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Type 'q()' to quit R. > x <- array(list(206010 + ,0 + ,198112 + ,0 + ,194519 + ,0 + ,185705 + ,0 + ,180173 + ,0 + ,176142 + ,0 + ,203401 + ,0 + ,221902 + ,0 + ,197378 + ,0 + ,185001 + ,0 + ,176356 + ,0 + ,180449 + ,0 + ,180144 + ,0 + ,173666 + ,0 + ,165688 + ,0 + ,161570 + ,0 + ,156145 + ,0 + ,153730 + ,0 + ,182698 + ,0 + ,200765 + ,0 + ,176512 + ,0 + ,166618 + ,0 + ,158644 + ,0 + ,159585 + ,0 + ,163095 + ,0 + ,159044 + ,0 + ,155511 + ,0 + ,153745 + ,0 + ,150569 + ,0 + ,150605 + ,0 + ,179612 + ,0 + ,194690 + ,0 + ,189917 + ,0 + ,184128 + ,0 + ,175335 + ,0 + ,179566 + ,0 + ,181140 + ,0 + ,177876 + ,0 + ,175041 + ,0 + ,169292 + ,0 + ,166070 + ,0 + ,166972 + ,0 + ,206348 + ,0 + ,215706 + ,0 + ,202108 + ,0 + ,195411 + ,0 + ,193111 + ,0 + ,195198 + ,0 + ,198770 + ,0 + ,194163 + ,0 + ,190420 + ,0 + ,189733 + ,0 + ,186029 + ,0 + ,191531 + ,0 + ,232571 + ,0 + ,243477 + ,0 + ,227247 + ,0 + ,217859 + ,0 + ,208679 + ,0 + ,213188 + ,0 + ,216234 + ,0 + ,213586 + ,0 + ,209465 + ,0 + ,204045 + ,0 + ,200237 + ,0 + ,203666 + ,0 + ,241476 + ,0 + ,260307 + ,0 + ,243324 + ,0 + ,244460 + ,0 + ,233575 + ,0 + ,237217 + ,0 + ,235243 + ,0 + ,230354 + ,0 + ,227184 + ,0 + ,221678 + ,0 + ,217142 + ,0 + ,219452 + ,0 + ,256446 + ,0 + ,265845 + ,0 + ,248624 + ,0 + ,241114 + ,0 + ,229245 + ,0 + ,231805 + ,0 + ,219277 + ,0 + ,219313 + ,0 + ,212610 + ,0 + ,214771 + ,0 + ,211142 + ,0 + ,211457 + ,0 + ,240048 + ,0 + ,240636 + ,0 + ,230580 + ,0 + ,208795 + ,0 + ,197922 + ,0 + ,194596 + ,0 + ,194581 + ,0 + ,185686 + ,0 + ,178106 + ,0 + ,172608 + ,0 + ,167302 + ,0 + ,168053 + ,0 + ,202300 + ,0 + ,202388 + ,0 + ,182516 + ,0 + ,173476 + ,0 + ,166444 + ,0 + ,171297 + ,0 + ,169701 + ,0 + ,164182 + ,0 + ,161914 + ,0 + ,159612 + ,0 + ,151001 + ,0 + ,158114 + ,0 + ,186530 + ,1 + ,187069 + ,1 + ,174330 + ,1 + ,169362 + ,1 + ,166827 + ,1 + ,178037 + ,1 + ,186413 + ,1 + ,189226 + ,1 + ,191563 + ,1 + ,188906 + ,1 + ,186005 + ,1 + ,195309 + ,1 + ,223532 + ,1 + ,226899 + ,1 + ,214126 + ,1 + ,206903 + ,1 + ,204442 + ,1 + ,220375 + ,1 + ,214320 + ,1 + ,212588 + ,1 + ,205816 + ,1 + ,202196 + ,1 + ,195722 + ,1 + ,198563 + ,1 + ,229139 + ,1 + ,229527 + ,1 + ,211868 + ,1 + ,203555 + ,1 + ,195770 + ,1) + ,dim=c(2 + ,143) + ,dimnames=list(c('Werkloosheid' + ,'Dummy_crisis') + ,1:143)) > y <- array(NA,dim=c(2,143),dimnames=list(c('Werkloosheid','Dummy_crisis'),1:143)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Werkloosheid Dummy_crisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 206010 0 1 0 0 0 0 0 0 0 0 0 0 2 198112 0 0 1 0 0 0 0 0 0 0 0 0 3 194519 0 0 0 1 0 0 0 0 0 0 0 0 4 185705 0 0 0 0 1 0 0 0 0 0 0 0 5 180173 0 0 0 0 0 1 0 0 0 0 0 0 6 176142 0 0 0 0 0 0 1 0 0 0 0 0 7 203401 0 0 0 0 0 0 0 1 0 0 0 0 8 221902 0 0 0 0 0 0 0 0 1 0 0 0 9 197378 0 0 0 0 0 0 0 0 0 1 0 0 10 185001 0 0 0 0 0 0 0 0 0 0 1 0 11 176356 0 0 0 0 0 0 0 0 0 0 0 1 12 180449 0 0 0 0 0 0 0 0 0 0 0 0 13 180144 0 1 0 0 0 0 0 0 0 0 0 0 14 173666 0 0 1 0 0 0 0 0 0 0 0 0 15 165688 0 0 0 1 0 0 0 0 0 0 0 0 16 161570 0 0 0 0 1 0 0 0 0 0 0 0 17 156145 0 0 0 0 0 1 0 0 0 0 0 0 18 153730 0 0 0 0 0 0 1 0 0 0 0 0 19 182698 0 0 0 0 0 0 0 1 0 0 0 0 20 200765 0 0 0 0 0 0 0 0 1 0 0 0 21 176512 0 0 0 0 0 0 0 0 0 1 0 0 22 166618 0 0 0 0 0 0 0 0 0 0 1 0 23 158644 0 0 0 0 0 0 0 0 0 0 0 1 24 159585 0 0 0 0 0 0 0 0 0 0 0 0 25 163095 0 1 0 0 0 0 0 0 0 0 0 0 26 159044 0 0 1 0 0 0 0 0 0 0 0 0 27 155511 0 0 0 1 0 0 0 0 0 0 0 0 28 153745 0 0 0 0 1 0 0 0 0 0 0 0 29 150569 0 0 0 0 0 1 0 0 0 0 0 0 30 150605 0 0 0 0 0 0 1 0 0 0 0 0 31 179612 0 0 0 0 0 0 0 1 0 0 0 0 32 194690 0 0 0 0 0 0 0 0 1 0 0 0 33 189917 0 0 0 0 0 0 0 0 0 1 0 0 34 184128 0 