R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-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(4 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + 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,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,2 + ,1 + ,1 + ,0 + ,1 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0) + ,dim=c(6 + ,154) + ,dimnames=list(c('Weeks*T' + ,'UseLimit' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome ') + ,1:154)) > y <- array(NA,dim=c(6,154),dimnames=list(c('Weeks*T','UseLimit','Used','CorrectAnalysis','Useful','Outcome '),1:154)) > 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 = '6' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '6' > #'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, 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 Outcome\r Weeks*T UseLimit Used CorrectAnalysis Useful t 1 1 4 1 0 0 0 1 2 0 0 0 0 0 0 2 3 0 0 0 0 0 0 3 4 0 0 0 0 0 0 4 5 0 0 0 0 0 0 5 6 1 0 1 0 0 1 6 7 0 0 0 0 0 0 7 8 0 4 0 0 0 0 8 9 1 0 0 0 0 0 9 10 0 0 1 0 0 0 10 11 0 4 1 0 0 0 11 12 0 0 0 0 0 0 12 13 0 0 0 1 0 1 13 14 0 4 1 0 0 0 14 15 1 0 0 1 0 1 15 16 1 4 0 1 0 1 16 17 0 4 1 1 1 1 17 18 0 4 1 0 0 0 18 19 1 0 0 0 0 0 19 20 1 4 0 1 1 1 20 21 0 0 1 0 0 1 21 22 1 0 1 1 0 1 22 23 1 0 0 0 0 1 23 24 1 0 1 0 0 1 24 25 1 4 0 1 0 0 25 26 0 0 0 1 0 1 26 27 1 0 1 0 0 0 27 28 0 0 0 1 0 0 28 29 1 0 0 0 0 0 29 30 0 0 0 0 0 1 30 31 0 0 0 0 0 0 31 32 0 0 1 0 0 0 32 33 0 0 1 0 0 1 33 34 1 4 0 0 0 0 34 35 0 0 0 0 0 0 35 36 0 0 0 0 0 0 36 37 0 4 1 1 0 1 37 38 1 0 0 1 0 0 38 39 1 0 0 0 0 1 39 40 0 4 0 0 0 1 40 41 1 0 0 1 1 1 41 42 1 0 0 1 0 0 42 43 1 0 1 0 0 1 43 44 0 4 1 0 0 0 44 45 0 0 0 0 0 1 45 46 1 0 0 0 0 1 46 47 0 0 0 0 0 0 47 48 1 0 0 0 0 0 48 49 1 0 0 0 0 1 49 50 0 0 0 0 0 0 50 51 0 4 0 1 0 0 51 52 0 4 1 1 1 1 52 53 1 0 0 0 0 0 53 54 0 0 0 1 1 0 54 55 0 0 0 0 0 0 55 56 1 4 0 1 0 0 56 57 1 0 0 1 0 1 57 58 1 0 0 0 0 0 58 59 1 0 0 0 0 0 59 60 1 4 1 1 1 1 60 61 1 4 1 0 0 0 61 62 0 0 0 1 0 1 62 63 0 0 0 0 0 0 63 64 1 4 1 0 0 0 64 65 0 0 0 0 0 0 65 66 0 0 0 0 0 0 66 67 0 4 0 1 1 1 67 68 0 0 1 0 0 0 68 69 1 0 0 0 0 0 69 70 0 0 0 1 0 0 70 71 0 0 0 0 0 0 71 72 1 0 0 0 0 0 72 73 1 0 0 1 0 0 73 74 0 0 1 1 0 0 74 75 1 0 0 0 0 0 75 76 1 4 0 0 0 1 76 77 1 0 0 0 0 0 77 78 1 0 0 1 0 1 78 79 1 4 0 1 1 0 79 80 0 4 0 0 0 1 80 81 0 0 0 0 0 0 81 82 1 0 1 1 0 0 82 83 0 0 0 0 0 0 83 84 0 0 0 1 1 0 84 85 1 0 0 0 0 1 85 86 0 0 1 0 0 0 86 87 1 0 1 0 0 0 87 88 1 2 1 1 0 0 88 89 0 0 0 0 0 0 89 90 1 0 0 0 0 0 90 91 0 0 0 0 0 1 91 92 0 2 1 0 0 0 92 93 0 0 1 0 0 1 93 94 0 0 0 0 0 0 94 95 0 2 0 0 0 0 95 96 1 0 0 0 0 0 96 97 0 2 1 0 0 0 97 98 0 0 0 0 0 0 98 99 0 0 1 0 0 0 99 100 1 0 0 0 0 0 100 101 1 0 1 0 0 0 101 102 0 0 0 0 0 0 102 103 0 0 0 0 0 0 103 104 0 0 0 0 0 0 104 105 0 2 0 1 0 0 105 106 0 0 0 0 0 0 106 107 0 0 0 0 0 0 107 108 0 2 1 1 0 0 108 109 0 0 0 0 0 0 109 110 0 0 1 0 0 0 110 111 0 2 1 1 0 1 111 112 0 2 0 0 0 0 112 113 0 0 0 1 0 0 113 114 0 2 1 1 0 0 114 115 0 0 1 0 0 0 115 116 0 0 0 0 0 0 116 117 1 0 1 0 0 0 117 118 0 0 1 0 0 0 118 119 0 0 0 0 0 0 119 120 1 0 0 0 0 0 120 121 0 0 1 0 0 0 121 122 0 0 0 0 0 0 122 123 0 2 1 1 0 0 123 124 1 0 0 1 0 1 124 125 1 0 0 0 0 0 125 126 0 2 0 0 0 0 126 127 0 0 0 0 0 1 127 