##The command ##source("http://parker.ad.siu.edu/Olive/robdata.txt") ##is an easy way to get these data sets into R. # For references, see Olive (2020) Robust Statistics # or Olive (2008), Applied Robust Statistics. # data sets are listed alphabetically as # Abraham and Ledolter (2006, pp. 360-364), death data # Agresti (2002, p. 127), crab data # Beaton, Martin, Mullis, Gonzales, Smith and Kelly (1996), timss data: # Box and Cox (1964), wool textile data 3^3 design # Buxton (1920) data # Collett (1999, pp. 216-299), rotifer data # Cook and Weisberg (1999a), mussels data # Cook and Weisberg (1999a), species data # Gladstone (1905), brain data and glado data # Hawkins data # Hebbler, B. (1847), marry data``Statistics of Prussia," # Kuehl (1994, p. 128), one way anova crab data # Myers, Montgomery and Vining (2002): ceriod Poisson regression data # Myers, Montgomery and Vining (2002): popcorn data # Olive (2008), M580 homework data # Olive (2008), M580 totals data # Olive (2008, table 12.1), sinc data # Olive (2010) insulation data, contributed by Ms. Spector # Rousseeuw and Leroy (1987, p. 26), Belgian telephone data # Schaaffhausen (1878), museum data # Staudte and Sheather (1990, p. 97), Cushny Peebles data # Abraham and Ledolter (2006, pp. 360-364), death data dmv <- c(62,182,17,22,13,11,12,6,9,7,17,4) aggrav<- c(1,1,2,2,3,3,4,4,5,5,6,6) race<- c(1,0,1,0,1,0,1,0,1,0,1,0) dy<-c(2,1,2,1,6,2,9,2,9,4,17,4) dx <- cbind(aggrav,race) rm(aggrav,race) #Agresti (2002, p. 127), crab data #Y = number of satellites (number of male crabs near the female crab) #color (2: light medium, 3: medium, 4: dark medium, 5: dark) #spine (1: both good, 2: one worn or broken, 3 both worn or broken) #width (carapace width in cm) #weight (of female crab in grams) craby <- c(8, 0, 9, 0, 4, 0, 0, 0, 0, 0, 0, 0, 11, 0, 14, 8, 1, 1, 0, 5, 4, 3, 1, 2, 3, 0, 3, 5, 0, 0, 4, 0, 0, 8, 5, 0, 0, 6, 0, 6, 3, 5, 6, 5, 9, 4, 6, 4, 3, 3, 5, 5, 6, 4, 5, 15,3, 3, 0, 0, 0, 5, 3, 5, 1, 8, 10,0, 0, 3, 7, 1, 0, 6, 0, 0, 3, 4, 0, 5, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 2, 4, 3, 6, 0, 2, 2, 0, 12,0, 5, 6, 6, 2, 0, 2, 3, 0, 3, 4, 2, 6, 6, 0, 4, 10,7, 0, 5, 5, 6, 6, 7, 3, 3, 0, 0, 8, 4, 4, 10, 9, 4, 0, 0, 0, 0, 4, 0, 2, 0, 4, 4, 3, 8, 0, 7, 0, 0, 2, 3, 4, 0, 0, 0) color <- c(3, 4, 2, 4, 4, 3, 2, 4, 3, 4, 4, 3, 3, 5, 3, 2, 3, 3, 5, 3, 3, 2, 3, 4, 5, 5, 3, 3, 5, 3, 2, 2, 3, 3, 3, 5, 3, 3, 5, 3, 4, 2, 2, 3, 4, 4, 3, 3, 3, 3, 5, 3, 2, 3, 3, 3, 3, 4, 3, 5, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 4, 3, 4, 3, 4, 3, 4, 5, 4, 4, 3, 5, 3, 5, 5, 3, 3, 3, 5, 3, 4, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 5, 5, 4, 3, 4, 4, 2, 4, 3, 3, 3, 4, 5, 3, 3, 2, 3, 3, 5, 3, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 5, 3, 3, 4, 3, 3, 3, 3, 3, 5, 3, 3, 3, 4, 4, 3, 3, 3, 5, 3, 3, 4, 4, 4, 4, 2, 5, 3) spine <- c(3, 3, 1, 3, 3, 3, 1, 2, 1, 3, 3, 3, 3, 2, 1, 1, 3, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 1, 3, 3, 1, 3, 2, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 3, 3, 1, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 1, 3, 3, 3, 1, 3, 2, 1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 2, 3, 3, 3, 1, 2, 3, 1, 3, 3, 3, 1, 3, 2, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 2) weight <- c(3050, 1550, 2300, 2100, 2600, 2100, 2350, 1900, 1950, 2150, 2150, 2650, 3050, 1850, 2300, 2950, 2000, 3000, 2200, 2700, 1950, 2300, 1600, 2600, 2000, 1300, 3150, 2700, 2600, 2100, 3200, 2600, 2000, 2000, 2700, 1850, 2650, 3150, 1900, 2800, 3100, 2800, 2500, 3300, 3250, 2800, 2600, 2100, 3000, 3600, 2100, 2900, 2700, 1600, 2000, 3000, 2700, 2300, 2750, 2250, 2550, 2050, 2450, 2150, 2800, 3050, 3200, 2400, 1300, 2400, 2800, 1650, 1800, 2250, 1900, 1600, 2200, 2250, 1200, 2100, 2250, 2900, 1650, 2550, 2300, 2250, 3050, 2750, 1900, 1700, 3850, 2550, 2450, 3200, 1550, 2800, 2250, 1967, 2200, 3000, 2867, 1600, 2550, 2550, 2000, 2900, 2400, 3100, 1900, 2300, 3250, 2500, 2100, 2100, 3325, 1800, 3225, 1400, 2400, 2500, 1800, 3275, 2225, 1650, 2900, 2300, 3200, 1475, 2025, 2300, 1950, 1800, 2900, 2250, 3050, 2200, 3100, 2400, 2250, 2625, 5200, 3325, 2925, 2000, 2400, 2100, 3725, 3025, 1900, 3000, 2850, 2300, 2000, 1600, 1900, 1950, 3200, 1850, 1800, 3500, 2350, 2275, 3050, 2150, 2750, 2200, 1800, 2175, 2750, 3275, 2625, 2625, 2000) width <- c(28.3,22.5, 26.0, 24.8, 26.0, 23.8, 26.5, 24.7, 23.7, 25.6, 24.3, 25.8, 28.2, 21.0, 26.0, 27.1, 25.2, 29.0, 24.7, 27.4, 23.2, 25.0, 22.5, 26.7, 25.8, 26.2, 28.7, 26.8, 27.5, 24.9, 29.3, 25.8, 25.7, 25.7, 26.7, 23.7, 26.8, 27.5, 23.4, 27.9, 27.5, 26.1, 27.7, 30.0, 28.5, 28.9, 28.2, 25.0, 28.5, 30.3, 24.7, 27.7, 27.4, 22.9, 25.7, 28.3, 27.2, 26.2, 27.8, 25.5, 27.1, 24.5, 27.0, 26.0, 28.0, 30.0, 29.0, 26.2, 26.5, 26.2, 25.6, 23.0, 23.0, 25.4, 24.2, 22.9, 26.0, 25.4, 25.7, 25.1, 24.5, 27.5, 23.1, 25.9, 25.8, 27.0, 28.5, 25.5, 23.5, 24.0, 29.7, 26.8, 26.7, 28.7, 23.1, 29.0, 25.5, 26.5, 24.5, 28.5, 28.2, 24.5, 27.5, 24.7, 25.2, 27.3, 26.3, 29.0, 25.3, 26.5, 27.8, 27.0, 25.7, 25.0, 31.9, 23.7, 29.3, 22.0, 25.0, 27.0, 23.8, 30.2, 26.2, 24.2, 27.4, 25.4, 28.4, 22.5, 26.2, 24.9, 24.5, 25.1, 28.0, 25.8, 27.9, 24.9, 28.4, 27.2, 25.0, 27.5, 33.5, 30.5, 29.0, 24.3, 25.8, 25.0, 31.7, 29.5, 24.0, 30.0, 27.6, 26.2, 23.1, 22.9, 24.5, 24.7, 28.3, 23.9, 23.8, 29.8, 26.5, 26.0, 28.2, 25.7, 26.5, 25.8, 24.1, 26.2, 26.1, 29.0, 28.0, 27.0, 24.5) crabx <- cbind(color,spine,weight,width) rm(color,spine,weight,width) # Beaton, Martin, Mullis, Gonzales, Smith and Kelly (1996), timss data: ytimss <- c(0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0) m8s <- c(550, 566, 558, 479, 537, 418, 461, 586, 494, 506, 542, 505, 535, 563, 501, 477, 544, 576, 492, 484, 570, 538, 534, 490, 492, 544, 527, 612, 552, 573, 526, 543, 529, 524, 539) g8s <- c(540, 549, 543, 463, 525, 405, 465, 562, 463, 490, 524, 489, 507, 545, 486, 461, 532, 551, 478, 470, 550, 512, 520, 468, 480, 533, 507, 603, 537, 548, 508, 528, 514, 526, 530) m7s <- c(507, 522, 536, 453, 505, 396, 420, 543, 452, 461, 505, 452, 503, 525, 468, 443, 504, 545, 440, 405, 523, 489, 489, 436, 456, 493, 477, 548, 520, 539, 487, 493, 492, 495, 514) g7s <- c(502, 516, 521, 432, 493, 378, 420, 523, 427, 443, 495, 446, 485, 510, 456, 428, 487, 521, 430, 401, 512, 472, 477, 420, 448, 475, 459, 541, 499, 521, 467, 484, 475, 492, 502) edaids <- c(66, 56, 64, 58, 57, 10, 37, 33, 66, 49, 66, 28, 33, 32, 72, 1, 67, 38, 13, 35, 83, 56, 63, 35, 8, 30, 74, 47, 27, 43, 40, 58, 63, 4, 56) colgrad <- c(28, 10, 20, 27, 37, 15, 15, 21, 13, 13, 11, 18, 7, 24, 25, 3, 17, 22, 27, 37, 12, 25, 25, 9, 10, 34, 14, 8, 20, 19, 15, 22, 11, 9, 33) fun<- c(94, 90, 94, 95, 96, 93, 91, 90, 98, 91, 88, 89, 74, 96, 95, 79, 94, 58, 90, 86, 96, 95, 97, 87, 83, 92, 94, 79, 95, 88, 96, 97, 83, 84, 93) dowell<-c(64, 45, 70, 78, 68, 93, 71, 61, 82, 53, 35, 82, 74, 66, 65, 95, 59, 79, 53, 55, 82, 66, 72, 88, 80, 81, 70, 96, 60, 56, 89, 61, 40, 94, 69) study <-c(2.0, 2.4, 3.4, 3.0, 2.2, 4.6, 3.6, 1.8, 1.4, 2.7, 2.0, 4.4, 2.5, 3.1, 2.4, 6.4, 2.7, 2.5, 2.7, 2.7, 2.2, 2.1, 2.3, 3.0, 5.0, 2.9, 1.8, 4.6, 2.4, 2.9, 3.6, 2.3, 2.7, 3.5, 2.3) fteach<- c(39, 52, 55, 56, 37, 39, 52, 76, 23, 51, 39, 43, 32, 74, 44, 40, 54, 48, 75, 78, 20, 40, 