R version 2.9.0 (2009-04-17) Copyright (C) 2009 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. Type 'q()' to quit R. > x <- array(list(23 + ,10 + ,0 + ,0 + ,53 + ,7 + ,12 + ,2 + ,4 + ,21 + ,6 + ,0 + ,0 + ,86 + ,4 + ,11 + ,4 + ,3 + ,21 + ,13 + ,0 + ,0 + ,66 + ,6 + ,14 + ,7 + ,5 + ,21 + ,12 + ,1 + ,0 + ,67 + ,5 + ,12 + ,3 + ,3 + ,24 + ,8 + ,0 + ,0 + ,76 + ,4 + ,21 + ,7 + ,6 + ,22 + ,6 + ,0 + ,0 + ,78 + ,3 + ,12 + ,2 + ,5 + ,21 + ,10 + ,0 + ,0 + ,53 + ,5 + ,22 + ,7 + ,6 + ,22 + ,10 + ,0 + ,0 + ,80 + ,6 + ,11 + ,2 + ,6 + ,21 + ,9 + ,0 + ,0 + ,74 + ,5 + ,10 + ,1 + ,5 + ,20 + ,9 + ,0 + ,0 + ,76 + ,6 + ,13 + ,2 + ,5 + ,22 + ,7 + ,1 + ,0 + ,79 + ,7 + ,10 + ,6 + ,3 + ,21 + ,5 + ,0 + ,0 + ,54 + ,6 + ,8 + ,1 + ,5 + ,21 + ,14 + ,1 + ,0 + ,67 + ,7 + ,15 + ,1 + ,7 + ,23 + ,6 + ,0 + ,0 + ,87 + ,6 + ,10 + ,1 + ,5 + ,22 + ,10 + ,1 + ,0 + ,58 + ,4 + ,14 + ,2 + ,5 + ,23 + ,10 + ,1 + ,0 + ,75 + ,6 + ,14 + ,2 + ,3 + ,22 + ,7 + ,0 + ,0 + ,88 + ,4 + ,11 + ,2 + ,5 + ,24 + ,10 + ,1 + ,0 + ,64 + ,5 + ,10 + ,1 + ,6 + ,23 + ,8 + ,0 + ,0 + ,57 + ,3 + ,13 + ,7 + ,5 + ,21 + ,6 + ,1 + ,0 + ,66 + ,3 + ,7 + ,1 + 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+ ,1 + ,54 + ,6 + ,23 + ,2 + ,6 + ,23 + ,10 + ,1 + ,1 + ,63 + ,5 + ,14 + ,5 + ,5 + ,23 + ,13 + ,0 + ,1 + ,54 + ,5 + ,16 + ,5 + ,6 + ,22 + ,9 + ,0 + ,1 + ,64 + ,6 + ,11 + ,7 + ,2 + ,21 + ,11 + ,1 + ,1 + ,69 + ,5 + ,12 + ,4 + ,5 + ,21 + ,8 + ,1 + ,1 + ,84 + ,7 + ,14 + ,5 + ,5 + ,21 + ,10 + ,0 + ,1 + ,86 + ,5 + ,12 + ,1 + ,1 + ,21 + ,9 + ,1 + ,1 + ,77 + ,3 + ,12 + ,4 + ,4 + ,22 + ,8 + ,0 + ,1 + ,89 + ,5 + ,11 + ,1 + ,2 + ,20 + ,8 + ,0 + ,1 + ,76 + ,1 + ,12 + ,4 + ,2 + ,21 + ,13 + ,1 + ,1 + ,60 + ,5 + ,13 + ,6 + ,7 + ,23 + ,11 + ,0 + ,1 + ,79 + ,7 + ,17 + ,7 + ,6 + ,32 + ,8 + ,1 + ,0 + ,76 + ,7 + ,11 + ,1 + ,5 + ,22 + ,12 + ,0 + ,1 + ,72 + ,6 + ,12 + ,3 + ,5 + ,24 + ,15 + ,0 + ,0 + ,69 + ,4 + ,19 + ,5 + ,5 + ,21 + ,11 + ,0 + ,1 + ,54 + ,2 + ,15 + ,2 + ,4 + ,22 + ,10 + ,0 + ,1 + ,69 + ,6 + ,14 + ,4 + ,3 + ,22 + ,5 + ,0 + ,1 + ,81 + ,5 + ,11 + ,5 + ,3 + ,23 + ,11 + ,0 + ,1 + ,84 + ,1 + ,9 + ,1 + ,3) + ,dim=c(9 + ,142) + ,dimnames=list(c('AGE' + ,'PStress' + ,'Pstress_M' + ,'Pstress_OKT' + ,'BelInSprt' + ,'KunnenRekRel' + ,'Depressie' + ,'Slaapgebrek' + ,'ToekZorgen') + ,1:142)) > y <- array(NA,dim=c(9,142),dimnames=list(c('AGE','PStress','Pstress_M','Pstress_OKT','BelInSprt','KunnenRekRel','Depressie','Slaapgebrek','ToekZorgen'),1:142)) > 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 = '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 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 PStress AGE Pstress_M Pstress_OKT BelInSprt KunnenRekRel Depressie 1 10 23 0 0 53 7 12 2 6 21 0 0 86 4 11 3 13 21 0 0 66 6 14 4 12 21 1 0 67 5 12 5 8 24 0 0 76 4 21 6 6 22 0 0 78 3 12 7 10 21 0 0 53 5 22 8 10 22 0 0 80 6 11 9 9 21 0 0 74 5 10 10 9 20 0 0 76 6 13 11 7 22 1 0 79 7 10 12 5 21 0 0 54 6 8 13 14 21 1 0 67 7 15 14 6 23 0 0 87 6 10 15 10 22 1 0 58 4 14 16 10 23 1 0 75 6 14 17 7 22 0 0 88 4 11 18 10 24 1 0 64 5 10 19 8 23 0 0 57 3 13 20 6 21 1 0 66 3 7 21 10 23 0 0 54 4 12 22 12 23 0 0 56 5 14 23 7 21 1 0 86 3 11 24 15 20 0 0 80 7 9 25 8 32 1 0 76 7 11 26 10 22 0 0 69 4 15 27 13 21 1 0 67 4 13 28 8 21 0 0 80 5 9 29 11 21 1 0 54 6 15 30 7 22 0 0 71 5 10 31 9 21 0 0 84 4 11 32 10 21 1 0 74 6 13 33 8 21 1 0 71 5 8 34 15 22 1 0 63 5 20 35 9 21 1 0 71 6 12 36 7 21 0 0 76 2 10 37 11 21 1 0 69 6 10 38 9 21 1 0 74 7 9 39 8 23 0 0 75 5 14 40 8 21 1 0 54 5 8 41 12 23 0 0 69 5 11 42 13 23 0 0 68 6 13 43 9 21 0 0 75 4 11 44 11 21 1 0 75 6 11 45 8 20 0 0 72 5 10 46 10 21 1 0 67 5 14 