R version 2.6.0 (2007-10-03) Copyright (C) 2007 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(106.54 + ,107.89 + ,1 + ,106.44 + ,107.26 + ,1 + ,106.57 + ,107.76 + ,1 + ,106.12 + ,107.32 + ,1 + ,106.13 + ,107.15 + ,1 + ,106.26 + ,108.04 + ,1 + ,105.78 + ,106.52 + ,1 + ,105.77 + ,106.62 + ,0 + ,105.2 + ,106.47 + ,0 + ,105.15 + ,105.46 + ,0 + ,105.01 + ,106.13 + ,0 + ,104.75 + ,105.15 + ,0 + ,104.96 + ,105.39 + ,0 + ,105.26 + ,104.57 + ,0 + ,105.13 + ,104.29 + ,0 + ,104.77 + ,104.09 + ,0 + ,104.79 + ,104.51 + ,0 + ,104.4 + ,103.39 + ,0 + ,103.89 + ,102.71 + ,0 + ,103.93 + ,102.62 + ,0 + ,103.48 + ,101.94 + ,0 + ,103.45 + ,101.65 + ,0 + ,103.47 + ,101.86 + ,0 + ,103.5 + ,101.27 + ,0 + ,103.69 + ,101.21 + ,0 + ,103.57 + ,102.15 + ,0 + ,103.47 + ,102.07 + ,0 + ,102.85 + ,102.8 + ,0 + ,102.54 + ,103.39 + ,0 + ,102.39 + ,102.71 + ,0 + ,102.16 + ,102.65 + ,0 + ,101.51 + ,101.12 + ,0 + ,100.83 + ,100.29 + ,0 + ,100.55 + ,99.79 + ,0 + ,100.88 + ,100.11 + ,0 + ,101 + ,99.76 + ,0 + ,100.51 + ,99.96 + ,0 + ,100.44 + ,99.98 + ,0 + ,100.32 + ,100.49 + ,0 + ,99.98 + ,100.75 + ,0 + ,100.03 + ,100.84 + ,0 + ,99.64 + ,100.44 + ,0 + ,99.11 + ,99.57 + ,0 + ,98.97 + ,99.22 + ,0 + ,98.6 + ,99.08 + ,0 + ,98.31 + ,98.04 + ,0 + ,98.37 + ,98.73 + ,0 + ,98.19 + ,98.72 + ,0 + ,98.51 + ,100.07 + ,0 + ,98.23 + ,99.02 + ,0 + ,97.96 + ,98.94 + ,0 + ,97.77 + ,99 + ,0 + ,97.49 + ,98.54 + ,0 + ,97.76 + ,98.42 + ,0 + ,98.01 + ,97.9 + ,0 + ,97.73 + ,97.46 + ,0 + ,97.06 + ,97 + ,0 + ,96.63 + ,95.97 + ,0 + ,96.58 + ,96.55 + ,0 + ,96.66 + ,96.51 + ,0 + ,96.77 + ,96.76 + ,0 + ,96.5 + ,96.05 + ,0 + ,96.53 + ,96.47 + ,0 + ,96.22 + ,96.38 + ,0 + ,96.49 + ,97.27 + ,0 + ,96.34 + ,96.67 + ,0 + ,96.31 + ,96.59 + ,0 + ,96.06 + ,96.06 + ,0 + ,95.9 + ,96.92 + ,0 + ,95.33 + ,94.96 + ,0 + ,95.53 + ,95.59 + ,0 + ,95.42 + ,95.68 + ,0 + ,95.57 + ,95.35 + ,0 + ,95.3 + ,95.41 + ,0 + ,95.31 + ,95.32 + ,0 + ,95.38 + ,95.8 + ,0 + ,95.22 + ,95.46 + ,0 + ,94.62 + ,94.16 + ,0 + ,93.81 + ,92.49 + ,0 + ,93.6 + ,91.58 + ,0 + ,93.2 + ,91.5 + ,0 + ,93.29 + ,90.83 + ,0 + ,93.54 + ,91.28 + ,0 + ,93.23 + ,90.57 + ,0 + ,93.46 + ,90.93 + ,0 + ,92.82 + ,90.9 + ,0 + ,92.85 + ,91.49 + ,0 + ,92.67 + ,91.38 + ,0 + ,92.32 + ,90.91 + ,0 + ,92.06 + ,90.72 + ,0 + ,91.88 + ,89.53 + ,0 + ,91.53 + ,89.47 + ,0 + ,91.19 + ,89.28 + ,0) + ,dim=c(3 + ,93) + ,dimnames=list(c('X' + ,'Y' + ,'D') + ,1:93)) > y <- array(NA,dim=c(3,93),dimnames=list(c('X','Y','D'),1:93)) > 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 = '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) > 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 D M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 107.89 106.54 