decathlon12 <- structure(c(1011L, 994L, 801L, 850L, 980L, 940L, 961L, 910L, 841L, 847L, 894L, 838L, 821L, 906L, 791L, 858L, 919L, 881L, 929L, 791L, 865L, 863L, 885L, 827L, 872L, 780L, 1068L, 942L, 940L, 970L, 945L, 864L, 947L, 922L, 854L, 850L, 903L, 900L, 767L, 755L, 854L, 874L, 799L, 864L, 852L, 781L, 878L, 809L, 790L, 804L, 842L, 677L, 769L, 807L, 759L, 819L, 712L, 782L, 756L, 754L, 715L, 831L, 739L, 795L, 758L, 758L, 731L, 661L, 774L, 880L, 699L, 703L, 714L, 695L, 788L, 724L, 800L, 735L, 850L, 794L, 906L, 850L, 850L, 714L, 794L, 794L, 850L, 767L, 740L, 767L, 850L, 794L, 850L, 822L, 740L, 794L, 740L, 794L, 767L, 740L, 740L, 714L, 767L, 740L, 963L, 904L, 859L, 853L, 899L, 906L, 888L, 866L, 882L, 813L, 868L, 804L, 766L, 873L, 873L, 823L, 889L, 822L, 897L, 779L, 832L, 786L, 878L, 777L, 809L, 729L, 1032L, 1035L, 917L, 863L, 926L, 989L, 963L, 799L, 955L, 932L, 920L, 847L, 959L, 944L, 780L, 925L, 955L, 924L, 875L, 794L, 897L, 823L, 945L, 859L, 865L, 881L, 716L, 834L, 782L, 835L, 785L, 852L, 802L, 817L, 738L, 761L, 509L, 791L, 690L, 711L, 689L, 736L, 755L, 765L, 762L, 801L, 711L, 769L, 779L, 770L, 796L, 737L, 972L, 849L, 819L, 849L, 819L, 880L, 790L, 790L, 760L, 819L, 1004L, 819L, 1035L, 790L, 819L, 941L, 731L, 941L, 790L, 880L, 760L, 760L, 673L, 790L, 673L, 731L, 767L, 838L, 996L, 763L, 780L, 698L, 661L, 865L, 810L, 810L, 720L, 735L, 697L, 736L, 774L, 645L, 673L, 588L, 612L, 834L, 762L, 698L, 594L, 720L, 591L, 687L, 721L, 674L, 744L, 795L, 746L, 695L, 721L, 702L, 768L, 696L, 737L, 692L, 629L, 689L, 791L, 663L, 693L, 467L, 693L, 685L, 619L, 706L, 514L, 596L, 542L, 506L, 8869L, 8671L, 8523L, 8447L, 8442L, 8320L, 8283L, 8219L, 8173L, 8126L, 8034L, 7988L, 7972L, 7956L, 7952L, 7948L, 7928L, 7926L, 7849L, 7842L, 7805L, 7649L, 7586L, 7581L, 7557L, 7203L), .Dim = c(26L, 11L), .Dimnames = list(c("Eaton", "Hardee", "Suarez", "VanAlphen", "Warner", "Freimuth", "Kasyanov", "Sviridov", "Coertzen", "Behrenbruch", "Sintnicolaas", "Newdick", "Barroilhet", "Garcia", "Mayer", "Shkurenev", "Mikhan", "Karpov", "DeAraujo", "Ushiro", "Vos", "Erins", "Addy", "Szabo", "Draudvila", "Artikov" ), c("Meter100", "LongJump", "ShotPut", "HighJump", "Meter400", "Hurdles110", "Discus", "PoleVault", "Javelin", "Meter1500", "Total"))) `planets` <- structure(c(35960000, 67200000, 92900000, 141500000, 483300000, 886700000, 1.782e+09, 2.793e+09, 3.664e+09, 59, 243, 1, 1, 0.413, 0.444, 0.7, 0.669, 6.388, 88, 224.7, 365.26, 687, 4332.6, 10759.2, 30685.4, 60189, 90465, 3030, 7517, 7921, 4215, 88803, 74520, 31600, 30200, 1423, 332, 854, 59, -67, -162, -208, -344, -261, -355, 0, 0, 1, 2, 16, 18, 15, 8, 1), .Dim = c(9L, 6L), .Dimnames = list( c("Mercury", "Venus", "Earth", "Mars", "Jupiter", "Saturn", "Uranus", "Neptune", "Pluto"), c("Distance", "Day", "Year", "Diameter", "Temperature", "Moons"))) `Spam` <- structure(c(0, 0.07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.73, 0, 0, 0, 0, 0, 0, 0.8, 0, 0.18, 0, 0, 0, 0, 0, 0.74, 0, 0, 0.16, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0.18, 0.9, 0.51, 0.32, 0, 0.06, 0, 0, 0, 1.75, 0, 0.35, 0, 0, 0.08, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.41, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0.41, 0, 0.34, 0.47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.63, 0, 0.33, 0, 0.55, 0, 0.31, 0.3, 0.12, 0.07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.03, 0, 0, 0, 0, 0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0.08, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.11, 0, 0.25, 0, 0, 0, 0.76, 0.71, 0.07, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0.17, 0, 0, 1.03, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0.1, 0.59, 0, 0, 0.95, 0, 0, 0.45, 0, 0.42, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.76, 0, 0.46, 0, 0, 0, 0, 0, 0, 0, 0.14, 0, 0.08, 0, 0, 0, 0, 0, 0.19, 0.15, 0.67, 0, 0.96, 0, 0.34, 0, 0, 0.08, 0.32, 0, 0, 0, 1.17, 0.17, 0, 0, 0, 0.47, 0, 0, 1.18, 0.08, 0, 0, 0, 0.21, 0.05, 0, 0, 0.14, 0, 0, 0.58, 0.17, 0, 0, 0, 0, 0, 0, 0.64, 0.17, 0.17, 0.15, 0, 0.89, 1.42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.44, 0, 0, 0.32, 0, 0, 2.85, 0.1, 1.03, 0, 0, 0.49, 0, 0, 0.54, 0, 0, 0, 0.54, 0, 0, 0, 0.31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.9, 0.43, 0.46, 0, 0.02, 0.22, 0, 2.32, 0, 0, 0, 0.58, 0, 0, 1.26, 0, 0, 0, 0.33, 0, 0, 0, 0, 0, 0.17, 0.34, 0.1, 0.22, 0, 0, 0, 0.27, 0, 0, 0.1, 0, 0, 0.28, 0.58, 0, 0, 0, 0, 0, 0.08, 1.24, 0, 0.16, 0, 0, 0.19, 0, 0, 0.57, 0, 0.06, 0, 0, 0, 0, 0.5, 4, 0.23, 0.78, 0.07, 0, 0, 0, 0.06, 0, 0, 0, 0.44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.24, 0, 0.08, 0, 0, 0, 0.27, 0, 0, 0, 0, 0, 0.1, 0, 2, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0.42, 0, 0, 0.42, 0, 0.39, 0, 0.44, 0, 0, 0, 0.24, 0, 0, 0.1, 0, 0, 0.82, 0.9, 0, 0, 0.17, 0, 0.17, 0, 0, 0, 0, 0.82, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.43, 0, 0, 0, 0, 0, 0.47, 0, 0, 0, 0, 0.12, 0, 0, 1.04, 0.74, 0.16, 0.46, 0, 0, 0, 0, 0, 0, 1.17, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.34, 0, 0.17, 0, 0, 0.53, 0, 0, 0.26, 0, 0, 0, 0, 0, 0, 0.31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0.54, 1.63, 0, 0, 0, 0, 0, 0, 0.42, 0, 0, 0, 0, 0, 0, 0.89, 0.38, 0.6, 0, 0, 0, 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"1277", "1382", "3145", "3232", "1894", "4088", "2784", "2363", "1895", "1832", "2727", "13", "1467", "1135", "1062", "3935", "3677", "3701", "411", "669", "2894", "3608", "3771", "2881", "2964", "1713", "4257", "166", "1043", "1580", "1084", "3181", "2570", "3768", "2597", "632", "3752", "321", "3870", "62", "462", "2013", "1242", "3279", "2607", "3927", "737", "304", "4048", "317", "2158", "1667", "212", "3953", "1733", "1372", "1846", "1167", "996", "4102", "286", "1854", "125", "3000", "716", "3178", "1031", "4218", "384", "612", "1356", "3766", "1422", "2398", "1750", "3446", "2066", "3318", "1508", "3295", "1177", "3440", "1168", "3590", "941", "2695", "2591", "4213", "432", "829", "3291", "721", "2224", "517", "652", "2113", "577", "3147", "3700", "2906", "1650", "1349", "4423", "2274", "1767", "3693", "3269", "4564", "1099", "2472", "4000", "3396", "1794", "2305", "4151", "442", "3533", "606", "21", "4386", "2061", "690", "1534", "3564", "2547", "1459", "1417", "3462", "991", 