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Type 'q()' to quit R. > x <- array(list(106.3 + ,0 + ,97.7 + ,98.3 + ,91.6 + ,104.6 + ,111.6 + ,102.3 + ,0 + ,106.3 + ,97.7 + ,98.3 + ,91.6 + ,104.6 + ,106.6 + ,0 + ,102.3 + ,106.3 + ,97.7 + ,98.3 + ,91.6 + ,108.1 + ,0 + ,106.6 + ,102.3 + ,106.3 + ,97.7 + ,98.3 + ,93.8 + ,0 + ,108.1 + ,106.6 + ,102.3 + ,106.3 + ,97.7 + ,88.2 + ,0 + ,93.8 + ,108.1 + ,106.6 + ,102.3 + ,106.3 + ,108.9 + ,0 + ,88.2 + ,93.8 + ,108.1 + ,106.6 + ,102.3 + ,114.2 + ,0 + ,108.9 + ,88.2 + ,93.8 + ,108.1 + ,106.6 + ,102.5 + ,0 + ,114.2 + ,108.9 + ,88.2 + ,93.8 + ,108.1 + ,94.2 + ,0 + ,102.5 + ,114.2 + ,108.9 + ,88.2 + ,93.8 + ,97.4 + ,0 + ,94.2 + ,102.5 + ,114.2 + ,108.9 + ,88.2 + ,98.5 + ,0 + ,97.4 + ,94.2 + ,102.5 + ,114.2 + ,108.9 + ,106.5 + ,0 + ,98.5 + ,97.4 + ,94.2 + ,102.5 + ,114.2 + ,102.9 + ,0 + ,106.5 + ,98.5 + ,97.4 + ,94.2 + ,102.5 + ,97.1 + ,0 + ,102.9 + ,106.5 + ,98.5 + ,97.4 + ,94.2 + ,103.7 + ,0 + ,97.1 + ,102.9 + ,106.5 + ,98.5 + ,97.4 + ,93.4 + ,0 + ,103.7 + ,97.1 + ,102.9 + ,106.5 + ,98.5 + 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+ ,114.9 + ,135.4 + ,115.0 + ,0 + ,129.4 + ,138.9 + ,128.9 + ,114.3 + ,114.9 + ,128.0 + ,0 + ,115.0 + ,129.4 + ,138.9 + ,128.9 + ,114.3 + ,127.0 + ,0 + ,128.0 + ,115.0 + ,129.4 + ,138.9 + ,128.9 + ,128.8 + ,0 + ,127.0 + ,128.0 + ,115.0 + ,129.4 + ,138.9 + ,137.9 + ,0 + ,128.8 + ,127.0 + ,128.0 + ,115.0 + ,129.4 + ,128.4 + ,0 + ,137.9 + ,128.8 + ,127.0 + ,128.0 + ,115.0 + ,135.9 + ,0 + ,128.4 + ,137.9 + ,128.8 + ,127.0 + ,128.0 + ,122.2 + ,0 + ,135.9 + ,128.4 + ,137.9 + ,128.8 + ,127.0 + ,113.1 + ,0 + ,122.2 + ,135.9 + ,128.4 + ,137.9 + ,128.8 + ,136.2 + ,1 + ,113.1 + ,122.2 + ,135.9 + ,128.4 + ,137.9 + ,138.0 + ,1 + ,136.2 + ,113.1 + ,122.2 + ,135.9 + ,128.4 + ,115.2 + ,1 + ,138.0 + ,136.2 + ,113.1 + ,122.2 + ,135.9 + ,111.0 + ,1 + ,115.2 + ,138.0 + ,136.2 + ,113.1 + ,122.2 + ,99.2 + ,1 + ,111.0 + ,115.2 + ,138.0 + ,136.2 + ,113.1 + ,102.4 + ,1 + ,99.2 + ,111.0 + ,115.2 + ,138.0 + ,136.2 + ,112.7 + ,1 + ,102.4 + ,99.2 + ,111.0 + ,115.2 + ,138.0 + ,105.5 + ,1 + ,112.7 + ,102.4 + ,99.2 + ,111.0 + ,115.2 + ,98.3 + ,1 + ,105.5 + ,112.7 + ,102.4 + ,99.2 + ,111.0 + ,116.4 + ,1 + ,98.3 + ,105.5 + ,112.7 + ,102.4 + ,99.2 + ,97.4 + ,1 + ,116.4 + ,98.3 + ,105.5 + ,112.7 + ,102.4 + ,93.3 + ,1 + ,97.4 + ,116.4 + ,98.3 + ,105.5 + ,112.7 + ,117.4 + ,1 + ,93.3 + ,97.4 + ,116.4 + ,98.3 + ,105.5) + ,dim=c(7 + ,91) + ,dimnames=list(c('y' + ,'x' + ,'y1' + ,'y2' + ,'y3' + ,'y4' + ,'y5') + ,1:91)) > y <- array(NA,dim=c(7,91),dimnames=list(c('y','x','y1','y2','y3','y4','y5'),1:91)) > 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 y5 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 106.3 0 97.7 98.3 91.6 104.6 111.6 1 0 0 0 0 0 0 0 