Fitting Bimodal Data to Histogram
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I am having trouble fitting the below bimodal data, which I plotted on a histogram. I am using the suggested MathWorks code to fit bivariate distributions (below), but for some reason, when I plot probability density on the yaxis, it appear to the 10^4 power, while the maximum value of the original histogram is 14, so I cannot overlay the two.
min_pts =
1.0e-04 *
0.0341 -0.1578 -0.1203
0.0198 -0.1459 -0.1284
0.0014 -0.1314 -0.1343
-0.0106 -0.1208 -0.1378
-0.0157 -0.1243 -0.1385
-0.0146 -0.1235 -0.1360
-0.0085 -0.1184 -0.1302
0.0015 -0.1092 -0.1216
0.0140 -0.0964 -0.1355
0.0278 -0.0807 -0.1465
0.0417 -0.0698 -0.1551
0.0554 -0.0655 -0.1618
0.0687 -0.0601 -0.1673
0.0816 -0.0550 -0.1721
0.0937 -0.0535 -0.1764
0.0860 -0.0726 -0.1803
0.0746 -0.1059 -0.1838
0.0443 -0.1343 -0.2418
0.0175 -0.1572 -0.2958
-0.0059 -0.1753 -0.3404
-0.0267 -0.1894 -0.3754
-0.0455 -0.2006 -0.4009
-0.0646 -0.2097 -0.4175
-0.0860 -0.2176 -0.4259
-0.1122 -0.2252 -0.4271
-0.1635 -0.2325 -0.4222
-0.2402 -0.2406 -0.4124
-0.3529 -0.2804 -0.3989
-0.4898 -0.3394 -0.3829
-0.6217 -0.4144 -0.4013
-0.7379 -0.4785 -0.4637
-0.8333 -0.5280 -0.5109
-0.9040 -0.5600 -0.5454
-0.9467 -0.5744 -0.5877
-0.9629 -0.5723 -0.6217
-0.9552 -0.5548 -0.6455
-0.9265 -0.5251 -0.6580
-0.9125 -0.4869 -0.6580
-0.8938 -0.4436 -0.6452
-0.8624 -0.3977 -0.6207
-0.8205 -0.3534 -0.5859
-0.7651 -0.3244 -0.5425
-0.7324 -0.2901 -0.4927
-0.7186 -0.2511 -0.4389
-0.6929 -0.2083 -0.3833
-0.6558 -0.1624 -0.3274
-0.6081 -0.1434 -0.3061
-0.5509 -0.1350 -0.3289
-0.4862 -0.1283 -0.3494
-0.4163 -0.1228 -0.3659
-0.4981 -0.1175 -0.3768
-0.6118 -0.1146 -0.3808
-0.7117 -0.1273 -0.3766
-0.7933 -0.1395 -0.3633
-0.8527 -0.1499 -0.3409
-0.8893 -0.1573 -0.3095
-0.9035 -0.1608 -0.2699
-0.8962 -0.1593 -0.2241
-0.8706 -0.1520 -0.1857
-0.8303 -0.1394 -0.2045
-0.7791 -0.1221 -0.2232
-0.7207 -0.1010 -0.2405
-0.6610 -0.0774 -0.2552
-0.6147 -0.0663 -0.2662
-0.5666 -0.0562 -0.2724
-0.5184 -0.0455 -0.2727
-0.4712 -0.0417 -0.2670
-0.4261 -0.0796 -0.2551
-0.3832 -0.1217 -0.2371
-0.3425 -0.1664 -0.2138
-0.3041 -0.2118 -0.1923
-0.2677 -0.2561 -0.2120
-0.2332 -0.2973 -0.2276
-0.2003 -0.3335 -0.2378
-0.1686 -0.3632 -0.2417
-0.1377 -0.3855 -0.2384
-0.1071 -0.3998 -0.2282
-0.0769 -0.4057 -0.2114
-0.0469 -0.4033 -0.1889
-0.0175 -0.3931 -0.1620
0.0106 -0.3755 -0.1446
0.0364 -0.3510 -0.1289
0.0252 -0.3204 -0.1119
-0.0195 -0.2842 -0.1305
-0.0644 -0.2435 -0.1471
-0.1089 -0.1992 -0.1612
-0.1521 -0.1523 -0.1726
-0.1932 -0.1620 -0.1811
-0.2311 -0.1738 -0.1865
-0.2646 -0.1840 -0.1892
-0.2927 -0.1924 -0.1897
-0.3141 -0.1986 -0.1885
-0.3280 -0.2023 -0.1864
-0.3335 -0.2031 -0.1900
-0.3300 -0.2010 -0.2451
-0.3174 -0.2063 -0.2989
-0.2960 -0.2274 -0.3493
x = min_pts(:);
figure;
hist(x,100);
pdf_normmixture = @(x,p,mu1,mu2,sigma1,sigma2) ...
p*normpdf(x,mu1,sigma1) + (1-p)*normpdf(x,mu2,sigma2);
pStart = .5;
muStart = quantile(x,[.25 .75]);
sigmaStart = sqrt(var(x) - .25*diff(muStart).^2);
start = [pStart muStart sigmaStart sigmaStart];
lb = [0 -Inf -Inf 0 0];
ub = [1 Inf Inf Inf Inf];
paramEsts = mle(x, 'pdf',pdf_normmixture, 'start',start, ...
'lower',lb, 'upper',ub);
bins = 100;
%h = bar(bins,histc(x,bins)/(length(x)*.5),'histc');
%h.FaceColor = [.9 .9 .9];
xgrid = linspace(1.1*min(x),1.1*max(x),300);
pdfgrid = pdf_normmixture(xgrid,paramEsts(1),paramEsts(2),paramEsts(3),paramEsts(4),paramEsts(5));
hold on
plot(xgrid,pdfgrid,'-')
hold off
xlabel('x')
ylabel('Probability Density')
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