47 13 21 1 0 63 3 18 48 12 22 0 0 62 4 14 49 12 21 1 0 63 4 11 50 9 21 0 0 76 2 12 51 8 22 0 0 74 3 13 52 9 20 0 0 67 6 9 53 12 22 1 0 73 5 10 54 12 22 0 0 70 6 15 55 16 21 1 0 53 2 20 56 11 23 1 0 77 3 12 57 13 22 0 0 77 6 12 58 10 24 0 0 52 3 14 59 9 23 0 0 54 6 13 60 14 21 1 1 80 6 11 61 13 22 0 1 66 4 17 62 12 22 1 1 73 7 12 63 9 21 0 1 63 6 13 64 9 21 1 1 69 3 14 65 10 21 1 1 67 7 13 66 8 21 0 1 54 2 15 67 9 20 0 1 81 4 13 68 9 22 1 1 69 6 10 69 11 22 1 1 84 4 11 70 7 22 0 1 70 1 13 71 11 23 0 1 69 4 17 72 9 21 1 1 77 7 13 73 11 23 1 1 54 4 9 74 9 22 1 1 79 4 11 75 8 21 1 1 30 4 10 76 9 21 0 1 71 6 9 77 8 20 1 1 73 2 12 78 9 24 0 1 72 3 12 79 10 24 0 1 77 4 13 80 9 21 1 1 75 4 13 81 17 20 0 1 70 4 22 82 7 21 0 1 73 6 13 83 11 21 0 1 54 2 15 84 9 21 0 1 77 4 13 85 10 21 0 1 82 3 15 86 11 22 0 1 80 7 10 87 8 22 0 1 80 4 11 88 12 21 0 1 69 5 16 89 10 22 0 1 78 6 11 90 7 21 1 1 81 5 11 91 9 23 1 1 76 4 10 92 7 21 0 1 76 5 10 93 12 22 1 1 73 4 16 94 8 22 0 1 85 5 12 95 13 22 1 1 66 7 11 96 9 20 0 1 79 7 16 97 15 21 1 1 68 4 19 98 8 21 0 1 76 6 11 99 14 22 1 1 54 4 15 100 14 25 0 1 46 1 24 101 9 22 0 1 82 3 14 102 13 22 0 1 74 6 15 103 11 21 0 1 88 7 11 104 10 22 1 1 38 6 15 105 6 21 0 1 76 6 12 106 8 24 1 1 86 6 10 107 10 23 0 1 54 4 14 108 10 23 0 1 69 1 9 109 10 22 0 1 90 3 15 110 12 22 0 1 54 7 15 111 10 25 0 1 76 2 14 112 9 23 0 1 89 7 11 113 9 22 0 1 76 4 8 114 11 21 0 1 79 5 11 115 7 21 1 1 90 6 8 116 7 22 0 1 74 6 10 117 5 22 0 1 81 5 11 118 9 21 0 1 72 5 13 119 11 0 1 1 71 4 11 120 15 21 1 1 66 2 20 121 9 22 0 1 77 2 10 122 9 21 1 1 74 4 12 123 8 24 0 1 82 4 14 124 13 21 1 1 54 6 23 125 10 23 1 1 63 5 14 126 13 23 0 1 54 5 16 127 9 22 0 1 64 6 11 128 11 21 1 1 69 5 12 129 8 21 1 1 84 7 14 130 10 21 0 1 86 5 12 131 9 21 1 1 77 3 12 132 8 22 0 1 89 5 11 133 8 20 0 1 76 1 12 134 13 21 1 1 60 5 13 135 11 23 0 1 79 7 17 136 8 32 1 0 76 7 11 137 12 22 0 1 72 6 12 138 15 24 0 0 69 4 19 139 11 21 0 1 54 2 15 140 10 22 0 1 69 6 14 141 5 22 0 1 81 5 11 142 11 23 0 1 84 1 9 Slaapgebrek ToekZorgen t 1 2 4 1 2 4 3 2 3 7 5 3 4 3 3 4 5 7 6 5 6 2 5 6 7 7 6 7 8 2 6 8 9 1 5 9 10 2 5 10 11 6 3 11 12 1 5 12 13 1 7 13 14 1 5 14 15 2 5 15 16 2 3 16 17 2 5 17 18 1 6 18 19 7 5 19 20 1 2 20 21 2 5 21 22 4 4 22 23 2 6 23 24 1 3 24 25 1 5 25 26 5 4 26 27 2 5 27 28 1 2 28 29 3 2 29 30 1 5 30 31 2 2 31 32 5 2 32 33 2 2 33 34 6 5 34 35 4 5 35 36 1 1 36 37 3 5 37 38 6 2 38 39 7 6 39 40 4 1 40 41 5 3 41 42 3 2 42 43 2 5 43 44 2 3 44 45 2 4 45 46 2 3 46 47 1 6 47 48 2 4 48 49 1 5 49 50 2 2 50 51 2 5 51 52 5 5 52 53 5 3 53 54 2 5 54 55 1 7 55 56 1 4 56 57 2 2 57 58 3 3 58 59 7 6 59 60 4 7 60 61 4 4 61 62 1 4 62 63 2 4 63 64 2 5 64 65 2 2 65 66 5 3 66 67 1 3 67 68 6 4 68 69 2 3 69 70 2 4 70 71 4 6 71 72 6 2 72 73 2 4 73 74 2 5 74 75 2 2 75 76 1 1 76 77 1 2 77 78 2 5 78 79 2 4 79 80 3 4 80 81 3 6 81 82 5 1 82 83 2 4 83 84 5 5 84 85 3 2 85 86 1 3 86 87 2 3 87 88 2 6 88 89 1 5 89 90 2 4 90 91 2 4 91 92 5 5 92 93 5 5 93 94 2 6 94 95 3 6 95 96 5 5 96 97 5 7 97 98 6 5 98 99 2 5 99 100 7 7 100 101 1 5 101 102 1 6 102 103 6 6 103 104 6 4 104 105 2 5 105 106 1 1 106 107 2 6 107 108 1 5 108 109 2 2 109 110 1 1 110 111 3 5 111 112 3 6 112 113 6 5 113 114 4 5 114 115 1 4 115 116 2 2 116 117 5 3 117 118 6 3 118 119 3 5 119 120 5 3 120 121 3 2 121 122 2 2 122 123 3 3 123 124 2 6 124 125 5 5 125 126 5 6 126 127 7 2 127 128 4 5 128 129 5 5 129 130 1 1 130 131 4 4 131 132 1 2 132 133 4 2 133 134 6 7 134 135 7 6 135 136 1 5 136 137 3 5 137 138 5 5 138 139 2 4 139 140 4 3 140 141 5 3 141 142 1 3 142 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) AGE