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(100.36 + ,0 + ,100.21 + ,100.62 + ,0 + ,100.36 + ,100.78 + ,0 + ,100.62 + ,100.93 + ,0 + ,100.78 + ,100.70 + ,0 + ,100.93 + ,100.00 + ,0 + ,100.70 + ,100.20 + ,0 + ,100.00 + ,99.68 + ,0 + ,100.20 + ,99.56 + ,0 + ,99.68 + ,100.06 + ,0 + ,99.56 + ,100.50 + ,0 + ,100.06 + ,99.30 + ,0 + ,100.50 + ,99.37 + ,0 + ,99.30 + ,99.20 + ,0 + ,99.37 + ,98.11 + ,0 + ,99.20 + ,97.60 + ,0 + ,98.11 + ,97.76 + ,0 + ,97.60 + ,98.06 + ,0 + ,97.76 + ,98.25 + ,0 + ,98.06 + ,98.50 + ,0 + ,98.25 + ,97.39 + ,0 + ,98.50 + ,98.09 + ,0 + ,97.39 + ,97.78 + ,0 + ,98.09 + ,98.12 + ,0 + ,97.78 + ,97.50 + ,0 + ,98.12 + ,97.30 + ,0 + ,97.50 + ,97.64 + ,0 + ,97.30 + ,96.88 + ,0 + ,97.64 + ,97.40 + ,0 + ,96.88 + ,98.27 + ,0 + ,97.40 + ,97.94 + ,0 + ,98.27 + ,98.61 + ,0 + ,97.94 + ,98.72 + ,0 + ,98.61 + ,98.62 + ,0 + ,98.72 + ,98.56 + ,0 + ,98.62 + ,98.06 + ,0 + ,98.56 + ,97.40 + ,0 + ,98.06 + ,97.76 + ,0 + ,97.40 + ,97.05 + ,0 + ,97.76 + ,97.85 + ,0 + ,97.05 + ,97.40 + ,0 + ,97.85 + 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,145.03 + ,147.39 + ,0 + ,146.05 + ,149.58 + ,0 + ,147.39 + ,151.02 + ,0 + ,149.58 + ,153.57 + ,0 + ,151.02 + ,155.60 + ,0 + ,153.57 + ,157.18 + ,0 + ,155.60 + ,158.77 + ,0 + ,157.18 + ,159.95 + ,0 + ,158.77 + ,161.34 + ,0 + ,159.95 + ,161.95 + ,0 + ,161.34 + ,163.36 + ,0 + ,161.95 + ,165.00 + ,0 + ,163.36 + ,166.65 + ,0 + ,165.00 + ,168.65 + ,0 + ,166.65 + ,170.29 + ,0 + ,168.65 + ,172.70 + ,0 + ,170.29 + ,173.79 + ,0 + ,172.70 + ,176.45 + ,0 + ,173.79 + ,177.58 + ,0 + ,176.45 + ,179.19 + ,0 + ,177.58 + ,181.01 + ,0 + ,179.19 + ,184.08 + ,0 + ,181.01 + ,185.63 + ,0 + ,184.08 + ,188.51 + ,0 + ,185.63 + ,190.18 + ,0 + ,188.51 + ,192.19 + ,0 + ,190.18 + ,193.47 + ,0 + ,192.19 + ,196.73 + ,0 + ,193.47 + ,200.39 + ,0 + ,196.73 + ,203.24 + ,0 + ,200.39 + ,205.53 + ,0 + ,203.24 + ,208.21 + ,0 + ,205.53 + ,208.88 + ,0 + ,208.21 + ,212.85 + ,0 + ,208.88 + ,216.41 + ,0 + ,212.85 + ,216.23 + ,0 + ,216.41 + ,219.27 + ,0 + ,216.23 + ,222.02 + ,0 + ,219.27 + ,224.89 + ,0 + ,222.02 + ,230.37 + ,0 + ,224.89 + ,232.29 + ,0 + ,230.37 + ,235.53 + ,0 + ,232.29 + ,236.92 + ,0 + ,235.53 + ,242.37 + ,0 + ,236.92 + ,242.75 + ,0 + ,242.37 + ,244.19 + ,0 + ,242.75 + ,247.94 + ,0 + ,244.19 + ,248.80 + ,0 + ,247.94 + ,250.18 + ,0 + ,248.80 + ,251.55 + ,0 + ,250.18 + ,254.40 + ,0 + ,251.55 + ,255.72 + ,0 + ,254.40 + ,257.69 + ,0 + ,255.72 + ,258.37 + ,0 + ,257.69 + ,258.22 + ,0 + ,258.37 + ,258.59 + ,0 + ,258.22 + ,257.45 + ,0 + ,258.59 + ,257.45 + ,0 + ,257.45 + ,256.73 + ,0 + ,257.45 + ,258.82 + ,0 + ,256.73 + ,257.99 + ,0 + ,258.82 + ,262.85 + ,0 + ,257.99 + ,262.58 + ,0 + ,262.85 + ,261.55 + ,0 + ,262.58 + ,261.25 + ,0 + ,261.55 + ,259.78 + ,1 + ,261.25 + ,256.26 + ,1 + ,259.78 + ,254.29 + ,1 + ,256.26 + ,248.50 + ,1 + ,254.29 + ,241.88 + ,1 + ,248.50 + ,238.53 + ,1 + ,241.88 + ,232.24 + ,1 + ,238.53 + ,232.46 + ,1 + ,232.24 + ,225.79 + ,1 + ,232.46 + ,221.63 + ,1 + ,225.79 + ,219.62 + ,1 + ,221.63 + ,215.94 + ,1 + ,219.62 + ,211.81 + ,1 + ,215.94 + ,205.57 + ,1 + ,211.81 + ,201.25 + ,1 + ,205.57 + ,194.70 + ,1 + ,201.25 + ,187.94 + ,1 + ,194.70 + ,185.61 + ,1 + ,187.94 + ,181.15 + ,1 + ,185.61 + ,186.50 + ,1 + ,181.15 + ,183.21 + ,1 + ,186.50 + ,182.61 + ,1 + ,183.21 + ,187.09 + ,1 + ,182.61 + ,189.10 + ,1 + ,187.09 + ,191.25 + ,1 + ,189.10 + ,190.74 + ,1 + ,191.25 + ,190.79 + ,1 + ,190.74) + ,dim=c(3 + ,215) + ,dimnames=list(c('Huizenprijs_Pacific' + ,'Dummy_Crisis' + ,'Y1') + ,1:215)) > y <- array(NA,dim=c(3,215),dimnames=list(c('Huizenprijs_Pacific','Dummy_Crisis','Y1'),1:215)) > 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 Huizenprijs_Pacific Dummy_Crisis Y1 t 1 100.36 0 100.21 1 2 100.62 0 100.36 2 3 100.78 0 100.62 3 4 100.93 0 100.78 4 5 100.70 0 100.93 5 6 100.00 0 100.70 6 7 100.20 0 100.00 7 8 99.68 0 100.20 8 9 99.56 0 99.68 9 10 100.06 0 99.56 10 11 100.50 0 100.06 11 12 99.30 0 100.50 12 13 99.37 0 99.30 13 14 99.20 0 99.37 14 15 98.11 0 99.20 15 16 97.60 0 98.11 16 17 97.76 0 97.60 17 18 98.06 0 97.76 18 19 98.25 0 98.06 19 20 98.50 0 98.25 20 21 97.39 0 98.50 21 22 98.09 0 97.39 22 23 97.78 0 98.09 23 24 98.12 0 97.78 24 25 97.50 0 98.12 25 26 97.30 0 97.50 26 27 97.64 0 97.30 27 28 96.88 0 97.64 28 29 97.40 0 96.88 29 30 98.27 0 97.40 30 31 97.94 0 98.27 31 32 98.61 0 97.94 32 33 98.72 0 98.61 33 34 98.62 0 98.72 34 35 98.56 0 98.62 35 36 98.06 0 98.56 36 37 97.40 0 98.06 37 38 97.76 0 97.40 38 39 97.05 0 97.76 39 40 97.85 0 97.05 40 41 97.40 0 97.85 41 42 97.27 0 97.40 42 43 97.93 0 97.27 43 44 98.60 0 97.93 44 45 98.70 0 98.60 45 46 98.88 0 98.70 46 47 98.27 0 98.88 47 48 97.85 0 98.27 48 49 97.70 0 97.85 49 50 96.97 0 97.70 50 51 97.72 0 96.97 51 52 97.66 0 97.72 52 53 99.00 0 97.66 53 54 98.86 0 99.00 54 55 99.56 0 98.86 55 56 100.19 0 99.56 56 57 100.37 0 100.19 57 58 100.01 0 100.37 58 59 99.68 0 100.01 59 60 99.78 0 99.68 60 61 99.36 0 99.78 61 62 99.21 0 99.36 62 63 99.26 0 99.21 63 64 99.26 0 99.26 64 65 100.43 0 99.26 65 66 101.50 0 100.43 66 67 102.27 0 101.50 67 68 102.69 0 102.27 68 69 103.47 0 102.69 69 70 104.02 0 103.47 70 71 103.55 0 104.02 71 72 103.77 0 103.55 72 73 104.19 0 103.77 73 74 103.64 0 104.19 74 75 103.68 0 103.64 75 76 105.39 0 103.68 76 77 106.61 0 105.39 77 78 108.12 0 106.61 78 79 109.22 0 108.12 79 80 110.17 0 109.22 80 81 110.31 0 110.17 81 82 111.06 0 110.31 82 83 111.14 0 111.06 83 84 111.39 0 111.14 84 85 112.51 0 111.39 85 86 111.28 0 112.51 86 87 112.22 0 111.28 87 88 113.19 0 112.22 88 89 114.32 