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(4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0) + ,dim=c(8 + ,86) + ,dimnames=list(c('Weeks' + ,'UseLimit' + ,'T4NA' + ,'T1NA' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome') + ,1:86)) > y <- array(NA,dim=c(8,86),dimnames=list(c('Weeks','UseLimit','T4NA','T1NA','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 = '6' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '6' > #'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 CorrectAnalysis Weeks UseLimit T4NA T1NA Used Useful Outcome 1 0 4 1 0 0 0 0 1 2 0 4 0 1 0 0 0 0 3 0 4 0 1 0 0 0 0 4 0 4 0 1 0 0 0 0 5 0 4 0 1 0 0 0 0 6 0 4 1 1 0 0 1 1 7 0 4 0 1 0 0 0 0 8 0 4 0 0 0 0 0 0 9 0 4 0 1 0 0 0 1 10 0 4 1 1 0 0 0 0 11 0 4 1 0 0 0 0 0 12 0 4 0 1 0 0 0 0 13 0 4 0 1 0 1 1 0 14 0 4 1 0 0 0 0 0 15 0 4 0 1 0 1 1 1 16 0 4 0 0 0 1 1 1 17 1 4 1 0 0 1 1 0 18 0 4 1 0 0 0 0 0 19 0 4 0 1 0 0 0 1 20 1 4 0 0 0 1 1 1 21 0 4 1 1 0 0 1 0 22 0 4 1 1 0 1 1 1 23 0 4 0 1 0 0 1 1 24 0 4 1 1 0 0 1 1 25 0 4 0 0 0 1 0 1 26 0 4 0 1 0 1 1 0 27 0 4 1 1 0 0 0 1 28 0 4 0 1 0 1 0 0 29 0 4 0 1 0 0 0 1 30 0 4 0 1 0 0 1 0 31 0 4 0 1 0 0 0 0 32 0 4 1 1 0 0 0 0 33 0 4 1 1 0 0 1 0 34 0 4 0 0 0 0 0 1 35 0 4 0 1 0 0 0 0 36 0 4 0 1 0 0 0 0 37 0 4 1 0 0 1 1 0 38 0 4 0 1 0 1 0 1 39 0 4 0 1 0 0 1 1 40 0 4 0 0 0 0 1 0 41 1 4 0 1 0 1 1 1 42 0 4 0 1 0 1 0 1 43 0 4 1 1 0 0 1 1 44 0 4 1 0 0 0 0 0 45 0 4 0 1 0 0 1 0 46 0 4 0 1 0 0 1 1 47 0 4 0 1 0 0 0 0 48 0 4 0 1 0 0 0 1 49 0 4 0 1 0 0 1 1 50 0 4 0 1 0 0 0 0 51 0 4 0 0 0 1 0 0 52 1 4 1 0 0 1 1 0 53 0 4 0 1 0 0 0 1 54 1 4 0 1 0 1 0 0 55 0 4 0 1 0 0 0 0 56 0 4 0 0 0 1 0 1 57 0 4 0 1 0 1 1 1 58 0 4 0 1 0 0 0 1 59 0 4 0 1 0 0 0 1 60 1 4 1 0 0 1 1 1 61 0 4 1 0 0 0 0 1 62 0 4 0 1 0 1 1 0 63 0 4 0 1 0 0 0 0 64 0 4 1 0 0 0 0 1 65 0 4 0 1 0 0 0 0 66 0 4 0 1 0 0 0 0 67 1 4 0 0 0 1 1 0 68 0 4 1 1 0 0 0 0 69 0 4 0 1 0 0 0 1 70 0 4 0 1 0 1 0 0 71 0 4 0 1 0 0 0 0 72 0 4 0 1 0 0 0 1 73 0 4 0 1 0 1 0 1 74 0 4 1 1 0 1 0 0 75 0 4 0 1 0 0 0 1 76 0 4 0 0 0 0 1 1 77 0 4 0 1 0 0 0 1 78 0 4 0 1 0 1 1 1 79 1 4 0 0 0 1 0 1 80 0 4 0 0 0 0 1 0 81 0 4 0 1 0 0 0 0 82 0 4 1 1 0 1 0 1 83 0 4 0 1 0 0 0 0 84 1 4 0 1 0 1 0 0 85 0 4 0 1 0 0 1 1 86 0 4 1 1 0 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UseLimit T4NA T1NA Used 0.124690 NA -0.007484 -0.150191 NA 0.280285 Useful Outcome 0.063988 -0.046232 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.46148 -0.13801 0.02037 0.03299 0.74522 Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 0.124690 0.077712 1.605 0.1125 Weeks NA NA NA NA UseLimit -0.007484 0.067009 -0.112 0.9113 T4NA -0.150191 0.068758 -2.184 0.0319 * T1NA NA NA NA NA Used 0.280285 0.065026 4.310 4.6e-05 *** Useful 0.063988 0.063324 1.010 0.3153 Outcome -0.046232 0.058251 -0.794 0.4297 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2653 on 80 degrees of freedom Multiple R-squared: 0.3014, Adjusted R-squared: 0.2578 F-statistic: 6.904 on 5 and 80 DF, p-value: 2.104e-05 > 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,] 0.00000000 0.00000000 