Pstress_M Pstress_OKT BelInSprt 7.78523 -0.11007 0.69938 -0.88485 -0.03379 KunnenRekRel Depressie Slaapgebrek ToekZorgen t 0.20248 0.40161 -0.21340 0.18862 0.01345 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.9828 -1.2861 -0.1169 1.2262 6.4128 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.785235 2.124281 3.665 0.000357 *** AGE -0.110075 0.066625 -1.652 0.100881 Pstress_M 0.699383 0.334342 2.092 0.038371 * Pstress_OKT -0.884853 0.545914 -1.621 0.107433 BelInSprt -0.033794 0.016095 -2.100 0.037669 * KunnenRekRel 0.202478 0.106642 1.899 0.059790 . Depressie 0.401608 0.061003 6.583 9.92e-10 *** Slaapgebrek -0.213401 0.091801 -2.325 0.021620 * ToekZorgen 0.188623 0.109684 1.720 0.087833 . t 0.013446 0.006606 2.035 0.043803 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.883 on 132 degrees of freedom Multiple R-squared: 0.4172, Adjusted R-squared: 0.3775 F-statistic: 10.5 on 9 and 132 DF, p-value: 3.797e-12 > 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,] 0.4980896 0.99617916 0.50191042 [2,] 0.5861155 0.82776909 0.41388455 [3,] 0.7239606 0.55207878 0.27603939 [4,] 0.7843690 0.43126205 0.21563103 [5,] 0.8252483 0.34950330 0.17475165 [6,] 0.7796007 0.44079866 0.22039933 [7,] 0.8388455 0.32230899 0.16115449 [8,] 0.7834642 0.43307164 0.21653582 [9,] 0.8465122 0.30697562 0.15348781 [10,] 0.9065312 0.18693763 0.09346882 [11,] 0.8884485 0.22310308 0.11155154 [12,] 0.9812065 0.03758707 0.01879353 [13,] 0.9734754 0.05304916 0.02652458 [14,] 0.9610099 0.07798013 0.03899006 [15,] 0.9584708 0.08305847 0.04152924 [16,] 0.9522462 0.09550761 0.04775381 [17,] 0.9608831 0.07823387 0.03911693 [18,] 0.9702177 0.05956465 0.02978233 [19,] 0.9584976 0.08300474 0.04150237 [20,] 0.9504648 0.09907039 0.04953519 [21,] 0.9388735 0.12225291 0.06112646 [22,] 0.9304664 0.13906718 0.06953359 [23,] 0.9398439 0.12031212 0.06015606 [24,] 0.9228545 0.15429104 0.07714552 [25,] 0.8993518 0.20129640 0.10064820 [26,] 0.8765917 0.24681652 0.12340826 [27,] 0.8712719 0.25745626 0.12872813 [28,] 0.8466582 0.30668354 0.15334177 [29,] 0.9013100 0.19738004 0.09869002 [30,] 0.9101201 0.17975976 0.08987988 [31,] 0.8889194 0.22216116 0.11108058 [32,] 0.8629675 0.27406491 0.13703246 [33,] 0.8655889 0.26882213 0.13441106 [34,] 0.8650732 0.26985364 0.13492682 [35,] 0.8411836 0.31763276 0.15881638 [36,] 0.8138707 0.37225860 0.18612930 [37,] 0.7927290 0.41454194 0.20727097 [38,] 0.7535386 0.49292272 0.24646136 [39,] 0.7743177 0.45136469 0.22568235 [40,] 0.7401210 0.51975797 0.25987899 [41,] 0.7774452 0.44510955 0.22255478 [42,] 0.7402090 0.51958193 0.25979097 [43,] 0.7274907 0.54501867 0.27250934 [44,] 0.6903196 0.61936074 0.30968037 [45,] 0.7418732 0.51625356 0.25812678 [46,] 0.7032395 0.59352096 0.29676048 [47,] 0.6992675 0.60146506 0.30073253 [48,] 0.7455870 0.50882608 0.25441304 [49,] 0.7479243 0.50415138 0.25207569 [50,] 0.7784379 0.44312418 0.22156209 [51,] 0.8250666 0.34986673 0.17493337 [52,] 0.8339427 0.33211455 0.16605727 [53,] 0.8391925 0.32161501 0.16080751 [54,] 0.8343982 0.33120365 0.16560183 [55,] 0.8053589 0.38928216 0.19464108 [56,] 0.7721758 0.45564842 0.22782421 [57,] 0.7934351 0.41312977 0.20656488 [58,] 0.8113620 0.37727591 0.18863795 [59,] 0.7791909 0.44161813 0.22080907 [60,] 0.7756792 0.44864155 0.22432077 [61,] 0.7884097 0.42318069 0.21159035 [62,] 0.7520217 0.49595665 0.24797832 [63,] 0.7543113 0.49137737 0.24568869 [64,] 0.7329286 0.53414273 0.26707137 [65,] 0.7127526 0.57449487 0.28724743 [66,] 0.6753566 0.64928670 0.32464335 [67,] 0.6308590 0.73828199 0.36914099 [68,] 0.6042484 0.79150328 