0 0 0 1 2 102.3 0 106.3 97.7 98.3 91.6 104.6 0 1 0 0 0 0 0 0 0 0 0 2 3 106.6 0 102.3 106.3 97.7 98.3 91.6 0 0 1 0 0 0 0 0 0 0 0 3 4 108.1 0 106.6 102.3 106.3 97.7 98.3 0 0 0 1 0 0 0 0 0 0 0 4 5 93.8 0 108.1 106.6 102.3 106.3 97.7 0 0 0 0 1 0 0 0 0 0 0 5 6 88.2 0 93.8 108.1 106.6 102.3 106.3 0 0 0 0 0 1 0 0 0 0 0 6 7 108.9 0 88.2 93.8 108.1 106.6 102.3 0 0 0 0 0 0 1 0 0 0 0 7 8 114.2 0 108.9 88.2 93.8 108.1 106.6 0 0 0 0 0 0 0 1 0 0 0 8 9 102.5 0 114.2 108.9 88.2 93.8 108.1 0 0 0 0 0 0 0 0 1 0 0 9 10 94.2 0 102.5 114.2 108.9 88.2 93.8 0 0 0 0 0 0 0 0 0 1 0 10 11 97.4 0 94.2 102.5 114.2 108.9 88.2 0 0 0 0 0 0 0 0 0 0 1 11 12 98.5 0 97.4 94.2 102.5 114.2 108.9 0 0 0 0 0 0 0 0 0 0 0 12 13 106.5 0 98.5 97.4 94.2 102.5 114.2 1 0 0 0 0 0 0 0 0 0 0 13 14 102.9 0 106.5 98.5 97.4 94.2 102.5 0 1 0 0 0 0 0 0 0 0 0 14 15 97.1 0 102.9 106.5 98.5 97.4 94.2 0 0 1 0 0 0 0 0 0 0 0 15 16 103.7 0 97.1 102.9 106.5 98.5 97.4 0 0 0 1 0 0 0 0 0 0 0 16 17 93.4 0 103.7 97.1 102.9 106.5 98.5 0 0 0 0 1 0 0 0 0 0 0 17 18 85.8 0 93.4 103.7 97.1 102.9 106.5 0 0 0 0 0 1 0 0 0 0 0 18 19 108.6 0 85.8 93.4 103.7 97.1 102.9 0 0 0 0 0 0 1 0 0 0 0 19 20 110.2 0 108.6 85.8 93.4 103.7 97.1 0 0 0 0 0 0 0 1 0 0 0 20 21 101.2 0 110.2 108.6 85.8 93.4 103.7 0 0 0 0 0 0 0 0 1 0 0 21 22 101.2 0 101.2 110.2 108.6 85.8 93.4 0 0 0 0 0 0 0 0 0 1 0 22 23 96.9 0 101.2 101.2 110.2 108.6 85.8 0 0 0 0 0 0 0 0 0 0 1 23 24 99.4 0 96.9 101.2 101.2 110.2 108.6 0 0 0 0 0 0 0 0 0 0 0 24 25 118.7 0 99.4 96.9 101.2 101.2 110.2 1 0 0 0 0 0 0 0 0 0 0 25 26 108.0 0 118.7 99.4 96.9 101.2 101.2 0 1 0 0 0 0 0 0 0 0 0 26 27 101.2 0 108.0 118.7 99.4 96.9 101.2 0 0 1 0 0 0 0 0 0 0 0 27 28 119.9 0 101.2 108.0 118.7 99.4 96.9 0 0 0 1 0 0 0 0 0 0 0 28 29 94.8 0 119.9 101.2 108.0 118.7 99.4 0 0 0 0 1 0 0 0 0 0 0 29 30 95.3 0 94.8 119.9 101.2 108.0 118.7 0 0 0 0 0 1 0 0 0 0 0 30 31 118.0 0 95.3 94.8 119.9 101.2 108.0 0 0 0 0 0 0 1 0 0 0 0 31 32 115.9 0 118.0 95.3 94.8 119.9 101.2 0 0 0 0 0 0 0 1 0 0 0 32 33 111.4 0 115.9 118.0 95.3 94.8 119.9 0 0 0 0 0 0 0 0 1 0 0 33 34 108.2 0 111.4 115.9 118.0 95.3 94.8 0 0 0 0 0 0 0 0 0 1 0 34 35 108.8 0 108.2 111.4 115.9 118.0 95.3 0 0 0 0 0 0 0 0 0 0 1 35 36 109.5 0 108.8 108.2 111.4 115.9 118.0 0 0 0 0 0 0 0 0 0 0 0 36 37 124.8 0 109.5 108.8 108.2 111.4 115.9 1 0 0 0 0 0 0 0 0 0 0 37 38 115.3 0 124.8 109.5 108.8 108.2 111.4 0 1 0 0 0 0 0 0 0 0 0 38 39 109.5 0 115.3 124.8 109.5 108.8 108.2 0 0 1 0 0 0 0 0 0 0 0 39 40 124.2 0 109.5 115.3 124.8 109.5 108.8 0 0 0 1 0 0 0 0 0 0 0 40 41 92.9 0 124.2 109.5 115.3 124.8 109.5 0 0 0 0 1 0 0 0 0 0 0 41 42 98.4 0 92.9 124.2 109.5 115.3 124.8 0 0 0 0 0 1 0 0 0 0 0 42 43 120.9 0 98.4 92.9 124.2 109.5 115.3 0 0 0 0 0 0 1 0 0 0 0 43 44 111.7 0 120.9 98.4 92.9 124.2 109.5 0 0 0 0 0 0 0 1 0 0 0 44 45 116.1 