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Type 'q()' to quit R. > x <- array(list(2.3 + ,0 + ,2.0 + ,1.9 + ,2.3 + ,2.7 + ,2.8 + ,0 + ,2.3 + ,2.0 + ,1.9 + ,2.3 + ,2.4 + ,0 + ,2.8 + ,2.3 + ,2.0 + ,1.9 + ,2.3 + ,0 + ,2.4 + ,2.8 + ,2.3 + ,2.0 + ,2.7 + ,0 + ,2.3 + ,2.4 + ,2.8 + ,2.3 + ,2.7 + ,0 + ,2.7 + ,2.3 + ,2.4 + ,2.8 + ,2.9 + ,0 + ,2.7 + ,2.7 + ,2.3 + ,2.4 + ,3.0 + ,0 + ,2.9 + ,2.7 + ,2.7 + ,2.3 + ,2.2 + ,0 + ,3.0 + ,2.9 + ,2.7 + ,2.7 + ,2.3 + ,0 + ,2.2 + ,3.0 + ,2.9 + ,2.7 + ,2.8 + ,0 + ,2.3 + ,2.2 + ,3.0 + ,2.9 + ,2.8 + ,0 + ,2.8 + ,2.3 + ,2.2 + ,3.0 + ,2.8 + ,0 + ,2.8 + ,2.8 + ,2.3 + ,2.2 + ,2.2 + ,0 + ,2.8 + ,2.8 + ,2.8 + ,2.3 + ,2.6 + ,0 + ,2.2 + ,2.8 + ,2.8 + ,2.8 + ,2.8 + ,0 + ,2.6 + ,2.2 + ,2.8 + ,2.8 + ,2.5 + ,0 + ,2.8 + ,2.6 + ,2.2 + ,2.8 + ,2.4 + ,0 + ,2.5 + ,2.8 + ,2.6 + ,2.2 + ,2.3 + ,0 + ,2.4 + ,2.5 + ,2.8 + ,2.6 + ,1.9 + ,0 + ,2.3 + ,2.4 + ,2.5 + ,2.8 + ,1.7 + ,0 + ,1.9 + ,2.3 + ,2.4 + ,2.5 + ,2.0 + ,0 + ,1.7 + ,1.9 + ,2.3 + ,2.4 + ,2.1 + ,0 + ,2.0 + ,1.7 + ,1.9 + ,2.3 + ,1.7 + ,0 + ,2.1 + ,2.0 + ,1.7 + ,1.9 + ,1.8 + ,0 + ,1.7 + ,2.1 + ,2.0 + ,1.7 + ,1.8 + ,0 + ,1.8 + ,1.7 + ,2.1 + ,2.0 + ,1.8 + ,0 + ,1.8 + ,1.8 + ,1.7 + ,2.1 + ,1.3 + ,0 + ,1.8 + ,1.8 + ,1.8 + ,1.7 + ,1.3 + ,0 + ,1.3 + ,1.8 + ,1.8 + ,1.8 + ,1.3 + ,1 + ,1.3 + ,1.3 + ,1.8 + ,1.8 + ,1.2 + ,1 + ,1.3 + ,1.3 + ,1.3 + ,1.8 + ,1.4 + ,1 + ,1.2 + ,1.3 + ,1.3 + ,1.3 + ,2.2 + ,1 + ,1.4 + ,1.2 + ,1.3 + ,1.3 + ,2.9 + ,1 + ,2.2 + ,1.4 + ,1.2 + ,1.3 + ,3.1 + ,1 + ,2.9 + ,2.2 + ,1.4 + ,1.2 + ,3.5 + ,1 + ,3.1 + ,2.9 + ,2.2 + ,1.4 + ,3.6 + ,1 + ,3.5 + ,3.1 + ,2.9 + ,2.2 + ,4.4 + ,1 + ,3.6 + ,3.5 + ,3.1 + ,2.9 + ,4.1 + ,1 + ,4.4 + ,3.6 + ,3.5 + ,3.1 + ,5.1 + ,1 + ,4.1 + ,4.4 + ,3.6 + ,3.5 + ,5.8 + ,1 + ,5.1 + ,4.1 + ,4.4 + ,3.6 + ,5.9 + ,1 + ,5.8 + ,5.1 + ,4.1 + ,4.4 + ,5.4 + ,1 + ,5.9 + ,5.8 + ,5.1 + ,4.1 + ,5.5 + ,1 + ,5.4 + ,5.9 + ,5.8 + ,5.1 + ,4.8 + ,1 + ,5.5 + ,5.4 + ,5.9 + ,5.8 + ,3.2 + ,1 + ,4.8 + ,5.5 + ,5.4 + ,5.9 + ,2.7 + ,1 + ,3.2 + ,4.8 + ,5.5 + ,5.4 + ,2.1 + ,1 + ,2.7 + ,3.2 + ,4.8 + ,5.5 + ,1.9 + ,1 + ,2.1 + ,2.7 + ,3.2 + ,4.8 + ,0.6 + ,1 + ,1.9 + ,2.1 + ,2.7 + ,3.2 + ,0.7 + ,1 + ,0.6 + ,1.9 + ,2.1 + ,2.7 + ,-0.2 + ,1 + ,0.7 + ,0.6 + ,1.9 + ,2.1 + ,-1.0 + ,1 + ,-0.2 + ,0.7 + ,0.6 + ,1.9 + ,-1.7 + ,1 + ,-1.0 + ,-0.2 + ,0.7 + ,0.6 + ,-0.7 + ,1 + ,-1.7 + ,-1.0 + ,-0.2 + ,0.7 + ,-1.0 + ,1 + ,-0.7 + ,-1.7 + ,-1.0 + ,-0.2) + ,dim=c(6 + ,56) + ,dimnames=list(c('Y' + ,'X' + ,'Y1' + ,'Y2' + ,'Y3' + ,'Y4') + ,1:56)) > y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),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 