R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) 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(7 + ,3 + ,2 + ,3 + ,7 + ,6 + ,7 + ,5 + ,6 + ,0 + ,7 + ,7 + ,6 + ,6 + ,6 + ,0 + ,8 + ,8 + ,6 + ,6 + ,6 + ,6 + ,9 + ,8 + ,8 + ,7 + ,8 + ,5 + ,5 + ,9 + ,8 + ,3 + ,1 + ,0 + ,7 + ,8 + ,8 + ,2 + ,9 + ,8 + ,8 + ,8 + ,5 + ,4 + ,4 + ,0 + ,7 + ,7 + ,4 + ,7 + ,7 + ,0 + ,8 + ,7 + ,9 + ,4 + ,4 + ,9 + ,8 + ,4 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,6 + ,4 + ,7 + ,5 + ,7 + ,7 + ,5 + ,8 + ,5 + ,6 + ,4 + ,5 + ,4 + ,8 + ,8 + ,2 + ,6 + ,6 + ,0 + ,7 + ,5 + ,4 + ,5 + ,5 + ,0 + ,9 + ,4 + ,2 + ,0 + ,2 + ,2 + ,2 + ,9 + ,6 + ,9 + ,9 + ,6 + ,8 + ,8 + ,7 + ,4 + ,4 + ,0 + ,8 + ,4 + ,8 + ,2 + ,4 + ,4 + ,4 + ,6 + ,5 + ,2 + ,5 + ,5 + ,5 + ,6 + ,7 + ,7 + ,7 + ,7 + ,7 + ,7 + ,5 + ,5 + ,5 + ,5 + ,8 + ,3 + ,4 + ,9 + ,9 + ,4 + ,4 + ,4 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,7 + ,7 + ,3 + ,0 + ,9 + ,7 + ,7 + ,3 + ,3 + ,1 + ,7 + ,5 + ,8 + ,6 + ,5 + ,0 + ,6 + ,8 + ,4 + ,6 + ,5 + ,4 + ,4 + ,6 + ,4 + ,4 + ,4 + ,4 + ,8 + ,4 + ,7 + ,7 + ,7 + ,7 + ,3 + ,9 + 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,9 + ,4 + ,4 + ,9 + ,4 + ,7 + ,5 + ,6 + ,6 + ,5 + ,5 + ,9 + ,6 + ,2 + ,5 + ,0 + ,6 + ,8 + ,4 + ,4 + ,4 + ,0 + ,4 + ,4 + ,6 + ,2 + ,2 + ,0 + ,6 + ,6 + ,3 + ,3 + ,3 + ,3 + ,7 + ,9 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,5 + ,5 + ,0 + ,5 + ,5 + ,4 + ,4 + ,4 + ,4 + ,9 + ,8 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,1 + ,1 + ,0 + ,9 + ,6 + ,4 + ,4 + ,5 + ,4 + ,3 + ,6 + ,7 + ,4 + ,2 + ,7 + ,7 + ,7 + ,6 + ,6 + ,6 + ,0 + ,6 + ,7 + ,7 + ,5 + ,5 + ,5 + ,5 + ,9 + ,6 + ,9 + ,2 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,9 + ,6 + ,8 + ,8 + ,8 + ,8 + ,8 + ,6 + ,7 + ,7 + ,7 + ,2 + ,7 + ,4 + ,7 + ,7 + ,7 + ,7 + ,7 + ,7 + ,4 + ,0 + ,9 + ,0 + ,4 + ,8 + ,6 + ,2 + ,2 + ,0 + ,8 + ,7 + ,5 + ,6 + ,6 + ,5 + ,5 + ,9 + ,2 + ,5 + ,5 + ,0 + ,9 + ,6) + ,dim=c(6 + ,156) + ,dimnames=list(c('Schoolprestaties' + ,'Sport' + ,'GoingOut' + ,'Relation' + ,'Friends' + ,'Job') + ,1:156)) > y <- array(NA,dim=c(6,156),dimnames=list(c('Schoolprestaties','Sport','GoingOut','Relation','Friends','Job'),1:156)) > 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 = '1' > 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 Schoolprestaties Sport GoingOut Relation Friends Job 1 7 3 2 3 7 6 2 7 5 6 0 7 7 3 6 6 6 0 8 8 4 6 6 6 6 9 8 5 8 7 8 5 5 9 6 8 3 1 0 7 8 7 8 2 9 8 8 8 8 5 4 4 0 7 7 9 4 7 7 0 8 7 10 9 4 4 9 8 4 11 6 6 6 6 6 6 12 6 6 5 6 4 7 13 5 7 7 5 8 5 14 6 4 5 4 8 8 15 2 6 6 0 7 5 16 4 5 5 0 9 4 17 2 0 2 2 2 9 18 6 9 9 6 8 8 19 7 4 4 0 8 4 20 8 2 4 4 4 6 21 5 2 5 5 5 6 22 7 7 7 7 7 7 23 5 5 5 5 8 3 24 4 9 9 4 4 4 25 6 6 6 6 6 6 26 6 6 6 6 6 6 27 7 7 3 0 9 7 28 7 3 3 1 7 5 29 8 6 5 0 6 8 30 4 6 5 4 4 6 31 4 4 4 4 8 4 32 7 7 7 7 3 9 33 7 7 6 7 7 7 34 4 2 7 0 4 4 35 7 4 4 4 7 6 36 5 5 5 5 8 8 37 6 6 6 0 6 6 38 5 5 5 5 5 5 39 6 6 0 1 6 6 40 7 6 6 2 9 6 41 6 6 5 0 8 4 42 9 3 3 9 7 7 43 7 3 3 3 3 9 44 4 3 3 0 4 8 45 6 6 7 6 6 6 46 5 7 7 1 8 6 47 5 5 1 5 5 5 48 4 5 5 0 7 7 49 7 5 5 0 7 5 50 6 6 6 0 9 8 51 6 2 2 6 6 6 52 7 6 6 7 8 8 53 5 5 5 0 5 5 54 4 4 2 4 4 4 55 5 7 7 5 8 5 56 5 5 5 1 9 6 57 4 3 3 4 4 4 58 9 6 6 9 8 6 59 8 2 2 2 2 9 60 8 8 8 8 8 7 61 3 3 5 3 7 3 62 6 0 2 1 7 6 63 6 2 6 0 6 6 64 6 8 2 6 6 6 65 5 4 1 0 5 5 66 5 5 5 0 8 5 67 6 6 6 6 4 5 68 7 5 2 2 9 9 69 6 6 6 1 6 8 70 5 2 2 5 5 5 71 5 6 6 5 5 6 72 7 2 5 5 7 7 73 5 5 0 5 8 5 74 6 6 2 6 9 6 75 6 4 4 6 6 6 76 9 6 1 0 6 6 77 8 5 5 0 5 6 78 5 5 5 1 3 9 79 7 4 2 7 7 7 80 7 2 2 2 9 9 81 4 7 7 4 7 4 82 6 5 5 0 8 8 83 5 6 2 5 5 5 84 5 5 5 5 5 8 85 3 3 3 3 8 9 86 6 6 6 0 6 6 87 4 4 1 4 9 4 88 9 5 5 9 5 7 89 8 7 7 0 8 8 90 4 4 2 4 8 9 91 2 6 6 2 7 9 92 7 8 8 7 7 7 93 