0 113.19 89 90 115.34 0 114.32 90 91 116.61 0 115.34 91 92 117.83 0 116.61 92 93 117.70 0 117.83 93 94 118.51 0 117.70 94 95 118.82 0 118.51 95 96 119.49 0 118.82 96 97 119.57 0 119.49 97 98 120.00 0 119.57 98 99 121.96 0 120.00 99 100 121.45 0 121.96 100 101 123.41 0 121.45 101 102 124.44 0 123.41 102 103 126.25 0 124.44 103 104 127.41 0 126.25 104 105 127.63 0 127.41 105 106 129.19 0 127.63 106 107 129.82 0 129.19 107 108 130.45 0 129.82 108 109 132.02 0 130.45 109 110 132.72 0 132.02 110 111 132.96 0 132.72 111 112 135.06 0 132.96 112 113 137.04 0 135.06 113 114 137.83 0 137.04 114 115 139.17 0 137.83 115 116 140.35 0 139.17 116 117 141.01 0 140.35 117 118 141.89 0 141.01 118 119 143.28 0 141.89 119 120 142.90 0 143.28 120 121 143.37 0 142.90 121 122 145.03 0 143.37 122 123 146.05 0 145.03 123 124 147.39 0 146.05 124 125 149.58 0 147.39 125 126 151.02 0 149.58 126 127 153.57 0 151.02 127 128 155.60 0 153.57 128 129 157.18 0 155.60 129 130 158.77 0 157.18 130 131 159.95 0 158.77 131 132 161.34 0 159.95 132 133 161.95 0 161.34 133 134 163.36 0 161.95 134 135 165.00 0 163.36 135 136 166.65 0 165.00 136 137 168.65 0 166.65 137 138 170.29 0 168.65 138 139 172.70 0 170.29 139 140 173.79 0 172.70 140 141 176.45 0 173.79 141 142 177.58 0 176.45 142 143 179.19 0 177.58 143 144 181.01 0 179.19 144 145 184.08 0 181.01 145 146 185.63 0 184.08 146 147 188.51 0 185.63 147 148 190.18 0 188.51 148 149 192.19 0 190.18 149 150 193.47 0 192.19 150 151 196.73 0 193.47 151 152 200.39 0 196.73 152 153 203.24 0 200.39 153 154 205.53 0 203.24 154 155 208.21 0 205.53 155 156 208.88 0 208.21 156 157 212.85 0 208.88 157 158 216.41 0 212.85 158 159 216.23 0 216.41 159 160 219.27 0 216.23 160 161 222.02 0 219.27 161 162 224.89 0 222.02 162 163 230.37 0 224.89 163 164 232.29 0 230.37 164 165 235.53 0 232.29 165 166 236.92 0 235.53 166 167 242.37 0 236.92 167 168 242.75 0 242.37 168 169 244.19 0 242.75 169 170 247.94 0 244.19 170 171 248.80 0 247.94 171 172 250.18 0 248.80 172 173 251.55 0 250.18 173 174 254.40 0 251.55 174 175 255.72 0 254.40 175 176 257.69 0 255.72 176 177 258.37 0 257.69 177 178 258.22 0 258.37 178 179 258.59 0 258.22 179 180 257.45 0 258.59 180 181 257.45 0 257.45 181 182 256.73 0 257.45 182 183 258.82 0 256.73 183 184 257.99 0 258.82 184 185 262.85 0 257.99 185 186 262.58 0 262.85 186 187 261.55 0 262.58 187 188 261.25 0 261.55 188 189 259.78 1 261.25 189 190 256.26 1 259.78 190 191 254.29 1 256.26 191 192 248.50 1 254.29 192 193 241.88 1 248.50 193 194 238.53 1 241.88 194 195 232.24 1 238.53 195 196 232.46 1 232.24 196 197 225.79 1 232.46 197 198 221.63 1 225.79 198 199 219.62 1 221.63 199 200 215.94 1 219.62 200 201 211.81 1 215.94 201 202 205.57 1 211.81 202 203 201.25 1 205.57 203 204 194.70 1 201.25 204 205 187.94 1 194.70 205 206 185.61 1 187.94 206 207 181.15 1 185.61 207 208 186.50 1 181.15 208 209 183.21 1 186.50 209 210 182.61 1 183.21 210 211 187.09 1 182.61 211 212 189.10 1 187.09 212 213 191.25 1 189.10 213 214 190.74 1 191.25 214 215 190.79 1 190.74 215 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Dummy_Crisis Y1 t 0.41155 -5.21336 0.98647 0.02521 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -4.49156 -0.70916 -0.01912 0.63361 7.35943 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.411545 0.325567 1.264 0.208 Dummy_Crisis -5.213361 0.383016 -13.611 < 2e-16 *** Y1 0.986467 0.004117 239.621 < 2e-16 *** t 0.025211 0.004152 6.073 5.78e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.472 on 211 degrees of freedom Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993 F-statistic: 1.051e+05 on 3 and 211 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,] 1.890904e-02 3.781809e-02 0.9810910 [2,] 5.316838e-03 1.063368e-02 0.9946832 [3,] 1.060670e-03 2.121340e-03 0.9989393 [4,] 7.546597e-04 1.509319e-03 0.9992453 [5,] 5.652010e-04 1.130402e-03 0.9994348 [6,] 4.919006e-04 9.838013e-04 0.9995081 [7,] 1.349836e-04 2.699672e-04 0.9998650 [8,] 3.653447e-05 7.306894e-05 0.9999635 [9,] 7.794070e-05 1.558814e-04 0.9999221 [10,] 4.212037e-05 8.424075e-05 0.9999579 [11,] 1.309853e-05 2.619707e-05 0.9999869 [12,] 5.028742e-06 1.005748e-05 0.9999950 [13,] 2.037183e-06 4.074367e-06 0.9999980 [14,] 1.120366e-06 2.240732e-06 0.9999989 [15,] 7.299925e-07 1.459985e-06 0.9999993 [16,] 6.259179e-07 1.251836e-06 0.9999994 [17,] 1.944982e-07 3.889965e-07 0.9999998 [18,] 1.284096e-07 2.568192e-07 0.9999999 [19,] 3.950499e-08 7.900998e-08 1.0000000 [20,] 1.170006e-08 2.340012e-08 1.0000000 [21,] 6.313783e-09 1.262757e-08 1.0000000 [22,] 2.273199e-09 4.546397e-09 1.0000000 [23,] 1.473049e-09 2.946098e-09 1.0000000 [24,] 7.539821e-09 1.507964e-08 1.0000000 [25,] 2.978807e-09 5.957614e-09 1.0000000 [26,] 6.622411e-09 1.324482e-08 1.0000000 [27,] 3.587450e-09 7.174900e-09 1.0000000 [28,] 1.343997e-09 2.687994e-09 1.0000000 [29,] 4.875997e-10 9.751994e-10 1.0000000 [30,] 1.725797e-10 3.451594e-10 1.0000000 [31,] 7.546820e-11 1.509364e-10 1.0000000 [32,] 3.605287e-11 7.210574e-11 1.0000000 [33,] 1.766131e-11 3.532262e-11 1.0000000 [34,] 1.993788e-11 3.987575e-11 1.0000000 [35,] 6.978990e-12 1.395798e-11 1.0000000 [36,] 2.254262e-12 4.508524e-12 1.0000000 [37,] 2.028418e-12 4.056836e-12 1.0000000 [38,] 2.347795e-12 4.695589e-12 1.0000000 [39,] 9.639211e-13 1.927842e-12 1.0000000 [40,] 4.138812e-13 8.277625e-13 1.0000000 [41,] 1.651451e-13 3.302902e-13 1.0000000 [42,] 5.777336e-14 1.155467e-13 1.0000000 [43,] 1.834823e-14 3.669646e-14 1.0000000 [44,] 1.062195e-14 2.124390e-14 1.0000000 [45,] 8.942500e-15 1.788500e-14 1.0000000 [46,] 2.853994e-15 5.707989e-15 1.0000000 [47,] 2.410416e-14 4.820832e-14 1.0000000 [48,] 8.174455e-15 1.634891e-14 1.0000000 [49,] 8.672894e-15 1.734579e-14 1.0000000 [50,] 7.090965e-15 1.418193e-14 1.0000000 [51,] 2.698817e-15 5.397634e-15 1.0000000 [52,] 9.835384e-16 1.967077e-15 1.0000000 [53,] 3.466779e-16 6.933557e-16 1.0000000 [54,] 1.188571e-16 2.377143e-16 1.0000000 [55,] 4.495788e-17 8.991576e-17 1.0000000 [56,] 1.443048e-17 2.886095e-17 1.0000000 [57,] 4.605479e-18 9.210958e-18 1.0000000 [58,] 1.433474e-18 2.866948e-18 1.0000000 [59,] 4.250790e-18 8.501581e-18 1.0000000 [60,] 8.144363e-18 1.628873e-17 1.0000000 [61,] 5.806601e-18 1.161320e-17 1.0000000 [62,] 2.195437e-18 4.390874e-18 1.0000000 [63,] 1.220739e-18 2.441478e-18 1.0000000 [64,] 4.509666e-19 9.019332e-19 1.0000000 [65,] 3.002119e-19 6.004237e-19 1.0000000 [66,] 9.633348e-20 1.926670e-19 1.0000000 [67,] 3.288244e-20 6.576487e-20 1.0000000 [68,] 2.280419e-20 4.560838e-20 1.0000000 [69,] 7.265117e-21 1.453023e-20 1.0000000 [70,] 6.378323e-20 1.275665e-19 1.0000000 [71,] 6.414923e-20 1.282985e-19 1.0000000 [72,] 9.235516e-20 1.847103e-19 1.0000000 [73,] 4.220221e-20 8.440442e-20 1.0000000 [74,] 1.542365e-20 3.084731e-20 1.0000000 [75,] 8.362607e-21 1.672521e-20 1.0000000 [76,] 2.815294e-21 5.630589e-21 1.0000000 [77,] 1.493345e-21 2.986690e-21 1.0000000 [78,] 5.794476e-22 1.158895e-21 1.0000000 [79,] 2.655052e-22 5.310104e-22 1.0000000 [80,] 5.940471e-21 1.188094e-20 1.0000000 [81,] 2.574201e-21 5.148402e-21 1.0000000 [82,] 1.085613e-21 2.171225e-21 1.0000000 [83,] 5.159635e-22 1.031927e-21 1.0000000 [84,] 2.011924e-22 4.023849e-22 1.0000000 [85,] 9.684304e-23 1.936861e-22 1.0000000 [86,] 3.987166e-23 7.974332e-23 1.0000000 [87,] 3.940892e-23 7.881783e-23 1.0000000 [88,] 1.287277e-23 2.574554e-23 1.0000000 [89,] 5.353018e-24 1.070604e-23 1.0000000 [90,] 1.721374e-24 3.442747e-24 1.0000000 [91,] 9.448262e-25 1.889652e-24 1.0000000 [92,] 3.294167e-25 6.588333e-25 1.0000000 [93,] 6.925550e-25 1.385110e-24 1.0000000 [94,] 1.711713e-24 3.423426e-24 1.0000000 [95,] 3.047402e-24 6.094805e-24 1.0000000 [96,] 1.035999e-24 2.071999e-24 1.0000000 [97,] 8.762686e-25 1.752537e-24 1.0000000 [98,] 2.940201e-25 5.880402e-25 1.0000000 [99,] 1.910322e-25 3.820645e-25 1.0000000 [100,] 9.030528e-26 1.806106e-25 1.0000000 [101,] 3.481848e-26 6.963696e-26 1.0000000 [102,] 1.321446e-26 2.642892e-26 1.0000000 [103,] 5.893092e-27 1.178618e-26 1.0000000 [104,] 2.189093e-27 4.378185e-27 1.0000000 [105,] 1.523636e-27 3.047272e-27 1.0000000 [106,] 1.725407e-27 3.450814e-27 1.0000000 [107,] 1.181476e-27 2.362952e-27 1.0000000 [108,] 4.592912e-28 9.185824e-28 1.0000000 [109,] 1.447552e-28 2.895104e-28 1.0000000 [110,] 4.484307e-29 8.968614e-29 1.0000000 [111,] 2.123555e-29 4.247109e-29 1.0000000 [112,] 7.666289e-30 1.533258e-29 1.0000000 [113,] 2.392441e-30 4.784881e-30 1.0000000 [114,] 1.775954e-29 3.551909e-29 1.0000000 [115,] 1.069239e-29 2.138478e-29 1.0000000 [116,] 4.546485e-30 9.092970e-30 1.0000000 [117,] 1.595886e-30 3.191773e-30 1.0000000 [118,] 5.359709e-31 1.071942e-30 1.0000000 [119,] 4.389082e-31 8.778165e-31 1.0000000 [120,] 1.430877e-31 2.861753e-31 1.0000000 [121,] 1.941118e-31 3.882236e-31 1.0000000 [122,] 8.047777e-32 1.609555e-31 1.0000000 [123,] 2.471500e-32 4.943000e-32 1.0000000 [124,] 7.537879e-33 1.507576e-32 1.0000000 [125,] 2.968366e-33 5.936732e-33 1.0000000 [126,] 9.882455e-34 1.976491e-33 1.0000000 [127,] 1.140604e-33 2.281208e-33 1.0000000 [128,] 3.960690e-34 7.921380e-34 1.0000000 [129,] 1.300778e-34 2.601555e-34 1.0000000 [130,] 4.292752e-35 8.585503e-35 1.0000000 [131,] 1.522206e-35 3.044413e-35 1.0000000 [132,] 5.099965e-36 1.019993e-35 1.0000000 [133,] 2.470554e-36 4.941109e-36 1.0000000 [134,] 1.749430e-36 3.498860e-36 1.0000000 [135,] 1.142359e-36 2.284717e-36 1.0000000 [136,] 8.852085e-37 1.770417e-36 1.0000000 [137,] 3.586775e-37 7.173550e-37 1.0000000 [138,] 1.323234e-37 2.646467e-37 1.0000000 [139,] 1.604795e-37 3.209591e-37 1.0000000 [140,] 8.266004e-38 1.653201e-37 1.0000000 [141,] 5.125796e-38 1.025159e-37 1.0000000 [142,] 2.535941e-38 5.071882e-38 1.0000000 [143,] 9.441971e-39 1.888394e-38 1.0000000 [144,] 1.217740e-38 2.435481e-38 1.0000000 [145,] 1.321097e-38 2.642193e-38 1.0000000 [146,] 2.658495e-38 5.316991e-38 1.0000000 [147,] 9.138171e-39 1.827634e-38 1.0000000 [148,] 2.986395e-39 5.972790e-39 1.0000000 [149,] 8.667854e-40 1.733571e-39 1.0000000 [150,] 2.445381e-38 4.890761e-38 1.0000000 [151,] 5.508743e-38 1.101749e-37 1.0000000 [152,] 3.264981e-38 6.529962e-38 1.0000000 [153,] 8.034404e-35 1.606881e-34 1.0000000 [154,] 2.981990e-35 5.963981e-35 1.0000000 [155,] 9.663172e-36 1.932634e-35 1.0000000 [156,] 2.985208e-36 5.970415e-36 1.0000000 [157,] 5.359013e-34 1.071803e-33 1.0000000 [158,] 3.677493e-34 7.354985e-34 1.0000000 [159,] 1.167041e-34 2.334083e-34 1.0000000 [160,] 2.337396e-34 4.674792e-34 1.0000000 [161,] 3.252735e-32 6.505470e-32 1.0000000 [162,] 1.098202e-30 2.196404e-30 1.0000000 [163,] 1.217014e-30 2.434029e-30 1.0000000 [164,] 1.642699e-30 3.285398e-30 1.0000000 [165,] 5.492944e-30 1.098589e-29 1.0000000 [166,] 5.537471e-30 1.107494e-29 1.0000000 [167,] 5.185273e-30 1.037055e-29 1.0000000 [168,] 5.966102e-30 1.193220e-29 1.0000000 [169,] 8.312302e-30 1.662460e-29 1.0000000 [170,] 1.264187e-29 2.528373e-29 1.0000000 [171,] 4.282246e-29 8.564492e-29 1.0000000 [172,] 4.168171e-28 8.336341e-28 1.0000000 [173,] 1.110387e-27 2.220774e-27 