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Type 'q()' to quit R. > x <- array(list(110.3672031 + ,0 + ,102.1880309 + ,114.0150276 + ,108.1560276 + ,0 + ,0 + ,0 + ,96.8602511 + ,0 + ,110.3672031 + ,102.1880309 + ,114.0150276 + ,0 + ,0 + ,0 + ,94.1944583 + ,0 + ,96.8602511 + ,110.3672031 + ,102.1880309 + ,0 + ,0 + ,0 + ,99.51621961 + ,0 + ,94.1944583 + ,96.8602511 + ,110.3672031 + ,0 + ,0 + ,0 + ,94.06333487 + ,0 + ,99.51621961 + ,94.1944583 + ,96.8602511 + ,0 + ,0 + ,0 + ,97.5541476 + ,0 + ,94.06333487 + ,99.51621961 + ,94.1944583 + ,0 + ,0 + ,0 + ,78.15062422 + ,0 + ,97.5541476 + ,94.06333487 + ,99.51621961 + ,0 + ,0 + ,0 + ,81.2434643 + ,0 + ,78.15062422 + ,97.5541476 + ,94.06333487 + ,0 + ,0 + ,0 + ,92.36262465 + ,0 + ,81.2434643 + ,78.15062422 + ,97.5541476 + ,0 + ,0 + ,0 + ,96.06324371 + ,0 + ,92.36262465 + ,81.2434643 + ,78.15062422 + ,0 + ,0 + ,0 + ,114.0523777 + ,0 + ,96.06324371 + ,92.36262465 + ,81.2434643 + ,0 + ,0 + ,0 + ,110.6616666 + ,0 + ,114.0523777 + ,96.06324371 + ,92.36262465 + ,0 + ,0 + ,0 + ,104.9171949 + ,0 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,116.8726598 + ,1 + ,102.0350019 + ,111.5039454 + ,124.4434777 + ,0 + ,0 + ,0 + ,112.2073122 + ,1 + ,116.8726598 + ,102.0350019 + ,111.5039454 + ,0 + ,0 + ,0 + ,101.1513902 + ,1 + ,112.2073122 + ,116.8726598 + ,102.0350019 + ,0 + ,0 + ,0 + ,124.4255108 + ,1 + ,101.1513902 + ,112.2073122 + ,116.8726598 + ,0 + ,0 + ,0) + ,dim=c(8 + ,104) + ,dimnames=list(c('BouwV' + ,'X' + ,'Y1' + ,'Y2' + ,'Y3' + ,'D1' + ,'D2' + ,'D3') + ,1:104)) > y <- array(NA,dim=c(8,104),dimnames=list(c('BouwV','X','Y1','Y2','Y3','D1','D2','D3'),1:104)) > 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 BouwV X Y1 Y2 Y3 D1 D2 D3 M1 M2 M3 M4 M5 M6 M7 M8 1 110.36720 0 102.18803 114.01503 108.15603 0 0 0 1 0 0 0 0 0 0 0 2 96.86025 0 110.36720 102.18803 114.01503 0 0 0 0 1 0 0 0 0 0 0 3 94.19446 0 96.86025 110.36720 102.18803 0 0 0 0 0 1 0 0 0 0 0 4 99.51622 0 94.19446 96.86025 110.36720 0 0 0 0 0 0 1 0 0 0 0 5 94.06333 0 99.51622 94.19446 96.86025 0 0 0 0 0 0 0 1 0 0 0 6 97.55415 0 94.06333 99.51622 94.19446 0 0 0 0 0 0 0 0 1 0 0 7 78.15062 0 97.55415 94.06333 99.51622 0 0 0 0 0 0 0 0 0 1 0 8 81.24346 0 78.15062 97.55415 94.06333 0 0 0 0 0 0 0 0 0 0 1 9 92.36262 0 81.24346 78.15062 97.55415 0 0 0 0 0 0 0 0 0 0 0 10 96.06324 0 92.36262 81.24346 78.15062 0 0 0 0 0 0 0 0 0 0 0 11 114.05238 0 96.06324 92.36262 81.24346 0 0 0 0 0 0 0 0 0 0 0 12 110.66167 0 114.05238 96.06324 92.36262 0 0 0 0 0 0 0 0 0 0 0 13 104.91719 0 110.66167 114.05238 96.06324 0 0 0 1 0 0 0 0 0 0 0 14 90.00187 0 104.91719 110.66167 114.05238 0 0 0 0 1 0 0 0 0 0 0 15 95.70081 0 90.00187 104.91719 110.66167 0 0 0 0 0 1 0 0 0 0 0 16 86.02741 0 95.70081 90.00187 104.91719 0 0 0 0 0 0 1 0 0 0 0 17 84.85288 0 86.02741 95.70081 90.00187 0 0 0 0 0 0 0 1 0 0 0 18 100.04328 0 84.85288 86.02741 95.70081 0 0 0 0 0 0 0 0 1 0 0 19 80.91714 0 100.04328 84.85288 86.02741 0 0 0 0 0 0 0 0 0 1 0 20 74.06540 0 80.91714 100.04328 84.85288 0 0 0 0 0 0 0 0 0 0 1 21 77.30281 0 74.06540 80.91714 100.04328 0 0 0 0 0 0 0 0 0 0 0 22 97.23043 0 77.30281 74.06540 80.91714 0 0 0 0 0 0 0 0 0 0 0 23 90.75516 0 97.23043 77.30281 74.06540 0 0 0 0 0 0 0 0 0 0 0 24 100.56145 0 90.75516 97.23043 77.30281 0 0 0 0 0 0 0 0 0 0 0 25 92.01293 0 100.56145 90.75516 97.23043 0 0 0 1 0 0 0 0 0 0 0 26 99.24012 0 92.01293 100.56145 