R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(10.81 + ,-0.2643 + ,0 + ,0 + ,24563400 + ,24.45 + ,9.12 + ,-0.2643 + ,0 + ,0 + ,14163200 + ,23.62 + ,11.03 + ,-0.2643 + ,0 + ,0 + ,18184800 + ,21.90 + ,12.74 + ,-0.1918 + ,0 + ,0 + ,20810300 + ,27.12 + ,9.98 + ,-0.1918 + ,0 + ,0 + ,12843000 + ,27.70 + ,11.62 + ,-0.1918 + ,0 + ,0 + ,13866700 + ,29.23 + ,9.40 + ,-0.2246 + ,0 + ,0 + ,15119200 + ,26.50 + ,9.27 + ,-0.2246 + ,0 + ,0 + ,8301600 + ,22.84 + ,7.76 + ,-0.2246 + ,0 + ,0 + ,14039600 + ,20.49 + ,8.78 + ,0.3654 + ,0 + ,0 + ,12139700 + ,23.28 + ,10.65 + ,0.3654 + ,0 + ,0 + ,9649000 + ,25.71 + ,10.95 + ,0.3654 + ,0 + ,0 + ,8513600 + ,26.52 + ,12.36 + ,0.0447 + ,0 + ,0 + ,15278600 + ,25.51 + ,10.85 + ,0.0447 + ,0 + ,0 + ,15590900 + ,23.36 + ,11.84 + ,0.0447 + ,0 + ,0 + ,9691100 + ,24.15 + ,12.14 + ,-0.0312 + ,0 + ,0 + ,10882700 + ,20.92 + ,11.65 + ,-0.0312 + ,0 + ,0 + ,10294800 + ,20.38 + ,8.86 + ,-0.0312 + ,0 + ,0 + ,16031900 + ,21.90 + ,7.63 + ,-0.0048 + ,0 + ,0 + ,13683600 + ,19.21 + ,7.38 + ,-0.0048 + ,0 + ,0 + ,8677200 + ,19.65 + ,7.25 + ,-0.0048 + ,0 + ,0 + ,9874100 + ,17.51 + ,8.03 + ,0.0705 + ,0 + ,0 + ,10725500 + ,21.41 + ,7.75 + ,0.0705 + ,0 + ,0 + ,8348400 + ,23.09 + ,7.16 + ,0.0705 + ,0 + ,0 + ,8046200 + ,20.70 + ,7.18 + ,-0.0134 + ,0 + ,0 + ,10862300 + ,19.00 + ,7.51 + ,-0.0134 + ,0 + ,0 + ,8100300 + ,19.04 + ,7.07 + ,-0.0134 + ,0 + ,0 + ,7287500 + ,19.45 + ,7.11 + ,0.0812 + ,0 + ,0 + ,14002500 + ,20.54 + ,8.98 + ,0.0812 + ,0 + ,0 + ,19037900 + ,19.77 + ,9.53 + ,0.0812 + ,0 + ,0 + ,10774600 + ,20.60 + ,10.54 + ,0.1885 + ,0 + ,0 + ,8960600 + ,21.21 + ,11.31 + ,0.1885 + ,0 + ,0 + ,7773300 + ,21.30 + ,10.36 + ,0.1885 + ,0 + ,0 + ,9579700 + ,22.33 + ,11.44 + ,0.3628 + ,0 + ,0 + ,11270700 + ,21.12 + ,10.45 + ,0.3628 + ,0 + ,0 + ,9492800 + ,20.77 + ,10.69 + ,0.3628 + ,0 + ,0 + ,9136800 + ,22.11 + ,11.28 + ,0.2942 + ,0 + ,0 + ,14487600 + ,22.34 + ,11.96 + ,0.2942 + ,0 + ,0 + ,10133200 + ,21.43 + ,13.52 + ,0.2942 + ,0 + ,0 + ,18659700 + ,20.14 + ,12.89 + ,0.3036 + ,0 + 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,0 + ,23279600 + ,23.90 + ,75.51 + ,0.3441 + ,0 + ,0 + ,40699800 + ,25.73 + ,68.49 + ,0.3441 + ,0 + ,0 + ,37646000 + ,24.64 + ,62.72 + ,0.3441 + ,0 + ,0 + ,37277000 + ,24.95 + ,70.39 + ,0.2415 + ,0 + ,0 + ,39246800 + ,22.15 + ,59.77 + ,0.2415 + ,0 + ,0 + ,27418400 + ,20.85 + ,57.27 + ,0.2415 + ,0 + ,0 + ,30318700 + ,21.45 + ,67.96 + ,0.3151 + ,0 + ,0 + ,32808100 + ,22.15 + ,67.85 + ,0.3151 + ,0 + ,0 + ,28668200 + ,23.75 + ,76.98 + ,0.3151 + ,0 + ,0 + ,32370300 + ,25.27 + ,81.08 + ,0.239 + ,0 + ,0 + ,24171100 + ,26.53 + ,91.66 + ,0.239 + ,0 + ,0 + ,25009100 + ,27.22 + ,84.84 + ,0.239 + ,0 + ,0 + ,32084300 + ,27.69 + ,85.73 + ,0.2127 + ,0 + ,0 + ,50117500 + ,28.61 + ,84.61 + ,0.2127 + ,0 + ,0 + ,27522200 + ,26.21 + ,92.91 + ,0.2127 + ,0 + ,0 + ,26816800 + ,25.93 + ,99.80 + ,0.273 + ,0 + ,0 + ,25136100 + ,27.86 + ,121.19 + ,0.273 + ,0 + ,0 + ,30295600 + ,28.65 + ,122.04 + ,0.273 + ,0.273 + ,0 + ,41526100 + ,27.51 + ,131.76 + ,0.3657 + ,0.3657 + ,0 + ,43845100 + ,27.06 + ,138.48 + ,0.3657 + ,0.3657 + ,0 + ,39188900 + ,26.91 + ,153.47 + ,0.3657 + ,0.3657 + ,0 + ,40496400 + ,27.60 + ,189.95 + ,0.4643 + ,0.4643 + ,0 + ,37438400 + ,34.48 + ,182.22 + ,0.4643 + ,0.4643 + ,0 + ,46553700 + ,31.58 + ,198.08 + ,0.4643 + ,0.4643 + ,0 + ,31771400 + ,33.46 + ,135.36 + ,0.5096 + ,0.5096 + ,0 + ,62108100 + ,30.64 + ,125.02 + ,0.5096 + ,0.5096 + ,0 + ,46645400 + ,25.66 + ,143.50 + ,0.5096 + ,0.5096 + ,0 + ,42313100 + ,26.78 + ,173.95 + ,0.3592 + ,0.3592 + ,0 + ,38841700 + ,26.91 + ,188.75 + ,0.3592 + ,0.3592 + ,0 + ,32650300 + ,26.82 + ,167.44 + ,0.3592 + ,0.3592 + ,0 + ,34281100 + ,26.05 + ,158.95 + ,0.7439 + ,0.7439 + ,0 + ,33096200 + ,24.36 + ,169.53 + ,0.7439 + ,0.7439 + ,0 + ,23273800 + ,25.94 + ,113.66 + ,0.7439 + ,0.7439 + ,0 + ,43697600 + ,25.37 + ,107.59 + ,0.139 + ,0.139 + ,0 + ,66902300 + ,21.23 + ,92.67 + ,0.139 + ,0.139 + ,0 + ,44957200 + ,19.35 + ,85.35 + ,0.139 + ,0.139 + ,0 + ,33800900 + ,18.61 + ,90.13 + ,0.1383 + ,0.1383 + ,0 + ,33487900 + ,16.37 + ,89.31 + ,0.1383 + ,0.1383 + ,0 + ,27394900 + ,15.56 + ,105.12 + ,0.1383 + ,0.1383 + ,0 + ,25963400 + ,17.70 + ,125.83 + ,0.2874 + ,0.2874 + ,0 + ,20952600 + ,19.52 + ,135.81 + ,0.2874 + ,0.2874 + ,0 + ,17702900 + ,20.26 + ,142.43 + ,0.2874 + ,0.2874 + ,0 + ,21282100 + ,23.05 + ,163.39 + ,0.0596 + ,0.0596 + ,0 + ,18449100 + ,22.81 + ,168.21 + ,0.0596 + ,0.0596 + ,0 + ,14415700 + ,24.04 + ,185.35 + ,0.0596 + ,0.0596 + ,0 + ,17906300 + ,25.08 + ,188.50 + ,0.3201 + ,0.3201 + ,0 + ,22197500 + ,27.04 + ,199.91 + ,0.3201 + ,0.3201 + ,0 + ,15856500 + ,28.81 + ,210.73 + ,0.3201 + ,0.3201 + ,0 + ,19068700 + ,29.86 + ,192.06 + ,0.486 + ,0.486 + ,0 + ,30855100 + ,27.61 + ,204.62 + ,0.486 + ,0.486 + ,0 + ,21209000 + ,28.22 + ,235.00 + ,0.486 + ,0.486 + ,0 + ,19541600 + ,28.83 + ,261.09 + ,0.6129 + ,0.6129 + ,0.6129 + ,21955000 + ,30.06 + ,256.88 + ,0.6129 + ,0.6129 + ,0.6129 + ,33725900 + ,25.51 + ,251.53 + ,0.6129 + ,0.6129 + ,0.6129 + ,28192800 + ,22.75 + ,257.25 + ,0.6665 + ,0.6665 + ,0.6665 + ,27377000 + ,25.52 + ,243.10 + ,0.6665 + ,0.6665 + ,0.6665 + ,16228100 + ,23.33 + ,283.75 + ,0.6665 + ,0.6665 + ,0.6665 + ,21278900 + ,24.34) + ,dim=c(6 + ,117) + ,dimnames=list(c('Apple' + ,'Omzetgroei' + ,'Omzetgroei_iPhone' + ,'Omzetgroei_iPad' + ,'Volume' + ,'Microsoft') + ,1:117)) > y <- array(NA,dim=c(6,117),dimnames=list(c('Apple','Omzetgroei','Omzetgroei_iPhone','Omzetgroei_iPad','Volume','Microsoft'),1:117)) > 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 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Apple Omzetgroei Omzetgroei_iPhone Omzetgroei_iPad Volume Microsoft t 1 10.81 -0.2643 0.0000 0.0000 24563400 24.45 1 2 9.12 -0.2643 0.0000 0.0000 14163200 23.62 2 3 11.03 -0.2643 0.0000 0.0000 18184800 21.90 3 4 12.74 -0.1918 0.0000 0.0000 20810300 27.12 4 5 9.98 -0.1918 0.0000 0.0000 12843000 27.70 5 6 11.62 -0.1918 0.0000 0.0000 13866700 29.23 6 7 9.40 -0.2246 0.0000 0.0000 15119200 26.50 7 8 9.27 -0.2246 0.0000 0.0000 8301600 22.84 8 9 7.76 -0.2246 0.0000 0.0000 14039600 20.49 9 10 8.78 0.3654 0.0000 0.0000 12139700 23.28 10 11 10.65 0.3654 0.0000 0.0000 9649000 25.71 11 12 10.95 0.3654 0.0000 0.0000 8513600 26.52 12 13 12.36 0.0447 0.0000 0.0000 15278600 25.51 13 14 10.85 0.0447 0.0000 0.0000 15590900 23.36 14 15 11.84 0.0447 0.0000 0.0000 9691100 24.15 15 16 12.14 -0.0312 0.0000 0.0000 10882700 20.92 16 17 11.65 -0.0312 0.0000 0.0000 10294800 20.38 17 18 8.86 -0.0312 0.0000 0.0000 16031900 21.90 18 19 7.63 -0.0048 0.0000 0.0000 13683600 19.21 19 20 7.38 -0.0048 0.0000 0.0000 8677200 19.65 20 21 7.25 -0.0048 0.0000 0.0000 9874100 17.51 21 22 8.03 0.0705 0.0000 0.0000 10725500 21.41 22 23 7.75 0.0705 0.0000 0.0000 8348400 23.09 23 24 7.16 0.0705 0.0000 0.0000 8046200 20.70 24 25 7.18 -0.0134 0.0000 0.0000 10862300 19.00 25 26 7.51 -0.0134 0.0000 0.0000 8100300 19.04 26 27 7.07 -0.0134 0.0000 0.0000 7287500 19.45 27 28 7.11 0.0812 0.0000 0.0000 14002500 20.54 28 29 8.98 0.0812 0.0000 0.0000 19037900 19.77 29 30 9.53 0.0812 0.0000 0.0000 10774600 20.60 30 31 10.54 0.1885 0.0000 0.0000 8960600 21.21 31 32 11.31 0.1885 0.0000 0.0000 7773300 21.30 32 33 10.36 0.1885 0.0000 0.0000 9579700 22.33 33 34 11.44 0.3628 0.0000 0.0000 11270700 21.12 34 35 10.45 0.3628 0.0000 0.0000 9492800 20.77 35 36 10.69 0.3628 0.0000 0.0000 9136800 22.11 36 37 11.28 0.2942 0.0000 0.0000 14487600 22.34 37 38 11.96 