1.0000000 [2,] 0.00000000 0.00000000 1.0000000 [3,] 0.00000000 0.00000000 1.0000000 [4,] 0.00000000 0.00000000 1.0000000 [5,] 0.00000000 0.00000000 1.0000000 [6,] 0.00000000 0.00000000 1.0000000 [7,] 0.28751017 0.57502034 0.7124898 [8,] 0.23249564 0.46499127 0.7675044 [9,] 0.19294571 0.38589142 0.8070543 [10,] 0.62118502 0.75762997 0.3788150 [11,] 0.53468896 0.93062208 0.4653110 [12,] 0.50523241 0.98953519 0.4947676 [13,] 0.42026562 0.84053125 0.5797344 [14,] 0.34125285 0.68250571 0.6587471 [15,] 0.33491391 0.66982782 0.6650861 [16,] 0.33198193 0.66396385 0.6680181 [17,] 0.28202565 0.56405129 0.7179744 [18,] 0.23823069 0.47646138 0.7617693 [19,] 0.19084590 0.38169180 0.8091541 [20,] 0.14734978 0.29469956 0.8526502 [21,] 0.10977471 0.21954941 0.8902253 [22,] 0.08009337 0.16018675 0.9199066 [23,] 0.05706272 0.11412543 0.9429373 [24,] 0.04087722 0.08175443 0.9591228 [25,] 0.02770702 0.05541404 0.9722930 [26,] 0.01828457 0.03656914 0.9817154 [27,] 0.03362650 0.06725300 0.9663735 [28,] 0.02540285 0.05080571 0.9745971 [29,] 0.01670718 0.03341435 0.9832928 [30,] 0.01458246 0.02916492 0.9854175 [31,] 0.17946752 0.35893504 0.8205325 [32,] 0.15460742 0.30921483 0.8453926 [33,] 0.11860226 0.23720451 0.8813977 [34,] 0.09565660 0.19131319 0.9043434 [35,] 0.07057981 0.14115963 0.9294202 [36,] 0.05099041 0.10198081 0.9490096 [37,] 0.03576706 0.07153412 0.9642329 [38,] 0.02492494 0.04984989 0.9750751 [39,] 0.01683136 0.03366273 0.9831686 [40,] 0.01094545 0.02189090 0.9890546 [41,] 0.02719358 0.05438716 0.9728064 [42,] 0.08080683 0.16161366 0.9191932 [43,] 0.05994655 0.11989311 0.9400534 [44,] 0.28009088 0.56018175 0.7199091 [45,] 0.22332750 0.44665500 0.7766725 [46,] 0.41648200 0.83296399 0.5835180 [47,] 0.38462335 0.76924670 0.6153767 [48,] 0.32118077 0.64236153 0.6788192 [49,] 0.26261871 0.52523743 0.7373813 [50,] 0.56749169 0.86501663 0.4325083 [51,] 0.50420859 0.99158281 0.4957914 [52,] 0.48205450 0.96410900 0.5179455 [53,] 0.40420589 0.80841179 0.5957941 [54,] 0.34688662 0.69377324 0.6531134 [55,] 0.27434961 0.54869923 0.7256504 [56,] 0.20963907 0.41927815 0.7903609 [57,] 0.32405930 0.64811860 0.6759407 [58,] 0.26320934 0.52641868 0.7367907 [59,] 0.19103191 0.38206382 0.8089681 [60,] 0.26900094 0.53800188 0.7309991 [61,] 0.20059172 0.40118344 0.7994083 [62,] 0.13092023 0.26184046 0.8690798 [63,] 0.22898726 0.45797452 0.7710127 [64,] 0.20229863 0.40459725 0.7977014 [65,] 0.11276025 0.22552050 0.8872397 > postscript(file="/var/fisher/rcomp/tmp/1k9yq1356089331.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2l9841356089331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3u66q1356089331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4qb4s1356089331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5ysql1356089331.