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 + ,1 + ,0 + ,2 + ,2 + ,2 + ,1 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,1 + ,2 + ,0 + ,2 + ,2 + ,1 + ,1 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,1 + ,4 + ,1 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,1 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,1 + ,2 + ,1 + ,2 + ,4 + ,1 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,1 + ,2 + ,1 + ,1 + ,4 + ,2 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,2 + ,4 + ,1 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,1 + ,4 + ,2 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,2 + ,2 + ,1 + ,2 + ,4 + ,1 + ,2 + ,0 + ,1 + ,2 + ,1 + ,1 + ,4 + ,2 + ,2 + ,0 + ,2 + ,2 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,2 + ,2 + ,1 + ,1 + 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,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,1 + ,2 + ,2 + ,0 + ,1 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,1 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,1 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,1 + ,2 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,1 + ,2 + ,1 + ,0 + ,2 + ,1 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,1 + ,0 + ,2 + ,1 + ,2 + ,1 + ,1 + ,2 + ,1 + ,0 + ,1 + ,1 + ,2 + ,1 + ,1 + ,2 + ,2 + ,0 + ,1 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,1 + ,1 + ,2 + ,1 + ,2 + ,2 + ,0 + ,1 + ,1 + ,2 + ,2 + ,1 + ,2 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,1 + ,1 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,1 + ,2 + ,2 + ,2 + ,0 + ,1 + ,2 + ,2 + ,2 + ,1 + ,2 + ,2 + ,0 + ,1 + ,1 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,1 + ,2 + ,2 + ,2 + ,2 + ,2 + ,1 + ,0 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,1 + ,1 + ,2 + ,2 + ,0 + ,2 + ,2 + ,2 + ,2 + ,1 + ,2 + ,1 + ,0 + ,2 + ,1 + ,1 + ,2 + ,2 + ,2 + ,1 + ,0 + ,2 + ,1 + ,1 + ,1 + ,2 + ,2 + ,1 + ,0 + ,2 + ,1 + ,2 + ,2 + ,2) + ,dim=c(8 + ,154) + ,dimnames=list(c('Weeks' + ,'UseLimit' + ,'T40' + ,'T20' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome') + ,1:154)) > y <- array(NA,dim=c(8,154),dimnames=list(c('Weeks','UseLimit','T40','T20','Used','CorrectAnalysis','Useful','Outcome'),1:154)) > 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 = '8' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '8' > #'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 Outcome Weeks UseLimit T40 T20 Used CorrectAnalysis Useful 1 1 4 1 1 0 2 2 2 2 2 4 2 2 0 2 2 2 3 2 4 2 2 0 2 2 2 4 2 4 2 2 0 2 2 2 5 2 4 2 2 0 2 2 2 6 1 4 1 2 0 2 2 1 7 2 4 2 2 0 2 2 2 8 2 4 2 1 0 2 2 2 9 1 4 2 2 0 2 2 2 10 2 4 1 2 0 2 2 2 11 2 4 1 1 0 2 2 2 12 2 4 2 2 0 2 2 2 13 2 4 2 2 0 1 2 1 14 2 4 1 1 0 2 2 2 15 1 4 2 2 0 1 2 1 16 1 4 2 1 0 1 2 1 17 2 4 1 1 0 1 1 1 18 2 4 1 1 0 2 2 2 19 1 4 2 2 0 2 2 2 20 1 4 2 1 0 1 1 1 21 2 4 1 2 0 2 2 1 22 1 4 1 2 0 1 2 1 23 1 4 2 2 0 2 2 1 24 1 4 1 2 0 2 2 1 25 1 4 2 1 0 1 2 2 26 2 4 2 2 0 1 2 1 27 1 4 1 2 0 2 2 2 28 2 4 2 2 0 1 2 2 29 1 4 2 2 0 2 2 2 30 2 4 2 2 0 2 2 1 31 2 4 2 2 0 2 2 2 32 2 4 1 2 0 2 2 2 33 2 4 1 2 0 2 2 1 34 1 4 2 1 0 2 2 2 35 2 4 2 2 0 2 2 2 36 2 4 2 2 0 2 2 2 37 2 4 1 1 0 1 2 1 38 1 4 2 2 0 1 2 2 39 1 4 2 2 0 2 2 1 40 2 4 2 1 0 2 2 1 41 1 4 2 2 0 1 1 1 42 1 4 2 2 0 1 2 2 43 1 4 1 2 0 2 2 1 44 2 4 1 1 0 2 2 2 45 2 4 2 2 0 2 2 1 46 1 4 2 2 0 2 2 1 47 2 4 2 2 0 2 2 2 48 1 4 2 2 0 2 2 2 49 1 4 2 2 0 2 