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Type 'q()' to quit R. > x <- array(list(13363 + ,0 + ,12530 + ,0 + ,11420 + ,0 + ,10948 + ,0 + ,10173 + ,0 + ,10602 + ,0 + ,16094 + ,0 + ,19631 + ,0 + ,17140 + ,0 + ,14345 + ,0 + ,12632 + ,0 + ,12894 + ,0 + ,11808 + ,0 + ,10673 + ,0 + ,9939 + ,0 + ,9890 + ,0 + ,9283 + ,0 + ,10131 + ,0 + ,15864 + ,0 + ,19283 + ,0 + ,16203 + ,0 + ,13919 + ,0 + ,11937 + ,0 + ,11795 + ,0 + ,11268 + ,0 + ,10522 + ,0 + ,9929 + ,0 + ,9725 + ,0 + ,9372 + ,0 + ,10068 + ,0 + ,16230 + ,0 + ,19115 + ,0 + ,18351 + ,0 + ,16265 + ,0 + ,14103 + ,0 + ,14115 + ,0 + ,13327 + ,0 + ,12618 + ,0 + ,12129 + ,0 + ,11775 + ,0 + ,11493 + ,0 + ,12470 + ,0 + ,20792 + ,0 + ,22337 + ,0 + ,21325 + ,0 + ,18581 + ,0 + ,16475 + ,0 + ,16581 + ,0 + ,15745 + ,0 + ,14453 + ,0 + ,13712 + ,0 + ,13766 + ,0 + ,13336 + ,0 + ,15346 + ,0 + ,24446 + ,0 + ,26178 + ,0 + ,24628 + ,0 + ,21282 + ,0 + ,18850 + ,0 + ,18822 + ,0 + ,18060 + ,0 + ,17536 + ,0 + ,16417 + ,0 + ,15842 + ,0 + ,15188 + ,0 + ,16905 + ,0 + ,25430 + ,0 + ,27962 + ,0 + ,26607 + ,0 + ,23364 + ,0 + ,20827 + ,0 + ,20506 + ,0 + ,19181 + ,0 + ,18016 + ,0 + ,17354 + ,0 + ,16256 + ,0 + ,15770 + ,0 + ,17538 + ,0 + ,26899 + ,0 + ,28915 + ,0 + ,25247 + ,0 + ,22856 + ,0 + ,19980 + ,0 + ,19856 + ,0 + ,16994 + ,0 + ,16839 + ,0 + ,15618 + ,0 + ,15883 + ,0 + ,15513 + ,0 + ,17106 + ,0 + ,25272 + ,0 + ,26731 + ,0 + ,22891 + ,0 + ,19583 + ,0 + ,16939 + ,0 + ,16757 + ,0 + ,15435 + ,0 + ,14786 + ,0 + ,13680 + ,0 + ,13208 + ,0 + ,12707 + ,0 + ,14277 + ,0 + ,22436 + ,1 + ,23229 + ,1 + ,18241 + ,1 + ,16145 + ,1 + ,13994 + ,1 + ,14780 + ,1 + ,13100 + ,1 + ,12329 + ,1 + ,12463 + ,1 + ,11532 + ,1 + ,10784 + ,1 + ,13106 + ,1 + ,19491 + ,1 + ,20418 + ,1 + ,16094 + ,1 + ,14491 + ,1 + ,13067 + ,1) + ,dim=c(2 + ,119) + ,dimnames=list(c('NWWZPB' + ,'Dummy') + ,1:119)) > y <- array(NA,dim=c(2,119),dimnames=list(c('NWWZPB','Dummy'),1:119)) > 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 = 'Include Monthly 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 NWWZPB Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 13363 0 1 0 0 0 0 0 0 0 0 0 0 2 12530 0 0 1 0 0 0 0 0 0 0 0 0 3 11420 0 0 0 1 0 0 0 0 0 0 0 0 4 10948 0 0 0 0 1 0 0 0 0 0 0 0 5 10173 0 0 0 0 0 1 0 0 0 0 0 0 6 10602 0 0 0 0 0 0 1 0 0 0 0 0 7 16094 0 0 0 0 0 0 0 1 0 0 0 0 8 19631 0 0 0 0 0 0 0 0 1 0 0 0 9 17140 0 0 0 0 0 0 0 0 0 1 0 0 10 14345 0 0 0 0 0 0 0 0 0 0 1 0 11 12632 0 0 0 0 0 0 0 0 0 0 0 1 12 12894 0 0 0 0 0 0 0 0 0 0 0 0 13 11808 0 1 0 0 0 0 0 0 0 0 0 0 14 10673 0 0 1 0 0 0 0 0 0 0 0 0 15 9939 0 0 0 1 0 0 0 0 0 0 0 0 16 9890 0 0 0 0 1 0 0 0 0 0 0 0 17 9283 0 0 0 0 0 1 0 0 0 0 0 0 18 10131 0 0 0 0 0 0 1 0 0 0 0 0 19 15864 0 0 0 0 0 0 0 1 0 0 0 0 20 19283 0 0 0 0 0 0 0 0 1 0 0 0 21 16203 0 0 0 0 0 0 0 0 0 1 0 0 22 13919 0 0 0 0 0 0 0 0 0 0 1 0 23 11937 0 0 0 0 0 0 0 0 0 0 0 1 24 11795 0 0 0 0 0 0 0 0 0 0 0 0 25 11268 0 1 0 0 0 0 0 0 0 0 0 0 26 10522 0 0 1 0 0 0 0 0 0 0 0 0 27 9929 0 0 0 1 0 0 0 0 0 0 0 0 28 9725 0 0 0 0 1 0 0 0 0 0 0 0 29 9372 0 0 0 0 0 1 0 0 0 0 0 0 30 