R version 2.11.1 (2010-05-31) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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(3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,5 + ,5 + ,5 + ,1 + ,5 + ,5 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,5 + ,4 + ,4 + ,1 + ,4 + ,4 + ,5 + ,3 + ,5 + ,1 + ,5 + ,5 + ,2 + ,1 + ,3 + ,5 + ,3 + ,2 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,5 + ,4 + ,5 + ,4 + ,5 + ,4 + ,5 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,5 + ,4 + ,5 + ,1 + ,5 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,5 + ,4 + ,2 + ,4 + ,4 + ,5 + ,4 + ,5 + ,1 + ,5 + ,5 + ,4 + ,3 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,3 + ,2 + ,4 + ,3 + ,4 + ,3 + ,4 + ,2 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,4 + ,5 + ,5 + ,1 + ,4 + ,4 + ,5 + ,5 + ,5 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,5 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,5 + ,1 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + 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,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,5 + ,5 + ,5 + ,5 + ,1 + ,4 + ,5 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,4 + ,2 + ,5 + ,3 + ,3 + ,4 + ,4 + ,1 + ,4 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,5 + ,5 + ,5 + ,1 + ,5 + ,5 + ,5 + ,4 + ,5 + ,1 + ,5 + ,5 + ,5 + ,4 + ,4 + ,1 + ,4 + ,4 + ,5 + ,4 + ,5 + ,1 + ,5 + ,5 + ,5 + ,4 + ,4 + ,3 + ,5 + ,3 + ,4 + ,3 + ,3 + ,2 + ,3 + ,3 + ,4 + ,5 + ,5 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,1 + ,4 + ,5 + ,5 + ,4 + ,5 + ,1 + ,5 + ,5 + ,5 + ,5 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,5 + ,4 + ,2 + ,4 + ,4 + ,5 + ,5 + ,5 + ,2 + ,5 + ,4 + ,3 + ,3 + ,3 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,5 + ,4 + ,5 + ,4 + ,5 + ,4 + ,5 + ,4 + ,4 + ,1 + ,5 + ,5 + ,5 + ,5 + ,5 + ,1 + ,5 + ,5 + ,5 + ,5 + ,4 + ,3 + ,4 + ,5) + ,dim=c(6 + ,161) + ,dimnames=list(c('Part_of_team' + ,'Respect_of_coach' + ,'Respect_of_team' + ,'Be_on_different_team' + ,'Be_liked' + ,'Proudness') + ,1:161)) > y <- array(NA,dim=c(6,161),dimnames=list(c('Part_of_team','Respect_of_coach','Respect_of_team','Be_on_different_team','Be_liked','Proudness'),1:161)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '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 > 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 Part_of_team Respect_of_coach Respect_of_team Be_on_different_team Be_liked 1 3 3 3 3 3 2 5 5 5 1 5 3 4 4 4 3 3 4 4 4 4 3 4 5 5 4 4 1 4 6 5 3 5 1 5 7 2 1 3 5 3 8 5 4 4 2 4 9 4 4 4 2 4 10 4 4 4 2 5 11 5 4 5 4 5 12 3 3 3 3 3 13 5 4 4 2 4 14 3 3 3 3 3 15 5 4 5 1 5 16 3 3 3 3 3 17 4 5 4 2 4 18 5 4 5 1 5 19 4 3 3 3 4 20 3 3 3 3 3 21 4 4 3 2 4 22 4 3 4 2 4 23 3 3 3 3 3 24 3 3 3 3 4 25 4 5 5 1 4 26 5 5 5 1 4 27 4 4 4 1 4 28 4 4 4 1 4 29 4 5 4 1 4 30 4 4 4 3 4 31 4 4 5 1 4 32 3 3 3 3 3 33 4 4 4 1 4 34 5 4 5 2 5 35 4 4 4 1 4 36 4 4 4 2 4 37 3 4 4 2 4 38 4 4 4 2 3 39 4 3 4 1 5 40 4 4 4 3 4 41 5 2 4 1 5 42 4 4 4 1 4 43 3 3 3 3 3 44 3 3 3 3 3 45 4 4 4 1 3 46 4 3 4 2 4 47 4 4 4 4 4 48 5 4 4 2 4 49 4 4 4 1 4 50 5 4 4 1 4 51 4 4 4 2 4 52 4 4 4 2 4 53 4 3 3 3 4 54 4 4 5 5 4 55 4 3 4 2 3 56 5 4 4 1 4 57 4 4 4 1 4 58 4 3 5 1 4 59 4 4 4 1 4 60 4 4 4 2 4 61 3 3 2 2 3 62 4 4 4 2 4 63 5 4 1 4 4 64 3 2 3 3 3 65 3 3 3 3 3 66 4 5 1 4 4 67 4 3 2 4 4 68 4 4 1 4 4 69 4 3 3 3 4 70 3 4 2 4 4 71 4 4 2 4 4 72 3 3 3 3 3 73 3 4 1 4 4 74 3 3 1 4 3 75 5 4 1 5 5 76 4 5 2 5 5 77 4 4 2 4 4 78 2 4 1 4 4 79 4 4 1 4 4 80 3 3 3 3 3 81 4 4 1 5 4 82 4 4 2 4 3 83 4 3 1 4 5 84 4 4 2 4 4 85 4 5 1 5 4 86 4 4 1 4 4 87 4 4 2 4 4 88 3 3 3 3 3 89 4 4 2 4 4 90 3 4 2 5 4 91 3 3 3 3 3 92 5 4 2 4 4 93 4 4 1 4 4 94 5 4 1 4 5 95 4 4 1 4 4 96 3 4 2 4 4 97 3 4 1 4 4 98 4 4 1 4 5 99 4 4 1 4 5 100 4 4 2 4 4 101 5 4 1 4 4 102 5 5 1 5 4 103 3 4 3 4 4 104 5 4 1 5 4 105 4 4 2 4 4 106 4 4 3 4 4 107 4 4 2 4 3 108 3 3 3 3 3 109 4 4 4 2 3 110 5 1 5 5 4 111 4 1 4 4 5 112 5 2 5 5 1 113 1 1 1 1 5 114 4 2 4 4 5 115 5 1 