90.75516 0 0 0 0 1 0 0 0 0 0 0 27 105.86728 0 99.24012 92.01293 100.56145 0 0 0 0 0 1 0 0 0 0 0 28 90.99205 0 105.86728 99.24012 92.01293 0 0 0 0 0 0 1 0 0 0 0 29 93.30624 0 90.99205 105.86728 99.24012 0 0 0 0 0 0 0 1 0 0 0 30 91.17419 0 93.30624 90.99205 105.86728 0 0 0 0 0 0 0 0 1 0 0 31 77.33295 0 91.17419 93.30624 90.99205 0 0 0 0 0 0 0 0 0 1 0 32 91.12777 0 77.33295 91.17419 93.30624 0 0 0 0 0 0 0 0 0 0 1 33 85.01250 0 91.12777 77.33295 91.17419 0 0 0 0 0 0 0 0 0 0 0 34 83.90390 0 85.01250 91.12777 77.33295 0 0 0 0 0 0 0 0 0 0 0 35 104.86263 0 83.90390 85.01250 91.12777 0 0 0 0 0 0 0 0 0 0 0 36 110.90391 0 104.86263 83.90390 85.01250 0 0 0 0 0 0 0 0 0 0 0 37 95.43714 0 110.90391 104.86263 83.90390 0 0 0 1 0 0 0 0 0 0 0 38 111.62387 0 95.43714 110.90391 104.86263 0 0 0 0 1 0 0 0 0 0 0 39 108.89254 0 111.62387 95.43714 110.90391 0 0 0 0 0 1 0 0 0 0 0 40 96.17512 0 108.89254 111.62387 95.43714 0 0 0 0 0 0 1 0 0 0 0 41 101.97402 0 96.17512 108.89254 111.62387 0 0 0 0 0 0 0 1 0 0 0 42 99.11953 0 101.97402 96.17512 108.89254 0 0 0 0 0 0 0 0 1 0 0 43 86.78158 0 99.11953 101.97402 96.17512 0 0 0 0 0 0 0 0 0 1 0 44 118.41950 0 86.78158 99.11953 101.97402 0 0 0 0 0 0 0 0 0 0 1 45 118.74414 0 118.41950 86.78158 99.11953 0 0 0 0 0 0 0 0 0 0 0 46 106.52962 0 118.74414 118.41950 86.78158 0 0 0 0 0 0 0 0 0 0 0 47 134.77727 0 106.52962 118.74414 118.41950 0 0 0 0 0 0 0 0 0 0 0 48 104.67787 0 134.77727 106.52962 118.74414 0 0 0 0 0 0 0 0 0 0 0 49 105.29543 0 104.67787 134.77727 106.52962 0 0 0 1 0 0 0 0 0 0 0 50 139.41398 0 105.29543 104.67787 134.77727 0 0 0 0 1 0 0 0 0 0 0 51 103.60605 0 139.41398 105.29543 104.67787 0 0 0 0 0 1 0 0 0 0 0 52 99.78183 0 103.60605 139.41398 105.29543 0 0 0 0 0 0 1 0 0 0 0 53 103.46103 0 99.78183 103.60605 139.41398 0 0 0 0 0 0 0 1 0 0 0 54 120.05949 0 103.46103 99.78183 103.60605 0 0 0 0 0 0 0 0 1 0 0 55 96.71377 0 120.05949 103.46103 99.78183 0 0 0 0 0 0 0 0 0 1 0 56 107.13089 0 96.71377 120.05949 103.46103 0 0 0 0 0 0 0 0 0 0 1 57 105.36084 0 107.13089 96.71377 120.05949 0 0 0 0 0 0 0 0 0 0 0 58 111.69424 0 105.36084 107.13089 96.71377 0 0 0 0 0 0 0 0 0 0 0 59 132.05200 0 111.69424 105.36084 107.13089 0 0 0 0 0 0 0 0 0 0 0 60 126.80379 0 132.05200 111.69424 105.36084 0 0 0 0 0 0 0 0 0 0 0 61 154.48243 0 126.80379 132.05200 111.69424 1 0 0 1 0 0 0 0 0 0 0 62 141.55710 0 154.48243 126.80379 132.05200 0 0 0 0 1 0 0 0 0 0 0 63 109.95069 0 141.55710 154.48243 126.80379 0 0 0 0 0 1 0 0 0 0 0 64 127.90420 0 109.95069 141.55710 154.48243 0 0 0 0 0 0 1 0 0 0 0 65 133.08886 0 127.90420 109.95069 141.55710 0 0 0 0 0 0 0 1 0 0 0 66 120.07963 0 133.08886 127.90420 109.95069 0 0 0 0 0 0 0 0 1 0 0 67 117.55571 0 120.07963 133.08886 127.90420 0 0 0 0 0 0 0 0 0 1 0 68 143.03623 0 117.55571 120.07963 133.08886 0 0 0 0 0 0 0 0 0 0 1 69 159.98293 1 143.03623 117.55571 120.07963 0 1 0 0 0 0 0 0 0 0 0 70 128.59911 1 159.98293 143.03623 117.55571 0 0 0 0 0 0 0 0 0 0 0 71 149.73733 1 128.59911 159.98293 143.03623 0 0 0 0 0 0 0 0 0 0 0 72 126.81693 1 149.73733 128.59911 159.98293 0 0 0 0 0 0 0 0 0 0 0 73 140.96397 1 126.81693 149.73733 128.59911 0 0 0 1 0 0 0 0 0 0 0 74 137.66920 1 140.96397 126.81693 149.73733 0 0 0 0 1 0 0 0 0 0 0 75 117.94023 1 137.66920 140.96397 126.81693 0 0 0 0 0 1 0 0 0 0 0 76 122.30952 1 117.94023 137.66920 140.96397 0 0 0 0 0 0 1 0 0 0 0 77 127.78042 1 122.30952 117.94023 137.66920 