31, 78, 74, 86, 37, 69, 63, 77, 44, 37, 14, 64, 54) comp <- c(73, 59, 67, 60, 61, 11, 39, 36, 76, 50, 71, 29, 39, 37, 77, 4, 78, 39, 13, 42, 85, 60, 64, 39, 19, 35, 90, 49, 31, 47, 42, 60, 66, 4, 59) rate<-c(75, 43, 61, 57, 90, 91, 100, 96, 88, 87, 70, 87, 83, 99, 97, 100, 82, 100, 83, 96, 23, 90, 84, 94, 94, 97, 79, 100, 91, 81, 96, 96, 90, 99, 77) xtimss <- cbind(m8s, g8s,m7s,g7s,edaids,colgrad,fun,dowell,study,fteach,comp,rate) rm(m8s,g8s,m7s,g7s,edaids,colgrad,fun,dowell,study,fteach,comp,rate) # Box and Cox (1964), wool textile data 3^3 design cycles <- c(674, 370, 292, 338, 266, 210, 170, 118, 90, 1414, 1198, 634, 1022, 620, 438, 443, 332, 220, 3636, 3184, 2000, 1568, 1070, 566, 1140, 884, 360) amp <- rep(c(8,8,8,9,9,9,10,10,10),3) length <- rep(c(250,300,350),c(9,9,9)) load <- rep(c(40,45,50),9) wooly <- cycles woolx <- cbind(amp,length,load) amp <- factor(amp) length <- factor(length) load <- factor(load) wool <- data.frame(amp,length,load,cycles) rm(amp,length,load,cycles) # Buxton (1920) data buxy <- c(1720,1698,1727,1739,1622,1684,1583,1697,1711,1672,1617,1772,1792,1696,1656,1770, 1617,1679,1631,1814,1668,1796,1620,1587,1735,1670,1672,1660,1610,1773,1571,1763,1635,1613, 1690,1770,1771,1640,1770,1740,1683,1696,1730,1722,1637,1716,1790,1694,1742,1712,1637,1645, 1715,1610,1700,1664,1723,1755,1697,1670,18,18,19,19,19,1786,1685,1750,1587,1686,1663,1693, 1750,1598,1710,1590,1790,1758,1835,1720,1684,1631,1758,1661,1790,1704,1590) len <- c(168,176,179,176,177,175,170,177,179,178,171,189,183,190,180,173,180,178,179,178, 188,173, 182,162,187,189,170,176,173,169,172,178,173, 185, 175, 176, 184, 184, 182, 178, 175, 189, 174, 173, 178, 186, 173,168, 172, 185,175,196,181, 177,173,171, 184, 178, 174, 181, 1755, 1537, 1650, 1675, 1610, 175,183, 180, 187, 181, 176, 185, 191, 179, 184, 172,175, 179, 177,167, 179,170, 170, 182, 173, 185, 189) nasal<- c(51, 55, 55, 46, 48, 54, 48, 54, 51, 50, 51, 61, 57, 51, 54, 55, 50, 50, 53, 57, 45, 60, 45, 45, 63, 49, 49, 50, 50, 47, 43, 58, 55, 51, 52, 52, 54, 47, 56, 50, 51, 52, 50, 46, 49, 49, 57, 56, 52, 47, 53, 55, 54, 51, 54, 47, 46, 59, 54, 55, 53, 44, 55, 47, 48, 44, 53, 49, 50, 59, 51,58, 49, 45, 49, 56, 58, 53, 48, 52, 57, 55, 56, 46, 57, 55, 50) bigonal<-c( 104, 106, 104, 114, 101, 102, 116, 110, 97, 107, 108, 114, 115, 108, 103, 108, 109, 110, 97, 108, 105, 102, 105, 92, 108, 110, 118, 112, 103, 112, 105, 109, 114, 106, 108, 110, 104, 106, 108, 108, 112, 110, 109, 103, 99, 110, 92, 117, 107, 106, 103, 108, 111, 108, 93, 100, 103, 100, 110, 106, 102, 124, 90, 107, 104, 111, 112, 97, 115, 112, 102, 101, 105, 103, 113, 105, 105, 103, 105, 107, 114, 103, 105, 108, 92, 107, 110) cephalic <- c( 89.29, 83.52, 83.80, 88.64, 80.79, 88.57, 90.00, 85.88, 83.24, 83.59, 85.96, 83.07, 80.87, 82.63, 87.22, 83.82, 81.67, 83.15, 84.92, 83.71, 77.66, 90.75, 81.32, 86.42, 79.68, 79.37, 86.47, 85.23, 86.71, 91.72, 91.28, 85.39, 89.60, 86.49, 90.86, 86.36, 80.98, 78.80, 82.97, 86.52, 85.71, 76.72, 85.63, 93.06, 85.39, 78.49, 80.35, 90.48, 87.79, 82.70, 79.43, 73.68, 79.01, 80.79, 84.39, 88.89, 83.70, 79.78, 83.33, 75.69, 86.63, 79.01, 76.09, 88.20, 83.63, 86.29, 79.23, 81.11, 79.68, 85.64, 86.36, 79.46, 80.10, 78.21, 84.78, 87.79, 85.14, 87.15, 85.31, 95.21, 85.47, 88.82, 87.06, 84.32, 80.35, 85.41, 74.60) buxx <- cbind(len,nasal,bigonal,cephalic) rm(len,nasal,bigonal,cephalic) # Collett (1999, pp. 216-299), rotifer data rotmv <- c(58,86,76,83,56,73,29,44,31,56,27,59,22,14,17,22,66,86,492,89, 161,248,234,283,129,161,167,286,117,162,42,48,49,160,74,45,101,68,190, 154) density<- c(1.019,1.020,1.021,1.03,1.03,1.03,1.031,1.04,1.04,1.041, 1.048,1.049,1.05,1.05,1.06,1.061,1.063,1.07,1.07,1.07,1.019,1.020, 1.021,1.03,1.03,1.03,1.031,1.04,1.04,1.041,1.048,1.049,1.05,1.05, 1.06,1.061,1.063,1.07,1.07,1.07) species <- 0*1:40 species[1:20] <- species[1:20]+1 roty<-c(11,7,10,19,9,21,13,34,10,36,20,54,20,9,14,10,64,68,488,88, 13,14,30,10,14,35,26,32,22,23,7,22,9,34,71,25,94,63,178,154) rotx <- cbind(density,species) rm(density,species) # Cook and Weisberg (1999a), mussels data #L Shell length in mm #W Shell width in mm #H Shell height in mm #S Shell mass in g #M Muscle mass in g L<-c(318, 312, 265, 222, 274, 216, 217, 202, 272, 273, 260, 276, 270, 280, 262, 312, 220, 212, 196, 226, 284, 320, 331, 276, 186, 213, 291, 298, 287, 230, 293, 298, 290, 282, 221, 287, 228, 210, 308, 265, 270, 208, 277, 241, 219, 170, 150, 132, 175, 150, 162, 252, 275, 224, 211, 254, 234, 221, 167, 220, 227, 177, 230, 288, 275, 273, 246, 250, 290, 226, 269, 267, 263, 217, 188, 152, 227, 216, 242, 260, 196, 220) W<-c(68, 56, 46, 38, 51, 35, 34, 32, 44, 49, 48, 47, 50, 52, 50, 61, 34, 32, 28, 38, 61, 60, 60, 46, 30, 35, 47, 54, 55, 40, 57, 48, 47, 52, 37, 54, 46, 33, 58, 48, 44, 33, 45, 39, 38, 27, 21, 20, 30, 22, 25, 47, 48, 36, 33, 46, 37, 37, 27, 36, 35, 25, 47, 46, 54, 42, 37, 43, 48, 35, 45, 48, 48, 36, 33, 25, 38, 25, 45, 44, 35, 36) H<-c(158, 148, 124, 104, 143, 99, 109, 96, 119, 123, 135, 133, 126, 130, 134, 120, 94, 102, 85, 104, 134, 137, 140, 126, 92, 98, 130, 137, 140, 106, 135, 135, 134, 135, 104, 135, 129, 107, 131, 124, 124, 99, 123, 110, 105, 87, 75, 65, 86, 69, 79, 124, 131, 107, 100, 126, 114, 108, 80, 106, 118, 83, 112, 132, 127, 120, 110, 115, 131, 111, 121, 121, 123, 104, 93, 76, 112, 110, 112, 123, 101, 105) S<-c(345, 290, 167, 67, 238, 68, 75, 54, 128, 150, 117, 190, 160, 212, 208, 235, 52, 74, 42, 69, 268, 323, 359, 167, 33, 51, 170, 224, 238, 68, 208, 167, 187, 191, 58, 180, 188, 65, 299, 159, 145, 54, 129, 104, 66, 24, 19, 10, 36, 18, 20, 133, 179, 69, 59, 120, 72, 74, 27, 52, 76, 25, 125, 138, 191, 148, 90, 120, 203, 64, 124, 153, 151, 68, 51, 19, 88, 53, 61, 133, 68, 64) M<-c(47, 52, 27, 13, 31, 14, 15, 4, 23, 32, 30, 26, 24, 31, 31, 42, 9, 13, 7, 13, 50, 39, 47, 40, 5, 12, 26, 32, 40, 16, 33, 28, 28, 42, 15, 27, 33, 14, 29, 26, 25, 9, 18, 23, 13, 6, 6, 1, 8, 5, 6, 22, 24, 13, 11, 18, 17, 15, 7, 14, 14, 8, 18, 24, 29, 21, 17, 17, 34, 16, 22, 24, 19, 13, 10, 5, 15, 12, 12, 24, 15, 16) mussels <- cbind(L,W,H,S,M) rm(L,W,H,S,M) #species data is from Cook and Weisberg (1999, pp. 285-286) #Y = number of species, endem = number of endemic species #area = area of island, elev = elevation of island #distnear = distance of nearest island, distsc = distance to Santa Cruz #areanear = area of nearest island Y <- c(58, 31, 3, 25, 2, 18, 10, 8, 2, 97, 93, 58, 5, 40, 347, 51, 2, 104, 108, 12, 70, 280, 237, 444, 62, 285, 44, 16, 21) endem <- c(23, 21, 3, 9, 1, 11, 7, 4, 2, 26, 35, 17, 4, 19, 89, 23, 2, 37, 33, 9, 30, 65, 81, 95, 28, 73, 16, 8, 12) area <- c(25.09, 1.24, 0.21, 0.10, 0.05, 0.34, 2.33, 0.03, 0.18, 58.27, 634.49, 0.57, 0.78, 17.35, 4669.32, 129.49, 0.01, 59.56, 17.95, 0.23, 4.89, 551.62, 572.33, 903.82, 24.08, 170.92, 1.84, 1.24, 2.85) elev <- c(NA, 109, 114, 46, NA, NA, 168, NA, 112, 198, 1494, 49, 227, 76, 1707, 343, 25, 777, 458, NA, 367, 716, 906, 864, 259, 640, NA, 186, 253) distnear <- c(0.6, 0.6, 2.8, 1.9, 1.9, 8.0, 34.1, 0.4, 2.6, 1.1, 4.3, 1.1, 4.6, 47.4, 0.7, 29.1, 3.3, 29.1, 10.7, 0.5, 4.4, 45.2, 0.2, 0.6, 16.5, 2.6, 0.6, 6.8, 34.1) distsc <- c(0.6, 26.3, 58.7, 47.4, 1.9, 8.0, 290.2, 0.4, 50.2, 88.3, 95.3, 93.1, 62.2, 