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Type 'q()' to quit R. > x <- array(list(6392.3 + ,0 + ,8686.4 + ,0 + ,9244.7 + ,0 + ,8182.7 + ,0 + ,7451.4 + ,0 + ,7988.8 + ,0 + ,8243.5 + ,0 + ,8843 + ,0 + ,9092.7 + ,0 + ,8246.7 + ,0 + ,9311.7 + ,0 + ,8341.2 + ,0 + ,7116.7 + ,0 + ,9635.7 + ,0 + ,9815.4 + ,0 + ,8611.3 + ,0 + ,8297.8 + ,0 + ,8715.1 + ,0 + ,8919.9 + ,0 + ,10085.8 + ,0 + ,9511.7 + ,0 + ,8991.3 + ,0 + ,10311.2 + ,0 + ,8895.4 + ,0 + ,7449.8 + ,0 + ,10084 + ,0 + ,9859.4 + ,0 + ,9100.1 + ,0 + ,8920.8 + ,0 + ,8502.7 + ,0 + ,8599.6 + ,0 + ,10394.4 + ,0 + ,9290.4 + ,0 + ,8742.2 + ,0 + ,10217.3 + ,0 + ,8639 + ,0 + ,8139.6 + ,0 + ,10779.1 + ,0 + ,10427.7 + ,0 + ,10349.1 + ,0 + ,10036.4 + ,0 + ,9492.1 + ,0 + ,10638.8 + ,0 + ,12054.5 + ,0 + ,10324.7 + ,0 + ,11817.3 + ,0 + ,11008.9 + ,0 + ,9996.6 + ,0 + ,9419.5 + ,0 + ,11958.8 + ,0 + ,12594.6 + ,0 + ,11890.6 + ,0 + ,10871.7 + ,0 + ,11835.7 + ,0 + ,11542.2 + ,0 + ,13093.7 + ,0 + ,11180.2 + ,0 + ,12035.7 + ,0 + ,12112 + ,0 + ,10875.2 + ,0 + ,9897.3 + ,0 + ,11672.1 + ,1 + ,12385.7 + ,1 + ,11405.6 + ,1 + ,9830.9 + ,1 + ,11025.1 + ,1 + ,10853.8 + ,1 + ,12252.6 + ,1 + ,11839.4 + ,1 + ,11669.1 + ,1 + ,11601.4 + ,1 + ,11178.4 + ,1 + ,9516.4 + ,1 + ,12102.8 + ,1 + ,12989 + ,1 + ,11610.2 + ,1 + ,10205.5 + ,1 + ,11356.2 + ,1 + ,11307.1 + ,1 + ,12648.6 + ,1 + ,11947.2 + ,1 + ,11714.1 + ,1 + ,12192.5 + ,1 + ,11268.8 + ,1 + ,9097.4 + ,1 + ,12639.8 + ,1 + ,13040.1 + ,1 + ,11687.3 + ,1 + ,11191.7 + ,1 + ,11391.9 + ,1 + ,11793.1 + ,1 + ,13933.2 + ,1 + ,12778.1 + ,1 + ,11810.3 + ,1 + ,13698.4 + ,1 + ,11956.6 + ,1 + ,10723.8 + ,1 + ,13938.9 + ,1 + ,13979.8 + ,1 + ,13807.4 + ,1 + ,12973.9 + ,1 + ,12509.8 + ,1 + ,12934.1 + ,1 + ,14908.3 + ,1 + ,13772.1 + ,1 + ,13012.6 + ,1 + ,14049.9 + ,1 + ,11816.5 + ,1 + ,11593.2 + ,1 + ,14466.2 + ,1 + ,13615.9 + ,1 + ,14733.9 + ,1 + ,13880.7 + ,1 + ,13527.5 + ,1 + ,13584 + ,1 + ,16170.2 + ,1 + ,13260.6 + ,1 + ,14741.9 + ,1 + ,15486.5 + ,1 + ,13154.5 + ,1 + ,12621.2 + ,1) + ,dim=c(2 + ,121) + ,dimnames=list(c('y' + ,'x') + ,1:121)) > y <- array(NA,dim=c(2,121),dimnames=list(c('y','x'),1:121)) > 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 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : 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 y x 1 6392.3 0 2 8686.4 0 3 9244.7 0 4 8182.7 0 5 7451.4 0 6 7988.8 0 7 8243.5 0 8 8843.0 0 9 9092.7 0 10 8246.7 0 11 9311.7 0 12 8341.2 0 13 7116.7 0 14 9635.7 0 15 9815.4 0 16 8611.3 0 17 8297.8 0 18 8715.1 0 19 8919.9 0 20 10085.8 0 21 9511.7 0 22 8991.3 0 23 10311.2 0 24 8895.4 0 25 7449.8 0 26 10084.0 0 27 9859.4 0 28 9100.1 0 29 8920.8 0 30 8502.7 0 31 8599.6 0 32 10394.4 0 33 9290.4 0 34 8742.2 0 35 10217.3 0 36 8639.0 0 37 8139.6 0 38 10779.1 0 39 10427.7 0 40 10349.1 0 41 10036.4 