R version 2.11.1 (2010-05-31) Copyright (C) 2010 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. 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. 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,150) + ,dimnames=list(c('Gender' + ,'Selfconfidence' + ,'ConcernMistakes' + ,'ConcernMistakes_G' + ,'DoubtsActions' + ,'DoubtsActions_G' + ,'ParentalCriticism' + ,'ParentalCriticism_G' + ,'PersonalStandards' + ,'PersonalStandards_G' + ,'Organization' + ,'Organization_G') + ,1:150)) > y <- array(NA,dim=c(12,150),dimnames=list(c('Gender','Selfconfidence','ConcernMistakes','ConcernMistakes_G','DoubtsActions','DoubtsActions_G','ParentalCriticism','ParentalCriticism_G','PersonalStandards','PersonalStandards_G','Organization','Organization_G'),1:150)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > #'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 > 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 Selfconfidence Gender ConcernMistakes ConcernMistakes_G DoubtsActions 1 13 0 26 0 9 2 16 0 20 0 9 3 19 0 21 0 9 4 15 1 31 31 14 5 14 0 21 0 8 6 13 0 18 0 8 7 19 0 26 0 11 8 15 0 22 0 10 9 14 0 22 0 9 10 15 0 29 0 15 11 16 1 15 15 14 12 16 0 16 0 11 13 16 1 24 24 14 14 17 0 17 0 6 15 15 1 19 19 20 16 15 1 22 22 9 17 20 0 31 0 10 18 18 1 28 28 8 19 16 0 38 0 11 20 16 1 26 26 14 21 19 0 25 0 11 22 16 0 25 0 16 23 17 1 29 29 14 24 17 0 28 0 11 25 16 1 15 15 11 26 15 0 18 0 12 27 14 1 21 21 9 28 15 0 25 0 7 29 12 1 23 23 13 30 14 0 23 0 10 31 16 0 19 0 9 32 14 1 18 18 9 33 7 1 18 18 13 34 10 1 26 26 16 35 14 1 18 18 12 36 16 0 18 0 6 37 16 1 28 28 14 38 16 1 17 17 14 39 14 0 29 0 10 40 20 1 12 12 4 41 14 1 25 25 12 42 14 0 28 0 12 43 11 0 20 0 14 44 15 0 17 0 9 45 16 0 17 0 9 46 14 1 20 20 10 47 16 0 31 0 14 48 14 1 21 21 10 49 12 1 19 19 9 50 16 0 23 0 14 51 9 1 15 15 8 52 14 0 24 0 9 53 16 0 28 0 8 54 16 0 16 0 9 55 15 1 19 19 9 56 16 0 21 0 9 57 12 1 21 21 15 58 16 1 20 20 8 59 16 0 16 0 10 60 14 0 25 0 8 61 16 0 30 0 14 62 17 1 29 29 11 63 18 0 22 0 10 64 18 1 19 19 12 65 12 0 33 0 14 66 16 1 17 17 9 67 10 1 9 9 13 68 14 0 14 0 15 69 18 0 15 0 8 70 18 1 12 12 7 71 16 1 21 21 10 72 16 0 20 0 10 73 16 0 29 0 13 74 13 1 33 33 11 75 16 1 21 21 8 76 16 1 15 15 12 77 20 1 19 19 9 78 16 0 23 0 10 79 15 1 20 20 11 80 15 0 20 0 11 81 16 0 18 0 10 82 14 1 31 31 16 83 15 0 18 0 16 84 12 0 13 0 8 85 17 0 9 0 6 86 16 0 20 0 11 87 15 0 18 0 12 88 13 0 23 0 14 89 16 0 17 0 9 90 16 0 17 0 11 91 16 0 16 0 8 92 16 1 31 31 8 93 14 1 15 15 7 94 16 0 28 0 16 95 16 1 26 26 13 96 20 0 20 0 8 97 15 1 19 19 11 98 16 0 25 0 14 99 13 1 18 18 10 100 17 0 20 0 10 101 16 1 33 33 14 102 12 0 24 0 14 103 16 0 22 0 10 104 16 0 32 0 12 105 17 0 31 0 9 106 13 1 13 13 16 107 12 0 18 0 8 108 18 1 17 17 9 109 14 0 29 0 16 110 14 0 22 0 13 111 13 0 18 0 13 112 16 0 22 0 8 113 13 0 25 0 14 114 16 0 20 0 11 115 13 0 20 0 9 116 16 0 17 0 8 117 15 0 21 0 13 118 16 0 26 0 13 119 15 1 10 10 10 120 17 0 15 0 8 121 15 0 20 0 7 122 12 0 14 0 11 123 16 1 16 16 11 124 10 1 23 23 14 125 16 0 11 0 6 126 14 1 19 19 10 127 15 0 30 0 9 128 13 1 21 21 12 129 15 1 20 20 11 130 11 0 22 0 14 131 12 0 30 0 12 132 8 1 25 25 14 133 16 0 28 0 8 134 15 1 23 23 14 135 17 0 23 0 8 136 16 1 21 21 11 137 10 0 30 0 12 138 18 0 22 0 9 139 13 1 32 32 16 140 15 0 22 0 11 141 16 1 15 15 11 142 16 0 21 0 12 143 14 0 27 0 15 144 10 0 22 0 13 145 17 0 9 0 6 146 13 0 29 0 11 147 15 0 20 0 7 148 16 0 16 0 8 149 12 0 16 0 8 150 13 0 16 0 9 DoubtsActions_G ParentalCriticism ParentalCriticism_G PersonalStandards 1 0 6 0 25 2 0 6 0 25 3 0 13 0 19 4 14 8 8 18 5 0 7 0 18 6 0 9 0 22 7 0 5 0 29 8 0 8 0 26 9 0 9 0 25 10 0 11 0 23 11 14 8 8 23 12 0 11 0 23 13 14 12 12 24 14 0 8 0 30 15 20 7 7 19 16 9 9 9 24 17 0 12 0 32 18 8 20 20 30 19 