1 1 0 0 0 0 0 0 0 0 0 0 2 107.26 106.44 1 0 1 0 0 0 0 0 0 0 0 0 3 107.76 106.57 1 0 0 1 0 0 0 0 0 0 0 0 4 107.32 106.12 1 0 0 0 1 0 0 0 0 0 0 0 5 107.15 106.13 1 0 0 0 0 1 0 0 0 0 0 0 6 108.04 106.26 1 0 0 0 0 0 1 0 0 0 0 0 7 106.52 105.78 1 0 0 0 0 0 0 1 0 0 0 0 8 106.62 105.77 0 0 0 0 0 0 0 0 1 0 0 0 9 106.47 105.20 0 0 0 0 0 0 0 0 0 1 0 0 10 105.46 105.15 0 0 0 0 0 0 0 0 0 0 1 0 11 106.13 105.01 0 0 0 0 0 0 0 0 0 0 0 1 12 105.15 104.75 0 0 0 0 0 0 0 0 0 0 0 0 13 105.39 104.96 0 1 0 0 0 0 0 0 0 0 0 0 14 104.57 105.26 0 0 1 0 0 0 0 0 0 0 0 0 15 104.29 105.13 0 0 0 1 0 0 0 0 0 0 0 0 16 104.09 104.77 0 0 0 0 1 0 0 0 0 0 0 0 17 104.51 104.79 0 0 0 0 0 1 0 0 0 0 0 0 18 103.39 104.40 0 0 0 0 0 0 1 0 0 0 0 0 19 102.71 103.89 0 0 0 0 0 0 0 1 0 0 0 0 20 102.62 103.93 0 0 0 0 0 0 0 0 1 0 0 0 21 101.94 103.48 0 0 0 0 0 0 0 0 0 1 0 0 22 101.65 103.45 0 0 0 0 0 0 0 0 0 0 1 0 23 101.86 103.47 0 0 0 0 0 0 0 0 0 0 0 1 24 101.27 103.50 0 0 0 0 0 0 0 0 0 0 0 0 25 101.21 103.69 0 1 0 0 0 0 0 0 0 0 0 0 26 102.15 103.57 0 0 1 0 0 0 0 0 0 0 0 0 27 102.07 103.47 0 0 0 1 0 0 0 0 0 0 0 0 28 102.80 102.85 0 0 0 0 1 0 0 0 0 0 0 0 29 103.39 102.54 0 0 0 0 0 1 0 0 0 0 0 0 30 102.71 102.39 0 0 0 0 0 0 1 0 0 0 0 0 31 102.65 102.16 0 0 0 0 0 0 0 1 0 0 0 0 32 101.12 101.51 0 0 0 0 0 0 0 0 1 0 0 0 33 100.29 100.83 0 0 0 0 0 0 0 0 0 1 0 0 34 99.79 100.55 0 0 0 0 0 0 0 0 0 0 1 0 35 100.11 100.88 0 0 0 0 0 0 0 0 0 0 0 1 36 99.76 101.00 0 0 0 0 0 0 0 0 0 0 0 0 37 99.96 100.51 0 1 0 0 0 0 0 0 0 0 0 0 38 99.98 100.44 0 0 1 0 0 0 0 0 0 0 0 0 39 100.49 100.32 0 0 0 1 0 0 0 0 0 0 0 0 40 100.75 99.98 0 0 0 0 1 0 0 0 0 0 0 0 41 100.84 100.03 0 0 0 0 0 1 0 0 0 0 0 0 42 100.44 99.64 0 0 0 0 0 0 1 0 0 0 0 0 43 99.57 99.11 0 0 0 0 0 0 0 1 0 0 0 0 44 99.22 98.97 0 0 0 0 0 0 0 0 1 0 0 0 45 99.08 98.60 0 0 0 0 0 0 0 0 0 1 0 0 46 98.04 98.31 0 0 0 0 0 0 0 0 0 0 1 0 47 98.73 98.37 0 0 0 0 0 0 0 0 0 0 0 1 48 98.72 98.19 0 0 0 0 0 0 0 0 0 0 0 0 49 100.07 98.51 0 1 0 0 0 0 0 0 0 0 0 0 50 99.02 98.23 0 0 1 0 0 0 0 0 0 0 0 0 51 98.94 97.96 0 0 0 1 0 0 0 0 0 0 0 0 52 99.00 97.77 0 0 0 0 1 0 0 0 0 0 0 0 53 98.54 97.49 0 0 0 0 0 1 0 0 0 0 0 0 54 98.42 97.76 0 0 0 0 0 0 1 0 0 0 0 0 55 97.90 98.01 0 0 0 0 0 0 0 1 0 0 0 0 56 97.46 97.73 0 0 0 0 0 0 0 0 1 0 0 0 57 97.00 97.06 0 0 0 0 0 0 0 0 0 1 0 0 58 95.97 96.63 0 0 0 0 0 0 0 0 0 0 1 0 59 96.55 96.58 0 0 0 0 0 0 0 0 0 0 0 1 60 96.51 96.66 0 0 0 0 0 0 0 0 0 0 0 0 61 96.76 96.77 0 1 0 0 0 0 0 0 0 0 0 0 62 96.05 96.50 0 0 1 0 0 0 0 0 0 0 0 0 63 96.47 96.53 0 0 0 1 0 0 0 0 0 0 0 0 64 96.38 96.22 0 0 0 0 1 0 0 0 0 0 0 0 65 97.27 96.49 0 0 0 0 0 1 0 0 0 0 0 0 66 96.67 96.34 0 0 0 0 0 0 1 0 0 0 0 0 67 96.59 96.31 0 0 0 0 0 0 0 1 0 0 0 