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Type 'q()' to quit R. > x <- array(list(7.2 + ,2.4 + ,7.5 + ,8.3 + ,8.9 + ,7.4 + ,2 + ,7.2 + ,7.5 + ,8.8 + ,8.8 + ,2.1 + ,7.4 + ,7.2 + ,8.3 + ,9.3 + ,2 + ,8.8 + ,7.4 + ,7.5 + ,9.3 + ,1.8 + ,9.3 + ,8.8 + ,7.2 + ,8.7 + ,2.7 + ,9.3 + ,9.3 + ,7.4 + ,8.2 + ,2.3 + ,8.7 + ,9.3 + ,8.8 + ,8.3 + ,1.9 + ,8.2 + ,8.7 + ,9.3 + ,8.5 + ,2 + ,8.3 + ,8.2 + ,9.3 + ,8.6 + ,2.3 + ,8.5 + ,8.3 + ,8.7 + ,8.5 + ,2.8 + ,8.6 + ,8.5 + ,8.2 + ,8.2 + ,2.4 + ,8.5 + ,8.6 + ,8.3 + ,8.1 + ,2.3 + ,8.2 + ,8.5 + ,8.5 + ,7.9 + ,2.7 + ,8.1 + ,8.2 + ,8.6 + ,8.6 + ,2.7 + ,7.9 + ,8.1 + ,8.5 + ,8.7 + ,2.9 + ,8.6 + ,7.9 + ,8.2 + ,8.7 + ,3 + ,8.7 + ,8.6 + ,8.1 + ,8.5 + ,2.2 + ,8.7 + ,8.7 + ,7.9 + ,8.4 + ,2.3 + ,8.5 + ,8.7 + ,8.6 + ,8.5 + ,2.8 + ,8.4 + ,8.5 + ,8.7 + ,8.7 + ,2.8 + ,8.5 + ,8.4 + ,8.7 + ,8.7 + ,2.8 + ,8.7 + ,8.5 + ,8.5 + ,8.6 + ,2.2 + ,8.7 + ,8.7 + ,8.4 + ,8.5 + ,2.6 + ,8.6 + ,8.7 + ,8.5 + ,8.3 + ,2.8 + ,8.5 + ,8.6 + ,8.7 + ,8 + ,2.5 + ,8.3 + ,8.5 + ,8.7 + ,8.2 + ,2.4 + ,8 + ,8.3 + ,8.6 + ,8.1 + ,2.3 + ,8.2 + ,8 + ,8.5 + ,8.1 + ,1.9 + ,8.1 + ,8.2 + ,8.3 + ,8 + ,1.7 + ,8.1 + ,8.1 + ,8 + ,7.9 + ,2 + ,8 + ,8.1 + ,8.2 + ,7.9 + ,2.1 + ,7.9 + ,8 + ,8.1 + ,8 + ,1.7 + ,7.9 + ,7.9 + ,8.1 + ,8 + ,1.8 + ,8 + ,7.9 + ,8 + ,7.9 + ,1.8 + ,8 + ,8 + ,7.9 + ,8 + ,1.8 + ,7.9 + ,8 + ,7.9 + ,7.7 + ,1.3 + ,8 + ,7.9 + ,8 + ,7.2 + ,1.3 + ,7.7 + ,8 + ,8 + ,7.5 + ,1.3 + ,7.2 + ,7.7 + ,7.9 + ,7.3 + ,1.2 + ,7.5 + ,7.2 + ,8 + ,7 + ,1.4 + ,7.3 + ,7.5 + ,7.7 + ,7 + ,2.2 + ,7 + ,7.3 + ,7.2 + ,7 + ,2.9 + ,7 + ,7 + ,7.5 + ,7.2 + ,3.1 + ,7 + ,7 + ,7.3 + ,7.3 + ,3.5 + ,7.2 + ,7 + ,7 + ,7.1 + ,3.6 + ,7.3 + ,7.2 + ,7 + ,6.8 + ,4.4 + ,7.1 + ,7.3 + ,7 + ,6.4 + ,4.1 + ,6.8 + ,7.1 + ,7.2 + ,6.1 + ,5.1 + ,6.4 + ,6.8 + ,7.3 + ,6.5 + ,5.8 + ,6.1 + ,6.4 + ,7.1 + ,7.7 + ,5.9 + ,6.5 + ,6.1 + ,6.8 + ,7.9 + ,5.4 + ,7.7 + ,6.5 + ,6.4 + ,7.5 + ,5.5 + ,7.9 + ,7.7 + ,6.1 + ,6.9 + ,4.8 + ,7.5 + ,7.9 + ,6.5 + ,6.6 + ,3.2 + ,6.9 + ,7.5 + ,7.7 + ,6.9 + ,2.7 + ,6.6 + ,6.9 + ,7.9) + ,dim=c(5 + ,56) + ,dimnames=list(c('Y(t)' + ,'X(t)' + ,'Y(t-1)' + ,'Y(t-2)' + ,'Y(t-4) ') + ,1:56)) > y <- array(NA,dim=c(5,56),dimnames=list(c('Y(t)','X(t)','Y(t-1)','Y(t-2)','Y(t-4) '),1:56)) > 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 Y(t) X(t) Y(t-1) Y(t-2) Y(t-4)\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 7.2 2.4 7.5 8.3 8.9 1 0 0 0 0 0 0 0 0 0 0 1 2 7.4 2.0 7.2 7.5 8.8 0 1 0 0 0 0 0 0 0 0 0 2 3 8.8 2.1 7.4 7.2 8.3 0 0 1 0 0 0 0 0 0 0 0 3 4 9.3 2.0 8.8 7.4 7.5 0 0 0 1 0 0 0 0 0 0 0 4 5 9.3 1.8 9.3 8.8 7.2 0 0 0 0 1 0 0 0 0 0 0 5 6 8.7 2.7 9.3 9.3 7.4 0 0 0 0 0 1 0 0 0 0 0 6 7 8.2 2.3 8.7 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 7 8 8.3 1.9 8.2 8.7 9.3 0 0 0 0 0 0 0 1 0 0 0 8 9 8.5 2.0 8.3 8.2 9.3 0 0 0 0 0 0 0 0 1 0 0 9 10 8.6 2.3 8.5 8.3 8.7 0 0 0 0 0 0 0 0 0 1 0 10 11 8.5 2.8 8.6 8.5 8.2 0 0 0 0 0 0 0 0 0 0 1 11 12 8.2 2.4 8.5 8.6 8.3 0 0 0 0 0 0 0 0 0 0 0 12 13 8.1 2.3 8.2 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 13 14 7.9 2.7 8.1 8.2 8.6 0 1 0 0 