92.2, 28.1, 85.9, 45.9, 119.6, 10.7, 0.6, 24.4, 66.5, 19.8, 0.0, 16.5, 49.2, 9.6, 50.9, 254.7) areanear <- c(1.84, 572.33, 0.78, 0.18, 903.82, 1.84, 2.85, 17.95, 0.10, 0.57, 4669.32, 58.27, 0.21, 129.49, 634.49, 59.56, 0.10, 129.49, 0.03, 25.09, 572.33, 0.57, 4.89, 0.52, 0.52, 0.10, 25.09, 17.95, 2.33) species <- cbind(Y,endem,area,elev,distnear,distsc, areanear) rm(Y,endem,area,elev,distnear,distsc, areanear) #Gladstone (1905), brain data cbrainy<-c( 1297, 1335, 1282, 1590, 1300, 1400, 1255, 1355, 1375, 1340, 1380, 1355, 1522, 1208, 1405, 1358,1292, 1340, 1400, 1357, 1287, 1275, 1270, 1505, 1490, 1485, 1310, 1420, 1318, 1432, 1364, 1405, 1432, 1207, 1375, 1350, 1236, 1250, 1350, 1320, 1525, 1570, 1340, 1422, 1506, 1215, 1311, 1300, 1224, 1350, 1335, 1390, 1400, 1225, 1310, 1560, 1330, 1222, 1415, 1175, 1330, 1485, 1470, 1135, 1310, 1154, 1510, 1415, 1468, 1390, 1380, 1432, 1240, 1225, 1188, 1252, 1315, 1245, 1430, 1279, 1245, 1309, 1412, 1120, 1220, 1440, 1370, 1192, 1230, 1346, 1290, 1165, 1240, 1132, 1242, 1270, 1218, 1430, 1586, 1320, 1290, 1260, 1425, 1226, 1360, 1620, 1310, 1250, 1295, 1290, 1290, 1275, 1250, 1270, 1362, 1300, 1173, 1256, 1440, 1180, 1306, 1350, 1125, 1165, 1312, 1300, 1270, 1335, 1450, 1310, 1235, 1260, 1165, 1080, 1127, 1252, 1200, 1290, 1334, 1380, 1140, 1243, 1340, 1168, 1322, 1249, 1321, 1192, 1373, 1170, 1265, 1235, 1302, 1241, 1078, 1520, 1460, 1075, 1280, 1180, 1250, 1190, 1374, 1306, 1202, 1240, 1316, 1280, 1350, 1180, 1210, 1127, 1324, 1210, 1290, 1100, 1280, 1175, 1160, 1205, 1163, 1243, 1350, 1237, 1204, 1090, 1355, 1250, 1076, 1120, 1220, 1240, 1220, 1095, 1235, 1105, 1405, 1150, 1305, 1220, 1296, 1175, 955, 1070, 1320, 1060, 1130, 1250, 1225, 1180, 1178, 1142, 1130, 1185, 1012, 1280, 1103, 1408, 1300, 1246, 1350, 1060, 1350, 1220, 1110, 1215, 1104, 1170, 1120, 450, 1052, 1015, 1120, 1240, 1310, 1095, 1080, 1202, 1200, 1375, 1450, 1417, 1412, 1379, 1322, 1454, 1618, 1095, 1230, 1460, 1278, 1412, 1450, 385, 490, 512, 503, 960, 995, 1100, 1120, 1205, 1205, 1180, 1147, 1419, 1245) age <-c(39.00, 35.00, 35.00, 37.00, 34.00, 38.00, 45.00, 45.00, 40.00, 21.00, 42.00, 25.00, 23.00, 45.00, 41.00, 28.00, 45.00, 20.00, 43.00, 43.00, 25.00, 29.00, 40.00, 40.00, 29.00, 35.00, 27.00, 20.00, 38.00,24.00, 41.00, 45.00, 41.00, 37.00, 29.00, 36.00, 21.00, 38.00, 42.00, 43.00, 37.00, 31.00, 34.00, 44.00, 24.00, 28.00, 44.00, 43.00, 44.00, 38.00, 35.00, 39.00, 36.00, 43.00, 42.00, 50.00, 47.00, 47.00, 49.00, 50.00,48.00, 47.00, 50.00, 48.00, 47.00, 50.00, 48.00, 49.00, 48.00, 49.00, 48.00, 50.00, 69.00, 58.00, 57.00, 67.00, 61.00, 53.00, 60.00, 58.00, 55.00, 57.00, 75.00, 63.00, 69.00, 52.00, 66.00, 67.00, 67.00, 53.00, 55.00, 56.00, 54.00, 59.00, 65.00, 59.00, 60.00, 60.00, 60.00, 62.00, 70.00, 59.00, 51.00, 57.00, 51.00, 65.00, 56.00, 75.00, 47.00, 57.00, 62.00, 84.00, 48.00, 58.00, 63.00, 52.00, 57.00, 52.00, 58.00, 63.00, 57.00, 46.00, 61.00, 58.00, 55.00, 50.00, 52.00, 51.00, 63.00, 72.00, 20.00, 35.00, 40.00, 30.00, 33.00, 42.00, 35.00, 31.00, 35.00, 23.00, 38.00, 23.00, 32.00, 34.00, 37.00, 39.00, 34.00, 41.00, 43.00, 33.00, 28.00, 33.00, 34.00, 38.00, 45.00, 25.00, 39.00, 36.00, 36.00, 40.00, 22.00, 40.00, 40.00, 40.00, 30.00, 42.00, 34.00, 42.00, 39.00, 39.00, 24.00, 44.00, 24.00, 30.00, 37.00, 33.00, 38.00, 26.00, 25.00, 35.00, 21.00, 50.00, 46.00, 50.00, 46.00, 49.00, 49.00, 48.00, 47.00, 53.00, 56.00, 64.00, 69.00, 60.00, 55.00, 55.00, 53.00, 60.00, 54.00, 60.00, 62.00, 53.00, 68.00, 63.00, 51.00, 78.00, 57.00, 68.00, 58.00, 59.00, 49.00, 56.00, 58.00, 62.00, 67.00, 47.00, 64.00, 47.00, 74.00, 67.00, 63.00, 74.00, 49.00, 46.00, 46.00, 50.00, 63.00, 56.00, 63.00, 0.21, 1.70, 2.50, 2.50, 2.70, 3.00, 3.50, 3.80, 4.00, 5.00, 7.00, 8.00, 10.00, 11.00, 12.00, 12.00, 12.50, 15.00, 17.00, 18.00, 19.00, 19.00, 19.00, 19.00, 0.06, 0.21, 0.42, 0.50, 0.67, 2.50, 3.50, 7.00, 7.00, 15.00, 17.00, 18.00, 18.00, 19.00) ageclass<-c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) breadth <-c( 149.5, 152.5, 145.5, 153.0, 146.0, 143.0, 140.0, 147.0, 150.5, 141.0, 148.5, 142.0, 154.0, 152.0, 148.0, 147.0, 144.5, 138.0, 160.0, 149.0, 158.0, 146.0, 151.0, 168.0, 151.0, 151.0, 139.0, 147.0, 153.0, 148.0, 159.0, 159.0, 153.0, 149.0, 142.0, 153.0, 142.0, 141.0, 152.0, 143.0, 155.0, 154.0, 153.0, 156.0, 158.0, 141.0, 143.0, 144.0, 154.5, 148.5, 161.0, 162.0, 154.0, 152.0, 152.0, 155.0, 149.0, 144.0, 153.0, 145.0, 159.0, 155.0, 148.0, 146.5, 141.0, 147.0, 154.0, 160.0, 151.5, 152.0, 160.0, 158.0, 148.0, 148.0, 140.0, 153.0, 150.0, 150.0, 146.0, 141.0, 146.0, 152.0, 150.0, 141.0, 141.0, 151.0, 151.0, 144.0, 145.0, 149.0, 143.0, 148.0, 144.0, 141.0, 156.0, 143.0, 148.0, 153.0, 148.0, 150.0, 148.0, 151.0, 143.5, 143.0, 152.0, 157.0, 150.0, 145.5, 150.0, 149.0, 149.5, 145.0, 147.0, 154.0, 152.0, 155.0, 147.0, 164.0, 158.0, 149.0, 157.0, 144.0, 144.0, 145.0, 154.0, 151.0, 145.0, 146.0, 156.0, 151.0, 139.5, 153.0, 144.0, 138.0, 143.0, 143.0, 140.0, 145.0, 158.0, 156.0, 142.5, 141.0, 141.0, 137.0, 139.0, 141.0, 144.0, 147.0, 151.5, 137.0, 142.0, 151.0, 152.0, 141.0, 138.0, 153.5, 145.0, 140.0, 147.0, 143.0, 144.0, 146.0, 151.0, 150.0, 141.0, 145.0, 145.0, 151.0, 151.0, 143.0, 147.0, 147.0, 154.0, 141.0, 144.0, 147.0, 154.0, 142.0, 145.0, 148.0, 148.0, 137.5, 144.0, 141.0, 141.0, 141.0, 151.0, 149.0, 130.0, 140.0, 136.5, 145.0, 142.0, 142.0, 139.0, 146.0, 154.0, 136.0, 157.5, 144.0, 153.0, 144.0, 131.0, 135.0, 146.0, 138.0, 135.0, 149.0, 143.0, 142.0, 139.0, 144.0, 140.0, 143.0, 132.0, 152.0, 147.0, 145.0, 152.0, 142.0, 152.0, 143.0, 145.0, 140.0, 146.0, 150.0, 143.0, 144.0, 148.0, 95.0, 125.0, 131.0, 135.5, 139.0, 137.0, 129.5, 135.0, 134.0, 132.0, 140.0, 140.0, 149.5, 145.0, 141.0, 142.0, 143.0, 151.0, 150.5, 142.0, 146.0, 157.0, 147.5, 149.0, 88.5, 99.0, 95.0, 104.5, 126.0, 132.0, 126.0, 131.0, 133.0, 139.0, 143.5, 135.0, 151.0, 141.0) cause <- c(1, 3, 3, 1, 2, 3, 3, 2, 2, 3, 2, 2, 1, 2, 1, 1, 3, 1, 3, 3, 2, 3, 3, 1, 2, 2, 2, 1, 3, 2, 2, 3, 3, 2, 2, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 1, 3, 3, 2, 3, 2, 3, 1, 3, 2, 1, 1, 2, 2, 3, 2, 3, 2, 2, 1, 3, 1, 2, 3, 1, 2, 1, 3, 3, 1, 2, 2, 2, 3, 3, 3, 2, 1, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 1, 1, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 1, 2, 3, 1, 1, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 1, 1, 2, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3, 1, 3, 3, 1, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 1, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 1, 3, 3, 2, 2, 3, 2, 2, 3, 1, 3, 2, 3, 3, 3, 3, 1, 1, 2, 2, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 3, 2) cephalic<-c(81.9, 75.9, 75.4, 78.5, 78.5, 74.2, 74.1, 79.2, 76.7, 76.2, 75.4, 75.3, 77.8, 79.6, 76.3, 80.3, 78.3, 77.5, 83.3, 78.4, 80.2, 77.0, 83.0, 86.2, 76.3, 78.2, 75.5, 73.5, 84.1, 77.9, 85.0, 83.2, 78.3, 78.0, 77.2, 81.8, 78.0, 73.4, 78.1, 75.3, 77.1, 81.1, 79.7, 80.8, 82.3, 77.9, 75.3, 75.4, 80.1, 76.7, 82.6, 85.3, 79.8, 83.5, 79.6, 80.7, 78.4, 74.6, 80.5, 80.6, 79.1, 77.1, 75.9, 80.3, 76.8, 78.2, 75.5, 81.2, 79.3, 80.9, 85.1, 80.6, 77.9, 80.7, 76.1, 81.6, 79.8, 81.1, 76.8, 78.3, 78.1, 77.6, 77.7, 75.4, 75.0, 77.0, 77.8, 80.0, 79.2, 78.0, 73.0, 