0 68 96.06 96.06 0 0 0 0 0 0 0 0 1 0 0 0 69 96.92 95.90 0 0 0 0 0 0 0 0 0 1 0 0 70 94.96 95.33 0 0 0 0 0 0 0 0 0 0 1 0 71 95.59 95.53 0 0 0 0 0 0 0 0 0 0 0 1 72 95.68 95.42 0 0 0 0 0 0 0 0 0 0 0 0 73 95.35 95.57 0 1 0 0 0 0 0 0 0 0 0 0 74 95.41 95.30 0 0 1 0 0 0 0 0 0 0 0 0 75 95.32 95.31 0 0 0 1 0 0 0 0 0 0 0 0 76 95.80 95.38 0 0 0 0 1 0 0 0 0 0 0 0 77 95.46 95.22 0 0 0 0 0 1 0 0 0 0 0 0 78 94.16 94.62 0 0 0 0 0 0 1 0 0 0 0 0 79 92.49 93.81 0 0 0 0 0 0 0 1 0 0 0 0 80 91.58 93.60 0 0 0 0 0 0 0 0 1 0 0 0 81 91.50 93.20 0 0 0 0 0 0 0 0 0 1 0 0 82 90.83 93.29 0 0 0 0 0 0 0 0 0 0 1 0 83 91.28 93.54 0 0 0 0 0 0 0 0 0 0 0 1 84 90.57 93.23 0 0 0 0 0 0 0 0 0 0 0 0 85 90.93 93.46 0 1 0 0 0 0 0 0 0 0 0 0 86 90.90 92.82 0 0 1 0 0 0 0 0 0 0 0 0 87 91.49 92.85 0 0 0 1 0 0 0 0 0 0 0 0 88 91.38 92.67 0 0 0 0 1 0 0 0 0 0 0 0 89 90.91 92.32 0 0 0 0 0 1 0 0 0 0 0 0 90 90.72 92.06 0 0 0 0 0 0 1 0 0 0 0 0 91 89.53 91.88 0 0 0 0 0 0 0 1 0 0 0 0 92 89.47 91.53 0 0 0 0 0 0 0 0 1 0 0 0 93 89.28 91.19 0 0 0 0 0 0 0 0 0 1 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X D M1 M2 M3 -7.2530 1.0659 0.8957 0.2405 0.1563 0.3985 M4 M5 M6 M7 M8 M9 0.8018 0.9705 0.7357 0.2477 0.1299 0.4062 M10 M11 -0.1311 0.2741 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.3050 -0.9179 0.2834 0.7308 2.0765 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.25303 2.65600 -2.731 0.00779 ** X 1.06594 0.02656 40.137 < 2e-16 *** D 0.89571 0.45806 1.955 0.05407 . M1 0.24055 0.52698 0.456 0.64930 M2 0.15625 0.52698 0.297 0.76762 M3 0.39846 0.52699 0.756 0.45183 M4 0.80183 0.52712 1.521 0.13221 M5 0.97051 0.52718 1.841 0.06938 . M6 0.73571 0.52735 1.395 0.16689 M7 0.24773 0.52773 0.469 0.64006 M8 0.12994 0.52455 0.248 0.80499 M9 0.40620 0.52489 0.774 0.44132 M10 -0.13105 0.54168 -0.242 0.80946 M11 0.27407 0.54169 0.506 0.61430 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.013 on 79 degrees of freedom Multiple R-Squared: 0.9647, Adjusted R-squared: 0.9588 F-statistic: 165.9 on 13 and 79 DF, p-value: < 2.2e-16 > postscript(file="/var/www/html/rcomp/tmp/1vhed1195470118.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/2ypyp1195470118.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/32cov1195470118.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4dgbp1195470118.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5ygaj1195470118.