1.0000000 [174,] 4.236628e-26 8.473256e-26 1.0000000 [175,] 8.180313e-26 1.636063e-25 1.0000000 [176,] 4.361009e-25 8.722017e-25 1.0000000 [177,] 3.766592e-25 7.533183e-25 1.0000000 [178,] 1.848757e-24 3.697513e-24 1.0000000 [179,] 2.242445e-21 4.484891e-21 1.0000000 [180,] 3.574267e-21 7.148534e-21 1.0000000 [181,] 1.193811e-20 2.387622e-20 1.0000000 [182,] 1.258095e-20 2.516190e-20 1.0000000 [183,] 1.177211e-19 2.354423e-19 1.0000000 [184,] 1.709210e-19 3.418420e-19 1.0000000 [185,] 2.248015e-18 4.496030e-18 1.0000000 [186,] 5.318114e-18 1.063623e-17 1.0000000 [187,] 1.338748e-17 2.677496e-17 1.0000000 [188,] 1.611732e-17 3.223465e-17 1.0000000 [189,] 1.624492e-17 3.248983e-17 1.0000000 [190,] 3.621238e-13 7.242476e-13 1.0000000 [191,] 4.396047e-13 8.792093e-13 1.0000000 [192,] 3.186420e-13 6.372840e-13 1.0000000 [193,] 1.477019e-11 2.954039e-11 1.0000000 [194,] 1.271554e-10 2.543109e-10 1.0000000 [195,] 2.851300e-09 5.702600e-09 1.0000000 [196,] 9.073200e-09 1.814640e-08 1.0000000 [197,] 1.405781e-06 2.811562e-06 0.9999986 [198,] 3.273476e-05 6.546952e-05 0.9999673 [199,] 7.074199e-05 1.414840e-04 0.9999293 [200,] 1.393485e-03 2.786970e-03 0.9986065 [201,] 6.449473e-04 1.289895e-03 0.9993551 [202,] 5.741210e-02 1.148242e-01 0.9425879 > postscript(file="/var/www/html/rcomp/tmp/1ju101261250737.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/24m8k1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3dohb1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/43dch1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5uxab1261250737.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 = 215 Frequency = 1 1 2 3 4 5 1.0694106546 1.1562294405 1.0345368843 1.0014910028 0.5983097887 6 7 8 9 10 0.0999859383 0.9653014588 0.2227969073 0.5905484135 1.1837132209 11 12 13 14 15 1.1052686455 -0.5539879251 0.6745609687 0.4102970943 -0.5372147608 16 17 18 19 20 0.0028227908 0.6407096294 0.7576637479 0.6265125218 0.6638726378 21 22 23 24 25 -0.7179552509 1.0518116357 0.0260737110 0.6466672003 -0.3339426956 26 27 28 29 30 0.0524554851 0.5645376323 -0.5560722636 0.6884312617 1.0202573513 31 32 33 34 35 -0.1931799203 0.7771429040 0.2009989817 -0.0327235625 -0.0192880900 36 37 38 39 40 -0.4853112873 -0.6772891161 0.3085677345 -0.7817714964 0.6934086916 41 42 43 44 45 -0.5709759078 -0.2822770739 0.4807524010 0.4744731462 -0.1116707761 46 47 48 49 50 -0.0555286529 -0.8683038694 -0.7117703561 -0.4726655247 -1.0799067148 51 52 53 54 55 0.3650028081 -0.4600584539 0.9139183488 -0.5731582937 0.2397358487 56 57 58 59 60 0.1539979240 -0.3126873285 -0.8754625450 -0.8755457183 -0.4752228940 61 62 63 64 65 -1.0190807708 -0.7799759394 -0.6072171295 -0.6817516689 0.4630371290 66 67 68 69 70 0.3536598334 0.0429292125 -0.3218613845 0.0186113798 -0.2260438846 71 72 73 74 75 -1.2638117973 -0.6053836285 -0.4276175149 -1.4171447505 -0.8597992420 76 77 78 79 80 0.7855308860 0.2934615473 0.5747609144 0.1599849250 -0.0003396984 81 82 83 84 85 -0.8226943097 -0.2360108563 -0.9210721184 -0.7752006602 0.0729714511 86 87 88 89 90 -2.2870825072 -0.1589396110 -0.1414295549 0.0064864988 -0.1134321269 91 92 93 94 95 0.1251605895 0.0671366193 -1.2915640136 -0.3785345387 -0.8927838055 96 97 98 99 100 -0.5537996991 -1.1599436214 -0.8340721632 0.6765359337 -1.7921500917 101 102 103 104 105 0.6457367469 -0.2829492784 0.4857787705 -0.1649372429 -1.1144498710 106 107 108 109 110 0.2033162427 -0.7307830841 -0.7474683365 0.1758464110 -0.6981175832 111 112 113 114 115 -1.1738555079 0.6641812709 0.5473899010 -0.6410254593 -0.1055453912 116 117 118 119 120 -0.2726220337 -0.8018639968 -0.5981432516 -0.1014451907 -1.8778451705 121 122 123 124 125 -1.0581990089 0.1129504181 -0.5297955833 -0.2212028669 0.6217204906 126 127 128 129 130 -0.1238528865 0.9804237964 0.4697223905 0.0219836929 0.0281550312 131 132 133 134 135 -0.3855382979 -0.1847802609 -0.9711802408 -0.1881361583 0.0357345270 136 137 138 139 140 0.0427178605 0.3898365266 0.0316918313 0.7986751649 -0.5139208964 141 142 143 144 145 1.0456191477 -0.4735936002 -0.0035122260 0.2030651100 1.4524844291 146 147 148 149 150 -0.0511796849 1.2745856558 0.0783502236 0.4157395547 -0.3122698080 151 152 153 154 155 1.6598415543 2.0787487584 1.2930692639 0.7464278341 1.1422077824 156 157 158 159 160 -0.8567343005 2.4271217772 2.0456375913 -1.6713952286 1.5209575837 161 162 163 164 165 1.2468874720 1.3788927169 4.0025219521 0.4914729789 1.8122456234 166 167 168 169 170 -0.0191178376 4.0344821826 -0.9869727883 0.0529586459 2.3572353288 171 172 173 174 175 -0.5072261729 -0.0007987771 -0.0173340894 1.4559952657 -0.0606461641 176 177 178 179 180 0.5820065283 -0.7065441645 -1.5525527543 -1.0597939444 -2.5899978427 181 182 183 184 185 -1.4906369538 -2.2358481559 0.5391966995 -2.3777300029 3.2758261947 186 187 188 189 190 -1.8136133957 -2.6024785763 -1.9116290294 2.1024607671 0.0073556824 191 192 193 194 195 1.4845074282 -2.3873642832 -3.3209330227 -0.1657343626 -3.1762819637 196 197 198 199 200 3.2233826700 -3.6888512163 -1.2943292189 0.7741612447 -0.9482517968 201 202 203 204 205 -1.4732653716 -3.6643689104 -1.8540276140 -4.1677024709 -4.4915564831 206 207 208 209 210 -0.1782524785 -2.3649961611 7.3594343265 -1.2333739697 1.3868904243 211 212 213 214 215 6.4335592702 3.9989770435 4.1409676808 1.4848529736 2.0127398122 > postscript(file="/var/www/html/rcomp/tmp/6b8031261250737.