0.2942 0.0000 0.0000 10133200 21.43 38 39 13.52 0.2942 0.0000 0.0000 18659700 20.14 39 40 12.89 0.3036 0.0000 0.0000 15980700 21.11 40 41 14.03 0.3036 0.0000 0.0000 9732100 21.19 41 42 16.27 0.3036 0.0000 0.0000 14626300 23.07 42 43 16.17 0.3703 0.0000 0.0000 16904000 23.01 43 44 17.25 0.3703 0.0000 0.0000 13616700 22.12 44 45 19.38 0.3703 0.0000 0.0000 13772900 22.40 45 46 26.20 0.7398 0.0000 0.0000 28749200 22.66 46 47 33.53 0.7398 0.0000 0.0000 31408300 24.21 47 48 32.20 0.7398 0.0000 0.0000 26342800 24.13 48 49 38.45 0.6988 0.0000 0.0000 48909500 23.73 49 50 44.86 0.6988 0.0000 0.0000 41542400 22.79 50 51 41.67 0.6988 0.0000 0.0000 24857200 21.89 51 52 36.06 0.7478 0.0000 0.0000 34093700 22.92 52 53 39.76 0.7478 0.0000 0.0000 22555200 23.44 53 54 36.81 0.7478 0.0000 0.0000 19067500 22.57 54 55 42.65 0.5651 0.0000 0.0000 19029100 23.27 55 56 46.89 0.5651 0.0000 0.0000 15223200 24.95 56 57 53.61 0.5651 0.0000 0.0000 21903700 23.45 57 58 57.59 0.6473 0.0000 0.0000 33306600 23.42 58 59 67.82 0.6473 0.0000 0.0000 23898100 25.30 59 60 71.89 0.6473 0.0000 0.0000 23279600 23.90 60 61 75.51 0.3441 0.0000 0.0000 40699800 25.73 61 62 68.49 0.3441 0.0000 0.0000 37646000 24.64 62 63 62.72 0.3441 0.0000 0.0000 37277000 24.95 63 64 70.39 0.2415 0.0000 0.0000 39246800 22.15 64 65 59.77 0.2415 0.0000 0.0000 27418400 20.85 65 66 57.27 0.2415 0.0000 0.0000 30318700 21.45 66 67 67.96 0.3151 0.0000 0.0000 32808100 22.15 67 68 67.85 0.3151 0.0000 0.0000 28668200 23.75 68 69 76.98 0.3151 0.0000 0.0000 32370300 25.27 69 70 81.08 0.2390 0.0000 0.0000 24171100 26.53 70 71 91.66 0.2390 0.0000 0.0000 25009100 27.22 71 72 84.84 0.2390 0.0000 0.0000 32084300 27.69 72 73 85.73 0.2127 0.0000 0.0000 50117500 28.61 73 74 84.61 0.2127 0.0000 0.0000 27522200 26.21 74 75 92.91 0.2127 0.0000 0.0000 26816800 25.93 75 76 99.80 0.2730 0.0000 0.0000 25136100 27.86 76 77 121.19 0.2730 0.0000 0.0000 30295600 28.65 77 78 122.04 0.2730 0.2730 0.0000 41526100 27.51 78 79 131.76 0.3657 0.3657 0.0000 43845100 27.06 79 80 138.48 0.3657 0.3657 0.0000 39188900 26.91 80 81 153.47 0.3657 0.3657 0.0000 40496400 27.60 81 82 189.95 0.4643 0.4643 0.0000 37438400 34.48 82 83 182.22 0.4643 0.4643 0.0000 46553700 31.58 83 84 198.08 0.4643 0.4643 0.0000 31771400 33.46 84 85 135.36 0.5096 0.5096 0.0000 62108100 30.64 85 86 125.02 0.5096 0.5096 0.0000 46645400 25.66 86 87 143.50 0.5096 0.5096 0.0000 42313100 26.78 87 88 173.95 0.3592 0.3592 0.0000 38841700 26.91 88 89 188.75 0.3592 0.3592 0.0000 32650300 26.82 89 90 167.44 0.3592 0.3592 0.0000 34281100 26.05 90 91 158.95 0.7439 0.7439 0.0000 33096200 24.36 91 92 169.53 0.7439 0.7439 0.0000 23273800 25.94 92 93 113.66 0.7439 0.7439 0.0000 43697600 25.37 93 94 107.59 0.1390 0.1390 0.0000 66902300 21.23 94 95 92.67 0.1390 0.1390 0.0000 44957200 19.35 95 96 85.35 0.1390 0.1390 0.0000 33800900 18.61 96 97 90.13 0.1383 0.1383 0.0000 33487900 16.37 97 98 89.31 0.1383 0.1383 0.0000 27394900 15.56 98 99 105.12 0.1383 0.1383 0.0000 25963400 17.70 99 100 125.83 0.2874 0.2874 0.0000 20952600 19.52 100 101 135.81 0.2874 0.2874 0.0000 17702900 20.26 101 102 142.43 0.2874 0.2874 0.0000 21282100 23.05 102 103 163.39 0.0596 0.0596 0.0000 18449100 22.81 103 104 168.21 0.0596 0.0596 0.0000 14415700 24.04 104 105 185.35 0.0596 0.0596 0.0000 17906300 25.08 105 106 188.50 0.3201 0.3201 0.0000 22197500 27.04 106 107 199.91 0.3201 0.3201 0.0000 15856500 28.81 107 108 210.73 0.3201 0.3201 0.0000 19068700 29.86 108 109 192.06 0.4860 0.4860 0.0000 30855100 27.61 109 110 204.62 0.4860 0.4860 0.0000 21209000 28.22 110 111 235.00 0.4860 0.4860 0.0000 19541600 28.83 111 112 261.09 0.6129 0.6129 0.6129 21955000 30.06 112 113 256.88 0.6129 0.6129 0.6129 33725900 25.51 113 114 