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(1 + ,1 + ,4 + ,4 + ,5 + ,5 + ,3 + ,3 + ,1 + ,1 + ,2 + ,2 + ,7 + ,7 + ,4 + ,4 + ,1 + ,2 + ,2 + ,2 + ,7 + ,7 + ,3 + ,3 + ,1 + ,1 + ,4 + ,4 + ,7 + ,7 + ,4 + ,4 + ,1 + ,1 + ,3 + ,3 + ,3 + ,3 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,2 + ,2 + ,4 + ,4 + ,1 + ,1 + ,2 + ,2 + ,1 + ,1 + ,2 + ,2 + ,1 + ,1 + ,3 + ,3 + ,7 + ,7 + ,6 + ,6 + ,1 + ,1 + ,5 + ,5 + ,5 + ,5 + ,2 + ,2 + ,1 + ,1 + ,2 + ,2 + ,5 + ,5 + ,4 + ,4 + ,1 + ,2 + ,4 + ,4 + ,6 + ,6 + ,2 + ,2 + ,1 + ,1 + ,3 + ,3 + ,2 + ,2 + ,2 + ,2 + ,1 + ,1 + ,2 + ,2 + ,6 + ,6 + ,2 + ,2 + ,1 + ,2 + ,4 + ,4 + ,6 + ,6 + ,6 + ,6 + ,1 + ,1 + ,2 + ,2 + ,6 + ,6 + ,2 + ,2 + ,1 + ,1 + ,5 + ,5 + ,6 + ,6 + ,4 + ,4 + ,1 + ,1 + ,5 + ,5 + ,6 + ,6 + ,3 + ,3 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,2 + ,2 + ,7 + ,7 + ,4 + ,4 + ,1 + ,1 + ,1 + ,1 + ,4 + ,4 + ,1 + ,1 + ,1 + ,1 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,1 + ,2 + ,2 + ,2 + ,7 + ,7 + ,1 + ,1 + ,1 + ,1 + ,4 + ,4 + ,5 + ,5 + ,4 + ,4 + ,1 + ,1 + ,6 + ,6 + ,2 + ,2 + ,3 + ,3 + 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,0 + ,1 + ,3 + ,0 + ,5 + ,0 + ,5 + ,0 + ,0 + ,3 + ,6 + ,0 + ,5 + ,0 + ,4 + ,0 + ,0 + ,1 + ,1 + ,0 + ,5 + ,0 + ,1 + ,0) + ,dim=c(8 + ,152) + ,dimnames=list(c('Gender' + ,'Depressed' + ,'Cannotdo' + ,'Cannotdo_G' + ,'Worrytoomuch' + ,'Worrytoomuch_G' + ,'Limitactivity' + ,'Limitactivity_G') + ,1:152)) > y <- array(NA,dim=c(8,152),dimnames=list(c('Gender','Depressed','Cannotdo','Cannotdo_G','Worrytoomuch','Worrytoomuch_G','Limitactivity','Limitactivity_G'),1:152)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo 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 Depressed Gender Cannotdo Cannotdo_G Worrytoomuch Worrytoomuch_G 1 1 1 4 4 5 5 2 1 1 2 2 7 7 3 2 1 2 2 7 7 4 1 1 4 4 7 7 5 1 1 3 3 3 3 6 1 1 1 1 2 2 7 1 1 2 2 1 1 8 1 1 3 3 7 7 9 1 1 5 5 5 5 10 1 1 2 2 5 5 11 2 1 4 4 6 6 12 1 1 3 3 2 2 13 1 1 2 2 6 6 14 2 1 4 4 6 6 15 1 1 2 2 6 6 16 1 1 5 5 6 6 17 1 1 5 5 6 6 18 1 1 1 1 1 1 19 1 1 2 2 7 7 20 1 1 1 1 4 4 21 1 1 3 3 3 3 22 2 1 2 2 7 7 23 1 1 4 4 5 5 24 1 1 6 6 2 2 25 2 1 3 3 7 7 26 1 1 2 2 2 2 27 1 1 6 6 7 7 28 1 1 2 2 3 3 29 2 1 2 2 3 3 30 1 1 1 1 2 2 31 1 1 3 3 5 5 32 1 1 4 4 2 2 33 1 1 3 3 5 5 34 1 1 2 2 2 2 35 1 1 2 2 5 5 36 1 1 3 3 2 2 37 1 1 7 7 2 2 38 1 1 2 2 5 5 39 1 1 4 4 3 3 40 1 1 2 2 5 5 41 2 1 4 4 5 5 42 1 1 5 5 5 5 43 2 1 6 6 6 6 44 2 1 5 5 5 5 45 2 1 1 1 5 5 46 1 1 4 4 3 3 47 1 1 1 1 3 3 48 1 1 3 3 2 2 49 2 1 5 5 4 4 50 2 1 6 6 5 5 51 1 1 2 2 5 5 52 1 1 2 2 5 5 53 4 1 2 2 6 6 54 1 1 5 5 5 5 55 1 1 6 6 5 5 56 1 1 5 5 7 7 57 1 1 2 2 3 3 58 1 1 6 6 6 6 59 1 1 2 2 4 4 60 1 1 5 5 5 5 61 1 0 4 0 4 0 62 1 0 2 0 2 0 63 1 0 5 0 5 0 64 2 0 1 0 1 0 65 1 0 2 0 4 0 66 4 0 5 0 6 0 67 1 0 1 0 5 0 68 1 0 3 0 4 0 69 2 0 5 0 5 0 70 1 0 2 0 2 0 71 1 0 2 0 5 0 72 1 0 1 0 5 0 73 1 0 6 0 6 0 74 2 0 3 0 6 0 75 2 0 5 0 5 0 76 1 0 5 0 4 0 77 2 0 3 0 4 0 78 1 0 5 0 7 0 79 2 0 4 0 6 0 80 1 0 1 0 1 0 81 1 0 6 0 4 0 82 1 0 2 0 2 0 83 1 0 1 0 1 0 84 1 0 1 0 1 0 85 1 0 4 0 2 0 86 1 0 5 0 3 0 87 1 0 3 0 5 0 88 1 0 3 0 3 0 89 1 0 2 0 2 0 90 2 0 5 0 7 0 91 1 0 4 0 1 0 92 1 0 2 0 2 0 93 2 0 3 0 5 0 94 1 0 2 0 4 0 95 1 0 5 0 5 0 96 1 0 5 0 6 0 97 1 0 5 0 3 0 98 2 0 4 0 4 0 99 1 0 5 0 5 0 100 1 0 6 0 6 0 101 1 0 6 0 6 0 102 3 0 5 0 3 0 103 2 0 2 0 4 0 104 2 0 4 0 6 0 105 1 0 3 0 1 0 106 1 0 2 0 4 0 107 1 0 2 0 5 0 108 1 0 5 0 3 0 109 1 0 1 0 2 0 110 3 0 5 0 7 0 111 1 0 2 0 1 0 112 1 0 1 0 5 0 113 1 0 2 0 5 0 114 1 0 2 0 2 0 115 1 0 0 0 6 0 116 1 0 5 0 2 0 117 1 0 3 0 5 0 118 1 0 2 0 3 0 119 1 0 2 0 5 0 120 1 0 1 0 6 0 121 1 0 4 0 5 0 122 1 0 2 0 2 0 123 3 0 7 0 1 0 124 2 0 5 0 5 0 125 2 0 3 0 6 0 126 1 0 4 0 6 0 127 1 0 2 0 3 0 128 1 0 6 0 5 0 129 1 0 4 0 5 0 130 1 0 2 0 