2 1 50 2 4 2 2 0 2 2 2 51 2 4 2 1 0 1 2 2 52 2 4 1 1 0 1 1 1 53 1 4 2 2 0 2 2 2 54 2 4 2 2 0 1 1 2 55 2 4 2 2 0 2 2 2 56 1 4 2 1 0 1 2 2 57 1 4 2 2 0 1 2 1 58 1 4 2 2 0 2 2 2 59 1 4 2 2 0 2 2 2 60 1 4 1 1 0 1 1 1 61 1 4 1 1 0 2 2 2 62 2 4 2 2 0 1 2 1 63 2 4 2 2 0 2 2 2 64 1 4 1 1 0 2 2 2 65 2 4 2 2 0 2 2 2 66 2 4 2 2 0 2 2 2 67 2 4 2 1 0 1 1 1 68 2 4 1 2 0 2 2 2 69 1 4 2 2 0 2 2 2 70 2 4 2 2 0 1 2 2 71 2 4 2 2 0 2 2 2 72 1 4 2 2 0 2 2 2 73 1 4 2 2 0 1 2 2 74 2 4 1 2 0 1 2 2 75 1 4 2 2 0 2 2 2 76 1 4 2 1 0 2 2 1 77 1 4 2 2 0 2 2 2 78 1 4 2 2 0 1 2 1 79 1 4 2 1 0 1 1 2 80 2 4 2 1 0 2 2 1 81 2 4 2 2 0 2 2 2 82 1 4 1 2 0 1 2 2 83 2 4 2 2 0 2 2 2 84 2 4 2 2 0 1 1 2 85 1 4 2 2 0 2 2 1 86 2 4 1 2 0 2 2 2 87 1 2 1 0 2 2 2 2 88 1 2 1 0 1 1 2 2 89 2 2 2 0 2 2 2 2 90 1 2 2 0 2 2 2 2 91 2 2 2 0 2 2 2 1 92 2 2 1 0 1 2 2 2 93 2 2 1 0 2 2 2 1 94 2 2 2 0 2 2 2 2 95 2 2 2 0 1 2 2 2 96 1 2 2 0 2 2 2 2 97 2 2 1 0 1 2 2 2 98 2 2 2 0 2 2 2 2 99 2 2 1 0 2 2 2 2 100 1 2 2 0 2 2 2 2 101 1 2 1 0 2 2 2 2 102 2 2 2 0 2 2 2 2 103 2 2 2 0 2 2 2 2 104 2 2 2 0 2 2 2 2 105 2 2 2 0 1 1 2 2 106 2 2 2 0 2 2 2 2 107 2 2 2 0 2 2 2 2 108 2 2 1 0 1 1 2 2 109 2 2 2 0 2 2 2 2 110 2 2 1 0 2 2 2 2 111 2 2 1 0 1 1 2 1 112 2 2 2 0 1 2 2 2 113 2 2 2 0 2 1 2 2 114 2 2 1 0 1 1 2 2 115 2 2 1 0 2 2 2 2 116 2 2 2 0 2 2 2 2 117 1 2 1 0 2 2 2 2 118 2 2 1 0 2 2 2 2 119 2 2 2 0 2 2 2 2 120 1 2 2 0 2 2 2 2 121 2 2 1 0 2 2 2 2 122 2 2 2 0 2 2 2 2 123 2 2 1 0 1 1 2 2 124 1 2 2 0 2 1 2 1 125 1 2 2 0 2 2 2 2 126 2 2 2 0 1 2 2 2 127 2 2 2 0 2 2 2 1 128 1 2 2 0 2 2 2 2 129 2 2 2 0 2 2 2 2 130 1 2 2 0 2 2 2 2 131 2 2 1 0 2 2 2 2 132 1 2 1 0 2 2 2 2 133 2 2 1 0 2 1 2 2 134 2 2 2 0 2 2 2 2 135 2 2 2 0 2 2 2 2 136 2 2 2 0 2 2 2 2 137 1 2 1 0 2 1 2 1 138 1 2 1 0 1 1 2 1 139 2 2 2 0 1 2 2 2 140 2 2 2 0 2 2 2 2 141 1 2 2 0 2 1 1 2 142 1 2 2 0 1 1 2 2 143 2 2 1 0 2 2 2 2 144 1 2 2 0 2 2 2 1 145 2 2 2 0 2 2 2 1 146 1 2 2 0 1 2 2 2 147 2 2 2 0 1 1 2 2 148 2 2 2 0 1 2 2 2 149 2 2 1 0 2 2 2 2 150 1 2 2 0 2 2 2 1 151 1 2 2 0 2 2 2 2 152 2 2 1 0 2 1 1 2 153 2 2 1 0 2 1 1 1 154 2 2 1 0 2 1 2 2 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UseLimit T40 2.28301 -0.20302 -0.08170 0.03118 T20 Used CorrectAnalysis Useful -0.13246 0.11742 -0.15558 0.15068 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.8062 -0.5252 0.2446 0.4051 0.6732 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.28301 0.71064 3.213 0.00162 ** Weeks -0.20302 0.17181 -1.182 0.23925 UseLimit -0.08170 0.08637 -0.946 0.34575 T40 0.03118 0.12499 0.249 0.80334 T20 -0.13246 0.14369 -0.922 0.35812 Used 0.11742 0.10359 1.134 0.25884 CorrectAnalysis -0.15558 0.17227 -0.903 0.36796 Useful 0.15068 0.09454 1.594 0.11314 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4862 on 146 degrees of freedom Multiple R-squared: 0.06298, Adjusted R-squared: 0.01805 F-statistic: 1.402 on 7 and 146 DF, p-value: 0.2088 > 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,] 0.8968382 0.2063236 0.1031618 [2,] 0.8187887 0.3624227 0.1812113 [3,] 0.7236976 0.5526049 0.2763024 [4,] 0.6542912 0.6914177 0.3457088 [5,] 0.7288266 0.5423469 0.2711734 [6,] 0.6641435 0.6717130 0.3358565 [7,] 0.5723517 0.8552965 0.4276483 [8,] 0.5057190 0.9885621 0.4942810 [9,] 0.6569774 0.6860452 0.3430226 [10,] 0.6680995 0.6638010 0.3319005 [11,] 0.7242249 0.5515502 0.2757751 [12,] 0.7180520 0.5638961 0.2819480 [13,] 0.6742223 0.6515554 0.3257777 [14,] 