R version 2.7.0 (2008-04-22) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(110.40,0,96.40,0,101.90,0,106.20,0,81.00,0,94.70,0,101.00,1,109.40,1,102.30,1,90.70,1,96.20,1,96.10,1,106.00,1,103.10,1,102.00,1,104.70,1,86.00,1,92.10,1,106.90,1,112.60,1,101.70,1,92.00,1,97.40,1,97.00,1,105.40,1,102.70,1,98.10,1,104.50,1,87.40,1,89.90,1,109.80,1,111.70,1,98.60,1,96.90,1,95.10,1,97.00,1,112.70,1,102.90,1,97.40,1,111.40,1,87.40,1,96.80,1,114.10,1,110.30,1,103.90,1,101.60,1,94.60,1,95.90,1,104.70,1,102.80,1,98.10,1,113.90,1,80.90,1,95.70,1,113.20,1,105.90,1,108.80,1,102.30,1,99.00,1,100.70,1,115.50,1),dim=c(2,61),dimnames=list(c('IP','d'),1:61)) > y <- array(NA,dim=c(2,61),dimnames=list(c('IP','d'),1:61)) > 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 IP d M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 110.4 0 1 0 0 0 0 0 0 0 0 0 0 1 2 96.4 0 0 1 0 0 0 0 0 0 0 0 0 2 3 101.9 0 0 0 1 0 0 0 0 0 0 0 0 3 4 106.2 0 0 0 0 1 0 0 0 0 0 0 0 4 5 81.0 0 0 0 0 0 1 0 0 0 0 0 0 5 6 94.7 0 0 0 0 0 0 1 0 0 0 0 0 6 7 101.0 1 0 0 0 0 0 0 1 0 0 0 0 7 8 109.4 1 0 0 0 0 0 0 0 1 0 0 0 8 9 102.3 1 0 0 0 0 0 0 0 0 1 0 0 9 10 90.7 1 0 0 0 0 0 0 0 0 0 1 0 10 11 96.2 1 0 0 0 0 0 0 0 0 0 0 1 11 12 96.1 1 0 0 0 0 0 0 0 0 0 0 0 12 13 106.0 1 1 0 0 0 0 0 0 0 0 0 0 13 14 103.1 1 0 1 0 0 0 0 0 0 0 0 0 14 15 102.0 1 0 0 1 0 0 0 0 0 0 0 0 15 16 104.7 1 0 0 0 1 0 0 0 0 0 0 0 16 17 86.0 1 0 0 0 0 1 0 0 0 0 0 0 17 18 92.1 1 0 0 0 0 0 1 0 0 0 0 0 18 19 106.9 1 0 0 0 0 0 0 1 0 0 0 0 19 20 112.6 1 0 0 0 0 0 0 0 1 0 0 0 20 21 101.7 1 0 0 0 0 0 0 0 0 1 0 0 21 22 92.0 1 0 0 0 0 0 0 0 0 0 1 0 22 23 97.4 1 0 0 0 0 0 0 0 0 0 0 1 23 24 97.0 1 0 0 0 0 0 0 0 0 0 0 0 24 25 105.4 1 1 0 0 0 0 0 0 0 0 0 0 25 26 102.7 1 0 1 0 0 0 0 0 0 0 0 0 26 27 98.1 1 0 0 1 0 0 0 0 0 0 0 0 27 28 104.5 1 0 0 0 1 0 0 0 0 0 0 0 28 29 87.4 1 0 0 0 0 1 0 0 0 0 0 0 29 30 89.9 1 0 0 0 0 0 1 0 0 0 0 0 30 31 109.8 1 0 0 0 0 0 0 1 0 0 0 0 31 32 111.7 1 0 0 0 0 0 0 0 1 0 0 0 32 33 98.6 1 0 0 0 0 0 0 0 0 1 0 0 33 34 96.9 1 0 0 0 0 0 0 0 0 0 1 0 34 35 95.1 1 0 0 0 0 0 0 0 0 0 0 1 35 36 97.0 1 0 0 0 0 0 0 0 0 0 0 0 36 37 112.7 1 1 0 0 0 0 0 0 0 0 0 0 37 38 102.9 1 0 1 0 0 0 0 0 0 0 0 0 38 39 97.4 1 0 0 1 0 0 0 0 0 0 0 0 39 40 111.4 1 0 0 0 1 0 0 0 0 0 0 0 40 41 87.4 1 0 0 0 0 1 0 0 0 0 0 0 41 42 96.8 1 0 0 0 0 0 1 0 0 0 0 0 42 43 114.1 1 0 0 0 0 0 0 1 0 0 0 0 43 44 110.3 1 0 0 0 0 0 0 0 1 0 0 0 44 45 103.9 1 0 0 0 0 0 0 0 0 1 0 0 45 46 101.6 1 0 0 0 0 0 0 0 0 0 1 0 46 47 94.6 1 0 0 0 0 0 0 0 0 0 0 1 47 48 95.9 1 0 0 0 0 0 0 0 0 0 0 0 48 49 104.7 1 1 0 0 0 0 0 0 0 0 0 0 49 50 102.8 1 0 1 0 0 0 0 0 0 0 0 0 50 51 98.1 1 0 0 1 0 0 0 0 0 0 0 0 51 52 113.9 1 0 0 0 1 0 0 0 0 0 0 0 52 53 80.9 1 0 0 0 0 1 0 0 0 0 0 0 53 54 95.7 1 0 0 0 0 0 1 0 0 0 0 0 54 55 113.2 1 0 0 0 0 0 0 1 0 0 0 0 55 56 105.9 1 0 0 0 0 0 0 0 1 0 0 0 56 57 108.8 1 0 0 0 0 0 0 0 0 1 0 0 57 58 102.3 1 0 0 0 0 0 0 0 0 0 1 0 58 59 99.0 1 0 0 0 0 0 0 0 0 0 0 1 59 60 100.7 1 0 0 0 0 0 0 0 0 0 0 0 60 61 115.5 1 1 0 0 0 0 0 0 0 0 0 0 61 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) d M1 M2 M3 M4 95.59522 -1.72989 11.97095 4.85921 2.68269 11.22617 M5 M6 M7 M8 M9 M10 -12.47035 -3.26687 12.14259 13.02607 6.00956 -0.44696 M11 t -0.78348 0.09652 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.3964 -2.5982 0.2236 2.1478 4.0042 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 95.59522 2.08969 45.746 < 2e-16 *** d -1.72989 1.72091 -1.005 0.31994 M1 11.97095 1.99479 6.001 2.68e-07 *** M2 4.85921 2.09130 2.324 0.02453 * M3 2.68269 2.09007 1.284 0.20560 M4 11.22617 2.08922 5.373 2.36e-06 *** M5 -12.47035 2.08874 -5.970 2.99e-07 *** M6 -3.26687 2.08863 -1.564 0.12450 M7 12.14259 2.07205 5.860 4.38e-07 *** M8 13.02607 2.07036 6.292 9.73e-08 *** M9 6.00956 2.06904 2.905 0.00559 ** M10 -0.44696 2.06810 -0.216 0.82983 M11 -0.78348 2.06753 -0.379 0.70643 t 0.09652 0.02792 3.457 0.00117 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.269 on 47 degrees of freedom Multiple R-squared: 0.8718, Adjusted R-squared: 0.8364 F-statistic: 24.59 on 13 and 47 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,] 0.71281355 0.5743729 0.2871865 [2,] 0.62013930 0.7597214 0.3798607 [3,] 0.47967392 0.9593478 0.5203261 [4,] 0.40894284 0.8178857 0.5910572 [5,] 0.37591083 0.7518217 0.6240892 [6,] 0.31763663 0.6352733 0.6823634 [7,] 0.24453782 0.4890756 0.7554622 [8,] 0.17251051 0.3450210 0.8274895 [9,] 0.19807701 0.3961540 0.8019230 [10,] 0.14377564 0.2875513 0.8562244 [11,] 0.17138620 0.3427724 0.8286138 [12,] 0.16413606 0.3282721 0.8358639 [13,] 0.17901024 0.3580205 0.8209898 [14,] 0.19972152 0.3994430 0.8002785 [15,] 0.20155969 0.4031194 0.7984403 [16,] 0.20357659 0.4071532 0.7964234 [17,] 0.28353994 0.5670799 0.7164601 [18,] 0.29990534 0.5998107 0.7000947 [19,] 0.24044895 0.4808979 0.7595510 [20,] 0.16792077 0.3358415 0.8320792 [21,] 0.18334725 0.3666945 0.8166527 [22,] 0.12423624 0.2484725 0.8757638 [23,] 0.10435986 0.2087197 0.8956401 [24,] 0.08521831 0.1704366 0.9147817 [25,] 0.15387032 0.3077406 0.8461297 [26,] 0.12570019 0.2514004 0.8742998 [27,] 0.12624550 0.2524910 0.8737545 [28,] 0.37880286 0.7576057 0.6211971 > postscript(file="/var/www/html/rcomp/tmp/17bwc1227799916.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/22qqc1227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3axi41227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/41l821227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/53wt01227799916.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 = 61 Frequency = 1 1 2 3 4 5 6 7 2.7373188 -4.2474638 3.3325362 -1.0074638 -2.6074638 1.7925362 -5.6835507 8 9 10 11 12 13 14 1.7364493 1.5564493 -3.6835507 2.0564493 1.0764493 -1.0910145 3.0242029 15 16 17 18 19 20 21 4.0042029 -1.9357971 2.9642029 -0.2357971 -0.9417754 3.7782246 -0.2017754 22 23 24 25 26 27 28 -3.5417754 2.0982246 0.8182246 -2.8492391 1.4659783 -1.0540217 -3.2940217 29 30 31 32 33 34 35 3.2059783 -3.5940217 0.8000000 1.7200000 -4.4600000 0.2000000 -1.3600000 36 37 38 39 40 41 42 -0.3400000 3.2925362 0.5077536 -2.9122464 2.4477536 2.0477536 2.1477536 43 44 45 46 47 48 49 3.9417754 -0.8382246 -0.3182246 3.7417754 -3.0182246 -2.5982246 -5.8656884 50 51 52 53 54 55 56 -0.7504710 -3.3704710 3.7895290 -5.6104710 -0.1104710 1.8835507 -6.3964493 57 58 59 60 61 3.4235507 3.2835507 0.2235507 1.0435507 3.7760870 > postscript(file="/var/www/html/rcomp/tmp/6nd271227799916.