X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 2.3 0 2.0 1.9 2.3 2.7 1 0 0 0 0 0 0 0 0 0 0 1 2 2.8 0 2.3 2.0 1.9 2.3 0 1 0 0 0 0 0 0 0 0 0 2 3 2.4 0 2.8 2.3 2.0 1.9 0 0 1 0 0 0 0 0 0 0 0 3 4 2.3 0 2.4 2.8 2.3 2.0 0 0 0 1 0 0 0 0 0 0 0 4 5 2.7 0 2.3 2.4 2.8 2.3 0 0 0 0 1 0 0 0 0 0 0 5 6 2.7 0 2.7 2.3 2.4 2.8 0 0 0 0 0 1 0 0 0 0 0 6 7 2.9 0 2.7 2.7 2.3 2.4 0 0 0 0 0 0 1 0 0 0 0 7 8 3.0 0 2.9 2.7 2.7 2.3 0 0 0 0 0 0 0 1 0 0 0 8 9 2.2 0 3.0 2.9 2.7 2.7 0 0 0 0 0 0 0 0 1 0 0 9 10 2.3 0 2.2 3.0 2.9 2.7 0 0 0 0 0 0 0 0 0 1 0 10 11 2.8 0 2.3 2.2 3.0 2.9 0 0 0 0 0 0 0 0 0 0 1 11 12 2.8 0 2.8 2.3 2.2 3.0 0 0 0 0 0 0 0 0 0 0 0 12 13 2.8 0 2.8 2.8 2.3 2.2 1 0 0 0 0 0 0 0 0 0 0 13 14 2.2 0 2.8 2.8 2.8 2.3 0 1 0 0 0 0 0 0 0 0 0 14 15 2.6 0 2.2 2.8 2.8 2.8 0 0 1 0 0 0 0 0 0 0 0 15 16 2.8 0 2.6 2.2 2.8 2.8 0 0 0 1 0 0 0 0 0 0 0 16 17 2.5 0 2.8 2.6 2.2 2.8 0 0 0 0 1 0 0 0 0 0 0 17 18 2.4 0 2.5 2.8 2.6 2.2 0 0 0 0 0 1 0 0 0 0 0 18 19 2.3 0 2.4 2.5 2.8 2.6 0 0 0 0 0 0 1 0 0 0 0 19 20 1.9 0 2.3 2.4 2.5 2.8 0 0 0 0 0 0 0 1 0 0 0 20 21 1.7 0 1.9 2.3 2.4 2.5 0 0 0 0 0 0 0 0 1 0 0 21 22 2.0 0 1.7 1.9 2.3 2.4 0 0 0 0 0 0 0 0 0 1 0 22 23 2.1 0 2.0 1.7 1.9 2.3 0 0 0 0 0 0 0 0 0 0 1 23 24 1.7 0 2.1 2.0 1.7 1.9 0 0 0 0 0 0 0 0 0 0 0 24 25 1.8 0 1.7 2.1 2.0 1.7 1 0 0 0 0 0 0 0 0 0 0 25 26 1.8 0 1.8 1.7 2.1 2.0 0 1 0 0 0 0 0 0 0 0 0 26 27 1.8 0 1.8 1.8 1.7 2.1 0 0 1 0 0 0 0 0 0 0 0 27 28 1.3 0 1.8 1.8 1.8 1.7 0 0 0 1 0 0 0 0 0 0 0 28 29 1.3 0 1.3 1.8 1.8 1.8 0 0 0 0 1 0 0 0 0 0 0 29 30 1.3 1 1.3 1.3 1.8 1.8 0 0 0 0 0 1 0 0 0 0 0 30 31 1.2 1 1.3 1.3 1.3 1.8 0 0 0 0 0 0 1 0 0 0 0 31 32 1.4 1 1.2 1.3 1.3 1.3 0 0 0 0 0 0 0 1 0 0 0 32 33 2.2 1 1.4 1.2 1.3 1.3 0 0 0 0 0 0 0 0 1 0 0 33 34 2.9 1 2.2 1.4 1.2 1.3 0 0 0 0 0 0 0 0 0 1 0 34 35 3.1 1 2.9 2.2 1.4 1.2 0 0 0 0 0 0 0 0 0 0 1 35 36 3.5 1 3.1 2.9 2.2 1.4 0 0 0 0 0 0 0 0 0 0 0 36 37 3.6 1 3.5 3.1 2.9 2.2 1 0 0 0 0 0 0 0 0 0 0 37 38 4.4 1 3.6 3.5 3.1 2.9 0 1 0 0 0 0 0 0 0 0 0 38 39 4.1 1 4.4 3.6 3.5 3.1 0 0 1 0 0 0 0 0 0 0 0 39 40 5.1 1 4.1 4.4 3.6 3.5 0 0 0 1 0 0 0 0 0 0 0 40 41 5.8 1 5.1 4.1 4.4 3.6 0 0 0 0 1 0 0 0 0 0 0 41 42 5.9 1 5.8 5.1 4.1 4.4 0 0 0 0 0 1 0 0 0 0 0 42 43 5.4 1 5.9 5.8 5.1 4.1 0 0 0 0 0 0 1 0 0 0 0 43 44 5.5 1 5.4 5.9 5.8 5.1 0 0 0 0 0 0 0 1 0 0 0 44 45 4.8 1 5.5 5.4 5.9 5.8 0 0 0 0 0 0 0 0 1 0 0 45 46 3.2 1 4.8 5.5 5.4 5.9 0 0 0 0 0 0 0 0 0 1 0 46 47 2.7 1 3.2 4.8 5.5 5.4 0 0 0 0 0 0 0 0 0 0 1 47 48 2.1 1 2.7 3.2 4.8 5.5 0 0 0 0 