251.53 0.6129 0.6129 0.6129 28192800 22.75 114 115 257.25 0.6665 0.6665 0.6665 27377000 25.52 115 116 243.10 0.6665 0.6665 0.6665 16228100 23.33 116 117 283.75 0.6665 0.6665 0.6665 21278900 24.34 117 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Omzetgroei Omzetgroei_iPhone Omzetgroei_iPad -1.356e+02 -3.729e+01 8.288e+01 9.440e+01 Volume Microsoft t -4.245e-07 5.429e+00 1.558e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -48.7473 -9.8155 0.2771 8.5517 37.5508 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.356e+02 1.008e+01 -13.454 < 2e-16 *** Omzetgroei -3.729e+01 5.857e+00 -6.367 4.58e-09 *** Omzetgroei_iPhone 8.288e+01 9.656e+00 8.584 6.71e-14 *** Omzetgroei_iPad 9.440e+01 1.131e+01 8.344 2.32e-13 *** Volume -4.245e-07 1.324e-07 -3.206 0.00176 ** Microsoft 5.429e+00 4.324e-01 12.556 < 2e-16 *** t 1.558e+00 6.094e-02 25.566 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.83 on 110 degrees of freedom Multiple R-squared: 0.9686, Adjusted R-squared: 0.9669 F-statistic: 565.5 on 6 and 110 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,] 1.837443e-03 3.674885e-03 0.99816256 [2,] 2.217018e-04 4.434036e-04 0.99977830 [3,] 2.334519e-05 4.669039e-05 0.99997665 [4,] 3.573365e-06 7.146730e-06 0.99999643 [5,] 3.063940e-07 6.127881e-07 0.99999969 [6,] 4.995615e-08 9.991231e-08 0.99999995 [7,] 1.143234e-08 2.286468e-08 0.99999999 [8,] 1.295166e-09 2.590332e-09 1.00000000 [9,] 1.491772e-09 2.983544e-09 1.00000000 [10,] 6.202923e-10 1.240585e-09 1.00000000 [11,] 1.524988e-10 3.049976e-10 1.00000000 [12,] 3.076873e-11 6.153746e-11 1.00000000 [13,] 5.927149e-12 1.185430e-11 1.00000000 [14,] 1.249531e-12 2.499061e-12 1.00000000 [15,] 2.045363e-13 4.090726e-13 1.00000000 [16,] 3.066882e-14 6.133764e-14 1.00000000 [17,] 4.021786e-15 8.043573e-15 1.00000000 [18,] 5.123631e-16 1.024726e-15 1.00000000 [19,] 8.258695e-17 1.651739e-16 1.00000000 [20,] 1.313911e-17 2.627823e-17 1.00000000 [21,] 2.644378e-18 5.288756e-18 1.00000000 [22,] 8.211128e-19 1.642226e-18 1.00000000 [23,] 3.937039e-19 7.874079e-19 1.00000000 [24,] 4.986557e-20 9.973114e-20 1.00000000 [25,] 1.131717e-20 2.263435e-20 1.00000000 [26,] 1.574900e-21 3.149801e-21 1.00000000 [27,] 1.753907e-22 3.507814e-22 1.00000000 [28,] 1.888411e-23 3.776823e-23 1.00000000 [29,] 3.875633e-24 7.751266e-24 1.00000000 [30,] 1.729335e-24 3.458671e-24 1.00000000 [31,] 2.698674e-25 5.397348e-25 1.00000000 [32,] 2.150783e-25 4.301566e-25 1.00000000 [33,] 1.414701e-25 2.829403e-25 1.00000000 [34,] 3.151133e-26 6.302267e-26 1.00000000 [35,] 3.519715e-26 7.039430e-26 1.00000000 [36,] 1.697487e-25 3.394974e-25 1.00000000 [37,] 7.602304e-25 1.520461e-24 1.00000000 [38,] 2.748565e-23 5.497130e-23 1.00000000 [39,] 1.057996e-22 2.115991e-22 1.00000000 [40,] 2.205603e-23 4.411206e-23 1.00000000 [41,] 3.810517e-21 7.621033e-21 1.00000000 [42,] 2.595516e-17 5.191032e-17 1.00000000 [43,] 7.793788e-18 1.558758e-17 1.00000000 [44,] 1.381050e-16 2.762100e-16 1.00000000 [45,] 5.589180e-16 1.117836e-15 1.00000000 [46,] 3.891208e-14 7.782416e-14 1.00000000 [47,] 3.237090e-12 6.474179e-12 1.00000000 [48,] 1.493828e-10 2.987656e-10 1.00000000 [49,] 9.036852e-10 1.807370e-09 1.00000000 [50,] 5.635422e-08 1.127084e-07 0.99999994 [51,] 9.287184e-06 1.857437e-05 0.99999071 [52,] 1.352889e-05 2.705778e-05 0.99998647 [53,] 1.194355e-05 2.388710e-05 0.99998806 [54,] 7.196056e-06 1.439211e-05 0.99999280 [55,] 1.179236e-05 2.358472e-05 0.99998821 [56,] 9.346317e-06 1.869263e-05 0.99999065 [57,] 5.541265e-06 1.108253e-05 0.99999446 [58,] 1.242726e-05 2.485451e-05 0.99998757 [59,] 1.068684e-05 2.137369e-05 0.99998931 [60,] 1.225412e-05 2.450825e-05 0.99998775 [61,] 8.782982e-06 1.756596e-05 0.99999122 [62,] 