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(405.7 + ,0 + ,403.3 + ,403.5 + ,395.1 + ,395.3 + ,406.7 + ,0 + ,405.7 + ,403.3 + ,403.5 + ,395.1 + ,407.2 + ,0 + ,406.7 + ,405.7 + ,403.3 + ,403.5 + ,412.4 + ,0 + ,407.2 + ,406.7 + ,405.7 + ,403.3 + ,415.9 + ,0 + ,412.4 + ,407.2 + ,406.7 + ,405.7 + ,414.0 + ,0 + ,415.9 + ,412.4 + ,407.2 + ,406.7 + ,411.8 + ,0 + ,414.0 + ,415.9 + ,412.4 + ,407.2 + ,409.9 + ,0 + ,411.8 + ,414.0 + ,415.9 + ,412.4 + ,412.4 + ,0 + ,409.9 + ,411.8 + ,414.0 + ,415.9 + ,415.9 + ,0 + ,412.4 + ,409.9 + ,411.8 + ,414.0 + ,416.3 + ,0 + ,415.9 + ,412.4 + ,409.9 + ,411.8 + ,417.2 + ,0 + ,416.3 + ,415.9 + ,412.4 + ,409.9 + ,421.8 + ,0 + ,417.2 + ,416.3 + ,415.9 + ,412.4 + ,421.4 + ,0 + ,421.8 + ,417.2 + ,416.3 + ,415.9 + ,415.1 + ,0 + ,421.4 + ,421.8 + ,417.2 + ,416.3 + ,412.4 + ,0 + ,415.1 + ,421.4 + ,421.8 + ,417.2 + ,411.8 + ,0 + ,412.4 + ,415.1 + ,421.4 + ,421.8 + ,408.8 + ,0 + ,411.8 + ,412.4 + ,415.1 + ,421.4 + ,404.5 + ,0 + ,408.8 + ,411.8 + ,412.4 + ,415.1 + ,402.5 + ,0 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+ ,656.1 + ,656.7 + ,643.0 + ,638.8 + ,659.9 + ,0 + ,654.1 + ,656.1 + ,656.7 + ,643.0 + ,662.1 + ,0 + ,659.9 + ,654.1 + ,656.1 + ,656.7 + ,669.2 + ,0 + ,662.1 + ,659.9 + ,654.1 + ,656.1 + ,673.1 + ,0 + ,669.2 + ,662.1 + ,659.9 + ,654.1 + ,678.3 + ,0 + ,673.1 + ,669.2 + ,662.1 + ,659.9 + ,677.4 + ,0 + ,678.3 + ,673.1 + ,669.2 + ,662.1 + ,678.5 + ,0 + ,677.4 + ,678.3 + ,673.1 + ,669.2 + ,672.4 + ,0 + ,678.5 + ,677.4 + ,678.3 + ,673.1 + ,665.3 + ,0 + ,672.4 + ,678.5 + ,677.4 + ,678.3 + ,667.9 + ,0 + ,665.3 + ,672.4 + ,678.5 + ,677.4 + ,672.1 + ,0 + ,667.9 + ,665.3 + ,672.4 + ,678.5 + ,662.5 + ,0 + ,672.1 + ,667.9 + ,665.3 + ,672.4 + ,682.3 + ,0 + ,662.5 + ,672.1 + ,667.9 + ,665.3 + ,692.1 + ,0 + ,682.3 + ,662.5 + ,672.1 + ,667.9 + ,702.7 + ,0 + ,692.1 + ,682.3 + ,662.5 + ,672.1 + ,721.4 + ,0 + ,702.7 + ,692.1 + ,682.3 + ,662.5 + ,733.2 + ,0 + ,721.4 + ,702.7 + ,692.1 + ,682.3 + ,747.7 + ,0 + ,733.2 + ,721.4 + ,702.7 + ,692.1 + ,737.6 + ,0 + ,747.7 + ,733.2 + ,721.4 + ,702.7 + ,729.3 + ,0 + ,737.6 + ,747.7 + ,733.2 + ,721.4 + ,706.1 + ,0 + ,729.3 + ,737.6 + ,747.7 + ,733.2 + ,674.3 + ,0 + ,706.1 + ,729.3 + ,737.6 + ,747.7 + ,659.0 + ,0 + ,674.3 + ,706.1 + ,729.3 + ,737.6 + ,645.7 + ,0 + ,659.0 + ,674.3 + ,706.1 + ,729.3 + ,646.1 + ,0 + ,645.7 + ,659.0 + ,674.3 + ,706.1 + ,633.0 + ,1 + ,646.1 + ,645.7 + ,659.0 + ,674.3 + ,622.3 + ,1 + ,633.0 + ,646.1 + ,645.7 + ,659.0 + ,628.2 + ,1 + ,622.3 + ,633.0 + ,646.1 + ,645.7 + ,637.3 + ,1 + ,628.2 + ,622.3 + ,633.0 + ,646.1 + ,639.6 + ,1 + ,637.3 + ,628.2 + ,622.3 + ,633.0 + ,638.5 + ,1 + ,639.6 + ,637.3 + ,628.2 + ,622.3 + ,650.5 + ,1 + ,638.5 + ,639.6 + ,637.3 + ,628.2 + ,655.4 + ,1 + ,650.5 + ,638.5 + ,639.6 + ,637.3) + ,dim=c(6 + ,113) + ,dimnames=list(c('Y' + ,'X' + ,'Y1' + ,'Y2' + ,'Y3' + ,'Y4') + ,1:113)) > y <- array(NA,dim=c(6,113),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:113)) > 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 = '1' > #'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 