2 0 131 2 0 7 0 3 0 132 1 0 4 0 3 0 133 1 0 4 0 6 0 134 1 0 4 0 5 0 135 1 0 2 0 2 0 136 1 0 5 0 4 0 137 1 0 2 0 2 0 138 1 0 3 0 5 0 139 1 0 4 0 5 0 140 1 0 2 0 1 0 141 1 0 2 0 5 0 142 2 0 2 0 5 0 143 1 0 3 0 6 0 144 1 0 4 0 5 0 145 1 0 1 0 1 0 146 1 0 2 0 5 0 147 1 0 2 0 5 0 148 1 0 2 0 2 0 149 2 0 1 0 4 0 150 1 0 3 0 5 0 151 3 0 6 0 5 0 152 1 0 1 0 5 0 Limitactivity Limitactivity_G 1 3 3 2 4 4 3 3 3 4 4 4 5 1 1 6 4 4 7 2 2 8 6 6 9 2 2 10 4 4 11 2 2 12 2 2 13 2 2 14 6 6 15 2 2 16 4 4 17 3 3 18 1 1 19 4 4 20 1 1 21 4 4 22 1 1 23 4 4 24 3 3 25 2 2 26 4 4 27 5 5 28 5 5 29 2 2 30 3 3 31 2 2 32 2 2 33 2 2 34 2 2 35 2 2 36 2 2 37 1 1 38 3 3 39 2 2 40 2 2 41 4 4 42 3 3 43 3 3 44 4 4 45 2 2 46 5 5 47 1 1 48 3 3 49 3 3 50 2 2 51 4 4 52 4 4 53 4 4 54 4 4 55 4 4 56 2 2 57 3 3 58 3 3 59 3 3 60 2 2 61 2 0 62 2 0 63 4 0 64 2 0 65 1 0 66 4 0 67 3 0 68 1 0 69 4 0 70 4 0 71 4 0 72 1 0 73 4 0 74 3 0 75 6 0 76 5 0 77 4 0 78 1 0 79 4 0 80 1 0 81 1 0 82 2 0 83 1 0 84 3 0 85 3 0 86 4 0 87 3 0 88 2 0 89 5 0 90 2 0 91 3 0 92 2 0 93 3 0 94 2 0 95 4 0 96 2 0 97 2 0 98 4 0 99 5 0 100 4 0 101 2 0 102 5 0 103 2 0 104 3 0 105 2 0 106 3 0 107 3 0 108 3 0 109 1 0 110 4 0 111 1 0 112 1 0 113 1 0 114 3 0 115 2 0 116 3 0 117 5 0 118 3 0 119 3 0 120 4 0 121 2 0 122 3 0 123 5 0 124 2 0 125 2 0 126 4 0 127 0 0 128 6 0 129 1 0 130 2 0 131 1 0 132 4 0 133 2 0 134 4 0 135 1 0 136 4 0 137 1 0 138 2 0 139 5 0 140 2 0 141 4 0 142 4 0 143 2 0 144 2 0 145 1 0 146 2 0 147 1 0 148 2 0 149 5 0 150 5 0 151 4 0 152 1 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Gender Cannotdo Cannotdo_G 0.66254 0.25238 0.08535 -0.10007 Worrytoomuch Worrytoomuch_G Limitactivity Limitactivity_G 0.02725 0.06611 0.08788 -0.10040 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.8381 -0.3213 -0.1332 0.0912 2.6044 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.66254 0.17810 3.720 0.000285 *** Gender 0.25238 0.30885 0.817 0.415181 Cannotdo 0.08535 0.03878 2.201 0.029349 * Cannotdo_G -0.10007 0.05975 -1.675 0.096155 . Worrytoomuch 0.02725 0.03550 0.768 0.444035 Worrytoomuch_G 0.06611 0.05542 1.193 0.234896 Limitactivity 0.08788 0.04492 1.956 0.052388 . Limitactivity_G -0.10040 0.07554 -1.329 0.185931 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5506 on 144 degrees of freedom Multiple R-squared: 0.1338, Adjusted R-squared: 0.09168 F-statistic: 3.177 on 7 and 144 DF, p-value: 0.003719 > 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,] 0.560084776 0.879830447 0.43991522 [2,] 0.390916678 0.781833356 0.60908332 [3,] 0.423990511 0.847981021 0.57600949 [4,] 0.609390008 0.781219985 0.39060999 [5,] 0.512084831 0.975830337 0.48791517 [6,] 0.440372675 0.880745350 0.55962732 [7,] 0.360677074 0.721354147 0.63932293 [8,] 0.271404723 0.542809447 0.72859528 [9,] 0.220928182 0.441856363 0.77907182 [10,] 0.159238715 0.318477430 0.84076129 [11,] 0.110626898 0.221253797 0.88937310 [12,] 0.142353665 0.284707329 0.85764634 [13,] 0.103332034 0.206664068 0.89666797 [14,] 0.071381250 0.142762500 0.92861875 [15,] 0.080168821 0.160337642 0.91983118 [16,] 0.055942572 0.111885145 0.94405743 [17,] 0.043237131 0.086474262 0.95676287 [18,] 0.029219282 0.058438563 0.97078072 [19,] 0.066534191 0.133068382 0.93346581 [20,] 0.046396376 0.092792752 0.95360362 [21,] 0.036263700 0.072527400 0.96373630 [22,] 0.024441719 0.048883438 0.97555828 [23,] 0.018569355 0.037138710 0.98143065 [24,] 0.012175125 0.024350250 0.98782487 [25,] 0.009290641 0.018581283 0.99070936 [26,] 0.005890585 0.011781171 0.99410941 [27,] 0.003852422 0.007704843 0.99614758 [28,] 0.002824488 0.005648975 0.99717551 [29,] 0.001734387 0.003468773 0.99826561 [30,] 0.001290463 0.002580927 0.99870954 [31,] 0.003500058 0.007000116 0.99649994 [32,] 0.002408137 