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Type 'q()' to quit R. > x <- array(list(95.1 + ,93.8 + ,96.9 + ,98.6 + ,111.7 + ,109.8 + ,97 + ,93.8 + ,95.1 + ,96.9 + ,98.6 + ,111.7 + ,112.7 + ,107.6 + ,97 + ,95.1 + ,96.9 + ,98.6 + ,102.9 + ,101 + ,112.7 + ,97 + ,95.1 + ,96.9 + ,97.4 + ,95.4 + ,102.9 + ,112.7 + ,97 + ,95.1 + ,111.4 + ,96.5 + ,97.4 + ,102.9 + ,112.7 + ,97 + ,87.4 + ,89.2 + ,111.4 + ,97.4 + ,102.9 + ,112.7 + ,96.8 + ,87.1 + ,87.4 + ,111.4 + ,97.4 + ,102.9 + ,114.1 + ,110.5 + ,96.8 + ,87.4 + ,111.4 + ,97.4 + ,110.3 + ,110.8 + ,114.1 + ,96.8 + ,87.4 + ,111.4 + ,103.9 + ,104.2 + ,110.3 + ,114.1 + ,96.8 + ,87.4 + ,101.6 + ,88.9 + ,103.9 + ,110.3 + ,114.1 + ,96.8 + ,94.6 + ,89.8 + ,101.6 + ,103.9 + ,110.3 + ,114.1 + ,95.9 + ,90 + ,94.6 + ,101.6 + ,103.9 + ,110.3 + ,104.7 + ,93.9 + ,95.9 + ,94.6 + ,101.6 + ,103.9 + ,102.8 + ,91.3 + ,104.7 + ,95.9 + ,94.6 + ,101.6 + ,98.1 + ,87.8 + ,102.8 + ,104.7 + ,95.9 + ,94.6 + ,113.9 + ,99.7 + ,98.1 + ,102.8 + ,104.7 + ,95.9 + ,80.9 + ,73.5 + ,113.9 + ,98.1 + ,102.8 + ,104.7 + ,95.7 + ,79.2 + ,80.9 + ,113.9 + ,98.1 + ,102.8 + ,113.2 + ,96.9 + ,95.7 + ,80.9 + ,113.9 + ,98.1 + ,105.9 + ,95.2 + ,113.2 + ,95.7 + ,80.9 + ,113.9 + ,108.8 + ,95.6 + ,105.9 + ,113.2 + ,95.7 + ,80.9 + ,102.3 + ,89.7 + ,108.8 + ,105.9 + ,113.2 + ,95.7 + ,99 + ,92.8 + ,102.3 + ,108.8 + ,105.9 + ,113.2 + ,100.7 + ,88 + ,99 + ,102.3 + ,108.8 + ,105.9 + ,115.5 + ,101.1 + ,100.7 + ,99 + ,102.3 + ,108.8 + ,100.7 + ,92.7 + ,115.5 + ,100.7 + ,99 + ,102.3 + ,109.9 + ,95.8 + ,100.7 + ,115.5 + ,100.7 + ,99 + ,114.6 + ,103.8 + ,109.9 + ,100.7 + ,115.5 + ,100.7 + ,85.4 + ,81.8 + ,114.6 + ,109.9 + ,100.7 + ,115.5 + ,100.5 + ,87.1 + ,85.4 + ,114.6 + ,109.9 + ,100.7 + ,114.8 + ,105.9 + ,100.5 + ,85.4 + ,114.6 + ,109.9 + ,116.5 + ,108.1 + ,114.8 + ,100.5 + ,85.4 + ,114.6 + ,112.9 + ,102.6 + ,116.5 + ,114.8 + ,100.5 + ,85.4 + ,102 + ,93.7 + ,112.9 + ,116.5 + ,114.8 + ,100.5 + ,106 + ,103.5 + ,102 + ,112.9 + ,116.5 + ,114.8 + ,105.3 + ,100.6 + ,106 + ,102 + ,112.9 + ,116.5 + ,118.8 + ,113.3 + ,105.3 + ,106 + ,102 + ,112.9 + ,106.1 + ,102.4 + ,118.8 + ,105.3 + ,106 + ,102 + ,109.3 + ,102.1 + ,106.1 + ,118.8 + ,105.3 + ,106 + ,117.2 + ,106.9 + ,109.3 + ,106.1 + ,118.8 + ,105.3 + ,92.5 + ,87.3 + ,117.2 + ,109.3 + ,106.1 + ,118.8 + ,104.2 + ,93.1 + ,92.5 + ,117.2 + ,109.3 + ,106.1 + ,112.5 + ,109.1 + ,104.2 + ,92.5 + ,117.2 + ,109.3 + ,122.4 + ,120.3 + ,112.5 + ,104.2 + ,92.5 + ,117.2 + ,113.3 + ,104.9 + ,122.4 + ,112.5 + ,104.2 + ,92.5 + ,100 + ,92.6 + ,113.3 + ,122.4 + ,112.5 + ,104.2 + ,110.7 + ,109.8 + ,100 + ,113.3 + ,122.4 + ,112.5 + ,112.8 + ,111.4 + ,110.7 + ,100 + ,113.3 + ,122.4 + ,109.8 + ,117.9 + ,112.8 + ,110.7 + ,100 + ,113.3 + ,117.3 + ,121.6 + ,109.8 + ,112.8 + ,110.7 + ,100 + ,109.1 + ,117.8 + ,117.3 + ,109.8 + ,112.8 + ,110.7 + ,115.9 + ,124.2 + ,109.1 + ,117.3 + ,109.8 + ,112.8 + ,96 + ,106.8 + ,115.9 + ,109.1 + ,117.3 + ,109.8 + ,99.8 + ,102.7 + ,96 + ,115.9 + ,109.1 + ,117.3 + ,116.8 + ,116.8 + ,99.8 + ,96 + ,115.9 + ,109.1 + ,115.7 + ,113.6 + ,116.8 + ,99.8 + ,96 + ,115.9 + ,99.4 + ,96.1 + ,115.7 + ,116.8 + ,99.8 + ,96 + ,94.3 + ,85 + ,99.4 + ,115.7 + ,116.8 + ,99.8) + ,dim=c(6 + ,60) + ,dimnames=list(c('TIA' + ,'IAidM' + ,'TIA(t-1)' + ,'TIA(t-2)' + ,'TIA(t-3)' + ,'TIA(t-4)') + ,1:60)) > y <- array(NA,dim=c(6,60),dimnames=list(c('TIA','IAidM','TIA(t-1)','TIA(t-2)','TIA(t-3)','TIA(t-4)'),1:60)) > 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 = '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 TIA IAidM TIA(t-1) TIA(t-2) TIA(t-3) TIA(t-4) M1 M2 M3 M4 M5 M6 M7 M8 M9 1 95.1 93.8 96.9 98.6 111.7 109.8 1 0 0 0 0 0 0 0 0 2 97.0 93.8 95.1 96.9 98.6 111.7 0 1 0 0 0 0 0 0 0 3 112.7 107.6 97.0 95.1 96.9 98.6 0 0 1 0 0 0 0 0 0 4 102.9 101.0 112.7 97.0 95.1 96.9 0 0 0 1 0 0 0 0 0 5 97.4 95.4 102.9 112.7 97.0 95.1 0 0 0 0 1 0 0 0 0 6 111.4 96.5 97.4 102.9 112.7 97.0 0 0 0 0 0 1 0 0 0 7 87.4 89.2 111.4 97.4 102.9 112.7 0 0 0 0 0 0 1 0 0 8 96.8 87.1 87.4 111.4 97.4 102.9 0 0 0 0 0 0 0 1 0 9 114.1 110.5 96.8 87.4 111.4 97.4 0 0 0 0 0 0 0 0 1 10 110.3 110.8 114.1 96.8 87.4 111.4 0 0 0 0 0 0 0 0 0 11 103.9 104.2 110.3 114.1 96.8 87.4 0 0 0 0 0 0 0 0 0 12 101.6 88.9 103.9 110.3 114.1 96.8 0 0 0 0 0 0 0 0 0 13 94.6 89.8 101.6 103.9 110.3 114.1 1 0 0 0 0 0 0 0 0 14 95.9 90.0 94.6 101.6 103.9 110.3 0 1 0 0 0 0 0 0 0 15 104.7 93.9 95.9 94.6 101.6 103.9 0 0 1 0 0 0 0 0 0 16 102.8 91.3 104.7 95.9 94.6 101.6 0 0 0 1 0 0 0 0 0 17 98.1 87.8 102.8 104.7 95.9 94.6 0 0 0 0 1 0 0 0 0 18 113.9 99.7 98.1 102.8 104.7 95.9 0 0 0 0 0 1 0 0 0 19 80.9 73.5 113.9 98.1 102.8 104.7 0 0 0 0 0 0 1 0 0 20 95.7 79.2 80.9 113.9 98.1 102.8 0 0 0 0 0 0 0 1 0 21 113.2 96.9 95.7 80.9 113.9 98.1 0 0 0 0 0 0 0 0 1 22 105.9 95.2 113.2 95.7 80.9 113.9 0 0 0 0 0 0 0 0 0 23 108.8 95.6 105.9 113.2 95.7 80.9 0 0 0 0 0 0 0 0 0 24 102.3 89.7 108.8 105.9 113.2 95.7 0 0 0 0 0 0 0 0 0 25 99.0 92.8 102.3 108.8 105.9 113.2 1 0 0 0 0 0 0 0 0 26 100.7 88.0 