0 0 0 0 0 0 0 14 15 8.6 2.7 7.9 8.1 8.5 0 0 1 0 0 0 0 0 0 0 0 15 16 8.7 2.9 8.6 7.9 8.2 0 0 0 1 0 0 0 0 0 0 0 16 17 8.7 3.0 8.7 8.6 8.1 0 0 0 0 1 0 0 0 0 0 0 17 18 8.5 2.2 8.7 8.7 7.9 0 0 0 0 0 1 0 0 0 0 0 18 19 8.4 2.3 8.5 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 19 20 8.5 2.8 8.4 8.5 8.7 0 0 0 0 0 0 0 1 0 0 0 20 21 8.7 2.8 8.5 8.4 8.7 0 0 0 0 0 0 0 0 1 0 0 21 22 8.7 2.8 8.7 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 22 23 8.6 2.2 8.7 8.7 8.4 0 0 0 0 0 0 0 0 0 0 1 23 24 8.5 2.6 8.6 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 24 25 8.3 2.8 8.5 8.6 8.7 1 0 0 0 0 0 0 0 0 0 0 25 26 8.0 2.5 8.3 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 26 27 8.2 2.4 8.0 8.3 8.6 0 0 1 0 0 0 0 0 0 0 0 27 28 8.1 2.3 8.2 8.0 8.5 0 0 0 1 0 0 0 0 0 0 0 28 29 8.1 1.9 8.1 8.2 8.3 0 0 0 0 1 0 0 0 0 0 0 29 30 8.0 1.7 8.1 8.1 8.0 0 0 0 0 0 1 0 0 0 0 0 30 31 7.9 2.0 8.0 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 31 32 7.9 2.1 7.9 8.0 8.1 0 0 0 0 0 0 0 1 0 0 0 32 33 8.0 1.7 7.9 7.9 8.1 0 0 0 0 0 0 0 0 1 0 0 33 34 8.0 1.8 8.0 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 34 35 7.9 1.8 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 1 35 36 8.0 1.8 7.9 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 36 37 7.7 1.3 8.0 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 37 38 7.2 1.3 7.7 8.0 8.0 0 1 0 0 0 0 0 0 0 0 0 38 39 7.5 1.3 7.2 7.7 7.9 0 0 1 0 0 0 0 0 0 0 0 39 40 7.3 1.2 7.5 7.2 8.0 0 0 0 1 0 0 0 0 0 0 0 40 41 7.0 1.4 7.3 7.5 7.7 0 0 0 0 1 0 0 0 0 0 0 41 42 7.0 2.2 7.0 7.3 7.2 0 0 0 0 0 1 0 0 0 0 0 42 43 7.0 2.9 7.0 7.0 7.5 0 0 0 0 0 0 1 0 0 0 0 43 44 7.2 3.1 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 44 45 7.3 3.5 7.2 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 45 46 7.1 3.6 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 1 0 46 47 6.8 4.4 7.1 7.3 7.0 0 0 0 0 0 0 0 0 0 0 1 47 48 6.4 4.1 6.8 7.1 7.2 0 0 0 0 0 0 0 0 0 0 0 48 49 6.1 5.1 6.4 6.8 7.3 1 0 0 0 0 0 0 0 0 0 0 49 50 6.5 5.8 6.1 6.4 7.1 0 1 0 0 0 0 0 0 0 0 0 50 51 7.7 5.9 6.5 6.1 6.8 0 0 1 0 0 0 0 0 0 0 0 51 52 7.9 5.4 7.7 6.5 6.4 0 0 0 1 0 0 0 0 0 0 0 52 53 7.5 5.5 7.9 7.7 6.1 0 0 0 0 1 0 0 0 0 0 0 53 54 6.9 4.8 7.5 7.9 6.5 0 0 0 0 0 1 0 0 0 0 0 54 55 6.6 3.2 6.9 7.5 7.7 0 0 0 0 0 0 1 0 0 0 0 55 56 6.9 2.7 6.6 6.9 7.9 0 0 0 0 0 0 0 1 0 0 0 56 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `X(t)` `Y(t-1)` `Y(t-2)` `Y(t-4)\r` M1 1.021279 0.036041 1.510845 -0.906468 0.274643 -0.139705 M2 M3 M4 M5 M6 M7 -0.114153 0.615649 -0.411606 0.060368 0.091291 0.022185 M8 M9 M10 M11 t 0.172457 0.013630 -0.083274 -0.010991 -0.006787 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.265197 -0.075476 -0.001807 0.076234 0.358442 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.021279 0.659961 1.547 0.129824 `X(t)` 0.036041 0.024169 1.491 0.143947 `Y(t-1)` 1.510845 0.099900 15.124 < 2e-16 *** `Y(t-2)` -0.906468 0.111302 -8.144 6.08e-10 *** `Y(t-4)\r` 0.274643 0.069650 3.943 0.000324 *** M1 -0.139705 0.102037 -1.369 