0.39575164 [69,] 0.6745064 0.65098726 0.32549363 [70,] 0.6935625 0.61287505 0.30643752 [71,] 0.6549411 0.69011777 0.34505888 [72,] 0.6112016 0.77759683 0.38879841 [73,] 0.5616734 0.87665317 0.43832658 [74,] 0.6082456 0.78350877 0.39175439 [75,] 0.5628398 0.87432032 0.43716016 [76,] 0.5154796 0.96904089 0.48452045 [77,] 0.4777239 0.95544775 0.52227613 [78,] 0.5412020 0.91759594 0.45879797 [79,] 0.4871423 0.97428455 0.51285772 [80,] 0.4759810 0.95196195 0.52401902 [81,] 0.4278333 0.85566662 0.57216669 [82,] 0.4281168 0.85623361 0.57188319 [83,] 0.4880110 0.97602203 0.51198899 [84,] 0.5164715 0.96705698 0.48352849 [85,] 0.5187223 0.96255538 0.48127769 [86,] 0.4741517 0.94830344 0.52584828 [87,] 0.5055380 0.98892398 0.49446199 [88,] 0.4740509 0.94810180 0.52594910 [89,] 0.4621188 0.92423755 0.53788123 [90,] 0.4573109 0.91462184 0.54268908 [91,] 0.5246328 0.95073444 0.47536722 [92,] 0.5252569 0.94948612 0.47474306 [93,] 0.7085085 0.58298302 0.29149151 [94,] 0.6870359 0.62592826 0.31296413 [95,] 0.7146601 0.57067984 0.28533992 [96,] 0.6867353 0.62652943 0.31326472 [97,] 0.6317656 0.73646881 0.36823441 [98,] 0.6308981 0.73820384 0.36910192 [99,] 0.6015554 0.79688911 0.39844456 [100,] 0.5370385 0.92592298 0.46296149 [101,] 0.4784587 0.95691748 0.52154126 [102,] 0.4809221 0.96184422 0.51907789 [103,] 0.4412173 0.88243459 0.55878270 [104,] 0.3833333 0.76666660 0.61666670 [105,] 0.4961191 0.99223820 0.50388090 [106,] 0.4250336 0.85006714 0.57496643 [107,] 0.4131003 0.82620050 0.58689975 [108,] 0.7840044 0.43199116 0.21599558 [109,] 0.7242052 0.55158963 0.27579481 [110,] 0.6538768 0.69224646 0.34612323 [111,] 0.5847650 0.83047000 0.41523500 [112,] 0.5101246 0.97975087 0.48987544 [113,] 0.4133593 0.82671864 0.58664068 [114,] 0.3427722 0.68554437 0.65722782 [115,] 0.3064340 0.61286799 0.69356600 [116,] 0.2084675 0.41693491 0.79153255 [117,] 0.2379338 0.47586759 0.76206620 > postscript(file="/var/www/html/rcomp/tmp/19ujw1291565270.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/29ujw1291565270.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/39ujw1291565270.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4jl0h1291565270.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5jl0h1291565270.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 = 142 Frequency = 1 1 2 3 4 5 6 -0.04021989 -1.53416041 3.42970162 2.27999840 -3.52395007 -2.75139803 7 8 9 10 11 12 -3.26240600 0.89484783 0.14787140 -1.10196156 -1.26006464 -3.96760296 13 14 15 16 17 18 1.36790899 -2.46237173 -1.25336390 -0.60995417 -1.36224280 0.13113644 19 20 21 22 23 24 -0.86039307 -1.79412501 0.14345595 1.80732946 -2.30611130 6.41275918 25 26 27 28 29 30 -1.29480647 0.09705758 2.18095420 0.06262662 -0.71415677 -2.12580944 31 32 33 34 35 36 0.77012600 0.15139409 -0.39312209 1.90160497 -1.36798797 -0.78567069 37 38 39 40 41 42 1.12734556 0.68806919 -1.51622218 -0.44631302 3.59801104 3.30689809 43 44 45 46 47 48 -0.26124166 0.99821876 -1.11183783 -1.30136774 -0.43073252 1.32608428 49 50 51 52 53 54 1.33977206 0.24764311 -1.89326900 0.27578038 3.16397810 0.52056892 55 56 57 58 59 60 1.33440004 0.92840044 3.48747552 -0.32166448 -1.29568683 4.50920959 61 62 63 64 65 66 2.39328898 1.67741487 -1.07038795 -1.56325247 -0.48672171 -1.57934675 67 68 69 70 71 72 -0.24578659 0.53426167 2.36608462 -1.80549380 0.09303512 -0.38930490 73 74 75 76 77 78 2.02315766 -0.24736093 -2.05929386 0.98405415 -1.35479695 0.18270107 79 80 81 82 83 84 0.92276012 -0.97447973 3.44070095 -1.78186156 0.36323719 -0.02311511 85 86 87 88 89 90 0.67073457 2.28247670 -0.31174248 0.41662310 