0 0 0 0 0 0 0 0 0 1 0 35 175335 0 0 0 0 0 0 0 0 0 0 0 1 36 179566 0 0 0 0 0 0 0 0 0 0 0 0 37 181140 0 1 0 0 0 0 0 0 0 0 0 0 38 177876 0 0 1 0 0 0 0 0 0 0 0 0 39 175041 0 0 0 1 0 0 0 0 0 0 0 0 40 169292 0 0 0 0 1 0 0 0 0 0 0 0 41 166070 0 0 0 0 0 1 0 0 0 0 0 0 42 166972 0 0 0 0 0 0 1 0 0 0 0 0 43 206348 0 0 0 0 0 0 0 1 0 0 0 0 44 215706 0 0 0 0 0 0 0 0 1 0 0 0 45 202108 0 0 0 0 0 0 0 0 0 1 0 0 46 195411 0 0 0 0 0 0 0 0 0 0 1 0 47 193111 0 0 0 0 0 0 0 0 0 0 0 1 48 195198 0 0 0 0 0 0 0 0 0 0 0 0 49 198770 0 1 0 0 0 0 0 0 0 0 0 0 50 194163 0 0 1 0 0 0 0 0 0 0 0 0 51 190420 0 0 0 1 0 0 0 0 0 0 0 0 52 189733 0 0 0 0 1 0 0 0 0 0 0 0 53 186029 0 0 0 0 0 1 0 0 0 0 0 0 54 191531 0 0 0 0 0 0 1 0 0 0 0 0 55 232571 0 0 0 0 0 0 0 1 0 0 0 0 56 243477 0 0 0 0 0 0 0 0 1 0 0 0 57 227247 0 0 0 0 0 0 0 0 0 1 0 0 58 217859 0 0 0 0 0 0 0 0 0 0 1 0 59 208679 0 0 0 0 0 0 0 0 0 0 0 1 60 213188 0 0 0 0 0 0 0 0 0 0 0 0 61 216234 0 1 0 0 0 0 0 0 0 0 0 0 62 213586 0 0 1 0 0 0 0 0 0 0 0 0 63 209465 0 0 0 1 0 0 0 0 0 0 0 0 64 204045 0 0 0 0 1 0 0 0 0 0 0 0 65 200237 0 0 0 0 0 1 0 0 0 0 0 0 66 203666 0 0 0 0 0 0 1 0 0 0 0 0 67 241476 0 0 0 0 0 0 0 1 0 0 0 0 68 260307 0 0 0 0 0 0 0 0 1 0 0 0 69 243324 0 0 0 0 0 0 0 0 0 1 0 0 70 244460 0 0 0 0 0 0 0 0 0 0 1 0 71 233575 0 0 0 0 0 0 0 0 0 0 0 1 72 237217 0 0 0 0 0 0 0 0 0 0 0 0 73 235243 0 1 0 0 0 0 0 0 0 0 0 0 74 230354 0 0 1 0 0 0 0 0 0 0 0 0 75 227184 0 0 0 1 0 0 0 0 0 0 0 0 76 221678 0 0 0 0 1 0 0 0 0 0 0 0 77 217142 0 0 0 0 0 1 0 0 0 0 0 0 78 219452 0 0 0 0 0 0 1 0 0 0 0 0 79 256446 0 0 0 0 0 0 0 1 0 0 0 0 80 265845 0 0 0 0 0 0 0 0 1 0 0 0 81 248624 0 0 0 0 0 0 0 0 0 1 0 0 82 241114 0 0 0 0 0 0 0 0 0 0 1 0 83 229245 0 0 0 0 0 0 0 0 0 0 0 1 84 231805 0 0 0 0 0 0 0 0 0 0 0 0 85 219277 0 1 0 0 0 0 0 0 0 0 0 0 86 219313 0 0 1 0 0 0 0 0 0 0 0 0 87 212610 0 0 0 1 0 0 0 0 0 0 0 0 88 214771 0 0 0 0 1 0 0 0 0 0 0 0 89 211142 0 0 0 0 0 1 0 0 0 0 0 0 90 211457 0 0 0 0 0 0 1 0 0 0 0 0 91 240048 0 0 0 0 0 0 0 1 0 0 0 0 92 240636 0 0 0 0 0 0 0 0 1 0 0 0 93 230580 0 0 0 0 0 0 0 0 0 1 0 0 94 208795 0 0 0 0 0 0 0 0 0 0 1 0 95 197922 0 0 0 0 0 0 0 0 0 0 0 1 96 194596 0 0 0 0 0 0 0 0 0 0 0 0 97 194581 0 1 0 0 0 0 0 0 0 0 0 0 98 185686 0 0 1 0 0 0 0 0 0 0 0 0 99 178106 0 0 0 1 0 0 0 0 0 0 0 0 100 172608 0 0 0 0 1 0 0 0 0 0 0 0 101 167302 0 0 0 0 0 1 0 0 0 0 0 0 102 168053 0 0 0 0 0 0 1 0 0 0 0 0 103 202300 0 0 0 0 0 0 0 1 0 0 0 0 104 202388 0 0 0 0 0 0 0 0 1 0 0 0 105 182516 0 0 0 0 0 0 0 0 0 1 0 0 106 173476 0 0 0 0 0 0 0 0 0 0 1 0 107 166444 0 0 0 0 0 0 0 0 0 0 0 1 108 171297 0 0 0 0 0 0 0 0 0 0 0 0 109 169701 0 1 0 0 0 0 0 0 0 0 0 0 110 164182 0 0 1 0 0 0 0 0 0 0 0 0 111 161914 0 0 0 1 0 0 0 0 0 0 0 0 112 159612 0 0 0 0 1 0 0 0 0 0 0 0 113 151001 0 0 0 0 0 1 0 0 0 0 0 0 114 158114 0 0 0 0 0 0 1 0 0 0 0 0 115 186530 1 0 0 0 0 0 0 1 0 0 0 0 116 187069 1 0 0 0 0 0 0 0 1 0 0 0 117 174330 1 0 0 0 0 0 0 0 0 1 0 0 118 169362 1 0 0 0 0 0 0 0 0 0 1 0 119 166827 1 0 0 0 0 0 0 0 0 0 0 1 120 178037 1 0 0 0 0 0 0 0 0 0 0 0 121 186413 1 1 0 0 0 0 0 0 0 0 0 0 122 189226 1 0 1 0 0 0 0 0 0 0 0 0 123 191563 1 0 0 1 0 0 0 0 0 0 0 0 124 188906 1 0 0 0 1 0 0 0 0 0 0 0 125 186005 1 0 0 0 0 1 0 0 0 0 0 0 126 195309 1 0 0 0 0 0 1 0 0 0 0 0 127 223532 1 0 0 0 0 0 0 1 0 0 0 0 128 226899 1 0 0 0 0 0 0 0 1 0 0 0 129 214126 1 0 0 0 0 0 0 0 0 1 0 0 130 206903 1 0 0 0 0 0 0 0 0 0 1 0 131 204442 1 0 0 0 0 0 0 0 0 0 0 1 132 220375 1 0 0 0 0 0 0 0 0 0 0 0 133 214320 1 1 0 0 0 0 0 0 0 0 0 0 134 212588 1 0 1 0 0 0 0 0 0 0 0 0 135 205816 1 0 0 1 0 0 0 0 0 0 0 0 136 202196 1 0 0 0 1 0 0 0 0 0 0 0 137 195722 1 0 0 0 0 1 0 0 0 0 0 0 138 198563 1 0 0 0 0 0 1 0 0 0 0 0 139 229139 1 0 0 0 0 0 0 1 0 0 0 0 140 229527 1 0 0 0 0 