128 1 0 0 0 0 0 128 129 0 0 0 0 0 0 129 130 1 0 0 0 0 0 130 131 0 0 1 0 0 0 131 132 1 0 1 0 0 0 132 133 0 0 1 1 0 0 133 134 0 0 0 0 0 0 134 135 0 0 0 0 0 0 135 136 0 0 0 0 0 0 136 137 1 0 1 1 0 1 137 138 1 2 1 1 0 1 138 139 0 2 0 0 0 0 139 140 0 0 0 0 0 0 140 141 1 0 0 1 1 0 141 142 1 2 0 1 0 0 142 143 0 0 1 0 0 0 143 144 1 0 0 0 0 1 144 145 0 0 0 0 0 1 145 146 1 2 0 0 0 0 146 147 0 2 0 1 0 0 147 148 0 2 0 0 0 0 148 149 0 0 1 0 0 0 149 150 1 0 0 0 0 1 150 151 1 0 0 0 0 0 151 152 0 0 1 1 1 0 152 153 0 0 1 1 1 1 153 154 0 0 1 1 0 0 154 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `Weeks*T` UseLimit Used 0.4090198 -0.0001892 -0.0950776 0.0936293 CorrectAnalysis Useful t -0.1000590 0.1734867 -0.0006147 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.6681 -0.3701 -0.3034 0.5263 0.7672 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.4090198 0.0952702 4.293 3.18e-05 *** `Weeks*T` -0.0001892 0.0289683 -0.007 0.9948 UseLimit -0.0950776 0.0860313 -1.105 0.2709 Used 0.0936293 0.1005303 0.931 0.3532 CorrectAnalysis -0.1000590 0.1674606 -0.598 0.5511 Useful 0.1734867 0.0945469 1.835 0.0685 . t -0.0006147 0.0009252 -0.664 0.5074 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4888 on 147 degrees of freedom Multiple R-squared: 0.0466, Adjusted R-squared: 0.007689 F-statistic: 1.198 on 6 and 147 DF, p-value: 0.3108 > 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,] 0.8410637 0.3178725 0.1589363 [2,] 0.7888480 0.4223040 0.2111520 [3,] 0.6808118 0.6383763 0.3191882 [4,] 0.5702337 0.8595326 0.4297663 [5,] 0.4652722 0.9305444 0.5347278 [6,] 0.5930098 0.8139804 0.4069902 [7,] 0.5313480 0.9373039 0.4686520 [8,] 0.4393499 0.8786998 0.5606501 [9,] 0.3536612 0.7073225 0.6463388 [10,] 0.5558718 0.8882563 0.4441282 [11,] 0.5566710 0.8866579 0.4433290 [12,] 0.6491300 0.7017400 0.3508700 [13,] 0.6447126 0.7105747 0.3552874 [14,] 0.5924172 0.8151655 0.4075828 [15,] 0.5500731 0.8998539 0.4499269 [16,] 0.5308723 0.9382554 0.4691277 [17,] 0.6523482 0.6953036 0.3476518 [18,] 0.6759545 0.6480909 0.3240455 [19,] 0.6682523 0.6634955 0.3317477 [20,] 0.6497169 0.7005661 0.3502831 [21,] 0.7403133 0.5193735 0.2596867 [22,] 0.7289841 0.5420318 0.2710159 [23,] 0.7039299 0.5921402 0.2960701 [24,] 0.7114387 0.5771226 0.2885613 [25,] 0.7088149 0.5823702 0.2911851 [26,] 0.6874621 0.6250758 0.3125379 [27,] 0.6609165 0.6781670 0.3390835 [28,] 0.6963128 0.6073743 0.3036872 [29,] 0.7112506 0.5774989 0.2887494 [30,] 0.6949360 0.6101281 0.3050640 [31,] 0.7168164 0.5663671 0.2831836 [32,] 0.6928562 0.6142875 0.3071438 [33,] 0.6783795 0.6432410 0.3216205 [34,] 0.6740160 0.6519681 0.3259840 [35,] 0.6514811 0.6970378 0.3485189 [36,] 0.6640583 0.6718835 0.3359417 [37,] 0.6521564 0.6956873 0.3478436 [38,] 0.6396211 0.7207577 0.3603789 [39,] 0.6507405 0.6985190 0.3492595 [40,] 0.6316237 0.7367525 0.3683763 [41,] 0.6244181 0.7511637 0.3755819 [42,] 0.6290035 0.7419930 0.3709965 [43,] 0.6365801 0.7268399 0.3634199 [44,] 0.6496646 0.7006708 0.3503354 [45,] 0.6355791 0.7288417 0.3644209 [46,] 0.6215955 0.7568089 0.3784045 [47,] 0.6195781 0.7608438 0.3804219 [48,] 0.5860267 0.8279466 0.4139733 [49,] 0.5971289 0.8057422 0.4028711 [50,] 0.6047903 0.7904194 0.3952097 [51,] 0.6009242 0.7981516 