8.912141e-06 1.782428e-05 0.99999109 [63,] 5.508653e-06 1.101731e-05 0.99999449 [64,] 6.988380e-06 1.397676e-05 0.99999301 [65,] 5.397007e-06 1.079401e-05 0.99999460 [66,] 5.048015e-06 1.009603e-05 0.99999495 [67,] 6.625993e-06 1.325199e-05 0.99999337 [68,] 2.920187e-05 5.840373e-05 0.99997080 [69,] 2.870379e-05 5.740759e-05 0.99997130 [70,] 1.653054e-05 3.306107e-05 0.99998347 [71,] 1.051299e-05 2.102598e-05 0.99998949 [72,] 1.105705e-05 2.211409e-05 0.99998894 [73,] 1.384355e-05 2.768710e-05 0.99998616 [74,] 9.684836e-06 1.936967e-05 0.99999032 [75,] 1.245265e-05 2.490529e-05 0.99998755 [76,] 1.364633e-03 2.729266e-03 0.99863537 [77,] 3.029142e-03 6.058284e-03 0.99697086 [78,] 2.713378e-03 5.426755e-03 0.99728662 [79,] 5.974074e-03 1.194815e-02 0.99402593 [80,] 5.668247e-02 1.133649e-01 0.94331753 [81,] 1.142841e-01 2.285681e-01 0.88571595 [82,] 2.320544e-01 4.641088e-01 0.76794562 [83,] 8.682908e-01 2.634183e-01 0.13170917 [84,] 9.679961e-01 6.400782e-02 0.03200391 [85,] 9.648128e-01 7.037443e-02 0.03518722 [86,] 9.507864e-01 9.842711e-02 0.04921355 [87,] 9.380055e-01 1.239891e-01 0.06199454 [88,] 9.048982e-01 1.902035e-01 0.09510177 [89,] 8.732622e-01 2.534756e-01 0.12673778 [90,] 8.506877e-01 2.986246e-01 0.14931228 [91,] 7.970502e-01 4.058996e-01 0.20294981 [92,] 8.040099e-01 3.919802e-01 0.19599010 [93,] 7.663505e-01 4.672989e-01 0.23364947 [94,] 7.434220e-01 5.131560e-01 0.25657799 [95,] 6.520444e-01 6.959112e-01 0.34795561 [96,] 5.913963e-01 8.172073e-01 0.40860367 [97,] 5.097712e-01 9.804576e-01 0.49022878 [98,] 4.094380e-01 8.188760e-01 0.59056201 > postscript(file="/var/www/html/freestat/rcomp/tmp/1dce31293008334.ps",horizontal=F,onefile=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/25md61293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/freestat/rcomp/tmp/35md61293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/45md61293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/5gdu91293008334.ps",horizontal=F,onefile=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 = 117 Frequency = 1 1 2 3 4 5 6 12.6566779 9.5000594 20.8969863 -3.4714341 -14.3200696 -22.1096191 7 8 9 10 11 12 -11.7581910 3.5297261 15.6554429 21.1679604 7.2305962 1.0933485 13 14 15 16 17 18 -2.6599185 6.0768946 -1.2841880 12.6684632 13.3026315 3.1381351 19 20 21 22 23 24 14.9417331 8.6199987 19.0580216 0.2770909 -11.6903792 -0.9914764 25 26 27 28 29 30 4.7661846 2.1487214 -2.4200289 -3.4769777 3.1528392 -5.8686678 31 32 33 34 35 36 -6.4965377 -8.2770104 -15.6098676 -2.3006253 -3.7030837 -12.4468037 37 38 39 40 41 42 -14.9503744 -12.7363341 -2.1115738 -10.3521147 -13.8567405 -21.3034376 43 44 45 46 47 48 -19.1812235 -16.2227941 -17.1044625 6.8835038 5.3695780 0.7657829 49 50 51 52 53 54 15.6796122 22.5076756 15.5631963 8.5517089 2.9728891 1.7076739 55 56 57 58 59 60 -4.6403618 -12.6943351 3.4468957 13.9378047 8.4098366 18.2598670 61 62 63 64 65 66 6.4741436 2.5174695 -6.6500050 11.6728025 1.5315172 -4.5525710 67 68 69 70 71 72 4.5808738 -7.5305654 -6.6388873 -17.2556890 -11.6237918 -19.5499441 73 74 75 76 77 78 -18.5384354 -17.7783137 -9.8155355 -13.4257868 4.3076294 -8.0710629 79 80 81 82 83 84 -0.7076101 3.2923591 13.5335516 5.3117662 15.6369869 13.4579253 85 86 87 88 89 90 -24.6981716 -16.1238194 -7.1210589 26.4482491 37.5508162 19.5554340 91 92 93 94 95 96 0.6415449 -3.0834482 -48.7472770 3.5269502 -12.0599549 -21.6561266 97 98 99 100 101 102 -6.3742309 -6.9410887 -4.9144591 -4.5671750 -1.5418870 -10.1070771 103 104 105 106 107 108 19.7804585 14.6529187 26.0706889 6.9679281 4.5192441 9.4445489 109 110 111 112 113 114 -1.1282951 2.4675784 27.2702845 -17.4907279 6.4394332 12.1665931 115 116 117 -6.5587545 -15.1098845 20.6430330 > postscript(file="/var/www/html/freestat/rcomp/tmp/6gdu91293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 117 Frequency = 1 lag(myerror, k = 1) myerror 0 12.6566779 NA 1 9.5000594 12.6566779 2 20.8969863 9.5000594 3 -3.4714341 20.8969863 4 -14.3200696 -3.4714341 5 -22.1096191 -14.3200696 6 -11.7581910 -22.1096191 7 3.5297261 -11.7581910 8 15.6554429 3.5297261 9 21.1679604 15.6554429 10 7.2305962 21.1679604 11 1.0933485 7.2305962 12 -2.6599185 1.0933485 13 6.0768946 -2.6599185 14 -1.2841880 6.0768946 15 12.6684632 -1.2841880 16 13.3026315 12.6684632 17 3.1381351 13.3026315 18 14.9417331 3.1381351 19 8.6199987 14.9417331 20 19.0580216 8.6199987 21 0.2770909 19.0580216 22 -11.6903792 0.2770909 23 -0.9914764 -11.6903792 24 4.7661846 -0.9914764 25 2.1487214 4.7661846 26 -2.4200289 2.1487214 27 -3.4769777 -2.4200289 28 3.1528392 -3.4769777 29 -5.8686678 3.1528392 30 -6.4965377 -5.8686678 31 -8.2770104 -6.4965377 32 -15.6098676 -8.2770104 33 -2.3006253 -15.6098676 34 -3.7030837 -2.3006253 35 -12.4468037 -3.7030837 36 -14.9503744 -12.4468037 37 -12.7363341 -14.9503744 38 -2.1115738 -12.7363341 39 -10.3521147 -2.1115738 40 -13.8567405 -10.3521147 41 -21.3034376 -13.8567405 42 -19.1812235 -21.3034376 43 -16.2227941 -19.1812235 44 -17.1044625 -16.2227941 45 6.8835038 -17.1044625 46 5.3695780 6.8835038 47 0.7657829 5.3695780 48 15.6796122 0.7657829 49 22.5076756 15.6796122 50 15.5631963 22.5076756 51 8.5517089 15.5631963 52 2.9728891 8.5517089 53 1.7076739 2.9728891 54 -4.6403618 1.7076739 55 -12.6943351 -4.6403618 56 3.4468957 -12.6943351 57 13.9378047 3.4468957 58 8.4098366 13.9378047 59 18.2598670 8.4098366 60 6.4741436 18.2598670 61 2.5174695 6.4741436 62 -6.6500050 2.5174695 63 11.6728025 -6.6500050 64 1.5315172 11.6728025 65 -4.5525710 1.5315172 66 4.5808738 -4.5525710 67 -7.5305654 4.5808738 68 -6.6388873 -7.5305654 69 -17.2556890 -6.6388873 70 -11.6237918 -17.2556890 71 -19.5499441 -11.6237918 72 -18.5384354 -19.5499441 73 -17.7783137 -18.5384354 74 -9.8155355 -17.7783137 75 -13.4257868 -9.8155355 76 4.3076294 -13.4257868 77 -8.0710629 4.3076294 78 -0.7076101 -8.0710629 79 3.2923591 -0.7076101 80 13.5335516 3.2923591 81 5.3117662 13.5335516 82 15.6369869 5.3117662 83 13.4579253 15.6369869 84 -24.6981716 13.4579253 85 -16.1238194 -24.6981716 86 -7.1210589 -16.1238194 87 26.4482491 -7.1210589 88 37.5508162 26.4482491 89 19.5554340 37.5508162 90 0.6415449 19.5554340 91 -3.0834482 0.6415449 92 -48.7472770 -3.0834482 93 3.5269502 -48.7472770 94 -12.0599549 3.5269502 95 -21.6561266 -12.0599549 96 -6.3742309 -21.6561266 97 -6.9410887 -6.3742309 98 -4.9144591 -6.9410887 99 -4.5671750 -4.9144591 100 -1.5418870 -4.5671750 101 -10.1070771 -1.5418870 102 19.7804585 -10.1070771 103 14.6529187 19.7804585 104 26.0706889 14.6529187 105 6.9679281 26.0706889 106 4.5192441 6.9679281 107 9.4445489 4.5192441 108 -1.1282951 9.4445489 109 2.4675784 -1.1282951 110 27.2702845 2.4675784 111 -17.4907279 27.2702845 112 6.4394332 -17.4907279 113 12.1665931 6.4394332 114 -6.5587545 12.1665931 115 -15.1098845 -6.5587545 116 20.6430330 -15.1098845 117 NA 20.6430330 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 9.5000594 12.6566779 [2,] 20.8969863 9.5000594 [3,] -3.4714341 20.8969863 [4,] -14.3200696 -3.4714341 [5,] -22.1096191 -14.3200696 [6,] -11.7581910 -22.1096191 [7,] 3.5297261 -11.7581910 [8,] 15.6554429 3.5297261 [9,] 21.1679604 15.6554429 [10,] 7.2305962 21.1679604 [11,] 1.0933485 7.2305962 [12,] -2.6599185 1.0933485 [13,] 6.0768946 -2.6599185 [14,] -1.2841880 6.0768946 [15,] 12.6684632 -1.2841880 [16,] 13.3026315 12.6684632 [17,] 3.1381351 13.3026315 [18,] 14.9417331 3.1381351 [19,] 8.6199987 14.9417331 [20,] 19.0580216 8.6199987 [21,] 0.2770909 