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Type 'q()' to quit R. > x <- array(list(128332 + ,0 + ,128332 + ,133639 + ,142773 + ,149657 + ,120297 + ,0 + ,120297 + ,128332 + ,133639 + ,142773 + ,118632 + ,0 + ,118632 + ,120297 + ,128332 + ,133639 + ,155276 + ,0 + ,155276 + ,118632 + ,120297 + ,128332 + ,169316 + ,0 + ,169316 + ,155276 + ,118632 + ,120297 + ,167395 + ,0 + ,167395 + ,169316 + ,155276 + ,118632 + ,157939 + ,0 + ,157939 + ,167395 + ,169316 + ,155276 + ,149601 + ,0 + ,149601 + ,157939 + ,167395 + ,169316 + ,146310 + ,0 + ,146310 + ,149601 + ,157939 + ,167395 + ,141579 + ,0 + ,141579 + ,146310 + ,149601 + ,157939 + ,136473 + ,0 + ,136473 + ,141579 + ,146310 + ,149601 + ,129818 + ,0 + ,129818 + ,136473 + ,141579 + ,146310 + ,124226 + ,0 + ,124226 + ,129818 + ,136473 + ,141579 + ,116428 + ,0 + ,116428 + ,124226 + ,129818 + ,136473 + ,116440 + ,0 + ,116440 + ,116428 + ,124226 + ,129818 + ,147747 + ,0 + ,147747 + ,116440 + ,116428 + ,124226 + ,160069 + ,0 + ,160069 + ,147747 + ,116440 + ,116428 + ,163129 + ,0 + ,163129 + ,160069 + ,147747 + ,116440 + ,151108 + ,0 + ,151108 + ,163129 + ,160069 + ,147747 + ,141481 + ,0 + ,141481 + ,151108 + ,163129 + ,160069 + ,139174 + ,0 + ,139174 + ,141481 + ,151108 + ,163129 + ,134066 + ,0 + ,134066 + ,139174 + ,141481 + ,151108 + ,130104 + ,0 + ,130104 + ,134066 + ,139174 + ,141481 + ,123090 + ,0 + ,123090 + ,130104 + ,134066 + ,139174 + ,116598 + ,0 + ,116598 + ,123090 + ,130104 + ,134066 + ,109627 + ,0 + ,109627 + ,116598 + ,123090 + ,130104 + ,105428 + ,0 + ,105428 + ,109627 + ,116598 + ,123090 + ,137272 + ,0 + ,137272 + ,105428 + ,109627 + ,116598 + ,159836 + ,0 + ,159836 + ,137272 + ,105428 + ,109627 + ,155283 + ,0 + ,155283 + ,159836 + ,137272 + ,105428 + ,141514 + ,0 + ,141514 + ,155283 + ,159836 + ,137272 + ,131852 + ,0 + ,131852 + ,141514 + ,155283 + ,159836 + ,130691 + ,0 + ,130691 + ,131852 + ,141514 + ,155283 + ,128461 + ,0 + ,128461 + ,130691 + ,131852 + ,141514 + ,123066 + ,0 + ,123066 + ,128461 + ,130691 + ,131852 + ,117599 + ,0 + ,117599 + ,123066 + 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,123913 + ,1 + ,123913 + ,133739 + ,139261 + ,119321 + ,113438 + ,1 + ,113438 + ,123913 + ,133739 + ,139261 + ,109416 + ,1 + ,109416 + ,113438 + ,123913 + ,133739 + ,109406 + ,1 + ,109406 + ,109416 + ,113438 + ,123913 + ,105645 + ,1 + ,105645 + ,109406 + ,109416 + ,113438 + ,101328 + ,1 + ,101328 + ,105645 + ,109406 + ,109416 + ,97686 + ,1 + ,97686 + ,101328 + ,105645 + ,109406 + ,93093 + ,1 + ,93093 + ,97686 + ,101328 + ,105645 + ,91382 + ,1 + ,91382 + ,93093 + ,97686 + ,101328 + ,122257 + ,1 + ,122257 + ,91382 + ,93093 + ,97686 + ,139183 + ,1 + ,139183 + ,122257 + ,91382 + ,93093 + ,139887 + ,1 + ,139887 + ,139183 + ,122257 + ,91382 + ,131822 + ,1 + ,131822 + ,139887 + ,139183 + ,122257 + ,116805 + ,1 + ,116805 + ,131822 + ,139887 + ,139183 + ,113706 + ,1 + ,113706 + ,116805 + ,131822 + ,139887 + ,113012 + ,1 + ,113012 + ,113706 + ,116805 + ,131822 + ,110452 + ,1 + ,110452 + ,113012 + ,113706 + ,116805 + ,107005 + ,1 + ,107005 + ,110452 + ,113012 + ,113706 + ,102841 + ,1 + ,102841 + ,107005 + ,110452 + ,113012 + ,98173 + ,1 + ,98173 + ,102841 + ,107005 + ,110452 + ,98181 + ,1 + ,98181 + ,98173 + ,102841 + ,107005 + ,137277 + ,1 + ,137277 + ,98181 + ,98173 + ,102841 + ,147579 + ,1 + ,147579 + ,137277 + ,98181 + ,98173 + ,146571 + ,1 + ,146571 + ,147579 + ,137277 + ,98181 + ,138920 + ,1 + ,138920 + ,146571 + ,147579 + ,137277 + ,130340 + ,1 + ,130340 + ,138920 + ,146571 + ,147579 + ,128140 + ,1 + ,128140 + ,130340 + ,138920 + ,146571 + ,127059 + ,1 + ,127059 + ,128140 + ,130340 + ,138920 + ,122860 + ,1 + ,122860 + ,127059 + ,128140 + ,130340 + ,117702 + ,1 + ,117702 + ,122860 + ,127059 + ,128140 + ,113537 + ,1 + ,113537 + ,117702 + ,122860 + ,127059 + ,108366 + ,1 + ,108366 + ,113537 + ,117702 + ,122860 + ,111078 + ,1 + ,111078 + ,108366 + ,113537 + ,117702 + ,150739 + ,1 + ,150739 + ,111078 + ,108366 + ,113537 + ,159129 + ,1 + ,159129 + ,150739 + ,111078 + ,108366 + ,157928 + ,1 + ,157928 + ,159129 + ,150739 + ,111078 + ,147768 + ,1 + ,147768 + ,157928 + ,159129 + ,150739 + ,137507 + ,1 + ,137507 + ,147768 + ,157928 + ,159129 + ,136919 + ,1 + ,136919 + ,137507 + ,147768 + ,157928) + ,dim=c(6 + ,93) + ,dimnames=list(c('Y' + ,'X' + ,'Y1' + ,'Y2' + ,'Y3' + ,'Y4') + ,1:93)) > y <- array(NA,dim=c(6,93),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:93)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly 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 Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 128332 0 128332 133639 142773 149657 1 0 0 0 0 0 0 0 0 0 0 1 2 120297 0 120297 128332 133639 142773 0 1 0 0 0 0 0 0 0 0 0 2 3 118632 0 118632 120297 128332 133639 0 0 1 0 0 0 0 0 0 0 0 3 4 155276 0 155276 118632 120297 128332 0 0 0 1 0 0 0 0 0 0 0 4 5 169316 0 169316 155276 118632 120297 0 0 0 0 1 0 0 0 0 0 0 5 6 167395 0 167395 169316 155276 118632 0 0 0 0 0 1 0 0 0 0 0 6 7 157939 0 157939 167395 169316 155276 0 0 0 0 0 0 1 0 0 0 0 7 8 149601 0 149601 157939 167395 169316 0 0 0 0 0 0 0 1 0 0 0 8 9 146310 0 146310 149601 157939 167395 0 0 0 0 0 0 0 0 1 0 0 9 10 141579 0 141579 146310 149601 157939 0 0 0 0 0 0 0 0 0 1 0 10 11 136473 0 136473 141579 146310 149601 0 0 0 0 0 0 0 0 0 0 1 11 12 129818 0 129818 136473 141579 146310 0 0 0 0 0 0 0 0 0 0 0 12 13 124226 0 124226 129818 136473 141579 1 0 0 0 0 0 0 0 0 0 0 13 14 116428 0 116428 124226 129818 136473 0 1 0 0 0 0 0 0 0 0 0 14 15 116440 0 116440 116428 124226 129818 0 0 1 0 0 0 0 0 0 0 0 15 16 147747 0 147747 116440 116428 124226 0 0 0 1 0 0 0 0 0 0 0 16 17 160069 0 160069 147747 116440 116428 0 0 0 0 1 0 0 0 0 0 0 17 18 163129 0 163129 160069 147747 116440 0 0 0 0 0 1 0 0 0 0 0 18 19 151108 0 151108 163129 160069 147747 0 0 0 0 0 0 1 0 0 0 0 19 20 141481 0 141481 151108 163129 160069 0 0 0 0 0 0 0 1 0 0 0 20 21 139174 0 139174 141481 151108 163129 0 0 0 0 0 0 0 0 1 0 0 21 22 134066 0 134066 139174 141481 151108 0 0 0 0 0 0 0 0 0 1 0 22 23 130104 0 130104 134066 139174 141481 0 0 0 0 0 0 0 0 0 0 1 23 24 123090 0 123090 130104 134066 139174 0 0 0 0 0 0 0 0 0 0 0 24 25 116598 0 116598 123090 130104 134066 1 0 0 0 0 0 0 0 0 0 0 25 26 109627 0 109627 116598 123090 130104 0 1 0 0 0 0 0 0 0 0 0 26 27 105428 0 105428 109627 116598 123090 0 0 1 0 0 0 0 0 0 0 0 27 28 137272 0 137272 105428 109627 116598 0 0 0 1 0 0 0 0 0 0 0 28 29 159836 0 159836 137272 105428 109627 0 0 0 0 1 0 0 0 0 0 0 29 30 155283 0 155283 159836 137272 105428 0 0 0 0 0 1 0 0 0 0 0 30 31 141514 0 141514 155283 159836 137272 0 0 0 0 0 0 1 0 0 0 0 31 32 131852 0 131852 141514 155283 159836 0 0 0 0 0 0 