0.3990758 [52,] 0.6193934 0.7612133 0.3806066 [53,] 0.6758846 0.6482308 0.3241154 [54,] 0.6727466 0.6545068 0.3272534 [55,] 0.7031441 0.5937119 0.2968559 [56,] 0.6994779 0.6010443 0.3005221 [57,] 0.6930456 0.6139088 0.3069544 [58,] 0.7025699 0.5948602 0.2974301 [59,] 0.6847598 0.6304805 0.3152402 [60,] 0.6951119 0.6097762 0.3048881 [61,] 0.7040200 0.5919600 0.2959800 [62,] 0.6948895 0.6102211 0.3051105 [63,] 0.7052801 0.5894398 0.2947199 [64,] 0.6975055 0.6049889 0.3024945 [65,] 0.6901062 0.6197876 0.3098938 [66,] 0.7010908 0.5978184 0.2989092 [67,] 0.6944393 0.6111213 0.3055607 [68,] 0.7085485 0.5829030 0.2914515 [69,] 0.6815640 0.6368719 0.3184360 [70,] 0.7398752 0.5202496 0.2601248 [71,] 0.7466380 0.5067240 0.2533620 [72,] 0.7359121 0.5281758 0.2640879 [73,] 0.7498864 0.5002272 0.2501136 [74,] 0.7371933 0.5256134 0.2628067 [75,] 0.7162084 0.5675832 0.2837916 [76,] 0.7124208 0.5751584 0.2875792 [77,] 0.6886005 0.6227991 0.3113995 [78,] 0.7398830 0.5202340 0.2601170 [79,] 0.7952330 0.4095341 0.2047670 [80,] 0.7798796 0.4402408 0.2201204 [81,] 0.8184494 0.3631012 0.1815506 [82,] 0.8204012 0.3591976 0.1795988 [83,] 0.7999369 0.4001263 0.2000631 [84,] 0.7898301 0.4203399 0.2101699 [85,] 0.7699262 0.4601476 0.2300738 [86,] 0.7437931 0.5124138 0.2562069 [87,] 0.7877669 0.4244663 0.2122331 [88,] 0.7587408 0.4825184 0.2412592 [89,] 0.7332296 0.5335408 0.2667704 [90,] 0.6985468 0.6029064 0.3014532 [91,] 0.7550241 0.4899517 0.2449759 [92,] 0.8464158 0.3071683 0.1535842 [93,] 0.8231445 0.3537111 0.1768555 [94,] 0.7972289 0.4055422 0.2027711 [95,] 0.7688666 0.4622669 0.2311334 [96,] 0.7453409 0.5093182 0.2546591 [97,] 0.7132409 0.5735182 0.2867591 [98,] 0.6801518 0.6396963 0.3198482 [99,] 0.6423672 0.7152655 0.3576328 [100,] 0.6077296 0.7845407 0.3922704 [101,] 0.5599145 0.8801710 0.4400855 [102,] 0.5410122 0.9179756 0.4589878 [103,] 0.5007261 0.9985478 0.4992739 [104,] 0.4948348 0.9896696 0.5051652 [105,] 0.4561752 0.9123504 0.5438248 [106,] 0.4088217 0.8176434 0.5911783 [107,] 0.3901290 0.7802579 0.6098710 [108,] 0.4870829 0.9741658 0.5129171 [109,] 0.4328019 0.8656038 0.5671981 [110,] 0.4126244 0.8252489 0.5873756 [111,] 0.4404712 0.8809425 0.5595288 [112,] 0.3849643 0.7699286 0.6150357 [113,] 0.3626209 0.7252419 0.6373791 [114,] 0.3304677 0.6609353 0.6695323 [115,] 0.2852704 0.5705407 0.7147296 [116,] 0.3069347 0.6138694 0.6930653 [117,] 0.2845538 0.5691075 0.7154462 [118,] 0.3513364 0.7026727 0.6486636 [119,] 0.3650533 0.7301065 0.6349467 [120,] 0.3470077 0.6940153 0.6529923 [121,] 0.3696745 0.7393490 0.6303255 [122,] 0.3120203 0.6240405 0.6879797 [123,] 0.4911331 0.9822662 0.5088669 [124,] 0.4246366 0.8492733 0.5753634 [125,] 0.3691041 0.7382083 0.6308959 [126,] 0.3282466 0.6564931 0.6717534 [127,] 0.3194166 0.6388331 0.6805834 [128,] 0.2650564 0.5301127 0.7349436 [129,] 0.3744310 0.7488621 0.6255690 [130,] 0.3188353 0.6376706 0.6811647 [131,] 0.4298198 0.8596396 0.5701802 [132,] 0.3312376 0.6624751 0.6687624 [133,] 0.4174215 0.8348429 0.5825785 [134,] 0.2911477 0.5822953 0.7088523 [135,] 0.3231865 0.6463730 0.6768135 > postscript(file="/var/fisher/rcomp/tmp/1ehqp1355687671.