19.0580216 [22,] -11.6903792 0.2770909 [23,] -0.9914764 -11.6903792 [24,] 4.7661846 -0.9914764 [25,] 2.1487214 4.7661846 [26,] -2.4200289 2.1487214 [27,] -3.4769777 -2.4200289 [28,] 3.1528392 -3.4769777 [29,] -5.8686678 3.1528392 [30,] -6.4965377 -5.8686678 [31,] -8.2770104 -6.4965377 [32,] -15.6098676 -8.2770104 [33,] -2.3006253 -15.6098676 [34,] -3.7030837 -2.3006253 [35,] -12.4468037 -3.7030837 [36,] -14.9503744 -12.4468037 [37,] -12.7363341 -14.9503744 [38,] -2.1115738 -12.7363341 [39,] -10.3521147 -2.1115738 [40,] -13.8567405 -10.3521147 [41,] -21.3034376 -13.8567405 [42,] -19.1812235 -21.3034376 [43,] -16.2227941 -19.1812235 [44,] -17.1044625 -16.2227941 [45,] 6.8835038 -17.1044625 [46,] 5.3695780 6.8835038 [47,] 0.7657829 5.3695780 [48,] 15.6796122 0.7657829 [49,] 22.5076756 15.6796122 [50,] 15.5631963 22.5076756 [51,] 8.5517089 15.5631963 [52,] 2.9728891 8.5517089 [53,] 1.7076739 2.9728891 [54,] -4.6403618 1.7076739 [55,] -12.6943351 -4.6403618 [56,] 3.4468957 -12.6943351 [57,] 13.9378047 3.4468957 [58,] 8.4098366 13.9378047 [59,] 18.2598670 8.4098366 [60,] 6.4741436 18.2598670 [61,] 2.5174695 6.4741436 [62,] -6.6500050 2.5174695 [63,] 11.6728025 -6.6500050 [64,] 1.5315172 11.6728025 [65,] -4.5525710 1.5315172 [66,] 4.5808738 -4.5525710 [67,] -7.5305654 4.5808738 [68,] -6.6388873 -7.5305654 [69,] -17.2556890 -6.6388873 [70,] -11.6237918 -17.2556890 [71,] -19.5499441 -11.6237918 [72,] -18.5384354 -19.5499441 [73,] -17.7783137 -18.5384354 [74,] -9.8155355 -17.7783137 [75,] -13.4257868 -9.8155355 [76,] 4.3076294 -13.4257868 [77,] -8.0710629 4.3076294 [78,] -0.7076101 -8.0710629 [79,] 3.2923591 -0.7076101 [80,] 13.5335516 3.2923591 [81,] 5.3117662 13.5335516 [82,] 15.6369869 5.3117662 [83,] 13.4579253 15.6369869 [84,] -24.6981716 13.4579253 [85,] -16.1238194 -24.6981716 [86,] -7.1210589 -16.1238194 [87,] 26.4482491 -7.1210589 [88,] 37.5508162 26.4482491 [89,] 19.5554340 37.5508162 [90,] 0.6415449 19.5554340 [91,] -3.0834482 0.6415449 [92,] -48.7472770 -3.0834482 [93,] 3.5269502 -48.7472770 [94,] -12.0599549 3.5269502 [95,] -21.6561266 -12.0599549 [96,] -6.3742309 -21.6561266 [97,] -6.9410887 -6.3742309 [98,] -4.9144591 -6.9410887 [99,] -4.5671750 -4.9144591 [100,] -1.5418870 -4.5671750 [101,] -10.1070771 -1.5418870 [102,] 19.7804585 -10.1070771 [103,] 14.6529187 19.7804585 [104,] 26.0706889 14.6529187 [105,] 6.9679281 26.0706889 [106,] 4.5192441 6.9679281 [107,] 9.4445489 4.5192441 [108,] -1.1282951 9.4445489 [109,] 2.4675784 -1.1282951 [110,] 27.2702845 2.4675784 [111,] -17.4907279 27.2702845 [112,] 6.4394332 -17.4907279 [113,] 12.1665931 6.4394332 [114,] -6.5587545 12.1665931 [115,] -15.1098845 -6.5587545 [116,] 20.6430330 -15.1098845 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 9.5000594 12.6566779 2 20.8969863 9.5000594 3 -3.4714341 20.8969863 4 -14.3200696 -3.4714341 5 -22.1096191 -14.3200696 6 -11.7581910 -22.1096191 7 3.5297261 -11.7581910 8 15.6554429 3.5297261 9 21.1679604 15.6554429 10 7.2305962 21.1679604 11 1.0933485 7.2305962 12 -2.6599185 1.0933485 13 6.0768946 -2.6599185 14 -1.2841880 6.0768946 15 12.6684632 -1.2841880 16 13.3026315 12.6684632 17 3.1381351 13.3026315 18 14.9417331 3.1381351 19 8.6199987 14.9417331 20 19.0580216 8.6199987 21 0.2770909 19.0580216 22 -11.6903792 0.2770909 23 -0.9914764 -11.6903792 24 4.7661846 -0.9914764 25 2.1487214 4.7661846 26 -2.4200289 2.1487214 27 -3.4769777 -2.4200289 28 3.1528392 -3.4769777 29 -5.8686678 3.1528392 30 -6.4965377 -5.8686678 31 -8.2770104 -6.4965377 32 -15.6098676 -8.2770104 33 -2.3006253 -15.6098676 34 -3.7030837 -2.3006253 35 -12.4468037 -3.7030837 36 -14.9503744 -12.4468037 37 -12.7363341 -14.9503744 38 -2.1115738 -12.7363341 39 -10.3521147 -2.1115738 40 -13.8567405 -10.3521147 41 -21.3034376 -13.8567405 42 -19.1812235 -21.3034376 43 -16.2227941 -19.1812235 44 -17.1044625 -16.2227941 45 6.8835038 -17.1044625 46 5.3695780 6.8835038 47 0.7657829 5.3695780 48 15.6796122 0.7657829 49 22.5076756 15.6796122 50 15.5631963 22.5076756 51 8.5517089 15.5631963 52 2.9728891 8.5517089 53 1.7076739 2.9728891 54 -4.6403618 1.7076739 55 -12.6943351 -4.6403618 56 3.4468957 -12.6943351 57 13.9378047 3.4468957 58 8.4098366 13.9378047 59 18.2598670 8.4098366 60 6.4741436 18.2598670 61 2.5174695 6.4741436 62 -6.6500050 2.5174695 63 11.6728025 -6.6500050 64 1.5315172 11.6728025 65 -4.5525710 1.5315172 66 4.5808738 -4.5525710 67 -7.5305654 4.5808738 68 -6.6388873 -7.5305654 69 -17.2556890 -6.6388873 70 -11.6237918 -17.2556890 71 -19.5499441 -11.6237918 72 -18.5384354 -19.5499441 73 -17.7783137 -18.5384354 74 -9.8155355 -17.7783137 75 -13.4257868 -9.8155355 76 4.3076294 -13.4257868 77 -8.0710629 4.3076294 78 -0.7076101 -8.0710629 79 3.2923591 -0.7076101 80 13.5335516 3.2923591 81 5.3117662 13.5335516 82 15.6369869 5.3117662 83 13.4579253 15.6369869 84 -24.6981716 13.4579253 85 -16.1238194 -24.6981716 86 -7.1210589 -16.1238194 87 26.4482491 -7.1210589 88 37.5508162 26.4482491 89 19.5554340 37.5508162 90 0.6415449 19.5554340 91 -3.0834482 0.6415449 92 -48.7472770 -3.0834482 93 3.5269502 -48.7472770 94 -12.0599549 3.5269502 95 -21.6561266 -12.0599549 96 -6.3742309 -21.6561266 97 -6.9410887 -6.3742309 98 -4.9144591 -6.9410887 99 -4.5671750 -4.9144591 100 -1.5418870 -4.5671750 101 -10.1070771 -1.5418870 102 19.7804585 -10.1070771 103 14.6529187 19.7804585 104 26.0706889 14.6529187 105 6.9679281 26.0706889 106 4.5192441 6.9679281 107 9.4445489 4.5192441 108 -1.1282951 9.4445489 109 2.4675784 -1.1282951 110 27.2702845 2.4675784 111 -17.4907279 27.2702845 112 6.4394332 -17.4907279 113 12.1665931 6.4394332 114 -6.5587545 12.1665931 115 -15.1098845 -6.5587545 116 20.6430330 -15.1098845 > 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/freestat/rcomp/tmp/7r4cc1293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8r4cc1293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/9r4cc1293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/10ketx1293008334.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/freestat/rcomp/tmp/11nwr31293008334.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/freestat/rcomp/tmp/12qeqr1293008334.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/freestat/rcomp/tmp/134o6h1293008334.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/freestat/rcomp/tmp/14q7451293008334.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/freestat/rcomp/tmp/15b73t1293008334.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/freestat/rcomp/tmp/16fq1h1293008334.tab") + } > > try(system("convert tmp/1dce31293008334.ps tmp/1dce31293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/25md61293008334.ps tmp/25md61293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/35md61293008334.ps tmp/35md61293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/45md61293008334.ps tmp/45md61293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/5gdu91293008334.ps tmp/5gdu91293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/6gdu91293008334.ps tmp/6gdu91293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/7r4cc1293008334.ps tmp/7r4cc1293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/8r4cc1293008334.ps tmp/8r4cc1293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/9r4cc1293008334.ps tmp/9r4cc1293008334.png",intern=TRUE)) character(0) > try(system("convert tmp/10ketx1293008334.ps tmp/10ketx1293008334.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.015 2.673 12.423