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(1579 + ,0 + ,4.0 + ,45.7 + ,17.0 + ,2146 + ,0 + ,5.9 + ,81.9 + ,21.0 + ,2462 + ,0 + ,7.1 + ,56.8 + ,21.0 + ,3695 + ,0 + ,10.5 + ,65.1 + ,18.0 + ,4831 + ,0 + ,15.1 + ,86.2 + ,20.0 + ,5134 + ,0 + ,16.8 + ,35.1 + ,11.0 + ,6250 + ,0 + ,15.3 + ,133.8 + ,20.0 + ,5760 + ,0 + ,18.4 + ,34.5 + ,13.0 + ,6249 + ,0 + ,16.1 + ,69.9 + ,14.0 + ,2917 + ,0 + ,11.3 + ,98.3 + ,23.0 + ,1741 + ,0 + ,7.9 + ,86.7 + ,24.0 + ,2359 + ,0 + ,5.6 + ,58.2 + ,22.0 + ,1511 + ,1 + ,3.4 + ,83.6 + ,17.0 + ,2059 + ,0 + ,4.8 + ,83.5 + ,18.0 + ,2635 + ,0 + ,6.5 + ,112.3 + ,24.0 + ,2867 + ,0 + ,8.5 + ,134.3 + ,23.0 + ,4403 + ,0 + ,15.1 + ,30.0 + ,8.0 + ,5720 + ,0 + ,15.7 + ,44.5 + ,10.0 + ,4502 + ,0 + ,18.7 + ,120.1 + ,18.0 + ,5749 + ,0 + ,19.2 + ,43.4 + ,13.0 + ,5627 + ,0 + ,12.9 + ,199.4 + ,23.0 + ,2846 + ,0 + ,14.4 + ,68.1 + ,14.0 + ,1762 + ,0 + ,6.2 + ,99.8 + ,15.0 + ,2429 + ,0 + ,3.3 + ,69.5 + ,18.0 + ,1169 + ,0 + ,4.6 + ,71.3 + ,18.0 + ,2154 + ,1 + ,7.2 + ,167.8 + ,20.0 + ,2249 + ,0 + ,7.8 + ,66.3 + ,14.0 + ,2687 + ,0 + ,9.9 + ,41.9 + ,12.0 + ,4359 + ,0 + ,13.6 + ,57.2 + ,20.0 + ,5382 + ,0 + ,17.1 + ,72.3 + ,14.0 + ,4459 + ,0 + ,17.8 + ,96.5 + ,16.0 + ,6398 + ,0 + ,18.6 + ,172.1 + ,19.0 + ,4596 + ,0 + ,14.7 + ,25.8 + ,12.0 + ,3024 + ,0 + ,10.5 + ,105.1 + ,17.0 + ,1887 + ,0 + ,8.6 + ,92.2 + ,16.0 + ,2070 + ,0 + ,4.4 + ,109.3 + ,18.0 + ,1351 + ,0 + ,2.3 + ,101.7 + ,19.0 + ,2218 + ,0 + ,2.8 + ,29.1 + ,8.0 + ,2461 + ,1 + ,8.8 + ,34.6 + ,10.0 + ,3028 + ,0 + ,10.7 + ,46.7 + ,10.0 + ,4784 + ,0 + ,13.9 + ,82.0 + ,19.0 + ,4975 + ,0 + ,19.3 + ,34.4 + ,8.0 + ,4607 + ,0 + ,19.5 + ,72.7 + ,13.0 + ,6249 + ,0 + ,20.4 + ,44.4 + ,8.0 + ,4809 + ,0 + ,15.3 + ,31.0 + ,12.0 + ,3157 + ,0 + ,7.9 + ,64.0 + ,15.0 + ,1910 + ,0 + ,8.3 + ,65.4 + ,18.0 + ,2228 + ,0 + ,4.5 + ,64.5 + ,17.0 + ,1594 + ,0 + ,3.2 + ,153.8 + ,24.0 + ,2467 + ,0 + ,5.0 + ,48.8 + ,14.0 + ,2222 + ,0 + ,6.6 + ,25.0 + ,15.0 + ,3607 + ,1 + ,11.1 + ,37.2 + ,15.0 + ,4685 + ,0 + ,12.8 + ,40.8 + ,11.0 + ,4962 + ,0 + ,16.3 + ,78.4 + ,18.0 + ,5770 + ,0 + ,17.4 + ,112.4 + ,18.0 + ,5480 + ,0 + ,18.9 + ,122.7 + ,21.0 + ,5000 + ,0 + ,15.8 + ,82.9 + ,13.0 + ,3228 + ,0 + ,11.7 + ,67.6 + ,15.0 + ,1993 + ,0 + ,6.4 + ,78.4 + ,17.0 + ,2288 + ,0 + ,2.9 + ,65.7 + ,17.0 + ,1580 + ,0 + ,4.7 + ,44.9 + ,22.0 + ,2111 + ,0 + ,2.4 + ,80.9 + ,19.0 + ,2192 + ,0 + ,7.2 + ,38.8 + ,17.0 + ,3601 + ,0 + ,10.7 + ,46.1 + ,17.0 + ,4665 + ,1 + ,13.4 + ,60.0 + ,19.0 + ,4876 + ,0 + ,18.5 + ,53.9 + ,11.0 + ,5813 + ,0 + ,18.3 + ,123.5 + ,16.0 + ,5589 + ,0 + ,16.8 + ,69.5 + ,15.0 + ,5331 + ,0 + ,16.6 + ,74.2 + ,11.0 + ,3075 + ,0 + ,14.1 + ,47.0 + ,13.0 + ,2002 + ,0 + ,6.1 + ,60.9 + ,18.0 + ,2306 + ,0 + ,3.5 + ,51.4 + ,22.0 + ,1507 + ,0 + ,1.7 + ,18.7 + ,9.0 + ,1992 + ,0 + ,2.3 + ,88.1 + ,19.0 + ,2487 + ,0 + ,4.5 + ,65.3 + ,16.0 + ,3490 + ,0 + ,9.3 + ,46.0 + ,16.0 + ,4647 + ,0 + ,14.2 + ,115.6 + ,20.0 + ,5594 + ,1 + ,17.3 + ,25.8 + ,7.0 + ,5611 + ,0 + ,23.0 + ,48.1 + ,8.0 + ,5788 + ,0 + ,16.3 + ,202.3 + ,21.0 + ,6204 + ,0 + ,18.4 + ,9.2 + ,8.0 + ,3013 + ,0 + ,14.2 + ,56.3 + ,17.0 + ,1931 + ,0 + ,9.1 + ,71.6 + ,20.0 + ,2549 + ,0 + ,5.9 + ,93.0 + ,18.0 + ,1504 + ,0 + ,7.2 + ,82.3 + ,26.0 + ,2090 + ,0 + ,6.8 + ,95.4 + ,18.0 + ,2702 + ,0 + ,8.0 + ,61.9 + ,20.0 + ,2939 + ,0 + ,14.3 + ,0.0 + ,0.0 + ,4500 + ,0 + ,14.6 + ,103.4 + ,22.0 + ,6208 + ,0 + ,17.5 + ,99.2 + ,19.0 + ,6415 + ,1 + ,17.2 + ,96.7 + ,18.0 + ,5657 + ,0 + ,17.2 + ,56.9 + ,13.0 + ,5964 + ,0 + ,14.1 + ,57.6 + ,16.0 + ,3163 + ,0 + ,10.5 + ,65.2 + ,11.0 + ,1997 + ,0 + ,6.8 + ,71.7 + ,22.0 + ,2422 + ,0 + ,4.1 + ,89.2 + ,19.0 + ,1376 + ,0 + ,6.5 + ,70.7 + ,23.0 + ,2202 + ,0 + ,6.1 + ,35.4 + ,11.0 + ,2683 + ,0 + ,6.3 + ,140.5 + ,24.0 + ,3303 + ,0 + ,9.3 + ,45.4 + ,14.0 + ,5202 + ,0 + ,16.4 + ,53.9 + ,11.0 + ,5231 + ,0 + ,16.1 + ,69.9 + ,17.0 + ,4880 + ,0 + ,18.0 + ,101.9 + ,20.0 + ,7998 + ,1 + ,17.6 + ,89.3 + ,19.0 + ,4977 + ,0 + ,14.0 + ,70.7 + ,12.0 + ,3531 + ,0 + ,10.5 + ,72.4 + ,19.0 + ,2025 + ,0 + ,6.9 + ,67.6 + ,26.0 + ,2205 + ,0 + ,2.8 + ,43.3 + ,13.0 + ,1442 + ,0 + ,0.7 + ,62.9 + ,12.0 + ,2238 + ,0 + ,3.6 + ,57.1 + ,20.0 + ,2179 + ,0 + ,6.7 + ,68.2 + ,15.0 + ,3218 + ,0 + ,12.5 + ,47.1 + ,15.0 + ,5139 + ,0 + ,14.4 + ,43.1 + ,17.0 + ,4990 + ,0 + ,16.5 + ,64.5 + ,11.0 + ,4914 + ,0 + ,18.7 + ,73.1 + ,20.0 + ,6084 + ,0 + ,19.4 + ,37.7 + ,9.0 + ,5672 + ,1 + ,15.8 + ,29.1 + ,10.0 + ,3548 + ,0 + ,11.3 + ,105.0 + ,17.0 + ,1793 + ,0 + ,9.7 + ,98.0 + ,25.0 + ,2086 + ,0 + ,2.9 + ,80.8 + ,19.0) + ,dim=c(5 + ,120) + ,dimnames=list(c('Huwelijken' + ,'Specialedag' + ,'Temperatuur' + ,'Neerslag' + ,'Neerslagdagen ') + ,1:120)) > y <- array(NA,dim=c(5,120),dimnames=list(c('Huwelijken','Specialedag','Temperatuur','Neerslag','Neerslagdagen '),1:120)) > 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 = '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 Huwelijken Specialedag Temperatuur Neerslag Neerslagdagen\r 1 1579 0 4.0 45.7 17 2 2146 0 5.9 81.9 21 3 2462 0 7.1 56.8 21 4 3695 0 10.5 65.1 18 5 4831 0 15.1 86.2 20 6 5134 0 16.8 35.1 11 7 6250 0 15.3 133.8 20 8 5760 0 18.4 34.5 13 9 6249 0 16.1 69.9 14 10 2917 0 11.3 98.3 23 11 1741 0 7.9 86.7 24 12 2359 0 5.6 58.2 22 13 1511 1 3.4 83.6 17 14 2059 0 4.8 83.5 18 15 2635 0 6.5 112.3 24 16 2867 0 8.5 134.3 23 17 4403 0 15.1 30.0 8 18 5720 0 15.7 44.5 10 19 4502 0 18.7 120.1 18 20 5749 0 19.2 43.4 13 21 5627 0 12.9 199.4 23 22 2846 0 14.4 68.1 14 23 1762 0 6.2 99.8 15 24 2429 0 3.3 69.5 18 25 1169 0 4.6 71.3 18 26 2154 1 7.2 167.8 20 27 2249 0 7.8 66.3 14 28 2687 0 9.9 41.9 12 29 4359 0 13.6 57.2 20 30 5382 0 17.1 72.3 14 31 4459 0 17.8 96.5 16 32 6398 0 18.6 172.1 19 33 4596 0 14.7 25.8 12 34 3024 0 10.5 105.1 17 35 1887 0 8.6 92.2 16 36 2070 0 4.4 109.3 18 37 1351 0 2.3 101.7 19 38 2218 0 2.8 29.1 8 39 2461 1 8.8 34.6 10 40 3028 0 10.7 46.7 10 41 4784 0 13.9 82.0 19 42 4975 0 19.3 34.4 8 43 4607 0 19.5 72.7 13 44 6249 0 20.4 44.4 8 45 4809 0 15.3 31.0 12 46 3157 0 7.9 64.0 15 47 1910 0 8.3 65.4 18 48 2228 0 4.5 64.5 17 49 1594 0 3.2 153.8 24 50 2467 0 5.0 48.8 14 51 2222 0 6.6 25.0 15 52 3607 1 11.1 37.2 15 53 4685 0 12.8 40.8 11 54 4962 0 16.3 78.4 18 55 5770 0 17.4 112.4 18 56 5480 0 18.9 122.7 21 57 5000 0 15.8 82.9 13 58 3228 0 11.7 67.6 15 59 1993 0 6.4 78.4 17 60 2288 0 2.9 65.7 17 61 1580 0 4.7 44.9 22 62 2111 0 2.4 80.9 19 63 2192 0 7.2 38.8 17 64 3601 0 10.7 46.1 17 65 4665 1 13.4 60.0 19 66 4876 0 18.5 53.9 11 67 5813 0 18.3 123.5 16 68 5589 0 16.8 69.5 15 69 5331 0 16.6 74.2 11 70 3075 0 14.1 47.0 13 71 