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"209", "2706", "655", "514", "4595", "3991", "1149", "1027", "2593", "459", "227", "3333", "3971", "1045", "1402", "1054", "1170", "3395", "4594", "1960", "155", "2121", "938", "47", "3630", "683", "4470", "4106", "4585", "2024", "2511", "1211", "1943", "3484", "1510", "2335", "2362", "2388", "2413", "2815", "2878", "3528", "1681", "3823", "2985", "4403", "2261", "2329", "2067", "1357", "2071", "3436", "2460", "3151", "503", "88", "3812", "2790", "3724", "2007", "2908", "2618", "474", "2461", "4323", "3754", "3134", "1426", "1520", "3769", "2673", "1397", "562", "3872", "1982", "665", "2699", "3958", "1359", "15", "522", "3655", "3141", "3787", "2848", "3427", "228", "1869", "4376", "988", "3465", "2532", "365", "2487", "400", "3910", "4447", "3476", "500", "3011", "1778", "2995", "3350", "2325", "4193", "3402", "320", "3503", "1297", "4058", "4123", "2346", "1633", "2193", "582", "3196", "306", "1282", "586", "2748", "3189", "2288", "884", "2049", "2431", "546", "2347", "2373", "877", "2690", "349", "4537", "3924", "2455", "3290", "1264", "3307", "1976", "2336", "1755", "1455", "1758", "338", "2253", "2833", "4010", "3146", "2643", "935", "2478", "793", "3267", "4508", "2231", "1734", "3806", "477", "2494", "3757", "3154", "1448", "3777", "1922", "4161", "3169", "3353", "3089", "944", "2655", "136", "3623", "1188", "3305", "181", "1100", "2657", "4598", "3084", "3188", "2226"), c("WFmake", "WFaddress", "WFall", "WF3d", "WFour", "WFover", "WFremove", "WFinternet", "WForder", "WFmail", "WFreceive", "WFwill", "WFpeople", "WFreport", "WFaddresses", "WFfree", "WFbusiness", "WFemail", "WFyou", "WFcredit", "WFyour", "WFfont", "WF000", "WFmoney", "WFhp", "WFhpl", "WFgeorge", "WF650", "WFlab", "WFlabs", "WFtelnet", "WF857", "WFdata", "WF415", "WF85", "WFtechnology", "WF1999", "WFparts", "WFpm", "WFdirect", "WFcs", "WFmeeting", "WForiginal", "WFproject", "WFre", "WFedu", "WFtable", "WFconference", "CFsemicolon", "CFparen", "CFbracket", "CFexclam", "CFdollar", "CFpound", "CRLaverage", "CRLlongest", "CRLtotal", "spam"))) `sportsranks` <- structure(c(1, 1, 1, 4, 2, 6, 5, 5, 6, 7, 4, 1, 5, 4, 2, 7, 7, 1, 1, 3, 3, 4, 1, 7, 7, 1, 6, 6, 2, 6, 6, 6, 2, 3, 1, 3, 4, 1, 3, 7, 4, 7, 1, 3, 7, 2, 1, 1, 2, 2, 7, 3, 1, 3, 4, 2, 2, 2, 1, 2, 3, 6, 6, 5, 4, 1, 4, 5, 5, 2, 4, 4, 6, 4, 4, 7, 1, 5, 1, 6, 5, 1, 6, 1, 2, 6, 3, 3, 7, 4, 4, 4, 4, 3, 4, 6, 4, 2, 2, 4, 6, 7, 4, 5, 1, 1, 7, 7, 6, 2, 6, 7, 6, 6, 1, 5, 3, 7, 3, 3, 5, 4, 6, 1, 3, 1, 3, 3, 5, 2, 3, 3, 3, 7, 3, 3, 7, 4, 7, 5, 6, 4, 7, 1, 1, 5, 6, 3, 2, 2, 7, 1, 5, 6, 6, 2, 3, 7, 4, 7, 7, 7, 5, 1, 5, 1, 6, 3, 5, 6, 6, 6, 6, 4, 1, 7, 5, 3, 5, 5, 5, 1, 2, 7, 3, 1, 3, 3, 7, 3, 1, 3, 7, 4, 1, 3, 5, 7, 6, 7, 6, 7, 2, 6, 6, 4, 2, 1, 5, 7, 6, 2, 4, 4, 1, 1, 6, 7, 4, 6, 1, 6, 5, 7, 7, 7, 1, 1, 4, 2, 2, 6, 5, 1, 3, 4, 5, 6, 7, 3, 7, 2, 1, 7, 7, 6, 1, 4, 2, 7, 6, 7, 7, 3, 4, 2, 2, 2, 7, 1, 7, 2, 2, 3, 1, 4, 3, 7, 5, 3, 5, 5, 4, 3, 3, 1, 5, 4, 3, 4, 6, 6, 4, 3, 1, 3, 1, 4, 5, 5, 5, 5, 1, 2, 2, 2, 7, 2, 2, 2, 7, 5, 2, 5, 3, 6, 3, 2, 3, 3, 6, 2, 7, 5, 5, 3, 4, 4, 6, 1, 2, 1, 4, 7, 7, 4, 7, 6, 4, 4, 5, 6, 4, 1, 1, 5, 3, 2, 4, 5, 3, 3, 5, 7, 5, 5, 5, 2, 1, 3, 3, 7, 7, 4, 3, 3, 2, 4, 3, 1, 4, 4, 3, 2, 2, 3, 3, 4, 2, 1, 4, 5, 2, 4, 4, 4, 2, 6, 1, 4, 4, 2, 5, 4, 6, 5, 1, 1, 6, 6, 2, 5, 5, 1, 7, 7, 4, 1, 3, 1, 3, 6, 1, 2, 5, 6, 1, 7, 4, 1, 5, 2, 3, 2, 4, 5, 5, 3, 1, 4, 4, 4, 3, 4, 7, 7, 1, 6, 1, 1, 2, 1, 3, 7, 2, 4, 2, 5, 1, 1, 1, 5, 5, 4, 7, 7, 1, 1, 5, 5, 5, 5, 1, 6, 2, 6, 1, 1, 3, 5, 3, 2, 5, 5, 7, 6, 4, 4, 6, 3, 7, 4, 2, 3, 6, 3, 4, 4, 3, 5, 5, 5, 6, 6, 5, 1, 3, 3, 7, 3, 7, 3, 1, 6, 5, 2, 4, 5, 4, 4, 3, 3, 5, 1, 3, 2, 7, 3, 4, 5, 3, 3, 4, 5, 7, 6, 4, 4, 4, 7, 4, 4, 4, 5, 6, 2, 2, 2, 4, 4, 2, 7, 2, 6, 6, 3, 3, 5, 5, 5, 4, 5, 2, 1, 2, 4, 4, 2, 6, 2, 2, 2, 6, 5, 3, 4, 3, 5, 7, 5, 5, 2, 4, 6, 5, 5, 4, 7, 4, 4, 2, 6, 3, 2, 2, 5, 5, 6, 3, 6, 6, 7, 2, 2, 6, 2, 3, 4, 2, 1, 1, 3, 1, 3, 3, 1, 5, 3, 2, 2, 1, 6, 1, 2, 3, 4, 1, 6, 5, 2, 2, 1, 2, 2, 1, 2, 5, 6, 1, 5, 3, 1, 6, 4, 6, 5, 1, 3, 1, 6, 1, 6, 3, 3, 5, 3, 5, 5, 6, 2, 1, 5, 1, 6, 2, 3, 7, 5, 1, 3, 5, 7, 7, 6, 5, 1, 1, 3, 1, 2, 1, 3, 6, 5, 7, 4, 2, 6, 6, 6, 2, 3, 6, 4, 3, 7, 2, 1, 3, 3, 3, 3, 4, 7, 6, 6, 2, 7, 6, 4, 3, 4, 7, 2, 4, 1, 6, 6, 6, 6, 3, 4, 4, 6, 1, 6, 6, 5, 2, 4, 7, 4, 5, 1, 5, 5, 6, 2, 1, 3, 2, 1, 3, 2, 2, 3, 7, 6, 3, 1, 4, 5, 3, 5, 4, 2, 2, 1, 2, 7, 7, 2, 1, 1, 2, 4, 6, 7, 5, 6, 1, 2, 7, 3, 4, 7, 2, 2, 3, 5, 2, 1, 4, 2, 2, 1, 4, 1, 5, 1, 2, 1, 2, 2, 5, 4, 5, 6, 3, 5, 6, 6, 6, 2, 4, 5, 6, 6, 2, 6, 7, 2, 3, 7, 4, 2, 4, 2, 7, 7, 1, 7, 7, 5, 5, 6, 7, 5, 7, 1, 1, 1, 7, 6, 4, 5, 5, 4, 4, 3, 1, 3, 5, 1, 6, 3, 7, 4, 7, 7, 4, 7, 6, 1, 6, 4, 7, 7, 4, 7, 4, 2, 3, 3, 3, 7, 2, 3, 7, 6, 7, 5, 7, 7, 5, 2, 6, 7, 7, 4, 2, 7, 7, 6, 7, 7, 7, 5, 6, 1, 6, 3, 3, 5, 6, 5, 7, 5, 6, 7, 5, 5, 2, 7, 7, 6, 6, 1, 5, 7, 5, 4, 7, 5, 6, 7, 6, 2, 7, 6, 7, 6, 3, 7, 1, 7, 6, 7, 2, 4), .Dim = c(130L, 7L), .Dimnames = list( c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""), c("Baseball", "Football", "Basketball", "Tennis", "Cycling", "Swimming", "Jogging"))) `election` <- structure(c(-0.151710567794972, -0.358738971274584, -0.0656519833427971, -0.0560137902084161, 0.124059799981436, -0.0897167152084006, 0.18511316097709, 0.134801295705769, 0.80975388955557, -9.21881606710886e-05, -0.119675072458053, 0.196545925608587, -0.41696980911488, 0.123600206316792, -0.160095650651639, 0.00325558375036335, -0.218338149270562, -0.154555740080323, -0.0787849193269446, 0.0549568308200083, 0.169416492011805, 0.295786875212676, 0.0526921296346819, 0.0257282395162264, -0.172017002762233, 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-0.190100015392427, -0.072394253308797, -0.210001523513267, -0.336946745191894, -0.02635191449723, 0.0138057724027463, 0.0674107263768441, -0.00800671772709769, 0.185755314080398, -0.124839440036822, -0.278187082998866, -0.021214825854456, -0.311408033295121, 0.0421725154890016, 0.0251656419412478, 0.211550211517563, -0.172707172028283, -0.218265816021874, -0.143708909094815, -0.230203837942198, -0.466928783202441, 0.206020042785023, -0.0826739342706012, 0.0729307536690591, -0.12959421259491, 0.00383606548538494, -0.406254063463434, -0.217818055071453, -0.222603739575268, -0.0862780143923264, -0.20426259682637, 0.243811176074568, 0.087914318207828, 0.22831372130364, 0.252742025261207, 0.868015366668313, 0.0283932006179859, -0.0525592742238974, 0.459874517569108, -0.260491796100411, 0.252448921259642, 0.0104390689075618, 0.096975555789347, -0.156523719549227, -0.164668551754092, -0.189143483231707, 0.174557482667142, 0.25854824584954, 0.262671986437722, 0.16742603152309, 0.104587499907855, -0.137735177505732, -0.00135158299353297, -0.0233880656788146, -0.153829442895468, 0.127734762503922, 0.09737082240219, 0.153824992792016, 