R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(1901 + ,61 + ,17 + ,56 + ,84 + ,4 + ,21 + ,51 + ,9 + ,2509 + ,74 + ,19 + ,73 + ,47 + ,3 + ,15 + ,45 + ,9 + ,2114 + ,57 + ,18 + ,62 + ,63 + ,3 + ,17 + ,44 + ,9 + ,1331 + ,50 + ,15 + ,42 + ,28 + ,3 + ,20 + ,42 + ,9 + ,1399 + ,48 + ,15 + ,59 + ,22 + ,2 + ,12 + ,38 + ,9 + ,7333 + ,2 + ,12 + ,27 + ,18 + ,6 + ,4 + ,38 + ,9 + ,1170 + ,31 + ,20 + ,78 + ,27 + ,5 + ,11 + ,35 + ,9 + ,1507 + ,61 + ,14 + ,56 + ,37 + ,5 + ,12 + ,35 + ,9 + ,1107 + ,36 + ,15 + ,59 + ,20 + ,5 + ,9 + ,34 + ,9 + ,2051 + ,46 + ,13 + ,51 + ,67 + ,5 + ,14 + ,33 + ,9 + ,1290 + ,30 + ,17 + ,47 + ,28 + ,4 + ,11 + ,32 + ,9 + ,820 + ,49 + ,10 + ,35 + ,45 + ,3 + ,14 + ,31 + ,9 + ,1502 + ,14 + ,13 + ,47 + ,15 + ,5 + ,4 + ,30 + ,9 + ,1451 + ,12 + ,12 + ,47 + ,23 + ,6 + ,7 + ,30 + ,9 + ,1178 + ,54 + ,16 + ,55 + ,30 + ,6 + ,9 + ,30 + ,9 + ,1514 + ,44 + ,15 + ,54 + ,27 + ,2 + ,14 + ,29 + ,9 + ,883 + ,40 + ,15 + ,60 + ,43 + ,5 + ,13 + ,29 + ,9 + ,1405 + ,57 + ,15 + ,55 + ,36 + ,5 + ,11 + ,29 + ,9 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,0 + ,12 + ,40 + ,0 + ,0 + ,0 + ,5 + ,11 + ,244 + ,8 + ,0 + ,0 + ,13 + ,4 + ,4 + ,5 + ,11 + ,269 + ,3 + ,9 + ,23 + ,1 + ,0 + ,1 + ,5 + ,11 + ,242 + ,0 + ,4 + ,13 + ,0 + ,0 + ,0 + ,4 + ,11 + ,291 + ,0 + ,14 + ,6 + ,39 + ,0 + ,2 + ,4 + ,11 + ,213 + ,0 + ,9 + ,31 + ,10 + ,0 + ,0 + ,4 + ,11 + ,135 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,3 + ,11 + ,210 + ,3 + ,1 + ,3 + ,3 + ,3 + ,3 + ,3 + ,11) + ,dim=c(9 + ,140) + ,dimnames=list(c('month' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:140)) > y <- array(NA,dim=c(9,140),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:140)) > 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 = '4' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Learning month Connected Separate Software Happiness Depression Belonging 1 56 1901 61 17 84 4 21 51 2 73 2509 74 19 47 3 15 45 3 62 2114 57 18 63 3 17 44 4 42 1331 50 15 28 3 20 42 5 59 1399 48 15 22 2 12 38 6 27 7333 2 12 18 6 4 38 7 78 1170 31 20 27 5 11 35 8 56 1507 61 14 37 5 12 35 9 59 1107 36 15 20 5 9 34 10 51 2051 46 13 67 5 14 33 11 47 1290 30 17 28 4 11 32 12 35 820 49 10 45 3 14 31 13 47 1502 14 13 15 5 4 30 14 47 1451 12 12 23 6 7 30 15 55 1178 54 16 30 6 9 30 16 54 1514 44 15 27 2 14 29 17 60 883 40 15 43 5 13 29 18 55 1405 57 15 36 5 11 29 19 48 927 29 12 28 5 9 28 20 47 1352 32 13 28 9 8 27 21 47 1314 28 12 22 4 9 27 22 52 1307 40 15 27 4 11 27 23 48 1243 54 12 24 5 7 26 24 48 1232 56 12 52 3 15 26 25 27 1097 19 9 12 0 4 26 26 12 1100 67 12 24 5 10 26 27 51 1316 25 13 10 3 10 26 28 