0 7 0 29 20 14 8 8 17 21 0 8 0 25 22 0 16 0 26 23 14 10 10 26 24 0 6 0 25 25 11 8 8 23 26 0 9 0 21 27 9 9 9 19 28 0 11 0 35 29 13 12 12 19 30 0 8 0 20 31 0 7 0 21 32 9 8 8 21 33 13 9 9 24 34 16 4 4 23 35 12 8 8 19 36 0 8 0 17 37 14 8 8 24 38 14 6 6 15 39 0 8 0 25 40 4 4 4 27 41 12 7 7 29 42 0 14 0 27 43 0 10 0 18 44 0 9 0 25 45 0 6 0 22 46 10 8 8 26 47 0 11 0 23 48 10 8 8 16 49 9 8 8 27 50 0 10 0 25 51 8 8 8 14 52 0 10 0 19 53 0 7 0 20 54 0 8 0 16 55 9 7 7 18 56 0 9 0 22 57 15 5 5 21 58 8 7 7 22 59 0 7 0 22 60 0 7 0 32 61 0 9 0 23 62 11 5 5 31 63 0 8 0 18 64 12 8 8 23 65 0 8 0 26 66 9 9 9 24 67 13 6 6 19 68 0 8 0 14 69 0 6 0 20 70 7 4 4 22 71 10 6 6 24 72 0 4 0 25 73 0 12 0 21 74 11 6 6 28 75 8 11 11 24 76 12 8 8 20 77 9 10 10 21 78 0 10 0 23 79 11 4 4 13 80 0 8 0 24 81 0 9 0 21 82 16 9 9 21 83 0 7 0 17 84 0 7 0 14 85 0 11 0 29 86 0 8 0 25 87 0 8 0 16 88 0 7 0 25 89 0 5 0 25 90 0 7 0 21 91 0 9 0 23 92 8 8 8 22 93 7 6 6 19 94 0 8 0 24 95 13 10 10 26 96 0 10 0 25 97 11 8 8 20 98 0 11 0 22 99 10 8 8 14 100 0 8 0 20 101 14 6 6 32 102 0 20 0 21 103 0 6 0 22 104 0 12 0 28 105 0 9 0 25 106 16 5 5 17 107 0 10 0 21 108 9 5 5 23 109 0 6 0 27 110 0 10 0 22 111 0 6 0 19 112 0 10 0 20 113 0 5 0 17 114 0 13 0 24 115 0 7 0 21 116 0 9 0 21 117 0 11 0 23 118 0 8 0 24 119 10 5 5 19 120 0 4 0 22 121 0 9 0 26 122 0 7 0 17 123 11 5 5 17 124 14 5 5 19 125 0 4 0 15 126 10 7 7 17 127 0 9 0 27 128 12 8 8 19 129 11 8 8 21 130 0 11 0 25 131 0 10 0 19 132 14 9 9 22 133 0 12 0 18 134 14 10 10 20 135 0 10 0 15 136 11 7 7 20 137 0 10 0 29 138 0 6 0 19 139 16 6 6 29 140 0 11 0 24 141 11 8 8 23 142 0 9 0 22 143 0 9 0 23 144 0 13 0 22 145 0 11 0 29 146 0 4 0 26 147 0 9 0 26 148 0 5 0 21 149 0 4 0 18 150 0 9 0 10 PersonalStandards_G Organization Organization_G 1 0 25 0 2 0 24 0 3 0 21 0 4 18 23 23 5 0 17 0 6 0 19 0 7 0 18 0 8 0 27 0 9 0 23 0 10 0 23 0 11 23 29 29 12 0 21 0 13 24 26 26 14 0 25 0 15 19 25 25 16 24 23 23 17 0 26 0 18 30 20 20 19 0 29 0 20 17 24 24 21 0 23 0 22 0 24 0 23 26 30 30 24 0 22 0 25 23 22 22 26 0 13 0 27 19 24 24 28 0 17 0 29 19 24 24 30 0 21 0 31 0 23 0 32 21 24 24 33 24 24 24 34 23 24 24 35 19 23 23 36 0 26 0 37 24 24 24 38 15 21 21 39 0 23 0 40 27 28 28 41 29 23 23 42 0 22 0 43 0 24 0 44 0 21 0 45 0 23 0 46 26 23 23 47 0 20 0 48 16 23 23 49 27 21 21 50 0 27 0 51 14 12 12 52 0 15 0 53 0 22 0 54 0 21 0 55 18 21 21 56 0 20 0 57 21 24 24 58 22 24 24 59 0 29 0 60 0 25 0 61 0 14 0 62 31 30 30 63 0 19 0 64 23 29 29 65 0 25 0 66 24 25 25 67 19 25 25 68 0 16 0 69 0 25 0 70 22 28 28 71 24 24 24 72 0 25 0 73 0 21 0 74 28 22 22 75 24 20 20 76 20 25 25 77 21 27 27 78 0 21 0 79 13 13 13 80 0 26 0 81 0 26 0 82 21 25 25 83 0 22 0 84 0 19 0 85 0 23 0 86 0 25 0 87 0 15 0 88 0 21 0 89 0 23 0 90 0 25 0 91 0 24 0 92 22 24 24 93 19 21 21 94 0 24 0 95 26 22 22 96 0 24 0 97 20 28 28 98 0 21 0 99 14 17 17 100 0 28 0 101 32 24 24 102 0 10 0 103 0 20 0 104 0 22 0 105 0 19 0 106 17 22 22 107 0 22 0 108 23 26 26 109 0 24 0 110 0 22 0 111 0 20 0 112 0 20 0 113 0 15 0 114 0 20 0 115 0 20 0 116 0 24 0 117 0 22 0 118 0 29 0 119 19 23 23 120 0 24 0 121 0 22 0 122 0 16 0 123 17 23 23 124 19 27 27 125 0 16 0 126 17 21 21 127 0 26 0 128 19 22 22 129 21 23 23 130 0 19 0 131 0 18 0 132 22 24 24 133 0 24 0 134 20 29 29 135 0 22 0 136 20 24 24 137 0 22 0 138 0 12 0 139 29 26 26 140 0 18 0 141 23 22 22 142 0 24 0 143 0 21 0 144 0 15 0 145 0 23 0 146 0 22 0 147 0 22 0 148 0 24 0 149 0 23 0 150 0 13 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Gender ConcernMistakes 13.690460 -3.238577 0.008773 ConcernMistakes_G DoubtsActions DoubtsActions_G 0.023914 -0.212820 -0.186727 ParentalCriticism ParentalCriticism_G PersonalStandards 0.027288 0.056977 0.032324 PersonalStandards_G Organization Organization_G -0.038112 