0.178784 M2 -0.114153 0.104870 -1.089 0.283045 M3 0.615649 0.106117 5.802 9.77e-07 *** M4 -0.411606 0.131738 -3.124 0.003355 ** M5 0.060368 0.104663 0.577 0.567402 M6 0.091291 0.108199 0.844 0.403965 M7 0.022185 0.099983 0.222 0.825561 M8 0.172457 0.102790 1.678 0.101392 M9 0.013630 0.111607 0.122 0.903429 M10 -0.083274 0.109081 -0.763 0.449809 M11 -0.010991 0.105006 -0.105 0.917176 t -0.006787 0.002399 -2.828 0.007348 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1477 on 39 degrees of freedom Multiple R-squared: 0.972, Adjusted R-squared: 0.9606 F-statistic: 84.75 on 16 and 39 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,] 0.14151855 0.28303710 0.8584814 [2,] 0.18733009 0.37466018 0.8126699 [3,] 0.09139235 0.18278470 0.9086076 [4,] 0.04125363 0.08250727 0.9587464 [5,] 0.05131280 0.10262561 0.9486872 [6,] 0.02575481 0.05150961 0.9742452 [7,] 0.01429216 0.02858431 0.9857078 [8,] 0.29278439 0.58556877 0.7072156 [9,] 0.20371367 0.40742735 0.7962863 [10,] 0.16332756 0.32665513 0.8366724 [11,] 0.18158795 0.36317589 0.8184121 [12,] 0.14031543 0.28063087 0.8596846 [13,] 0.12445766 0.24891532 0.8755423 [14,] 0.08956881 0.17913761 0.9104312 [15,] 0.05982342 0.11964683 0.9401766 [16,] 0.03230956 0.06461912 0.9676904 [17,] 0.19927988 0.39855976 0.8007201 > postscript(file="/var/www/html/rcomp/tmp/1qcwb1258566964.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2ay951258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/39fuo1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4juhr1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5jj211258566964.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 = 56 Frequency = 1 1 2 3 4 5 6 -0.013267998 -0.062072625 0.174519392 -0.002009903 0.136036530 -0.122232133 7 8 9 10 11 12 -0.009915538 0.035236260 -0.207072605 -0.060930966 -0.076917714 -0.152438036 13 14 15 16 17 18 0.205336219 -0.176165424 0.039805140 0.010146460 0.052262559 0.002534579 19 20 21 22 23 24 0.084742499 -0.034436828 0.089445525 0.036542370 0.101428538 0.106428264 25 26 27 28 29 30 0.051221024 -0.045209212 -0.265196743 0.125804262 0.062340623 -0.062841471 31 32 33 34 35 36 -0.001604824 -0.060792173 0.128591298 0.105057443 0.057671955 0.304552583 37 38 39 40 41 42 -0.100130362 -0.074994982 0.012935623 -0.083370052 -0.199262977 0.157048877 43 44 45 46 47 48 -0.146620206 -0.042385150 -0.010964218 -0.080668847 -0.082182778 -0.258542810 49 50 51 52 53 54 -0.143158883 0.358442242 0.037936589 -0.050570766 -0.051376736 0.025490148 55 56 0.073398068 0.102377891 > postscript(file="/var/www/html/rcomp/tmp/67e3n1258566964.