7 7 7 7 8 8 94 6 6 6 6 4 4 95 5 7 7 0 5 6 96 8 4 4 5 9 7 97 6 0 5 6 6 6 98 3 3 2 0 7 7 99 5 5 5 5 5 5 100 9 6 2 9 2 9 101 7 5 5 0 7 7 102 7 7 7 7 7 7 103 6 6 5 1 6 6 104 3 8 8 3 8 6 105 7 7 2 7 9 9 106 8 8 8 8 8 9 107 3 3 3 0 3 8 108 5 8 2 5 5 8 109 8 3 3 3 7 3 110 7 4 5 0 8 6 111 5 2 2 5 5 5 112 7 7 2 7 9 7 113 6 6 6 0 6 6 114 7 2 2 0 7 7 115 9 7 7 0 7 7 116 6 6 6 6 6 6 117 6 6 2 0 3 8 118 6 6 2 6 9 9 119 6 6 5 6 6 6 120 2 6 6 2 2 9 121 5 4 4 5 5 5 122 5 2 5 0 5 6 123 4 7 7 4 9 4 124 7 6 6 0 7 7 125 6 6 6 6 6 6 126 5 5 5 5 8 8 127 8 8 2 8 8 8 128 7 6 6 6 6 9 129 5 0 3 5 3 8 130 4 4 2 0 7 4 131 8 8 8 8 9 6 132 6 6 6 0 7 6 133 9 4 4 9 4 7 134 5 6 6 5 5 9 135 6 2 5 0 6 8 136 4 4 4 0 4 4 137 6 2 2 0 6 6 138 3 3 3 3 7 9 139 6 6 6 6 6 6 140 5 5 5 0 5 5 141 4 4 4 4 9 8 142 6 6 6 6 6 6 143 5 1 1 0 9 6 144 4 4 5 4 3 6 145 7 4 2 7 7 7 146 6 6 6 0 6 7 147 7 5 5 5 5 9 148 6 9 2 6 6 6 149 6 6 6 6 9 6 150 8 8 8 8 8 6 151 7 7 7 2 7 4 152 7 7 7 7 7 7 153 4 0 9 0 4 8 154 6 2 2 0 8 7 155 5 6 6 5 5 9 156 2 5 5 0 9 6 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Sport GoingOut Relation Friends Job 3.40800 0.04877 -0.02654 0.16906 0.12010 0.14835 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -4.0553 -0.8354 -0.0266 0.8554 3.7152 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.40800 0.74136 4.597 9.04e-06 *** Sport 0.04877 0.07168 0.680 0.497279 GoingOut -0.02654 0.06481 -0.410 0.682722 Relation 0.16906 0.04329 3.905 0.000142 *** Friends 0.12010 0.06875 1.747 0.082729 . Job 0.14835 0.07739 1.917 0.057149 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.505 on 150 degrees of freedom Multiple R-squared: 0.1527, Adjusted R-squared: 0.1244 F-statistic: 5.405 on 5 and 150 DF, p-value: 0.0001337 > 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,] 0.35612095 0.71224189 0.64387905 [2,] 0.40895758 0.81791515 0.59104242 [3,] 0.43066035 0.86132070 0.56933965 [4,] 0.45347725 0.90695451 0.54652275 [5,] 0.34489585 0.68979170 0.65510415 [6,] 0.30692854 0.61385708 0.69307146 [7,] 0.46009731 0.92019462 0.53990269 [8,] 0.36981078 0.73962156 0.63018922 [9,] 0.82434790 0.35130421 0.17565210 [10,] 0.79787822 0.40424355 0.20212178 [11,] 0.85543670 0.28912659 0.14456330 [12,] 0.91516899 0.16966202 0.08483101 [13,] 0.89516712 0.20966576 0.10483288 [14,] 0.85844794 0.28310413 0.14155206 [15,] 0.83780116 0.32439768 0.16219884 [16,] 0.80199678 0.39600645 0.19800322 [17,] 0.75104021 0.49791958 0.24895979 [18,] 0.69463337 0.61073325 0.30536663 [19,] 0.65101431 0.69797137 0.34898569 [20,] 0.64824686 0.70350628 0.35175314 [21,] 0.76006922 0.47986157 0.23993078 [22,] 0.74764030 0.50471940 0.25235970 [23,] 0.78196289 0.43607422 0.21803711 [24,] 0.74488088 0.51023823 0.25511912 [25,] 0.69461780 0.61076441 0.30538220 [26,] 0.65750493 0.68499013 0.34249507 [27,] 0.61644889 0.76710221 0.38355111 [28,] 0.65890751 0.68218498 0.34109249 [29,] 0.63329131 0.73341738 0.36670869 [30,] 0.58706683 0.82586633 0.41293317 [31,] 0.53360133 0.93279734 0.46639867 [32,] 0.50341062 0.99317876 0.49658938 [33,] 0.46838922 0.93677845 0.53161078 [34,] 0.47132606 0.94265212 0.52867394 [35,] 0.44229671 0.88459342 0.55770329 [36,] 0.43238867 0.86477733 0.56761133 [37,] 0.38035004 0.76070008 0.61964996 [38,] 0.33513102 0.67026204 0.66486898 [39,] 0.31451060 0.62902120 0.68548940 [40,] 0.31329932 0.62659864 0.68670068 [41,] 0.34938413 0.69876826 0.65061587 [42,] 0.30343914 0.60687828 0.69656086 [43,] 0.26840292 0.53680584 0.73159708 [44,] 0.22962405 0.45924811 0.77037595 [45,] 0.19534095 0.39068191 0.80465905 [46,] 0.18454259 0.36908519 0.81545741 [47,] 0.16795359 0.33590717 0.83204641 [48,] 0.14892052 0.29784103 0.85107948 [49,] 0.13593477 0.27186954 0.86406523 [50,] 0.15476261 0.30952523 0.84523739 [51,] 0.21347924 0.42695848 0.78652076 [52,] 0.19424231 0.38848463 0.80575769 [53,] 0.22656475 0.45312950 0.77343525 [54,] 0.19674946 0.39349892 0.80325054 [55,] 0.18325976 0.36651952 0.81674024 [56,] 0.15621775 0.31243550 0.84378225 [57,] 0.12896046 0.25792093 0.87103954 [58,] 0.10569226 0.21138453 0.89430774 [59,] 0.08870993 0.17741986 0.91129007 [60,] 0.07619602 0.15239204 0.92380398 [61,] 0.06113981 0.12227963 0.93886019 [62,] 0.05132144 0.10264288 0.94867856 [63,] 0.04324086 0.08648173 0.95675914 [64,] 0.03603650 0.07207301 0.96396350 [65,] 0.03559134 0.07118267 0.96440866 [66,] 0.02978849 0.05957698 0.97021151 [67,] 0.02270485 0.04540969 0.97729515 [68,] 0.08368694 0.16737389 0.91631306 [69,] 0.15647832 0.31295665 0.84352168 [70,] 0.13513978 0.27027956 0.86486022 [71,] 0.11315320 0.22630639 0.88684680 [72,] 0.10557439 0.21114878 0.89442561 [73,] 0.11392796 0.22785593 0.88607204 [74,] 0.09679983 0.19359965 0.90320017 [75,] 0.08461522 0.16923043 0.91538478 [76,] 0.07942757 0.15885515 0.92057243 [77,] 0.18119147 0.36238294 0.81880853 [78,] 0.16061498 0.32122997 0.83938502 [79,] 0.18379644 0.36759288 0.81620356 [80,] 0.22508517 0.45017033 0.77491483 [81,] 0.28698366 0.57396731 0.71301634 [82,] 0.35923177 0.71846355 0.64076823 [83,] 0.62211542 0.75576915 0.37788458 [84,] 0.57756417 0.84487166 0.42243583 [85,] 0.53017451 0.93965099 0.46982549 [86,] 0.48477043 0.96954085 0.51522957 [87,] 0.43656216 0.87312432 0.56343784 [88,] 0.44176271 0.88352541 0.55823729 [89,] 0.39471471 0.78942942 0.60528529 [90,] 0.45276472 0.90552944 0.54723528 [91,] 0.41997808 0.83995616 0.58002192 [92,] 0.49788811 0.99577622 0.50211189 [93,] 0.50755469 0.98489062 0.49244531 [94,] 0.46089297 0.92178594 0.53910703 [95,] 0.41997002 0.83994004 0.58002998 [96,] 0.58516777 0.82966446 0.41483223 [97,] 0.53791329 0.92417342 0.46208671 [98,] 0.50679745 0.98640510 0.49320255 [99,] 0.52596053 0.94807893 0.47403947 [100,] 0.50826356 0.98347288 0.49173644 [101,] 0.59765386 0.80469228 0.40234614 [102,] 0.60912184 0.78175632 0.39087816 [103,] 0.56283204 0.87433593 0.43716796 [104,] 0.50991458 0.98017083 0.49008542 [105,] 0.46842495 0.93684989 0.53157505 [106,] 0.51264965 0.97470070 0.48735035 [107,] 0.77554189 0.44891622 0.22445811 [108,] 0.73195328 0.53609344 0.26804672 [109,] 0.72731915 0.54536170 0.27268085 [110,] 0.68597877 0.62804246 0.31402123 [111,] 0.63434805 0.73130390 0.36565195 [112,] 0.81487080 0.37025839 0.18512920 [113,] 0.78729315 0.42541369 0.21270685 [114,] 0.74407424 0.51185152 0.25592576 [115,] 0.79196860 0.41606279 0.20803140 [116,] 0.83537664 0.32924671 0.16462336 [117,] 0.79877984 0.40244032 0.20122016 [118,] 0.77914220 0.44171559 0.22085780 [119,] 0.75604958 0.48790084 0.24395042 [120,] 0.72133114 0.55733773 0.27866886 [121,] 0.67781270 0.64437460 0.32218730 [122,] 0.64670404 0.70659193 0.35329596 [123,] 0.60298827 0.79402345 0.39701173 [124,] 0.58878154 0.82243693 0.41121846 [125,] 0.63835077 0.72329846 0.36164923 [126,] 0.57977806 0.84044388 0.42022194 [127,] 0.59657809 0.80684382 0.40342191 [128,] 0.57218736 0.85562529 0.42781264 [129,] 0.55696361 0.88607278 0.44303639 [130,] 0.61241952 0.77516097 0.38758048 [131,] 0.52816940 0.94366120 0.47183060 [132,] 0.43554301 0.87108603 0.56445699 [133,] 0.46927237 0.93854473 0.53072763 [134,] 0.37354846 0.74709692 0.62645154 [135,] 0.28160942 0.56321884 0.71839058 [136,] 0.36352939 0.72705878 0.63647061 [137,] 0.25594309 0.51188618 0.74405691 [138,] 0.36090940 0.72181881 0.63909060 [139,] 0.33532509 0.67065017 0.66467491 > postscript(file="/var/www/rcomp/tmp/11ha31324485973.