0.59817511 -2.51884643 91 92 93 94 95 96 0.12297411 -1.16213537 0.92656204 -1.40685269 2.44829257 -2.03921568 97 98 99 100 101 102 2.01166552 -0.63349779 1.96520811 0.39374782 -1.02539665 1.49314447 103 104 105 106 107 108 2.31369302 -2.00544913 -3.98283586 -0.68320010 -1.11992061 1.96422934 109 110 111 112 113 114 0.51504284 0.45033640 0.59688266 -0.19358560 1.88465883 2.02841528 115 116 117 118 119 120 -1.76192112 -1.71917580 -3.24361511 -0.26109256 -1.33104481 2.39264937 121 122 123 124 125 126 1.33828547 -1.00757161 -1.49949779 -3.28746560 -1.13084752 1.25910878 127 128 129 130 131 132 0.46037691 0.40123875 -3.10007260 0.76250686 -0.77517334 -0.83994544 133 134 135 136 137 138 -0.46435079 1.66436761 -0.39682888 -2.78735933 1.77518045 2.01615536 139 140 141 142 -0.38976246 -0.57910790 -3.56632924 3.39120059 > postscript(file="/var/www/html/rcomp/tmp/6jl0h1291565270.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 = 142 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.04021989 NA 1 -1.53416041 -0.04021989 2 3.42970162 -1.53416041 3 2.27999840 3.42970162 4 -3.52395007 2.27999840 5 -2.75139803 -3.52395007 6 -3.26240600 -2.75139803 7 0.89484783 -3.26240600 8 0.14787140 0.89484783 9 -1.10196156 0.14787140 10 -1.26006464 -1.10196156 11 -3.96760296 -1.26006464 12 1.36790899 -3.96760296 13 -2.46237173 1.36790899 14 -1.25336390 -2.46237173 15 -0.60995417 -1.25336390 16 -1.36224280 -0.60995417 17 0.13113644 -1.36224280 18 -0.86039307 0.13113644 19 -1.79412501 -0.86039307 20 0.14345595 -1.79412501 21 1.80732946 0.14345595 22 -2.30611130 1.80732946 23 6.41275918 -2.30611130 24 -1.29480647 6.41275918 25 0.09705758 -1.29480647 26 2.18095420 0.09705758 27 0.06262662 2.18095420 28 -0.71415677 0.06262662 29 -2.12580944 -0.71415677 30 0.77012600 -2.12580944 31 0.15139409 0.77012600 32 -0.39312209 0.15139409 33 1.90160497 -0.39312209 34 -1.36798797 1.90160497 35 -0.78567069 -1.36798797 36 1.12734556 -0.78567069 37 0.68806919 1.12734556 38 -1.51622218 0.68806919 39 -0.44631302 -1.51622218 40 3.59801104 -0.44631302 41 3.30689809 3.59801104 42 -0.26124166 3.30689809 43 0.99821876 -0.26124166 44 -1.11183783 0.99821876 45 -1.30136774 -1.11183783 46 -0.43073252 -1.30136774 47 1.32608428 -0.43073252 48 1.33977206 1.32608428 49 0.24764311 1.33977206 50 -1.89326900 0.24764311 51 0.27578038 -1.89326900 52 3.16397810 0.27578038 53 0.52056892 3.16397810 54 1.33440004 0.52056892 55 0.92840044 1.33440004 56 3.48747552 0.92840044 57 -0.32166448 3.48747552 58 -1.29568683 -0.32166448 59 4.50920959 -1.29568683 60 2.39328898 4.50920959 61 1.67741487 2.39328898 62 -1.07038795 1.67741487 63 -1.56325247 -1.07038795 64 -0.48672171 -1.56325247 65 -1.57934675 -0.48672171 66 -0.24578659 -1.57934675 67 0.53426167 -0.24578659 68 2.36608462 0.53426167 69 -1.80549380 2.36608462 70 0.09303512 -1.80549380 71 -0.38930490 0.09303512 72 2.02315766 -0.38930490 73 -0.24736093 2.02315766 74 -2.05929386 -0.24736093 75 0.98405415 -2.05929386 76 -1.35479695 0.98405415 77 0.18270107 -1.35479695 78 0.92276012 0.18270107 79 -0.97447973 0.92276012 80 3.44070095 -0.97447973 81 -1.78186156 3.44070095 82 0.36323719 -1.78186156 83 -0.02311511 0.36323719 84 0.67073457 -0.02311511 85 2.28247670 0.67073457 86 -0.31174248 2.28247670 87 0.41662310 -0.31174248 88 0.59817511 0.41662310 89 -2.51884643 0.59817511 90 0.12297411 -2.51884643 91 -1.16213537 0.12297411 92 0.92656204 -1.16213537 93 -1.40685269 0.92656204 94 2.44829257 -1.40685269 95 -2.03921568 2.44829257 96 2.01166552 -2.03921568 97 -0.63349779 2.01166552 98 1.96520811 -0.63349779 99 0.39374782 1.96520811 