0 0 0 1 0 0 0 141 211868 1 0 0 0 0 0 0 0 0 1 0 0 142 203555 1 0 0 0 0 0 0 0 0 0 1 0 143 195770 1 0 0 0 0 0 0 0 0 0 0 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Dummy_crisis M1 M2 M3 196266.1 1193.2 612.4 -3315.3 -7478.5 M4 M5 M6 M7 M8 -11143.2 -15836.8 -13665.4 18777.4 27536.6 M9 M10 M11 11646.5 3159.1 -4368.5 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -37927 -18640 1212 18439 45035 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 196266.1 7316.4 26.826 < 2e-16 *** Dummy_crisis 1193.2 5032.5 0.237 0.81295 M1 612.4 10049.8 0.061 0.95150 M2 -3315.3 10049.8 -0.330 0.74202 M3 -7478.5 10049.8 -0.744 0.45813 M4 -11143.2 10049.8 -1.109 0.26957 M5 -15836.8 10049.8 -1.576 0.11749 M6 -13665.4 10049.8 -1.360 0.17626 M7 18777.4 10055.4 1.867 0.06410 . M8 27536.6 10055.4 2.738 0.00704 ** M9 11646.5 10055.4 1.158 0.24889 M10 3159.1 10055.4 0.314 0.75389 M11 -4368.5 10055.4 -0.434 0.66468 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 24080 on 130 degrees of freedom Multiple R-squared: 0.2337, Adjusted R-squared: 0.163 F-statistic: 3.304 on 12 and 130 DF, p-value: 0.0003366 > 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,] 0.4491018314 0.8982036627 0.5508981686 [2,] 0.3670594815 0.7341189630 0.6329405185 [3,] 0.2974045254 0.5948090508 0.7025954746 [4,] 0.2379112265 0.4758224531 0.7620887735 [5,] 0.1899872823 0.3799745647 0.8100127177 [6,] 0.1547806956 0.3095613912 0.8452193044 [7,] 0.1219544972 0.2439089943 0.8780455028 [8,] 0.0958274037 0.1916548074 0.9041725963 [9,] 0.0824817060 0.1649634121 0.9175182940 [10,] 0.1038535911 0.2077071822 0.8961464089 [11,] 0.1147015223 0.2294030447 0.8852984777 [12,] 0.1185413819 0.2370827638 0.8814586181 [13,] 0.1099656084 0.2199312167 0.8900343916 [14,] 0.0978008676 0.1956017352 0.9021991324 [15,] 0.0845902332 0.1691804664 0.9154097668 [16,] 0.0756993229 0.1513986458 0.9243006771 [17,] 0.0684198585 0.1368397170 0.9315801415 [18,] 0.0501209343 0.1002418685 0.9498790657 [19,] 0.0373130019 0.0746260037 0.9626869981 [20,] 0.0276651154 0.0553302309 0.9723348846 [21,] 0.0209217276 0.0418434552 0.9790782724 [22,] 0.0145278225 0.0290556450 0.9854721775 [23,] 0.0099574539 0.0199149078 0.9900425461 [24,] 0.0067595230 0.0135190459 0.9932404770 [25,] 0.0046212067 0.0092424134 0.9953787933 [26,] 0.0031259989 0.0062519978 0.9968740011 [27,] 0.0022401710 0.0044803421 0.9977598290 [28,] 0.0020858951 0.0041717902 0.9979141049 [29,] 0.0014813098 0.0029626197 0.9985186902 [30,] 0.0011653266 0.0023306532 0.9988346734 [31,] 0.0009959819 0.0019919638 0.9990040181 [32,] 0.0010818151 0.0021636301 0.9989181849 [33,] 0.0011205431 0.0022410861 0.9988794569 [34,] 0.0009077481 0.0018154962 0.9990922519 [35,] 0.0007603552 0.0015207105 0.9992396448 [36,] 0.0006518320 0.0013036640 0.9993481680 [37,] 0.0006643483 0.0013286965 0.9993356517 [38,] 0.0006849307 0.0013698615 0.9993150693 [39,] 0.0009763533 0.0019527066 0.9990236467 [40,] 0.0022927316 0.0045854633 0.9977072684 [41,] 0.0037047329 0.0074094658 0.9962952671 [42,] 0.0056777140 0.0113554280 0.9943222860 [43,] 0.0079065625 0.0158131250 0.9920934375 [44,] 0.0096698626 0.0193397252 0.9903301374 [45,] 0.0121064685 0.0242129371 0.9878935315 [46,] 0.0134337229 0.0268674459 0.9865662771 [47,] 0.0157992408 0.0315984816 0.9842007592 [48,] 0.0182691826 0.0365383653 0.9817308174 [49,] 0.0199363721 0.0398727442 0.9800636279 [50,] 0.0217254417 0.0434508833 0.9782745583 [51,] 0.0252866972 0.0505733944 0.9747133028 [52,] 0.0328124028 0.0656248056 0.9671875972 [53,] 0.0510993044 0.1021986088 0.9489006956 [54,] 0.0731598414 0.1463196828 0.9268401586 [55,] 0.1338752464 0.2677504927 0.8661247536 [56,] 0.1986866003 0.3973732006 