0.6405040 0.7189921 0.3594960 [15,] 0.6601099 0.6797802 0.3398901 [16,] 0.7333859 0.5332283 0.2666141 [17,] 0.8016196 0.3967609 0.1983804 [18,] 0.7752850 0.4494299 0.2247150 [19,] 0.8092999 0.3814003 0.1907001 [20,] 0.8246963 0.3506073 0.1753037 [21,] 0.7979558 0.4040883 0.2020442 [22,] 0.7664098 0.4671805 0.2335902 [23,] 0.7644117 0.4711766 0.2355883 [24,] 0.7723256 0.4553488 0.2276744 [25,] 0.7444161 0.5111678 0.2555839 [26,] 0.7146025 0.5707949 0.2853975 [27,] 0.7462242 0.5075517 0.2537758 [28,] 0.7606405 0.4787190 0.2393595 [29,] 0.7521744 0.4956512 0.2478256 [30,] 0.7669128 0.4661745 0.2330872 [31,] 0.7522374 0.4955253 0.2477626 [32,] 0.7445807 0.5108386 0.2554193 [33,] 0.7535163 0.4929675 0.2464837 [34,] 0.7292670 0.5414659 0.2707330 [35,] 0.7299337 0.5401326 0.2700663 [36,] 0.7274935 0.5450131 0.2725065 [37,] 0.7083980 0.5832039 0.2916020 [38,] 0.7299608 0.5400783 0.2700392 [39,] 0.7226667 0.5546667 0.2773333 [40,] 0.7053337 0.5893326 0.2946663 [41,] 0.7182806 0.5634388 0.2817194 [42,] 0.7233458 0.5533084 0.2766542 [43,] 0.7422600 0.5154800 0.2577400 [44,] 0.7213782 0.5572436 0.2786218 [45,] 0.7050724 0.5898552 0.2949276 [46,] 0.6980802 0.6038396 0.3019198 [47,] 0.6696827 0.6606346 0.3303173 [48,] 0.6906255 0.6187489 0.3093745 [49,] 0.7103861 0.5792278 0.2896139 [50,] 0.7148648 0.5702704 0.2851352 [51,] 0.7367169 0.5265663 0.2632831 [52,] 0.7649770 0.4700459 0.2350230 [53,] 0.7510270 0.4979461 0.2489730 [54,] 0.7713194 0.4573613 0.2286806 [55,] 0.7583099 0.4833803 0.2416901 [56,] 0.7459948 0.5080103 0.2540052 [57,] 0.7541946 0.4916108 0.2458054 [58,] 0.7344614 0.5310772 0.2655386 [59,] 0.7478079 0.5043841 0.2521921 [60,] 0.7559760 0.4880480 0.2440240 [61,] 0.7480164 0.5039672 0.2519836 [62,] 0.7579143 0.4841715 0.2420857 [63,] 0.7506732 0.4986537 0.2493268 [64,] 0.7486535 0.5026929 0.2513465 [65,] 0.7588895 0.4822210 0.2411105 [66,] 0.7408280 0.5183440 0.2591720 [67,] 0.7574237 0.4851526 0.2425763 [68,] 0.7369011 0.5261977 0.2630989 [69,] 0.7798313 0.4403374 0.2201687 [70,] 0.7684925 0.4630150 0.2315075 [71,] 0.7529571 0.4940858 0.2470429 [72,] 0.7693378 0.4613244 0.2306622 [73,] 0.7493769 0.5012463 0.2506231 [74,] 0.7310600 0.5378799 0.2689400 [75,] 0.7299885 0.5400231 0.2700115 [76,] 0.6959621 0.6080758 0.3040379 [77,] 0.7177052 0.5645896 0.2822948 [78,] 0.7463429 0.5073142 0.2536571 [79,] 0.7530648 0.4938705 0.2469352 [80,] 0.7696355 0.4607290 0.2303645 [81,] 0.7885674 0.4228653 0.2114326 [82,] 0.7717986 0.4564029 0.2282014 [83,] 0.7669233 0.4661534 0.2330767 [84,] 0.7455346 0.5089307 0.2544654 [85,] 0.7163660 0.5672679 0.2836340 [86,] 0.7504157 0.4991686 0.2495843 [87,] 0.7119952 0.5760096 0.2880048 [88,] 0.6885112 0.6229777 0.3114888 [89,] 0.6524847 0.6950305 0.3475153 [90,] 0.6924131 0.6151738 0.3075869 [91,] 0.7580394 0.4839212 0.2419606 [92,] 0.7360558 0.5278884 0.2639442 [93,] 0.7121287 0.5757426 0.2878713 [94,] 0.6867874 0.6264252 0.3132126 [95,] 0.6582286 0.6835427 0.3417714 [96,] 0.6312260 0.7375479 0.3687740 [97,] 0.6041482 0.7917036 0.3958518 [98,] 0.5575871 0.8848258 0.4424129 [99,] 0.5299134 0.9401732 0.4700866 [100,] 0.4830345 0.9660690 0.5169655 [101,] 0.4651484 0.9302968 0.5348516 [102,] 0.4242649 0.8485298 0.5757351 [103,] 0.4195681 0.8391362 0.5804319 [104,] 0.3761242 0.7522484 0.6238758 [105,] 0.3297576 0.6595153 0.6702424 [106,] 0.3050542 0.6101084 0.6949458 [107,] 0.4048480 0.8096960 