Hi Thanks, it work for my example, which is actually a subset of a bigger data (4000 rows) with the same characteristics. For the whole problem it does not give correct clustering. I tried to set G to 3 but it did not help either.
I attached the whole dataset (dput) that you can use, however after quick tour through Mclust it seems to me that it is designed for slightly different problem. Here is the result with whole data. fm <- Mclust(temp) g <- fm$classification plot(1/temp[,1], temp[,2], pch = g, col = g) I will go through the docs more thoroughly, to be 100% sure I did not miss anything. Cheers Petr > -----Original Message----- > From: Gabor Grothendieck [mailto:[email protected]] > Sent: Tuesday, February 02, 2016 1:20 PM > To: PIKAL Petr > Cc: R Help R > Subject: Re: [R] fancy linear model and grouping > > Try the mclust package: > > library(mclust) > temp.na <- na.omit(temp) > fm <- Mclust(temp.na) > g <- fm$classification > plot(temp.na, pch = g, col = g) > > > > On Tue, Feb 2, 2016 at 6:35 AM, PIKAL Petr <[email protected]> > wrote: > > Dear all > > > > I have data like this > > > >> dput(temp) > > > > temp <- structure(list(X1 = c(93, 82, NA, 93, 93, 79, 79, 93, 93, 85, > > 82, 93, 87, 93, 92, NA, 87, 93, 93, 93, 74, 77, 87, 93, 82, 87, 75, > > 82, 93, 92, 68, 93, 93, 73, NA, 85, 81, 79, 75, 87, 93, NA, 87, 87, > > 85, 92, 87, 92, 93, 87, 87, NA, 69, 87, 93, 87, 93, 87, 82, 79, 87, > > 93, 87, 80, 87, 87, 87, 92, 93, 69, 76, 87, 82, 93, 82, NA, 54, 87, > > 77, 73, 93, 82, 73, 93, 92, 82, 77, 93, 87, 75, 87, 87, 87, 60, 92, > > 87, 87, NA, 77, 78), X2 = c(224, 624, NA, 224, 224, 642, 642, 224, > > 224, 599, 622, 224, 239, 224, 225, NA, 239, 224, 224, 224, 688, 657, > > 239, 224, 624, 239, 672, 254, 224, 225, 499, 224, 224, 692, NA, 599, > > 627, 642, 677, 239, 224, NA, 239, 239, NA, 375, 239, 375, 224, 239, > > 239, NA, 299, 239, 224, 239, 224, 239, 621, 642, 239, 224, 239, 638, > > 239, 239, 239, 225, 224, 299, 672, 239, 618, 224, 620, NA, 626, 239, > > 657, 693, 224, 624, 693, 224, 225, 621, 657, 224, 239, 673, 239, 239, > > 239, 569, 224, 239, 239, NA, 657, 651)), .Names = c("X1", "X2"), > > row.names = c(NA, -100L), class = "data.frame") > >> > > > > You can see there are 3 distinct linear relationships of those 2 > variables. > > > > plot(1/temp[,1], temp[,2]) > > > > Is there any simple way how to evaluate such data without grouping > variable? I know that in case I have proper grouping variable I can > evaluate it with lme and get intercepts and/or slopes. > > > > My question is: > > > > Does anybody know about a way/package/function which can give me > appropriate grouping of such data or which can give me separate > slope/intercept for each set. > > > > I hope I expressed my problem clearly. > > > > Best regards > > Petr > > > > ________________________________ Tento e-mail a jakékoliv k němu připojené dokumenty jsou důvěrné a jsou určeny pouze jeho adresátům. Jestliže jste obdržel(a) tento e-mail omylem, informujte laskavě neprodleně jeho odesílatele. Obsah tohoto emailu i s přílohami a jeho kopie vymažte ze svého systému. Nejste-li zamýšleným adresátem tohoto emailu, nejste oprávněni tento email jakkoliv užívat, rozšiřovat, kopírovat či zveřejňovat. Odesílatel e-mailu neodpovídá za eventuální škodu způsobenou modifikacemi či zpožděním přenosu e-mailu. 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224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 225, 225, 225, 225, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 225, 225, 224, 224, 224, 224, 224, 224, 224, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 225, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 223, 223, 223, 223, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 223, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 223, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 223, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 223, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 223, 223, 224, 224, 223, 223, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 240, 240, 240, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 240, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 252, 269, 269, 269, 269, 269, 269, 269, 269, 269, 269, 269, 269, 269, 269, 269, 269, 268, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 247, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239, 239), .Dim = c(3793L, 2L), na.action = structure(c(27, 151, 528, 529, 530, 588, 589, 590, 591, 592, 593, 731, 732, 854, 1127, 1128, 1129, 1130, 1131, 1262, 1265, 1266, 1267, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1307, 1308, 1349, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1568, 1627, 1891, 1892, 1964, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2161, 2198, 2199, 2223, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2262, 2263, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2274, 2295, 2418, 2419, 2422, 2448, 2475, 2476, 2477, 2480, 2539, 2563, 2578, 2586, 2638, 2729, 2740, 2741, 2742, 2802, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2911, 2993, 3024, 3030, 3031, 3032, 3054, 3059, 3060, 3092, 3093, 3094, 3095, 3096, 3097, 3098, 3099, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3156, 3270, 3271, 3272, 3314, 3392, 3473, 3655, 3657, 3965, 3966, 3967, 3968, 3969, 3970, 3971, 3972, 73, 182, 197, 200, 208, 211, 212, 253, 254, 344, 362, 363, 422, 653, 654, 655, 656, 745, 1218, 1222, 1223, 1263, 1264, 1268, 1276, 1277, 2433, 2434, 4017), class = "omit"))
______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