0.8013133997 [57,] 0.2728270441 0.5456540882 0.7271729559 [58,] 0.3330350871 0.6660701743 0.6669649129 [59,] 0.3907224934 0.7814449868 0.6092775066 [60,] 0.4554361006 0.9108722012 0.5445638994 [61,] 0.5086252557 0.9827494887 0.4913747443 [62,] 0.5613598629 0.8772802742 0.4386401371 [63,] 0.6122439952 0.7755120097 0.3877560048 [64,] 0.6854848253 0.6290303493 0.3145151747 [65,] 0.7669652512 0.4660694975 0.2330347488 [66,] 0.8321650115 0.3356699771 0.1678349885 [67,] 0.8941039990 0.2117920019 0.1058960010 [68,] 0.9294809993 0.1410380014 0.0705190007 [69,] 0.9524959561 0.0950080878 0.0475040439 [70,] 0.9550972747 0.0898054506 0.0449027253 [71,] 0.9622272974 0.0755454051 0.0377727026 [72,] 0.9662880739 0.0674238521 0.0337119261 [73,] 0.9758658366 0.0482683267 0.0241341634 [74,] 0.9850394028 0.0299211943 0.0149605972 [75,] 0.9899361681 0.0201276639 0.0100638319 [76,] 0.9939418325 0.0121163349 0.0060581675 [77,] 0.9965839425 0.0068321149 0.0034160575 [78,] 0.9990103158 0.0019793684 0.0009896842 [79,] 0.9994355914 0.0011288171 0.0005644086 [80,] 0.9996086325 0.0007827350 0.0003913675 [81,] 0.9995337950 0.0009324100 0.0004662050 [82,] 0.9994568833 0.0010862335 0.0005431167 [83,] 0.9992193678 0.0015612644 0.0007806322 [84,] 0.9987760548 0.0024478905 0.0012239452 [85,] 0.9980542547 0.0038914905 0.0019457453 [86,] 0.9969959873 0.0060080254 0.0030040127 [87,] 0.9952270689 0.0095458622 0.0047729311 [88,] 0.9939786892 0.0120426215 0.0060213108 [89,] 0.9929308177 0.0141383646 0.0070691823 [90,] 0.9911103695 0.0177792611 0.0088896305 [91,] 0.9886521294 0.0226957412 0.0113478706 [92,] 0.9850551869 0.0298896261 0.0149448131 [93,] 0.9789371223 0.0421257553 0.0210628777 [94,] 0.9705086584 0.0589826832 0.0294913416 [95,] 0.9583985172 0.0832029656 0.0416014828 [96,] 0.9415128064 0.1169743871 0.0584871936 [97,] 0.9188498170 0.1623003660 0.0811501830 [98,] 0.8905184583 0.2189630834 0.1094815417 [99,] 0.8520546603 0.2958906794 0.1479453397 [100,] 0.8904753420 0.2190493159 0.1095246580 [101,] 0.9322516994 0.1354966013 0.0677483006 [102,] 0.9610227409 0.0779545182 0.0389772591 [103,] 0.9791263314 0.0417473372 0.0208736686 [104,] 0.9900194668 0.0199610663 0.0099805332 [105,] 0.9985892993 0.0028214013 0.0014107007 [106,] 0.9995246431 0.0009507139 0.0004753569 [107,] 0.9998584149 0.0002831702 0.0001415851 [108,] 0.9998496547 0.0003006906 0.0001503453 [109,] 0.9998616615 0.0002766771 0.0001383385 [110,] 0.9997722100 0.0004555799 0.0002277900 [111,] 0.9986608999 0.0026782002 0.0013391001 [112,] 0.9941566228 0.0116867544 0.0058433772 > postscript(file="/var/www/html/rcomp/tmp/17crb1293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/27crb1293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3i3qe1293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4i3qe1293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5i3qe1293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 143 Frequency = 1 1 2 3 4 5 6 9131.5333 5161.1999 5731.4499 582.1166 -256.2167 -6458.6334 7 8 9 10 11 12 -11642.4501 -1900.6168 -10534.5334 -14424.2001 -15541.5334 -15817.0546 13 14 15 16 17 18 -16734.4667 -19284.8001 -23099.5501 -23552.8834 -24284.2167 -28870.6334 19 20 21 22 23 24 -32345.4501 -23037.6168 -31400.5334 -32807.2001 -33253.5334 -36681.0546 25 26 27 28 29 30 -33783.4667 -33906.8001 -33276.5501 -31377.8834 -29860.2167 -31995.6334 31 32 33 34 35 36 -35431.4501 -29112.6168 -17995.5334 -15297.2001 -16562.5334 -16700.0546 37 38 39 40 41 42 -15738.4667 -15074.8001 -13746.5501 -15830.8834 -14359.2167 -15628.6334 43 44 45 46 47 48 -8695.4501 -8096.6168 -5804.5334 -4014.2001 1213.4666 -1068.0546 49 50 51 52 53 54 1891.5333 1212.1999 