77.1, 77.6, 77.5, 85.2, 75.3, 80.2, 79.7, 76.3, 80.2, 77.5, 79.0, 71.7, 77.3, 78.8, 77.3, 79.2, 75.3, 78.1, 79.7, 81.2, 76.3, 77.4, 81.5, 76.4, 81.2, 80.3, 85.4, 74.5, 79.3, 80.9, 73.5, 78.3, 84.4, 82.8, 79.9, 74.0, 77.2, 78.8, 82.1, 75.6, 81.8, 79.1, 77.3, 79.0, 76.5, 77.8, 80.1, 81.9, 88.6, 77.0, 77.9, 76.6, 76.5, 74.7, 77.5, 75.8, 80.1, 81.0, 76.1, 79.3, 81.2, 79.6, 79.7, 77.5, 84.8, 78.8, 78.0, 80.3, 81.9, 80.4, 79.3, 78.2, 81.1, 79.7, 77.1, 77.1, 79.9, 79.5, 81.7, 79.0, 78.0, 81.1, 75.4, 77.0, 82.1, 78.2, 81.1, 78.0, 77.9, 77.5, 73.5, 77.8, 77.9, 77.0, 81.5, 81.6, 80.5, 73.4, 83.3, 73.0, 79.7, 76.8, 78.0, 77.2, 83.9, 81.9, 76.0, 87.0, 80.4, 79.7, 80.0, 75.7, 78.5, 79.8, 78.4, 75.0, 79.3, 80.3, 78.9, 76.8, 77.4, 76.9, 77.7, 75.4, 82.2, 81.7, 75.5, 79.6, 76.8, 80.4, 76.9, 78.2, 71.4, 83.9, 82.9, 80.3, 78.3, 82.7, 76.0, 74.4, 81.9, 78.8, 85.8, 75.7, 76.2, 77.6, 77.5, 77.7, 80.0, 72.9, 79.9, 80.1, 77.1, 77.6, 77.7, 79.2, 79.6, 82.6, 78.1, 82.2, 78.2, 78.8, 77.6, 76.4, 73.6, 78.8, 87.5, 76.7, 76.8, 77.5, 76.4, 75.1, 80.6, 75.4, 81.2, 78.8) circum <- c( 550.0, 569.0, 542.0, 576.0, 547.0, 549.0, 535.0, 555.0, 565.0, 537.0, 570.0, 558.0, 576.0, 561.0, 569.0, 551.0, 541.0, 517.0, 580.0, 568.0, 582.0, 554.0, 541.0, 577.0, 564.0, 570.0, 537.0, 574.0, 567.0, 564.0, 574.0, 584.0, 574.0, 548.0, 530.0, 560.0, 528.0, 540.0, 558.0, 539.0, 587.0, 550.0, 563.0, 578.0, 569.0, 510.0, 545.0, 555.0, 570.0, 560.0, 558.0, 557.0, 569.0, 538.0, 549.0, 560.0, 544.0, 556.0, 553.0, 530.0, 585.0, 585.0, 568.0, 541.0, 548.0, 550.0, 583.0, 589.0, 564.0, 563.0, 576.0, 575.0, 560.0, 540.0, 544.0, 569.0, 552.0, 545.0, 564.0, 532.0, 572.0, 569.0, 553.0, 534.0, 540.0, 552.0, 570.0, 531.0, 524.0, 568.0, 551.0, 558.0, 530.0, 528.0, 544.0, 536.0, 525.0, 579.0, 555.0, 548.0, 565.0, 558.0, 565.0, 542.0, 556.0, 585.0, 549.0, 555.0, 561.0, 555.0, 536.0, 545.0, 549.0, 557.0, 588.0, 558.0, 544.0, 563.0, 585.0, 551.0, 573.0, 556.0, 531.0, 534.0, 558.0, 547.0, 556.0, 553.0, 583.0, 554.0, 523.0, 558.0, 532.0, 498.5, 531.0, 552.0, 521.0, 539.0, 554.0, 555.0, 542.0, 540.0, 532.0, 505.0, 532.0, 521.0, 552.0, 533.0, 565.0, 530.0, 528.0, 560.0, 546.0, 523.0, 514.0, 550.0, 535.0, 519.0, 535.0, 513.0, 513.0, 528.0, 548.0, 535.0, 502.0, 539.0, 540.0, 543.0, 555.0, 515.0, 560.0, 541.0, 552.0, 540.0, 530.0, 490.0, 578.0, 545.0, 542.0, 547.0, 541.0, 528.0, 532.0, 522.0, 522.0, 514.0, 545.0, 530.0, 516.0, 515.0, 531.0, 540.0, 525.0, 515.0, 518.0, 506.0, 577.0, 518.0, 548.0, 523.0, 561.0, 523.0, 502.0, 520.0, 532.0, 509.0, 525.0, 540.0, 515.0, 524.0, 512.0, 541.0, 520.0, 528.0, 509.0, 547.0, 532.0, 565.0, 564.0, 528.0, 548.0, 542.0, 535.0, 521.0, 532.0, 542.0, 522.0, 533.0, 537.0, 361.0, 481.0, 474.0, 486.0, 483.0, 514.0, 476.0, 501.0, 501.0, 490.0, 504.0, 530.0, 539.0, 532.0, 540.0, 528.0, 530.0, 547.0, 536.0, 520.0, 556.0, 558.0, 540.0, 547.0, 324.0, 369.0, 368.0, 385.0, 437.0, 496.0, 475.0, 493.0, 485.0, 518.0, 524.0, 507.0, 554.0, 512.0) headht<-c(137.0, 139.0, 134.5, 140.0, 132.0, 137.5, 134.5, 132.5, 135.0, 132.0, 136.5, 136.0, 138.0, 132.0, 135.0, 130.0, 130.0, 126.0, 144.0, 137.0, 130.0, 137.5, 135.0, 135.0, 135.0, 138.0, 135.0, 142.0, 136.0, 135.0, 138.0, 137.0, 139.0, 134.0, 135.0, 131.0, 129.0, 129.0, 134.0, 129.0, 138.5, 130.0, 131.5, 134.0, 142.0, 124.0, 134.0, 132.5, 130.5, 132.0, 136.0, 132.0, 135.0, 125.0, 134.0, 140.0, 139.0, 132.0, 133.0, 130.0, 139.0, 136.5, 129.5, 124.5, 132.0, 122.0, 141.0, 139.0, 138.5, 135.0, 137.0, 131.0, 136.0, 131.0, 130.5, 137.0, 136.0, 138.0, 139.0, 128.0, 131.0, 132.0, 133.0, 125.5, 128.5, 132.0, 142.0, 128.0, 138.0, 137.0, 132.0, 133.0, 130.0, 136.0, 128.0, 128.0, 128.0, 130.5, 135.0, 130.5, 128.0, 126.5, 140.5, 128.5, 133.5, 139.0, 130.0, 125.5, 124.0, 129.0, 123.0, 127.0, 128.5, 134.0, 136.0, 133.0, 126.0, 132.0, 134.0, 129.0, 133.0, 134.0, 128.0, 126.0, 141.5, 125.5, 129.0, 128.0, 131.0, 132.0, 133.5, 132.5, 126.0, 126.0, 122.5, 132.0, 126.0, 131.0, 128.0, 142.0, 129.0, 128.0, 133.0, 126.0, 132.0, 129.5, 134.5, 127.5, 136.0, 128.0, 129.0, 132.0, 138.0, 131.0, 125.0, 136.0, 133.0, 131.0, 130.0, 123.5, 126.0, 125.0, 130.5, 128.5, 126.0, 128.5, 131.0, 130.5, 126.0, 128.0, 132.0, 128.5, 136.0, 134.0, 137.0, 120.0, 132.0, 128.0, 129.0, 129.5, 128.5, 132.0, 139.0, 124.0, 130.0, 123.0, 132.0, 129.0, 120.5, 130.0, 131.0, 132.0, 125.5, 130.0, 129.0, 129.0, 133.0, 126.0, 129.5, 132.0, 136.0, 128.0, 120.0, 126.5, 134.0, 121.0, 123.0, 128.0, 124.0, 127.0, 126.0, 122.0, 133.0, 128.5, 124.0, 133.0, 131.5, 131.0, 128.0, 125.0, 130.0, 121.0, 137.0, 135.0, 126.5, 125.0, 127.0, 126.5, 128.0, 89.5, 119.0, 111.0, 121.0, 116.0, 124.4, 121.0, 127.0, 127.0, 125.0, 127.0, 131.5, 137.5, 130.0, 136.0, 122.5, 134.0, 141.0, 129.0, 131.0, 143.0, 135.0, 133.0, 138.0, 92.5, 94.5, 88.5, 100.0, 118.0, 124.0, 114.0, 121.0, 118.0, 128.0, 125.5, 125.0, 139.0, 131.0) height<-c(68.00, 71.00, 70.00, 68.00, 65.00, 68.00, 69.50, 68.00, 64.00, 66.00, 63.00, 69.75, 66.50, 71.00, 60.50, 63.50, 65.00, 71.50, 68.00, 68.50, 68.50, 68.50, 68.50, 72.50, 69.25, 70.50, 69.50, 69.00, 68.50, 69.75, 70.50, 71.00, 67.00, 61.00, 65.00, 61.50, 61.00, 62.50, 61.50, 62.50, 66.00, 67.50, 69.00, 69.00, 69.50, 68.50, 67.00, 66.50, 68.50, 68.50, 70.00, 69.00, 69.00, 66.00, 60.50, 68.00, 69.00, 67.00, 64.50, 69.00, 69.50, 69.00, 72.25, 64.50, 67.00, 68.00, 68.00, 68.50, 69.50, 69.00, 66.00, 69.50, 69.50, 67.00, 69.00, 68.50, 66.00, 69.00, 68.50, 70.00, 67.50, 67.00, 67.50, 61.00, 67.25, 63.25, 71.50, 69.50, 67.00, 63.00, 63.00, 65.00, 65.00, 63.00, 65.50, 64.00, 65.50, 69.00, 68.00, 65.00, 67.00, 67.50, 68.00, 66.00, 68.50, 70.50, 66.00, 72.50, 69.50, 67.25, 69.75, 50.50, 61.50, 68.50, 67.50, 67.00, 66.00, 69.00, 72.00, 65.00, 67.50, 69.00, 64.00, 66.00, 65.00, 66.00, 66.00, 67.00, 69.00, 66.50, 63.00, 63.50, 60.00, 51.00, 58.00, 62.00, 66.00, 66.00, 64.00, 69.00, 65.50, 67.50, 70.00, 58.00, 60.00, 64.00, 63.00, 66.50, 66.50, 59.00, 64.00, 67.00, 66.00, 65.50, 68.00, 67.00, 62.00, 61.00, 62.50, 60.00, 58.50, 63.50, 61.50, 65.00, 63.00, 64.50, 63.50, 62.50, 63.50, 62.50, 64.00, 64.50, 64.50, 63.50, 63.75, 55.00, 67.00, 64.50, 61.50, 63.00, 66.50, 60.00, 66.00, 63.00, 67.50, 60.50, 65.50, 64.00, 66.50, 64.00, 60.00, 61.50, 62.50, 61.00, 61.50, 59.00, 61.00, 60.50, 60.50, 64.50, 62.50, 61.00, 61.00, 62.50, 66.00, 58.50, 61.00, 64.00, 60.50, 65.00, 57.00, 60.00, 59.00, 62.50, 63.00, 64.00, 59.00, 65.00, 63.00, 62.00, 63.00, 62.00, 65.50, 67.00, 62.50, 64.50, 58.50, 64.50, 62.00, 22.00, 37.50, 31.75, 33.00, 38.00, 37.00, 35.50, 34.00, 39.00, 37.50, 46.00, 43.00, 52.00, 55.00, 65.00, 48.50, 56.50, 62.50, 67.00, 71.00, 67.00, 67.50, 65.50, 69.50, 21.50, 22.00, 22.50, 24.50, 25.00, 33.50, 38.50, 47.00, 48.00, 61.50, 61.00, 53.50, 66.00, 65.00) len <-c( 182.5, 201.0, 193.0, 195.0, 186.0, 192.5, 189.0, 185.5, 196.0, 185.0, 197.0, 188.5, 198.0, 191.0, 194.0, 183.0, 184.5, 178.0, 192.0, 190.0, 197.0, 189.5, 182.0, 195.0, 198.0, 193.0, 184.0, 200.0, 182.0, 190.0, 187.0, 191.0, 195.5, 191.0, 184.0, 187.0, 182.0, 192.0, 194.5, 190.0, 201.0, 190.0, 192.0, 193.0, 192.0, 181.0, 190.0, 191.0, 193.0, 193.5, 195.0, 190.0, 193.0, 182.0, 191.0, 192.0, 190.0, 193.0, 190.0, 180.0, 201.0, 201.0, 195.0, 182.5, 183.5, 188.0, 204.0, 