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 93 Frequency = 1 1 2 3 4 5 6 0.441324370 0.002216592 0.121432200 -0.242261664 -0.591603133 0.394630613 7 8 9 10 11 12 -0.125738978 0.998419587 1.179752902 0.760297028 1.174411899 0.745622017 13 14 15 16 17 18 0.521224764 -0.534259763 -0.917899268 -1.137527901 -0.907528789 -1.377005269 19 20 21 22 23 24 -1.025396604 -1.040247366 -1.516827075 -1.237601787 -1.454037616 -1.801950641 25 26 27 28 29 30 -2.305029057 -1.152817997 -1.368435758 -0.380919504 0.370840427 0.085537897 31 32 33 34 35 36 0.758682838 0.039331968 -0.342081110 -0.006370353 -0.443248164 -0.647095957 37 38 39 40 41 42 -0.165333899 0.013580068 0.409281144 0.628333674 0.496354529 0.746878049 43 44 45 46 47 48 0.929805552 0.846824327 0.824969268 0.631339444 0.852265939 1.308200708 49 50 51 52 53 54 2.076549848 1.409311608 1.374903966 1.234065214 0.903846888 0.730848772 55 56 57 58 59 60 0.432341613 0.408592250 0.386519753 0.352121791 0.580301893 0.729091774 61 62 63 64 65 66 0.621288708 0.283391050 0.429200845 0.266275118 0.699788762 0.494486232 67 68 69 70 71 72 0.934442798 0.788715179 1.543012326 0.727846227 0.739540860 1.220859698 73 74 75 76 77 78 0.490418957 0.922521298 0.579649931 0.581666292 0.243534941 -0.182093745 79 80 81 82 83 84 -0.500702518 -1.069067812 -0.998944615 -1.227632351 -1.449234811 -1.554727599 85 86 87 88 89 90 -1.680443690 -0.943942856 -0.628133060 -0.949631230 -1.215233625 -0.893282549 91 92 93 -1.403434702 -0.972568133 -1.076401449 > postscript(file="/var/www/html/rcomp/tmp/63hoc1195470118.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 = 93 Frequency = 1 lag(myerror, k = 1) myerror 0 0.441324370 NA 1 0.002216592 0.441324370 2 0.121432200 0.002216592 3 -0.242261664 0.121432200 4 -0.591603133 -0.242261664 5 0.394630613 -0.591603133 6 -0.125738978 0.394630613 7 0.998419587 -0.125738978 8 1.179752902 0.998419587 9 0.760297028 1.179752902 10 1.174411899 0.760297028 11 0.745622017 1.174411899 12 0.521224764 0.745622017 13 -0.534259763 0.521224764 14 -0.917899268 -0.534259763 15 -1.137527901 -0.917899268 16 -0.907528789 -1.137527901 17 -1.377005269 -0.907528789 18 -1.025396604 -1.377005269 19 -1.040247366 -1.025396604 20 -1.516827075 -1.040247366 21 -1.237601787 -1.516827075 22 -1.454037616 -1.237601787 23 -1.801950641 -1.454037616 24 -2.305029057 -1.801950641 25 -1.152817997 -2.305029057 26 -1.368435758 -1.152817997 27 -0.380919504 -1.368435758 28 0.370840427 -0.380919504 29 0.085537897 0.370840427 30 0.758682838 0.085537897 31 0.039331968 0.758682838 32 -0.342081110 0.039331968 33 -0.006370353 -0.342081110 34 -0.443248164 -0.006370353 35 -0.647095957 -0.443248164 36 -0.165333899 -0.647095957 37 0.013580068 -0.165333899 38 0.409281144 0.013580068 39 0.628333674 0.409281144 40 0.496354529 0.628333674 41 0.746878049 0.496354529 42 0.929805552 0.746878049 43 0.846824327 0.929805552 44 0.824969268 0.846824327 45 0.631339444 0.824969268 46 0.852265939 0.631339444 47 1.308200708 0.852265939 48 