10068 0 0 0 0 0 0 1 0 0 0 0 0 31 16230 0 0 0 0 0 0 0 1 0 0 0 0 32 19115 0 0 0 0 0 0 0 0 1 0 0 0 33 18351 0 0 0 0 0 0 0 0 0 1 0 0 34 16265 0 0 0 0 0 0 0 0 0 0 1 0 35 14103 0 0 0 0 0 0 0 0 0 0 0 1 36 14115 0 0 0 0 0 0 0 0 0 0 0 0 37 13327 0 1 0 0 0 0 0 0 0 0 0 0 38 12618 0 0 1 0 0 0 0 0 0 0 0 0 39 12129 0 0 0 1 0 0 0 0 0 0 0 0 40 11775 0 0 0 0 1 0 0 0 0 0 0 0 41 11493 0 0 0 0 0 1 0 0 0 0 0 0 42 12470 0 0 0 0 0 0 1 0 0 0 0 0 43 20792 0 0 0 0 0 0 0 1 0 0 0 0 44 22337 0 0 0 0 0 0 0 0 1 0 0 0 45 21325 0 0 0 0 0 0 0 0 0 1 0 0 46 18581 0 0 0 0 0 0 0 0 0 0 1 0 47 16475 0 0 0 0 0 0 0 0 0 0 0 1 48 16581 0 0 0 0 0 0 0 0 0 0 0 0 49 15745 0 1 0 0 0 0 0 0 0 0 0 0 50 14453 0 0 1 0 0 0 0 0 0 0 0 0 51 13712 0 0 0 1 0 0 0 0 0 0 0 0 52 13766 0 0 0 0 1 0 0 0 0 0 0 0 53 13336 0 0 0 0 0 1 0 0 0 0 0 0 54 15346 0 0 0 0 0 0 1 0 0 0 0 0 55 24446 0 0 0 0 0 0 0 1 0 0 0 0 56 26178 0 0 0 0 0 0 0 0 1 0 0 0 57 24628 0 0 0 0 0 0 0 0 0 1 0 0 58 21282 0 0 0 0 0 0 0 0 0 0 1 0 59 18850 0 0 0 0 0 0 0 0 0 0 0 1 60 18822 0 0 0 0 0 0 0 0 0 0 0 0 61 18060 0 1 0 0 0 0 0 0 0 0 0 0 62 17536 0 0 1 0 0 0 0 0 0 0 0 0 63 16417 0 0 0 1 0 0 0 0 0 0 0 0 64 15842 0 0 0 0 1 0 0 0 0 0 0 0 65 15188 0 0 0 0 0 1 0 0 0 0 0 0 66 16905 0 0 0 0 0 0 1 0 0 0 0 0 67 25430 0 0 0 0 0 0 0 1 0 0 0 0 68 27962 0 0 0 0 0 0 0 0 1 0 0 0 69 26607 0 0 0 0 0 0 0 0 0 1 0 0 70 23364 0 0 0 0 0 0 0 0 0 0 1 0 71 20827 0 0 0 0 0 0 0 0 0 0 0 1 72 20506 0 0 0 0 0 0 0 0 0 0 0 0 73 19181 0 1 0 0 0 0 0 0 0 0 0 0 74 18016 0 0 1 0 0 0 0 0 0 0 0 0 75 17354 0 0 0 1 0 0 0 0 0 0 0 0 76 16256 0 0 0 0 1 0 0 0 0 0 0 0 77 15770 0 0 0 0 0 1 0 0 0 0 0 0 78 17538 0 0 0 0 0 0 1 0 0 0 0 0 79 26899 0 0 0 0 0 0 0 1 0 0 0 0 80 28915 0 0 0 0 0 0 0 0 1 0 0 0 81 25247 0 0 0 0 0 0 0 0 0 1 0 0 82 22856 0 0 0 0 0 0 0 0 0 0 1 0 83 19980 0 0 0 0 0 0 0 0 0 0 0 1 84 19856 0 0 0 0 0 0 0 0 0 0 0 0 85 16994 0 1 0 0 0 0 0 0 0 0 0 0 86 16839 0 0 1 0 0 0 0 0 0 0 0 0 87 15618 0 0 0 1 0 0 0 0 0 0 0 0 88 15883 0 0 0 0 1 0 0 0 0 0 0 0 89 15513 0 0 0 0 0 1 0 0 0 0 0 0 90 17106 0 0 0 0 0 0 1 0 0 0 0 0 91 25272 0 0 0 0 0 0 0 1 0 0 0 0 92 26731 0 0 0 0 0 0 0 0 1 0 0 0 93 22891 0 0 0 0 0 0 0 0 0 1 0 0 94 19583 0 0 0 0 0 0 0 0 0 0 1 0 95 16939 0 0 0 0 0 0 0 0 0 0 0 1 96 16757 0 0 0 0 0 0 0 0 0 0 0 0 97 15435 0 1 0 0 0 0 0 0 0 0 0 0 98 14786 0 0 1 0 0 0 0 0 0 0 0 0 99 13680 0 0 0 1 0 0 0 0 0 0 0 0 100 13208 0 0 0 0 1 0 0 0 0 0 0 0 101 12707 0 0 0 0 0 1 0 0 0 0 0 0 102 14277 0 0 0 0 0 0 1 0 0 0 0 0 103 22436 1 0 0 0 0 0 0 1 0 0 0 0 104 23229 1 0 0 0 0 0 0 0 1 0 0 0 105 18241 1 0 0 0 0 0 0 0 0 1 0 0 106 16145 1 0 0 0 0 0 0 0 0 0 1 0 107 13994 1 0 0 0 0 0 0 0 0 0 0 1 108 14780 1 0 0 0 0 0 0 0 0 0 0 0 109 13100 1 1 0 0 0 0 0 0 0 0 0 0 110 12329 1 0 1 0 0 0 0 0 0 0 0 0 111 12463 1 0 0 1 0 0 0 0 0 0 0 0 112 11532 1 0 0 0 1 0 0 0 0 0 0 0 113 10784 1 0 0 0 0 1 0 0 0 0 0 0 114 13106 1 0 0 0 0 0 1 0 0 0 0 0 115 19491 1 0 0 0 0 0 0 1 0 0 0 0 116 20418 1 0 0 0 0 0 0 0 1 0 0 0 117 16094 1 0 0 0 0 0 0 0 0 1 0 0 118 14491 1 0 0 0 0 0 0 0 0 0 1 0 119 13067 1 0 0 0 0 0 0 0 0 0 0 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Dummy