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Type 'q()' to quit R. > x <- array(list(277 + ,7.7 + ,0 + ,260.6 + ,7.5 + ,0 + ,291.6 + ,8.3 + ,0 + ,275.4 + ,7.8 + ,0 + ,275.3 + ,7.9 + ,0 + ,231.7 + ,6.6 + ,0 + ,238.8 + ,7 + ,0 + ,274.2 + ,8.2 + ,0 + ,277.8 + ,8.2 + ,0 + ,299.1 + ,9.1 + ,0 + ,286.6 + ,9 + ,0 + ,232.3 + ,7.1 + ,0 + ,294.1 + ,8.9 + ,0 + ,267.5 + ,8.5 + ,0 + ,309.7 + ,9.8 + ,0 + ,280.7 + ,8.8 + ,0 + ,287.3 + ,9.2 + ,0 + ,235.7 + ,7.4 + ,0 + ,256.4 + ,8.3 + ,0 + ,289 + ,9.7 + ,0 + ,290.8 + ,9.7 + ,0 + ,321.9 + ,10.8 + ,0 + ,291.8 + ,9.8 + ,0 + ,241.4 + ,7.9 + ,0 + ,295.5 + ,9.8 + ,0 + ,258.2 + ,9 + ,0 + ,306.1 + ,10.5 + ,0 + ,281.5 + ,9.5 + ,0 + ,283.1 + ,9.7 + ,0 + ,237.4 + ,8.1 + ,0 + ,274.8 + ,10.1 + ,0 + ,299.3 + ,11.1 + ,0 + ,300.4 + ,11.2 + ,0 + ,340.9 + ,12.6 + ,0 + ,318.8 + ,12.2 + ,0 + ,265.7 + ,9.9 + ,0 + ,322.7 + ,11.8 + ,0 + ,281.6 + ,11.1 + ,0 + ,323.5 + ,12.6 + ,0 + ,312.6 + ,11.9 + ,0 + ,310.8 + ,11.9 + ,0 + ,262.8 + ,10 + ,0 + ,273.8 + ,10.8 + ,0 + ,320 + ,12.9 + ,0 + ,310.3 + ,12.5 + ,0 + ,342.2 + ,13.8 + ,0 + ,320.1 + ,13.1 + ,0 + ,265.6 + ,10.5 + ,0 + ,327 + ,12.9 + ,0 + ,300.7 + ,12.9 + ,0 + ,346.4 + ,14.4 + ,0 + ,317.3 + ,12.7 + ,0 + ,326.2 + ,13.3 + ,0 + ,270.7 + ,11 + ,0 + ,278.2 + ,11.9 + ,0 + ,324.6 + ,14.1 + ,0 + ,321.8 + ,14.4 + ,0 + ,343.5 + ,14.9 + ,0 + ,354 + ,15.7 + ,0 + ,278.2 + ,12 + ,0 + ,330.2 + ,14.3 + ,0 + ,307.3 + ,14.2 + ,0 + ,375.9 + ,17.4 + ,0 + ,335.3 + ,15.1 + ,0 + ,339.3 + ,15.3 + ,0 + ,280.3 + ,12.6 + ,0 + ,293.7 + ,14 + ,0 + ,341.2 + ,16.6 + ,0 + ,345.1 + ,16.7 + ,0 + ,368.7 + ,17.6 + ,0 + ,369.4 + ,18.3 + ,0 + ,288.4 + ,13.6 + ,0 + ,341 + ,15.8 + ,0 + ,319.1 + ,16.1 + ,0 + ,374.2 + ,18.6 + ,0 + ,344.5 + ,17.3 + ,0 + ,337.3 + ,17 + ,0 + ,281 + ,13.9 + ,0 + ,282.2 + ,15.2 + ,0 + ,321 + ,17.8 + ,0 + ,325.4 + ,18 + ,0 + ,366.3 + ,19.4 + ,0 + ,380.3 + ,21.8 + ,0 + ,300.7 + ,16.2 + ,0 + ,359.3 + ,19.2 + ,0 + ,327.6 + ,19.5 + ,0 + ,383.6 + ,22 + ,0 + ,352.4 + ,20 + ,0 + ,329.4 + ,19.2 + ,0 + ,294.5 + ,16.9 + ,0 + ,333.5 + ,20 + ,0 + ,334.3 + ,20.4 + ,0 + ,358 + ,21.8 + ,0 + ,396.1 + ,25 + ,0 + ,387 + ,25.8 + ,0 + ,307.2 + ,19.4 + ,0 + ,363.9 + ,22.6 + ,0 + ,344.7 + ,24.1 + ,0 + ,397.6 + ,26.9 + ,0 + ,376.8 + ,24.9 + ,0 + ,337.1 + ,23.3 + ,0 + ,299.3 + ,20.3 + ,0 + ,323.1 + ,22.3 + ,0 + ,329.1 + ,23.7 + ,0 + ,347 + ,24.3 + ,0 + ,462 + ,31.7 + ,1 + ,436.5 + ,32.2 + ,0 + ,360.4 + ,25.4 + ,0 + ,415.5 + ,28.6 + ,0 + ,382.1 + ,28.7 + ,0 + ,432.2 + ,30.9 + ,0 + ,424.3 + ,31.4 + ,0 + ,386.7 + ,29.1 + ,0 + ,354.5 + ,26.3 + ,0 + ,375.8 + ,28.9 + ,0 + ,368 + ,28.9 + ,0 + ,402.4 + ,31 + ,0 + ,426.5 + ,33.4 + ,0 + ,433.3 + ,35.9 + ,0 + ,338.5 + ,25.8 + ,0 + ,416.8 + ,31.2 + ,0 + ,381.1 + ,31.7 + ,0 + ,445.7 + ,36.2 + ,0 + ,412.4 + ,32 + ,0 + ,394 + ,32.1 + ,0 + ,348.2 + ,28.1 + ,0 + ,380.1 + ,31.1 + ,0 + ,373.7 + ,31.9 + ,0 + ,393.6 + ,32 + ,0 + ,434.2 + ,36.6 + ,0 + ,430.7 + ,38.1 + ,0 + ,344.5 + ,28.1 + ,0 + ,411.9 + ,32.9 + ,0 + ,370.5 + ,30.7 + ,0 + ,437.3 + ,35.4 + ,0 + ,411.3 + ,33.7 + ,0 + ,385.5 + ,31.6 + ,0 + ,341.3 + ,27.9 + ,0 + ,384.2 + ,32.2 + ,0 + ,373.2 + ,32.3 + ,0 + ,415.8 + ,35.3 + ,0 + ,448.6 + ,37.2 + ,0 + ,454.3 + ,39.6 + ,0 + ,350.3 + ,28.4 + ,0 + ,419.1 + ,33.9 + ,0 + ,398 + ,33.7 + ,0 + ,456.1 + ,38.3 + ,0 + ,430.1 + ,34.6 + ,0 + ,399.8 + ,32.7 + ,0 + ,362.7 + ,29.5 + ,0 + ,384.9 + ,32 + ,0 + ,385.3 + ,33.2 + ,0 + ,432.3 + ,36.7 + ,0 + ,468.9 + ,38.6 + ,0 + ,442.7 + ,38.1 + ,0 + ,370.2 + ,29.8 + ,0 + ,439.4 + ,35.6 + ,0 + ,393.9 + ,33.2 + ,0 + ,468.7 + ,38.9 + ,0 + ,438.8 + ,34.8 + ,0 + ,430.1 + ,37.2 + ,0 + ,366.3 + ,29.7 + ,0 + ,391 + ,32.2 + ,0 + ,380.9 + ,32.1 + ,0 + ,431.4 + ,36.3 + ,0 + ,465.4 + ,38.4 + ,0 + ,471.5 + ,40.8 + ,0 + ,387.5 + ,31.3 + ,0 + ,446.4 + ,36.2 + ,0 + ,421.5 + ,35.1 + ,0 + ,504.8 + ,44.1 + ,0 + ,492.1 + ,39.3 + ,0 + ,421.3 + ,34.1 + ,0 + ,396.7 + ,32.4 + ,0 + ,428 + ,36.3 + ,0 + ,421.9 + ,36.8 + ,0 + ,465.6 + ,40.5 + ,0 + ,525.8 + ,46 + ,0 + ,499.9 + ,43.9 + ,0 + ,435.3 + ,37.2 + ,0 + ,479.5 + ,40.7 + ,0 + ,473 + ,42 + ,0 + ,554.4 + ,49.2 + ,0 + ,489.6 + ,42.3 + ,0 + ,462.2 + ,40.8 + ,0 + ,420.3 + ,37.6 + ,0) + ,dim=c(3 + ,186) + ,dimnames=list(c('Y[t]' + ,'X[t]' + ,'D[t]') + ,1:186)) > y <- array(NA,dim=c(3,186),dimnames=list(c('Y[t]','X[t]','D[t]'),1:186)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Warning messages: 1: package 'lmtest' was built under R version 2.8.1 and help may not work correctly 2: package 'zoo' was built under R version 2.8.1 and help may not work correctly > 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 Y[t] X[t] D[t] M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 277.0 7.7 0 1 0 0 0 0 0 0 0 0 0 0 1 2 260.6 7.5 0 0 1 0 0 0 0 0 0 0 0 0 2 3 291.6 8.3 0 0 0 1 0 0 0 0 0 0 0 0 3 4 275.4 7.8 0 0 0 0 1 0 0 0 0 0 0 0 4 5 275.3 7.9 0 0 0 0 0 1 0 0 0 0 0 0 5 6 231.7 6.6 0 0 0 0 0 0 1 0 0 0 0 0 6 7 238.8 7.0 0 0 0 0 0 0 0 1 0 0 0 0 7 8 274.2 8.2 0 0 0 0 0 0 0 0 1 0 0 0 8 9 277.8 8.2 0 0 0 0 0 0 0 0 0 1 0 0 9 10 299.1 9.1 0 0 0 0 0 0 0 0 0 0 1 0 10 11 286.6 9.0 0 0 0 0 0 0 0 0 0 0 0 1 11 12 232.3 7.1 0 0 0 0 0 0 0 0 0 0 0 0 12 13 294.1 8.9 0 1 0 0 0 0 0 0 0 0 0 0 13 14 267.5 8.5 0 0 1 0 0 0 0 0 0 0 0 0 14 15 309.7 9.8 0 0 0 1 0 0 0 0 0 0 0 0 15 16 280.7 8.8 0 0 0 0 1 0 0 0 0 0 0 0 16 17 287.3 9.2 0 0 0 0 0 1 0 0 0 0 0 0 17 18 235.7 7.4 0 0 0 0 0 0 1 0 0 0 0 0 18 19 256.4 8.3 0 0 0 0 0 0 0 1 0 0 0 0 19 20 289.0 9.7 0 0 0 0 0 0 0 0 1 0 0 0 20 21 290.8 9.7 0 0 0 0 0 0 0 0 0 1 0 0 21 22 321.9 10.8 0 0 0 0 