99.0 102.3 108.8 105.9 0 1 0 0 0 0 0 0 0 27 115.5 101.1 100.7 99.0 102.3 108.8 0 0 1 0 0 0 0 0 0 28 100.7 92.7 115.5 100.7 99.0 102.3 0 0 0 1 0 0 0 0 0 29 109.9 95.8 100.7 115.5 100.7 99.0 0 0 0 0 1 0 0 0 0 30 114.6 103.8 109.9 100.7 115.5 100.7 0 0 0 0 0 1 0 0 0 31 85.4 81.8 114.6 109.9 100.7 115.5 0 0 0 0 0 0 1 0 0 32 100.5 87.1 85.4 114.6 109.9 100.7 0 0 0 0 0 0 0 1 0 33 114.8 105.9 100.5 85.4 114.6 109.9 0 0 0 0 0 0 0 0 1 34 116.5 108.1 114.8 100.5 85.4 114.6 0 0 0 0 0 0 0 0 0 35 112.9 102.6 116.5 114.8 100.5 85.4 0 0 0 0 0 0 0 0 0 36 102.0 93.7 112.9 116.5 114.8 100.5 0 0 0 0 0 0 0 0 0 37 106.0 103.5 102.0 112.9 116.5 114.8 1 0 0 0 0 0 0 0 0 38 105.3 100.6 106.0 102.0 112.9 116.5 0 1 0 0 0 0 0 0 0 39 118.8 113.3 105.3 106.0 102.0 112.9 0 0 1 0 0 0 0 0 0 40 106.1 102.4 118.8 105.3 106.0 102.0 0 0 0 1 0 0 0 0 0 41 109.3 102.1 106.1 118.8 105.3 106.0 0 0 0 0 1 0 0 0 0 42 117.2 106.9 109.3 106.1 118.8 105.3 0 0 0 0 0 1 0 0 0 43 92.5 87.3 117.2 109.3 106.1 118.8 0 0 0 0 0 0 1 0 0 44 104.2 93.1 92.5 117.2 109.3 106.1 0 0 0 0 0 0 0 1 0 45 112.5 109.1 104.2 92.5 117.2 109.3 0 0 0 0 0 0 0 0 1 46 122.4 120.3 112.5 104.2 92.5 117.2 0 0 0 0 0 0 0 0 0 47 113.3 104.9 122.4 112.5 104.2 92.5 0 0 0 0 0 0 0 0 0 48 100.0 92.6 113.3 122.4 112.5 104.2 0 0 0 0 0 0 0 0 0 49 110.7 109.8 100.0 113.3 122.4 112.5 1 0 0 0 0 0 0 0 0 50 112.8 111.4 110.7 100.0 113.3 122.4 0 1 0 0 0 0 0 0 0 51 109.8 117.9 112.8 110.7 100.0 113.3 0 0 1 0 0 0 0 0 0 52 117.3 121.6 109.8 112.8 110.7 100.0 0 0 0 1 0 0 0 0 0 53 109.1 117.8 117.3 109.8 112.8 110.7 0 0 0 0 1 0 0 0 0 54 115.9 124.2 109.1 117.3 109.8 112.8 0 0 0 0 0 1 0 0 0 55 96.0 106.8 115.9 109.1 117.3 109.8 0 0 0 0 0 0 1 0 0 56 99.8 102.7 96.0 115.9 109.1 117.3 0 0 0 0 0 0 0 1 0 57 116.8 116.8 99.8 96.0 115.9 109.1 0 0 0 0 0 0 0 0 1 58 115.7 113.6 116.8 99.8 96.0 115.9 0 0 0 0 0 0 0 0 0 59 99.4 96.1 115.7 116.8 99.8 96.0 0 0 0 0 0 0 0 0 0 60 94.3 85.0 99.4 115.7 116.8 99.8 0 0 0 0 0 0 0 0 0 M10 M11 t 1 0 0 1 2 0 0 2 3 0 0 3 4 0 0 4 5 0 0 5 6 0 0 6 7 0 0 7 8 0 0 8 9 0 0 9 10 1 0 10 11 0 1 11 12 0 0 12 13 0 0 13 14 0 0 14 15 0 0 15 16 0 0 16 17 0 0 17 18 0 0 18 19 0 0 19 20 0 0 20 21 0 0 21 22 1 0 22 23 0 1 23 24 0 0 24 25 0 0 25 26 0 0 26 27 0 0 27 28 0 0 28 29 0 0 29 30 0 0 30 31 0 0 31 32 0 0 32 33 0 0 33 34 1 0 34 35 0 1 35 36 0 0 36 37 0 0 37 38 0 0 38 39 0 0 39 40 0 0 40 41 0 0 41 42 0 0 42 43 0 0 43 44 0 0 44 45 0 0 45 46 1 0 46 47 0 1 47 48 0 0 48 49 0 0 49 50 0 0 50 51 0 0 51 52 0 0 52 