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 = 215 Frequency = 1 lag(myerror, k = 1) myerror 0 1.0694106546 NA 1 1.1562294405 1.0694106546 2 1.0345368843 1.1562294405 3 1.0014910028 1.0345368843 4 0.5983097887 1.0014910028 5 0.0999859383 0.5983097887 6 0.9653014588 0.0999859383 7 0.2227969073 0.9653014588 8 0.5905484135 0.2227969073 9 1.1837132209 0.5905484135 10 1.1052686455 1.1837132209 11 -0.5539879251 1.1052686455 12 0.6745609687 -0.5539879251 13 0.4102970943 0.6745609687 14 -0.5372147608 0.4102970943 15 0.0028227908 -0.5372147608 16 0.6407096294 0.0028227908 17 0.7576637479 0.6407096294 18 0.6265125218 0.7576637479 19 0.6638726378 0.6265125218 20 -0.7179552509 0.6638726378 21 1.0518116357 -0.7179552509 22 0.0260737110 1.0518116357 23 0.6466672003 0.0260737110 24 -0.3339426956 0.6466672003 25 0.0524554851 -0.3339426956 26 0.5645376323 0.0524554851 27 -0.5560722636 0.5645376323 28 0.6884312617 -0.5560722636 29 1.0202573513 0.6884312617 30 -0.1931799203 1.0202573513 31 0.7771429040 -0.1931799203 32 0.2009989817 0.7771429040 33 -0.0327235625 0.2009989817 34 -0.0192880900 -0.0327235625 35 -0.4853112873 -0.0192880900 36 -0.6772891161 -0.4853112873 37 0.3085677345 -0.6772891161 38 -0.7817714964 0.3085677345 39 0.6934086916 -0.7817714964 40 -0.5709759078 0.6934086916 41 -0.2822770739 -0.5709759078 42 0.4807524010 -0.2822770739 43 0.4744731462 0.4807524010 44 -0.1116707761 0.4744731462 45 -0.0555286529 -0.1116707761 46 -0.8683038694 -0.0555286529 47 -0.7117703561 -0.8683038694 48 -0.4726655247 -0.7117703561 49 -1.0799067148 -0.4726655247 50 0.3650028081 -1.0799067148 51 -0.4600584539 0.3650028081 52 0.9139183488 -0.4600584539 53 -0.5731582937 0.9139183488 54 0.2397358487 -0.5731582937 55 0.1539979240 0.2397358487 56 -0.3126873285 0.1539979240 57 -0.8754625450 -0.3126873285 58 -0.8755457183 -0.8754625450 59 -0.4752228940 -0.8755457183 60 -1.0190807708 -0.4752228940 61 -0.7799759394 -1.0190807708 62 -0.6072171295 -0.7799759394 63 -0.6817516689 -0.6072171295 64 0.4630371290 -0.6817516689 65 0.3536598334 0.4630371290 66 0.0429292125 0.3536598334 67 -0.3218613845 0.0429292125 68 0.0186113798 -0.3218613845 69 -0.2260438846 0.0186113798 70 -1.2638117973 -0.2260438846 71 -0.6053836285 -1.2638117973 72 -0.4276175149 -0.6053836285 73 -1.4171447505 -0.4276175149 74 -0.8597992420 -1.4171447505 75 0.7855308860 -0.8597992420 76 0.2934615473 0.7855308860 77 0.5747609144 0.2934615473 78 0.1599849250 0.5747609144 79 -0.0003396984 0.1599849250 80 -0.8226943097 -0.0003396984 81 -0.2360108563 -0.8226943097 82 -0.9210721184 -0.2360108563 83 -0.7752006602 -0.9210721184 84 0.0729714511 -0.7752006602 85 -2.2870825072 0.0729714511 86 -0.1589396110 -2.2870825072 87 -0.1414295549 -0.1589396110 88 0.0064864988 -0.1414295549 89 -0.1134321269 0.0064864988 90 0.1251605895 -0.1134321269 91 0.0671366193 0.1251605895 92 -1.2915640136 0.0671366193 93 -0.3785345387 -1.2915640136 94 -0.8927838055 -0.3785345387 95 -0.5537996991 -0.8927838055 96 -1.1599436214 -0.5537996991 97 -0.8340721632 -1.1599436214 98 0.6765359337 -0.8340721632 99 -1.7921500917 0.6765359337 100 0.6457367469 -1.7921500917 101 -0.2829492784 0.6457367469 102 0.4857787705 -0.2829492784 103 -0.1649372429 0.4857787705 104 -1.1144498710 -0.1649372429 105 0.2033162427 -1.1144498710 106 -0.7307830841 0.2033162427 107 -0.7474683365 -0.7307830841 108 0.1758464110 -0.7474683365 109 -0.6981175832 0.1758464110 110 -1.1738555079 -0.6981175832 111 0.6641812709 -1.1738555079 112 0.5473899010 0.6641812709 113 -0.6410254593 0.5473899010 114 -0.1055453912 -0.6410254593 115 -0.2726220337 -0.1055453912 116 -0.8018639968 -0.2726220337 117 -0.5981432516 -0.8018639968 118 -0.1014451907 -0.5981432516 119 -1.8778451705 -0.1014451907 120 -1.0581990089 -1.8778451705 121 0.1129504181 -1.0581990089 122 -0.5297955833 0.1129504181 123 -0.2212028669 -0.5297955833 124 0.6217204906 -0.2212028669 125 -0.1238528865 0.6217204906 126 0.9804237964 -0.1238528865 127 0.4697223905 0.9804237964 128 0.0219836929 0.4697223905 129 0.0281550312 0.0219836929 130 -0.3855382979 0.0281550312 131 -0.1847802609 -0.3855382979 132 -0.9711802408 -0.1847802609 133 -0.1881361583 -0.9711802408 134 0.0357345270 -0.1881361583 135 0.0427178605 0.0357345270 136 0.3898365266 0.0427178605 137 0.0316918313 0.3898365266 138 0.7986751649 0.0316918313 139 -0.5139208964 0.7986751649 140 1.0456191477 -0.5139208964 141 -0.4735936002 1.0456191477 142 -0.0035122260 -0.4735936002 143 0.2030651100 -0.0035122260 144 1.4524844291 0.2030651100 145 -0.0511796849 1.4524844291 146 1.2745856558 -0.0511796849 147 0.0783502236 1.2745856558 148 0.4157395547 0.0783502236 149 -0.3122698080 0.4157395547 150 1.6598415543 -0.3122698080 151 2.0787487584 1.6598415543 152 1.2930692639 2.0787487584 153 0.7464278341 1.2930692639 154 1.1422077824 0.7464278341 155 -0.8567343005 1.1422077824 156 2.4271217772 -0.8567343005 157 2.0456375913 2.4271217772 158 -1.6713952286 2.0456375913 159 1.5209575837 -1.6713952286 160 1.2468874720 1.5209575837 161 1.3788927169 1.2468874720 162 4.0025219521 1.3788927169 163 0.4914729789 4.0025219521 164 1.8122456234 0.4914729789 165 -0.0191178376 1.8122456234 166 4.0344821826 -0.0191178376 167 -0.9869727883 4.0344821826 168 0.0529586459 -0.9869727883 169 2.3572353288 0.0529586459 170 -0.5072261729 2.3572353288 171 -0.0007987771 -0.5072261729 172 -0.0173340894 -0.0007987771 173 1.4559952657 -0.0173340894 174 -0.0606461641 1.4559952657 175 0.5820065283 -0.0606461641 176 -0.7065441645 0.5820065283 177 -1.5525527543 -0.7065441645 178 -1.0597939444 -1.5525527543 179 -2.5899978427 -1.0597939444 180 -1.4906369538 -2.5899978427 181 -2.2358481559 -1.4906369538 