R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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(14 + ,2 + ,3 + ,3 + ,3 + ,6 + ,8 + ,6 + ,0 + ,7 + ,7 + ,7 + ,12 + ,6 + ,0 + ,6 + ,8 + ,8 + ,7 + ,6 + ,6 + ,6 + ,9 + ,8 + ,10 + ,8 + ,5 + ,5 + ,5 + ,9 + ,9 + ,1 + ,0 + ,7 + ,7 + ,8 + ,16 + ,9 + ,8 + ,8 + ,8 + ,8 + ,7 + ,4 + ,0 + ,2 + ,3 + ,7 + ,14 + ,7 + ,0 + ,4 + ,8 + ,7 + ,6 + ,4 + ,9 + ,9 + ,4 + ,4 + ,16 + ,6 + ,6 + ,6 + ,6 + ,6 + ,11 + ,5 + ,6 + ,6 + ,4 + ,7 + ,17 + ,7 + ,5 + ,5 + ,8 + ,5 + ,12 + ,5 + ,4 + ,4 + ,8 + ,8 + ,7 + ,6 + ,0 + ,2 + ,2 + ,5 + ,13 + ,5 + ,0 + ,4 + ,9 + ,4 + ,9 + ,2 + ,2 + ,2 + ,2 + ,9 + ,15 + ,9 + ,6 + ,6 + ,8 + ,8 + ,7 + ,4 + ,0 + ,4 + ,8 + ,4 + ,9 + ,4 + ,4 + ,4 + ,4 + ,6 + ,7 + ,5 + ,5 + ,5 + ,5 + ,6 + ,14 + ,7 + ,7 + ,7 + ,7 + ,7 + ,15 + ,5 + ,5 + ,5 + ,3 + ,3 + ,7 + ,9 + ,4 + ,4 + ,4 + ,4 + ,13 + ,6 + ,6 + ,6 + ,6 + ,6 + ,17 + ,6 + ,6 + ,6 + ,6 + ,6 + ,15 + ,3 + ,0 + ,7 + ,9 + ,7 + ,14 + ,3 + ,1 + ,2 + ,2 + ,5 + ,14 + ,5 + ,0 + ,6 + ,6 + ,8 + ,8 + ,5 + ,4 + ,4 + ,4 + ,6 + ,8 + ,4 + ,4 + ,4 + ,8 + ,4 + ,12 + ,7 + ,7 + ,7 + ,3 + ,9 + ,14 + ,6 + ,7 + ,7 + ,7 + ,7 + ,8 + ,7 + ,0 + ,4 + ,4 + ,4 + ,11 + ,4 + ,4 + ,4 + ,4 + ,6 + ,16 + ,5 + ,5 + ,5 + ,8 + ,8 + ,11 + ,6 + ,0 + ,6 + ,6 + ,6 + ,8 + ,5 + ,5 + ,5 + ,5 + ,5 + ,14 + ,0 + ,1 + ,6 + ,6 + ,6 + ,16 + ,6 + ,2 + ,2 + ,9 + ,6 + ,14 + ,5 + ,0 + ,6 + ,4 + ,4 + ,5 + ,3 + ,9 + ,9 + ,7 + ,7 + ,8 + ,3 + ,3 + ,3 + ,3 + ,9 + ,10 + ,3 + ,0 + ,4 + ,4 + ,8 + ,8 + ,7 + ,6 + ,6 + ,6 + ,6 + ,13 + ,7 + ,1 + ,5 + ,8 + ,6 + ,15 + ,1 + ,5 + ,5 + ,5 + ,5 + ,6 + ,5 + ,0 + ,4 + ,4 + ,7 + ,12 + ,5 + ,0 + ,2 + ,2 + ,5 + ,14 + ,6 + ,0 + ,6 + ,9 + ,8 + ,5 + ,2 + ,6 + ,6 + ,6 + ,6 + ,15 + ,6 + ,7 + ,7 + ,8 + ,8 + ,11 + ,5 + ,0 + ,5 + ,5 + ,5 + ,8 + ,2 + ,4 + ,4 + ,4 + ,4 + ,13 + ,7 + ,5 + ,5 + ,5 + ,5 + ,14 + ,5 + ,1 + ,5 + ,9 + ,6 + ,12 + ,3 + ,4 + ,4 + ,4 + ,4 + ,16 + ,6 + ,9 + ,9 + ,8 + ,6 + ,10 + ,2 + ,2 + ,2 + ,2 + ,9 + ,15 + ,8 + ,8 + ,8 + ,8 + ,7 + ,8 + ,5 + ,3 + ,3 + ,3 + ,3 + ,16 + ,2 + ,1 + ,6 + ,3 + ,6 + ,19 + ,6 + ,0 + ,6 + ,6 + ,6 + ,14 + ,2 + ,6 + ,6 + ,6 + ,6 + ,7 + ,1 + ,0 + ,5 + ,5 + ,5 + ,13 + ,5 + ,0 + ,5 + ,5 + ,5 + ,15 + ,6 + ,6 + ,6 + ,4 + ,5 + ,7 + ,2 + ,2 + ,2 + ,9 + ,9 + ,13 + ,6 + ,1 + ,6 + ,6 + ,8 + ,4 + ,2 + ,5 + ,5 + ,5 + ,5 + ,14 + ,6 + ,5 + ,5 + ,5 + ,6 + ,13 + ,5 + ,5 + ,5 + ,3 + ,7 + ,11 + ,0 + ,5 + ,5 + ,8 + ,5 + ,14 + ,2 + ,6 + ,6 + ,9 + ,6 + ,12 + ,4 + ,6 + ,6 + ,6 + ,6 + ,15 + ,1 + ,0 + ,9 + ,6 + ,6 + ,14 + ,5 + ,0 + ,5 + ,5 + ,6 + ,13 + ,5 + ,1 + ,5 + ,3 + ,9 + ,7 + ,2 + ,7 + ,7 + ,4 + ,7 + ,5 + ,2 + ,2 + ,2 + ,9 + ,9 + ,7 + ,7 + ,4 + ,4 + ,4 + ,4 + ,13 + ,5 + ,0 + ,6 + ,8 + ,8 + ,13 + ,2 + ,5 + ,5 + ,5 + ,5 + ,11 + ,5 + ,5 + ,5 + ,5 + ,8 + ,6 + ,3 + ,3 + ,3 + ,8 + ,9 + ,12 + ,6 + ,0 + ,6 + ,6 + ,6 + ,8 + ,1 + ,4 + ,4 + ,9 + ,4 + ,11 + ,5 + ,9 + ,9 + ,5 + ,7 + ,12 + ,7 + ,0 + ,8 + ,8 + ,8 + ,9 + ,2 + ,4 + ,4 + ,3 + ,9 + ,12 + ,6 + ,2 + ,2 + ,2 + ,9 + ,13 + ,8 + ,7 + ,7 + ,7 + ,7 + ,16 + ,7 + ,7 + ,7 + ,7 + ,8 + ,16 + ,6 + ,6 + ,6 + ,4 + ,4 + ,11 + ,7 + ,0 + ,5 + ,5 + ,6 + ,8 + ,4 + ,5 + ,5 + ,9 + ,7 + ,4 + ,5 + ,6 + ,6 + ,6 + ,6 + ,7 + ,2 + ,0 + ,3 + ,3 + ,7 + ,14 + ,5 + ,5 + ,5 + ,5 + ,5 + ,11 + ,2 + ,9 + ,9 + ,2 + ,9 + ,17 + ,5 + ,0 + ,7 + ,7 + ,7 + ,15 + ,7 + ,7 + ,7 + ,7 + ,7 + ,14 + ,5 + ,1 + ,6 + ,6 + ,6 + ,5 + ,8 + ,3 + ,3 + ,8 + ,6 + ,4 + ,2 + ,7 + ,7 + ,9 + ,9 + ,19 + ,8 + ,8 + ,8 + ,8 + ,9 + ,11 + ,3 + ,0 + ,3 + ,3 + ,8 + ,15 + ,2 + ,5 + ,5 + ,5 + ,8 + ,10 + ,3 + ,3 + ,3 + ,3 + ,3 + ,9 + ,5 + ,0 + ,4 + ,4 + ,6 + ,12 + ,2 + ,5 + ,5 + ,5 + ,5 + ,15 + ,2 + ,7 + ,7 + ,9 + ,7 + ,7 + ,6 + ,0 + ,6 + ,6 + ,6 + ,13 + ,2 + ,0 + ,7 + ,7 + ,7 + ,14 + ,7 + ,0 + ,9 + ,7 + ,7 + ,14 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,2 + ,0 + ,6 + ,3 + ,8 + ,8 + ,2 + ,6 + ,6 + ,9 + ,9 + ,15 + ,5 + ,6 + ,6 + ,6 + ,6 + ,15 + ,6 + ,2 + ,2 + ,2 + ,9 + ,9 + ,4 + ,5 + ,5 + ,5 + ,5 + ,16 + ,5 + ,0 + ,5 + ,5 + ,6 + ,9 + ,7 + ,4 + ,4 + ,9 + ,4 + ,15 + ,6 + ,0 + ,7 + ,7 + ,7 + ,15 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,5 + ,5 + ,8 + ,8 + ,8 + ,2 + ,8 + ,8 + ,8 + ,8 + ,15 + ,6 + ,6 + ,6 + ,6 + ,9 + ,10 + ,3 + ,5 + ,5 + ,3 + ,8 + ,9 + ,2 + ,0 + ,4 + ,4 + ,4 + ,14 + ,8 + ,8 + ,8 + ,9 + ,6 + ,12 + ,6 + ,0 + ,6 + ,6 + ,6 + ,8 + ,4 + ,9 + ,9 + ,4 + ,7 + ,11 + ,6 + ,5 + ,5 + ,5 + ,9 + ,13 + ,5 + ,0 + ,6 + ,6 + ,8 + ,9 + ,4 + ,0 + ,4 + ,4 + ,4 + ,15 + ,2 + ,0 + ,6 + ,6 + ,6 + ,13 + ,3 + ,3 + ,3 + ,3 + ,9 + ,15 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,5 + ,0 + ,5 + ,5 + ,5 + ,16 + ,4 + ,4 + ,4 + ,9 + ,8 + ,12 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,1 + ,0 + ,5 + ,9 + ,6 + ,10 + ,5 + ,4 + ,4 + ,3 + ,6 + ,10 + ,2 + ,7 + ,7 + ,7 + ,7 + ,4 + ,6 + ,0 + ,6 + ,6 + ,7 + ,8 + ,5 + ,5 + ,5 + ,5 + ,9 + ,17 + ,2 + ,6 + ,6 + ,6 + ,6 + ,16 + ,6 + ,6 + ,6 + ,9 + ,6 + ,12 + ,8 + ,8 + ,8 + ,8 + ,6 + ,12 + ,7 + ,2 + ,2 + ,4 + ,4 + ,15 + ,7 + ,7 + ,7 + ,7 + ,7 + ,9 + ,9 + ,0 + ,4 + ,4 + ,8 + ,13 + ,2 + ,0 + ,6 + ,8 + ,7 + ,14 + ,6 + ,5 + ,5 + ,5 + ,9 + ,11 + ,5 + ,0 + ,2 + ,9 + ,6) + ,dim=c(6 + ,156) + ,dimnames=list(c('Schoolprestaties' + ,'Goingout' + ,'Relation' + ,'Family' + ,'Friends' + ,'Job') + ,1:156)) > y <- array(NA,dim=c(6,156),dimnames=list(c('Schoolprestaties','Goingout','Relation','Family','Friends','Job'),1:156)) > 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 = 'Do not include Seasonal Dummies' > par1 = '1' > library(lattice) > library(lmtest) Loading required package: zoo > 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 Schoolprestaties Goingout Relation Family Friends Job t 1 14 2 3 3 3 6 1 2 8 6 0 7 7 7 2 3 12 6 0 6 8 8 3 4 7 6 6 6 9 8 4 5 10 8 5 5 5 9 5 6 9 1 0 7 7 8 6 7 16 9 8 8 8 8 7 8 7 4 0 2 3 7 8 9 14 7 0 4 8 7 9 10 6 4 9 9 4 4 10 11 16 6 6 6 6 6 11 12 11 5 6 6 4 7 12 13 17 7 5 5 8 5 13 14 12 5 4 4 8 8 14 15 7 6 0 2 2 5 15 16 13 5 0 4 9 4 16 17 9 2 2 2 2 9 17 18 15 9 6 6 8 8 18 19 7 4 0 4 8 4 19 20 9 4 4 4 4 6 20 21 7 5 5 5 5 6 21 22 14 7 7 7 7 7 22 23 15 5 5 5 3 3 23 24 7 9 4 4 4 4 24 25 13 6 6 6 6 6 25 26 17 6 6 6 6 6 26 27 15 3 0 7 9 7 27 28 14 3 1 2 2 5 28 29 14 5 0 6 6 8 29 30 8 5 4 4 4 6 30 31 8 4 4 4 8 4 31 32 12 7 7 7 3 9 32 33 14 6 7 7 7 7 33 34 8 7 0 4 4 4 34 35 11 4 4 4 4 6 35 36 16 5 5 5 8 8 36 37 11 6 0 6 6 6 37 38 8 5 5 5 5 5 38 39 14 0 1 6 6 6 39 40 16 6 2 2 9 6 40 41 14 5 0 6 4 4 41 42 5 3 9 9 7 7 42 43 8 3 3 3 3 9 43 44 10 3 0 4 4 8 44 45 8 7 6 6 6 6 45 46 13 7 1 5 8 6 46 47 15 1 5 5 5 5 47 48 6 5 0 4 4 7 48 49 12 5 0 2 2 5 49 50 14 6 0 6 9 8 50 51 5 2 6 6 6 6 51 52 15 6 7 7 8 8 52 53 11 5 0 5 5 5 53 54 8 2 4 4 4 4 54 55 13 7 5 5 5 5 55 56 14 5 1 5 9 6 56 57 12 3 4 4 4 4 57 58 16 6 9 9 8 6 58 59 10 2 2 2 2 9 59 60 15 8 8 8 8 7 60 61 8 5 3 3 3 3 61 62 16 2 1 6 3 6 62 63 19 6 0 6 6 6 63 64 14 2 6 6 6 6 64 65 7 1 0 5 5 5 65 66 13 5 0 5 5 5 66 67 15 6 6 6 4 5 67 68 7 2 2 2 9 9 68 69 13 6 1 6 6 8 69 70 4 2 5 5 5 5 70 71 14 6 5 5 5 6 71 72 13 5 5 5 3 7 72 73 11 0 5 5 8 5 73 74 14 2 6 6 9 6 74 75 12 4 6 6 6 6 75 76 15 1 0 9 6 6 76 77 14 5 0 5 5 6 77 78 13 5 1 5 3 9 78 79 7 2 7 7 4 7 79 80 5 2 2 2 9 9 80 81 7 7 4 4 4 4 81 82 13 5 0 6 8 8 82 83 13 2 5 5 5 5 83 84 11 5 5 5 5 8 84 85 6 3 3 3 8 9 85 86 12 6 0 6 6 6 86 87 8 1 4 4 9 4 87 88 11 5 9 9 5 7 88 89 12 7 0 8 8 8 89 90 9 2 4 4 3 9 90 91 12 6 2 2 2 9 91 92 13 8 7 7 7 7 92 93 16 7 7 7 7 8 93 94 16 6 6 6 4 4 94 95 11 7 0 5 5 6 95 96 8 4 5 5 9 7 96 97 4 5 6 6 6 6 97 98 7 2 0 3 3 7 98 99 14 5 5 5 5 5 99 100 11 2 9 9 2 9 100 101 17 5 0 7 7 7 101 102 15 7 7 7 7 7 102 103 14 5 1 6 6 6 103 104 5 8 3 3 8 6 104 105 4 2 7 7 9 9 105 106 19 8 8 8 8 9 106 107 11 3 0 3 3 8 107 108 15 2 5 5 5 8 108 109 10 3 3 3 3 3 109 110 9 5 0 4 4 6 110 111 12 2 5 5 5 5 111 112 15 2 7 7 9 7 112 113 7 6 0 6 6 6 113 114 13 2 0 7 7 7 114 115 14 7 0 9 7 7 115 116 14 6 6 6 6 6 116 117 14 2 0 6 3 8 117 118 8 2 6 6 9 9 118 119 15 5 6 6 6 6 119 120 15 6 2 2 2 9 120 121 9 4 5 5 5 5 121 122 16 5 0 5 5 6 122 123 9 7 4 4 9 4 123 124 15 6 0 7 7 7 124 125 15 6 6 6 6 6 125 126 6 5 5 5 8 8 126 127 8 2 8 8 8 8 127 128 15 6 6 6 6 9 128 129 10 3 5 5 3 8 129 130 9 2 0 4 4 4 130 131 14 8 8 8 9 6 131 132 