0 0 0 0 0 0 0 1 0 0 0 78 136.16772 1 127.78042 122.30952 117.94023 0 0 0 0 0 0 0 0 1 0 0 79 116.24059 1 136.16772 127.78042 122.30952 0 0 0 0 0 0 0 0 0 1 0 80 123.15769 1 116.24059 136.16772 127.78042 0 0 0 0 0 0 0 0 0 0 1 81 116.34002 1 123.15769 116.24059 136.16772 0 0 0 0 0 0 0 0 0 0 0 82 108.61193 1 116.34002 123.15769 116.24059 0 0 0 0 0 0 0 0 0 0 0 83 125.89823 1 108.61193 116.34002 123.15769 0 0 0 0 0 0 0 0 0 0 0 84 112.80031 1 125.89823 108.61193 116.34002 0 0 0 0 0 0 0 0 0 0 0 85 107.51824 1 112.80031 125.89823 108.61193 0 0 0 1 0 0 0 0 0 0 0 86 135.09554 1 107.51824 112.80031 125.89823 0 0 0 0 1 0 0 0 0 0 0 87 115.50965 1 135.09554 107.51824 112.80031 0 0 0 0 0 1 0 0 0 0 0 88 115.86408 1 115.50965 135.09554 107.51824 0 0 0 0 0 0 1 0 0 0 0 89 104.58839 1 115.86408 115.50965 135.09554 0 0 0 0 0 0 0 1 0 0 0 90 163.72134 1 104.58839 115.86408 115.50965 0 0 1 0 0 0 0 0 1 0 0 91 113.44823 1 163.72134 104.58839 115.86408 0 0 0 0 0 0 0 0 0 1 0 92 98.04288 1 113.44823 163.72134 104.58839 0 0 0 0 0 0 0 0 0 0 1 93 116.78685 1 98.04288 113.44823 163.72134 0 0 0 0 0 0 0 0 0 0 0 94 126.53304 1 116.78685 98.04288 113.44823 0 0 0 0 0 0 0 0 0 0 0 95 113.03366 1 126.53304 116.78685 98.04288 0 0 0 0 0 0 0 0 0 0 0 96 124.33922 1 113.03366 126.53304 116.78685 0 0 0 0 0 0 0 0 0 0 0 97 109.82988 1 124.33922 113.03366 126.53304 0 0 0 1 0 0 0 0 0 0 0 98 124.44348 1 109.82988 124.33922 113.03366 0 0 0 0 1 0 0 0 0 0 0 99 111.50395 1 124.44348 109.82988 124.33922 0 0 0 0 0 1 0 0 0 0 0 100 102.03500 1 111.50395 124.44348 109.82988 0 0 0 0 0 0 1 0 0 0 0 101 116.87266 1 102.03500 111.50395 124.44348 0 0 0 0 0 0 0 1 0 0 0 102 112.20731 1 116.87266 102.03500 111.50395 0 0 0 0 0 0 0 0 1 0 0 103 101.15139 1 112.20731 116.87266 102.03500 0 0 0 0 0 0 0 0 0 1 0 104 124.42551 1 101.15139 112.20731 116.87266 0 0 0 0 0 0 0 0 0 0 1 M9 M10 M11 t 1 0 0 0 1 2 0 0 0 2 3 0 0 0 3 4 0 0 0 4 5 0 0 0 5 6 0 0 0 6 7 0 0 0 7 8 0 0 0 8 9 1 0 0 9 10 0 1 0 10 11 0 0 1 11 12 0 0 0 12 13 0 0 0 13 14 0 0 0 14 15 0 0 0 15 16 0 0 0 16 17 0 0 0 17 18 0 0 0 18 19 0 0 0 19 20 0 0 0 20 21 1 0 0 21 22 0 1 0 22 23 0 0 1 23 24 0 0 0 24 25 0 0 0 25 26 0 0 0 26 27 0 0 0 27 28 0 0 0 28 29 0 0 0 29 30 0 0 0 30 31 0 0 0 31 32 0 0 0 32 33 1 0 0 33 34 0 1 0 34 35 0 0 1 35 36 0 0 0 36 37 0 0 0 37 38 0 0 0 38 39 0 0 0 39 40 0 0 0 40 41 0 0 0 41 42 0 0 0 42 43 0 0 0 43 44 0 0 0 44 45 1 0 0 45 46 0 1 0 46 47 0 0 1 47 48 0 0 0 48 49 0 0 0 49 50 0 0 0 50 51 0 0 0 51 52 0 0 0 52 53 0 0 0 53 54 0 0 0 54 55 0 0 0 55 56 0 0 0 56 57 1 0 0 57 58 0 1 0 58 59 0 0 1 59 60 0 0 0 60 61 0 0 0 61 62 0 0 0 62 63 0 0 0 63 64 0 0 0 64 65 0 0 0 65 66 0 0 0 66 67 0 0 0 67 68 0 0 0 68 69 1 0 0 69 70 0 1 0 70 71 0 0 1 71 72 0 0 0 72 73 0 0 0 73 74 0 0 0 74 75 0 0 0 75 76 0 0 0 76 77 0 0 0 77 78 0 0 0 78 79 0 0 0 79 80 0 0 0 80 81 1 0 0 81 82 0 1 0 82 83 0 0 1 83 84 0 0 0 84 85 0 0 0 85 86 0 0 0 86 87 0 0 0 87 88 0 0 0 88 89 0 0 0 89 90 0 0 0 90 91 0 0 0 91 92 0 0 0 92 93 1 0 0 93 94 0 1 0 94 95 0 0 1 95 96 0 0 0 96 97 0 0 0 97 98 0 0 0 98 99 0 0 0 99 100 0 0 0 100 101 0 0 0 101 102 0 0 0 102 103 0 0 0 103 104 0 0 0 104 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X Y1 Y2 Y3 D1 31.80825 -7.02864 0.23897 0.02737 0.40219 35.16469 D2 D3 M1 M2 M3 M4 46.44886 48.36934 -2.53493 2.46210 -9.05816 -8.97052 M5 M6 M7 M8 M9 M10 -8.52014 -0.58179 -15.66246 -2.13163 -9.61420 1.19846 M11 t 11.05018 