0 1 0 0 0 32 33 130691 0 130691 131852 141514 155283 0 0 0 0 0 0 0 0 1 0 0 33 34 128461 0 128461 130691 131852 141514 0 0 0 0 0 0 0 0 0 1 0 34 35 123066 0 123066 128461 130691 131852 0 0 0 0 0 0 0 0 0 0 1 35 36 117599 0 117599 123066 128461 130691 0 0 0 0 0 0 0 0 0 0 0 36 37 111599 0 111599 117599 123066 128461 1 0 0 0 0 0 0 0 0 0 0 37 38 105395 0 105395 111599 117599 123066 0 1 0 0 0 0 0 0 0 0 0 38 39 102334 0 102334 105395 111599 117599 0 0 1 0 0 0 0 0 0 0 0 39 40 131305 0 131305 102334 105395 111599 0 0 0 1 0 0 0 0 0 0 0 40 41 149033 0 149033 131305 102334 105395 0 0 0 0 1 0 0 0 0 0 0 41 42 144954 0 144954 149033 131305 102334 0 0 0 0 0 1 0 0 0 0 0 42 43 132404 0 132404 144954 149033 131305 0 0 0 0 0 0 1 0 0 0 0 43 44 122104 0 122104 132404 144954 149033 0 0 0 0 0 0 0 1 0 0 0 44 45 118755 0 118755 122104 132404 144954 0 0 0 0 0 0 0 0 1 0 0 45 46 116222 1 116222 118755 122104 132404 0 0 0 0 0 0 0 0 0 1 0 46 47 110924 1 110924 116222 118755 122104 0 0 0 0 0 0 0 0 0 0 1 47 48 103753 1 103753 110924 116222 118755 0 0 0 0 0 0 0 0 0 0 0 48 49 99983 1 99983 103753 110924 116222 1 0 0 0 0 0 0 0 0 0 0 49 50 93302 1 93302 99983 103753 110924 0 1 0 0 0 0 0 0 0 0 0 50 51 91496 1 91496 93302 99983 103753 0 0 1 0 0 0 0 0 0 0 0 51 52 119321 1 119321 91496 93302 99983 0 0 0 1 0 0 0 0 0 0 0 52 53 139261 1 139261 119321 91496 93302 0 0 0 0 1 0 0 0 0 0 0 53 54 133739 1 133739 139261 119321 91496 0 0 0 0 0 1 0 0 0 0 0 54 55 123913 1 123913 133739 139261 119321 0 0 0 0 0 0 1 0 0 0 0 55 56 113438 1 113438 123913 133739 139261 0 0 0 0 0 0 0 1 0 0 0 56 57 109416 1 109416 113438 123913 133739 0 0 0 0 0 0 0 0 1 0 0 57 58 109406 1 109406 109416 113438 123913 0 0 0 0 0 0 0 0 0 1 0 58 59 105645 1 105645 109406 109416 113438 0 0 0 0 0 0 0 0 0 0 1 59 60 101328 1 101328 105645 109406 109416 0 0 0 0 0 0 0 0 0 0 0 60 61 97686 1 97686 101328 105645 109406 1 0 0 0 0 0 0 0 0 0 0 61 62 93093 1 93093 97686 101328 105645 0 1 0 0 0 0 0 0 0 0 0 62 63 91382 1 91382 93093 97686 101328 0 0 1 0 0 0 0 0 0 0 0 63 64 122257 1 122257 91382 93093 97686 0 0 0 1 0 0 0 0 0 0 0 64 65 139183 1 139183 122257 91382 93093 0 0 0 0 1 0 0 0 0 0 0 65 66 139887 1 139887 139183 122257 91382 0 0 0 0 0 1 0 0 0 0 0 66 67 131822 1 131822 139887 139183 122257 0 0 0 0 0 0 1 0 0 0 0 67 68 116805 1 116805 131822 139887 139183 0 0 0 0 0 0 0 1 0 0 0 68 69 113706 1 113706 116805 131822 139887 0 0 0 0 0 0 0 0 1 0 0 69 70 113012 1 113012 113706 116805 131822 0 0 0 0 0 0 0 0 0 1 0 70 71 110452 1 110452 113012 113706 116805 0 0 0 0 0 0 0 0 0 0 1 71 72 107005 1 107005 110452 113012 113706 0 0 0 0 0 0 0 0 0 0 0 72 73 102841 1 102841 107005 110452 113012 1 0 0 0 0 0 0 0 0 0 0 73 74 98173 1 98173 102841 107005 110452 0 1 0 0 0 0 0 0 0 0 0 74 75 98181 1 98181 98173 102841 107005 0 0 1 0 0 0 0 0 0 0 0 75 76 137277 1 137277 98181 98173 102841 0 0 0 1 0 0 0 0 0 0 0 76 77 147579 1 147579 137277 98181 98173 0 0 0 0 1 0 0 0 0 0 0 77 78 146571 1 146571 147579 137277 98181 0 0 0 0 0 1 0 0 0 0 0 78 79 138920 1 138920 146571 147579 137277 0 0 0 0 0 0 1 0 0 0 0 79 80 130340 1 130340 138920 146571 147579 0 0 0 0 0 0 0 1 0 0 0 80 81 128140 1 128140 130340 138920 146571 0 0 0 0 0 0 0 0 1 0 0 81 82 127059 1 127059 128140 130340 138920 0 0 0 0 0 0 0 0 0 1 0 82 83 122860 1 122860 127059 128140 130340 0 0 0 0 0 0 0 0 0 0 1 83 84 117702 1 117702 