0 0 0 0 0 0 1 0 22 23 291.8 9.8 0 0 0 0 0 0 0 0 0 0 0 1 23 24 241.4 7.9 0 0 0 0 0 0 0 0 0 0 0 0 24 25 295.5 9.8 0 1 0 0 0 0 0 0 0 0 0 0 25 26 258.2 9.0 0 0 1 0 0 0 0 0 0 0 0 0 26 27 306.1 10.5 0 0 0 1 0 0 0 0 0 0 0 0 27 28 281.5 9.5 0 0 0 0 1 0 0 0 0 0 0 0 28 29 283.1 9.7 0 0 0 0 0 1 0 0 0 0 0 0 29 30 237.4 8.1 0 0 0 0 0 0 1 0 0 0 0 0 30 31 274.8 10.1 0 0 0 0 0 0 0 1 0 0 0 0 31 32 299.3 11.1 0 0 0 0 0 0 0 0 1 0 0 0 32 33 300.4 11.2 0 0 0 0 0 0 0 0 0 1 0 0 33 34 340.9 12.6 0 0 0 0 0 0 0 0 0 0 1 0 34 35 318.8 12.2 0 0 0 0 0 0 0 0 0 0 0 1 35 36 265.7 9.9 0 0 0 0 0 0 0 0 0 0 0 0 36 37 322.7 11.8 0 1 0 0 0 0 0 0 0 0 0 0 37 38 281.6 11.1 0 0 1 0 0 0 0 0 0 0 0 0 38 39 323.5 12.6 0 0 0 1 0 0 0 0 0 0 0 0 39 40 312.6 11.9 0 0 0 0 1 0 0 0 0 0 0 0 40 41 310.8 11.9 0 0 0 0 0 1 0 0 0 0 0 0 41 42 262.8 10.0 0 0 0 0 0 0 1 0 0 0 0 0 42 43 273.8 10.8 0 0 0 0 0 0 0 1 0 0 0 0 43 44 320.0 12.9 0 0 0 0 0 0 0 0 1 0 0 0 44 45 310.3 12.5 0 0 0 0 0 0 0 0 0 1 0 0 45 46 342.2 13.8 0 0 0 0 0 0 0 0 0 0 1 0 46 47 320.1 13.1 0 0 0 0 0 0 0 0 0 0 0 1 47 48 265.6 10.5 0 0 0 0 0 0 0 0 0 0 0 0 48 49 327.0 12.9 0 1 0 0 0 0 0 0 0 0 0 0 49 50 300.7 12.9 0 0 1 0 0 0 0 0 0 0 0 0 50 51 346.4 14.4 0 0 0 1 0 0 0 0 0 0 0 0 51 52 317.3 12.7 0 0 0 0 1 0 0 0 0 0 0 0 52 53 326.2 13.3 0 0 0 0 0 1 0 0 0 0 0 0 53 54 270.7 11.0 0 0 0 0 0 0 1 0 0 0 0 0 54 55 278.2 11.9 0 0 0 0 0 0 0 1 0 0 0 0 55 56 324.6 14.1 0 0 0 0 0 0 0 0 1 0 0 0 56 57 321.8 14.4 0 0 0 0 0 0 0 0 0 1 0 0 57 58 343.5 14.9 0 0 0 0 0 0 0 0 0 0 1 0 58 59 354.0 15.7 0 0 0 0 0 0 0 0 0 0 0 1 59 60 278.2 12.0 0 0 0 0 0 0 0 0 0 0 0 0 60 61 330.2 14.3 0 1 0 0 0 0 0 0 0 0 0 0 61 62 307.3 14.2 0 0 1 0 0 0 0 0 0 0 0 0 62 63 375.9 17.4 0 0 0 1 0 0 0 0 0 0 0 0 63 64 335.3 15.1 0 0 0 0 1 0 0 0 0 0 0 0 64 65 339.3 15.3 0 0 0 0 0 1 0 0 0 0 0 0 65 66 280.3 12.6 0 0 0 0 0 0 1 0 0 0 0 0 66 67 293.7 14.0 0 0 0 0 0 0 0 1 0 0 0 0 67 68 341.2 16.6 0 0 0 0 0 0 0 0 1 0 0 0 68 69 345.1 16.7 0 0 0 0 0 0 0 0 0 1 0 0 69 70 368.7 17.6 0 0 0 0 0 0 0 0 0 0 1 0 70 71 369.4 18.3 0 0 0 0 0 0 0 0 0 0 0 1 71 72 288.4 13.6 0 0 0 0 0 0 0 0 0 0 0 0 72 73 341.0 15.8 0 1 0 0 0 0 0 0 0 0 0 0 73 74 319.1 16.1 0 0 1 0 0 0 0 0 0 0 0 0 74 75 374.2 18.6 0 0 0 1 0 0 0 0 0 0 0 0 75 76 344.5 17.3 0 0 0 0 1 0 0 0 0 0 0 0 76 77 337.3 17.0 0 0 0 0 0 1 0 0 0 0 0 0 77 78 281.0 13.9 0 0 0 0 0 0 1 0 0 0 0 0 78 79 282.2 15.2 0 0 0 0 0 0 0 1 0 0 0 0 79 80 321.0 17.8 0 0 0 0 0 0 0 0 1 0 0 0 80 81 325.4 18.0 0 0 0 0 0 0 0 0 0 1 0 0 81 82 366.3 19.4 0 0 0 0 0 0 0 0 0 0 1 0 82 83 380.3 21.8 0 0 0 0 0 0 0 0 0 0 0 1 83 84 300.7 16.2 0 0 0 0 0 0 0 0 0 0 0 0 84 85 359.3 19.2 0 1 0 0 0 0 0 0 0 0 0 0 85 86 327.6 19.5 0 0 1 0 0 0 0 0 0 0 0 0 86 87 383.6 22.0 0 0 0 1 0 0 0 0 0 0 0 0 87 88 352.4 20.0 0 0 0 0 1 0 0 0 0 0 0 0 88 89 329.4 19.2 0 0 0 0 0 1 0 0 0 0 0 0 89 90 294.5 16.9 0 0 0 0 0 0 1 0 0 0 0 0 90 91 333.5 20.0 0 0 0 0 0 0 0 1 0 0 0 0 91 92 334.3 20.4 0 0 0 0 0 0 0 0 1 0 0 0 92 93 358.0 21.8 0 0 0 0 0 0 0 0 0 1 0 0 93 94 396.1 25.0 0 0 0 0 0 0 0 0 0 0 1 0 94 95 387.0 25.8 0 0 0 0 0 0 0 0 0 0 0 1 95 96 307.2 19.4 0 0 0 0 0 0 0 0 0 0 0 0 96 97 363.9 22.6 0 1 0 0 0 0 0 0 0 0 0 0 97 98 344.7 24.1 0 0 1 0 0 0 0 0 0 0 0 0 98 99 397.6 26.9 0 0 0 1 0 0 0 0 0 0 0 0 99 100 376.8 24.9 0 0 0 0 1 0 0 0 0 0 0 0 100 101 337.1 23.3 0 0 0 0 0 1 0 0 0 0 0 0 101 102 299.3 20.3 0 0 0 0 0 0 1 0 0 0 0 0 102 103 323.1 22.3 0 0 0 0 0 0 0 1 0 0 0 0 103 104 329.1 23.7 0 0 0 0 0 0 0 0 1 0 0 0 104 105 347.0 24.3 0 0 0 0 0 0 0 0 0 1 0 0 105 106 462.0 31.7 1 0 0 0 0 0 0 0 0 0 1 0 106 107 436.5 32.2 0 0 0 0 0 0 0 0 0 0 0 1 107 108 360.4 25.4 0 0 0 0 0 0 0 0 0 0 0 0 108 109 415.5 28.6 0 1 0 0 0 0 0 0 0 0 0 0 109 110 382.1 28.7 0 0 1 0 0 0 0 0 0 0 0 0 110 111 432.2 30.9 0 0 0 1 0 0 0 0 0 0 0 0 111 112 424.3 31.4 0 0 0 0 1 0 0 0 0 0 0 0 112 113 386.7 29.1 0 0 0 0 0 1 0 0 0 0 0 0 113 114 354.5 26.3 0 0 0 0 0 0 1 0 0 0 0 0 114 115 375.8 28.9 0 0 0 0 0 0 0 1 0 0 0 0 115 116 368.0 28.9 0 0 0 0 0 0 0 0 1 0 0 0 116 117 402.4 31.0 0 0 0 0 0 0 0 0 0 1 0 0 117 118 426.5 33.4 0 0 0 0 0 0 0 0 0 0 1 0 118 119 433.3 35.9 0 0 0 0 0 0 0 0 0 0 0 1 119 120 338.5 25.8 0 0 0 0 0 0 0 0 0 0 0 0 120 121 416.8 31.2 0 1 0 0 0 0 0 0 0 0 0 0 121 122 381.1 31.7 0 0 1 0 0 0 0 0 0 0 0 0 122 123 445.7 36.2 0 0 0 1 0 0 0 0 0 0 0 0 123 124 412.4 32.0 0 0 0 0 1 0 0 0 0 0 0 0 124 125 394.0 32.1 0 0 0 0 0 1 0 0 0 0 0 0 125 126 348.2 28.1 0 0 0 0 0 0 1 0 0 0 0 0 126 127 380.1 31.1 0 0 0 0 0 0 0 1 0 0 0 0 127 128 373.7 31.9 0 0 0 0 0 0 0 0 1 0 0 0 128 129 393.6 32.0 0 0 0 0 0 0 0 0 0 1 0 0 129 130 434.2 36.6 0 0 0 0 0 0 0 0 0 0 1 0 130 131 430.7 38.1 0 0 0 0 0 0 0 0 0 0 0 1 131 132 344.5 28.1 0 0 0 0 0 0 0 0 0 0 0 0 132 133 411.9 32.9 0 1 0 0 0 0 0 0 0 0 0 0 133 134 370.5 30.7 0 0 1 0 0 0 0 0 0 0 0 0 134 135 437.3 35.4 0 0 0 1 0 0 0 0 0 0 0 0 135 136 411.3 33.7 0 0 0 0 1 0 0 0 0 0 0 0 136 137 385.5 31.6 0 0 0 0 0 1 0 0 0 0 0 0 137 138 341.3 27.9 0 0 0 0 0 0 1 0 0 0 0 0 138 139 384.2 32.2 0 0 0 0 0 0 0 1 0 0 0 0 139 140 373.2 32.3 0 0 0 0 0 0 0 0 1 0 0 0 140 141 415.8 35.3 0 0 0 0 0 0 0 0 0 1 0 0 141 142 448.6 37.2 0 0 0 0 0 0 0 0 0 0 1 0 142 143 454.3 39.6 0 0 0 0 0 0 0 0 0 0 0 1 143 144 350.3 28.4 0 0 0 0 0 0 0 0 0 0 0 0 144 145 419.1 33.9 0 1 0 0 0 0 0 0 0 0 0 0 145 146 398.0 33.7 0 0 1 0 0 0 0 0 0 0 0 0 146 147 456.1 38.3 0 0 0 1 0 0 0 0 0 0 0 0 147 148 430.1 34.6 0 0 0 0 1 0 0 0 0 0 0 0 148 149 399.8 32.7 0 0 0 0 0 1 0 0 0 0 0 0 149 150 362.7 29.5 0 0 0 0 0 0 1 0 0 0 0 0 150 151 384.9 32.0 0 0 0 0 0 0 0 1 0 0 0 0 151 152 385.3 33.2 0 0 0 0 0 0 0 0 