5 5 3 116 3 3 3 3 4 117 4 4 4 5 4 118 3 4 3 5 5 119 1 5 5 3 3 120 3 3 3 4 3 121 1 4 3 5 5 122 1 5 4 4 3 123 1 5 4 4 5 124 3 4 4 4 4 125 2 5 4 5 5 126 1 5 5 4 4 127 2 4 4 5 4 128 4 5 4 4 4 129 2 4 4 4 4 130 1 4 4 4 4 131 2 4 4 4 4 132 1 4 4 3 2 133 2 4 4 4 4 134 2 4 5 5 5 135 1 4 5 3 3 136 3 3 3 4 3 137 3 3 4 3 3 138 4 3 3 4 2 139 2 5 3 3 4 140 1 4 4 3 3 141 3 3 3 5 5 142 1 5 5 5 4 143 1 5 5 5 4 144 1 4 4 5 4 145 1 5 5 5 4 146 3 5 3 4 3 147 2 3 3 4 5 148 2 4 4 4 4 149 1 4 5 5 4 150 1 5 5 5 5 151 2 4 4 4 4 152 1 4 4 4 4 153 2 4 4 4 5 154 2 4 4 5 5 155 2 5 4 3 3 156 3 3 3 4 4 157 1 4 4 5 4 158 4 5 4 5 4 159 1 5 5 5 5 160 1 5 5 5 5 161 3 4 5 3 3 Proudness 1 3 2 5 3 4 4 4 5 4 6 5 7 2 8 4 9 4 10 4 11 4 12 3 13 4 14 3 15 4 16 3 17 4 18 5 19 3 20 3 21 3 22 3 23 3 24 3 25 4 26 4 27 4 28 4 29 4 30 4 31 4 32 3 33 4 34 5 35 4 36 4 37 4 38 4 39 4 40 4 41 3 42 4 43 3 44 3 45 4 46 3 47 4 48 4 49 3 50 4 51 4 52 4 53 4 54 5 55 3 56 4 57 4 58 4 59 4 60 4 61 3 62 4 63 1 64 3 65 5 66 4 67 4 68 3 69 4 70 4 71 3 72 5 73 4 74 5 75 4 76 4 77 4 78 4 79 3 80 5 81 5 82 4 83 4 84 5 85 4 86 4 87 3 88 4 89 4 90 3 91 5 92 5 93 5 94 4 95 4 96 4 97 4 98 4 99 4 100 4 101 5 102 4 103 4 104 3 105 5 106 4 107 3 108 2 109 5 110 4 111 5 112 1 113 5 114 5 115 3 116 4 117 4 118 5 119 3 120 4 121 5 122 4 123 4 124 4 125 5 126 4 127 5 128 4 129 4 130 4 131 4 132 4 133 4 134 5 135 3 136 4 137 3 138 4 139 4 140 3 141 5 142 5 143 4 144 5 145 4 146 3 147 5 148 4 149 5 150 4 151 4 152 4 153 4 154 5 155 3 156 4 157 5 158 4 159 5 160 4 161 3 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Respect_of_coach Respect_of_team 5.8105 -0.1566 -0.2958 Be_on_different_team Be_liked Proudness -0.4237 0.2918 -0.1564 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -4.611657 -0.588529 -0.008263 0.716900 2.964783 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.81049 0.64826 8.963 9.52e-16 *** Respect_of_coach -0.15661 0.10182 -1.538 0.1261 Respect_of_team -0.29580 0.07044 -4.199 4.50e-05 *** Be_on_different_team -0.42370 0.06709 -6.315 2.71e-09 *** Be_liked 0.29184 0.13300 2.194 0.0297 * Proudness -0.15638 0.12587 -1.242 0.2160 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.054 on 155 degrees of freedom Multiple R-squared: 0.2687, Adjusted R-squared: 0.2451 F-statistic: 11.39 on 5 and 155 DF, p-value: 2.269e-09 > 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,] 1.384579e-01 2.769158e-01 0.86154208 [2,] 5.558553e-02 1.111711e-01 0.94441447 [3,] 4.361000e-02 8.722000e-02 0.95639000 [4,] 1.733123e-02 3.466245e-02 0.98266877 [5,] 1.668793e-02 3.337586e-02 0.98331207 [6,] 6.870776e-03 1.374155e-02 0.99312922 [7,] 5.290472e-03 1.058094e-02 0.99470953 [8,] 2.179515e-03 4.359030e-03 0.99782048 [9,] 1.626525e-03 3.253050e-03 0.99837348 [10,] 8.104270e-04 1.620854e-03 0.99918957 [11,] 1.197331e-03 2.394663e-03 0.99880267 [12,] 5.338068e-04 1.067614e-03 0.99946619 [13,] 2.360068e-04 4.720137e-04 0.99976399 [14,] 9.315530e-05 1.863106e-04 0.99990684 [15,] 3.854790e-05 7.709581e-05 0.99996145 [16,] 2.121135e-05 4.242271e-05 0.99997879 [17,] 4.361794e-05 8.723587e-05 0.99995638 [18,] 2.691093e-05 5.382185e-05 0.99997309 [19,] 1.398156e-05 2.796312e-05 0.99998602 [20,] 6.828068e-06 1.365614e-05 0.99999317 [21,] 4.082286e-06 8.164571e-06 0.99999592 [22,] 1.758587e-06 3.517175e-06 0.99999824 [23,] 1.134350e-06 2.268700e-06 0.99999887 [24,] 4.619126e-07 9.238252e-07 0.99999954 [25,] 1.969252e-07 3.938505e-07 0.99999980 [26,] 1.153853e-07 2.307705e-07 0.99999988 [27,] 4.829540e-08 9.659080e-08 0.99999995 [28,] 1.998992e-08 3.997985e-08 0.99999998 [29,] 1.631121e-07 3.262243e-07 0.99999984 [30,] 8.401771e-08 1.680354e-07 0.99999992 [31,] 4.219974e-08 8.439948e-08 0.99999996 [32,] 1.898218e-08 3.796436e-08 0.99999998 [33,] 4.417878e-08 8.835756e-08 0.99999996 [34,] 1.992719e-08 3.985438e-08 0.99999998 [35,] 8.518950e-09 1.703790e-08 0.99999999 [36,] 3.616554e-09 7.233108e-09 1.00000000 [37,] 1.727594e-09 