197.0, 191.0, 188.0, 188.0, 196.0, 190.0, 183.5, 184.0, 187.5, 188.0, 185.0, 190.0, 180.0, 187.0, 196.0, 193.0, 187.0, 188.0, 196.0, 194.0, 180.0, 183.0, 191.0, 196.0, 192.0, 185.5, 182.0, 183.0, 190.0, 184.5, 192.0, 194.0, 187.0, 191.0, 191.0, 200.0, 185.0, 193.0, 203.0, 189.5, 193.0, 192.0, 187.0, 184.0, 190.0, 190.0, 189.0, 199.0, 191.0, 183.0, 192.0, 212.0, 188.0, 194.0, 196.0, 184.0, 171.0, 186.0, 189.0, 196.0, 189.0, 198.0, 184.0, 184.5, 187.0, 182.0, 178.5, 181.0, 187.0, 180.0, 181.0, 193.0, 176.0, 185.0, 181.0, 184.0, 179.0, 186.0, 182.0, 190.0, 183.5, 187.0, 180.0, 179.0, 186.0, 191.0, 177.0, 178.0, 181.0, 184.0, 179.5, 183.0, 174.5, 179.0, 184.0, 193.0, 185.0, 177.0, 188.0, 188.0, 189.0, 190.0, 175.0, 186.0, 188.5, 190.0, 187.0, 187.0, 179.0, 197.0, 175.0, 186.0, 190.0, 191.0, 187.0, 185.0, 181.0, 183.0, 173.0, 185.0, 185.0, 177.0, 168.0, 187.0, 182.5, 185.0, 182.0, 180.0, 174.0, 188.0, 179.0, 181.0, 179.0, 192.0, 180.0, 173.0, 172.0, 183.0, 176.0, 180.0, 188.0, 178.0, 180.0, 181.0, 186.0, 182.0, 184.0, 175.0, 185.0, 180.0, 192.0, 191.0, 185.0, 189.0, 186.0, 185.5, 196.0, 174.0, 181.0, 178.0, 184.0, 179.0, 125.0, 168.0, 160.0, 172.0, 162.0, 181.0, 170.0, 174.0, 173.0, 170.0, 175.0, 192.0, 187.0, 181.0, 183.0, 183.0, 184.0, 190.5, 189.0, 172.0, 187.0, 191.0, 188.5, 189.0, 114.0, 129.5, 129.0, 132.5, 144.0, 172.0, 164.0, 169.0, 174.0, 185.0, 178.0, 179.0, 186.0, 179.0) sex<-c(1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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) size <- c(3738, 4261, 3777, 4177, 3585, 3785, 3559, 3613, 3982, 3443, 3993, 3640, 4208, 3832, 3876, 3497, 3466, 3095, 4424, 3878, 4046, 3804, 3710, 4423, 4036, 4022, 3454, 4175, 3787, 3796, 4103, 4161, 4158, 3814, 3527, 3748, 3334, 3492, 3962, 3505, 4315, 3804, 3863, 4034, 4308, 3165, 3641, 3644, 3891, 3793, 4270, 4063, 4012, 3458, 3890, 4166, 3935, 3669, 3866, 3393, 4442, 4253, 3737, 3329, 3415, 3372, 4430, 4381, 4008, 3858, 4121, 4057, 3824, 3558, 3362, 3930, 3835, 3830, 3856, 3249, 3577, 3933, 3850, 3309, 3406, 3907, 4160, 3318, 3662, 3899, 3700, 3779, 3473, 3490, 3654, 3478, 3495, 3834, 3876, 3661, 3618, 3648, 4032, 3399, 3916, 4430, 3695, 3524, 3571, 3594, 3383, 3499, 3589, 3900, 4114, 3937, 3399, 4200, 4488, 3614, 4051, 3782, 3391, 3124, 4053, 3582, 3666, 3532, 4046, 3667, 3436, 3791, 3302, 3104, 3171, 3530, 3175, 3438, 3903, 3899, 3401, 3267, 3451, 3090, 3413, 3323, 3680, 3439, 3853, 3156, 3279, 3707, 4006, 3269, 3071, 3779, 3548, 3292, 3497, 3082, 3248, 3358, 3803, 3566, 3145, 3503, 3571, 3724, 3615, 3203, 3609, 3561, 3979, 3533, 3689, 3158, 4005, 3181, 3479, 3642, 3632, 3394, 3703, 3165, 3354, 3000, 3687, 3556, 2773, 3058, 3344, 3493, 3297, 3360, 3228, 3277, 3851, 3067, 3692, 3402, 3995, 3318, 2720, 2937, 3580, 2939, 2989, 3586, 3156, 3246, 3170, 3268, 3389, 3381, 2864, 3740, 3479, 3647, 3716, 3284, 3735, 3218, 3685, 3704, 3214, 3394, 3233, 3352, 3391, 1063, 2499, 2327, 2820, 2612, 3087, 2664, 2983, 2944, 2805, 3112, 3535, 3844, 3412, 3509, 3183, 3526, 4056, 3669, 3200, 3904, 4048, 3698, 3886, 933, 1212, 1085, 1385, 2141, 2815, 2356, 2679, 2731, 3292, 3206, 3021, 3904,3306) cbrainx <- cbind(age,ageclass,breadth,cause,cephalic,circum,headht,height,len,sex,size) #Glado data is Gladstone cbrain data where head length for case 119 in the paper was 109 #instead of 199 the case is 115 in the data set since 4 cases were deleted gladoy <- cbrainy gladox <- cbrainx gladox[115,9] <- 109 rm(age,ageclass,breadth,cause,cephalic,circum,headht,height,len,sex,size) # Hawkins data x1 <- c( -15, 9, -3, -19, -3, 11, 11, -11, -3, 9, -3, -9, 5, -11, -3, 7, 9, 11, -1, -7, 1, -3, -11, 13, -21, -1, 1, -1, 5, 7, 3, 15, 5, -5, -13, 7, -7, -1, -3, -9, -3, -9, 7, 7, -5, 7, -3, -15, -5, 3, 3, -11, 11, -15, -5, 3, 5, -9, 5, -11, -9, -3, 11, 17, -1, -15, 13, 3, -17, 9, 1, 3, 13, -7, 3, 9, 17, 13, 15, 1, -7, -9, -17, -9, 21, 9, -13, 1, 3, 23, -1, -3, 11, -7, -13, -1, -5, -9, 5, 19, 7, -1, -23, 15, -7, 17, 1, 11, 1, 5, -13, -17, -5, -11, -7, -5, 9, 17, 3, 15, -17, 1, 3, -5, 9, 5, -11, 3) x2 <- c( -10, 0, 4, 6, 0, -32, 2, 32, -2, 14, -6, 12, -24, 16, 8, 0, 18, -6, 12, 16, -12, -20, -14, -2, 12, 6, 8, 8, -10, 4, 16, 10, -28, -10, -2, -16, 0, -20, 12, 0, -16, -14, -14, 6, -6, -10, 10, 8, 8, -8, 2, -2, -2, 4, 10, -4, 4, -18, 10, 12, 2, 24, -16, -20, 14, 24, 2, -10, -6, -16, -6, 22, -4, 2, 0, -2, 10, -18, -24, 6, -4, 20, 10, -12, -12, -12, 2, 2, 20, 2, -8, -22, 14, 10, 18, -6, 28, 16, 2, -6, -2, -2, -2, -8, -6, 6, -8, -12, -14, -8, 4, -10, -4, 2, 14, 24, 12, -10, -12, -4, 20, 20, 6, -2, -20, 6, 6, -24) x3 <- c( -14, 8, 10, 12, 6, -38, 0, 38, -16, 30, -2, 12, -36, 8, -4, 18, 16, 4, -4, 12, 4, -10, -20, 4, 10, 8, 20, 10, -14, 4, 24, 14, -22, -2, 10, -12, -18, -20, 6, -8, -24, -30, -10, 6, -16, -24, 18, -6, 6, 4, 16, 0, -10, 8, 14, -10, -6, -16, 2, 22, 0, 26, -8, -24, 18, 24, -10, -18, -18, -22, -8, 32, 2, 4, -6, 0, 4, -26, -24, -2, -4, 8, 12, -8, -10, -12, -4, 12, 10, 2, -20, -32, 20, 24, 26, 2, 22, 22, 6, -12, -4, -12, -2, 6, -6, 18, -10, -22, -18, -6, -2, -4, 6, 10, 10, 36, 8, -12, -6, -8, 24, 20, 2, -6, -8, 16, -4, -26) x4 <- c(-8, -8, 0, -16, 4, 10, 18, -10, -12, 12, 4, -12, -2, -14, -16, 6, -4, 10, -6, -2, 6, 16, 2, 20, 0, -8, -6, 10, 18, -10, 0, 8, 14, -6, -4, 2, -6, 2, 8, 8, 0, -12, 20, 8, -22, 4, 0, -4, -2, 16, 12, -18, -6, 12, -18, 0, 6, 4, 6, 2, -8, -12, 14, 10, 10, 0, 4, 0, -10, -12, 8, 0, 2, 10, -4, 8, -6, 16, 0, -22, 10, -4, -6, 4, 0, 12, -20, -6, -16, 6, 6, 0, -2, -4, -16, 22, -14, 12, -2, 16, -10, 6, -6, 4, -8, 10, -10, -2, -10, 2, -2, 6, -6, 6, -20, 2, -4, 6, -8, -12, -10, -2, -4, 2, 4, 22, -10, 12) x5 <- c(2,18, 16, 8, -8, -16, -18, 16, -6, 6, -8, 26, -6, 8, 18, -2, 8, 16, 2, 10, -2, -18, -26, 0, 0, -10, 8, 0, -18, 0, 16, -2, -8, 8, -2, -10, 2, 2, -18, -18, -16, -6, 0, 10, 10, 2, 16,-8, -2, -18, 6, 18, 18, -10, 18, -16, -8, -8, -8, 6, 2, 26, -8, -16, 0, 0, 2, -16, -16, 10, 10, 16, -10, 0, 8,-2, 18, -8, 0, 10, 0, -8, -8, -8, 0, -26, 0, 8, 18, 8, -8, -16, 26, -2, 8, -10, 8, -10, 26, -8, 0, -8, -8, 8, -10, 16,0, -6, 0, 2, 10,-18, 8, -18, 0, 6,8, 8,18, 10, 16, -2, 8, -26, 8, -10, -16, -26) x6 <-c(-4, 8, -14,-6, 22, -2, 12, 2, -8, 12, -6, -8, 4, -10, -16, 8, 10, 2, -4, 4, 4, 12, -12, 14, 6, -16, 14, -2, 8, 6, -10, 4, 6, -18, -12, -4, -8, 12, -4, -8, 10, -12, 10, 20, -20, 8, 14, -2,-16, 16, 8, -12, 0, 0, -8, 14, -10, -10, 18, -8, 0, -4, 10, 2, 26, 10, 12,-14, -6, -4, 16, 18, 0, 22,-22, 0, 4, 2, -10, -16, -6, 2, 6, 18, -6, 8, -14, -14,-12, -2,-14,-18, 12,-8,-2, 16,-6, 4, 8, 6, -22, 14, 2, 2, -20, 6, 2, 8, -26, 16, 0,-4, 10, 0, -10, -4, -18, -6, 4, 0, -2, -12, 6, -8, -2, 20, -2, 4) x7 <- c(-10, -18, 6, 4, -16, 10, 4, -10, -10, 0, -6, -8, -4, 10, 2, 4, -4, 2, -14, -24, 14, 8, 22, -14, -6, 18, -10, -10, 14, 0, -4, 10, 0, 10, 18, 24, -4, -14, 8, 18, 4, 0, -4, -28, 6, -8, -4, 4, 24, -2, 10, -4, -2, 8, -14, -6, 0, 4, -10, 14, -20, -18, -10, 0, -20, -16, -18, 4, 8, 2, -18, -14, 14, -10, 16, 20, -12, 6, 16, -4, 0, -6, -12, -6, 6, 8, 14, 0, -8, 2, 10, 14, -22, 8, -6, 4, 0, -2, -12, -4, 10, 0, -2, -4, 28, -8, 10, -14, 20, -24, -14, 12, 0, 2, 4, 4, 6, 12, -8, -8, 0, 14, 6, 12, 6, -6, -2, 18) x8 <- c(59, 74, 49, 95, 57, 97, 27, 62, 56, 60, 43, 53, 72, 67, 24, 61, 68, 7, 10, 58, 76, 