53 0 0 53 54 0 0 54 55 0 0 55 56 0 0 56 57 0 0 57 58 1 0 58 59 0 1 59 60 0 0 60 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) IAidM `TIA(t-1)` `TIA(t-2)` `TIA(t-3)` `TIA(t-4)` 49.38518 0.32823 -0.05692 0.05504 0.31532 -0.15972 M1 M2 M3 M4 M5 M6 1.07936 5.04024 12.99101 7.48174 5.69905 10.74423 M7 M8 M9 M10 M11 t -5.15003 2.42700 10.14582 19.48964 7.43513 0.02241 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.2643 -2.3977 0.1846 2.1578 6.1557 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 49.38518 34.91299 1.415 0.164580 IAidM 0.32823 0.09199 3.568 0.000915 *** `TIA(t-1)` -0.05692 0.14603 -0.390 0.698695 `TIA(t-2)` 0.05504 0.16080 0.342 0.733835 `TIA(t-3)` 0.31532 0.17445 1.807 0.077860 . `TIA(t-4)` -0.15972 0.16860 -0.947 0.348911 M1 1.07936 4.00449 0.270 0.788837 M2 5.04024 4.50496 1.119 0.269575 M3 12.99101 5.10573 2.544 0.014712 * M4 7.48174 4.44599 1.683 0.099834 . M5 5.69905 3.36571 1.693 0.097811 . M6 10.74423 3.41014 3.151 0.003000 ** M7 -5.15003 3.33108 -1.546 0.129595 M8 2.42700 4.13539 0.587 0.560423 M9 10.14582 5.90111 1.719 0.092921 . M10 19.48964 6.83273 2.852 0.006705 ** M11 7.43513 5.02651 1.479 0.146553 t 0.02241 0.06782 0.330 0.742721 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.579 on 42 degrees of freedom Multiple R-squared: 0.8874, Adjusted R-squared: 0.8419 F-statistic: 19.48 on 17 and 42 DF, p-value: 1.17e-14 > 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,] 0.25336745 0.50673490 0.7466326 [2,] 0.13986947 0.27973895 0.8601305 [3,] 0.21991089 0.43982177 0.7800891 [4,] 0.13297191 0.26594381 0.8670281 [5,] 0.09679228 0.19358455 0.9032077 [6,] 0.10434263 0.20868527 0.8956574 [7,] 0.13125681 0.26251363 0.8687432 [8,] 0.11641378 0.23282755 0.8835862 [9,] 0.12320167 0.24640335 0.8767983 [10,] 0.08534424 0.17068849 0.9146558 [11,] 0.06565658 0.13131315 0.9343434 [12,] 0.08405828 0.16811656 0.9159417 [13,] 0.05677225 0.11354449 0.9432278 [14,] 0.03086388 0.06172775 0.9691361 [15,] 0.02245114 0.04490228 0.9775489 [16,] 0.02189442 0.04378884 0.9781056 [17,] 0.02055761 0.04111522 0.9794424 [18,] 0.07442313 0.14884625 0.9255769 [19,] 0.04024514 0.08049027 0.9597549 > postscript(file="/var/www/html/rcomp/tmp/1n1711258745755.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/22gzg1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/359l01258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4312q1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/56afl1258745755.