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 = 61 Frequency = 1 lag(myerror, k = 1) myerror 0 2.7373188 NA 1 -4.2474638 2.7373188 2 3.3325362 -4.2474638 3 -1.0074638 3.3325362 4 -2.6074638 -1.0074638 5 1.7925362 -2.6074638 6 -5.6835507 1.7925362 7 1.7364493 -5.6835507 8 1.5564493 1.7364493 9 -3.6835507 1.5564493 10 2.0564493 -3.6835507 11 1.0764493 2.0564493 12 -1.0910145 1.0764493 13 3.0242029 -1.0910145 14 4.0042029 3.0242029 15 -1.9357971 4.0042029 16 2.9642029 -1.9357971 17 -0.2357971 2.9642029 18 -0.9417754 -0.2357971 19 3.7782246 -0.9417754 20 -0.2017754 3.7782246 21 -3.5417754 -0.2017754 22 2.0982246 -3.5417754 23 0.8182246 2.0982246 24 -2.8492391 0.8182246 25 1.4659783 -2.8492391 26 -1.0540217 1.4659783 27 -3.2940217 -1.0540217 28 3.2059783 -3.2940217 29 -3.5940217 3.2059783 30 0.8000000 -3.5940217 31 1.7200000 0.8000000 32 -4.4600000 1.7200000 33 0.2000000 -4.4600000 34 -1.3600000 0.2000000 35 -0.3400000 -1.3600000 36 3.2925362 -0.3400000 37 0.5077536 3.2925362 38 -2.9122464 0.5077536 39 2.4477536 -2.9122464 40 2.0477536 2.4477536 41 2.1477536 2.0477536 42 3.9417754 2.1477536 43 -0.8382246 3.9417754 44 -0.3182246 -0.8382246 45 3.7417754 -0.3182246 46 -3.0182246 3.7417754 47 -2.5982246 -3.0182246 48 -5.8656884 -2.5982246 49 -0.7504710 -5.8656884 50 -3.3704710 -0.7504710 51 3.7895290 -3.3704710 52 -5.6104710 3.7895290 53 -0.1104710 -5.6104710 54 1.8835507 -0.1104710 55 -6.3964493 1.8835507 56 3.4235507 -6.3964493 57 3.2835507 3.4235507 58 0.2235507 3.2835507 59 1.0435507 0.2235507 60 3.7760870 1.0435507 61 NA 3.7760870 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -4.2474638 2.7373188 [2,] 3.3325362 -4.2474638 [3,] -1.0074638 3.3325362 [4,] -2.6074638 -1.0074638 [5,] 1.7925362 -2.6074638 [6,] -5.6835507 1.7925362 [7,] 1.7364493 -5.6835507 [8,] 1.5564493 1.7364493 [9,] -3.6835507 1.5564493 [10,] 2.0564493 -3.6835507 [11,] 1.0764493 2.0564493 [12,] -1.0910145 1.0764493 [13,] 3.0242029 -1.0910145 [14,] 4.0042029 3.0242029 [15,] -1.9357971 4.0042029 [16,] 2.9642029 -1.9357971 [17,] -0.2357971 2.9642029 [18,] -0.9417754 -0.2357971 [19,] 3.7782246 -0.9417754 [20,] -0.2017754 3.7782246 [21,] -3.5417754 -0.2017754 [22,] 2.0982246 -3.5417754 [23,] 0.8182246 2.0982246 [24,] -2.8492391 0.8182246 [25,] 1.4659783 -2.8492391 [26,] -1.0540217 1.4659783 [27,] -3.2940217 -1.0540217 [28,] 3.2059783 -3.2940217 [29,] -3.5940217 3.2059783 [30,] 0.8000000 -3.5940217 [31,] 1.7200000 0.8000000 [32,] -4.4600000 1.7200000 [33,] 0.2000000 -4.4600000 [34,] -1.3600000 0.2000000 [35,] -0.3400000 -1.3600000 [36,] 3.2925362 -0.3400000 [37,] 0.5077536 3.2925362 [38,] -2.9122464 0.5077536 [39,] 2.4477536 -2.9122464 [40,] 2.0477536 2.4477536 [41,] 2.1477536 2.0477536 [42,] 3.9417754 2.1477536 [43,] -0.8382246 3.9417754 [44,] -0.3182246 -0.8382246 [45,] 3.7417754 -0.3182246 [46,] -3.0182246 3.7417754 [47,] -2.5982246 -3.0182246 [48,] -5.8656884 -2.5982246 [49,] -0.7504710 -5.8656884 [50,] -3.3704710 -0.7504710 [51,] 3.7895290 -3.3704710 [52,] -5.6104710 