3.455188e-09 1.00000000 [38,] 6.877044e-10 1.375409e-09 1.00000000 [39,] 3.321879e-10 6.643757e-10 1.00000000 [40,] 1.334158e-09 2.668317e-09 1.00000000 [41,] 5.572798e-10 1.114560e-09 1.00000000 [42,] 1.443333e-09 2.886667e-09 1.00000000 [43,] 7.634914e-10 1.526983e-09 1.00000000 [44,] 4.147000e-10 8.293999e-10 1.00000000 [45,] 2.173676e-10 4.347351e-10 1.00000000 [46,] 2.240049e-10 4.480099e-10 1.00000000 [47,] 1.733903e-10 3.467806e-10 1.00000000 [48,] 6.246428e-10 1.249286e-09 1.00000000 [49,] 5.503238e-10 1.100648e-09 1.00000000 [50,] 8.480595e-10 1.696119e-09 1.00000000 [51,] 1.350891e-09 2.701782e-09 1.00000000 [52,] 2.171017e-09 4.342034e-09 1.00000000 [53,] 1.029303e-09 2.058605e-09 1.00000000 [54,] 2.550615e-09 5.101231e-09 1.00000000 [55,] 6.528195e-08 1.305639e-07 0.99999993 [56,] 3.880243e-08 7.760485e-08 0.99999996 [57,] 1.902451e-08 3.804902e-08 0.99999998 [58,] 9.702137e-09 1.940427e-08 0.99999999 [59,] 6.239883e-09 1.247977e-08 0.99999999 [60,] 3.027541e-09 6.055082e-09 1.00000000 [61,] 2.310828e-09 4.621656e-09 1.00000000 [62,] 3.962874e-09 7.925748e-09 1.00000000 [63,] 1.947742e-09 3.895483e-09 1.00000000 [64,] 9.205401e-10 1.841080e-09 1.00000000 [65,] 1.201305e-09 2.402611e-09 1.00000000 [66,] 1.472958e-09 2.945916e-09 1.00000000 [67,] 1.849283e-09 3.698567e-09 1.00000000 [68,] 2.092734e-09 4.185468e-09 1.00000000 [69,] 1.180596e-09 2.361192e-09 1.00000000 [70,] 9.282141e-08 1.856428e-07 0.99999991 [71,] 5.662704e-08 1.132541e-07 0.99999994 [72,] 2.912714e-08 5.825428e-08 0.99999997 [73,] 2.378886e-08 4.757772e-08 0.99999998 [74,] 2.948520e-08 5.897039e-08 0.99999997 [75,] 1.540316e-08 3.080632e-08 0.99999998 [76,] 1.027777e-08 2.055555e-08 0.99999999 [77,] 5.291897e-09 1.058379e-08 0.99999999 [78,] 2.930508e-09 5.861015e-09 1.00000000 [79,] 1.500705e-09 3.001409e-09 1.00000000 [80,] 7.425253e-10 1.485051e-09 1.00000000 [81,] 4.100331e-10 8.200662e-10 1.00000000 [82,] 8.559956e-10 1.711991e-09 1.00000000 [83,] 4.167111e-10 8.334222e-10 1.00000000 [84,] 4.762319e-09 9.524638e-09 1.00000000 [85,] 2.589313e-09 5.178626e-09 1.00000000 [86,] 3.729981e-09 7.459962e-09 1.00000000 [87,] 1.912865e-09 3.825729e-09 1.00000000 [88,] 2.349888e-09 4.699776e-09 1.00000000 [89,] 3.707275e-09 7.414549e-09 1.00000000 [90,] 2.165505e-09 4.331010e-09 1.00000000 [91,] 1.235610e-09 2.471220e-09 1.00000000 [92,] 7.249632e-10 1.449926e-09 1.00000000 [93,] 4.499607e-09 8.999214e-09 1.00000000 [94,] 1.392178e-08 2.784355e-08 0.99999999 [95,] 1.661472e-08 3.322944e-08 0.99999998 [96,] 4.488858e-08 8.977717e-08 0.99999996 [97,] 6.231926e-08 1.246385e-07 0.99999994 [98,] 1.086087e-07 2.172175e-07 0.99999989 [99,] 1.209703e-07 2.419405e-07 0.99999988 [100,] 6.919013e-08 1.383803e-07 0.99999993 [101,] 1.663453e-05 3.326907e-05 0.99998337 [102,] 6.930133e-05 1.386027e-04 0.99993070 [103,] 8.277676e-05 1.655535e-04 0.99991722 [104,] 1.410675e-03 2.821351e-03 0.99858932 [105,] 5.662658e-03 1.132532e-02 0.99433734 [106,] 1.313781e-02 2.627562e-02 0.98686219 [107,] 1.701158e-02 3.402315e-02 0.98298842 [108,] 1.634806e-02 3.269612e-02 0.98365194 [109,] 2.717673e-02 5.435346e-02 0.97282327 [110,] 3.878321e-02 7.756642e-02 0.96121679 [111,] 1.655497e-01 3.310994e-01 0.83445031 [112,] 1.367791e-01 2.735583e-01 0.86322086 [113,] 4.167095e-01 8.334191e-01 0.58329047 [114,] 5.673656e-01 8.652687e-01 0.43263436 [115,] 7.360197e-01 5.279606e-01 0.26398031 [116,] 7.506454e-01 4.987091e-01 0.24935457 [117,] 7.576692e-01 4.846617e-01 0.24233083 [118,] 7.886989e-01 4.226021e-01 0.21130106 [119,] 7.608833e-01 4.782335e-01 0.23911674 [120,] 9.409151e-01 1.181698e-01 0.05908488 [121,] 9.294661e-01 1.410679e-01 0.07053394 [122,] 9.526242e-01 9.475152e-02 0.04737576 [123,] 9.402540e-01 1.194920e-01 0.05974600 [124,] 9.385974e-01 1.228052e-01 0.06140262 [125,] 9.214819e-01 1.570362e-01 0.07851811 [126,] 9.371767e-01 1.256465e-01 0.06282327 [127,] 9.369510e-01 1.260980e-01 0.06304899 [128,] 