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 + ,3 + ,2 + ,3 + ,3 + ,7 + ,8 + ,5 + ,6 + ,0 + ,7 + ,2 + ,12 + ,6 + ,6 + ,0 + ,6 + ,3 + ,7 + ,6 + ,6 + ,6 + ,6 + ,8 + ,10 + ,7 + ,8 + ,5 + ,5 + ,7 + ,9 + ,3 + ,1 + ,0 + ,7 + ,7 + ,16 + ,8 + ,9 + ,8 + ,8 + ,9 + ,7 + ,4 + ,4 + ,0 + ,2 + ,2 + ,14 + ,7 + ,7 + ,0 + ,4 + ,4 + ,6 + ,4 + ,4 + ,9 + ,9 + ,4 + ,16 + ,6 + ,6 + ,6 + ,6 + ,6 + ,11 + ,6 + ,5 + ,6 + ,6 + ,4 + ,17 + ,7 + ,7 + ,5 + ,5 + ,9 + ,12 + ,4 + ,5 + ,4 + ,4 + ,8 + ,7 + ,6 + ,6 + ,0 + ,2 + ,7 + ,13 + ,5 + ,5 + ,0 + ,4 + ,4 + ,9 + ,0 + ,2 + ,2 + ,2 + ,2 + ,15 + ,9 + ,9 + ,6 + ,6 + ,8 + ,7 + ,4 + ,4 + ,0 + ,4 + ,4 + ,9 + ,4 + ,4 + ,4 + ,4 + ,4 + ,7 + ,2 + ,5 + ,5 + ,5 + ,2 + ,14 + ,7 + ,7 + ,7 + ,7 + ,9 + ,15 + ,5 + ,5 + ,5 + ,5 + ,3 + ,7 + ,9 + ,9 + ,4 + ,4 + ,4 + ,13 + ,6 + ,6 + ,6 + ,6 + ,6 + ,17 + ,6 + ,6 + ,6 + ,6 + ,6 + ,15 + ,7 + ,3 + ,0 + ,7 + ,7 + ,14 + ,3 + ,3 + ,1 + ,2 + ,2 + ,14 + ,6 + ,5 + ,0 + ,6 + ,6 + ,8 + ,6 + ,5 + ,4 + ,4 + ,4 + ,8 + ,4 + ,4 + ,4 + ,4 + ,2 + ,12 + ,7 + ,7 + ,7 + ,7 + ,9 + ,14 + ,7 + ,6 + ,7 + ,7 + ,7 + ,8 + ,7 + ,7 + ,0 + ,4 + ,4 + ,11 + ,4 + ,4 + ,4 + ,4 + ,4 + ,16 + ,5 + ,5 + ,5 + ,5 + ,7 + ,11 + ,6 + ,6 + ,0 + ,6 + ,6 + ,8 + ,5 + ,5 + ,5 + ,5 + ,5 + ,14 + ,6 + ,0 + ,1 + ,6 + ,6 + ,16 + ,6 + ,6 + ,2 + ,2 + ,2 + ,14 + ,6 + ,5 + ,0 + ,6 + ,2 + ,5 + ,3 + ,3 + ,9 + ,9 + ,7 + ,8 + ,3 + ,3 + ,3 + ,3 + ,3 + ,10 + ,3 + ,3 + ,0 + ,4 + ,4 + ,8 + ,6 + ,7 + ,6 + ,6 + ,6 + ,13 + ,7 + ,7 + ,1 + ,5 + ,5 + ,15 + ,5 + ,1 + ,5 + ,5 + ,7 + ,6 + ,5 + ,5 + ,0 + ,4 + ,4 + ,12 + ,5 + ,5 + ,0 + ,2 + ,2 + ,14 + ,6 + ,6 + ,0 + ,6 + ,6 + ,5 + ,6 + ,2 + ,6 + ,6 + ,9 + ,15 + ,6 + ,6 + ,7 + ,7 + ,8 + ,11 + ,5 + ,5 + ,0 + ,5 + ,5 + ,8 + ,4 + ,2 + ,4 + ,4 + ,4 + ,13 + ,7 + ,7 + ,5 + ,5 + ,2 + ,14 + ,5 + ,5 + ,1 + ,5 + ,9 + ,12 + ,3 + ,3 + ,4 + ,4 + ,4 + ,16 + ,6 + ,6 + ,9 + ,9 + ,6 + ,10 + ,2 + ,2 + ,2 + ,2 + ,2 + ,15 + ,8 + ,8 + ,8 + ,8 + ,8 + ,8 + ,3 + ,5 + ,3 + ,3 + ,3 + ,16 + ,0 + ,2 + ,1 + ,6 + ,3 + ,19 + ,6 + ,6 + ,0 + ,6 + ,7 + ,14 + ,8 + ,2 + ,6 + ,6 + ,2 + ,7 + ,4 + ,1 + ,0 + ,5 + ,9 + ,13 + ,5 + ,5 + ,0 + ,5 + ,5 + ,15 + ,6 + ,6 + ,6 + ,6 + ,4 + ,7 + ,5 + ,2 + ,2 + ,2 + ,2 + ,13 + ,6 + ,6 + ,1 + ,6 + ,6 + ,4 + ,2 + ,2 + ,5 + ,5 + ,5 + ,14 + ,6 + ,6 + ,5 + ,5 + ,5 + ,13 + ,5 + ,5 + ,5 + ,5 + ,9 + ,11 + ,5 + ,0 + ,5 + ,5 + ,2 + ,14 + ,6 + ,2 + ,6 + ,6 + ,6 + ,12 + ,4 + ,4 + ,6 + ,6 + ,6 + ,15 + ,6 + ,1 + ,0 + ,9 + ,6 + ,14 + ,5 + ,5 + ,0 + ,5 + ,5 + ,13 + ,5 + ,5 + ,1 + ,5 + ,3 + ,7 + ,4 + ,2 + ,7 + ,7 + ,2 + ,5 + ,2 + ,2 + ,2 + ,2 + ,2 + ,7 + ,7 + ,7 + ,4 + ,4 + ,4 + ,13 + ,5 + ,5 + ,0 + ,6 + ,8 + ,13 + ,6 + ,2 + ,5 + ,5 + ,5 + ,11 + ,5 + ,5 + ,5 + ,5 + ,9 + ,6 + ,3 + ,3 + ,3 + ,3 + ,2 + ,12 + ,6 + ,6 + ,0 + ,6 + ,6 + ,8 + ,4 + ,1 + ,4 + ,4 + ,4 + ,11 + ,5 + ,5 + ,9 + ,9 + ,5 + ,12 + ,7 + ,7 + ,0 + ,8 + ,8 + ,9 + ,4 + ,2 + ,4 + ,4 + ,3 + ,12 + ,6 + ,6 + ,2 + ,2 + ,2 + ,13 + ,8 + ,8 + ,7 + ,7 + ,7 + ,16 + ,7 + ,7 + ,7 + ,7 + ,7 + ,16 + ,6 + ,6 + ,6 + ,6 + ,9 + ,11 + ,7 + ,7 + ,0 + ,5 + ,5 + ,8 + ,4 + ,4 + ,5 + ,5 + ,5 + ,4 + ,0 + ,5 + ,6 + ,6 + ,2 + ,7 + ,3 + ,2 + ,0 + ,3 + ,3 + ,14 + ,5 + ,5 + ,5 + ,5 + ,5 + ,11 + ,6 + ,2 + ,9 + ,9 + ,2 + ,17 + ,5 + ,5 + ,0 + ,7 + ,7 + ,15 + ,7 + ,7 + ,7 + ,7 + ,7 + ,14 + ,6 + ,5 + ,1 + ,6 + ,6 + ,5 + ,8 + ,8 + ,3 + ,3 + ,3 + ,4 + ,7 + ,2 + ,7 + ,7 + ,3 + ,19 + ,8 + ,8 + ,8 + ,8 + ,2 + ,11 + ,3 + ,3 + ,0 + ,3 + ,3 + ,15 + ,8 + ,2 + ,5 + ,5 + ,5 + ,10 + ,3 + ,3 + ,3 + ,3 + ,3 + ,9 + ,4 + ,5 + ,0 + ,4 + ,4 + ,12 + ,2 + ,2 + ,5 + ,5 + ,5 + ,15 + ,7 + ,2 + ,7 + ,7 + ,7 + ,7 + ,6 + ,6 + ,0 + ,6 + ,6 + ,13 + ,2 + ,2 + ,0 + ,7 + ,7 + ,14 + ,7 + ,7 + ,0 + ,9 + ,2 + ,14 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,6 + ,2 + ,0 + ,6 + ,9 + ,8 + ,6 + ,2 + ,6 + ,6 + ,4 + ,15 + ,6 + ,5 + ,6 + ,6 + ,6 + ,15 + ,6 + ,6 + ,2 + ,2 + ,2 + ,9 + ,4 + ,4 + ,5 + ,5 + ,2 + ,16 + ,5 + ,5 + ,0 + ,5 + ,5 + ,9 + ,7 + ,7 + ,4 + ,4 + ,4 + ,15 + ,6 + ,6 + ,0 + ,7 + ,7 + ,15 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,5 + ,5 + ,5 + ,5 + ,7 + ,8 + ,8 + ,2 + ,8 + ,8 + ,8 + ,15 + ,6 + ,6 + ,6 + ,6 + ,6 + ,10 + ,0 + ,3 + ,5 + ,5 + ,3 + ,9 + ,4 + ,2 + ,0 + ,4 + ,4 + ,14 + ,8 + ,8 + ,8 + ,8 + ,8 + ,12 + ,6 + ,6 + ,0 + ,6 + ,9 + ,8 + ,4 + ,4 + ,9 + ,9 + ,2 + ,11 + ,6 + ,6 + ,5 + ,5 + ,5 + ,13 + ,2 + ,5 + ,0 + ,6 + ,6 + ,9 + ,4 + ,4 + ,0 + ,4 + ,4 + ,15 + ,6 + ,2 + ,0 + ,6 + ,6 + ,13 + ,3 + ,3 + ,3 + ,3 + ,3 + ,15 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,5 + ,5 + ,0 + ,5 + ,5 + ,16 + ,4 + ,4 + ,4 + ,4 + ,8 + ,12 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,1 + ,1 + ,0 + ,5 + ,5 + ,10 + ,4 + ,5 + ,4 + ,4 + ,3 + ,10 + ,4 + ,2 + ,7 + ,7 + ,2 + ,4 + ,6 + ,6 + ,0 + ,6 + ,6 + ,8 + ,5 + ,5 + ,5 + ,5 + ,5 + ,17 + ,9 + ,2 + ,6 + ,6 + ,6 + ,16 + ,6 + ,6 + ,6 + ,6 + ,6 + ,12 + ,8 + ,8 + ,8 + ,8 + ,9 + ,12 + ,7 + ,7 + ,2 + ,2 + ,4 + ,15 + ,7 + ,7 + ,7 + ,7 + ,7 + ,9 + ,0 + ,9 + ,0 + ,4 + ,4 + ,13 + ,6 + ,2 + ,0 + ,6 + ,7 + ,14 + ,6 + ,6 + ,5 + ,5 + ,5 + ,11 + ,5 + ,5 + ,0 + ,2 + ,2) + ,dim=c(6 + ,156) + ,dimnames=list(c('Schoolprestaties' + ,'Sport' + ,'Goingout' + ,'Relation' + ,'Family' + ,'Coach') + ,1:156)) > y <- array(NA,dim=c(6,156),dimnames=list(c('Schoolprestaties','Sport','Goingout','Relation','Family','Coach'),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 Sport Goingout Relation Family Coach t 1 14 3 2 3 3 7 1 2 8 5 6 0 7 2 2 3 12 6 6 0 6 3 3 4 7 6 6 6 6 8 4 5 10 7 8 5 5 7 5 6 9 3 1 0 7 7 6 7 16 8 9 8 8 9 7 8 7 4 4 0 2 2 8 9 14 7 7 0 4 4 9 10 6 4 4 9 9 4 10 11 16 6 6 6 6 6 11 12 11 6 5 6 6 4 12 13 17 7 7 5 5 9 13 14 12 4 5 4 4 8 14 15 7 6 6 0 2 7 15 16 13 5 5 0 4 4 16 17 9 0 2 2 2 2 17 18 15 9 9 6 6 8 18 19 7 4 4 0 4 4 19 20 9 4 4 4 4 4 20 21 7 2 5 5 5 2 21 22 14 7 7 7 7 9 22 23 15 5 5 5 5 3 23 24 7 9 9 4 4 4 24 25 13 6 6 6 6 6 25 26 17 6 6 6 6 6 26 27 15 7 3 0 7 7 27 28 14 3 3 1 2 2 28 29 14 6 5 0 6 6 29 30 8 6 5 4 4 4 30 31 8 4 4 4 4 2 31 32 12 7 7 7 7 9 32 33 14 7 6 7 7 7 33 34 8 7 7 0 4 4 34 35 11 4 4 4 4 4 35 36 16 5 5 5 5 7 36 37 11 6 6 0 6 6 37 38 8 5 5 5 5 5 38 39 14 6 0 1 6 6 39 40 16 6 6 2 2 2 40 41 14 6 5 0 6 2 41 42 5 3 3 9 9 7 42 43 8 3 3 3 3 3 43 44 10 3 3 0 4 4 44 45 8 6 7 6 6 6 45 46 13 7 7 1 5 5 46 47 15 5 1 5 5 7 47 48 6 5 5 0 4 4 48 49 12 5 5 0 2 2 49 50 14 6 6 0 6 6 50 51 5 6 2 6 6 9 51 52 15 6 6 7 7 8 52 53 11 5 5 0 5 5 53 54 8 4 2 4 4 4 54 55 13 7 7 5 5 2 55 56 14 5 5 1 5 9 56 57 12 3 3 4 4 4 57 58 16 6 6 9 9 6 58 59 10 2 2 2 2 2 59 60 15 8 8 8 8 8 60 61 8 3 5 3 3 3 61 62 16 0 2 1 6 3 62 63 19 6 6 0 6 7 63 64 14 8 2 6 6 2 64 65 7 4 1 0 5 9 65 66 13 5 5 0 5 5 66 67 15 6 6 6 6 4 67 68 7 5 2 2 2 2 68 69 13 6 6 1 6 6 69 70 4 2 2 5 5 5 70 71 14 6 6 5 5 5 71 72 13 5 5 5 5 9 72 73 11 5 0 5 5 2 73 74 14 6 2 6 6 6 74 75 12 4 4 6 6 6 75 76 15 6 1 0 9 6 76 77 14 5 5 0 5 5 77 78 13 5 5 1 5 3 78 79 7 4 2 7 7 2 79 80 5 2 2 2 2 2 80 81 7 7 7 4 4 4 81 82 13 5 5 0 6 8 82 83 13 6 2 5 5 5 83 84 11 5 5 5 5 9 84 85 6 3 3 3 3 2 85 86 12 6 6 0 6 6 86 87 8 4 1 4 4 4 87 88 11 5 5 9 9 5 88 89 12 7 7 0 8 8 89 90 9 4 2 4 4 3 90 91 12 6 6 2 2 2 91 92 13 8 8 7 7 7 92 93 16 7 7 7 7 7 93 94 16 6 6 6 6 9 94 95 11 7 7 0 5 5 95 96 8 4 4 5 5 5 96 97 4 0 5 6 6 2 97 98 7 3 2 0 3 3 98 99 14 5 5 5 5 5 99 100 11 6 2 9 9 2 100 101 17 5 5 0 7 7 101 102 15 7 7 7 7 7 102 103 14 6 5 1 6 6 103 104 5 8 8 3 3 3 104 105 4 7 2 7 7 3 105 106 19 8 8 8 8 2 106 107 11 3 3 0 3 3 107 108 15 8 2 5 5 5 108 109 10 3 3 3 3 3 109 110 9 4 5 0 4 4 110 111 12 2 2 5 5 5 111 112 15 7 2 7 7 7 112 113 7 6 6 0 6 6 113 114 13 2 2 0 7 7 114 115 14 7 7 0 9 2 115 116 14 6 6 6 6 6 116 117 14 6 2 0 6 9 117 118 8 6 2 6 6 4 118 119 15 6 5 6 6 6 119 120 15 6 6 2 2 2 120 121 9 4 4 5 5 2 121 122 16 5 5 0 5 5 122 123 9 7 7 4 4 4 123 124 15 6 6 0 7 7 124 125 15 6 6 6 6 6 125 126 6 5 5 5 5 7 126 127 8 8 2 8 8 8 127 128 15 6 6 6 6 6 128 129 10 0 3 5 5 3 129 130 9 4 2 0 4 4 130 131 14 8 8 8 8 8 131 132 12 6 6 0 6 9 132 133 8 4 4 9 9 2 133 134 11 6 6 5 5 5 134 135 13 2 5 0 6 6 135 136 9 4 4 0 4 4 