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Data X:
102750 2.75 42.6 45.498 95276 2.73 42.9 46.1773 112053 2.82 43.3 46.1937 98841 2.83 43.6 46.1272 123102 2.9 43.9 46.4199 118152 3.05 44.2 46.4535 101752 3.15 44.3 46.648 148219 3.26 45.1 46.5669 124966 3.38 45.2 46.9866 134741 3.54 45.6 47.2997 132168 3.81 45.9 47.548 100950 5.27 46.2 47.4375 96418 6.71 46.6 47.1083 86891 9.09 47.2 46.9634 89796 11.08 47.8 46.9733 119663 11.91 48 46.83 130539 11.81 48.6 47.1848 120851 11.81 49 47.1292 145422 12.09 49.4 47.1505 150583 11.95 50 46.6882 127054 11.67 50.6 46.7161 137473 11.6 51.1 46.536 127094 11.71 51.5 45.0062 132080 11.62 51.9 43.4204 188311 11.64 52.1 42.8246 107487 11.66 52.5 41.8301 84669 11.67 52.7 41.3862 149184 11.69 52.9 41.4258 121026 11.58 53.2 41.3326 81073 11.4 53.6 41.6042 132947 11.44 54.2 42.0025 141294 11.38 54.3 42.4426 155077 11.31 54.6 42.9708 145154 11.45 54.9 43.1611 127094 11.73 55.3 43.2561 151414 12.11 55.5 43.7944 167858 12.23 55.6 44.4309 127070 12.39 55.8 44.8644 154692 12.34 55.9 44.916 170905 12.42 56.1 45.1733 127751 12.37 56.5 45.3729 173795 12.37 56.8 45.3841 190181 12.39 57.1 45.6491 198417 12.43 57.4 45.9698 183018 12.48 57.6 46.1015 171608 12.45 57.9 46.1172 188087 12.58 58 46.7939 197042 12.59 58.2 47.2798 208788 12.54 58.5 47.023 178111 13.01 59.1 47.7335 236455 13.31 59.5 48.3415 233219 13.45 60 48.7789 188106 13.28 60.3 49.2046 238876 13.38 60.7 49.5627 205148 13.36 61 49.6389 214727 13.4 61.2 49.6517 213428 13.49 61.4 49.8872 195128 13.47 61.6 49.9859 206047 13.62 61.9 50.0357 201773 13.57 62.1 50.1135 192772 13.59 62.5 49.4201 198230 13.48 62.9 49.6618 181172 13.47 63.4 50.6053 189079 13.47 63.9 51.6639 179073 13.36 64.5 51.8472 197421 13.37 65.2 52.2056 195244 13.4 65.7 52.1834 219826 13.41 66 52.3807 211793 13.37 66.5 52.5124 203394 13.42 67.1 52.9384 209578 13.41 67.4 53.3363 214769 13.46 67.7 53.6296 226177 13.64 68.3 53.2837 191449 13.93 69.1 53.5675 200989 14.46 69.8 53.7364 216707 14.92 70.6 53.1571 192882 16.27 71.5 53.5566 199736 17.36 72.3 53.5534 202349 19.07 73.1 53.4808 204137 21.1 73.8 53.1195 215588 22.39 74.6 53.1786 229454 23.13 75.2 53.4617 175048 23.27 75.9 53.409 212799 24.57 76.7 53.4536 181727 26.32 77.8 53.7071 211607 28.57 78.9 53.7262 185853 30.44 80.1 53.5481 158277 31.4 81 52.4571 180695 31.84 81.8 51.1904 175959 31.86 82.7 50.5575 139550 32.3 82.7 50.166 155810 32.93 83.3 50.353 138305 32.73 84 51.1727 147014 33.1 84.8 51.8129 135994 33.23 85.5 52.7175 166455 33.94 86.3 53.0142 177737 34.27 87 52.7119 167021 35.96 87.9 52.4633 132134 36.25 88.5 52.7501 169834 36.92 89.1 52.5233 130599 36.16 89.8 52.8211 156836 36.59 90.6 53.0699 119749 35.05 91.6 53.4044 148996 34.53 92.3 53.3959 147491 34.07 93.2 53.0761 147216 33.65 93.4 52.6972 153455 33.84 93.7 52.0996 112004 33.99 94 51.5219 158512 35.41 94.3 50.4933 104139 35.53 94.6 51.4979 102536 34.71 94.5 51.1159 93017 33.2 94.9 50.6623 91988 32.25 95.8 50.3505 123616 32.92 97 50.1943 134498 33.27 97.5 50.0395 149812 32.91 97.7 49.6075 110334 32.39 97.9 49.4584 136639 32.44 98.2 49.011 102712 32.84 98 48.8232 112951 32.44 97.6 48.4682 107897 32.5 97.8 49.3992 73242 31.12 97.9 49.089 72800 30.28 97.9 49.4906 78767 28.76 98.6 50.0805 114791 28.59 99.2 50.4295 109351 28.83 99.5 50.7333 122520 28.93 99.9 51.5016 137338 29.31 100.2 52.0679 132061 29.27 100.7 52.8472 130607 29.36 101 53.2874 118570 29.05 101.2 53.4759 95873 29 101.3 53.7593 103116 27.65 101.9 54.8216 98619 27.64 102.4 55.0698 104178 27.8 102.6 55.3384 123468 27.84 103.1 55.6911 99651 27.85 103.4 55.9506 120264 27.76 103.7 56.1549 122795 28.05 104.1 56.3326 108524 27.66 104.5 56.3847 105760 27.39 105 56.2832 117191 27.56 105.3 56.1943 122882 27.55 105.3 56.4108 93275 27.3 105.3 56.4759 99842 27.38 105.5 56.3801 83803 26.91 106 56.5796 61132 26.05 106.4 56.6645 118563 26.52 