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Type 'q()' to quit R. > x <- array(list(70.5 + ,0 + ,71.3 + ,0 + ,71.4 + ,0 + ,70.1 + ,0 + ,69.4 + ,0 + ,69.8 + ,0 + ,69.8 + ,0 + ,70.7 + ,0 + ,69.4 + ,0 + ,69.8 + ,0 + ,69.3 + ,0 + ,72.9 + ,0 + ,70.0 + ,0 + ,64.4 + ,0 + ,63.5 + ,0 + ,69.8 + ,0 + ,69.9 + ,0 + ,69.3 + ,0 + ,69.7 + ,0 + ,69.8 + ,0 + ,70.2 + ,0 + ,69.8 + ,0 + ,70.7 + ,0 + ,71.4 + ,0 + ,70.3 + ,0 + ,70.9 + ,0 + ,70.6 + ,0 + ,69.0 + ,0 + ,71.0 + ,0 + ,74.7 + ,0 + ,77.5 + ,0 + ,78.6 + ,0 + ,75.3 + ,0 + ,72.1 + ,0 + ,73.8 + ,0 + ,73.7 + ,0 + ,75.2 + ,0 + ,75.2 + ,0 + ,74.5 + ,0 + ,74.4 + ,0 + ,75.4 + ,0 + ,73.7 + ,0 + ,74.3 + ,0 + ,75.0 + ,0 + ,75.8 + ,0 + ,76.7 + ,0 + ,76.8 + ,0 + ,76.8 + ,0 + ,76.4 + ,0 + ,76.4 + ,0 + ,77.2 + ,0 + ,77.2 + ,0 + ,77.4 + ,0 + ,78.1 + ,0 + ,78.5 + ,0 + ,77.9 + ,0 + ,78.6 + ,0 + ,79.8 + ,0 + ,80.3 + ,0 + ,80.8 + ,0 + ,80.5 + ,0 + ,79.4 + ,0 + ,79.3 + ,0 + ,79.6 + ,0 + ,79.2 + ,0 + ,79.1 + ,0 + ,79.8 + ,0 + ,80.0 + ,0 + ,80.5 + ,0 + ,80.4 + ,0 + ,81.1 + ,0 + ,82.2 + ,0 + ,81.5 + ,0 + ,84.2 + ,0 + ,84.3 + ,0 + ,83.3 + ,0 + ,84.2 + ,0 + ,84.9 + ,0 + ,85.0 + ,0 + ,85.3 + ,0 + ,85.4 + ,0 + ,85.8 + ,0 + ,85.2 + ,0 + ,86.4 + ,0 + ,88.2 + ,0 + ,88.3 + ,0 + ,88.0 + ,0 + ,87.8 + ,0 + ,87.4 + ,0 + ,87.4 + ,0 + ,88.0 + ,0 + ,88.0 + ,0 + ,89.9 + ,0 + ,88.4 + ,0 + ,89.7 + ,0 + ,89.9 + ,0 + ,90.5 + ,0 + ,90.7 + ,0 + ,89.5 + ,0 + ,91.2 + ,0 + ,91.2 + ,0 + ,89.8 + ,0 + ,89.6 + ,0 + ,92.3 + ,0 + ,90.1 + ,0 + ,92.9 + ,0 + ,93.3 + ,0 + ,93.5 + ,0 + ,93.4 + ,0 + ,93.6 + ,0 + ,93.7 + ,0 + ,93.6 + ,0 + ,93.0 + ,0 + ,94.1 + ,0 + ,95.7 + ,0 + ,95.6 + ,0 + ,97.2 + ,0 + ,98.1 + ,0 + ,98.8 + ,0 + ,95.3 + ,0 + ,95.3 + ,0 + ,96.7 + ,0 + ,99.2 + ,0 + ,99.0 + ,0 + ,100.9 + ,0 + ,100.1 + ,0 + ,100.4 + ,0 + ,100.5 + ,0 + ,102.6 + ,0 + ,101.8 + ,0 + ,102.6 + ,0 + ,101.0 + ,0 + ,101.6 + ,0 + ,100.6 + ,0 + ,100.4 + ,0 + ,100.7 + ,0 + ,100.6 + ,0 + ,100.3 + ,0 + ,101.4 + ,0 + ,103.2 + ,0 + ,79.2 + ,1 + ,83.4 + ,1 + ,86.5 + ,1 + ,91.3 + ,1 + ,91.5 + ,1 + ,93.1 + ,1 + ,93.1 + ,1 + ,93.3 + ,1 + ,94.4 + ,1 + ,94.4 + ,1 + ,94.1 + ,1 + ,95.3 + ,1 + ,93.8 + ,1 + ,96.3 + ,1 + ,96.0 + ,1 + ,97.6 + ,1 + ,96.8 + ,1 + ,95.0 + ,1 + ,93.7 + ,1 + ,91.0 + ,1 + ,92.2 + ,1 + ,93.6 + ,1 + ,97.2 + ,1 + ,97.1 + ,1 + ,98.2 + ,1 + ,98.3 + ,1 + ,99.8 + ,1 + ,100.5 + ,1 + ,99.2 + ,1 + ,101.0 + ,1 + ,102.1 + ,1 + ,102.8 + ,1 + ,102.5 + ,1 + ,104.2 + ,1 + ,104.3 + ,1 + ,105.3 + ,1 + ,105.1 + ,1 + ,107.4 + ,1 + ,106.4 + ,1 + ,106.4 + ,1 + ,107.9 + ,1 + ,107.8 + ,1 + ,108.3 + ,1 + ,108.3 + ,1 + ,109.2 + ,1 + ,109.3 + ,1 + ,109.3 + ,1 + ,109.6 + ,1 + ,111.1 + ,1 + ,109.0 + ,1 + ,109.8 + ,1 + ,108.8 + ,1 + ,110.9 + ,1 + ,110.2 + ,1 + ,111.3 + ,1 + ,111.6 + ,1 + ,112.3 + ,1 + ,111.2 + ,1 + ,111.7 + ,1 + ,111.7 + ,1 + ,112.7 + ,1 + ,113.2 + ,1 + ,113.0 + ,1 + ,114.2 + ,1 + ,114.0 + ,1 + ,111.7 + ,1 + ,114.2 + ,1 + ,114.7 + ,1 + ,116.5 + ,1 + ,116.2 + ,1 + ,116.2 + ,1 + ,117.4 + ,1 + ,117.4 + ,1 + ,118.2 + ,1 + ,116.4 + ,1 + ,117.3 + ,1 + ,117.1 + ,1 + ,116.5 + ,1 + ,117.4 + ,1 + ,118.2 + ,1 + ,118.4 + ,1 + ,116.9 + ,1 + ,116.3 + ,1 + ,116.8 + ,1 + ,114.9 + ,1) + ,dim=c(2 + ,225) + ,dimnames=list(c('Y' + ,'D') + ,1:225)) > y <- array(NA,dim=c(2,225),dimnames=list(c('Y','D'),1:225)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Y D t 1 70.5 0 1 2 71.3 0 2 3 71.4 0 3 4 70.1 0 4 5 69.4 0 5 6 69.8 0 6 7 69.8 0 7 8 70.7 0 8 9 69.4 0 9 10 69.8 0 10 11 69.3 0 11 12 72.9 0 12 13 70.0 0 13 14 64.4 0 14 15 63.5 0 15 16 69.8 0 16 17 69.9 0 17 18 69.3 0 18 19 69.7 0 19 20 69.8 0 20 21 70.2 0 21 22 69.8 0 22 23 70.7 0 23 24 71.4 0 24 25 70.3 0 25 26 70.9 0 26 27 70.6 0 27 28 69.0 0 28 29 71.0 0 29 30 74.7 0 30 31 77.5 0 31 32 78.6 0 32 33 75.3 0 33 34 72.1 0 34 35 73.8 0 35 36 73.7 0 36 37 75.2 0 37 38 75.2 0 38 39 74.5 0 39 40 74.4 0 40 41 75.4 0 41 42 73.7 0 42 43 74.3 0 43 44 75.0 0 44 45 75.8 0 45 46 76.7 0 46 47 76.8 0 47 48 76.8 0 48 49 76.4 0 49 50 76.4 0 50 51 77.2 0 51 52 77.2 0 52 53 77.4 0 53 54 78.1 0 54 55 78.5 0 55 56 77.9 0 56 57 78.6 0 57 58 79.8 0 58 59 80.3 0 59 60 80.8 0 60 61 80.5 0 61 62 79.4 0 62 63 79.3 0 63 64 79.6 0 64 65 79.2 0 65 66 79.1 0 66 67 79.8 0 67 68 80.0 0 68 69 80.5 0 69 70 80.4 0 70 71 81.1 0 71 72 82.2 0 72 73 81.5 0 73 74 84.2 0 74 75 84.3 0 75 76 83.3 0 76 77 84.2 0 77 78 84.9 0 78 79 85.0 0 79 80 85.3 0 80 81 85.4 0 81 82 85.8 0 82 83 85.2 0 83 84 86.4 0 84 85 88.2 0 85 86 88.3 0 86 87 88.0 0 87 88 87.8 0 88 89 87.4 0 89 90 87.4 0 90 91 88.0 0 91 92 88.0 0 92 93 89.9 0 93 94 88.4 0 94 95 89.7 0 95 96 89.9 0 96 97 90.5 0 97 98 90.7 0 98 99 89.5 0 99 100 91.2 0 100 101 91.2 0 101 102 89.8 0 102 103 89.6 0 103 104 92.3 0 104 105 90.1 0 105 106 92.9 0 106 107 93.3 0 107 108 93.5 0 108 109 93.4 0 109 110 93.6 0 110 111 93.7 0 111 112 93.6 0 112 113 93.0 0 113 114 94.1 0 114 115 95.7 0 115 116 95.6 0 116 117 97.2 0 117 118 98.1 0 118 119 98.8 0 119 120 95.3 0 120 121 95.3 0 121 122 96.7 0 122 123 99.2 0 123 124 99.0 0 124 125 100.9 0 125 126 100.1 0 126 127 100.4 0 127 128 100.5 0 128 129 102.6 0 129 130 101.8 0 130 131 102.6 0 131 132 101.0 0 132 133 101.6 0 133 134 100.6 0 134 135 100.4 0 135 136 100.7 0 136 137 100.6 0 137 138 100.3 0 138 139 101.4 0 139 140 103.2 0 140 141 79.2 1 141 142 83.4 1 142 143 86.5 1 143 144 91.3 1 144 145 91.5 1 145 146 93.1 1 146 147 93.1 1 147 148 93.3 1 148 149 94.4 1 149 150 94.4 1 150 151 94.1 1 151 152 95.3 1 152 153 93.8 1 153 154 96.3 1 154 155 96.0 1 155 156 97.6 1 156 157 96.8 1 157 158 95.0 1 158 159 93.7 1 159 160 91.0 1 160 161 92.2 1 161 162 93.6 1 162 