136 137 15 6 2 0 6 6 137 138 13 3 3 3 3 3 138 139 15 6 6 6 6 6 139 140 14 5 5 0 5 5 140 141 16 4 4 4 4 8 141 142 12 6 6 6 6 6 142 143 14 1 1 0 5 5 143 144 10 4 5 4 4 3 144 145 10 4 2 7 7 2 145 146 4 6 6 0 6 6 146 147 8 5 5 5 5 5 147 148 17 9 2 6 6 6 148 149 16 6 6 6 6 6 149 150 12 8 8 8 8 9 150 151 12 7 7 2 2 4 151 152 15 7 7 7 7 7 152 153 9 0 9 0 4 4 153 154 13 6 2 0 6 7 154 155 14 6 6 5 5 5 155 156 11 5 5 0 2 2 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Sport Goingout Relation Family Coach 5.944512 0.431702 0.096186 -0.134927 0.260355 0.312539 t 0.005587 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -9.3649 -2.1367 0.5614 2.0830 7.1519 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.944512 1.099974 5.404 2.53e-07 *** Sport 0.431702 0.171964 2.510 0.0131 * Goingout 0.096186 0.142764 0.674 0.5015 Relation -0.134927 0.099817 -1.352 0.1785 Family 0.260355 0.185270 1.405 0.1620 Coach 0.312539 0.136615 2.288 0.0236 * t 0.005587 0.005743 0.973 0.3322 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.207 on 149 degrees of freedom Multiple R-squared: 0.1897, Adjusted R-squared: 0.1571 F-statistic: 5.815 on 6 and 149 DF, p-value: 1.813e-05 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.8719442 0.2561115 0.12805577 [2,] 0.9039689 0.1920623 0.09603114 [3,] 0.8468079 0.3063842 0.15319209 [4,] 0.7787362 0.4425276 0.22126378 [5,] 0.6857938 0.6284125 0.31420624 [6,] 0.8774082 0.2451836 0.12259181 [7,] 0.8585268 0.2829464 0.14147322 [8,] 0.8339695 0.3320609 0.16603046 [9,] 0.7731394 0.4537213 0.22686065 [10,] 0.7647241 0.4705518 0.23527590 [11,] 0.7018476 0.5963048 0.29815240 [12,] 0.6330874 0.7338251 0.36691257 [13,] 0.5582165 0.8835669 0.44178345 [14,] 0.6107153 0.7785694 0.38928468 [15,] 0.7539765 0.4920470 0.24602352 [16,] 0.7006950 0.5986101 0.29930505 [17,] 0.7499470 0.5001059 0.25005297 [18,] 0.6951286 0.6097427 0.30487137 [19,] 0.7262449 0.5475102 0.27375508 [20,] 0.6711115 0.6577769 0.32888847 [21,] 0.7094392 0.5811217 0.29056085 [22,] 0.6756546 0.6486909 0.32434545 [23,] 0.6533805 0.6932391 0.34661954 [24,] 0.5964711 0.8070579 0.40352893 [25,] 0.6135991 0.7728017 0.38640087 [26,] 0.5557927 0.8884146 0.44420732 [27,] 0.5601280 0.8797440 0.43987200 [28,] 0.5145182 0.9709637 0.48548184 [29,] 0.5284061 0.9431878 0.47159388 [30,] 0.4755383 0.9510766 0.52446170 [31,] 0.5783235 0.8433530 0.42167652 [32,] 0.5642498 0.8715003 0.43575017 [33,] 0.6941632 0.6116736 0.30583679 [34,] 0.6611258 0.6777483 0.33887416 [35,] 0.6100859 0.7798281 0.38991406 [36,] 0.6224343 0.7551315 0.37756573 [37,] 0.5739238 0.8521524 0.42607619 [38,] 0.5498209 0.9003582 0.45017908 [39,] 0.6130709 0.7738583 0.38692914 [40,] 0.5758056 0.8483889 0.42419443 [41,] 0.5521301 0.8957399 0.44786994 [42,] 0.8000514 0.3998973 0.19994864 [43,] 0.7928581 0.4142837 0.20714186 [44,] 0.7565533 0.4868934 0.24344671 [45,] 0.7343179 0.5313642 0.26568208 [46,] 0.7034107 0.5931786 0.29658929 [47,] 0.6688742 0.6622517 0.33112584 [48,] 0.6459713 0.7080575 0.35402875 [49,] 0.6648529 0.6702941 0.33514705 [50,] 0.6280660 0.7438679 0.37193397 [51,] 0.5840542 0.8318917 0.41594585 [52,] 0.5475696 0.9048607 0.45243035 [53,] 0.7159859 0.5680282 0.28401410 [54,] 0.7811107 0.4377786 0.21888932 [55,] 0.7658619 0.4682762 0.23413810 [56,] 0.8295144 0.3409712 0.17048562 [57,] 0.8011961 0.3976077 0.19880387 [58,] 0.8006238 0.3987524 0.19937619 [59,] 0.7942051 0.4115897 0.20579486 [60,] 0.7600643 0.4798714 0.23993569 [61,] 0.8333111 0.3333779 0.16668894 [62,] 0.8165272 0.3669455 0.18347275 [63,] 0.7835219 0.4329562 0.21647809 [64,] 0.7529926 0.4940149 0.24700743 [65,] 0.7310264 0.5379473 0.26897363 [66,] 0.6932259 0.6135481 0.30677406 [67,] 0.6635957 0.6728086 0.33640428 [68,] 0.6418463 0.7163074 0.35815371 [69,] 0.6212339 0.7575321 0.37876605 [70,] 0.6139720 0.7720560 0.38602801 [71,] 0.6156303 0.7687395 0.38436974 [72,] 0.6688876 0.6622249 0.33111243 [73,] 0.6254603 0.7490793 0.37453966 [74,] 