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 -0.070973338 0.025501350 0.025501350 0.025501350 0.025501350 0.015229741 7 8 9 10 11 12 0.025501350 -0.124689670 0.071733275 0.032985758 -0.117205263 0.025501350 13 14 15 16 17 18 -0.318771848 -0.117205263 -0.272539923 -0.422730944 0.538521539 -0.117205263 19 20 21 22 23 24 0.071733275 0.577269056 -0.031002184 -0.265055515 0.007745333 0.015229741 25 26 27 28 29 30 -0.358743002 -0.318771848 0.079217683 -0.254783906 0.071733275 -0.038486591 31 32 33 34 35 36 0.025501350 0.032985758 -0.031002184 -0.078457745 0.025501350 0.025501350 37 38 39 40 41 42 -0.461478461 -0.208551981 0.007745333 -0.188677612 0.727460077 -0.208551981 43 44 45 46 47 48 0.015229741 -0.117205263 -0.038486591 0.007745333 0.025501350 0.071733275 49 50 51 52 53 54 0.007745333 0.025501350 -0.404974927 0.538521539 0.071733275 0.745216094 55 56 57 58 59 60 0.025501350 -0.358743002 -0.272539923 0.071733275 0.071733275 0.584753464 61 62 63 64 65 66 -0.070973338 -0.318771848 0.025501350 -0.070973338 0.025501350 0.025501350 67 68 69 70 71 72 0.531037131 0.032985758 0.071733275 -0.254783906 0.025501350 0.071733275 73 74 75 76 77 78 -0.208551981 -0.247299498 0.071733275 -0.142445687 0.071733275 -0.272539923 79 80 81 82 83 84 0.641256998 -0.188677612 0.025501350 -0.201067574 0.025501350 0.745216094 85 86 0.007745333 0.032985758 > postscript(file="/var/fisher/rcomp/tmp/65t7e1356089331.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 -0.070973338 NA 1 0.025501350 -0.070973338 2 0.025501350 0.025501350 3 0.025501350 0.025501350 4 0.025501350 0.025501350 5 0.015229741 0.025501350 6 0.025501350 0.015229741 7 -0.124689670 0.025501350 8 0.071733275 -0.124689670 9 0.032985758 0.071733275 10 -0.117205263 0.032985758 11 0.025501350 -0.117205263 12 -0.318771848 0.025501350 13 -0.117205263 -0.318771848 14 -0.272539923 -0.117205263 15 -0.422730944 -0.272539923 16 0.538521539 -0.422730944 17 -0.117205263 0.538521539 18 0.071733275 -0.117205263 19 0.577269056 0.071733275 20 -0.031002184 0.577269056 21 -0.265055515 -0.031002184 22 0.007745333 -0.265055515 23 0.015229741 0.007745333 24 -0.358743002 0.015229741 25 -0.318771848 -0.358743002 26 0.079217683 -0.318771848 27 -0.254783906 0.079217683 28 0.071733275 -0.254783906 29 -0.038486591 0.071733275 30 0.025501350 -0.038486591 31 0.032985758 0.025501350 32 -0.031002184 0.032985758 33 -0.078457745 -0.031002184 34 0.025501350 -0.078457745 35 0.025501350 0.025501350 36 -0.461478461 0.025501350 37 -0.208551981 -0.461478461 38 0.007745333 -0.208551981 39 -0.188677612 0.007745333 40 0.727460077 -0.188677612 41 -0.208551981 0.727460077 42 0.015229741 -0.208551981 43 -0.117205263 0.015229741 44 -0.038486591 -0.117205263 45 0.007745333 -0.038486591 46 0.025501350 0.007745333 47 0.071733275 0.025501350 48 0.007745333 0.071733275 49 0.025501350 0.007745333 50 -0.404974927 0.025501350 51 0.538521539 -0.404974927 52 0.071733275 0.538521539 53 0.745216094 0.071733275 54 0.025501350 0.745216094 55 -0.358743002 0.025501350 56 -0.272539923 -0.358743002 57 0.071733275 -0.272539923 58 0.071733275 0.071733275 59 0.584753464 0.071733275 60 -0.070973338 0.584753464 61 -0.318771848 -0.070973338 62 0.025501350 -0.318771848 63 -0.070973338 0.025501350 64 0.025501350 -0.070973338 65 0.025501350 0.025501350 66 0.531037131 