12 6 0 6 6 6 132 133 8 4 9 9 4 7 133 134 11 6 5 5 5 9 134 135 13 5 0 6 6 8 135 136 9 4 0 4 4 4 136 137 15 2 0 6 6 6 137 138 13 3 3 3 3 9 138 139 15 6 6 6 6 6 139 140 14 5 0 5 5 5 140 141 16 4 4 4 9 8 141 142 12 6 6 6 6 6 142 143 14 1 0 5 9 6 143 144 10 5 4 4 3 6 144 145 10 2 7 7 7 7 145 146 4 6 0 6 6 7 146 147 8 5 5 5 5 9 147 148 17 2 6 6 6 6 148 149 16 6 6 6 9 6 149 150 12 8 8 8 8 6 150 151 12 7 2 2 4 4 151 152 15 7 7 7 7 7 152 153 9 9 0 4 4 8 153 154 13 2 0 6 8 7 154 155 14 6 5 5 5 9 155 156 11 5 0 2 9 6 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Goingout Relation Family Friends Job 7.094770 0.318908 -0.124223 0.524420 0.062418 -0.013376 t 0.004759 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -9.1304 -2.3301 0.5571 2.4368 6.2512 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.094770 1.524217 4.655 7.12e-06 *** Goingout 0.318908 0.131488 2.425 0.01649 * Relation -0.124223 0.103833 -1.196 0.23345 Family 0.524420 0.184181 2.847 0.00503 ** Friends 0.062418 0.138570 0.450 0.65305 Job -0.013376 0.174313 -0.077 0.93894 t 0.004759 0.006037 0.788 0.43173 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.356 on 149 degrees of freedom Multiple R-squared: 0.1129, Adjusted R-squared: 0.07722 F-statistic: 3.162 on 6 and 149 DF, p-value: 0.006004 > 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,] 0.9471933 0.10561335 0.05280667 [2,] 0.9569045 0.08619107 0.04309554 [3,] 0.9206979 0.15860422 0.07930211 [4,] 0.8824925 0.23501494 0.11750747 [5,] 0.8317874 0.33642527 0.16821264 [6,] 0.8336377 0.33272467 0.16636233 [7,] 0.7723372 0.45532567 0.22766283 [8,] 0.7024497 0.59510069 0.29755035 [9,] 0.6325403 0.73491941 0.36745970 [10,] 0.6829099 0.63418025 0.31709012 [11,] 0.6082753 0.78344945 0.39172472 [12,] 0.5917435 0.81651296 0.40825648 [13,] 0.5567150 0.88657000 0.44328500 [14,] 0.6467210 0.70655796 0.35327898 [15,] 0.7056134 0.58877315 0.29438658 [16,] 0.6551071 0.68978586 0.34489293 [17,] 0.7217081 0.55658370 0.27829185 [18,] 0.7086695 0.58266101 0.29133050 [19,] 0.7334036 0.53319281 0.26659640 [20,] 0.6886347 0.62273066 0.31136533 [21,] 0.7125176 0.57496478 0.28748239 [22,] 0.7562363 0.48752731 0.24376366 [23,] 0.7083192 0.58336153 0.29168077 [24,] 0.6560955 0.68780908 0.34390454 [25,] 0.6420518 0.71589631 0.35794816 [26,] 0.5854020 0.82919597 0.41459799 [27,] 0.5639563 0.87208735 0.43604367 [28,] 0.5112568 0.97748632 0.48874316 [29,] 0.5248227 0.95035469 0.47517734 [30,] 0.5033379 0.99332424 0.49666212 [31,] 0.5029330 0.99413407 0.49706704 [32,] 0.4950232 0.99004632 0.50497684 [33,] 0.7352439 0.52951226 0.26475613 [34,] 0.7198731 0.56025375 0.28012688 [35,] 0.6752801 0.64943977 0.32471989 [36,] 0.7040339 0.59193228 0.29596614 [37,] 0.6568998 0.68620031 0.34310016 [38,] 0.6931009 0.61379829 0.30689914 [39,] 0.7412660 0.51746796 0.25873398 [40,] 0.7145015 0.57099706 0.28549853 [41,] 0.6727292 0.65454169 0.32727085 [42,] 0.7674943 0.46501139 0.23250569 [43,] 0.7459303 0.50813938 0.25406969 [44,] 0.7056164 0.58876712 0.29438356 [45,] 0.6767979 0.64640429 0.32320214 [46,] 0.6367180 0.72656400 0.36328200 [47,] 0.5993524 0.80129520 0.40064760 [48,] 0.5621686 0.87566276 0.43783138 [49,] 0.5463336 0.90733273 0.45366637 [50,] 0.4993607 0.99872135 0.50063932 [51,] 0.4565071 0.91301418 0.54349291 [52,] 0.4350661 0.87013211 0.56493395 [53,] 0.5092440 0.98151210 0.49075605 [54,] 0.6172190 0.76556207 0.38278104 [55,] 0.6002774 0.79944524 0.39972262 [56,] 0.6197038 0.76059247 0.38029624 [57,] 0.5771881 0.84562389 0.42281194 [58,] 0.5650772 0.86984564 0.43492282 [59,] 0.5900209 0.81995822 0.40997911 [60,] 0.5456257 0.90874867 0.45437433 [61,] 0.6700345 0.65993094 0.32996547 [62,] 0.6451900 0.70962001 0.35481000 [63,] 0.6112897 0.77742068 0.38871034 [64,] 0.5694280 0.86114405 0.43057202 [65,] 0.5604697 0.87906067 0.43953033 [66,] 0.5169670 0.96606595 0.48303297 [67,] 0.4910588 0.98211761 0.50894120 [68,] 0.4644254 0.92885072 0.53557464 [69,] 0.4265667 0.85313333 0.57343334 [70,] 0.4494975 0.89899499 0.55050250 [71,] 0.4907604 