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 + ,3 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,4 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,3 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,4 + ,1 + ,4 + ,6 + ,0 + ,8 + ,0 + ,4 + ,1 + ,3 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,5 + ,1 + ,8 + ,0 + ,4 + ,1 + ,3 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,5 + ,1 + ,7 + ,0 + ,4 + ,0 + ,3 + ,5 + ,1 + ,7 + ,0 + ,4 + ,1 + ,3 + ,5 + ,1 + ,8 + ,1 + ,4 + ,1 + ,3 + ,6 + ,0 + ,8 + ,0 + ,4 + ,0 + ,4 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,3 + ,5 + ,1 + ,7 + ,1 + ,4 + ,1 + ,4 + ,6 + ,1 + ,8 + ,0 + ,4 + ,1 + ,4 + ,5 + ,1 + ,7 + ,0 + ,4 + ,0 + ,4 + ,6 + ,1 + ,7 + ,0 + ,4 + ,1 + ,4 + ,6 + ,1 + ,7 + ,0 + ,4 + ,0 + ,3 + ,5 + ,0 + ,7 + ,0 + ,4 + ,0 + ,4 + ,5 + ,1 + ,8 + ,0 + ,4 + ,1 + ,4 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,4 + 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,7 + ,0 + ,2 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,2 + ,0 + ,0 + ,6 + ,1 + ,8 + ,0 + ,2 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,2 + ,0 + ,0 + ,5 + ,0 + ,8 + ,0 + ,2 + ,0 + ,0 + ,6 + ,0 + ,8 + ,0 + ,2 + ,1 + ,0 + ,6 + ,0 + ,8 + ,0 + ,2 + ,0 + ,0 + ,6 + ,1 + ,7 + ,0 + ,2 + ,0 + ,0 + ,6 + ,0 + ,7 + ,0 + ,2 + ,1 + ,0 + ,5 + ,0 + ,8 + ,1 + ,2 + ,1 + ,0 + ,5 + ,1 + ,8 + ,1 + ,2 + ,1 + ,0 + ,5 + ,0 + ,8 + ,0) + ,dim=c(7 + ,154) + ,dimnames=list(c('Weeks' + ,'UsedLimit' + ,'T40' + ,'Used' + ,'Useful' + ,'Outcome' + ,'CorrectAnalysis') + ,1:154)) > y <- array(NA,dim=c(7,154),dimnames=list(c('Weeks','UsedLimit','T40','Used','Useful','Outcome','CorrectAnalysis'),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 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '7' > 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 CorrectAnalysis Weeks UsedLimit T40 Used Useful Outcome 1 0 4 1 3 6 0 7 2 0 4 0 4 6 0 8 3 0 4 0 4 6 0 8 4 0 4 0 4 6 0 8 5 0 4 0 4 6 0 8 6 0 4 1 4 6 1 7 7 0 4 0 4 6 0 8 8 0 4 0 3 6 0 8 9 0 4 0 4 6 0 7 10 0 4 1 4 6 0 8 11 0 4 1 3 6 0 8 12 0 4 0 4 6 0 8 13 0 4 0 4 5 1 8 14 0 4 1 3 6 0 8 15 0 4 0 4 5 1 7 16 0 4 0 3 5 1 7 17 1 4 1 3 5 1 8 18 0 4 1 3 6 0 8 19 0 4 0 4 6 0 7 20 1 4 0 3 5 1 7 21 0 4 1 4 6 1 8 22 0 4 1 4 5 1 7 23 0 4 0 4 6 1 7 24 0 4 1 4 6 1 7 25 0 4 0 3 5 0 7 26 0 4 0 4 5 1 8 27 0 4 1 4 6 0 7 28 0 4 0 4 5 0 8 29 0 4 0 4 6 0 7 30 0 4 0 4 6 1 8 31 0 4 0 4 6 0 8 32 0 4 1 4 6 0 8 33 0 4 1 4 6 1 8 34 0 4 0 3 6 0 7 35 0 4 0 4 6 0 8 36 0 4 0 4 6 0 8 37 0 4 1 3 5 1 8 38 0 4 0 4 5 0 7 39 0 4 0 4 6 1 7 40 0 4 0 3 6 1 8 41 1 4 0 4 5 1 7 42 0 4 0 4 5 0 7 43 0 4 1 4 6 1 7 44 0 4 1 3 6 0 8 45 0 4 0 4 6 1 8 46 0 4 0 4 6 1 7 47 0 4 0 4 6 0 8 48 0 4 0 4 6 0 7 49 0 4 0 4 6 1 7 50 0 4 0 4 6 0 8 51 0 4 0 3 5 0 8 52 1 4 1 3 5 1 8 53 0 4 0 4 6 0 7 54 1 4 0 4 5 0 8 55 0 4 0 4 6 0 8 56 0 4 0 3 5 0 7 57 0 4 0 4 5 1 7 58 0 4 0 4 6 0 7 59 0 4 0 4 6 0 7 60 1 4 1 3 5 1 7 61 0 4 1 3 6 0 7 62 0 4 0 4 5 1 8 63 0 4 0 4 6 0 8 64 0 4 1 3 6 0 7 65 0 4 0 4 6 0 8 66 0 4 0 4 6 0 8 67 1 4 0 3 5 1 8 68 0 4 1 4 6 0 8 69 0 4 0 4 6 0 7 70 0 4 0 4 5 0 8 71 0 4 0 4 6 0 8 72 0 4 0 4 6 0 7 73 0 4 0 4 5 0 7 74 0 4 1 4 5 0 8 75 0 4 0 4 6 0 7 76 0 4 0 3 6 1 7 77 0 4 0 4 6 0 7 78 0 4 0 4 5 1 7 79 1 4 0 3 5 0 7 80 0 4 0 3 6 1 8 81 0 4 0 4 6 0 8 82 0 4 1 4 5 0 7 83 0 4 0 4 6 0 8 84 1 4 0 4 5 0 8 85 0 4 0 4 6 1 7 86 0 4 1 4 6 0 8 87 0 2 1 0 6 0 7 88 0 2 1 0 5 0 7 89 0 2 0 0 6 0 8 90 0 2 0 0 6 0 7 91 0 2 0 0 6 1 8 92 0 2 1 0 6 0 8 93 0 2 1 0 6 1 8 94 0 2 0 0 6 0 8 95 0 2 0 0 6 0 8 96 0 2 0 0 6 0 7 97 0 2 1 0 6 0 8 98 0 2 0 0 6 0 8 99 0 2 1 0 6 0 8 100 0 2 0 0 6 0 7 101 0 2 1 0 6 0 7 102 0 2 0 0 6 0 8 103 0 2 0 0 6 0 8 104 0 2 0 0 6 0 8 105 0 2 0 0 5 0 8 106 0 2 0 0 6 0 8 107 0 2 