0.5951520 [108,] 0.3525669 0.7051339 0.6474331 [109,] 0.3287661 0.6575322 0.6712339 [110,] 0.3604633 0.7209265 0.6395367 [111,] 0.3084001 0.6168002 0.6915999 [112,] 0.2836618 0.5673236 0.7163382 [113,] 0.2440396 0.4880792 0.7559604 [114,] 0.2070959 0.4141919 0.7929041 [115,] 0.2331303 0.4662606 0.7668697 [116,] 0.2037496 0.4074991 0.7962504 [117,] 0.2265392 0.4530785 0.7734608 [118,] 0.2599550 0.5199100 0.7400450 [119,] 0.2273351 0.4546703 0.7726649 [120,] 0.2696489 0.5392977 0.7303511 [121,] 0.2154402 0.4308804 0.7845598 [122,] 0.3872121 0.7744243 0.6127879 [123,] 0.3276212 0.6552425 0.6723788 [124,] 0.2750662 0.5501324 0.7249338 [125,] 0.2301657 0.4603314 0.7698343 [126,] 0.1953765 0.3907531 0.8046235 [127,] 0.1698384 0.3396769 0.8301616 [128,] 0.2538320 0.5076640 0.7461680 [129,] 0.2167256 0.4334512 0.7832744 [130,] 0.3036003 0.6072006 0.6963997 [131,] 0.2126993 0.4253986 0.7873007 [132,] 0.2554098 0.5108196 0.7445902 [133,] 0.1520763 0.3041526 0.8479237 > postscript(file="/var/fisher/rcomp/tmp/1vu4i1356009081.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2mr261356009081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/339jt1356009081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4nros1356009081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/56pnn1356009081.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 = 154 Frequency = 1 1 2 3 4 5 6 7 -0.6454478 0.4050643 0.4050643 0.4050643 0.4050643 -0.5259533 0.4050643 8 9 10 11 12 13 14 0.4362479 -0.5949357 0.3233685 0.3545522 0.4050643 0.6731640 0.3545522 15 16 17 18 19 20 21 -0.3268360 -0.2956524 0.4670739 0.3545522 -0.5949357 -0.4512303 0.4740467 22 23 24 25 26 27 28 -0.4085318 -0.4442575 -0.5259533 -0.4463306 0.6731640 -0.6766315 0.5224858 29 30 31 32 33 34 35 -0.5949357 0.5557425 0.4050643 0.3233685 0.4740467 -0.5637521 0.4050643 36 37 38 39 40 41 42 0.4050643 0.6226519 -0.4775142 -0.4442575 0.5869261 -0.4824140 -0.4775142 43 44 45 46 47 48 49 -0.5259533 0.3545522 0.5557425 -0.4442575 0.4050643 -0.5949357 -0.4442575 50 51 52 53 54 55 56 0.4050643 0.5536694 0.4670739 -0.5949357 0.3669078 0.4050643 -0.4463306 57 58 59 60 61 62 63 -0.3268360 -0.5949357 -0.5949357 -0.5329261 -0.6454478 0.6731640 0.4050643 64 65 66 67 68 69 70 -0.6454478 0.4050643 0.4050643 0.5487697 0.3233685 -0.5949357 0.5224858 71 72 73 74 75 76 77 0.4050643 -0.5949357 -0.4775142 0.4407900 -0.5949357 -0.4130739 -0.5949357 78 79 80 81 82 83 84 -0.3268360 -0.6019085 0.5869261 0.4050643 -0.5592100 0.4050643 0.3669078 85 86 87 88 89 90 91 -0.4442575 0.3233685 -0.7553863 -0.7704262 0.3263094 -0.6736906 0.4769876 92 93 94 95 96 97 98 0.1121523 0.3952919 0.3263094 0.1938480 -0.6736906 0.1121523 0.3263094 99 100 101 102 103 104 105 0.2446137 -0.6736906 -0.7553863 0.3263094 0.3263094 0.3263094 0.3112695 106 107 108 109 110 111 112 0.3263094 0.3263094 0.2295738 0.3263094 0.2446137 0.3802520 0.1938480 113 114 115 116 117 118 119 0.4437309 0.2295738 0.2446137 0.3263094 -0.7553863 0.2446137 0.3263094 120 121 122 123 124 125 126 -0.6736906 0.2446137 0.3263094 0.2295738 -0.4055909 -0.6736906 0.1938480 127 128 129 130 131 132 133 0.4769876 -0.6736906 0.3263094 -0.6736906 0.2446137 -0.7553863 0.3620352 134 135 136 137 138 139 140 0.3263094 0.3263094 0.3263094 -0.4872866 -0.6197480 0.1938480 0.3263094 141 142 143 144 145 146 147 -0.7118470 -0.6887305 0.2446137 -0.5230124 0.4769876 -0.8061520 0.3112695 148 149 150 151 152 153 154 0.1938480 0.2446137 -0.5230124 -0.6736906 0.2064573 0.3571355 0.3620352 > postscript(file="/var/fisher/rcomp/tmp/6d61e1356009081.