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Type 'q()' to quit R. > x <- array(list(0,34,41,0,9,39,35,0,1,40,34,0,4,45,36,0,6,43,39,0,21,42,40,0,24,49,30,0,23,43,33,0,22,50,30,0,21,44,32,0,20,40,41,0,16,41,40,0,18,45,41,0,18,45,40,0,24,48,39,0,16,54,34,0,15,47,34,0,24,35,46,0,18,28,45,0,15,28,44,0,4,34,40,0,3,23,39,0,6,33,37,0,5,38,39,0,12,41,35,0,12,47,26,0,12,46,26,0,14,45,33,0,12,47,27,0,17,49,30,0,12,50,26,0,20,56,27,0,21,50,18,0,15,56,19,0,22,58,13,0,19,59,14,0,19,51,41,0,26,59,21,0,25,60,16,0,19,60,17,0,20,68,9,0,30,62,14,0,31,62,14,0,35,58,16,0,33,56,11,0,26,50,10,0,25,52,6,0,17,36,9,0,14,33,5,0,8,26,7,0,12,28,2,0,7,27,0,0,4,20,8,0,10,16,13,0,8,11,11,0,16,0,19,1,14,3,23,1,20,10,23,1,9,0,43,1,10,3,59,1),dim=c(4,60),dimnames=list(c('Spa','Eco','Wer','Val'),1:60)) > y <- array(NA,dim=c(4,60),dimnames=list(c('Spa','Eco','Wer','Val'),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 = '2' > #'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 Eco Spa Wer Val M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 34 0 41 0 1 0 0 0 0 0 0 0 0 0 0 1 2 39 9 35 0 0 1 0 0 0 0 0 0 0 0 0 2 3 40 1 34 0 0 0 1 0 0 0 0 0 0 0 0 3 4 45 4 36 0 0 0 0 1 0 0 0 0 0 0 0 4 5 43 6 39 0 0 0 0 0 1 0 0 0 0 0 0 5 6 42 21 40 0 0 0 0 0 0 1 0 0 0 0 0 6 7 49 24 30 0 0 0 0 0 0 0 1 0 0 0 0 7 8 43 23 33 0 0 0 0 0 0 0 0 1 0 0 0 8 9 50 22 30 0 0 0 0 0 0 0 0 0 1 0 0 9 10 44 21 32 0 0 0 0 0 0 0 0 0 0 1 0 10 11 40 20 41 0 0 0 0 0 0 0 0 0 0 0 1 11 12 41 16 40 0 0 0 0 0 0 0 0 0 0 0 0 12 13 45 18 41 0 1 0 0 0 0 0 0 0 0 0 0 13 14 45 18 40 0 0 1 0 0 0 0 0 0 0 0 0 14 15 48 24 39 0 0 0 1 0 0 0 0 0 0 0 0 15 16 54 16 34 0 0 0 0 1 0 0 0 0 0 0 0 16 17 47 15 34 0 0 0 0 0 1 0 0 0 0 0 0 17 18 35 24 46 0 0 0 0 0 0 1 0 0 0 0 0 18 19 28 18 45 0 0 0 0 0 0 0 1 0 0 0 0 19 20 28 15 44 0 0 0 0 0 0 0 0 1 0 0 0 20 21 34 4 40 0 0 0 0 0 0 0 0 0 1 0 0 21 22 23 3 39 0 0 0 0 0 0 0 0 0 0 1 0 22 23 33 6 37 0 0 0 0 0 0 0 0 0 0 0 1 23 24 38 5 39 0 0 0 0 0 0 0 0 0 0 0 0 24 25 41 12 35 0 1 0 0 0 0 0 0 0 0 0 0 25 26 47 12 26 0 0 1 0 0 0 0 0 0 0 0 0 26 27 46 12 26 0 0 0 1 0 0 0 0 0 0 0 0 27 28 45 14 33 0 0 0 0 1 0 0 0 0 0 0 0 28 29 47 12 27 0 0 0 0 0 1 0 0 0 0 0 0 29 30 49 17 30 0 0 0 0 0 0 1 0 0 0 0 0 30 31 50 12 26 0 0 0 0 0 0 0 1 0 0 0 0 31 32 56 20 27 0 0 0 0 0 0 0 0 1 0 0 0 32 33 50 21 18 0 0 0 0 0 0 0 0 0 1 0 0 33 34 56 15 19 0 0 0 0 0 0 0 0 0 0 1 0 34 35 58 22 13 0 0 0 0 0 0 0 0 0 0 0 1 35 36 59 19 14 0 0 0 0 0 0 0 0 0 0 0 0 36 37 51 19 41 0 1 0 0 0 0 0 0 0 0 0 0 37 38 59 26 21 0 0 1 0 0 0 0 0 0 0 0 0 38 39 60 25 16 0 0 0 1 0 0 0 0 0 0 0 0 39 40 60 19 17 0 0 