182 0.5391966995 -2.2358481559 183 -2.3777300029 0.5391966995 184 3.2758261947 -2.3777300029 185 -1.8136133957 3.2758261947 186 -2.6024785763 -1.8136133957 187 -1.9116290294 -2.6024785763 188 2.1024607671 -1.9116290294 189 0.0073556824 2.1024607671 190 1.4845074282 0.0073556824 191 -2.3873642832 1.4845074282 192 -3.3209330227 -2.3873642832 193 -0.1657343626 -3.3209330227 194 -3.1762819637 -0.1657343626 195 3.2233826700 -3.1762819637 196 -3.6888512163 3.2233826700 197 -1.2943292189 -3.6888512163 198 0.7741612447 -1.2943292189 199 -0.9482517968 0.7741612447 200 -1.4732653716 -0.9482517968 201 -3.6643689104 -1.4732653716 202 -1.8540276140 -3.6643689104 203 -4.1677024709 -1.8540276140 204 -4.4915564831 -4.1677024709 205 -0.1782524785 -4.4915564831 206 -2.3649961611 -0.1782524785 207 7.3594343265 -2.3649961611 208 -1.2333739697 7.3594343265 209 1.3868904243 -1.2333739697 210 6.4335592702 1.3868904243 211 3.9989770435 6.4335592702 212 4.1409676808 3.9989770435 213 1.4848529736 4.1409676808 214 2.0127398122 1.4848529736 215 NA 2.0127398122 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.1562294405 1.0694106546 [2,] 1.0345368843 1.1562294405 [3,] 1.0014910028 1.0345368843 [4,] 0.5983097887 1.0014910028 [5,] 0.0999859383 0.5983097887 [6,] 0.9653014588 0.0999859383 [7,] 0.2227969073 0.9653014588 [8,] 0.5905484135 0.2227969073 [9,] 1.1837132209 0.5905484135 [10,] 1.1052686455 1.1837132209 [11,] -0.5539879251 1.1052686455 [12,] 0.6745609687 -0.5539879251 [13,] 0.4102970943 0.6745609687 [14,] -0.5372147608 0.4102970943 [15,] 0.0028227908 -0.5372147608 [16,] 0.6407096294 0.0028227908 [17,] 0.7576637479 0.6407096294 [18,] 0.6265125218 0.7576637479 [19,] 0.6638726378 0.6265125218 [20,] -0.7179552509 0.6638726378 [21,] 1.0518116357 -0.7179552509 [22,] 0.0260737110 1.0518116357 [23,] 0.6466672003 0.0260737110 [24,] -0.3339426956 0.6466672003 [25,] 0.0524554851 -0.3339426956 [26,] 0.5645376323 0.0524554851 [27,] -0.5560722636 0.5645376323 [28,] 0.6884312617 -0.5560722636 [29,] 1.0202573513 0.6884312617 [30,] -0.1931799203 1.0202573513 [31,] 0.7771429040 -0.1931799203 [32,] 0.2009989817 0.7771429040 [33,] -0.0327235625 0.2009989817 [34,] -0.0192880900 -0.0327235625 [35,] -0.4853112873 -0.0192880900 [36,] -0.6772891161 -0.4853112873 [37,] 0.3085677345 -0.6772891161 [38,] -0.7817714964 0.3085677345 [39,] 0.6934086916 -0.7817714964 [40,] -0.5709759078 0.6934086916 [41,] -0.2822770739 -0.5709759078 [42,] 0.4807524010 -0.2822770739 [43,] 0.4744731462 0.4807524010 [44,] -0.1116707761 0.4744731462 [45,] -0.0555286529 -0.1116707761 [46,] -0.8683038694 -0.0555286529 [47,] -0.7117703561 -0.8683038694 [48,] -0.4726655247 -0.7117703561 [49,] -1.0799067148 -0.4726655247 [50,] 0.3650028081 -1.0799067148 [51,] -0.4600584539 0.3650028081 [52,] 0.9139183488 -0.4600584539 [53,] -0.5731582937 0.9139183488 [54,] 0.2397358487 -0.5731582937 [55,] 0.1539979240 0.2397358487 [56,] -0.3126873285 0.1539979240 [57,] -0.8754625450 -0.3126873285 [58,] -0.8755457183 -0.8754625450 [59,] -0.4752228940 -0.8755457183 [60,] -1.0190807708 -0.4752228940 [61,] -0.7799759394 -1.0190807708 [62,] -0.6072171295 -0.7799759394 [63,] -0.6817516689 -0.6072171295 [64,] 0.4630371290 -0.6817516689 [65,] 0.3536598334 0.4630371290 [66,] 0.0429292125 0.3536598334 [67,] -0.3218613845 0.0429292125 [68,] 0.0186113798 -0.3218613845 [69,] -0.2260438846 0.0186113798 [70,] -1.2638117973 -0.2260438846 [71,] -0.6053836285 -1.2638117973 [72,] -0.4276175149 -0.6053836285 [73,] -1.4171447505 -0.4276175149 [74,] -0.8597992420 -1.4171447505 [75,] 0.7855308860 -0.8597992420 [76,] 0.2934615473 0.7855308860 [77,] 0.5747609144 0.2934615473 [78,] 0.1599849250 0.5747609144 [79,] -0.0003396984 0.1599849250 [80,] -0.8226943097 -0.0003396984 [81,] -0.2360108563 -0.8226943097 [82,] -0.9210721184 -0.2360108563 [83,] -0.7752006602 -0.9210721184 [84,] 0.0729714511 -0.7752006602 [85,] -2.2870825072 0.0729714511 [86,] -0.1589396110 -2.2870825072 [87,] -0.1414295549 -0.1589396110 [88,] 0.0064864988 -0.1414295549 [89,] -0.1134321269 0.0064864988 [90,] 0.1251605895 -0.1134321269 [91,] 0.0671366193 0.1251605895 [92,] -1.2915640136 0.0671366193 [93,] -0.3785345387 -1.2915640136 [94,] -0.8927838055 -0.3785345387 [95,] -0.5537996991 -0.8927838055 [96,] -1.1599436214 -0.5537996991 [97,] -0.8340721632 -1.1599436214 [98,] 0.6765359337 -0.8340721632 [99,] -1.7921500917 0.6765359337 [100,] 0.6457367469 -1.7921500917 [101,] -0.2829492784 0.6457367469 [102,] 0.4857787705 -0.2829492784 [103,] -0.1649372429 0.4857787705 [104,] -1.1144498710 -0.1649372429 [105,] 0.2033162427 -1.1144498710 [106,] -0.7307830841 0.2033162427 [107,] -0.7474683365 -0.7307830841 [108,] 0.1758464110 -0.7474683365 [109,] -0.6981175832 0.1758464110 [110,] -1.1738555079 -0.6981175832 [111,] 0.6641812709 -1.1738555079 [112,] 0.5473899010 0.6641812709 [113,] -0.6410254593 0.5473899010 [114,] -0.1055453912 -0.6410254593 [115,] -0.2726220337 -0.1055453912 [116,] -0.8018639968 -0.2726220337 [117,] -0.5981432516 -0.8018639968 [118,] -0.1014451907 -0.5981432516 [119,] -1.8778451705 -0.1014451907 [120,] -1.0581990089 -1.8778451705 [121,] 0.1129504181 -1.0581990089 [122,] -0.5297955833 0.1129504181 [123,] -0.2212028669 -0.5297955833 [124,] 0.6217204906 -0.2212028669 [125,] -0.1238528865 0.6217204906 [126,] 0.9804237964 -0.1238528865 [127,] 0.4697223905 0.9804237964 [128,] 0.0219836929 0.4697223905 [129,] 0.0281550312 0.0219836929 [130,] -0.3855382979 0.0281550312 [131,] -0.1847802609 -0.3855382979 [132,] -0.9711802408 -0.1847802609 [133,] -0.1881361583 -0.9711802408 [134,] 0.0357345270 -0.1881361583 [135,] 0.0427178605 0.0357345270 [136,] 0.3898365266 0.0427178605 [137,] 0.0316918313 0.3898365266 [138,] 0.7986751649 0.0316918313 [139,] -0.5139208964 0.7986751649 [140,] 1.0456191477 -0.5139208964 [141,] -0.4735936002 1.0456191477 [142,] -0.0035122260 -0.4735936002 [143,] 0.2030651100 -0.0035122260 [144,] 1.4524844291 0.2030651100 [145,] -0.0511796849 1.4524844291 [146,] 1.2745856558 -0.0511796849 [147,] 0.0783502236 1.2745856558 [148,] 0.4157395547 0.0783502236 [149,] -0.3122698080 0.4157395547 [150,] 1.6598415543 -0.3122698080 [151,] 2.0787487584 1.6598415543 [152,] 1.2930692639 2.0787487584 [153,] 0.7464278341 1.2930692639 [154,] 1.1422077824 0.7464278341 [155,] -0.8567343005 1.1422077824 [156,] 2.4271217772 -0.8567343005 [157,] 2.0456375913 2.4271217772 [158,] -1.6713952286 2.0456375913 [159,] 1.5209575837 -1.6713952286 [160,] 1.2468874720 1.5209575837 [161,] 1.3788927169 1.2468874720 [162,] 4.0025219521 1.3788927169 [163,] 0.4914729789 4.0025219521 [164,] 1.8122456234 0.4914729789 [165,] -0.0191178376 1.8122456234 [166,] 4.0344821826 -0.0191178376 [167,] -0.9869727883 4.0344821826 [168,] 0.0529586459 -0.9869727883 [169,] 2.3572353288 0.0529586459 [170,] -0.5072261729 2.3572353288 [171,] -0.0007987771 -0.5072261729 [172,] -0.0173340894 -0.0007987771 [173,] 1.4559952657 -0.0173340894 [174,] -0.0606461641 1.4559952657 [175,] 0.5820065283 -0.0606461641 [176,] -0.7065441645 0.5820065283 [177,] -1.5525527543 -0.7065441645 [178,] -1.0597939444 -1.5525527543 [179,] -2.5899978427 -1.0597939444 [180,] -1.4906369538 -2.5899978427 [181,] -2.2358481559 -1.4906369538 [182,] 0.5391966995 -2.2358481559 [183,] -2.3777300029 0.5391966995 [184,] 3.2758261947 -2.3777300029 [185,] -1.8136133957 3.2758261947 [186,] -2.6024785763 -1.8136133957 [187,] -1.9116290294 -2.6024785763 [188,] 2.1024607671 -1.9116290294 [189,] 0.0073556824 2.1024607671 [190,] 1.4845074282 0.0073556824 [191,] -2.3873642832 1.4845074282 [192,] -3.3209330227 -2.3873642832 [193,] -0.1657343626 -3.3209330227 [194,] -3.1762819637 -0.1657343626 [195,] 3.2233826700 -3.1762819637 [196,] -3.6888512163 3.2233826700 [197,] -1.2943292189 -3.6888512163 [198,] 0.7741612447 -1.2943292189 [199,] -0.9482517968 0.7741612447 [200,] -1.4732653716 -0.9482517968 [201,] -3.6643689104 -1.4732653716 [202,] -1.8540276140 -3.6643689104 [203,] -4.1677024709 -1.8540276140 [204,] -4.4915564831 -4.1677024709 [205,] -0.1782524785 -4.4915564831 [206,] -2.3649961611 -0.1782524785 [207,] 7.3594343265 -2.3649961611 [208,] -1.2333739697 7.3594343265 [209,] 1.3868904243 -1.2333739697 [210,] 6.4335592702 1.3868904243 [211,] 3.9989770435 6.4335592702 [212,] 4.1409676808 3.9989770435 [213,] 1.4848529736 4.1409676808 [214,] 2.0127398122 1.4848529736 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.1562294405 1.0694106546 2 1.0345368843 1.1562294405 3 1.0014910028 1.0345368843 4 0.5983097887 1.0014910028 5 0.0999859383 0.5983097887 6 0.9653014588 0.0999859383 7 0.2227969073 0.9653014588 8 0.5905484135 0.2227969073 9 1.1837132209 0.5905484135 10 1.1052686455 1.1837132209 11 -0.5539879251 1.1052686455 12 0.6745609687 -0.5539879251 13 0.4102970943 0.6745609687 14 -0.5372147608 0.4102970943 15 0.0028227908 -0.5372147608 16 0.6407096294 0.0028227908 17 0.7576637479 0.6407096294 18 0.6265125218 0.7576637479 19 0.6638726378 0.6265125218 20 -0.7179552509 0.6638726378 21 1.0518116357 -0.7179552509 22 0.0260737110 1.0518116357 23 0.6466672003 0.0260737110 24 -0.3339426956 0.6466672003 25 0.0524554851 -0.3339426956 26 0.5645376323 0.0524554851 27 -0.5560722636 0.5645376323 28 0.6884312617 -0.5560722636 29 1.0202573513 0.6884312617 30 -0.1931799203 1.0202573513 31 0.7771429040 -0.1931799203 32 0.2009989817 0.7771429040 33 -0.0327235625 0.2009989817 34 -0.0192880900 -0.0327235625 35 -0.4853112873 -0.0192880900 36 -0.6772891161 -0.4853112873 37 0.3085677345 -0.6772891161 38 -0.7817714964 0.3085677345 39 0.6934086916 -0.7817714964 40 -0.5709759078 0.6934086916 41 -0.2822770739 -0.5709759078 42 0.4807524010 -0.2822770739 43 0.4744731462 0.4807524010 44 -0.1116707761 0.4744731462 45 -0.0555286529 -0.1116707761 46 -0.8683038694 -0.0555286529 47 -0.7117703561 -0.8683038694 48 -0.4726655247 -0.7117703561 49 -1.0799067148 -0.4726655247 50 0.3650028081 -1.0799067148 51 -0.4600584539 0.3650028081 52 0.9139183488 -0.4600584539 53 -0.5731582937 0.9139183488 54 0.2397358487 -0.5731582937 55 0.1539979240 0.2397358487 56 -0.3126873285 0.1539979240 57 -0.8754625450 -0.3126873285 58 -0.8755457183 -0.8754625450 59 -0.4752228940 -0.8755457183 60 -1.0190807708 -0.4752228940 61 -0.7799759394 -1.0190807708 62 -0.6072171295 -0.7799759394 63 -0.6817516689 -0.6072171295 64 0.4630371290 -0.6817516689 65 0.3536598334 0.4630371290 66 0.0429292125 0.3536598334 67 -0.3218613845 0.0429292125 68 0.0186113798 -0.3218613845 69 -0.2260438846 0.0186113798 70 -1.2638117973 -0.2260438846 71 -0.6053836285 -1.2638117973 72 -0.4276175149 -0.6053836285 73 -1.4171447505 -0.4276175149 74 -0.8597992420 -1.4171447505 75 0.7855308860 -0.8597992420 76 0.2934615473 0.7855308860 77 0.5747609144 0.2934615473 78 0.1599849250 0.5747609144 79 -0.0003396984 0.1599849250 80 -0.8226943097 -0.0003396984 81 -0.2360108563 -0.8226943097 82 -0.9210721184 -0.2360108563 83 -0.7752006602 -0.9210721184 84 0.0729714511 -0.7752006602 85 -2.2870825072 0.0729714511 86 -0.1589396110 -2.2870825072 87 -0.1414295549 -0.1589396110 88 0.0064864988 -0.1414295549 89 -0.1134321269 