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/rcomp/tmp/24zzi1324485973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3ip6a1324485973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4r2q41324485973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5swnw1324485973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 156 Frequency = 1 1 2 3 4 5 6 1.260808296 1.628252751 0.311032748 -0.823412893 1.681984888 2.444736863 7 8 9 10 11 12 1.233279081 -0.376058148 -1.562845373 2.427378625 -0.166422057 -0.101125638 13 14 15 16 17 18 -1.111432290 -0.294197973 -3.123814390 -1.193421708 -3.268398002 -0.770009608 19 20 21 22 23 24 1.948904204 2.553890986 -0.708721111 0.373841098 -0.770265906 -1.358101651 25 26 27 28 29 30 -0.166422057 -0.166422057 1.210893151 1.773819182 2.524681703 -1.614656307 31 32 33 34 35 36 -1.727329387 0.557517047 0.347299543 -0.393545686 1.096060797 -1.512028583 37 38 39 40 41 42 0.847928329 -0.706685212 0.519620600 1.149525769 0.877901335 2.124646930 43 44 45 46 47 48 1.322673265 -1.141894262 -0.139880502 -0.583551235 -0.812851433 -1.398288804 49 50 51 52 53 54 1.898416267 0.190937494 -0.077499430 0.127623965 0.138606776 -1.300031478 55 56 57 58 59 60 -1.111432290 -0.659185176 -1.224717711 2.086212240 2.634057574 1.062456789 61 62 63 64 65 66 -2.214509431 0.745241728 1.043017178 -0.370132702 0.081212767 -0.221678988 67 68 69 70 71 72 0.222120988 0.647074154 0.382164861 -0.639993242 -0.877268404 0.902735843 73 74 75 76 77 78 -1.199678753 -0.632874043 -0.121960743 3.715220553 2.990254241 -0.383671254 79 80 81 82 83 84 0.387449958 0.793390790 -1.673926102 0.333263405 -0.835082090 -1.151742819 85 86 87 88 89 90 -3.277803010 0.847928329 -1.927049308 2.320376126 2.288802092 -2.522175175 91 92 93 94 95 96 -4.055341328 0.351610441 0.105393308 0.370473524 -0.054207073 1.538459354 97 98 99 100 101 102 0.099669660 -2.380369046 -0.706685212 2.255559942 1.601711196 0.373841098 103 104 105 106 107 108 0.652328376 -2.943898687 -0.295762259 0.765751718 -2.021799007 -1.377684121 109 110 111 112 113 114 2.732407458 1.678740688 -0.639993242 0.000942812 0.847928329 1.668403166 115 116 117 118 119 120 3.557249882 -0.166422057 0.805342801 -1.077931649 -0.192963612 -3.454865053 121 122 123 124 125 126 -0.684454555 0.136570877 -1.914116612 1.579480539 -0.166422057 -1.512028583 127 128 129 130 131 132 0.754854921 0.388520337 -0.720774359 -0.984083652 1.090714070 0.727833074 133 134 135 136 137 138 2.462702038 -1.322326011 0.719770551 -0.570714777 0.936850956 -3.157707755 139 140 141 142 143 144 -0.166422057 0.138606776 -2.440834784 -0.166422057 -0.401204152 -1.397016628 145 146 147 148 149 150 0.387449958 0.699575794 0.699904646 -0.418904915 -0.526707822 1.210809324 151 152 153 154 155 156 1.664190693 0.373841098 -0.836328294 0.548307911 -1.322326011 -3.490126779 > postscript(file="/var/www/rcomp/tmp/6pt1l1324485973.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 = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 1.260808296 NA 1 1.628252751 1.260808296 2 0.311032748 1.628252751 3 -0.823412893 0.311032748 4 1.681984888 -0.823412893 5 2.444736863 1.681984888 6 1.233279081 2.444736863 7 -0.376058148 1.233279081 8 -1.562845373 -0.376058148 9 2.427378625 -1.562845373 10 -0.166422057 2.427378625 11 -0.101125638 -0.166422057 12 -1.111432290 -0.101125638 13 -0.294197973 -1.111432290 14 -3.123814390 -0.294197973 15 -1.193421708 -3.123814390 16 -3.268398002 -1.193421708 17 -0.770009608 -3.268398002 18 1.948904204 -0.770009608 19 2.553890986 1.948904204 20 -0.708721111 2.553890986 21 0.373841098 -0.708721111 22 -0.770265906 0.373841098 23 -1.358101651 -0.770265906 24 -0.166422057 -1.358101651 25 -0.166422057 -0.166422057 26 1.210893151 -0.166422057 27 1.773819182 1.210893151 