0 0 1 0 0 0 0 0 0 0 40 41 68 20 9 0 0 0 0 0 1 0 0 0 0 0 0 41 42 62 30 14 0 0 0 0 0 0 1 0 0 0 0 0 42 43 62 31 14 0 0 0 0 0 0 0 1 0 0 0 0 43 44 58 35 16 0 0 0 0 0 0 0 0 1 0 0 0 44 45 56 33 11 0 0 0 0 0 0 0 0 0 1 0 0 45 46 50 26 10 0 0 0 0 0 0 0 0 0 0 1 0 46 47 52 25 6 0 0 0 0 0 0 0 0 0 0 0 1 47 48 36 17 9 0 0 0 0 0 0 0 0 0 0 0 0 48 49 33 14 5 0 1 0 0 0 0 0 0 0 0 0 0 49 50 26 8 7 0 0 1 0 0 0 0 0 0 0 0 0 50 51 28 12 2 0 0 0 1 0 0 0 0 0 0 0 0 51 52 27 7 0 0 0 0 0 1 0 0 0 0 0 0 0 52 53 20 4 8 0 0 0 0 0 1 0 0 0 0 0 0 53 54 16 10 13 0 0 0 0 0 0 1 0 0 0 0 0 54 55 11 8 11 0 0 0 0 0 0 0 1 0 0 0 0 55 56 0 16 19 1 0 0 0 0 0 0 0 1 0 0 0 56 57 3 14 23 1 0 0 0 0 0 0 0 0 1 0 0 57 58 10 20 23 1 0 0 0 0 0 0 0 0 0 1 0 58 59 0 9 43 1 0 0 0 0 0 0 0 0 0 0 1 59 60 3 10 59 1 0 0 0 0 0 0 0 0 0 0 0 60 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Spa Wer Val M1 M2 36.3121 1.2533 -0.1127 -28.4495 -1.8051 -2.4452 M3 M4 M5 M6 M7 M8 -1.5333 4.0766 3.7940 -10.8670 -9.5612 -10.3559 M9 M10 M11 t -5.1461 -4.6346 -3.2664 -0.2330 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -11.869 -5.641 -1.071 5.404 18.362 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 36.3121 9.7342 3.730 0.000544 *** Spa 1.2533 0.1639 7.648 1.3e-09 *** Wer -0.1127 0.1560 -0.722 0.473864 Val -28.4495 6.6671 -4.267 0.000104 *** M1 -1.8051 5.6642 -0.319 0.751475 M2 -2.4452 5.7739 -0.423 0.674006 M3 -1.5333 5.8304 -0.263 0.793786 M4 4.0766 5.8049 0.702 0.486209 M5 3.7940 5.8114 0.653 0.517246 M6 -10.8670 5.7294 -1.897 0.064445 . M7 -9.5612 5.7189 -1.672 0.101648 M8 -10.3559 5.8318 -1.776 0.082688 . M9 -5.1461 5.8257 -0.883 0.381850 M10 -4.6346 5.7598 -0.805 0.425357 M11 -3.2664 5.6375 -0.579 0.565275 t -0.2330 0.1325 -1.759 0.085531 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 8.81 on 44 degrees of freedom Multiple R-squared: 0.7872, Adjusted R-squared: 0.7146 F-statistic: 10.85 on 15 and 44 DF, p-value: 2.915e-10 > 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,] 0.119021159 0.238042318 0.8809788 [2,] 0.073091752 0.146183503 0.9269082 [3,] 0.057691169 0.115382339 0.9423088 [4,] 0.035802001 0.071604003 0.9641980 [5,] 0.017353820 0.034707639 0.9826462 [6,] 0.011666933 0.023333865 0.9883331 [7,] 0.008695842 0.017391684 0.9913042 [8,] 0.003640069 0.007280137 0.9963599 [9,] 0.001960947 0.003921893 0.9980391 [10,] 0.009246464 0.018492927 0.9907535 [11,] 0.047865422 0.095730845 0.9521346 [12,] 0.173813860 0.347627720 0.8261861 [13,] 0.307793371 0.615586741 0.6922066 [14,] 0.522709609 0.954580782 0.4772904 [15,] 0.642240328 0.715519344 0.3577597 [16,] 0.793339179 0.413321642 0.2066608 [17,] 0.751954687 0.496090627 0.2480453 [18,] 0.693666585 0.612666830 0.3063334 [19,] 0.685194343 0.629611315 0.3148057 [20,] 0.836183858 0.327632285 0.1638161 [21,] 0.825778955 0.348442091 0.1742210 [22,] 0.858806100 0.282387800 0.1411939 [23,] 0.880858018 0.238283964 0.1191420 > postscript(file="/var/www/html/rcomp/tmp/1wp5q1228688787.