1632.4499 4610.1166 5599.7833 8930.3666 55 56 57 58 59 60 17527.5499 19674.3832 19334.4666 18433.7999 16781.4666 16921.9454 61 62 63 64 65 66 19355.5333 20635.1999 20677.4499 18922.1166 19807.7833 21065.3666 67 68 69 70 71 72 26432.5499 36504.3832 35411.4666 45034.7999 41677.4666 40950.9454 73 74 75 76 77 78 38364.5333 37403.1999 38396.4499 36555.1166 36712.7833 36851.3666 79 80 81 82 83 84 41402.5499 42042.3832 40711.4666 41688.7999 37347.4666 35538.9454 85 86 87 88 89 90 22398.5333 26362.1999 23822.4499 29648.1166 30712.7833 28856.3666 91 92 93 94 95 96 25004.5499 16833.3832 22667.4666 9369.7999 6024.4666 -1670.0546 97 98 99 100 101 102 -2297.4667 -7264.8001 -10681.5501 -12514.8834 -13127.2167 -14547.6334 103 104 105 106 107 108 -12743.4501 -21414.6168 -25396.5334 -25949.2001 -25453.5334 -24969.0546 109 110 111 112 113 114 -27177.4667 -28768.8001 -26873.5501 -25510.8834 -29428.2167 -24486.6334 115 116 117 118 119 120 -29706.6497 -37926.8164 -34775.7330 -31256.3997 -26263.7330 -19422.2542 121 122 123 124 125 126 -11658.6663 -4917.9997 1582.2503 2589.9170 4382.5837 11515.1670 127 128 129 130 131 132 7295.3503 1903.1836 5020.2670 6284.6003 11351.2670 22915.7458 133 134 135 136 137 138 16248.3337 18444.0003 15835.2503 15879.9170 14099.5837 14769.1670 139 140 141 142 143 12902.3503 4531.1836 2762.2670 2936.6003 2679.2670 > postscript(file="/var/www/html/rcomp/tmp/6i3qe1293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 143 Frequency = 1 lag(myerror, k = 1) myerror 0 9131.5333 NA 1 5161.1999 9131.5333 2 5731.4499 5161.1999 3 582.1166 5731.4499 4 -256.2167 582.1166 5 -6458.6334 -256.2167 6 -11642.4501 -6458.6334 7 -1900.6168 -11642.4501 8 -10534.5334 -1900.6168 9 -14424.2001 -10534.5334 10 -15541.5334 -14424.2001 11 -15817.0546 -15541.5334 12 -16734.4667 -15817.0546 13 -19284.8001 -16734.4667 14 -23099.5501 -19284.8001 15 -23552.8834 -23099.5501 16 -24284.2167 -23552.8834 17 -28870.6334 -24284.2167 18 -32345.4501 -28870.6334 19 -23037.6168 -32345.4501 20 -31400.5334 -23037.6168 21 -32807.2001 -31400.5334 22 -33253.5334 -32807.2001 23 -36681.0546 -33253.5334 24 -33783.4667 -36681.0546 25 -33906.8001 -33783.4667 26 -33276.5501 -33906.8001 27 -31377.8834 -33276.5501 28 -29860.2167 -31377.8834 29 -31995.6334 -29860.2167 30 -35431.4501 -31995.6334 31 -29112.6168 -35431.4501 32 -17995.5334 -29112.6168 33 -15297.2001 -17995.5334 34 -16562.5334 -15297.2001 35 -16700.0546 -16562.5334 36 -15738.4667 -16700.0546 37 -15074.8001 -15738.4667 38 -13746.5501 -15074.8001 39 -15830.8834 -13746.5501 40 -14359.2167 -15830.8834 41 -15628.6334 -14359.2167 42 -8695.4501 -15628.6334 43 -8096.6168 -8695.4501 44 -5804.5334 -8096.6168 45 -4014.2001 -5804.5334 46 1213.4666 -4014.2001 47 -1068.0546 1213.4666 48 1891.5333 -1068.0546 49 1212.1999 1891.5333 50 1632.4499 1212.1999 51 4610.1166 1632.4499 52 5599.7833 4610.1166 53 8930.3666 5599.7833 54 17527.5499 8930.3666 55 19674.3832 17527.5499 56 19334.4666 19674.3832 57 18433.7999 19334.4666 58 16781.4666 18433.7999 59 16921.9454 16781.4666 60 19355.5333 16921.9454 61 20635.1999 19355.5333 62 20677.4499 20635.1999 63 18922.1166 20677.4499 64 19807.7833 18922.1166 65 21065.3666 19807.7833 66 26432.5499 21065.3666 67 36504.3832 26432.5499 68 35411.4666 36504.3832 69 45034.7999 35411.4666 70 41677.4666 45034.7999 71 40950.9454 41677.4666 72 38364.5333 40950.9454 73 37403.1999 38364.5333 74 38396.4499 37403.1999 75 36555.1166 38396.4499 76 36712.7833 36555.1166 77 36851.3666 36712.7833 78 41402.5499 36851.3666 79 42042.3832 41402.5499 80 40711.4666 42042.3832 81 41688.7999 40711.4666 82 37347.4666 41688.7999 83 35538.9454 37347.4666 84 22398.5333 35538.9454 85 26362.1999 