M1 M2 M3 M4 16469.5 -2119.2 -1429.4 -2227.3 -2991.4 -3375.0 M5 M6 M7 M8 M9 M10 -3895.6 -2502.6 5249.8 7334.3 4627.1 2037.5 M11 -165.2 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5855.2 -2377.6 206.1 2761.3 5510.5 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16469.5 1044.8 15.763 < 2e-16 *** Dummy -2119.2 826.0 -2.566 0.011697 * M1 -1429.4 1434.6 -0.996 0.321329 M2 -2227.3 1434.6 -1.553 0.123508 M3 -2991.4 1434.6 -2.085 0.039455 * M4 -3375.0 1434.6 -2.353 0.020490 * M5 -3895.6 1434.6 -2.715 0.007729 ** M6 -2502.6 1434.6 -1.744 0.083977 . M7 5249.8 1436.5 3.655 0.000402 *** M8 7334.3 1436.5 5.106 1.46e-06 *** M9 4627.1 1436.5 3.221 0.001696 ** M10 2037.5 1436.5 1.418 0.159012 M11 -165.2 1436.5 -0.115 0.908644 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3122 on 106 degrees of freedom Multiple R-squared: 0.5985, Adjusted R-squared: 0.5531 F-statistic: 13.17 on 12 and 106 DF, p-value: 3.688e-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,] 8.303826e-02 1.660765e-01 9.169617e-01 [2,] 3.255486e-02 6.510972e-02 9.674451e-01 [3,] 1.126869e-02 2.253737e-02 9.887313e-01 [4,] 3.998508e-03 7.997016e-03 9.960015e-01 [5,] 1.334125e-03 2.668250e-03 9.986659e-01 [6,] 5.861314e-04 1.172263e-03 9.994139e-01 [7,] 2.075043e-04 4.150085e-04 9.997925e-01 [8,] 8.231651e-05 1.646330e-04 9.999177e-01 [9,] 4.650739e-05 9.301478e-05 9.999535e-01 [10,] 3.869344e-05 7.738688e-05 9.999613e-01 [11,] 2.525606e-05 5.051212e-05 9.999747e-01 [12,] 1.284198e-05 2.568397e-05 9.999872e-01 [13,] 6.366203e-06 1.273241e-05 9.999936e-01 [14,] 2.688458e-06 5.376917e-06 9.999973e-01 [15,] 1.338280e-06 2.676561e-06 9.999987e-01 [16,] 1.377907e-06 2.755813e-06 9.999986e-01 [17,] 1.296582e-06 2.593164e-06 9.999987e-01 [18,] 3.535856e-06 7.071712e-06 9.999965e-01 [19,] 1.604542e-05 3.209085e-05 9.999840e-01 [20,] 3.593480e-05 7.186960e-05 9.999641e-01 [21,] 7.617545e-05 1.523509e-04 9.999238e-01 [22,] 8.593250e-05 1.718650e-04 9.999141e-01 [23,] 1.174737e-04 2.349474e-04 9.998825e-01 [24,] 2.062627e-04 4.125253e-04 9.997937e-01 [25,] 3.144826e-04 6.289652e-04 9.996855e-01 [26,] 5.833544e-04 1.166709e-03 9.994166e-01 [27,] 1.725297e-03 3.450594e-03 9.982747e-01 [28,] 5.408366e-02 1.081673e-01 9.459163e-01 [29,] 1.542137e-01 3.084275e-01 8.457863e-01 [30,] 3.364142e-01 6.728285e-01 6.635858e-01 [31,] 5.059780e-01 9.880441e-01 4.940220e-01 [32,] 6.397668e-01 7.204664e-01 3.602332e-01 [33,] 7.562462e-01 4.875076e-01 2.437538e-01 [34,] 8.153194e-01 3.693613e-01 1.846806e-01 [35,] 8.598947e-01 2.802105e-01 1.401053e-01 [36,] 8.945775e-01 2.108450e-01 1.054225e-01 [37,] 9.191094e-01 1.617813e-01 8.089064e-02 [38,] 9.376370e-01 1.247261e-01 6.236305e-02 [39,] 9.658990e-01 6.820201e-02 3.410101e-02 [40,] 9.944460e-01 1.110794e-02 5.553971e-03 [41,] 9.981700e-01 3.660039e-03 1.830019e-03 [42,] 9.993520e-01 1.295964e-03 6.479820e-04 [43,] 9.996108e-01 7.784901e-04 3.892450e-04 [44,] 9.997061e-01 5.878601e-04 2.939300e-04 [45,] 9.997614e-01 4.772930e-04 2.386465e-04 [46,] 