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2u8e01228688787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3moko1228688787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4qqsp1228688787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5qtnq1228688787.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 4.3468908 -1.7363094 8.4988465 4.5872744 0.9343336 -3.8590528 7 8 9 10 11 12 -2.8189253 -6.1997260 -3.2613195 -8.0611211 -10.9286393 -8.0613664 13 14 15 16 17 18 -4.4172459 -3.6568828 -8.9684548 1.1177546 -4.1132875 -11.1468950 19 20 21 22 23 24 -11.8123852 -7.1373295 7.2217494 -2.9161665 1.9632119 5.4085860 25 26 27 28 29 30 1.2224941 7.0812190 5.4023780 -2.6923325 1.6537345 12.6191349 31 32 33 34 35 36 18.3621927 15.4759431 2.2314455 15.5856277 7.0008362 8.8401810 37 38 39 40 41 42 5.9213009 3.7669094 4.7778823 7.0336449 13.3942892 10.3184105 43 44 45 46 47 48 7.9922611 0.2320671 -4.8015982 -2.4194878 -0.7521681 -9.4207252 49 50 51 52 53 54 -7.0734399 -5.4549362 -9.7106519 -10.0463414 -11.8690698 -7.9315976 55 56 57 58 59 60 -11.7231434 -2.3709546 -1.3902771 -2.1888522 2.7167592 3.2333247 > postscript(file="/var/www/html/rcomp/tmp/61ww41228688787.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 4.3468908 NA 1 -1.7363094 4.3468908 2 8.4988465 -1.7363094 3 4.5872744 8.4988465 4 0.9343336 4.5872744 5 -3.8590528 0.9343336 6 -2.8189253 -3.8590528 7 -6.1997260 -2.8189253 8 -3.2613195 -6.1997260 9 -8.0611211 -3.2613195 10 -10.9286393 -8.0611211 11 -8.0613664 -10.9286393 12 -4.4172459 -8.0613664 13 -3.6568828 -4.4172459 14 -8.9684548 -3.6568828 15 1.1177546 -8.9684548 16 -4.1132875 1.1177546 17 -11.1468950 -4.1132875 18 -11.8123852 -11.1468950 19 -7.1373295 -11.8123852 20 7.2217494 -7.1373295 21 -2.9161665 7.2217494 22 1.9632119 -2.9161665 23 5.4085860 1.9632119 24 1.2224941 5.4085860 25 7.0812190 1.2224941 26 5.4023780 7.0812190 27 -2.6923325 5.4023780 28 1.6537345 -2.6923325 29 12.6191349 1.6537345 30 18.3621927 12.6191349 31 15.4759431 18.3621927 32 2.2314455 15.4759431 33 15.5856277 2.2314455 34 7.0008362 15.5856277 35 8.8401810 7.0008362 36 5.9213009 8.8401810 37 3.7669094 5.9213009 38 4.7778823 3.7669094 39 7.0336449 