22398.5333 86 23822.4499 26362.1999 87 29648.1166 23822.4499 88 30712.7833 29648.1166 89 28856.3666 30712.7833 90 25004.5499 28856.3666 91 16833.3832 25004.5499 92 22667.4666 16833.3832 93 9369.7999 22667.4666 94 6024.4666 9369.7999 95 -1670.0546 6024.4666 96 -2297.4667 -1670.0546 97 -7264.8001 -2297.4667 98 -10681.5501 -7264.8001 99 -12514.8834 -10681.5501 100 -13127.2167 -12514.8834 101 -14547.6334 -13127.2167 102 -12743.4501 -14547.6334 103 -21414.6168 -12743.4501 104 -25396.5334 -21414.6168 105 -25949.2001 -25396.5334 106 -25453.5334 -25949.2001 107 -24969.0546 -25453.5334 108 -27177.4667 -24969.0546 109 -28768.8001 -27177.4667 110 -26873.5501 -28768.8001 111 -25510.8834 -26873.5501 112 -29428.2167 -25510.8834 113 -24486.6334 -29428.2167 114 -29706.6497 -24486.6334 115 -37926.8164 -29706.6497 116 -34775.7330 -37926.8164 117 -31256.3997 -34775.7330 118 -26263.7330 -31256.3997 119 -19422.2542 -26263.7330 120 -11658.6663 -19422.2542 121 -4917.9997 -11658.6663 122 1582.2503 -4917.9997 123 2589.9170 1582.2503 124 4382.5837 2589.9170 125 11515.1670 4382.5837 126 7295.3503 11515.1670 127 1903.1836 7295.3503 128 5020.2670 1903.1836 129 6284.6003 5020.2670 130 11351.2670 6284.6003 131 22915.7458 11351.2670 132 16248.3337 22915.7458 133 18444.0003 16248.3337 134 15835.2503 18444.0003 135 15879.9170 15835.2503 136 14099.5837 15879.9170 137 14769.1670 14099.5837 138 12902.3503 14769.1670 139 4531.1836 12902.3503 140 2762.2670 4531.1836 141 2936.6003 2762.2670 142 2679.2670 2936.6003 143 NA 2679.2670 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 5161.1999 9131.5333 [2,] 5731.4499 5161.1999 [3,] 582.1166 5731.4499 [4,] -256.2167 582.1166 [5,] -6458.6334 -256.2167 [6,] -11642.4501 -6458.6334 [7,] -1900.6168 -11642.4501 [8,] -10534.5334 -1900.6168 [9,] -14424.2001 -10534.5334 [10,] -15541.5334 -14424.2001 [11,] -15817.0546 -15541.5334 [12,] -16734.4667 -15817.0546 [13,] -19284.8001 -16734.4667 [14,] -23099.5501 -19284.8001 [15,] -23552.8834 -23099.5501 [16,] -24284.2167 -23552.8834 [17,] -28870.6334 -24284.2167 [18,] -32345.4501 -28870.6334 [19,] -23037.6168 -32345.4501 [20,] -31400.5334 -23037.6168 [21,] -32807.2001 -31400.5334 [22,] -33253.5334 -32807.2001 [23,] -36681.0546 -33253.5334 [24,] -33783.4667 -36681.0546 [25,] -33906.8001 -33783.4667 [26,] -33276.5501 -33906.8001 [27,] -31377.8834 -33276.5501 [28,] -29860.2167 -31377.8834 [29,] -31995.6334 -29860.2167 [30,] -35431.4501 -31995.6334 [31,] -29112.6168 -35431.4501 [32,] -17995.5334 -29112.6168 [33,] -15297.2001 -17995.5334 [34,] -16562.5334 -15297.2001 [35,] -16700.0546 -16562.5334 [36,] -15738.4667 -16700.0546 [37,] -15074.8001 -15738.4667 [38,] -13746.5501 -15074.8001 [39,] -15830.8834 -13746.5501 [40,] -14359.2167 -15830.8834 [41,] -15628.6334 -14359.2167 [42,] -8695.4501 -15628.6334 [43,] -8096.6168 -8695.4501 [44,] -5804.5334 -8096.6168 [45,] -4014.2001 -5804.5334 [46,] 1213.4666 -4014.2001 [47,] -1068.0546 1213.4666 [48,] 1891.5333 -1068.0546 [49,] 1212.1999 1891.5333 [50,] 1632.4499 1212.1999 [51,] 4610.1166 1632.4499 [52,] 5599.7833 4610.1166 [53,] 8930.3666 5599.7833 [54,] 17527.5499 8930.3666 [55,] 19674.3832 17527.5499 [56,] 19334.4666 19674.3832 [57,] 18433.7999 19334.4666 [58,] 16781.4666 18433.7999 [59,] 16921.9454 16781.4666 [60,] 19355.5333 16921.9454 [61,] 20635.1999 19355.5333 [62,] 20677.4499 20635.1999 [63,] 18922.1166 20677.4499 [64,] 19807.7833 18922.1166 [65,] 21065.3666 19807.7833 [66,] 26432.5499 21065.3666 [67,] 36504.3832 26432.5499 [68,] 35411.4666 36504.3832 [69,] 45034.7999 35411.4666 [70,] 41677.4666 45034.7999 [71,] 40950.9454 41677.4666 [72,] 38364.5333 40950.9454 [73,] 37403.1999 38364.5333 [74,] 