0.004816273 0.99759186 [33,] 0.003954517 0.007909034 0.99604548 [34,] 0.007020663 0.014041326 0.99297934 [35,] 0.010148463 0.020296926 0.98985154 [36,] 0.006875095 0.013750190 0.99312490 [37,] 0.004720254 0.009440509 0.99527975 [38,] 0.003102300 0.006204600 0.99689770 [39,] 0.006704698 0.013409396 0.99329530 [40,] 0.011949517 0.023899034 0.98805048 [41,] 0.011698274 0.023396547 0.98830173 [42,] 0.016042271 0.032084542 0.98395773 [43,] 0.633947040 0.732105919 0.36605296 [44,] 0.595321640 0.809356720 0.40467836 [45,] 0.554840676 0.890318648 0.44515932 [46,] 0.533363967 0.933272066 0.46663603 [47,] 0.484286474 0.968572949 0.51571353 [48,] 0.448154309 0.896308618 0.55184569 [49,] 0.403516621 0.807033241 0.59648338 [50,] 0.363462155 0.726924309 0.63653785 [51,] 0.321673547 0.643347095 0.67832645 [52,] 0.278279523 0.556559046 0.72172048 [53,] 0.253527436 0.507054872 0.74647256 [54,] 0.293488002 0.586976004 0.70651200 [55,] 0.252189710 0.504379420 0.74781029 [56,] 0.826750243 0.346499513 0.17324976 [57,] 0.903460279 0.193079441 0.09653972 [58,] 0.881444112 0.237111776 0.11855589 [59,] 0.868484877 0.263030246 0.13151512 [60,] 0.866217224 0.267565551 0.13378278 [61,] 0.855960707 0.288078587 0.14403929 [62,] 0.827453600 0.345092800 0.17254640 [63,] 0.865090884 0.269818232 0.13490912 [64,] 0.869898486 0.260203028 0.13010151 [65,] 0.847103275 0.305793449 0.15289672 [66,] 0.859008797 0.281982407 0.14099120 [67,] 0.862150714 0.275698571 0.13784929 [68,] 0.846775926 0.306448147 0.15322407 [69,] 0.836317048 0.327365904 0.16368295 [70,] 0.806091489 0.387817022 0.19390851 [71,] 0.783601278 0.432797445 0.21639872 [72,] 0.746679329 0.506641342 0.25332067 [73,] 0.708242695 0.583514610 0.29175730 [74,] 0.667086653 0.665826695 0.33291335 [75,] 0.633362415 0.733275169 0.36663758 [76,] 0.621263743 0.757472513 0.37873626 [77,] 0.594567816 0.810864367 0.40543218 [78,] 0.550049573 0.899900854 0.44995043 [79,] 0.519855082 0.960289836 0.48014492 [80,] 0.513559520 0.972880960 0.48644048 [81,] 0.478002178 0.956004355 0.52199782 [82,] 0.429487479 0.858974957 0.57051252 [83,] 0.447800941 0.895601882 0.55219906 [84,] 0.403281120 0.806562241 0.59671888 [85,] 0.403572450 0.807144901 0.59642755 [86,] 0.381318295 0.762636590 0.61868171 [87,] 0.353304834 0.706609668 0.64669517 [88,] 0.348351599 0.696703198 0.65164840 [89,] 0.367079440 0.734158880 0.63292056 [90,] 0.396015968 0.792031936 0.60398403 [91,] 0.403171621 0.806343242 0.59682838 [92,] 0.674827136 0.650345727 0.32517286 [93,] 0.746334289 0.507331422 0.25366571 [94,] 0.743275535 0.513448931 0.25672447 [95,] 0.700312816 0.599374367 0.29968718 [96,] 0.659776323 0.680447354 0.34022368 [97,] 0.619836354 0.760327291 0.38016365 [98,] 0.608710567 0.782578867 0.39128943 [99,] 0.557718156 0.884563689 0.44228184 [100,] 0.819765416 0.360469168 0.18023458 [101,] 0.781383088 0.437233824 0.21861691 [102,] 0.740221397 0.519557206 0.25977860 [103,] 0.691444302 0.617111396 0.30855570 [104,] 0.641792745 0.716414511 0.35820726 [105,] 0.605115428 0.789769143 0.39488457 [106,] 0.608914085 0.782171830 0.39108592 [107,] 0.582833364 0.834333273 0.41716664 [108,] 0.527079191 0.945841618 0.47292081 [109,] 0.470790804 0.941581608 0.52920920 [110,] 0.420603650 0.841207300 0.57939635 [111,] 0.377533415 0.755066830 0.62246659 [112,] 0.324103752 0.648207504 0.67589625 [113,] 0.517883788 0.964232423 0.48211621 [114,] 0.528818841 0.942362318 0.47118116 [115,] 0.611878226 0.776243549 0.38812177 [116,] 0.569680956 0.860638088 0.43031904 [117,] 0.496650596 0.993301191 0.50334940 [118,] 0.549520567 0.900958866 0.45047943 [119,] 0.479787615 0.959575231 0.52021238 [120,] 0.402843040 0.805686079 0.59715696 [121,] 0.403277559 0.806555118 0.59672244 [122,] 0.366680468 0.733360936 0.63331953 [123,] 0.296434654 0.592869309 0.70356535 [124,] 0.268471794 0.536943589 0.73152821 [125,] 0.197020220 0.394040440 0.80297978 [126,] 0.214832853 0.429665706 0.78516715 [127,] 0.146864458 0.293728915 0.85313554 [128,] 0.097730384 0.195460768 0.90226962 [129,] 0.148607330 0.297214659 0.85139267 [130,] 0.094376689 0.188753378 0.90562331 [131,] 0.066579459 0.133158918 0.93342054 > postscript(file="/var/www/html/rcomp/tmp/1kebm1292769564.