0 0 6 0 8 108 0 2 1 0 5 0 8 109 0 2 0 0 6 0 8 110 0 2 1 0 6 0 8 111 0 2 1 0 5 1 8 112 0 2 0 0 6 0 8 113 0 2 0 0 5 0 8 114 0 2 1 0 5 0 8 115 0 2 1 0 6 0 8 116 0 2 0 0 6 0 8 117 0 2 1 0 6 0 7 118 0 2 1 0 6 0 8 119 0 2 0 0 6 0 8 120 0 2 0 0 6 0 7 121 0 2 1 0 6 0 8 122 0 2 0 0 6 0 8 123 0 2 1 0 5 0 8 124 0 2 0 0 5 1 7 125 0 2 0 0 6 0 7 126 0 2 0 0 6 0 8 127 0 2 0 0 6 1 8 128 0 2 0 0 6 0 7 129 0 2 0 0 6 0 8 130 0 2 0 0 6 0 7 131 0 2 1 0 6 0 8 132 0 2 1 0 6 0 7 133 0 2 1 0 5 0 8 134 0 2 0 0 6 0 8 135 0 2 0 0 6 0 8 136 0 2 0 0 6 0 8 137 0 2 1 0 5 1 7 138 0 2 1 0 5 1 7 139 0 2 0 0 6 0 8 140 0 2 0 0 6 0 8 141 1 2 0 0 5 0 7 142 0 2 0 0 5 0 7 143 0 2 1 0 6 0 8 144 0 2 0 0 6 1 7 145 0 2 0 0 6 1 8 146 0 2 0 0 6 0 7 147 0 2 0 0 5 0 8 148 0 2 0 0 6 0 8 149 0 2 1 0 6 0 8 150 0 2 0 0 6 1 7 151 0 2 0 0 6 0 7 152 1 2 1 0 5 0 8 153 1 2 1 0 5 1 8 154 0 2 1 0 5 0 8 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UsedLimit T40 Used Useful 0.53369 0.32090 -0.01391 -0.16220 -0.23725 0.05050 Outcome 0.02990 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.42024 -0.13249 0.00881 0.02971 0.80146 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.53369 0.47061 1.134 0.25863 Weeks 0.32090 0.11245 2.854 0.00495 ** UsedLimit -0.01391 0.04205 -0.331 0.74119 T40 -0.16220 0.05929 -2.736 0.00699 ** Used -0.23725 0.04397 -5.396 2.67e-07 *** Useful 0.05050 0.04602 1.097 0.27435 Outcome 0.02990 0.04004 0.747 0.45641 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2364 on 147 degrees of freedom Multiple R-squared: 0.2577, Adjusted R-squared: 0.2274 F-statistic: 8.504 on 6 and 147 DF, p-value: 6.226e-08 > 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,] 0.0000000000 0.0000000000 1.000000000 [2,] 0.0000000000 0.0000000000 1.000000000 [3,] 0.0000000000 0.0000000000 1.000000000 [4,] 0.0000000000 0.0000000000 1.000000000 [5,] 0.0000000000 0.0000000000 1.000000000 [6,] 0.0000000000 0.0000000000 1.000000000 [7,] 0.0000000000 0.0000000000 1.000000000 [8,] 0.4349212398 0.8698424796 0.565078760 [9,] 0.3860876608 0.7721753215 0.613912339 [10,] 0.3512631843 0.7025263686 0.648736816 [11,] 0.8426405370 0.3147189260 0.157359463 [12,] 0.7912499187 0.4175001626 0.208750081 [13,] 0.7775542659 0.4448914683 0.222445734 [14,] 0.7174996446 0.5650007108 0.282500355 [15,] 0.6530403334 0.6939193331 0.346959667 [16,] 0.6551921231 0.6896157539 0.344807877 [17,] 0.6564633291 0.6870733418 0.343536671 [18,] 0.6147820131 0.7704359738 0.385217987 [19,] 0.5602022219 0.8795955561 0.439797778 [20,] 0.5065322493 0.9869355014 0.493467751 [21,] 0.4484660499 0.8969320998 0.551533950 [22,] 0.3888826663 0.7777653327 0.611117334 [23,] 0.3326229799 0.6652459599 0.667377020 [24,] 0.2808026990 0.5616053980 0.719197301 [25,] 0.2402218822 0.4804437644 0.759778118 [26,] 0.1969491893 0.3938983786 0.803050811 [27,] 0.1588149993 0.3176299986 0.841185001 [28,] 0.2218880778 0.4437761556 0.778111922 [29,] 0.1874762890 0.3749525780 0.812523711 [30,] 0.1510425749 0.3020851498 0.848957425 [31,] 0.1465410345 0.2930820691 0.853458965 [32,] 0.6646945995 0.6706108010 0.335305400 [33,] 0.6335846805 0.7328306389 0.366415319 [34,] 0.5825625049 0.8348749902 0.417437495 [35,] 0.5447477858 0.9105044284 0.455252214 [36,] 0.4929928384 0.9859856767 0.507007162 [37,] 0.4416757546 0.8833515092 0.558324245 [38,] 0.3915561901 0.7831123802 0.608443810 [39,] 0.3429487485 0.6858974969 0.657051252 [40,] 0.2973150307 0.5946300613 0.702684969 [41,] 0.2547866363 0.5095732726 0.745213364 [42,] 0.3109201250 0.6218402500 0.689079875 [43,] 0.5842462236 0.8315075529 0.415753776 [44,] 0.5379345232 0.9241309535 0.462065477 [45,] 0.9068913542 0.1862172915 0.093108646 [46,] 0.8845356419 0.2309287163 0.115464358 [47,] 