0 111.7 120.9 98.4 92.9 124.2 0 0 0 0 0 0 0 0 1 0 0 45 46 109.4 0 116.1 111.7 120.9 98.4 92.9 0 0 0 0 0 0 0 0 0 1 0 46 47 111.7 0 109.4 116.1 111.7 120.9 98.4 0 0 0 0 0 0 0 0 0 0 1 47 48 114.3 0 111.7 109.4 116.1 111.7 120.9 0 0 0 0 0 0 0 0 0 0 0 48 49 133.7 0 114.3 111.7 109.4 116.1 111.7 1 0 0 0 0 0 0 0 0 0 0 49 50 114.3 0 133.7 114.3 111.7 109.4 116.1 0 1 0 0 0 0 0 0 0 0 0 50 51 126.5 0 114.3 133.7 114.3 111.7 109.4 0 0 1 0 0 0 0 0 0 0 0 51 52 131.0 0 126.5 114.3 133.7 114.3 111.7 0 0 0 1 0 0 0 0 0 0 0 52 53 104.0 0 131.0 126.5 114.3 133.7 114.3 0 0 0 0 1 0 0 0 0 0 0 53 54 108.9 0 104.0 131.0 126.5 114.3 133.7 0 0 0 0 0 1 0 0 0 0 0 54 55 128.5 0 108.9 104.0 131.0 126.5 114.3 0 0 0 0 0 0 1 0 0 0 0 55 56 132.4 0 128.5 108.9 104.0 131.0 126.5 0 0 0 0 0 0 0 1 0 0 0 56 57 128.0 0 132.4 128.5 108.9 104.0 131.0 0 0 0 0 0 0 0 0 1 0 0 57 58 116.4 0 128.0 132.4 128.5 108.9 104.0 0 0 0 0 0 0 0 0 0 1 0 58 59 120.9 0 116.4 128.0 132.4 128.5 108.9 0 0 0 0 0 0 0 0 0 0 1 59 60 118.6 0 120.9 116.4 128.0 132.4 128.5 0 0 0 0 0 0 0 0 0 0 0 60 61 133.1 0 118.6 120.9 116.4 128.0 132.4 1 0 0 0 0 0 0 0 0 0 0 61 62 121.1 0 133.1 118.6 120.9 116.4 128.0 0 1 0 0 0 0 0 0 0 0 0 62 63 127.6 0 121.1 133.1 118.6 120.9 116.4 0 0 1 0 0 0 0 0 0 0 0 63 64 135.4 0 127.6 121.1 133.1 118.6 120.9 0 0 0 1 0 0 0 0 0 0 0 64 65 114.9 0 135.4 127.6 121.1 133.1 118.6 0 0 0 0 1 0 0 0 0 0 0 65 66 114.3 0 114.9 135.4 127.6 121.1 133.1 0 0 0 0 0 1 0 0 0 0 0 66 67 128.9 0 114.3 114.9 135.4 127.6 121.1 0 0 0 0 0 0 1 0 0 0 0 67 68 138.9 0 128.9 114.3 114.9 135.4 127.6 0 0 0 0 0 0 0 1 0 0 0 68 69 129.4 0 138.9 128.9 114.3 114.9 135.4 0 0 0 0 0 0 0 0 1 0 0 69 70 115.0 0 129.4 138.9 128.9 114.3 114.9 0 0 0 0 0 0 0 0 0 1 0 70 71 128.0 0 115.0 129.4 138.9 128.9 114.3 0 0 0 0 0 0 0 0 0 0 1 71 72 127.0 0 128.0 115.0 129.4 138.9 128.9 0 0 0 0 0 0 0 0 0 0 0 72 73 128.8 0 127.0 128.0 115.0 129.4 138.9 1 0 0 0 0 0 0 0 0 0 0 73 74 137.9 0 128.8 127.0 128.0 115.0 129.4 0 1 0 0 0 0 0 0 0 0 0 74 75 128.4 0 137.9 128.8 127.0 128.0 115.0 0 0 1 0 0 0 0 0 0 0 0 75 76 135.9 0 128.4 137.9 128.8 127.0 128.0 0 0 0 1 0 0 0 0 0 0 0 76 77 122.2 0 135.9 128.4 137.9 128.8 127.0 0 0 0 0 1 0 0 0 0 0 0 77 78 113.1 0 122.2 135.9 128.4 137.9 128.8 0 0 0 0 0 1 0 0 0 0 0 78 79 136.2 1 113.1 122.2 135.9 128.4 137.9 0 0 0 0 0 0 1 0 0 0 0 79 80 138.0 1 136.2 113.1 122.2 135.9 128.4 0 0 0 0 0 0 0 1 0 0 0 80 81 115.2 1 138.0 136.2 113.1 122.2 135.9 0 0 0 0 0 0 0 0 1 0 0 81 82 111.0 1 115.2 138.0 136.2 113.1 122.2 0 0 0 0 0 0 0 0 0 1 0 82 83 99.2 1 111.0 115.2 138.0 136.2 113.1 0 0 0 0 0 0 0 0 0 0 1 83 84 102.4 1 99.2 111.0 115.2 138.0 136.2 0 0 0 0 0 0 0 0 0 0 0 84 85 112.7 1 102.4 99.2 111.0 115.2 138.0 1 0 0 0 0 0 0 0 0 0 0 85 86 105.5 1 112.7 102.4 99.2 111.0 115.2 0 1 0 0 0 0 0 0 0 0 0 