0.18369 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -20.8121 -5.2559 0.0677 5.8684 19.1843 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 31.80825 9.39588 3.385 0.001083 ** X -7.02864 3.59364 -1.956 0.053806 . Y1 0.23897 0.08333 2.868 0.005229 ** Y2 0.02737 0.08401 0.326 0.745350 Y3 0.40219 0.08121 4.952 3.74e-06 *** D1 35.16469 10.23979 3.434 0.000926 *** D2 46.44886 10.67411 4.352 3.78e-05 *** D3 48.36934 10.19150 4.746 8.42e-06 *** M1 -2.53493 4.92466 -0.515 0.608085 M2 2.46210 4.75244 0.518 0.605771 M3 -9.05816 4.62051 -1.960 0.053261 . M4 -8.97052 4.91392 -1.826 0.071475 . M5 -8.52014 4.90451 -1.737 0.086016 . M6 -0.58179 4.81218 -0.121 0.904059 M7 -15.66246 4.60884 -3.398 0.001039 ** M8 -2.13163 5.15491 -0.414 0.680283 M9 -9.61420 5.28955 -1.818 0.072693 . M10 1.19846 4.84207 0.248 0.805117 M11 11.05018 4.84643 2.280 0.025137 * t 0.18369 0.06358 2.889 0.004915 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 9.424 on 84 degrees of freedom Multiple R-squared: 0.7899, Adjusted R-squared: 0.7424 F-statistic: 16.63 on 19 and 84 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,] 0.6996706 0.6006588 0.3003294 [2,] 0.5632169 0.8735661 0.4367831 [3,] 0.4460069 0.8920138 0.5539931 [4,] 0.5110251 0.9779499 0.4889749 [5,] 0.6952467 0.6095066 0.3047533 [6,] 0.5961847 0.8076307 0.4038153 [7,] 0.5316725 0.9366551 0.4683275 [8,] 0.4686677 0.9373353 0.5313323 [9,] 0.3823929 0.7647857 0.6176071 [10,] 0.4809493 0.9618987 0.5190507 [11,] 0.3994795 0.7989591 0.6005205 [12,] 0.3928922 0.7857843 0.6071078 [13,] 0.3362328 0.6724656 0.6637672 [14,] 0.3065985 0.6131969 0.6934015 [15,] 0.2439661 0.4879322 0.7560339 [16,] 0.3555754 0.7111507 0.6444246 [17,] 0.3158327 0.6316655 0.6841673 [18,] 0.2594776 0.5189551 0.7405224 [19,] 0.2083197 0.4166393 0.7916803 [20,] 0.2133322 0.4266643 0.7866678 [21,] 0.1900699 0.3801398 0.8099301 [22,] 0.4450870 0.8901741 0.5549130 [23,] 0.5722933 0.8554133 0.4277067 [24,] 0.5210688 0.9578624 0.4789312 [25,] 0.4665799 0.9331597 0.5334201 [26,] 0.7203728 0.5592543 0.2796272 [27,] 0.6764725 0.6470551 0.3235275 [28,] 0.7552684 0.4894632 0.2447316 [29,] 0.7238811 0.5522378 0.2761189 [30,] 0.6798629 0.6402742 0.3201371 [31,] 0.7644558 0.4710884 0.2355442 [32,] 0.7504190 0.4991621 0.2495810 [33,] 0.7422681 0.5154639 0.2577319 [34,] 0.7565970 0.4868060 0.2434030 [35,] 0.7318587 0.5362825 0.2681413 [36,] 0.7166198 0.5667604 0.2833802 [37,] 0.6715980 0.6568041 0.3284020 [38,] 0.6114013 0.7771975 0.3885987 [39,] 0.5383345 0.9233310 0.4616655 [40,] 0.4808980 0.9617959 0.5191020 [41,] 0.4815654 0.9631308 0.5184346 [42,] 0.4202091 0.8404181 0.5797909 [43,] 0.3709846 0.7419692 0.6290154 [44,] 0.3385411 0.6770821 0.6614589 [45,] 0.3501212 0.7002424 0.6498788 [46,] 0.3259787 0.6519575 0.6740213 [47,] 0.2552852 0.5105704 0.7447148 [48,] 0.2054630 0.4109261 0.7945370 [49,] 0.2492285 0.4984570 0.7507715 [50,] 0.2413426 0.4826853 0.7586574 [51,] 0.5709194 0.8581611 0.4290806 [52,] 0.4763247 0.9526495 0.5236753 [53,] 0.3893744 0.7787488 0.6106256 [54,] 0.2931704 0.5863409 0.7068296 [55,] 0.2452259 0.4904519 0.7547741 [56,] 0.3596273 0.7192547 0.6403727 [57,] 0.2936487 0.5872973 0.7063513 [58,] 0.2072898 0.4145795 0.7927102 [59,] 0.1242408 0.2484816 0.8757592 > postscript(file="/var/www/html/rcomp/tmp/1aqhz1258652951.