0.117975 0.201262 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -7.127 -1.065 0.294 1.196 4.225 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.690460 1.818071 7.530 5.98e-12 *** Gender -3.238577 2.978129 -1.087 0.2787 ConcernMistakes 0.008773 0.047808 0.184 0.8547 ConcernMistakes_G 0.023914 0.077534 0.308 0.7582 DoubtsActions -0.212820 0.095570 -2.227 0.0276 * DoubtsActions_G -0.186727 0.146837 -1.272 0.2056 ParentalCriticism 0.027288 0.087395 0.312 0.7553 ParentalCriticism_G 0.056977 0.144895 0.393 0.6948 PersonalStandards 0.032324 0.061268 0.528 0.5986 PersonalStandards_G -0.038112 0.104694 -0.364 0.7164 Organization 0.117975 0.063028 1.872 0.0634 . Organization_G 0.201262 0.114274 1.761 0.0804 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.074 on 138 degrees of freedom Multiple R-squared: 0.2286, Adjusted R-squared: 0.1671 F-statistic: 3.717 on 11 and 138 DF, p-value: 0.0001176 > 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,] 0.92525409 0.14949182 0.07474591 [2,] 0.85818948 0.28362105 0.14181052 [3,] 0.78434515 0.43130969 0.21565485 [4,] 0.69213704 0.61572593 0.30786296 [5,] 0.58421995 0.83156010 0.41578005 [6,] 0.48463488 0.96926977 0.51536512 [7,] 0.64242008 0.71515984 0.35757992 [8,] 0.65135341 0.69729317 0.34864659 [9,] 0.58443195 0.83113611 0.41556805 [10,] 0.53068064 0.93863873 0.46931936 [11,] 0.47595493 0.95190986 0.52404507 [12,] 0.43449445 0.86898890 0.56550555 [13,] 0.38626292 0.77252584 0.61373708 [14,] 0.57982589 0.84034822 0.42017411 [15,] 0.60054364 0.79891271 0.39945636 [16,] 0.55089432 0.89821137 0.44910568 [17,] 0.49328159 0.98656319 0.50671841 [18,] 0.43317963 0.86635926 0.56682037 [19,] 0.96751357 0.06497285 0.03248643 [20,] 0.97374030 0.05251940 0.02625970 [21,] 0.96303274 0.07393453 0.03696726 [22,] 0.95232277 0.09535445 0.04767723 [23,] 0.95131773 0.09736455 0.04868227 [24,] 0.96697079 0.06605842 0.03302921 [25,] 0.96322671 0.07354659 0.03677329 [26,] 0.97728841 0.04542319 0.02271159 [27,] 0.96829872 0.06340256 0.03170128 [28,] 0.96600460 0.06799080 0.03399540 [29,] 0.98151849 0.03696303 0.01848151 [30,] 0.97449082 0.05101836 0.02550918 [31,] 0.96626036 0.06747928 0.03373964 [32,] 0.95661651 0.08676697 0.04338349 [33,] 0.94765863 0.10468273 0.05234137 [34,] 0.93565010 0.12869980 0.06434990 [35,] 0.94315368 0.11369265 0.05684632 [36,] 0.92799429 0.14401142 0.07200571 [37,] 0.94169066 0.11661868 0.05830934 [38,] 0.92606345 0.14787311 0.07393655 [39,] 0.90764349 0.18471303 0.09235651 [40,] 0.89256378 0.21487243 0.10743622 [41,] 0.87075542 0.25848916 0.12924458 [42,] 0.84473689 0.31052623 0.15526311 [43,] 0.81765687 0.36468626 0.18234313 [44,] 0.78378939 0.43242122 0.21621061 [45,] 0.74499773 0.51000454 0.25500227 [46,] 0.75521727 0.48956547 0.24478273 [47,] 0.75786514 0.48426971 0.24213486 [48,] 0.71617770 0.56764460 0.28382230 [49,] 0.76490913 0.47018173 0.23509087 [50,] 0.76076345 0.47847311 0.23923655 [51,] 0.81013854 0.37972291 0.18986146 [52,] 0.77595061 0.44809878 0.22404939 [53,] 0.86095717 0.27808565 0.13904283 [54,] 0.83634074 0.32731853 0.16365926 [55,] 0.84021698 0.31956605 0.15978302 [56,] 0.81319955 0.37360090 0.18680045 [57,] 0.78389009 0.43221982 0.21610991 [58,] 0.74661075 0.50677851 0.25338925 [59,] 0.72155681 0.55688638 0.27844319 [60,] 0.70599669 0.58800661 0.29400331 [61,] 0.68626816 0.62746368 0.31373184 [62,] 0.65580225 0.68839551 0.34419775 [63,] 0.72077920 0.55844159 0.27922080 [64,] 0.68370996 0.63258008 0.31629004 [65,] 0.78450445 0.43099110 0.21549555 [66,] 0.74877205 0.50245590 0.25122795 [67,] 0.70719857 0.58560287 0.29280143 [68,] 0.70815536 0.58368929 0.29184464 [69,] 0.68029541 0.63940919 0.31970459 [70,] 0.73532812 0.52934376 0.26467188 [71,] 0.69561991 0.60876019 0.30438009 [72,] 0.65287349 0.69425302 0.34712651 [73,] 0.62642981 0.74714039 0.37357019 [74,] 0.60067696 0.79864607 0.39932304 [75,] 0.55333182 0.89333636 0.44666818 [76,] 0.50802639 0.98394723 0.49197361 [77,] 0.45598202 0.91196404 0.54401798 [78,] 0.42503407 0.85006813 0.57496593 [79,] 0.44013752 0.88027505 0.55986248 [80,] 0.44110580 0.88221160 0.55889420 [81,] 0.43741572 0.87483143 0.56258428 [82,] 0.59075137 0.81849727 0.40924863 [83,] 0.54976908 0.90046184 0.45023092 [84,] 0.55661535 0.88676929 0.44338465 [85,] 0.50664000 0.98672001 0.49336000 [86,] 0.47775761 0.95551521 0.52224239 [87,] 0.47348494 0.94696988 0.52651506 [88,] 0.44759725 0.89519449 0.55240275 [89,] 0.41363618 0.82727236 0.58636382 [90,] 0.39229630 0.78459259 0.60770370 [91,] 0.40668051 0.81336102 0.59331949 [92,] 0.43085676 0.86171353 0.56914324 [93,] 0.54758168 0.90483664 0.45241832 [94,] 0.59818742 0.80362516 0.40181258 [95,] 0.57547938 0.84904125 0.42452062 [96,] 0.52062733 0.95874535 0.47937267 [97,] 0.46986388 0.93972775 0.53013612 [98,] 0.41676201 0.83352402 0.58323799 [99,] 0.37193571 0.74387143 0.62806429 [100,] 0.36192475 0.72384951 0.63807525 [101,] 0.34940506 0.69881012 0.65059494 [102,] 0.29083705 0.58167410 0.70916295 [103,] 0.26786733 0.53573465 0.73213267 [104,] 0.26352857 0.52705714 0.73647143 [105,] 0.25493588 0.50987177 0.74506412 [106,] 0.23128006 0.46256012 0.76871994 [107,] 0.18750623 0.37501247 0.81249377 [108,] 0.16477027 0.32954053 0.83522973 [109,] 0.17396434 0.34792867 0.82603566 [110,] 0.25201507 0.50403015 0.74798493 [111,] 0.19625943 0.39251886 0.80374057 [112,] 0.14841599 0.29683197 0.85158401 [113,] 0.10748624 0.21497247 0.89251376 [114,] 0.09617154 0.19234307 0.90382846 [115,] 0.06306166 0.12612331 0.93693834 [116,] 0.04901951 0.09803901 0.95098049 [117,] 0.03517917 0.07035833 0.96482083 [118,] 0.02891854 0.05783707 0.97108146 [119,] 0.01535978 0.03071956 0.98464022 [120,] 0.00702238 0.01404476 0.99297762 [121,] 0.01248050 0.02496100 0.98751950 > postscript(file="/var/www/rcomp/tmp/1lpoy1290479068.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2ezn11290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/3ezn11290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4ezn11290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5ezn11290479068.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 = 150 Frequency = 1 1 2 3 4 5 6 -2.92440338 0.24621162 3.59429162 1.21607205 -0.95057305 -2.34407800 7 8 9 10 11 12 4.22505476 -0.99934149 -1.73522493 0.49035615 0.85258210 0.98907801 13 14 15 16 17 18 1.18483652 0.29989643 3.45718254 -0.53701914 3.73657412 1.93283285 19 20 21 22 23 24 -0.23252910 2.05448342 3.69138430 1.38687929 0.92455535 1.83761644 25 26 27 28 29 30 1.88860188 1.24737921 -1.85250751 -0.85715490 -2.57248694 -1.10631703 31 32 33 34 35 36 0.47496933 -1.65860534 -7.12731726 -2.77463568 -0.15230051 -0.40663495 37 38 39 40 41 42 2.02962229 3.46333629 -1.55652924 1.63461661 -0.23897023 -1.23251917 43 44 45 46 47 48 -3.57256940 -0.45540800 0.48747992 -0.97625673 1.61391490 -1.06682040 49 50 51 52 53 54 -2.69885436 0.82091425 -3.16975461 -0.64231170 0.33348895 0.87157326 55 56 57 58 59 60 0.33332190 0.72444724 -1.10658700 -0.03347485 -0.02606620 -2.38200961 61 62 63 64 65 66 2.38511636 0.17617590 3.20305552 1.92273782 -3.00861584 -0.01205762 67 68 69 70 71 72 -3.92851226 0.82056694 2.12090449 0.80432105 0.82877321 0.39563354 73 74 75 76 77 78 1.33802629 -1.50230131 0.88530309 1.31307405 3.18246490 0.74213299 79 80 81 82 83 84 3.87748638 -0.58635052 0.28806050 0.30979007 1.22075795 -2.98703956 85 86 87 88 89 90 0.55649245 0.49930031 1.20033913 -1.38936918 0.41779513 0.68220618 91 92 93 94 95 96 0.05126804 -0.47730013 -1.24497135 1.64351537 2.17696960 3.92423768 