Y X Y1 Y2 Y3 Y4 t 1 405.7 0 403.3 403.5 395.1 395.3 1 2 406.7 0 405.7 403.3 403.5 395.1 2 3 407.2 0 406.7 405.7 403.3 403.5 3 4 412.4 0 407.2 406.7 405.7 403.3 4 5 415.9 0 412.4 407.2 406.7 405.7 5 6 414.0 0 415.9 412.4 407.2 406.7 6 7 411.8 0 414.0 415.9 412.4 407.2 7 8 409.9 0 411.8 414.0 415.9 412.4 8 9 412.4 0 409.9 411.8 414.0 415.9 9 10 415.9 0 412.4 409.9 411.8 414.0 10 11 416.3 0 415.9 412.4 409.9 411.8 11 12 417.2 0 416.3 415.9 412.4 409.9 12 13 421.8 0 417.2 416.3 415.9 412.4 13 14 421.4 0 421.8 417.2 416.3 415.9 14 15 415.1 0 421.4 421.8 417.2 416.3 15 16 412.4 0 415.1 421.4 421.8 417.2 16 17 411.8 0 412.4 415.1 421.4 421.8 17 18 408.8 0 411.8 412.4 415.1 421.4 18 19 404.5 0 408.8 411.8 412.4 415.1 19 20 402.5 0 404.5 408.8 411.8 412.4 20 21 409.4 0 402.5 404.5 408.8 411.8 21 22 410.7 0 409.4 402.5 404.5 408.8 22 23 413.4 0 410.7 409.4 402.5 404.5 23 24 415.2 0 413.4 410.7 409.4 402.5 24 25 417.7 0 415.2 413.4 410.7 409.4 25 26 417.8 0 417.7 415.2 413.4 410.7 26 27 417.9 0 417.8 417.7 415.2 413.4 27 28 418.4 0 417.9 417.8 417.7 415.2 28 29 418.2 0 418.4 417.9 417.8 417.7 29 30 416.6 0 418.2 418.4 417.9 417.8 30 31 418.9 0 416.6 418.2 418.4 417.9 31 32 421.0 0 418.9 416.6 418.2 418.4 32 33 423.5 0 421.0 418.9 416.6 418.2 33 34 432.3 0 423.5 421.0 418.9 416.6 34 35 432.3 0 432.3 423.5 421.0 418.9 35 36 428.6 0 432.3 432.3 423.5 421.0 36 37 426.7 0 428.6 432.3 432.3 423.5 37 38 427.3 0 426.7 428.6 432.3 432.3 38 39 428.5 0 427.3 426.7 428.6 432.3 39 40 437.0 0 428.5 427.3 426.7 428.6 40 41 442.0 0 437.0 428.5 427.3 426.7 41 42 444.9 0 442.0 437.0 428.5 427.3 42 43 441.4 0 444.9 442.0 437.0 428.5 43 44 440.3 0 441.4 444.9 442.0 437.0 44 45 447.1 0 440.3 441.4 444.9 442.0 45 46 455.3 0 447.1 440.3 441.4 444.9 46 47 478.6 0 455.3 447.1 440.3 441.4 47 48 486.5 0 478.6 455.3 447.1 440.3 48 49 487.8 0 486.5 478.6 455.3 447.1 49 50 485.9 0 487.8 486.5 478.6 455.3 50 51 483.8 0 485.9 487.8 486.5 478.6 51 52 488.4 0 483.8 485.9 487.8 486.5 52 53 494.0 0 488.4 483.8 485.9 487.8 53 54 493.6 0 494.0 488.4 483.8 485.9 54 55 487.3 0 493.6 494.0 488.4 483.8 55 56 482.1 0 487.3 493.6 494.0 488.4 56 57 484.2 0 482.1 487.3 493.6 494.0 57 58 496.8 0 484.2 482.1 487.3 493.6 58 59 501.1 0 496.8 484.2 482.1 487.3 59 60 499.8 0 501.1 496.8 484.2 482.1 60 61 495.5 0 499.8 501.1 496.8 484.2 61 62 498.1 0 495.5 499.8 501.1 496.8 62 63 503.8 0 498.1 495.5 499.8 501.1 63 64 516.2 0 503.8 498.1 495.5 499.8 64 65 526.1 0 516.2 503.8 498.1 495.5 65 66 527.1 0 526.1 516.2 503.8 498.1 66 67 525.1 0 527.1 526.1 516.2 503.8 67 68 528.9 0 525.1 527.1 526.1 516.2 68 69 540.1 0 528.9 525.1 527.1 526.1 69 70 549.0 0 540.1 528.9 525.1 527.1 70 71 556.0 0 549.0 540.1 528.9 525.1 71 72 568.9 0 556.0 549.0 540.1 528.9 72 73 589.1 0 568.9 556.0 549.0 540.1 73 74 590.3 0 589.1 568.9 556.0 549.0 74 75 603.3 0 590.3 589.1 568.9 556.0 75 76 638.8 0 603.3 590.3 589.1 568.9 76 77 643.0 0 638.8 603.3 590.3 589.1 77 78 656.7 0 643.0 638.8 603.3 590.3 78 79 656.1 0 656.7 643.0 638.8 603.3 79 