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2pdl61258652951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3sw111258652951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4nop51258652951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5tiu51258652951.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 = 104 Frequency = 1 1 2 3 4 5 9.870142e+00 -1.280475e+01 3.626531e+00 6.394140e+00 4.540842e+00 6 7 8 9 10 2.139140e+00 -5.192694e+00 -9.080052e+00 7.726093e+00 5.492542e+00 11 12 13 14 15 1.101364e+01 9.617298e+00 5.053520e+00 -2.081207e+01 1.308643e+00 16 17 18 19 20 -7.279246e+00 -9.334198e-01 4.388351e+00 4.519216e-01 -1.548730e+01 21 22 23 24 25 -8.899563e+00 7.138019e+00 -1.146759e+01 8.904982e+00 -7.473131e+00 26 27 28 29 30 -1.048005e+00 1.147866e+01 -2.011256e+00 1.354025e-01 -1.292989e+01 31 32 33 34 35 -5.445321e+00 -2.929827e+00 -3.806310e+00 -9.260716e+00 -3.453255e+00 36 37 38 39 40 1.093597e+01 -3.751082e+00 2.356126e+00 5.086930e+00 -1.471625e+00 41 42 43 44 45 2.968270e-01 -1.061879e+01 -2.421532e+00 1.619608e+01 1.774504e+01 46 47 48 49 50 -1.447276e+00 6.950392e+00 -1.882892e+01 -4.528096e+00 1.372514e+01 51 52 53 54 55 -6.810548e+00 -3.531611e+00 -1.231461e+01 9.788975e+00 -1.188877e+00 56 57 58 59 60 -8.415789e-01 -3.838779e+00 1.025547e+00 5.693202e+00 6.985211e+00 61 62 63 64 65 3.330669e-16 2.400343e+00 -1.342771e+01 1.028997e+00 7.353014e+00 66 67 68 69 70 -2.796833e+00 5.322286e+00 1.596231e+01 1.887379e-15 3.365943e-01 71 72 73 74 75 8.227062e+00 -1.483486e+01 1.918428e+01 -5.460559e-01 6.800143e-01 76 77 78 79 80 3.892874e+00 9.550802e+00 1.632393e+01 7.382439e+00 2.916968e+00 81 82 83 84 85 -1.082574e+00 -1.035266e+01 -3.850401e+00 -7.259083e+00 -4.424990e+00 86 87 88 89 90 1.263995e+01 3.213078e+00 9.346006e+00 -1.320366e+01 -2.109424e-15 91 92 93 94 95 -9.714036e-01 -1.516149e+01 -7.843906e+00 7.067947e+00 -1.311305e+01 96 97 98 99 100 4.479398e+00 -1.393064e+01 4.089330e+00 -5.155603e+00 -6.368279e+00 101 102 103 104 4.574800e+00 -6.294886e+00 2.063180e+00 8.424888e+00 > postscript(file="/var/www/html/rcomp/tmp/6m9m71258652951.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 = 104 Frequency = 1 lag(myerror, k = 1) myerror 0 9.870142e+00 NA 1 -1.280475e+01 9.870142e+00 2 3.626531e+00 -1.280475e+01 3 6.394140e+00 3.626531e+00 4 4.540842e+00 6.394140e+00 5 2.139140e+00 4.540842e+00 6 -5.192694e+00 2.139140e+00 7 -9.080052e+00 -5.192694e+00 8 7.726093e+00 -9.080052e+00 9 5.492542e+00 7.726093e+00 10 1.101364e+01 5.492542e+00 11 9.617298e+00 1.101364e+01 12 5.053520e+00 9.617298e+00 13 -2.081207e+01 5.053520e+00 14 1.308643e+00 -2.081207e+01 15 -7.279246e+00 1.308643e+00 16 -9.334198e-01 -7.279246e+00 17 4.388351e+00 -9.334198e-01 18 4.519216e-01 4.388351e+00 19 -1.548730e+01 4.519216e-01 20 -8.899563e+00 -1.548730e+01 21 7.138019e+00 -8.899563e+00 22 -1.146759e+01 7.138019e+00 23 8.904982e+00 -1.146759e+01 24 -7.473131e+00 8.904982e+00 25 -1.048005e+00 -7.473131e+00 26 1.147866e+01 -1.048005e+00 27 -2.011256e+00 1.147866e+01 28 1.354025e-01 -2.011256e+00 29 -1.292989e+01 1.354025e-01 30 -5.445321e+00 -1.292989e+01 31 -2.929827e+00 -5.445321e+00 32 -3.806310e+00 -2.929827e+00 33 -9.260716e+00 -3.806310e+00 34 -3.453255e+00 -9.260716e+00 35 1.093597e+01 -3.453255e+00 36 -3.751082e+00 1.093597e+01 37 2.356126e+00 -3.751082e+00 38 5.086930e+00 2.356126e+00 39 -1.471625e+00 5.086930e+00 40 2.968270e-01 -1.471625e+00 41 -1.061879e+01 