0.98152087 0.50923956 [72,] 0.5303852 0.93922963 0.46961482 [73,] 0.4908055 0.98161095 0.50919453 [74,] 0.4721876 0.94437523 0.52781238 [75,] 0.4262270 0.85245404 0.57377298 [76,] 0.4373959 0.87479176 0.56260412 [77,] 0.3954111 0.79082217 0.60458892 [78,] 0.3619062 0.72381248 0.63809376 [79,] 0.3310527 0.66210544 0.66894728 [80,] 0.3051892 0.61037846 0.69481077 [81,] 0.2673109 0.53462183 0.73268909 [82,] 0.2395415 0.47908309 0.76045845 [83,] 0.2040820 0.40816406 0.79591797 [84,] 0.2036236 0.40724720 0.79637640 [85,] 0.2190893 0.43817852 0.78091074 [86,] 0.1908729 0.38174577 0.80912712 [87,] 0.1827099 0.36541981 0.81729010 [88,] 0.3339195 0.66783910 0.66608045 [89,] 0.3259331 0.65186617 0.67406692 [90,] 0.3061534 0.61230681 0.69384659 [91,] 0.2712629 0.54252582 0.72873709 [92,] 0.2911227 0.58224533 0.70887733 [93,] 0.2709263 0.54185269 0.72907366 [94,] 0.2452076 0.49041511 0.75479245 [95,] 0.3517612 0.70352235 0.64823883 [96,] 0.5913300 0.81733996 0.40866998 [97,] 0.6734630 0.65307394 0.32653697 [98,] 0.6272684 0.74546324 0.37273162 [99,] 0.6425550 0.71489003 0.35744502 [100,] 0.5948255 0.81034905 0.40517452 [101,] 0.5720035 0.85599300 0.42799650 [102,] 0.5231725 0.95365509 0.47682755 [103,] 0.5124170 0.97516607 0.48758303 [104,] 0.6062962 0.78740755 0.39370378 [105,] 0.5535825 0.89283497 0.44641748 [106,] 0.5020681 0.99586377 0.49793188 [107,] 0.4630008 0.92600155 0.53699923 [108,] 0.4387115 0.87742309 0.56128846 [109,] 0.4592310 0.91846197 0.54076902 [110,] 0.4481186 0.89623729 0.55188136 [111,] 0.4895175 0.97903492 0.51048254 [112,] 0.4541141 0.90822827 0.54588586 [113,] 0.5124042 0.97519154 0.48759577 [114,] 0.5275719 0.94485617 0.47242809 [115,] 0.5381268 0.92374634 0.46187317 [116,] 0.5405388 0.91892238 0.45946119 [117,] 0.7177433 0.56451341 0.28225671 [118,] 0.8286593 0.34268139 0.17134070 [119,] 0.8088730 0.38225408 0.19112704 [120,] 0.7620835 0.47583292 0.23791646 [121,] 0.7453191 0.50936173 0.25468086 [122,] 0.6830555 0.63388893 0.31694447 [123,] 0.6214287 0.75714262 0.37857131 [124,] 0.6676436 0.66471274 0.33235637 [125,] 0.6069331 0.78613373 0.39306687 [126,] 0.5633897 0.87322056 0.43661028 [127,] 0.5487238 0.90255239 0.45127620 [128,] 0.5404336 0.91913287 0.45956644 [129,] 0.4829439 0.96588789 0.51705606 [130,] 0.4321147 0.86422948 0.56788526 [131,] 0.5559360 0.88812809 0.44406405 [132,] 0.6158259 0.76834824 0.38417412 [133,] 0.5371301 0.92573983 0.46286992 [134,] 0.7853922 0.42921553 0.21460776 [135,] 0.6852532 0.62949359 0.31474679 [136,] 0.6628561 0.67428785 0.33714393 [137,] 0.5674292 0.86514168 0.43257084 > postscript(file="/var/wessaorg/rcomp/tmp/1vk421321988951.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/wessaorg/rcomp/tmp/2s7mq1321988951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3iquy1321988951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4ynp71321988951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5nk211321988951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 156 Frequency = 1 1 2 3 4 5 6 4.95506552 -5.03196986 -0.56135030 -4.88319056 -1.86252001 -2.44309132 7 8 9 10 11 12 2.40783149 -2.55093553 2.12665199 -6.21593768 4.24399706 -0.30364243 13 14 15 16 17 18 5.17755686 1.25093928 -3.18639915 1.62860758 0.38166378 2.15587661 19 20 21 22 23 24 -4.00434410 -1.23578837 -4.02207071 1.42349986 4.05311843 -4.87611584 25 26 27 28 29 30 1.17736966 5.17261056 2.68094021 4.83267862 1.75865658 -2.60228713 31 32 33 34 35 36 -2.56456217 -0.34766745 1.69005752 -3.78278245 0.69282513 4.74604147 37 38 39 40 41 42 -1.62507623 -3.11635153 3.40307492 5.51952003 1.77287859 -7.19644631 43 44 45 46 47 48 -1.52359605 -0.50123788 -4.23672010 0.53699156 5.11644762 -5.17146594 49 50 51 52 53 54 1.97069922 1.15255414 -5.67073589 2.55059289 -0.80885194 -1.78653450 55 56 57 58 59 60 1.16492825 2.06479823 1.88028044 2.69489081 1.18178158 1.46113083 61 62 63 64 65 66 -2.32733214 4.84305368 6.25118717 3.26739582 -3.59033009 1.12927976 67 68 69 70 71 72 3.08894706 -2.29797534 0.37360761 -6.31191944 