122860 127059 128140 0 0 0 0 0 0 0 0 0 0 0 84 85 113537 1 113537 117702 122860 127059 1 0 0 0 0 0 0 0 0 0 0 85 86 108366 1 108366 113537 117702 122860 0 1 0 0 0 0 0 0 0 0 0 86 87 111078 1 111078 108366 113537 117702 0 0 1 0 0 0 0 0 0 0 0 87 88 150739 1 150739 111078 108366 113537 0 0 0 1 0 0 0 0 0 0 0 88 89 159129 1 159129 150739 111078 108366 0 0 0 0 1 0 0 0 0 0 0 89 90 157928 1 157928 159129 150739 111078 0 0 0 0 0 1 0 0 0 0 0 90 91 147768 1 147768 157928 159129 150739 0 0 0 0 0 0 1 0 0 0 0 91 92 137507 1 137507 147768 157928 159129 0 0 0 0 0 0 0 1 0 0 0 92 93 136919 1 136919 137507 147768 157928 0 0 0 0 0 0 0 0 1 0 0 93 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X Y1 Y2 Y3 Y4 4.985e-11 -3.019e-12 1.000e+00 -2.498e-16 4.417e-16 -9.483e-17 M1 M2 M3 M4 M5 M6 -1.302e-12 1.402e-12 1.795e-11 2.060e-12 9.606e-12 -2.834e-13 M7 M8 M9 M10 M11 t -3.559e-12 -4.093e-12 -1.366e-12 9.013e-14 4.517e-13 -1.236e-13 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.956e-11 -2.085e-12 6.811e-13 2.117e-12 1.069e-10 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.985e-11 2.733e-11 1.824e+00 0.0722 . X -3.019e-12 6.557e-12 -4.610e-01 0.6465 Y1 1.000e+00 6.432e-16 1.555e+15 <2e-16 *** Y2 -2.498e-16 7.685e-16 -3.250e-01 0.7461 Y3 4.417e-16 7.698e-16 5.740e-01 0.5679 Y4 -9.483e-17 6.530e-16 -1.450e-01 0.8849 M1 -1.302e-12 7.351e-12 -1.770e-01 0.8599 M2 1.402e-12 7.608e-12 1.840e-01 0.8543 M3 1.795e-11 8.078e-12 2.223e+00 0.0293 * M4 2.060e-12 2.666e-11 7.700e-02 0.9386 M5 9.606e-12 3.263e-11 2.940e-01 0.7693 M6 -2.834e-13 3.081e-11 -9.000e-03 0.9927 M7 -3.559e-12 1.370e-11 -2.600e-01 0.7958 M8 -4.093e-12 1.032e-11 -3.970e-01 0.6928 M9 -1.366e-12 1.072e-11 -1.270e-01 0.8989 M10 9.013e-14 9.066e-12 1.000e-02 0.9921 M11 4.517e-13 7.531e-12 6.000e-02 0.9523 t -1.236e-13 1.099e-13 -1.125e+00 0.2643 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.372e-11 on 75 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.077e+31 on 17 and 75 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,] 1.988130e-01 3.976260e-01 8.011870e-01 [2,] 5.569832e-01 8.860336e-01 4.430168e-01 [3,] 1.000000e+00 2.966678e-28 1.483339e-28 [4,] 1.000000e+00 9.097091e-18 4.548546e-18 [5,] 1.920381e-08 3.840762e-08 1.000000e+00 [6,] 9.372758e-01 1.254484e-01 6.272422e-02 [7,] 8.945498e-01 2.109003e-01 1.054502e-01 [8,] 2.016930e-06 4.033860e-06 9.999980e-01 [9,] 2.830618e-01 5.661237e-01 7.169382e-01 [10,] 1.000000e+00 5.859789e-08 2.929895e-08 [11,] 8.091120e-05 1.618224e-04 9.999191e-01 [12,] 8.774910e-10 1.754982e-09 1.000000e+00 [13,] 9.860908e-01 2.781834e-02 1.390917e-02 [14,] 1.699967e-01 3.399934e-01 8.300033e-01 [15,] 8.543097e-08 1.708619e-07 9.999999e-01 [16,] 1.877045e-04 3.754090e-04 9.998123e-01 [17,] 8.946327e-01 2.107347e-01 1.053673e-01 [18,] 1.000000e+00 4.007946e-10 2.003973e-10 [19,] 1.109643e-07 2.219286e-07 9.999999e-01 [20,] 9.999999e-01 2.040632e-07 1.020316e-07 [21,] 1.000000e+00 8.282285e-11 4.141142e-11 [22,] 1.393906e-04 2.787812e-04 9.998606e-01 [23,] 1.759231e-13 3.518462e-13 1.000000e+00 [24,] 9.999477e-01 1.046713e-04 5.233566e-05 [25,] 8.975782e-02 1.795156e-01 9.102422e-01 [26,] 9.997919e-01 4.161205e-04 2.080602e-04 [27,] 6.154995e-01 7.690009e-01 3.845005e-01 [28,] 3.632035e-06 7.264070e-06 9.999964e-01 [29,] 8.145306e-01 3.709389e-01 