2.968270e-01 42 -2.421532e+00 -1.061879e+01 43 1.619608e+01 -2.421532e+00 44 1.774504e+01 1.619608e+01 45 -1.447276e+00 1.774504e+01 46 6.950392e+00 -1.447276e+00 47 -1.882892e+01 6.950392e+00 48 -4.528096e+00 -1.882892e+01 49 1.372514e+01 -4.528096e+00 50 -6.810548e+00 1.372514e+01 51 -3.531611e+00 -6.810548e+00 52 -1.231461e+01 -3.531611e+00 53 9.788975e+00 -1.231461e+01 54 -1.188877e+00 9.788975e+00 55 -8.415789e-01 -1.188877e+00 56 -3.838779e+00 -8.415789e-01 57 1.025547e+00 -3.838779e+00 58 5.693202e+00 1.025547e+00 59 6.985211e+00 5.693202e+00 60 3.330669e-16 6.985211e+00 61 2.400343e+00 3.330669e-16 62 -1.342771e+01 2.400343e+00 63 1.028997e+00 -1.342771e+01 64 7.353014e+00 1.028997e+00 65 -2.796833e+00 7.353014e+00 66 5.322286e+00 -2.796833e+00 67 1.596231e+01 5.322286e+00 68 1.887379e-15 1.596231e+01 69 3.365943e-01 1.887379e-15 70 8.227062e+00 3.365943e-01 71 -1.483486e+01 8.227062e+00 72 1.918428e+01 -1.483486e+01 73 -5.460559e-01 1.918428e+01 74 6.800143e-01 -5.460559e-01 75 3.892874e+00 6.800143e-01 76 9.550802e+00 3.892874e+00 77 1.632393e+01 9.550802e+00 78 7.382439e+00 1.632393e+01 79 2.916968e+00 7.382439e+00 80 -1.082574e+00 2.916968e+00 81 -1.035266e+01 -1.082574e+00 82 -3.850401e+00 -1.035266e+01 83 -7.259083e+00 -3.850401e+00 84 -4.424990e+00 -7.259083e+00 85 1.263995e+01 -4.424990e+00 86 3.213078e+00 1.263995e+01 87 9.346006e+00 3.213078e+00 88 -1.320366e+01 9.346006e+00 89 -2.109424e-15 -1.320366e+01 90 -9.714036e-01 -2.109424e-15 91 -1.516149e+01 -9.714036e-01 92 -7.843906e+00 -1.516149e+01 93 7.067947e+00 -7.843906e+00 94 -1.311305e+01 7.067947e+00 95 4.479398e+00 -1.311305e+01 96 -1.393064e+01 4.479398e+00 97 4.089330e+00 -1.393064e+01 98 -5.155603e+00 4.089330e+00 99 -6.368279e+00 -5.155603e+00 100 4.574800e+00 -6.368279e+00 101 -6.294886e+00 4.574800e+00 102 2.063180e+00 -6.294886e+00 103 8.424888e+00 2.063180e+00 104 NA 8.424888e+00 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.280475e+01 9.870142e+00 [2,] 3.626531e+00 -1.280475e+01 [3,] 6.394140e+00 3.626531e+00 [4,] 4.540842e+00 6.394140e+00 [5,] 2.139140e+00 4.540842e+00 [6,] -5.192694e+00 2.139140e+00 [7,] -9.080052e+00 -5.192694e+00 [8,] 7.726093e+00 -9.080052e+00 [9,] 5.492542e+00 7.726093e+00 [10,] 1.101364e+01 5.492542e+00 [11,] 9.617298e+00 1.101364e+01 [12,] 5.053520e+00 9.617298e+00 [13,] -2.081207e+01 5.053520e+00 [14,] 1.308643e+00 -2.081207e+01 [15,] -7.279246e+00 1.308643e+00 [16,] -9.334198e-01 -7.279246e+00 [17,] 4.388351e+00 -9.334198e-01 [18,] 4.519216e-01 4.388351e+00 [19,] -1.548730e+01 4.519216e-01 [20,] -8.899563e+00 -1.548730e+01 [21,] 7.138019e+00 -8.899563e+00 [22,] -1.146759e+01 7.138019e+00 [23,] 8.904982e+00 -1.146759e+01 [24,] -7.473131e+00 8.904982e+00 [25,] -1.048005e+00 -7.473131e+00 [26,] 1.147866e+01 -1.048005e+00 [27,] -2.011256e+00 1.147866e+01 [28,] 1.354025e-01 -2.011256e+00 [29,] -1.292989e+01 1.354025e-01 [30,] -5.445321e+00 -1.292989e+01 [31,] -2.929827e+00 -5.445321e+00 [32,] -3.806310e+00 -2.929827e+00 [33,] -9.260716e+00 -3.806310e+00 [34,] -3.453255e+00 -9.260716e+00 [35,] 1.093597e+01 -3.453255e+00 [36,] -3.751082e+00 1.093597e+01 [37,] 2.356126e+00 -3.751082e+00 [38,] 5.086930e+00 2.356126e+00 [39,] -1.471625e+00 5.086930e+00 [40,] 2.968270e-01 -1.471625e+00 [41,] -1.061879e+01 2.968270e-01 [42,] -2.421532e+00 -1.061879e+01 [43,] 1.619608e+01 -2.421532e+00 [44,] 1.774504e+01 