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2w7i91355687671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3b79g1355687671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4r3j51355687671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5pa4d1355687671.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 = 154 Frequency = 1 1 2 3 4 5 6 7 0.6874295 -0.4077904 -0.4071756 -0.4065609 -0.4059462 0.5162595 -0.4047167 8 9 10 11 12 13 14 -0.4033450 0.5965128 -0.3077949 -0.3064232 -0.4016430 -0.6681442 -0.3045789 15 16 17 18 19 20 21 0.3330853 0.3344570 -0.4697917 -0.3021200 0.6026602 0.4369749 -0.4745194 22 23 24 25 26 27 28 0.4324661 0.4316325 0.5273248 0.5134763 -0.6601526 0.7026557 -0.4854365 29 30 31 32 33 34 35 0.6088075 -0.5640644 -0.3899630 -0.2942706 -0.4671426 0.6126382 -0.3875040 36 37 38 39 40 41 42 -0.3868893 -0.5575559 0.5207109 0.4414683 -0.5571600 0.4491274 0.5231699 43 44 45 46 47 48 49 0.5390048 -0.2861368 -0.5548433 0.4457714 -0.3801272 0.6204876 0.4476156 50 51 52 53 54 55 56 -0.3782830 -0.4705405 -0.4482759 0.6235613 -0.3693943 -0.3752093 0.5325332 57 58 59 60 61 62 63 0.3589043 0.6266349 0.6272497 0.5566420 0.7243137 -0.6380220 -0.3702914 64 65 66 67 68 69 70 0.7261579 -0.3690619 -0.3684472 -0.5341324 -0.2721401 0.6333971 -0.4596175 71 72 73 74 75 76 77 -0.3653735 0.6352413 0.5422267 -0.3620809 0.6370855 0.4649705 0.6383150 78 79 80 81 82 83 84 0.3718138 0.6467311 -0.5325705 -0.3592261 0.6428370 -0.3579966 -0.3509522 85 86 87 88 89 90 91 0.4697462 -0.2610748 0.7395399 0.6469039 -0.3543082 0.6463065 -0.5265654 92 93 94 95 96 97 98 -0.2570079 -0.4302583 -0.3512345 -0.3502413 0.6499950 -0.2539342 -0.3487756 99 100 101 102 103 104 105 -0.2530832 0.6524539 0.7481463 -0.3463166 -0.3457019 -0.3450871 -0.4377232 106 107 108 109 110 111 112 -0.3438577 -0.3432429 -0.3408014 -0.3420134 -0.2463211 -0.5124438 -0.3397907 113 114 115 116 117 118 119 -0.4331838 -0.3371129 -0.2432474 -0.3377103 0.7579821 -0.2414032 -0.3358661 120 121 122 123 124 125 126 0.6647487 -0.2395590 -0.3340218 -0.3315803 0.4000917 0.6678224 -0.3311844 127 128 129 130 131 132 133 -0.5044348 0.6696666 -0.3297187 0.6708961 -0.2334116 0.7672031 -0.3258114 134 135 136 137 138 139 140 -0.3266450 -0.3260303 -0.3254155 0.5031609 0.5041541 -0.3231928 -0.3229566 141 142 143 144 145 146 147 0.6840879 0.5850221 -0.2260348 0.5060157 -0.4933695 0.6811103 -0.4119042 148 149 150 151 152 153 154 -0.3176602 -0.2223463 0.5097042 0.6838055 -0.2140724 -0.3869443 -0.3129019 > postscript(file="/var/fisher/rcomp/tmp/6a5y31355687671.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 0.6874295 NA 1 -0.4077904 0.6874295 2 -0.4071756 -0.4077904 3 -0.4065609 -0.4071756 4 -0.4059462 -0.4065609 5 0.5162595 -0.4059462 6 -0.4047167 0.5162595 7 -0.4033450 -0.4047167 8 0.5965128 -0.4033450 9 -0.3077949 0.5965128 10 -0.3064232 -0.3077949 11 -0.4016430 -0.3064232 12 -0.6681442 -0.4016430 13 -0.3045789 -0.6681442 14 0.3330853 -0.3045789 15 0.3344570 0.3330853 16 -0.4697917 0.3344570 17 -0.3021200 -0.4697917 18 0.6026602 -0.3021200 19 0.4369749 0.6026602 20 -0.4745194 0.4369749 21 0.4324661 -0.4745194 22 0.4316325 0.4324661 23 0.5273248 0.4316325 24 0.5134763 0.5273248 25 -0.6601526 0.5134763 26 0.7026557 -0.6601526 27 -0.4854365 0.7026557 28 0.6088075 -0.4854365 29 -0.5640644 0.6088075 30 -0.3899630 -0.5640644 31 -0.2942706 -0.3899630 32 -0.4671426 -0.2942706 33 0.6126382 -0.4671426 34 -0.3875040 0.6126382 35 -0.3868893 -0.3875040 36 -0.5575559 -0.3868893 37 0.5207109 -0.5575559 38 0.4414683 0.5207109 39 -0.5571600 0.4414683 40 0.4491274 -0.5571600 41 0.5231699 0.4491274 42 0.5390048 0.5231699 43 -0.2861368 0.5390048 44 -0.5548433 -0.2861368 45 0.4457714 -0.5548433 46 -0.3801272 0.4457714 47 0.6204876 -0.3801272 48 0.4476156 0.6204876 49 -0.3782830 0.4476156 50 -0.4705405 -0.3782830 51 -0.4482759 -0.4705405 52 0.6235613 -0.4482759 53 -0.3693943 0.6235613 54 -0.3752093 -0.3693943 55 0.5325332 -0.3752093 56 0.3589043 0.5325332 57 0.6266349 0.3589043 58 0.6272497 0.6266349 59 0.5566420 0.6272497 60 0.7243137 0.5566420 61 -0.6380220 0.7243137 62 -0.3702914 -0.6380220 63 0.7261579 -0.3702914 64 -0.3690619 0.7261579 65 -0.3684472 -0.3690619 66 -0.5341324 -0.3684472 67 -0.2721401 -0.5341324 68 0.6333971 -0.2721401 69 -0.4596175 0.6333971 70 -0.3653735 -0.4596175 71 0.6352413 -0.3653735 72 0.5422267 0.6352413 73 -0.3620809 0.5422267 74 0.6370855 -0.3620809 75 0.4649705 0.6370855 76 0.6383150 0.4649705 77 0.3718138 0.6383150 78 0.6467311 0.3718138 79 -0.5325705 0.6467311 80 -0.3592261 -0.5325705 81 0.6428370 -0.3592261 82 -0.3579966 0.6428370 83 -0.3509522 -0.3579966 84 0.4697462 -0.3509522 85 -0.2610748 0.4697462 86 0.7395399 -0.2610748 87 0.6469039 0.7395399 88 -0.3543082 0.6469039 89 0.6463065 -0.3543082 90 -0.5265654 0.6463065 91 -0.2570079 -0.5265654 92 -0.4302583 -0.2570079 93 -0.3512345 -0.4302583 94 -0.3502413 -0.3512345 95 0.6499950 -0.3502413 96 -0.2539342 0.6499950 97 -0.3487756 -0.2539342 98 -0.2530832 -0.3487756 99 0.6524539 -0.2530832 100 0.7481463 0.6524539 101 -0.3463166 0.7481463 102 -0.3457019 -0.3463166 103 -0.3450871 -0.3457019 104 -0.4377232 -0.3450871 105 -0.3438577 -0.4377232 106 -0.3432429 -0.3438577 107 -0.3408014 -0.3432429 108 -0.3420134 -0.3408014 109 -0.2463211 -0.3420134 110 -0.5124438 -0.2463211 111 -0.3397907 -0.5124438 112 -0.4331838 -0.3397907 113 -0.3371129 -0.4331838 114 -0.2432474 -0.3371129 115 -0.3377103 -0.2432474 116 0.7579821 -0.3377103 117 -0.2414032 0.7579821 118 -0.3358661 -0.2414032 119 0.6647487 -0.3358661 120 -0.2395590 0.6647487 121 -0.3340218 -0.2395590 122 -0.3315803 -0.3340218 123 0.4000917 -0.3315803 124 0.6678224 0.4000917 125 -0.3311844 0.6678224 126 -0.5044348 -0.3311844 127 0.6696666 -0.5044348 128 -0.3297187 0.6696666 129 0.6708961 -0.3297187 130 -0.2334116 0.6708961 131 0.7672031 -0.2334116 132 -0.3258114 0.7672031 133 -0.3266450 -0.3258114 134 -0.3260303 -0.3266450 135 -0.3254155 -0.3260303 136 0.5031609 -0.3254155 137 0.5041541 0.5031609 138 -0.3231928 0.5041541 139 -0.3229566 -0.3231928 140 0.6840879 -0.3229566 141 0.5850221 0.6840879 142 -0.2260348 0.5850221 143 0.5060157 -0.2260348 144 -0.4933695 0.5060157 145 0.6811103 -0.4933695 146 -0.4119042 0.6811103 147 -0.3176602 -0.4119042 148 -0.2223463 -0.3176602 149 0.5097042 -0.2223463 150 0.6838055 0.5097042 151 -0.2140724 0.6838055 152 -0.3869443 -0.2140724 153 -0.3129019 -0.3869443 154 NA -0.3129019 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.4077904 0.6874295 [2,] -0.4071756 -0.4077904 [3,] -0.4065609 -0.4071756 [4,] -0.4059462 -0.4065609 [5,] 0.5162595 -0.4059462 [6,] -0.4047167 0.5162595 [7,] -0.4033450 -0.4047167 [8,] 0.5965128 -0.4033450 [9,] -0.3077949 0.5965128 [10,] -0.3064232 -0.3077949 [11,] -0.4016430 -0.3064232 [12,] -0.6681442 -0.4016430 [13,] -0.3045789 -0.6681442 [14,] 0.3330853 -0.3045789 [15,] 0.3344570 0.3330853 [16,] -0.4697917 0.3344570 [17,] -0.3021200 -0.4697917 [18,] 0.6026602 -0.3021200 [19,] 0.4369749 0.6026602 [20,] -0.4745194 0.4369749 [21,] 0.4324661 -0.4745194 [22,] 0.4316325 0.4324661 [23,] 0.5273248 0.4316325 [24,] 0.5134763 0.5273248 [25,] -0.6601526 0.5134763 [26,] 0.7026557 -0.6601526 [27,] -0.4854365 0.7026557 [28,] 0.6088075 -0.4854365 [29,] -0.5640644 0.6088075 [30,] -0.3899630 -0.5640644 [31,] -0.2942706 -0.3899630 [32,] -0.4671426 -0.2942706 [33,] 0.6126382 -0.4671426 [34,] -0.3875040 0.6126382 [35,] -0.3868893 -0.3875040 [36,] -0.5575559 -0.3868893 [37,] 0.5207109 -0.5575559 [38,] 0.4414683 0.5207109 [39,] -0.5571600 0.4414683 [40,] 0.4491274 -0.5571600 [41,] 0.5231699 0.4491274 [42,] 0.5390048 0.5231699 [43,] -0.2861368 0.5390048 [44,] -0.5548433 -0.2861368 [45,] 0.4457714 -0.5548433 [46,] -0.3801272 0.4457714 [47,] 0.6204876 -0.3801272 [48,] 0.4476156 0.6204876 [49,] -0.3782830 0.4476156 [50,] -0.4705405 -0.3782830 [51,] -0.4482759 -0.4705405 [52,] 0.6235613 -0.4482759 [53,] -0.3693943 0.6235613 [54,] -0.3752093 -0.3693943 [55,] 0.5325332 -0.3752093 [56,] 0.3589043 0.5325332 [57,] 0.6266349 0.3589043 [58,] 0.6272497 0.6266349 [59,] 0.5566420 0.6272497 [60,] 0.7243137 0.5566420 [61,] -0.6380220 0.7243137 [62,] -0.3702914 -0.6380220 [63,] 0.7261579 -0.3702914 [64,] -0.3690619 0.7261579 [65,] -0.3684472 -0.3690619 [66,] -0.5341324 -0.3684472 [67,] -0.2721401 -0.5341324 [68,] 0.6333971 -0.2721401 [69,] -0.4596175 0.6333971 [70,] -0.3653735 -0.4596175 [71,] 0.6352413 -0.3653735 [72,] 0.5422267 0.6352413 [73,] -0.3620809 0.5422267 [74,] 0.6370855 -0.3620809 [75,] 0.4649705 0.6370855 [76,] 0.6383150 0.4649705 [77,] 0.3718138 0.6383150 [78,] 0.6467311 0.3718138 [79,] -0.5325705 0.6467311 [80,] -0.3592261 -0.5325705 [81,] 0.6428370 -0.3592261 [82,] -0.3579966 0.6428370 [83,] -0.3509522 -0.3579966 [84,] 0.4697462 -0.3509522 [85,] -0.2610748 0.4697462 [86,] 0.7395399 -0.2610748 [87,] 0.6469039 0.7395399 [88,] -0.3543082 0.6469039 [89,] 0.6463065 -0.3543082 [90,] -0.5265654 0.6463065 [91,] -0.2570079 -0.5265654 [92,] -0.4302583 -0.2570079 [93,] -0.3512345 -0.4302583 [94,] -0.3502413 -0.3512345 [95,] 0.6499950 -0.3502413 [96,] -0.2539342 0.6499950 [97,] -0.3487756 -0.2539342 [98,] -0.2530832 -0.3487756 [99,] 0.6524539 -0.2530832 [100,] 0.7481463 0.6524539 [101,] -0.3463166 0.7481463 [102,] -0.3457019 -0.3463166 [103,] -0.3450871 -0.3457019 [104,] -0.4377232 -0.3450871 [105,] -0.3438577 -0.4377232 [106,] -0.3432429 -0.3438577 [107,] -0.3408014 -0.3432429 [108,] -0.3420134 -0.3408014 [109,] -0.2463211 -0.3420134 [110,] -0.5124438 -0.2463211 [111,] -0.3397907 -0.5124438 [112,] -0.4331838 -0.3397907 [113,] -0.3371129 -0.4331838 [114,] -0.2432474 -0.3371129 [115,] -0.3377103 -0.2432474 [116,] 0.7579821 -0.3377103 [117,] -0.2414032 0.7579821 [118,] -0.3358661 -0.2414032 [119,] 0.6647487 -0.3358661 [120,] -0.2395590 0.6647487 [121,] -0.3340218 -0.2395590 [122,] -0.3315803 -0.3340218 [123,] 0.4000917 -0.3315803 [124,] 0.6678224 0.4000917 [125,] -0.3311844 0.6678224 [126,] -0.5044348 -0.3311844 [127,] 0.6696666 -0.5044348 [128,] -0.3297187 0.6696666 [129,] 0.6708961 -0.3297187 [130,] -0.2334116 0.6708961 [131,] 0.7672031 -0.2334116 [132,] -0.3258114 0.7672031 [133,] -0.3266450 -0.3258114 [134,] -0.3260303 -0.3266450 [135,] -0.3254155 -0.3260303 [136,] 0.5031609 -0.3254155 [137,] 0.5041541 0.5031609 [138,] -0.3231928 0.5041541 [139,] -0.3229566 -0.3231928 [140,] 0.6840879 -0.3229566 [141,] 0.5850221 0.6840879 [142,] -0.2260348 0.5850221 [143,] 0.5060157 -0.2260348 [144,] -0.4933695 0.5060157 [145,] 0.6811103 -0.4933695 [146,] -0.4119042 0.6811103 [147,] -0.3176602 -0.4119042 [148,] -0.2223463 -0.3176602 [149,] 0.5097042 -0.2223463 [150,] 0.6838055 0.5097042 [151,] -0.2140724 0.6838055 [152,] -0.3869443 -0.2140724 [153,] -0.3129019 -0.3869443 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.4077904 0.6874295 2 -0.4071756 -0.4077904 3 -0.4065609 -0.4071756 4 -0.4059462 -0.4065609 5 0.5162595 -0.4059462 6 -0.4047167 0.5162595 7 -0.4033450 -0.4047167 8 0.5965128 -0.4033450 9 -0.3077949 0.5965128 10 -0.3064232 -0.3077949 11 -0.4016430 -0.3064232 12 -0.6681442 -0.4016430 13 -0.3045789 -0.6681442 14 0.3330853 -0.3045789 15 0.3344570 0.3330853 16 -0.4697917 0.3344570 17 -0.3021200 -0.4697917 18 0.6026602 -0.3021200 19 0.4369749 0.6026602 20 -0.4745194 0.4369749 21 0.4324661 -0.4745194 22 0.4316325 0.4324661 23 0.5273248 0.4316325 24 0.5134763 0.5273248 25 -0.6601526 0.5134763 26 0.7026557 -0.6601526 27 -0.4854365 0.7026557 28 0.6088075 -0.4854365 29 -0.5640644 0.6088075 30 -0.3899630 -0.5640644 31 -0.2942706 -0.3899630 32 -0.4671426 -0.2942706 33 0.6126382 -0.4671426 34 -0.3875040 0.6126382 35 -0.3868893 -0.3875040 36 -0.5575559 -0.3868893 37 0.5207109 -0.5575559 38 0.4414683 0.5207109 39 -0.5571600 0.4414683 40 0.4491274 -0.5571600 41 0.5231699 0.4491274 42 0.5390048 0.5231699 43 -0.2861368 0.5390048 44 -0.5548433 -0.2861368 45 0.4457714 -0.5548433 46 -0.3801272 0.4457714 47 0.6204876 -0.3801272 48 0.4476156 0.6204876 49 -0.3782830 0.4476156 50 -0.4705405 -0.3782830 51 -0.4482759 -0.4705405 52 0.6235613 -0.4482759 53 -0.3693943 0.6235613 54 -0.3752093 -0.3693943 55 0.5325332 -0.3752093 56 0.3589043 0.5325332 57 0.6266349 0.3589043 58 0.6272497 0.6266349 59 0.5566420 0.6272497 60 0.7243137 0.5566420 61 -0.6380220 0.7243137 62 -0.3702914 -0.6380220 63 0.7261579 -0.3702914 64 -0.3690619 0.7261579 65 -0.3684472 -0.3690619 66 -0.5341324 -0.3684472 67 -0.2721401 -0.5341324 68 0.6333971 -0.2721401 69 -0.4596175 0.6333971 70 -0.3653735 -0.4596175 71 0.6352413 -0.3653735 72 0.5422267 0.6352413 73 -0.3620809 0.5422267 74 0.6370855 -0.3620809 75 0.4649705 0.6370855 76 0.6383150 0.4649705 77 0.3718138 0.6383150 78 0.6467311 0.3718138 79 -0.5325705 0.6467311 80 -0.3592261 -0.5325705 81 0.6428370 -0.3592261 82 -0.3579966 0.6428370 83 -0.3509522 -0.3579966 84 0.4697462 -0.3509522 85 -0.2610748 0.4697462 86 0.7395399 -0.2610748 87 0.6469039 0.7395399 88 -0.3543082 0.6469039 89 0.6463065 -0.3543082 90 -0.5265654 0.6463065 91 -0.2570079 -0.5265654 92 -0.4302583 -0.2570079 93 -0.3512345 -0.4302583 94 -0.3502413 -0.3512345 95 0.6499950 -0.3502413 96 -0.2539342 0.6499950 97 -0.3487756 -0.2539342 98 -0.2530832 -0.3487756 99 0.6524539 -0.2530832 100 0.7481463 0.6524539 101 -0.3463166 0.7481463 102 -0.3457019 -0.3463166 103 -0.3450871 -0.3457019 104 -0.4377232 -0.3450871 105 -0.3438577 -0.4377232 106 -0.3432429 -0.3438577 107 -0.3408014 -0.3432429 108 -0.3420134 -0.3408014 109 -0.2463211 -0.3420134 110 -0.5124438 -0.2463211 111 -0.3397907 -0.5124438 112 -0.4331838 -0.3397907 113 -0.3371129 -0.4331838 114 -0.2432474 -0.3371129 115 -0.3377103 -0.2432474 116 0.7579821 -0.3377103 117 -0.2414032 0.7579821 118 -0.3358661 -0.2414032 119 0.6647487 -0.3358661 120 -0.2395590 0.6647487 121 -0.3340218 -0.2395590 122 -0.3315803 -0.3340218 123 0.4000917 -0.3315803 124 0.6678224 0.4000917 125 -0.3311844 0.6678224 126 -0.5044348 -0.3311844 127 0.6696666 -0.5044348 128 -0.3297187 0.6696666 129 0.6708961 -0.3297187 130 -0.2334116 0.6708961 131 0.7672031 -0.2334116 132 -0.3258114 0.7672031 133 -0.3266450 -0.3258114 134 -0.3260303 -0.3266450 135 -0.3254155 -0.3260303 136 0.5031609 -0.3254155 137 0.5041541 0.5031609 138 -0.3231928 0.5041541 139 -0.3229566 -0.3231928 140 0.6840879 -0.3229566 141 0.5850221 0.6840879 142 -0.2260348 0.5850221 143 0.5060157 -0.2260348 144 -0.4933695 0.5060157 145 0.6811103 -0.4933695 146 -0.4119042 0.6811103 147 -0.3176602 -0.4119042 148 -0.2223463 -0.3176602 149 0.5097042 -0.2223463 150 0.6838055 0.5097042 151 -0.2140724 0.6838055 152 -0.3869443 -0.2140724 153 -0.3129019 -0.3869443 > 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/fisher/rcomp/tmp/7ard11355687671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8tekr1355687671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9b5bv1355687671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10bs051355687671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/fisher/rcomp/tmp/11tqtm1355687671.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/fisher/rcomp/tmp/121gfk1355687671.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/fisher/rcomp/tmp/13p4mv1355687672.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/fisher/rcomp/tmp/14t7131355687672.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/fisher/rcomp/tmp/15zqs21355687672.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/fisher/rcomp/tmp/16f2zb1355687672.tab") + } > > try(system("convert tmp/1ehqp1355687671.ps tmp/1ehqp1355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/2w7i91355687671.ps tmp/2w7i91355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/3b79g1355687671.ps tmp/3b79g1355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/4r3j51355687671.ps tmp/4r3j51355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/5pa4d1355687671.ps tmp/5pa4d1355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/6a5y31355687671.ps tmp/6a5y31355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/7ard11355687671.ps tmp/7ard11355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/8tekr1355687671.ps tmp/8tekr1355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/9b5bv1355687671.ps tmp/9b5bv1355687671.png",intern=TRUE)) character(0) > try(system("convert tmp/10bs051355687671.ps tmp/10bs051355687671.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.831 1.649 9.481