1 0 0 0 152 153 432.3 36.7 0 0 0 0 0 0 0 0 0 1 0 0 153 154 468.9 38.6 0 0 0 0 0 0 0 0 0 0 1 0 154 155 442.7 38.1 0 0 0 0 0 0 0 0 0 0 0 1 155 156 370.2 29.8 0 0 0 0 0 0 0 0 0 0 0 0 156 157 439.4 35.6 0 1 0 0 0 0 0 0 0 0 0 0 157 158 393.9 33.2 0 0 1 0 0 0 0 0 0 0 0 0 158 159 468.7 38.9 0 0 0 1 0 0 0 0 0 0 0 0 159 160 438.8 34.8 0 0 0 0 1 0 0 0 0 0 0 0 160 161 430.1 37.2 0 0 0 0 0 1 0 0 0 0 0 0 161 162 366.3 29.7 0 0 0 0 0 0 1 0 0 0 0 0 162 163 391.0 32.2 0 0 0 0 0 0 0 1 0 0 0 0 163 164 380.9 32.1 0 0 0 0 0 0 0 0 1 0 0 0 164 165 431.4 36.3 0 0 0 0 0 0 0 0 0 1 0 0 165 166 465.4 38.4 0 0 0 0 0 0 0 0 0 0 1 0 166 167 471.5 40.8 0 0 0 0 0 0 0 0 0 0 0 1 167 168 387.5 31.3 0 0 0 0 0 0 0 0 0 0 0 0 168 169 446.4 36.2 0 1 0 0 0 0 0 0 0 0 0 0 169 170 421.5 35.1 0 0 1 0 0 0 0 0 0 0 0 0 170 171 504.8 44.1 0 0 0 1 0 0 0 0 0 0 0 0 171 172 492.1 39.3 0 0 0 0 1 0 0 0 0 0 0 0 172 173 421.3 34.1 0 0 0 0 0 1 0 0 0 0 0 0 173 174 396.7 32.4 0 0 0 0 0 0 1 0 0 0 0 0 174 175 428.0 36.3 0 0 0 0 0 0 0 1 0 0 0 0 175 176 421.9 36.8 0 0 0 0 0 0 0 0 1 0 0 0 176 177 465.6 40.5 0 0 0 0 0 0 0 0 0 1 0 0 177 178 525.8 46.0 0 0 0 0 0 0 0 0 0 0 1 0 178 179 499.9 43.9 0 0 0 0 0 0 0 0 0 0 0 1 179 180 435.3 37.2 0 0 0 0 0 0 0 0 0 0 0 0 180 181 479.5 40.7 0 1 0 0 0 0 0 0 0 0 0 0 181 182 473.0 42.0 0 0 1 0 0 0 0 0 0 0 0 0 182 183 554.4 49.2 0 0 0 1 0 0 0 0 0 0 0 0 183 184 489.6 42.3 0 0 0 0 1 0 0 0 0 0 0 0 184 185 462.2 40.8 0 0 0 0 0 1 0 0 0 0 0 0 185 186 420.3 37.6 0 0 0 0 0 0 1 0 0 0 0 0 186 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `X[t]` `D[t]` M1 M2 M3 209.716 4.161 35.614 44.488 17.060 59.901 M4 M5 M6 M7 M8 M9 42.332 28.267 -5.121 7.384 18.120 31.427 M10 M11 t 58.171 48.862 0.251 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -24.1664 -8.6177 -0.7808 6.7515 34.1444 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 209.71551 3.46008 60.610 < 2e-16 *** `X[t]` 4.16065 0.40193 10.352 < 2e-16 *** `D[t]` 35.61396 12.39356 2.874 0.004573 ** M1 44.48811 4.48990 9.908 < 2e-16 *** M2 17.05951 4.43623 3.846 0.000170 *** M3 59.90125 4.95736 12.083 < 2e-16 *** M4 42.33201 4.52459 9.356 < 2e-16 *** M5 28.26722 4.41446 6.403 1.41e-09 *** M6 -5.12092 4.26983 -1.199 0.232060 M7 7.38450 4.38773 1.683 0.094202 . M8 18.12047 4.46203 4.061 7.43e-05 *** M9 31.42704 4.58063 6.861 1.20e-10 *** M10 58.17085 4.96215 11.723 < 2e-16 *** M11 48.86224 5.02460 9.725 < 2e-16 *** t 0.25101 0.08216 3.055 0.002611 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 11.88 on 171 degrees of freedom Multiple R-squared: 0.9711, Adjusted R-squared: 0.9687 F-statistic: 410.4 on 14 and 171 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 1.154177e-02 2.308354e-02 9.884582e-01 [2,] 2.939459e-03 5.878918e-03 9.970605e-01 [3,] 9.886685e-04 1.977337e-03 9.990113e-01 [4,] 3.759150e-04 7.518300e-04 9.996241e-01 [5,] 8.413804e-05 1.682761e-04 9.999159e-01 [6,] 1.648069e-05 3.296138e-05 9.999835e-01 [7,] 6.376027e-06 1.275205e-05 9.999936e-01 [8,] 1.859701e-06 3.719403e-06 9.999981e-01 [9,] 3.917427e-06 7.834854e-06 9.999961e-01 [10,] 1.490040e-06 2.980080e-06 9.999985e-01 [11,] 3.753704e-07 7.507408e-07 9.999996e-01 [12,] 8.025422e-08 1.605084e-07 9.999999e-01 [13,] 2.391961e-08 4.783921e-08 1.000000e+00 [14,] 6.840236e-09 1.368047e-08 1.000000e+00 [15,] 4.731557e-09 9.463114e-09 1.000000e+00 [16,] 8.304497e-09 1.660899e-08 1.000000e+00 [17,] 2.010327e-09 4.020655e-09 1.000000e+00 [18,] 6.071447e-10 1.214289e-09 1.000000e+00 [19,] 2.347643e-10 4.695286e-10 1.000000e+00 [20,] 8.801330e-11 1.760266e-10 1.000000e+00 [21,] 1.190562e-10 2.381125e-10 1.000000e+00 [22,] 6.445710e-10 1.289142e-09 1.000000e+00 [23,] 1.809555e-10 3.619110e-10 1.000000e+00 [24,] 5.148529e-11 1.029706e-10 1.000000e+00 [25,] 3.137878e-11 6.275755e-11 1.000000e+00 [26,] 8.173356e-12 1.634671e-11 1.000000e+00 [27,] 3.986650e-12 7.973301e-12 1.000000e+00 [28,] 2.305318e-12 4.610635e-12 1.000000e+00 [29,] 2.278090e-12 4.556181e-12 1.000000e+00 [30,] 8.886941e-13 1.777388e-12 1.000000e+00 [31,] 4.167401e-13 8.334801e-13 1.000000e+00 [32,] 1.148150e-13 2.296300e-13 1.000000e+00 [33,] 4.728812e-14 9.457623e-14 1.000000e+00 [34,] 1.390308e-14 2.780617e-14 1.000000e+00 [35,] 5.724172e-15 1.144834e-14 1.000000e+00 [36,] 3.702575e-15 7.405151e-15 1.000000e+00 [37,] 2.969622e-15 5.939243e-15 1.000000e+00 [38,] 1.283948e-15 2.567895e-15 1.000000e+00 [39,] 1.753294e-15 3.506589e-15 1.000000e+00 [40,] 2.653147e-14 5.306293e-14 1.000000e+00 [41,] 1.012458e-13 2.024916e-13 1.000000e+00 [42,] 3.982757e-14 7.965513e-14 1.000000e+00 [43,] 1.365153e-14 2.730305e-14 1.000000e+00 [44,] 1.521850e-14 3.043700e-14 1.000000e+00 [45,] 9.288631e-15 1.857726e-14 1.000000e+00 [46,] 8.089567e-15 1.617913e-14 1.000000e+00 [47,] 3.729122e-15 7.458245e-15 1.000000e+00 [48,] 3.495025e-15 6.990050e-15 1.000000e+00 [49,] 1.227187e-15 2.454375e-15 1.000000e+00 [50,] 1.748143e-15 3.496285e-15 1.000000e+00 [51,] 8.835505e-14 1.767101e-13 1.000000e+00 [52,] 1.566515e-13 3.133030e-13 1.000000e+00 [53,] 4.318597e-13 8.637193e-13 1.000000e+00 [54,] 4.831824e-13 9.663648e-13 1.000000e+00 [55,] 2.048521e-13 4.097043e-13 1.000000e+00 [56,] 1.681622e-13 3.363243e-13 1.000000e+00 [57,] 2.153496e-13 4.306992e-13 1.000000e+00 [58,] 3.503461e-13 7.006923e-13 1.000000e+00 [59,] 8.721875e-13 1.744375e-12 1.000000e+00 [60,] 1.847015e-11 3.694030e-11 1.000000e+00 [61,] 1.571674e-11 3.143348e-11 1.000000e+00 [62,] 4.633131e-09 9.266261e-09 1.000000e+00 [63,] 6.731919e-06 