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 = 56 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.013267998 NA 1 -0.062072625 -0.013267998 2 0.174519392 -0.062072625 3 -0.002009903 0.174519392 4 0.136036530 -0.002009903 5 -0.122232133 0.136036530 6 -0.009915538 -0.122232133 7 0.035236260 -0.009915538 8 -0.207072605 0.035236260 9 -0.060930966 -0.207072605 10 -0.076917714 -0.060930966 11 -0.152438036 -0.076917714 12 0.205336219 -0.152438036 13 -0.176165424 0.205336219 14 0.039805140 -0.176165424 15 0.010146460 0.039805140 16 0.052262559 0.010146460 17 0.002534579 0.052262559 18 0.084742499 0.002534579 19 -0.034436828 0.084742499 20 0.089445525 -0.034436828 21 0.036542370 0.089445525 22 0.101428538 0.036542370 23 0.106428264 0.101428538 24 0.051221024 0.106428264 25 -0.045209212 0.051221024 26 -0.265196743 -0.045209212 27 0.125804262 -0.265196743 28 0.062340623 0.125804262 29 -0.062841471 0.062340623 30 -0.001604824 -0.062841471 31 -0.060792173 -0.001604824 32 0.128591298 -0.060792173 33 0.105057443 0.128591298 34 0.057671955 0.105057443 35 0.304552583 0.057671955 36 -0.100130362 0.304552583 37 -0.074994982 -0.100130362 38 0.012935623 -0.074994982 39 -0.083370052 0.012935623 40 -0.199262977 -0.083370052 41 0.157048877 -0.199262977 42 -0.146620206 0.157048877 43 -0.042385150 -0.146620206 44 -0.010964218 -0.042385150 45 -0.080668847 -0.010964218 46 -0.082182778 -0.080668847 47 -0.258542810 -0.082182778 48 -0.143158883 -0.258542810 49 0.358442242 -0.143158883 50 0.037936589 0.358442242 51 -0.050570766 0.037936589 52 -0.051376736 -0.050570766 53 0.025490148 -0.051376736 54 0.073398068 0.025490148 55 0.102377891 0.073398068 56 NA 0.102377891 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.062072625 -0.013267998 [2,] 0.174519392 -0.062072625 [3,] -0.002009903 0.174519392 [4,] 0.136036530 -0.002009903 [5,] -0.122232133 0.136036530 [6,] -0.009915538 -0.122232133 [7,] 0.035236260 -0.009915538 [8,] -0.207072605 0.035236260 [9,] -0.060930966 -0.207072605 [10,] -0.076917714 -0.060930966 [11,] -0.152438036 -0.076917714 [12,] 0.205336219 -0.152438036 [13,] -0.176165424 0.205336219 [14,] 0.039805140 -0.176165424 [15,] 0.010146460 0.039805140 [16,] 0.052262559 0.010146460 [17,] 0.002534579 0.052262559 [18,] 0.084742499 0.002534579 [19,] -0.034436828 0.084742499 [20,] 0.089445525 -0.034436828 [21,] 0.036542370 0.089445525 [22,] 0.101428538 0.036542370 [23,] 0.106428264 0.101428538 [24,] 0.051221024 0.106428264 [25,] -0.045209212 0.051221024 [26,] -0.265196743 -0.045209212 [27,] 0.125804262 -0.265196743 [28,] 0.062340623 0.125804262 [29,] -0.062841471 0.062340623 [30,] -0.001604824 -0.062841471 [31,] -0.060792173 -0.001604824 [32,] 0.128591298 -0.060792173 [33,] 0.105057443 0.128591298 [34,] 0.057671955 0.105057443 [35,] 0.304552583 0.057671955 [36,] -0.100130362 0.304552583 [37,] -0.074994982 -0.100130362 [38,] 0.012935623 -0.074994982 [39,] -0.083370052 0.012935623 [40,] -0.199262977 -0.083370052 [41,] 0.157048877 -0.199262977 [42,] -0.146620206 0.157048877 [43,] -0.042385150 -0.146620206 [44,] -0.010964218 -0.042385150 [45,] -0.080668847 -0.010964218 [46,] -0.082182778 -0.080668847 [47,] -0.258542810 -0.082182778 [48,] -0.143158883 -0.258542810 [49,] 0.358442242 -0.143158883 [50,] 0.037936589 0.358442242 [51,] -0.050570766 0.037936589 [52,] -0.051376736 -0.050570766 [53,] 0.025490148 -0.051376736 [54,] 0.073398068 0.025490148 [55,] 0.102377891 0.073398068 