2.42106654 1.87342705 73 74 75 76 77 78 1.12436520 3.03255124 0.57723019 2.21059650 2.09030579 1.37473357 79 80 81 82 83 84 -4.06597644 -4.35508454 -4.50956901 0.38158857 2.62621226 -0.29514174 85 86 87 88 89 90 -4.03556755 -0.85827213 -1.93676634 -1.92834471 -2.33838144 -0.82856360 91 92 93 94 95 96 1.75385934 -0.22854490 3.09897989 3.94707523 -1.63317354 -3.29639075 97 98 99 100 101 102 -7.84637777 -2.86585939 2.59334337 -0.81472479 3.81578701 2.04277187 103 104 105 106 107 108 1.50395372 -6.76065764 -7.47505009 5.26896449 0.78577708 4.54736315 109 110 111 112 113 114 0.08204659 -2.47990626 1.49295746 3.46488396 -5.98676783 0.71064200 115 116 117 118 119 120 -0.93749670 1.74429157 2.48383266 -3.13672077 3.04892203 4.61584544 121 122 123 124 125 126 -2.19244906 3.87614629 -3.02154051 1.38741995 2.70145967 -5.68227752 127 128 129 130 131 132 -3.93090616 2.72731076 -0.74664999 -1.64511722 0.04744075 -1.07719072 133 134 135 136 137 138 -4.76117858 -0.83862837 0.25419199 -2.31148735 3.17464483 3.02428946 139 140 141 142 143 144 2.63483227 1.77710636 4.90302350 -0.37944503 2.80216480 -1.08240666 145 146 147 148 149 150 -1.56733062 -9.13044199 -3.58158891 5.86763142 3.39998769 -1.98056429 151 152 153 154 155 156 0.95768922 1.80481687 -3.93342634 0.98228054 2.06143053 0.03792663 > postscript(file="/var/wessaorg/rcomp/tmp/69b4h1321988951.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 = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 4.95506552 NA 1 -5.03196986 4.95506552 2 -0.56135030 -5.03196986 3 -4.88319056 -0.56135030 4 -1.86252001 -4.88319056 5 -2.44309132 -1.86252001 6 2.40783149 -2.44309132 7 -2.55093553 2.40783149 8 2.12665199 -2.55093553 9 -6.21593768 2.12665199 10 4.24399706 -6.21593768 11 -0.30364243 4.24399706 12 5.17755686 -0.30364243 13 1.25093928 5.17755686 14 -3.18639915 1.25093928 15 1.62860758 -3.18639915 16 0.38166378 1.62860758 17 2.15587661 0.38166378 18 -4.00434410 2.15587661 19 -1.23578837 -4.00434410 20 -4.02207071 -1.23578837 21 1.42349986 -4.02207071 22 4.05311843 1.42349986 23 -4.87611584 4.05311843 24 1.17736966 -4.87611584 25 5.17261056 1.17736966 26 2.68094021 5.17261056 27 4.83267862 2.68094021 28 1.75865658 4.83267862 29 -2.60228713 1.75865658 30 -2.56456217 -2.60228713 31 -0.34766745 -2.56456217 32 1.69005752 -0.34766745 33 -3.78278245 1.69005752 34 0.69282513 -3.78278245 35 4.74604147 0.69282513 36 -1.62507623 4.74604147 37 -3.11635153 -1.62507623 38 3.40307492 -3.11635153 39 5.51952003 3.40307492 40 1.77287859 5.51952003 41 -7.19644631 1.77287859 42 -1.52359605 -7.19644631 43 -0.50123788 -1.52359605 44 -4.23672010 -0.50123788 45 0.53699156 -4.23672010 46 5.11644762 0.53699156 47 -5.17146594 5.11644762 48 1.97069922 -5.17146594 49 1.15255414 1.97069922 50 -5.67073589 1.15255414 51 2.55059289 -5.67073589 52 -0.80885194 2.55059289 53 -1.78653450 -0.80885194 54 1.16492825 -1.78653450 55 2.06479823 1.16492825 56 1.88028044 2.06479823 57 2.69489081 1.88028044 58 1.18178158 2.69489081 59 1.46113083 1.18178158 60 -2.32733214 1.46113083 61 4.84305368 -2.32733214 62 6.25118717 4.84305368 63 3.26739582 6.25118717 64 -3.59033009 3.26739582 65 1.12927976 -3.59033009 66 3.08894706 1.12927976 67 -2.29797534 3.08894706 68 0.37360761 -2.29797534 69 -6.31191944 0.37360761 70 2.42106654 -6.31191944 71 1.87342705 2.42106654 72 1.12436520 1.87342705 73 3.03255124 1.12436520 74 0.57723019 3.03255124 75 2.21059650 0.57723019 76 2.09030579 2.21059650 77 1.37473357 2.09030579 78 -4.06597644 1.37473357 79 -4.35508454 -4.06597644 80 -4.50956901 -4.35508454 81 0.38158857 -4.50956901 82 2.62621226 0.38158857 83 -0.29514174 2.62621226 84 -4.03556755 -0.29514174 85 -0.85827213 -4.03556755 86 -1.93676634 -0.85827213 87 -1.92834471 -1.93676634 88 -2.33838144 -1.92834471 89 -0.82856360 -2.33838144 90 1.75385934 -0.82856360 91 -0.22854490 1.75385934 92 3.09897989 -0.22854490 93 3.94707523 3.09897989 94 -1.63317354 3.94707523 95 -3.29639075 -1.63317354 96 -7.84637777 -3.29639075 97 -2.86585939 -7.84637777 98 2.59334337 -2.86585939 99 -0.81472479 2.59334337 100 3.81578701 -0.81472479 101 2.04277187 3.81578701 102 1.50395372 2.04277187 103 -6.76065764 1.50395372 104 -7.47505009 -6.76065764 105 5.26896449 -7.47505009 106 0.78577708 5.26896449 107 4.54736315 0.78577708 108 0.08204659 4.54736315 109 -2.47990626 0.08204659 110 1.49295746 -2.47990626 111 3.46488396 1.49295746 112 -5.98676783 3.46488396 113 0.71064200 -5.98676783 114 -0.93749670 0.71064200 115 1.74429157 -0.93749670 116 2.48383266 1.74429157 117 -3.13672077 2.48383266 118 3.04892203 -3.13672077 119 4.61584544 3.04892203 120 -2.19244906 4.61584544 121 3.87614629 -2.19244906 122 -3.02154051 3.87614629 123 1.38741995 -3.02154051 124 2.70145967 1.38741995 125 -5.68227752 2.70145967 126 -3.93090616 -5.68227752 127 2.72731076 -3.93090616 128 -0.74664999 2.72731076 129 -1.64511722 -0.74664999 130 0.04744075 -1.64511722 131 -1.07719072 0.04744075 132 -4.76117858 -1.07719072 133 -0.83862837 -4.76117858 134 0.25419199 -0.83862837 135 -2.31148735 0.25419199 136 3.17464483 -2.31148735 137 3.02428946 3.17464483 138 2.63483227 3.02428946 139 1.77710636 2.63483227 140 4.90302350 1.77710636 141 -0.37944503 4.90302350 142 2.80216480 -0.37944503 143 -1.08240666 2.80216480 144 -1.56733062 -1.08240666 145 -9.13044199 -1.56733062 146 -3.58158891 -9.13044199 147 5.86763142 -3.58158891 148 3.39998769 5.86763142 149 -1.98056429 3.39998769 150 0.95768922 -1.98056429 151 1.80481687 0.95768922 152 -3.93342634 1.80481687 153 0.98228054 -3.93342634 154 2.06143053 0.98228054 155 0.03792663 2.06143053 156 NA 0.03792663 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -5.03196986 4.95506552 [2,] -0.56135030 -5.03196986 [3,] -4.88319056 -0.56135030 [4,] -1.86252001 -4.88319056 [5,] -2.44309132 -1.86252001 [6,] 2.40783149 -2.44309132 [7,] -2.55093553 2.40783149 [8,] 2.12665199 -2.55093553 [9,] -6.21593768 2.12665199 [10,] 4.24399706 -6.21593768 [11,] -0.30364243 4.24399706 [12,] 5.17755686 -0.30364243 [13,] 1.25093928 5.17755686 [14,] -3.18639915 1.25093928 [15,] 1.62860758 -3.18639915 [16,] 0.38166378 1.62860758 [17,] 2.15587661 0.38166378 [18,] -4.00434410 2.15587661 [19,] -1.23578837 -4.00434410 [20,] -4.02207071 -1.23578837 [21,] 1.42349986 -4.02207071 [22,] 4.05311843 1.42349986 [23,] -4.87611584 4.05311843 [24,] 1.17736966 -4.87611584 [25,] 5.17261056 1.17736966 [26,] 2.68094021 5.17261056 [27,] 4.83267862 2.68094021 [28,] 1.75865658 4.83267862 [29,] -2.60228713 1.75865658 [30,] -2.56456217 -2.60228713 [31,] -0.34766745 -2.56456217 [32,] 1.69005752 -0.34766745 [33,] -3.78278245 1.69005752 [34,] 0.69282513 -3.78278245 [35,] 4.74604147 0.69282513 [36,] -1.62507623 4.74604147 [37,] -3.11635153 -1.62507623 [38,] 3.40307492 -3.11635153 [39,] 5.51952003 3.40307492 [40,] 1.77287859 5.51952003 [41,] -7.19644631 1.77287859 [42,] -1.52359605 -7.19644631 [43,] -0.50123788 -1.52359605 [44,] -4.23672010 -0.50123788 [45,] 0.53699156 -4.23672010 [46,] 5.11644762 0.53699156 [47,] -5.17146594 5.11644762 [48,] 1.97069922 -5.17146594 [49,] 1.15255414 1.97069922 [50,] -5.67073589 1.15255414 [51,] 2.55059289 -5.67073589 [52,] -0.80885194 2.55059289 [53,] -1.78653450 -0.80885194 [54,] 1.16492825 -1.78653450 [55,] 2.06479823 1.16492825 [56,] 1.88028044 2.06479823 [57,] 2.69489081 1.88028044 [58,] 1.18178158 2.69489081 [59,] 1.46113083 1.18178158 [60,] -2.32733214 1.46113083 [61,] 4.84305368 -2.32733214 [62,] 6.25118717 4.84305368 [63,] 3.26739582 6.25118717 [64,] -3.59033009 3.26739582 [65,] 1.12927976 -3.59033009 [66,] 3.08894706 1.12927976 [67,] -2.29797534 3.08894706 [68,] 0.37360761 -2.29797534 [69,] -6.31191944 0.37360761 [70,] 2.42106654 -6.31191944 [71,] 1.87342705 2.42106654 [72,] 1.12436520 1.87342705 [73,] 3.03255124 1.12436520 [74,] 0.57723019 3.03255124 [75,] 2.21059650 0.57723019 [76,] 2.09030579 2.21059650 [77,] 1.37473357 2.09030579 [78,] -4.06597644 1.37473357 [79,] -4.35508454 -4.06597644 [80,] -4.50956901 -4.35508454 [81,] 0.38158857 -4.50956901 [82,] 2.62621226 0.38158857 [83,] -0.29514174 2.62621226 [84,] -4.03556755 -0.29514174 [85,] -0.85827213 -4.03556755 [86,] -1.93676634 -0.85827213 [87,] -1.92834471 -1.93676634 [88,] -2.33838144 -1.92834471 [89,] -0.82856360 -2.33838144 [90,] 1.75385934 -0.82856360 [91,] -0.22854490 1.75385934 [92,] 3.09897989 -0.22854490 [93,] 3.94707523 3.09897989 [94,] -1.63317354 3.94707523 [95,] -3.29639075 -1.63317354 [96,] -7.84637777 -3.29639075 [97,] -2.86585939 -7.84637777 [98,] 2.59334337 -2.86585939 [99,] -0.81472479 2.59334337 [100,] 3.81578701 -0.81472479 [101,] 2.04277187 3.81578701 [102,] 1.50395372 2.04277187 [103,] -6.76065764 1.50395372 [104,] -7.47505009 -6.76065764 [105,] 5.26896449 -7.47505009 [106,] 0.78577708 5.26896449 [107,] 4.54736315 0.78577708 [108,] 0.08204659 4.54736315 [109,] -2.47990626 0.08204659 [110,] 1.49295746 -2.47990626 [111,] 3.46488396 1.49295746 [112,] -5.98676783 3.46488396 [113,] 0.71064200 -5.98676783 [114,] -0.93749670 0.71064200 [115,] 1.74429157 -0.93749670 [116,] 2.48383266 1.74429157 [117,] -3.13672077 2.48383266 [118,] 3.04892203 -3.13672077 [119,] 4.61584544 3.04892203 [120,] -2.19244906 4.61584544 [121,] 3.87614629 -2.19244906 [122,] -3.02154051 3.87614629 [123,] 1.38741995 -3.02154051 [124,] 2.70145967 1.38741995 [125,] -5.68227752 2.70145967 [126,] -3.93090616 -5.68227752 [127,] 2.72731076 -3.93090616 [128,] -0.74664999 2.72731076 [129,] -1.64511722 -0.74664999 [130,] 0.04744075 -1.64511722 [131,] -1.07719072 0.04744075 [132,] -4.76117858 -1.07719072 [133,] -0.83862837 -4.76117858 [134,] 0.25419199 -0.83862837 [135,] -2.31148735 0.25419199 [136,] 3.17464483 -2.31148735 [137,] 3.02428946 3.17464483 [138,] 2.63483227 3.02428946 [139,] 1.77710636 2.63483227 [140,] 4.90302350 1.77710636 [141,] -0.37944503 4.90302350 [142,] 2.80216480 -0.37944503 [143,] -1.08240666 2.80216480 [144,] -1.56733062 -1.08240666 [145,] -9.13044199 -1.56733062 [146,] -3.58158891 -9.13044199 [147,] 5.86763142 -3.58158891 [148,] 3.39998769 5.86763142 [149,] -1.98056429 3.39998769 [150,] 0.95768922 -1.98056429 [151,] 1.80481687 0.95768922 [152,] -3.93342634 1.80481687 [153,] 0.98228054 -3.93342634 [154,] 2.06143053 0.98228054 [155,] 0.03792663 2.06143053 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -5.03196986 4.95506552 2 -0.56135030 -5.03196986 3 -4.88319056 -0.56135030 4 -1.86252001 -4.88319056 5 -2.44309132 -1.86252001 6 2.40783149 -2.44309132 7 -2.55093553 2.40783149 8 2.12665199 -2.55093553 9 -6.21593768 2.12665199 10 4.24399706 -6.21593768 11 -0.30364243 4.24399706 12 5.17755686 -0.30364243 13 1.25093928 5.17755686 14 -3.18639915 1.25093928 15 1.62860758 -3.18639915 16 0.38166378 1.62860758 17 2.15587661 0.38166378 18 -4.00434410 2.15587661 19 -1.23578837 -4.00434410 20 -4.02207071 -1.23578837 21 1.42349986 -4.02207071 22 4.05311843 1.42349986 23 -4.87611584 4.05311843 24 1.17736966 -4.87611584 25 5.17261056 1.17736966 26 2.68094021 5.17261056 27 4.83267862 2.68094021 28 1.75865658 4.83267862 29 -2.60228713 1.75865658 30 -2.56456217 -2.60228713 31 -0.34766745 -2.56456217 32 1.69005752 -0.34766745 33 -3.78278245 1.69005752 34 0.69282513 -3.78278245 35 4.74604147 0.69282513 36 -1.62507623 4.74604147 37 -3.11635153 -1.62507623 38 3.40307492 -3.11635153 39 5.51952003 3.40307492 40 1.77287859 5.51952003 41 -7.19644631 1.77287859 42 -1.52359605 -7.19644631 43 -0.50123788 -1.52359605 44 -4.23672010 -0.50123788 45 0.53699156 -4.23672010 46 5.11644762 0.53699156 47 -5.17146594 5.11644762 48 1.97069922 -5.17146594 49 1.15255414 1.97069922 50 -5.67073589 1.15255414 51 2.55059289 -5.67073589 52 -0.80885194 2.55059289 53 -1.78653450 -0.80885194 54 1.16492825 -1.78653450 55 2.06479823 1.16492825 56 1.88028044 2.06479823 57 2.69489081 1.88028044 58 1.18178158 2.69489081 59 1.46113083 1.18178158 60 -2.32733214 1.46113083 61 4.84305368 -2.32733214 62 6.25118717 4.84305368 63 3.26739582 6.25118717 64 -3.59033009 3.26739582 65 1.12927976 -3.59033009 66 3.08894706 1.12927976 67 -2.29797534 3.08894706 68 0.37360761 -2.29797534 69 -6.31191944 0.37360761 70 2.42106654 -6.31191944 71 1.87342705 