163 97.2 1 163 164 97.1 1 164 165 98.2 1 165 166 98.3 1 166 167 99.8 1 167 168 100.5 1 168 169 99.2 1 169 170 101.0 1 170 171 102.1 1 171 172 102.8 1 172 173 102.5 1 173 174 104.2 1 174 175 104.3 1 175 176 105.3 1 176 177 105.1 1 177 178 107.4 1 178 179 106.4 1 179 180 106.4 1 180 181 107.9 1 181 182 107.8 1 182 183 108.3 1 183 184 108.3 1 184 185 109.2 1 185 186 109.3 1 186 187 109.3 1 187 188 109.6 1 188 189 111.1 1 189 190 109.0 1 190 191 109.8 1 191 192 108.8 1 192 193 110.9 1 193 194 110.2 1 194 195 111.3 1 195 196 111.6 1 196 197 112.3 1 197 198 111.2 1 198 199 111.7 1 199 200 111.7 1 200 201 112.7 1 201 202 113.2 1 202 203 113.0 1 203 204 114.2 1 204 205 114.0 1 205 206 111.7 1 206 207 114.2 1 207 208 114.7 1 208 209 116.5 1 209 210 116.2 1 210 211 116.2 1 211 212 117.4 1 212 213 117.4 1 213 214 118.2 1 214 215 116.4 1 215 216 117.3 1 216 217 117.1 1 217 218 116.5 1 218 219 117.4 1 219 220 118.2 1 220 221 118.4 1 221 222 116.9 1 222 223 116.3 1 223 224 116.8 1 224 225 114.9 1 225 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) D t 63.6322 -9.4325 0.2804 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -14.5352 -1.4343 -0.2608 1.7510 7.1070 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 63.632236 0.426025 149.36 <2e-16 *** D -9.432540 0.683670 -13.80 <2e-16 *** t 0.280394 0.005103 54.94 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.7 on 222 degrees of freedom Multiple R-squared: 0.9673, Adjusted R-squared: 0.967 F-statistic: 3284 on 2 and 222 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,] 0.0337790248 6.755805e-02 9.662210e-01 [2,] 0.0093708590 1.874172e-02 9.906291e-01 [3,] 0.0070786142 1.415723e-02 9.929214e-01 [4,] 0.0021215697 4.243139e-03 9.978784e-01 [5,] 0.0006179999 1.236000e-03 9.993820e-01 [6,] 0.0001598004 3.196008e-04 9.998402e-01 [7,] 0.0124147691 2.482954e-02 9.875852e-01 [8,] 0.0060582954 1.211659e-02 9.939417e-01 [9,] 0.1757314445 3.514629e-01 8.242686e-01 [10,] 0.3878951942 7.757904e-01 6.121048e-01 [11,] 0.4024330811 8.048662e-01 5.975669e-01 [12,] 0.4003811542 8.007623e-01 5.996188e-01 [13,] 0.3552742609 7.105485e-01 6.447257e-01 [14,] 0.3235188760 6.470378e-01 6.764811e-01 [15,] 0.2904793775 5.809588e-01 7.095206e-01 [16,] 0.2680382100 5.360764e-01 7.319618e-01 [17,] 0.2278399080 4.556798e-01 7.721601e-01 [18,] 0.2145138282 4.290277e-01 7.854862e-01 [19,] 0.2195985897 4.391972e-01 7.804014e-01 [20,] 0.1819155566 3.638311e-01 8.180844e-01 [21,] 0.1577187994 3.154376e-01 8.422812e-01 [22,] 0.1279787234 2.559574e-01 8.720213e-01 [23,] 0.1000810859 2.001622e-01 8.999189e-01 [24,] 0.0830320415 1.660641e-01 9.169680e-01 [25,] 0.1965515296 3.931031e-01 8.034485e-01 [26,] 0.6009871534 7.980257e-01 3.990128e-01 [27,] 0.9060734187 1.878532e-01 9.392658e-02 [28,] 0.9230390833 1.539218e-01 7.696092e-02 [29,] 0.9052822973 1.894354e-01 9.471770e-02 [30,] 0.8911971957 2.176056e-01 1.088028e-01 [31,] 0.8726482396 2.547035e-01 1.273518e-01 [32,] 0.8725851448 2.548297e-01 1.274149e-01 [33,] 0.8675616961 2.648766e-01 1.324383e-01 [34,] 0.8485242877 3.029514e-01 1.514757e-01 [35,] 0.8248986898 3.502026e-01 1.751013e-01 [36,] 0.8102657304 3.794685e-01 1.897343e-01 [37,] 0.7813221853 4.373556e-01 2.186778e-01 [38,] 0.7480421720 5.039157e-01 2.519578e-01 [39,] 0.7146631991 5.706736e-01 2.853368e-01 [40,] 0.6882303749 6.235393e-01 3.117696e-01 [41,] 0.6783659118 6.432682e-01 3.216341e-01 [42,] 0.6640730589 6.718539e-01 3.359269e-01 [43,] 0.6431255089 7.137490e-01 3.568745e-01 [44,] 0.6091236486 7.817527e-01 3.908764e-01 [45,] 0.5714289130 8.571422e-01 4.285711e-01 [46,] 0.5428996409 9.142007e-01 4.571004e-01 [47,] 0.5095457817 9.809084e-01 4.904542e-01 [48,] 0.4756854532 9.513709e-01 5.243145e-01 [49,] 0.4524814684 9.049629e-01 5.475185e-01 [50,] 0.4339291446 8.678583e-01 5.660709e-01 [51,] 0.3978154397 7.956309e-01 6.021846e-01 [52,] 0.3706112590 7.412225e-01 6.293887e-01 [53,] 0.3728181706 7.456363e-01 6.271818e-01 [54,] 0.3845977120 7.691954e-01 6.154023e-01 [55,] 0.4068719660 8.137439e-01 5.931280e-01 [56,] 0.4048364289 8.096729e-01 5.951636e-01 [57,] 0.3706072194 7.412144e-01 6.293928e-01 [58,] 0.3345652990 6.691306e-01 6.654347e-01 [59,] 0.3000189575 6.000379e-01 6.999810e-01 [60,] 0.2664108398 5.328217e-01 7.335892e-01 [61,] 0.2362592354 4.725185e-01 7.637408e-01 [62,] 0.2054561943 4.109124e-01 7.945438e-01 [63,] 0.1769849131 3.539698e-01 8.230151e-01 [64,] 0.1512238756 3.024478e-01 8.487761e-01 [65,] 0.1279013269 2.558027e-01 8.720987e-01 [66,] 0.1076893955 2.153788e-01 8.923106e-01 [67,] 0.0954653590 1.909307e-01 9.045346e-01 [68,] 0.0790547284 1.581095e-01 9.209453e-01 [69,] 0.0908196913 1.816394e-01 9.091803e-01 [70,] 0.0986862622 1.973725e-01 9.013137e-01 [71,] 0.0870021159 1.740042e-01 9.129979e-01 [72,] 0.0831169859 1.662340e-01 9.168830e-01 [73,] 0.0853369919 1.706740e-01 9.146630e-01 [74,] 0.0842992957 1.685986e-01 9.157007e-01 [75,] 0.0832527726 1.665055e-01 9.167472e-01 [76,] 0.0794829549 1.589659e-01 9.205170e-01 [77,] 0.0771588555 1.543177e-01 9.228411e-01 [78,] 0.0665290500 1.330581e-01 9.334710e-01 [79,] 0.0647772451 1.295545e-01 9.352228e-01 [80,] 0.0897985438 1.795971e-01 9.102015e-01 [81,] 0.1136656861 2.273314e-01 8.863343e-01 [82,] 0.1236141917 2.472284e-01 8.763858e-01 [83,] 0.1227187044 2.454374e-01 8.772813e-01 [84,] 0.1111786014 2.223572e-01 8.888214e-01 [85,] 0.0976945628 1.953891e-01 9.023054e-01 [86,] 0.0881769218 1.763538e-01 9.118231e-01 [87,] 0.0770140746 1.540281e-01 9.229859e-01 [88,] 0.0866538625 1.733077e-01 9.133461e-01 [89,] 0.0743831957 1.487664e-01 9.256168e-01 [90,] 0.0716146802 1.432294e-01 9.283853e-01 [91,] 0.0677615125 1.355230e-01 9.322385e-01 [92,] 0.0672903278 1.345807e-01 9.327097e-01 [93,] 0.0655159343 1.310319e-01 9.344841e-01 [94,] 0.0543998267 1.087997e-01 9.456002e-01 [95,] 0.0520158325 1.040317e-01 9.479842e-01 [96,] 0.0473559280 9.471186e-02 9.526441e-01 [97,] 0.0386392575 7.727851e-02 9.613607e-01 [98,] 0.0320359510 6.407190e-02 9.679640e-01 [99,] 0.0301209774 6.024195e-02 9.698790e-01 [100,] 0.0250562738 5.011255e-02 9.749437e-01 [101,] 0.0235665480 4.713310e-02 9.764335e-01 [102,] 0.0224831869 4.496637e-02 9.775168e-01 [103,] 0.0209555012 4.191100e-02 9.790445e-01 [104,] 0.0182552553 3.651051e-02 9.817447e-01 [105,] 0.0156312443 3.126249e-02 9.843688e-01 [106,] 0.0130242827 2.604857e-02 9.869757e-01 [107,] 0.0104597033 2.091941e-02 9.895403e-01 [108,] 0.0083421581 1.668432e-02 9.916578e-01 [109,] 0.0066421688 1.328434e-02 9.933578e-01 [110,] 0.0062165895 1.243318e-02 9.937834e-01 [111,] 0.0053817142 1.076343e-02 9.946183e-01 [112,] 0.0062501754 1.250035e-02 9.937498e-01 [113,] 0.0086401225 1.728025e-02 9.913599e-01 [114,] 0.0132576361 2.651527e-02 9.867424e-01 [115,] 0.0106889897 2.137798e-02 9.893110e-01 [116,] 0.0088339598 1.766792e-02 9.911660e-01 [117,] 0.0071258183 1.425164e-02 9.928742e-01 [118,] 0.0081667005 1.633340e-02 9.918333e-01 [119,] 0.0081212071 1.624241e-02 9.918788e-01 [120,] 0.0128384814 2.567696e-02 9.871615e-01 [121,] 0.0139301402 2.786028e-02 9.860699e-01 [122,] 0.0149557327 2.991147e-02 9.850443e-01 [123,] 0.0152116276 3.042326e-02 9.847884e-01 [124,] 0.0262735401 5.254708e-02 9.737265e-01 [125,] 0.0312623281 6.252466e-02 9.687377e-01 [126,] 0.0428894418 8.577888e-02 9.571106e-01 [127,] 0.0391720019 7.834400e-02 9.608280e-01 [128,] 0.0377757614 7.555152e-02 9.622242e-01 [129,] 0.0312890828 6.257817e-02 9.687109e-01 [130,] 0.0251456238 5.029125e-02 9.748544e-01 [131,] 0.0200460166 4.009203e-02 9.799540e-01 [132,] 0.0158010854 3.160217e-02 9.841989e-01 [133,] 0.0128734339 2.574687e-02 9.871266e-01 [134,] 0.0102660163 2.053203e-02 9.897340e-01 [135,] 0.0085831798 1.716636e-02 9.914168e-01 [136,] 0.1240619027 2.481238e-01 8.759381e-01 [137,] 0.3355648538 6.711297e-01 6.644351e-01 [138,] 0.4970527344 9.941055e-01 5.029473e-01 [139,] 0.6102639870 7.794720e-01 3.897360e-01 [140,] 0.6556273868 6.887452e-01 3.443726e-01 [141,] 0.6993425734 6.013149e-01 3.006574e-01 [142,] 0.7112481444 5.775037e-01 2.887519e-01 [143,] 0.7094581565 5.810837e-01 2.905418e-01 [144,] 0.7149295608 5.701409e-01 2.850704e-01 [145,] 0.7063919214 5.872162e-01 2.936081e-01 [146,] 0.6858524686 6.282951e-01 3.141475e-01 [147,] 0.6739356167 6.521288e-01 3.260644e-01 [148,] 0.6508275419 6.983449e-01 3.491725e-01 [149,] 0.6410656172 7.178688e-01 3.589344e-01 [150,] 0.6170349922 7.659300e-01 3.829650e-01 [151,] 0.6196986555 7.606027e-01 3.803013e-01 [152,] 0.5933609928 8.132780e-01 4.066390e-01 [153,] 0.5746360201 8.507280e-01 4.253640e-01 [154,] 0.6221642101 7.556716e-01 3.778358e-01 [155,] 0.8789735877 2.420528e-01 1.210264e-01 [156,] 0.9776764354 4.464713e-02 2.232356e-02 [157,] 0.9971794824 5.641035e-03 2.820518e-03 [158,] 0.9981895328 3.620934e-03 1.810467e-03 [159,] 0.9992501467 1.499707e-03 7.498533e-04 [160,] 0.9996116776 7.766448e-04 3.883224e-04 [161,] 0.9998680087 2.639825e-04 1.319913e-04 [162,] 0.9999218345 1.563311e-04 7.816555e-05 [163,] 0.9999493825 1.012350e-04 5.061751e-05 [164,] 0.9999947131 1.057390e-05 5.286948e-06 [165,] 0.9999983414 3.317225e-06 1.658613e-06 [166,] 0.9999991642 1.671663e-06 8.358314e-07 [167,] 0.9999994979 1.004249e-06 5.021247e-07 [168,] 0.9999998616 2.767755e-07 1.383877e-07 [169,] 0.9999998881 2.238960e-07 1.119480e-07 [170,] 0.9999999232 1.535177e-07 7.675887e-08 [171,] 0.9999999227 1.546221e-07 7.731105e-08 [172,] 0.9999999417 1.165376e-07 5.826878e-08 [173,] 0.9999999381 1.237585e-07 6.187924e-08 [174,] 0.9999999245 1.510900e-07 7.554502e-08 [175,] 0.9999999178 1.643911e-07 8.219557e-08 [176,] 0.9999998858 2.283755e-07 1.141877e-07 [177,] 0.9999998305 3.390057e-07 1.695028e-07 [178,] 0.9999997416 5.168695e-07 2.584347e-07 [179,] 0.9999995932 8.136437e-07 4.068218e-07 [180,] 0.9999993875 1.225092e-06 6.125461e-07 [181,] 0.9999990191 1.961743e-06 9.808713e-07 [182,] 0.9999983392 3.321583e-06 1.660792e-06 [183,] 0.9999971582 5.683600e-06 2.841800e-06 [184,] 0.9999971011 5.797763e-06 2.898882e-06 [185,] 0.9999955167 8.966636e-06 4.483318e-06 [186,] 0.9999920826 1.583476e-05 7.917379e-06 [187,] 0.9999929343 1.413131e-05 7.065655e-06 [188,] 0.9999869769 2.604615e-05 1.302308e-05 [189,] 0.9999811280 3.774396e-05 1.887198e-05 [190,] 0.9999656470 6.870596e-05 3.435298e-05 [191,] 0.9999373980 1.252039e-04 6.260195e-05 [192,] 0.9998861153 2.277694e-04 1.138847e-04 [193,] 0.9998418316 3.163368e-04 1.581684e-04 [194,] 0.9997695866 4.608268e-04 2.304134e-04 [195,] 0.9997291935 5.416130e-04 2.708065e-04 [196,] 0.9995740305 8.519389e-04 4.259695e-04 [197,] 0.9992929738 1.414052e-03 7.070262e-04 [198,] 0.9990525331 1.894934e-03 9.474669e-04 [199,] 0.9983211752 3.357650e-03 1.678825e-03 [200,] 0.9973584301 5.283140e-03 2.641570e-03 [201,] 0.9998247640 3.504720e-04 1.752360e-04 [202,] 0.9999068036 1.863928e-04 9.319642e-05 [203,] 0.9999635105 7.297906e-05 3.648953e-05 [204,] 0.9999166694 1.666612e-04 8.333062e-05 [205,] 0.9998704073 2.591854e-04 1.295927e-04 [206,] 0.9998600883 2.798234e-04 1.399117e-04 [207,] 0.9996093229 7.813543e-04 3.906771e-04 [208,] 0.9989259810 2.148038e-03 1.074019e-03 [209,] 0.9974337661 5.132468e-03 2.566234e-03 [210,] 0.9960233456 7.953309e-03 3.976654e-03 [211,] 0.9895525422 2.089492e-02 1.044746e-02 [212,] 0.9765863435 4.682731e-02 2.341366e-02 [213,] 0.9818616262 3.627675e-02 1.813837e-02 [214,] 0.9780935881 4.381282e-02 2.190641e-02 > postscript(file="/var/www/html/rcomp/tmp/12zbl1227803728.