58 903 42 16 71 4 13 25 29 60 929 28 15 12 2 10 25 30 46 1049 57 13 24 5 10 25 31 45 1372 28 12 22 11 6 24 32 42 1470 35 13 21 5 8 24 33 41 821 10 12 13 3 7 24 34 47 1239 30 12 28 4 11 24 35 32 1384 23 8 19 5 10 24 36 56 820 32 15 29 5 11 24 37 42 1462 24 12 12 2 10 24 38 41 1202 42 12 32 6 8 23 39 47 1091 33 12 21 3 10 23 40 47 1228 19 14 19 4 5 23 41 49 707 17 15 15 8 5 23 42 52 868 49 15 14 14 5 23 43 42 1165 30 12 34 11 9 22 44 55 1106 3 13 8 8 2 22 45 48 1429 56 12 27 3 9 22 46 48 1671 37 13 31 3 13 22 47 38 1579 26 12 21 11 7 22 48 48 774 19 12 10 3 5 21 49 50 934 22 13 21 4 7 21 50 39 825 53 12 19 3 8 21 51 48 1375 35 12 27 5 8 21 52 36 968 12 9 17 6 5 21 53 49 1156 34 13 30 8 5 21 54 39 1374 28 13 19 3 10 21 55 41 1224 38 12 17 3 5 21 56 45 804 38 15 24 5 10 21 57 60 998 45 15 36 5 10 21 58 45 1112 15 13 16 3 7 21 59 41 1153 35 14 16 3 10 20 60 52 613 27 14 30 3 9 20 61 46 729 23 12 18 5 10 20 62 39 813 33 12 26 3 10 20 63 32 912 23 9 17 3 5 20 64 52 1178 26 14 28 6 8 20 65 54 1201 32 16 20 4 6 19 66 51 1165 35 15 27 3 7 19 67 52 705 18 13 13 13 6 18 68 57 814 18 16 10 5 3 17 69 47 1082 41 12 29 6 9 17 70 45 885 39 12 34 5 11 17 71 41 837 56 12 30 3 9 17 72 43 586 35 12 16 4 10 16 73 31 913 37 10 22 4 9 16 74 32 547 26 15 22 7 7 15 75 41 758 33 12 31 4 6 15 76 27 848 7 9 10 5 6 15 77 40 634 16 10 7 7 5 15 78 46 501 13 13 10 3 5 15 79 32 849 54 12 55 6 8 15 80 9 733 30 13 25 8 7 15 81 64 634 9 16 9 5 5 15 82 30 1010 35 15 31 5 10 15 83 46 778 0 12 0 0 0 15 84 37 480 40 12 24 3 10 15 85 22 848 22 12 14 5 6 15 86 20 714 29 12 11 3 6 14 87 21 871 25 12 8 8 4 14 88 44 776 17 14 9 9 3 14 89 24 815 32 12 18 9 7 14 90 33 811 40 12 14 4 5 14 91 45 529 24 12 27 2 8 13 92 35 642 18 13 10 0 0 13 93 31 562 15 8 16 3 5 13 94 20 626 17 16 13 7 5 13 95 13 636 28 12 10 5 5 13 96 33 935 18 11 16 3 5 13 97 58 473 16 15 11 3 6 12 98 26 836 28 13 8 3 5 12 99 36 938 17 12 29 7 6 12 100 32 656 25 13 12 4 4 12 101 34 566 2 13 1 0 0 12 102 15 765 10 12 26 5 8 12 103 40 705 9 12 5 5 2 11 104 37 558 7 12 5 5 2 11 105 26 582 27 14 24 6 8 11 106 31 608 25 12 19 6 3 11 107 47 567 16 16 10 5 3 11 108 21 434 28 8 6 6 3 11 109 21 479 7 8 61 0 3 11 110 9 488 0 5 25 25 1 10 111 28 507 16 9 7 2 2 10 112 24 394 10 11 10 5 2 10 113 15 504 0 4 3 3 1 9 114 19 368 2 8 1 1 2 9 115 35 386 5 13 38 5 7 9 116 45 451 36 13 13 4 4 9 117 20 580 10 12 2 0 1 9 118 1 565 43 13 8 4 6 9 119 29 510 14 12 30 10 3 9 120 33 495 12 12 11 6 2 8 121 32 596 15 10 69 23 3 8 122 11 412 8 12 2 0 2 8 123 10 338 39 5 23 6 5 7 124 18 446 10 13 8 4 4 7 125 41 418 0 12 0 0 0 7 126 0 335 7 6 2 0 0 6 127 10 