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.6454478 NA 1 0.4050643 -0.6454478 2 0.4050643 0.4050643 3 0.4050643 0.4050643 4 0.4050643 0.4050643 5 -0.5259533 0.4050643 6 0.4050643 -0.5259533 7 0.4362479 0.4050643 8 -0.5949357 0.4362479 9 0.3233685 -0.5949357 10 0.3545522 0.3233685 11 0.4050643 0.3545522 12 0.6731640 0.4050643 13 0.3545522 0.6731640 14 -0.3268360 0.3545522 15 -0.2956524 -0.3268360 16 0.4670739 -0.2956524 17 0.3545522 0.4670739 18 -0.5949357 0.3545522 19 -0.4512303 -0.5949357 20 0.4740467 -0.4512303 21 -0.4085318 0.4740467 22 -0.4442575 -0.4085318 23 -0.5259533 -0.4442575 24 -0.4463306 -0.5259533 25 0.6731640 -0.4463306 26 -0.6766315 0.6731640 27 0.5224858 -0.6766315 28 -0.5949357 0.5224858 29 0.5557425 -0.5949357 30 0.4050643 0.5557425 31 0.3233685 0.4050643 32 0.4740467 0.3233685 33 -0.5637521 0.4740467 34 0.4050643 -0.5637521 35 0.4050643 0.4050643 36 0.6226519 0.4050643 37 -0.4775142 0.6226519 38 -0.4442575 -0.4775142 39 0.5869261 -0.4442575 40 -0.4824140 0.5869261 41 -0.4775142 -0.4824140 42 -0.5259533 -0.4775142 43 0.3545522 -0.5259533 44 0.5557425 0.3545522 45 -0.4442575 0.5557425 46 0.4050643 -0.4442575 47 -0.5949357 0.4050643 48 -0.4442575 -0.5949357 49 0.4050643 -0.4442575 50 0.5536694 0.4050643 51 0.4670739 0.5536694 52 -0.5949357 0.4670739 53 0.3669078 -0.5949357 54 0.4050643 0.3669078 55 -0.4463306 0.4050643 56 -0.3268360 -0.4463306 57 -0.5949357 -0.3268360 58 -0.5949357 -0.5949357 59 -0.5329261 -0.5949357 60 -0.6454478 -0.5329261 61 0.6731640 -0.6454478 62 0.4050643 0.6731640 63 -0.6454478 0.4050643 64 0.4050643 -0.6454478 65 0.4050643 0.4050643 66 0.5487697 0.4050643 67 0.3233685 0.5487697 68 -0.5949357 0.3233685 69 0.5224858 -0.5949357 70 0.4050643 0.5224858 71 -0.5949357 0.4050643 72 -0.4775142 -0.5949357 73 0.4407900 -0.4775142 74 -0.5949357 0.4407900 75 -0.4130739 -0.5949357 76 -0.5949357 -0.4130739 77 -0.3268360 -0.5949357 78 -0.6019085 -0.3268360 79 0.5869261 -0.6019085 80 0.4050643 0.5869261 81 -0.5592100 0.4050643 82 0.4050643 -0.5592100 83 0.3669078 0.4050643 84 -0.4442575 0.3669078 85 0.3233685 -0.4442575 86 -0.7553863 0.3233685 87 -0.7704262 -0.7553863 88 0.3263094 -0.7704262 89 -0.6736906 0.3263094 90 0.4769876 -0.6736906 91 0.1121523 0.4769876 92 0.3952919 0.1121523 93 0.3263094 0.3952919 94 0.1938480 0.3263094 95 -0.6736906 0.1938480 96 0.1121523 -0.6736906 97 0.3263094 0.1121523 98 0.2446137 0.3263094 99 -0.6736906 0.2446137 100 -0.7553863 -0.6736906 101 0.3263094 -0.7553863 102 0.3263094 0.3263094 103 0.3263094 0.3263094 104 0.3112695 0.3263094 105 0.3263094 0.3112695 106 0.3263094 0.3263094 107 0.2295738 0.3263094 108 0.3263094 0.2295738 109 0.2446137 0.3263094 110 0.3802520 0.2446137 111 0.1938480 0.3802520 112 0.4437309 0.1938480 113 0.2295738 0.4437309 114 0.2446137 0.2295738 115 0.3263094 0.2446137 116 -0.7553863 0.3263094 117 0.2446137 -0.7553863 118 0.3263094 0.2446137 119 -0.6736906 0.3263094 120 0.2446137 -0.6736906 121 0.3263094 0.2446137 122 0.2295738 0.3263094 123 -0.4055909 0.2295738 124 -0.6736906 -0.4055909 125 0.1938480 -0.6736906 126 0.4769876 0.1938480 127 -0.6736906 0.4769876 128 0.3263094 -0.6736906 129 -0.6736906 0.3263094 130 0.2446137 -0.6736906 131 -0.7553863 0.2446137 