9.998037e-01 3.926093e-04 1.963047e-04 [47,] 9.998520e-01 2.960471e-04 1.480236e-04 [48,] 9.998630e-01 2.739518e-04 1.369759e-04 [49,] 9.998554e-01 2.891598e-04 1.445799e-04 [50,] 9.998361e-01 3.278264e-04 1.639132e-04 [51,] 9.998358e-01 3.283868e-04 1.641934e-04 [52,] 9.998861e-01 2.277649e-04 1.138824e-04 [53,] 9.999116e-01 1.768626e-04 8.843130e-05 [54,] 9.999773e-01 4.530817e-05 2.265408e-05 [55,] 9.999891e-01 2.175970e-05 1.087985e-05 [56,] 9.999934e-01 1.322921e-05 6.614607e-06 [57,] 9.999939e-01 1.225072e-05 6.125360e-06 [58,] 9.999954e-01 9.192351e-06 4.596176e-06 [59,] 9.999951e-01 9.846324e-06 4.923162e-06 [60,] 9.999949e-01 1.020833e-05 5.104165e-06 [61,] 9.999923e-01 1.535118e-05 7.675591e-06 [62,] 9.999886e-01 2.283219e-05 1.141610e-05 [63,] 9.999838e-01 3.241019e-05 1.620509e-05 [64,] 9.999840e-01 3.205775e-05 1.602887e-05 [65,] 9.999887e-01 2.250973e-05 1.125486e-05 [66,] 9.999948e-01 1.044925e-05 5.224623e-06 [67,] 9.999982e-01 3.656496e-06 1.828248e-06 [68,] 9.999989e-01 2.255413e-06 1.127706e-06 [69,] 9.999988e-01 2.367846e-06 1.183923e-06 [70,] 9.999973e-01 5.499658e-06 2.749829e-06 [71,] 9.999951e-01 9.742704e-06 4.871352e-06 [72,] 9.999886e-01 2.288493e-05 1.144247e-05 [73,] 9.999827e-01 3.468721e-05 1.734361e-05 [74,] 9.999784e-01 4.327013e-05 2.163506e-05 [75,] 9.999679e-01 6.427658e-05 3.213829e-05 [76,] 9.999372e-01 1.255693e-04 6.278465e-05 [77,] 9.999142e-01 1.716149e-04 8.580743e-05 [78,] 9.999619e-01 7.618312e-05 3.809156e-05 [79,] 9.999529e-01 9.422508e-05 4.711254e-05 [80,] 9.999066e-01 1.867662e-04 9.338310e-05 [81,] 9.996932e-01 6.136807e-04 3.068403e-04 [82,] 9.990862e-01 1.827549e-03 9.137743e-04 [83,] 9.975291e-01 4.941869e-03 2.470935e-03 [84,] 9.930052e-01 1.398960e-02 6.994799e-03 [85,] 9.811809e-01 3.763826e-02 1.881913e-02 [86,] 9.539389e-01 9.212217e-02 4.606109e-02 [87,] 8.930289e-01 2.139422e-01 1.069711e-01 [88,] 8.671512e-01 2.656975e-01 1.328488e-01 > postscript(file="/var/www/html/rcomp/tmp/1a8ul1229267023.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/202ya1229267023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3slf11229267023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4fhgg1229267023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/50v441229267023.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 = 119 Frequency = 1 1 2 3 4 5 6 -1677.01781 -1712.11781 -2058.01781 -2146.41781 -2400.81781 -3364.81781 7 8 9 10 11 12 -5625.23561 -4172.73561 -3956.53561 -4161.93561 -3672.23561 -3575.46423 13 14 15 16 17 18 -3232.01781 -3569.11781 -3539.01781 -3204.41781 -3290.81781 -3835.81781 19 20 21 22 23 24 -5855.23561 -4520.73561 -4893.53561 -4587.93561 -4367.23561 -4674.46423 25 26 27 28 29 30 -3772.01781 -3720.11781 -3549.01781 -3369.41781 -3201.81781 -3898.81781 31 32 33 34 35 36 -5489.23561 -4688.73561 -2745.53561 -2241.93561 -2201.23561 -2354.46423 37 38 39 40 41 42 -1713.01781 -1624.11781 -1349.01781 -1319.41781 -1080.81781 -1496.81781 43 44 45 46 47 48 -927.23561 -1466.73561 228.46439 74.06439 170.76439 