R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,111.1 + ,111.5 + ,101.7 + ,60.3 + ,37.3 + ,21.5 + ,31.3 + ,27.2 + ,18.0 + ,10.399 + ,26.0 + ,72 + ,190.75 + ,70.50 + ,38.9 + ,108.3 + ,101.3 + ,97.8 + ,56.0 + ,41.6 + ,22.7 + ,30.5 + ,29.4 + ,19.8 + ,10.271 + ,31.9 + ,74 + ,207.50 + ,70.00 + ,40.8 + ,112.4 + ,108.5 + ,107.1 + ,59.3 + ,42.2 + ,24.6 + ,33.7 + ,30.0 + ,20.9) + ,dim=c(15 + ,252) + ,dimnames=list(c('Density' + ,'Fat' + ,'Age' + ,'Weight' + ,'Height' + ,'Neck' + ,'Chest' + ,'Abdomen' + ,'Hip' + ,'Thigh' + ,'Knee' + ,'Ankle' + ,'Biceps' + ,'Forearm' + ,'Wrist') + ,1:252)) > y <- array(NA,dim=c(15,252),dimnames=list(c('Density','Fat','Age','Weight','Height','Neck','Chest','Abdomen','Hip','Thigh','Knee','Ankle','Biceps','Forearm','Wrist'),1:252)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Fat Density Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle 1 12.3 10.708 23 154.25 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 2 6.1 10.853 22 173.25 72.25 38.5 93.6 83.0 98.7 58.7 37.3 23.4 3 25.3 10.414 22 154.00 66.25 34.0 95.8 87.9 99.2 59.6 38.9 24.0 4 10.4 10.751 26 184.75 72.25 37.4 101.8 86.4 101.2 60.1 37.3 22.8 5 28.7 10.340 24 184.25 71.25 34.4 97.3 100.0 101.9 63.2 42.2 24.0 6 20.9 10.502 24 210.25 74.75 39.0 104.5 94.4 107.8 66.0 42.0 25.6 7 19.2 10.549 26 181.00 69.75 36.4 105.1 90.7 100.3 58.4 38.3 22.9 8 12.4 10.704 25 176.00 72.50 37.8 99.6 88.5 97.1 60.0 39.4 23.2 9 4.1 10.900 25 191.00 74.00 38.1 100.9 82.5 99.9 62.9 38.3 23.8 10 11.7 10.722 23 198.25 73.50 42.1 99.6 88.6 104.1 63.1 41.7 25.0 11 7.1 10.830 26 186.25 74.50 38.5 101.5 83.6 98.2 59.7 39.7 25.2 12 7.8 10.812 27 216.00 76.00 39.4 103.6 90.9 107.7 66.2 39.2 25.9 13 20.8 10.513 32 180.50 69.50 38.4 102.0 91.6 103.9 63.4 38.3 21.5 14 21.2 10.505 30 205.25 71.25 39.4 104.1 101.8 108.6 66.0 41.5 23.7 15 22.1 10.484 35 187.75 69.50 40.5 101.3 96.4 100.1 69.0 39.0 23.1 16 20.9 10.512 35 162.75 66.00 36.4 99.1 92.8 99.2 63.1 38.7 21.7 17 29.0 10.333 34 195.75 71.00 38.9 101.9 96.4 105.2 64.8 40.8 23.1 18 22.9 10.468 32 209.25 71.00 42.1 107.6 97.5 107.0 66.9 40.0 24.4 19 16.0 10.622 28 183.75 67.75 38.0 106.8 89.6 102.4 64.2 38.7 22.9 20 16.5 10.610 33 211.75 73.50 40.0 106.2 100.5 109.0 65.8 40.6 24.0 21 19.1 10.551 28 179.00 68.00 39.1 103.3 95.9 104.9 63.5 38.0 22.1 22 15.2 10.640 28 200.50 69.75 41.3 111.4 98.8 104.8 63.4 40.6 24.6 23 15.6 10.631 31 140.25 68.25 33.9 86.0 76.4 94.6 57.4 35.3 22.2 24 17.7 10.584 32 148.75 70.00 35.5 86.7 80.0 93.4 54.9 36.2 22.1 25 14.0 10.668 28 151.25 67.75 34.5 90.2 76.3 95.8 58.4 35.5 22.9 26 3.7 10.911 27 159.25 71.50 35.7 89.6 79.7 96.5 55.0 36.7 22.5 27 7.9 10.811 34 131.50 67.50 36.2 88.6 74.6 85.3 51.7 34.7 21.4 28 22.9 10.468 31 148.00 67.50 38.8 97.4 88.7 94.7 57.5 36.0 21.0 29 3.7 10.910 27 133.25 64.75 36.4 93.5 73.9 88.5 50.1 34.5 21.3 30 8.8 10.790 29 160.75 69.00 36.7 97.4 83.5 98.7 58.9 35.3 22.6 31 11.9 10.716 32 182.00 73.75 38.7 100.5 88.7 99.8 57.5 38.7 33.9 32 5.7 10.862 29 160.25 71.25 37.3 93.5 84.5 100.6 58.5 38.8 21.5 33 11.8 10.719 27 168.00 71.25 38.1 93.0 79.1 94.5 57.3 36.2 24.5 34 21.3 10.502 41 218.50 71.00 39.8 111.7 100.5 108.3 67.1 44.2 25.2 35 32.3 10.263 41 247.25 73.50 42.1 117.0 115.6 116.1 71.2 43.3 26.3 36 40.1 10.101 49 191.75 65.00 38.4 118.5 113.1 113.8 61.9 38.3 21.9 37 24.2 10.438 40 202.25 70.00 38.5 106.5 100.9 106.2 63.5 39.9 22.6 38 28.4 10.346 50 196.75 68.25 42.1 105.6 98.8 104.8 66.0 41.5 24.7 39 35.2 10.202 46 363.15 72.25 51.2 136.2 148.1 147.7 87.3 49.1 29.6 40 32.6 10.258 50 203.00 67.00 40.2 114.8 108.1 102.5 61.3 41.1 24.7 41 34.5 10.217 45 262.75 68.75 43.2 128.3 126.2 125.6 72.5 39.6 26.6 42 32.9 10.250 44 205.00 29.50 36.6 106.0 104.3 115.5 70.6 42.5 23.7 43 31.6 10.279 48 217.00 70.00 37.3 113.3 111.2 114.1 67.7 40.9 25.0 44 32.0 10.269 41 212.00 71.50 41.5 106.6 104.3 106.0 65.0 40.2 23.0 45 7.7 10.814 39 125.25 68.00 31.5 85.1 76.0 88.2 50.0 34.7 21.0 46 13.9 10.670 43 164.25 73.25 35.7 96.6 81.5 97.2 58.4 38.2 23.4 47 10.8 10.742 40 133.50 67.50 33.6 88.2 73.7 88.5 53.3 34.5 22.5 48 5.6 10.665 39 148.50 71.25 34.6 89.8 79.5 92.7 52.7 37.5 21.9 49 13.6 10.678 45 135.75 68.50 32.8 92.3 83.4 90.4 52.0 35.8 20.6 50 4.0 10.903 47 127.50 66.75 34.0 83.4 70.4 87.2 50.6 34.4 21.9 51 10.2 10.756 47 158.25 72.25 34.9 90.2 86.7 98.3 52.6 37.2 22.4 52 6.6 10.840 40 139.25 69.00 34.3 89.2 77.9 91.0 51.4 34.9 21.0 53 8.0 10.807 51 137.25 67.75 36.5 89.7 82.0 89.1 49.3 33.7 21.4 54 6.3 10.848 49 152.75 73.50 35.1 93.3 79.6 91.6 52.6 37.6 22.6 55 3.9 10.906 42 136.25 67.50 37.8 87.6 77.6 88.6 51.9 34.9 22.5 56 22.6 10.473 54 198.00 72.00 39.9 107.6 100.0 99.6 57.2 38.0 22.0 57 20.4 10.524 58 181.50 68.00 39.1 100.0 99.8 102.5 62.1 39.6 22.5 58 28.0 10.356 62 201.25 69.50 40.5 111.5 104.2 105.8 61.8 39.8 22.7 59 31.5 10.280 54 202.50 70.75 40.5 115.4 105.3 97.0 59.1 38.0 22.5 60 24.6 10.430 61 179.75 65.75 38.4 104.8 98.3 99.6 60.6 37.7 22.9 61 26.1 10.396 62 216.00 73.25 41.4 112.3 104.8 103.1 61.6 40.9 23.1 62 29.8 10.317 56 178.75 68.50 35.6 102.9 94.7 100.8 60.9 38.0 22.1 63 30.7 10.298 54 193.25 70.25 38.0 107.6 102.4 99.4 61.0 39.4 23.6 64 25.8 10.403 61 178.00 67.00 37.4 105.3 99.7 99.7 60.8 40.1 22.7 65 32.3 10.264 57 205.50 70.00 40.1 105.3 105.5 108.3 65.0 41.2 24.7 66 30.0 10.313 55 183.50 67.50 40.9 103.0 100.3 104.2 64.8 40.2 22.7 67 21.5 10.499 54 151.50 70.75 35.6 90.0 83.9 93.9 55.0 36.1 21.7 68 13.8 10.673 55 154.75 71.50 36.9 95.4 86.6 91.8 54.3 35.4 21.5 69 6.3 10.847 54 155.25 69.25 37.5 89.3 78.4 96.1 56.0 37.4 22.4 70 12.9 10.693 55 156.75 71.50 36.3 94.4 84.6 94.3 51.2 37.4 21.6 71 24.3 10.439 62 167.50 71.50 35.5 97.6 91.5 98.5 56.6 38.6 22.4 72 8.8 10.788 55 146.75 68.75 38.7 88.5 82.8 95.5 58.9 37.6 21.6 73 8.5 10.796 56 160.75 73.75 36.4 93.6 82.9 96.3 52.9 37.5 23.1 74 13.5 10.680 55 125.00 64.00 33.2 87.7 76.0 88.6 50.9 35.4 19.1 75 11.8 10.720 61 143.00 65.75 36.5 93.4 83.3 93.0 55.5 35.2 20.9 76 18.5 10.666 61 148.25 67.50 36.0 91.6 81.8 94.8 54.5 37.0 21.4 77 8.8 10.790 57 162.50 69.50 38.7 91.6 78.8 94.3 56.7 39.7 24.2 78 22.2 10.483 69 177.75 68.50 38.7 102.0 95.0 98.3 55.0 38.3 21.8 79 21.5 10.498 81 161.25 70.25 37.8 96.4 95.4 99.3 53.5 37.5 21.5 80 18.8 10.560 66 171.25 69.25 37.4 102.7 98.6 100.2 56.5 39.3 22.7 81 31.4 10.283 67 163.75 67.75 38.4 97.7 95.8 97.1 54.8 38.2 23.7 82 26.8 10.382 64 150.25 67.25 38.1 97.1 89.0 96.9 54.8 38.0 22.0 83 18.4 10.568 64 190.25 72.75 39.3 103.1 97.8 99.6 58.9 39.0 23.0 84 27.0 10.377 70 170.75 70.00 38.7 101.8 94.9 95.0 56.0 36.5 24.1 85 27.0 10.378 72 168.00 69.25 38.5 101.4 99.8 96.2 56.3 36.6 22.0 86 26.6 10.386 67 167.00 67.50 36.5 98.9 89.7 96.2 54.7 37.8 33.7 87 14.9 10.648 72 157.75 67.25 37.7 97.5 88.1 96.9 57.2 37.7 21.8 88 23.1 10.462 64 160.00 65.75 36.5 104.3 90.9 93.8 57.8 39.5 23.3 89 8.3 10.800 46 176.75 72.50 38.0 97.3 86.0 99.3 61.0 38.4 23.8 90 14.1 10.666 48 176.00 73.00 36.7 96.7 86.5 98.3 60.4 39.9 24.4 91 20.5 10.520 46 177.00 70.00 37.2 99.7 95.6 102.2 58.3 38.2 22.5 92 18.2 10.573 44 179.75 69.50 39.2 101.9 93.2 100.6 58.9 39.7 23.1 93 8.5 10.795 47 165.25 70.50 37.5 97.2 83.1 95.4 56.9 38.3 22.1 94 24.9 10.424 46 192.50 71.75 38.0 106.6 97.5 100.6 58.9 40.5 24.5 95 9.0 10.785 47 184.25 74.50 37.3 99.6 88.8 101.4 57.4 39.6 24.6 96 17.4 10.991 53 224.50 77.75 41.1 113.2 99.2 107.5 61.7 42.3 23.2 97 9.6 10.770 38 188.75 73.25 37.5 99.1 91.6 102.4 60.6 39.4 22.9 98 11.3 10.730 50 162.50 66.50 38.7 99.4 86.7 96.2 62.1 39.3 23.3 99 17.8 10.582 46 156.50 68.25 35.9 95.1 88.2 92.8 54.7 37.3 21.9 100 22.2 10.484 47 197.00 72.00 40.0 107.5 94.0 103.7 62.7 39.0 22.3 101 