2.076549848 1.308200708 49 1.409311608 2.076549848 50 1.374903966 1.409311608 51 1.234065214 1.374903966 52 0.903846888 1.234065214 53 0.730848772 0.903846888 54 0.432341613 0.730848772 55 0.408592250 0.432341613 56 0.386519753 0.408592250 57 0.352121791 0.386519753 58 0.580301893 0.352121791 59 0.729091774 0.580301893 60 0.621288708 0.729091774 61 0.283391050 0.621288708 62 0.429200845 0.283391050 63 0.266275118 0.429200845 64 0.699788762 0.266275118 65 0.494486232 0.699788762 66 0.934442798 0.494486232 67 0.788715179 0.934442798 68 1.543012326 0.788715179 69 0.727846227 1.543012326 70 0.739540860 0.727846227 71 1.220859698 0.739540860 72 0.490418957 1.220859698 73 0.922521298 0.490418957 74 0.579649931 0.922521298 75 0.581666292 0.579649931 76 0.243534941 0.581666292 77 -0.182093745 0.243534941 78 -0.500702518 -0.182093745 79 -1.069067812 -0.500702518 80 -0.998944615 -1.069067812 81 -1.227632351 -0.998944615 82 -1.449234811 -1.227632351 83 -1.554727599 -1.449234811 84 -1.680443690 -1.554727599 85 -0.943942856 -1.680443690 86 -0.628133060 -0.943942856 87 -0.949631230 -0.628133060 88 -1.215233625 -0.949631230 89 -0.893282549 -1.215233625 90 -1.403434702 -0.893282549 91 -0.972568133 -1.403434702 92 -1.076401449 -0.972568133 93 NA -1.076401449 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.002216592 0.441324370 [2,] 0.121432200 0.002216592 [3,] -0.242261664 0.121432200 [4,] -0.591603133 -0.242261664 [5,] 0.394630613 -0.591603133 [6,] -0.125738978 0.394630613 [7,] 0.998419587 -0.125738978 [8,] 1.179752902 0.998419587 [9,] 0.760297028 1.179752902 [10,] 1.174411899 0.760297028 [11,] 0.745622017 1.174411899 [12,] 0.521224764 0.745622017 [13,] -0.534259763 0.521224764 [14,] -0.917899268 -0.534259763 [15,] -1.137527901 -0.917899268 [16,] -0.907528789 -1.137527901 [17,] -1.377005269 -0.907528789 [18,] -1.025396604 -1.377005269 [19,] -1.040247366 -1.025396604 [20,] -1.516827075 -1.040247366 [21,] -1.237601787 -1.516827075 [22,] -1.454037616 -1.237601787 [23,] -1.801950641 -1.454037616 [24,] -2.305029057 -1.801950641 [25,] -1.152817997 -2.305029057 [26,] -1.368435758 -1.152817997 [27,] -0.380919504 -1.368435758 [28,] 0.370840427 -0.380919504 [29,] 0.085537897 0.370840427 [30,] 0.758682838 0.085537897 [31,] 0.039331968 0.758682838 [32,] -0.342081110 0.039331968 [33,] -0.006370353 -0.342081110 [34,] -0.443248164 -0.006370353 [35,] -0.647095957 -0.443248164 [36,] -0.165333899 -0.647095957 [37,] 0.013580068 -0.165333899 [38,] 0.409281144 0.013580068 [39,] 0.628333674 0.409281144 [40,] 0.496354529 0.628333674 [41,] 0.746878049 0.496354529 [42,] 0.929805552 0.746878049 [43,] 0.846824327 0.929805552 [44,] 0.824969268 0.846824327 [45,] 0.631339444 0.824969268 [46,] 0.852265939 0.631339444 [47,] 1.308200708 0.852265939 [48,] 2.076549848 1.308200708 [49,] 1.409311608 2.076549848 [50,] 1.374903966 1.409311608 [51,] 1.234065214 1.374903966 [52,] 0.903846888 1.234065214 [53,] 0.730848772 0.903846888 [54,] 0.432341613 0.730848772 [55,] 0.408592250 0.432341613 [56,] 0.386519753 0.408592250 [57,] 0.352121791 0.386519753 [58,] 0.580301893 0.352121791 [59,] 0.729091774 0.580301893 [60,] 0.621288708 0.729091774 [61,] 0.283391050 0.621288708 [62,] 0.429200845 0.283391050 [63,] 0.266275118 0.429200845 [64,] 0.699788762 0.266275118 [65,] 0.494486232 0.699788762 [66,] 0.934442798 0.494486232 [67,] 0.788715179 0.934442798 [68,] 1.543012326 0.788715179 [69,] 0.727846227 1.543012326 [70,] 0.739540860 0.727846227 [71,] 1.220859698 0.739540860 [72,] 0.490418957 1.220859698 [73,] 0.922521298 0.490418957 [74,] 0.579649931 0.922521298 [75,] 0.581666292 0.579649931 [76,] 0.243534941 0.581666292 [77,] -0.182093745 0.243534941 [78,] -0.500702518 -0.182093745 [79,] -1.069067812 -0.500702518 [80,] -0.998944615 -1.069067812 [81,] -1.227632351 -0.998944615 [82,] -1.449234811 -1.227632351 [83,] -1.554727599 -1.449234811 [84,] -1.680443690 -1.554727599 [85,] -0.943942856 -1.680443690 [86,] -0.628133060 -0.943942856 [87,] -0.949631230 -0.628133060 [88,] -1.215233625 -0.949631230 [89,] -0.893282549 -1.215233625 [90,] -1.403434702 -0.893282549 [91,] -0.972568133 -1.403434702 [92,] -1.076401449 -0.972568133 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.002216592 0.441324370 2 0.121432200 0.002216592 3 -0.242261664 0.121432200 4 -0.591603133 -0.242261664 5 0.394630613 -0.591603133 6 -0.125738978 0.394630613 7 0.998419587 -0.125738978 8 1.179752902 0.998419587 9 0.760297028 1.179752902 10 1.174411899 0.760297028 11 0.745622017 1.174411899 12 0.521224764 0.745622017 13 -0.534259763 0.521224764 14 -0.917899268 -0.534259763 15 -1.137527901 -0.917899268 16 -0.907528789 -1.137527901 17 -1.377005269 -0.907528789 18 -1.025396604 -1.377005269 19 -1.040247366 -1.025396604 20 -1.516827075 -1.040247366 21 -1.237601787 -1.516827075 22 -1.454037616 -1.237601787 23 -1.801950641 -1.454037616 24 -2.305029057 -1.801950641 25 -1.152817997 -2.305029057 26 -1.368435758 -1.152817997 27 -0.380919504 -1.368435758 28 0.370840427 -0.380919504 29 0.085537897 0.370840427 30 0.758682838 0.085537897 31 0.039331968 0.758682838 32 -0.342081110 0.039331968 33 -0.006370353 -0.342081110 34 -0.443248164 -0.006370353 35 -0.647095957 -0.443248164 36 -0.165333899 -0.647095957 37 0.013580068 -0.165333899 38 0.409281144 0.013580068 39 0.628333674 0.409281144 40 0.496354529 0.628333674 41 0.746878049 0.496354529 42 