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 = 60 Frequency = 1 1 2 3 4 5 6 -3.77029127 -1.42837686 0.41988604 -0.64190984 -4.85206589 -0.70132340 7 8 9 10 11 12 0.26375131 0.78606797 -0.77252037 -3.76637133 -3.93362361 2.09223757 13 14 15 16 17 18 -2.12228113 -3.73194226 -4.02288881 2.68652832 -1.22482683 2.87162839 19 20 21 22 23 24 -2.49444310 1.26590789 2.14112401 -0.85678692 2.62801663 2.88991476 25 26 27 28 29 30 2.03792097 -0.58028985 4.73788639 -1.06698511 6.15574071 0.10542306 31 32 33 34 35 36 0.78983847 -0.63451165 2.20763243 3.75994788 3.88198180 0.92016434 37 38 39 40 41 42 1.92746557 0.43028341 4.39058410 -1.44016698 3.01228945 0.78180734 43 44 45 46 47 48 4.82106102 2.13988130 -2.50744488 3.22865835 3.68787602 0.02651226 49 50 51 52 53 54 1.92718584 5.31032557 -5.52546772 0.46253361 -3.09113744 -3.05753539 55 56 57 58 59 60 -3.38020771 -3.55734552 -1.06879119 -2.36544798 -6.26425083 -5.92882893 > postscript(file="/var/www/html/rcomp/tmp/6appa1258745755.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 = 60 Frequency = 1 lag(myerror, k = 1) myerror 0 -3.77029127 NA 1 -1.42837686 -3.77029127 2 0.41988604 -1.42837686 3 -0.64190984 0.41988604 4 -4.85206589 -0.64190984 5 -0.70132340 -4.85206589 6 0.26375131 -0.70132340 7 0.78606797 0.26375131 8 -0.77252037 0.78606797 9 -3.76637133 -0.77252037 10 -3.93362361 -3.76637133 11 2.09223757 -3.93362361 12 -2.12228113 2.09223757 13 -3.73194226 -2.12228113 14 -4.02288881 -3.73194226 15 2.68652832 -4.02288881 16 -1.22482683 2.68652832 17 2.87162839 -1.22482683 18 -2.49444310 2.87162839 19 1.26590789 -2.49444310 20 2.14112401 1.26590789 21 -0.85678692 2.14112401 22 2.62801663 -0.85678692 23 2.88991476 2.62801663 24 2.03792097 2.88991476 25 -0.58028985 2.03792097 26 4.73788639 -0.58028985 27 -1.06698511 4.73788639 28 6.15574071 -1.06698511 29 0.10542306 6.15574071 30 0.78983847 0.10542306 31 -0.63451165 0.78983847 32 2.20763243 -0.63451165 33 3.75994788 2.20763243 34 3.88198180 3.75994788 35 0.92016434 3.88198180 36 1.92746557 0.92016434 37 0.43028341 1.92746557 38 4.39058410 0.43028341 39 -1.44016698 4.39058410 40 3.01228945 -1.44016698 41 0.78180734 3.01228945 42 4.82106102 0.78180734 43 2.13988130 4.82106102 44 -2.50744488 2.13988130 45 3.22865835 -2.50744488 46 3.68787602 3.22865835 47 0.02651226 3.68787602 48 1.92718584 0.02651226 49 5.31032557 1.92718584 50 -5.52546772 5.31032557 51 0.46253361 -5.52546772 52 -3.09113744 0.46253361 53 -3.05753539 -3.09113744 54 -3.38020771 -3.05753539 55 -3.55734552 -3.38020771 56 -1.06879119 -3.55734552 57 -2.36544798 -1.06879119 58 -6.26425083 -2.36544798 59 -5.92882893 -6.26425083 60 NA -5.92882893 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.42837686 -3.77029127 [2,] 0.41988604 -1.42837686 [3,] -0.64190984 0.41988604 [4,] -4.85206589 -0.64190984 [5,] -0.70132340 -4.85206589 [6,] 0.26375131 -0.70132340 [7,] 0.78606797 0.26375131 [8,] -0.77252037 0.78606797 [9,] -3.76637133 -0.77252037 [10,] -3.93362361 -3.76637133 [11,] 2.09223757 -3.93362361 [12,] -2.12228113 2.09223757 [13,] -3.73194226 -2.12228113 [14,] -4.02288881 -3.73194226 [15,] 2.68652832 -4.02288881 [16,] -1.22482683 2.68652832 [17,] 2.87162839 -1.22482683 [18,] -2.49444310 2.87162839 [19,] 1.26590789 -2.49444310 [20,] 2.14112401 1.26590789 [21,] -0.85678692 2.14112401 [22,] 2.62801663 -0.85678692 [23,] 2.88991476 2.62801663 [24,] 2.03792097 2.88991476 [25,] -0.58028985 2.03792097 [26,] 4.73788639 -0.58028985 [27,] -1.06698511 4.73788639 [28,] 6.15574071 -1.06698511 [29,] 0.10542306 6.15574071 [30,] 0.78983847 0.10542306 [31,] -0.63451165 0.78983847 [32,] 2.20763243 -0.63451165 [33,] 3.75994788 2.20763243 [34,] 3.88198180 3.75994788 [35,] 0.92016434 3.88198180 [36,] 1.92746557 0.92016434 [37,] 0.43028341 1.92746557 [38,] 4.39058410 0.43028341 [39,] -1.44016698 4.39058410 [40,] 3.01228945 -1.44016698 [41,] 0.78180734 3.01228945 [42,] 4.82106102 0.78180734 [43,] 2.13988130 4.82106102 [44,] -2.50744488 2.13988130 [45,] 3.22865835 -2.50744488 [46,] 3.68787602 3.22865835 [47,] 0.02651226 3.68787602 [48,] 1.92718584 0.02651226 [49,] 5.31032557 1.92718584 [50,] -5.52546772 5.31032557 [51,] 0.46253361 -5.52546772 [52,] -3.09113744 0.46253361 [53,] -3.05753539 -3.09113744 [54,] -3.38020771 -3.05753539 [55,] -3.55734552 -3.38020771 [56,] -1.06879119 -3.55734552 [57,] -2.36544798 -1.06879119 [58,] -6.26425083 -2.36544798 [59,] -5.92882893 -6.26425083 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.42837686 -3.77029127 2 0.41988604 -1.42837686 3 -0.64190984 0.41988604 4 -4.85206589 -0.64190984 5 -0.70132340 -4.85206589 6 0.26375131 -0.70132340 7 0.78606797 0.26375131 8 -0.77252037 0.78606797 9 -3.76637133 -0.77252037 10 -3.93362361 -3.76637133 11 2.09223757 -3.93362361 12 -2.12228113 2.09223757 13 -3.73194226 -2.12228113 14 -4.02288881 -3.73194226 15 2.68652832 -4.02288881 16 -1.22482683 2.68652832 17 2.87162839 -1.22482683 18 -2.49444310 2.87162839 19 