100 -1.02539665 0.39374782 101 1.49314447 -1.02539665 102 2.31369302 1.49314447 103 -2.00544913 2.31369302 104 -3.98283586 -2.00544913 105 -0.68320010 -3.98283586 106 -1.11992061 -0.68320010 107 1.96422934 -1.11992061 108 0.51504284 1.96422934 109 0.45033640 0.51504284 110 0.59688266 0.45033640 111 -0.19358560 0.59688266 112 1.88465883 -0.19358560 113 2.02841528 1.88465883 114 -1.76192112 2.02841528 115 -1.71917580 -1.76192112 116 -3.24361511 -1.71917580 117 -0.26109256 -3.24361511 118 -1.33104481 -0.26109256 119 2.39264937 -1.33104481 120 1.33828547 2.39264937 121 -1.00757161 1.33828547 122 -1.49949779 -1.00757161 123 -3.28746560 -1.49949779 124 -1.13084752 -3.28746560 125 1.25910878 -1.13084752 126 0.46037691 1.25910878 127 0.40123875 0.46037691 128 -3.10007260 0.40123875 129 0.76250686 -3.10007260 130 -0.77517334 0.76250686 131 -0.83994544 -0.77517334 132 -0.46435079 -0.83994544 133 1.66436761 -0.46435079 134 -0.39682888 1.66436761 135 -2.78735933 -0.39682888 136 1.77518045 -2.78735933 137 2.01615536 1.77518045 138 -0.38976246 2.01615536 139 -0.57910790 -0.38976246 140 -3.56632924 -0.57910790 141 3.39120059 -3.56632924 142 NA 3.39120059 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.53416041 -0.04021989 [2,] 3.42970162 -1.53416041 [3,] 2.27999840 3.42970162 [4,] -3.52395007 2.27999840 [5,] -2.75139803 -3.52395007 [6,] -3.26240600 -2.75139803 [7,] 0.89484783 -3.26240600 [8,] 0.14787140 0.89484783 [9,] -1.10196156 0.14787140 [10,] -1.26006464 -1.10196156 [11,] -3.96760296 -1.26006464 [12,] 1.36790899 -3.96760296 [13,] -2.46237173 1.36790899 [14,] -1.25336390 -2.46237173 [15,] -0.60995417 -1.25336390 [16,] -1.36224280 -0.60995417 [17,] 0.13113644 -1.36224280 [18,] -0.86039307 0.13113644 [19,] -1.79412501 -0.86039307 [20,] 0.14345595 -1.79412501 [21,] 1.80732946 0.14345595 [22,] -2.30611130 1.80732946 [23,] 6.41275918 -2.30611130 [24,] -1.29480647 6.41275918 [25,] 0.09705758 -1.29480647 [26,] 2.18095420 0.09705758 [27,] 0.06262662 2.18095420 [28,] -0.71415677 0.06262662 [29,] -2.12580944 -0.71415677 [30,] 0.77012600 -2.12580944 [31,] 0.15139409 0.77012600 [32,] -0.39312209 0.15139409 [33,] 1.90160497 -0.39312209 [34,] -1.36798797 1.90160497 [35,] -0.78567069 -1.36798797 [36,] 1.12734556 -0.78567069 [37,] 0.68806919 1.12734556 [38,] -1.51622218 0.68806919 [39,] -0.44631302 -1.51622218 [40,] 3.59801104 -0.44631302 [41,] 3.30689809 3.59801104 [42,] -0.26124166 3.30689809 [43,] 0.99821876 -0.26124166 [44,] -1.11183783 0.99821876 [45,] -1.30136774 -1.11183783 [46,] -0.43073252 -1.30136774 [47,] 1.32608428 -0.43073252 [48,] 1.33977206 1.32608428 [49,] 0.24764311 1.33977206 [50,] -1.89326900 0.24764311 [51,] 0.27578038 -1.89326900 [52,] 3.16397810 0.27578038 [53,] 0.52056892 3.16397810 [54,] 1.33440004 0.52056892 [55,] 0.92840044 1.33440004 [56,] 3.48747552 0.92840044 [57,] -0.32166448 3.48747552 [58,] -1.29568683 -0.32166448 [59,] 4.50920959 -1.29568683 [60,] 2.39328898 4.50920959 [61,] 1.67741487 2.39328898 [62,] -1.07038795 1.67741487 [63,] -1.56325247 -1.07038795 [64,] -0.48672171 -1.56325247 [65,] -1.57934675 -0.48672171 [66,] -0.24578659 -1.57934675 [67,] 0.53426167 -0.24578659 [68,] 2.36608462 0.53426167 [69,] -1.80549380 2.36608462 [70,] 0.09303512 -1.80549380 [71,] -0.38930490 0.09303512 [72,] 2.02315766 -0.38930490 [73,] -0.24736093 2.02315766 [74,] -2.05929386 -0.24736093 [75,] 0.98405415 -2.05929386 [76,] -1.35479695 0.98405415 [77,] 0.18270107 -1.35479695 [78,] 0.92276012 0.18270107 [79,] -0.97447973 0.92276012 [80,] 3.44070095 -0.97447973 [81,] -1.78186156 3.44070095 [82,] 0.36323719 -1.78186156 [83,] -0.02311511 0.36323719 [84,] 0.67073457 -0.02311511 [85,] 2.28247670 0.67073457 [86,] -0.31174248 2.28247670 [87,] 0.41662310 -0.31174248 [88,] 0.59817511 0.41662310 [89,] -2.51884643 0.59817511 [90,] 0.12297411 -2.51884643 [91,] -1.16213537 0.12297411 [92,] 0.92656204 -1.16213537 [93,] -1.40685269 0.92656204 [94,] 2.44829257 -1.40685269 [95,] -2.03921568 2.44829257 [96,] 2.01166552 -2.03921568 [97,] -0.63349779 2.01166552 [98,] 1.96520811 -0.63349779 [99,] 0.39374782 1.96520811 [100,] -1.02539665 0.39374782 [101,] 1.49314447 -1.02539665 [102,] 2.31369302 1.49314447 [103,] -2.00544913 2.31369302 [104,] -3.98283586 -2.00544913 [105,] -0.68320010 -3.98283586 [106,] -1.11992061 -0.68320010 [107,] 1.96422934 -1.11992061 [108,] 0.51504284 1.96422934 [109,] 0.45033640 0.51504284 [110,] 0.59688266 0.45033640 [111,] -0.19358560 0.59688266 [112,] 1.88465883 -0.19358560 [113,] 2.02841528 1.88465883 [114,] -1.76192112 2.02841528 [115,] -1.71917580 -1.76192112 [116,] -3.24361511 -1.71917580 [117,] -0.26109256 -3.24361511 [118,] -1.33104481 -0.26109256 [119,] 2.39264937 -1.33104481 [120,] 1.33828547 2.39264937 [121,] -1.00757161 1.33828547 [122,] -1.49949779 -1.00757161 [123,] -3.28746560 -1.49949779 [124,] -1.13084752 -3.28746560 [125,] 1.25910878 -1.13084752 [126,] 0.46037691 1.25910878 [127,] 0.40123875 0.46037691 [128,] -3.10007260 0.40123875 [129,] 0.76250686 -3.10007260 [130,] -0.77517334 0.76250686 [131,] -0.83994544 -0.77517334 [132,] -0.46435079 -0.83994544 [133,] 1.66436761 -0.46435079 [134,] -0.39682888 1.66436761 [135,] -2.78735933 -0.39682888 [136,] 1.77518045 -2.78735933 [137,] 2.01615536 1.77518045 [138,] -0.38976246 2.01615536 [139,] -0.57910790 -0.38976246 [140,] -3.56632924 -0.57910790 [141,] 3.39120059 -3.56632924 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.53416041 -0.04021989 2 3.42970162 -1.53416041 3 2.27999840 3.42970162 4 -3.52395007 2.27999840 5 -2.75139803 -3.52395007 6 -3.26240600 -2.75139803 7 0.89484783 -3.26240600 8 0.14787140 0.89484783 9 -1.10196156 0.14787140 10 -1.26006464 -1.10196156 11 -3.96760296 -1.26006464 12 1.36790899 -3.96760296 13 -2.46237173 1.36790899 14 -1.25336390 -2.46237173 15 -0.60995417 -1.25336390 16 -1.36224280 -0.60995417 17 0.13113644 -1.36224280 18 -0.86039307 0.13113644 19 -1.79412501 -0.86039307 20 0.14345595 -1.79412501 21 1.80732946 0.14345595 22 -2.30611130 1.80732946 23 6.41275918 -2.30611130 24 -1.29480647 6.41275918 25 0.09705758 -1.29480647 26 2.18095420 0.09705758 27 0.06262662 2.18095420 28 -0.71415677 0.06262662 29 -2.12580944 -0.71415677 30 0.77012600 -2.12580944 31 0.15139409 0.77012600 32 -0.39312209 0.15139409 33 1.90160497 -0.39312209 34 -1.36798797 1.90160497 35 -0.78567069 -1.36798797 36 1.12734556 -0.78567069 37 0.68806919 1.12734556 38 -1.51622218 0.68806919 39 -0.44631302 -1.51622218 40 3.59801104 -0.44631302 41 3.30689809 3.59801104 42 -0.26124166 3.30689809 43 0.99821876 -0.26124166 44 -1.11183783 0.99821876 45 -1.30136774 -1.11183783 46 -0.43073252 -1.30136774 47 1.32608428 -0.43073252 48 1.33977206 1.32608428 49 0.24764311 1.33977206 50 -1.89326900 0.24764311 51 0.27578038 -1.89326900 52 3.16397810 0.27578038 53 0.52056892 3.16397810 54 1.33440004 0.52056892 55 0.92840044 1.33440004 56 3.48747552 0.92840044 57 -0.32166448 3.48747552 58 -1.29568683 -0.32166448 59 4.50920959 -1.29568683 60 2.39328898 4.50920959 61 1.67741487 2.39328898 62 -1.07038795 1.67741487 63 -1.56325247 -1.07038795 64 -0.48672171 -1.56325247 65 -1.57934675 -0.48672171 66 -0.24578659 -1.57934675 67 0.53426167 -0.24578659 68 2.36608462 