0.929805552 0.746878049 43 0.846824327 0.929805552 44 0.824969268 0.846824327 45 0.631339444 0.824969268 46 0.852265939 0.631339444 47 1.308200708 0.852265939 48 2.076549848 1.308200708 49 1.409311608 2.076549848 50 1.374903966 1.409311608 51 1.234065214 1.374903966 52 0.903846888 1.234065214 53 0.730848772 0.903846888 54 0.432341613 0.730848772 55 0.408592250 0.432341613 56 0.386519753 0.408592250 57 0.352121791 0.386519753 58 0.580301893 0.352121791 59 0.729091774 0.580301893 60 0.621288708 0.729091774 61 0.283391050 0.621288708 62 0.429200845 0.283391050 63 0.266275118 0.429200845 64 0.699788762 0.266275118 65 0.494486232 0.699788762 66 0.934442798 0.494486232 67 0.788715179 0.934442798 68 1.543012326 0.788715179 69 0.727846227 1.543012326 70 0.739540860 0.727846227 71 1.220859698 0.739540860 72 0.490418957 1.220859698 73 0.922521298 0.490418957 74 0.579649931 0.922521298 75 0.581666292 0.579649931 76 0.243534941 0.581666292 77 -0.182093745 0.243534941 78 -0.500702518 -0.182093745 79 -1.069067812 -0.500702518 80 -0.998944615 -1.069067812 81 -1.227632351 -0.998944615 82 -1.449234811 -1.227632351 83 -1.554727599 -1.449234811 84 -1.680443690 -1.554727599 85 -0.943942856 -1.680443690 86 -0.628133060 -0.943942856 87 -0.949631230 -0.628133060 88 -1.215233625 -0.949631230 89 -0.893282549 -1.215233625 90 -1.403434702 -0.893282549 91 -0.972568133 -1.403434702 92 -1.076401449 -0.972568133 > 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/76o1n1195470118.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/80hhe1195470118.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9su0h1195470118.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > load(file='/var/www/html/rcomp/createtable') > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/10o5n91195470118.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/11gxrf1195470118.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/12les11195470119.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/13cygh1195470119.tab") > > system("convert tmp/1vhed1195470118.ps tmp/1vhed1195470118.png") > system("convert tmp/2ypyp1195470118.ps tmp/2ypyp1195470118.png") > system("convert tmp/32cov1195470118.ps tmp/32cov1195470118.png") > system("convert tmp/4dgbp1195470118.ps tmp/4dgbp1195470118.png") > system("convert tmp/5ygaj1195470118.ps tmp/5ygaj1195470118.png") > system("convert tmp/63hoc1195470118.ps tmp/63hoc1195470118.png") > system("convert tmp/76o1n1195470118.ps tmp/76o1n1195470118.png") > system("convert tmp/80hhe1195470118.ps tmp/80hhe1195470118.png") > system("convert tmp/9su0h1195470118.ps tmp/9su0h1195470118.png") > > > proc.time() user system elapsed 2.414 1.497 2.832