0.5929925 0.8140151 0.40700753 [75,] 0.5569943 0.8860114 0.44300572 [76,] 0.5465146 0.9069708 0.45348541 [77,] 0.5035846 0.9928309 0.49641543 [78,] 0.4737242 0.9474484 0.52627581 [79,] 0.4287519 0.8575039 0.57124806 [80,] 0.4096478 0.8192957 0.59035217 [81,] 0.3662070 0.7324139 0.63379303 [82,] 0.3339514 0.6679029 0.66604857 [83,] 0.2930308 0.5860615 0.70696924 [84,] 0.2811354 0.5622709 0.71886456 [85,] 0.2693209 0.5386418 0.73067912 [86,] 0.2413363 0.4826727 0.75866365 [87,] 0.2295208 0.4590416 0.77047918 [88,] 0.2651720 0.5303441 0.73482796 [89,] 0.2564589 0.5129178 0.74354110 [90,] 0.2393168 0.4786337 0.76068316 [91,] 0.2026606 0.4053212 0.79733940 [92,] 0.2200787 0.4401573 0.77992134 [93,] 0.1963110 0.3926219 0.80368905 [94,] 0.1709813 0.3419626 0.82901868 [95,] 0.2979585 0.5959170 0.70204149 [96,] 0.5343351 0.9313298 0.46566490 [97,] 0.7057841 0.5884317 0.29421586 [98,] 0.6624174 0.6751653 0.33758263 [99,] 0.6396991 0.7206019 0.36030094 [100,] 0.5925387 0.8149227 0.40746134 [101,] 0.5655426 0.8689149 0.43445743 [102,] 0.5231890 0.9536220 0.47681099 [103,] 0.4918590 0.9837180 0.50814101 [104,] 0.6143850 0.7712299 0.38561496 [105,] 0.5668966 0.8662068 0.43310342 [106,] 0.5571239 0.8857522 0.44287611 [107,] 0.5170689 0.9658622 0.48293108 [108,] 0.4603357 0.9206714 0.53966431 [109,] 0.4715546 0.9431093 0.52844535 [110,] 0.4509402 0.9018805 0.54905975 [111,] 0.4871722 0.9743443 0.51282784 [112,] 0.4310078 0.8620157 0.56899217 [113,] 0.5070308 0.9859384 0.49296920 [114,] 0.4669253 0.9338506 0.53307469 [115,] 0.5042332 0.9915336 0.49576678 [116,] 0.5364682 0.9270635 0.46353176 [117,] 0.7304761 0.5390479 0.26952393 [118,] 0.9117921 0.1764159 0.08820793 [119,] 0.9062295 0.1875409 0.09377046 [120,] 0.8809826 0.2380347 0.11901735 [121,] 0.8755123 0.2489755 0.12448774 [122,] 0.8462217 0.3075565 0.15377826 [123,] 0.8027163 0.3945674 0.19728372 [124,] 0.7492870 0.5014259 0.25071297 [125,] 0.6906547 0.6186907 0.30934534 [126,] 0.6605825 0.6788349 0.33941746 [127,] 0.6152685 0.7694630 0.38473148 [128,] 0.5952192 0.8095616 0.40478078 [129,] 0.5156080 0.9687840 0.48439199 [130,] 0.4920124 0.9840247 0.50798763 [131,] 0.8283364 0.3433272 0.17166359 [132,] 0.7841967 0.4316066 0.21580330 [133,] 0.7151132 0.5697736 0.28488680 [134,] 0.6801173 0.6397654 0.31988269 [135,] 0.5713282 0.8573436 0.42867180 [136,] 0.4267282 0.8534564 0.57327180 [137,] 0.3886525 0.7773051 0.61134745 > postscript(file="/var/wessaorg/rcomp/tmp/16b251321990957.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/23x241321990957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/30b271321990957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4j7z51321990957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5nz0i1321990957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 3.99836622 -3.13887671 0.37164960 -5.38706762 -2.57876358 -2.37958175 7 8 9 10 11 12 1.88081456 -2.24654916 2.01841021 -4.49093574 4.19890303 -0.08541927 13 14 15 16 17 18 3.84765068 0.76751033 -4.90412941 2.03508064 1.89220755 0.95105092 19 20 21 22 23 24 -3.45379076 -0.91966778 -1.65838515 0.54651552 4.72279490 -5.58145937 25 26 27 28 29 30 1.12068829 5.11510152 1.58391339 5.30453183 1.38496303 -2.93512683 31 32 33 34 35 36 -1.35604420 -1.50935216 1.20632554 -4.12125898 0.99653070 4.40001086 37 38 39 40 41 42 -1.75591754 -2.98608464 1.94495497 5.78875134 2.56807788 -6.07944045 43 44 45 46 47 48 -1.08230839 -0.06557105 -4.08723350 0.37373366 3.72330216 -5.14369592 49 50 51 52 53 54 1.99650447 1.17145448 -7.57743897 2.21934041 -0.74452334 -1.91724501 55 56 57 58 59 60 1.80077954 1.12348773 2.40151072 3.56004357 1.79415837 0.99344133 61 62 63 64 65 66 -1.37524308 7.15191830 5.78628749 2.67430121 -5.24527240 1.18284868 67 68 69 70 71 72 3.51112208 -2.55122994 0.20023333 -5.58119450 2.30166312 0.57380920 73 74 75 76 77 78 1.23692771 2.23168242 0.89712771 1.72606700 2.12139424 1.87581293 79 80 81 82 83 84 -2.80811755 -3.32316375 -4.84412732 -0.10451121 1.61936765 -1.49323201 85 86 87 88 89 90 -3.00441362 -1.02966915 -2.00542191 -0.76713154 -2.72010552 -0.80582963 91 92 93 94 95 96 1.50382620 -0.74736909 2.77493305 2.79758428 -2.03494539 -2.78222826 97 98 99 100 101 102 -4.34500168 -2.69817648 2.67312253 -0.03969887 3.84152466 1.72465214 103 104 105 106 107 108 1.10646967 -7.06254573 -7.56101993 6.61168411 1.15535617 2.61629352 109 110 111 112 113 114 0.54896495 -2.05837305 2.18974803 2.14971664 -6.18051188 1.35256339 115 116 117 118 119 120 0.74951805 1.61229244 0.24426975 -3.38905733 2.69171857 4.34180994 121 122 123 124 125 126 -0.98428041 3.86998969 -3.07877155 1.18514010 2.56201153 -6.10279822 127 128 129 130 131 132 -5.80375349 2.54525123 1.48148274 -1.88154909 -0.40321917 -2.22427751 133 134 135 136 137 138 -2.55303013 -1.05030324 1.51957554 -2.10744257 2.07015144 3.38694869 139 140 141 142 143 144 2.48379678 1.76942788 4.15417728 -0.53296352 3.86422319 -0.39607438 145 146 147 148 149 150 -0.17684422 -9.36487521 -3.59504231 3.52315421 3.42792911 -2.82190677 151 152 153 154 155 156 0.01565321 1.44531376 -0.95653992 -0.33736263 1.83237464 0.39872033 > postscript(file="/var/wessaorg/rcomp/tmp/6v8b91321990957.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 3.99836622 NA 1 -3.13887671 3.99836622 2 0.37164960 -3.13887671 3 -5.38706762 0.37164960 4 -2.57876358 -5.38706762 5 -2.37958175 -2.57876358 6 1.88081456 -2.37958175 7 -2.24654916 1.88081456 8 2.01841021 -2.24654916 9 -4.49093574 2.01841021 10 4.19890303 -4.49093574 11 -0.08541927 4.19890303 12 3.84765068 -0.08541927 13 0.76751033 3.84765068 14 -4.90412941 0.76751033 15 2.03508064 -4.90412941 16 1.89220755 2.03508064 17 0.95105092 1.89220755 18 -3.45379076 0.95105092 19 -0.91966778 -3.45379076 20 -1.65838515 -0.91966778 21 0.54651552 -1.65838515 22 4.72279490 0.54651552 23 -5.58145937 4.72279490 24 1.12068829 -5.58145937 25 5.11510152 1.12068829 26 1.58391339 5.11510152 27 5.30453183 1.58391339 28 1.38496303 5.30453183 29 -2.93512683 1.38496303 30 -1.35604420 -2.93512683 31 -1.50935216 -1.35604420 32 1.20632554 -1.50935216 33 -4.12125898 1.20632554 34 0.99653070 -4.12125898 35 4.40001086 0.99653070 36 -1.75591754 4.40001086 37 -2.98608464 -1.75591754 38 1.94495497 -2.98608464 39 5.78875134 1.94495497 40 2.56807788 5.78875134 41 -6.07944045 2.56807788 42 -1.08230839 -6.07944045 43 -0.06557105 -1.08230839 44 -4.08723350 -0.06557105 45 0.37373366 -4.08723350 46 3.72330216 0.37373366 47 -5.14369592 3.72330216 48 1.99650447 -5.14369592 49 1.17145448 1.99650447 50 -7.57743897 1.17145448 51 2.21934041 -7.57743897 52 -0.74452334 2.21934041 53 -1.91724501 -0.74452334 54 1.80077954 -1.91724501 55 1.12348773 1.80077954 56 2.40151072 1.12348773 57 3.56004357 2.40151072 58 1.79415837 3.56004357 59 0.99344133 1.79415837 60 -1.37524308 0.99344133 61 7.15191830 -1.37524308 62 5.78628749 7.15191830 63 2.67430121 5.78628749 64 -5.24527240 2.67430121 65 1.18284868 -5.24527240 66 3.51112208 1.18284868 67 -2.55122994 3.51112208 68 0.20023333 -2.55122994 69 -5.58119450 0.20023333 70 2.30166312 -5.58119450 71 0.57380920 2.30166312 72 1.23692771 0.57380920 73 2.23168242 1.23692771 74 0.89712771 2.23168242 75 1.72606700 0.89712771 76 2.12139424 1.72606700 77 1.87581293 2.12139424 78 -2.80811755 1.87581293 79 -3.32316375 -2.80811755 80 -4.84412732 -3.32316375 81 -0.10451121 -4.84412732 82 1.61936765 -0.10451121 83 -1.49323201 1.61936765 84 -3.00441362 -1.49323201 85 -1.02966915 -3.00441362 86 -2.00542191 -1.02966915 87 -0.76713154 -2.00542191 88 -2.72010552 -0.76713154 89 -0.80582963 -2.72010552 90 1.50382620 -0.80582963 91 -0.74736909 1.50382620 92 2.77493305 -0.74736909 93 2.79758428 2.77493305 94 -2.03494539 2.79758428 95 -2.78222826 -2.03494539 96 -4.34500168 -2.78222826 97 -2.69817648 -4.34500168 98 2.67312253 -2.69817648 99 -0.03969887 2.67312253 100 3.84152466 -0.03969887 101 1.72465214 3.84152466 102 1.10646967 1.72465214 103 -7.06254573 1.10646967 104 -7.56101993 -7.06254573 105 6.61168411 -7.56101993 106 1.15535617 6.61168411 107 2.61629352 1.15535617 108 0.54896495 2.61629352 109 -2.05837305 0.54896495 110 2.18974803 -2.05837305 111 2.14971664 2.18974803 112 -6.18051188 2.14971664 113 1.35256339 -6.18051188 114 0.74951805 1.35256339 115 1.61229244 0.74951805 116 0.24426975 1.61229244 117 -3.38905733 0.24426975 118 2.69171857 -3.38905733 119 4.34180994 2.69171857 120 -0.98428041 4.34180994 121 3.86998969 -0.98428041 122 -3.07877155 3.86998969 123 1.18514010 -3.07877155 124 2.56201153 1.18514010 125 -6.10279822 2.56201153 126 -5.80375349 -6.10279822 127 2.54525123 -5.80375349 128 1.48148274 2.54525123 129 -1.88154909 1.48148274 130 -0.40321917 -1.88154909 131 -2.22427751 -0.40321917 132 -2.55303013 -2.22427751 133 -1.05030324 -2.55303013 134 1.51957554 -1.05030324 135 -2.10744257 1.51957554 136 2.07015144 -2.10744257 137 3.38694869 2.07015144 138 2.48379678 3.38694869 139 1.76942788 2.48379678 140 4.15417728 1.76942788 141 -0.53296352 4.15417728 142 3.86422319 -0.53296352 143 -0.39607438 3.86422319 144 -0.17684422 -0.39607438 145 -9.36487521 -0.17684422 146 -3.59504231 -9.36487521 147 3.52315421 -3.59504231 148 3.42792911 3.52315421 149 -2.82190677 3.42792911 150 0.01565321 -2.82190677 151 1.44531376 0.01565321 152 -0.95653992 1.44531376 153 -0.33736263 -0.95653992 154 1.83237464 -0.33736263 155 0.39872033 1.83237464 156 NA 0.39872033 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -3.13887671 3.99836622 [2,] 0.37164960 -3.13887671 [3,] -5.38706762 0.37164960 [4,] -2.57876358 -5.38706762 [5,] -2.37958175 -2.57876358 [6,] 1.88081456 -2.37958175 [7,] -2.24654916 1.88081456 [8,] 2.01841021 -2.24654916 [9,] -4.49093574 2.01841021 [10,] 4.19890303 -4.49093574 [11,] -0.08541927 4.19890303 [12,] 3.84765068 -0.08541927 [13,] 0.76751033 3.84765068 [14,] -4.90412941 0.76751033 [15,] 2.03508064 -4.90412941 [16,] 1.89220755 2.03508064 [17,] 0.95105092 1.89220755 [18,] -3.45379076 0.95105092 [19,] -0.91966778 -3.45379076 [20,] -1.65838515 -0.91966778 [21,] 0.54651552 -1.65838515 [22,] 4.72279490 0.54651552 [23,] -5.58145937 4.72279490 [24,] 1.12068829 -5.58145937 [25,] 5.11510152 1.12068829 [26,] 1.58391339 5.11510152 [27,] 5.30453183 1.58391339 [28,] 1.38496303 5.30453183 [29,] -2.93512683 1.38496303 [30,] -1.35604420 -2.93512683 [31,] -1.50935216 -1.35604420 [32,] 1.20632554 -1.50935216 [33,] -4.12125898 1.20632554 [34,] 0.99653070 -4.12125898 [35,] 4.40001086 0.99653070 [36,] -1.75591754 4.40001086 [37,] -2.98608464 -1.75591754 [38,] 1.94495497 -2.98608464 [39,] 5.78875134 1.94495497 [40,] 2.56807788 5.78875134 [41,] -6.07944045 2.56807788 [42,] -1.08230839 -6.07944045 [43,] -0.06557105 -1.08230839 [44,] -4.08723350 -0.06557105 [45,] 0.37373366 -4.08723350 [46,] 3.72330216 0.37373366 [47,] -5.14369592 3.72330216 [48,] 1.99650447 -5.14369592 [49,] 1.17145448 1.99650447 [50,] -7.57743897 1.17145448 [51,] 2.21934041 -7.57743897 [52,] -0.74452334 2.21934041 [53,] -1.91724501 -0.74452334 [54,] 1.80077954 -1.91724501 [55,] 1.12348773 1.80077954 [56,] 2.40151072 1.12348773 [57,] 3.56004357 2.40151072 [58,] 1.79415837 3.56004357 [59,] 0.99344133 1.79415837 [60,] -1.37524308 0.99344133 [61,] 7.15191830 -1.37524308 [62,] 5.78628749 7.15191830 [63,] 2.67430121 5.78628749 [64,] -5.24527240 2.67430121 [65,] 1.18284868 -5.24527240 [66,] 3.51112208 1.18284868 [67,] -2.55122994 3.51112208 [68,] 0.20023333 -2.55122994 [69,] -5.58119450 0.20023333 [70,] 2.30166312 -5.58119450 [71,] 0.57380920 2.30166312 [72,] 1.23692771 0.57380920 [73,] 2.23168242 1.23692771 [74,] 0.89712771 2.23168242 [75,] 1.72606700 0.89712771 [76,] 2.12139424 1.72606700 [77,] 1.87581293 2.12139424 [78,] -2.80811755 1.87581293 [79,] -3.32316375 -2.80811755 [80,] -4.84412732 -3.32316375 [81,] -0.10451121 -4.84412732 [82,] 1.61936765 -0.10451121 [83,] -1.49323201 1.61936765 [84,] -3.00441362 -1.49323201 [85,] -1.02966915 -3.00441362 [86,] -2.00542191 -1.02966915 [87,] -0.76713154 -2.00542191 [88,] -2.72010552 -0.76713154 [89,] -0.80582963 -2.72010552 [90,] 1.50382620 -0.80582963 [91,] -0.74736909 1.50382620 [92,] 2.77493305 -0.74736909 [93,] 2.79758428 2.77493305 [94,] -2.03494539 2.79758428 [95,] -2.78222826 -2.03494539 [96,] -4.34500168 -2.78222826 [97,] -2.69817648 -4.34500168 [98,] 2.67312253 -2.69817648 [99,] -0.03969887 2.67312253 [100,] 3.84152466 -0.03969887 [101,] 1.72465214 3.84152466 [102,] 1.10646967 1.72465214 [103,] -7.06254573 1.10646967 [104,] -7.56101993 -7.06254573 [105,] 6.61168411 -7.56101993 [106,] 1.15535617 6.61168411 [107,] 2.61629352 1.15535617 [108,] 0.54896495 2.61629352 [109,] -2.05837305 0.54896495 [110,] 2.18974803 -2.05837305 [111,] 2.14971664 2.18974803 [112,] -6.18051188 2.14971664 [113,] 1.35256339 -6.18051188 [114,] 0.74951805 1.35256339 [115,] 1.61229244 0.74951805 [116,] 0.24426975 1.61229244 [117,] -3.38905733 0.24426975 [118,] 2.69171857 -3.38905733 [119,] 4.34180994 2.69171857 [120,] -0.98428041 4.34180994 [121,] 3.86998969 -0.98428041 [122,] -3.07877155 3.86998969 [123,] 1.18514010 -3.07877155 [124,] 2.56201153 1.18514010 [125,] -6.10279822 2.56201153 [126,] -5.80375349 -6.10279822 [127,] 2.54525123 -5.80375349 [128,] 1.48148274 2.54525123 [129,] -1.88154909 1.48148274 [130,] -0.40321917 -1.88154909 [131,] -2.22427751 -0.40321917 [132,] -2.55303013 -2.22427751 [133,] -1.05030324 -2.55303013 [134,] 1.51957554 -1.05030324 [135,] -2.10744257 1.51957554 [136,] 2.07015144 -2.10744257 [137,] 3.38694869 2.07015144 [138,] 2.48379678 3.38694869 [139,] 1.76942788 2.48379678 [140,] 4.15417728 1.76942788 [141,] -0.53296352 4.15417728 [142,] 3.86422319 -0.53296352 [143,] -0.39607438 3.86422319 [144,] -0.17684422 -0.39607438 [145,] -9.36487521 -0.17684422 [146,] -3.59504231 -9.36487521 [147,] 3.52315421 -3.59504231 [148,] 3.42792911 3.52315421 [149,] -2.82190677 3.42792911 [150,] 0.01565321 -2.82190677 [151,] 1.44531376 0.01565321 [152,] -0.95653992 1.44531376 [153,] -0.33736263 -0.95653992 [154,] 1.83237464 -0.33736263 [155,] 0.39872033 1.83237464 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -3.13887671 3.99836622 2 0.37164960 -3.13887671 3 -5.38706762 0.37164960 4 -2.57876358 -5.38706762 5 -2.37958175 -2.57876358 6 1.88081456 -2.37958175 7 -2.24654916 1.88081456 8 2.01841021 -2.24654916 9 -4.49093574 2.01841021 10 4.19890303 -4.49093574 11 -0.08541927 4.19890303 12 3.84765068 -0.08541927 13 0.76751033 3.84765068 14 -4.90412941 0.76751033 15 2.03508064 -4.90412941 16 1.89220755 2.03508064 17 0.95105092 1.89220755 18 -3.45379076 0.95105092 19 -0.91966778 -3.45379076 20 -1.65838515 -0.91966778 21 0.54651552 -1.65838515 22 4.72279490 0.54651552 23 -5.58145937 4.72279490 24 1.12068829 -5.58145937 25 5.11510152 1.12068829 26 1.58391339 5.11510152 27 5.30453183 1.58391339 28 1.38496303 5.30453183 29 -2.93512683 1.38496303 30 -1.35604420 -2.93512683 31 -1.50935216 -1.35604420 32 1.20632554 -1.50935216 33 -4.12125898 1.20632554 34 0.99653070 -4.12125898 35 4.40001086 0.99653070 36 -1.75591754 4.40001086 37 -2.98608464 -1.75591754 38 1.94495497 -2.98608464 39 5.78875134 1.94495497 40 2.56807788 5.78875134 41 -6.07944045 2.56807788 42 -1.08230839 -6.07944045 43 -0.06557105 -1.08230839 44 -4.08723350 -0.06557105 45 0.37373366 -4.08723350 46 3.72330216 0.37373366 47 -5.14369592 3.72330216 48 1.99650447 -5.14369592 49 1.17145448 1.99650447 50 -7.57743897 1.17145448 51 2.21934041 -7.57743897 52 -0.74452334 2.21934041 53 -1.91724501 -0.74452334 54 1.80077954 -1.91724501 55 1.12348773 1.80077954 56 2.40151072 1.12348773 57 3.56004357 2.40151072 58 1.79415837 3.56004357 59 0.99344133 1.79415837 60 -1.37524308 0.99344133 61 7.15191830 -1.37524308 62 5.78628749 7.15191830 63 2.67430121 5.78628749 64 -5.24527240 2.67430121 65 1.18284868 -5.24527240 66 3.51112208 1.18284868 67 -2.55122994 3.51112208 68 0.20023333 -2.55122994 69 -5.58119450 0.20023333 70 