0.9213881932 0.1572236135 0.078611807 [48,] 0.9200770523 0.1598458954 0.079922948 [49,] 0.9019011667 0.1961976666 0.098098833 [50,] 0.8807678627 0.2384642746 0.119232137 [51,] 0.9596842700 0.0806314600 0.040315730 [52,] 0.9544453564 0.0911092873 0.045554644 [53,] 0.9564002348 0.0871995304 0.043599765 [54,] 0.9442901420 0.1114197160 0.055709858 [55,] 0.9425493759 0.1149012483 0.057450624 [56,] 0.9275878347 0.1448243306 0.072412165 [57,] 0.9097956256 0.1804087488 0.090204374 [58,] 0.9627613188 0.0744773624 0.037238681 [59,] 0.9519671731 0.0960656537 0.048032827 [60,] 0.9396885452 0.1206229096 0.060311455 [61,] 0.9377293666 0.1245412668 0.062270633 [62,] 0.9219104194 0.1561791613 0.078089581 [63,] 0.9040464438 0.1919071124 0.095953556 [64,] 0.8990967115 0.2018065770 0.100903288 [65,] 0.8978907139 0.2042185723 0.102109286 [66,] 0.8766394737 0.2467210526 0.123360526 [67,] 0.8769671780 0.2460656439 0.123032822 [68,] 0.8531102070 0.2937795860 0.146889793 [69,] 0.8596615389 0.2806769222 0.140338461 [70,] 0.9597576003 0.0804847994 0.040242400 [71,] 0.9516226537 0.0967546926 0.048377346 [72,] 0.9410101423 0.1179797154 0.058989858 [73,] 0.9478226260 0.1043547479 0.052177374 [74,] 0.9435363741 0.1129272519 0.056463626 [75,] 0.9943282737 0.0113434526 0.005671726 [76,] 0.9919984148 0.0160031705 0.008001585 [77,] 0.9888811653 0.0222376695 0.011118835 [78,] 0.9847168807 0.0305662385 0.015283119 [79,] 0.9829227761 0.0341544479 0.017077224 [80,] 0.9770792096 0.0458415807 0.022920790 [81,] 0.9697515117 0.0604969766 0.030248488 [82,] 0.9602184431 0.0795631139 0.039781557 [83,] 0.9483797970 0.1032404061 0.051620203 [84,] 0.9337657655 0.1324684690 0.066234235 [85,] 0.9160683127 0.1678633745 0.083931687 [86,] 0.8949450442 0.2101099115 0.105054956 [87,] 0.8706492642 0.2587014716 0.129350736 [88,] 0.8420774991 0.3158450017 0.157922501 [89,] 0.8093975255 0.3812049490 0.190602474 [90,] 0.7728383148 0.4543233704 0.227161685 [91,] 0.7329317014 0.5341365972 0.267068299 [92,] 0.6899397092 0.6201205816 0.310060291 [93,] 0.6429321539 0.7141356922 0.357067846 [94,] 0.5934399632 0.8131200735 0.406560037 [95,] 0.5421858986 0.9156282028 0.457814101 [96,] 0.5287621955 0.9424756091 0.471237805 [97,] 0.4762373878 0.9524747755 0.523762612 [98,] 0.4238607541 0.8477215082 0.576139246 [99,] 0.4110561009 0.8221122017 0.588943899 [100,] 0.3598919188 0.7197838376 0.640108081 [101,] 0.3111241317 0.6222482634 0.688875868 [102,] 0.3089696569 0.6179393138 0.691030343 [103,] 0.2626181498 0.5252362996 0.737381850 [104,] 0.2568994621 0.5137989241 0.743100538 [105,] 0.2574435082 0.5148870164 0.742556492 [106,] 0.2150520228 0.4301040456 0.784947977 [107,] 0.1763104564 0.3526209127 0.823689544 [108,] 0.1430750341 0.2861500683 0.856924966 [109,] 0.1134800909 0.2269601817 0.886519909 [110,] 0.0881499584 0.1762999168 0.911850042 [111,] 0.0674170537 0.1348341075 0.932582946 [112,] 0.0504119235 0.1008238470 0.949588076 [113,] 0.0368095048 0.0736190096 0.963190495 [114,] 0.0395951263 0.0791902526 0.960404874 [115,] 0.0402958965 0.0805917931 0.959704103 [116,] 0.0288117824 0.0576235648 0.971188218 [117,] 0.0199599984 0.0399199968 0.980040002 [118,] 0.0135237331 0.0270474663 0.986476267 [119,] 0.0089511184 0.0179022368 0.991048882 [120,] 0.0057060849 0.0114121697 0.994293915 [121,] 0.0035643309 0.0071286617 0.996435669 [122,] 0.0021325814 0.0042651628 0.997867419 [123,] 0.0012832798 0.0025665596 0.998716720 [124,] 0.0017130521 0.0034261042 0.998286948 [125,] 0.0009532549 0.0019065099 0.999046745 [126,] 0.0005088941 0.0010177883 0.999491106 [127,] 0.0002599680 0.0005199360 0.999740032 [128,] 0.0003423959 0.0006847918 0.999657604 [129,] 0.0020310590 0.0040621179 0.997968941 [130,] 0.0011627972 0.0023255944 0.998837203 [131,] 0.0007154460 0.0014308919 0.999284554 [132,] 0.0238647878 0.0477295756 0.976135212 [133,] 0.0177422232 0.0354844464 0.982257777 [134,] 0.0094093366 0.0188186733 0.990590663 [135,] 0.0043682206 0.0087364411 0.995631779 > postscript(file="/var/wessaorg/rcomp/tmp/19ujw1355681359.