0 42 9492.1 0 43 10638.8 0 44 12054.5 0 45 10324.7 0 46 11817.3 0 47 11008.9 0 48 9996.6 0 49 9419.5 0 50 11958.8 0 51 12594.6 0 52 11890.6 0 53 10871.7 0 54 11835.7 0 55 11542.2 0 56 13093.7 0 57 11180.2 0 58 12035.7 0 59 12112.0 0 60 10875.2 0 61 9897.3 0 62 11672.1 1 63 12385.7 1 64 11405.6 1 65 9830.9 1 66 11025.1 1 67 10853.8 1 68 12252.6 1 69 11839.4 1 70 11669.1 1 71 11601.4 1 72 11178.4 1 73 9516.4 1 74 12102.8 1 75 12989.0 1 76 11610.2 1 77 10205.5 1 78 11356.2 1 79 11307.1 1 80 12648.6 1 81 11947.2 1 82 11714.1 1 83 12192.5 1 84 11268.8 1 85 9097.4 1 86 12639.8 1 87 13040.1 1 88 11687.3 1 89 11191.7 1 90 11391.9 1 91 11793.1 1 92 13933.2 1 93 12778.1 1 94 11810.3 1 95 13698.4 1 96 11956.6 1 97 10723.8 1 98 13938.9 1 99 13979.8 1 100 13807.4 1 101 12973.9 1 102 12509.8 1 103 12934.1 1 104 14908.3 1 105 13772.1 1 106 13012.6 1 107 14049.9 1 108 11816.5 1 109 11593.2 1 110 14466.2 1 111 13615.9 1 112 14733.9 1 113 13880.7 1 114 13527.5 1 115 13584.0 1 116 16170.2 1 117 13260.6 1 118 14741.9 1 119 15486.5 1 120 13154.5 1 121 12621.2 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) x 9723 2758 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3383.5 -1007.9 -211.3 1056.1 3689.3 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9723.0 185.1 52.52 <2e-16 *** x 2757.9 262.9 10.49 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1446 on 119 degrees of freedom Multiple R-squared: 0.4805, Adjusted R-squared: 0.4761 F-statistic: 110 on 1 and 119 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,] 0.517272432 0.96545514 0.4827276 [2,] 0.353139982 0.70627996 0.6468600 [3,] 0.228850193 0.45770039 0.7711498 [4,] 0.170199492 0.34039898 0.8298005 [5,] 0.138069645 0.27613929 0.8619304 [6,] 0.083763085 0.16752617 0.9162369 [7,] 0.075280756 0.15056151 0.9247192 [8,] 0.045012364 0.09002473 0.9549876 [9,] 0.054436430 0.10887286 0.9455636 [10,] 0.064963607 0.12992721 0.9350364 [11,] 0.078669199 0.15733840 0.9213308 [12,] 0.053122970 0.10624594 0.9468770 [13,] 0.036070896 0.07214179 0.9639291 [14,] 0.023840071 0.04768014 0.9761599 [15,] 0.016178994 0.03235799 0.9838210 [16,] 0.024600317 0.04920063 0.9753997 [17,] 0.020878446 0.04175689 0.9791216 [18,] 0.014249639 0.02849928 0.9857504 [19,] 0.021987551 0.04397510 0.9780124 [20,] 0.015070348 0.03014070 0.9849297 [21,] 0.020751052 0.04150210 0.9792489 [22,] 0.023914578 0.04782916 0.9760854 [23,] 0.022619363 0.04523873 0.9773806 [24,] 0.016537438 0.03307488 0.9834626 [25,] 0.012009530 0.02401906 0.9879905 [26,] 0.009513610 0.01902722 0.9904864 [27,] 0.007438769 0.01487754 0.9925612 [28,] 0.010561600 0.02112320 0.9894384 [29,] 0.008102113 0.01620423 0.9918979 [30,] 0.006491693 0.01298339 0.9935083 [31,] 0.007515437 0.01503087 0.9924846 [32,] 0.006558299 