38396.4499 37403.1999 [75,] 36555.1166 38396.4499 [76,] 36712.7833 36555.1166 [77,] 36851.3666 36712.7833 [78,] 41402.5499 36851.3666 [79,] 42042.3832 41402.5499 [80,] 40711.4666 42042.3832 [81,] 41688.7999 40711.4666 [82,] 37347.4666 41688.7999 [83,] 35538.9454 37347.4666 [84,] 22398.5333 35538.9454 [85,] 26362.1999 22398.5333 [86,] 23822.4499 26362.1999 [87,] 29648.1166 23822.4499 [88,] 30712.7833 29648.1166 [89,] 28856.3666 30712.7833 [90,] 25004.5499 28856.3666 [91,] 16833.3832 25004.5499 [92,] 22667.4666 16833.3832 [93,] 9369.7999 22667.4666 [94,] 6024.4666 9369.7999 [95,] -1670.0546 6024.4666 [96,] -2297.4667 -1670.0546 [97,] -7264.8001 -2297.4667 [98,] -10681.5501 -7264.8001 [99,] -12514.8834 -10681.5501 [100,] -13127.2167 -12514.8834 [101,] -14547.6334 -13127.2167 [102,] -12743.4501 -14547.6334 [103,] -21414.6168 -12743.4501 [104,] -25396.5334 -21414.6168 [105,] -25949.2001 -25396.5334 [106,] -25453.5334 -25949.2001 [107,] -24969.0546 -25453.5334 [108,] -27177.4667 -24969.0546 [109,] -28768.8001 -27177.4667 [110,] -26873.5501 -28768.8001 [111,] -25510.8834 -26873.5501 [112,] -29428.2167 -25510.8834 [113,] -24486.6334 -29428.2167 [114,] -29706.6497 -24486.6334 [115,] -37926.8164 -29706.6497 [116,] -34775.7330 -37926.8164 [117,] -31256.3997 -34775.7330 [118,] -26263.7330 -31256.3997 [119,] -19422.2542 -26263.7330 [120,] -11658.6663 -19422.2542 [121,] -4917.9997 -11658.6663 [122,] 1582.2503 -4917.9997 [123,] 2589.9170 1582.2503 [124,] 4382.5837 2589.9170 [125,] 11515.1670 4382.5837 [126,] 7295.3503 11515.1670 [127,] 1903.1836 7295.3503 [128,] 5020.2670 1903.1836 [129,] 6284.6003 5020.2670 [130,] 11351.2670 6284.6003 [131,] 22915.7458 11351.2670 [132,] 16248.3337 22915.7458 [133,] 18444.0003 16248.3337 [134,] 15835.2503 18444.0003 [135,] 15879.9170 15835.2503 [136,] 14099.5837 15879.9170 [137,] 14769.1670 14099.5837 [138,] 12902.3503 14769.1670 [139,] 4531.1836 12902.3503 [140,] 2762.2670 4531.1836 [141,] 2936.6003 2762.2670 [142,] 2679.2670 2936.6003 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 5161.1999 9131.5333 2 5731.4499 5161.1999 3 582.1166 5731.4499 4 -256.2167 582.1166 5 -6458.6334 -256.2167 6 -11642.4501 -6458.6334 7 -1900.6168 -11642.4501 8 -10534.5334 -1900.6168 9 -14424.2001 -10534.5334 10 -15541.5334 -14424.2001 11 -15817.0546 -15541.5334 12 -16734.4667 -15817.0546 13 -19284.8001 -16734.4667 14 -23099.5501 -19284.8001 15 -23552.8834 -23099.5501 16 -24284.2167 -23552.8834 17 -28870.6334 -24284.2167 18 -32345.4501 -28870.6334 19 -23037.6168 -32345.4501 20 -31400.5334 -23037.6168 21 -32807.2001 -31400.5334 22 -33253.5334 -32807.2001 23 -36681.0546 -33253.5334 24 -33783.4667 -36681.0546 25 -33906.8001 -33783.4667 26 -33276.5501 -33906.8001 27 -31377.8834 -33276.5501 28 -29860.2167 -31377.8834 29 -31995.6334 -29860.2167 30 -35431.4501 -31995.6334 31 -29112.6168 -35431.4501 32 -17995.5334 -29112.6168 33 -15297.2001 -17995.5334 34 -16562.5334 -15297.2001 35 -16700.0546 -16562.5334 36 -15738.4667 -16700.0546 37 -15074.8001 -15738.4667 38 -13746.5501 -15074.8001 39 -15830.8834 -13746.5501 40 -14359.2167 -15830.8834 41 -15628.6334 -14359.2167 42 -8695.4501 -15628.6334 43 -8096.6168 -8695.4501 44 -5804.5334 -8096.6168 45 -4014.2001 -5804.5334 46 1213.4666 -4014.2001 47 -1068.0546 1213.4666 48 1891.5333 -1068.0546 49 1212.1999 1891.5333 50 1632.4499 1212.1999 51 4610.1166 1632.4499 52 5599.7833 4610.1166 53 8930.3666 5599.7833 54 17527.5499 8930.3666 55 19674.3832 17527.5499 56 19334.4666 19674.3832 57 18433.7999 19334.4666 58 16781.4666 18433.7999 59 16921.9454 16781.4666 60 19355.5333 16921.9454 61 20635.1999 19355.5333 62 20677.4499 20635.1999 63 18922.1166 20677.4499 