9.123557e-01 1.752886e-01 0.08764432 [129,] 8.831033e-01 2.337934e-01 0.11689672 [130,] 8.828426e-01 2.343148e-01 0.11715741 [131,] 8.502991e-01 2.994017e-01 0.14970086 [132,] 9.173186e-01 1.653629e-01 0.08268144 [133,] 9.009133e-01 1.981734e-01 0.09908668 [134,] 8.716060e-01 2.567881e-01 0.12839403 [135,] 8.433979e-01 3.132042e-01 0.15660210 [136,] 8.144469e-01 3.711062e-01 0.18555308 [137,] 7.888013e-01 4.223974e-01 0.21119872 [138,] 7.406728e-01 5.186545e-01 0.25932723 [139,] 6.792346e-01 6.415308e-01 0.32076539 [140,] 5.764006e-01 8.471987e-01 0.42359937 [141,] 4.671625e-01 9.343250e-01 0.53283751 [142,] 4.248337e-01 8.496675e-01 0.57516626 [143,] 2.964779e-01 5.929558e-01 0.70352210 [144,] 3.012924e-01 6.025847e-01 0.69870765 > postscript(file="/var/www/rcomp/tmp/1w23q1290522067.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2pt3t1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3pt3t1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4pt3t1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5h22w1290522067.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 = 161 Frequency = 1 1 2 3 4 5 6 -0.588528534 1.197979585 1.020263273 0.728425293 0.881025758 0.884768555 7 8 9 10 11 12 -1.210722664 1.304725525 0.304725525 0.012887545 2.156090737 -0.588528534 13 14 15 16 17 18 1.304725525 -0.588528534 0.884991434 -0.588528534 0.461331040 1.041374070 19 20 21 22 23 24 0.119633486 -0.588528534 -0.147460767 -0.008262625 -0.588528534 -0.880366514 25 26 27 28 29 30 0.333434929 1.333434929 -0.118974242 -0.118974242 0.037631272 0.728425293 31 32 33 34 35 36 0.176829414 -0.588528534 -0.118974242 1.465073838 -0.118974242 0.304725525 37 38 39 40 41 42 -0.695274475 0.596563506 -0.567417737 0.728425293 0.119594112 -0.118974242 43 44 45 46 47 48 -0.588528534 -0.588528534 0.172863738 -0.008262625 1.152125061 1.304725525 49 50 51 52 53 54 -0.275356878 0.881025758 0.304725525 0.304725525 0.276016122 2.028011121 55 56 57 58 59 60 0.283575355 0.881025758 -0.118974242 0.020223899 -0.118974242 0.304725525 61 62 63 64 65 66 -1.308031957 0.304725525 0.795566184 -0.745134048 -0.275763261 0.421319607 67 68 69 70 71 72 0.403912234 0.108331456 0.276016122 -0.439482252 0.404135112 -0.275763261 73 74 75 76 77 78 -0.735285908 -0.443670806 1.396575879 0.848985050 0.560517748 -1.735285908 79 80 81 82 83 84 0.108331456 -0.275763261 0.844796496 0.852355729 -0.183729403 0.716900384 85 86 87 88 89 90 0.845019374 0.264714092 0.404135112 -0.432145897 0.560517748 -0.172165120 91 92 93 94 95 96 -0.275763261 1.716900384 0.421096728 0.972876112 0.264714092 -0.439482252 97 98 99 100 101 102 -0.735285908 -0.027123888 -0.027123888 0.560517748 1.421096728 1.845019374 103 104 105 106 107 108 -0.143678595 1.532031224 0.716900384 0.856321405 0.695973092 -0.744911170 109 110 111 112 113 114 0.752946142 2.401811941 0.546853173 2.964783488 -4.611657099 0.703458687 115 116 117 118 119 120 2.537267285 -0.723983878 1.575824828 0.144565828 -1.683710192 -0.008446130 121 122 123 124 125 126 -1.855434172 -1.399431444 -1.983107405 0.152125061 -0.403025001 -1.395465768 127 128 129 130 131 132 -0.267792535 1.308730575 -0.847874939 -1.847874939 -0.847874939 -1.687898746 133 134 135 136 137 138 -0.847874939 -0.263826860 -1.840315707 -0.008446130 -0.292724877 1.283391851 139 140 141 142 143 144 -1.410772848 -2.136119363 -0.012039687 -0.815383365 -0.971766001 -1.267792535 145 146 147 148 149 150 -0.971766001 0.148382263 -1.435739454 -0.847874939 -0.971988879 -1.263603981 151 152 153 154 155 156 -0.847874939 -1.847874939 -1.139712920 -0.559630516 -0.979513848 -0.300284110 157 158 159 160 161 -1.267792535 1.732430343 -1.107221345 -1.263603981 0.159684293 > postscript(file="/var/www/rcomp/tmp/6h22w1290522067.