R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) 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(1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0) + ,dim=c(6 + ,86) + ,dimnames=list(c('UseLimit' + ,'T40' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome') + ,1:86)) > y <- array(NA,dim=c(6,86),dimnames=list(c('UseLimit','T40','Used','CorrectAnalysis','Useful','Outcome'),1:86)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal 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, 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 T40 UseLimit Used CorrectAnalysis Useful Outcome 1 1 1 0 0 0 1 2 0 0 0 0 0 0 3 0 0 0 0 0 0 4 0 0 0 0 0 0 5 0 0 0 0 0 0 6 0 1 0 0 1 1 7 0 0 0 0 0 0 8 1 0 0 0 0 0 9 0 0 0 0 0 1 10 0 1 0 0 0 0 11 1 1 0 0 0 0 12 0 0 0 0 0 0 13 0 0 1 0 1 0 14 1 1 0 0 0 0 15 0 0 1 0 1 1 16 1 0 1 0 1 1 17 1 1 1 1 1 0 18 1 1 0 0 0 0 19 0 0 0 0 0 1 20 1 0 1 1 1 1 21 0 1 0 0 1 0 22 0 1 1 0 1 1 23 0 0 0 0 1 1 24 0 1 0 0 1 1 25 1 0 1 0 0 1 26 0 0 1 0 1 0 27 0 1 0 0 0 1 28 0 0 1 0 0 0 29 0 0 0 0 0 1 30 0 0 0 0 1 0 31 0 0 0 0 0 0 32 0 1 0 0 0 0 33 0 1 0 0 1 0 34 1 0 0 0 0 1 35 0 0 0 0 0 0 36 0 0 0 0 0 0 37 1 1 1 0 1 0 38 0 0 1 0 0 1 39 0 0 0 0 1 1 40 1 0 0 0 1 0 41 0 0 1 1 1 1 42 0 0 1 0 0 1 43 0 1 0 0 1 1 44 1 1 0 0 0 0 45 0 0 0 0 1 0 46 0 0 0 0 1 1 47 0 0 0 0 0 0 48 0 0 0 0 0 1 49 0 0 0 0 1 1 50 0 0 0 0 0 0 51 1 0 1 0 0 0 52 1 1 1 1 1 0 53 0 0 0 0 0 1 54 0 0 1 1 0 0 55 0 0 0 0 0 0 56 1 0 1 0 0 1 57 0 0 1 0 1 1 58 0 0 0 0 0 1 59 0 0 0 0 0 1 60 1 1 1 1 1 1 61 1 1 0 0 0 1 62 0 0 1 0 1 0 63 0 0 0 0 0 0 64 1 1 0 0 0 1 65 0 0 0 0 0 0 66 0 0 0 0 0 0 67 1 0 1 1 1 0 68 0 1 0 0 0 0 69 0 0 0 0 0 1 70 0 0 1 0 0 0 71 0 0 0 0 0 0 72 0 0 0 0 0 1 73 0 0 1 0 0 1 74 0 1 1 0 0 0 75 0 0 0 0 0 1 76 1 0 0 0 1 1 77 0 0 0 0 0 1 78 0 0 1 0 1 1 79 1 0 1 1 0 1 80 1 0 0 0 1 0 81 0 0 0 0 0 0 82 0 1 1 0 0 1 83 0 0 0 0 0 0 84 0 0 1 1 0 0 85 0 0 0 0 1 1 86 0 1 0 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) UseLimit Used CorrectAnalysis 0.120068 0.259220 0.073801 0.374753 Useful Outcome 0.001126 0.024498 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.5942 -0.2125 -0.1329 0.1649 0.8799 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.120068 0.076648 1.566 0.1212 UseLimit 0.259220 0.101811 2.546 0.0128 * Used 0.073801 0.113722 0.649 0.5182 CorrectAnalysis 0.374753 0.171564 2.184 0.0319 * Useful 0.001126 0.100664 0.011 0.9911 Outcome 0.024498 0.092335 0.265 0.7914 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.419 on 80 degrees of freedom Multiple R-squared: 0.1664, Adjusted R-squared: 0.1143 F-statistic: 3.193 on 5 and 80 DF, p-value: 0.01115 > 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,] 0.7933614 0.4132773 0.20663865 [2,] 0.8549192 0.2901615 0.14508076 [3,] 0.8564356 0.2871288 0.14356439 [4,] 0.7799945 0.4400110 0.22000548 [5,] 0.6870468 0.6259063 0.31295317 [6,] 0.6589689 0.6820622 0.34103108 [7,] 0.5647419 0.8705162 0.43525810 [8,] 0.7428852 0.5142295 0.25711475 [9,] 0.6644998 0.6710003 0.33550017 [10,] 0.6484682 0.7030635 0.35153177 [11,] 0.5854259 0.8291481 