97 98 99 100 101 102 -1.17493514 1.58090383 -0.06490886 1.09417625 2.08101688 -1.32586652 103 104 105 106 107 108 1.01035950 0.75464005 1.65771634 1.16978335 -3.69296769 1.99997725 109 110 111 112 113 114 -0.40765432 -0.69628348 -1.21911306 0.54021399 -0.38589243 0.98505888 115 116 117 118 119 120 -2.17987827 0.10714354 0.25287703 0.43272474 0.56289779 1.22880772 121 122 123 124 125 126 -1.05766822 -2.10039928 1.75474626 -4.54079688 1.00833279 -0.27291819 127 128 129 130 131 132 -1.22398577 -0.93112492 0.39435263 -3.25379901 -2.41041608 -5.96815609 133 134 135 136 137 138 0.02574530 -0.59480920 1.45711221 1.12090520 -5.20556097 3.83831417 139 140 141 142 143 144 -0.74303862 0.25803954 1.88860188 0.89100740 -0.20157000 -3.95232206 145 146 147 148 149 150 0.55649245 -2.14890446 -1.05766822 0.22507042 -3.53269276 -1.01796692 > postscript(file="/var/www/rcomp/tmp/6785m1290479068.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 = 150 Frequency = 1 lag(myerror, k = 1) myerror 0 -2.92440338 NA 1 0.24621162 -2.92440338 2 3.59429162 0.24621162 3 1.21607205 3.59429162 4 -0.95057305 1.21607205 5 -2.34407800 -0.95057305 6 4.22505476 -2.34407800 7 -0.99934149 4.22505476 8 -1.73522493 -0.99934149 9 0.49035615 -1.73522493 10 0.85258210 0.49035615 11 0.98907801 0.85258210 12 1.18483652 0.98907801 13 0.29989643 1.18483652 14 3.45718254 0.29989643 15 -0.53701914 3.45718254 16 3.73657412 -0.53701914 17 1.93283285 3.73657412 18 -0.23252910 1.93283285 19 2.05448342 -0.23252910 20 3.69138430 2.05448342 21 1.38687929 3.69138430 22 0.92455535 1.38687929 23 1.83761644 0.92455535 24 1.88860188 1.83761644 25 1.24737921 1.88860188 26 -1.85250751 1.24737921 27 -0.85715490 -1.85250751 28 -2.57248694 -0.85715490 29 -1.10631703 -2.57248694 30 0.47496933 -1.10631703 31 -1.65860534 0.47496933 32 -7.12731726 -1.65860534 33 -2.77463568 -7.12731726 34 -0.15230051 -2.77463568 35 -0.40663495 -0.15230051 36 2.02962229 -0.40663495 37 3.46333629 2.02962229 38 -1.55652924 3.46333629 39 1.63461661 -1.55652924 40 -0.23897023 1.63461661 41 -1.23251917 -0.23897023 42 -3.57256940 -1.23251917 43 -0.45540800 -3.57256940 44 0.48747992 -0.45540800 45 -0.97625673 0.48747992 46 1.61391490 -0.97625673 47 -1.06682040 1.61391490 48 -2.69885436 -1.06682040 49 0.82091425 -2.69885436 50 -3.16975461 0.82091425 51 -0.64231170 -3.16975461 52 0.33348895 -0.64231170 53 0.87157326 0.33348895 54 0.33332190 0.87157326 55 0.72444724 0.33332190 56 -1.10658700 0.72444724 57 -0.03347485 -1.10658700 58 -0.02606620 -0.03347485 59 -2.38200961 -0.02606620 60 2.38511636 -2.38200961 61 0.17617590 2.38511636 62 3.20305552 0.17617590 63 1.92273782 3.20305552 64 -3.00861584 1.92273782 65 -0.01205762 -3.00861584 66 -3.92851226 -0.01205762 67 0.82056694 -3.92851226 68 2.12090449 0.82056694 69 0.80432105 2.12090449 70 0.82877321 0.80432105 71 0.39563354 0.82877321 72 1.33802629 0.39563354 73 -1.50230131 1.33802629 74 0.88530309 -1.50230131 75 1.31307405 0.88530309 76 3.18246490 1.31307405 77 0.74213299 3.18246490 78 3.87748638 0.74213299 79 -0.58635052 3.87748638 80 0.28806050 -0.58635052 81 0.30979007 0.28806050 82 1.22075795 0.30979007 83 -2.98703956 1.22075795 84 0.55649245 -2.98703956 85 0.49930031 0.55649245 86 1.20033913 0.49930031 87 -1.38936918 1.20033913 88 0.41779513 -1.38936918 89 0.68220618 0.41779513 90 0.05126804 0.68220618 91 -0.47730013 0.05126804 92 -1.24497135 -0.47730013 93 1.64351537 -1.24497135 94 2.17696960 1.64351537 95 3.92423768 2.17696960 96 -1.17493514 3.92423768 97 1.58090383 -1.17493514 98 -0.06490886 1.58090383 99 1.09417625 -0.06490886 100 2.08101688 1.09417625 101 -1.32586652 2.08101688 102 1.01035950 -1.32586652 103 0.75464005 1.01035950 104 1.65771634 0.75464005 105 1.16978335 1.65771634 106 -3.69296769 