0.025501350 67 0.032985758 0.531037131 68 0.071733275 0.032985758 69 -0.254783906 0.071733275 70 0.025501350 -0.254783906 71 0.071733275 0.025501350 72 -0.208551981 0.071733275 73 -0.247299498 -0.208551981 74 0.071733275 -0.247299498 75 -0.142445687 0.071733275 76 0.071733275 -0.142445687 77 -0.272539923 0.071733275 78 0.641256998 -0.272539923 79 -0.188677612 0.641256998 80 0.025501350 -0.188677612 81 -0.201067574 0.025501350 82 0.025501350 -0.201067574 83 0.745216094 0.025501350 84 0.007745333 0.745216094 85 0.032985758 0.007745333 86 NA 0.032985758 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.025501350 -0.070973338 [2,] 0.025501350 0.025501350 [3,] 0.025501350 0.025501350 [4,] 0.025501350 0.025501350 [5,] 0.015229741 0.025501350 [6,] 0.025501350 0.015229741 [7,] -0.124689670 0.025501350 [8,] 0.071733275 -0.124689670 [9,] 0.032985758 0.071733275 [10,] -0.117205263 0.032985758 [11,] 0.025501350 -0.117205263 [12,] -0.318771848 0.025501350 [13,] -0.117205263 -0.318771848 [14,] -0.272539923 -0.117205263 [15,] -0.422730944 -0.272539923 [16,] 0.538521539 -0.422730944 [17,] -0.117205263 0.538521539 [18,] 0.071733275 -0.117205263 [19,] 0.577269056 0.071733275 [20,] -0.031002184 0.577269056 [21,] -0.265055515 -0.031002184 [22,] 0.007745333 -0.265055515 [23,] 0.015229741 0.007745333 [24,] -0.358743002 0.015229741 [25,] -0.318771848 -0.358743002 [26,] 0.079217683 -0.318771848 [27,] -0.254783906 0.079217683 [28,] 0.071733275 -0.254783906 [29,] -0.038486591 0.071733275 [30,] 0.025501350 -0.038486591 [31,] 0.032985758 0.025501350 [32,] -0.031002184 0.032985758 [33,] -0.078457745 -0.031002184 [34,] 0.025501350 -0.078457745 [35,] 0.025501350 0.025501350 [36,] -0.461478461 0.025501350 [37,] -0.208551981 -0.461478461 [38,] 0.007745333 -0.208551981 [39,] -0.188677612 0.007745333 [40,] 0.727460077 -0.188677612 [41,] -0.208551981 0.727460077 [42,] 0.015229741 -0.208551981 [43,] -0.117205263 0.015229741 [44,] -0.038486591 -0.117205263 [45,] 0.007745333 -0.038486591 [46,] 0.025501350 0.007745333 [47,] 0.071733275 0.025501350 [48,] 0.007745333 0.071733275 [49,] 0.025501350 0.007745333 [50,] -0.404974927 0.025501350 [51,] 0.538521539 -0.404974927 [52,] 0.071733275 0.538521539 [53,] 0.745216094 0.071733275 [54,] 0.025501350 0.745216094 [55,] -0.358743002 0.025501350 [56,] -0.272539923 -0.358743002 [57,] 0.071733275 -0.272539923 [58,] 0.071733275 0.071733275 [59,] 0.584753464 0.071733275 [60,] -0.070973338 0.584753464 [61,] -0.318771848 -0.070973338 [62,] 0.025501350 -0.318771848 [63,] -0.070973338 0.025501350 [64,] 0.025501350 -0.070973338 [65,] 0.025501350 0.025501350 [66,] 0.531037131 0.025501350 [67,] 0.032985758 0.531037131 [68,] 0.071733275 0.032985758 [69,] -0.254783906 0.071733275 [70,] 0.025501350 -0.254783906 [71,] 0.071733275 0.025501350 [72,] -0.208551981 0.071733275 [73,] -0.247299498 -0.208551981 [74,] 0.071733275 -0.247299498 [75,] -0.142445687 0.071733275 [76,] 0.071733275 -0.142445687 [77,] -0.272539923 0.071733275 [78,] 0.641256998 -0.272539923 [79,] -0.188677612 0.641256998 [80,] 0.025501350 -0.188677612 [81,] -0.201067574 0.025501350 [82,] 0.025501350 -0.201067574 [83,] 0.745216094 0.025501350 [84,] 0.007745333 0.745216094 [85,] 0.032985758 0.007745333 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.025501350 -0.070973338 2 0.025501350 0.025501350 3 0.025501350 0.025501350 4 0.025501350 0.025501350 5 0.015229741 0.025501350 6 0.025501350 0.015229741 7 -0.124689670 0.025501350 8 0.071733275 -0.124689670 9 0.032985758 0.071733275 10 -0.117205263 0.032985758 11 0.025501350 -0.117205263 12 -0.318771848 0.025501350 13 -0.117205263 -0.318771848 14 -0.272539923 -0.117205263 15 -0.422730944 -0.272539923 16 0.538521539 -0.422730944 17 -0.117205263 0.538521539 18 0.071733275 -0.117205263 19 0.577269056 0.071733275 20 -0.031002184 0.577269056 21 -0.265055515 -0.031002184 22 0.007745333 -0.265055515 23 0.015229741 0.007745333 24 -0.358743002 0.015229741 25 -0.318771848 -0.358743002 26 0.079217683 -0.318771848 27 -0.254783906 0.079217683 28 0.071733275 -0.254783906 29 -0.038486591 0.071733275 30 0.025501350 -0.038486591 31 0.032985758 0.025501350 32 -0.031002184 0.032985758 33 -0.078457745 -0.031002184 34 0.025501350 -0.078457745 35 0.025501350 0.025501350 36 -0.461478461 0.025501350 37 -0.208551981 -0.461478461 38 0.007745333 -0.208551981 39 -0.188677612 0.007745333 40 0.727460077 -0.188677612 41 -0.208551981 0.727460077 42 0.015229741 -0.208551981 43 -0.117205263 0.015229741 44 -0.038486591 -0.117205263 45 0.007745333 -0.038486591 46 0.025501350 0.007745333 47 0.071733275 0.025501350 48 0.007745333 0.071733275 49 0.025501350 0.007745333 50 -0.404974927 0.025501350 51 0.538521539 -0.404974927 52 0.071733275 0.538521539 53 0.745216094 0.071733275 54 0.025501350 0.745216094 55 -0.358743002 0.025501350 56 -0.272539923 -0.358743002 57 0.071733275 -0.272539923 58 0.071733275 0.071733275 59 0.584753464 0.071733275 60 -0.070973338 0.584753464 61 -0.318771848 -0.070973338 62 0.025501350 -0.318771848 63 -0.070973338 0.025501350 64 0.025501350 -0.070973338 65 0.025501350 0.025501350 66 0.531037131 0.025501350 67 0.032985758 0.531037131 68 0.071733275 0.032985758 69 -0.254783906 0.071733275 70 0.025501350 -0.254783906 71 0.071733275 0.025501350 72 -0.208551981 0.071733275 73 -0.247299498 -0.208551981 74 0.071733275 -0.247299498 75 -0.142445687 0.071733275 76 0.071733275 -0.142445687 77 -0.272539923 0.071733275 78 0.641256998 -0.272539923 79 -0.188677612 0.641256998 80 0.025501350 -0.188677612 81 -0.201067574 0.025501350 82 0.025501350 -0.201067574 83 0.745216094 0.025501350 84 0.007745333 0.745216094 85 0.032985758 0.007745333 > 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/7lzrh1356089331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8pj8k1356089331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9tbam1356089331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/100t491356089331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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='') + } + } Error: subscript out of bounds Execution halted