0.01311660 0.9934417 [33,] 0.007970568 0.01594114 0.9920294 [34,] 0.014241813 0.02848363 0.9857582 [35,] 0.017297740 0.03459548 0.9827023 [36,] 0.019229420 0.03845884 0.9807706 [37,] 0.018426808 0.03685362 0.9815732 [38,] 0.016331111 0.03266222 0.9836689 [39,] 0.020232666 0.04046533 0.9797673 [40,] 0.065217636 0.13043527 0.9347824 [41,] 0.063430195 0.12686039 0.9365698 [42,] 0.113259055 0.22651811 0.8867409 [43,] 0.124610892 0.24922178 0.8753891 [44,] 0.114785144 0.22957029 0.8852149 [45,] 0.113159963 0.22631993 0.8868400 [46,] 0.172141165 0.34428233 0.8278588 [47,] 0.298893536 0.59778707 0.7011065 [48,] 0.352606510 0.70521302 0.6473935 [49,] 0.340857531 0.68171506 0.6591425 [50,] 0.378280364 0.75656073 0.6217196 [51,] 0.389556777 0.77911355 0.6104432 [52,] 0.550715282 0.89856944 0.4492847 [53,] 0.531774386 0.93645123 0.4682256 [54,] 0.564392627 0.87121475 0.4356074 [55,] 0.608644671 0.78271066 0.3913553 [56,] 0.579265657 0.84146869 0.4207343 [57,] 0.527608516 0.94478297 0.4723915 [58,] 0.483386252 0.96677250 0.5166137 [59,] 0.434325721 0.86865144 0.5656743 [60,] 0.400792405 0.80158481 0.5992076 [61,] 0.477603477 0.95520695 0.5223965 [62,] 0.458438733 0.91687747 0.5415613 [63,] 0.450006225 0.90001245 0.5499938 [64,] 0.410792589 0.82158518 0.5892074 [65,] 0.370859653 0.74171931 0.6291403 [66,] 0.334976422 0.66995284 0.6650236 [67,] 0.302455518 0.60491104 0.6975445 [68,] 0.286239231 0.57247846 0.7137608 [69,] 0.420823844 0.84164769 0.5791762 [70,] 0.382986841 0.76597368 0.6170132 [71,] 0.358593271 0.71718654 0.6414067 [72,] 0.330459856 0.66091971 0.6695401 [73,] 0.406374690 0.81274938 0.5936253 [74,] 0.394502674 0.78900535 0.6054973 [75,] 0.388394065 0.77678813 0.6116059 [76,] 0.352616725 0.70523345 0.6473833 [77,] 0.321972233 0.64394447 0.6780278 [78,] 0.300810373 0.60162075 0.6991896 [79,] 0.268555271 0.53711054 0.7314447 [80,] 0.274461094 0.54892219 0.7255389 [81,] 0.618578720 0.76284256 0.3814213 [82,] 0.582613953 0.83477209 0.4173860 [83,] 0.546584555 0.90683089 0.4534154 [84,] 0.548166913 0.90366617 0.4518331 [85,] 0.605347354 0.78930529 0.3946526 [86,] 0.651904057 0.69619189 0.3480959 [87,] 0.668568641 0.66286272 0.3314314 [88,] 0.659764305 0.68047139 0.3402357 [89,] 0.622709731 0.75458054 0.3772903 [90,] 0.645097190 0.70980562 0.3549028 [91,] 0.613267135 0.77346573 0.3867329 [92,] 0.628373435 0.74325313 0.3716266 [93,] 0.831133545 0.33773291 0.1688665 [94,] 0.807736217 0.38452757 0.1922638 [95,] 0.780992618 0.43801476 0.2190074 [96,] 0.743228462 0.51354308 0.2567715 [97,] 0.703153150 0.59369370 0.2968468 [98,] 0.690895234 0.61820953 0.3091048 [99,] 0.652372865 0.69525427 0.3476271 [100,] 0.671802636 0.65639473 0.3281974 [101,] 0.609075731 0.78184854 0.3909243 [102,] 0.553586704 0.89282659 0.4464133 [103,] 0.488316268 0.97663254 0.5116837 [104,] 0.579788104 0.84042379 0.4202119 [105,] 0.776253215 0.44749357 0.2237468 [106,] 0.716661946 0.56667611 0.2833381 [107,] 0.638648545 0.72270291 0.3613515 [108,] 0.574336041 0.85132792 0.4256640 [109,] 0.464069216 0.92813843 0.5359308 [110,] 0.362166972 0.72433394 0.6378330 [111,] 0.261634859 0.52326972 0.7383651 [112,] 0.419245747 0.83849149 0.5807543 > postscript(file="/var/www/html/rcomp/tmp/1ika81229527061.ps",horizontal=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2duo01229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3pw471229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4fyaw1229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5dkaz1229527061.ps",horizontal=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 = 121 Frequency = 1 1 2 3 4 5 6 -3330.71803 -1036.61803 -478.31803 -1540.31803 -2271.61803 -1734.21803 7 8 9 10 11 12 -1479.51803 -880.01803 -630.31803 -1476.31803 -411.31803 -1381.81803 13 14 15 16 17 18 -2606.31803 -87.31803 92.38197 -1111.71803 -1425.21803 -1007.91803 19 20 21 22 23 24 -803.11803 362.78197 -211.31803 -731.71803 588.18197 -827.61803 25 26 27 28 29 30 -2273.21803 360.98197 136.38197 -622.91803 -802.21803 -1220.31803 31 32 33 34 35 36 -1123.41803 671.38197 -432.61803 -980.81803 494.28197 -1084.01803 37 38 39 40 41 42 -1583.41803 1056.08197 704.68197 626.08197 313.38197 -230.91803 43 44 45 46 47 48 915.78197 2331.48197 601.68197 2094.28197 1285.88197 273.58197 49 50 51 52 53 54 -303.51803 2235.78197 2871.58197 2167.58197 1148.68197 2112.68197 55 56 57 58 59 60 1819.18197 3370.68197 1457.18197 2312.68197 2388.98197 1152.18197 61 62 63 64 65 66 174.28197 -808.79667 -95.19667 -1075.29667 -2649.99667 -1455.79667 67 68 69 70 71 72 -1627.09667 -228.29667 -641.49667 -811.79667 -879.49667 -1302.49667 73 74 75 76 77 78 -2964.49667 -378.09667 508.10333 -870.69667 -2275.39667 -1124.69667 79 80 81 82 83 84 -1173.79667 167.70333 -533.69667 -766.79667 -288.39667 -1212.09667 85 86 87 88 89 90 -3383.49667 158.90333 559.20333 -793.59667 -1289.19667 -1088.99667 91 92 93 94 95 96 -687.79667 1452.30333 297.20333 -670.59667 1217.50333 -524.29667 97 98 99 100 101 102 -1757.09667 1458.00333 1498.90333 1326.50333 493.00333 28.90333 103 104 105 106 107 108 453.20333 2427.40333 1291.20333 531.70333 1569.00333 -664.39667 109 110 111 112 113 114 -887.69667 1985.30333 1135.00333 2253.00333 1399.80333 1046.60333 115 116 117 118 119 120 1103.10333 3689.30333 779.70333 2261.00333 3005.60333 673.60333 121 140.30333 > postscript(file="/var/www/html/rcomp/tmp/6ekjx1229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 121 Frequency = 1 lag(myerror, k = 1) myerror 0 -3330.71803 NA 1 -1036.61803 -3330.71803 2 -478.31803 -1036.61803 3 -1540.31803 -478.31803 4 -2271.61803 -1540.31803 5 -1734.21803 -2271.61803 6 -1479.51803 -1734.21803 7 -880.01803 -1479.51803 8 -630.31803 -880.01803 9 -1476.31803 -630.31803 10 -411.31803 -1476.31803 11 -1381.81803 -411.31803 12 -2606.31803 -1381.81803 13 -87.31803 -2606.31803 14 92.38197 -87.31803 15 -1111.71803 92.38197 16 -1425.21803 -1111.71803 17 -1007.91803 -1425.21803 18 -803.11803 -1007.91803 19 362.78197 -803.11803 20 -211.31803 362.78197 21 -731.71803 -211.31803 22 588.18197 -731.71803 23 -827.61803 588.18197 24 -2273.21803 -827.61803 25 360.98197 -2273.21803 26 136.38197 360.98197 27 -622.91803 136.38197 28 -802.21803 -622.91803 29 -1220.31803 -802.21803 30 -1123.41803 -1220.31803 31 671.38197 -1123.41803 32 -432.61803 671.38197 33 -980.81803 -432.61803 34 494.28197 -980.81803 35 -1084.01803 494.28197 36 -1583.41803 -1084.01803 37 1056.08197 -1583.41803 38 704.68197 1056.08197 39 626.08197 704.68197 40 313.38197 626.08197 41 -230.91803 313.38197 42 915.78197 -230.91803 43 2331.48197 915.78197 44 601.68197 2331.48197 45 2094.28197 601.68197 46 1285.88197 2094.28197 47 273.58197 1285.88197 48 -303.51803 273.58197 49 2235.78197 -303.51803 50 2871.58197 2235.78197 51 2167.58197 2871.58197 52 1148.68197 2167.58197 53 2112.68197 1148.68197 54 1819.18197 2112.68197 55 3370.68197 1819.18197 56 1457.18197 3370.68197 57 2312.68197 1457.18197 58 2388.98197 2312.68197 59 1152.18197 2388.98197 60 174.28197 1152.18197 61 -808.79667 174.28197 62 -95.19667 -808.79667 63 -1075.29667 -95.19667 64 -2649.99667 -1075.29667 65 -1455.79667 -2649.99667 66 -1627.09667 -1455.79667 67 -228.29667 -1627.09667 68 -641.49667 -228.29667 69 -811.79667 -641.49667 70 -879.49667 -811.79667 71 -1302.49667 -879.49667 72 -2964.49667 -1302.49667 73 -378.09667 -2964.49667 74 508.10333 -378.09667 75 -870.69667 508.10333 76 -2275.39667 -870.69667 77 -1124.69667 -2275.39667 78 -1173.79667 -1124.69667 79 167.70333 -1173.79667 80 -533.69667 167.70333 81 -766.79667 -533.69667 82 -288.39667 -766.79667 83 -1212.09667 -288.39667 84 -3383.49667 -1212.09667 85 158.90333 -3383.49667 86 559.20333 158.90333 87 -793.59667 559.20333 88 -1289.19667 -793.59667 89 -1088.99667 -1289.19667 90 -687.79667 -1088.99667 91 1452.30333 -687.79667 92 297.20333 1452.30333 93 -670.59667 297.20333 94 1217.50333 -670.59667 95 -524.29667 1217.50333 96 -1757.09667 -524.29667 97 1458.00333 -1757.09667 98 1498.90333 1458.00333 99 1326.50333 1498.90333 100 493.00333 1326.50333 101 28.90333 493.00333 102 453.20333 28.90333 103 2427.40333 453.20333 104 1291.20333 2427.40333 105 531.70333 1291.20333 106 1569.00333 531.70333 107 -664.39667 1569.00333 108 -887.69667 -664.39667 109 1985.30333 -887.69667 110 1135.00333 1985.30333 111 2253.00333 1135.00333 112 1399.80333 2253.00333 