0 0 0 0 0 0 0 48 49 1.9 1 2.1 2.7 3.2 4.8 1 0 0 0 0 0 0 0 0 0 0 49 50 0.6 1 1.9 2.1 2.7 3.2 0 1 0 0 0 0 0 0 0 0 0 50 51 0.7 1 0.6 1.9 2.1 2.7 0 0 1 0 0 0 0 0 0 0 0 51 52 -0.2 1 0.7 0.6 1.9 2.1 0 0 0 1 0 0 0 0 0 0 0 52 53 -1.0 1 -0.2 0.7 0.6 1.9 0 0 0 0 1 0 0 0 0 0 0 53 54 -1.7 1 -1.0 -0.2 0.7 0.6 0 0 0 0 0 1 0 0 0 0 0 54 55 -0.7 1 -1.7 -1.0 -0.2 0.7 0 0 0 0 0 0 1 0 0 0 0 55 56 -1.0 1 -0.7 -1.7 -1.0 -0.2 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 Y1 Y2 Y3 Y4 0.539091 0.573439 1.028299 0.026967 0.032818 -0.210413 M1 M2 M3 M4 M5 M6 0.136005 -0.060537 0.039992 0.021028 0.120045 -0.137284 M7 M8 M9 M10 M11 t 0.119909 -0.032093 -0.136439 -0.006291 0.196690 -0.019678 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.02099 -0.27025 0.04085 0.29608 1.07877 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.539091 0.375288 1.436 0.1591 X 0.573439 0.325110 1.764 0.0858 . Y1 1.028299 0.160039 6.425 1.49e-07 *** Y2 0.026967 0.242223 0.111 0.9119 Y3 0.032818 0.254511 0.129 0.8981 Y4 -0.210413 0.175993 -1.196 0.2393 M1 0.136005 0.367371 0.370 0.7133 M2 -0.060537 0.366645 -0.165 0.8697 M3 0.039992 0.368788 0.108 0.9142 M4 0.021028 0.369717 0.057 0.9549 M5 0.120045 0.367397 0.327 0.7457 M6 -0.137284 0.369555 -0.371 0.7123 M7 0.119909 0.371871 0.322 0.7489 M8 -0.032093 0.369313 -0.087 0.9312 M9 -0.136439 0.386666 -0.353 0.7261 M10 -0.006291 0.388555 -0.016 0.9872 M11 0.196690 0.387853 0.507 0.6150 t -0.019678 0.010447 -1.884 0.0673 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5427 on 38 degrees of freedom Multiple R-squared: 0.9199, Adjusted R-squared: 0.884 F-statistic: 25.66 on 17 and 38 DF, p-value: 1.007e-15 > 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,] 0.3851129391 0.7702258782 0.6148871 [2,] 0.2389822984 0.4779645968 0.7610177 [3,] 0.1315248439 0.2630496878 0.8684752 [4,] 0.0725129608 0.1450259217 0.9274870 [5,] 0.0330092435 0.0660184869 0.9669908 [6,] 0.0140635349 0.0281270698 0.9859365 [7,] 0.0054677273 0.0109354546 0.9945323 [8,] 0.0028084123 0.0056168246 0.9971916 [9,] 0.0009342180 0.0018684359 0.9990658 [10,] 0.0002794597 0.0005589193 0.9997205 [11,] 0.0001967524 0.0003935049 0.9998032 [12,] 0.0009998830 0.0019997659 0.9990001 [13,] 0.0167651152 0.0335302303 0.9832349 [14,] 0.0090827635 0.0181655270 0.9909172 [15,] 0.0068347396 0.0136694792 0.9931653 > postscript(file="/var/www/html/rcomp/tmp/1t2pg1259323372.