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/wessaorg/rcomp/tmp/2al751355681359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/37ta91355681359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4vomy1355681359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/51u4g1355681359.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -0.102587035 0.015796654 0.015796654 0.015796654 0.015796654 0.009115597 7 8 9 10 11 12 0.015796654 -0.146401251 0.045697898 0.029709625 -0.132488280 0.015796654 13 14 15 16 17 18 -0.271950880 -0.132488280 -0.242049636 -0.404247540 0.579764186 -0.132488280 19 20 21 22 23 24 0.045697898 0.595752460 -0.020785647 -0.228136665 -0.004797374 0.009115597 25 26 27 28 29 30 -0.353752268 -0.271950880 0.059610869 -0.221455609 0.045697898 -0.034698618 31 32 33 34 35 36 0.015796654 0.029709625 -0.020785647 -0.116500006 0.015796654 0.015796654 37 38 39 40 41 42 -0.420235814 -0.191554364 -0.004797374 -0.196896523 0.757950364 -0.191554364 43 44 45 46 47 48 0.009115597 -0.132488280 -0.034698618 -0.004797374 0.015796654 0.045697898 49 50 51 52 53 54 -0.004797374 0.015796654 -0.383653513 0.579764186 0.045697898 0.778544391 55 56 57 58 59 60 0.015796654 -0.353752268 -0.242049636 0.045697898 0.045697898 0.609665431 61 62 63 64 65 66 -0.102587035 -0.271950880 0.015796654 -0.102587035 0.015796654 0.015796654 67 68 69 70 71 72 0.565851215 0.029709625 0.045697898 -0.221455609 0.015796654 0.045697898 73 74 75 76 77 78 -0.191554364 -0.207542638 0.045697898 -0.166995278 0.045697898 -0.242049636 79 80 81 82 83 84 0.646247732 -0.196896523 0.015796654 -0.177641393 0.015796654 0.778544391 85 86 87 88 89 90 -0.004797374 0.029709625 0.052624114 -0.184628148 0.008809898 0.038711143 91 92 93 94 95 96 -0.041685374 0.022722869 -0.027772403 0.008809898 0.008809898 0.038711143 97 98 99 100 101 102 0.022722869 0.008809898 0.022722869 0.038711143 0.052624114 0.008809898 103 104 105 106 107 108 0.008809898 0.008809898 -0.228442364 0.008809898 0.008809898 -0.214529393 109 110 111 112 113 114 0.008809898 0.022722869 -0.265024665 0.008809898 -0.228442364 -0.214529393 115 116 117 118 119 120 0.022722869 0.008809898 0.052624114 0.022722869 0.008809898 0.038711143 121 122 123 124 125 126 0.022722869 0.008809898 -0.214529393 -0.249036391 0.038711143 0.008809898 127 128 129 130 131 132 -0.041685374 0.038711143 0.008809898 0.038711143 0.022722869 0.052624114 133 134 135 136 137 138 -0.214529393 0.008809898 0.008809898 0.008809898 -0.235123420 -0.235123420 139 140 141 142 143 144 0.008809898 0.008809898 0.801458881 -0.198541119 0.022722869 -0.011784129 145 146 147 148 149 150 -0.041685374 0.038711143 -0.228442364 0.008809898 0.022722869 -0.011784129 151 152 153 154 0.038711143 0.785470607 0.734975335 -0.214529393 > postscript(file="/var/wessaorg/rcomp/tmp/67tej1355681359.