80 654.1 0 656.1 656.7 643.0 638.8 80 81 659.9 0 654.1 656.1 656.7 643.0 81 82 662.1 0 659.9 654.1 656.1 656.7 82 83 669.2 0 662.1 659.9 654.1 656.1 83 84 673.1 0 669.2 662.1 659.9 654.1 84 85 678.3 0 673.1 669.2 662.1 659.9 85 86 677.4 0 678.3 673.1 669.2 662.1 86 87 678.5 0 677.4 678.3 673.1 669.2 87 88 672.4 0 678.5 677.4 678.3 673.1 88 89 665.3 0 672.4 678.5 677.4 678.3 89 90 667.9 0 665.3 672.4 678.5 677.4 90 91 672.1 0 667.9 665.3 672.4 678.5 91 92 662.5 0 672.1 667.9 665.3 672.4 92 93 682.3 0 662.5 672.1 667.9 665.3 93 94 692.1 0 682.3 662.5 672.1 667.9 94 95 702.7 0 692.1 682.3 662.5 672.1 95 96 721.4 0 702.7 692.1 682.3 662.5 96 97 733.2 0 721.4 702.7 692.1 682.3 97 98 747.7 0 733.2 721.4 702.7 692.1 98 99 737.6 0 747.7 733.2 721.4 702.7 99 100 729.3 0 737.6 747.7 733.2 721.4 100 101 706.1 0 729.3 737.6 747.7 733.2 101 102 674.3 0 706.1 729.3 737.6 747.7 102 103 659.0 0 674.3 706.1 729.3 737.6 103 104 645.7 0 659.0 674.3 706.1 729.3 104 105 646.1 0 645.7 659.0 674.3 706.1 105 106 633.0 1 646.1 645.7 659.0 674.3 106 107 622.3 1 633.0 646.1 645.7 659.0 107 108 628.2 1 622.3 633.0 646.1 645.7 108 109 637.3 1 628.2 622.3 633.0 646.1 109 110 639.6 1 637.3 628.2 622.3 633.0 110 111 638.5 1 639.6 637.3 628.2 622.3 111 112 650.5 1 638.5 639.6 637.3 628.2 112 113 655.4 1 650.5 638.5 639.6 637.3 113 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X Y1 Y2 Y3 Y4 17.02549 -2.97334 1.41304 -0.37043 -0.02263 -0.06788 t 0.16320 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -19.5182 -3.7711 -0.2091 3.9554 27.4984 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 17.02549 6.60145 2.579 0.0113 * X -2.97334 3.31529 -0.897 0.3718 Y1 1.41304 0.09665 14.620 <2e-16 *** Y2 -0.37043 0.16803 -2.205 0.0296 * Y3 -0.02263 0.16854 -0.134 0.8935 Y4 -0.06788 0.09745 -0.697 0.4876 t 0.16320 0.06921 2.358 0.0202 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 7.276 on 106 degrees of freedom Multiple R-squared: 0.9959, Adjusted R-squared: 0.9957 F-statistic: 4339 on 6 and 106 DF, p-value: < 2.2e-16 > 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,] 5.327201e-02 1.065440e-01 0.9467280 [2,] 1.652368e-02 3.304735e-02 0.9834763 [3,] 5.648206e-03 1.129641e-02 0.9943518 [4,] 4.311786e-03 8.623573e-03 0.9956882 [5,] 1.456244e-03 2.912489e-03 0.9985438 [6,] 1.496524e-03 2.993048e-03 0.9985035 [7,] 5.472342e-04 1.094468e-03 0.9994528 [8,] 4.250353e-04 8.500706e-04 0.9995750 [9,] 4.179727e-04 8.359453e-04 0.9995820 [10,] 1.907468e-04 3.814936e-04 0.9998093 [11,] 6.386278e-05 1.277256e-04 0.9999361 [12,] 1.494196e-04 2.988392e-04 0.9998506 [13,] 7.500418e-05 1.500084e-04 0.9999250 [14,] 5.135595e-05 1.027119e-04 0.9999486 [15,] 1.866089e-05 3.732177e-05 0.9999813 [16,] 8.676375e-06 1.735275e-05 0.9999913 [17,] 3.005521e-06 6.011042e-06 0.9999970 [18,] 1.077782e-06 2.155564e-06 0.9999989 [19,] 3.753098e-07 7.506197e-07 0.9999996 [20,] 1.212539e-07 2.425079e-07 0.9999999 [21,] 3.863482e-08 7.726965e-08 1.0000000 [22,] 2.283020e-08 4.566039e-08 1.0000000 [23,] 7.909742e-09 1.581948e-08 