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2dnap1292769564.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3dnap1292769564.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4dnap1292769564.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5dnap1292769564.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 = 152 Frequency = 1 1 2 3 4 5 6 -0.285299278 -0.488931227 0.498547545 -0.459499553 -0.138336068 -0.036843304 7 8 9 10 11 12 0.046190828 -0.449172933 -0.283104669 -0.302209723 0.608818742 -0.032454087 13 14 15 16 17 18 -0.420612932 0.658903656 -0.420612932 -0.351422964 -0.363944193 0.018953762 19 20 21 22 23 24 -0.488931227 -0.261128493 -0.100772382 0.473505088 -0.272778049 0.024214652 25 26 27 28 29 30 0.500742153 -0.022127467 -0.417546651 -0.102966991 0.859469324 -0.049364533 31 32 33 34 35 36 -0.312536343 -0.017738250 -0.312536343 -0.047169924 -0.327252180 -0.032454087 37 38 39 40 41 42 0.013888032 -0.314730951 -0.111099002 -0.327252180 0.727221951 -0.270583441 43 44 45 46 47 48 0.650771644 0.741937788 0.658031983 -0.073535317 -0.167767741 -0.019932859 49 50 51 52 53 54 0.822777311 0.731611168 -0.302209723 -0.302209723 2.604429525 -0.258062212 55 56 57 58 59 60 -0.243346375 -0.469826173 -0.128009448 -0.349228356 -0.221370200 -0.283104669 61 62 63 64 65 66 -0.288691349 -0.063490701 -0.577043885 1.049109622 -0.030113533 2.395706646 67 68 69 70 71 72 -0.147764360 -0.115464388 0.422956115 -0.239242913 -0.320991321 0.027987852 73 74 75 76 77 78 -0.689644208 0.654284462 0.247203903 -0.637670522 0.620907294 -0.367914504 79 80 81 82 83 84 0.481057501 0.136985728 -0.371516952 -0.063490701 0.136985728 -0.038766484 85 86 87 88 89 90 -0.322068517 -0.522544946 -0.318466069 -0.176091025 -0.327119019 0.544209389 91 92 93 94 95 96 -0.294819048 -0.063490701 0.681533931 -0.117989639 -0.577043885 -0.428541141 97 98 99 100 101 102 -0.346792734 0.535556439 -0.664919991 -0.689644208 -0.513891996 1.389578948 103 104 105 106 107 108 0.882010361 0.568933607 -0.121592087 -0.205865746 -0.233115215 -0.434668840 109 110 111 112 113 114 0.109736259 1.368457177 0.051634874 0.027987852 -0.057363002 -0.151366807 115 116 117 118 119 120 -0.001786868 -0.407419371 -0.494218281 -0.178616276 -0.233115215 -0.262889935 121 122 123 124 125 126 -0.315940818 -0.151366807 1.273376176 0.598708328 0.742160568 -0.518942499 127 128 129 130 131 132 0.085012042 -0.838146951 -0.228064712 -0.063490701 0.570381663 -0.437194092 133 134 135 136 137 138 -0.343190287 -0.491693030 0.024385405 -0.549794415 0.024385405 -0.230589963 139 140 141 142 143 144 -0.579569136 -0.036241232 -0.320991321 0.679008679 -0.257839432 -0.315940818 145 146 147 148 149 150 0.136985728 -0.145239109 -0.057363002 -0.063490701 0.703732897 -0.494218281 151 152 1.337605261 0.027987852 > postscript(file="/var/www/html/rcomp/tmp/65wrs1292769564.