0.0064864988 90 0.1251605895 -0.1134321269 91 0.0671366193 0.1251605895 92 -1.2915640136 0.0671366193 93 -0.3785345387 -1.2915640136 94 -0.8927838055 -0.3785345387 95 -0.5537996991 -0.8927838055 96 -1.1599436214 -0.5537996991 97 -0.8340721632 -1.1599436214 98 0.6765359337 -0.8340721632 99 -1.7921500917 0.6765359337 100 0.6457367469 -1.7921500917 101 -0.2829492784 0.6457367469 102 0.4857787705 -0.2829492784 103 -0.1649372429 0.4857787705 104 -1.1144498710 -0.1649372429 105 0.2033162427 -1.1144498710 106 -0.7307830841 0.2033162427 107 -0.7474683365 -0.7307830841 108 0.1758464110 -0.7474683365 109 -0.6981175832 0.1758464110 110 -1.1738555079 -0.6981175832 111 0.6641812709 -1.1738555079 112 0.5473899010 0.6641812709 113 -0.6410254593 0.5473899010 114 -0.1055453912 -0.6410254593 115 -0.2726220337 -0.1055453912 116 -0.8018639968 -0.2726220337 117 -0.5981432516 -0.8018639968 118 -0.1014451907 -0.5981432516 119 -1.8778451705 -0.1014451907 120 -1.0581990089 -1.8778451705 121 0.1129504181 -1.0581990089 122 -0.5297955833 0.1129504181 123 -0.2212028669 -0.5297955833 124 0.6217204906 -0.2212028669 125 -0.1238528865 0.6217204906 126 0.9804237964 -0.1238528865 127 0.4697223905 0.9804237964 128 0.0219836929 0.4697223905 129 0.0281550312 0.0219836929 130 -0.3855382979 0.0281550312 131 -0.1847802609 -0.3855382979 132 -0.9711802408 -0.1847802609 133 -0.1881361583 -0.9711802408 134 0.0357345270 -0.1881361583 135 0.0427178605 0.0357345270 136 0.3898365266 0.0427178605 137 0.0316918313 0.3898365266 138 0.7986751649 0.0316918313 139 -0.5139208964 0.7986751649 140 1.0456191477 -0.5139208964 141 -0.4735936002 1.0456191477 142 -0.0035122260 -0.4735936002 143 0.2030651100 -0.0035122260 144 1.4524844291 0.2030651100 145 -0.0511796849 1.4524844291 146 1.2745856558 -0.0511796849 147 0.0783502236 1.2745856558 148 0.4157395547 0.0783502236 149 -0.3122698080 0.4157395547 150 1.6598415543 -0.3122698080 151 2.0787487584 1.6598415543 152 1.2930692639 2.0787487584 153 0.7464278341 1.2930692639 154 1.1422077824 0.7464278341 155 -0.8567343005 1.1422077824 156 2.4271217772 -0.8567343005 157 2.0456375913 2.4271217772 158 -1.6713952286 2.0456375913 159 1.5209575837 -1.6713952286 160 1.2468874720 1.5209575837 161 1.3788927169 1.2468874720 162 4.0025219521 1.3788927169 163 0.4914729789 4.0025219521 164 1.8122456234 0.4914729789 165 -0.0191178376 1.8122456234 166 4.0344821826 -0.0191178376 167 -0.9869727883 4.0344821826 168 0.0529586459 -0.9869727883 169 2.3572353288 0.0529586459 170 -0.5072261729 2.3572353288 171 -0.0007987771 -0.5072261729 172 -0.0173340894 -0.0007987771 173 1.4559952657 -0.0173340894 174 -0.0606461641 1.4559952657 175 0.5820065283 -0.0606461641 176 -0.7065441645 0.5820065283 177 -1.5525527543 -0.7065441645 178 -1.0597939444 -1.5525527543 179 -2.5899978427 -1.0597939444 180 -1.4906369538 -2.5899978427 181 -2.2358481559 -1.4906369538 182 0.5391966995 -2.2358481559 183 -2.3777300029 0.5391966995 184 3.2758261947 -2.3777300029 185 -1.8136133957 3.2758261947 186 -2.6024785763 -1.8136133957 187 -1.9116290294 -2.6024785763 188 2.1024607671 -1.9116290294 189 0.0073556824 2.1024607671 190 1.4845074282 0.0073556824 191 -2.3873642832 1.4845074282 192 -3.3209330227 -2.3873642832 193 -0.1657343626 -3.3209330227 194 -3.1762819637 -0.1657343626 195 3.2233826700 -3.1762819637 196 -3.6888512163 3.2233826700 197 -1.2943292189 -3.6888512163 198 0.7741612447 -1.2943292189 199 -0.9482517968 0.7741612447 200 -1.4732653716 -0.9482517968 201 -3.6643689104 -1.4732653716 202 -1.8540276140 -3.6643689104 203 -4.1677024709 -1.8540276140 204 -4.4915564831 -4.1677024709 205 -0.1782524785 -4.4915564831 206 -2.3649961611 -0.1782524785 207 7.3594343265 -2.3649961611 208 -1.2333739697 7.3594343265 209 1.3868904243 -1.2333739697 210 6.4335592702 1.3868904243 211 3.9989770435 6.4335592702 212 4.1409676808 3.9989770435 213 1.4848529736 4.1409676808 214 2.0127398122 1.4848529736 > 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/7yxrs1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8wtwi1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9cstz1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10mt9q1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/110mb61261250737.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/12m8pk1261250737.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/13z4tq1261250737.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/14v7f21261250737.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/15je4r1261250737.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/16owy21261250737.tab") + } > > try(system("convert tmp/1ju101261250737.ps tmp/1ju101261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/24m8k1261250737.ps tmp/24m8k1261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/3dohb1261250737.ps tmp/3dohb1261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/43dch1261250737.ps tmp/43dch1261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/5uxab1261250737.ps tmp/5uxab1261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/6b8031261250737.ps tmp/6b8031261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/7yxrs1261250737.ps tmp/7yxrs1261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/8wtwi1261250737.ps tmp/8wtwi1261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/9cstz1261250737.ps tmp/9cstz1261250737.png",intern=TRUE)) character(0) > try(system("convert tmp/10mt9q1261250737.ps tmp/10mt9q1261250737.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.971 1.789 6.409