2002 0 6.1 60.9 18 72 2306 0 3.5 51.4 22 73 1507 0 1.7 18.7 9 74 1992 0 2.3 88.1 19 75 2487 0 4.5 65.3 16 76 3490 0 9.3 46.0 16 77 4647 0 14.2 115.6 20 78 5594 1 17.3 25.8 7 79 5611 0 23.0 48.1 8 80 5788 0 16.3 202.3 21 81 6204 0 18.4 9.2 8 82 3013 0 14.2 56.3 17 83 1931 0 9.1 71.6 20 84 2549 0 5.9 93.0 18 85 1504 0 7.2 82.3 26 86 2090 0 6.8 95.4 18 87 2702 0 8.0 61.9 20 88 2939 0 14.3 0.0 0 89 4500 0 14.6 103.4 22 90 6208 0 17.5 99.2 19 91 6415 1 17.2 96.7 18 92 5657 0 17.2 56.9 13 93 5964 0 14.1 57.6 16 94 3163 0 10.5 65.2 11 95 1997 0 6.8 71.7 22 96 2422 0 4.1 89.2 19 97 1376 0 6.5 70.7 23 98 2202 0 6.1 35.4 11 99 2683 0 6.3 140.5 24 100 3303 0 9.3 45.4 14 101 5202 0 16.4 53.9 11 102 5231 0 16.1 69.9 17 103 4880 0 18.0 101.9 20 104 7998 1 17.6 89.3 19 105 4977 0 14.0 70.7 12 106 3531 0 10.5 72.4 19 107 2025 0 6.9 67.6 26 108 2205 0 2.8 43.3 13 109 1442 0 0.7 62.9 12 110 2238 0 3.6 57.1 20 111 2179 0 6.7 68.2 15 112 3218 0 12.5 47.1 15 113 5139 0 14.4 43.1 17 114 4990 0 16.5 64.5 11 115 4914 0 18.7 73.1 20 116 6084 0 19.4 37.7 9 117 5672 1 15.8 29.1 10 118 3548 0 11.3 105.0 17 119 1793 0 9.7 98.0 25 120 2086 0 2.9 80.8 19 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Specialedag Temperatuur Neerslag 783.758 510.481 253.377 4.649 `Neerslagdagen\r` -20.739 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1612.6 -496.0 93.9 439.7 2223.3 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 783.758 329.571 2.378 0.0191 * Specialedag 510.481 245.913 2.076 0.0401 * Temperatuur 253.377 12.923 19.606 <2e-16 *** Neerslag 4.649 2.463 1.887 0.0617 . `Neerslagdagen\r` -20.739 19.712 -1.052 0.2949 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 706.4 on 115 degrees of freedom Multiple R-squared: 0.8169, Adjusted R-squared: 0.8106 F-statistic: 128.3 on 4 and 115 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,] 0.09815852 0.1963170 0.9018415 [2,] 0.13633150 0.2726630 0.8636685 [3,] 0.25157355 0.5031471 0.7484264 [4,] 0.20197447 0.4039489 0.7980255 [5,] 0.33024797 0.6604959 0.6697520 [6,] 0.23740711 0.4748142 0.7625929 [7,] 0.20255118 0.4051024 0.7974488 [8,] 0.13847034 0.2769407 0.8615297 [9,] 0.13546767 0.2709353 0.8645323 [10,] 0.19280529 0.3856106 0.8071947 [11,] 0.16229410 0.3245882 0.8377059 [12,] 0.52157105 0.9568579 0.4784289 [13,] 0.44285521 0.8857104 0.5571448 [14,] 0.44955053 0.8991011 0.5504495 [15,] 0.81840755 0.3631849 0.1815924 [16,] 0.84219843 0.3156031 0.1578016 [17,] 0.85999141 0.2800172 0.1400086 [18,] 0.85697287 0.2860543 0.1430271 [19,] 0.87995956 0.2400809 0.1200404 [20,] 0.86438229 0.2712354 0.1356177 [21,] 0.84551013 0.3089797 0.1544899 [22,] 0.81012284 0.3797543 0.1898772 [23,] 0.76615944 0.4676811 0.2338406 [24,] 0.81434689 0.3713062 0.1856531 [25,] 0.78036599 0.4392680 0.2196340 [26,] 0.73842394 0.5231521 0.2615761 [27,] 0.71950702 0.5609860 0.2804930 [28,] 0.78841073 0.4231785 0.2115893 [29,] 0.74842293 0.5031541 0.2515771 [30,] 0.70381046 0.5923791 0.2961895 [31,] 0.72608227 0.5478355 0.2739177 [32,] 0.75390849 0.4921830 0.2460915 [33,] 0.73056151 0.5388770 0.2694385 [34,] 0.70826127 0.5834775 0.2917387 [35,] 0.70407138 0.5918572 0.2959286 [36,] 0.78048558 0.4390288 0.2195144 [37,] 0.74732116 0.5053577 0.2526788 [38,] 0.71162153 