1.854694e-01 [30,] 4.198512e-01 8.397023e-01 5.801488e-01 [31,] 9.956250e-01 8.750048e-03 4.375024e-03 [32,] 9.429568e-01 1.140863e-01 5.704317e-02 [33,] 4.801435e-02 9.602870e-02 9.519857e-01 [34,] 1.825629e-01 3.651257e-01 8.174371e-01 [35,] 7.974155e-01 4.051689e-01 2.025845e-01 [36,] 9.984289e-01 3.142242e-03 1.571121e-03 [37,] 9.797687e-02 1.959537e-01 9.020231e-01 [38,] 1.000000e+00 3.233873e-17 1.616937e-17 [39,] 8.740843e-02 1.748169e-01 9.125916e-01 [40,] 9.997123e-01 5.753438e-04 2.876719e-04 [41,] 2.271194e-01 4.542388e-01 7.728806e-01 [42,] 9.999931e-01 1.383009e-05 6.915046e-06 [43,] 9.999981e-01 3.828235e-06 1.914118e-06 [44,] 9.999991e-01 1.881381e-06 9.406906e-07 [45,] 3.683558e-01 7.367117e-01 6.316442e-01 [46,] 4.128132e-01 8.256264e-01 5.871868e-01 [47,] 1.285623e-07 2.571247e-07 9.999999e-01 [48,] 2.685243e-09 5.370487e-09 1.000000e+00 [49,] 9.961352e-01 7.729653e-03 3.864826e-03 [50,] 5.108969e-07 1.021794e-06 9.999995e-01 [51,] 4.145027e-01 8.290054e-01 5.854973e-01 [52,] 9.548500e-01 9.029994e-02 4.514997e-02 > postscript(file="/var/www/html/rcomp/tmp/13ewq1258546716.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2apc01258546716.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3qfrw1258546716.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4yevk1258546716.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5o23v1258546716.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 = 93 Frequency = 1 1 2 3 4 5 -1.608597e-11 -1.376058e-11 1.068879e-10 -6.397474e-12 -1.276148e-12 6 7 8 9 10 -2.575780e-12 -5.797745e-12 -5.985644e-12 -4.067262e-12 -3.321806e-12 11 12 13 14 15 -4.122048e-12 -3.223862e-12 -4.028610e-13 -3.016388e-12 -1.956389e-11 16 17 18 19 20 -1.809069e-12 -4.652573e-12 -4.177070e-12 -2.300974e-13 -2.399461e-12 21 22 23 24 25 -2.706549e-12 -8.628738e-13 -2.084510e-12 -3.560463e-13 2.742259e-13 26 27 28 29 30 2.504091e-12 -1.740987e-11 2.356433e-13 2.256382e-12 -2.230352e-13 31 32 33 34 35 -1.264298e-12 3.665479e-14 6.811054e-13 2.424732e-12 9.572479e-13 36 37 38 39 40 1.757437e-12 5.108813e-12 4.215124e-12 -1.648033e-11 2.526189e-12 41 42 43 44 45 8.680475e-13 3.638694e-12 1.680018e-12 3.691852e-12 4.509130e-12 46 47 48 49 50 -1.656893e-12 -1.461566e-12 -2.997467e-12 1.726230e-12 2.529664e-12 51 52 53 54 55 -1.678878e-11 -3.165826e-15 -4.359464e-13 1.018770e-12 -1.704088e-12 56 57 58 59 60 9.508747e-13 9.181292e-13 4.648996e-13 2.171177e-12 1.215631e-12 61 62 63 64 65 2.473086e-12 4.175786e-12 -1.084437e-11 1.044498e-12 -1.764557e-12 66 67 68 69 70 1.756709e-12 1.722437e-12 1.745404e-12 -3.411757e-13 2.151751e-12 71 72 73 74 75 2.805823e-12 1.849972e-12 3.143353e-12 8.667903e-13 -1.158427e-11 76 77 78 79 80 2.383529e-12 1.567064e-12 1.323334e-12 2.419560e-12 2.116661e-12 81 82 83 84 85 -3.297577e-13 8.001905e-13 1.733876e-12 1.754337e-12 3.763127e-12 86 87 88 89 90 2.485515e-12 -1.421635e-11 2.019849e-12 3.437729e-12 -7.616217e-13 91 92 93 3.174214e-12 -1.563424e-13 1.336380e-12 > postscript(file="/var/www/html/rcomp/tmp/6rak41258546716.