69, 78, 6, 43, 49, 2, 49, 67, 68, 77, 1, 97, 1, 7, 94, 89, 28, 92, 94, 7, 11, 1, 1, 93, 38, 16, 96, 23, 68, 89, 88, 73, 80, 84, 80, 98, 19, 79, 21, 94, 69, 31, 59, 31, 29, 73, 48, 81, 25, 58, 25, 24, 44, 83, 49, 33, 6, 22, 14, 78, 28, 82, 75, 90, 40, 94, 6, 12, 1, 61, 30, 2, 53, 23, 57, 14, 91, 95, 67, 9, 5, 58, 97, 18, 8, 23, 87, 58, 76, 9, 89, 70, 81, 82, 98, 25, 9, 86, 11, 59, 91, 62, 91, 87, 92, 64, 53) hy<-c( 8.88, 12.18, 5.75, 11.75, 10.52, 10.57, 1.70, 5.31, 8.51, 1.21, 3.36, 8.26, 10.14, -0.58, 7.10, -0.63, 5.87, -0.25, -9.45, 8.93, 18.88, 4.01, 8.48, -0.16, 7.26, 1.69, -4.46, 3.36, 7.53, 3.91, 6.73, -2.91, 8.80, 1.80, -2.40, 6.25, 15.60, 1.06, 9.99, 2.10, 1.63, 5.84,-2.30, 1.42, 2.67, -6.93, 0.75, 14.31, 2.93, 2.06, 5.97, 9.78, 10.20, 8.90, 7.55, 7.11,12.60, 2.80, 5.88, 3.38, 7.10, 4.43, 9.47, 4.92, 2.44, 2.03, 10.35, 5.65, 2.02, 3.45, 8.94, 9.69, 13.81, 2.66, 2.55, 5.61, 3.21, 3.41, 3.95, 2.28, 10.65, 5.70, 7.35, 6.69, 6.01, 1.01, 10.14, -2.33, 4.05, -0.90, 10.72, -2.72, -0.52, 16.00, -0.55, 4.77, 2.27, 8.13, 7.36, 4.71, 2.93, 3.42, 6.78, 4.97, 0.47, 7.64, 4.90, 6.91, 6.46, 6.94, -8.69, 11.03, 4.18, 5.16, 8.70, 6.83, 3.27, 1.71, 7.78, 0.20, 6.86, 12.06, 7.10, 11.21, 5.79, 15.30, 7.33, 7.76) hx <- cbind(x1,x2,x3,x4,x5,x6,x7,x8) rm(x1,x2,x3,x4,x5,x6,x7,x8) # Hebbler, B. (1847), marry data``Statistics of Prussia," # mmen = number of married men #mmilmen = number of married military men #milwmn = number of women married to military men #mwmn = number of married women pop <-c(821946, 619553, 387306, 577575, 857230, 432957, 353149, 782186, 799772, 517522, 413106, 175722, 1117204, 939624, 892056, 647326, 701037, 335543, 418765, 452877, 549801, 465363, 851456, 489900, 478338, 394451) mmen <- c(133217, 104159, 61670, 97363, 142600, 71710, 43455, 130111, 139617, 85671, 67357, 28389, 189939, 158427, 162699, 109785, 117142, 55557, 65510, 74236, 86664, 69870, 134882, 77345, 74970, 60449) mwmn <- c( 133217, 104245, 61688, 97456, 142538, 71374, 43639, 130663, 140088, 85892, 67681, 28623, 190063, 158456, 163279, 110621, 118078, 56305, 65989, 74698, 87629, 70040, 136012, 77755, 74970, 60534) mmilmen<- c(1129, 290, 651, 752, 931, 192, 1524, 1822, 1066, 1202, 649, 269, 1522, 1076, 1018, 941, 879, 610, 229, 410, 156, 464, 519, 781, 678, 159) milwmn <- c( 1061, 290, 612, 711, 925, 189, 1476, 1750, 955, 1089, 617, 253, 1455, 1046, 1019, 929, 792, 585, 226, 381, 156, 413, 493, 716, 591, 128) marry <- cbind(pop,mmen,mwmn,mmilmen,milwmn) rm(pop,mmen,mwmn,mmilmen,milwmn) # Kuehl (1994, p. 128), one way anova crab data, Y = count of hermit #crabs on 25 transects in each of 6 different coastline habitats crabhab <- rep(1:6,c(25,25,25,25,25,25)) ycrab<-c(0,0,22,3,17,0,0,7,11,11,73,33,0,65,13,44,20,27,48,104,233,81,22,9,2, 415,466,6,14,12,0,3,1,16,55,142,10,2,145,6,4,5,124,24,204,0,0,56,0,8, 0,0,4,13,5,1,1,4,4,36,407,0,0,18,4,14,0,24,52,314,245,107,5,6,2, 0,0,0,4,2,2,5,4,2,1,0,12,1,30,0,3,28,2,21,8,82,12,10,2,0, 0,1,1,2,2,1,2,29,2,2,0,13,0,19,1,3,26,30,5,4,94,1,9,3,0, 0,0,0,2,3,0,0,4,0,5,4,22,0,64,4,4,43,3,16,19,95,6,22,0,0) #Myers, Montgomery and Vining (2002), ceriod Poisson regression data #conc: concentration of a jet fuel that impairs reproduction #strain: type of organism #y number of Ceriodaphnia organisms counted ceriody <- c(82,58, 106, 58, 63, 62, 99, 58, 101, 73, 45, 27, 34, 28, 26, 31, 44, 28, 42, 38, 31, 19, 22, 20, 16, 22, 30, 20, 29, 28, 22, 14, 14, 14, 10, 15, 21, 14, 20, 21, 15, 9, 8, 10, 6, 12, 14, 10, 13, 16, 10, 7, 8, 3, 11, 1, 10, 8, 10, 7, 8, 4, 8, 3, 3, 2, 8, 8, 1, 4) conc <- c(0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.50, 1.50, 1.50, 1.50, 1.50, 1.50, 1.50, 1.50, 1.50, 1.50, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75, 1.75) strain <- c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) ceriodx <- cbind(conc,strain) rm(conc,strain) ##Myers, Montgomery and Vining (2002), popcorn data ## y = number of kernels that did not pop popy <- c(24, 28, 40, 42, 11, 16, 126, 34, 32, 32, 34, 17, 30, 17, 50) oil <- c(4, 3, 3, 2, 4, 3, 3, 2, 4, 2, 2, 3, 3, 3, 4) temp <- c(7, 5, 7, 7, 6, 6, 5, 6, 5, 6, 5, 7, 6, 6, 6) time <- c(90, 105, 105, 90, 105, 90, 75, 105, 90, 75, 90, 75, 90, 90, 75) popx <- cbind(oil, temp, time) rm(temp,oil,time) # Olive (2008), M580 homework data m580hwy <- c(66.7,76.0,89.7,90.0,94.0,94.0,95.0,95.3,97.0,97.7) # Olive (2008), M580 totals data m580toty <- c(66.5,84.1,84.6,84.7,90.7,91.8,93.4,95.2,96.3,98.5) #Olive (2008, table 12.1), sinc data: artificial lnsinc data = ARC data in file sinc.lsp. #predictors are iid lognormal(0,1) sincy <- c( -0.0028821674, 0.2890844565, -0.0081247662, 0.0215128476, 0.1283180354, -0.0313035088, 0.1701067014, 0.6620241567, 0.1485533713, 0.0010814439, 0.1082185514, -0.1373508414, 0.0348479121, -0.1271030906, -0.0847906761, 0.1217390276, 0.1283264411, -0.0267359337, -0.0480461634, 0.0841809740, 0.0553645449, 0.0658390820, 0.0250642521, 0.1274119552, 0.0555172502, -0.0777657468, 0.0643479882, -0.2069999083, -0.0820373141, 0.3415226347, -0.0467193915, 0.0610753393, -0.0610721638, -0.1710705847, 0.0626815282, -0.0905060084, -0.0578484194, -0.1013178695, -0.0730087933, -0.0800819373, 0.0509914573, -0.1892833492, -0.1646483125, -0.0424791680, 0.0291957612, 0.0339200339, -0.0037224662, -0.2153601962, -0.0150491128, -0.0197933829, 0.0163254372, -0.1918047434, -0.0795193957, -0.0050357705, 0.0383855089, -0.2132998245, 0.0719676836, 0.0672089340, -0.1928597478, -0.0760393477, -0.1011459395, -0.2063959898, 0.0052411853, -0.0540323523, -0.1619213670, 0.1056028659, -0.1305676960, 0.1282284589, -0.2150646665, 0.0127933615, 0.1155746276, 0.1105433083, -0.1604480056, 0.1005693127, -0.1230651908, 0.0230895770, -0.2145524477, 0.1278376090, 0.0468634023, 0.0683769738, -0.0477689517, -0.0091407306, 0.1282505304, -0.1057710078, -0.2171630267, -0.2151835125, -0.0896056321, 0.0702024631, 0.0300139410, -0.0115433705, -0.2109894885, -0.0693700000, 0.1253562949, -0.0264754557, 0.1076423512, -0.0289798917, 0.0370326060, -0.1608046667, 0.0058830634, -0.0907731417, -0.0871686771, 0.0334702265, 0.0017633618, -0.1416883937, -0.0269648806, -0.1061325008, 0.0507985637, 0.0685092554, -0.2170971667, -0.2159165764, 0.0374834281, 0.3938776376, 0.0214936471, 0.0514798183, 0.0420556740, 0.0199026042, 0.0616069746, -0.1146763518, 0.0195012331, 0.0467691350, -0.1827079711, 0.1214223380, -0.0185169172, -0.1742503531, 0.6495275708, 0.0655686459, 0.0916743814, 0.1210846072, 0.1271038944, 0.0343843854, -0.0241528882, -0.1120652480, 0.0747637899, 0.1101258953, -0.0408824535, 0.0304449679, 0.1256029734, -0.1298150431, 0.1264355476, -0.0233372295, -0.2016464417, 0.0142501460, 0.1217589461, 0.0001474315, 0.1131972396, -0.0847578357, 0.1189040575, 0.0347127579, -0.0783649358, 0.0497414573, 0.0284206909, -0.0726449926, -0.0265951848, -0.0121832077, 0.0737206254, 0.0936601646, 0.0788140405, -0.1854621672, 0.0672293395, 0.0561837358, -0.1946888925, -0.2133803206, 0.0255226801, 0.0080295811, -0.0327019439, -0.1717080813, -0.0086593656, 0.0650610877, 0.0214832524, -0.2096341815, -0.1864280717, -0.1536143117, -0.1813989703, -0.0573180366, 0.0782198699, 0.0043632996, 0.0170804848, 0.1260734335, 0.0611008383, 0.0601559682, 