1.16978335 107 1.99997725 -3.69296769 108 -0.40765432 1.99997725 109 -0.69628348 -0.40765432 110 -1.21911306 -0.69628348 111 0.54021399 -1.21911306 112 -0.38589243 0.54021399 113 0.98505888 -0.38589243 114 -2.17987827 0.98505888 115 0.10714354 -2.17987827 116 0.25287703 0.10714354 117 0.43272474 0.25287703 118 0.56289779 0.43272474 119 1.22880772 0.56289779 120 -1.05766822 1.22880772 121 -2.10039928 -1.05766822 122 1.75474626 -2.10039928 123 -4.54079688 1.75474626 124 1.00833279 -4.54079688 125 -0.27291819 1.00833279 126 -1.22398577 -0.27291819 127 -0.93112492 -1.22398577 128 0.39435263 -0.93112492 129 -3.25379901 0.39435263 130 -2.41041608 -3.25379901 131 -5.96815609 -2.41041608 132 0.02574530 -5.96815609 133 -0.59480920 0.02574530 134 1.45711221 -0.59480920 135 1.12090520 1.45711221 136 -5.20556097 1.12090520 137 3.83831417 -5.20556097 138 -0.74303862 3.83831417 139 0.25803954 -0.74303862 140 1.88860188 0.25803954 141 0.89100740 1.88860188 142 -0.20157000 0.89100740 143 -3.95232206 -0.20157000 144 0.55649245 -3.95232206 145 -2.14890446 0.55649245 146 -1.05766822 -2.14890446 147 0.22507042 -1.05766822 148 -3.53269276 0.22507042 149 -1.01796692 -3.53269276 150 NA -1.01796692 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.24621162 -2.92440338 [2,] 3.59429162 0.24621162 [3,] 1.21607205 3.59429162 [4,] -0.95057305 1.21607205 [5,] -2.34407800 -0.95057305 [6,] 4.22505476 -2.34407800 [7,] -0.99934149 4.22505476 [8,] -1.73522493 -0.99934149 [9,] 0.49035615 -1.73522493 [10,] 0.85258210 0.49035615 [11,] 0.98907801 0.85258210 [12,] 1.18483652 0.98907801 [13,] 0.29989643 1.18483652 [14,] 3.45718254 0.29989643 [15,] -0.53701914 3.45718254 [16,] 3.73657412 -0.53701914 [17,] 1.93283285 3.73657412 [18,] -0.23252910 1.93283285 [19,] 2.05448342 -0.23252910 [20,] 3.69138430 2.05448342 [21,] 1.38687929 3.69138430 [22,] 0.92455535 1.38687929 [23,] 1.83761644 0.92455535 [24,] 1.88860188 1.83761644 [25,] 1.24737921 1.88860188 [26,] -1.85250751 1.24737921 [27,] -0.85715490 -1.85250751 [28,] -2.57248694 -0.85715490 [29,] -1.10631703 -2.57248694 [30,] 0.47496933 -1.10631703 [31,] -1.65860534 0.47496933 [32,] -7.12731726 -1.65860534 [33,] -2.77463568 -7.12731726 [34,] -0.15230051 -2.77463568 [35,] -0.40663495 -0.15230051 [36,] 2.02962229 -0.40663495 [37,] 3.46333629 2.02962229 [38,] -1.55652924 3.46333629 [39,] 1.63461661 -1.55652924 [40,] -0.23897023 1.63461661 [41,] -1.23251917 -0.23897023 [42,] -3.57256940 -1.23251917 [43,] -0.45540800 -3.57256940 [44,] 0.48747992 -0.45540800 [45,] -0.97625673 0.48747992 [46,] 1.61391490 -0.97625673 [47,] -1.06682040 1.61391490 [48,] -2.69885436 -1.06682040 [49,] 0.82091425 -2.69885436 [50,] -3.16975461 0.82091425 [51,] -0.64231170 -3.16975461 [52,] 0.33348895 -0.64231170 [53,] 0.87157326 0.33348895 [54,] 0.33332190 0.87157326 [55,] 0.72444724 0.33332190 [56,] -1.10658700 0.72444724 [57,] -0.03347485 -1.10658700 [58,] -0.02606620 -0.03347485 [59,] -2.38200961 -0.02606620 [60,] 2.38511636 -2.38200961 [61,] 0.17617590 2.38511636 [62,] 3.20305552 0.17617590 [63,] 1.92273782 3.20305552 [64,] -3.00861584 1.92273782 [65,] -0.01205762 -3.00861584 [66,] -3.92851226 -0.01205762 [67,] 0.82056694 -3.92851226 [68,] 2.12090449 0.82056694 [69,] 0.80432105 2.12090449 [70,] 0.82877321 0.80432105 [71,] 0.39563354 0.82877321 [72,] 1.33802629 0.39563354 [73,] -1.50230131 1.33802629 [74,] 0.88530309 -1.50230131 [75,] 1.31307405 0.88530309 [76,] 3.18246490 1.31307405 [77,] 0.74213299 3.18246490 [78,] 3.87748638 0.74213299 [79,] -0.58635052 3.87748638 [80,] 0.28806050 -0.58635052 [81,] 0.30979007 0.28806050 [82,] 1.22075795 0.30979007 [83,] -2.98703956 1.22075795 [84,] 0.55649245 -2.98703956 [85,] 0.49930031 0.55649245 [86,] 1.20033913 0.49930031 [87,] -1.38936918 