.0698046},{67.3503,-0.0452734},{67.4509,-0.109995},{67.551,-0.144111},{67.6488,-0.0767324},{67.7514,-0.104839},{67.8498,-0.043911},{67.9504,-0.0875963},{68.0527,-0.106565},{68.1505,-0.0352017},{68.2529,-0.0995127},{68.3522,-0.105364},{68.4524,0.0275967},{68.5548,-0.0881422},{68.6534,-0.120237},{68.7538,-0.0829747},{68.8549,-0.135021},{68.9576,-0.186265},{69.0557,-0.0837615},{69.1571,-0.210476},{69.2541,-0.103774},{69.3557,-0.197768},{69.4549,-0.176159},{69.5554,-0.16489},{69.6563,-0.145311},{69.7568,-0.129697},{69.8581,-0.178585},{69.9582,-0.0514388},{70.0609,-0.119592},{70.159,-0.155045},{70.2588,-0.046378},{70.3599,-0.141985},{70.4599,-0.122845},{70.5603,-0.0442577},{70.661,-0.0841938},{70.7612,-0.0780952},{70.8623,-0.196571},{70.9596,-0.0420082},{71.0612,-0.089715},{71.1626,-0.0875202},{71.2624,-0.0848227},{71.3632,-0.0510948},{71.4646,-0.0747354},{71.5653,-0.0647276},{71.6637,-0.0593889},{71.7651,0.015856},{71.8655,-0.08077},{71.9656,0.0328062},{72.0663,-0.0861278},{72.1666,-0.0136021},{72.2666,-0.0388442},{72.367,0.00699998},{72.4675,-0.0403441},{72.5671,0.0209988},{72.6682,-0.053782},{72.7689,-0.0424938},{72.8687,0.0153571},{72.9697,-0.105135},{73.0685,-0.0408923},{73.1695,-0.0689926},{73.2691,-0.114818},{73.3694,-0.0556425},{73.47,-0.113179},{73.5686,-0.0279214},{73.6706,-0.0711992},{73.7712,-0.0754252},{73.8719,-0.0714618},{73.9714,-0.104854},{74.0701,-0.0184052},{74.1729,-0.0242854},{74.2733,-0.0403457},{74.3723,-0.025646},{74.4731,-0.0500356},{74.5728,-0.0199442},{74.6733,0.0168603},{74.7742,0.04879},{74.8737,0.023149},{74.9739,0.0393329},{75.0749,0.0430668},{75.1756,0.0853634},{75.2754,0.0430295},{75.3755,0.0833915},{75.4767,0.0775784},{75.576,0.0736538},{75.6765,0.0771978},{75.7763,0.0444032},{75.8773,0.0591804},{75.977,0.0673182},{76.0777,0.0711651},{76.1774,0.101622},{76.2783,0.102251},{76.3784,0.0688463},{76.4781,0.076515},{76.5777,0.0826821},{76.6782,0.0839145},{76.7795,0.0867357},{76.8803,0.0295746},{76.9804,0.103667},{77.0799,0.0527557},{77.1808,0.0660514},{77.2816,0.0295279},{77.3809,0.129854},{77.4815,0.0984308},{77.5829,-0.00994034},{77.6826,0.0925383},{77.7832,0.0285631},{77.8827,0.0342876},{77.9833,0.0514509},{78.0839,-0.0306254},{78.1825,0.0245728},{78.2832,-0.00395284},{78.384,0.0701531},{78.4835,0.0560318},{78.5839,0.0296539},{78.6838,0.0252336},{78.7839,0.0452132},{78.8845,0.0555628},{78.9845,0.115994},{79.0854,0.0294528},{79.1845,0.102611},{79.2849,0.149225},{79.3865,0.00471552},{79.4858,0.102026},{79.5866,0.0593717},{79.6856,0.111954},{79.7864,0.120631},{79.8866,0.0939448},{79.986,0.124297},{80.0874,0.0483595},{80.1867,0.104533},{80.2866,0.127849},{80.3893,0.01285},{80.4878,0.0938407},{80.5891,0.0480354},{80.6881,0.0484184},{80.7882,0.0100602},{80.8901,0.0102189},{80.9897,0.0459907},{81.0896,0.047609},{81.19,0.0192626},{81.2902,0.0437195},{81.3916,0.000117753},{81.4905,0.0674037},{81.5909,0.0625754},{81.6925,-0.0421826},{81.7916,0.0660971},{81.892,0.0609279},{81.9913,0.0946857},{82.092,0.102265},{82.1942,0.0184685},{82.2935,0.0348381},{82.3947,0.0431542},{82.4943,0.0370449},{82.594,0.0936491},{82.6955,-0.0225001},{82.7945,0.0538391},{82.8967,-0.00553105},{82.9972,0.0277808},{83.0962,0.0528907},{83.1965,0.0365817},{83.2962,-0.00254658},{83.3963,0.0806524},{83.4983,-0.0324423},{83.5967,0.0