1.0000000 [24,] 3.795596e-09 7.591193e-09 1.0000000 [25,] 4.681482e-08 9.362964e-08 1.0000000 [26,] 2.118858e-08 4.237715e-08 1.0000000 [27,] 7.721146e-09 1.544229e-08 1.0000000 [28,] 2.526516e-09 5.053033e-09 1.0000000 [29,] 1.036858e-09 2.073716e-09 1.0000000 [30,] 3.992844e-10 7.985687e-10 1.0000000 [31,] 4.376121e-09 8.752242e-09 1.0000000 [32,] 1.811808e-09 3.623616e-09 1.0000000 [33,] 8.266377e-10 1.653275e-09 1.0000000 [34,] 4.882493e-10 9.764987e-10 1.0000000 [35,] 1.938633e-10 3.877267e-10 1.0000000 [36,] 5.030638e-10 1.006128e-09 1.0000000 [37,] 3.886396e-10 7.772791e-10 1.0000000 [38,] 1.839662e-06 3.679325e-06 0.9999982 [39,] 1.506030e-06 3.012061e-06 0.9999985 [40,] 8.289508e-07 1.657902e-06 0.9999992 [41,] 4.277653e-07 8.555305e-07 0.9999996 [42,] 2.384736e-07 4.769473e-07 0.9999998 [43,] 1.277416e-07 2.554832e-07 0.9999999 [44,] 6.222076e-08 1.244415e-07 0.9999999 [45,] 5.754042e-08 1.150808e-07 0.9999999 [46,] 7.543323e-08 1.508665e-07 0.9999999 [47,] 4.239914e-08 8.479829e-08 1.0000000 [48,] 2.047885e-08 4.095771e-08 1.0000000 [49,] 3.703465e-08 7.406930e-08 1.0000000 [50,] 2.658065e-08 5.316130e-08 1.0000000 [51,] 1.891348e-08 3.782695e-08 1.0000000 [52,] 1.696214e-08 3.392429e-08 1.0000000 [53,] 9.251303e-09 1.850261e-08 1.0000000 [54,] 4.026030e-09 8.052060e-09 1.0000000 [55,] 4.240611e-09 8.481222e-09 1.0000000 [56,] 2.147495e-09 4.294989e-09 1.0000000 [57,] 2.470843e-09 4.941686e-09 1.0000000 [58,] 4.010961e-09 8.021922e-09 1.0000000 [59,] 3.935979e-09 7.871958e-09 1.0000000 [60,] 3.015669e-09 6.031337e-09 1.0000000 [61,] 1.710082e-09 3.420164e-09 1.0000000 [62,] 2.122232e-09 4.244463e-09 1.0000000 [63,] 7.625660e-09 1.525132e-08 1.0000000 [64,] 2.243017e-08 4.486034e-08 1.0000000 [65,] 2.018380e-06 4.036760e-06 0.9999980 [66,] 1.912282e-05 3.824565e-05 0.9999809 [67,] 7.759057e-04 1.551811e-03 0.9992241 [68,] 1.300357e-02 2.600714e-02 0.9869964 [69,] 1.793464e-02 3.586929e-02 0.9820654 [70,] 1.133845e-01 2.267690e-01 0.8866155 [71,] 1.534071e-01 3.068142e-01 0.8465929 [72,] 1.205386e-01 2.410772e-01 0.8794614 [73,] 1.005552e-01 2.011103e-01 0.8994448 [74,] 7.926718e-02 1.585344e-01 0.9207328 [75,] 6.068482e-02 1.213696e-01 0.9393152 [76,] 4.378497e-02 8.756995e-02 0.9562150 [77,] 3.926793e-02 7.853585e-02 0.9607321 [78,] 2.732978e-02 5.465957e-02 0.9726702 [79,] 3.365257e-02 6.730514e-02 0.9663474 [80,] 3.927171e-02 7.854342e-02 0.9607283 [81,] 2.688399e-02 5.376797e-02 0.9731160 [82,] 1.731337e-02 3.462673e-02 0.9826866 [83,] 2.072065e-01 4.144130e-01 0.7927935 [84,] 2.411962e-01 4.823923e-01 0.7588038 [85,] 2.849703e-01 5.699406e-01 0.7150297 [86,] 2.643633e-01 5.287265e-01 0.7356367 [87,] 2.745849e-01 5.491698e-01 0.7254151 [88,] 2.209564e-01 4.419128e-01 0.7790436 [89,] 3.478633e-01 6.957266e-01 0.6521367 [90,] 3.610572e-01 7.221145e-01 0.6389428 [91,] 7.300221e-01 5.399557e-01 0.2699779 [92,] 7.151800e-01 5.696399e-01 0.2848200 [93,] 6.875017e-01 6.249966e-01 0.3124983 [94,] 8.217324e-01 3.565351e-01 0.1782676 > postscript(file="/var/www/html/rcomp/tmp/162r11258472413.ps",horizontal=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2sh481258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3ao7b1258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4sfbf1258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/53u551258472413.ps",horizontal=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 = 113 Frequency = 1 1 2 3 4 5 6 3.8754556 1.4233787 1.8018164 6.5432651 2.9030224 -2.1003545 7 8 9 10 11 12 -0.3306609 0.4431447 4.8443313 3.7659705 -0.2090884 1.1866251 13 14 15 16 17 18 4.7487529 -1.7344074 -5.8808767 0.1750563 1.1964943 -2.2887604 19 20 21 22 23 24 -3.2238212 -0.6191075 7.2422894 -2.4126571 0.5060758 -1.1703775 25 26 27 28 29 30 0.1208893 -2.6587857 -1.7132075 -1.3019213 -2.1626432 -3.4489670 31 32 33 34 35 36 0.8927072 -0.9837591 -0.8121142 5.0134509 -6.4547514 -6.8590167 37 38 39 40 41 42 -3.3251681 -0.9769003 -1.5754692 4.9938166 -1.8510589 -2.9628670 43 44 45 46 47 48 -8.5979129 -3.1511421 4.1484760 2.2867927 16.0931948 -5.9769877 49 50 51 52 53 54 -6.7249459 -6.6148547 -3.9514587 3.3145267 1.5186924 -5.4299933 55 56 57 58 59 60 -9.2919948 -5.4622944 1.8596108 9.2330703 -4.2017568 -7.3789706 61 62 63 64 65 66 -7.9847059 0.9991191 1.5316050 6.4916908 0.5852826 -7.6681340 67 68 69 70 71 72 -6.9095922 0.9893879 6.6103767 0.9514403 -0.6886817 5.9650888 73 74 75 76 77 78 11.3283535 -10.6370906 8.7538738 27.4983937 -12.4137126 8.7143776 79 80 81 82 83 84 -8.1659341 -1.9017345 6.9339548 0.9505937 6.8412552 1.3559417 85 86 87 88 89 90 3.9554461 -2.7008653 2.0040940 -5.7644579 -3.6680667 6.5054448 91 92 93 94 95 96 4.1748990 -11.1346205 23.2000729 1.5740882 5.5655910 12.5508850 97 98 99 100 101 102 3.2561976 8.7513300 -16.4871521 -3.7710976 -18.0184508 -19.5181507 103 104 105 106 107 108 0.4857760 -4.2261152 6.8421494 -11.4443556 -4.9880450 10.1218581 109 110 111 112 113 6.4888203 -3.1787327 -4.9137259 9.9357928 -2.0216120 > postscript(file="/var/www/html/rcomp/tmp/6nax31258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 113 Frequency = 1 lag(myerror, k = 1) myerror 0 3.8754556 NA 1 1.4233787 3.8754556 2 1.8018164 1.4233787 3 6.5432651 1.8018164 4 2.9030224 6.5432651 5 -2.1003545 2.9030224 6 -0.3306609 -2.1003545 7 0.4431447 -0.3306609 8 4.8443313 0.4431447 9 3.7659705 4.8443313 10 -0.2090884 3.7659705 11 1.1866251 -0.2090884 12 4.7487529 1.1866251 13 -1.7344074 4.7487529 14 -5.8808767 -1.7344074 15 0.1750563 -5.8808767 16 1.1964943 0.1750563 17 -2.2887604 1.1964943 18 -3.2238212 -2.2887604 19 -0.6191075 -3.2238212 20 7.2422894 -0.6191075 21 -2.4126571 7.2422894 22 0.5060758 -2.4126571 23 -1.1703775 0.5060758 24 0.1208893 -1.1703775 25 -2.6587857 0.1208893 26 -1.7132075 -2.6587857 27 -1.3019213 -1.7132075 28 -2.1626432 -1.3019213 29 -3.4489670 -2.1626432 30 0.8927072 -3.4489670 31 -0.9837591 0.8927072 32 -0.8121142 -0.9837591 33 5.0134509 -0.8121142 34 -6.4547514 5.0134509 35 -6.8590167 -6.4547514 36 -3.3251681 -6.8590167 37 -0.9769003 -3.3251681 38 -1.5754692 -0.9769003 39 4.9938166 -1.5754692 40 -1.8510589 