0.5767569 0.2883785 [39,] 0.67829313 0.6434137 0.3217069 [40,] 0.70912501 0.5817500 0.2908750 [41,] 0.67340459 0.6531908 0.3265954 [42,] 0.63962649 0.7207470 0.3603735 [43,] 0.61102037 0.7779593 0.3889796 [44,] 0.55733109 0.8853378 0.4426689 [45,] 0.56761818 0.8647636 0.4323818 [46,] 0.57337769 0.8532446 0.4266223 [47,] 0.52097062 0.9580588 0.4790294 [48,] 0.48917211 0.9783442 0.5108279 [49,] 0.43929972 0.8785994 0.5607003 [50,] 0.38738325 0.7747665 0.6126168 [51,] 0.36571947 0.7314389 0.6342805 [52,] 0.34085649 0.6817130 0.6591435 [53,] 0.35148964 0.7029793 0.6485104 [54,] 0.30399108 0.6079822 0.6960089 [55,] 0.29918894 0.5983779 0.7008111 [56,] 0.25874744 0.5174949 0.7412526 [57,] 0.22401297 0.4480259 0.7759870 [58,] 0.22997136 0.4599427 0.7700286 [59,] 0.21504208 0.4300842 0.7849579 [60,] 0.17946953 0.3589391 0.8205305 [61,] 0.16966826 0.3393365 0.8303317 [62,] 0.14145722 0.2829144 0.8585428 [63,] 0.20890507 0.4178101 0.7910949 [64,] 0.17728471 0.3545694 0.8227153 [65,] 0.19216825 0.3843365 0.8078318 [66,] 0.16474584 0.3294917 0.8352542 [67,] 0.14944377 0.2988875 0.8505562 [68,] 0.13687010 0.2737402 0.8631299 [69,] 0.12263559 0.2452712 0.8773644 [70,] 0.09713451 0.1942690 0.9028655 [71,] 0.11119252 0.2223850 0.8888075 [72,] 0.14957463 0.2991493 0.8504254 [73,] 0.12405237 0.2481047 0.8759476 [74,] 0.15015573 0.3003115 0.8498443 [75,] 0.22810040 0.4562008 0.7718996 [76,] 0.28718841 0.5743768 0.7128116 [77,] 0.24062210 0.4812442 0.7593779 [78,] 0.27268268 0.5453654 0.7273173 [79,] 0.25742397 0.5148479 0.7425760 [80,] 0.21071002 0.4214200 0.7892900 [81,] 0.48221534 0.9644307 0.5177847 [82,] 0.41940696 0.8388139 0.5805930 [83,] 0.47657419 0.9531484 0.5234258 [84,] 0.47051263 0.9410253 0.5294874 [85,] 0.43035584 0.8607117 0.5696442 [86,] 0.76012532 0.4797494 0.2398747 [87,] 0.75243948 0.4951210 0.2475605 [88,] 0.70413964 0.5917207 0.2958604 [89,] 0.67065390 0.6586922 0.3293461 [90,] 0.70096657 0.5980669 0.2990334 [91,] 0.65937238 0.6812552 0.3406276 [92,] 0.59615411 0.8076918 0.4038459 [93,] 0.51921049 0.9615790 0.4807895 [94,] 0.44116327 0.8823265 0.5588367 [95,] 0.40164257 0.8032851 0.5983574 [96,] 0.33155675 0.6631135 0.6684433 [97,] 0.87505148 0.2498970 0.1249485 [98,] 0.86489192 0.2702162 0.1351081 [99,] 0.81701411 0.3659718 0.1829859 [100,] 0.73678110 0.5264378 0.2632189 [101,] 0.64250157 0.7149969 0.3574984 [102,] 0.54234851 0.9153030 0.4576515 [103,] 0.45610659 0.9122132 0.5438934 [104,] 0.40144514 0.8028903 0.5985549 [105,] 0.66732170 0.6653566 0.3326783 > postscript(file="/var/www/html/rcomp/tmp/1l94s1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2eild1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3eild1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4eild1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/56r2g1292285051.