3.7895290 [53,] -0.1104710 -5.6104710 [54,] 1.8835507 -0.1104710 [55,] -6.3964493 1.8835507 [56,] 3.4235507 -6.3964493 [57,] 3.2835507 3.4235507 [58,] 0.2235507 3.2835507 [59,] 1.0435507 0.2235507 [60,] 3.7760870 1.0435507 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -4.2474638 2.7373188 2 3.3325362 -4.2474638 3 -1.0074638 3.3325362 4 -2.6074638 -1.0074638 5 1.7925362 -2.6074638 6 -5.6835507 1.7925362 7 1.7364493 -5.6835507 8 1.5564493 1.7364493 9 -3.6835507 1.5564493 10 2.0564493 -3.6835507 11 1.0764493 2.0564493 12 -1.0910145 1.0764493 13 3.0242029 -1.0910145 14 4.0042029 3.0242029 15 -1.9357971 4.0042029 16 2.9642029 -1.9357971 17 -0.2357971 2.9642029 18 -0.9417754 -0.2357971 19 3.7782246 -0.9417754 20 -0.2017754 3.7782246 21 -3.5417754 -0.2017754 22 2.0982246 -3.5417754 23 0.8182246 2.0982246 24 -2.8492391 0.8182246 25 1.4659783 -2.8492391 26 -1.0540217 1.4659783 27 -3.2940217 -1.0540217 28 3.2059783 -3.2940217 29 -3.5940217 3.2059783 30 0.8000000 -3.5940217 31 1.7200000 0.8000000 32 -4.4600000 1.7200000 33 0.2000000 -4.4600000 34 -1.3600000 0.2000000 35 -0.3400000 -1.3600000 36 3.2925362 -0.3400000 37 0.5077536 3.2925362 38 -2.9122464 0.5077536 39 2.4477536 -2.9122464 40 2.0477536 2.4477536 41 2.1477536 2.0477536 42 3.9417754 2.1477536 43 -0.8382246 3.9417754 44 -0.3182246 -0.8382246 45 3.7417754 -0.3182246 46 -3.0182246 3.7417754 47 -2.5982246 -3.0182246 48 -5.8656884 -2.5982246 49 -0.7504710 -5.8656884 50 -3.3704710 -0.7504710 51 3.7895290 -3.3704710 52 -5.6104710 3.7895290 53 -0.1104710 -5.6104710 54 1.8835507 -0.1104710 55 -6.3964493 1.8835507 56 3.4235507 -6.3964493 57 3.2835507 3.4235507 58 0.2235507 3.2835507 59 1.0435507 0.2235507 60 3.7760870 1.0435507 > 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/7prkn1227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8hvs01227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9w7fo1227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10izl01227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11j4361227799916.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/1282s31227799916.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/13tm4a1227799916.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/14v7uv1227799916.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/15qpjw1227799916.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/16zahz1227799916.tab") + } > > system("convert tmp/17bwc1227799916.ps tmp/17bwc1227799916.png") > system("convert tmp/22qqc1227799916.ps tmp/22qqc1227799916.png") > system("convert tmp/3axi41227799916.ps tmp/3axi41227799916.png") > system("convert tmp/41l821227799916.ps tmp/41l821227799916.png") > system("convert tmp/53wt01227799916.ps tmp/53wt01227799916.png") > system("convert tmp/6nd271227799916.ps tmp/6nd271227799916.png") > system("convert tmp/7prkn1227799916.ps tmp/7prkn1227799916.png") > system("convert tmp/8hvs01227799916.ps tmp/8hvs01227799916.png") > system("convert tmp/9w7fo1227799916.ps tmp/9w7fo1227799916.png") > system("convert tmp/10izl01227799916.ps tmp/10izl01227799916.png") > > > proc.time() user system elapsed 4.987 2.766 5.343