4.7778823 40 13.3942892 7.0336449 41 10.3184105 13.3942892 42 7.9922611 10.3184105 43 0.2320671 7.9922611 44 -4.8015982 0.2320671 45 -2.4194878 -4.8015982 46 -0.7521681 -2.4194878 47 -9.4207252 -0.7521681 48 -7.0734399 -9.4207252 49 -5.4549362 -7.0734399 50 -9.7106519 -5.4549362 51 -10.0463414 -9.7106519 52 -11.8690698 -10.0463414 53 -7.9315976 -11.8690698 54 -11.7231434 -7.9315976 55 -2.3709546 -11.7231434 56 -1.3902771 -2.3709546 57 -2.1888522 -1.3902771 58 2.7167592 -2.1888522 59 3.2333247 2.7167592 60 NA 3.2333247 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.7363094 4.3468908 [2,] 8.4988465 -1.7363094 [3,] 4.5872744 8.4988465 [4,] 0.9343336 4.5872744 [5,] -3.8590528 0.9343336 [6,] -2.8189253 -3.8590528 [7,] -6.1997260 -2.8189253 [8,] -3.2613195 -6.1997260 [9,] -8.0611211 -3.2613195 [10,] -10.9286393 -8.0611211 [11,] -8.0613664 -10.9286393 [12,] -4.4172459 -8.0613664 [13,] -3.6568828 -4.4172459 [14,] -8.9684548 -3.6568828 [15,] 1.1177546 -8.9684548 [16,] -4.1132875 1.1177546 [17,] -11.1468950 -4.1132875 [18,] -11.8123852 -11.1468950 [19,] -7.1373295 -11.8123852 [20,] 7.2217494 -7.1373295 [21,] -2.9161665 7.2217494 [22,] 1.9632119 -2.9161665 [23,] 5.4085860 1.9632119 [24,] 1.2224941 5.4085860 [25,] 7.0812190 1.2224941 [26,] 5.4023780 7.0812190 [27,] -2.6923325 5.4023780 [28,] 1.6537345 -2.6923325 [29,] 12.6191349 1.6537345 [30,] 18.3621927 12.6191349 [31,] 15.4759431 18.3621927 [32,] 2.2314455 15.4759431 [33,] 15.5856277 2.2314455 [34,] 7.0008362 15.5856277 [35,] 8.8401810 7.0008362 [36,] 5.9213009 8.8401810 [37,] 3.7669094 5.9213009 [38,] 4.7778823 3.7669094 [39,] 7.0336449 4.7778823 [40,] 13.3942892 7.0336449 [41,] 10.3184105 13.3942892 [42,] 7.9922611 10.3184105 [43,] 0.2320671 7.9922611 [44,] -4.8015982 0.2320671 [45,] -2.4194878 -4.8015982 [46,] -0.7521681 -2.4194878 [47,] -9.4207252 -0.7521681 [48,] -7.0734399 -9.4207252 [49,] -5.4549362 -7.0734399 [50,] -9.7106519 -5.4549362 [51,] -10.0463414 -9.7106519 [52,] -11.8690698 -10.0463414 [53,] -7.9315976 -11.8690698 [54,] -11.7231434 -7.9315976 [55,] -2.3709546 -11.7231434 [56,] -1.3902771 -2.3709546 [57,] -2.1888522 -1.3902771 [58,] 2.7167592 -2.1888522 [59,] 3.2333247 2.7167592 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.7363094 4.3468908 2 8.4988465 -1.7363094 3 4.5872744 8.4988465 4 0.9343336 4.5872744 5 -3.8590528 0.9343336 6 -2.8189253 -3.8590528 7 -6.1997260 -2.8189253 8 -3.2613195 -6.1997260 9 -8.0611211 -3.2613195 10 -10.9286393 -8.0611211 11 -8.0613664 -10.9286393 12 -4.4172459 -8.0613664 13 -3.6568828 -4.4172459 14 -8.9684548 -3.6568828 15 1.1177546 -8.9684548 16 -4.1132875 1.1177546 17 -11.1468950 -4.1132875 18 -11.8123852 -11.1468950 19 -7.1373295 -11.8123852 20 7.2217494 -7.1373295 21 -2.9161665 7.2217494 22 1.9632119 -2.9161665 23 5.4085860 1.9632119 24 1.2224941 5.4085860 25 7.0812190 1.2224941 26 5.4023780 7.0812190 27 -2.6923325 5.4023780 28 1.6537345 -2.6923325 29 12.6191349 1.6537345 30 18.3621927 12.6191349 31 15.4759431 18.3621927 32 2.2314455 15.4759431 33 15.5856277 2.2314455 34 7.0008362 15.5856277 35 8.8401810 7.0008362 36 5.9213009 8.8401810 37 3.7669094 5.9213009 38 4.7778823 3.7669094 39 7.0336449 4.7778823 40 13.3942892 7.0336449 41 10.3184105 13.3942892 42 7.9922611 10.3184105 43 0.2320671 7.9922611 44 -4.8015982 0.2320671 45 -2.4194878 -4.8015982 46 -0.7521681 -2.4194878 47 -9.4207252 -0.7521681 48 -7.0734399 -9.4207252 49 -5.4549362 -7.0734399 50 -9.7106519 -5.4549362 51 -10.0463414 -9.7106519 52 -11.8690698 -10.0463414 53 -7.9315976 -11.8690698 54 -11.7231434 -7.9315976 55 -2.3709546 -11.7231434 56 -1.3902771 -2.3709546 57 -2.1888522 -1.3902771 58 2.7167592 -2.1888522 59 3.2333247 2.7167592 > 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/74qro1228688787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/80msg1228688787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9usni1228688787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10bydd1228688787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/1120w51228688788.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/12xkye1228688788.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/13c0151228688788.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/14ao4p1228688788.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/15chj21228688788.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/16ef721228688788.tab") + } > > system("convert tmp/1wp5q1228688787.ps tmp/1wp5q1228688787.png") > system("convert tmp/2u8e01228688787.ps tmp/2u8e01228688787.png") > system("convert tmp/3moko1228688787.ps tmp/3moko1228688787.png") > system("convert tmp/4qqsp1228688787.ps tmp/4qqsp1228688787.png") > system("convert tmp/5qtnq1228688787.ps tmp/5qtnq1228688787.png") > system("convert tmp/61ww41228688787.ps tmp/61ww41228688787.png") > system("convert tmp/74qro1228688787.ps tmp/74qro1228688787.png") > system("convert tmp/80msg1228688787.ps tmp/80msg1228688787.png") > system("convert tmp/9usni1228688787.ps tmp/9usni1228688787.png") > system("convert tmp/10bydd1228688787.ps tmp/10bydd1228688787.png") > > > proc.time() user system elapsed 4.924 2.749 5.297