0.153091137741366, 0.257505966356694, 0.00331986329065674, -0.0881731256393625, 0.0432865517109296, -0.31290162245387, 0.168299644942686, 0.102373053011519, 0.283962750182916, -0.0908885837841826, -0.0859141446823075, -0.152815410875396, -0.118718735018814, -0.290527227454801, 0.377983634786762, 0.0636516881225511, 0.175040536042179, -0.13288821635267, 0.140778958258996, -0.331240467101918), .Dim = c(51L, 3L), .Dimnames = list(c("Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delaware", "DC", "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana", "Nebraska", "Nevada", "NewHampshire", "NewJersey", "NewMexico", "NewYork", "NorthCarolina", "NorthDakota", "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "RhodeIsland", "SouthCarolina", "SouthDakota", "Tennessee", "Texas", "Utah", "Vermont", "Virginia", "Washington", "WestVirginia", "Wisconsin", "Wyoming"), c("2000", "2004", "2008"))) `decathlon08` <- structure(c(989L, 870L, 883L, 845L, 804L, 814L, 968L, 834L, 894L, 906L, 827L, 856L, 885L, 819L, 942L, 793L, 797L, 827L, 861L, 915L, 847L, 878L, 814L, 825L, 1005L, 962L, 893L, 903L, 833L, 980L, 950L, 883L, 823L, 985L, 823L, 869L, 883L, 859L, 830L, 781L, 811L, 866L, 826L, 905L, 842L, 767L, 874L, 818L, 868L, 752L, 758L, 884L, 816L, 776L, 799L, 681L, 795L, 708L, 750L, 867L, 777L, 715L, 840L, 782L, 820L, 692L, 734L, 784L, 608L, 852L, 631L, 727L, 794L, 906L, 850L, 878L, 767L, 906L, 767L, 850L, 794L, 794L, 906L, 822L, 767L, 794L, 767L, 794L, 740L, 740L, 714L, 740L, 610L, 714L, 850L, 767L, 865L, 943L, 913L, 773L, 837L, 836L, 924L, 831L, 911L, 886L, 774L, 744L, 862L, 815L, 830L, 814L, 782L, 843L, 875L, 885L, 950L, 780L, 810L, 724L, 984L, 948L, 955L, 915L, 948L, 885L, 927L, 927L, 964L, 885L, 910L, 789L, 967L, 882L, 987L, 908L, 842L, 899L, 934L, 935L, 887L, 894L, 846L, 777L, 950L, 758L, 756L, 871L, 770L, 777L, 837L, 775L, 889L, 673L, 857L, 817L, 661L, 748L, 598L, 783L, 772L, 797L, 765L, 711L, 606L, 877L, 649L, 662L, 910L, 910L, 819L, 910L, 910L, 849L, 702L, 972L, 790L, 849L, 849L, 941L, 849L, 819L, 819L, 790L, 819L, 702L, 819L, 673L, 880L, 617L, 617L, 617L, 904L, 741L, 950L, 798L, 819L, 797L, 612L, 741L, 611L, 742L, 845L, 777L, 846L, 911L, 822L, 769L, 807L, 784L, 520L, 626L, 636L, 582L, 548L, 494L, 522L, 761L, 750L, 551L, 749L, 621L, 752L, 726L, 734L, 766L, 637L, 672L, 621L, 693L, 557L, 692L, 648L, 685L, 666L, 491L, 650L, 522L, 697L, 610L, 8791L, 8551L, 8527L, 8328L, 8253L, 8241L, 8238L, 8220L, 8205L, 8194L, 8178L, 8154L, 8118L, 8055L, 7992L, 7906L, 7838L, 7835L, 7714L, 7665L, 7516L, 7483L, 7336L, 7021L), .Dim = c(24L, 11L), .Dimnames = list(c("BryanClay", "AndreiKrauchanka", "LeonelSuarez", "AlexanderPogorelov", "RomainBarras", "RomanSebrle", "OleksiyKasyanov", "AndreNiklaus", "MauriceSmith", "MichaelSchrader", "MikkPahapill", "AlekseyDrozdov", "AndresRaja", "EugeneMartineau", "YordaniGarcia", "MikalaiShubianok", "AliaksandrParkhomenka", "HaifengQi", "MassimoBertocchi", "JangyAddy", "DanielAwde", "HadiSepehrzad", "DamjanSitar", "SlavenDizdarevic" ), c("X_100meter", "LongJump", "Shotput", "HighJump", "X_400meter", "Hurdles", "Discus", "PoleVault", "Javelin", "X_1500meter", "Total" ))) `cars` <- structure(c(-0.2222222, -2.2222222, -1.7777778, -1, -1.1111111, -4.2222222, -3.2222222, -0.8888889, -1.1111111, -1.2222222, -1.6666667, -0.7777778, -1.8888889, -2, -0.7777778, -0.7777778, -3.6666667, -0.4444444, -1, -1.3333333, -1.7777778, -2.3333333, -0.7777778, 1.4444444, -0.2222222, 0, 0.1111111, -0.2222222, -1.7777778, -3.3333333, -0.1111111, -2.6666667, -0.2222222, -1, -0.1111111, -1, -1.3333333, -0.8888889, -1.1111111, -0.1111111, -0.5555556, 0.3333333, -2.2222222, -2.3333333, -0.3333333, 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1.5, 1.5, 0.5, 0, 0.5, 0, 2.5, 0, 0.5, 0.5, 0, -1.5, 2, -1, -1, 1.5, 1, 1, 2.5, 1, 3, 4, 4, 0.5, -8, -8, -8, -8, -8, -8, -8, -8.5, -8, -8), .Dim = c(111L, 11L), .Dimnames = list( c("AcuraIntegra", "DodgeColt", "DodgeOmni", "EagleSummit", "FordEscort", "FordFestiva", "GEOMetro", "GEOPrizm", "HondaCivic", "HyundaiExcel", "Mazda323", "MazdaProtege", "MercuryTracer", "MitsubishiPrecis", "NissanSentra", "PontiacLeMans", "SubaruJusty", "SubaruLoyale", "ToyotaCorolla", "ToyotaTercel", "VolkswagenFox", "VolkswagenGolf", "VolkswagenJetta", "ChevroletCamaro", "ChevroletCorvette", "DodgeDaytona", "FordMustang", "FordProbe", "GEOStorm", "HondaCivicCRX", "HondaPrelude", "MazdaMX5Miata", "MazdaMX6", "MazdaRX7", "Nissan240SX", "Nissan300ZX", "NissanPulsarNX", "PlymouthLaser", "Porsche944", "SubaruXT", "ToyotaCelica", "ToyotaSupra", "VolkswagenCorrado", "VolkswagenGTI", "Audi80", "BMW325i", "ChevroletBeretta", "ChevroletCavalier", "ChevroletCorsica", "ChryslerLeBaron", "DodgeShadow", "DodgeSpirit", "FordTempo", "HondaAccord", "Mazda626", "Mercedes-Benz190", "MitsubishiGalant", "MitsubishiSigma", "NissanStanza", "Peugeot405", "PontiacGrandAm", "Saab900", "SubaruLegacy", "ToyotaCamry", "Volvo240", "AcuraLegend", "Audi100", "BMW535i", "BuickCentury", "BuickRegal", "BuickRiviera", "CadillacEldorado", "CadillacSeville", "ChevroletLumina", "ChryslerImperial", "ChryslerLeBaronCoupe", "DodgeDynasty", "EaglePremier", "FordTaurus", "FordThunderbird", "HyundaiSonata", "InfinitiQ45", "LexusLS400", "LincolnContinental", "LincolnMarkVII", "Mazda929", "MercedesBenz300E", "NissanMaxima", "Peugeot505", "Saab9000", "Sterling827", "ToyotaCressida", "Volvo740", "BuickElectra", "BuickLeSabre", "CadillacBrougham", "CadillacDeVille", "ChevroletCaprice", "FordLTDCrownVictoria", "LincolnTownCar", "PontiacBonneville", "ChevroletAstro", "ChevroletLuminaAPV", "DodgeCaravan", "DodgeGrandCaravan", "FordAerostar", "MazdaMPV", "MitsubishiWagon", "NissanAxxess", "NissanVan", "VolkswagenVanagon" ), c("Length", "Wheelbase", "Width", "Height", "FrontHd", "RearHd", "FrtLegRoom", "RearSeating", "FrtShld", "RearShld", "Luggage"))) `grades` <- structure(c(0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 30.47, 37.72, 65.56, 65.5, 72.36, 76.84, 88.52, 60.23, 88.38, 63.11, 79.22, 83.47, 74.32, 79.82, 93.85, 70.99, 75.36, 73.39, 74.37, 49.24, 83.16, 73.02, 85.88, 52.46, 93.37, 65.48, 72.88, 81.25, 81.55, 78.78, 89.47, 77.89, 74.28, 78.15, 71.23, 72.04, 78.48, 81.05, 44.46, 81.83, 79.92, 89.21, 90.17, 92.11, 85.41, 82.19, 89.83, 79.39, 83.78, 87.29, 80.43, 77.99, 95.09, 98.22, 81.99, 88.44, 92.26, 79.43, 84.08, 75.12, 87, 85.29, 86.67, 88.9, 93.3, 86.61, 90, 80.99, 86.64, 77.35, 90.45, 83.61, 87.98, 91.84, 85.55, 88.79, 76.64, 86.32, 81.08, 82.48, 86.62, 88.98, 86.67, 86.66, 75.55, 84.1, 81.31, 87.99, 82.97, 