106.9 56.5122 106993 26.79 107.3 56.5982 118108 26.52 107.6 56.6317 99017 25.91 107.8 56.2637 99852 25.76 108 56.496 112720 25.42 108.3 56.7412 113636 25.65 108.7 56.508 118220 25.69 109 56.6984 128854 26.04 109.3 57.2954 123898 25.8 109.6 57.5555 100823 23.13 109.3 57.1707 115107 18.1 108.8 56.7784 90624 12.78 108.6 56.8228 132001 12.24 108.9 56.938 157969 12.04 109.5 56.7427 169333 11.03 109.5 57.0569 144907 10.09 109.7 56.9807 169346 11.08 110.2 57.0954 144666 11.79 110.3 57.3542 158829 12.23 110.4 57.623 127286 12.4 110.5 58.1006 120578 13.86 111.2 57.9173 129293 15.47 111.6 58.663 122371 15.87 112.1 58.7602 115176 16.57 112.7 59.1416 142168 16.92 113.1 59.517 153260 17.31 113.5 59.7996 173906 17.77 113.8 60.2152 178446 18.07 114.4 60.7146 155962 17.49 115 60.8781 168257 17.21 115.3 61.7569 149456 17.12 115.4 62.091 136105 16.46 115.4 62.394 141507 22.4 115.7 62.4207 152084 15.2 116 62.6908 145138 14.24 116.5 62.8421 146548 14.21 117.1 63.1885 173098 14.69 117.5 63.1203 165471 14.68 118 63.2843 152271 14.02 118.5 63.3155 163201 13.38 119 63.5859 157823 13.08 119.8 63.405 166167 11.92 120.2 63.7184 154253 11.52 120.3 63.8175 170299 12.34 120.5 64.1273 166388 13.91 121.1 64.3162 141051 14.84 121.6 64.026 160254 15.54 122.3 64.166 164995 17.33 123.1 64.222 195971 17.97 123.8 63.7707 182635 17.27 124.1 63.8022 189829 16.93 124.4 63.236 209476 15.95 124.6 63.8059 189848 16.14 125 63.576 183746 16.61 125.6 63.5346 192682 17.08 125.9 63.7465 169677 17.72 126.1 64.1419 201823 18.85 127.4 63.7117 172643 18.79 128 64.3504 202931 17.75 128.7 64.6721 175863 16.02 128.9 64.5975 222061 14.61 129.2 64.7028 199797 13.83 129.9 64.9174 214638 13.92 130.4 64.8436 200106 19.57 131.6 65.043 166077 25.63 132.7 65.1372 160586 30.08 133.5 64.6442 158330 29.51 133.8 63.8853 141749 25.75 133.8 63.4658 170795 22.98 134.6 63.1915 153286 18.39 134.8 62.7585 163426 16.75 135 62.4265 172562 16.39 135.2 62.5503 197474 16.57 135.6 63.1756 189822 16.4 136 63.742 188511 16.15 136.2 63.8029 207437 16.8 136.6 63.8503 192128 17.14 137.2 64.4151 175716 17.97 137.4 64.2992 159108 18.06 137.8 64.2209 175801 16.6 137.9 63.9602 186723 14.87 138.1 63.596 154970 14.42 138.6 64.0409 172446 14.48 139.3 64.5973 185965 15.5 139.5 65.0756 195525 16.74 139.7 65.2831 193156 18.27 140.2 65.2957 212705 18.2 140.5 65.8801 201357 18.03 140.9 65.5581 189971 17.86 141.3 65.715 216523 18.22 141.8 66.2013 193233 17.63 142 66.4879 191996 16.22 141.9 66.5431 211974 15.5 142.6 66.8264 175907 15.71 143.1 67.1172 206109 16.49 143.6 67.0479 220275 16.69 144 67.2498 211342 16.71 144.2 67.0325 222528 16.07 144.4 67.1532 229523 14.96 144.4 67.3586 204153 14.51 144.8 67.2888 206735 14.37 145.1 67.6092 223416 14.59 145.7 68.1214 228292 13.72 145.8 68.4089 203121 12.2 145.8 68.7737 205957 11.64 146.2 69.0299 176918 12.09 146.7 69.0418 219839 11.76 147.2 69.7582 217213 12.85 147.4 70.125 216618 14.05 147.5 70.4978 248057 15.18 148 70.948 245642 16.09 148.4 71.0595 242485 15.97 149 71.4749 260423 15 149.4 71.7333 221030 14.8 149.5 72.3479 229157 15.31 149.7 72.8018 220858 14.7 149.7 73.5563 212270 15.06 150.3 73.6891 195944 15.53 150.9 73.5889 239741 15.78 151.4 73.6895 212013 16.76 151.9 73.676 240514 17.4 152.2 73.8858 241982 16.78 152.5 74.1391 245447 15.51 152.5 73.8447 240839 15.22 152.9 74.7803 244875 15.44 153.2 75.0755 226375 15.25 153.7 74.9925 231567 15.1 153.6 75.1822 235746 15.82 153.5 75.4725 238990 16.43 154.4 74.9823 198120 16.1 154.9 76.153 201663 17.31 155.7 76.0724 238198 19.27 156.3 76.7608 261641 18.9 156.6 77.3269 253014 17.96 156.7 77.9694 275225 18.16 157 77.8351 250957 18.65 157.3 78.3005 260375 19.97 157.8 78.8378 250694 21.41 158.3 78.7843 216953 21.38 158.6 79.4683 247816 21.63 158.6 79.9829 224135 21.86 159.1 80.0837 211073 20.48 159.6 81.0483 245623 18.76 160 81.6195 250947 17.13 160.2 81.6408 