132 0.3620352 -0.7553863 133 0.3263094 0.3620352 134 0.3263094 0.3263094 135 0.3263094 0.3263094 136 -0.4872866 0.3263094 137 -0.6197480 -0.4872866 138 0.1938480 -0.6197480 139 0.3263094 0.1938480 140 -0.7118470 0.3263094 141 -0.6887305 -0.7118470 142 0.2446137 -0.6887305 143 -0.5230124 0.2446137 144 0.4769876 -0.5230124 145 -0.8061520 0.4769876 146 0.3112695 -0.8061520 147 0.1938480 0.3112695 148 0.2446137 0.1938480 149 -0.5230124 0.2446137 150 -0.6736906 -0.5230124 151 0.2064573 -0.6736906 152 0.3571355 0.2064573 153 0.3620352 0.3571355 154 NA 0.3620352 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.4050643 -0.6454478 [2,] 0.4050643 0.4050643 [3,] 0.4050643 0.4050643 [4,] 0.4050643 0.4050643 [5,] -0.5259533 0.4050643 [6,] 0.4050643 -0.5259533 [7,] 0.4362479 0.4050643 [8,] -0.5949357 0.4362479 [9,] 0.3233685 -0.5949357 [10,] 0.3545522 0.3233685 [11,] 0.4050643 0.3545522 [12,] 0.6731640 0.4050643 [13,] 0.3545522 0.6731640 [14,] -0.3268360 0.3545522 [15,] -0.2956524 -0.3268360 [16,] 0.4670739 -0.2956524 [17,] 0.3545522 0.4670739 [18,] -0.5949357 0.3545522 [19,] -0.4512303 -0.5949357 [20,] 0.4740467 -0.4512303 [21,] -0.4085318 0.4740467 [22,] -0.4442575 -0.4085318 [23,] -0.5259533 -0.4442575 [24,] -0.4463306 -0.5259533 [25,] 0.6731640 -0.4463306 [26,] -0.6766315 0.6731640 [27,] 0.5224858 -0.6766315 [28,] -0.5949357 0.5224858 [29,] 0.5557425 -0.5949357 [30,] 0.4050643 0.5557425 [31,] 0.3233685 0.4050643 [32,] 0.4740467 0.3233685 [33,] -0.5637521 0.4740467 [34,] 0.4050643 -0.5637521 [35,] 0.4050643 0.4050643 [36,] 0.6226519 0.4050643 [37,] -0.4775142 0.6226519 [38,] -0.4442575 -0.4775142 [39,] 0.5869261 -0.4442575 [40,] -0.4824140 0.5869261 [41,] -0.4775142 -0.4824140 [42,] -0.5259533 -0.4775142 [43,] 0.3545522 -0.5259533 [44,] 0.5557425 0.3545522 [45,] -0.4442575 0.5557425 [46,] 0.4050643 -0.4442575 [47,] -0.5949357 0.4050643 [48,] -0.4442575 -0.5949357 [49,] 0.4050643 -0.4442575 [50,] 0.5536694 0.4050643 [51,] 0.4670739 0.5536694 [52,] -0.5949357 0.4670739 [53,] 0.3669078 -0.5949357 [54,] 0.4050643 0.3669078 [55,] -0.4463306 0.4050643 [56,] -0.3268360 -0.4463306 [57,] -0.5949357 -0.3268360 [58,] -0.5949357 -0.5949357 [59,] -0.5329261 -0.5949357 [60,] -0.6454478 -0.5329261 [61,] 0.6731640 -0.6454478 [62,] 0.4050643 0.6731640 [63,] -0.6454478 0.4050643 [64,] 0.4050643 -0.6454478 [65,] 0.4050643 0.4050643 [66,] 0.5487697 0.4050643 [67,] 0.3233685 0.5487697 [68,] -0.5949357 0.3233685 [69,] 0.5224858 -0.5949357 [70,] 0.4050643 0.5224858 [71,] -0.5949357 0.4050643 [72,] -0.4775142 -0.5949357 [73,] 0.4407900 -0.4775142 [74,] -0.5949357 0.4407900 [75,] -0.4130739 -0.5949357 [76,] -0.5949357 -0.4130739 [77,] -0.3268360 -0.5949357 [78,] -0.6019085 -0.3268360 [79,] 0.5869261 -0.6019085 [80,] 0.4050643 0.5869261 [81,] -0.5592100 0.4050643 [82,] 0.4050643 -0.5592100 [83,] 0.3669078 0.4050643 [84,] -0.4442575 0.3669078 [85,] 0.3233685 -0.4442575 [86,] -0.7553863 0.3233685 [87,] -0.7704262 -0.7553863 [88,] 0.3263094 -0.7704262 [89,] -0.6736906 0.3263094 [90,] 0.4769876 -0.6736906 [91,] 0.1121523 0.4769876 [92,] 0.3952919 0.1121523 [93,] 0.3263094 0.3952919 [94,] 0.1938480 0.3263094 [95,] -0.6736906 0.1938480 [96,] 0.1121523 -0.6736906 [97,] 0.3263094 0.1121523 [98,] 0.2446137 0.3263094 [99,] -0.6736906 0.2446137 [100,] -0.7553863 -0.6736906 [101,] 0.3263094 -0.7553863 [102,] 0.3263094 0.3263094 [103,] 0.3263094 0.3263094 [104,] 0.3112695 0.3263094 [105,] 0.3263094 0.3112695 [106,] 0.3263094 0.3263094 [107,] 0.2295738 0.3263094 [108,] 0.3263094 0.2295738 [109,] 0.2446137 0.3263094 [110,] 0.3802520 0.2446137 [111,] 0.1938480 0.3802520 [112,] 0.4437309 0.1938480 [113,] 0.2295738 0.4437309 [114,] 0.2446137 0.2295738 [115,] 0.3263094 0.2446137 [116,] -0.7553863 0.3263094 [117,] 0.2446137 -0.7553863 [118,] 0.3263094 0.2446137 [119,] -0.6736906 0.3263094 [120,] 0.2446137 -0.6736906 [121,] 0.3263094 0.2446137 [122,] 0.2295738 0.3263094 [123,] -0.4055909 0.2295738 [124,] -0.6736906 -0.4055909 [125,] 0.1938480 -0.6736906 [126,] 0.4769876 0.1938480 [127,] -0.6736906 0.4769876 [128,] 0.3263094 -0.6736906 [129,] -0.6736906 0.3263094 [130,] 0.2446137 -0.6736906 [131,] -0.7553863 0.2446137 [132,] 0.3620352 -0.7553863 [133,] 0.3263094 0.3620352 [134,] 0.3263094 0.3263094 [135,] 0.3263094 0.3263094 [136,] -0.4872866 0.3263094 [137,] -0.6197480 -0.4872866 [138,] 0.1938480 -0.6197480 [139,] 0.3263094 0.1938480 [140,] -0.7118470 0.3263094 [141,] -0.6887305 -0.7118470 [142,] 0.2446137 -0.6887305 [143,] -0.5230124 0.2446137 [144,] 0.4769876 -0.5230124 [145,] -0.8061520 0.4769876 [146,] 0.3112695 -0.8061520 [147,] 0.1938480 0.3112695 [148,] 0.2446137 0.1938480 [149,] -0.5230124 0.2446137 [150,] -0.6736906 -0.5230124 [151,] 0.2064573 -0.6736906 [152,] 0.3571355 0.2064573 [153,] 0.3620352 0.3571355 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.4050643 -0.6454478 2 0.4050643 0.4050643 3 0.4050643 0.4050643 4 0.4050643 0.4050643 5 -0.5259533 0.4050643 6 0.4050643 -0.5259533 7 0.4362479 0.4050643 8 -0.5949357 0.4362479 9 0.3233685 -0.5949357 10 0.3545522 0.3233685 11 0.4050643 0.3545522 12 0.6731640 0.4050643 13 0.3545522 0.6731640 14 -0.3268360 0.3545522 15 -0.2956524 -0.3268360 16 0.4670739 -0.2956524 17 0.3545522 0.4670739 18 -0.5949357 0.3545522 19 -0.4512303 -0.5949357 20 0.4740467 -0.4512303 21 -0.4085318 0.4740467 22 -0.4442575 -0.4085318 23 -0.5259533 -0.4442575 24 -0.4463306 -0.5259533 25 0.6731640 -0.4463306 26 -0.6766315 0.6731640 27 0.5224858 -0.6766315 28 -0.5949357 0.5224858 29 0.5557425 -0.5949357 30 0.4050643 0.5557425 31 0.3233685 0.4050643 32 0.4740467 0.3233685 33 -0.5637521 0.4740467 34 0.4050643 -0.5637521 35 0.4050643 0.4050643 36 0.6226519 0.4050643 37 -0.4775142 0.6226519 38 -0.4442575 -0.4775142 39 0.5869261 -0.4442575 40 -0.4824140 0.5869261 41 -0.4775142 -0.4824140 42 -0.5259533 -0.4775142 43 0.3545522 -0.5259533 44 0.5557425 0.3545522 45 -0.4442575 0.5557425 46 0.4050643 -0.4442575 47 -0.5949357 0.4050643 48 -0.4442575 -0.5949357 49 0.4050643 -0.4442575 50 0.5536694 0.4050643 51 0.4670739 0.5536694 52 -0.5949357 0.4670739 53 0.3669078 -0.5949357 54 0.4050643 0.3669078 55 -0.4463306 0.4050643 56 -0.3268360 -0.4463306 57 -0.5949357 -0.3268360 58 -0.5949357 -0.5949357 59 -0.5329261 -0.5949357 60 -0.6454478 -0.5329261 61 0.6731640 -0.6454478 62 0.4050643 0.6731640 63 -0.6454478 0.4050643 64 0.4050643 -0.6454478 65 0.4050643 0.4050643 66 0.5487697 0.4050643 67 0.3233685 0.5487697 68 -0.5949357 0.3233685 69 0.5224858 -0.5949357 70 0.4050643 0.5224858 71 -0.5949357 0.4050643 72 -0.4775142 -0.5949357 73 0.4407900 -0.4775142 74 -0.5949357 0.4407900 75 -0.4130739 -0.5949357 76 -0.5949357 -0.4130739 77 -0.3268360 -0.5949357 78 -0.6019085 -0.3268360 79 0.5869261 -0.6019085 80 0.4050643 0.5869261 81 -0.5592100 0.4050643 82 0.4050643 -0.5592100 83 0.3669078 0.4050643 84 -0.4442575 0.3669078 85 0.3233685 -0.4442575 86 -0.7553863 0.3233685 87 -0.7704262 -0.7553863 88 0.3263094 -0.7704262 89 -0.6736906 0.3263094 90 0.4769876 -0.6736906 91 0.1121523 0.4769876 92 0.3952919 0.1121523 93 0.3263094 0.3952919 94 0.1938480 0.3263094 95 -0.6736906 0.1938480 96 0.1121523 -0.6736906 97 0.3263094 0.1121523 98 0.2446137 0.3263094 99 -0.6736906 0.2446137 100 -0.7553863 -0.6736906 101 0.3263094 -0.7553863 102 0.3263094 0.3263094 103 0.3263094 0.3263094 104 0.3112695 0.3263094 105 0.3263094 0.3112695 106 0.3263094 0.3263094 107 0.2295738 0.3263094 108 0.3263094 0.2295738 109 0.2446137 0.3263094 110 0.3802520 0.2446137 111 0.1938480 0.3802520 112 0.4437309 0.1938480 113 0.2295738 0.4437309 114 0.2446137 0.2295738 115 0.3263094 0.2446137 116 -0.7553863 0.3263094 117 0.2446137 -0.7553863 118 0.3263094 0.2446137 119 -0.6736906 0.3263094 120 0.2446137 -0.6736906 121 0.3263094 0.2446137 122 0.2295738 0.3263094 123 -0.4055909 0.2295738 124 -0.6736906 -0.4055909 125 0.1938480 -0.6736906 126 0.4769876 0.1938480 127 -0.6736906 0.4769876 128 0.3263094 -0.6736906 129 -0.6736906 0.3263094 130 0.2446137 -0.6736906 131 -0.7553863 0.2446137 132 0.3620352 -0.7553863 133 0.3263094 0.3620352 134 0.3263094 0.3263094 135 0.3263094 0.3263094 136 -0.4872866 0.3263094 137 -0.6197480 -0.4872866 138 0.1938480 -0.6197480 139 0.3263094 0.1938480 140 -0.7118470 0.3263094 141 -0.6887305 -0.7118470 142 0.2446137 -0.6887305 143 -0.5230124 0.2446137 144 0.4769876 -0.5230124 145 -0.8061520 0.4769876 146 0.3112695 -0.8061520 147 0.1938480 0.3112695 148 0.2446137 0.1938480 149 -0.5230124 0.2446137 150 -0.6736906 -0.5230124 151 0.2064573 -0.6736906 152 0.3571355 0.2064573 153 0.3620352 0.3571355 > 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/7ya2w1356009081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8dxo41356009081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/92p311356009081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10cpp01356009081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/116dgr1356009081.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/12l2j91356009081.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/13xwwo1356009081.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/14cy3i1356009081.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/15pmar1356009081.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/165zqb1356009081.tab") + } > > try(system("convert tmp/1vu4i1356009081.ps tmp/1vu4i1356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/2mr261356009081.ps tmp/2mr261356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/339jt1356009081.ps tmp/339jt1356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/4nros1356009081.ps tmp/4nros1356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/56pnn1356009081.ps tmp/56pnn1356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/6d61e1356009081.ps tmp/6d61e1356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/7ya2w1356009081.ps tmp/7ya2w1356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/8dxo41356009081.ps tmp/8dxo41356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/92p311356009081.ps tmp/92p311356009081.png",intern=TRUE)) character(0) > try(system("convert tmp/10cpp01356009081.ps tmp/10cpp01356009081.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.807 1.686 9.574