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 = 120 Frequency = 1 1 2 3 4 5 6 -78.133449 -77.868044 50.757212 321.475412 235.337130 158.481228 7 8 9 10 11 12 1382.392944 423.346020 1351.295010 -709.859827 -949.716596 342.053885 13 14 15 16 17 18 -680.766213 44.190922 180.009264 -217.750836 -180.288720 958.760493 19 20 21 22 23 24 -1204.882438 168.272856 1124.773414 -1612.597052 -745.525232 859.335248 25 26 27 28 29 30 -738.421954 -1329.784344 -528.942592 -551.089081 278.209025 219.761757 31 32 33 34 35 36 -951.617208 495.472325 216.543005 -556.204089 -1172.561699 36.610241 37 38 39 40 41 42 -94.230441 755.430081 -1016.399653 -476.582011 491.173742 -692.924874 43 44 45 46 47 48 -1185.941381 255.875555 253.344676 385.150602 -907.490089 356.786244 49 50 51 52 53 54 -217.760100 479.861394 -39.167826 -361.555913 696.492117 57.064622 55 56 57 58 59 60 428.300941 -227.425966 97.138185 -523.416011 -424.243968 816.610985 61 62 63 64 65 66 -147.081847 737.120770 -243.864659 244.382313 90.648718 -617.651288 67 68 69 70 71 72 150.184748 536.529940 224.400067 -1230.239870 -237.142768 852.755082 73 74 75 76 77 78 392.228371 609.989222 591.328123 467.835424 142.710253 -58.413915 79 80 81 82 83 84 -1058.103699 369.332856 881.256599 -1277.851378 -1076.533656 211.315603 85 86 87 88 89 90 -947.420752 -486.879965 18.271364 -1468.046649 -7.450093 923.062834 91 92 93 94 95 96 686.477285 520.271727 1671.703933 -356.164660 -386.753137 578.797552 97 98 99 100 101 102 -906.352262 -63.781122 147.596803 242.145892 240.440079 395.512962 103 104 105 106 107 108 -523.437231 2223.264800 566.188938 144.280646 -282.074683 780.117902 109 110 111 112 113 114 437.359262 691.442287 -308.320908 -640.823140 858.833515 -46.171754 115 116 117 118 119 120 -532.924088 396.136695 446.529234 -234.440711 -1385.583695 585.897200 > postscript(file="/var/www/html/rcomp/tmp/66r2g1292285051.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 = 120 Frequency = 1 lag(myerror, k = 1) myerror 0 -78.133449 NA 1 -77.868044 -78.133449 2 50.757212 -77.868044 3 321.475412 50.757212 4 235.337130 321.475412 5 158.481228 235.337130 6 1382.392944 158.481228 7 423.346020 1382.392944 8 1351.295010 423.346020 9 -709.859827 1351.295010 10 -949.716596 -709.859827 11 342.053885 -949.716596 12 -680.766213 342.053885 13 44.190922 -680.766213 14 180.009264 44.190922 15 -217.750836 180.009264 16 -180.288720 -217.750836 17 958.760493 -180.288720 18 -1204.882438 958.760493 19 168.272856 -1204.882438 20 1124.773414 168.272856 21 -1612.597052 1124.773414 22 -745.525232 -1612.597052 23 859.335248 -745.525232 24 -738.421954 859.335248 25 -1329.784344 -738.421954 26 -528.942592 -1329.784344 27 -551.089081 -528.942592 28 278.209025 -551.089081 29 219.761757 278.209025 30 -951.617208 219.761757 31 495.472325 -951.617208 32 216.543005 495.472325 33 -556.204089 216.543005 34 -1172.561699 -556.204089 35 36.610241 -1172.561699 36 -94.230441 36.610241 37 755.430081 -94.230441 38 -1016.399653 755.430081 39 -476.582011 -1016.399653 40 491.173742 -476.582011 41 -692.924874 491.173742 42 -1185.941381 -692.924874 43 255.875555 -1185.941381 44 253.344676 255.875555 45 385.150602 253.344676 46 -907.490089 385.150602 47 356.786244 -907.490089 48 -217.760100 356.786244 49 479.861394 -217.760100 50 -39.167826 479.861394 51 -361.555913 -39.167826 52 696.492117 -361.555913 53 57.064622 696.492117 54 428.300941 57.064622 55 -227.425966 428.300941 56 97.138185 -227.425966 57 -523.416011 97.138185 58 -424.243968 -523.416011 59 816.610985 -424.243968 60 -147.081847 816.610985 61 737.120770 -147.081847 62 -243.864659 737.120770 63 244.382313 -243.864659 64 90.648718 244.382313 65 -617.651288 