278223 17.06 160.1 82.1311 254232 16.85 160.3 82.5332 266293 16.41 160.5 83.1538 280897 16.95 160.8 84.0293 274565 16.73 161.2 84.7873 280555 17.71 161.6 85.5125 252757 17.25 161.5 86.2601 250131 16.05 161.3 86.5262 271208 14.31 161.6 86.9662 230593 13.02 161.9 87.0687 263407 11.88 162.2 87.1414 289968 11.77 162.5 87.4497 282846 11.8 162.8 88.0124 271314 11.12 163 87.4571 289718 10.78 163.2 87.1484 300227 10.55 163.4 88.936 259951 10.99 163.6 88.778 263149 11.66 164 89.4857 267953 10.79 164 89.4358 252378 9.38 163.9 89.7761 280356 9.21 164.3 90.1893 234298 9.48 164.5 90.6683 271574 10.5 165 90.831 262378 12.88 166.2 91.0632 289457 14.6 166.2 91.7311 278274 14.52 166.2 91.5818 288932 16.11 166.7 92.1587 283813 17.88 167.1 92.5363 267600 19.69 167.9 92.1699 267574 20.76 168.2 93.3786 254862 21.05 168.3 93.824 248974 22.79 168.3 94.5441 256840 23.31 168.8 94.5458 250914 25.14 169.8 94.8185 279334 26.41 171.2 95.1983 286549 24.41 171.3 95.8921 302266 24.28 171.5 96.0691 298205 26.78 172.4 96.1568 300843 27.73 172.8 96.0239 312955 26.59 172.8 95.7182 275962 29.03 173.7 96.1105 299561 28.57 174 95.8225 260975 28.34 174.1 95.8391 274836 26.4 174 95.5791 284112 23.19 175.1 94.9499 247331 23.85 175.8 94.369 298120 22.75 176.2 94.1259 306008 21.66 176.9 93.9061 306813 22.65 177.7 93.2803 288550 23.09 178 92.7057 301636 22.33 177.5 92.1721 293215 22.14 177.5 92.0023 270713 23.02 178.3 91.6795 311803 19.88 177.7 91.2682 281316 17 177.4 90.7894 281450 15.46 176.7 90.8311 295494 16.29 177.1 91.3471 246411 16.58 177.8 91.3672 267037 19.27 178.8 92.1054 296134 22.53 179.8 92.479 296505 23.75 179.8 92.8824 270677 23.35 179.9 93.7637 290855 23.73 180.1 93.5461 296068 24.58 180.7 93.5765 272653 25.49 181 93.7116 315720 26.25 181.3 93.4006 286298 24.19 181.3 93.8758 284170 24.15 180.9 93.4191 273338 27.76 181.7 93.9571 250262 30.37 183.1 94.2558 294768 30.39 184.2 94.0416 318088 26.01 183.8 93.3666 319111 24.05 183.5 93.3852 312982 25.5 183.7 93.5219 335511 26.75 183.9 93.9144 319674 27.56 184.6 93.7371 316796 26.43 185.2 94.3262 329992 26.28 185 94.4442 291352 26.54 184.5 95.2224 314131 27.17 184.3 95.1545 309876 28.57 185.2 95.3434 288494 29.17 186.2 95.9228 329991 30.66 187.4 95.4538 311663 31 188 95.8653 317854 33.14 189.1 96.6472 344729 33.74 189.7 95.8588 324108 33.38 189.4 96.5901 333756 36.54 189.5 96.6687 297013 37.52 189.9 96.745 313249 41.84 190.9 97.6604 329660 41.19 191 97.8427 320586 36.46 190.3 98.5495 325786 35.27 190.7 99.002 293425 36.93 191.8 99.6741 324180 41.28 193.3 99.5181 315528 44.78 194.6 99.6518 319982 43.04 194.4 99.8158 327865 44.41 194.5 100.2232 312106 49.07 195.4 99.8997 329039 52.85 196.4 100.1025 277589 57.42 198.8 98.2644 300884 56.21 199.2 99.4949 314028 52.16 197.6 100.5129 314259 49.79 196.8 101.1118 303472 51.8 198.3 101.2313 290744 53.86 198.7 101.2755 313340 52.32 199.8 101.4651 294281 56.65 201.5 101.9012 325796 62.04 202.5 101.7589 329839 62.12 202.9 102.1304 322588 64.93 203.5 102.0989 336528 66.13 203.9 102.4526 316381 62.4 202.9 102.2753 308602 55.47 201.8 102.2299 299010 52.22 201.5 102.1419 293645 53.84 201.8 103.2191 320108 52.23 202.4 102.7129 252869 50.71 203.5 103.7659 324248 53 205.4 103.9538 304775 57.28 206.7 104.7077 320208 59.36 207.9 104.7507 321260 60.95 208.4 104.7581 310320 65.56 208.3 104.7111 319197 68.21 207.9 104.9122 297503 68.51 208.5 105.2764 316184 72.49 208.9 104.772 303411 79.65 210.2 105.3295 300841 82.76 210 105.3213
Names of X columns:
barrels_purchased unit_price cpi US_IND_PROD
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