0.41457405 [12,] 0.5656438 0.8687124 0.43435621 [13,] 0.4956447 0.9912895 0.50435526 [14,] 0.6481470 0.7037059 0.35185297 [15,] 0.6054654 0.7890691 0.39453455 [16,] 0.5546222 0.8907555 0.44537777 [17,] 0.5675461 0.8649078 0.43245390 [18,] 0.5015485 0.9969030 0.49845149 [19,] 0.5753482 0.8493037 0.42465184 [20,] 0.6068479 0.7863041 0.39315206 [21,] 0.5538167 0.8923666 0.44618330 [22,] 0.5030920 0.9938160 0.49690799 [23,] 0.4418279 0.8836557 0.55817213 [24,] 0.4435637 0.8871274 0.55643629 [25,] 0.4163661 0.8327323 0.58363385 [26,] 0.6042877 0.7914245 0.39571226 [27,] 0.5461154 0.9077692 0.45388461 [28,] 0.4863008 0.9726016 0.51369922 [29,] 0.5314655 0.9370690 0.46853448 [30,] 0.5347996 0.9304009 0.46520043 [31,] 0.4795002 0.9590004 0.52049981 [32,] 0.7223435 0.5553130 0.27765649 [33,] 0.7934693 0.4130614 0.20653071 [34,] 0.7618988 0.4762024 0.23810122 [35,] 0.7708826 0.4582348 0.22911741 [36,] 0.8214546 0.3570908 0.17854538 [37,] 0.7812705 0.4374591 0.21872953 [38,] 0.7508264 0.4983473 0.24917365 [39,] 0.7006454 0.5987092 0.29935458 [40,] 0.6470787 0.7058426 0.35292132 [41,] 0.6208456 0.7583087 0.37915437 [42,] 0.5607050 0.8785900 0.43929499 [43,] 0.8157667 0.3684666 0.18423328 [44,] 0.7672447 0.4655107 0.23275533 [45,] 0.7223732 0.5552536 0.27762679 [46,] 0.7550370 0.4899259 0.24496297 [47,] 0.6992194 0.6015612 0.30078061 [48,] 0.9323013 0.1353974 0.06769871 [49,] 0.9155517 0.1688967 0.08444834 [50,] 0.8882809 0.2234381 0.11171907 [51,] 0.8561702 0.2876596 0.14382980 [52,] 0.8485869 0.3028261 0.15141305 [53,] 0.8860321 0.2279357 0.11396785 [54,] 0.8536869 0.2926262 0.14631312 [55,] 0.8033062 0.3933876 0.19669382 [56,] 0.9238279 0.1523443 0.07617213 [57,] 0.8881778 0.2236444 0.11182220 [58,] 0.8408365 0.3183271 0.15916353 [59,] 0.7967341 0.4065318 0.20326591 [60,] 0.7454287 0.5091426 0.25457128 [61,] 0.6652626 0.6694749 0.33473743 [62,] 0.6022474 0.7955052 0.39775258 [63,] 0.5017112 0.9965776 0.49828880 [64,] 0.4007649 0.8015299 0.59923505 [65,] 0.3365246 0.6730492 0.66347541 [66,] 0.2809732 0.5619463 0.71902684 [67,] 0.1887160 0.3774321 0.81128396 [68,] 0.2002963 0.4005926 0.79970371 [69,] 0.1113102 0.2226204 0.88868978 > postscript(file="/var/fisher/rcomp/tmp/16emt1356113649.ps",horizontal=F,onefile=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2ycn51356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3z5mg1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4cjhk1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5vqz41356113649.ps",horizontal=F,onefile=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 = 86 Frequency = 1 1 2 3 4 5 6 7 0.5962144 -0.1200677 -0.1200677 -0.1200677 -0.1200677 -0.4049111 -0.1200677 8 9 10 11 12 13 14 0.8799323 -0.1445660 -0.3792873 0.6207127 -0.1200677 -0.1949945 0.6207127 15 16 17 18 19 20 21 -0.2194928 0.7805072 0.1710332 0.6207127 -0.1445660 0.4057545 -0.3804128 22 23 24 25 26 27 28 -0.4787124 -0.1456916 -0.4049111 0.7816327 -0.1949945 -0.4037856 -0.1938690 29 30 31 32 33 34 35 -0.1445660 -0.1211933 -0.1200677 -0.3792873 -0.3804128 0.8554340 -0.1200677 