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 -0.102587035 NA 1 0.015796654 -0.102587035 2 0.015796654 0.015796654 3 0.015796654 0.015796654 4 0.015796654 0.015796654 5 0.009115597 0.015796654 6 0.015796654 0.009115597 7 -0.146401251 0.015796654 8 0.045697898 -0.146401251 9 0.029709625 0.045697898 10 -0.132488280 0.029709625 11 0.015796654 -0.132488280 12 -0.271950880 0.015796654 13 -0.132488280 -0.271950880 14 -0.242049636 -0.132488280 15 -0.404247540 -0.242049636 16 0.579764186 -0.404247540 17 -0.132488280 0.579764186 18 0.045697898 -0.132488280 19 0.595752460 0.045697898 20 -0.020785647 0.595752460 21 -0.228136665 -0.020785647 22 -0.004797374 -0.228136665 23 0.009115597 -0.004797374 24 -0.353752268 0.009115597 25 -0.271950880 -0.353752268 26 0.059610869 -0.271950880 27 -0.221455609 0.059610869 28 0.045697898 -0.221455609 29 -0.034698618 0.045697898 30 0.015796654 -0.034698618 31 0.029709625 0.015796654 32 -0.020785647 0.029709625 33 -0.116500006 -0.020785647 34 0.015796654 -0.116500006 35 0.015796654 0.015796654 36 -0.420235814 0.015796654 37 -0.191554364 -0.420235814 38 -0.004797374 -0.191554364 39 -0.196896523 -0.004797374 40 0.757950364 -0.196896523 41 -0.191554364 0.757950364 42 0.009115597 -0.191554364 43 -0.132488280 0.009115597 44 -0.034698618 -0.132488280 45 -0.004797374 -0.034698618 46 0.015796654 -0.004797374 47 0.045697898 0.015796654 48 -0.004797374 0.045697898 49 0.015796654 -0.004797374 50 -0.383653513 0.015796654 51 0.579764186 -0.383653513 52 0.045697898 0.579764186 53 0.778544391 0.045697898 54 0.015796654 0.778544391 55 -0.353752268 0.015796654 56 -0.242049636 -0.353752268 57 0.045697898 -0.242049636 58 0.045697898 0.045697898 59 0.609665431 0.045697898 60 -0.102587035 0.609665431 61 -0.271950880 -0.102587035 62 0.015796654 -0.271950880 63 -0.102587035 0.015796654 64 0.015796654 -0.102587035 65 0.015796654 0.015796654 66 0.565851215 0.015796654 67 0.029709625 0.565851215 68 0.045697898 0.029709625 69 -0.221455609 0.045697898 70 0.015796654 -0.221455609 71 0.045697898 0.015796654 72 -0.191554364 0.045697898 73 -0.207542638 -0.191554364 74 0.045697898 -0.207542638 75 -0.166995278 0.045697898 76 0.045697898 -0.166995278 77 -0.242049636 0.045697898 78 0.646247732 -0.242049636 79 -0.196896523 0.646247732 80 0.015796654 -0.196896523 81 -0.177641393 0.015796654 82 0.015796654 -0.177641393 83 0.778544391 0.015796654 84 -0.004797374 0.778544391 85 0.029709625 -0.004797374 86 0.052624114 0.029709625 87 -0.184628148 0.052624114 88 0.008809898 -0.184628148 89 0.038711143 0.008809898 90 -0.041685374 0.038711143 91 0.022722869 -0.041685374 92 -0.027772403 0.022722869 93 0.008809898 -0.027772403 94 0.008809898 0.008809898 95 0.038711143 0.008809898 96 0.022722869 0.038711143 97 0.008809898 0.022722869 98 0.022722869 0.008809898 99 0.038711143 0.022722869 100 0.052624114 0.038711143 101 0.008809898 0.052624114 102 0.008809898 0.008809898 103 0.008809898 0.008809898 104 -0.228442364 0.008809898 105 0.008809898 -0.228442364 106 0.008809898 0.008809898 107 -0.214529393 0.008809898 108 0.008809898 -0.214529393 109 0.022722869 0.008809898 110 -0.265024665 0.022722869 111 0.008809898 -0.265024665 112 -0.228442364 0.008809898 113 -0.214529393 -0.228442364 114 0.022722869 -0.214529393 115 0.008809898 0.022722869 116 0.052624114 0.008809898 117 0.022722869 0.052624114 118 0.008809898 0.022722869 119 0.038711143 0.008809898 120 0.022722869 0.038711143 121 0.008809898 0.022722869 122 -0.214529393 0.008809898 123 -0.249036391 -0.214529393 124 0.038711143 -0.249036391 125 0.008809898 0.038711143 126 -0.041685374 0.008809898 127 0.038711143 -0.041685374 128 0.008809898 0.038711143 129 0.038711143 0.008809898 130 0.022722869 0.038711143 131 0.052624114 0.022722869 132 -0.214529393 0.052624114 133 0.008809898 -0.214529393 134 0.008809898 0.008809898 135 0.008809898 0.008809898 136 -0.235123420 0.008809898 137 -0.235123420 -0.235123420 138 0.008809898 -0.235123420 139 0.008809898 0.008809898 140 0.801458881 0.008809898 141 -0.198541119 0.801458881 142 0.022722869 -0.198541119 143 -0.011784129 0.022722869 144 -0.041685374 -0.011784129 145 0.038711143 -0.041685374 146 -0.228442364 0.038711143 147 0.008809898 -0.228442364 148 0.022722869 0.008809898 149 -0.011784129 