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2es1u1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3us671259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4hv6s1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5c58f1259323372.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 0.029381297 0.363376170 -0.727161627 -0.379487511 0.101504603 0.088222222 7 8 9 10 11 12 -0.040962452 -0.009111438 -0.709145872 0.093763203 0.368004636 0.114821819 13 14 15 16 17 18 -0.186600940 -0.565749435 0.475584913 0.319088208 -0.257007111 0.083720459 19 20 21 22 23 24 -0.065272229 -0.136138396 0.142059190 0.530276837 0.135963320 -0.236190900 25 26 27 28 29 30 0.104176900 0.288195258 0.238815727 -0.309988936 0.145862088 -0.137086342 31 32 33 34 35 36 -0.458191685 -0.088889167 0.632171449 0.396951584 -0.355339329 0.052319598 37 38 39 40 41 42 -0.235362203 0.807965860 -0.369265576 1.037176075 0.632415074 0.440821375 43 44 45 46 47 48 -0.514341802 0.456230499 -0.065084767 -1.020991623 -0.148628627 0.069049484 49 50 51 52 53 54 0.288404947 -0.893787853 0.382026563 -0.666787837 -0.622774654 -0.475677714 55 56 1.078768168 -0.222091499 > postscript(file="/var/www/html/rcomp/tmp/6oaxb1259323372.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 0.029381297 NA 1 0.363376170 0.029381297 2 -0.727161627 0.363376170 3 -0.379487511 -0.727161627 4 0.101504603 -0.379487511 5 0.088222222 0.101504603 6 -0.040962452 0.088222222 7 -0.009111438 -0.040962452 8 -0.709145872 -0.009111438 9 0.093763203 -0.709145872 10 0.368004636 0.093763203 11 0.114821819 0.368004636 12 -0.186600940 0.114821819 13 -0.565749435 -0.186600940 14 0.475584913 -0.565749435 15 0.319088208 0.475584913 16 -0.257007111 0.319088208 17 0.083720459 -0.257007111 18 -0.065272229 0.083720459 19 -0.136138396 -0.065272229 20 0.142059190 -0.136138396 21 0.530276837 0.142059190 22 0.135963320 0.530276837 23 -0.236190900 0.135963320 24 0.104176900 -0.236190900 25 0.288195258 0.104176900 26 0.238815727 0.288195258 27 -0.309988936 0.238815727 28 0.145862088 -0.309988936 29 -0.137086342 0.145862088 30 -0.458191685 -0.137086342 31 -0.088889167 -0.458191685 32 0.632171449 -0.088889167 33 0.396951584 0.632171449 34 -0.355339329 0.396951584 35 0.052319598 -0.355339329 36 -0.235362203 0.052319598 37 0.807965860 -0.235362203 38 -0.369265576 0.807965860 39 1.037176075 -0.369265576 40 0.632415074 1.037176075 41 0.440821375 0.632415074 42 -0.514341802 0.440821375 43 0.456230499 -0.514341802 44 -0.065084767 0.456230499 45 -1.020991623 -0.065084767 46 -0.148628627 -1.020991623 47 0.069049484 -0.148628627 48 