2.42106654 72 1.12436520 1.87342705 73 3.03255124 1.12436520 74 0.57723019 3.03255124 75 2.21059650 0.57723019 76 2.09030579 2.21059650 77 1.37473357 2.09030579 78 -4.06597644 1.37473357 79 -4.35508454 -4.06597644 80 -4.50956901 -4.35508454 81 0.38158857 -4.50956901 82 2.62621226 0.38158857 83 -0.29514174 2.62621226 84 -4.03556755 -0.29514174 85 -0.85827213 -4.03556755 86 -1.93676634 -0.85827213 87 -1.92834471 -1.93676634 88 -2.33838144 -1.92834471 89 -0.82856360 -2.33838144 90 1.75385934 -0.82856360 91 -0.22854490 1.75385934 92 3.09897989 -0.22854490 93 3.94707523 3.09897989 94 -1.63317354 3.94707523 95 -3.29639075 -1.63317354 96 -7.84637777 -3.29639075 97 -2.86585939 -7.84637777 98 2.59334337 -2.86585939 99 -0.81472479 2.59334337 100 3.81578701 -0.81472479 101 2.04277187 3.81578701 102 1.50395372 2.04277187 103 -6.76065764 1.50395372 104 -7.47505009 -6.76065764 105 5.26896449 -7.47505009 106 0.78577708 5.26896449 107 4.54736315 0.78577708 108 0.08204659 4.54736315 109 -2.47990626 0.08204659 110 1.49295746 -2.47990626 111 3.46488396 1.49295746 112 -5.98676783 3.46488396 113 0.71064200 -5.98676783 114 -0.93749670 0.71064200 115 1.74429157 -0.93749670 116 2.48383266 1.74429157 117 -3.13672077 2.48383266 118 3.04892203 -3.13672077 119 4.61584544 3.04892203 120 -2.19244906 4.61584544 121 3.87614629 -2.19244906 122 -3.02154051 3.87614629 123 1.38741995 -3.02154051 124 2.70145967 1.38741995 125 -5.68227752 2.70145967 126 -3.93090616 -5.68227752 127 2.72731076 -3.93090616 128 -0.74664999 2.72731076 129 -1.64511722 -0.74664999 130 0.04744075 -1.64511722 131 -1.07719072 0.04744075 132 -4.76117858 -1.07719072 133 -0.83862837 -4.76117858 134 0.25419199 -0.83862837 135 -2.31148735 0.25419199 136 3.17464483 -2.31148735 137 3.02428946 3.17464483 138 2.63483227 3.02428946 139 1.77710636 2.63483227 140 4.90302350 1.77710636 141 -0.37944503 4.90302350 142 2.80216480 -0.37944503 143 -1.08240666 2.80216480 144 -1.56733062 -1.08240666 145 -9.13044199 -1.56733062 146 -3.58158891 -9.13044199 147 5.86763142 -3.58158891 148 3.39998769 5.86763142 149 -1.98056429 3.39998769 150 0.95768922 -1.98056429 151 1.80481687 0.95768922 152 -3.93342634 1.80481687 153 0.98228054 -3.93342634 154 2.06143053 0.98228054 155 0.03792663 2.06143053 > 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/wessaorg/rcomp/tmp/7qf7x1321988951.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/wessaorg/rcomp/tmp/88ec31321988951.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/wessaorg/rcomp/tmp/9yg6r1321988951.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/wessaorg/rcomp/tmp/10nth61321988951.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11jc4i1321988951.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12zupn1321988951.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13frpo1321988951.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/143t5u1321988951.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/wessaorg/rcomp/tmp/158drh1321988951.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/wessaorg/rcomp/tmp/16r3lq1321988951.tab") + } > > try(system("convert tmp/1vk421321988951.ps tmp/1vk421321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/2s7mq1321988951.ps tmp/2s7mq1321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/3iquy1321988951.ps tmp/3iquy1321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/4ynp71321988951.ps tmp/4ynp71321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/5nk211321988951.ps tmp/5nk211321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/69b4h1321988951.ps tmp/69b4h1321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/7qf7x1321988951.ps tmp/7qf7x1321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/88ec31321988951.ps tmp/88ec31321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/9yg6r1321988951.ps tmp/9yg6r1321988951.png",intern=TRUE)) character(0) > try(system("convert tmp/10nth61321988951.ps tmp/10nth61321988951.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.876 0.550 5.465