28 2.524681703 1.773819182 29 -1.614656307 2.524681703 30 -1.727329387 -1.614656307 31 0.557517047 -1.727329387 32 0.347299543 0.557517047 33 -0.393545686 0.347299543 34 1.096060797 -0.393545686 35 -1.512028583 1.096060797 36 0.847928329 -1.512028583 37 -0.706685212 0.847928329 38 0.519620600 -0.706685212 39 1.149525769 0.519620600 40 0.877901335 1.149525769 41 2.124646930 0.877901335 42 1.322673265 2.124646930 43 -1.141894262 1.322673265 44 -0.139880502 -1.141894262 45 -0.583551235 -0.139880502 46 -0.812851433 -0.583551235 47 -1.398288804 -0.812851433 48 1.898416267 -1.398288804 49 0.190937494 1.898416267 50 -0.077499430 0.190937494 51 0.127623965 -0.077499430 52 0.138606776 0.127623965 53 -1.300031478 0.138606776 54 -1.111432290 -1.300031478 55 -0.659185176 -1.111432290 56 -1.224717711 -0.659185176 57 2.086212240 -1.224717711 58 2.634057574 2.086212240 59 1.062456789 2.634057574 60 -2.214509431 1.062456789 61 0.745241728 -2.214509431 62 1.043017178 0.745241728 63 -0.370132702 1.043017178 64 0.081212767 -0.370132702 65 -0.221678988 0.081212767 66 0.222120988 -0.221678988 67 0.647074154 0.222120988 68 0.382164861 0.647074154 69 -0.639993242 0.382164861 70 -0.877268404 -0.639993242 71 0.902735843 -0.877268404 72 -1.199678753 0.902735843 73 -0.632874043 -1.199678753 74 -0.121960743 -0.632874043 75 3.715220553 -0.121960743 76 2.990254241 3.715220553 77 -0.383671254 2.990254241 78 0.387449958 -0.383671254 79 0.793390790 0.387449958 80 -1.673926102 0.793390790 81 0.333263405 -1.673926102 82 -0.835082090 0.333263405 83 -1.151742819 -0.835082090 84 -3.277803010 -1.151742819 85 0.847928329 -3.277803010 86 -1.927049308 0.847928329 87 2.320376126 -1.927049308 88 2.288802092 2.320376126 89 -2.522175175 2.288802092 90 -4.055341328 -2.522175175 91 0.351610441 -4.055341328 92 0.105393308 0.351610441 93 0.370473524 0.105393308 94 -0.054207073 0.370473524 95 1.538459354 -0.054207073 96 0.099669660 1.538459354 97 -2.380369046 0.099669660 98 -0.706685212 -2.380369046 99 2.255559942 -0.706685212 100 1.601711196 2.255559942 101 0.373841098 1.601711196 102 0.652328376 0.373841098 103 -2.943898687 0.652328376 104 -0.295762259 -2.943898687 105 0.765751718 -0.295762259 106 -2.021799007 0.765751718 107 -1.377684121 -2.021799007 108 2.732407458 -1.377684121 109 1.678740688 2.732407458 110 -0.639993242 1.678740688 111 0.000942812 -0.639993242 112 0.847928329 0.000942812 113 1.668403166 0.847928329 114 3.557249882 1.668403166 115 -0.166422057 3.557249882 116 0.805342801 -0.166422057 117 -1.077931649 0.805342801 118 -0.192963612 -1.077931649 119 -3.454865053 -0.192963612 120 -0.684454555 -3.454865053 121 0.136570877 -0.684454555 122 -1.914116612 0.136570877 123 1.579480539 -1.914116612 124 -0.166422057 1.579480539 125 -1.512028583 -0.166422057 126 0.754854921 -1.512028583 127 0.388520337 0.754854921 128 -0.720774359 0.388520337 129 -0.984083652 -0.720774359 130 1.090714070 -0.984083652 131 0.727833074 1.090714070 132 2.462702038 0.727833074 133 -1.322326011 2.462702038 134 0.719770551 -1.322326011 135 -0.570714777 0.719770551 136 0.936850956 -0.570714777 137 -3.157707755 0.936850956 138 -0.166422057 -3.157707755 139 0.138606776 -0.166422057 140 -2.440834784 0.138606776 141 -0.166422057 -2.440834784 142 -0.401204152 -0.166422057 143 -1.397016628 -0.401204152 144 0.387449958 -1.397016628 145 0.699575794 0.387449958 146 0.699904646 0.699575794 147 -0.418904915 0.699904646 148 -0.526707822 -0.418904915 149 1.210809324 -0.526707822 150 1.664190693 1.210809324 151 0.373841098 1.664190693 152 -0.836328294 0.373841098 153 0.548307911 -0.836328294 154 -1.322326011 0.548307911 155 -3.490126779 -1.322326011 156 NA -3.490126779 