111.53577 49 50 51 52 53 54 704.98219 210.88219 233.98219 671.58219 762.18219 1379.18219 55 56 57 58 59 60 2726.76439 2374.26439 3531.46439 2775.06439 2545.76439 2352.53577 61 62 63 64 65 66 3019.98219 3293.88219 2938.98219 2747.58219 2614.18219 2938.18219 67 68 69 70 71 72 3710.76439 4158.26439 5510.46439 4857.06439 4522.76439 4036.53577 73 74 75 76 77 78 4140.98219 3773.88219 3875.98219 3161.58219 3196.18219 3571.18219 79 80 81 82 83 84 5179.76439 5111.26439 4150.46439 4349.06439 3675.76439 3386.53577 85 86 87 88 89 90 1953.98219 2596.88219 2139.98219 2788.58219 2939.18219 3139.18219 91 92 93 94 95 96 3552.76439 2927.26439 1794.46439 1076.06439 634.76439 287.53577 97 98 99 100 101 102 394.98219 543.88219 201.98219 113.58219 133.18219 310.18219 103 104 105 106 107 108 2835.94246 1544.44246 -736.35754 -242.75754 -191.05754 429.71384 109 110 111 112 113 114 179.16026 206.06026 1104.16026 556.76026 329.36026 1258.36026 115 116 117 118 119 -109.05754 -1266.55754 -2883.35754 -1896.75754 -1118.05754 > postscript(file="/var/www/html/rcomp/tmp/659lt1229267023.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 = 119 Frequency = 1 lag(myerror, k = 1) myerror 0 -1677.01781 NA 1 -1712.11781 -1677.01781 2 -2058.01781 -1712.11781 3 -2146.41781 -2058.01781 4 -2400.81781 -2146.41781 5 -3364.81781 -2400.81781 6 -5625.23561 -3364.81781 7 -4172.73561 -5625.23561 8 -3956.53561 -4172.73561 9 -4161.93561 -3956.53561 10 -3672.23561 -4161.93561 11 -3575.46423 -3672.23561 12 -3232.01781 -3575.46423 13 -3569.11781 -3232.01781 14 -3539.01781 -3569.11781 15 -3204.41781 -3539.01781 16 -3290.81781 -3204.41781 17 -3835.81781 -3290.81781 18 -5855.23561 -3835.81781 19 -4520.73561 -5855.23561 20 -4893.53561 -4520.73561 21 -4587.93561 -4893.53561 22 -4367.23561 -4587.93561 23 -4674.46423 -4367.23561 24 -3772.01781 -4674.46423 25 -3720.11781 -3772.01781 26 -3549.01781 -3720.11781 27 -3369.41781 -3549.01781 28 -3201.81781 -3369.41781 29 -3898.81781 -3201.81781 30 -5489.23561 -3898.81781 31 -4688.73561 -5489.23561 32 -2745.53561 -4688.73561 33 -2241.93561 -2745.53561 34 -2201.23561 -2241.93561 35 -2354.46423 -2201.23561 36 -1713.01781 -2354.46423 37 -1624.11781 -1713.01781 38 -1349.01781 -1624.11781 39 -1319.41781 -1349.01781 40 -1080.81781 -1319.41781 41 -1496.81781 -1080.81781 42 -927.23561 -1496.81781 43 -1466.73561 -927.23561 44 228.46439 -1466.73561 45 74.06439 228.46439 46 170.76439 74.06439 47 111.53577 170.76439 48 704.98219 111.53577 49 210.88219 704.98219 50 233.98219 210.88219 51 671.58219 233.98219 52 762.18219 671.58219 53 1379.18219 762.18219 54 2726.76439 1379.18219 55 2374.26439 2726.76439 56 3531.46439 2374.26439 57 2775.06439 3531.46439 58 2545.76439 2775.06439 59 2352.53577 2545.76439 60 3019.98219 2352.53577 61 3293.88219 3019.98219 62 2938.98219 3293.88219 63 2747.58219 2938.98219 64 2614.18219 2747.58219 65 2938.18219 2614.18219 66 3710.76439 2938.18219 67 4158.26439 3710.76439 68 5510.46439 4158.26439 69 4857.06439 5510.46439 70 4522.76439 4857.06439 71 4036.53577 4522.76439 72 