21.2 10.506 49 198.50 73.50 40.1 106.5 95.0 101.7 59.0 39.4 22.3 102 20.4 10.524 48 173.75 72.00 37.0 99.1 92.0 98.3 59.3 38.4 22.4 103 20.1 10.530 41 172.75 71.25 36.3 96.7 89.2 98.3 60.0 38.4 23.2 104 22.3 10.480 49 196.75 73.75 40.7 103.5 95.5 101.6 59.1 39.8 25.4 105 25.4 10.412 43 177.00 69.25 39.6 104.0 98.6 99.5 59.5 36.1 22.0 106 18.0 10.578 43 165.50 68.50 31.1 93.1 87.3 96.6 54.7 39.0 24.8 107 19.3 10.547 43 200.25 73.50 38.6 105.2 102.8 103.6 61.2 39.3 23.5 108 18.3 10.569 52 203.25 74.25 42.0 110.0 101.6 100.7 55.8 38.7 23.4 109 17.3 10.593 43 194.00 75.50 38.5 110.1 88.7 102.1 57.5 40.0 24.8 110 21.4 10.500 40 168.50 69.25 34.2 97.8 92.3 100.6 57.5 36.8 22.8 111 19.7 10.538 43 170.75 68.50 37.2 96.3 90.6 99.3 61.9 38.0 22.3 112 28.0 10.355 43 183.25 70.00 37.1 108.0 105.0 103.0 63.7 40.0 23.6 113 22.1 10.486 47 178.25 70.00 40.2 99.7 95.0 98.6 62.3 38.1 23.9 114 21.3 10.503 42 163.00 70.25 35.3 93.5 89.6 99.8 61.5 37.8 21.9 115 26.7 10.384 48 175.25 71.75 38.0 100.7 92.4 97.5 59.3 38.1 21.8 116 16.7 10.607 40 158.00 69.25 36.3 97.0 86.6 92.6 55.9 36.3 22.1 117 20.1 10.529 48 177.25 72.75 36.8 96.0 90.0 99.7 58.8 38.4 22.8 118 13.9 10.671 51 179.00 72.00 41.0 99.2 90.0 96.4 56.8 38.8 23.3 119 25.8 10.404 40 191.00 74.00 38.3 95.4 92.4 104.3 64.6 41.1 24.8 120 18.1 10.575 44 187.50 72.25 38.0 101.8 87.5 101.0 58.5 39.2 24.5 121 27.9 10.358 52 206.50 74.50 40.8 104.3 99.2 104.1 58.5 39.3 24.6 122 25.3 10.414 44 185.25 71.50 39.5 99.2 98.1 101.4 57.1 40.5 23.2 123 14.7 10.652 40 160.25 68.75 36.9 99.3 83.3 97.5 60.5 38.7 22.6 124 16.0 10.623 47 151.50 66.75 36.9 94.0 86.1 95.2 58.1 36.5 22.1 125 13.8 10.674 50 161.00 66.50 37.7 98.9 84.1 94.0 58.5 36.6 23.5 126 17.5 10.587 46 167.00 67.00 36.6 101.0 89.9 100.0 60.7 36.0 21.9 127 27.2 10.373 42 177.50 68.75 38.9 98.7 92.1 98.5 60.7 36.8 22.2 128 17.4 10.590 43 152.25 67.75 37.5 95.9 78.0 93.2 53.5 35.8 20.8 129 20.8 10.515 40 192.25 73.25 39.8 103.9 93.5 99.5 61.7 39.0 21.8 130 14.9 10.648 42 165.25 69.75 38.3 96.2 87.0 97.8 57.4 36.9 22.2 131 18.1 10.575 49 171.75 71.50 35.5 97.8 90.1 95.8 57.0 38.7 23.2 132 22.7 10.472 40 171.25 70.50 36.3 94.6 90.3 99.1 60.3 38.5 23.0 133 23.6 10.452 47 197.00 73.25 37.8 103.6 99.8 103.2 61.2 38.1 22.6 134 26.1 10.398 50 157.00 66.75 37.8 100.4 89.4 92.3 56.1 35.6 20.5 135 24.4 10.435 41 168.25 69.50 36.5 98.4 87.2 98.4 56.0 36.9 23.0 136 27.1 10.374 44 186.00 69.75 37.8 104.6 101.1 102.1 58.9 37.9 22.7 137 21.8 10.491 39 166.75 70.75 37.0 92.9 86.1 95.6 58.8 36.1 22.4 138 29.4 10.325 43 187.75 74.00 37.7 97.8 98.6 100.6 63.6 39.2 23.8 139 22.4 10.481 40 168.25 71.25 34.3 98.3 88.5 98.3 58.1 38.4 22.5 140 20.4 10.522 49 212.75 75.00 40.8 104.7 106.6 107.7 66.5 42.5 24.5 141 24.9 10.422 40 176.75 71.00 37.4 98.6 93.1 101.6 59.1 39.6 21.6 142 18.3 10.571 40 173.25 69.50 36.5 99.5 93.0 99.3 60.4 38.2 22.0 143 23.3 10.459 52 167.00 67.75 37.5 102.7 91.0 98.9 57.1 36.7 22.3 144 9.4 10.775 23 159.75 72.25 35.5 92.1 77.1 93.9 56.1 36.1 22.7 145 10.3 10.754 23 188.15 77.50 38.0 96.6 85.3 102.5 59.1 37.6 23.2 146 14.2 10.664 24 156.00 70.75 35.7 92.7 81.9 95.3 56.4 36.5 22.0 147 19.2 10.550 24 208.50 72.75 39.2 102.0 99.1 110.1 71.2 43.5 25.2 148 29.6 10.322 25 206.50 69.75 40.9 110.9 100.5 106.2 68.4 40.8 24.6 149 5.3 10.873 25 143.75 72.50 35.2 92.3 76.5 92.1 51.9 35.7 22.0 150 25.2 10.416 26 223.00 70.25 40.6 114.1 106.8 113.9 67.6 42.7 24.7 151 9.4 10.776 26 152.25 69.00 35.4 92.9 77.6 93.5 56.9 35.9 20.4 152 19.6 10.542 26 241.75 74.50 41.8 108.3 102.9 114.4 72.9 43.5 25.1 153 10.1 10.758 27 146.00 72.25 34.1 88.5 72.8 91.1 53.6 36.8 23.8 154 16.5 10.610 27 156.75 67.25 37.9 94.0 88.2 95.2 56.8 37.4 22.8 155 21.0 10.510 27 200.25 73.50 38.2 101.1 100.1 105.0 62.1 40.0 24.9 156 17.3 10.594 28 171.50 75.25 35.6 92.1 83.5 98.3 57.3 37.8 21.7 157 31.2 10.287 28 205.75 69.00 38.5 105.6 105.0 106.4 68.6 40.0 25.2 158 10.0 10.761 28 182.50 72.25 37.0 98.5 90.8 102.5 60.8 38.5 25.0 159 12.5 10.704 30 136.50 68.75 35.9 88.7 76.6 89.8 50.1 34.8 21.8 160 22.5 10.477 31 177.25 71.50 36.2 101.1 92.4 99.3 59.4 39.0 24.6 161 9.4 10.775 31 151.25 72.25 35.0 94.0 81.2 91.5 52.5 36.6 21.0 162 14.6 10.653 33 196.00 73.00 38.5 103.8 95.6 105.1 61.4 40.6 25.0 163 13.0 10.690 33 184.25 68.75 40.7 98.9 92.1 103.5 64.0 37.3 23.5 164 15.1 10.644 34 140.00 70.50 36.0 89.2 83.4 89.6 52.4 35.6 20.4 165 27.3 10.370 34 218.75 72.00 39.5 111.4 106.0 108.8 63.8 42.0 23.4 166 19.2 10.549 35 217.00 73.75 40.5 107.5 95.1 104.5 64.8 41.3 25.6 167 21.8 10.492 35 166.25 68.00 38.5 99.1 90.4 95.6 55.5 34.2 21.9 168 20.3 10.525 35 224.75 72.25 43.9 108.2 100.4 106.8 63.3 41.7 24.6 169 34.3 10.180 35 228.25 69.50 40.4 114.9 115.9 111.9 74.4 40.6 24.0 170 16.5 10.610 35 172.75 69.50 37.6 99.1 90.8 98.1 60.1 39.1 23.4 171 3.0 10.926 35 152.25 67.75 37.0 92.2 81.9 92.8 54.7 36.2 22.1 172 0.7 10.983 35 125.75 65.50 34.0 90.8 75.0 89.2 50.0 34.8 22.0 173 20.5 10.521 35 177.25 71.00 38.4 100.5 90.3 98.7 57.8 37.3 22.4 174 16.9 10.603 36 176.25 71.50 38.7 98.2 90.3 99.9 59.2 37.7 21.5 175 25.3 10.414 36 226.75 71.75 41.5 115.3 108.8 114.4 69.2 42.4 24.0 176 9.9 10.763 37 145.25 69.25 36.0 96.8 79.4 89.2 50.3 34.8 22.2 177 13.1 10.689 37 151.00 67.00 35.3 92.6 83.2 96.4 60.0 38.1 22.0 178 29.9 10.316 37 241.25 71.50 42.1 119.2 110.3 113.9 69.8 42.6 24.8 179 22.5 10.477 38 187.25 69.25 38.0 102.7 92.7 101.9 64.7 39.5 24.7 180 16.9 10.603 39 234.75 74.50 42.8 109.5 104.5 109.9 69.5 43.1 25.8 181 26.6 10.387 39 219.25 74.25 40.0 108.5 104.6 109.8 68.1 42.8 24.1 182 0.0 11.089 40 118.50 68.00 33.8 79.3 69.4 85.0 47.2 33.5 20.2 183 11.5 10.725 40 145.75 67.25 35.5 95.5 83.6 91.6 54.1 36.2 21.8 184 12.1 10.713 40 159.25 69.75 35.3 92.3 86.8 96.1 58.0 39.4 22.7 185 17.5 10.587 40 170.50 74.25 37.7 98.9 90.4 95.5 55.4 38.9 22.4 186 8.6 10.794 40 167.50 71.50 39.4 89.5 83.7 98.1 57.3 39.7 22.6 187 23.6 10.453 41 232.75 74.25 41.9 117.5 109.3 108.8 67.7 41.3 24.7 188 20.4 10.524 41 210.50 72.00 38.5 107.4 98.9 104.1 63.5 39.8 23.5 189 20.5 10.520 41 202.25 72.50 40.8 109.2 98.0 101.8 62.8 41.3 24.8 190 24.4 10.434 41 185.00 68.25 38.0 103.4 101.2 103.1 61.5 40.4 22.9 191 11.4 10.728 41 153.00 69.25 36.4 91.4 80.6 92.3 54.3 36.3 21.8 192 38.1 10.140 42 244.25 76.00 41.8 115.2 113.7 112.4 68.5 45.0 25.5 193 15.9 10.624 42 193.50 70.50 40.7 104.9 94.1 102.7 60.6 38.6 24.7 194 24.7 10.429 42 224.75 74.75 38.5 106.7 105.7 111.8 65.3 43.3 26.0 195 22.8 10.470 42 162.75 72.75 35.4 92.2 85.6 96.5 60.2 38.9 22.4 196 25.5 10.411 42 180.00 68.25 38.5 101.6 96.6 100.6 61.1 38.4 24.1 197 22.0 10.488 42 156.25 69.00 35.5 97.8 86.0 96.2 57.7 38.6 24.0 198 17.7 10.583 42 168.00 71.50 36.5 92.0 89.7 101.0 62.3 38.0 22.3 199 6.6 10.841 42 167.25 72.75 37.6 94.0 78.0 99.0 57.5 40.0 22.5 200 23.6 10.462 43 170.75 67.50 37.4 103.7 89.7 94.2 58.5 39.0 24.1 201 12.2 10.709 43 178.25 70.25 37.8 102.7 89.2 99.2 60.2 39.2 23.8 202 22.1 10.484 43 150.00 69.25 35.2 91.1 85.7 96.9 55.5 35.7 22.0 203 28.7 10.340 43 200.50 71.50 37.9 107.2 103.1 105.5 68.8 38.3 23.7 204 6.0 10.854 44 184.00 74.00 37.9 100.8 89.1 102.6 60.6 39.0 24.0 205 34.8 10.209 44 223.00 69.75 40.9 121.6 113.9 107.1 63.5 40.3 21.8 206 16.6 10.610 44 208.75 73.00 41.9 105.6 96.3 102.0 63.3 39.8 24.1 207 32.9 10.250 44 166.00 65.50 39.1 100.6 93.9 100.1 58.9 37.6 21.4 208 32.8 10.254 47 195.00 72.50 40.2 102.7 101.3 101.7 60.7 39.4 23.3 209 9.6 10.771 47 160.50 70.25 36.0 99.8 83.9 91.8 53.0 36.2 22.5 210 10.8 10.742 47 159.75 70.75 34.5 92.9 84.4 94.0 56.0 38.2 22.6 211 7.1 10.829 49 140.50 68.00 35.8 91.2 79.4 89.0 51.1 35.0 21.7 212 27.2 10.373 49 216.25 74.50 40.2 