4.9938166 41 -2.9628670 -1.8510589 42 -8.5979129 -2.9628670 43 -3.1511421 -8.5979129 44 4.1484760 -3.1511421 45 2.2867927 4.1484760 46 16.0931948 2.2867927 47 -5.9769877 16.0931948 48 -6.7249459 -5.9769877 49 -6.6148547 -6.7249459 50 -3.9514587 -6.6148547 51 3.3145267 -3.9514587 52 1.5186924 3.3145267 53 -5.4299933 1.5186924 54 -9.2919948 -5.4299933 55 -5.4622944 -9.2919948 56 1.8596108 -5.4622944 57 9.2330703 1.8596108 58 -4.2017568 9.2330703 59 -7.3789706 -4.2017568 60 -7.9847059 -7.3789706 61 0.9991191 -7.9847059 62 1.5316050 0.9991191 63 6.4916908 1.5316050 64 0.5852826 6.4916908 65 -7.6681340 0.5852826 66 -6.9095922 -7.6681340 67 0.9893879 -6.9095922 68 6.6103767 0.9893879 69 0.9514403 6.6103767 70 -0.6886817 0.9514403 71 5.9650888 -0.6886817 72 11.3283535 5.9650888 73 -10.6370906 11.3283535 74 8.7538738 -10.6370906 75 27.4983937 8.7538738 76 -12.4137126 27.4983937 77 8.7143776 -12.4137126 78 -8.1659341 8.7143776 79 -1.9017345 -8.1659341 80 6.9339548 -1.9017345 81 0.9505937 6.9339548 82 6.8412552 0.9505937 83 1.3559417 6.8412552 84 3.9554461 1.3559417 85 -2.7008653 3.9554461 86 2.0040940 -2.7008653 87 -5.7644579 2.0040940 88 -3.6680667 -5.7644579 89 6.5054448 -3.6680667 90 4.1748990 6.5054448 91 -11.1346205 4.1748990 92 23.2000729 -11.1346205 93 1.5740882 23.2000729 94 5.5655910 1.5740882 95 12.5508850 5.5655910 96 3.2561976 12.5508850 97 8.7513300 3.2561976 98 -16.4871521 8.7513300 99 -3.7710976 -16.4871521 100 -18.0184508 -3.7710976 101 -19.5181507 -18.0184508 102 0.4857760 -19.5181507 103 -4.2261152 0.4857760 104 6.8421494 -4.2261152 105 -11.4443556 6.8421494 106 -4.9880450 -11.4443556 107 10.1218581 -4.9880450 108 6.4888203 10.1218581 109 -3.1787327 6.4888203 110 -4.9137259 -3.1787327 111 9.9357928 -4.9137259 112 -2.0216120 9.9357928 113 NA -2.0216120 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.4233787 3.8754556 [2,] 1.8018164 1.4233787 [3,] 6.5432651 1.8018164 [4,] 2.9030224 6.5432651 [5,] -2.1003545 2.9030224 [6,] -0.3306609 -2.1003545 [7,] 0.4431447 -0.3306609 [8,] 4.8443313 0.4431447 [9,] 3.7659705 4.8443313 [10,] -0.2090884 3.7659705 [11,] 1.1866251 -0.2090884 [12,] 4.7487529 1.1866251 [13,] -1.7344074 4.7487529 [14,] -5.8808767 -1.7344074 [15,] 0.1750563 -5.8808767 [16,] 1.1964943 0.1750563 [17,] -2.2887604 1.1964943 [18,] -3.2238212 -2.2887604 [19,] -0.6191075 -3.2238212 [20,] 7.2422894 -0.6191075 [21,] -2.4126571 7.2422894 [22,] 0.5060758 -2.4126571 [23,] -1.1703775 0.5060758 [24,] 0.1208893 -1.1703775 [25,] -2.6587857 0.1208893 [26,] -1.7132075 -2.6587857 [27,] -1.3019213 -1.7132075 [28,] -2.1626432 -1.3019213 [29,] -3.4489670 -2.1626432 [30,] 0.8927072 -3.4489670 [31,] -0.9837591 0.8927072 [32,] -0.8121142 -0.9837591 [33,] 5.0134509 -0.8121142 [34,] -6.4547514 5.0134509 [35,] -6.8590167 -6.4547514 [36,] -3.3251681 -6.8590167 [37,] -0.9769003 -3.3251681 [38,] -1.5754692 -0.9769003 [39,] 4.9938166 -1.5754692 [40,] -1.8510589 4.9938166 [41,] -2.9628670 -1.8510589 [42,] -8.5979129 -2.9628670 [43,] -3.1511421 -8.5979129 [44,] 4.1484760 -3.1511421 [45,] 2.2867927 4.1484760 [46,] 16.0931948 2.2867927 [47,] -5.9769877 16.0931948 [48,] -6.7249459 -5.9769877 [49,] -6.6148547 -6.7249459 [50,] -3.9514587 -6.6148547 [51,] 3.3145267 -3.9514587 [52,] 1.5186924 3.3145267 [53,] -5.4299933 1.5186924 [54,] -9.2919948 -5.4299933 [55,] -5.4622944 -9.2919948 [56,] 1.8596108 -5.4622944 [57,] 9.2330703 1.8596108 [58,] -4.2017568 9.2330703 [59,] -7.3789706 -4.2017568 [60,] -7.9847059 -7.3789706 [61,] 0.9991191 -7.9847059 [62,] 1.5316050 0.9991191 [63,] 6.4916908 1.5316050 [64,] 0.5852826 6.4916908 [65,] -7.6681340 0.5852826 [66,] -6.9095922 -7.6681340 [67,] 0.9893879 -6.9095922 [68,] 6.6103767 0.9893879 [69,] 0.9514403 6.6103767 [70,] -0.6886817 0.9514403 [71,] 5.9650888 -0.6886817 [72,] 11.3283535 5.9650888 [73,] -10.6370906 11.3283535 [74,] 8.7538738 -10.6370906 [75,] 27.4983937 8.7538738 [76,] -12.4137126 27.4983937 [77,] 8.7143776 -12.4137126 [78,] -8.1659341 8.7143776 [79,] -1.9017345 -8.1659341 [80,] 6.9339548 -1.9017345 [81,] 0.9505937 6.9339548 [82,] 6.8412552 0.9505937 [83,] 1.3559417 6.8412552 [84,] 3.9554461 1.3559417 [85,] -2.7008653 3.9554461 [86,] 2.0040940 -2.7008653 [87,] -5.7644579 2.0040940 [88,] -3.6680667 -5.7644579 [89,] 6.5054448 -3.6680667 [90,] 4.1748990 6.5054448 [91,] -11.1346205 4.1748990 [92,] 23.2000729 -11.1346205 [93,] 1.5740882 23.2000729 [94,] 5.5655910 1.5740882 [95,] 12.5508850 5.5655910 [96,] 3.2561976 12.5508850 [97,] 8.7513300 3.2561976 [98,] -16.4871521 8.7513300 [99,] -3.7710976 -16.4871521 [100,] -18.0184508 -3.7710976 [101,] -19.5181507 -18.0184508 [102,] 0.4857760 -19.5181507 [103,] -4.2261152 0.4857760 [104,] 6.8421494 -4.2261152 [105,] -11.4443556 6.8421494 [106,] -4.9880450 -11.4443556 [107,] 10.1218581 -4.9880450 [108,] 6.4888203 10.1218581 [109,] -3.1787327 6.4888203 [110,] -4.9137259 -3.1787327 [111,] 9.9357928 -4.9137259 [112,] -2.0216120 9.9357928 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.4233787 3.8754556 2 1.8018164 1.4233787 3 6.5432651 1.8018164 4 2.9030224 6.5432651 5 -2.1003545 2.9030224 6 -0.3306609 -2.1003545 7 0.4431447 -0.3306609 8 4.8443313 0.4431447 9 3.7659705 4.8443313 10 -0.2090884 3.7659705 11 1.1866251 -0.2090884 12 4.7487529 1.1866251 13 -1.7344074 4.7487529 14 -5.8808767 -1.7344074 15 0.1750563 -5.8808767 16 1.1964943 0.1750563 17 -2.2887604 1.1964943 18 -3.2238212 -2.2887604 19 -0.6191075 -3.2238212 20 7.2422894 -0.6191075 21 -2.4126571 7.2422894 22 0.5060758 -2.4126571 23 -1.1703775 0.5060758 24 0.1208893 -1.1703775 25 -2.6587857 0.1208893 26 -1.7132075 -2.6587857 27 -1.3019213 -1.7132075 28 -2.1626432 -1.3019213 29 -3.4489670 -2.1626432 30 0.8927072 -3.4489670 31 -0.9837591 0.8927072 32 -0.8121142 -0.9837591 33 5.0134509 -0.8121142 34 -6.4547514 5.0134509 35 -6.8590167 -6.4547514 36 -3.3251681 -6.8590167 37 -0.9769003 -3.3251681 38 -1.5754692 -0.9769003 39 4.9938166 -1.5754692 40 -1.8510589 4.9938166 41 -2.9628670 -1.8510589 42 -8.5979129 -2.9628670 43 -3.1511421 -8.5979129 44 4.1484760 -3.1511421 45 2.2867927 4.1484760 46 16.0931948 2.2867927 47 -5.9769877 