0.288404947 0.069049484 49 -0.893787853 0.288404947 50 0.382026563 -0.893787853 51 -0.666787837 0.382026563 52 -0.622774654 -0.666787837 53 -0.475677714 -0.622774654 54 1.078768168 -0.475677714 55 -0.222091499 1.078768168 56 NA -0.222091499 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.363376170 0.029381297 [2,] -0.727161627 0.363376170 [3,] -0.379487511 -0.727161627 [4,] 0.101504603 -0.379487511 [5,] 0.088222222 0.101504603 [6,] -0.040962452 0.088222222 [7,] -0.009111438 -0.040962452 [8,] -0.709145872 -0.009111438 [9,] 0.093763203 -0.709145872 [10,] 0.368004636 0.093763203 [11,] 0.114821819 0.368004636 [12,] -0.186600940 0.114821819 [13,] -0.565749435 -0.186600940 [14,] 0.475584913 -0.565749435 [15,] 0.319088208 0.475584913 [16,] -0.257007111 0.319088208 [17,] 0.083720459 -0.257007111 [18,] -0.065272229 0.083720459 [19,] -0.136138396 -0.065272229 [20,] 0.142059190 -0.136138396 [21,] 0.530276837 0.142059190 [22,] 0.135963320 0.530276837 [23,] -0.236190900 0.135963320 [24,] 0.104176900 -0.236190900 [25,] 0.288195258 0.104176900 [26,] 0.238815727 0.288195258 [27,] -0.309988936 0.238815727 [28,] 0.145862088 -0.309988936 [29,] -0.137086342 0.145862088 [30,] -0.458191685 -0.137086342 [31,] -0.088889167 -0.458191685 [32,] 0.632171449 -0.088889167 [33,] 0.396951584 0.632171449 [34,] -0.355339329 0.396951584 [35,] 0.052319598 -0.355339329 [36,] -0.235362203 0.052319598 [37,] 0.807965860 -0.235362203 [38,] -0.369265576 0.807965860 [39,] 1.037176075 -0.369265576 [40,] 0.632415074 1.037176075 [41,] 0.440821375 0.632415074 [42,] -0.514341802 0.440821375 [43,] 0.456230499 -0.514341802 [44,] -0.065084767 0.456230499 [45,] -1.020991623 -0.065084767 [46,] -0.148628627 -1.020991623 [47,] 0.069049484 -0.148628627 [48,] 0.288404947 0.069049484 [49,] -0.893787853 0.288404947 [50,] 0.382026563 -0.893787853 [51,] -0.666787837 0.382026563 [52,] -0.622774654 -0.666787837 [53,] -0.475677714 -0.622774654 [54,] 1.078768168 -0.475677714 [55,] -0.222091499 1.078768168 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.363376170 0.029381297 2 -0.727161627 0.363376170 3 -0.379487511 -0.727161627 4 0.101504603 -0.379487511 5 0.088222222 0.101504603 6 -0.040962452 0.088222222 7 -0.009111438 -0.040962452 8 -0.709145872 -0.009111438 9 0.093763203 -0.709145872 10 0.368004636 0.093763203 11 0.114821819 0.368004636 12 -0.186600940 0.114821819 13 -0.565749435 -0.186600940 14 0.475584913 -0.565749435 15 0.319088208 0.475584913 16 -0.257007111 0.319088208 17 0.083720459 -0.257007111 18 -0.065272229 0.083720459 19 -0.136138396 -0.065272229 20 0.142059190 -0.136138396 21 0.530276837 0.142059190 22 0.135963320 0.530276837 23 -0.236190900 0.135963320 24 0.104176900 -0.236190900 25 0.288195258 0.104176900 26 0.238815727 0.288195258 27 -0.309988936 0.238815727 28 0.145862088 -0.309988936 29 -0.137086342 0.145862088 30 -0.458191685 -0.137086342 31 -0.088889167 -0.458191685 32 0.632171449 -0.088889167 33 0.396951584 0.632171449 34 -0.355339329 0.396951584 35 0.052319598 -0.355339329 36 -0.235362203 0.052319598 37 0.807965860 -0.235362203 38 -0.369265576 0.807965860 39 1.037176075 -0.369265576 40 0.632415074 1.037176075 41 0.440821375 0.632415074 42 -0.514341802 0.440821375 43 0.456230499 -0.514341802 44 -0.065084767 0.456230499 45 -1.020991623 -0.065084767 46 -0.148628627 -1.020991623 47 0.069049484 -0.148628627 48 0.288404947 0.069049484 49 -0.893787853 0.288404947 50 0.382026563 -0.893787853 51 -0.666787837 0.382026563 52 -0.622774654 -0.666787837 53 -0.475677714 -0.622774654 54 1.078768168 -0.475677714 55 -0.222091499 1.078768168 > 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/779hv1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8jec41259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9d69w1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10cano1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11qzdy1259323372.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/12yt5y1259323372.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/13fies1259323372.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/14flhp1259323372.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/15qyhj1259323372.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/16ddhm1259323372.tab") + } > > system("convert tmp/1t2pg1259323372.ps tmp/1t2pg1259323372.png") > system("convert tmp/2es1u1259323372.ps tmp/2es1u1259323372.png") > system("convert tmp/3us671259323372.ps tmp/3us671259323372.png") > system("convert tmp/4hv6s1259323372.ps tmp/4hv6s1259323372.png") > system("convert tmp/5c58f1259323372.ps tmp/5c58f1259323372.png") > system("convert tmp/6oaxb1259323372.ps tmp/6oaxb1259323372.png") > system("convert tmp/779hv1259323372.ps tmp/779hv1259323372.png") > system("convert tmp/8jec41259323372.ps tmp/8jec41259323372.png") > system("convert tmp/9d69w1259323372.ps tmp/9d69w1259323372.png") > system("convert tmp/10cano1259323372.ps tmp/10cano1259323372.png") > > > proc.time() user system elapsed 2.305 1.526 3.289