681519},{83.6992,0.00392629},{83.7983,0.0334884},{83.8987,0.101576},{83.9984,-0.0605161},{84.0987,0.0527921},{84.2,0.0372952},{84.2993,-0.00451583},{84.3992,0.0741938},{84.5002,-0.0306354},{84.5997,0.0126465},{84.7004,0.0374626},{84.8007,0.00627066},{84.9004,0.0819417},{85.0015,0.024233},{85.101,0.0305066},{85.2013,0.0903355},{85.3017,0.0302726},{85.401,0.0794114},{85.503,0.0828585},{85.6022,0.0936857},{85.703,0.156533},{85.8045,0.114789},{85.9029,0.161412},{86.0036,0.127524},{86.1033,0.184279},{86.2044,0.29168},{86.3062,0.157925},{86.4051,0.218502},{86.5055,0.186206},{86.6056,0.259661},{86.7062,0.314921},{86.8068,0.193787},{86.9069,0.255744},{87.0078,0.288432},{87.1083,0.18052},{87.2081,0.233166},{87.3089,0.16766},{87.4083,0.208161},{87.5093,0.154674},{87.6099,0.0820034},{87.7092,0.159547},{87.8108,0.118381},{87.9091,0.11305},{88.01,0.160598},{88.1106,0.0625675},{88.2107,0.114323},{88.3115,0.0607155},{88.4115,0.0626875},{88.512,0.0787813},{88.6134,0.00629379},{88.7134,0.0386982},{88.8132,0.0420906},{88.913,0.0361965},{89.0137,0.0962618},{89.115,0.0188612},{89.2137,0.04278},{89.3149,0.0147721},{89.4146,0.00564302},{89.5146,0.0647129},{89.6153,-0.00390606},{89.7144,0.0366069},{89.8154,0.0381451},{89.9166,-0.0410749},{90.0157,0.0214114},{90.1161,0.00122263},{90.2161,0.0382576},{90.3169,0.079943},{90.4183,-0.00338674},{90.5168,0.0335765},{90.6174,-0.0349709},{90.7172,-0.00686173},{90.818,-0.0033936},{90.9191,-0.117864},{91.0187,0.0194805},{91.1198,-0.0709443},{91.2194,-0.0316406},{91.319,-0.0548936},{91.4195,-0.108552},{91.5195,-0.0341622},{91.6199,-0.0563115},{91.7203,-0.0976142},{91.8211,-0.0160093},{91.9215,-0.106219},{92.0206,-0.0578895},{92.1208,-0.0591874},{92.2218,-0.0432252},{92.3214,-0.071423},{92.422,-0.0605619},{92.5222,0.0110079},{92.6239,0.0473027},{92.7232,0.00557733},{92.8237,0.0210314},{92.9248,-0.00555508},{93.0241,-0.0188044},{93.1242,0.0522193},{93.2252,0.0136839},{93.3255,-0.0484867},{93.4255,0.00890995},{93.5254,0.0000760234},{93.6256,0.0187633},{93.7273,-0.0508971},{93.8266,0.0108169},{93.9278,0.0161969},{94.0276,-0.0144502},{94.1282,0.00724041},{94.2294,-0.0130664},{94.3287,0.04053},{94.4281,0.0917202},{94.5294,-0.0108698},{94.6304,0.0854767},{94.7308,0.0508835},{94.8304,0.043496},{94.9306,0.096094},{95.0311,0.0425868},{95.1305,0.00981259},{95.2308,0.0876683},{95.3307,0.0227264},{95.4308,0.0550309},{95.5316,-0.00476025},{95.6326,0.0820943},{95.7321,0.0203032},{95.8324,0.0186398},{95.9331,0.0494208},{96.0331,0.0608241},{96.1334,0.0435086},{96.2344,0.032548},{96.3348,0.0403473},{96.4355,0.0635307},{96.5359,0.0483974},{96.6346,0.0725407},{96.735,0.117848},{96.8367,0.0483234},{96.9358,0.0593262},{97.0373,0.0992934},{97.1371,0.0700262},{97.2363,0.100931},{97.3363,0.0700837},{97.437,0.065287},{97.5371,0.0438809},{97.6378,0.0788256},{97.7386,0.0645919},{97.8383,0.0529107},{97.9391,0.0857188},{98.0398,0.00363135},{98.1403,0.0827982},{98.2402,-0.0380434},{98.3403,0.0504304},{98.4404,0.0870947},{98.5405,0.0286105},{98.6405,0.0907113},{98.7412,0.0976311},{98.842,0.0387416},{98.9416,0.0822455},{99.0424,0.0892565},{99.143,0.0105733},{99.2421,0.0435315},{99.3424,0.0243316},{99.4426,0.0955495},{99.5437,0.106561},{99.6442,0.0523241},{99.744,0.0289709},{99.8442,0.0680112},{99.9441,0.0466789},{100.044,0.139357}}