1.619608e+01 [45,] -1.447276e+00 1.774504e+01 [46,] 6.950392e+00 -1.447276e+00 [47,] -1.882892e+01 6.950392e+00 [48,] -4.528096e+00 -1.882892e+01 [49,] 1.372514e+01 -4.528096e+00 [50,] -6.810548e+00 1.372514e+01 [51,] -3.531611e+00 -6.810548e+00 [52,] -1.231461e+01 -3.531611e+00 [53,] 9.788975e+00 -1.231461e+01 [54,] -1.188877e+00 9.788975e+00 [55,] -8.415789e-01 -1.188877e+00 [56,] -3.838779e+00 -8.415789e-01 [57,] 1.025547e+00 -3.838779e+00 [58,] 5.693202e+00 1.025547e+00 [59,] 6.985211e+00 5.693202e+00 [60,] 3.330669e-16 6.985211e+00 [61,] 2.400343e+00 3.330669e-16 [62,] -1.342771e+01 2.400343e+00 [63,] 1.028997e+00 -1.342771e+01 [64,] 7.353014e+00 1.028997e+00 [65,] -2.796833e+00 7.353014e+00 [66,] 5.322286e+00 -2.796833e+00 [67,] 1.596231e+01 5.322286e+00 [68,] 1.887379e-15 1.596231e+01 [69,] 3.365943e-01 1.887379e-15 [70,] 8.227062e+00 3.365943e-01 [71,] -1.483486e+01 8.227062e+00 [72,] 1.918428e+01 -1.483486e+01 [73,] -5.460559e-01 1.918428e+01 [74,] 6.800143e-01 -5.460559e-01 [75,] 3.892874e+00 6.800143e-01 [76,] 9.550802e+00 3.892874e+00 [77,] 1.632393e+01 9.550802e+00 [78,] 7.382439e+00 1.632393e+01 [79,] 2.916968e+00 7.382439e+00 [80,] -1.082574e+00 2.916968e+00 [81,] -1.035266e+01 -1.082574e+00 [82,] -3.850401e+00 -1.035266e+01 [83,] -7.259083e+00 -3.850401e+00 [84,] -4.424990e+00 -7.259083e+00 [85,] 1.263995e+01 -4.424990e+00 [86,] 3.213078e+00 1.263995e+01 [87,] 9.346006e+00 3.213078e+00 [88,] -1.320366e+01 9.346006e+00 [89,] -2.109424e-15 -1.320366e+01 [90,] -9.714036e-01 -2.109424e-15 [91,] -1.516149e+01 -9.714036e-01 [92,] -7.843906e+00 -1.516149e+01 [93,] 7.067947e+00 -7.843906e+00 [94,] -1.311305e+01 7.067947e+00 [95,] 4.479398e+00 -1.311305e+01 [96,] -1.393064e+01 4.479398e+00 [97,] 4.089330e+00 -1.393064e+01 [98,] -5.155603e+00 4.089330e+00 [99,] -6.368279e+00 -5.155603e+00 [100,] 4.574800e+00 -6.368279e+00 [101,] -6.294886e+00 4.574800e+00 [102,] 2.063180e+00 -6.294886e+00 [103,] 8.424888e+00 2.063180e+00 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.280475e+01 9.870142e+00 2 3.626531e+00 -1.280475e+01 3 6.394140e+00 3.626531e+00 4 4.540842e+00 6.394140e+00 5 2.139140e+00 4.540842e+00 6 -5.192694e+00 2.139140e+00 7 -9.080052e+00 -5.192694e+00 8 7.726093e+00 -9.080052e+00 9 5.492542e+00 7.726093e+00 10 1.101364e+01 5.492542e+00 11 9.617298e+00 1.101364e+01 12 5.053520e+00 9.617298e+00 13 -2.081207e+01 5.053520e+00 14 1.308643e+00 -2.081207e+01 15 -7.279246e+00 1.308643e+00 16 -9.334198e-01 -7.279246e+00 17 4.388351e+00 -9.334198e-01 18 4.519216e-01 4.388351e+00 19 -1.548730e+01 4.519216e-01 20 -8.899563e+00 -1.548730e+01 21 7.138019e+00 -8.899563e+00 22 -1.146759e+01 7.138019e+00 23 8.904982e+00 -1.146759e+01 24 -7.473131e+00 8.904982e+00 25 -1.048005e+00 -7.473131e+00 26 1.147866e+01 -1.048005e+00 27 -2.011256e+00 1.147866e+01 28 1.354025e-01 -2.011256e+00 29 -1.292989e+01 1.354025e-01 30 -5.445321e+00 -1.292989e+01 31 -2.929827e+00 -5.445321e+00 32 -3.806310e+00 -2.929827e+00 33 -9.260716e+00 -3.806310e+00 34 -3.453255e+00 -9.260716e+00 35 1.093597e+01 -3.453255e+00 36 -3.751082e+00 1.093597e+01 37 2.356126e+00 -3.751082e+00 38 5.086930e+00 2.356126e+00 39 -1.471625e+00 5.086930e+00 40 2.968270e-01 -1.471625e+00 41 -1.061879e+01 2.968270e-01 42 -2.421532e+00 -1.061879e+01 43 1.619608e+01 -2.421532e+00 44 1.774504e+01 1.619608e+01 45 -1.447276e+00 1.774504e+01 46 