36 37 38 39 40 41 42 -0.1200677 0.5457859 -0.2183673 -0.1456916 0.8788067 -0.5942455 -0.2183673 43 44 45 46 47 48 49 -0.4049111 0.6207127 -0.1211933 -0.1456916 -0.1200677 -0.1445660 -0.1456916 50 51 52 53 54 55 56 -0.1200677 0.8061310 0.1710332 -0.1445660 -0.5686216 -0.1200677 0.7816327 57 58 59 60 61 62 63 -0.2194928 -0.1445660 -0.1445660 0.1465349 0.5962144 -0.1949945 -0.1200677 64 65 66 67 68 69 70 0.5962144 -0.1200677 -0.1200677 0.4302528 -0.3792873 -0.1445660 -0.1938690 71 72 73 74 75 76 77 -0.1200677 -0.1445660 -0.2183673 -0.4530885 -0.1445660 0.8543084 -0.1445660 78 79 80 81 82 83 84 -0.2194928 0.4068801 0.8788067 -0.1200677 -0.4775868 -0.1200677 -0.5686216 85 86 -0.1456916 -0.3792873 > postscript(file="/var/fisher/rcomp/tmp/6u8jp1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 86 Frequency = 1 lag(myerror, k = 1) myerror 0 0.5962144 NA 1 -0.1200677 0.5962144 2 -0.1200677 -0.1200677 3 -0.1200677 -0.1200677 4 -0.1200677 -0.1200677 5 -0.4049111 -0.1200677 6 -0.1200677 -0.4049111 7 0.8799323 -0.1200677 8 -0.1445660 0.8799323 9 -0.3792873 -0.1445660 10 0.6207127 -0.3792873 11 -0.1200677 0.6207127 12 -0.1949945 -0.1200677 13 0.6207127 -0.1949945 14 -0.2194928 0.6207127 15 0.7805072 -0.2194928 16 0.1710332 0.7805072 17 0.6207127 0.1710332 18 -0.1445660 0.6207127 19 0.4057545 -0.1445660 20 -0.3804128 0.4057545 21 -0.4787124 -0.3804128 22 -0.1456916 -0.4787124 23 -0.4049111 -0.1456916 24 0.7816327 -0.4049111 25 -0.1949945 0.7816327 26 -0.4037856 -0.1949945 27 -0.1938690 -0.4037856 28 -0.1445660 -0.1938690 29 -0.1211933 -0.1445660 30 -0.1200677 -0.1211933 31 -0.3792873 -0.1200677 32 -0.3804128 -0.3792873 33 0.8554340 -0.3804128 34 -0.1200677 0.8554340 35 -0.1200677 -0.1200677 36 0.5457859 -0.1200677 37 -0.2183673 0.5457859 38 -0.1456916 -0.2183673 39 0.8788067 -0.1456916 40 -0.5942455 0.8788067 41 -0.2183673 -0.5942455 42 -0.4049111 -0.2183673 43 0.6207127 -0.4049111 44 -0.1211933 0.6207127 45 -0.1456916 -0.1211933 46 -0.1200677 -0.1456916 47 -0.1445660 -0.1200677 48 -0.1456916 -0.1445660 49 -0.1200677 -0.1456916 50 0.8061310 -0.1200677 51 0.1710332 0.8061310 52 -0.1445660 0.1710332 53 -0.5686216 -0.1445660 54 -0.1200677 -0.5686216 55 0.7816327 -0.1200677 56 -0.2194928 0.7816327 57 -0.1445660 -0.2194928 58 -0.1445660 -0.1445660 59 0.1465349 -0.1445660 60 0.5962144 0.1465349 61 -0.1949945 0.5962144 62 -0.1200677 -0.1949945 63 0.5962144 -0.1200677 64 -0.1200677 0.5962144 65 -0.1200677 -0.1200677 66 0.4302528 -0.1200677 67 -0.3792873 0.4302528 68 -0.1445660 -0.3792873 69 -0.1938690 -0.1445660 70 -0.1200677 -0.1938690 71 -0.1445660 -0.1200677 72 -0.2183673 -0.1445660 73 -0.4530885 -0.2183673 74 -0.1445660 -0.4530885 75 0.8543084 -0.1445660 76 -0.1445660 0.8543084 77 -0.2194928 -0.1445660 78 0.4068801 -0.2194928 79 0.8788067 0.4068801 80 -0.1200677 0.8788067 81 -0.4775868 -0.1200677 82 -0.1200677 -0.4775868 83 -0.5686216 -0.1200677 84 -0.1456916 -0.5686216 85 -0.3792873 -0.1456916 86 NA -0.3792873 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.1200677 0.5962144 [2,] -0.1200677 -0.1200677 [3,] -0.1200677 -0.1200677 [4,] -0.1200677 -0.1200677 [5,] -0.4049111 -0.1200677 [6,] -0.1200677 -0.4049111 [7,] 0.8799323 -0.1200677 [8,] -0.1445660 0.8799323 [9,] -0.3792873 -0.1445660 [10,] 0.6207127 -0.3792873 [11,] -0.1200677 0.6207127 [12,] -0.1949945 -0.1200677 [13,] 0.6207127 -0.1949945 [14,] -0.2194928 0.6207127 [15,] 0.7805072 -0.2194928 [16,] 0.1710332 0.7805072 [17,] 0.6207127 0.1710332 [18,] -0.1445660 0.6207127 [19,] 0.4057545 -0.1445660 [20,] -0.3804128 0.4057545 [21,] -0.4787124 -0.3804128 [22,] -0.1456916 -0.4787124 [23,] -0.4049111 -0.1456916 [24,] 0.7816327 -0.4049111 [25,] -0.1949945 0.7816327 [26,] -0.4037856 -0.1949945 [27,] -0.1938690 -0.4037856 [28,] -0.1445660 -0.1938690 [29,] -0.1211933 -0.1445660 [30,] -0.1200677 -0.1211933 [31,] -0.3792873 -0.1200677 [32,] -0.3804128 -0.3792873 [33,] 0.8554340 -0.3804128 [34,] -0.1200677 0.8554340 [35,] -0.1200677 -0.1200677 [36,] 0.5457859 -0.1200677 [37,] -0.2183673 0.5457859 [38,] -0.1456916 -0.2183673 [39,] 0.8788067 -0.1456916 [40,] -0.5942455 0.8788067 [41,] -0.2183673 -0.5942455 [42,] -0.4049111 -0.2183673 [43,] 0.6207127 -0.4049111 [44,] -0.1211933 0.6207127 [45,] -0.1456916 -0.1211933 [46,] -0.1200677 -0.1456916 [47,] -0.1445660 -0.1200677 [48,] -0.1456916 -0.1445660 [49,] -0.1200677 -0.1456916 [50,] 0.8061310 -0.1200677 [51,] 0.1710332 0.8061310 [52,] -0.1445660 0.1710332 [53,] -0.5686216 -0.1445660 [54,] -0.1200677 -0.5686216 [55,] 0.7816327 -0.1200677 [56,] -0.2194928 0.7816327 [57,] -0.1445660 -0.2194928 [58,] -0.1445660 -0.1445660 [59,] 0.1465349 -0.1445660 [60,] 0.5962144 0.1465349 [61,] -0.1949945 0.5962144 [62,] -0.1200677 -0.1949945 [63,] 0.5962144 -0.1200677 [64,] -0.1200677 0.5962144 [65,] -0.1200677 -0.1200677 [66,] 0.4302528 -0.1200677 [67,] -0.3792873 0.4302528 [68,] -0.1445660 -0.3792873 [69,] -0.1938690 -0.1445660 [70,] -0.1200677 -0.1938690 [71,] -0.1445660 -0.1200677 [72,] -0.2183673 -0.1445660 [73,] -0.4530885 -0.2183673 [74,] -0.1445660 -0.4530885 [75,] 0.8543084 -0.1445660 [76,] -0.1445660 0.8543084 [77,] -0.2194928 -0.1445660 [78,] 0.4068801 -0.2194928 [79,] 0.8788067 0.4068801 [80,] -0.1200677 0.8788067 [81,] -0.4775868 -0.1200677 [82,] -0.1200677 -0.4775868 [83,] -0.5686216 -0.1200677 [84,] -0.1456916 -0.5686216 [85,] -0.3792873 -0.1456916 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.1200677 0.5962144 2 -0.1200677 -0.1200677 3 -0.1200677 -0.1200677 4 -0.1200677 -0.1200677 5 -0.4049111 -0.1200677 6 -0.1200677 -0.4049111 7 0.8799323 -0.1200677 8 -0.1445660 0.8799323 9 -0.3792873 -0.1445660 10 0.6207127 -0.3792873 11 -0.1200677 0.6207127 12 -0.1949945 -0.1200677 13 0.6207127 -0.1949945 14 -0.2194928 0.6207127 15 0.7805072 -0.2194928 16 0.1710332 0.7805072 17 0.6207127 0.1710332 18 -0.1445660 0.6207127 19 0.4057545 -0.1445660 20 -0.3804128 0.4057545 21 -0.4787124 -0.3804128 22 -0.1456916 -0.4787124 23 -0.4049111 -0.1456916 24 0.7816327 -0.4049111 25 -0.1949945 0.7816327 26 -0.4037856 -0.1949945 27 -0.1938690 -0.4037856 28 -0.1445660 -0.1938690 29 -0.1211933 -0.1445660 30 -0.1200677 -0.1211933 31 -0.3792873 -0.1200677 32 -0.3804128 -0.3792873 33 0.8554340 -0.3804128 34 -0.1200677 0.8554340 35 -0.1200677 -0.1200677 36 0.5457859 -0.1200677 37 -0.2183673 0.5457859 38 -0.1456916 -0.2183673 39 0.8788067 -0.1456916 40 -0.5942455 0.8788067 41 -0.2183673 -0.5942455 42 -0.4049111 -0.2183673 43 0.6207127 -0.4049111 44 -0.1211933 0.6207127 45 -0.1456916 -0.1211933 46 -0.1200677 -0.1456916 47 -0.1445660 -0.1200677 48 -0.1456916 -0.1445660 49 -0.1200677 -0.1456916 50 0.8061310 -0.1200677 51 0.1710332 0.8061310 52 -0.1445660 0.1710332 53 -0.5686216 -0.1445660 54 -0.1200677 -0.5686216 55 0.7816327 -0.1200677 56 -0.2194928 0.7816327 57 -0.1445660 -0.2194928 58 -0.1445660 -0.1445660 59 0.1465349 -0.1445660 60 0.5962144 0.1465349 61 -0.1949945 0.5962144 62 -0.1200677 -0.1949945 63 0.5962144 -0.1200677 64 -0.1200677 0.5962144 65 -0.1200677 -0.1200677 66 0.4302528 -0.1200677 67 -0.3792873 0.4302528 68 -0.1445660 -0.3792873 69 -0.1938690 -0.1445660 70 -0.1200677 -0.1938690 71 -0.1445660 -0.1200677 72 -0.2183673 -0.1445660 73 -0.4530885 -0.2183673 74 -0.1445660 -0.4530885 75 0.8543084 -0.1445660 76 -0.1445660 0.8543084 77 -0.2194928 -0.1445660 78 0.4068801 -0.2194928 79 0.8788067 0.4068801 80 -0.1200677 0.8788067 81 -0.4775868 -0.1200677 82 -0.1200677 -0.4775868 83 -0.5686216 -0.1200677 84 -0.1456916 -0.5686216 85 -0.3792873 -0.1456916 > 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/fisher/rcomp/tmp/7pqdm1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8orbh1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9h08m1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10rlt81356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/fisher/rcomp/tmp/114sj31356113649.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/fisher/rcomp/tmp/120zv91356113649.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/fisher/rcomp/tmp/139sq61356113649.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/fisher/rcomp/tmp/14jksz1356113649.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/fisher/rcomp/tmp/15vmrs1356113649.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/fisher/rcomp/tmp/16tbrn1356113649.tab") + } > > try(system("convert tmp/16emt1356113649.ps tmp/16emt1356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/2ycn51356113649.ps tmp/2ycn51356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/3z5mg1356113649.ps tmp/3z5mg1356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/4cjhk1356113649.ps tmp/4cjhk1356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/5vqz41356113649.ps tmp/5vqz41356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/6u8jp1356113649.ps tmp/6u8jp1356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/7pqdm1356113649.ps tmp/7pqdm1356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/8orbh1356113649.ps tmp/8orbh1356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/9h08m1356113649.ps tmp/9h08m1356113649.png",intern=TRUE)) character(0) > try(system("convert tmp/10rlt81356113649.ps tmp/10rlt81356113649.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.467 1.812 8.295