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.628252751 1.260808296 [2,] 0.311032748 1.628252751 [3,] -0.823412893 0.311032748 [4,] 1.681984888 -0.823412893 [5,] 2.444736863 1.681984888 [6,] 1.233279081 2.444736863 [7,] -0.376058148 1.233279081 [8,] -1.562845373 -0.376058148 [9,] 2.427378625 -1.562845373 [10,] -0.166422057 2.427378625 [11,] -0.101125638 -0.166422057 [12,] -1.111432290 -0.101125638 [13,] -0.294197973 -1.111432290 [14,] -3.123814390 -0.294197973 [15,] -1.193421708 -3.123814390 [16,] -3.268398002 -1.193421708 [17,] -0.770009608 -3.268398002 [18,] 1.948904204 -0.770009608 [19,] 2.553890986 1.948904204 [20,] -0.708721111 2.553890986 [21,] 0.373841098 -0.708721111 [22,] -0.770265906 0.373841098 [23,] -1.358101651 -0.770265906 [24,] -0.166422057 -1.358101651 [25,] -0.166422057 -0.166422057 [26,] 1.210893151 -0.166422057 [27,] 1.773819182 1.210893151 [28,] 2.524681703 1.773819182 [29,] -1.614656307 2.524681703 [30,] -1.727329387 -1.614656307 [31,] 0.557517047 -1.727329387 [32,] 0.347299543 0.557517047 [33,] -0.393545686 0.347299543 [34,] 1.096060797 -0.393545686 [35,] -1.512028583 1.096060797 [36,] 0.847928329 -1.512028583 [37,] -0.706685212 0.847928329 [38,] 0.519620600 -0.706685212 [39,] 1.149525769 0.519620600 [40,] 0.877901335 1.149525769 [41,] 2.124646930 0.877901335 [42,] 1.322673265 2.124646930 [43,] -1.141894262 1.322673265 [44,] -0.139880502 -1.141894262 [45,] -0.583551235 -0.139880502 [46,] -0.812851433 -0.583551235 [47,] -1.398288804 -0.812851433 [48,] 1.898416267 -1.398288804 [49,] 0.190937494 1.898416267 [50,] -0.077499430 0.190937494 [51,] 0.127623965 -0.077499430 [52,] 0.138606776 0.127623965 [53,] -1.300031478 0.138606776 [54,] -1.111432290 -1.300031478 [55,] -0.659185176 -1.111432290 [56,] -1.224717711 -0.659185176 [57,] 2.086212240 -1.224717711 [58,] 2.634057574 2.086212240 [59,] 1.062456789 2.634057574 [60,] -2.214509431 1.062456789 [61,] 0.745241728 -2.214509431 [62,] 1.043017178 0.745241728 [63,] -0.370132702 1.043017178 [64,] 0.081212767 -0.370132702 [65,] -0.221678988 0.081212767 [66,] 0.222120988 -0.221678988 [67,] 0.647074154 0.222120988 [68,] 0.382164861 0.647074154 [69,] -0.639993242 0.382164861 [70,] -0.877268404 -0.639993242 [71,] 0.902735843 -0.877268404 [72,] -1.199678753 0.902735843 [73,] -0.632874043 -1.199678753 [74,] -0.121960743 -0.632874043 [75,] 3.715220553 -0.121960743 [76,] 2.990254241 3.715220553 [77,] -0.383671254 2.990254241 [78,] 0.387449958 -0.383671254 [79,] 0.793390790 0.387449958 [80,] -1.673926102 0.793390790 [81,] 0.333263405 -1.673926102 [82,] -0.835082090 0.333263405 [83,] -1.151742819 -0.835082090 [84,] -3.277803010 -1.151742819 [85,] 0.847928329 -3.277803010 [86,] -1.927049308 0.847928329 [87,] 2.320376126 -1.927049308 [88,] 2.288802092 2.320376126 [89,] -2.522175175 2.288802092 [90,] -4.055341328 -2.522175175 [91,] 0.351610441 -4.055341328 [92,] 0.105393308 0.351610441 [93,] 0.370473524 0.105393308 [94,] -0.054207073 0.370473524 [95,] 1.538459354 -0.054207073 [96,] 0.099669660 1.538459354 [97,] -2.380369046 0.099669660 [98,] -0.706685212 -2.380369046 [99,] 2.255559942 -0.706685212 [100,] 1.601711196 2.255559942 [101,] 0.373841098 1.601711196 [102,] 0.652328376 0.373841098 [103,] -2.943898687 0.652328376 [104,] -0.295762259 -2.943898687 [105,] 0.765751718 -0.295762259 [106,] -2.021799007 0.765751718 [107,] -1.377684121 -2.021799007 [108,] 2.732407458 -1.377684121 [109,] 1.678740688 2.732407458 [110,] -0.639993242 1.678740688 [111,] 0.000942812 -0.639993242 [112,] 0.847928329 0.000942812 [113,] 1.668403166 0.847928329 [114,] 3.557249882 1.668403166 [115,] -0.166422057 3.557249882 [116,] 0.805342801 -0.166422057 [117,] -1.077931649 0.805342801 [118,] -0.192963612 -1.077931649 [119,] -3.454865053 -0.192963612 [120,] -0.684454555 -3.454865053 [121,] 0.136570877 -0.684454555 [122,] -1.914116612 0.136570877 [123,] 1.579480539 -1.914116612 [124,] -0.166422057 1.579480539 [125,] -1.512028583 -0.166422057 [126,] 0.754854921 -1.512028583 [127,] 0.388520337 0.754854921 [128,] -0.720774359 0.388520337 [129,] -0.984083652 -0.720774359 [130,] 1.090714070 -0.984083652 [131,] 0.727833074 1.090714070 [132,] 2.462702038 0.727833074 [133,] -1.322326011 2.462702038 [134,] 0.719770551 -1.322326011 [135,] -0.570714777 0.719770551 [136,] 0.936850956 -0.570714777 [137,] -3.157707755 0.936850956 [138,] -0.166422057 -3.157707755 [139,] 0.138606776 -0.166422057 [140,] -2.440834784 0.138606776 [141,] -0.166422057 -2.440834784 [142,] -0.401204152 -0.166422057 [143,] -1.397016628 -0.401204152 [144,] 0.387449958 -1.397016628 [145,] 0.699575794 0.387449958 [146,] 0.699904646 0.699575794 [147,] -0.418904915 0.699904646 [148,] -0.526707822 -0.418904915 [149,] 1.210809324 -0.526707822 [150,] 1.664190693 1.210809324 [151,] 0.373841098 1.664190693 [152,] -0.836328294 0.373841098 [153,] 0.548307911 -0.836328294 [154,] -1.322326011 0.548307911 [155,] -3.490126779 -1.322326011 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.628252751 1.260808296 2 0.311032748 1.628252751 3 -0.823412893 0.311032748 4 1.681984888 -0.823412893 5 2.444736863 1.681984888 6 1.233279081 2.444736863 7 -0.376058148 1.233279081 8 -1.562845373 -0.376058148 9 2.427378625 -1.562845373 10 -0.166422057 2.427378625 11 -0.101125638 -0.166422057 12 -1.111432290 -0.101125638 13 -0.294197973 -1.111432290 14 -3.123814390 -0.294197973 15 -1.193421708 -3.123814390 16 -3.268398002 -1.193421708 17 -0.770009608 -3.268398002 18 1.948904204 -0.770009608 19 2.553890986 1.948904204 20 -0.708721111 2.553890986 21 0.373841098 -0.708721111 22 -0.770265906 0.373841098 23 -1.358101651 -0.770265906 24 -0.166422057 -1.358101651 25 -0.166422057 -0.166422057 26 1.210893151 -0.166422057 27 1.773819182 1.210893151 28 2.524681703 1.773819182 29 -1.614656307 2.524681703 30 -1.727329387 -1.614656307 31 0.557517047 -1.727329387 32 0.347299543 0.557517047 33 -0.393545686 0.347299543 34 1.096060797 -0.393545686 35 -1.512028583 1.096060797 36 0.847928329 -1.512028583 37 -0.706685212 0.847928329 38 0.519620600 -0.706685212 39 1.149525769 0.519620600 40 0.877901335 1.149525769 41 2.124646930 0.877901335 42 1.322673265 2.124646930 43 -1.141894262 1.322673265 44 -0.139880502 -1.141894262 45 -0.583551235 -0.139880502 46 -0.812851433 -0.583551235 47 -1.398288804 -0.812851433 48 1.898416267 -1.398288804 49 0.190937494 1.898416267 50 -0.077499430 0.190937494 51 0.127623965 -0.077499430 52 0.138606776 0.127623965 53 -1.300031478 0.138606776 54 -1.111432290 -1.300031478 55 -0.659185176 -1.111432290 56 -1.224717711 -0.659185176 57 2.086212240 -1.224717711 58 2.634057574 2.086212240 59 1.062456789 2.634057574 60 -2.214509431 1.062456789 61 0.745241728 -2.214509431 62 1.043017178 0.745241728 63 -0.370132702 1.043017178 64 0.081212767 -0.370132702 65 -0.221678988 0.081212767 66 0.222120988 -0.221678988 67 0.647074154 0.222120988 68 0.382164861 0.647074154 69 -0.639993242 0.382164861 70 -0.877268404 -0.639993242 71 0.902735843 -0.877268404 72 -1.199678753 0.902735843 73 -0.632874043 -1.199678753 74 -0.121960743 -0.632874043 75 3.715220553 -0.121960743 76 2.990254241 3.715220553 77 -0.383671254 2.990254241 78 0.387449958 -0.383671254 79 0.793390790 0.387449958 80 -1.673926102 0.793390790 81 0.333263405 -1.673926102 82 -0.835082090 0.333263405 83 -1.151742819 -0.835082090 84 -3.277803010 -1.151742819 85 0.847928329 -3.277803010 86 -1.927049308 0.847928329 87 2.320376126 -1.927049308 88 