par6 <- '12' par5 <- '' par4 <- '' par3 <- 'Linear Trend' par2 <- 'Include Seasonal Dummies' par1 <- '1' library(lattice) library(lmtest) library(car) library(MASS) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test mywarning <- '' par6 <- as.numeric(par6) if(is.na(par6)) { par6 <- 12 mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.' } par1 <- as.numeric(par1) if(is.na(par1)) { par1 <- 1 mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.' } if (par4=='') par4 <- 0 par4 <- as.numeric(par4) if (!is.numeric(par4)) par4 <- 0 if (par5=='') par5 <- 0 par5 <- as.numeric(par5) if (!is.numeric(par5)) par5 <- 0 x <- na.omit(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'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par3 == 'Seasonal Differences (s)'){ (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s)'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if(par4 > 0) { x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep=''))) for (i in 1:(n-par4)) { for (j in 1:par4) { x2[i,j] <- x[i+par4-j,par1] } } x <- cbind(x[(par4+1):n,], x2) n <- n - par4 } if(par5 > 0) { x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*par6)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*par6-j*par6,par1] } } x <- cbind(x[(par5*par6+1):n,], x2) n <- n - par5*par6 } if (par2 == 'Include Seasonal Dummies'){ x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep =''))) for (i in 1:(par6-1)){ x2[seq(i,n,par6),i] <- 1 } x <- cbind(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[n,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } print(x) (k <- length(x[n,])) head(x) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') sresid <- studres(mylm) hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals') xfit<-seq(min(sresid),max(sresid),length=40) yfit<-dnorm(xfit) lines(xfit, yfit) grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqPlot(mylm, main='QQ Plot') grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) print(z) plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$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.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'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<br />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$coefficients)[i],header=TRUE) a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') myr <- as.numeric(mysum$resid) myr a <-table.start() a <- table.row.start(a) a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Description',1,TRUE) a <- table.element(a,'Link',1,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Histogram',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'QQ Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Kernel Density Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Spectral Analysis',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Summary Statistics',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a<-table.end(a) table.save(a,file='mytable7.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.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,signif(numsignificant1,6)) a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' ')) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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='mytable6.tab') } } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_fitted <- resettest(mylm,power=2:3,type='fitted') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_fitted'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_regressors <- resettest(mylm,power=2:3,type='regressor') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_regressors'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_principal_components'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable8.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) vif <- vif(mylm) a<-table.element(a,paste('<pre>',RC.texteval('vif'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable9.tab')
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R Server
Big Analytics Cloud Computing Center
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