92.66, 86.06, 83.42, 89.92, 93.04, 95.55, 92.93, 89.63, 94.35, 87.3, 86.01, 90.75, 89.62, 91.84, 96.59, 93.18, 97.54, 94.17, 0, 20.56, 77.33, 75.83, 65.83, 84.17, 86.39, 41.67, 87.78, 56.67, 30.83, 76.67, 85.83, 61.67, 80, 81.11, 85.83, 88.33, 61.67, 45, 75.67, 75, 94.17, 55, 89.72, 76.67, 100, 84.44, 86, 88.06, 90.28, 85, 71.67, 86.39, 88.33, 89.17, 91.67, 90.83, 60, 81.67, 85.83, 91.39, 90, 88.06, 91.67, 85.28, 93.33, 83.06, 97.92, 96.67, 80, 91, 90, 90.83, 88.89, 87.5, 87.5, 86.67, 90.28, 83.33, 85, 93.33, 95, 89.44, 83.33, 85, 98.33, 87.78, 87.78, 92.78, 91.94, 87.5, 85.83, 91.94, 79.44, 90, 94.17, 82.5, 94.17, 91.67, 90.67, 89.17, 90.28, 93, 82.5, 90, 91.11, 93.89, 71.11, 91.67, 86.94, 93.33, 98.33, 96.67, 100, 98.33, 90, 95.83, 96.67, 92.22, 96.67, 87.5, 90.83, 93.61, 97.78, 99.17, 97.5, 0, 75, 75, 100, 25, 100, 50, 50, 50, 25, 75, 100, 100, 50, 75, 50, 100, 100, 50, 75, 50, 50, 50, 100, 100, 100, 50, 100, 100, 100, 100, 100, 50, 100, 75, 75, 75, 100, 100, 50, 100, 100, 100, 100, 50, 100, 75, 100, 25, 100, 100, 100, 50, 75, 100, 75, 100, 100, 100, 100, 25, 100, 75, 100, 75, 100, 75, 100, 100, 100, 75, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 50, 100, 100, 100, 100, 100, 100, 100, 75, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 60.38, 69.84, 68.81, 58.88, 74.93, 53.55, 59.04, 78.59, 63.18, 83.07, 74.82, 63.16, 67.97, 81.72, 76.04, 70.44, 64.29, 71.47, 79.98, 88.56, 76.21, 80.93, 79.89, 87.26, 72.13, 79.54, 86.61, 74.4, 75.49, 75.73, 76.61, 75.62, 85.34, 81.59, 77.45, 78.37, 77.71, 76.37, 80.38, 81.75, 79.48, 76.76, 76.28, 78.59, 87.57, 73.7, 81.17, 84.9, 91.38, 74.34, 88.99, 83.6, 88.76, 82.32, 78.44, 85.03, 82.36, 86.9, 86.33, 86.18, 90.98, 81.06, 83.95, 79.59, 84.79, 86.13, 82.12, 86.38, 83.4, 92.37, 89.59, 85.49, 89.11, 87.17, 85.49, 85.54, 88.76, 90.72, 87.48, 88.1, 88.45, 92.66, 90.98, 91.62, 92.72, 92.17, 92.33, 88.25, 95.24, 90.3, 88.76, 93.52, 90.98, 96.23, 89.64, 89.59, 93.61, 93.52, 92.22, 93.65, 95.99, 97.42, 96.19, 93.56, 94.75, 91.23, 94.64, 52, 62, 42, 56, 60, 70, 72, 72, 70, 68, 72, 64, 58, 58, 46, 76, 70, 60, 74, 70, 72, 70, 60, 64, 60, 66, 56, 66, 64, 64, 58, 68, 74, 58, 76, 74, 72, 66, 90, 78, 66, 70, 72, 68, 70, 84, 72, 68, 72, 78, 62, 68, 68, 70, 78, 72, 68, 66, 64, 72, 82, 74, 76, 78, 78, 72, 80, 76, 80, 66, 74, 80, 74, 74, 86, 80, 80, 78, 82, 82, 80, 90, 78, 76, 84, 78, 80, 84, 84, 88, 88, 78, 82, 74, 84, 86, 84, 80, 88, 90, 82, 88, 88, 90, 92, 96, 96, 43.52, 59.34, 63.18, 64.04, 65.92, 67.31, 67.81, 68.58, 69.31, 69.56, 69.78, 70.04, 70.7, 70.84, 70.84, 71.43, 72.16, 72.95, 73.46, 73.72, 73.77, 74.2, 74.76, 75.09, 75.6, 75.93, 75.99, 76.21, 76.4, 76.43, 76.75, 76.95, 76.96, 77.32, 77.4, 77.5, 77.69, 77.77, 78.16, 78.27, 78.5, 79.91, 80.14, 80.19, 80.38, 80.59, 80.68, 81.07, 81.11, 81.19, 81.19, 81.21, 81.3, 81.34, 81.59, 81.62, 81.84, 81.88, 82.03, 82.21, 82.36, 82.63, 82.76, 82.93, 83.08, 83.64, 83.67, 84.43, 84.72, 84.78, 84.88, 85.29, 85.72, 85.94, 86.12, 86.16, 86.73, 86.89, 87.14, 87.3, 87.3, 87.79, 87.91, 87.99, 88.11, 88.13, 88.51, 88.72, 88.81, 88.91, 88.97, 89.03, 90.15, 90.67, 90.85, 90.86, 90.88, 91, 91.73, 92.25, 92.31, 93.32, 93.38, 93.53, 94.64, 94.69, 95.67), .Dim = c(107L, 7L), .Dimnames = list( c("Obs1", "Obs2", "Obs3", "Obs4", "Obs5", "Obs6", "Obs7", "Obs8", "Obs9", "Obs10", "Obs11", "Obs12", "Obs13", "Obs14", "Obs15", "Obs16", "Obs17", "Obs18", "Obs19", "Obs20", "Obs21", "Obs22", "Obs23", "Obs24", "Obs25", "Obs26", "Obs27", "Obs28", "Obs29", "Obs30", "Obs31", "Obs32", "Obs33", "Obs34", "Obs35", "Obs36", "Obs37", "Obs38", "Obs39", "Obs40", "Obs41", "Obs42", "Obs43", "Obs44", "Obs45", "Obs46", "Obs47", "Obs48", "Obs49", "Obs50", "Obs51", "Obs52", "Obs53", "Obs54", "Obs55", "Obs56", "Obs57", "Obs58", "Obs59", "Obs60", "Obs61", "Obs62", "Obs63", "Obs64", "Obs65", "Obs66", "Obs67", "Obs68", "Obs69", "Obs70", "Obs71", "Obs72", "Obs73", "Obs74", "Obs75", "Obs76", "Obs77", "Obs78", "Obs79", "Obs80", "Obs81", "Obs82", "Obs83", "Obs84", "Obs85", "Obs86", "Obs87", "Obs88", "Obs89", "Obs90", "Obs91", "Obs92", "Obs93", "Obs94", "Obs95", "Obs96", "Obs97", "Obs98", "Obs99", "Obs100", "Obs101", "Obs102", "Obs103", "Obs104", "Obs105", "Obs106", "Obs107"), c("Gender", "HW", "Labs", "InClass", "Midterms", "Final", "Total")), class = "matrix") `mouths` <- structure(c(21, 21, 20.5, 23.5, 21.5, 20, 21.5, 23, 20, 16.5, 24.5, 26, 21.5, 23, 25.5, 20, 24.5, 22, 24, 23, 27.5, 23, 21.5, 17, 22.5, 23, 22, 20, 21.5, 24, 24.5, 23, 21, 22.5, 23, 21, 19, 25, 25, 22.5, 22.5, 27.5, 23.5, 25.5, 22, 21.5, 20.5, 28, 23, 23.5, 24.5, 25.5, 24.5, 21.5, 21.5, 24, 24.5, 25, 22.5, 21, 23, 23.5, 22, 19, 28, 29, 23, 24, 26.5, 22.5, 27, 24.5, 24.5, 31, 31, 23.5, 24, 26, 25.5, 26, 23.5, 23, 25.5, 26, 26.5, 23.5, 22.5, 25, 24, 21.5, 19.5, 28, 31, 26.5, 27.5, 27, 26, 28.5, 26.5, 25.5, 26, 31.5, 25, 28, 29.5, 26, 30, 25, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(27L, 5L), .Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27"), c("Age8", "Age10", "Age12", "Age14", "Sex"))) `histamine` <- structure(c(0.04, 0.02, 0.07, 0.17, 0.1, 0.12, 0.07, 0.05, 0.03, 0.03, 0.07, 0.09, 0.1, 0.08, 0.13, 0.06, 0.2, 0.06, 1.4, 0.57, 0.09, 0.11, 0.07, 0.07, 0.62, 1.05, 0.83, 3.13, 0.09, 0.09, 0.1, 0.05, 0.1, 0.02, 0.48, 0.35, 0.13, 0.1, 0.07, 0.06, 0.31, 0.73, 1.07, 2.06, 0.09, 0.09, 0.12, 0.05, 0.08, 0.02, 0.24, 0.24, 0.14, 0.1, 0.07, 0.07, 0.22, 0.6, 0.8, 1.23, 0.08, 0.1, 0.12, 0.05), .Dim = c(16L, 4L), .Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16"), c("Before", "After1", "After3", "After5"))) `births` <- structure(c(13.56, 14.39, 14.63, 14.97, 15.13, 14.25, 14.14, 13.71, 14.93, 14.21, 13.89, 13.6, 12.81, 13.27, 13.15, 12.29, 12.92, 13.64, 13.04, 13, 12.77, 12.37, 13.45, 13.53, 19.24, 18.68, 18.89, 20.27, 20.54, 21.38, 20.37, 19.95, 20.62, 20.86, 20.15, 19.54, 19.52, 18.89, 18.41, 17.55, 18.84, 17.18, 17.2, 17.09, 18.19, 18.41, 17.58, 18.19, 20.52, 20.37, 20.83, 21.14, 20.98, 21.77, 20.66, 21.17, 21.21, 21.68, 20.37, 20.49, 19.7, 18.36, 18.87, 17.32, 18.79, 18.55, 18.19, 17.38, 18.41, 19.1, 19.49, 19.1, 21.14, 21.14, 21.79, 22.54, 21.66, 22.32, 22.47, 20.88, 22.14, 21.86, 22.38, 20.71, 20.54, 20.66, 20.32, 19.36, 20.02, 18.84, 20.4, 18.44, 20.83, 21, 19.57, 21.35), .Dim = c(24L, 4L ), .Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24"), c("V1", "V2", "V3", "V4"))) `prostaglandin` <- structure(c(146L, 140L, 288L, 121L, 116L, 143L, 174L, 177L, 294L, 76L, 280L, 265L, 281L, 150L, 132L, 172L, 276L, 313L, 193L, 151L, 285L, 289L, 271L, 101L, 150L, 175L, 317L, 237L, 306L, 333L, 215L, 231L, 227L, 139L, 125L, 222L, 306L, 135L, 204L, 144L, 218L, 188L, 272L, 99L, 100L, 180L, 139L, 257L, 207L, 135L, 161L, 69L, 150L, 103L, 86L, 126L, 120L, 152L, 148L, 99L), .Dim = c(10L, 6L), .Dimnames = list( c("Person1", "Person2", "Person3", "Person4", "Person5", "Person6", "Person7", "Person8", "Person9", "Person10"), c("T1", "T2", "T3", "T4", "T5", "T6"))) `skulls` <- structure(c(131L, 125L, 131L, 119L, 136L, 138L, 139L, 125L, 131L, 134L, 129L, 134L, 126L, 132L, 141L, 131L, 135L, 132L, 139L, 132L, 126L, 135L, 134L, 128L, 130L, 138L, 128L, 127L, 131L, 124L, 124L, 133L, 138L, 148L, 126L, 135L, 132L, 133L, 131L, 133L, 133L, 131L, 131L, 138L, 130L, 131L, 138L, 123L, 130L, 134L, 137L, 126L, 135L, 129L, 134L, 131L, 132L, 130L, 135L, 130L, 137L, 129L, 132L, 130L, 134L, 140L, 138L, 136L, 136L, 126L, 137L, 137L, 136L, 137L, 129L, 135L, 129L, 134L, 138L, 136L, 132L, 133L, 138L, 130L, 136L, 134L, 136L, 133L, 138L, 138L, 137L, 141L, 141L, 135L, 133L, 131L, 140L, 139L, 140L, 138L, 132L, 134L, 135L, 133L, 136L, 134L, 131L, 129L, 136L, 131L, 139L, 144L, 141L, 130L, 133L, 138L, 131L, 136L, 132L, 135L, 137L, 136L, 128L, 130L, 138L, 126L, 136L, 126L, 132L, 139L, 143L, 141L, 135L, 137L, 142L, 139L, 138L, 137L, 133L, 145L, 138L, 131L, 143L, 134L, 132L, 137L, 129L, 140L, 147L, 136L, 138L, 131L, 132L, 132L, 143L, 137L, 130L, 136L, 134L, 134L, 138L, 121L, 129L, 136L, 140L, 134L, 137L, 133L, 136L, 131L, 133L, 135L, 124L, 134L, 130L, 135L, 132L, 129L, 136L, 138L, 138L, 134L, 134L, 129L, 124L, 136L, 145L, 130L, 134L, 125L, 136L, 139L, 136L, 134L, 136L, 128L, 129L, 131L, 129L, 130L, 136L, 131L, 136L, 126L, 139L, 134L, 130L, 132L, 132L, 128L, 141L, 133L, 138L, 134L, 134L, 133L, 138L, 145L, 131L, 136L, 129L, 139L, 126L, 133L, 142L, 138L, 135L, 125L, 134L, 135L, 130L, 131L, 137L, 127L, 133L, 123L, 137L, 131L, 133L, 133L, 134L, 128L, 130L, 131L, 120L, 135L, 137L, 130L, 134L, 140L, 133L, 134L, 135L, 136L, 130L, 137L, 141L, 135L, 128L, 125L, 130L, 124L, 131L, 131L, 128L, 126L, 142L, 138L, 136L, 130L, 123L, 131L, 126L, 134L, 127L, 138L, 138L, 126L, 132L, 135L, 120L, 136L, 135L, 134L, 135L, 134L, 125L, 135L, 125L, 129L, 136L, 129L, 126L, 124L, 127L, 125L, 128L, 135L, 129L, 133L, 89L, 92L, 99L, 96L, 100L, 89L, 108L, 93L, 102L, 99L, 95L, 95L, 109L, 100L, 100L, 97L, 103L, 93L, 96L, 101L, 102L, 103L, 93L, 103L, 104L, 100L, 93L, 106L, 114L, 101L, 101L, 97L, 98L, 104L, 95L, 98L, 100L, 102L, 96L, 94L, 103L, 98L, 99L, 98L, 104L, 98L, 107L, 101L, 105L, 93L, 106L, 100L, 97L, 91L, 101L, 90L, 104L, 93L, 98L, 101L, 96L, 93L, 87L, 106L, 96L, 98L, 95L, 99L, 92L, 95L, 100L, 97L, 101L, 90L, 104L, 102L, 92L, 90L, 96L, 94L, 91L, 100L, 94L, 99L, 91L, 95L, 101L, 96L, 100L, 91L, 107L, 95L, 87L, 99L, 91L, 90L, 94L, 90L, 90L, 100L, 90L, 97L, 99L, 95L, 99L, 93L, 99L, 95L, 93L, 88L, 94L, 86L, 97L, 98L, 92L, 97L, 95L, 94L, 92L, 100L, 91L, 95L, 91L, 92L, 86L, 101L, 97L, 92L, 99L, 92L, 95L, 101L, 95L, 93L, 96L, 95L, 99L, 96L, 92L, 89L, 92L, 97L, 88L, 91L, 97L, 85L, 81L, 103L, 87L, 97L, 49L, 48L, 50L, 44L, 54L, 56L, 48L, 48L, 51L, 51L, 50L, 53L, 51L, 50L, 51L, 54L, 50L, 53L, 50L, 49L, 51L, 47L, 53L, 50L, 49L, 55L, 53L, 48L, 54L, 46L, 48L, 48L, 45L, 51L, 45L, 52L, 54L, 48L, 50L, 46L, 53L, 51L, 56L, 49L, 53L, 45L, 53L, 51L, 47L, 54L, 49L, 48L, 52L, 50L, 49L, 53L, 50L, 52L, 54L, 51L, 52L, 47L, 48L, 50L, 45L, 50L, 47L, 55L, 46L, 56L, 53L, 50L, 50L, 49L, 47L, 55L, 50L, 60L, 51L, 53L, 52L, 50L, 51L, 45L, 49L, 52L, 54L, 49L, 55L, 46L, 54L, 53L, 49L, 51L, 46L, 50L, 60L, 48L, 51L, 52L, 53L, 54L, 50L, 52L, 55L, 52L, 55L, 47L, 54L, 48L, 53L, 50L, 53L, 53L, 51L, 54L, 53L, 55L, 52L, 51L, 50L, 49L, 57L, 52L, 47L, 52L, 58L, 45L, 55L, 54L, 51L, 54L, 56L, 53L, 52L, 47L, 51L, 54L, 50L, 47L, 46L, 44L, 54L, 55L, 52L, 57L, 52L, 48L, 48L, 51L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Dim = c(150L, 5L), .Dimnames = list(NULL, c("MaximalBreadth", "BasibregmaticHeight", "BasialveolarLength", "NasalHeight", "" ))) `caffeine` <- structure(c(8, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 9, 6, 8, 7, 6, 8, 6, 6, 6, 9, 4, 7, 6, 7, 6, 9, 10, 10, 7, 8, 9, 11, 5, 9, 9, 11, 10, 6, 8, 5, 9, 6, 6, 6, 8, 7, 3, 11, 4, 6, 8, 6, 8, 8, 7, 6, 7, 6, 6, 7, 5, 4, 7, 8, 9), .Dim = c(28L, 3L), .Dimnames = list( c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""), c("Grade", "Without", "With"))) `leprosy` <- structure(c(11, 8, 5, 14, 19, 6, 10, 6, 11, 3, 6, 6, 7, 8, 18, 8, 19, 8, 5, 15, 16, 13, 11, 9, 21, 16, 12, 12, 7, 12, 6, 0, 2, 8, 11, 4, 13, 1, 8, 0, 0, 2, 3, 1, 18, 4, 14, 9, 1, 9, 13, 10, 18, 5, 23, 12, 5, 16, 1, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), .Dim = c(30L, 3L), .Dimnames = list(c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""), c("Before", "After", "Group"))) `exams` <- structure(c(94.44, 94.44, 80.56, 91.67, 94.44, 100, 72.22, 91.67, 91.67, 91.67, 80.56, 86.11, 100, 88.89, 94.44, 88.89, 97.22, 94.44, 80.56, 86.11, 86.11, 100, 85.12, 94.44, 70.01, 88.89, 100, 100, 88.89, 94.44, 91.67, 91.67, 77.78, 97.22, 94.44, 88.89, 69.44, 91.67, 72.22, 91.67, 72.22, 97.22, 91.67, 80.56, 88.89, 69.44, 66.67, 91.67, 97.22, 91.67, 77.78, 75, 86.11, 94.44, 86.11, 97.22, 86.11, 100, 100, 86.11, 94.44, 88.89, 91.67, 91.67, 97.22, 97.22, 97.22, 86.11, 94.44, 88.89, 97.22, 94.44, 88.89, 94.44, 91.67, 79.14, 83.33, 75, 91.67, 88.89, 94.44, 88.89, 97.22, 77.78, 100, 80.56, 97.22, 100, 91.67, 97.22, 88.89, 77.78, 94.44, 100, 83.33, 72.22, 86.11, 77.78, 94.44, 86.11, 77.78, 91.67, 86.11, 88.89, 83.33, 100, 83.33, 91.67, 94.44, 83.33, 83.33, 94.44, 83.33, 94.44, 94.44, 94.44, 69.44, 91.67, 97.22, 66.67, 94.44, 97.22, 97.22, 97.22, 88.89, 80.56, 94.44, 69.44, 80.56, 94.44, 80.56, 94.44, 77.78, 91.67, 86.11, 88.89, 94.44, 97.22, 100, 100, 94.44, 77.78, 77.78, 94.44, 86.11, 63.89, 94.44, 88.89, 83.33, 100, 97.22, 91.67, 88.89, 88.89, 91.67, 97.22, 97.22, 83.33, 94.44, 75, 94.44, 94.44, 94.44, 91.67, 83.33, 97.22, 85.2, 97.22, 88.89, 91.67, 91.67, 88.89, 94.44, 94.44, 86.11, 88.89, 87.97, 97.22, 100, 36.11, 94.44, 83.33, 94.44, 97.22, 100, 100, 91.67, 94.44, 91.67, 97.22, 91.67, 78.79, 87.88, 63.64, 93.94, 93.94, 93.94, 75.76, 75.76, 60.61, 90.91, 69.7, 78.79, 87.88, 72.73, 78.79, 90.91, 78.79, 84.8499, 84.8499, 90.91, 57.58, 90.91, 87.88, 75.76, 60.61, 69.7, 90.91, 81.8199, 93.94, 90.91, 78.79, 93.94, 75.76, 90.91, 81.8199, 81.8199, 45.45, 93.94, 60.61, 78.79, 45.45, 81.8199, 72.73, 63.64, 90.91, 48.48, 63.64, 75.76, 93.94, 94.36, 63.64, 72.73, 90.91, 72.73, 75.76, 81.8199, 84.8499, 84.8499, 78.79, 84.8499, 81.8199, 90.91, 