1.20033913 [88,] 0.41779513 -1.38936918 [89,] 0.68220618 0.41779513 [90,] 0.05126804 0.68220618 [91,] -0.47730013 0.05126804 [92,] -1.24497135 -0.47730013 [93,] 1.64351537 -1.24497135 [94,] 2.17696960 1.64351537 [95,] 3.92423768 2.17696960 [96,] -1.17493514 3.92423768 [97,] 1.58090383 -1.17493514 [98,] -0.06490886 1.58090383 [99,] 1.09417625 -0.06490886 [100,] 2.08101688 1.09417625 [101,] -1.32586652 2.08101688 [102,] 1.01035950 -1.32586652 [103,] 0.75464005 1.01035950 [104,] 1.65771634 0.75464005 [105,] 1.16978335 1.65771634 [106,] -3.69296769 1.16978335 [107,] 1.99997725 -3.69296769 [108,] -0.40765432 1.99997725 [109,] -0.69628348 -0.40765432 [110,] -1.21911306 -0.69628348 [111,] 0.54021399 -1.21911306 [112,] -0.38589243 0.54021399 [113,] 0.98505888 -0.38589243 [114,] -2.17987827 0.98505888 [115,] 0.10714354 -2.17987827 [116,] 0.25287703 0.10714354 [117,] 0.43272474 0.25287703 [118,] 0.56289779 0.43272474 [119,] 1.22880772 0.56289779 [120,] -1.05766822 1.22880772 [121,] -2.10039928 -1.05766822 [122,] 1.75474626 -2.10039928 [123,] -4.54079688 1.75474626 [124,] 1.00833279 -4.54079688 [125,] -0.27291819 1.00833279 [126,] -1.22398577 -0.27291819 [127,] -0.93112492 -1.22398577 [128,] 0.39435263 -0.93112492 [129,] -3.25379901 0.39435263 [130,] -2.41041608 -3.25379901 [131,] -5.96815609 -2.41041608 [132,] 0.02574530 -5.96815609 [133,] -0.59480920 0.02574530 [134,] 1.45711221 -0.59480920 [135,] 1.12090520 1.45711221 [136,] -5.20556097 1.12090520 [137,] 3.83831417 -5.20556097 [138,] -0.74303862 3.83831417 [139,] 0.25803954 -0.74303862 [140,] 1.88860188 0.25803954 [141,] 0.89100740 1.88860188 [142,] -0.20157000 0.89100740 [143,] -3.95232206 -0.20157000 [144,] 0.55649245 -3.95232206 [145,] -2.14890446 0.55649245 [146,] -1.05766822 -2.14890446 [147,] 0.22507042 -1.05766822 [148,] -3.53269276 0.22507042 [149,] -1.01796692 -3.53269276 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.24621162 -2.92440338 2 3.59429162 0.24621162 3 1.21607205 3.59429162 4 -0.95057305 1.21607205 5 -2.34407800 -0.95057305 6 4.22505476 -2.34407800 7 -0.99934149 4.22505476 8 -1.73522493 -0.99934149 9 0.49035615 -1.73522493 10 0.85258210 0.49035615 11 0.98907801 0.85258210 12 1.18483652 0.98907801 13 0.29989643 1.18483652 14 3.45718254 0.29989643 15 -0.53701914 3.45718254 16 3.73657412 -0.53701914 17 1.93283285 3.73657412 18 -0.23252910 1.93283285 19 2.05448342 -0.23252910 20 3.69138430 2.05448342 21 1.38687929 3.69138430 22 0.92455535 1.38687929 23 1.83761644 0.92455535 24 1.88860188 1.83761644 25 1.24737921 1.88860188 26 -1.85250751 1.24737921 27 -0.85715490 -1.85250751 28 -2.57248694 -0.85715490 29 -1.10631703 -2.57248694 30 0.47496933 -1.10631703 31 -1.65860534 0.47496933 32 -7.12731726 -1.65860534 33 -2.77463568 -7.12731726 34 -0.15230051 -2.77463568 35 -0.40663495 -0.15230051 36 2.02962229 -0.40663495 37 3.46333629 2.02962229 38 -1.55652924 3.46333629 39 1.63461661 -1.55652924 40 -0.23897023 1.63461661 41 -1.23251917 -0.23897023 42 -3.57256940 -1.23251917 43 -0.45540800 -3.57256940 44 0.48747992 -0.45540800 45 -0.97625673 0.48747992 46 1.61391490 -0.97625673 47 -1.06682040 1.61391490 48 -2.69885436 -1.06682040 49 0.82091425 -2.69885436 50 -3.16975461 0.82091425 51 -0.64231170 -3.16975461 52 0.33348895 -0.64231170 53 0.87157326 0.33348895 54 0.33332190 0.87157326 55 0.72444724 0.33332190 56 -1.10658700 0.72444724 57 -0.03347485 -1.10658700 58 -0.02606620 -0.03347485 59 -2.38200961 -0.02606620 60 2.38511636 -2.38200961 61 0.17617590 2.38511636 62 3.20305552 0.17617590 63 1.92273782 3.20305552 64 -3.00861584 1.92273782 65 -0.01205762 -3.00861584 66 -3.92851226 -0.01205762 67 0.82056694 -3.92851226 68 2.12090449 0.82056694 69 0.80432105 2.12090449 70 0.82877321 0.80432105 71 0.39563354 