1.26590789 -2.49444310 20 2.14112401 1.26590789 21 -0.85678692 2.14112401 22 2.62801663 -0.85678692 23 2.88991476 2.62801663 24 2.03792097 2.88991476 25 -0.58028985 2.03792097 26 4.73788639 -0.58028985 27 -1.06698511 4.73788639 28 6.15574071 -1.06698511 29 0.10542306 6.15574071 30 0.78983847 0.10542306 31 -0.63451165 0.78983847 32 2.20763243 -0.63451165 33 3.75994788 2.20763243 34 3.88198180 3.75994788 35 0.92016434 3.88198180 36 1.92746557 0.92016434 37 0.43028341 1.92746557 38 4.39058410 0.43028341 39 -1.44016698 4.39058410 40 3.01228945 -1.44016698 41 0.78180734 3.01228945 42 4.82106102 0.78180734 43 2.13988130 4.82106102 44 -2.50744488 2.13988130 45 3.22865835 -2.50744488 46 3.68787602 3.22865835 47 0.02651226 3.68787602 48 1.92718584 0.02651226 49 5.31032557 1.92718584 50 -5.52546772 5.31032557 51 0.46253361 -5.52546772 52 -3.09113744 0.46253361 53 -3.05753539 -3.09113744 54 -3.38020771 -3.05753539 55 -3.55734552 -3.38020771 56 -1.06879119 -3.55734552 57 -2.36544798 -1.06879119 58 -6.26425083 -2.36544798 59 -5.92882893 -6.26425083 > 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/7ts951258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8frqx1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/99woy1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10mykf1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/111al01258745755.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/12vzw21258745755.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/13uc611258745755.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/14zjnw1258745755.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/159nz11258745755.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/16i0u51258745755.tab") + } > > system("convert tmp/1n1711258745755.ps tmp/1n1711258745755.png") > system("convert tmp/22gzg1258745755.ps tmp/22gzg1258745755.png") > system("convert tmp/359l01258745755.ps tmp/359l01258745755.png") > system("convert tmp/4312q1258745755.ps tmp/4312q1258745755.png") > system("convert tmp/56afl1258745755.ps tmp/56afl1258745755.png") > system("convert tmp/6appa1258745755.ps tmp/6appa1258745755.png") > system("convert tmp/7ts951258745755.ps tmp/7ts951258745755.png") > system("convert tmp/8frqx1258745755.ps tmp/8frqx1258745755.png") > system("convert tmp/99woy1258745755.ps tmp/99woy1258745755.png") > system("convert tmp/10mykf1258745755.ps tmp/10mykf1258745755.png") > > > proc.time() user system elapsed 2.425 1.607 2.813