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 = 152 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.285299278 NA 1 -0.488931227 -0.285299278 2 0.498547545 -0.488931227 3 -0.459499553 0.498547545 4 -0.138336068 -0.459499553 5 -0.036843304 -0.138336068 6 0.046190828 -0.036843304 7 -0.449172933 0.046190828 8 -0.283104669 -0.449172933 9 -0.302209723 -0.283104669 10 0.608818742 -0.302209723 11 -0.032454087 0.608818742 12 -0.420612932 -0.032454087 13 0.658903656 -0.420612932 14 -0.420612932 0.658903656 15 -0.351422964 -0.420612932 16 -0.363944193 -0.351422964 17 0.018953762 -0.363944193 18 -0.488931227 0.018953762 19 -0.261128493 -0.488931227 20 -0.100772382 -0.261128493 21 0.473505088 -0.100772382 22 -0.272778049 0.473505088 23 0.024214652 -0.272778049 24 0.500742153 0.024214652 25 -0.022127467 0.500742153 26 -0.417546651 -0.022127467 27 -0.102966991 -0.417546651 28 0.859469324 -0.102966991 29 -0.049364533 0.859469324 30 -0.312536343 -0.049364533 31 -0.017738250 -0.312536343 32 -0.312536343 -0.017738250 33 -0.047169924 -0.312536343 34 -0.327252180 -0.047169924 35 -0.032454087 -0.327252180 36 0.013888032 -0.032454087 37 -0.314730951 0.013888032 38 -0.111099002 -0.314730951 39 -0.327252180 -0.111099002 40 0.727221951 -0.327252180 41 -0.270583441 0.727221951 42 0.650771644 -0.270583441 43 0.741937788 0.650771644 44 0.658031983 0.741937788 45 -0.073535317 0.658031983 46 -0.167767741 -0.073535317 47 -0.019932859 -0.167767741 48 0.822777311 -0.019932859 49 0.731611168 0.822777311 50 -0.302209723 0.731611168 51 -0.302209723 -0.302209723 52 2.604429525 -0.302209723 53 -0.258062212 2.604429525 54 -0.243346375 -0.258062212 55 -0.469826173 -0.243346375 56 -0.128009448 -0.469826173 57 -0.349228356 -0.128009448 58 -0.221370200 -0.349228356 59 -0.283104669 -0.221370200 60 -0.288691349 -0.283104669 61 -0.063490701 -0.288691349 62 -0.577043885 -0.063490701 63 1.049109622 -0.577043885 64 -0.030113533 1.049109622 65 2.395706646 -0.030113533 66 -0.147764360 2.395706646 67 -0.115464388 -0.147764360 68 0.422956115 -0.115464388 69 -0.239242913 0.422956115 70 -0.320991321 -0.239242913 71 0.027987852 -0.320991321 72 -0.689644208 0.027987852 73 0.654284462 -0.689644208 74 0.247203903 0.654284462 75 -0.637670522 0.247203903 76 0.620907294 -0.637670522 77 -0.367914504 0.620907294 78 0.481057501 -0.367914504 79 0.136985728 0.481057501 80 -0.371516952 0.136985728 81 -0.063490701 -0.371516952 82 0.136985728 -0.063490701 83 -0.038766484 0.136985728 84 -0.322068517 -0.038766484 85 -0.522544946 -0.322068517 86 -0.318466069 -0.522544946 87 -0.176091025 -0.318466069 88 -0.327119019 -0.176091025 89 0.544209389 -0.327119019 90 -0.294819048 0.544209389 91 -0.063490701 -0.294819048 92 0.681533931 -0.063490701 93 -0.117989639 0.681533931 94 -0.577043885 -0.117989639 95 -0.428541141 -0.577043885 96 -0.346792734 -0.428541141 97 0.535556439 -0.346792734 98 -0.664919991 0.535556439 99 -0.689644208 -0.664919991 100 -0.513891996 -0.689644208 101 1.389578948 -0.513891996 102 0.882010361 1.389578948 103 0.568933607 0.882010361 104 -0.121592087 0.568933607 105 -0.205865746 -0.121592087 106 -0.233115215 -0.205865746 107 -0.434668840 -0.233115215 108 0.109736259 -0.434668840 109 1.368457177 0.109736259 110 0.051634874 1.368457177 111 0.027987852 0.051634874 112 -0.057363002 0.027987852 113 -0.151366807 -0.057363002 114 -0.001786868 -0.151366807 115 -0.407419371 -0.001786868 116 -0.494218281 -0.407419371 117 -0.178616276 -0.494218281 118 -0.233115215 -0.178616276 119 -0.262889935 -0.233115215 120 -0.315940818 -0.262889935 121 -0.151366807 -0.315940818 122 1.273376176 -0.151366807 123 0.598708328 1.273376176 124 0.742160568 0.598708328 125 -0.518942499 0.742160568 126 0.085012042 -0.518942499 127 -0.838146951 0.085012042 128 -0.228064712 -0.838146951 129 -0.063490701 -0.228064712 130 0.570381663 -0.063490701 131 -0.437194092 0.570381663 132 -0.343190287 -0.437194092 133 -0.491693030 -0.343190287 134 0.024385405 -0.491693030 135 -0.549794415 0.024385405 136 0.024385405 -0.549794415 137 -0.230589963 0.024385405 138 -0.579569136 -0.230589963 139 -0.036241232 -0.579569136 140 -0.320991321 -0.036241232 141 