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 = 161 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.588528534 NA 1 1.197979585 -0.588528534 2 1.020263273 1.197979585 3 0.728425293 1.020263273 4 0.881025758 0.728425293 5 0.884768555 0.881025758 6 -1.210722664 0.884768555 7 1.304725525 -1.210722664 8 0.304725525 1.304725525 9 0.012887545 0.304725525 10 2.156090737 0.012887545 11 -0.588528534 2.156090737 12 1.304725525 -0.588528534 13 -0.588528534 1.304725525 14 0.884991434 -0.588528534 15 -0.588528534 0.884991434 16 0.461331040 -0.588528534 17 1.041374070 0.461331040 18 0.119633486 1.041374070 19 -0.588528534 0.119633486 20 -0.147460767 -0.588528534 21 -0.008262625 -0.147460767 22 -0.588528534 -0.008262625 23 -0.880366514 -0.588528534 24 0.333434929 -0.880366514 25 1.333434929 0.333434929 26 -0.118974242 1.333434929 27 -0.118974242 -0.118974242 28 0.037631272 -0.118974242 29 0.728425293 0.037631272 30 0.176829414 0.728425293 31 -0.588528534 0.176829414 32 -0.118974242 -0.588528534 33 1.465073838 -0.118974242 34 -0.118974242 1.465073838 35 0.304725525 -0.118974242 36 -0.695274475 0.304725525 37 0.596563506 -0.695274475 38 -0.567417737 0.596563506 39 0.728425293 -0.567417737 40 0.119594112 0.728425293 41 -0.118974242 0.119594112 42 -0.588528534 -0.118974242 43 -0.588528534 -0.588528534 44 0.172863738 -0.588528534 45 -0.008262625 0.172863738 46 1.152125061 -0.008262625 47 1.304725525 1.152125061 48 -0.275356878 1.304725525 49 0.881025758 -0.275356878 50 0.304725525 0.881025758 51 0.304725525 0.304725525 52 0.276016122 0.304725525 53 2.028011121 0.276016122 54 0.283575355 2.028011121 55 0.881025758 0.283575355 56 -0.118974242 0.881025758 57 0.020223899 -0.118974242 58 -0.118974242 0.020223899 59 0.304725525 -0.118974242 60 -1.308031957 0.304725525 61 0.304725525 -1.308031957 62 0.795566184 0.304725525 63 -0.745134048 0.795566184 64 -0.275763261 -0.745134048 65 0.421319607 -0.275763261 66 0.403912234 0.421319607 67 0.108331456 0.403912234 68 0.276016122 0.108331456 69 -0.439482252 0.276016122 70 0.404135112 -0.439482252 71 -0.275763261 0.404135112 72 -0.735285908 -0.275763261 73 -0.443670806 -0.735285908 74 1.396575879 -0.443670806 75 0.848985050 1.396575879 76 0.560517748 0.848985050 77 -1.735285908 0.560517748 78 0.108331456 -1.735285908 79 -0.275763261 0.108331456 80 0.844796496 -0.275763261 81 0.852355729 0.844796496 82 -0.183729403 0.852355729 83 0.716900384 -0.183729403 84 0.845019374 0.716900384 85 0.264714092 0.845019374 86 0.404135112 0.264714092 87 -0.432145897 0.404135112 88 0.560517748 -0.432145897 89 -0.172165120 0.560517748 90 -0.275763261 -0.172165120 91 1.716900384 -0.275763261 92 0.421096728 1.716900384 93 0.972876112 0.421096728 94 0.264714092 0.972876112 95 -0.439482252 0.264714092 96 -0.735285908 -0.439482252 97 -0.027123888 -0.735285908 98 -0.027123888 -0.027123888 99 0.560517748 -0.027123888 100 1.421096728 0.560517748 101 1.845019374 1.421096728 102 -0.143678595 1.845019374 103 1.532031224 -0.143678595 104 0.716900384 1.532031224 105 0.856321405 0.716900384 106 0.695973092 0.856321405 107 -0.744911170 0.695973092 108 0.752946142 -0.744911170 109 2.401811941 0.752946142 110 0.546853173 2.401811941 111 2.964783488 0.546853173 112 -4.611657099 2.964783488 113 0.703458687 -4.611657099 114 2.537267285 0.703458687 115 -0.723983878 2.537267285 116 1.575824828 -0.723983878 117 0.144565828 1.575824828 118 -1.683710192 0.144565828 119 -0.008446130 -1.683710192 120 -1.855434172 -0.008446130 121 -1.399431444 -1.855434172 122 -1.983107405 -1.399431444 123 0.152125061 -1.983107405 124 -0.403025001 0.152125061 125 -1.395465768 -0.403025001 126 -0.267792535 -1.395465768 127 1.308730575 -0.267792535 128 -0.847874939 1.308730575 129 -1.847874939 -0.847874939 130 -0.847874939 -1.847874939 131 -1.687898746 -0.847874939 132 -0.847874939 -1.687898746 133 -0.263826860 -0.847874939 134 -1.840315707 -0.263826860 135 -0.008446130 -1.840315707 136 -0.292724877 -0.008446130 137 1.283391851 -0.292724877 138 -1.410772848 1.283391851 139 -2.136119363 -1.410772848 140 -0.012039687 -2.136119363 141 -0.815383365 -0.012039687 142 -0.971766001 -0.815383365 143 -1.267792535 -0.971766001 144 -0.971766001 -1.267792535 145 0.148382263 -0.971766001 146 -1.435739454 0.148382263 147 -0.847874939 -1.435739454 148 -0.971988879 -0.847874939 149 -1.263603981 -0.971988879 150 -0.847874939 -1.263603981 151 -1.847874939 -0.847874939 152 -1.139712920 -1.847874939 153 -0.559630516 -1.139712920 154 -0.979513848 -0.559630516 155 -0.300284110 -0.979513848 156 -1.267792535 -0.300284110 157 1.732430343 -1.267792535 158 -1.107221345 1.732430343 159 -1.263603981 -1.107221345 160 0.159684293 -1.263603981 161 NA 0.159684293 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.197979585 -0.588528534 [2,] 1.020263273 1.197979585 [3,] 0.728425293 1.020263273 [4,] 0.881025758 0.728425293 [5,] 0.884768555 0.881025758 [6,] -1.210722664 0.884768555 [7,] 1.304725525 -1.210722664 [8,] 0.304725525 1.304725525 [9,] 0.012887545 0.304725525 [10,] 2.156090737 0.012887545 [11,] -0.588528534 2.156090737 [12,] 1.304725525 -0.588528534 [13,] -0.588528534 1.304725525 [14,] 0.884991434 -0.588528534 [15,] -0.588528534 0.884991434 [16,] 0.461331040 -0.588528534 [17,] 1.041374070 0.461331040 [18,] 0.119633486 1.041374070 [19,] -0.588528534 0.119633486 [20,] -0.147460767 -0.588528534 [21,] -0.008262625 -0.147460767 [22,] -0.588528534 -0.008262625 [23,] -0.880366514 -0.588528534 [24,] 0.333434929 -0.880366514 [25,] 1.333434929 0.333434929 [26,] -0.118974242 1.333434929 [27,] -0.118974242 -0.118974242 [28,] 0.037631272 -0.118974242 [29,] 0.728425293 0.037631272 [30,] 0.176829414 0.728425293 [31,] -0.588528534 0.176829414 [32,] -0.118974242 -0.588528534 [33,] 1.465073838 -0.118974242 [34,] -0.118974242 1.465073838 [35,] 0.304725525 -0.118974242 [36,] -0.695274475 0.304725525 [37,] 0.596563506 -0.695274475 [38,] -0.567417737 0.596563506 [39,] 0.728425293 -0.567417737 [40,] 0.119594112 0.728425293 [41,] -0.118974242 0.119594112 [42,] -0.588528534 -0.118974242 [43,] -0.588528534 -0.588528534 [44,] 0.172863738 -0.588528534 [45,] -0.008262625 0.172863738 [46,] 1.152125061 -0.008262625 [47,] 1.304725525 1.152125061 [48,] -0.275356878 1.304725525 [49,] 0.881025758 -0.275356878 [50,] 0.304725525 0.881025758 [51,] 0.304725525 0.304725525 [52,] 0.276016122 0.304725525 [53,] 2.028011121 0.276016122 [54,] 0.283575355 2.028011121 [55,] 0.881025758 0.283575355 [56,] -0.118974242 0.881025758 [57,] 0.020223899 -0.118974242 [58,] -0.118974242 0.020223899 [59,] 0.304725525 -0.118974242 [60,] -1.308031957 0.304725525 [61,] 0.304725525 -1.308031957 [62,] 0.795566184 0.304725525 [63,] -0.745134048 0.795566184 [64,] -0.275763261 -0.745134048 [65,] 0.421319607 -0.275763261 [66,] 0.403912234 0.421319607 [67,] 0.108331456 0.403912234 [68,] 0.276016122 0.108331456 [69,] -0.439482252 0.276016122 [70,] 0.404135112 -0.439482252 [71,] -0.275763261 0.404135112 [72,] -0.735285908 -0.275763261 [73,] -0.443670806 -0.735285908 [74,] 1.396575879 -0.443670806 [75,] 0.848985050 1.396575879 [76,] 0.560517748 0.848985050 [77,] -1.735285908 0.560517748 [78,] 0.108331456 -1.735285908 [79,] -0.275763261 0.108331456 [80,] 0.844796496 -0.275763261 [81,] 0.852355729 0.844796496 [82,] -0.183729403 0.852355729 [83,] 0.716900384 -0.183729403 [84,] 0.845019374 0.716900384 [85,] 0.264714092 0.845019374 [86,] 0.404135112 0.264714092 [87,] -0.432145897 0.404135112 [88,] 0.560517748 -0.432145897 [89,] -0.172165120 0.560517748 [90,] -0.275763261 -0.172165120 [91,] 1.716900384 -0.275763261 [92,] 0.421096728 1.716900384 [93,] 0.972876112 0.421096728 [94,] 0.264714092 0.972876112 [95,] -0.439482252 0.264714092 [96,] -0.735285908 -0.439482252 [97,] -0.027123888 -0.735285908 [98,] -0.027123888 -0.027123888 [99,] 0.560517748 -0.027123888 [100,] 1.421096728 0.560517748 [101,] 1.845019374 1.421096728 [102,] -0.143678595 1.845019374 [103,] 1.532031224 -0.143678595 [104,] 0.716900384 1.532031224 [105,] 0.856321405 0.716900384 [106,] 0.695973092 0.856321405 [107,] -0.744911170 0.695973092 [108,] 0.752946142 -0.744911170 [109,] 