92.9, 90.91, 78.79, 84.8499, 96.97, 75.76, 82.52, 66.67, 87.88, 87.88, 69.7, 93.94, 75.76, 81.8199, 48.48, 63.64, 87.88, 72.73, 81.8199, 87.88, 92.73, 69.7, 96.97, 78.79, 81.8199, 96.97, 78.79, 81.8199, 84.8499, 72.73, 72.73, 87.88, 90.91, 84.8499, 81.8199, 87.88, 93.94, 78.79, 66.67, 87.88, 75.76, 96.97, 69.7, 78.79, 87.88, 66.67, 84.8499, 78.79, 84.8499, 84.8499, 87.88, 84.8499, 90.91, 87.88, 48.48, 90.91, 84.8499, 69.7, 93.94, 90.91, 81.8199, 85.37, 72.73, 63.64, 87.88, 63.64, 63.64, 81.8199, 78.79, 87.88, 84.8499, 81.8199, 93.94, 75.76, 63.64, 72.73, 81.8199, 84.8499, 75.76, 69.7, 75.76, 93.94, 84.8499, 57.58, 96.97, 69.7, 81.8199, 81.8199, 72.73, 90.91, 63.64, 66.67, 84.8499, 87.88, 93.94, 81.8199, 81.8199, 84.8499, 84.8499, 93.94, 87.88, 100, 66.67, 78.79, 93.94, 78.79, 63.64, 87.88, 90.91, 90.91, 72.73, 84.8499, 90.91, 81.8199, 81.8199, 96.97, 100, 78.79, 84.8499, 90.91, 81.8199, 84.8499, 90.91, 90.91, 84.8499, 90.91, 75.76, 84.8499, 90.91, 85.29, 91.18, 64.7099, 100, 97.06, 97.06, 79.41, 67.65, 85.29, 85.29, 58.82, 76.47, 91.18, 79.41, 91.18, 89.9, 97.06, 85.29, 70.59, 94.12, 85.29, 82.3499, 82.3499, 88.2399, 79.41, 76.47, 100, 88.2399, 76.47, 94.12, 82.3499, 94.12, 73.53, 97.06, 85.29, 79.41, 82.3499, 94.12, 82.3499, 64.7099, 55.88, 85.29, 91.18, 73.53, 88.2399, 70.59, 67.65, 91.18, 91.18, 97.06, 55.88, 82.3499, 91.18, 76.47, 94.12, 94.12, 79.41, 94.12, 100, 79.41, 91.18, 85.29, 94.12, 91.18, 88.2399, 94.12, 76.47, 76.47, 70.59, 64.7099, 0, 67.65, 82.3499, 100, 79.41, 76.47, 61.76, 64.7099, 91.18, 85.29, 79.41, 67.65, 88.2399, 67.65, 88.2399, 70.59, 91.18, 100, 85.29, 70.59, 94.12, 76.47, 82.3499, 97.06, 85.29, 58.82, 91.18, 58.82, 97.06, 85.29, 73.53, 97.06, 82.3499, 88.2399, 73.53, 94.12, 79.41, 82.3499, 91.18, 73.53, 85.29, 82.3499, 88.2399, 88.2399, 94.12, 91.18, 61.76, 91.18, 76.47, 61.76, 94.12, 91.18, 97.06, 73.53, 80.81, 82.3499, 88.2399, 73.53, 73.53, 88.2399, 79.41, 82.3499, 79.41, 91.18, 82.3499, 94.12, 67.65, 91.18, 82.3499, 82.3499, 85.29, 76.47, 76.77, 88.2399, 85.48, 0, 97.06, 88.2399, 79.41, 94.12, 88.2399, 88.2399, 91.18, 77.78, 88.2399, 91.18, 94.12, 88.2399, 79.41, 94.12, 94.12, 97.06, 82.3499, 82.3499, 88.2399, 88.2399, 76.47, 91.18, 73.53, 88.2399, 82.3499, 91.18, 82.3499, 79.41, 76.47, 85.29, 94.12, 100, 97.06, 76.47, 94.12, 100, 82.3499, 79.41, 88.2399, 97.06, 91.18, 94.12, 83.72, 85.29, 94.12, 94, 88, 64, 90, 100, 92, 80, 68, 82, 90, 62, 76, 74, 72, 84, 96, 96, 84, 82, 90, 80, 72, 94, 74, 76, 68, 96, 88, 84, 100, 80, 100, 78, 94, 80, 88, 64, 98, 80, 78, 44, 90, 80, 86, 78, 86, 60, 96, 94, 98, 50, 66, 86, 90, 96, 96, 80, 90, 98, 86, 98, 72, 88, 86, 92, 98, 92, 60, 82, 66, 82, 84, 74, 96, 90, 80, 56, 72, 78, 70, 64, 80, 92, 74, 100, 70, 90, 100, 90, 72, 84, 74, 86, 94, 92, 76, 90, 78, 94, 66, 62, 80, 80, 78, 68, 96, 96, 90, 86, 82, 66, 96, 92, 84, 94, 80, 56, 94, 96, 66, 90, 98, 92, 80, 80, 82, 100, 66, 94, 72, 78, 96, 82, 76, 94, 94, 84, 86, 92, 88, 82, 82, 94, 92, 72, 0, 96, 70, 78, 86, 98, 98, 88, 84, 94, 90, 90, 74, 90, 90, 98, 98, 74, 78, 74, 92, 0, 80, 58, 84, 98, 80, 86, 88, 82, 76, 82, 98, 96, 86, 86, 100, 74, 80, 92, 88, 84, 78, 92, 76, 96), .Dim = c(191L, 4L), .Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96", "97", "98", "99", "100", "101", "102", "103", "104", "105", "106", "107", "108", "109", "110", "111", "112", "113", "114", "115", "116", "117", "118", "119", "120", "121", "122", "123", "124", "125", "126", "127", "128", "129", "130", "131", "132", "133", "134", "135", "136", "137", "138", "139", "140", "141", "142", "143", "144", "145", "146", "147", "148", "149", "150", "151", "152", "153", "154", "155", "156", "157", "158", "159", "160", "161", "162", "163", "164", "165", "166", "167", "168", "169", "170", "171", "172", "173", "174", "175", "176", "177", "178", "179", "180", "181", "182", "183", "184", "185", "186", "187", "188", "189", "190", "191"), c("Midterm1", "Midterm2", "Midterm3", "FinalExam"))) `softdrinks` <- structure(c(1, 1, 1, 6, 8, 5, 1, 2, 1, 2, 1, 3, 5, 1, 6, 1, 2, 1, 2, 4, 1, 2, 4, 2, 2, 2, 5, 6, 6, 5, 1, 2, 1, 3, 1, 7, 2, 5, 3, 1, 2, 1, 3, 2, 1, 1, 4, 4, 4, 1, 5, 7, 2, 4, 5, 3, 5, 2, 2, 3, 8, 4, 4, 3, 3, 1, 4, 6, 2, 3, 3, 3, 2, 7, 8, 3, 3, 6, 7, 6, 4, 1, 7, 7, 2, 3, 4, 4, 2, 3, 4, 3, 5, 5, 5, 8, 3, 1, 4, 5, 3, 5, 2, 8, 6, 4, 2, 5, 6, 5, 7, 8, 6, 5, 6, 6, 6, 6, 7, 2, 2, 8, 6, 4, 4, 4, 7, 8, 5, 1, 6, 5, 6, 5, 7, 5, 3, 5, 8, 8, 8, 3, 1, 4, 6, 8, 7, 6, 7, 6, 4, 6, 4, 8, 7, 7, 6, 5, 8, 7, 7, 7, 7, 7, 4, 4, 3, 7, 7, 8, 8, 8, 5, 3, 8, 3, 7, 8, 8, 8, 6, 7, 8, 8), .Dim = c(23L, 8L)) `states` <- structure(list(Population = c(4253L, 604L, 4218L, 2484L, 31589L, 3747L, 3275L, 717L, 554L, 14166L, 7201L, 1187L, 1163L, 11830L, 5803L, 2842L, 2565L, 3860L, 4342L, 1241L, 5042L, 6074L, 9549L, 4610L, 2697L, 5324L, 870L, 1637L, 1530L, 1148L, 7945L, 1685L, 18136L, 7195L, 641L, 11151L, 3278L, 3141L, 12072L, 990L, 3673L, 729L, 5256L, 18724L, 1951L, 585L, 6618L, 5431L, 1828L, 5123L, 480L), PctCities = c(67.5, 42.1, 87.2, 45, 96.8, 84.3, 95.7, 82.5, 100, 92.9, 68.1, 74.2, 30.7, 84, 71.7, 44, 55, 48.3, 75.1, 35.9, 92.8, 96.1, 82.6, 69.4, 35, 68, 23.8, 50.7, 85, 59.6, 100, 56.3, 91.8, 66.6, 42.1, 81.2, 60.2, 70, 84.6, 94, 70.1, 32.9, 67.8, 83.9, 77.3, 27.2, 77.7, 82.9, 41.8, 67.9, 29.8), Doctors = c(175L, 143L, 201L, 166L, 241L, 226L, 325L, 214L, 641L, 219L, 187L, 245L, 136L, 235L, 173L, 160L, 188L, 185L, 210L, 196L, 341L, 373L, 200L, 237L, 132L, 213L, 171L, 192L, 159L, 212L, 269L, 196L, 343L, 205L, 192L, 213L, 155L, 212L, 262L, 278L, 179L, 160L, 218L, 183L, 190L, 266L, 220L, 225L, 190L, 205L, 141L), SchoolEnroll = c(94.3, 90.3, 99.6, 92.3, 91.4, 91.5, 90.4, 86.3, 106, 91.7, 94.6, 87.6, 95.5, 88.5, 91.2, 92.1, 91, 92.5, 87.1, 94.5, 89.5, 89.7, 87.9, 90.5, 91.7, 85.9, 92, 87.9, 96, 88.1, 86.9, 88.2, 89.2, 92, 92.6, 88.2, 95.1, 91, 84.8, 87.4, 94.6, 92.9, 93, 98.3, 96.1, 99.6, 93.4, 92.2, 96.5, 86, 97.2), TeacherSalary = c(31144L, 47951L, 32175L, 28934L, 41078L, 34571L, 50045L, 39076L, 43700L, 32588L, 32633L, 38518L, 29783L, 39431L, 36785L, 31511L, 34652L, 32257L, 26461L, 31972L, 40661L, 42174L, 47360L, 35948L, 26818L, 31189L, 28785L, 30922L, 34836L, 34720L, 46087L, 28491L, 47612L, 30793L, 26327L, 36802L, 28172L, 38590L, 44510L, 40729L, 30279L, 25994L, 32477L, 31223L, 29082L, 35406L, 33998L, 36151L, 31944L, 37746L, 31285L ), CollegeEnroll = c(66.2, 43.3, 46.4, 69.5, 47, 53.2, 51.2, 58.7, 63.4, 47.6, 65.7, 54.8, 66.8, 50.9, 65, 69.9, 57.4, 65.4, 70.9, 56.5, 46.8, 62.7, 50.5, 55.9, 74.8, 56.9, 76.7, 57.5, 33.1, 63.5, 51.4, 50.4, 63.8, 63.7, 