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.062072625 -0.013267998 2 0.174519392 -0.062072625 3 -0.002009903 0.174519392 4 0.136036530 -0.002009903 5 -0.122232133 0.136036530 6 -0.009915538 -0.122232133 7 0.035236260 -0.009915538 8 -0.207072605 0.035236260 9 -0.060930966 -0.207072605 10 -0.076917714 -0.060930966 11 -0.152438036 -0.076917714 12 0.205336219 -0.152438036 13 -0.176165424 0.205336219 14 0.039805140 -0.176165424 15 0.010146460 0.039805140 16 0.052262559 0.010146460 17 0.002534579 0.052262559 18 0.084742499 0.002534579 19 -0.034436828 0.084742499 20 0.089445525 -0.034436828 21 0.036542370 0.089445525 22 0.101428538 0.036542370 23 0.106428264 0.101428538 24 0.051221024 0.106428264 25 -0.045209212 0.051221024 26 -0.265196743 -0.045209212 27 0.125804262 -0.265196743 28 0.062340623 0.125804262 29 -0.062841471 0.062340623 30 -0.001604824 -0.062841471 31 -0.060792173 -0.001604824 32 0.128591298 -0.060792173 33 0.105057443 0.128591298 34 0.057671955 0.105057443 35 0.304552583 0.057671955 36 -0.100130362 0.304552583 37 -0.074994982 -0.100130362 38 0.012935623 -0.074994982 39 -0.083370052 0.012935623 40 -0.199262977 -0.083370052 41 0.157048877 -0.199262977 42 -0.146620206 0.157048877 43 -0.042385150 -0.146620206 44 -0.010964218 -0.042385150 45 -0.080668847 -0.010964218 46 -0.082182778 -0.080668847 47 -0.258542810 -0.082182778 48 -0.143158883 -0.258542810 49 0.358442242 -0.143158883 50 0.037936589 0.358442242 51 -0.050570766 0.037936589 52 -0.051376736 -0.050570766 53 0.025490148 -0.051376736 54 0.073398068 0.025490148 55 0.102377891 0.073398068 > 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/7r5it1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8hogv1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9oi7d1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10bxzm1258566965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11jp2u1258566965.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/12glza1258566965.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/13w14q1258566965.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/14w01z1258566965.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/15jaqh1258566965.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/16d9l51258566965.tab") + } > > system("convert tmp/1qcwb1258566964.ps tmp/1qcwb1258566964.png") > system("convert tmp/2ay951258566964.ps tmp/2ay951258566964.png") > system("convert tmp/39fuo1258566964.ps tmp/39fuo1258566964.png") > system("convert tmp/4juhr1258566964.ps tmp/4juhr1258566964.png") > system("convert tmp/5jj211258566964.ps tmp/5jj211258566964.png") > system("convert tmp/67e3n1258566964.ps tmp/67e3n1258566964.png") > system("convert tmp/7r5it1258566964.ps tmp/7r5it1258566964.png") > system("convert tmp/8hogv1258566964.ps tmp/8hogv1258566964.png") > system("convert tmp/9oi7d1258566964.ps tmp/9oi7d1258566964.png") > system("convert tmp/10bxzm1258566965.ps tmp/10bxzm1258566965.png") > > > proc.time() user system elapsed 2.365 1.562 3.169