0.022722869 150 0.038711143 -0.011784129 151 0.785470607 0.038711143 152 0.734975335 0.785470607 153 -0.214529393 0.734975335 154 NA -0.214529393 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.015796654 -0.102587035 [2,] 0.015796654 0.015796654 [3,] 0.015796654 0.015796654 [4,] 0.015796654 0.015796654 [5,] 0.009115597 0.015796654 [6,] 0.015796654 0.009115597 [7,] -0.146401251 0.015796654 [8,] 0.045697898 -0.146401251 [9,] 0.029709625 0.045697898 [10,] -0.132488280 0.029709625 [11,] 0.015796654 -0.132488280 [12,] -0.271950880 0.015796654 [13,] -0.132488280 -0.271950880 [14,] -0.242049636 -0.132488280 [15,] -0.404247540 -0.242049636 [16,] 0.579764186 -0.404247540 [17,] -0.132488280 0.579764186 [18,] 0.045697898 -0.132488280 [19,] 0.595752460 0.045697898 [20,] -0.020785647 0.595752460 [21,] -0.228136665 -0.020785647 [22,] -0.004797374 -0.228136665 [23,] 0.009115597 -0.004797374 [24,] -0.353752268 0.009115597 [25,] -0.271950880 -0.353752268 [26,] 0.059610869 -0.271950880 [27,] -0.221455609 0.059610869 [28,] 0.045697898 -0.221455609 [29,] -0.034698618 0.045697898 [30,] 0.015796654 -0.034698618 [31,] 0.029709625 0.015796654 [32,] -0.020785647 0.029709625 [33,] -0.116500006 -0.020785647 [34,] 0.015796654 -0.116500006 [35,] 0.015796654 0.015796654 [36,] -0.420235814 0.015796654 [37,] -0.191554364 -0.420235814 [38,] -0.004797374 -0.191554364 [39,] -0.196896523 -0.004797374 [40,] 0.757950364 -0.196896523 [41,] -0.191554364 0.757950364 [42,] 0.009115597 -0.191554364 [43,] -0.132488280 0.009115597 [44,] -0.034698618 -0.132488280 [45,] -0.004797374 -0.034698618 [46,] 0.015796654 -0.004797374 [47,] 0.045697898 0.015796654 [48,] -0.004797374 0.045697898 [49,] 0.015796654 -0.004797374 [50,] -0.383653513 0.015796654 [51,] 0.579764186 -0.383653513 [52,] 0.045697898 0.579764186 [53,] 0.778544391 0.045697898 [54,] 0.015796654 0.778544391 [55,] -0.353752268 0.015796654 [56,] -0.242049636 -0.353752268 [57,] 0.045697898 -0.242049636 [58,] 0.045697898 0.045697898 [59,] 0.609665431 0.045697898 [60,] -0.102587035 0.609665431 [61,] -0.271950880 -0.102587035 [62,] 0.015796654 -0.271950880 [63,] -0.102587035 0.015796654 [64,] 0.015796654 -0.102587035 [65,] 0.015796654 0.015796654 [66,] 0.565851215 0.015796654 [67,] 0.029709625 0.565851215 [68,] 0.045697898 0.029709625 [69,] -0.221455609 0.045697898 [70,] 0.015796654 -0.221455609 [71,] 0.045697898 0.015796654 [72,] -0.191554364 0.045697898 [73,] -0.207542638 -0.191554364 [74,] 0.045697898 -0.207542638 [75,] -0.166995278 0.045697898 [76,] 0.045697898 -0.166995278 [77,] -0.242049636 0.045697898 [78,] 0.646247732 -0.242049636 [79,] -0.196896523 0.646247732 [80,] 0.015796654 -0.196896523 [81,] -0.177641393 0.015796654 [82,] 0.015796654 -0.177641393 [83,] 0.778544391 0.015796654 [84,] -0.004797374 0.778544391 [85,] 0.029709625 -0.004797374 [86,] 0.052624114 0.029709625 [87,] -0.184628148 0.052624114 [88,] 0.008809898 -0.184628148 [89,] 0.038711143 0.008809898 [90,] -0.041685374 0.038711143 [91,] 0.022722869 -0.041685374 [92,] -0.027772403 0.022722869 [93,] 0.008809898 -0.027772403 [94,] 0.008809898 0.008809898 [95,] 0.038711143 0.008809898 [96,] 0.022722869 0.038711143 [97,] 0.008809898 0.022722869 [98,] 0.022722869 0.008809898 [99,] 0.038711143 0.022722869 [100,] 0.052624114 0.038711143 [101,] 0.008809898 0.052624114 [102,] 0.008809898 0.008809898 [103,] 0.008809898 0.008809898 [104,] -0.228442364 0.008809898 [105,] 0.008809898 -0.228442364 [106,] 0.008809898 0.008809898 [107,] -0.214529393 0.008809898 [108,] 0.008809898 -0.214529393 [109,] 0.022722869 0.008809898 [110,] -0.265024665 0.022722869 [111,] 0.008809898 -0.265024665 [112,] -0.228442364 0.008809898 [113,] -0.214529393 -0.228442364 [114,] 0.022722869 -0.214529393 [115,] 0.008809898 0.022722869 [116,] 0.052624114 0.008809898 [117,] 0.022722869 0.052624114 [118,] 0.008809898 0.022722869 [119,] 0.038711143 0.008809898 [120,] 0.022722869 0.038711143 [121,] 0.008809898 0.022722869 [122,] -0.214529393 0.008809898 [123,] -0.249036391 -0.214529393 [124,] 0.038711143 -0.249036391 [125,] 0.008809898 0.038711143 [126,] -0.041685374 0.008809898 [127,] 0.038711143 -0.041685374 [128,] 0.008809898 0.038711143 [129,] 0.038711143 0.008809898 [130,] 0.022722869 0.038711143 [131,] 0.052624114 0.022722869 [132,] -0.214529393 0.052624114 [133,] 0.008809898 -0.214529393 [134,] 0.008809898 0.008809898 [135,] 0.008809898 0.008809898 [136,] -0.235123420 0.008809898 [137,] -0.235123420 -0.235123420 [138,] 0.008809898 -0.235123420 [139,] 0.008809898 0.008809898 [140,] 0.801458881 0.008809898 [141,] -0.198541119 0.801458881 [142,] 0.022722869 -0.198541119 [143,] -0.011784129 0.022722869 [144,] -0.041685374 -0.011784129 [145,] 0.038711143 -0.041685374 [146,] -0.228442364 0.038711143 [147,] 0.008809898 -0.228442364 [148,] 0.022722869 0.008809898 [149,] -0.011784129 0.022722869 [150,] 0.038711143 -0.011784129 [151,] 0.785470607 0.038711143 [152,] 0.734975335 0.785470607 [153,] -0.214529393 0.734975335 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.015796654 -0.102587035 2 0.015796654 0.015796654 3 0.015796654 0.015796654 4 0.015796654 0.015796654 5 0.009115597 0.015796654 6 0.015796654 0.009115597 7 -0.146401251 0.015796654 8 0.045697898 -0.146401251 9 0.029709625 0.045697898 10 -0.132488280 0.029709625 11 0.015796654 -0.132488280 12 -0.271950880 0.015796654 13 -0.132488280 -0.271950880 14 -0.242049636 -0.132488280 15 -0.404247540 -0.242049636 16 0.579764186 -0.404247540 17 -0.132488280 0.579764186 18 0.045697898 -0.132488280 19 0.595752460 0.045697898 20 -0.020785647 0.595752460 21 -0.228136665 -0.020785647 22 -0.004797374 -0.228136665 23 0.009115597 -0.004797374 24 -0.353752268 0.009115597 25 -0.271950880 -0.353752268 26 0.059610869 -0.271950880 27 -0.221455609 0.059610869 28 0.045697898 -0.221455609 29 -0.034698618 0.045697898 30 0.015796654 -0.034698618 31 0.029709625 0.015796654 32 -0.020785647 0.029709625 33 -0.116500006 -0.020785647 34 0.015796654 -0.116500006 35 0.015796654 0.015796654 36 -0.420235814 0.015796654 37 -0.191554364 -0.420235814 38 -0.004797374 -0.191554364 39 -0.196896523 -0.004797374 40 0.757950364 -0.196896523 41 -0.191554364 0.757950364 42 0.009115597 -0.191554364 43 -0.132488280 0.009115597 44 -0.034698618 -0.132488280 45 -0.004797374 -0.034698618 46 0.015796654 -0.004797374 47 0.045697898 0.015796654 48 -0.004797374 0.045697898 49 0.015796654 -0.004797374 50 -0.383653513 0.015796654 51 0.579764186 -0.383653513 52 0.045697898 0.579764186 53 0.778544391 0.045697898 54 0.015796654 0.778544391 55 -0.353752268 0.015796654 56 -0.242049636 -0.353752268 57 0.045697898 -0.242049636 58 0.045697898 0.045697898 59 0.609665431 0.045697898 60 -0.102587035 0.609665431 61 -0.271950880 -0.102587035 62 0.015796654 -0.271950880 63 -0.102587035 0.015796654 64 0.015796654 -0.102587035 65 0.015796654 0.015796654 66 0.565851215 0.015796654 67 0.029709625 0.565851215 68 0.045697898 0.029709625 69 -0.221455609 0.045697898 70 0.015796654 -0.221455609 71 0.045697898 0.015796654 72 -0.191554364 0.045697898 73 -0.207542638 -0.191554364 74 0.045697898 -0.207542638 75 -0.166995278 0.045697898 76 0.045697898 -0.166995278 77 -0.242049636 0.045697898 78 0.646247732 -0.242049636 79 -0.196896523 0.646247732 80 0.015796654 -0.196896523 81 -0.177641393 0.015796654 82 0.015796654 -0.177641393 83 0.778544391 0.015796654 84 -0.004797374 0.778544391 85 0.029709625 -0.004797374 