0.0291926024, -0.2172207765, -0.0594929833, 0.0846110629, -0.0611639274, 0.1279743324, 0.1180477004, 0.0452430666, -0.2153926256, 0.0663555324, 0.0065644849, 0.0797701340, 0.0025444100, 0.1038507902, -0.0427317749,0.2848815734, -0.1701818529, -0.0134997116, -0.1065103080, 0.0078233897) x1 <- c( 0.20672511, 0.62290732, 0.19807451, 1.98361771, 4.36977605, 1.45886008, 1.08672308, 0.52494089, 0.19143479, 3.82034546, 4.10108359, 0.60356730, 3.52670243, 1.20914638, 3.54699500, 0.89032228, 1.20257419, 1.18765744, 0.33011306, 0.90273021, 0.89686746, 0.34010200, 7.39350018, 4.54569984, 1.13610729, 1.60522770, 0.94630255, 1.72297340, 1.80439034, 0.31764386, 2.32719567, 3.21060447, 0.78906996, 0.71167073, 4.71668066, 1.84660590, 0.99940854, 2.61504941, 1.82903748, 1.31658053, 2.57038948, 1.42990250, 0.36740995, 1.22595461, 10.74544422, 0.41120430, 0.49418631, 0.34301786, 1.37791732, 6.37333379, 0.39003110, 0.21200772, 1.19673040, 9.48549033, 0.66963043, 0.62844351, 0.87599847, 0.24051425, 1.28459648, 0.16568908, 0.54814800, 1.34847954, 1.20407895, 1.21916491, 0.43364911, 0.04589704, 0.36326031, 3.94269004, 1.12192231, 0.41466353, 0.38334095, 0.42919126, 2.85461115, 1.54177512, 1.49008366, 3.87309831, 0.66145610, 0.22212880, 1.55050397, 1.09119253, 7.37369049, 0.47339670, 0.41703586, 0.13712570, 0.28867720, 3.33974990, 0.62109557, 3.76449125, 5.91536585, 1.41930636, 0.25098993, 2.54255926, 0.68757924, 10.56786887, 3.24098851, 0.29482579, 0.76562854, 2.66006216, 3.48049805, 0.55868387, 2.44974040, 1.99070648, 1.79760883, 0.59280588, 0.93981176, 0.58421144, 2.04962249, 0.31754219, 1.12501905, 0.63481078, 1.55558569, 0.30898299, 0.92822064, 0.40358609, 7.34010086, 2.35134957, 8.54008641, 1.92822564, 0.39006936, 0.59276421, 1.21989966, 2.05842094, 4.52749785, 2.34639869, 0.38768654, 0.99148448, 0.28503702, 1.83971817, 5.26543189, 1.66854456, 0.96018317, 1.89786261, 1.19663199, 0.23736736, 0.22233782, 0.40250403, 6.15225584, 1.68036766, 0.32739487, 2.80632027, 0.88062512, 1.30961432, 3.06256122, 1.10947349, 0.70178793, 0.44552088, 0.46911987, 2.37923659, 0.49821153, 0.82425332, 0.50668566, 0.24832221, 0.42151044, 2.65718552, 0.22752465, 2.03660895, 0.16196615, 0.53975376, 0.33079254, 0.37980041, 1.97472442, 0.63329452, 4.46371227, 0.67485238, 1.84869200, 0.60581459, 5.48894751, 0.27572585, 0.48587739, 1.75641040, 2.51462863, 0.82634301, 0.26188583, 0.94367579, 2.30838716, 7.28096375, 0.37863598, 1.21102957, 3.74376594, 0.42787304, 4.73680746, 0.39513750, 4.28094170, 0.17274637, 0.83435234, 0.08279293, 3.70274293, 11.38592586, 0.71561836, 0.22071824, 0.58616560, 0.63920280, 2.22390768, 0.82899805, 0.42226816, 0.53569823, 0.38961313, 1.00964133, 0.75047254, 4.11136605) x2 <- c( 0.55444874, 0.58836933, 0.17220385, 4.90644479, 0.72690244, 3.97389471, 0.26829651, 0.33584434, 0.96111332, 0.73206284, 1.14050948, 0.71548158, 0.09767094, 0.88513865, 3.22312318, 2.35071272, 0.98510979, 0.32037331, 0.73983438, 0.57959638, 0.13545758, 1.20410001, 0.87043463, 1.21717343, 0.79922917, 1.74230832, 3.63936970, 0.58779203, 2.92495291, 0.24068963, 1.75359844, 2.12907051, 1.60089613, 0.96380695, 1.99358683, 1.47489434, 1.08012371, 0.83093250, 1.88057454, 3.97872471, 0.41735236, 0.74326798, 1.75575002, 0.37056703, 10.72977208, 2.15899779, 5.91792012, 0.18424986, 1.98102061, 1.19849683, 6.44785311, 0.66306528, 0.55958680, 0.49667554, 9.58472697, 1.02672887, 0.99072966, 0.24154097, 0.22765513, 1.48862002, 0.28556245, 1.01568558, 1.60537632, 2.59466980, 1.13034039, 1.22693290, 0.64868520, 1.25945356, 0.77325131, 0.84550135, 0.34268290, 0.90606556, 0.13048149, 1.20608659, 0.61457837, 16.03693971, 1.29382313, 2.05880679, 5.12118118, 3.35464562, 1.18753373, 1.56682537, 3.06204219, 2.00770809, 1.20265634, 0.36229206, 1.08235714, 2.20376428, 9.08536982, 0.94994874, 1.33273071, 0.21903843, 1.30793415, 0.26946015, 1.64065119, 3.92661445, 0.27533739, 0.22543267, 2.41913368, 2.03223474, 2.78453223, 1.18290470, 6.52094224, 0.96504155, 1.04770219, 0.72865200, 0.51638003, 1.12291507, 1.09836277, 1.22092775, 8.81215533, 0.77094509, 0.65024025, 0.79126528, 2.38652915, 0.09542528, 1.68690296, 1.67511767, 0.45286664, 2.66811663, 0.97473253, 2.30358109, 2.31228015, 0.80354026, 0.29929606, 4.79583864, 0.98282197, 0.67781739, 1.09002355, 3.65107083, 0.16492451, 0.98739819, 0.47273080, 2.22086336, 1.29584633, 1.47752046, 0.21544401, 0.89290121, 0.47963635, 19.64955170, 1.75066860, 0.27493236, 1.29901764, 2.07407136, 0.46779140, 0.25525613, 1.30773866, 0.96717771, 2.56337687, 2.89910227, 0.95438405, 0.49382506, 0.62107197, 5.69770613, 0.68207018, 0.35122262, 1.47052433, 0.65254874, 0.24008857, 0.73643811, 0.78006933, 0.36545248, 1.04915063, 0.45480938, 3.23339743, 1.24117058, 6.67425502, 0.58991943, 0.81721304, 0.84232267, 0.70790546, 0.80605387, 0.33957608, 5.30199393, 0.49353014, 0.43787739, 4.71013048, 2.21428608, 0.06477493, 2.21080312, 11.26766631, 1.41906137, 0.33510482, 0.60933500, 0.52784934, 1.99433592, 0.81031328, 0.57077447, 0.66149250, 0.68867633, 4.84317286, 0.36956226, 3.11653589, 2.52678593, 3.64408659, 0.12064295, 0.56303991, 4.65076495, 0.71346171, 0.42344912) x3 <- c( 7.91494242, 0.19412687, 10.20583043, 3.23785157, 0.62401143, 0.10925820, 0.34893258, 0.10362127, 0.20372902, 2.43183481, 0.64172931, 0.54511936, 0.93002297, 0.21341470, 0.17826381, 0.82030962, 1.50837048, 2.61944811, 1.39380602, 1.61379144, 4.52814205, 3.64692499, 1.25370903, 0.28946323, 3.55707324, 1.75731529, 1.80399485, 0.63667593, 0.93378912, 0.48696211, 2.04612154, 2.37910418, 2.59128954, 0.85036486, 1.95154540, 0.31360510, 4.70881211, 0.46472551, 0.08410636, 0.71310015, 3.30247943, 0.70311195, 0.45432213, 7.18258889, 0.34213668, 1.46046769, 9.55462496, 1.30486497, 1.40977707, 0.28198592, 2.78150135, 0.82828400, 1.16253518, 4.84254590, 0.40375544, 0.54121042, 1.31220587, 4.55658719, 0.76424702, 2.39502896, 0.79486471, 0.47939642, 0.63382155, 1.19559769, 0.85622962, 1.94875705, 0.65856052, 0.40534678, 0.65596391, 1.41967842, 2.37189601, 1.65440054, 0.71956930, 1.48724000, 0.93825307, 0.95435123, 0.36295986, 1.09812592, 0.48085855, 0.32676320, 0.73653302, 0.87306490, 0.40939178, 0.49699497, 0.60832122, 0.09766429, 2.77127635, 2.01212513, 2.90873052, 0.96407304, 0.60718832, 0.96119955, 1.40219422, 0.37655441, 0.59790353, 7.39783081, 1.73700802, 0.22926784, 2.43239947, 2.05710972, 0.86176258, 7.59293172, 0.28027213, 0.96230436, 4.37452845, 1.19992132, 1.18137491, 3.74777043, 0.40240165, 0.43582520, 0.63339735, 0.09617269, 1.39765031, 1.54848897, 0.97029062, 0.17934970, 0.54690580, 0.10307251, 1.70422789, 2.43491809, 0.63994834, 0.24461365, 4.42239395, 0.40346128, 0.18417115, 1.29246893, 1.57550739, 1.39876579, 0.14048065, 5.76284705, 0.64313252, 0.57716822, 2.19100838, 0.83979317, 3.07905677, 3.95613882, 0.31193959, 0.67457270, 2.08862427, 0.14258461, 0.16776561, 1.50488803, 0.58225549, 7.67084991, 0.39322424, 3.44470272, 1.67787637, 0.73252807, 1.58300834, 2.22201657, 2.24863875, 0.71816017, 5.55879446, 1.52270194, 1.73913101, 1.41962661, 1.25027319, 0.70799543, 4.52715693, 2.34946416, 0.17168436, 0.98108097, 0.87535199, 0.51069806, 2.65115615, 0.25993574, 1.11566531, 4.06851326, 0.31838995, 0.44094467, 0.37452572, 