79.3, 62.3, 59.5, 56.2, 63.2, 62.9, 62.1, 72.8, 64.2, 53.9, 65.6, 67.5, 56.3, 60.6, 69.1, 59.6, 57.2 ), Crime = c(684L, 766L, 703L, 595L, 1013L, 510L, 456L, 561L, 2663L, 1147L, 668L, 262L, 286L, 961L, 525L, 315L, 479L, 605L, 982L, 130L, 948L, 708L, 766L, 359L, 494L, 744L, 177L, 390L, 1002L, 117L, 614L, 889L, 966L, 655L, 82L, 486L, 652L, 521L, 427L, 376L, 1031L, 228L, 748L, 707L, 305L, 97L, 358L, 511L, 216L, 271L, 273L ), Prisoners = c(48.7, 58.1, 50.6, 37.8, 42.9, 29.5, 45.2, 67, 176.8, 45.1, 47.6, 30, 28.6, 31.8, 27.8, 20.8, 27.5, 31.2, 58.6, 11.7, 42.5, 19.1, 43.1, 10.5, 48.2, 36, 20.5, 19, 51.1, 17.5, 34.1, 24.9, 37.8, 40.8, 9.5, 40.1, 55.4, 25.1, 26.8, 29.3, 53.4, 25.7, 28.9, 68.2, 17.7, 18.3, 41.9, 21.4, 13.7, 21.9, 29.3), Poverty = c(16.4, 10.2, 15.9, 15.3, 17.9, 9, 10.8, 8.3, 21.2, 14.9, 14, 8.7, 12, 12.4, 13.7, 10.7, 14.9, 18.5, 25.7, 9.4, 10.7, 9.7, 14.1, 11.7, 19.9, 15.6, 11.5, 8.8, 11.1, 7.7, 9.2, 21.1, 17, 14.2, 10.4, 14.1, 16.7, 11.8, 12.5, 10.3, 13.8, 14.5, 14.6, 19.1, 8, 7.6, 10.7, 11.7, 18.6, 9, 9.3), Employment = c(59.3, 67.3, 64.4, 61.7, 60.3, 70.7, 64.1, 66.7, 59.6, 58.6, 63.8, 63.3, 66.3, 64.8, 67.8, 69.8, 67.2, 59.6, 57, 63, 67.1, 63.9, 62.3, 72.5, 59.1, 67.3, 62.4, 72, 65.2, 69.6, 62.3, 59.6, 57, 63.9, 68.5, 62.8, 60.3, 64.7, 59.2, 60.1, 63.3, 69.9, 63.5, 65.3, 69.2, 68.3, 66.9, 64, 50.1, 71, 68.1), Income = c(27196L, 45367L, 31293L, 25565L, 35331L, 37833L, 41097L, 35873L, 30116L, 29294L, 31467L, 42255L, 31536L, 35081L, 27858L, 33079L, 28322L, 26595L, 25676L, 30316L, 39198L, 40500L, 35284L, 33644L, 25400L, 30190L, 27631L, 31794L, 35871L, 35245L, 42280L, 26905L, 31899L, 30114L, 28278L, 31855L, 26991L, 31456L, 32066L, 31928L, 29846L, 29733L, 28639L, 30755L, 35716L, 35802L, 37647L, 33533L, 23564L, 35388L, 33140L)), .Names = c("Population", "PctCities", "Doctors", "SchoolEnroll", "TeacherSalary", "CollegeEnroll", "Crime", "Prisoners", "Poverty", "Employment", "Income"), class = "data.frame", row.names = c("Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delaware", "DC", "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana", "Nebraska", "Nevada", "NewHampshire", "NewJersey", "NewMexico", "NewYork", "NorthCarolina", "NorthDakota", "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "RhodeIsland", "SouthCarolina", "SouthDakota", "Tennessee", "Texas", "Utah", "Vermont", "Virginia", "Washington", "WestVirginia", "Wisconsin", "Wyoming")) `cereal` <- structure(c(23.33, 3.15, -5.94, -4.4, -7.03, -3.31, -3.76, -2.03, 10.08, -0.1, 3.81, -3.65, 4.72, -4.56, -0.01, -10.28, 14.58, -0.6, 1.31, -4.15, 0.22, -5.06, 0.49, -6.78, 8.33, 4.15, -1.94, -6.4, -2.03, 0.69, -0.76, -2.03, -10.17, 13.65, -7.44, 8.1, -2.53, 1.19, -4.26, 1.47, 1.7, 7.52, -8.57, 9.98, -9.66, 2.07, -8.39, 5.34, 18.33, 9.15, -6.94, -2.4, -7.03, -3.31, -4.76, -3.03, -15.92, 8.9, -8.19, 10.35, -4.28, 6.44, -4.01, 6.72, 1.95, -20.23, 8.68, 5.23, 2.59, 0.32, 2.86, -1.41, -21.42, -3.6, -9.69, 2.85, -6.78, 15.94, -4.51, 27.22, -30.8, -21.98, 34.93, -15.52, 31.84, -10.43, 27.11, -15.16), .Dim = c(8L, 11L), .Dimnames = list(c("CornFlakes", "Weetabix", "RiceKrispies", "ShreadedWheat", "SugarPuffs", "SpecialK", "FrostedFlakes", "AllBran"), c("Return", "Tasty", "Popular", "Digestable", "Nourishing", "NaturalFlavor", "Affordable", "GoodValue", "Crispy", "Fit", "Fun"))) `negent` <- function(x,K=ceiling(log2(length(x))+1)) { p <- table(cut(x,breaks=K))/length(x) sigma2 <- sum((1:K)^2*p)-sum((1:K)*p)^2 p <- p[(p>0)] (1+log(2*pi*(sigma2+1/12)))/2+sum(p*log(p)) } `negent2D` <- function(y,m=100) { thetas <- (1:m)*pi/m ngnt <- NULL for(theta in thetas) { x <- y%*%c(cos(theta),sin(theta)) ngnt <- c(ngnt,negent(x)) } i <- imax(ngnt) g <- c(cos(thetas[i]),sin(thetas[i])) g <- cbind(g,c(-g[2],g[1])) list(vectors = g,values = c(ngnt[i],negent(y%*%g[,2]))) } `negent3D` <- function(y,nstart=1,m=100,...) { f <- function(thetas) { cs <- cos(thetas) sn <- sin(thetas) negent(y%*%c(cs[1],sn[1]*c(cs[2],sn[2]))) } tt <- NULL nn <- NULL for(i in 1:nstart) { thetas <- runif(3)*pi o <- optim(thetas,f,method='SANN',control=list(fnscale=-1),...) tt <- rbind(tt,o$par) nn <- c(nn,o$value) } i <- imax(nn) # The index of best negentropy cs<-cos(tt[i,]) sn<-sin(tt[i,]) g.opt <- c(cs[1],sn[1]*cs[2],sn[1]*sn[2]) g.opt <- cbind(g.opt,c(-sn[1],cs[1]*cs[2],sn[2]*cs[1])) g.opt <- cbind(g.opt,c(0,-sn[2],cs[2])) x <- y%*%g.opt[,2:3] n2 <- negent2D(x,m=m) g.opt[,2:3] <- g.opt[,2:3]%*%n2$vectors list(vectors=g.opt,values = c(nn[i],n2$values)) } bothsidesmodel.df <- function(xx,n,pattern) { l <- ncol(pattern) tt <- pattern==1 pj <- apply(pattern,2,sum) df <- matrix(0,l,l) diag(df) <- n-pj a <- vector("list",l) a[[1]] <- solve(xx[tt[,1],tt[,1]]) for(i in 2:l) { if(pj[i]>0) a[[i]] <- solve(xx[tt[,i],tt[,i]]) for(j in 1:(i-1)) { if(pj[i]==0|pj[j]==0) {pij<-0} else { b <- xx[tt[,i],tt[,j]] if(is.vector(b)) b <- matrix(b,nrow=pj[i]) pij <- tr(a[[i]]%*%b%*%a[[j]]%*%t(b)) } df[j,i] <- df[i,j] <- n-pj[i]-pj[j]+pij } } df } logdet <- function(a) {determinant(a)$modulus[1]} tr <- function(x) sum(diag(x)) bothsidesmodel.mle <- function(x,y,z=diag(qq),pattern=matrix(1,nrow=p,ncol=l)) { x <- cbind(x) y <- cbind(y) n <- nrow(y) p <- ncol(x) qq <- ncol(y) z <- cbind(z) l <- ncol(z) if(length(x)==1&&x==0) { bsm <- list(Beta=0,SE=0,T=0,Covbeta=0,df=n) resid <- y psum <- 0 } else { bsm <- list() beta <- se <- tt <- matrix(0,nrow=p,ncol=l) psum <- sum(pattern) if(psum>0) { rowin <- apply(pattern,1,max)==1 colin <- apply(pattern,2,max)==1 x1 <- x[,rowin,drop=FALSE] z1 <- z[,colin,drop=FALSE] yz <- cbind(y%*%solve(t(fillout(z1)))) l1 <- ncol(z1) p1 <- ncol(x1) yza <- yz[,1:l1,drop=FALSE] xyzb <- x[,rowin,drop=FALSE] if(l1qq) { z <- t(fillout(t(z))) } z } reverse.kronecker <- function(ab,p,qq) { m <- nrow(ab)/p n <- ncol(ab)/qq rr <- c(outer((0:(p-1))*m,1:m,"+")) cc <- c(outer((0:(qq-1))*n,1:n,"+")) ab[rr,cc] } bothsidesmodel <- function(x,y,z=diag(qq),pattern=matrix(1,nrow=p,ncol=l)) { x <- cbind(x) y <- cbind(y) n <- nrow(y) p <- ncol(x) qq <- ncol(y) z <- cbind(z) l <- ncol(z) if((p*nrow(x)==1&&x==0) || (l*nrow(z)==1&&z==0)) { sigma <- t(y)%*%y/n output <- list(Beta = 0, SE = 0,T = 0, Covbeta = 0, df = n, Sigmaz=sigma) return(output) } yz <- t(lm(t(y)~z-1)$coef) residb <- lm(yz~x-1)$resid sigmab <- t(residb)%*%residb/(n-p) if(sum(pattern)==0) { residss <- ((n-p-l-1)/(n-p))*tr(solve(sigmab,t(yz)%*%yz)) output <- list(Beta = 0, SE = 0,T = 0, Covbeta = 0, df = n,Sigmaz=sigmab, ResidSS=residss,Dim=l*(l+1)/2,Cp=residss+l*(l+1)) return(output) } rowse <- rowt <- rowb <- rep(0,p*l) rowp <- c(t(pattern))==1 xx <- t(x)%*%x xyz <- t(x)%*%yz xyz <- c(t(xyz))[rowp] xxzz <- kronecker(xx,diag(l))[rowp,rowp] dstarinv <- solve(xxzz) g <- xyz%*%dstarinv rowb[rowp] <- g beta <- matrix(rowb,nrow=p,byrow=T) df <- bothsidesmodel.df(xx,n,pattern) residz <- yz-x%*%beta residsscp <- t(residz)%*%residz sigmaz <- residsscp/df xxzsz <- kronecker(xx,sigmaz)[rowp,rowp] covbeta <- dstarinv%*%xxzsz%*%dstarinv covbetab <- matrix(0,p*l,p*l) covbetab[rowp,rowp] <- covbeta se <- sqrt(diag(covbeta)) tt <- g/se rowse[rowp] <- se rowt[rowp] <- tt residss <- ((n-p-l-1)/(n-p))*tr(solve(sigmab,residsscp)) dd <- sum(pattern)+l*(l+1)/2 list(Beta = beta, SE = matrix(rowse,nrow=p,byrow=T), T = matrix(rowt,nrow=p,byrow=T), Covbeta = covbetab, df = diag(df),Sigmaz = sigmaz, ResidSS = residss,Dim=dd,Cp=residss+2*dd) } bothsidesmodel.chisquare <- function(x,y,z,pattern0,patternA=matrix(1,nrow=ncol(x),ncol=ncol(z))) { bsm <- bothsidesmodel(x,y,z,patternA) which <- patternA*(1-pattern0) which <- c(t(which)) == 1 theta <- c(t(bsm$Beta))[which] covtheta <- bsm$Covbeta[which,which] chisq <- theta%*%solve(covtheta,theta) df <- sum(which) list(Theta=theta,Covtheta = covtheta,df = df, Chisq=chisq,pvalue=1-pchisq(chisq,df)) } bothsidesmodel.hotelling <- function(x,y,z,rows,cols) { bsm <- bothsidesmodel(x,y,z) lstar <- length(cols) pstar <- length(rows) nu <- bsm$df[1] bstar <- bsm$Beta[rows,cols] if(lstar==1) bstar <- matrix(bstar,ncol=1) if(pstar==1) bstar <- matrix(bstar,nrow=1) W.nu <- bsm$Sigmaz[cols,cols] cx <- solve(t(x)%*%x) B <- t(bstar)%*%solve(cx[rows,rows])%*%bstar t2 <- tr(solve(W.nu)%*%B) f <- (nu-lstar+1)*t2/(lstar*pstar*nu) df <- c(lstar*pstar,nu-lstar+1) W <- W.nu*nu lambda <- ifelse(lstar==1,W/(W+B),det(W)/det(W+B)) chis <- -(nu-(lstar-pstar+1)/2)*log(lambda) Hotelling <- list(T2 = t2, F = f, df = df,pvalue = 1-pf(f,df[1],df[2])) Wilks <- list(Lambda=lambda,Chisq=chis,df=df[1],pvalue=1-pchisq(chis,df[1])) list(Hotelling = Hotelling,Wilks = Wilks) } bothsidesmodel.lrt <- function(x,y,z,pattern0,patternA=matrix(1,nrow=ncol(x),ncol=ncol(z))) { bsmA <- bothsidesmodel.mle(x,y,z,patternA) bsm0 <- bothsidesmodel.mle(x,y,z,pattern0) chisq <- bsm0$Deviance-bsmA$Deviance df <- bsmA$Dim - bsm0$Dim list(Chisq=chisq,df=df,pvalue=1-pchisq(chisq,df)) } bsm.fit <- function(x,y,z,pattern) { n <- nrow(y) p <- ncol(x) l <- ncol(z) xx <- t(x)%*%x xy <- t(x)%*%y rowp <- c(t(pattern))==1 lsreg <- lm(y~x-1) yqxy <- t(lsreg$resid)%*%lsreg$resid beta0 <- lsreg$coef sigma <- cov(y)*(n-1)/n rowse <- rowt <- rowb <- rep(0,p*l) dev0 <- n*logdet(sigma) maxiter = 25 iter = 0; repeat { sigmainvz <- solve(sigma,z) xyz <- c(t(xy%*%sigmainvz))[rowp] xxzz <- kronecker(xx,t(z)%*%sigmainvz)[rowp,rowp] dstarinv <- solve(xxzz) gamma <- xyz%*%dstarinv rowb[rowp] <- gamma beta <- matrix(rowb,nrow=p,byrow=T) betadiff <- beta0-beta%*%t(z) sigma <- (yqxy+t(betadiff)%*%xx%*%betadiff)/n dev <- n*logdet(sigma) iter <- iter+1 if(abs(dev-dev0) < 0.00001) break; if(iter>=maxiter) break; dev0 <- dev } df <- n-p covbeta <- solve(xxzz)*(n/df) se <- sqrt(diag(covbeta)) tt <- gamma/se rowse[rowp] <- se rowt[rowp] <- tt se <- matrix(rowse,nrow=p,byrow=T) tt <- matrix(rowt,nrow=p,byrow=T) list(Beta = beta, SE = se, T = tt, Covbeta = covbeta, df = df) } bsm.simple <- function(x,y,z) { yz <- y%*%z%*%solve(t(z)%*%z) cx <- solve(t(x)%*%x) beta <- cx%*%t(x)%*%yz residz <- yz - x%*%beta df <- nrow(yz)-ncol(x) sigmaz <- t(residz)%*%residz/df se <- sqrt(outer(diag(cx),diag(sigmaz),"*")) list(Beta = beta, SE = se, T = beta/se, Covbeta = kronecker(cx,sigmaz),df = df, Sigmaz = sigmaz, Cx=cx) } `lda` <- function(x,y) { if(is.vector(x)) {x <- matrix(x,ncol=1)} K <- max(y) p <- ncol(x) n <- nrow(x) m <- NULL v <- matrix(0,ncol=p,nrow=p) for(k in 1:K) { xk <- x[y==k,] if(is.vector(xk)) {xk <- matrix(xk,ncol=1)} m <- rbind(m,apply(xk,2,mean)) v <- v + var(xk)*(nrow(xk)-1) } v <- v/n phat <- table(y)/n ck <- NULL ak <- NULL vi <- solve(v) for(k in 1:K) { c0 <- -(1/2)*(m[k,]%*%vi%*%m[k,]-m[K,]%*%vi%*%m[K,]) +log(phat[k]/phat[K]) ck <- c(ck,c0) a0 <- vi%*%(m[k,]-m[K,]) ak <- cbind(ak,a0) } list(a = ak, c = ck) } `qda` <- function(x,y) { K <- max(y) p <- ncol(x) n <- nrow(x) m <- NULL v <- array(0,c(K,p,p)) ck <- NULL phat <- table(y)/n for(k in 1:K) { xk <- x[y==k,] m <- rbind(m,apply(xk,2,mean)) nk <- nrow(xk) v[k,,] <- var(xk)*(nk-1)/nk ck <- c(ck,-log(det(v[k,,]))+2*log(phat[k])) } list(Mean = m,Sigma = v, c = ck) } `predict.qda` <- function(qd,newx) { newx <- c(newx) disc <- NULL K <- length(qd$c) for(k in 1:K) { dk <- -t(newx-qd$Mean[k,])%*% solve(qd$Sigma[k,,],newx-qd$Mean[k,])+qd$c[k] disc <- c(disc,dk) } disc } `silhouette.km` <- function(x,centers) { dd <- NULL k <- nrow(centers) for(i in 1:k) { xr <- sweep(x,2,centers[i,],'-') dd<-cbind(dd,apply(xr^2,1,sum)) } dd <- apply(dd,1,sort)[1:2,] (dd[2,]-dd[1,])/dd[2,] } `sort.silhouette` <- function(sil,cluster) { ss <- NULL ks <- sort(unique(cluster)) for(k in ks) { ss <- c(ss,sort(sil[cluster==k])) } ss } `pcbic` <- function(eigenvals,n,pattern) { p <- length(eigenvals) l <- eigenvals k <- length(pattern) istart <- 1 for(i in 1:k) { iend <- istart+pattern[i] l[istart:(iend-1)] = mean(l[istart:(iend-1)]) istart <- iend } dev <- n*sum(log(l)) dimen <- (p^2-sum(pattern^2))/2 + k bic <- dev + log(n)*dimen list(lambdaHat = l,Deviance = dev,Dimension = dimen,BIC = bic) } `pcbic.stepwise` <- function(eigenvals,n) { k <- length(eigenvals) p0 <- rep(1,k) b <- rep(0,k) pb <- vector('list',k) pb[[1]] <- p0 b[1] <- pcbic(eigenvals,n,p0)$BIC for(i in 2:k) { psb <- pcbic.subpatterns(eigenvals,n,pb[[i-1]]) b[i] <- min(psb$bic) pb[[i]] <- psb$pattern[,psb$bic==b[i]] } ib <- (1:k)[b==min(b)] list(Patterns = pb,BICs = b, BestBIC = b[ib],BestPattern = pb[[ib]], LambdaHat = pcbic(eigenvals,n,pb[[ib]])$lambdaHat) } `pcbic.unite` <- function(pattern,index1) { if(index1 < 1) stop("starting index must be positive") k <- length(pattern) if(index1 > k-1) stop("starting index must be less than length of pattern") if(k==1) return(pattern) if(k==2) return(sum(pattern)) if(index1==1) return(c(pattern[1]+pattern[2],pattern[3:k])) if(index1==k-1) return(c(pattern[1:(k-2)],pattern[k-1]+pattern[k])) c(pattern[1:(index1-1)],pattern[index1]+pattern[index1+1], pattern[(index1+2):k]) } `pcbic.subpatterns` <- function(eigenvals,n,pattern0) { b <- NULL pts <- NULL k <- length(pattern0) if(k==1) return(F) for(i in 1:(k-1)) { p1 <- pcbic.unite(pattern0,i) b2 <- pcbic(eigenvals,n,p1) b <- c(b,b2$BIC) pts <- cbind(pts,p1) } list(bic=b,pattern=pts) }