86 87 98.3 1 105.5 112.7 102.4 99.2 111.0 0 0 1 0 0 0 0 0 0 0 0 87 88 116.4 1 98.3 105.5 112.7 102.4 99.2 0 0 0 1 0 0 0 0 0 0 0 88 89 97.4 1 116.4 98.3 105.5 112.7 102.4 0 0 0 0 1 0 0 0 0 0 0 89 90 93.3 1 97.4 116.4 98.3 105.5 112.7 0 0 0 0 0 1 0 0 0 0 0 90 91 117.4 1 93.3 97.4 116.4 98.3 105.5 0 0 0 0 0 0 1 0 0 0 0 91 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) x y1 y2 y3 y4 38.24345 -9.64428 0.06950 0.44837 0.54834 -0.22818 y5 M1 M2 M3 M4 M5 -0.21168 14.21327 1.26209 -7.01049 -0.04423 -13.50740 M6 M7 M8 M9 M10 M11 -16.96829 7.20522 21.34649 1.66102 -21.69985 -13.11919 t 0.16555 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -10.0388 -2.2730 0.3067 2.7118 8.6357 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 38.24345 8.67564 4.408 3.57e-05 *** x -9.64428 2.37106 -4.068 0.000120 *** y1 0.06950 0.11246 0.618 0.538538 y2 0.44837 0.10869 4.125 9.82e-05 *** y3 0.54834 0.10083 5.438 7.01e-07 *** y4 -0.22818 0.10417 -2.190 0.031735 * y5 -0.21168 0.11377 -1.861 0.066887 . M1 14.21327 2.47677 5.739 2.10e-07 *** M2 1.26209 3.45136 0.366 0.715677 M3 -7.01049 3.74377 -1.873 0.065188 . M4 -0.04423 3.33722 -0.013 0.989461 M5 -13.50740 2.92367 -4.620 1.64e-05 *** M6 -16.96829 2.92947 -5.792 1.69e-07 *** M7 7.20522 2.72179 2.647 0.009963 ** M8 21.34649 3.04820 7.003 1.09e-09 *** M9 1.66102 4.66724 0.356 0.722964 M10 -21.69985 4.80183 -4.519 2.38e-05 *** M11 -13.11919 3.93353 -3.335 0.001350 ** t 0.16555 0.04240 3.904 0.000211 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.085 on 72 degrees of freedom Multiple R-squared: 0.9219, Adjusted R-squared: 0.9023 F-statistic: 47.2 on 18 and 72 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,] 0.69581988 0.60836024 0.3041801 [2,] 0.54031771 0.91936459 0.4596823 [3,] 0.44647753 0.89295506 0.5535225 [4,] 0.57236668 0.85526665 0.4276333 [5,] 0.46436944 0.92873888 0.5356306 [6,] 0.37933820 0.75867639 0.6206618 [7,] 0.33677189 0.67354378 0.6632281 [8,] 0.34711771 0.69423542 0.6528823 [9,] 0.43519602 0.87039203 0.5648040 [10,] 0.34721151 0.69442302 0.6527885 [11,] 0.28207035 0.56414069 0.7179297 [12,] 0.21288500 0.42577000 0.7871150 [13,] 0.16989616 0.33979233 0.8301038 [14,] 0.16695785 0.33391570 0.8330422 [15,] 0.11759213 0.23518425 0.8824079 [16,] 0.08044285 0.16088569 0.9195572 [17,] 0.05735391 0.11470781 0.9426461 [18,] 0.05809743 0.11619486 0.9419026 [19,] 0.03803616 0.07607232 0.9619638 [20,] 0.13383431 0.26766862 0.8661657 [21,] 0.09573858 0.19147716 0.9042614 [22,] 0.06712510 0.13425020 0.9328749 [23,] 0.12253476 0.24506952 0.8774652 [24,] 0.09575414 0.19150827 0.9042459 [25,] 0.07545076 0.15090151 0.9245492 [26,] 0.13257739 0.26515479 0.8674226 [27,] 0.10022238 0.20044476 0.8997776 [28,] 0.11813867 0.23627735 0.8818613 [29,] 0.14002197 