4140.98219 4036.53577 73 3773.88219 4140.98219 74 3875.98219 3773.88219 75 3161.58219 3875.98219 76 3196.18219 3161.58219 77 3571.18219 3196.18219 78 5179.76439 3571.18219 79 5111.26439 5179.76439 80 4150.46439 5111.26439 81 4349.06439 4150.46439 82 3675.76439 4349.06439 83 3386.53577 3675.76439 84 1953.98219 3386.53577 85 2596.88219 1953.98219 86 2139.98219 2596.88219 87 2788.58219 2139.98219 88 2939.18219 2788.58219 89 3139.18219 2939.18219 90 3552.76439 3139.18219 91 2927.26439 3552.76439 92 1794.46439 2927.26439 93 1076.06439 1794.46439 94 634.76439 1076.06439 95 287.53577 634.76439 96 394.98219 287.53577 97 543.88219 394.98219 98 201.98219 543.88219 99 113.58219 201.98219 100 133.18219 113.58219 101 310.18219 133.18219 102 2835.94246 310.18219 103 1544.44246 2835.94246 104 -736.35754 1544.44246 105 -242.75754 -736.35754 106 -191.05754 -242.75754 107 429.71384 -191.05754 108 179.16026 429.71384 109 206.06026 179.16026 110 1104.16026 206.06026 111 556.76026 1104.16026 112 329.36026 556.76026 113 1258.36026 329.36026 114 -109.05754 1258.36026 115 -1266.55754 -109.05754 116 -2883.35754 -1266.55754 117 -1896.75754 -2883.35754 118 -1118.05754 -1896.75754 119 NA -1118.05754 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1712.11781 -1677.01781 [2,] -2058.01781 -1712.11781 [3,] -2146.41781 -2058.01781 [4,] -2400.81781 -2146.41781 [5,] -3364.81781 -2400.81781 [6,] -5625.23561 -3364.81781 [7,] -4172.73561 -5625.23561 [8,] -3956.53561 -4172.73561 [9,] -4161.93561 -3956.53561 [10,] -3672.23561 -4161.93561 [11,] -3575.46423 -3672.23561 [12,] -3232.01781 -3575.46423 [13,] -3569.11781 -3232.01781 [14,] -3539.01781 -3569.11781 [15,] -3204.41781 -3539.01781 [16,] -3290.81781 -3204.41781 [17,] -3835.81781 -3290.81781 [18,] -5855.23561 -3835.81781 [19,] -4520.73561 -5855.23561 [20,] -4893.53561 -4520.73561 [21,] -4587.93561 -4893.53561 [22,] -4367.23561 -4587.93561 [23,] -4674.46423 -4367.23561 [24,] -3772.01781 -4674.46423 [25,] -3720.11781 -3772.01781 [26,] -3549.01781 -3720.11781 [27,] -3369.41781 -3549.01781 [28,] -3201.81781 -3369.41781 [29,] -3898.81781 -3201.81781 [30,] -5489.23561 -3898.81781 [31,] -4688.73561 -5489.23561 [32,] -2745.53561 -4688.73561 [33,] -2241.93561 -2745.53561 [34,] -2201.23561 -2241.93561 [35,] -2354.46423 -2201.23561 [36,] -1713.01781 -2354.46423 [37,] -1624.11781 -1713.01781 [38,] -1349.01781 -1624.11781 [39,] -1319.41781 -1349.01781 [40,] -1080.81781 -1319.41781 [41,] -1496.81781 -1080.81781 [42,] -927.23561 -1496.81781 [43,] -1466.73561 -927.23561 [44,] 228.46439 -1466.73561 [45,] 74.06439 228.46439 [46,] 170.76439 74.06439 [47,] 111.53577 170.76439 [48,] 704.98219 111.53577 [49,] 210.88219 704.98219 [50,] 233.98219 210.88219 [51,] 671.58219 233.98219 [52,] 762.18219 671.58219 [53,] 1379.18219 762.18219 [54,] 2726.76439 1379.18219 [55,] 2374.26439 2726.76439 [56,] 3531.46439 2374.26439 [57,] 2775.06439 3531.46439 [58,] 2545.76439 2775.06439 [59,] 2352.53577 2545.76439 [60,] 3019.98219 2352.53577 [61,] 3293.88219 3019.98219 [62,] 2938.98219 3293.88219 [63,] 2747.58219 2938.98219 [64,] 2614.18219 2747.58219 [65,] 2938.18219 2614.18219 [66,] 3710.76439 2938.18219 [67,] 4158.26439 3710.76439 [68,] 5510.46439 4158.26439 [69,] 4857.06439 5510.46439 [70,] 4522.76439 4857.06439 [71,] 4036.53577 4522.76439 [72,] 4140.98219 4036.53577 [73,] 3773.88219 4140.98219 [74,] 3875.98219 3773.88219 [75,] 3161.58219 3875.98219 [76,] 3196.18219 3161.58219 [77,] 3571.18219 3196.18219 [78,] 5179.76439 3571.18219 [79,] 5111.26439 5179.76439 [80,] 4150.46439 5111.26439 [81,] 4349.06439 4150.46439 [82,] 3675.76439 4349.06439 [83,] 3386.53577 3675.76439 [84,] 1953.98219 3386.53577 [85,] 2596.88219 1953.98219 [86,] 2139.98219 2596.88219 [87,] 2788.58219 2139.98219 [88,] 2939.18219 2788.58219 [89,] 3139.18219 2939.18219 [90,] 3552.76439 3139.18219 [91,] 2927.26439 3552.76439 [92,] 1794.46439 2927.26439 [93,] 1076.06439 1794.46439 [94,] 634.76439 1076.06439 [95,] 287.53577 634.76439 [96,] 394.98219 287.53577 [97,] 543.88219 394.98219 [98,] 201.98219 543.88219 [99,] 113.58219 201.98219 [100,] 133.18219 113.58219 [101,] 310.18219 133.18219 [102,] 2835.94246 310.18219 [103,] 1544.44246 2835.94246 [104,] -736.35754 1544.44246 [105,] -242.75754 -736.35754 [106,] -191.05754 -242.75754 [107,] 429.71384 -191.05754 [108,] 179.16026 429.71384 [109,] 206.06026 179.16026 [110,] 1104.16026 206.06026 [111,] 556.76026 1104.16026 [112,] 329.36026 556.76026 [113,] 1258.36026 329.36026 [114,] -109.05754 1258.36026 [115,] -1266.55754 -109.05754 [116,] -2883.35754 -1266.55754 [117,] -1896.75754 -2883.35754 [118,] -1118.05754 -1896.75754 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1712.11781 -1677.01781 2 -2058.01781 -1712.11781 3 -2146.41781 -2058.01781 4 -2400.81781 -2146.41781 5 -3364.81781 -2400.81781 6 -5625.23561 -3364.81781 7 -4172.73561 -5625.23561 8 -3956.53561 -4172.73561 9 -4161.93561 -3956.53561 10 -3672.23561 -4161.93561 11 -3575.46423 -3672.23561 12 -3232.01781 -3575.46423 13 -3569.11781 -3232.01781 14 -3539.01781 -3569.11781 15 -3204.41781 -3539.01781 16 -3290.81781 -3204.41781 17 -3835.81781 -3290.81781 18 -5855.23561 -3835.81781 19 -4520.73561 -5855.23561 20 -4893.53561 -4520.73561 21 -4587.93561 -4893.53561 22 -4367.23561 -4587.93561 23 -4674.46423 -4367.23561 24 -3772.01781 -4674.46423 25 -3720.11781 -3772.01781 26 -3549.01781 -3720.11781 27 -3369.41781 -3549.01781 28 -3201.81781 -3369.41781 29 -3898.81781 -3201.81781 30 -5489.23561 -3898.81781 31 -4688.73561 -5489.23561 32 -2745.53561 -4688.73561 33 -2241.93561 -2745.53561 34 -2201.23561 -2241.93561 35 -2354.46423 -2201.23561 36 -1713.01781 -2354.46423 37 -1624.11781 -1713.01781 38 -1349.01781 -1624.11781 39 -1319.41781 -1349.01781 40 -1080.81781 -1319.41781 41 -1496.81781 -1080.81781 42 -927.23561 -1496.81781 43 -1466.73561 -927.23561 44 228.46439 -1466.73561 45 74.06439 228.46439 46 170.76439 74.06439 47 111.53577 170.76439 48 704.98219 111.53577 49 210.88219 704.98219 50 233.98219 210.88219 51 671.58219 233.98219 52 762.18219 671.58219 53 1379.18219 762.18219 54 2726.76439 1379.18219 