64 19807.7833 18922.1166 65 21065.3666 19807.7833 66 26432.5499 21065.3666 67 36504.3832 26432.5499 68 35411.4666 36504.3832 69 45034.7999 35411.4666 70 41677.4666 45034.7999 71 40950.9454 41677.4666 72 38364.5333 40950.9454 73 37403.1999 38364.5333 74 38396.4499 37403.1999 75 36555.1166 38396.4499 76 36712.7833 36555.1166 77 36851.3666 36712.7833 78 41402.5499 36851.3666 79 42042.3832 41402.5499 80 40711.4666 42042.3832 81 41688.7999 40711.4666 82 37347.4666 41688.7999 83 35538.9454 37347.4666 84 22398.5333 35538.9454 85 26362.1999 22398.5333 86 23822.4499 26362.1999 87 29648.1166 23822.4499 88 30712.7833 29648.1166 89 28856.3666 30712.7833 90 25004.5499 28856.3666 91 16833.3832 25004.5499 92 22667.4666 16833.3832 93 9369.7999 22667.4666 94 6024.4666 9369.7999 95 -1670.0546 6024.4666 96 -2297.4667 -1670.0546 97 -7264.8001 -2297.4667 98 -10681.5501 -7264.8001 99 -12514.8834 -10681.5501 100 -13127.2167 -12514.8834 101 -14547.6334 -13127.2167 102 -12743.4501 -14547.6334 103 -21414.6168 -12743.4501 104 -25396.5334 -21414.6168 105 -25949.2001 -25396.5334 106 -25453.5334 -25949.2001 107 -24969.0546 -25453.5334 108 -27177.4667 -24969.0546 109 -28768.8001 -27177.4667 110 -26873.5501 -28768.8001 111 -25510.8834 -26873.5501 112 -29428.2167 -25510.8834 113 -24486.6334 -29428.2167 114 -29706.6497 -24486.6334 115 -37926.8164 -29706.6497 116 -34775.7330 -37926.8164 117 -31256.3997 -34775.7330 118 -26263.7330 -31256.3997 119 -19422.2542 -26263.7330 120 -11658.6663 -19422.2542 121 -4917.9997 -11658.6663 122 1582.2503 -4917.9997 123 2589.9170 1582.2503 124 4382.5837 2589.9170 125 11515.1670 4382.5837 126 7295.3503 11515.1670 127 1903.1836 7295.3503 128 5020.2670 1903.1836 129 6284.6003 5020.2670 130 11351.2670 6284.6003 131 22915.7458 11351.2670 132 16248.3337 22915.7458 133 18444.0003 16248.3337 134 15835.2503 18444.0003 135 15879.9170 15835.2503 136 14099.5837 15879.9170 137 14769.1670 14099.5837 138 12902.3503 14769.1670 139 4531.1836 12902.3503 140 2762.2670 4531.1836 141 2936.6003 2762.2670 142 2679.2670 2936.6003 > 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/7ad8z1293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8l4721293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9l4721293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10wvo51293090861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11zwnt1293090861.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/12a5me1293090861.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/13ho1q1293090861.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/14sxjb1293090861.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/15vyhy1293090861.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/169px71293090861.tab") + } > > try(system("convert tmp/17crb1293090861.ps tmp/17crb1293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/27crb1293090861.ps tmp/27crb1293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/3i3qe1293090861.ps tmp/3i3qe1293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/4i3qe1293090861.ps tmp/4i3qe1293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/5i3qe1293090861.ps tmp/5i3qe1293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/6i3qe1293090861.ps tmp/6i3qe1293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/7ad8z1293090861.ps tmp/7ad8z1293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/8l4721293090861.ps tmp/8l4721293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/9l4721293090861.ps tmp/9l4721293090861.png",intern=TRUE)) character(0) > try(system("convert tmp/10wvo51293090861.ps tmp/10wvo51293090861.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.736 1.714 8.901