Which gives the following plot with ListPlot:

1) How can I best fit the peak that is around 85-90? 2) How can I find the area of that peak?

Edit: I tried @Antonov's quantile regression function as suggested by @JimB but I still do not know how to focus it only on the 85-90 region and how to obtain the area (I am very new in Mathematica). This is what I get using this method so far:

Is there an easier method perhaps using simply NonLinearFit?

Thank you !

Answer 1

To even attempt a fitting, one should consider a long list of caveats regarding the noisiness of your data, how you define your peak and what its functional shape should be, what you expect to be your baseline, etc. Nevertheless, you could try the following, with caution.

The peak is around 85ish, so select a region in the data that has a reasonably flat baseline and includes your "peak":

ListLinePlot[data[[120 ;;]]]

Fit a vaguely Gaussian peak shape to that region:

fit = NonlinearModelFit[
       data[[120 ;;]],
       height Exp[-(x - peakposition)^2/peakwidth^2] + baseline,
       {{height, 0.2}, {peakposition, 87}, {peakwidth, 1}, {baseline, 0.04}},
       x
     ];

fit["BestFitParameters"]

(* Out: 
  {height -> 0.209819, peakposition -> 86.7737, 
   peakwidth -> 1.06978, baseline -> 0.038016} 
*)

Plot the points, the fitted curve, and highlight the region of interest:

Show[
  Plot[
    fit[x], {x, data[[120, 1]], data[[-1, 1]]}, 
    PlotRange -> All, PlotStyle -> Black
  ],
  ListPlot[data[[120 ;;]]],
  Graphics[{
    Opacity[0.1],
    Rectangle[
      {peakposition - 3 peakwidth, baseline},
      {peakposition + 3 peakwidth, fit[peakposition]}
    ]
  }]
] /. fit["BestFitParameters"]

Now get the area of the peak after subtracting the baseline value:

NIntegrate[
 fit[x] - baseline /. fit["BestFitParameters"],
 {x, peakposition - 3 peakwidth, peakposition + 3 peakwidth} /. 
   fit["BestFitParameters"]
]

(* Out: 0.397838 *)