113 1046.60333 1399.80333 114 1103.10333 1046.60333 115 3689.30333 1103.10333 116 779.70333 3689.30333 117 2261.00333 779.70333 118 3005.60333 2261.00333 119 673.60333 3005.60333 120 140.30333 673.60333 121 NA 140.30333 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1036.61803 -3330.71803 [2,] -478.31803 -1036.61803 [3,] -1540.31803 -478.31803 [4,] -2271.61803 -1540.31803 [5,] -1734.21803 -2271.61803 [6,] -1479.51803 -1734.21803 [7,] -880.01803 -1479.51803 [8,] -630.31803 -880.01803 [9,] -1476.31803 -630.31803 [10,] -411.31803 -1476.31803 [11,] -1381.81803 -411.31803 [12,] -2606.31803 -1381.81803 [13,] -87.31803 -2606.31803 [14,] 92.38197 -87.31803 [15,] -1111.71803 92.38197 [16,] -1425.21803 -1111.71803 [17,] -1007.91803 -1425.21803 [18,] -803.11803 -1007.91803 [19,] 362.78197 -803.11803 [20,] -211.31803 362.78197 [21,] -731.71803 -211.31803 [22,] 588.18197 -731.71803 [23,] -827.61803 588.18197 [24,] -2273.21803 -827.61803 [25,] 360.98197 -2273.21803 [26,] 136.38197 360.98197 [27,] -622.91803 136.38197 [28,] -802.21803 -622.91803 [29,] -1220.31803 -802.21803 [30,] -1123.41803 -1220.31803 [31,] 671.38197 -1123.41803 [32,] -432.61803 671.38197 [33,] -980.81803 -432.61803 [34,] 494.28197 -980.81803 [35,] -1084.01803 494.28197 [36,] -1583.41803 -1084.01803 [37,] 1056.08197 -1583.41803 [38,] 704.68197 1056.08197 [39,] 626.08197 704.68197 [40,] 313.38197 626.08197 [41,] -230.91803 313.38197 [42,] 915.78197 -230.91803 [43,] 2331.48197 915.78197 [44,] 601.68197 2331.48197 [45,] 2094.28197 601.68197 [46,] 1285.88197 2094.28197 [47,] 273.58197 1285.88197 [48,] -303.51803 273.58197 [49,] 2235.78197 -303.51803 [50,] 2871.58197 2235.78197 [51,] 2167.58197 2871.58197 [52,] 1148.68197 2167.58197 [53,] 2112.68197 1148.68197 [54,] 1819.18197 2112.68197 [55,] 3370.68197 1819.18197 [56,] 1457.18197 3370.68197 [57,] 2312.68197 1457.18197 [58,] 2388.98197 2312.68197 [59,] 1152.18197 2388.98197 [60,] 174.28197 1152.18197 [61,] -808.79667 174.28197 [62,] -95.19667 -808.79667 [63,] -1075.29667 -95.19667 [64,] -2649.99667 -1075.29667 [65,] -1455.79667 -2649.99667 [66,] -1627.09667 -1455.79667 [67,] -228.29667 -1627.09667 [68,] -641.49667 -228.29667 [69,] -811.79667 -641.49667 [70,] -879.49667 -811.79667 [71,] -1302.49667 -879.49667 [72,] -2964.49667 -1302.49667 [73,] -378.09667 -2964.49667 [74,] 508.10333 -378.09667 [75,] -870.69667 508.10333 [76,] -2275.39667 -870.69667 [77,] -1124.69667 -2275.39667 [78,] -1173.79667 -1124.69667 [79,] 167.70333 -1173.79667 [80,] -533.69667 167.70333 [81,] -766.79667 -533.69667 [82,] -288.39667 -766.79667 [83,] -1212.09667 -288.39667 [84,] -3383.49667 -1212.09667 [85,] 158.90333 -3383.49667 [86,] 559.20333 158.90333 [87,] -793.59667 559.20333 [88,] -1289.19667 -793.59667 [89,] -1088.99667 -1289.19667 [90,] -687.79667 -1088.99667 [91,] 1452.30333 -687.79667 [92,] 297.20333 1452.30333 [93,] -670.59667 297.20333 [94,] 1217.50333 -670.59667 [95,] -524.29667 1217.50333 [96,] -1757.09667 -524.29667 [97,] 1458.00333 -1757.09667 [98,] 1498.90333 1458.00333 [99,] 1326.50333 1498.90333 [100,] 493.00333 1326.50333 [101,] 28.90333 493.00333 [102,] 453.20333 28.90333 [103,] 2427.40333 453.20333 [104,] 1291.20333 2427.40333 [105,] 531.70333 1291.20333 [106,] 1569.00333 531.70333 [107,] -664.39667 1569.00333 [108,] -887.69667 -664.39667 [109,] 1985.30333 -887.69667 [110,] 1135.00333 1985.30333 [111,] 2253.00333 1135.00333 [112,] 1399.80333 2253.00333 [113,] 1046.60333 1399.80333 [114,] 1103.10333 1046.60333 [115,] 3689.30333 1103.10333 [116,] 779.70333 3689.30333 [117,] 2261.00333 779.70333 [118,] 3005.60333 2261.00333 [119,] 673.60333 3005.60333 [120,] 140.30333 673.60333 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1036.61803 -3330.71803 2 -478.31803 -1036.61803 3 -1540.31803 -478.31803 4 -2271.61803 -1540.31803 5 -1734.21803 -2271.61803 6 -1479.51803 -1734.21803 7 -880.01803 -1479.51803 8 -630.31803 -880.01803 9 -1476.31803 -630.31803 10 -411.31803 -1476.31803 11 -1381.81803 -411.31803 12 -2606.31803 -1381.81803 13 -87.31803 -2606.31803 14 92.38197 -87.31803 15 -1111.71803 92.38197 16 -1425.21803 -1111.71803 17 -1007.91803 -1425.21803 18 -803.11803 -1007.91803 19 362.78197 -803.11803 20 -211.31803 362.78197 21 -731.71803 -211.31803 22 588.18197 -731.71803 23 -827.61803 588.18197 24 -2273.21803 -827.61803 25 360.98197 -2273.21803 26 136.38197 360.98197 27 -622.91803 136.38197 28 -802.21803 -622.91803 29 -1220.31803 -802.21803 30 -1123.41803 -1220.31803 31 671.38197 -1123.41803 32 -432.61803 671.38197 33 -980.81803 -432.61803 34 494.28197 -980.81803 35 -1084.01803 494.28197 36 -1583.41803 -1084.01803 37 1056.08197 -1583.41803 38 704.68197 1056.08197 39 626.08197 704.68197 40 313.38197 626.08197 41 -230.91803 313.38197 42 915.78197 -230.91803 43 2331.48197 915.78197 44 601.68197 2331.48197 45 2094.28197 601.68197 46 1285.88197 2094.28197 47 273.58197 1285.88197 48 -303.51803 273.58197 49 2235.78197 -303.51803 50 2871.58197 2235.78197 51 2167.58197 2871.58197 52 1148.68197 2167.58197 53 2112.68197 1148.68197 54 1819.18197 2112.68197 55 3370.68197 1819.18197 56 1457.18197 3370.68197 57 2312.68197 1457.18197 58 2388.98197 2312.68197 59 1152.18197 2388.98197 60 174.28197 1152.18197 61 -808.79667 174.28197 62 -95.19667 -808.79667 63 -1075.29667 -95.19667 64 -2649.99667 -1075.29667 65 -1455.79667 -2649.99667 66 -1627.09667 -1455.79667 67 -228.29667 -1627.09667 68 -641.49667 -228.29667 69 -811.79667 -641.49667 70 -879.49667 -811.79667 71 -1302.49667 -879.49667 72 -2964.49667 -1302.49667 73 -378.09667 -2964.49667 74 508.10333 -378.09667 75 -870.69667 508.10333 76 -2275.39667 -870.69667 77 -1124.69667 -2275.39667 78 -1173.79667 -1124.69667 79 167.70333 -1173.79667 80 -533.69667 167.70333 81 -766.79667 -533.69667 82 -288.39667 -766.79667 83 -1212.09667 -288.39667 84 -3383.49667 -1212.09667 85 158.90333 -3383.49667 86 559.20333 158.90333 87 -793.59667 559.20333 88 -1289.19667 -793.59667 89 -1088.99667 -1289.19667 90 -687.79667 -1088.99667 91 1452.30333 -687.79667 92 297.20333 1452.30333 93 -670.59667 297.20333 94 1217.50333 -670.59667 95 -524.29667 1217.50333 96 -1757.09667 -524.29667 97 1458.00333 -1757.09667 98 1498.90333 1458.00333 99 1326.50333 1498.90333 100 493.00333 1326.50333 101 28.90333 493.00333 102 453.20333 28.90333 103 2427.40333 453.20333 104 1291.20333 2427.40333 105 531.70333 1291.20333 106 1569.00333 531.70333 107 -664.39667 1569.00333 108 -887.69667 -664.39667 109 1985.30333 -887.69667 110 1135.00333 1985.30333 111 2253.00333 1135.00333 112 1399.80333 2253.00333 113 1046.60333 1399.80333 114 1103.10333 1046.60333 115 3689.30333 1103.10333 116 779.70333 3689.30333 117 2261.00333 779.70333 118 3005.60333 2261.00333 119 673.60333 3005.60333 120 140.30333 673.60333 > 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/www/html/rcomp/tmp/7jqnj1229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8pj531229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/93ap51229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/1027841229527061.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/www/html/rcomp/tmp/118tcb1229527061.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/www/html/rcomp/tmp/12i5vb1229527061.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/www/html/rcomp/tmp/13wge01229527061.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/www/html/rcomp/tmp/149m3l1229527061.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/www/html/rcomp/tmp/15jzyh1229527061.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/www/html/rcomp/tmp/16gp3b1229527061.tab") + } > > system("convert tmp/1ika81229527061.ps tmp/1ika81229527061.png") > system("convert tmp/2duo01229527061.ps tmp/2duo01229527061.png") > system("convert tmp/3pw471229527061.ps tmp/3pw471229527061.png") > system("convert tmp/4fyaw1229527061.ps tmp/4fyaw1229527061.png") > system("convert tmp/5dkaz1229527061.ps tmp/5dkaz1229527061.png") > system("convert tmp/6ekjx1229527061.ps tmp/6ekjx1229527061.png") > system("convert tmp/7jqnj1229527061.ps tmp/7jqnj1229527061.png") > system("convert tmp/8pj531229527061.ps tmp/8pj531229527061.png") > system("convert tmp/93ap51229527061.ps tmp/93ap51229527061.png") > system("convert tmp/1027841229527061.ps tmp/1027841229527061.png") > > > proc.time() user system elapsed 3.209 1.627 3.700