0.679008679 -0.320991321 142 -0.257839432 0.679008679 143 -0.315940818 -0.257839432 144 0.136985728 -0.315940818 145 -0.145239109 0.136985728 146 -0.057363002 -0.145239109 147 -0.063490701 -0.057363002 148 0.703732897 -0.063490701 149 -0.494218281 0.703732897 150 1.337605261 -0.494218281 151 0.027987852 1.337605261 152 NA 0.027987852 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.488931227 -0.285299278 [2,] 0.498547545 -0.488931227 [3,] -0.459499553 0.498547545 [4,] -0.138336068 -0.459499553 [5,] -0.036843304 -0.138336068 [6,] 0.046190828 -0.036843304 [7,] -0.449172933 0.046190828 [8,] -0.283104669 -0.449172933 [9,] -0.302209723 -0.283104669 [10,] 0.608818742 -0.302209723 [11,] -0.032454087 0.608818742 [12,] -0.420612932 -0.032454087 [13,] 0.658903656 -0.420612932 [14,] -0.420612932 0.658903656 [15,] -0.351422964 -0.420612932 [16,] -0.363944193 -0.351422964 [17,] 0.018953762 -0.363944193 [18,] -0.488931227 0.018953762 [19,] -0.261128493 -0.488931227 [20,] -0.100772382 -0.261128493 [21,] 0.473505088 -0.100772382 [22,] -0.272778049 0.473505088 [23,] 0.024214652 -0.272778049 [24,] 0.500742153 0.024214652 [25,] -0.022127467 0.500742153 [26,] -0.417546651 -0.022127467 [27,] -0.102966991 -0.417546651 [28,] 0.859469324 -0.102966991 [29,] -0.049364533 0.859469324 [30,] -0.312536343 -0.049364533 [31,] -0.017738250 -0.312536343 [32,] -0.312536343 -0.017738250 [33,] -0.047169924 -0.312536343 [34,] -0.327252180 -0.047169924 [35,] -0.032454087 -0.327252180 [36,] 0.013888032 -0.032454087 [37,] -0.314730951 0.013888032 [38,] -0.111099002 -0.314730951 [39,] -0.327252180 -0.111099002 [40,] 0.727221951 -0.327252180 [41,] -0.270583441 0.727221951 [42,] 0.650771644 -0.270583441 [43,] 0.741937788 0.650771644 [44,] 0.658031983 0.741937788 [45,] -0.073535317 0.658031983 [46,] -0.167767741 -0.073535317 [47,] -0.019932859 -0.167767741 [48,] 0.822777311 -0.019932859 [49,] 0.731611168 0.822777311 [50,] -0.302209723 0.731611168 [51,] -0.302209723 -0.302209723 [52,] 2.604429525 -0.302209723 [53,] -0.258062212 2.604429525 [54,] -0.243346375 -0.258062212 [55,] -0.469826173 -0.243346375 [56,] -0.128009448 -0.469826173 [57,] -0.349228356 -0.128009448 [58,] -0.221370200 -0.349228356 [59,] -0.283104669 -0.221370200 [60,] -0.288691349 -0.283104669 [61,] -0.063490701 -0.288691349 [62,] -0.577043885 -0.063490701 [63,] 1.049109622 -0.577043885 [64,] -0.030113533 1.049109622 [65,] 2.395706646 -0.030113533 [66,] -0.147764360 2.395706646 [67,] -0.115464388 -0.147764360 [68,] 0.422956115 -0.115464388 [69,] -0.239242913 0.422956115 [70,] -0.320991321 -0.239242913 [71,] 0.027987852 -0.320991321 [72,] -0.689644208 0.027987852 [73,] 0.654284462 -0.689644208 [74,] 0.247203903 0.654284462 [75,] -0.637670522 0.247203903 [76,] 0.620907294 -0.637670522 [77,] -0.367914504 0.620907294 [78,] 0.481057501 -0.367914504 [79,] 0.136985728 0.481057501 [80,] -0.371516952 0.136985728 [81,] -0.063490701 -0.371516952 [82,] 0.136985728 -0.063490701 [83,] -0.038766484 0.136985728 [84,] -0.322068517 -0.038766484 [85,] -0.522544946 -0.322068517 [86,] -0.318466069 -0.522544946 [87,] -0.176091025 -0.318466069 [88,] -0.327119019 -0.176091025 [89,] 0.544209389 -0.327119019 [90,] -0.294819048 0.544209389 [91,] -0.063490701 -0.294819048 [92,] 0.681533931 -0.063490701 [93,] -0.117989639 0.681533931 [94,] -0.577043885 -0.117989639 [95,] -0.428541141 -0.577043885 [96,] -0.346792734 -0.428541141 [97,] 0.535556439 -0.346792734 [98,] -0.664919991 0.535556439 [99,] -0.689644208 -0.664919991 [100,] -0.513891996 -0.689644208 [101,] 1.389578948 -0.513891996 [102,] 0.882010361 1.389578948 [103,] 0.568933607 0.882010361 [104,] -0.121592087 0.568933607 [105,] -0.205865746 -0.121592087 [106,] -0.233115215 -0.205865746 [107,] -0.434668840 -0.233115215 [108,] 0.109736259 -0.434668840 [109,] 1.368457177 0.109736259 [110,] 0.051634874 1.368457177 [111,] 0.027987852 0.051634874 [112,] -0.057363002 0.027987852 [113,] -0.151366807 -0.057363002 [114,] -0.001786868 -0.151366807 [115,] -0.407419371 -0.001786868 [116,] -0.494218281 -0.407419371 [117,] -0.178616276 -0.494218281 [118,] -0.233115215 -0.178616276 [119,] -0.262889935 -0.233115215 [120,] -0.315940818 -0.262889935 [121,] -0.151366807 -0.315940818 [122,] 1.273376176 -0.151366807 [123,] 0.598708328 1.273376176 [124,] 0.742160568 0.598708328 [125,] -0.518942499 0.742160568 [126,] 0.085012042 -0.518942499 [127,] -0.838146951 0.085012042 [128,] -0.228064712 -0.838146951 [129,] -0.063490701 -0.228064712 [130,] 0.570381663 -0.063490701 [131,] -0.437194092 0.570381663 [132,] -0.343190287 -0.437194092 [133,] -0.491693030 -0.343190287 [134,] 0.024385405 -0.491693030 [135,] -0.549794415 0.024385405 [136,] 0.024385405 -0.549794415 [137,] -0.230589963 0.024385405 [138,] -0.579569136 -0.230589963 [139,] -0.036241232 -0.579569136 [140,] -0.320991321 -0.036241232 [141,] 0.679008679 -0.320991321 [142,] -0.257839432 0.679008679 [143,] -0.315940818 -0.257839432 [144,] 0.136985728 -0.315940818 [145,] -0.145239109 0.136985728 [146,] -0.057363002 -0.145239109 [147,] -0.063490701 -0.057363002 [148,] 0.703732897 -0.063490701 [149,] -0.494218281 0.703732897 [150,] 1.337605261 -0.494218281 [151,] 0.027987852 1.337605261 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.488931227 -0.285299278 2 0.498547545 -0.488931227 3 -0.459499553 0.498547545 4 -0.138336068 -0.459499553 5 -0.036843304 -0.138336068 6 0.046190828 -0.036843304 7 -0.449172933 0.046190828 8 -0.283104669 -0.449172933 9 -0.302209723 -0.283104669 10 0.608818742 -0.302209723 11 -0.032454087 0.608818742 12 -0.420612932 -0.032454087 13 0.658903656 -0.420612932 14 -0.420612932 0.658903656 15 -0.351422964 -0.420612932 16 -0.363944193 -0.351422964 17 0.018953762 -0.363944193 18 -0.488931227 0.018953762 19 -0.261128493 -0.488931227 20 -0.100772382 -0.261128493 21 0.473505088 -0.100772382 22 -0.272778049 0.473505088 23 0.024214652 -0.272778049 24 0.500742153 0.024214652 25 -0.022127467 0.500742153 26 -0.417546651 -0.022127467 27 -0.102966991 -0.417546651 28 0.859469324 -0.102966991 29 -0.049364533 0.859469324 30 -0.312536343 -0.049364533 31 -0.017738250 -0.312536343 32 -0.312536343 -0.017738250 33 -0.047169924 -0.312536343 34 -0.327252180 -0.047169924 35 -0.032454087 -0.327252180 36 0.013888032 -0.032454087 37 -0.314730951 0.013888032 38 -0.111099002 -0.314730951 39 -0.327252180 -0.111099002 40 0.727221951 -0.327252180 41 -0.270583441 0.727221951 42 0.650771644 -0.270583441 43 0.741937788 0.650771644 44 0.658031983 0.741937788 45 -0.073535317 0.658031983 46 -0.167767741 -0.073535317 47 -0.019932859 -0.167767741 48 0.822777311 -0.019932859 49 0.731611168 0.822777311 50 -0.302209723 0.731611168 51 -0.302209723 -0.302209723 52 2.604429525 -0.302209723 53 -0.258062212 2.604429525 54 -0.243346375 -0.258062212 55 -0.469826173 -0.243346375 56 -0.128009448 -0.469826173 57 -0.349228356 -0.128009448 58 -0.221370200 -0.349228356 59 -0.283104669 -0.221370200 60 -0.288691349 -0.283104669 61 -0.063490701 -0.288691349 62 -0.577043885 -0.063490701 63 1.049109622 -0.577043885 64 -0.030113533 1.049109622 65 2.395706646 -0.030113533 66 -0.147764360 2.395706646 67 -0.115464388 -0.147764360 68 0.422956115 -0.115464388 69 -0.239242913 0.422956115 70 -0.320991321 -0.239242913 71 0.027987852 -0.320991321 72 -0.689644208 0.027987852 73 0.654284462 -0.689644208 74 0.247203903 0.654284462 75 -0.637670522 0.247203903 76 0.620907294 -0.637670522 77 -0.367914504 0.620907294 78 0.481057501 -0.367914504 79 0.136985728 