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 = 93 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.608597e-11 NA 1 -1.376058e-11 -1.608597e-11 2 1.068879e-10 -1.376058e-11 3 -6.397474e-12 1.068879e-10 4 -1.276148e-12 -6.397474e-12 5 -2.575780e-12 -1.276148e-12 6 -5.797745e-12 -2.575780e-12 7 -5.985644e-12 -5.797745e-12 8 -4.067262e-12 -5.985644e-12 9 -3.321806e-12 -4.067262e-12 10 -4.122048e-12 -3.321806e-12 11 -3.223862e-12 -4.122048e-12 12 -4.028610e-13 -3.223862e-12 13 -3.016388e-12 -4.028610e-13 14 -1.956389e-11 -3.016388e-12 15 -1.809069e-12 -1.956389e-11 16 -4.652573e-12 -1.809069e-12 17 -4.177070e-12 -4.652573e-12 18 -2.300974e-13 -4.177070e-12 19 -2.399461e-12 -2.300974e-13 20 -2.706549e-12 -2.399461e-12 21 -8.628738e-13 -2.706549e-12 22 -2.084510e-12 -8.628738e-13 23 -3.560463e-13 -2.084510e-12 24 2.742259e-13 -3.560463e-13 25 2.504091e-12 2.742259e-13 26 -1.740987e-11 2.504091e-12 27 2.356433e-13 -1.740987e-11 28 2.256382e-12 2.356433e-13 29 -2.230352e-13 2.256382e-12 30 -1.264298e-12 -2.230352e-13 31 3.665479e-14 -1.264298e-12 32 6.811054e-13 3.665479e-14 33 2.424732e-12 6.811054e-13 34 9.572479e-13 2.424732e-12 35 1.757437e-12 9.572479e-13 36 5.108813e-12 1.757437e-12 37 4.215124e-12 5.108813e-12 38 -1.648033e-11 4.215124e-12 39 2.526189e-12 -1.648033e-11 40 8.680475e-13 2.526189e-12 41 3.638694e-12 8.680475e-13 42 1.680018e-12 3.638694e-12 43 3.691852e-12 1.680018e-12 44 4.509130e-12 3.691852e-12 45 -1.656893e-12 4.509130e-12 46 -1.461566e-12 -1.656893e-12 47 -2.997467e-12 -1.461566e-12 48 1.726230e-12 -2.997467e-12 49 2.529664e-12 1.726230e-12 50 -1.678878e-11 2.529664e-12 51 -3.165826e-15 -1.678878e-11 52 -4.359464e-13 -3.165826e-15 53 1.018770e-12 -4.359464e-13 54 -1.704088e-12 1.018770e-12 55 9.508747e-13 -1.704088e-12 56 9.181292e-13 9.508747e-13 57 4.648996e-13 9.181292e-13 58 2.171177e-12 4.648996e-13 59 1.215631e-12 2.171177e-12 60 2.473086e-12 1.215631e-12 61 4.175786e-12 2.473086e-12 62 -1.084437e-11 4.175786e-12 63 1.044498e-12 -1.084437e-11 64 -1.764557e-12 1.044498e-12 65 1.756709e-12 -1.764557e-12 66 1.722437e-12 1.756709e-12 67 1.745404e-12 1.722437e-12 68 -3.411757e-13 1.745404e-12 69 2.151751e-12 -3.411757e-13 70 2.805823e-12 2.151751e-12 71 1.849972e-12 2.805823e-12 72 3.143353e-12 1.849972e-12 73 8.667903e-13 3.143353e-12 74 -1.158427e-11 8.667903e-13 75 2.383529e-12 -1.158427e-11 76 1.567064e-12 2.383529e-12 77 1.323334e-12 1.567064e-12 78 2.419560e-12 1.323334e-12 79 2.116661e-12 2.419560e-12 80 -3.297577e-13 2.116661e-12 81 8.001905e-13 -3.297577e-13 82 1.733876e-12 8.001905e-13 83 1.754337e-12 1.733876e-12 84 3.763127e-12 1.754337e-12 85 2.485515e-12 3.763127e-12 86 -1.421635e-11 2.485515e-12 87 2.019849e-12 -1.421635e-11 88 3.437729e-12 2.019849e-12 89 -7.616217e-13 3.437729e-12 90 3.174214e-12 -7.616217e-13 91 -1.563424e-13 3.174214e-12 92 1.336380e-12 -1.563424e-13 93 NA 1.336380e-12 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.376058e-11 -1.608597e-11 [2,] 1.068879e-10 -1.376058e-11 [3,] -6.397474e-12 1.068879e-10 [4,] -1.276148e-12 -6.397474e-12 [5,] -2.575780e-12 -1.276148e-12 [6,] -5.797745e-12 -2.575780e-12 [7,] -5.985644e-12 -5.797745e-12 [8,] -4.067262e-12 -5.985644e-12 [9,] -3.321806e-12 -4.067262e-12 [10,] -4.122048e-12 -3.321806e-12 [11,] -3.223862e-12 -4.122048e-12 [12,] -4.028610e-13 -3.223862e-12 [13,] -3.016388e-12 -4.028610e-13 [14,] -1.956389e-11 -3.016388e-12 [15,] -1.809069e-12 -1.956389e-11 [16,] -4.652573e-12 -1.809069e-12 [17,] -4.177070e-12 -4.652573e-12 [18,] -2.300974e-13 -4.177070e-12 [19,] -2.399461e-12 -2.300974e-13 [20,] -2.706549e-12 -2.399461e-12 [21,] -8.628738e-13 -2.706549e-12 [22,] -2.084510e-12 -8.628738e-13 [23,] -3.560463e-13 -2.084510e-12 [24,] 2.742259e-13 -3.560463e-13 [25,] 2.504091e-12 2.742259e-13 [26,] -1.740987e-11 2.504091e-12 [27,] 2.356433e-13 -1.740987e-11 [28,] 2.256382e-12 2.356433e-13 [29,] -2.230352e-13 2.256382e-12 [30,] -1.264298e-12 -2.230352e-13 [31,] 3.665479e-14 -1.264298e-12 [32,] 6.811054e-13 3.665479e-14 [33,] 2.424732e-12 6.811054e-13 [34,] 9.572479e-13 2.424732e-12 [35,] 1.757437e-12 9.572479e-13 [36,] 5.108813e-12 1.757437e-12 [37,] 4.215124e-12 5.108813e-12 [38,] -1.648033e-11 4.215124e-12 [39,] 2.526189e-12 -1.648033e-11 [40,] 8.680475e-13 2.526189e-12 [41,] 3.638694e-12 8.680475e-13 [42,] 1.680018e-12 3.638694e-12 [43,] 3.691852e-12 1.680018e-12 [44,] 4.509130e-12 3.691852e-12 [45,] -1.656893e-12 4.509130e-12 [46,] -1.461566e-12 -1.656893e-12 [47,] -2.997467e-12 -1.461566e-12 [48,] 1.726230e-12 -2.997467e-12 [49,] 2.529664e-12 1.726230e-12 [50,] -1.678878e-11 2.529664e-12 [51,] -3.165826e-15 -1.678878e-11 [52,] -4.359464e-13 -3.165826e-15 [53,] 1.018770e-12 -4.359464e-13 [54,] -1.704088e-12 1.018770e-12 [55,] 9.508747e-13 -1.704088e-12 [56,] 9.181292e-13 9.508747e-13 [57,] 4.648996e-13 9.181292e-13 [58,] 2.171177e-12 4.648996e-13 [59,] 1.215631e-12 2.171177e-12 [60,] 2.473086e-12 1.215631e-12 [61,] 4.175786e-12 2.473086e-12 [62,] -1.084437e-11 4.175786e-12 [63,] 1.044498e-12 -1.084437e-11 [64,] -1.764557e-12 1.044498e-12 [65,] 1.756709e-12 -1.764557e-12 [66,] 1.722437e-12 1.756709e-12 [67,] 1.745404e-12 1.722437e-12 [68,] -3.411757e-13 1.745404e-12 [69,] 2.151751e-12 -3.411757e-13 [70,] 2.805823e-12 2.151751e-12 [71,] 1.849972e-12 2.805823e-12 [72,] 3.143353e-12 1.849972e-12 [73,] 8.667903e-13 3.143353e-12 [74,] -1.158427e-11 8.667903e-13 [75,] 2.383529e-12 -1.158427e-11 [76,] 1.567064e-12 2.383529e-12 [77,] 1.323334e-12 1.567064e-12 [78,] 2.419560e-12 1.323334e-12 [79,] 2.116661e-12 2.419560e-12 [80,] -3.297577e-13 2.116661e-12 [81,] 8.001905e-13 -3.297577e-13 [82,] 1.733876e-12 8.001905e-13 [83,] 1.754337e-12 1.733876e-12 [84,] 3.763127e-12 1.754337e-12 [85,] 2.485515e-12 3.763127e-12 [86,] -1.421635e-11 2.485515e-12 [87,] 2.019849e-12 -1.421635e-11 [88,] 3.437729e-12 2.019849e-12 [89,] -7.616217e-13 3.437729e-12 [90,] 3.174214e-12 -7.616217e-13 [91,] -1.563424e-13 3.174214e-12 [92,] 1.336380e-12 -1.563424e-13 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.376058e-11 -1.608597e-11 2 1.068879e-10 -1.376058e-11 3 -6.397474e-12 1.068879e-10 4 -1.276148e-12 -6.397474e-12 5 -2.575780e-12 -1.276148e-12 6 -5.797745e-12 -2.575780e-12 7 -5.985644e-12 -5.797745e-12 8 -4.067262e-12 -5.985644e-12 9 -3.321806e-12 -4.067262e-12 10 -4.122048e-12 -3.321806e-12 11 -3.223862e-12 -4.122048e-12 12 -4.028610e-13 -3.223862e-12 13 -3.016388e-12 -4.028610e-13 14 -1.956389e-11 -3.016388e-12 15 -1.809069e-12 -1.956389e-11 16 -4.652573e-12 -1.809069e-12 17 -4.177070e-12 -4.652573e-12 18 -2.300974e-13 -4.177070e-12 19 -2.399461e-12 -2.300974e-13 20 -2.706549e-12 -2.399461e-12 21 -8.628738e-13 -2.706549e-12 22 -2.084510e-12 -8.628738e-13 23 -3.560463e-13 -2.084510e-12 24 2.742259e-13 -3.560463e-13 25 2.504091e-12 2.742259e-13 26 -1.740987e-11 2.504091e-12 27 2.356433e-13 -1.740987e-11 28 2.256382e-12 2.356433e-13 29 -2.230352e-13 2.256382e-12 30 -1.264298e-12 -2.230352e-13 31 3.665479e-14 -1.264298e-12 32 6.811054e-13 3.665479e-14 33 2.424732e-12 6.811054e-13 34 9.572479e-13 2.424732e-12 35 1.757437e-12 9.572479e-13 36 5.108813e-12 1.757437e-12 37 4.215124e-12 5.108813e-12 38 -1.648033e-11 4.215124e-12 39 2.526189e-12 -1.648033e-11 40 8.680475e-13 2.526189e-12 41 3.638694e-12 8.680475e-13 42 1.680018e-12 3.638694e-12 43 3.691852e-12 1.680018e-12 44 4.509130e-12 3.691852e-12 45 -1.656893e-12 4.509130e-12 46 -1.461566e-12 -1.656893e-12 47 -2.997467e-12 -1.461566e-12 48 1.726230e-12 -2.997467e-12 49 2.529664e-12 1.726230e-12 50 -1.678878e-11 2.529664e-12 51 -3.165826e-15 -1.678878e-11 52 -4.359464e-13 -3.165826e-15 53 1.018770e-12 -4.359464e-13 54 -1.704088e-12 1.018770e-12 55 9.508747e-13 -1.704088e-12 56 9.181292e-13 9.508747e-13 57 4.648996e-13 9.181292e-13 58 2.171177e-12 4.648996e-13 59 1.215631e-12 2.171177e-12 60 2.473086e-12 1.215631e-12 61 4.175786e-12 2.473086e-12 62 -1.084437e-11 4.175786e-12 63 1.044498e-12 -1.084437e-11 64 -1.764557e-12 1.044498e-12 65 1.756709e-12 -1.764557e-12 66 1.722437e-12 1.756709e-12 67 1.745404e-12 1.722437e-12 68 -3.411757e-13 1.745404e-12 69 2.151751e-12 -3.411757e-13 70 2.805823e-12 2.151751e-12 71 1.849972e-12 2.805823e-12 72 3.143353e-12 1.849972e-12 73 8.667903e-13 3.143353e-12 74 -1.158427e-11 8.667903e-13 75 2.383529e-12 -1.158427e-11 76 1.567064e-12 2.383529e-12 77 1.323334e-12 1.567064e-12 78 2.419560e-12 1.323334e-12 79 2.116661e-12 2.419560e-12 80 -3.297577e-13 2.116661e-12 81 8.001905e-13 -3.297577e-13 82 1.733876e-12 8.001905e-13 83 1.754337e-12 1.733876e-12 84 3.763127e-12 1.754337e-12 85 2.485515e-12 3.763127e-12 86 -1.421635e-11 2.485515e-12 87 2.019849e-12 -1.421635e-11 88 3.437729e-12 2.019849e-12 89 -7.616217e-13 3.437729e-12 90 3.174214e-12 -7.616217e-13 91 -1.563424e-13 3.174214e-12 92 1.336380e-12 -1.563424e-13 > 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/782zy1258546716.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/88pxo1258546716.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9kw6m1258546716.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/101mcd1258546716.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11a3v01258546716.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/12wjzu1258546716.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/13s1ys1258546716.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/14j7jv1258546716.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/15c8pp1258546716.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/167ptw1258546716.tab") + } > > system("convert tmp/13ewq1258546716.ps tmp/13ewq1258546716.png") > system("convert tmp/2apc01258546716.ps tmp/2apc01258546716.png") > system("convert tmp/3qfrw1258546716.ps tmp/3qfrw1258546716.png") > system("convert tmp/4yevk1258546716.ps tmp/4yevk1258546716.png") > system("convert tmp/5o23v1258546716.ps tmp/5o23v1258546716.png") > system("convert tmp/6rak41258546716.ps tmp/6rak41258546716.png") > system("convert tmp/782zy1258546716.ps tmp/782zy1258546716.png") > system("convert tmp/88pxo1258546716.ps tmp/88pxo1258546716.png") > system("convert tmp/9kw6m1258546716.ps tmp/9kw6m1258546716.png") > system("convert tmp/101mcd1258546716.ps tmp/101mcd1258546716.png") > > > proc.time() user system elapsed 2.928 1.614 3.779