1.346384e-05 9.999933e-01 [64,] 1.358170e-04 2.716341e-04 9.998642e-01 [65,] 3.132714e-04 6.265429e-04 9.996867e-01 [66,] 6.041391e-04 1.208278e-03 9.993959e-01 [67,] 5.040468e-04 1.008094e-03 9.994960e-01 [68,] 5.807275e-04 1.161455e-03 9.994193e-01 [69,] 9.762848e-04 1.952570e-03 9.990237e-01 [70,] 1.194772e-03 2.389545e-03 9.988052e-01 [71,] 1.095446e-03 2.190893e-03 9.989046e-01 [72,] 5.478201e-03 1.095640e-02 9.945218e-01 [73,] 5.134455e-03 1.026891e-02 9.948655e-01 [74,] 5.957358e-03 1.191472e-02 9.940426e-01 [75,] 3.003071e-02 6.006141e-02 9.699693e-01 [76,] 4.226388e-02 8.452776e-02 9.577361e-01 [77,] 6.201264e-02 1.240253e-01 9.379874e-01 [78,] 7.918016e-02 1.583603e-01 9.208198e-01 [79,] 7.654054e-02 1.530811e-01 9.234595e-01 [80,] 8.476465e-02 1.695293e-01 9.152354e-01 [81,] 8.091629e-02 1.618326e-01 9.190837e-01 [82,] 7.792828e-02 1.558566e-01 9.220717e-01 [83,] 6.782851e-02 1.356570e-01 9.321715e-01 [84,] 1.458241e-01 2.916483e-01 8.541759e-01 [85,] 1.496016e-01 2.992031e-01 8.503984e-01 [86,] 1.369535e-01 2.739070e-01 8.630465e-01 [87,] 3.002088e-01 6.004175e-01 6.997912e-01 [88,] 3.341314e-01 6.682628e-01 6.658686e-01 [89,] 2.933677e-01 5.867354e-01 7.066323e-01 [90,] 6.034997e-01 7.930006e-01 3.965003e-01 [91,] 7.559773e-01 4.880454e-01 2.440227e-01 [92,] 8.929934e-01 2.140131e-01 1.070066e-01 [93,] 9.259594e-01 1.480813e-01 7.404064e-02 [94,] 9.766668e-01 4.666641e-02 2.333320e-02 [95,] 9.865757e-01 2.684860e-02 1.342430e-02 [96,] 9.922157e-01 1.556870e-02 7.784348e-03 [97,] 9.974654e-01 5.069163e-03 2.534582e-03 [98,] 9.986222e-01 2.755551e-03 1.377776e-03 [99,] 9.997548e-01 4.903824e-04 2.451912e-04 [100,] 9.999493e-01 1.014076e-04 5.070380e-05 [101,] 9.999626e-01 7.470968e-05 3.735484e-05 [102,] 9.999795e-01 4.092752e-05 2.046376e-05 [103,] 9.999792e-01 4.160616e-05 2.080308e-05 [104,] 9.999989e-01 2.108152e-06 1.054076e-06 [105,] 9.999985e-01 3.064867e-06 1.532434e-06 [106,] 9.999986e-01 2.819940e-06 1.409970e-06 [107,] 9.999984e-01 3.162645e-06 1.581322e-06 [108,] 9.999990e-01 2.004665e-06 1.002333e-06 [109,] 9.999990e-01 1.958071e-06 9.790356e-07 [110,] 9.999993e-01 1.321222e-06 6.606109e-07 [111,] 9.999997e-01 5.082892e-07 2.541446e-07 [112,] 9.999997e-01 5.468547e-07 2.734273e-07 [113,] 9.999997e-01 6.547445e-07 3.273722e-07 [114,] 9.999996e-01 7.554262e-07 3.777131e-07 [115,] 9.999994e-01 1.204038e-06 6.020189e-07 [116,] 9.999991e-01 1.813959e-06 9.069793e-07 [117,] 9.999985e-01 3.054879e-06 1.527439e-06 [118,] 9.999975e-01 5.089879e-06 2.544939e-06 [119,] 9.999984e-01 3.246865e-06 1.623432e-06 [120,] 9.999974e-01 5.279351e-06 2.639676e-06 [121,] 9.999951e-01 9.890755e-06 4.945378e-06 [122,] 9.999902e-01 1.958685e-05 9.793427e-06 [123,] 9.999876e-01 2.486228e-05 1.243114e-05 [124,] 9.999744e-01 5.124316e-05 2.562158e-05 [125,] 9.999494e-01 1.012316e-04 5.061582e-05 [126,] 9.999051e-01 1.897561e-04 9.487804e-05 [127,] 9.998830e-01 2.340208e-04 1.170104e-04 [128,] 9.997808e-01 4.384317e-04 2.192159e-04 [129,] 9.995785e-01 8.430923e-04 4.215461e-04 [130,] 9.992924e-01 1.415119e-03 7.075595e-04 [131,] 9.989514e-01 2.097162e-03 1.048581e-03 [132,] 9.982345e-01 3.531013e-03 1.765507e-03 [133,] 9.970534e-01 5.893245e-03 2.946622e-03 [134,] 9.948871e-01 1.022585e-02 5.112925e-03 [135,] 9.921305e-01 1.573906e-02 7.869528e-03 [136,] 9.873036e-01 2.539272e-02 1.269636e-02 [137,] 9.823792e-01 3.524158e-02 1.762079e-02 [138,] 9.758141e-01 4.837188e-02 2.418594e-02 [139,] 9.628008e-01 7.439830e-02 3.719915e-02 [140,] 9.437894e-01 1.124213e-01 5.621063e-02 [141,] 9.396766e-01 1.206469e-01 6.032345e-02 [142,] 9.082086e-01 1.835829e-01 9.179145e-02 [143,] 8.769107e-01 2.461786e-01 1.230893e-01 [144,] 8.478950e-01 3.042100e-01 1.521050e-01 [145,] 7.939675e-01 4.120650e-01 2.060325e-01 [146,] 7.478941e-01 5.042117e-01 2.521059e-01 [147,] 7.020256e-01 5.959487e-01 2.979744e-01 [148,] 6.206784e-01 7.586431e-01 3.793216e-01 [149,] 4.924807e-01 9.849613e-01 5.075193e-01 [150,] 5.129600e-01 9.740800e-01 4.870400e-01 [151,] 3.841050e-01 7.682100e-01 6.158950e-01 > postscript(file="/var/www/rcomp/tmp/1vi191261082312.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2zlbw1261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/rcomp/tmp/3vge31261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/45qcd1261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5i7561261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 186 Frequency = 1 1 2 3 4 5 -9.491620e+00 2.118094e+00 -1.330317e+01 -1.010462e+01 3.193094e+00 6 7 8 9 10 -1.860932e+00 -9.181620e+00 1.023862e+01 2.810381e-01 -9.158370e+00 11 12 13 14 15 -1.218470e+01 -9.968238e+00 -3.965239e-01 1.845319e+00 -4.456273e+00 16 17 18 19 20 -1.197739e+01 6.772125e+00 -4.201577e+00 -2.589310e-03 1.578552e+01 21 22 23 24 25 4.027939e+00 3.556402e+00 -1.332535e+01 -7.208883e+00 -5.753233e+00 26 27 28 29 30 -1.254713e+01 -1.398085e+01 -1.710197e+01 -2.520325e+00 -8.426157e+00 31 32 33 34 35 7.896117e+00 1.724849e+01 4.374840e+00 1.205511e+01 6.769683e-01 36 37 38 39 40 5.757694e+00 1.011334e+01 -8.966189e-01 -8.330341e+00 1.000346e+00 41 42 43 44 45 1.301412e+01 6.056485e+00 9.715373e-01 2.744719e+01 5.853871e+00 46 47 48 49 50 5.350204e+00 -4.779741e+00 1.491791e-01 6.824504e+00 7.702088e+00 51 52 53 54 55 4.068365e+00 -6.402986e-01 1.957709e+01 6.783710e+00 -2.217302e+00 56 57 58 59 60 2.404229e+01 6.436513e+00 -9.386353e-01 1.529045e+01 3.496080e+00 61 62 63 64 65 1.187470e+00 5.881118e+00 