115.6 104.0 109.0 63.7 40.3 23.2 213 19.5 10.543 49 168.25 71.75 38.3 98.3 89.7 99.1 56.3 38.8 23.0 214 18.7 10.561 50 194.75 70.75 39.0 103.7 97.6 104.2 60.0 40.9 25.5 215 19.5 10.543 50 172.75 73.00 37.4 98.7 87.6 96.1 57.1 38.1 21.8 216 47.5 0.995 51 219.00 64.00 41.2 119.8 122.1 112.8 62.5 36.9 23.6 217 13.6 10.678 51 149.25 69.75 34.8 92.8 81.1 96.3 53.8 36.5 21.5 218 7.5 10.819 51 154.50 70.00 36.9 93.3 81.5 94.4 54.7 39.0 22.6 219 24.5 10.433 52 199.25 71.75 39.4 106.8 100.0 105.0 63.9 39.2 22.9 220 15.0 10.646 53 154.50 69.25 37.6 93.9 88.7 94.5 53.7 36.2 22.0 221 12.4 10.706 54 153.25 70.50 38.5 99.0 91.8 96.2 57.7 38.1 23.9 222 26.0 10.399 54 230.00 72.25 42.5 119.9 110.4 105.5 64.2 42.7 27.0 223 11.5 10.726 54 161.75 67.50 37.4 94.2 87.6 95.6 59.7 40.2 23.4 224 5.2 10.874 55 142.25 67.25 35.2 92.7 82.8 91.9 54.4 35.2 22.5 225 10.9 10.740 55 179.75 68.75 41.1 106.9 95.3 98.2 57.4 37.1 21.8 226 12.5 10.703 55 126.50 66.75 33.4 88.8 78.2 87.5 50.8 33.0 19.7 227 14.8 10.650 55 169.50 68.25 37.2 101.7 91.1 97.1 56.6 38.5 22.6 228 25.2 10.418 55 198.50 74.25 38.3 105.3 96.7 106.6 64.0 42.6 23.4 229 14.9 10.647 56 174.50 69.50 38.1 104.0 89.4 98.4 58.4 37.4 22.5 230 17.0 10.601 56 167.75 68.50 37.4 98.6 93.0 97.0 55.4 38.8 23.2 231 10.6 10.745 57 147.75 65.75 35.2 99.6 86.4 90.1 53.0 35.0 21.3 232 16.1 10.620 57 182.25 71.75 39.4 103.4 96.7 100.7 59.3 38.6 22.8 233 15.4 10.636 58 175.50 71.50 38.0 100.2 88.1 97.8 57.1 38.9 23.6 234 26.7 10.384 58 161.75 67.25 35.1 94.9 94.9 100.2 56.8 35.9 21.0 235 25.8 10.403 60 157.75 67.50 40.4 97.2 93.3 94.0 54.3 35.7 21.0 236 18.6 10.563 62 168.75 67.50 38.3 104.7 95.6 93.7 54.4 37.1 22.7 237 24.8 10.424 62 191.50 72.25 40.6 104.0 98.2 101.1 59.3 40.3 23.0 238 27.3 10.372 63 219.15 69.50 40.2 117.6 113.8 111.8 63.4 41.1 22.3 239 12.4 10.705 64 155.25 69.50 37.9 95.8 82.8 94.5 61.2 39.1 22.3 240 29.9 10.316 65 189.75 65.75 40.8 106.4 100.5 100.5 59.2 38.1 24.0 241 17.0 10.599 65 127.50 65.75 34.7 93.0 79.7 87.6 50.7 33.4 20.1 242 35.0 10.207 65 224.50 68.25 38.8 119.6 118.0 114.3 61.3 42.1 23.4 243 30.4 10.304 66 234.25 72.00 41.4 119.7 109.0 109.1 63.7 42.4 24.6 244 32.6 10.256 67 227.75 72.75 41.3 115.8 113.4 109.8 65.6 46.0 25.4 245 29.0 10.334 67 199.50 68.50 40.7 118.3 106.1 101.6 58.2 38.8 24.1 246 15.2 10.641 68 155.50 69.25 36.3 97.4 84.3 94.4 54.3 37.5 22.6 247 30.2 10.308 69 215.50 70.50 40.8 113.7 107.6 110.0 63.3 44.0 22.6 248 11.0 10.736 70 134.25 67.00 34.9 89.2 83.6 88.8 49.6 34.8 21.5 249 33.6 10.236 72 201.00 69.75 40.9 108.5 105.0 104.5 59.6 40.8 23.2 250 29.3 10.328 72 186.75 66.00 38.9 111.1 111.5 101.7 60.3 37.3 21.5 251 26.0 10.399 72 190.75 70.50 38.9 108.3 101.3 97.8 56.0 41.6 22.7 252 31.9 10.271 74 207.50 70.00 40.8 112.4 108.5 107.1 59.3 42.2 24.6 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 28.8 25.2 16.6 4 32.4 29.4 18.2 5 32.2 27.7 17.7 6 35.7 30.6 18.8 7 31.9 27.8 17.7 8 30.5 29.0 18.8 9 35.9 31.1 18.2 10 35.6 30.0 19.2 11 32.8 29.4 18.5 12 37.2 30.2 19.0 13 32.5 28.6 17.7 14 36.9 31.6 18.8 15 36.1 30.5 18.2 16 31.1 26.4 16.9 17 36.2 30.8 17.3 18 38.2 31.6 19.3 19 37.2 30.5 18.5 20 37.1 30.1 18.2 21 32.5 30.3 18.4 22 33.0 32.8 19.9 23 27.9 25.9 16.7 24 29.8 26.7 17.1 25 31.1 28.0 17.6 26 29.9 28.2 17.7 27 28.7 27.0 16.5 28 29.2 26.6 17.0 29 30.5 27.9 17.2 30 30.1 26.7 17.6 31 32.5 27.7 18.4 32 30.1 26.4 17.9 33 29.0 30.0 18.8 34 37.5 31.5 18.7 35 37.3 31.7 19.7 36 32.0 29.8 17.0 37 35.1 30.6 19.0 38 33.2 30.5 19.4 39 45.0 29.0 21.4 40 34.1 31.0 18.3 41 36.4 32.7 21.4 42 33.6 28.7 17.4 43 36.7 29.8 18.4 44 35.8 31.5 18.8 45 26.1 23.1 16.1 46 29.7 27.4 18.3 47 27.9 26.2 17.3 48 28.8 26.8 17.9 49 28.8 25.5 16.3 50 26.8 25.8 16.8 51 26.0 25.8 17.3 52 26.7 26.1 17.2 53 29.6 26.0 16.9 54 38.5 27.4 18.5 55 27.7 27.5 18.5 56 35.9 30.2 18.9 57 33.1 28.3 18.5 58 37.7 30.9 19.2 59 31.6 28.8 18.2 60 34.5 29.6 18.5 61 36.2 31.8 20.2 62 32.5 29.8 18.3 63 32.7 29.9 19.1 64 33.6 29.0 18.8 65 35.3 31.1 18.4 66 34.8 30.1 18.7 67 29.6 27.4 17.4 68 32.8 27.4 18.7 69 32.6 28.1 18.1 70 27.3 27.1 17.3 71 31.5 27.3 18.6 72 30.3 27.3 18.3 73 29.7 27.3 18.2 74 29.3 25.7 16.9 75 29.4 27.0 16.8 76 29.3 27.0 18.3 77 30.2 29.2 18.1 78 30.8 25.7 18.8 79 31.4 26.8 18.3 80 30.3 28.7 19.0 81 29.4 27.2 19.0 82 29.9 25.2 17.7 83 34.3 29.6 19.0 84 31.2 27.3 19.2 85 29.7 26.3 18.0 86 32.4 27.7 18.2 87 32.6 28.0 18.8 88 29.2 28.4 18.1 89 30.2 29.3 18.8 90 28.8 29.6 18.7 91 29.1 27.7 17.7 92 31.4 28.4 18.8 93 30.1 28.2 18.4 94 33.3 29.6 19.1 95 30.3 27.9 17.8 96 32.9 30.8 20.4 97 31.6 30.1 18.5 98 30.6 27.8 18.2 99 31.6 27.5 18.2 100 35.3 30.9 18.3 101 32.2 31.0 18.6 102 27.9 26.2 17.0 103 31.0 29.2 18.4 104 31.0 30.3 19.7 105 30.1 27.2 17.7 106 31.0 29.4 18.8 107 30.5 28.5 18.1 108 35.1 29.6 19.1 109 35.1 30.7 19.2 110 32.1 26.0 17.3 111 33.3 28.2 18.1 112 33.5 27.8 17.4 113 35.3 31.1 19.8 114 30.7 27.6 17.4 115 31.8 27.3 17.5 116 29.8 26.3 17.3 117 29.9 28.0 18.1 118 33.4 29.8 19.5 119 33.6 29.5 18.5 120 32.1 28.6 18.0 121 33.9 31.2 19.5 122 33.0 29.6 18.4 123 34.4 28.0 17.6 124 30.6 27.5 17.6 125 34.4 29.2 18.0 126 35.6 30.2 17.6 127 33.8 30.3 17.2 128 33.9 28.2 17.4 129 33.3 29.6 18.1 130 31.6 27.8 17.7 131 27.5 26.5 17.6 132 31.2 28.4 17.1 133 33.5 28.6 17.9 134 33.6 29.3 17.3 135 34.0 29.8 18.1 136 30.9 28.8 17.6 137 32.7 28.3 17.1 138 34.3 28.4 17.7 139 31.7 27.4 17.6 140 35.5 29.8 18.7 141 30.8 27.9 16.6 142 32.0 28.5 17.8 143 31.6 27.5 17.9 144 30.5 27.2 18.2 145 31.8 29.7 18.3 146 33.5 28.3 17.3 147 36.1 30.3 18.7 148 33.3 29.7 18.4 149 25.8 25.2 16.9 150 36.0 30.4 18.4 151 31.6 29.0 17.8 152 38.5 33.8 19.6 153 27.8 26.3 17.4 154 30.6 28.3 17.9 155 33.7 29.2 19.4 156 32.2 27.7 17.7 157 35.2 30.7 19.1 158 31.6 28.0 18.6 159 27.0 34.9 16.9 160 30.1 28.2 18.2 161 27.0 26.3 16.5 162 31.3 29.2 19.1 163 33.5 30.6 19.7 164 28.3 26.2 16.5 165 34.0 31.2 18.5 166 36.4 33.7 19.4 167 30.2 28.7 17.7 168 37.2 33.1 19.8 169 36.1 31.8 18.8 170 32.5 29.8 17.4 171 30.4 27.4 17.7 172 24.8 25.9 16.9 173 31.0 28.7 17.7 174 32.4 28.4 17.8 175 35.4 21.0 20.1 176 31.0 26.9 16.9 177 31.5 26.6 16.7 178 34.4 29.5 18.4 179 34.8 30.3 18.1 180 39.1 32.5 19.9 181 35.6 29.0 19.0 182 27.7 24.6 16.5 183 31.4 28.3 17.2 184 30.0 26.4 17.4 185 30.5 28.9 17.7 186 32.9 29.3 18.2 187 37.2 31.8 20.0 188 36.4 30.4 19.1 189 36.6 32.4 18.8 190 33.4 29.2 18.5 191 29.6 27.3 17.9 192 37.1 31.2 19.9 193 34.0 30.1 18.7 194 33.7 29.9 18.5 195 31.7 27.1 17.1 196 32.9 29.8 18.8 197 31.2 27.3 17.4 198 30.8 27.8 16.9 199 30.6 30.0 18.5 200 33.8 28.8 18.8 201 31.7 28.4 18.6 202 29.4 26.6 17.4 203 32.1 28.9 18.7 204 32.9 29.2 18.4 205 34.8 30.7 17.4 206 37.3 23.1 19.4 207 33.1 29.5 17.3 208 36.7 31.6 18.4 209 31.4 27.5 17.7 210 29.0 26.2 17.6 211 30.9 28.8 17.4 212 36.8 31.0 18.9 213 29.5 27.9 18.6 214 32.7 30.0 19.0 215 28.6 26.7 18.0 216 34.7 29.1 18.4 217 31.3 26.3 17.8 218 27.5 25.9 18.6 219 35.7 30.4 19.2 220 28.5 25.7 17.1 221 31.4 29.9 18.9 222 38.4 32.0 19.6 223 27.9 27.0 17.8 224 29.4 26.8 17.0 225 34.1 31.1 19.2 226 25.3 22.0 15.8 227 33.4 29.3 18.8 228 33.2 30.0 18.4 229 34.6 30.1 18.8 230 32.4 29.7 19.0 231 31.7 27.3 16.9 232 31.8 29.1 19.0 233 30.9 29.6 18.0 234 27.8 26.1 17.6 235 31.3 28.7 18.3 236 30.3 26.3 18.3 237 32.6 28.5 19.0 238 35.1 29.6 18.5 239 29.8 28.9 18.3 240 35.9 30.5 19.1 241 28.5 24.8 16.5 242 34.9 30.1 19.4 243 35.6 30.7 19.5 244 35.3 29.8 19.5 245 32.1 29.3 18.5 246 29.2 27.3 18.5 247 37.5 32.6 18.8 248 25.6 25.7 18.5 249 35.2 28.6 20.1 250 31.3 27.2 18.0 251 30.5 29.4 19.8 252 33.7 30.0 20.9 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Density Age Weight Height Neck 3.46447 -2.07443 0.07056 -0.07839 -0.04591 -0.47618 Chest Abdomen Hip Thigh Knee Ankle -0.01999 0.85662 -0.24680 0.28919 0.18585 0.11014 Biceps Forearm Wrist 0.15998 0.43388 -1.50455 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -10.4868 -2.8649 0.0307 2.9893 10.0685 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.46447 17.47591 0.198 0.84303 Density -2.07443 0.48074 -4.315 2.34e-05 *** Age 0.07056 0.03128 2.256 0.02498 * Weight -0.07839 0.05170 -1.516 0.13078 Height -0.04591 0.09280 -0.495 0.62125 Neck -0.47618 0.22432 -2.123 0.03481 * Chest -0.01999 0.09567 -0.209 0.83465 Abdomen 0.85662 0.08646 9.907 < 2e-16 *** Hip -0.24680 0.14109 -1.749 0.08153 . Thigh 0.28919 0.13984 2.068 0.03972 * Knee 0.18585 0.23681 0.785 0.43335 Ankle 0.11014 0.21421 0.514 0.60760 Biceps 0.15998 0.16520 0.968 0.33384 Forearm 0.43388 0.19219 2.258 0.02488 * Wrist -1.50455 0.51688 -2.911 0.00395 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.154 on 237 degrees of freedom Multiple R-squared: 0.7673, Adjusted R-squared: 0.7536 F-statistic: 55.83 on 14 and 237 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 1.274279e-04 2.548559e-04 9.998726e-01 [2,] 6.230331e-06 1.246066e-05 9.999938e-01 [3,] 3.619149e-07 7.238298e-07 9.999996e-01 [4,] 2.645844e-08 5.291688e-08 1.000000e+00 [5,] 1.041088e-09 2.082175e-09 1.000000e+00 [6,] 2.554107e-10 5.108215e-10 1.000000e+00 [7,] 1.342625e-11 2.685249e-11 1.000000e+00 [8,] 6.165420e-13 1.233084e-12 1.000000e+00 [9,] 3.618942e-14 7.237883e-14 1.000000e+00 [10,] 1.900625e-15 3.801250e-15 1.000000e+00 [11,] 1.660857e-16 3.321715e-16 1.000000e+00 [12,] 7.023433e-18 1.404687e-17 1.000000e+00 [13,] 3.587052e-19 7.174105e-19 1.000000e+00 [14,] 1.534914e-20 3.069829e-20 1.000000e+00 [15,] 7.478234e-22 1.495647e-21 1.000000e+00 [16,] 2.994002e-23 5.988004e-23 1.000000e+00 [17,] 1.125945e-24 2.251890e-24 1.000000e+00 [18,] 1.538133e-24 3.076267e-24 1.000000e+00 [19,] 4.714655e-24 9.429310e-24 1.000000e+00 [20,] 3.023988e-25 6.047976e-25 1.000000e+00 [21,] 2.237141e-26 4.474282e-26 1.000000e+00 [22,] 1.512190e-27 3.024380e-27 1.000000e+00 [23,] 7.294283e-29 1.458857e-28 1.000000e+00 [24,] 4.098441e-30 8.196882e-30 1.000000e+00 [25,] 2.031786e-31 4.063572e-31 1.000000e+00 [26,] 9.494245e-33 1.898849e-32 1.000000e+00 [27,] 6.129281e-34 1.225856e-33 1.000000e+00 [28,] 4.818787e-35 9.637574e-35 1.000000e+00 [29,] 2.257563e-36 4.515126e-36 1.000000e+00 [30,] 1.028453e-37 2.056906e-37 1.000000e+00 [31,] 1.447390e-11 2.894780e-11 1.000000e+00 [32,] 5.186888e-12 1.037378e-11 1.000000e+00 [33,] 1.999268e-12 3.998536e-12 1.000000e+00 [34,] 6.983959e-13 1.396792e-12 1.000000e+00 [35,] 2.295821e-13 4.591642e-13 1.000000e+00 [36,] 8.358554e-14 1.671711e-13 1.000000e+00 [37,] 1.213426e-13 2.426851e-13 1.000000e+00 [38,] 4.242760e-14 8.485520e-14 1.000000e+00 [39,] 1.301727e-14 2.603454e-14 1.000000e+00 [40,] 4.380868e-15 8.761737e-15 1.000000e+00 [41,] 1.311467e-15 2.622934e-15 1.000000e+00 [42,] 4.053312e-16 8.106623e-16 1.000000e+00 [43,] 1.162335e-16 2.324670e-16 1.000000e+00 [44,] 3.761934e-17 7.523869e-17 1.000000e+00 [45,] 1.620938e-17 3.241877e-17 1.000000e+00 [46,] 5.616681e-18 1.123336e-17 1.000000e+00 [47,] 1.539531e-18 3.079061e-18 1.000000e+00 [48,] 4.261220e-19 8.522439e-19 1.000000e+00 [49,] 1.330298e-19 2.660596e-19 1.000000e+00 [50,] 4.736440e-20 9.472880e-20 1.000000e+00 [51,] 1.315326e-20 2.630653e-20 1.000000e+00 [52,] 3.974150e-21 7.948300e-21 1.000000e+00 [53,] 1.052827e-21 2.105653e-21 1.000000e+00 [54,] 4.455031e-22 8.910062e-22 1.000000e+00 [55,] 1.299793e-22 2.599586e-22 1.000000e+00 [56,] 3.943936e-23 7.887872e-23 1.000000e+00 [57,] 1.084430e-23 2.168861e-23 1.000000e+00 [58,] 4.167109e-24 8.334217e-24 1.000000e+00 [59,] 2.442218e-21 4.884435e-21 1.000000e+00 [60,] 6.937823e-22 1.387565e-21 1.000000e+00 [61,] 1.983946e-22 3.967892e-22 1.000000e+00 [62,] 6.118506e-23 1.223701e-22 1.000000e+00 [63,] 2.151485e-23 4.302970e-23 1.000000e+00 [64,] 1.605307e-23 3.210615e-23 1.000000e+00 [65,] 9.947869e-24 1.989574e-23 1.000000e+00 [66,] 3.821994e-24 7.643987e-24 1.000000e+00 [67,] 1.519872e-24 3.039744e-24 1.000000e+00 [68,] 4.290526e-25 8.581052e-25 1.000000e+00 [69,] 2.631603e-25 5.263206e-25 1.000000e+00 [70,] 8.239750e-26 1.647950e-25 1.000000e+00 [71,] 2.851457e-26 5.702914e-26 1.000000e+00 [72,] 9.460992e-27 1.892198e-26 1.000000e+00 [73,] 2.480089e-27 4.960178e-27 1.000000e+00 [74,] 6.488130e-28 1.297626e-27 1.000000e+00 [75,] 1.679762e-28 3.359523e-28 1.000000e+00 [76,] 4.864377e-29 9.728754e-29 1.000000e+00 [77,] 1.595362e-29 3.190723e-29 1.000000e+00 [78,] 6.669404e-30 1.333881e-29 1.000000e+00 [79,] 7.657129e-13 1.531426e-12 1.000000e+00 [80,] 9.761789e-13 1.952358e-12 1.000000e+00 [81,] 6.493125e-13 1.298625e-12 1.000000e+00 [82,] 2.997964e-13 5.995928e-13 1.000000e+00 [83,] 1.398584e-13 2.797169e-13 1.000000e+00 [84,] 6.780504e-14 1.356101e-13 1.000000e+00 [85,] 3.087529e-14 6.175058e-14 1.000000e+00 [86,] 1.500931e-14 3.001862e-14 1.000000e+00 [87,] 9.746195e-15 1.949239e-14 1.000000e+00 [88,] 4.854416e-15 9.708832e-15 1.000000e+00 [89,] 2.127087e-15 4.254173e-15 1.000000e+00 [90,] 1.732907e-15 3.465813e-15 1.000000e+00 [91,] 1.192107e-15 2.384214e-15 1.000000e+00 [92,] 6.649867e-16 1.329973e-15 1.000000e+00 [93,] 2.980915e-16 5.961830e-16 1.000000e+00 [94,] 1.278118e-16 2.556236e-16 1.000000e+00 [95,] 5.713912e-17 1.142782e-16 1.000000e+00 [96,] 2.643645e-17 5.287290e-17 1.000000e+00 [97,] 1.151688e-17 2.303376e-17 1.000000e+00 [98,] 8.771955e-18 1.754391e-17 1.000000e+00 [99,] 3.610779e-18 7.221559e-18 1.000000e+00 [100,] 1.608381e-18 3.216761e-18 1.000000e+00 [101,] 6.688260e-19 1.337652e-18 1.000000e+00 [102,] 7.525201e-19 1.505040e-18 1.000000e+00 [103,] 4.140933e-19 8.281867e-19 1.000000e+00 [104,] 4.440108e-19 8.880216e-19 1.000000e+00 [105,] 2.131468e-19 4.262936e-19 1.000000e+00 [106,] 8.450031e-20 1.690006e-19 1.000000e+00 [107,] 3.312403e-20 6.624806e-20 1.000000e+00 [108,] 1.289631e-20 2.579262e-20 1.000000e+00 [109,] 6.134128e-21 1.226826e-20 1.000000e+00 [110,] 6.114779e-21 1.222956e-20 1.000000e+00 [111,] 6.728012e-21 1.345602e-20 1.000000e+00 [112,] 2.632703e-21 5.265406e-21 1.000000e+00 [113,] 1.004812e-21 2.009624e-21 1.000000e+00 [114,] 4.063042e-22 8.126084e-22 1.000000e+00 [115,] 2.042131e-22 4.084262e-22 1.000000e+00 [116,] 8.108752e-23 1.621750e-22 1.000000e+00 [117,] 6.659739e-23 1.331948e-22 1.000000e+00 [118,] 1.678972e-22 3.357944e-22 1.000000e+00 [119,] 6.542357e-23 1.308471e-22 1.000000e+00 [120,] 6.657003e-23 1.331401e-22 1.000000e+00 [121,] 5.110829e-23 1.022166e-22 1.000000e+00 [122,] 3.253578e-23 6.507156e-23 1.000000e+00 [123,] 7.253241e-23 1.450648e-22 1.000000e+00 [124,] 3.654741e-23 7.309482e-23 1.000000e+00 [125,] 1.627408e-23 3.254815e-23 1.000000e+00 [126,] 1.523456e-23 3.046911e-23 1.000000e+00 [127,] 8.031405e-24 1.606281e-23 1.000000e+00 [128,] 2.982208e-24 5.964417e-24 1.000000e+00 [129,] 1.357013e-24 2.714027e-24 1.000000e+00 [130,] 7.611127e-25 1.522225e-24 1.000000e+00 [131,] 1.424530e-24 2.849060e-24 1.000000e+00 [132,] 5.218161e-25 1.043632e-24 1.000000e+00 [133,] 1.978743e-25 3.957485e-25 1.000000e+00 [134,] 7.167872e-26 1.433574e-25 1.000000e+00 [135,] 4.237856e-26 8.475712e-26 1.000000e+00 [136,] 8.326989e-26 1.665398e-25 1.000000e+00 [137,] 3.247488e-26 6.494977e-26 1.000000e+00 [138,] 1.223280e-26 2.446559e-26 1.000000e+00 [139,] 8.494764e-27 1.698953e-26 1.000000e+00 [140,] 6.564976e-27 1.312995e-26 1.000000e+00 [141,] 4.122537e-27 8.245075e-27 1.000000e+00 [142,] 2.925403e-27 5.850807e-27 1.000000e+00 [143,] 3.642506e-27 7.285012e-27 1.000000e+00 [144,] 1.375653e-27 2.751307e-27 