55 2374.26439 2726.76439 56 3531.46439 2374.26439 57 2775.06439 3531.46439 58 2545.76439 2775.06439 59 2352.53577 2545.76439 60 3019.98219 2352.53577 61 3293.88219 3019.98219 62 2938.98219 3293.88219 63 2747.58219 2938.98219 64 2614.18219 2747.58219 65 2938.18219 2614.18219 66 3710.76439 2938.18219 67 4158.26439 3710.76439 68 5510.46439 4158.26439 69 4857.06439 5510.46439 70 4522.76439 4857.06439 71 4036.53577 4522.76439 72 4140.98219 4036.53577 73 3773.88219 4140.98219 74 3875.98219 3773.88219 75 3161.58219 3875.98219 76 3196.18219 3161.58219 77 3571.18219 3196.18219 78 5179.76439 3571.18219 79 5111.26439 5179.76439 80 4150.46439 5111.26439 81 4349.06439 4150.46439 82 3675.76439 4349.06439 83 3386.53577 3675.76439 84 1953.98219 3386.53577 85 2596.88219 1953.98219 86 2139.98219 2596.88219 87 2788.58219 2139.98219 88 2939.18219 2788.58219 89 3139.18219 2939.18219 90 3552.76439 3139.18219 91 2927.26439 3552.76439 92 1794.46439 2927.26439 93 1076.06439 1794.46439 94 634.76439 1076.06439 95 287.53577 634.76439 96 394.98219 287.53577 97 543.88219 394.98219 98 201.98219 543.88219 99 113.58219 201.98219 100 133.18219 113.58219 101 310.18219 133.18219 102 2835.94246 310.18219 103 1544.44246 2835.94246 104 -736.35754 1544.44246 105 -242.75754 -736.35754 106 -191.05754 -242.75754 107 429.71384 -191.05754 108 179.16026 429.71384 109 206.06026 179.16026 110 1104.16026 206.06026 111 556.76026 1104.16026 112 329.36026 556.76026 113 1258.36026 329.36026 114 -109.05754 1258.36026 115 -1266.55754 -109.05754 116 -2883.35754 -1266.55754 117 -1896.75754 -2883.35754 118 -1118.05754 -1896.75754 > 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/79p0y1229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8561k1229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/917651229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10bcdf1229267024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11fla11229267024.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/12699u1229267024.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/13wbrl1229267024.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/14wjzw1229267024.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/15r5wr1229267024.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/168efj1229267024.tab") + } > > system("convert tmp/1a8ul1229267023.ps tmp/1a8ul1229267023.png") > system("convert tmp/202ya1229267023.ps tmp/202ya1229267023.png") > system("convert tmp/3slf11229267023.ps tmp/3slf11229267023.png") > system("convert tmp/4fhgg1229267023.ps tmp/4fhgg1229267023.png") > system("convert tmp/50v441229267023.ps tmp/50v441229267023.png") > system("convert tmp/659lt1229267023.ps tmp/659lt1229267023.png") > system("convert tmp/79p0y1229267024.ps tmp/79p0y1229267024.png") > system("convert tmp/8561k1229267024.ps tmp/8561k1229267024.png") > system("convert tmp/917651229267024.ps tmp/917651229267024.png") > system("convert tmp/10bcdf1229267024.ps tmp/10bcdf1229267024.png") > > > proc.time() user system elapsed 3.350 1.629 3.709