1.807429e+01 4.362019e+00 2.134366e+01 66 67 68 69 70 6.714547e+00 1.533210e+00 2.722854e+01 1.715489e+01 1.001549e+01 71 72 73 74 75 1.686063e+01 4.026916e+00 2.734371e+00 6.763760e+00 8.369389e+00 76 77 78 79 80 1.396465e+00 9.258436e+00 -1.006422e+00 -1.797169e+01 -9.763621e-01 81 82 83 84 85 -1.096607e+01 -2.885806e+00 1.018624e+01 2.497104e+00 3.876039e+00 86 87 88 89 90 -1.894572e+00 6.110569e-01 -4.949412e+00 -1.080712e+01 -3.000495e+00 91 92 93 94 95 1.034507e+01 -1.506175e+00 2.811334e+00 6.024343e-01 -2.768484e+00 96 97 98 99 100 -7.329098e+00 -8.682293e+00 -6.945682e+00 -8.788248e+00 -3.948717e+00 101 102 103 104 105 -2.317790e+01 -1.535883e+01 -1.263655e+01 -2.344844e+01 -2.160241e+01 106 107 108 109 110 3.883883e-15 1.709124e+01 1.789488e+01 1.494169e+01 8.303208e+00 111 112 113 114 115 6.157031e+00 1.349494e+01 -7.217925e-01 1.186515e+01 9.591039e+00 116 117 118 119 120 -9.195941e+00 2.909114e+00 -9.971267e+00 -4.515289e+00 -8.681502e+00 121 122 123 124 125 2.411876e+00 -8.190865e+00 -5.406534e+00 -3.913575e+00 -8.915865e+00 126 127 128 129 130 -4.936139e+00 1.725486e+00 -1.899001e+01 -1.306366e+01 -1.859747e+01 131 132 133 134 135 -1.928084e+01 -1.526312e+01 -1.257335e+01 -1.764234e+01 -1.349014e+01 136 137 138 139 140 -1.509880e+01 -1.834767e+01 -1.401613e+01 -1.763353e+00 -2.416640e+01 141 142 143 144 145 -7.605928e+00 -9.705984e+00 -4.933941e+00 -1.372344e+01 -1.254613e+01 146 147 148 149 150 -5.636414e+00 -9.768148e+00 -3.055514e+00 -1.163651e+01 -2.285298e+00 151 152 153 154 155 -3.243349e+00 -1.882311e+01 5.703829e-02 1.756982e+00 -1.330509e+01 156 157 158 159 160 -2.660474e+00 -2.331356e+00 -1.066821e+01 -2.676663e+00 1.800231e+00 161 162 163 164 165 -3.071551e+00 -2.529554e+00 -9.876043e-01 -2.165852e+01 -2.190828e+00 166 167 168 169 170 -3.923014e+00 1.249029e+00 5.386427e+00 -8.398707e-01 6.014427e+00 171 172 173 174 175 8.775838e+00 3.336519e+01 -1.985665e+00 1.362457e+01 1.594161e+01 176 177 178 179 180 -3.225695e+00 1.152232e+01 2.184393e+01 1.373889e+01 2.562647e+01 181 182 183 184 185 1.052508e+01 2.579382e+01 3.414440e+01 1.537111e+01 8.025862e+00 186 1.257707e+01 > postscript(file="/var/www/rcomp/tmp/6x8sm1261082312.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 = 186 Frequency = 1 lag(myerror, k = 1) myerror 0 -9.491620e+00 NA 1 2.118094e+00 -9.491620e+00 2 -1.330317e+01 2.118094e+00 3 -1.010462e+01 -1.330317e+01 4 3.193094e+00 -1.010462e+01 5 -1.860932e+00 3.193094e+00 6 -9.181620e+00 -1.860932e+00 7 1.023862e+01 -9.181620e+00 8 2.810381e-01 1.023862e+01 9 -9.158370e+00 2.810381e-01 10 -1.218470e+01 -9.158370e+00 11 -9.968238e+00 -1.218470e+01 12 -3.965239e-01 -9.968238e+00 13 1.845319e+00 -3.965239e-01 14 -4.456273e+00 1.845319e+00 15 -1.197739e+01 -4.456273e+00 16 6.772125e+00 -1.197739e+01 17 -4.201577e+00 6.772125e+00 18 -2.589310e-03 -4.201577e+00 19 1.578552e+01 -2.589310e-03 20 4.027939e+00 1.578552e+01 21 3.556402e+00 4.027939e+00 22 -1.332535e+01 3.556402e+00 23 -7.208883e+00 -1.332535e+01 24 -5.753233e+00 -7.208883e+00 25 -1.254713e+01 -5.753233e+00 26 -1.398085e+01 -1.254713e+01 27 -1.710197e+01 -1.398085e+01 28 -2.520325e+00 -1.710197e+01 29 -8.426157e+00 -2.520325e+00 30 7.896117e+00 -8.426157e+00 31 1.724849e+01 7.896117e+00 32 4.374840e+00 1.724849e+01 33 1.205511e+01 4.374840e+00 34 6.769683e-01 1.205511e+01 35 5.757694e+00 6.769683e-01 36 1.011334e+01 5.757694e+00 37 -8.966189e-01 1.011334e+01 38 -8.330341e+00 -8.966189e-01 39 1.000346e+00 -8.330341e+00 40 1.301412e+01 1.000346e+00 41 6.056485e+00 1.301412e+01 42 9.715373e-01 6.056485e+00 43 2.744719e+01 9.715373e-01 44 5.853871e+00 2.744719e+01 45 5.350204e+00 5.853871e+00 46 -4.779741e+00 5.350204e+00 47 1.491791e-01 -4.779741e+00 48 6.824504e+00 1.491791e-01 49 7.702088e+00 6.824504e+00 50 4.068365e+00 7.702088e+00 51 -6.402986e-01 4.068365e+00 52 1.957709e+01 -6.402986e-01 53 6.783710e+00 1.957709e+01 54 -2.217302e+00 6.783710e+00 55 2.404229e+01 -2.217302e+00 56 6.436513e+00 2.404229e+01 57 -9.386353e-01 6.436513e+00 58 1.529045e+01 -9.386353e-01 59 3.496080e+00 1.529045e+01 60 1.187470e+00 3.496080e+00 61 5.881118e+00 1.187470e+00 62 1.807429e+01 5.881118e+00 63 4.362019e+00 1.807429e+01 64 2.134366e+01 4.362019e+00 65 6.714547e+00 2.134366e+01 66 1.533210e+00 6.714547e+00 67 2.722854e+01 1.533210e+00 68 1.715489e+01 2.722854e+01 69 1.001549e+01 1.715489e+01 70 1.686063e+01 1.001549e+01 71 4.026916e+00 1.686063e+01 72 2.734371e+00 4.026916e+00 73 6.763760e+00 2.734371e+00 74 8.369389e+00 6.763760e+00 75 1.396465e+00 8.369389e+00 76 9.258436e+00 1.396465e+00 77 -1.006422e+00 9.258436e+00 78 -1.797169e+01 -1.006422e+00 79 -9.763621e-01 -1.797169e+01 80 -1.096607e+01 -9.763621e-01 81 -2.885806e+00 -1.096607e+01 82 1.018624e+01 -2.885806e+00 83 2.497104e+00 1.018624e+01 84 3.876039e+00 2.497104e+00 85 -1.894572e+00 3.876039e+00 86 6.110569e-01 -1.894572e+00 87 -4.949412e+00 6.110569e-01 88 -1.080712e+01 -4.949412e+00 89 -3.000495e+00 -1.080712e+01 90 1.034507e+01 -3.000495e+00 91 -1.506175e+00 1.034507e+01 92 2.811334e+00 -1.506175e+00 93 6.024343e-01 2.811334e+00 94 -2.768484e+00 6.024343e-01 95 -7.329098e+00 -2.768484e+00 96 -8.682293e+00 -7.329098e+00 97 -6.945682e+00 -8.682293e+00 98 -8.788248e+00 -6.945682e+00 99 -3.948717e+00 -8.788248e+00 100 -2.317790e+01 -3.948717e+00 101 -1.535883e+01 -2.317790e+01 102 -1.263655e+01 -1.535883e+01 103 -2.344844e+01 -1.263655e+01 104 -2.160241e+01 -2.344844e+01 105 3.883883e-15 -2.160241e+01 106 1.709124e+01 3.883883e-15 107 1.789488e+01 1.709124e+01 108 1.494169e+01 1.789488e+01 109 8.303208e+00 1.494169e+01 110 6.157031e+00 8.303208e+00 111 1.349494e+01 6.157031e+00 112 -7.217925e-01 1.349494e+01 113 1.186515e+01 -7.217925e-01 114 9.591039e+00 1.186515e+01 115 -9.195941e+00 9.591039e+00 116 2.909114e+00 -9.195941e+00 117 -9.971267e+00 2.909114e+00 118 -4.515289e+00 -9.971267e+00 119 -8.681502e+00 -4.515289e+00 120 2.411876e+00 -8.681502e+00 121 -8.190865e+00 2.411876e+00 122 -5.406534e+00 -8.190865e+00 123 -3.913575e+00 -5.406534e+00 124 -8.915865e+00 -3.913575e+00 125 -4.936139e+00 -8.915865e+00 126 1.725486e+00 -4.936139e+00 127 -1.899001e+01 1.725486e+00 128 -1.306366e+01 -1.899001e+01 129 -1.859747e+01 -1.306366e+01 130 -1.928084e+01 -1.859747e+01 131 -1.526312e+01 -1.928084e+01 132 -1.257335e+01 -1.526312e+01 133 -1.764234e+01 -1.257335e+01 134 -1.349014e+01 -1.764234e+01 135 -1.509880e+01 -1.349014e+01 136 -1.834767e+01 -1.509880e+01 137 -1.401613e+01 -1.834767e+01 138 -1.763353e+00 -1.401613e+01 139 -2.416640e+01 -1.763353e+00 140 -7.605928e+00 -2.416640e+01 141 -9.705984e+00 -7.605928e+00 142 -4.933941e+00 -9.705984e+00 143 -1.372344e+01 -4.933941e+00 144 -1.254613e+01 -1.372344e+01 145 -5.636414e+00 -1.254613e+01 146 -9.768148e+00 -5.636414e+00 147 -3.055514e+00 -9.768148e+00 148 -1.163651e+01 -3.055514e+00 149 -2.285298e+00 -1.163651e+01 150 -3.243349e+00 -2.285298e+00 151 -1.882311e+01 -3.243349e+00 152 5.703829e-02 -1.882311e+01 153 1.756982e+00 5.703829e-02 154 -1.330509e+01 1.756982e+00 155 -2.660474e+00 -1.330509e+01 156 -2.331356e+00 -2.660474e+00 157 -1.066821e+01 -2.331356e+00 158 -2.676663e+00 -1.066821e+01 159 1.800231e+00 -2.676663e+00 160 -3.071551e+00 1.800231e+00 161 -2.529554e+00 -3.071551e+00 162 -9.876043e-01 -2.529554e+00 163 -2.165852e+01 -9.876043e-01 164 -2.190828e+00 -2.165852e+01 165 -3.923014e+00 -2.190828e+00 166 1.249029e+00 -3.923014e+00 167 5.386427e+00 1.249029e+00 168 -8.398707e-01 5.386427e+00 169 6.014427e+00 -8.398707e-01 170 8.775838e+00 6.014427e+00 171 3.336519e+01 8.775838e+00 172 -1.985665e+00 3.336519e+01 173 1.362457e+01 -1.985665e+00 174 1.594161e+01 1.362457e+01 175 -3.225695e+00 1.594161e+01 176 1.152232e+01 -3.225695e+00 177 2.184393e+01 1.152232e+01 178 1.373889e+01 2.184393e+01 179 2.562647e+01 1.373889e+01 180 1.052508e+01 2.562647e+01 181 2.579382e+01 1.052508e+01 182 3.414440e+01 2.579382e+01 183 1.537111e+01 3.414440e+01 184 8.025862e+00 1.537111e+01 185 1.257707e+01 8.025862e+00 186 NA 1.257707e+01 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 2.118094e+00 -9.491620e+00 [2,] -1.330317e+01 2.118094e+00 [3,] -1.010462e+01 -1.330317e+01 [4,] 3.193094e+00 -1.010462e+01 [5,] -1.860932e+00 3.193094e+00 [6,] -9.181620e+00 -1.860932e+00 [7,] 1.023862e+01 -9.181620e+00 [8,] 2.810381e-01 1.023862e+01 [9,] -9.158370e+00 2.810381e-01 [10,] -1.218470e+01 -9.158370e+00 [11,] -9.968238e+00 -1.218470e+01 [12,] -3.965239e-01 -9.968238e+00 [13,] 1.845319e+00 -3.965239e-01 [14,] -4.456273e+00 1.845319e+00 [15,] -1.197739e+01 -4.456273e+00 [16,] 6.772125e+00 -1.197739e+01 [17,] -4.201577e+00 6.772125e+00 [18,] -2.589310e-03 -4.201577e+00 [19,] 1.578552e+01 -2.589310e-03 [20,] 4.027939e+00 1.578552e+01 [21,] 3.556402e+00 4.027939e+00 [22,] -1.332535e+01 3.556402e+00 [23,] -7.208883e+00 -1.332535e+01 [24,] -5.753233e+00 -7.208883e+00 [25,] -1.254713e+01 -5.753233e+00 [26,] -1.398085e+01 -1.254713e+01 [27,] -1.710197e+01 -1.398085e+01 [28,] -2.520325e+00 -1.710197e+01 [29,] -8.426157e+00 -2.520325e+00 [30,] 7.896117e+00 -8.426157e+00 [31,] 1.724849e+01 7.896117e+00 [32,] 4.374840e+00 1.724849e+01 [33,] 1.205511e+01 4.374840e+00 [34,] 6.769683e-01 1.205511e+01 [35,] 5.757694e+00 6.769683e-01 [36,] 1.011334e+01 5.757694e+00 [37,] -8.966189e-01 1.011334e+01 [38,] -8.330341e+00 -8.966189e-01 [39,] 1.000346e+00 -8.330341e+00 [40,] 1.301412e+01 1.000346e+00 [41,] 6.056485e+00 1.301412e+01 [42,] 9.715373e-01 6.056485e+00 [43,] 2.744719e+01 9.715373e-01 [44,] 5.853871e+00 2.744719e+01 [45,] 5.350204e+00 5.853871e+00 [46,] -4.779741e+00 5.350204e+00 [47,] 1.491791e-01 -4.779741e+00 [48,] 6.824504e+00 1.491791e-01 [49,] 7.702088e+00 6.824504e+00 [50,] 4.068365e+00 7.702088e+00 [51,] -6.402986e-01 4.068365e+00 [52,] 1.957709e+01 -6.402986e-01 [53,] 6.783710e+00 1.957709e+01 [54,] -2.217302e+00 6.783710e+00 [55,] 2.404229e+01 -2.217302e+00 [56,] 6.436513e+00 2.404229e+01 [57,] -9.386353e-01 6.436513e+00 [58,] 1.529045e+01 -9.386353e-01 [59,] 3.496080e+00 1.529045e+01 [60,] 1.187470e+00 3.496080e+00 [61,] 5.881118e+00 1.187470e+00 [62,] 1.807429e+01 5.881118e+00 [63,] 4.362019e+00 1.807429e+01 [64,] 2.134366e+01 4.362019e+00 [65,] 6.714547e+00 2.134366e+01 [66,] 1.533210e+00 6.714547e+00 [67,] 2.722854e+01 1.533210e+00 [68,] 1.715489e+01 2.722854e+01 [69,] 1.001549e+01 1.715489e+01 [70,] 1.686063e+01 1.001549e+01 [71,] 4.026916e+00 1.686063e+01 [72,] 2.734371e+00 4.026916e+00 [73,] 6.763760e+00 2.734371e+00 [74,] 8.369389e+00 6.763760e+00 [75,] 1.396465e+00 8.369389e+00 [76,] 9.258436e+00 1.396465e+00 [77,] -1.006422e+00 9.258436e+00 [78,] -1.797169e+01 -1.006422e+00 [79,] -9.763621e-01 -1.797169e+01 [80,] -1.096607e+01 -9.763621e-01 [81,] -2.885806e+00 -1.096607e+01 [82,] 1.018624e+01 -2.885806e+00 [83,] 2.497104e+00 1.018624e+01 [84,] 3.876039e+00 2.497104e+00 [85,] -1.894572e+00 3.876039e+00 [86,] 6.110569e-01 -1.894572e+00 [87,] -4.949412e+00 6.110569e-01 [88,] -1.080712e+01 -4.949412e+00 [89,] -3.000495e+00 -1.080712e+01 [90,] 1.034507e+01 -3.000495e+00 [91,] -1.506175e+00 1.034507e+01 [92,] 2.811334e+00 -1.506175e+00 [93,] 6.024343e-01 2.811334e+00 [94,] -2.768484e+00 6.024343e-01 [95,] -7.329098e+00 -2.768484e+00 [96,] -8.682293e+00 -7.329098e+00 [97,] -6.945682e+00 -8.682293e+00 [98,] -8.788248e+00 -6.945682e+00 [99,] -3.948717e+00 -8.788248e+00 [100,] -2.317790e+01 -3.948717e+00 [101,] -1.535883e+01 -2.317790e+01 [102,] -1.263655e+01 -1.535883e+01 [103,] -2.344844e+01 -1.263655e+01 [104,] -2.160241e+01 -2.344844e+01 [105,] 3.883883e-15 -2.160241e+01 [106,] 1.709124e+01 3.883883e-15 [107,] 1.789488e+01 1.709124e+01 [108,] 1.494169e+01 1.789488e+01 [109,] 8.303208e+00 1.494169e+01 [110,] 6.157031e+00 8.303208e+00 [111,] 1.349494e+01 6.157031e+00 [112,] -7.217925e-01 1.349494e+01 [113,] 1.186515e+01 -7.217925e-01 [114,] 9.591039e+00 1.186515e+01 [115,] -9.195941e+00 9.591039e+00 [116,] 2.909114e+00 -9.195941e+00 [117,] -9.971267e+00 2.909114e+00 [118,] -4.515289e+00 -9.971267e+00 [119,] -8.681502e+00 -4.515289e+00 [120,] 2.411876e+00 -8.681502e+00 [121,] -8.190865e+00 2.411876e+00 [122,] -5.406534e+00 -8.190865e+00 [123,] -3.913575e+00 -5.406534e+00 [124,] -8.915865e+00 -3.913575e+00 [125,] -4.936139e+00 -8.915865e+00 [126,] 1.725486e+00 -4.936139e+00 [127,] -1.899001e+01 1.725486e+00 [128,] -1.306366e+01 -1.899001e+01 [129,] -1.859747e+01 -1.306366e+01 [130,] -1.928084e+01 -1.859747e+01 [131,] -1.526312e+01 -1.928084e+01 [132,] -1.257335e+01 -1.526312e+01 [133,] -1.764234e+01 -1.257335e+01 [134,] -1.349014e+01 -1.764234e+01 [135,] -1.509880e+01 -1.349014e+01 [136,] -1.834767e+01 -1.509880e+01 [137,] -1.401613e+01 -1.834767e+01 [138,] -1.763353e+00 -1.401613e+01 [139,] -2.416640e+01 -1.763353e+00 [140,] -7.605928e+00 -2.416640e+01 [141,] -9.705984e+00 -7.605928e+00 [142,] -4.933941e+00 -9.705984e+00 [143,] -1.372344e+01 -4.933941e+00 [144,] -1.254613e+01 -1.372344e+01 [145,] -5.636414e+00 -1.254613e+01 [146,] -9.768148e+00 -5.636414e+00 [147,] -3.055514e+00 -9.768148e+00 [148,] -1.163651e+01 -3.055514e+00 [149,] -2.285298e+00 -1.163651e+01 [150,] -3.243349e+00 -2.285298e+00 [151,] -1.882311e+01 -3.243349e+00 [152,] 5.703829e-02 -1.882311e+01 [153,] 1.756982e+00 5.703829e-02 [154,] -1.330509e+01 1.756982e+00 [155,] -2.660474e+00 -1.330509e+01 [156,] -2.331356e+00 -2.660474e+00 [157,] -1.066821e+01 -2.331356e+00 [158,] -2.676663e+00 -1.066821e+01 [159,] 1.800231e+00 -2.676663e+00 [160,] -3.071551e+00 1.800231e+00 [161,] -2.529554e+00 -3.071551e+00 [162,] -9.876043e-01 -2.529554e+00 [163,] -2.165852e+01 -9.876043e-01 [164,] -2.190828e+00 -2.165852e+01 [165,] -3.923014e+00 -2.190828e+00 [166,] 1.249029e+00 -3.923014e+00 [167,] 5.386427e+00 1.249029e+00 [168,] -8.398707e-01 5.386427e+00 [169,] 6.014427e+00 -8.398707e-01 [170,] 8.775838e+00 6.014427e+00 [171,] 3.336519e+01 8.775838e+00 [172,] -1.985665e+00 3.336519e+01 [173,] 1.362457e+01 -1.985665e+00 [174,] 1.594161e+01 1.362457e+01 [175,] -3.225695e+00 1.594161e+01 [176,] 1.152232e+01 -3.225695e+00 [177,] 2.184393e+01 1.152232e+01 [178,] 1.373889e+01 2.184393e+01 [179,] 2.562647e+01 1.373889e+01 [180,] 1.052508e+01 2.562647e+01 [181,] 2.579382e+01 1.052508e+01 [182,] 3.414440e+01 2.579382e+01 [183,] 1.537111e+01 3.414440e+01 [184,] 8.025862e+00 1.537111e+01 [185,] 1.257707e+01 8.025862e+00 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 2.118094e+00 -9.491620e+00 2 -1.330317e+01 2.118094e+00 3 -1.010462e+01 -1.330317e+01 4 3.193094e+00 -1.010462e+01 5 -1.860932e+00 3.193094e+00 6 -9.181620e+00 -1.860932e+00 7 1.023862e+01 -9.181620e+00 8 2.810381e-01 1.023862e+01 9 -9.158370e+00 2.810381e-01 10 -1.218470e+01 -9.158370e+00 11 -9.968238e+00 -1.218470e+01 12 -3.965239e-01 -9.968238e+00 13 1.845319e+00 -3.965239e-01 14 -4.456273e+00 1.845319e+00 15 -1.197739e+01 -4.456273e+00 16 6.772125e+00 -1.197739e+01 17 -4.201577e+00 6.772125e+00 18 -2.589310e-03 -4.201577e+00 19 1.578552e+01 -2.589310e-03 20 4.027939e+00 1.578552e+01 21 3.556402e+00 4.027939e+00 22 -1.332535e+01 3.556402e+00 23 -7.208883e+00 -1.332535e+01 24 -5.753233e+00 -7.208883e+00 25 -1.254713e+01 -5.753233e+00 26 -1.398085e+01 -1.254713e+01 27 -1.710197e+01 -1.398085e+01 28 -2.520325e+00 -1.710197e+01 29 -8.426157e+00 -2.520325e+00 30 7.896117e+00 -8.426157e+00 31 1.724849e+01 7.896117e+00 32 4.374840e+00 1.724849e+01 33 1.205511e+01 4.374840e+00 34 6.769683e-01 1.205511e+01 35 5.757694e+00 6.769683e-01 36 1.011334e+01 5.757694e+00 37 -8.966189e-01 1.011334e+01 38 -8.330341e+00 -8.966189e-01 39 1.000346e+00 -8.330341e+00 40 1.301412e+01 1.000346e+00 41 6.056485e+00 1.301412e+01 42 9.715373e-01 6.056485e+00 43 2.744719e+01 9.715373e-01 44 5.853871e+00 2.744719e+01 45 5.350204e+00 5.853871e+00 46 -4.779741e+00 5.350204e+00 47 1.491791e-01 -4.779741e+00 48 6.824504e+00 1.491791e-01 49 7.702088e+00 6.824504e+00 50 4.068365e+00 7.702088e+00 51 -6.402986e-01 4.068365e+00 52 1.957709e+01 -6.402986e-01 53 6.783710e+00 1.957709e+01 54 -2.217302e+00 6.783710e+00 55 2.404229e+01 -2.217302e+00 56 6.436513e+00 2.404229e+01 57 -9.386353e-01 6.436513e+00 58 1.529045e+01 -9.386353e-01 59 3.496080e+00 1.529045e+01 60 1.187470e+00 3.496080e+00 61 5.881118e+00 1.187470e+00 62 1.807429e+01 5.881118e+00 63 4.362019e+00 1.807429e+01 64 2.134366e+01 4.362019e+00 65 6.714547e+00 2.134366e+01 66 1.533210e+00 6.714547e+00 67 2.722854e+01 1.533210e+00 68 1.715489e+01 2.722854e+01 69 1.001549e+01 1.715489e+01 70 