0.82877321 72 1.33802629 0.39563354 73 -1.50230131 1.33802629 74 0.88530309 -1.50230131 75 1.31307405 0.88530309 76 3.18246490 1.31307405 77 0.74213299 3.18246490 78 3.87748638 0.74213299 79 -0.58635052 3.87748638 80 0.28806050 -0.58635052 81 0.30979007 0.28806050 82 1.22075795 0.30979007 83 -2.98703956 1.22075795 84 0.55649245 -2.98703956 85 0.49930031 0.55649245 86 1.20033913 0.49930031 87 -1.38936918 1.20033913 88 0.41779513 -1.38936918 89 0.68220618 0.41779513 90 0.05126804 0.68220618 91 -0.47730013 0.05126804 92 -1.24497135 -0.47730013 93 1.64351537 -1.24497135 94 2.17696960 1.64351537 95 3.92423768 2.17696960 96 -1.17493514 3.92423768 97 1.58090383 -1.17493514 98 -0.06490886 1.58090383 99 1.09417625 -0.06490886 100 2.08101688 1.09417625 101 -1.32586652 2.08101688 102 1.01035950 -1.32586652 103 0.75464005 1.01035950 104 1.65771634 0.75464005 105 1.16978335 1.65771634 106 -3.69296769 1.16978335 107 1.99997725 -3.69296769 108 -0.40765432 1.99997725 109 -0.69628348 -0.40765432 110 -1.21911306 -0.69628348 111 0.54021399 -1.21911306 112 -0.38589243 0.54021399 113 0.98505888 -0.38589243 114 -2.17987827 0.98505888 115 0.10714354 -2.17987827 116 0.25287703 0.10714354 117 0.43272474 0.25287703 118 0.56289779 0.43272474 119 1.22880772 0.56289779 120 -1.05766822 1.22880772 121 -2.10039928 -1.05766822 122 1.75474626 -2.10039928 123 -4.54079688 1.75474626 124 1.00833279 -4.54079688 125 -0.27291819 1.00833279 126 -1.22398577 -0.27291819 127 -0.93112492 -1.22398577 128 0.39435263 -0.93112492 129 -3.25379901 0.39435263 130 -2.41041608 -3.25379901 131 -5.96815609 -2.41041608 132 0.02574530 -5.96815609 133 -0.59480920 0.02574530 134 1.45711221 -0.59480920 135 1.12090520 1.45711221 136 -5.20556097 1.12090520 137 3.83831417 -5.20556097 138 -0.74303862 3.83831417 139 0.25803954 -0.74303862 140 1.88860188 0.25803954 141 0.89100740 1.88860188 142 -0.20157000 0.89100740 143 -3.95232206 -0.20157000 144 0.55649245 -3.95232206 145 -2.14890446 0.55649245 146 -1.05766822 -2.14890446 147 0.22507042 -1.05766822 148 -3.53269276 0.22507042 149 -1.01796692 -3.53269276 > 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/rcomp/tmp/70hmp1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/80hmp1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/90hmp1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10s8ls1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11hamv1290479069.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/rcomp/tmp/1291lg1290479069.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/rcomp/tmp/13ykia1290479069.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/rcomp/tmp/141lgy1290479069.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/rcomp/tmp/15ucxj1290479069.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/rcomp/tmp/16q4da1290479069.tab") + } > > try(system("convert tmp/1lpoy1290479068.ps tmp/1lpoy1290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/2ezn11290479068.ps tmp/2ezn11290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/3ezn11290479068.ps tmp/3ezn11290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/4ezn11290479068.ps tmp/4ezn11290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/5ezn11290479068.ps tmp/5ezn11290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/6785m1290479068.ps tmp/6785m1290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/70hmp1290479068.ps tmp/70hmp1290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/80hmp1290479068.ps tmp/80hmp1290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/90hmp1290479068.ps tmp/90hmp1290479068.png",intern=TRUE)) character(0) > try(system("convert tmp/10s8ls1290479068.ps tmp/10s8ls1290479068.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.00 1.81 7.82