2.288802092 2.320376126 89 -2.522175175 2.288802092 90 -4.055341328 -2.522175175 91 0.351610441 -4.055341328 92 0.105393308 0.351610441 93 0.370473524 0.105393308 94 -0.054207073 0.370473524 95 1.538459354 -0.054207073 96 0.099669660 1.538459354 97 -2.380369046 0.099669660 98 -0.706685212 -2.380369046 99 2.255559942 -0.706685212 100 1.601711196 2.255559942 101 0.373841098 1.601711196 102 0.652328376 0.373841098 103 -2.943898687 0.652328376 104 -0.295762259 -2.943898687 105 0.765751718 -0.295762259 106 -2.021799007 0.765751718 107 -1.377684121 -2.021799007 108 2.732407458 -1.377684121 109 1.678740688 2.732407458 110 -0.639993242 1.678740688 111 0.000942812 -0.639993242 112 0.847928329 0.000942812 113 1.668403166 0.847928329 114 3.557249882 1.668403166 115 -0.166422057 3.557249882 116 0.805342801 -0.166422057 117 -1.077931649 0.805342801 118 -0.192963612 -1.077931649 119 -3.454865053 -0.192963612 120 -0.684454555 -3.454865053 121 0.136570877 -0.684454555 122 -1.914116612 0.136570877 123 1.579480539 -1.914116612 124 -0.166422057 1.579480539 125 -1.512028583 -0.166422057 126 0.754854921 -1.512028583 127 0.388520337 0.754854921 128 -0.720774359 0.388520337 129 -0.984083652 -0.720774359 130 1.090714070 -0.984083652 131 0.727833074 1.090714070 132 2.462702038 0.727833074 133 -1.322326011 2.462702038 134 0.719770551 -1.322326011 135 -0.570714777 0.719770551 136 0.936850956 -0.570714777 137 -3.157707755 0.936850956 138 -0.166422057 -3.157707755 139 0.138606776 -0.166422057 140 -2.440834784 0.138606776 141 -0.166422057 -2.440834784 142 -0.401204152 -0.166422057 143 -1.397016628 -0.401204152 144 0.387449958 -1.397016628 145 0.699575794 0.387449958 146 0.699904646 0.699575794 147 -0.418904915 0.699904646 148 -0.526707822 -0.418904915 149 1.210809324 -0.526707822 150 1.664190693 1.210809324 151 0.373841098 1.664190693 152 -0.836328294 0.373841098 153 0.548307911 -0.836328294 154 -1.322326011 0.548307911 155 -3.490126779 -1.322326011 > 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/74u271324485973.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/rcomp/tmp/88csa1324485973.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/rcomp/tmp/9t3ey1324485973.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/rcomp/tmp/103dgm1324485973.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/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/112mwt1324485973.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/12pnyh1324485973.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/13jmd81324485973.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14feuk1324485973.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/158xsh1324485973.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/169lr21324485973.tab") + } > > try(system("convert tmp/11ha31324485973.ps tmp/11ha31324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/24zzi1324485973.ps tmp/24zzi1324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/3ip6a1324485973.ps tmp/3ip6a1324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/4r2q41324485973.ps tmp/4r2q41324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/5swnw1324485973.ps tmp/5swnw1324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/6pt1l1324485973.ps tmp/6pt1l1324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/74u271324485973.ps tmp/74u271324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/88csa1324485973.ps tmp/88csa1324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/9t3ey1324485973.ps tmp/9t3ey1324485973.png",intern=TRUE)) character(0) > try(system("convert tmp/103dgm1324485973.ps tmp/103dgm1324485973.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.420 0.240 4.633