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ly8t1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3ydu41227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4q9cn1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5rxod1227803728.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 = 225 Frequency = 1 1 2 3 4 5 6.587370e+00 7.106977e+00 6.926583e+00 5.346189e+00 4.365795e+00 6 7 8 9 10 4.485401e+00 4.205007e+00 4.824614e+00 3.244220e+00 3.363826e+00 11 12 13 14 15 2.583432e+00 5.903038e+00 2.722645e+00 -3.157749e+00 -4.338143e+00 16 17 18 19 20 1.681463e+00 1.501069e+00 6.206754e-01 7.402816e-01 5.598878e-01 21 22 23 24 25 6.794940e-01 -8.998481e-04 6.187063e-01 1.038313e+00 -3.420813e-01 26 27 28 29 30 -2.247512e-02 -6.028689e-01 -2.483263e+00 -7.636566e-01 2.655950e+00 31 32 33 34 35 5.175556e+00 5.995162e+00 2.414768e+00 -1.065626e+00 3.539805e-01 36 37 38 39 40 -2.641329e-02 1.193193e+00 9.127991e-01 -6.759475e-02 -4.479886e-01 41 42 43 44 45 2.716176e-01 -1.708776e+00 -1.389170e+00 -9.695638e-01 -4.499577e-01 46 47 48 49 50 1.696485e-01 -1.074529e-02 -2.911391e-01 -9.715329e-01 -1.251927e+00 51 52 53 54 55 -7.323206e-01 -1.012714e+00 -1.093108e+00 -6.735020e-01 -5.538958e-01 56 57 58 59 60 -1.434290e+00 -1.014683e+00 -9.507728e-02 1.245289e-01 3.441351e-01 61 62 63 64 65 -2.362587e-01 -1.616653e+00 -1.997046e+00 -1.977440e+00 -2.657834e+00 66 67 68 69 70 -3.038228e+00 -2.618622e+00 -2.699015e+00 -2.479409e+00 -2.859803e+00 71 72 73 74 75 -2.440197e+00 -1.620591e+00 -2.600985e+00 -1.813784e-01 -3.617722e-01 76 77 78 79 80 -1.642166e+00 -1.022560e+00 -6.029536e-01 -7.833474e-01 -7.637413e-01 81 82 83 84 85 -9.441351e-01 -8.245289e-01 -1.704923e+00 -7.853165e-01 7.342896e-01 86 87 88 89 90 5.538958e-01 -2.649799e-02 -5.068918e-01 -1.187286e+00 -1.467679e+00 91 92 93 94 95 -1.148073e+00 -1.428467e+00 1.911391e-01 -1.589255e+00 -5.696485e-01 96 97 98 99 100 -6.500423e-01 -3.304362e-01 -4.108300e-01 -1.891224e+00 -4.716176e-01 101 102 103 104 105 -7.520114e-01 -2.432405e+00 -2.912799e+00 -4.931929e-01 -2.973587e+00 106 107 108 109 110 -4.539805e-01 -3.343743e-01 -4.147682e-01 -7.951620e-01 -8.755558e-01 111 112 113 114 115 -1.055950e+00 -1.436343e+00 -2.316737e+00 -1.497131e+00 -1.775249e-01 116 117 118 119 120 -5.579187e-01 7.616875e-01 1.381294e+00 1.800900e+00 -1.979494e+00 121 122 123 124 125 -2.259888e+00 -1.140282e+00 1.079325e+00 5.989308e-01 2.218537e+00 126 127 128 129 130 1.138143e+00 1.157749e+00 9.773555e-01 2.796962e+00 1.716568e+00 131 132 133 134 135 2.236174e+00 3.557802e-01 6.753864e-01 -6.050074e-01 -1.085401e+00 136 137 138 139 140 -1.065795e+00 -1.446189e+00 -2.026583e+00 -1.206977e+00 3.126297e-01 141 142 143 144 145 -1.453522e+01 -1.061562e+01 -7.796012e+00 -3.276406e+00 -3.356800e+00 146 147 148 149 150 -2.037193e+00 -2.317587e+00 -2.397981e+00 -1.578375e+00 -1.858769e+00 151 152 153 154 155 -2.439163e+00 -1.519556e+00 -3.299950e+00 -1.080344e+00 -1.660738e+00 156 157 158 159 160 -3.411316e-01 -1.421525e+00 -3.501919e+00 -5.082313e+00 -8.062707e+00 161 162 163 164 165 -7.143101e+00 -6.023495e+00 -2.703888e+00 -3.084282e+00 -2.264676e+00 166 167 168 169 170 -2.445070e+00 -1.225464e+00 -8.058574e-01 -2.386251e+00 -8.666451e-01 171 172 173 174 175 -4.703890e-02 3.725673e-01 -2.078265e-01 1.211780e+00 1.031386e+00 176 177 178 179 180 1.750992e+00 1.270598e+00 3.290204e+00 2.009811e+00 1.729417e+00 181 182 183 184 185 2.949023e+00 2.568629e+00 2.788235e+00 2.507841e+00 3.127448e+00 186 187 188 189 190 2.947054e+00 2.666660e+00 2.686266e+00 3.905872e+00 1.525479e+00 191 192 193 194 195 2.045085e+00 7.646909e-01 2.584297e+00 1.603903e+00 2.423509e+00 196 197 198 199 200 2.443116e+00 2.862722e+00 1.482328e+00 1.701934e+00 1.421540e+00 201 202 203 204 205 2.141147e+00 2.360753e+00 1.880359e+00 2.799965e+00 2.319571e+00 206 207 208 209 210 -2.608225e-01 1.958784e+00 2.178390e+00 3.697996e+00 3.117602e+00 211 212 213 214 215 2.837208e+00 3.756815e+00 3.476421e+00 3.996027e+00 1.915633e+00 216 217 218 219 220 2.535239e+00 2.054845e+00 1.174452e+00 1.794058e+00 2.313664e+00 221 222 223 224 225 2.233270e+00 4.528764e-01 -4.275174e-01 -2.079112e-01 -2.388305e+00 > postscript(file="/var/www/html/rcomp/tmp/61z1t1227803728.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 = 225 Frequency = 1 lag(myerror, k = 1) myerror 0 6.587370e+00 NA 1 7.106977e+00 6.587370e+00 2 6.926583e+00 7.106977e+00 3 5.346189e+00 6.926583e+00 4 4.365795e+00 5.346189e+00 5 4.485401e+00 4.365795e+00 6 4.205007e+00 4.485401e+00 7 4.824614e+00 4.205007e+00 8 3.244220e+00 4.824614e+00 9 3.363826e+00 3.244220e+00 10 2.583432e+00 3.363826e+00 11 5.903038e+00 2.583432e+00 12 2.722645e+00 5.903038e+00 13 -3.157749e+00 2.722645e+00 14 -4.338143e+00 -3.157749e+00 15 1.681463e+00 -4.338143e+00 16 1.501069e+00 1.681463e+00 17 6.206754e-01 1.501069e+00 18 7.402816e-01 6.206754e-01 19 5.598878e-01 7.402816e-01 20 6.794940e-01 5.598878e-01 21 -8.998481e-04 6.794940e-01 22 6.187063e-01 -8.998481e-04 23 1.038313e+00 6.187063e-01 24 -3.420813e-01 1.038313e+00 25 -2.247512e-02 -3.420813e-01 26 -6.028689e-01 -2.247512e-02 27 -2.483263e+00 -6.028689e-01 28 -7.636566e-01 -2.483263e+00 29 2.655950e+00 -7.636566e-01 30 5.175556e+00 2.655950e+00 31 5.995162e+00 5.175556e+00 32 2.414768e+00 5.995162e+00 33 -1.065626e+00 2.414768e+00 34 3.539805e-01 -1.065626e+00 35 -2.641329e-02 3.539805e-01 36 1.193193e+00 -2.641329e-02 37 9.127991e-01 1.193193e+00 38 -6.759475e-02 9.127991e-01 39 -4.479886e-01 -6.759475e-02 40 2.716176e-01 -4.479886e-01 41 -1.708776e+00 2.716176e-01 42 -1.389170e+00 -1.708776e+00 43 -9.695638e-01 -1.389170e+00 44 -4.499577e-01 -9.695638e-01 45 1.696485e-01 -4.499577e-01 46 -1.074529e-02 1.696485e-01 47 -2.911391e-01 -1.074529e-02 48 -9.715329e-01 -2.911391e-01 49 -1.251927e+00 -9.715329e-01 50 -7.323206e-01 -1.251927e+00 51 -1.012714e+00 -7.323206e-01 52 -1.093108e+00 -1.012714e+00 53 -6.735020e-01 -1.093108e+00 54 -5.538958e-01 -6.735020e-01 55 -1.434290e+00 -5.538958e-01 56 -1.014683e+00 -1.434290e+00 57 -9.507728e-02 -1.014683e+00 58 1.245289e-01 -9.507728e-02 59 3.441351e-01 1.245289e-01 60 -2.362587e-01 3.441351e-01 61 -1.616653e+00 -2.362587e-01 62 -1.997046e+00 -1.616653e+00 63 -1.977440e+00 -1.997046e+00 64 -2.657834e+00 -1.977440e+00 65 -3.038228e+00 -2.657834e+00 66 -2.618622e+00 -3.038228e+00 67 -2.699015e+00 -2.618622e+00 68 -2.479409e+00 -2.699015e+00 69 -2.859803e+00 -2.479409e+00 70 -2.440197e+00 -2.859803e+00 71 -1.620591e+00 -2.440197e+00 72 -2.600985e+00 -1.620591e+00 73 -1.813784e-01 -2.600985e+00 74 -3.617722e-01 -1.813784e-01 75 -1.642166e+00 -3.617722e-01 76 -1.022560e+00 -1.642166e+00 77 -6.029536e-01 -1.022560e+00 78 -7.833474e-01 -6.029536e-01 79 -7.637413e-01 -7.833474e-01 80 -9.441351e-01 -7.637413e-01 81 -8.245289e-01 -9.441351e-01 82 -1.704923e+00 -8.245289e-01 83 -7.853165e-01 -1.704923e+00 84 7.342896e-01 -7.853165e-01 85 5.538958e-01 7.342896e-01 86 -2.649799e-02 5.538958e-01 87 -5.068918e-01 -2.649799e-02 88 -1.187286e+00 -5.068918e-01 89 -1.467679e+00 -1.187286e+00 90 -1.148073e+00 -1.467679e+00 91 -1.428467e+00 -1.148073e+00 92 1.911391e-01 -1.428467e+00 93 -1.589255e+00 1.911391e-01 94 -5.696485e-01 -1.589255e+00 95 -6.500423e-01 -5.696485e-01 96 -3.304362e-01 -6.500423e-01 97 -4.108300e-01 -3.304362e-01 98 -1.891224e+00 -4.108300e-01 99 -4.716176e-01 -1.891224e+00 100 -7.520114e-01 -4.716176e-01 101 -2.432405e+00 -7.520114e-01 102 -2.912799e+00 -2.432405e+00 103 -4.931929e-01 -2.912799e+00 104 -2.973587e+00 -4.931929e-01 105 -4.539805e-01 -2.973587e+00 106 -3.343743e-01 -4.539805e-01 107 -4.147682e-01 -3.343743e-01 108 -7.951620e-01 -4.147682e-01 109 -8.755558e-01 -7.951620e-01 110 -1.055950e+00 -8.755558e-01 111 -1.436343e+00 -1.055950e+00 112 -2.316737e+00 -1.436343e+00 113 -1.497131e+00 -2.316737e+00 114 -1.775249e-01 -1.497131e+00 115 -5.579187e-01 -1.775249e-01 116 7.616875e-01 -5.579187e-01 117 1.381294e+00 7.616875e-01 118 1.800900e+00 1.381294e+00 119 -1.979494e+00 1.800900e+00 120 -2.259888e+00 -1.979494e+00 121 -1.140282e+00 -2.259888e+00 122 1.079325e+00 -1.140282e+00 123 5.989308e-01 1.079325e+00 124 2.218537e+00 5.989308e-01 125 1.138143e+00 2.218537e+00 126 1.157749e+00 1.138143e+00 127 9.773555e-01 1.157749e+00 128 2.796962e+00 9.773555e-01 129 1.716568e+00 2.796962e+00 130 2.236174e+00 1.716568e+00 131 3.557802e-01 2.236174e+00 132 6.753864e-01 3.557802e-01 133 -6.050074e-01 6.753864e-01 134 -1.085401e+00 -6.050074e-01 135 -1.065795e+00 -1.085401e+00 136 -1.446189e+00 -1.065795e+00 137 -2.026583e+00 -1.446189e+00 138 -1.206977e+00 -2.026583e+00 139 3.126297e-01 -1.206977e+00 140 -1.453522e+01 3.126297e-01 141 -1.061562e+01 -1.453522e+01 142 -7.796012e+00 -1.061562e+01 143 -3.276406e+00 -7.796012e+00 144 -3.356800e+00 -3.276406e+00 145 -2.037193e+00 -3.356800e+00 146 -2.317587e+00 -2.037193e+00 147 -2.397981e+00 -2.317587e+00 148 -1.578375e+00 -2.397981e+00 149 -1.858769e+00 -1.578375e+00 150 -2.439163e+00 -1.858769e+00 151 -1.519556e+00 -2.439163e+00 152 -3.299950e+00 -1.519556e+00 153 -1.080344e+00 -3.299950e+00 154 -1.660738e+00 -1.080344e+00 155 -3.411316e-01 -1.660738e+00 156 -1.421525e+00 -3.411316e-01 157 -3.501919e+00 -1.421525e+00 158 -5.082313e+00 -3.501919e+00 159 -8.062707e+00 -5.082313e+00 160 -7.143101e+00 -8.062707e+00 161 -6.023495e+00 -7.143101e+00 162 -2.703888e+00 -6.023495e+00 163 -3.084282e+00 -2.703888e+00 164 -2.264676e+00 -3.084282e+00 165 -2.445070e+00 -2.264676e+00 166 -1.225464e+00 -2.445070e+00 167 -8.058574e-01 -1.225464e+00 168 -2.386251e+00 -8.058574e-01 169 -8.666451e-01 -2.386251e+00 170 -4.703890e-02 -8.666451e-01 171 3.725673e-01 -4.703890e-02 172 -2.078265e-01 3.725673e-01 173 1.211780e+00 -2.078265e-01 174 1.031386e+00 1.211780e+00 175 1.750992e+00 1.031386e+00 176 1.270598e+00 1.750992e+00 177 3.290204e+00 1.270598e+00 178 2.009811e+00 3.290204e+00 179 1.729417e+00 2.009811e+00 180 2.949023e+00 1.729417e+00 181 2.568629e+00 2.949023e+00 182 2.788235e+00 2.568629e+00 183 2.507841e+00 2.788235e+00 184 3.127448e+00 2.507841e+00 185 2.947054e+00 3.127448e+00 186 2.666660e+00 2.947054e+00 187 2.686266e+00 2.666660e+00 188 3.905872e+00 2.686266e+00 189 1.525479e+00 3.905872e+00 190 2.045085e+00 1.525479e+00 191 7.646909e-01 2.045085e+00 192 2.584297e+00 7.646909e-01 193 1.603903e+00 2.584297e+00 194 2.423509e+00 1.603903e+00 195 2.443116e+00 2.423509e+00 196 2.862722e+00 2.443116e+00 197 1.482328e+00 2.862722e+00 198 1.701934e+00 1.482328e+00 199 1.421540e+00 1.701934e+00 200 2.141147e+00 1.421540e+00 201 2.360753e+00 2.141147e+00 202 1.880359e+00 2.360753e+00 203 2.799965e+00 1.880359e+00 204 2.319571e+00 2.799965e+00 205 -2.608225e-01 2.319571e+00 206 1.958784e+00 -2.608225e-01 207 2.178390e+00 1.958784e+00 208 3.697996e+00 2.178390e+00 209 3.117602e+00 3.697996e+00 210 2.837208e+00 3.117602e+00 211 3.756815e+00 2.837208e+00 212 3.476421e+00 3.756815e+00 213 3.996027e+00 3.476421e+00 214 1.915633e+00 3.996027e+00 215 2.535239e+00 1.915633e+00 216 2.054845e+00 2.535239e+00 217 1.174452e+00 2.054845e+00 218 1.794058e+00 1.174452e+00 219 2.313664e+00 1.794058e+00 220 2.233270e+00 2.313664e+00 221 4.528764e-01 2.233270e+00 222 -4.275174e-01 4.528764e-01 223 -2.079112e-01 -4.275174e-01 224 -2.388305e+00 -2.079112e-01 225 NA -2.388305e+00 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 7.106977e+00 6.587370e+00 [2,] 6.926583e+00 7.106977e+00 [3,] 5.346189e+00 6.926583e+00 [4,] 4.365795e+00 5.346189e+00 [5,] 4.485401e+00 4.365795e+00 [6,] 4.205007e+00 4.485401e+00 [7,] 4.824614e+00 4.205007e+00 [8,] 3.244220e+00 4.824614e+00 [9,] 3.363826e+00 3.244220e+00 [10,] 2.583432e+00 3.363826e+00 [11,] 5.903038e+00 2.583432e+00 [12,] 2.722645e+00 5.903038e+00 [13,] -3.157749e+00 2.722645e+00 [14,] -4.338143e+00 -3.157749e+00 [15,] 1.681463e+00 -4.338143e+00 [16,] 1.501069e+00 1.681463e+00 [17,] 6.206754e-01 1.501069e+00 [18,] 7.402816e-01 6.206754e-01 [19,] 5.598878e-01 7.402816e-01 [20,] 6.794940e-01 5.598878e-01 [21,] -8.998481e-04 6.794940e-01 [22,] 6.187063e-01 -8.998481e-04 [23,] 1.038313e+00 6.187063e-01 [24,] -3.420813e-01 1.038313e+00 [25,] -2.247512e-02 -3.420813e-01 [26,] -6.028689e-01 -2.247512e-02 [27,] -2.483263e+00 -6.028689e-01 [28,] -7.636566e-01 -2.483263e+00 [29,] 2.655950e+00 -7.636566e-01 [30,] 5.175556e+00 2.655950e+00 [31,] 5.995162e+00 5.175556e+00 [32,] 2.414768e+00 5.995162e+00 [33,] -1.065626e+00 2.414768e+00 [34,] 3.539805e-01 -1.065626e+00 [35,] -2.641329e-02 3.539805e-01 [36,] 1.193193e+00 -2.641329e-02 [37,] 9.127991e-01 1.193193e+00 [38,] -6.759475e-02 9.127991e-01 [39,] -4.479886e-01 -6.759475e-02 [40,] 2.716176e-01 -4.479886e-01 [41,] -1.708776e+00 2.716176e-01 [42,] -1.389170e+00 -1.708776e+00 [43,] -9.695638e-01 -1.389170e+00 [44,] -4.499577e-01 -9.695638e-01 [45,] 1.696485e-01 -4.499577e-01 [46,] -1.074529e-02 1.696485e-01 [47,] -2.911391e-01 -1.074529e-02 [48,] -9.715329e-01 -2.911391e-01 [49,] -1.251927e+00 -9.715329e-01 [50,] -7.323206e-01 -1.251927e+00 [51,] -1.012714e+00 -7.323206e-01 [52,] -1.093108e+00 -1.012714e+00 [53,] -6.735020e-01 -1.093108e+00 [54,] -5.538958e-01 -6.735020e-01 [55,] -1.434290e+00 -5.538958e-01 [56,] -1.014683e+00 -1.434290e+00 [57,] -9.507728e-02 -1.014683e+00 [58,] 1.245289e-01 -9.507728e-02 [59,] 3.441351e-01 1.245289e-01 [60,] -2.362587e-01 3.441351e-01 [61,] -1.616653e+00 -2.362587e-01 [62,] -1.997046e+00 -1.616653e+00 [63,] -1.977440e+00 -1.997046e+00 [64,] -2.657834e+00 -1.977440e+00 [65,] -3.038228e+00 -2.657834e+00 [66,] -2.618622e+00 -3.038228e+00 [67,] -2.699015e+00 -2.618622e+00 [68,] -2.479409e+00 -2.699015e+00 [69,] -2.859803e+00 -2.479409e+00 [70,] -2.440197e+00 -2.859803e+00 [71,] -1.620591e+00 -2.440197e+00 [72,] -2.600985e+00 -1.620591e+00 [73,] -1.813784e-01 -2.600985e+00 [74,] -3.617722e-01 -1.813784e-01 [75,] -1.642166e+00 -3.617722e-01 [76,] -1.022560e+00 -1.642166e+00 [77,] -6.029536e-01 -1.022560e+00 [78,] -7.833474e-01 -6.029536e-01 [79,] -7.637413e-01 -7.833474e-01 [80,] -9.441351e-01 -7.637413e-01 [81,] -8.245289e-01 -9.441351e-01 [82,] -1.704923e+00 -8.245289e-01 [83,] -7.853165e-01 -1.704923e+00 [84,] 7.342896e-01 -7.853165e-01 [85,] 5.538958e-01 7.342896e-01 [86,] -2.649799e-02 5.538958e-01 [87,] -5.068918e-01 -2.649799e-02 [88,] -1.187286e+00 -5.068918e-01 [89,] -1.467679e+00 -1.187286e+00 [90,] -1.148073e+00 -1.467679e+00 [91,] -1.428467e+00 -1.148073e+00 [92,] 1.911391e-01 -1.428467e+00 [93,] -1.589255e+00 1.911391e-01 [94,] -5.696485e-01 -1.589255e+00 [95,] -6.500423e-01 -5.696485e-01 [96,] -3.304362e-01 -6.500423e-01 [97,] -4.108300e-01 -3.304362e-01 [98,] -1.891224e+00 -4.108300e-01 [99,] -4.716176e-01 -1.891224e+00 [100,] -7.520114e-01 -4.716176e-01 [101,] -2.432405e+00 -7.520114e-01 [102,] -2.912799e+00 -2.432405e+00 [103,] -4.931929e-01 -2.912799e+00 [104,] -2.973587e+00 -4.931929e-01 [105,] -4.539805e-01 -2.973587e+00 [106,] -3.343743e-01 -4.539805e-01 [107,] -4.147682e-01 -3.343743e-01 [108,] -7.951620e-01 -4.147682e-01 [109,] -8.755558e-01 -7.951620e-01 [110,] -1.055950e+00 -8.755558e-01 [111,] -1.436343e+00 -1.055950e+00 [112,] -2.316737e+00 -1.436343e+00 [113,] -1.497131e+00 -2.316737e+00 [114,] -1.775249e-01 -1.497131e+00 [115,] -5.579187e-01 -1.775249e-01 [116,] 7.616875e-01 -5.579187e-01 [117,] 1.381294e+00 7.616875e-01 [118,] 1.800900e+00 1.381294e+00 [119,] -1.979494e+00 1.800900e+00 [120,] -2.259888e+00 -1.979494e+00 [121,] -1.140282e+00 -2.259888e+00 [122,] 1.079325e+00 -1.140282e+00 [123,] 5.989308e-01 1.079325e+00 [124,] 2.218537e+00 5.989308e-01 [125,] 1.138143e+00 2.218537e+00 [126,] 1.157749e+00 1.138143e+00 [127,] 9.773555e-01 1.157749e+00 [128,] 2.796962e+00 9.773555e-01 [129,] 1.716568e+00 2.796962e+00 [130,] 2.236174e+00 1.716568e+00 [131,] 3.557802e-01 2.236174e+00 [132,] 6.753864e-01 3.557802e-01 [133,] -6.050074e-01 6.753864e-01 [134,] -1.085401e+00 -6.050074e-01 [135,] -1.065795e+00 -1.085401e+00 [136,] -1.446189e+00 -1.065795e+00 [137,] -2.026583e+00 -1.446189e+00 [138,] -1.206977e+00 -2.026583e+00 [139,] 3.126297e-01 -1.206977e+00 [140,] -1.453522e+01 3.126297e-01 [141,] -1.061562e+01 -1.453522e+01 [142,] -7.796012e+00 -1.061562e+01 [143,] -3.276406e+00 -7.796012e+00 [144,] -3.356800e+00 -3.276406e+00 [145,] -2.037193e+00 -3.356800e+00 [146,] -2.317587e+00 -2.037193e+00 [147,] -2.397981e+00 -2.317587e+00 [148,] -1.578375e+00 -2.397981e+00 [149,] -1.858769e+00 -1.578375e+00 [150,] -2.439163e+00 -1.858769e+00 [151,] -1.519556e+00 -2.439163e+00 [152,] -3.299950e+00 -1.519556e+00 [153,] -1.080344e+00 -3.299950e+00 [154,] -1.660738e+00 -1.080344e+00 [155,] -3.411316e-01 -1.660738e+00 [156,] -1.421525e+00 -3.411316e-01 [157,] -3.501919e+00 -1.421525e+00 [158,] -5.082313e+00 -3.501919e+00 [159,] -8.062707e+00 -5.082313e+00 [160,] -7.143101e+00 -8.062707e+00 [161,] -6.023495e+00 -7.143101e+00 [162,] -2.703888e+00 -6.023495e+00 [163,] -3.084282e+00 -2.703888e+00 [164,] -2.264676e+00 -3.084282e+00 [165,] -2.445070e+00 -2.264676e+00 [166,] -1.225464e+00 -2.445070e+00 [167,] -8.058574e-01 -1.225464e+00 [168,] -2.386251e+00 -8.058574e-01 [169,] -8.666451e-01 -2.386251e+00 [170,] -4.703890e-02 -8.666451e-01 [171,] 3.725673e-01 -4.703890e-02 [172,] -2.078265e-01 3.725673e-01 [173,] 1.211780e+00 -2.078265e-01 [174,] 1.031386e+00 1.211780e+00 [175,] 1.750992e+00 1.031386e+00 [176,] 1.270598e+00 1.750992e+00 [177,] 3.290204e+00 1.270598e+00 [178,] 2.009811e+00 3.290204e+00 [179,] 1.729417e+00 2.009811e+00 [180,] 2.949023e+00 1.729417e+00 [181,] 2.568629e+00 2.949023e+00 [182,] 2.788235e+00 2.568629e+00 [183,] 2.507841e+00 2.788235e+00 [184,] 3.127448e+00 2.507841e+00 [185,] 2.947054e+00 3.127448e+00 [186,] 2.666660e+00 2.947054e+00 [187,] 2.686266e+00 2.666660e+00 [188,] 3.905872e+00 2.686266e+00 [189,] 1.525479e+00 3.905872e+00 [190,] 2.045085e+00 1.525479e+00 [191,] 7.646909e-01 2.045085e+00 [192,] 2.584297e+00 7.646909e-01 [193,] 1.603903e+00 2.584297e+00 [194,] 2.423509e+00 1.603903e+00 [195,] 2.443116e+00 2.423509e+00 [196,] 2.862722e+00 2.443116e+00 [197,] 1.482328e+00 2.862722e+00 [198,] 1.701934e+00 1.482328e+00 [199,] 1.421540e+00 1.701934e+00 [200,] 2.141147e+00 1.421540e+00 [201,] 2.360753e+00 2.141147e+00 [202,] 1.880359e+00 2.360753e+00 [203,] 2.799965e+00 1.880359e+00 [204,] 2.319571e+00 2.799965e+00 [205,] -2.608225e-01 2.319571e+00 [206,] 1.958784e+00 -2.608225e-01 [207,] 2.178390e+00 1.958784e+00 [208,] 3.697996e+00 2.178390e+00 [209,] 3.117602e+00 3.697996e+00 [210,] 2.837208e+00 3.117602e+00 [211,] 3.756815e+00 2.837208e+00 [212,] 3.476421e+00 3.756815e+00 [213,] 3.996027e+00 3.476421e+00 [214,] 1.915633e+00 3.996027e+00 [215,] 2.535239e+00 1.915633e+00 [216,] 2.054845e+00 2.535239e+00 [217,] 1.174452e+00 2.054845e+00 [218,] 1.794058e+00 1.174452e+00 [219,] 2.313664e+00 1.794058e+00 [220,] 2.233270e+00 2.313664e+00 [221,] 4.528764e-01 2.233270e+00 [222,] -4.275174e-01 4.528764e-01 [223,] -2.079112e-01 -4.275174e-01 [224,] -2.388305e+00 -2.079112e-01 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 7.106977e+00 6.587370e+00 2 6.926583e+00 7.106977e+00 3 5.346189e+00 6.926583e+00 4 4.365795e+00 5.346189e+00 5 4.485401e+00 4.365795e+00 6 4.205007e+00 4.485401e+00 7 4.824614e+00 4.205007e+00 8 3.244220e+00 4.824614e+00 9 3.363826e+00 3.244220e+00 10 2.583432e+00 3.363826e+00 11 5.903038e+00 2.583432e+00 12 2.722645e+00 5.903038e+00 13 -3.157749e+00 2.722645e+00 14 -4.338143e+00 -3.157749e+00 15 1.681463e+00 -4.338143e+00 16 1.501069e+00 1.681463e+00 17 6.206754e-01 1.501069e+00 18 7.402816e-01 6.206754e-01 19 5.598878e-01 7.402816e-01 20 6.794940e-01 5.598878e-01 21 -8.998481e-04 6.794940e-01 22 6.187063e-01 -8.998481e-04 23 1.038313e+00 6.187063e-01 24 -3.420813e-01 1.038313e+00 25 -2.247512e-02 -3.420813e-01 26 -6.028689e-01 -2.247512e-02 27 -2.483263e+00 -6.028689e-01 28 -7.636566e-01 -2.483263e+00 29 2.655950e+00 -7.636566e-01 30 5.175556e+00 2.655950e+00 31 5.995162e+00 5.175556e+00 32 2.414768e+00 5.995162e+00 33 -1.065626e+00 2.414768e+00 34 3.539805e-01 -1.065626e+00 35 -2.641329e-02 3.539805e-01 36 1.193193e+00 -2.641329e-02 37 9.127991e-01 1.193193e+00 38 -6.759475e-02 9.127991e-01 39 -4.479886e-01 -6.759475e-02 40 2.716176e-01 -4.479886e-01 41 -1.708776e+00 2.716176e-01 42 -1.389170e+00 -1.708776e+00 43 -9.695638e-01 -1.389170e+00 44 -4.499577e-01 -9.695638e-01 45 1.696485e-01 -4.499577e-01 46 -1.074529e-02 1.696485e-01 47 -2.911391e-01 -1.074529e-02 48 -9.715329e-01 -2.911391e-01 49 -1.251927e+00 -9.715329e-01 50 -7.323206e-01 -1.251927e+00 51 -1.012714e+00 -7.323206e-01 52 -1.093108e+00 -1.012714e+00 53 -6.735020e-01 -1.093108e+00 54 -5.538958e-01 -6.735020e-01 55 -1.434290e+00 -5.538958e-01 56 -1.014683e+00 -1.434290e+00 57 -9.507728e-02 -1.014683e+00 58 1.245289e-01 -9.507728e-02 59 3.441351e-01 1.245289e-01 60 -2.362587e-01 3.441351e-01 61 -1.616653e+00 -2.362587e-01 62 -1.997046e+00 -1.616653e+00 63 -1.977440e+00 -1.997046e+00 64 -2.657834e+00 -1.977440e+00 65 -3.038228e+00 -2.657834e+00 66 -2.618622e+00 -3.038228e+00 67 -2.699015e+00 -2.618622e+00 68 -2.479409e+00 -2.699015e+00 69 -2.859803e+00 -2.479409e+00 70 -2.440197e+00 -2.859803e+00 71 -1.620591e+00 -2.440197e+00 72 -2.600985e+00 -1.620591e+00 73 -1.813784e-01 -2.600985e+00 74 -3.617722e-01 -1.813784e-01 75 -1.642166e+00 -3.617722e-01 76 -1.022560e+00 -1.642166e+00 77 -6.029536e-01 -1.022560e+00 78 -7.833474e-01 -6.029536e-01 79 -7.637413e-01 -7.833474e-01 80 -9.441351e-01 -7.637413e-01 81 -8.245289e-01 -9.441351e-01 82 -1.704923e+00 -8.245289e-01 83 -7.853165e-01 -1.704923e+00 84 7.342896e-01 -7.853165e-01 85 5.538958e-01 7.342896e-01 86 -2.649799e-02 5.538958e-01 87 -5.068918e-01 -2.649799e-02 88 -1.187286e+00 -5.068918e-01 89 -1.467679e+00 -1.187286e+00 90 -1.148073e+00 -1.467679e+00 91 -1.428467e+00 -1.148073e+00 92 1.911391e-01 -1.428467e+00 93 -1.589255e+00 1.911391e-01 94 -5.696485e-01 -1.589255e+00 95 -6.500423e-01 -5.696485e-01 96 -3.304362e-01 -6.500423e-01 97 -4.108300e-01 -3.304362e-01 98 -1.891224e+00 -4.108300e-01 99 -4.716176e-01 -1.891224e+00 100 -7.520114e-01 -4.716176e-01 101 -2.432405e+00 -7.520114e-01 102 -2.912799e+00 -2.432405e+00 103 -4.931929e-01 -2.912799e+00 104 -2.973587e+00 -4.931929e-01 105 -4.539805e-01 -2.973587e+00 106 -3.343743e-01 -4.539805e-01 107 -4.147682e-01 -3.343743e-01 108 -7.951620e-01 -4.147682e-01 109 -8.755558e-01 -7.951620e-01 110 -1.055950e+00 -8.755558e-01 111 -1.436343e+00 -1.055950e+00 112 -2.316737e+00 -1.436343e+00 113 -1.497131e+00 -2.316737e+00 114 -1.775249e-01 -1.497131e+00 115 -5.579187e-01 -1.775249e-01 116 7.616875e-01 -5.579187e-01 117 1.381294e+00 7.616875e-01 118 1.800900e+00 1.381294e+00 119 -1.979494e+00 1.800900e+00 120 -2.259888e+00 -1.979494e+00 121 -1.140282e+00 -2.259888e+00 122 1.079325e+00 -1.140282e+00 123 5.989308e-01 1.079325e+00 124 2.218537e+00 5.989308e-01 125 1.138143e+00 2.218537e+00 126 1.157749e+00 1.138143e+00 127 9.773555e-01 1.157749e+00 128 2.796962e+00 9.773555e-01 129 1.716568e+00 2.796962e+00 130 2.236174e+00 1.716568e+00 131 3.557802e-01 2.236174e+00 132 6.753864e-01 3.557802e-01 133 -6.050074e-01 6.753864e-01 134 -1.085401e+00 -6.050074e-01 135 -1.065795e+00 -1.085401e+00 136 -1.446189e+00 -1.065795e+00 137 -2.026583e+00 -1.446189e+00 138 -1.206977e+00 -2.026583e+00 139 3.126297e-01 -1.206977e+00 140 -1.453522e+01 3.126297e-01 141 -1.061562e+01 -1.453522e+01 142 -7.796012e+00 -1.061562e+01 143 -3.276406e+00 -7.796012e+00 144 -3.356800e+00 -3.276406e+00 145 -2.037193e+00 -3.356800e+00 146 -2.317587e+00 -2.037193e+00 147 -2.397981e+00 -2.317587e+00 148 -1.578375e+00 -2.397981e+00 149 -1.858769e+00 -1.578375e+00 150 -2.439163e+00 -1.858769e+00 151 -1.519556e+00 -2.439163e+00 152 -3.299950e+00 -1.519556e+00 153 -1.080344e+00 -3.299950e+00 154 -1.660738e+00 -1.080344e+00 155 -3.411316e-01 -1.660738e+00 156 -1.421525e+00 -3.411316e-01 157 -3.501919e+00 -1.421525e+00 158 -5.082313e+00 -3.501919e+00 159 -8.062707e+00 -5.082313e+00 160 -7.143101e+00 -8.062707e+00 161 -6.023495e+00 -7.143101e+00 162 -2.703888e+00 -6.023495e+00 163 -3.084282e+00 -2.703888e+00 164 -2.264676e+00 -3.084282e+00 165 -2.445070e+00 -2.264676e+00 166 -1.225464e+00 -2.445070e+00 167 -8.058574e-01 -1.225464e+00 168 -2.386251e+00 -8.058574e-01 169 -8.666451e-01 -2.386251e+00 170 -4.703890e-02 -8.666451e-01 171 3.725673e-01 -4.703890e-02 172 -2.078265e-01 3.725673e-01 173 1.211780e+00 -2.078265e-01 174 1.031386e+00 1.211780e+00 175 1.750992e+00 1.031386e+00 176 1.270598e+00 1.750992e+00 177 3.290204e+00 1.270598e+00 178 2.009811e+00 3.290204e+00 179 1.729417e+00 2.009811e+00 180 2.949023e+00 1.729417e+00 181 2.568629e+00 2.949023e+00 182 2.788235e+00 2.568629e+00 183 2.507841e+00 2.788235e+00 184 3.127448e+00 2.507841e+00 185 2.947054e+00 3.127448e+00 186 2.666660e+00 2.947054e+00 187 2.686266e+00 2.666660e+00 188 3.905872e+00 2.686266e+00 189 1.525479e+00 3.905872e+00 190 2.045085e+00 1.525479e+00 191 7.646909e-01 2.045085e+00 192 2.584297e+00 7.646909e-01 193 1.603903e+00 2.584297e+00 194 2.423509e+00 1.603903e+00 195 2.443116e+00 2.423509e+00 196 2.862722e+00 2.443116e+00 197 1.482328e+00 2.862722e+00 198 1.701934e+00 1.482328e+00 199 1.421540e+00 1.701934e+00 200 2.141147e+00 1.421540e+00 201 2.360753e+00 2.141147e+00 202 1.880359e+00 2.360753e+00 203 2.799965e+00 1.880359e+00 204 2.319571e+00 2.799965e+00 205 -2.608225e-01 2.319571e+00 206 1.958784e+00 -2.608225e-01 207 2.178390e+00 1.958784e+00 208 3.697996e+00 2.178390e+00 209 3.117602e+00 3.697996e+00 210 2.837208e+00 3.117602e+00 211 3.756815e+00 2.837208e+00 212 3.476421e+00 3.756815e+00 213 3.996027e+00 3.476421e+00 214 1.915633e+00 3.996027e+00 215 2.535239e+00 1.915633e+00 216 2.054845e+00 2.535239e+00 217 1.174452e+00 2.054845e+00 218 1.794058e+00 1.174452e+00 219 2.313664e+00 1.794058e+00 220 2.233270e+00 2.313664e+00 221 4.528764e-01 2.233270e+00 222 -4.275174e-01 4.528764e-01 223 -2.079112e-01 -4.275174e-01 224 -2.388305e+00 -2.079112e-01 > 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/7h78v1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/87d9r1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/983561227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10rdy21227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11zhcj1227803728.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/12ibti1227803728.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/1315eb1227803728.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/14g8gc1227803728.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/15don71227803728.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/167exw1227803728.tab") + } > > system("convert tmp/12zbl1227803728.ps tmp/12zbl1227803728.png") > system("convert tmp/2ly8t1227803728.ps tmp/2ly8t1227803728.png") > system("convert tmp/3ydu41227803728.ps tmp/3ydu41227803728.png") > system("convert tmp/4q9cn1227803728.ps tmp/4q9cn1227803728.png") > system("convert tmp/5rxod1227803728.ps tmp/5rxod1227803728.png") > system("convert tmp/61z1t1227803728.ps tmp/61z1t1227803728.png") > system("convert tmp/7h78v1227803728.ps tmp/7h78v1227803728.png") > system("convert tmp/87d9r1227803728.ps tmp/87d9r1227803728.png") > system("convert tmp/983561227803728.ps tmp/983561227803728.png") > system("convert tmp/10rdy21227803728.ps tmp/10rdy21227803728.png") > > > proc.time() user system elapsed 5.056 1.732 5.503