16.0931948 48 -6.7249459 -5.9769877 49 -6.6148547 -6.7249459 50 -3.9514587 -6.6148547 51 3.3145267 -3.9514587 52 1.5186924 3.3145267 53 -5.4299933 1.5186924 54 -9.2919948 -5.4299933 55 -5.4622944 -9.2919948 56 1.8596108 -5.4622944 57 9.2330703 1.8596108 58 -4.2017568 9.2330703 59 -7.3789706 -4.2017568 60 -7.9847059 -7.3789706 61 0.9991191 -7.9847059 62 1.5316050 0.9991191 63 6.4916908 1.5316050 64 0.5852826 6.4916908 65 -7.6681340 0.5852826 66 -6.9095922 -7.6681340 67 0.9893879 -6.9095922 68 6.6103767 0.9893879 69 0.9514403 6.6103767 70 -0.6886817 0.9514403 71 5.9650888 -0.6886817 72 11.3283535 5.9650888 73 -10.6370906 11.3283535 74 8.7538738 -10.6370906 75 27.4983937 8.7538738 76 -12.4137126 27.4983937 77 8.7143776 -12.4137126 78 -8.1659341 8.7143776 79 -1.9017345 -8.1659341 80 6.9339548 -1.9017345 81 0.9505937 6.9339548 82 6.8412552 0.9505937 83 1.3559417 6.8412552 84 3.9554461 1.3559417 85 -2.7008653 3.9554461 86 2.0040940 -2.7008653 87 -5.7644579 2.0040940 88 -3.6680667 -5.7644579 89 6.5054448 -3.6680667 90 4.1748990 6.5054448 91 -11.1346205 4.1748990 92 23.2000729 -11.1346205 93 1.5740882 23.2000729 94 5.5655910 1.5740882 95 12.5508850 5.5655910 96 3.2561976 12.5508850 97 8.7513300 3.2561976 98 -16.4871521 8.7513300 99 -3.7710976 -16.4871521 100 -18.0184508 -3.7710976 101 -19.5181507 -18.0184508 102 0.4857760 -19.5181507 103 -4.2261152 0.4857760 104 6.8421494 -4.2261152 105 -11.4443556 6.8421494 106 -4.9880450 -11.4443556 107 10.1218581 -4.9880450 108 6.4888203 10.1218581 109 -3.1787327 6.4888203 110 -4.9137259 -3.1787327 111 9.9357928 -4.9137259 112 -2.0216120 9.9357928 > 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/7e9ca1258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8wrrn1258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/929ow1258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10foa41258472413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11qsk41258472413.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/12v69r1258472413.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/13320j1258472413.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/147pwl1258472413.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/15bpyt1258472413.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/162ixc1258472413.tab") + } > > system("convert tmp/162r11258472413.ps tmp/162r11258472413.png") > system("convert tmp/2sh481258472413.ps tmp/2sh481258472413.png") > system("convert tmp/3ao7b1258472413.ps tmp/3ao7b1258472413.png") > system("convert tmp/4sfbf1258472413.ps tmp/4sfbf1258472413.png") > system("convert tmp/53u551258472413.ps tmp/53u551258472413.png") > system("convert tmp/6nax31258472413.ps tmp/6nax31258472413.png") > system("convert tmp/7e9ca1258472413.ps tmp/7e9ca1258472413.png") > system("convert tmp/8wrrn1258472413.ps tmp/8wrrn1258472413.png") > system("convert tmp/929ow1258472413.ps tmp/929ow1258472413.png") > system("convert tmp/10foa41258472413.ps tmp/10foa41258472413.png") > > > proc.time() user system elapsed 3.382 1.640 4.829