1.000000e+00 [145,] 6.362956e-28 1.272591e-27 1.000000e+00 [146,] 2.514636e-28 5.029272e-28 1.000000e+00 [147,] 8.982017e-29 1.796403e-28 1.000000e+00 [148,] 3.081418e-29 6.162837e-29 1.000000e+00 [149,] 1.441768e-29 2.883535e-29 1.000000e+00 [150,] 2.819454e-29 5.638909e-29 1.000000e+00 [151,] 9.909251e-30 1.981850e-29 1.000000e+00 [152,] 4.790505e-30 9.581011e-30 1.000000e+00 [153,] 1.677871e-30 3.355743e-30 1.000000e+00 [154,] 3.590189e-30 7.180378e-30 1.000000e+00 [155,] 2.275344e-30 4.550687e-30 1.000000e+00 [156,] 1.910235e-30 3.820470e-30 1.000000e+00 [157,] 6.116967e-31 1.223393e-30 1.000000e+00 [158,] 2.420721e-31 4.841441e-31 1.000000e+00 [159,] 9.066488e-32 1.813298e-31 1.000000e+00 [160,] 3.881975e-32 7.763949e-32 1.000000e+00 [161,] 1.491063e-32 2.982126e-32 1.000000e+00 [162,] 1.140324e-32 2.280647e-32 1.000000e+00 [163,] 5.246676e-32 1.049335e-31 1.000000e+00 [164,] 1.710598e-32 3.421196e-32 1.000000e+00 [165,] 1.569283e-31 3.138566e-31 1.000000e+00 [166,] 6.357307e-32 1.271461e-31 1.000000e+00 [167,] 9.610788e-32 1.922158e-31 1.000000e+00 [168,] 2.993988e-32 5.987975e-32 1.000000e+00 [169,] 8.762255e-32 1.752451e-31 1.000000e+00 [170,] 5.635916e-32 1.127183e-31 1.000000e+00 [171,] 2.757625e-32 5.515251e-32 1.000000e+00 [172,] 9.565383e-33 1.913077e-32 1.000000e+00 [173,] 9.063486e-33 1.812697e-32 1.000000e+00 [174,] 2.678670e-33 5.357340e-33 1.000000e+00 [175,] 2.477053e-33 4.954106e-33 1.000000e+00 [176,] 7.677473e-34 1.535495e-33 1.000000e+00 [177,] 3.067570e-34 6.135140e-34 1.000000e+00 [178,] 4.409219e-34 8.818439e-34 1.000000e+00 [179,] 2.558959e-34 5.117918e-34 1.000000e+00 [180,] 9.375844e-33 1.875169e-32 1.000000e+00 [181,] 3.707145e-33 7.414290e-33 1.000000e+00 [182,] 1.497824e-33 2.995648e-33 1.000000e+00 [183,] 1.702279e-30 3.404558e-30 1.000000e+00 [184,] 5.731628e-31 1.146326e-30 1.000000e+00 [185,] 9.371682e-30 1.874336e-29 1.000000e+00 [186,] 2.920326e-28 5.840652e-28 1.000000e+00 [187,] 1.428231e-26 2.856462e-26 1.000000e+00 [188,] 7.145002e-27 1.429000e-26 1.000000e+00 [189,] 3.684130e-26 7.368261e-26 1.000000e+00 [190,] 1.640886e-17 3.281772e-17 1.000000e+00 [191,] 7.245979e-16 1.449196e-15 1.000000e+00 [192,] 2.947937e-16 5.895874e-16 1.000000e+00 [193,] 1.255475e-16 2.510950e-16 1.000000e+00 [194,] 4.861616e-17 9.723232e-17 1.000000e+00 [195,] 3.162616e-17 6.325231e-17 1.000000e+00 [196,] 2.877327e-16 5.754654e-16 1.000000e+00 [197,] 1.093340e-16 2.186680e-16 1.000000e+00 [198,] 1.233659e-15 2.467319e-15 1.000000e+00 [199,] 1.000000e+00 1.886195e-29 9.430974e-30 [200,] 1.000000e+00 6.144795e-28 3.072397e-28 [201,] 1.000000e+00 1.057002e-26 5.285010e-27 [202,] 1.000000e+00 3.796870e-25 1.898435e-25 [203,] 1.000000e+00 1.285022e-23 6.425109e-24 [204,] 1.000000e+00 3.208530e-22 1.604265e-22 [205,] 1.000000e+00 8.903783e-21 4.451891e-21 [206,] 1.000000e+00 1.927859e-19 9.639296e-20 [207,] 1.000000e+00 1.583494e-19 7.917472e-20 [208,] 1.000000e+00 1.662458e-18 8.312291e-19 [209,] 1.000000e+00 2.061254e-17 1.030627e-17 [210,] 1.000000e+00 9.475180e-16 4.737590e-16 [211,] 1.000000e+00 4.455019e-14 2.227510e-14 [212,] 1.000000e+00 1.482561e-12 7.412807e-13 [213,] 1.000000e+00 4.965395e-11 2.482698e-11 [214,] 1.000000e+00 1.933588e-09 9.667940e-10 [215,] 1.000000e+00 7.922120e-08 3.961060e-08 [216,] 9.999986e-01 2.896496e-06 1.448248e-06 [217,] 9.999838e-01 3.247263e-05 1.623631e-05 > postscript(file="/var/fisher/rcomp/tmp/1dlvt1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/24btq1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3jg9p1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4fd5j1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/54c831353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 252 Frequency = 1 1 2 3 4 5 6 -3.61993512 -2.56045668 6.61775492 -1.36779180 1.21782512 3.42844372 7 8 9 10 11 12 2.60531089 -1.75868449 -5.78578106 1.02933235 -2.31442817 -4.92204415 13 14 15 16 17 18 2.92966840 -3.52405593 -2.11125248 -1.96927584 5.13069372 3.39012856 19 20 21 22 23 24 -0.77153156 -6.15688579 -1.39671488 -4.28311588 5.71332085 6.30534840 25 26 27 28 29 30 5.52019763 -4.52621456 -1.20961026 5.21064578 -2.23115896 -2.30588631 31 32 33 34 35 36 -1.94153689 -5.20450080 5.30664947 -2.98539129 0.38891486 4.10264183 37 38 39 40 41 42 0.30109384 5.94030523 -7.08603347 0.55294240 -0.23103121 1.15243294 43 44 45 46 47 48 -1.82220389 5.64486320 -2.97284094 3.20868196 2.72736333 -4.45690954 49 50 51 52 53 54 -3.87634863 -1.67412069 -4.04838551 -2.65193729 -5.31981789 -4.67161585 55 56 57 58 59 60 -3.48217468 -0.48853154 -5.08695963 0.65811903 3.20398465 -1.24743958 61 62 63 64 65 66 0.59716337 6.00536340 3.19337842 -1.45020505 2.35279389 3.66282089 67 68 69 70 71 72 5.73629019 -1.68970152 -2.70994077 -0.94944998 4.40585898 -4.22572362 73 74 75 76 77 78 -2.60003792 1.55974448 -4.69308819 6.01430910 -1.05099162 2.82490112 79 80 81 82 83 84 -1.25022796 -5.53826492 9.15012086 8.56295400 -4.38216939 5.75742079 85 86 87 88 89 90 0.35819626 6.03089906 -2.78135730 0.74962468 -4.60032411 -0.78940215 91 92 93 94 95 96 -0.74076798 0.52524265 -2.91006950 3.04081775 -5.67297963 1.36014195 97 98 99 100 101 102 -7.05048811 -5.15596666 0.02201857 2.82710885 2.49506567 0.35177274 103 104 105 106 107 108 2.48078861 4.89415186 2.26032398 0.06453744 -5.85773614 -3.17474499 109 110 111 112 113 114 4.35933109 0.92008518 0.37989555 -3.83383686 1.27030494 1.28991762 115 116 117 118 119 120 6.20849415 0.38173275 2.92711310 -0.62328715 6.80895525 4.22322460 121 122 123 124 125 126 7.53851950 2.88969618 0.14285419 -0.97826744 -1.68704527 -2.93751019 127 128 129 130 131 132 5.75977564 7.97994441 1.82222680 0.10080303 -0.46459967 2.36805652 133 134 135 136 137 138 -0.74717930 5.09803650 8.28370141 0.98367831 5.01941059 3.23078766 139 140 141 142 143 144 4.16810589 -8.08860367 3.56615289 -2.76720679 4.58651620 3.64933904 145 146 147 148 149 150 1.00820952 1.73137248 -3.63701059 6.38512160 -0.67065498 -1.01761837 151 152 153 154 155 156 0.64428685 -2.56669158 5.29821714 0.25360521 -0.12388652 5.73710860 157 158 159 160 161 162 2.89349300 -5.09982162 1.00274877 3.57141241 -2.35796508 -2.91794126 163 164 165 166 167 168 -2.03453799 0.03934274 0.51399728 0.81606453 4.78469442 1.26930790 169 170 171 172 173 174 -2.55388404 -3.54906229 -8.11634649 -6.93974072 3.95169815 0.46540654 175 176 177 178 179 180 4.00053205 -0.86770056 -3.01641355 2.02273502 1.58180003 -6.68728095 181 182 183 184 185 186 1.24667746 -4.04299487 -4.41551168 -4.92956109 -0.49496408 -2.84978886 187 188 189 190 191 192 -3.52533213 -1.49083108 -2.25904276 -1.75301052 0.86813598 7.38119440 193 194 195 196 197 198 -1.64984206 -1.85396379 5.17233365 2.98853113 4.51792788 -2.11047141 199 200 201 202 203 204 -0.10491637 5.14379884 -3.48397381 6.20068040 1.39840506 -8.83690931 205 206 207 208 209 210 -0.09907883 1.34302501 10.06848035 6.08518676 -3.96313436 -4.04947560 211 212 213 214 215 216 -5.33336670 2.06808537 3.88957552 -2.53633735 4.65991453 -10.15735997 217 218 219 220 221 222 1.96472995 -2.08227904 0.77161011 -2.36871736 -7.95802079 -4.26697572 223 224 225 226 227 228 -6.20842863 -10.48683651 -8.56681486 -0.18838313 -4.14058253 2.60669177 229 230 231 232 233 234 -2.26053296 -3.34497113 -8.68450242 -4.88849242 -1.33110012 3.79689770 235 236 237 238 239 240 5.02861303 -3.28764002 2.99179739 -6.17309027 -2.84919308 4.32781287 241 242 243 244 245 246 2.10245510 -0.73248651 2.29710405 -0.73826941 0.25326563 0.85526801 247 248 249 250 251 252 -0.77221608 -3.41872034 7.56708904 -6.28372171 0.99657190 4.50203056 > postscript(file="/var/fisher/rcomp/tmp/64jfz1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 252 Frequency = 1 lag(myerror, k = 1) myerror 0 -3.61993512 NA 1 -2.56045668 -3.61993512 2 6.61775492 -2.56045668 3 -1.36779180 6.61775492 4 1.21782512 -1.36779180 5 3.42844372 1.21782512 6 2.60531089 3.42844372 7 -1.75868449 2.60531089 8 -5.78578106 -1.75868449 9 1.02933235 -5.78578106 10 -2.31442817 1.02933235 11 -4.92204415 -2.31442817 12 2.92966840 -4.92204415 13 -3.52405593 2.92966840 14 -2.11125248 -3.52405593 15 -1.96927584 -2.11125248 16 5.13069372 -1.96927584 17 3.39012856 5.13069372 18 -0.77153156 3.39012856 19 -6.15688579 -0.77153156 20 -1.39671488 -6.15688579 21 -4.28311588 -1.39671488 22 5.71332085 -4.28311588 23 6.30534840 5.71332085 24 5.52019763 6.30534840 25 -4.52621456 5.52019763 26 -1.20961026 -4.52621456 27 5.21064578 -1.20961026 28 -2.23115896 5.21064578 29 -2.30588631 -2.23115896 30 -1.94153689 -2.30588631 31 -5.20450080 -1.94153689 32 5.30664947 -5.20450080 33 -2.98539129 5.30664947 34 0.38891486 -2.98539129 35 4.10264183 0.38891486 36 0.30109384 4.10264183 37 5.94030523 0.30109384 38 -7.08603347 5.94030523 39 0.55294240 -7.08603347 40 -0.23103121 0.55294240 41 1.15243294 -0.23103121 42 -1.82220389 1.15243294 43 5.64486320 -1.82220389 44 -2.97284094 5.64486320 45 3.20868196 -2.97284094 46 2.72736333 3.20868196 47 -4.45690954 2.72736333 48 -3.87634863 -4.45690954 49 -1.67412069 -3.87634863 50 -4.04838551 -1.67412069 51 -2.65193729 -4.04838551 52 -5.31981789 -2.65193729 53 -4.67161585 -5.31981789 54 -3.48217468 -4.67161585 55 -0.48853154 -3.48217468 56 -5.08695963 -0.48853154 57 0.65811903 -5.08695963 58 3.20398465 0.65811903 59 -1.24743958 3.20398465 60 0.59716337 -1.24743958 61 6.00536340 0.59716337 62 3.19337842 6.00536340 63 -1.45020505 3.19337842 64 2.35279389 -1.45020505 65 3.66282089 2.35279389 66 5.73629019 3.66282089 67 -1.68970152 5.73629019 68 -2.70994077 -1.68970152 69 -0.94944998 -2.70994077 70 4.40585898 -0.94944998 71 -4.22572362 4.40585898 72 -2.60003792 -4.22572362 73 1.55974448 -2.60003792 74 -4.69308819 1.55974448 75 6.01430910 -4.69308819 76 -1.05099162 6.01430910 77 2.82490112 -1.05099162 78 -1.25022796 2.82490112 79 -5.53826492 -1.25022796 80 9.15012086 -5.53826492 81 8.56295400 9.15012086 82 -4.38216939 8.56295400 83 5.75742079 -4.38216939 84 0.35819626 5.75742079 85 6.03089906 0.35819626 86 -2.78135730 6.03089906 87 0.74962468 -2.78135730 88 -4.60032411 0.74962468 89 -0.78940215 -4.60032411 90 -0.74076798 -0.78940215 91 0.52524265 -0.74076798 92 -2.91006950 0.52524265 93 3.04081775 -2.91006950 94 -5.67297963 3.04081775 95 1.36014195 -5.67297963 96 -7.05048811 1.36014195 97 -5.15596666 -7.05048811 98 0.02201857 -5.15596666 99 2.82710885 0.02201857 100 2.49506567 2.82710885 101 0.35177274 2.49506567 102 2.48078861 0.35177274 103 4.89415186 2.48078861 104 2.26032398 4.89415186 105 0.06453744 2.26032398 106 -5.85773614 0.06453744 107 -3.17474499 -5.85773614 108 4.35933109 -3.17474499 109 0.92008518 4.35933109 110 0.37989555 0.92008518 111 -3.83383686 0.37989555 112 1.27030494 -3.83383686 113 1.28991762 1.27030494 114 6.20849415 1.28991762 115 0.38173275 6.20849415 116 2.92711310 0.38173275 117 -0.62328715 2.92711310 118 6.80895525 -0.62328715 119 4.22322460 6.80895525 120 7.53851950 4.22322460 121 2.88969618 7.53851950 122 0.14285419 2.88969618 123 -0.97826744 0.14285419 124 -1.68704527 -0.97826744 125 -2.93751019 -1.68704527 126 5.75977564 -2.93751019 127 7.97994441 5.75977564 128 1.82222680 7.97994441 129 0.10080303 1.82222680 130 -0.46459967 0.10080303 131 2.36805652 -0.46459967 132 -0.74717930 2.36805652 133 5.09803650 -0.74717930 134 8.28370141 5.09803650 135 0.98367831 8.28370141 136 5.01941059 0.98367831 137 3.23078766 5.01941059 138 4.16810589 3.23078766 139 -8.08860367 4.16810589 140 3.56615289 -8.08860367 141 -2.76720679 3.56615289 142 4.58651620 -2.76720679 143 3.64933904 4.58651620 144 1.00820952 3.64933904 145 1.73137248 1.00820952 146 -3.63701059 1.73137248 147 6.38512160 -3.63701059 148 -0.67065498 6.38512160 149 -1.01761837 -0.67065498 150 0.64428685 -1.01761837 151 -2.56669158 0.64428685 152 5.29821714 -2.56669158 153 0.25360521 5.29821714 154 -0.12388652 0.25360521 155 5.73710860 -0.12388652 156 2.89349300 5.73710860 157 -5.09982162 2.89349300 158 1.00274877 -5.09982162 159 3.57141241 1.00274877 160 -2.35796508 3.57141241 161 -2.91794126 -2.35796508 162 -2.03453799 -2.91794126 163 0.03934274 -2.03453799 164 0.51399728 0.03934274 165 0.81606453 0.51399728 166 4.78469442 0.81606453 167 1.26930790 4.78469442 168 -2.55388404 1.26930790 169 -3.54906229 -2.55388404 170 -8.11634649 -3.54906229 171 -6.93974072 -8.11634649 172 3.95169815 -6.93974072 173 0.46540654 3.95169815 174 4.00053205 0.46540654 175 -0.86770056 4.00053205 176 -3.01641355 -0.86770056 177 2.02273502 -3.01641355 178 1.58180003 2.02273502 179 -6.68728095 1.58180003 180 1.24667746 -6.68728095 181 -4.04299487 1.24667746 182 -4.41551168 -4.04299487 183 -4.92956109 -4.41551168 184 -0.49496408 -4.92956109 185 -2.84978886 -0.49496408 186 -3.52533213 -2.84978886 187 -1.49083108 -3.52533213 188 -2.25904276 -1.49083108 189 -1.75301052 -2.25904276 190 0.86813598 -1.75301052 191 7.38119440 0.86813598 192 -1.64984206 7.38119440 193 -1.85396379 -1.64984206 194 5.17233365 -1.85396379 195 2.98853113 5.17233365 196 4.51792788 2.98853113 197 -2.11047141 4.51792788 198 -0.10491637 -2.11047141 199 5.14379884 -0.10491637 200 -3.48397381 5.14379884 201 6.20068040 -3.48397381 202 1.39840506 6.20068040 203 -8.83690931 1.39840506 204 -0.09907883 -8.83690931 205 1.34302501 -0.09907883 206 10.06848035 1.34302501 207 6.08518676 10.06848035 208 -3.96313436 6.08518676 209 -4.04947560 -3.96313436 210 -5.33336670 -4.04947560 211 2.06808537 -5.33336670 212 3.88957552 2.06808537 213 -2.53633735 3.88957552 214 4.65991453 -2.53633735 215 -10.15735997 4.65991453 216 1.96472995 -10.15735997 217 -2.08227904 1.96472995 218 0.77161011 -2.08227904 219 -2.36871736 0.77161011 220 -7.95802079 -2.36871736 221 -4.26697572 -7.95802079 222 -6.20842863 -4.26697572 223 -10.48683651 -6.20842863 224 -8.56681486 -10.48683651 225 -0.18838313 -8.56681486 226 -4.14058253 -0.18838313 227 2.60669177 -4.14058253 228 -2.26053296 2.60669177 229 -3.34497113 -2.26053296 230 -8.68450242 -3.34497113 231 -4.88849242 -8.68450242 232 -1.33110012 -4.88849242 233 3.79689770 -1.33110012 234 5.02861303 3.79689770 235 -3.28764002 5.02861303 236 2.99179739 -3.28764002 237 -6.17309027 2.99179739 238 -2.84919308 -6.17309027 239 4.32781287 -2.84919308 240 2.10245510 4.32781287 241 -0.73248651 2.10245510 242 2.29710405 -0.73248651 243 -0.73826941 2.29710405 244 0.25326563 -0.73826941 245 0.85526801 0.25326563 246 -0.77221608 0.85526801 247 -3.41872034 -0.77221608 248 7.56708904 -3.41872034 249 -6.28372171 7.56708904 250 0.99657190 -6.28372171 251 4.50203056 0.99657190 252 NA 4.50203056 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.56045668 -3.61993512 [2,] 6.61775492 -2.56045668 [3,] -1.36779180 6.61775492 [4,] 1.21782512 -1.36779180 [5,] 3.42844372 1.21782512 [6,] 2.60531089 3.42844372 [7,] -1.75868449 2.60531089 [8,] -5.78578106 -1.75868449 [9,] 1.02933235 -5.78578106 [10,] -2.31442817 1.02933235 [11,] -4.92204415 -2.31442817 [12,] 2.92966840 -4.92204415 [13,] -3.52405593 2.92966840 [14,] -2.11125248 -3.52405593 [15,] -1.96927584 -2.11125248 [16,] 5.13069372 -1.96927584 [17,] 3.39012856 5.13069372 [18,] -0.77153156 3.39012856 [19,] -6.15688579 -0.77153156 [20,] -1.39671488 -6.15688579 [21,] -4.28311588 -1.39671488 [22,] 5.71332085 -4.28311588 [23,] 6.30534840 5.71332085 [24,] 5.52019763 6.30534840 [25,] -4.52621456 5.52019763 [26,] -1.20961026 -4.52621456 [27,] 5.21064578 -1.20961026 [28,] -2.23115896 5.21064578 [29,] -2.30588631 -2.23115896 [30,] -1.94153689 -2.30588631 [31,] -5.20450080 -1.94153689 [32,] 5.30664947 -5.20450080 [33,] -2.98539129 5.30664947 [34,] 0.38891486 -2.98539129 [35,] 4.10264183 0.38891486 [36,] 0.30109384 4.10264183 [37,] 5.94030523 0.30109384 [38,] -7.08603347 5.94030523 [39,] 0.55294240 -7.08603347 [40,] -0.23103121 0.55294240 [41,] 1.15243294 -0.23103121 [42,] -1.82220389 1.15243294 [43,] 5.64486320 -1.82220389 [44,] -2.97284094 5.64486320 [45,] 3.20868196 -2.97284094 [46,] 2.72736333 3.20868196 [47,] -4.45690954 2.72736333 [48,] -3.87634863 -4.45690954 [49,] -1.67412069 -3.87634863 [50,] -4.04838551 -1.67412069 [51,] -2.65193729 -4.04838551 [52,] -5.31981789 -2.65193729 [53,] -4.67161585 -5.31981789 [54,] -3.48217468 -4.67161585 [55,] -0.48853154 -3.48217468 [56,] -5.08695963 -0.48853154 [57,] 0.65811903 -5.08695963 [58,] 3.20398465 0.65811903 [59,] -1.24743958 3.20398465 [60,] 0.59716337 -1.24743958 [61,] 6.00536340 0.59716337 [62,] 3.19337842 6.00536340 [63,] -1.45020505 3.19337842 [64,] 2.35279389 -1.45020505 [65,] 3.66282089 2.35279389 [66,] 5.73629019 3.66282089 [67,] -1.68970152 5.73629019 [68,] -2.70994077 -1.68970152 [69,] -0.94944998 -2.70994077 [70,] 4.40585898 -0.94944998 [71,] -4.22572362 4.40585898 [72,] -2.60003792 -4.22572362 [73,] 1.55974448 -2.60003792 [74,] -4.69308819 1.55974448 [75,] 6.01430910 -4.69308819 [76,] -1.05099162 6.01430910 [77,] 2.82490112 -1.05099162 [78,] -1.25022796 2.82490112 [79,] -5.53826492 -1.25022796 [80,] 9.15012086 -5.53826492 [81,] 8.56295400 9.15012086 [82,] -4.38216939 8.56295400 [83,] 5.75742079 -4.38216939 [84,] 0.35819626 5.75742079 [85,] 6.03089906 0.35819626 [86,] -2.78135730 6.03089906 [87,] 0.74962468 -2.78135730 [88,] -4.60032411 0.74962468 [89,] -0.78940215 -4.60032411 [90,] -0.74076798 -0.78940215 [91,] 0.52524265 -0.74076798 [92,] -2.91006950 0.52524265 [93,] 3.04081775 -2.91006950 [94,] -5.67297963 3.04081775 [95,] 1.36014195 -5.67297963 [96,] -7.05048811 1.36014195 [97,] -5.15596666 -7.05048811 [98,] 0.02201857 -5.15596666 [99,] 2.82710885 0.02201857 [100,] 2.49506567 2.82710885 [101,] 0.35177274 2.49506567 [102,] 2.48078861 0.35177274 [103,] 4.89415186 2.48078861 [104,] 2.26032398 4.89415186 [105,] 0.06453744 2.26032398 [106,] -5.85773614 0.06453744 [107,] -3.17474499 -5.85773614 [108,] 4.35933109 -3.17474499 [109,] 0.92008518 4.35933109 [110,] 0.37989555 0.92008518 [111,] -3.83383686 0.37989555 [112,] 1.27030494 -3.83383686 [113,] 1.28991762 1.27030494 [114,] 6.20849415 1.28991762 [115,] 0.38173275 6.20849415 [116,] 2.92711310 0.38173275 [117,] -0.62328715 2.92711310 [118,] 6.80895525 -0.62328715 [119,] 4.22322460 6.80895525 [120,] 7.53851950 4.22322460 [121,] 2.88969618 7.53851950 [122,] 0.14285419 2.88969618 [123,] -0.97826744 0.14285419 [124,] -1.68704527 -0.97826744 [125,] -2.93751019 -1.68704527 [126,] 5.75977564 -2.93751019 [127,] 7.97994441 5.75977564 [128,] 1.82222680 7.97994441 [129,] 0.10080303 1.82222680 [130,] -0.46459967 0.10080303 [131,] 2.36805652 -0.46459967 [132,] -0.74717930 2.36805652 [133,] 5.09803650 -0.74717930 [134,] 8.28370141 5.09803650 [135,] 0.98367831 8.28370141 [136,] 5.01941059 0.98367831 [137,] 3.23078766 5.01941059 [138,] 4.16810589 3.23078766 [139,] -8.08860367 4.16810589 [140,] 3.56615289 -8.08860367 [141,] -2.76720679 3.56615289 [142,] 4.58651620 -2.76720679 [143,] 3.64933904 4.58651620 [144,] 1.00820952 3.64933904 [145,] 1.73137248 1.00820952 [146,] -3.63701059 1.73137248 [147,] 6.38512160 -3.63701059 [148,] -0.67065498 6.38512160 [149,] -1.01761837 -0.67065498 [150,] 0.64428685 -1.01761837 [151,] -2.56669158 0.64428685 [152,] 5.29821714 -2.56669158 [153,] 0.25360521 5.29821714 [154,] -0.12388652 0.25360521 [155,] 5.73710860 -0.12388652 [156,] 2.89349300 5.73710860 [157,] -5.09982162 2.89349300 [158,] 1.00274877 -5.09982162 [159,] 3.57141241 1.00274877 [160,] -2.35796508 3.57141241 [161,] -2.91794126 -2.35796508 [162,] -2.03453799 -2.91794126 [163,] 0.03934274 -2.03453799 [164,] 0.51399728 0.03934274 [165,] 0.81606453 0.51399728 [166,] 4.78469442 0.81606453 [167,] 1.26930790 4.78469442 [168,] -2.55388404 1.26930790 [169,] -3.54906229 -2.55388404 [170,] -8.11634649 -3.54906229 [171,] -6.93974072 -8.11634649 [172,] 3.95169815 -6.93974072 [173,] 0.46540654 3.95169815 [174,] 4.00053205 0.46540654 [175,] -0.86770056 4.00053205 [176,] -3.01641355 -0.86770056 [177,] 2.02273502 -3.01641355 [178,] 1.58180003 2.02273502 [179,] -6.68728095 1.58180003 [180,] 1.24667746 -6.68728095 [181,] -4.04299487 1.24667746 [182,] -4.41551168 -4.04299487 [183,] -4.92956109 -4.41551168 [184,] -0.49496408 -4.92956109 [185,] -2.84978886 -0.49496408 [186,] -3.52533213 -2.84978886 [187,] -1.49083108 -3.52533213 [188,] -2.25904276 -1.49083108 [189,] -1.75301052 -2.25904276 [190,] 0.86813598 -1.75301052 [191,] 7.38119440 0.86813598 [192,] -1.64984206 7.38119440 [193,] -1.85396379 -1.64984206 [194,] 5.17233365 -1.85396379 [195,] 2.98853113 5.17233365 [196,] 4.51792788 2.98853113 [197,] -2.11047141 4.51792788 [198,] -0.10491637 -2.11047141 [199,] 5.14379884 -0.10491637 [200,] -3.48397381 5.14379884 [201,] 6.20068040 -3.48397381 [202,] 1.39840506 6.20068040 [203,] -8.83690931 1.39840506 [204,] -0.09907883 -8.83690931 [205,] 1.34302501 -0.09907883 [206,] 10.06848035 1.34302501 [207,] 6.08518676 10.06848035 [208,] -3.96313436 6.08518676 [209,] -4.04947560 -3.96313436 [210,] -5.33336670 -4.04947560 [211,] 2.06808537 -5.33336670 [212,] 3.88957552 2.06808537 [213,] -2.53633735 3.88957552 [214,] 4.65991453 -2.53633735 [215,] -10.15735997 4.65991453 [216,] 1.96472995 -10.15735997 [217,] -2.08227904 1.96472995 [218,] 0.77161011 -2.08227904 [219,] -2.36871736 0.77161011 [220,] -7.95802079 -2.36871736 [221,] -4.26697572 -7.95802079 [222,] -6.20842863 -4.26697572 [223,] -10.48683651 -6.20842863 [224,] -8.56681486 -10.48683651 [225,] -0.18838313 -8.56681486 [226,] -4.14058253 -0.18838313 [227,] 2.60669177 -4.14058253 [228,] -2.26053296 2.60669177 [229,] -3.34497113 -2.26053296 [230,] -8.68450242 -3.34497113 [231,] -4.88849242 -8.68450242 [232,] -1.33110012 -4.88849242 [233,] 3.79689770 -1.33110012 [234,] 5.02861303 3.79689770 [235,] -3.28764002 5.02861303 [236,] 2.99179739 -3.28764002 [237,] -6.17309027 2.99179739 [238,] -2.84919308 -6.17309027 [239,] 4.32781287 -2.84919308 [240,] 2.10245510 4.32781287 [241,] -0.73248651 2.10245510 [242,] 2.29710405 -0.73248651 [243,] -0.73826941 2.29710405 [244,] 0.25326563 -0.73826941 [245,] 0.85526801 0.25326563 [246,] -0.77221608 0.85526801 [247,] -3.41872034 -0.77221608 [248,] 7.56708904 -3.41872034 [249,] -6.28372171 7.56708904 [250,] 0.99657190 -6.28372171 [251,] 4.50203056 0.99657190 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.56045668 -3.61993512 2 6.61775492 -2.56045668 3 -1.36779180 6.61775492 4 1.21782512 -1.36779180 5 3.42844372 1.21782512 6 2.60531089 3.42844372 7 -1.75868449 2.60531089 8 -5.78578106 -1.75868449 9 1.02933235 -5.78578106 10 -2.31442817 1.02933235 11 -4.92204415 -2.31442817 12 2.92966840 -4.92204415 13 -3.52405593 2.92966840 14 -2.11125248 -3.52405593 15 -1.96927584 -2.11125248 16 5.13069372 -1.96927584 17 3.39012856 5.13069372 18 -0.77153156 3.39012856 19 -6.15688579 -0.77153156 20 -1.39671488 -6.15688579 21 -4.28311588 -1.39671488 22 5.71332085 -4.28311588 23 6.30534840 5.71332085 24 5.52019763 6.30534840 25 -4.52621456 5.52019763 26 -1.20961026 -4.52621456 27 5.21064578 -1.20961026 28 -2.23115896 5.21064578 29 -2.30588631 -2.23115896 30 -1.94153689 -2.30588631 31 -5.20450080 -1.94153689 32 5.30664947 -5.20450080 33 -2.98539129 5.30664947 34 0.38891486 -2.98539129 35 4.10264183 0.38891486 36 0.30109384 4.10264183 37 5.94030523 0.30109384 38 -7.08603347 5.94030523 39 0.55294240 -7.08603347 40 -0.23103121 0.55294240 41 1.15243294 -0.23103121 42 -1.82220389 1.15243294 43 5.64486320 -1.82220389 44 -2.97284094 5.64486320 45 3.20868196 -2.97284094 46 2.72736333 3.20868196 47 -4.45690954 2.72736333 48 -3.87634863 -4.45690954 49 -1.67412069 -3.87634863 50 -4.04838551 -1.67412069 51 -2.65193729 -4.04838551 52 -5.31981789 -2.65193729 53 -4.67161585 -5.31981789 54 -3.48217468 -4.67161585 55 -0.48853154 -3.48217468 56 -5.08695963 -0.48853154 57 0.65811903 -5.08695963 58 3.20398465 0.65811903 59 -1.24743958 3.20398465 60 0.59716337 -1.24743958 61 6.00536340 0.59716337 62 3.19337842 6.00536340 63 -1.45020505 3.19337842 64 2.35279389 -1.45020505 65 3.66282089 2.35279389 66 5.73629019 3.66282089 67 -1.68970152 5.73629019 68 -2.70994077 -1.68970152 69 -0.94944998 -2.70994077 70 4.40585898 -0.94944998 71 -4.22572362 4.40585898 72 -2.60003792 -4.22572362 73 1.55974448 -2.60003792 74 -4.69308819 1.55974448 75 6.01430910 -4.69308819 76 -1.05099162 6.01430910 77 2.82490112 -1.05099162 78 -1.25022796 2.82490112 79 -5.53826492 -1.25022796 80 9.15012086 -5.53826492 81 8.56295400 9.15012086 82 -4.38216939 8.56295400 83 5.75742079 -4.38216939 84 0.35819626 5.75742079 85 6.03089906 0.35819626 86 -2.78135730 6.03089906 87 0.74962468 -2.78135730 88 -4.60032411 0.74962468 89 -0.78940215 -4.60032411 90 -0.74076798 -0.78940215 91 0.52524265 -0.74076798 92 -2.91006950 0.52524265 93 3.04081775 -2.91006950 94 -5.67297963 3.04081775 95 1.36014195 -5.67297963 96 -7.05048811 1.36014195 97 -5.15596666 -7.05048811 98 0.02201857 -5.15596666 99 2.82710885 0.02201857 100 2.49506567 2.82710885 101 0.35177274 2.49506567 102 2.48078861 0.35177274 103 4.89415186 2.48078861 104 2.26032398 4.89415186 105 0.06453744 2.26032398 106 -5.85773614 0.06453744 107 -3.17474499 -5.85773614 108 4.35933109 -3.17474499 109 0.92008518 4.35933109 110 0.37989555 0.92008518 111 -3.83383686 0.37989555 112 1.27030494 -3.83383686 113 1.28991762 1.27030494 114 6.20849415 1.28991762 115 0.38173275 6.20849415 116 2.92711310 0.38173275 117 -0.62328715 2.92711310 118 6.80895525 -0.62328715 119 4.22322460 6.80895525 120 7.53851950 4.22322460 121 2.88969618 7.53851950 122 0.14285419 2.88969618 123 -0.97826744 0.14285419 124 -1.68704527 -0.97826744 125 -2.93751019 -1.68704527 126 5.75977564 -2.93751019 127 7.97994441 5.75977564 128 1.82222680 7.97994441 129 0.10080303 1.82222680 130 -0.46459967 0.10080303 131 2.36805652 -0.46459967 132 -0.74717930 2.36805652 133 5.09803650 -0.74717930 134 8.28370141 5.09803650 135 0.98367831 8.28370141 136 5.01941059 0.98367831 137 3.23078766 5.01941059 138 4.16810589 3.23078766 139 -8.08860367 4.16810589 140 3.56615289 -8.08860367 141 -2.76720679 3.56615289 142 4.58651620 -2.76720679 143 3.64933904 4.58651620 144 1.00820952 3.64933904 145 1.73137248 1.00820952 146 -3.63701059 1.73137248 147 6.38512160 -3.63701059 148 -0.67065498 6.38512160 149 -1.01761837 -0.67065498 150 0.64428685 -1.01761837 151 -2.56669158 0.64428685 152 5.29821714 -2.56669158 153 0.25360521 5.29821714 154 -0.12388652 0.25360521 155 5.73710860 -0.12388652 156 2.89349300 5.73710860 157 -5.09982162 2.89349300 158 1.00274877 -5.09982162 159 3.57141241 1.00274877 160 -2.35796508 3.57141241 161 -2.91794126 -2.35796508 162 -2.03453799 -2.91794126 163 0.03934274 -2.03453799 164 0.51399728 0.03934274 165 0.81606453 0.51399728 166 4.78469442 0.81606453 167 1.26930790 4.78469442 168 -2.55388404 1.26930790 169 -3.54906229 -2.55388404 170 -8.11634649 -3.54906229 171 -6.93974072 -8.11634649 172 3.95169815 -6.93974072 173 0.46540654 3.95169815 174 4.00053205 0.46540654 175 -0.86770056 4.00053205 176 -3.01641355 -0.86770056 177 2.02273502 -3.01641355 178 1.58180003 2.02273502 179 -6.68728095 1.58180003 180 1.24667746 -6.68728095 181 -4.04299487 1.24667746 182 -4.41551168 -4.04299487 183 -4.92956109 -4.41551168 184 -0.49496408 -4.92956109 185 -2.84978886 -0.49496408 186 -3.52533213 -2.84978886 187 -1.49083108 -3.52533213 188 -2.25904276 -1.49083108 189 -1.75301052 -2.25904276 190 0.86813598 -1.75301052 191 7.38119440 0.86813598 192 -1.64984206 7.38119440 193 -1.85396379 -1.64984206 194 5.17233365 -1.85396379 195 2.98853113 5.17233365 196 4.51792788 2.98853113 197 -2.11047141 4.51792788 198 -0.10491637 -2.11047141 199 5.14379884 -0.10491637 200 -3.48397381 5.14379884 201 6.20068040 -3.48397381 202 1.39840506 6.20068040 203 -8.83690931 1.39840506 204 -0.09907883 -8.83690931 205 1.34302501 -0.09907883 206 10.06848035 1.34302501 207 6.08518676 10.06848035 208 -3.96313436 6.08518676 209 -4.04947560 -3.96313436 210 -5.33336670 -4.04947560 211 2.06808537 -5.33336670 212 3.88957552 2.06808537 213 -2.53633735 3.88957552 214 4.65991453 -2.53633735 215 -10.15735997 4.65991453 216 1.96472995 -10.15735997 217 -2.08227904 1.96472995 218 0.77161011 -2.08227904 219 -2.36871736 0.77161011 220 -7.95802079 -2.36871736 221 -4.26697572 -7.95802079 222 -6.20842863 -4.26697572 223 -10.48683651 -6.20842863 224 -8.56681486 -10.48683651 225 -0.18838313 -8.56681486 226 -4.14058253 -0.18838313 227 2.60669177 -4.14058253 228 -2.26053296 2.60669177 229 -3.34497113 -2.26053296 230 -8.68450242 -3.34497113 231 -4.88849242 -8.68450242 232 -1.33110012 -4.88849242 233 3.79689770 -1.33110012 234 5.02861303 3.79689770 235 -3.28764002 5.02861303 236 2.99179739 -3.28764002 237 -6.17309027 2.99179739 238 -2.84919308 -6.17309027 239 4.32781287 -2.84919308 240 2.10245510 4.32781287 241 -0.73248651 2.10245510 242 2.29710405 -0.73248651 243 -0.73826941 2.29710405 244 0.25326563 -0.73826941 245 0.85526801 0.25326563 246 -0.77221608 0.85526801 247 -3.41872034 -0.77221608 248 7.56708904 -3.41872034 249 -6.28372171 7.56708904 250 0.99657190 -6.28372171 251 4.50203056 0.99657190 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/7iqp41353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8fj2z1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9zjtx1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') Warning messages: 1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced 2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10548t1353152822.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11rtww1353152822.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12dmwh1353152822.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13155j1353152822.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/147ki31353152822.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/15ofzk1353152822.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/16bumw1353152822.tab") + } > > try(system("convert tmp/1dlvt1353152822.ps tmp/1dlvt1353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/24btq1353152822.ps tmp/24btq1353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/3jg9p1353152822.ps tmp/3jg9p1353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/4fd5j1353152822.ps tmp/4fd5j1353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/54c831353152822.ps tmp/54c831353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/64jfz1353152822.ps tmp/64jfz1353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/7iqp41353152822.ps tmp/7iqp41353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/8fj2z1353152822.ps tmp/8fj2z1353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/9zjtx1353152822.ps tmp/9zjtx1353152822.png",intern=TRUE)) character(0) > try(system("convert tmp/10548t1353152822.ps tmp/10548t1353152822.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 14.471 1.322 15.795