0.96225318, 0.99866618, 0.09140138, 1.18435399, 5.69546329, 0.99588289, 0.63252879, 1.65996950, 2.88875606, 1.98654826, 0.41642680, 0.32397323, 2.40579293, 2.73327507, 1.19169098, 0.66840302, 2.48361183, 0.86211431, 0.44894826, 8.01923174, 0.50973335, 2.40370240, 0.82945545, 0.71641292, 0.53830713, 0.78070175, 1.87115376, 0.44975634, 0.45816162) sincx <- cbind(x1,x2,x3) rm(x1,x2,x3) # Olive (2010), insulation data, contributed by Ms. Spector #A box with insulation was heated for 20 minutes then allowed to cool down. #temperature taken in the middle of an insulated box at the 45 time type combinations #(insulation) type 1 = no insulation, 2 = corn pith, 3 = fiberglass, #4 = styrofoam and 5 = bubbles #time 0, 5, ..., 40 minutes after heating ytemp <- c(21.79, 24.36, 27.42, 29.12, 29.98, 28.28, 25.67, 23.93, 23.10, 21.18, 22.33, 24.41, 26.01, 27.09, 26.75, 25.29, 24.17, 23.25, 21.70, 22.59, 24.50, 26.00, 27.29, 27.02, 25.74, 24.58, 23.39, 21.57, 22.26, 24.46, 26.20, 27.52, 27.57, 25.85, 24.52, 23.49, 21.51, 22.69, 25.30, 27.09, 28.24, 27.65, 25.55, 24.09, 23.23) type <- rep(1:5,c(9,9,9,9,9)) time <- rep(c(0,5,10,15,20,25,30,35,40),5) insulation<-cbind(ytemp,type,time) rm(ytemp,type,time) # Rousseeuw and Leroy (1987, p. 26), Belgian telephone data belx <- 50:73 bely <- c(0.44,0.47,0.47,0.59,0.66,0.73,0.81,0.88,1.06,1.2,1.35,1.49,1.61,2.12,11.9, 12.4,14.2,15.9,18.2,21.2,4.3,2.4,2.7,2.9) # Schaaffhausen (1878) data ape <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1) crancap <- c(1485, 1450, 1460, 1425, 1430, 1290, 1225, 1280, 1325, 1225, 1090, 1510, 1440, 1220, 1310, 1400, 1225, 1340, 1410, 1200, 1460, 1575, 1335, 1250, 1325, 1050, 1270, 1355, 1405, 1305, 1195, 1310, 1300, 1350, 1300, 1295, 1430, 1325, 1455, 1325, 1450, 1190, 1330, 1280, 1065, 1125, 1520, 315, 340, 315, 485, 440, 480, 310, 345, 350, 385, 330, 420, 90) hdlen<- c(175, 191, 186, 191, 178, 180, 168, 179, 186, 185, 173, 184, 175, 177, 177, 185, 184, 184, 181, 174, 176, 186, 184, 168, 181, 163, 163, 168, 182, 160, 160, 165, 158, 171, 161, 171, 169, 162, 170, 168, 179, 174, 164, 175, 159, 166, 178, 113, 119, 108, 158, 131, 136, 111, 110, 117, 127, 103, 133, 75) hdbrdth<- c(148, 150, 142, 135, 148, 146, 138, 139, 136, 133, 128, 158, 153, 138, 142, 135, 128, 138, 154, 141, 146, 153, 135, 139, 135, 119, 133, 145, 141, 146, 141, 140, 142, 146, 137, 138, 141, 143, 145, 145, 137, 130, 148, 138, 132, 147, 145, 87, 95, 89, 106, 97, 103, 96, 97, 98, 98, 91, 97, 61) hdht <-c(132, 117, 122, 125, 120, 117, 120, 123, 123, 115, 120, 121, 126, 126, 130, 129, 126, 116, 119, 112, 132, 129, 131, 122, 128, 118, 126, 129, 131, 123, 128, 131, 126, 137, 129, 124, 130, 130, 134, 131, 134, 132, 122, 121, 127, 123, 128, 85, 87, 72, 85, 76, 85, 77, 79, 73, 76, 69, 84, 51) lowjaw <-c(95, 96, 101, 101, 95, 100, 96, 99, 99, 98, 88, 103, 101, 87, 90, 94, 95, 95, 99, 90, 101, 103, 98, 97, 106, 81, 105, 98, 93, 103, 97, 97, 90, 100, 94, 96, 98, 104, 97, 97, 94, 87, 97, 93, 98, 99, 99, 74, 85, 55, 110, 112, 122, 75, 79, 94, 90, 55, 85, 42) facelen<-c(100, 121, 123, 113, 127, 120, 108, 120, 115, 118, 89, 115, 109, 118, 107, 121, 118, 108, 117, 117, 109, 106, 119, 110, 118, 79, 118, 107, 119, 117, 102, 107, 121, 124, 107, 113, 112, 127, 108, 125, 114, 124, 114, 110, 116, 120, 111, 110, 132, 71, 146, 185, 178, 111, 88, 126, 130, 72, 112, 50) upjaw <-c(74, 83, 82, 74, 87, 83, 72, 85, 78, 77, 61, 75, 73, 81, 71, 81, 78, 68, 79, 79, 75, 72, 76, 74, 76, 53, 76, 73, 76, 75, 69, 71, 86, 78, 79, 74, 75, 84, 73, 83, 72, 85, 70, 75, 78, 79, 77, 75, 98, 50, 115, 141, 127, 75, 61, 91, 94, 51, 84, 33) htjaw <- c(34, 38, 40, 39, 46, 42, 36, 40, 41, 41, 29, 40, 35, 43, 40, 40, 43, 38, 44, 38, 38, 38, 43, 41, 43, 29, 45, 37, 45, 43, 36, 41, 45, 42, 39, 36, 35, 47, 41, 47, 38, 47, 42, 37, 38, 40, 42, 46, 53, 28, 65, 67, 82, 46, 33, 64, 62, 29, 56, 21) eyewdth <- c(96, 106, 103, 115, 109, 112, 110, 113, 109, 110, 95, 117, 109, 101, 100, 112, 121, 109, 119, 108, 112, 115, 109, 107, 113, 93, 109, 118, 116, 117, 107, 105, 100, 108, 106, 106, 121, 106, 115, 112, 105, 103, 108, 102, 106, 109, 109, 70, 93, 50, 130, 116, 117, 72, 67, 80, 97, 52, 97, 43) diaglen <-c(234, 237, 237, 231, 236, 223, 211, 230, 231, 230, 204, 237, 223, 229, 226, 238, 239, 227, 230, 226, 228, 228, 238, 224, 238, 201, 232, 232, 234, 233, 217, 221, 220, 240, 227, 226, 229, 238, 234, 240, 232, 235, 230, 218, 228, 238, 226, 181, 209, 128, 290, 289, 283, 178, 170, 202, 224, 102, 203, 99) museum <- cbind(ape,crancap,hdlen,hdbrdth,hdht,lowjaw,facelen,upjaw,htjaw,eyewdth,diaglen) rm(ape,crancap,hdlen,hdbrdth,hdht,lowjaw,facelen,upjaw,htjaw,eyewdth,diaglen) # Staudte and Sheather (1990, p. 97), Cushny Peebles data cushny <- c(1.2, 2.4, 1.3, 1.3, 0.0, 1.0, 1.8, 0.8, 4.6, 1.4) ##Getting the lsp files into R. ##Already included in this file. #bodfat <- matrix(scan(),nrow=252,ncol=15,byrow=T) #out<-lsfit(bodfat[,-2],bodfat[,2]) #ls.print(out) #boston2 <- matrix(scan(),nrow=506,ncol=14,byrow=T) #out<-lsfit(boston2[,-1],boston2[,1]) #ls.print(out) #buxton <- matrix(scan(),nrow=87,ncol=8,byrow=T) #buxton <- buxton[,-1] #out<-lsfit(buxton[,-1],buxton[,1]) #ls.print(out) ##Already in this file. #cbrain <- matrix(scan(),nrow=267,ncol=13,byrow=T) #cbrain <- cbrain[,-1] #out<-lsfit(cbrain[,-8],cbrain[,8]) #ls.print(out) #cyp <- matrix(scan(),nrow=76,ncol=8,byrow=T) #cyp<-cyp[,-1] #out<-lsfit(cyp[,-1],cyp[,1]) #ls.print(out) #gladstone <- matrix(scan(),nrow=274,ncol=13,byrow=T) #gladstone <- gladstone[,-1] #out<-lsfit(gladstone[,-8],gladstone[,8]) #ls.print(out) #hbk <- matrix(scan(),nrow=75,ncol=5,byrow=T) #hbk <- hbk[,-1] #out<-lsfit(hbk[,-4],hbk[,4]) #ls.print(out) #major <- matrix(scan(),nrow=112,ncol=7,byrow=T) #major <- major[,-1] #out<-lsfit(major[,-6],major[,6]) #ls.print(out) ##Already included in this file. #marry <- matrix(scan(),nrow=26,ncol=6,byrow=T) #marry <- marry[,-1] #out<-lsfit(marry[,-3],marry[,3]) #ls.print(out) #Already included in this file. #museum <- matrix(scan(),nrow=60,ncol=12,byrow=T) #museum <- museum[,-1] #out<-lsfit(museum[,-10],museum[,10]) #ls.print(out) ##has mahalanobis distances and weights #muss <- matrix(scan(),nrow=82,ncol=8,byrow=T) #out<-lsfit(muss[,-c(4,5,6,7)],museum[,10]) #ls.print(out) #nasty <- matrix(scan(),nrow=32,ncol=6,byrow=T) #nasty <- nasty[,-1] #out<-lsfit(nasty[,-5],nasty[,5]) #ls.print(out) #pollution <- matrix(scan(),nrow=60,ncol=16,byrow=T) #out<-lsfit(pollution[,-16],pollution[,16]) #ls.print(out) #pov <- matrix(scan(),nrow=97,ncol=7,byrow=T) #out<-lsfit(pov[,-4],pov[,4]) #ls.print(out) ##gnp missing values coded as -1 #povc <- matrix(scan(),nrow=91,ncol=7,byrow=T) #out<-lsfit(povc[,-4],povc[,4]) #ls.print(out) #Already included in this file. #sinc <- matrix(scan(),nrow=200,ncol=4,byrow=T) #out<-lsfit(sinc[,-4],sinc[,4]) #ls.print(out) #skeleton <- matrix(scan(),nrow=53,ncol=16,byrow=T) #skeleton <- skeleton[,-1] #out<-lsfit(skeleton[,-15],skeleton[,15]) #ls.print(out) #stackloss <- matrix(scan(),nrow=21,ncol=5,byrow=T) #stackloss <- stackloss[,-1] #out<-lsfit(stackloss[,-1],stackloss[,1]) #ls.print(out) #wood <- matrix(scan(),nrow=20,ncol=7,byrow=T) #wood <- wood[,-1] #out<-lsfit(wood[,-6],wood[,6]) #ls.print(out)