6.950392e+00 -1.447276e+00 47 -1.882892e+01 6.950392e+00 48 -4.528096e+00 -1.882892e+01 49 1.372514e+01 -4.528096e+00 50 -6.810548e+00 1.372514e+01 51 -3.531611e+00 -6.810548e+00 52 -1.231461e+01 -3.531611e+00 53 9.788975e+00 -1.231461e+01 54 -1.188877e+00 9.788975e+00 55 -8.415789e-01 -1.188877e+00 56 -3.838779e+00 -8.415789e-01 57 1.025547e+00 -3.838779e+00 58 5.693202e+00 1.025547e+00 59 6.985211e+00 5.693202e+00 60 3.330669e-16 6.985211e+00 61 2.400343e+00 3.330669e-16 62 -1.342771e+01 2.400343e+00 63 1.028997e+00 -1.342771e+01 64 7.353014e+00 1.028997e+00 65 -2.796833e+00 7.353014e+00 66 5.322286e+00 -2.796833e+00 67 1.596231e+01 5.322286e+00 68 1.887379e-15 1.596231e+01 69 3.365943e-01 1.887379e-15 70 8.227062e+00 3.365943e-01 71 -1.483486e+01 8.227062e+00 72 1.918428e+01 -1.483486e+01 73 -5.460559e-01 1.918428e+01 74 6.800143e-01 -5.460559e-01 75 3.892874e+00 6.800143e-01 76 9.550802e+00 3.892874e+00 77 1.632393e+01 9.550802e+00 78 7.382439e+00 1.632393e+01 79 2.916968e+00 7.382439e+00 80 -1.082574e+00 2.916968e+00 81 -1.035266e+01 -1.082574e+00 82 -3.850401e+00 -1.035266e+01 83 -7.259083e+00 -3.850401e+00 84 -4.424990e+00 -7.259083e+00 85 1.263995e+01 -4.424990e+00 86 3.213078e+00 1.263995e+01 87 9.346006e+00 3.213078e+00 88 -1.320366e+01 9.346006e+00 89 -2.109424e-15 -1.320366e+01 90 -9.714036e-01 -2.109424e-15 91 -1.516149e+01 -9.714036e-01 92 -7.843906e+00 -1.516149e+01 93 7.067947e+00 -7.843906e+00 94 -1.311305e+01 7.067947e+00 95 4.479398e+00 -1.311305e+01 96 -1.393064e+01 4.479398e+00 97 4.089330e+00 -1.393064e+01 98 -5.155603e+00 4.089330e+00 99 -6.368279e+00 -5.155603e+00 100 4.574800e+00 -6.368279e+00 101 -6.294886e+00 4.574800e+00 102 2.063180e+00 -6.294886e+00 103 8.424888e+00 2.063180e+00 > 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/7al6r1258652951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8f3v31258652951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/97yvt1258652951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') Warning messages: 1: Not plotting observations with leverage one: 61, 69, 90 2: Not plotting observations with leverage one: 61, 69, 90 > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/1017vp1258652951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11g91v1258652951.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/129zft1258652951.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/133d9m1258652951.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/14tpur1258652951.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/15e0lh1258652952.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/16rzoq1258652952.tab") + } > > system("convert tmp/1aqhz1258652951.ps tmp/1aqhz1258652951.png") > system("convert tmp/2pdl61258652951.ps tmp/2pdl61258652951.png") > system("convert tmp/3sw111258652951.ps tmp/3sw111258652951.png") > system("convert tmp/4nop51258652951.ps tmp/4nop51258652951.png") > system("convert tmp/5tiu51258652951.ps tmp/5tiu51258652951.png") > system("convert tmp/6m9m71258652951.ps tmp/6m9m71258652951.png") > system("convert tmp/7al6r1258652951.ps tmp/7al6r1258652951.png") > system("convert tmp/8f3v31258652951.ps tmp/8f3v31258652951.png") > system("convert tmp/97yvt1258652951.ps tmp/97yvt1258652951.png") > system("convert tmp/1017vp1258652951.ps tmp/1017vp1258652951.png") > > > proc.time() user system elapsed 3.215 1.598 3.594