0.53426167 69 -1.80549380 2.36608462 70 0.09303512 -1.80549380 71 -0.38930490 0.09303512 72 2.02315766 -0.38930490 73 -0.24736093 2.02315766 74 -2.05929386 -0.24736093 75 0.98405415 -2.05929386 76 -1.35479695 0.98405415 77 0.18270107 -1.35479695 78 0.92276012 0.18270107 79 -0.97447973 0.92276012 80 3.44070095 -0.97447973 81 -1.78186156 3.44070095 82 0.36323719 -1.78186156 83 -0.02311511 0.36323719 84 0.67073457 -0.02311511 85 2.28247670 0.67073457 86 -0.31174248 2.28247670 87 0.41662310 -0.31174248 88 0.59817511 0.41662310 89 -2.51884643 0.59817511 90 0.12297411 -2.51884643 91 -1.16213537 0.12297411 92 0.92656204 -1.16213537 93 -1.40685269 0.92656204 94 2.44829257 -1.40685269 95 -2.03921568 2.44829257 96 2.01166552 -2.03921568 97 -0.63349779 2.01166552 98 1.96520811 -0.63349779 99 0.39374782 1.96520811 100 -1.02539665 0.39374782 101 1.49314447 -1.02539665 102 2.31369302 1.49314447 103 -2.00544913 2.31369302 104 -3.98283586 -2.00544913 105 -0.68320010 -3.98283586 106 -1.11992061 -0.68320010 107 1.96422934 -1.11992061 108 0.51504284 1.96422934 109 0.45033640 0.51504284 110 0.59688266 0.45033640 111 -0.19358560 0.59688266 112 1.88465883 -0.19358560 113 2.02841528 1.88465883 114 -1.76192112 2.02841528 115 -1.71917580 -1.76192112 116 -3.24361511 -1.71917580 117 -0.26109256 -3.24361511 118 -1.33104481 -0.26109256 119 2.39264937 -1.33104481 120 1.33828547 2.39264937 121 -1.00757161 1.33828547 122 -1.49949779 -1.00757161 123 -3.28746560 -1.49949779 124 -1.13084752 -3.28746560 125 1.25910878 -1.13084752 126 0.46037691 1.25910878 127 0.40123875 0.46037691 128 -3.10007260 0.40123875 129 0.76250686 -3.10007260 130 -0.77517334 0.76250686 131 -0.83994544 -0.77517334 132 -0.46435079 -0.83994544 133 1.66436761 -0.46435079 134 -0.39682888 1.66436761 135 -2.78735933 -0.39682888 136 1.77518045 -2.78735933 137 2.01615536 1.77518045 138 -0.38976246 2.01615536 139 -0.57910790 -0.38976246 140 -3.56632924 -0.57910790 141 3.39120059 -3.56632924 > 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/7uczk1291565270.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/853hn1291565270.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/953hn1291565270.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/1053hn1291565270.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11jvfw1291565270.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/12t4ey1291565270.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/1305ta1291565270.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/14txav1291565270.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/15exrj1291565270.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/16ap6s1291565270.tab") + } > > try(system("convert tmp/19ujw1291565270.ps tmp/19ujw1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/29ujw1291565270.ps tmp/29ujw1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/39ujw1291565270.ps tmp/39ujw1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/4jl0h1291565270.ps tmp/4jl0h1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/5jl0h1291565270.ps tmp/5jl0h1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/6jl0h1291565270.ps tmp/6jl0h1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/7uczk1291565270.ps tmp/7uczk1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/853hn1291565270.ps tmp/853hn1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/953hn1291565270.ps tmp/953hn1291565270.png",intern=TRUE)) character(0) > try(system("convert tmp/1053hn1291565270.ps tmp/1053hn1291565270.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.112 1.919 9.372