0.28004395 0.8599780 [30,] 0.14233895 0.28467789 0.8576611 [31,] 0.10939818 0.21879636 0.8906018 [32,] 0.10661478 0.21322956 0.8933852 [33,] 0.08894537 0.17789074 0.9110546 [34,] 0.06074913 0.12149827 0.9392509 [35,] 0.07662196 0.15324392 0.9233780 [36,] 0.06122706 0.12245411 0.9387729 [37,] 0.04394259 0.08788518 0.9560574 [38,] 0.02986971 0.05973942 0.9701303 [39,] 0.03101335 0.06202669 0.9689867 [40,] 0.02191737 0.04383473 0.9780826 [41,] 0.03845952 0.07691904 0.9615405 [42,] 0.05378471 0.10756942 0.9462153 [43,] 0.03724313 0.07448626 0.9627569 [44,] 0.02243203 0.04486405 0.9775680 [45,] 0.01841880 0.03683760 0.9815812 [46,] 0.12298805 0.24597609 0.8770120 [47,] 0.10382298 0.20764595 0.8961770 [48,] 0.05171366 0.10342731 0.9482863 > postscript(file="/var/www/html/rcomp/tmp/1c5ve1262014961.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2gnij1262014961.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/33eei1262014961.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4n9wv1262014961.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5smzl1262014961.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 = 91 Frequency = 1 1 2 3 4 5 6 0.0759216 0.4108981 8.3459247 0.7743710 1.7684744 -1.6651035 7 8 9 10 11 12 0.8087237 1.9679929 0.2634713 -2.0599658 -1.1516002 2.1694761 13 14 15 16 17 18 -2.7171930 -0.7059831 -3.3656660 -5.3386084 3.8332364 1.3374871 19 20 21 22 23 24 -0.7597054 -5.7172159 -2.3172088 4.3695218 -1.9248507 -2.2843216 25 26 27 28 29 30 2.6761603 2.7523477 -6.2026476 -0.2870361 0.4603151 2.9880083 31 32 33 34 35 36 -1.5022195 -3.1198071 -0.1751972 3.4280593 3.9590365 -0.4391580 37 38 39 40 41 42 0.4476983 0.3444353 -4.4727213 -0.3447938 -7.9195914 0.7112400 43 44 45 46 47 48 1.1290058 -7.1178828 0.3066727 2.9130363 6.3027123 -1.2869983 49 50 51 52 53 54 5.2526906 -5.7343049 4.9034152 0.5644934 -3.3058305 -2.2617141 55 56 57 58 59 60 0.9745133 5.4230583 3.5885633 -1.6036921 0.3001862 -2.9446495 61 62 63 64 65 66 1.5010002 -3.7356487 5.0364786 3.1101972 1.8532117 -0.7571524 67 68 69 70 71 72 -6.5968714 2.7474269 2.8284746 -4.6818131 2.5533294 4.4032850 73 74 75 76 77 78 -6.0897228 3.6939762 1.3280726 -0.1870902 -1.6420121 -2.1906229 79 80 81 82 83 84 8.6357095 5.8164277 -4.4947758 -2.3651465 -10.0388135 0.3823663 85 86 87 88 89 90 -1.1465553 2.9742794 -5.5728563 1.7084668 4.9521963 1.8378574 91 -2.6891561 > postscript(file="/var/www/html/rcomp/tmp/6o1fh1262014961.