2.30166312 -5.58119450 71 0.57380920 2.30166312 72 1.23692771 0.57380920 73 2.23168242 1.23692771 74 0.89712771 2.23168242 75 1.72606700 0.89712771 76 2.12139424 1.72606700 77 1.87581293 2.12139424 78 -2.80811755 1.87581293 79 -3.32316375 -2.80811755 80 -4.84412732 -3.32316375 81 -0.10451121 -4.84412732 82 1.61936765 -0.10451121 83 -1.49323201 1.61936765 84 -3.00441362 -1.49323201 85 -1.02966915 -3.00441362 86 -2.00542191 -1.02966915 87 -0.76713154 -2.00542191 88 -2.72010552 -0.76713154 89 -0.80582963 -2.72010552 90 1.50382620 -0.80582963 91 -0.74736909 1.50382620 92 2.77493305 -0.74736909 93 2.79758428 2.77493305 94 -2.03494539 2.79758428 95 -2.78222826 -2.03494539 96 -4.34500168 -2.78222826 97 -2.69817648 -4.34500168 98 2.67312253 -2.69817648 99 -0.03969887 2.67312253 100 3.84152466 -0.03969887 101 1.72465214 3.84152466 102 1.10646967 1.72465214 103 -7.06254573 1.10646967 104 -7.56101993 -7.06254573 105 6.61168411 -7.56101993 106 1.15535617 6.61168411 107 2.61629352 1.15535617 108 0.54896495 2.61629352 109 -2.05837305 0.54896495 110 2.18974803 -2.05837305 111 2.14971664 2.18974803 112 -6.18051188 2.14971664 113 1.35256339 -6.18051188 114 0.74951805 1.35256339 115 1.61229244 0.74951805 116 0.24426975 1.61229244 117 -3.38905733 0.24426975 118 2.69171857 -3.38905733 119 4.34180994 2.69171857 120 -0.98428041 4.34180994 121 3.86998969 -0.98428041 122 -3.07877155 3.86998969 123 1.18514010 -3.07877155 124 2.56201153 1.18514010 125 -6.10279822 2.56201153 126 -5.80375349 -6.10279822 127 2.54525123 -5.80375349 128 1.48148274 2.54525123 129 -1.88154909 1.48148274 130 -0.40321917 -1.88154909 131 -2.22427751 -0.40321917 132 -2.55303013 -2.22427751 133 -1.05030324 -2.55303013 134 1.51957554 -1.05030324 135 -2.10744257 1.51957554 136 2.07015144 -2.10744257 137 3.38694869 2.07015144 138 2.48379678 3.38694869 139 1.76942788 2.48379678 140 4.15417728 1.76942788 141 -0.53296352 4.15417728 142 3.86422319 -0.53296352 143 -0.39607438 3.86422319 144 -0.17684422 -0.39607438 145 -9.36487521 -0.17684422 146 -3.59504231 -9.36487521 147 3.52315421 -3.59504231 148 3.42792911 3.52315421 149 -2.82190677 3.42792911 150 0.01565321 -2.82190677 151 1.44531376 0.01565321 152 -0.95653992 1.44531376 153 -0.33736263 -0.95653992 154 1.83237464 -0.33736263 155 0.39872033 1.83237464 > 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/7islv1321990957.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/890af1321990957.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/9cclg1321990957.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/10aoka1321990957.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11zmii1321990957.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12uioh1321990957.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13ho0d1321990957.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14wbnf1321990957.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/1548ad1321990958.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/168ktl1321990958.tab") + } > > try(system("convert tmp/16b251321990957.ps tmp/16b251321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/23x241321990957.ps tmp/23x241321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/30b271321990957.ps tmp/30b271321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/4j7z51321990957.ps tmp/4j7z51321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/5nz0i1321990957.ps tmp/5nz0i1321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/6v8b91321990957.ps tmp/6v8b91321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/7islv1321990957.ps tmp/7islv1321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/890af1321990957.ps tmp/890af1321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/9cclg1321990957.ps tmp/9cclg1321990957.png",intern=TRUE)) character(0) > try(system("convert tmp/10aoka1321990957.ps tmp/10aoka1321990957.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.863 0.514 5.448