1.686063e+01 1.001549e+01 71 4.026916e+00 1.686063e+01 72 2.734371e+00 4.026916e+00 73 6.763760e+00 2.734371e+00 74 8.369389e+00 6.763760e+00 75 1.396465e+00 8.369389e+00 76 9.258436e+00 1.396465e+00 77 -1.006422e+00 9.258436e+00 78 -1.797169e+01 -1.006422e+00 79 -9.763621e-01 -1.797169e+01 80 -1.096607e+01 -9.763621e-01 81 -2.885806e+00 -1.096607e+01 82 1.018624e+01 -2.885806e+00 83 2.497104e+00 1.018624e+01 84 3.876039e+00 2.497104e+00 85 -1.894572e+00 3.876039e+00 86 6.110569e-01 -1.894572e+00 87 -4.949412e+00 6.110569e-01 88 -1.080712e+01 -4.949412e+00 89 -3.000495e+00 -1.080712e+01 90 1.034507e+01 -3.000495e+00 91 -1.506175e+00 1.034507e+01 92 2.811334e+00 -1.506175e+00 93 6.024343e-01 2.811334e+00 94 -2.768484e+00 6.024343e-01 95 -7.329098e+00 -2.768484e+00 96 -8.682293e+00 -7.329098e+00 97 -6.945682e+00 -8.682293e+00 98 -8.788248e+00 -6.945682e+00 99 -3.948717e+00 -8.788248e+00 100 -2.317790e+01 -3.948717e+00 101 -1.535883e+01 -2.317790e+01 102 -1.263655e+01 -1.535883e+01 103 -2.344844e+01 -1.263655e+01 104 -2.160241e+01 -2.344844e+01 105 3.883883e-15 -2.160241e+01 106 1.709124e+01 3.883883e-15 107 1.789488e+01 1.709124e+01 108 1.494169e+01 1.789488e+01 109 8.303208e+00 1.494169e+01 110 6.157031e+00 8.303208e+00 111 1.349494e+01 6.157031e+00 112 -7.217925e-01 1.349494e+01 113 1.186515e+01 -7.217925e-01 114 9.591039e+00 1.186515e+01 115 -9.195941e+00 9.591039e+00 116 2.909114e+00 -9.195941e+00 117 -9.971267e+00 2.909114e+00 118 -4.515289e+00 -9.971267e+00 119 -8.681502e+00 -4.515289e+00 120 2.411876e+00 -8.681502e+00 121 -8.190865e+00 2.411876e+00 122 -5.406534e+00 -8.190865e+00 123 -3.913575e+00 -5.406534e+00 124 -8.915865e+00 -3.913575e+00 125 -4.936139e+00 -8.915865e+00 126 1.725486e+00 -4.936139e+00 127 -1.899001e+01 1.725486e+00 128 -1.306366e+01 -1.899001e+01 129 -1.859747e+01 -1.306366e+01 130 -1.928084e+01 -1.859747e+01 131 -1.526312e+01 -1.928084e+01 132 -1.257335e+01 -1.526312e+01 133 -1.764234e+01 -1.257335e+01 134 -1.349014e+01 -1.764234e+01 135 -1.509880e+01 -1.349014e+01 136 -1.834767e+01 -1.509880e+01 137 -1.401613e+01 -1.834767e+01 138 -1.763353e+00 -1.401613e+01 139 -2.416640e+01 -1.763353e+00 140 -7.605928e+00 -2.416640e+01 141 -9.705984e+00 -7.605928e+00 142 -4.933941e+00 -9.705984e+00 143 -1.372344e+01 -4.933941e+00 144 -1.254613e+01 -1.372344e+01 145 -5.636414e+00 -1.254613e+01 146 -9.768148e+00 -5.636414e+00 147 -3.055514e+00 -9.768148e+00 148 -1.163651e+01 -3.055514e+00 149 -2.285298e+00 -1.163651e+01 150 -3.243349e+00 -2.285298e+00 151 -1.882311e+01 -3.243349e+00 152 5.703829e-02 -1.882311e+01 153 1.756982e+00 5.703829e-02 154 -1.330509e+01 1.756982e+00 155 -2.660474e+00 -1.330509e+01 156 -2.331356e+00 -2.660474e+00 157 -1.066821e+01 -2.331356e+00 158 -2.676663e+00 -1.066821e+01 159 1.800231e+00 -2.676663e+00 160 -3.071551e+00 1.800231e+00 161 -2.529554e+00 -3.071551e+00 162 -9.876043e-01 -2.529554e+00 163 -2.165852e+01 -9.876043e-01 164 -2.190828e+00 -2.165852e+01 165 -3.923014e+00 -2.190828e+00 166 1.249029e+00 -3.923014e+00 167 5.386427e+00 1.249029e+00 168 -8.398707e-01 5.386427e+00 169 6.014427e+00 -8.398707e-01 170 8.775838e+00 6.014427e+00 171 3.336519e+01 8.775838e+00 172 -1.985665e+00 3.336519e+01 173 1.362457e+01 -1.985665e+00 174 1.594161e+01 1.362457e+01 175 -3.225695e+00 1.594161e+01 176 1.152232e+01 -3.225695e+00 177 2.184393e+01 1.152232e+01 178 1.373889e+01 2.184393e+01 179 2.562647e+01 1.373889e+01 180 1.052508e+01 2.562647e+01 181 2.579382e+01 1.052508e+01 182 3.414440e+01 2.579382e+01 183 1.537111e+01 3.414440e+01 184 8.025862e+00 1.537111e+01 185 1.257707e+01 8.025862e+00 > 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/rcomp/tmp/74n5y1261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8e3zy1261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/95pzq1261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') Warning messages: 1: Not plotting observations with leverage one: 106 2: Not plotting observations with leverage one: 106 > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/10wzh01261082312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/rcomp/tmp/11evan1261082312.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/rcomp/tmp/12cwnh1261082312.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/rcomp/tmp/13h16a1261082312.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/rcomp/tmp/1455rv1261082313.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/154j531261082313.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/161ni41261082313.tab") + } > > try(system("convert tmp/1vi191261082312.ps tmp/1vi191261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/2zlbw1261082312.ps tmp/2zlbw1261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/3vge31261082312.ps tmp/3vge31261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/45qcd1261082312.ps tmp/45qcd1261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/5i7561261082312.ps tmp/5i7561261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/6x8sm1261082312.ps tmp/6x8sm1261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/74n5y1261082312.ps tmp/74n5y1261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/8e3zy1261082312.ps tmp/8e3zy1261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/95pzq1261082312.ps tmp/95pzq1261082312.png",intern=TRUE)) character(0) > try(system("convert tmp/10wzh01261082312.ps tmp/10wzh01261082312.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.160 3.170 8.196