90.648718 66 150.184748 -617.651288 67 536.529940 150.184748 68 224.400067 536.529940 69 -1230.239870 224.400067 70 -237.142768 -1230.239870 71 852.755082 -237.142768 72 392.228371 852.755082 73 609.989222 392.228371 74 591.328123 609.989222 75 467.835424 591.328123 76 142.710253 467.835424 77 -58.413915 142.710253 78 -1058.103699 -58.413915 79 369.332856 -1058.103699 80 881.256599 369.332856 81 -1277.851378 881.256599 82 -1076.533656 -1277.851378 83 211.315603 -1076.533656 84 -947.420752 211.315603 85 -486.879965 -947.420752 86 18.271364 -486.879965 87 -1468.046649 18.271364 88 -7.450093 -1468.046649 89 923.062834 -7.450093 90 686.477285 923.062834 91 520.271727 686.477285 92 1671.703933 520.271727 93 -356.164660 1671.703933 94 -386.753137 -356.164660 95 578.797552 -386.753137 96 -906.352262 578.797552 97 -63.781122 -906.352262 98 147.596803 -63.781122 99 242.145892 147.596803 100 240.440079 242.145892 101 395.512962 240.440079 102 -523.437231 395.512962 103 2223.264800 -523.437231 104 566.188938 2223.264800 105 144.280646 566.188938 106 -282.074683 144.280646 107 780.117902 -282.074683 108 437.359262 780.117902 109 691.442287 437.359262 110 -308.320908 691.442287 111 -640.823140 -308.320908 112 858.833515 -640.823140 113 -46.171754 858.833515 114 -532.924088 -46.171754 115 396.136695 -532.924088 116 446.529234 396.136695 117 -234.440711 446.529234 118 -1385.583695 -234.440711 119 585.897200 -1385.583695 120 NA 585.897200 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -77.868044 -78.133449 [2,] 50.757212 -77.868044 [3,] 321.475412 50.757212 [4,] 235.337130 321.475412 [5,] 158.481228 235.337130 [6,] 1382.392944 158.481228 [7,] 423.346020 1382.392944 [8,] 1351.295010 423.346020 [9,] -709.859827 1351.295010 [10,] -949.716596 -709.859827 [11,] 342.053885 -949.716596 [12,] -680.766213 342.053885 [13,] 44.190922 -680.766213 [14,] 180.009264 44.190922 [15,] -217.750836 180.009264 [16,] -180.288720 -217.750836 [17,] 958.760493 -180.288720 [18,] -1204.882438 958.760493 [19,] 168.272856 -1204.882438 [20,] 1124.773414 168.272856 [21,] -1612.597052 1124.773414 [22,] -745.525232 -1612.597052 [23,] 859.335248 -745.525232 [24,] -738.421954 859.335248 [25,] -1329.784344 -738.421954 [26,] -528.942592 -1329.784344 [27,] -551.089081 -528.942592 [28,] 278.209025 -551.089081 [29,] 219.761757 278.209025 [30,] -951.617208 219.761757 [31,] 495.472325 -951.617208 [32,] 216.543005 495.472325 [33,] -556.204089 216.543005 [34,] -1172.561699 -556.204089 [35,] 36.610241 -1172.561699 [36,] -94.230441 36.610241 [37,] 755.430081 -94.230441 [38,] -1016.399653 755.430081 [39,] -476.582011 -1016.399653 [40,] 491.173742 -476.582011 [41,] -692.924874 491.173742 [42,] -1185.941381 -692.924874 [43,] 255.875555 -1185.941381 [44,] 253.344676 255.875555 [45,] 385.150602 253.344676 [46,] -907.490089 385.150602 [47,] 356.786244 -907.490089 [48,] -217.760100 356.786244 [49,] 479.861394 -217.760100 [50,] -39.167826 479.861394 [51,] -361.555913 -39.167826 [52,] 696.492117 -361.555913 [53,] 57.064622 696.492117 [54,] 428.300941 57.064622 [55,] -227.425966 428.300941 [56,] 97.138185 -227.425966 [57,] -523.416011 97.138185 [58,] -424.243968 -523.416011 [59,] 816.610985 -424.243968 [60,] -147.081847 816.610985 [61,] 737.120770 -147.081847 [62,] -243.864659 737.120770 [63,] 244.382313 -243.864659 [64,] 90.648718 244.382313 [65,] -617.651288 90.648718 [66,] 150.184748 -617.651288 [67,] 536.529940 150.184748 [68,] 224.400067 536.529940 [69,] -1230.239870 224.400067 [70,] -237.142768 -1230.239870 [71,] 852.755082 -237.142768 [72,] 392.228371 852.755082 [73,] 609.989222 392.228371 [74,] 591.328123 609.989222 [75,] 467.835424 591.328123 [76,] 142.710253 467.835424 [77,] -58.413915 142.710253 [78,] -1058.103699 -58.413915 [79,] 369.332856 -1058.103699 [80,] 881.256599 369.332856 [81,] -1277.851378 881.256599 [82,] -1076.533656 -1277.851378 [83,] 211.315603 -1076.533656 [84,] -947.420752 211.315603 [85,] -486.879965 -947.420752 [86,] 18.271364 -486.879965 [87,] -1468.046649 18.271364 [88,] -7.450093 -1468.046649 [89,] 923.062834 -7.450093 [90,] 686.477285 923.062834 [91,] 520.271727 686.477285 [92,] 1671.703933 520.271727 [93,] -356.164660 1671.703933 [94,] -386.753137 -356.164660 [95,] 578.797552 -386.753137 [96,] -906.352262 578.797552 [97,] -63.781122 -906.352262 [98,] 147.596803 -63.781122 [99,] 242.145892 147.596803 [100,] 240.440079 242.145892 [101,] 395.512962 240.440079 [102,] -523.437231 395.512962 [103,] 2223.264800 -523.437231 [104,] 566.188938 2223.264800 [105,] 144.280646 566.188938 [106,] -282.074683 144.280646 [107,] 780.117902 -282.074683 [108,] 437.359262 780.117902 [109,] 691.442287 437.359262 [110,] -308.320908 691.442287 [111,] -640.823140 -308.320908 [112,] 858.833515 -640.823140 [113,] -46.171754 858.833515 [114,] -532.924088 -46.171754 [115,] 396.136695 -532.924088 [116,] 446.529234 396.136695 [117,] -234.440711 446.529234 [118,] -1385.583695 -234.440711 [119,] 585.897200 -1385.583695 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -77.868044 -78.133449 2 50.757212 -77.868044 3 321.475412 50.757212 4 235.337130 321.475412 5 158.481228 235.337130 6 1382.392944 158.481228 7 423.346020 1382.392944 8 1351.295010 423.346020 9 -709.859827 1351.295010 10 -949.716596 -709.859827 11 342.053885 -949.716596 12 -680.766213 342.053885 13 44.190922 -680.766213 14 180.009264 44.190922 15 -217.750836 180.009264 16 -180.288720 -217.750836 17 958.760493 -180.288720 18 -1204.882438 958.760493 19 168.272856 -1204.882438 20 1124.773414 168.272856 21 -1612.597052 1124.773414 22 -745.525232 -1612.597052 23 859.335248 -745.525232 24 -738.421954 859.335248 25 -1329.784344 -738.421954 26 -528.942592 -1329.784344 27 -551.089081 -528.942592 28 278.209025 -551.089081 29 219.761757 278.209025 30 -951.617208 219.761757 31 495.472325 -951.617208 32 216.543005 495.472325 33 -556.204089 216.543005 34 -1172.561699 -556.204089 35 36.610241 -1172.561699 36 -94.230441 36.610241 37 755.430081 -94.230441 38 -1016.399653 755.430081 39 -476.582011 -1016.399653 40 491.173742 -476.582011 41 -692.924874 491.173742 42 -1185.941381 -692.924874 43 255.875555 -1185.941381 44 253.344676 255.875555 45 385.150602 253.344676 46 -907.490089 385.150602 47 356.786244 -907.490089 48 -217.760100 356.786244 49 479.861394 -217.760100 50 -39.167826 479.861394 51 -361.555913 -39.167826 52 696.492117 -361.555913 53 57.064622 696.492117 54 428.300941 57.064622 55 -227.425966 428.300941 56 97.138185 -227.425966 57 -523.416011 97.138185 58 -424.243968 -523.416011 59 816.610985 -424.243968 60 -147.081847 816.610985 61 737.120770 -147.081847 62 -243.864659 737.120770 63 244.382313 -243.864659 64 90.648718 244.382313 65 -617.651288 90.648718 66 150.184748 -617.651288 67 536.529940 150.184748 68 224.400067 536.529940 69 -1230.239870 224.400067 70 -237.142768 -1230.239870 71 852.755082 -237.142768 72 392.228371 852.755082 73 609.989222 392.228371 74 591.328123 609.989222 75 467.835424 591.328123 76 142.710253 467.835424 77 -58.413915 142.710253 78 -1058.103699 -58.413915 79 369.332856 -1058.103699 80 881.256599 369.332856 81 -1277.851378 881.256599 82 -1076.533656 -1277.851378 83 211.315603 -1076.533656 84 -947.420752 211.315603 85 -486.879965 -947.420752 86 18.271364 -486.879965 87 -1468.046649 18.271364 88 -7.450093 -1468.046649 89 923.062834 -7.450093 90 686.477285 923.062834 91 520.271727 686.477285 92 1671.703933 520.271727 93 -356.164660 1671.703933 94 -386.753137 -356.164660 95 578.797552 -386.753137 96 -906.352262 578.797552 97 -63.781122 -906.352262 98 147.596803 -63.781122 99 242.145892 147.596803 100 240.440079 242.145892 101 395.512962 240.440079 102 -523.437231 395.512962 103 2223.264800 -523.437231 104 566.188938 2223.264800 105 144.280646 566.188938 106 -282.074683 144.280646 107 780.117902 -282.074683 108 437.359262 780.117902 109 691.442287 437.359262 110 -308.320908 691.442287 111 -640.823140 -308.320908 112 858.833515 -640.823140 113 -46.171754 858.833515 114 -532.924088 -46.171754 115 396.136695 -532.924088 116 446.529234 396.136695 117 -234.440711 446.529234 118 -1385.583695 -234.440711 119 585.897200 -1385.583695 > 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/7hiji1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8hiji1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9ss1l1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10ss1l1292285051.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11dah91292285051.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/12ztgx1292285051.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/13nud91292285051.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/14y3uc1292285051.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/15j3s01292285051.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/16gd891292285051.tab") + } > > try(system("convert tmp/1l94s1292285051.ps tmp/1l94s1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/2eild1292285051.ps tmp/2eild1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/3eild1292285051.ps tmp/3eild1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/4eild1292285051.ps tmp/4eild1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/56r2g1292285051.ps tmp/56r2g1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/66r2g1292285051.ps tmp/66r2g1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/7hiji1292285051.ps tmp/7hiji1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/8hiji1292285051.ps tmp/8hiji1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/9ss1l1292285051.ps tmp/9ss1l1292285051.png",intern=TRUE)) character(0) > try(system("convert tmp/10ss1l1292285051.ps tmp/10ss1l1292285051.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.348 1.756 11.363