2.401811941 0.752946142 [110,] 0.546853173 2.401811941 [111,] 2.964783488 0.546853173 [112,] -4.611657099 2.964783488 [113,] 0.703458687 -4.611657099 [114,] 2.537267285 0.703458687 [115,] -0.723983878 2.537267285 [116,] 1.575824828 -0.723983878 [117,] 0.144565828 1.575824828 [118,] -1.683710192 0.144565828 [119,] -0.008446130 -1.683710192 [120,] -1.855434172 -0.008446130 [121,] -1.399431444 -1.855434172 [122,] -1.983107405 -1.399431444 [123,] 0.152125061 -1.983107405 [124,] -0.403025001 0.152125061 [125,] -1.395465768 -0.403025001 [126,] -0.267792535 -1.395465768 [127,] 1.308730575 -0.267792535 [128,] -0.847874939 1.308730575 [129,] -1.847874939 -0.847874939 [130,] -0.847874939 -1.847874939 [131,] -1.687898746 -0.847874939 [132,] -0.847874939 -1.687898746 [133,] -0.263826860 -0.847874939 [134,] -1.840315707 -0.263826860 [135,] -0.008446130 -1.840315707 [136,] -0.292724877 -0.008446130 [137,] 1.283391851 -0.292724877 [138,] -1.410772848 1.283391851 [139,] -2.136119363 -1.410772848 [140,] -0.012039687 -2.136119363 [141,] -0.815383365 -0.012039687 [142,] -0.971766001 -0.815383365 [143,] -1.267792535 -0.971766001 [144,] -0.971766001 -1.267792535 [145,] 0.148382263 -0.971766001 [146,] -1.435739454 0.148382263 [147,] -0.847874939 -1.435739454 [148,] -0.971988879 -0.847874939 [149,] -1.263603981 -0.971988879 [150,] -0.847874939 -1.263603981 [151,] -1.847874939 -0.847874939 [152,] -1.139712920 -1.847874939 [153,] -0.559630516 -1.139712920 [154,] -0.979513848 -0.559630516 [155,] -0.300284110 -0.979513848 [156,] -1.267792535 -0.300284110 [157,] 1.732430343 -1.267792535 [158,] -1.107221345 1.732430343 [159,] -1.263603981 -1.107221345 [160,] 0.159684293 -1.263603981 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.197979585 -0.588528534 2 1.020263273 1.197979585 3 0.728425293 1.020263273 4 0.881025758 0.728425293 5 0.884768555 0.881025758 6 -1.210722664 0.884768555 7 1.304725525 -1.210722664 8 0.304725525 1.304725525 9 0.012887545 0.304725525 10 2.156090737 0.012887545 11 -0.588528534 2.156090737 12 1.304725525 -0.588528534 13 -0.588528534 1.304725525 14 0.884991434 -0.588528534 15 -0.588528534 0.884991434 16 0.461331040 -0.588528534 17 1.041374070 0.461331040 18 0.119633486 1.041374070 19 -0.588528534 0.119633486 20 -0.147460767 -0.588528534 21 -0.008262625 -0.147460767 22 -0.588528534 -0.008262625 23 -0.880366514 -0.588528534 24 0.333434929 -0.880366514 25 1.333434929 0.333434929 26 -0.118974242 1.333434929 27 -0.118974242 -0.118974242 28 0.037631272 -0.118974242 29 0.728425293 0.037631272 30 0.176829414 0.728425293 31 -0.588528534 0.176829414 32 -0.118974242 -0.588528534 33 1.465073838 -0.118974242 34 -0.118974242 1.465073838 35 0.304725525 -0.118974242 36 -0.695274475 0.304725525 37 0.596563506 -0.695274475 38 -0.567417737 0.596563506 39 0.728425293 -0.567417737 40 0.119594112 0.728425293 41 -0.118974242 0.119594112 42 -0.588528534 -0.118974242 43 -0.588528534 -0.588528534 44 0.172863738 -0.588528534 45 -0.008262625 0.172863738 46 1.152125061 -0.008262625 47 1.304725525 1.152125061 48 -0.275356878 1.304725525 49 0.881025758 -0.275356878 50 0.304725525 0.881025758 51 0.304725525 0.304725525 52 0.276016122 0.304725525 53 2.028011121 0.276016122 54 0.283575355 2.028011121 55 0.881025758 0.283575355 56 -0.118974242 0.881025758 57 0.020223899 -0.118974242 58 -0.118974242 0.020223899 59 0.304725525 -0.118974242 60 -1.308031957 0.304725525 61 0.304725525 -1.308031957 62 0.795566184 0.304725525 63 -0.745134048 0.795566184 64 -0.275763261 -0.745134048 65 0.421319607 -0.275763261 66 0.403912234 0.421319607 67 0.108331456 0.403912234 68 0.276016122 0.108331456 69 -0.439482252 0.276016122 70 0.404135112 -0.439482252 71 -0.275763261 0.404135112 72 -0.735285908 -0.275763261 73 -0.443670806 -0.735285908 74 1.396575879 -0.443670806 75 0.848985050 1.396575879 76 0.560517748 0.848985050 77 -1.735285908 0.560517748 78 0.108331456 -1.735285908 79 -0.275763261 0.108331456 80 