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 = 91 Frequency = 1 lag(myerror, k = 1) myerror 0 0.0759216 NA 1 0.4108981 0.0759216 2 8.3459247 0.4108981 3 0.7743710 8.3459247 4 1.7684744 0.7743710 5 -1.6651035 1.7684744 6 0.8087237 -1.6651035 7 1.9679929 0.8087237 8 0.2634713 1.9679929 9 -2.0599658 0.2634713 10 -1.1516002 -2.0599658 11 2.1694761 -1.1516002 12 -2.7171930 2.1694761 13 -0.7059831 -2.7171930 14 -3.3656660 -0.7059831 15 -5.3386084 -3.3656660 16 3.8332364 -5.3386084 17 1.3374871 3.8332364 18 -0.7597054 1.3374871 19 -5.7172159 -0.7597054 20 -2.3172088 -5.7172159 21 4.3695218 -2.3172088 22 -1.9248507 4.3695218 23 -2.2843216 -1.9248507 24 2.6761603 -2.2843216 25 2.7523477 2.6761603 26 -6.2026476 2.7523477 27 -0.2870361 -6.2026476 28 0.4603151 -0.2870361 29 2.9880083 0.4603151 30 -1.5022195 2.9880083 31 -3.1198071 -1.5022195 32 -0.1751972 -3.1198071 33 3.4280593 -0.1751972 34 3.9590365 3.4280593 35 -0.4391580 3.9590365 36 0.4476983 -0.4391580 37 0.3444353 0.4476983 38 -4.4727213 0.3444353 39 -0.3447938 -4.4727213 40 -7.9195914 -0.3447938 41 0.7112400 -7.9195914 42 1.1290058 0.7112400 43 -7.1178828 1.1290058 44 0.3066727 -7.1178828 45 2.9130363 0.3066727 46 6.3027123 2.9130363 47 -1.2869983 6.3027123 48 5.2526906 -1.2869983 49 -5.7343049 5.2526906 50 4.9034152 -5.7343049 51 0.5644934 4.9034152 52 -3.3058305 0.5644934 53 -2.2617141 -3.3058305 54 0.9745133 -2.2617141 55 5.4230583 0.9745133 56 3.5885633 5.4230583 57 -1.6036921 3.5885633 58 0.3001862 -1.6036921 59 -2.9446495 0.3001862 60 1.5010002 -2.9446495 61 -3.7356487 1.5010002 62 5.0364786 -3.7356487 63 3.1101972 5.0364786 64 1.8532117 3.1101972 65 -0.7571524 1.8532117 66 -6.5968714 -0.7571524 67 2.7474269 -6.5968714 68 2.8284746 2.7474269 69 -4.6818131 2.8284746 70 2.5533294 -4.6818131 71 4.4032850 2.5533294 72 -6.0897228 4.4032850 73 3.6939762 -6.0897228 74 1.3280726 3.6939762 75 -0.1870902 1.3280726 76 -1.6420121 -0.1870902 77 -2.1906229 -1.6420121 78 8.6357095 -2.1906229 79 5.8164277 8.6357095 80 -4.4947758 5.8164277 81 -2.3651465 -4.4947758 82 -10.0388135 -2.3651465 83 0.3823663 -10.0388135 84 -1.1465553 0.3823663 85 2.9742794 -1.1465553 86 -5.5728563 2.9742794 87 1.7084668 -5.5728563 88 4.9521963 1.7084668 89 1.8378574 4.9521963 90 -2.6891561 1.8378574 91 NA -2.6891561 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.4108981 0.0759216 [2,] 8.3459247 0.4108981 [3,] 0.7743710 8.3459247 [4,] 1.7684744 0.7743710 [5,] -1.6651035 1.7684744 [6,] 0.8087237 -1.6651035 [7,] 1.9679929 0.8087237 [8,] 0.2634713 1.9679929 [9,] -2.0599658 0.2634713 [10,] -1.1516002 -2.0599658 [11,] 2.1694761 -1.1516002 [12,] -2.7171930 2.1694761 [13,] -0.7059831 -2.7171930 [14,] -3.3656660 -0.7059831 [15,] -5.3386084 -3.3656660 [16,] 3.8332364 -5.3386084 [17,] 1.3374871 3.8332364 [18,] -0.7597054 1.3374871 [19,] -5.7172159 -0.7597054 [20,] -2.3172088 -5.7172159 [21,] 4.3695218 -2.3172088 [22,] -1.9248507 4.3695218 [23,] -2.2843216 -1.9248507 [24,] 2.6761603 -2.2843216 [25,] 2.7523477 2.6761603 [26,] -6.2026476 2.7523477 [27,] -0.2870361 -6.2026476 [28,] 0.4603151 -0.2870361 [29,] 2.9880083 0.4603151 [30,] -1.5022195 2.9880083 [31,] -3.1198071 -1.5022195 [32,] -0.1751972 -3.1198071 [33,] 3.4280593 -0.1751972 [34,] 3.9590365 3.4280593 [35,] -0.4391580 3.9590365 [36,] 0.4476983 -0.4391580 [37,] 0.3444353 0.4476983 [38,] -4.4727213 0.3444353 [39,] -0.3447938 -4.4727213 [40,] -7.9195914 -0.3447938 [41,] 0.7112400 -7.9195914 [42,] 1.1290058 0.7112400 [43,] -7.1178828 1.1290058 [44,] 0.3066727 -7.1178828 [45,] 2.9130363 0.3066727 [46,] 6.3027123 2.9130363 [47,] -1.2869983 6.3027123 [48,] 5.2526906 -1.2869983 [49,] -5.7343049 5.2526906 [50,] 4.9034152 -5.7343049 [51,] 0.5644934 4.9034152 [52,] -3.3058305 0.5644934 [53,] -2.2617141 -3.3058305 [54,] 0.9745133 -2.2617141 [55,] 5.4230583 0.9745133 [56,] 3.5885633 5.4230583 [57,] -1.6036921 3.5885633 [58,] 0.3001862 -1.6036921 [59,] -2.9446495 0.3001862 [60,] 1.5010002 -2.9446495 [61,] -3.7356487 1.5010002 [62,] 5.0364786 -3.7356487 [63,] 3.1101972 5.0364786 [64,] 1.8532117 3.1101972 [65,] -0.7571524 1.8532117 [66,] -6.5968714 -0.7571524 [67,] 2.7474269 -6.5968714 [68,] 2.8284746 2.7474269 [69,] -4.6818131 2.8284746 [70,] 2.5533294 -4.6818131 [71,] 4.4032850 2.5533294 [72,] -6.0897228 4.4032850 [73,] 3.6939762 -6.0897228 [74,] 1.3280726 3.6939762 [75,] -0.1870902 1.3280726 [76,] -1.6420121 -0.1870902 [77,] -2.1906229 -1.6420121 [78,] 8.6357095 -2.1906229 [79,] 5.8164277 8.6357095 [80,] -4.4947758 5.8164277 [81,] -2.3651465 -4.4947758 [82,] -10.0388135 -2.3651465 [83,] 0.3823663 -10.0388135 [84,] -1.1465553 0.3823663 [85,] 2.9742794 -1.1465553 [86,] -5.5728563 2.9742794 [87,] 1.7084668 -5.5728563 [88,] 4.9521963 1.7084668 [89,] 1.8378574 4.9521963 [90,] -2.6891561 1.8378574 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.4108981 0.0759216 2 8.3459247 0.4108981 3 0.7743710 8.3459247 4 1.7684744 0.7743710 5 -1.6651035 1.7684744 6 0.8087237 -1.6651035 7 1.9679929 0.8087237 8 0.2634713 1.9679929 9 -2.0599658 0.2634713 10 -1.1516002 -2.0599658 11 2.1694761 -1.1516002 12 -2.7171930 2.1694761 13 -0.7059831 -2.7171930 14 -3.3656660 -0.7059831 15 -5.3386084 -3.3656660 16 3.8332364 -5.3386084 17 1.3374871 3.8332364 18 -0.7597054 1.3374871 19 -5.7172159 -0.7597054 20 -2.3172088 -5.7172159 21 4.3695218 -2.3172088 22 -1.9248507 4.3695218 23 -2.2843216 -1.9248507 24 2.6761603 -2.2843216 25 2.7523477 2.6761603 26 -6.2026476 2.7523477 27 -0.2870361 -6.2026476 28 0.4603151 -0.2870361 29 2.9880083 0.4603151 30 -1.5022195 2.9880083 31 -3.1198071 -1.5022195 32 -0.1751972 -3.1198071 33 3.4280593 -0.1751972 34 3.9590365 3.4280593 35 -0.4391580 3.9590365 36 0.4476983 -0.4391580 37 0.3444353 0.4476983 38 -4.4727213 0.3444353 39 -0.3447938 -4.4727213 40 -7.9195914 -0.3447938 41 0.7112400 -7.9195914 42 1.1290058 0.7112400 43 -7.1178828 1.1290058 44 0.3066727 -7.1178828 45 2.9130363 0.3066727 46 6.3027123 2.9130363 47 -1.2869983 6.3027123 48 5.2526906 -1.2869983 49 -5.7343049 5.2526906 50 4.9034152 -5.7343049 51 0.5644934 4.9034152 52 -3.3058305 0.5644934 53 -2.2617141 -3.3058305 54 0.9745133 -2.2617141 55 5.4230583 0.9745133 56 3.5885633 5.4230583 57 -1.6036921 3.5885633 58 0.3001862 -1.6036921 59 -2.9446495 0.3001862 60 1.5010002 -2.9446495 61 -3.7356487 1.5010002 62 5.0364786 -3.7356487 63 3.1101972 5.0364786 64 1.8532117 3.1101972 65 -0.7571524 1.8532117 66 -6.5968714 -0.7571524 67 2.7474269 -6.5968714 68 2.8284746 2.7474269 69 -4.6818131 2.8284746 70 2.5533294 -4.6818131 71 4.4032850 2.5533294 72 -6.0897228 4.4032850 73 3.6939762 -6.0897228 74 1.3280726 3.6939762 75 -0.1870902 1.3280726 76 -1.6420121 -0.1870902 77 -2.1906229 -1.6420121 78 8.6357095 -2.1906229 79 5.8164277 8.6357095 80 -4.4947758 5.8164277 81 -2.3651465 -4.4947758 82 -10.0388135 -2.3651465 83 0.3823663 -10.0388135 84 -1.1465553 0.3823663 85 2.9742794 -1.1465553 86 -5.5728563 2.9742794 87 1.7084668 -5.5728563 88 4.9521963 1.7084668 89 1.8378574 4.9521963 90 -2.6891561 1.8378574 > 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/7jsr91262014961.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8yep41262014961.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9ki151262014961.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10u5c61262014961.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11wjql1262014961.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/12e8681262014961.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/13hbne1262014961.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/14o7q21262014961.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/155a541262014961.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/16ei6a1262014961.tab") + } > > try(system("convert tmp/1c5ve1262014961.ps tmp/1c5ve1262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/2gnij1262014961.ps tmp/2gnij1262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/33eei1262014961.ps tmp/33eei1262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/4n9wv1262014961.ps tmp/4n9wv1262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/5smzl1262014961.ps tmp/5smzl1262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/6o1fh1262014961.ps tmp/6o1fh1262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/7jsr91262014961.ps tmp/7jsr91262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/8yep41262014961.ps tmp/8yep41262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/9ki151262014961.ps tmp/9ki151262014961.png",intern=TRUE)) character(0) > try(system("convert tmp/10u5c61262014961.ps tmp/10u5c61262014961.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.875 1.594 3.926