0.481057501 80 -0.371516952 0.136985728 81 -0.063490701 -0.371516952 82 0.136985728 -0.063490701 83 -0.038766484 0.136985728 84 -0.322068517 -0.038766484 85 -0.522544946 -0.322068517 86 -0.318466069 -0.522544946 87 -0.176091025 -0.318466069 88 -0.327119019 -0.176091025 89 0.544209389 -0.327119019 90 -0.294819048 0.544209389 91 -0.063490701 -0.294819048 92 0.681533931 -0.063490701 93 -0.117989639 0.681533931 94 -0.577043885 -0.117989639 95 -0.428541141 -0.577043885 96 -0.346792734 -0.428541141 97 0.535556439 -0.346792734 98 -0.664919991 0.535556439 99 -0.689644208 -0.664919991 100 -0.513891996 -0.689644208 101 1.389578948 -0.513891996 102 0.882010361 1.389578948 103 0.568933607 0.882010361 104 -0.121592087 0.568933607 105 -0.205865746 -0.121592087 106 -0.233115215 -0.205865746 107 -0.434668840 -0.233115215 108 0.109736259 -0.434668840 109 1.368457177 0.109736259 110 0.051634874 1.368457177 111 0.027987852 0.051634874 112 -0.057363002 0.027987852 113 -0.151366807 -0.057363002 114 -0.001786868 -0.151366807 115 -0.407419371 -0.001786868 116 -0.494218281 -0.407419371 117 -0.178616276 -0.494218281 118 -0.233115215 -0.178616276 119 -0.262889935 -0.233115215 120 -0.315940818 -0.262889935 121 -0.151366807 -0.315940818 122 1.273376176 -0.151366807 123 0.598708328 1.273376176 124 0.742160568 0.598708328 125 -0.518942499 0.742160568 126 0.085012042 -0.518942499 127 -0.838146951 0.085012042 128 -0.228064712 -0.838146951 129 -0.063490701 -0.228064712 130 0.570381663 -0.063490701 131 -0.437194092 0.570381663 132 -0.343190287 -0.437194092 133 -0.491693030 -0.343190287 134 0.024385405 -0.491693030 135 -0.549794415 0.024385405 136 0.024385405 -0.549794415 137 -0.230589963 0.024385405 138 -0.579569136 -0.230589963 139 -0.036241232 -0.579569136 140 -0.320991321 -0.036241232 141 0.679008679 -0.320991321 142 -0.257839432 0.679008679 143 -0.315940818 -0.257839432 144 0.136985728 -0.315940818 145 -0.145239109 0.136985728 146 -0.057363002 -0.145239109 147 -0.063490701 -0.057363002 148 0.703732897 -0.063490701 149 -0.494218281 0.703732897 150 1.337605261 -0.494218281 151 0.027987852 1.337605261 > 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/7y6rd1292769564.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8y6rd1292769564.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9rfqg1292769564.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10rfqg1292769564.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11uf641292769564.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/12yy5a1292769564.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/13bpk01292769564.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/14f8161292769564.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/15i90d1292769564.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/16w0f31292769564.tab") + } > > try(system("convert tmp/1kebm1292769564.ps tmp/1kebm1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/2dnap1292769564.ps tmp/2dnap1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/3dnap1292769564.ps tmp/3dnap1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/4dnap1292769564.ps tmp/4dnap1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/5dnap1292769564.ps tmp/5dnap1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/65wrs1292769564.ps tmp/65wrs1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/7y6rd1292769564.ps tmp/7y6rd1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/8y6rd1292769564.ps tmp/8y6rd1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/9rfqg1292769564.ps tmp/9rfqg1292769564.png",intern=TRUE)) character(0) > try(system("convert tmp/10rfqg1292769564.ps tmp/10rfqg1292769564.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.106 1.759 9.619