86 0.052624114 0.029709625 87 -0.184628148 0.052624114 88 0.008809898 -0.184628148 89 0.038711143 0.008809898 90 -0.041685374 0.038711143 91 0.022722869 -0.041685374 92 -0.027772403 0.022722869 93 0.008809898 -0.027772403 94 0.008809898 0.008809898 95 0.038711143 0.008809898 96 0.022722869 0.038711143 97 0.008809898 0.022722869 98 0.022722869 0.008809898 99 0.038711143 0.022722869 100 0.052624114 0.038711143 101 0.008809898 0.052624114 102 0.008809898 0.008809898 103 0.008809898 0.008809898 104 -0.228442364 0.008809898 105 0.008809898 -0.228442364 106 0.008809898 0.008809898 107 -0.214529393 0.008809898 108 0.008809898 -0.214529393 109 0.022722869 0.008809898 110 -0.265024665 0.022722869 111 0.008809898 -0.265024665 112 -0.228442364 0.008809898 113 -0.214529393 -0.228442364 114 0.022722869 -0.214529393 115 0.008809898 0.022722869 116 0.052624114 0.008809898 117 0.022722869 0.052624114 118 0.008809898 0.022722869 119 0.038711143 0.008809898 120 0.022722869 0.038711143 121 0.008809898 0.022722869 122 -0.214529393 0.008809898 123 -0.249036391 -0.214529393 124 0.038711143 -0.249036391 125 0.008809898 0.038711143 126 -0.041685374 0.008809898 127 0.038711143 -0.041685374 128 0.008809898 0.038711143 129 0.038711143 0.008809898 130 0.022722869 0.038711143 131 0.052624114 0.022722869 132 -0.214529393 0.052624114 133 0.008809898 -0.214529393 134 0.008809898 0.008809898 135 0.008809898 0.008809898 136 -0.235123420 0.008809898 137 -0.235123420 -0.235123420 138 0.008809898 -0.235123420 139 0.008809898 0.008809898 140 0.801458881 0.008809898 141 -0.198541119 0.801458881 142 0.022722869 -0.198541119 143 -0.011784129 0.022722869 144 -0.041685374 -0.011784129 145 0.038711143 -0.041685374 146 -0.228442364 0.038711143 147 0.008809898 -0.228442364 148 0.022722869 0.008809898 149 -0.011784129 0.022722869 150 0.038711143 -0.011784129 151 0.785470607 0.038711143 152 0.734975335 0.785470607 153 -0.214529393 0.734975335 > 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/wessaorg/rcomp/tmp/73ts51355681359.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/wessaorg/rcomp/tmp/89qgf1355681359.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/wessaorg/rcomp/tmp/9w5jv1355681359.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/wessaorg/rcomp/tmp/10i5x11355681359.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11atmc1355681359.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12e42p1355681359.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13frqx1355681359.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14lw8u1355681359.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/wessaorg/rcomp/tmp/151mxc1355681359.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/wessaorg/rcomp/tmp/16r5421355681359.tab") + } > > try(system("convert tmp/19ujw1355681359.ps tmp/19ujw1355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/2al751355681359.ps tmp/2al751355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/37ta91355681359.ps tmp/37ta91355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/4vomy1355681359.ps tmp/4vomy1355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/51u4g1355681359.ps tmp/51u4g1355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/67tej1355681359.ps tmp/67tej1355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/73ts51355681359.ps tmp/73ts51355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/89qgf1355681359.ps tmp/89qgf1355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/9w5jv1355681359.ps tmp/9w5jv1355681359.png",intern=TRUE)) character(0) > try(system("convert tmp/10i5x11355681359.ps tmp/10i5x11355681359.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.021 0.877 8.906