0.844796496 -0.275763261 81 0.852355729 0.844796496 82 -0.183729403 0.852355729 83 0.716900384 -0.183729403 84 0.845019374 0.716900384 85 0.264714092 0.845019374 86 0.404135112 0.264714092 87 -0.432145897 0.404135112 88 0.560517748 -0.432145897 89 -0.172165120 0.560517748 90 -0.275763261 -0.172165120 91 1.716900384 -0.275763261 92 0.421096728 1.716900384 93 0.972876112 0.421096728 94 0.264714092 0.972876112 95 -0.439482252 0.264714092 96 -0.735285908 -0.439482252 97 -0.027123888 -0.735285908 98 -0.027123888 -0.027123888 99 0.560517748 -0.027123888 100 1.421096728 0.560517748 101 1.845019374 1.421096728 102 -0.143678595 1.845019374 103 1.532031224 -0.143678595 104 0.716900384 1.532031224 105 0.856321405 0.716900384 106 0.695973092 0.856321405 107 -0.744911170 0.695973092 108 0.752946142 -0.744911170 109 2.401811941 0.752946142 110 0.546853173 2.401811941 111 2.964783488 0.546853173 112 -4.611657099 2.964783488 113 0.703458687 -4.611657099 114 2.537267285 0.703458687 115 -0.723983878 2.537267285 116 1.575824828 -0.723983878 117 0.144565828 1.575824828 118 -1.683710192 0.144565828 119 -0.008446130 -1.683710192 120 -1.855434172 -0.008446130 121 -1.399431444 -1.855434172 122 -1.983107405 -1.399431444 123 0.152125061 -1.983107405 124 -0.403025001 0.152125061 125 -1.395465768 -0.403025001 126 -0.267792535 -1.395465768 127 1.308730575 -0.267792535 128 -0.847874939 1.308730575 129 -1.847874939 -0.847874939 130 -0.847874939 -1.847874939 131 -1.687898746 -0.847874939 132 -0.847874939 -1.687898746 133 -0.263826860 -0.847874939 134 -1.840315707 -0.263826860 135 -0.008446130 -1.840315707 136 -0.292724877 -0.008446130 137 1.283391851 -0.292724877 138 -1.410772848 1.283391851 139 -2.136119363 -1.410772848 140 -0.012039687 -2.136119363 141 -0.815383365 -0.012039687 142 -0.971766001 -0.815383365 143 -1.267792535 -0.971766001 144 -0.971766001 -1.267792535 145 0.148382263 -0.971766001 146 -1.435739454 0.148382263 147 -0.847874939 -1.435739454 148 -0.971988879 -0.847874939 149 -1.263603981 -0.971988879 150 -0.847874939 -1.263603981 151 -1.847874939 -0.847874939 152 -1.139712920 -1.847874939 153 -0.559630516 -1.139712920 154 -0.979513848 -0.559630516 155 -0.300284110 -0.979513848 156 -1.267792535 -0.300284110 157 1.732430343 -1.267792535 158 -1.107221345 1.732430343 159 -1.263603981 -1.107221345 160 0.159684293 -1.263603981 > 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/7aujz1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8aujz1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/93l021290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/103l021290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11b76e1290522067.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/12amxw1290522067.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/13yndp1290522067.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/1426bd1290522067.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/15norj1290522067.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/16qpqp1290522067.tab") + } > > try(system("convert tmp/1w23q1290522067.ps tmp/1w23q1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/2pt3t1290522067.ps tmp/2pt3t1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/3pt3t1290522067.ps tmp/3pt3t1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/4pt3t1290522067.ps tmp/4pt3t1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/5h22w1290522067.ps tmp/5h22w1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/6h22w1290522067.ps tmp/6h22w1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/7aujz1290522067.ps tmp/7aujz1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/8aujz1290522067.ps tmp/8aujz1290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/93l021290522067.ps tmp/93l021290522067.png",intern=TRUE)) character(0) > try(system("convert tmp/103l021290522067.ps tmp/103l021290522067.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.480 2.280 7.689