349 10 9 4 2 3 6 128 24 308 3 12 4 4 2 5 129 28 466 8 15 0 0 0 5 130 38 228 0 11 9 9 1 5 131 4 428 8 3 5 5 3 5 132 25 242 1 8 0 0 0 5 133 40 352 0 12 0 0 0 5 134 0 244 8 0 13 4 4 5 135 23 269 3 9 1 0 1 5 136 13 242 0 4 0 0 0 4 137 6 291 0 14 39 0 2 4 138 31 213 0 9 10 0 0 4 139 0 135 0 0 1 0 1 3 140 3 210 3 1 3 3 3 3 Belonging_Final 1 9 2 9 3 9 4 9 5 9 6 9 7 9 8 9 9 9 10 9 11 9 12 9 13 9 14 9 15 9 16 9 17 9 18 9 19 9 20 9 21 9 22 9 23 9 24 9 25 9 26 9 27 9 28 9 29 9 30 9 31 9 32 9 33 9 34 9 35 9 36 9 37 9 38 9 39 9 40 9 41 9 42 9 43 9 44 9 45 9 46 9 47 10 48 10 49 10 50 10 51 10 52 10 53 10 54 10 55 10 56 10 57 10 58 10 59 10 60 10 61 10 62 10 63 10 64 10 65 10 66 10 67 10 68 10 69 10 70 10 71 10 72 10 73 10 74 10 75 10 76 10 77 10 78 10 79 10 80 10 81 10 82 10 83 10 84 10 85 10 86 10 87 10 88 10 89 10 90 10 91 10 92 10 93 10 94 11 95 11 96 11 97 11 98 11 99 11 100 11 101 11 102 11 103 11 104 11 105 11 106 11 107 11 108 11 109 11 110 11 111 11 112 11 113 11 114 11 115 11 116 11 117 11 118 11 119 11 120 11 121 11 122 11 123 11 124 11 125 11 126 11 127 11 128 11 129 11 130 11 131 11 132 11 133 11 134 11 135 11 136 11 137 11 138 11 139 11 140 11 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) month Connected Separate 63.377250 -0.004361 -0.104279 2.513469 Software Happiness Depression Belonging 0.053009 0.017854 -0.347745 0.634499 Belonging_Final -5.995953 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -30.173 -3.729 1.676 5.926 20.443 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 63.377250 23.362232 2.713 0.00757 ** month -0.004361 0.001629 -2.676 0.00839 ** Connected -0.104279 0.077024 -1.354 0.17811 Separate 2.513469 0.298703 8.415 5.92e-14 *** Software 0.053009 0.077075 0.688 0.49282 Happiness 0.017854 0.238579 0.075 0.94046 Depression -0.347745 0.432475 -0.804 0.42281 Belonging 0.634499 0.248472 2.554 0.01181 * Belonging_Final -5.995953 2.055088 -2.918 0.00415 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 9.353 on 131 degrees of freedom Multiple R-squared: 0.6768, Adjusted R-squared: 0.6571 F-statistic: 34.3 on 8 and 131 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 5.392595e-01 9.214810e-01 0.4607405 [2,] 3.873542e-01 7.747084e-01 0.6126458 [3,] 2.939762e-01 5.879524e-01 0.7060238 [4,] 3.474767e-01 6.949533e-01 0.6525233 [5,] 2.841921e-01 5.683841e-01 0.7158079 [6,] 2.367192e-01 4.734385e-01 0.7632808 [7,] 1.661132e-01 3.322265e-01 0.8338868 [8,] 1.135946e-01 2.271893e-01 0.8864054 [9,] 7.161748e-02 1.432350e-01 0.9283825 [10,] 4.644169e-02 9.288337e-02 0.9535583 [11,] 2.870489e-02 5.740979e-02 0.9712951 [12,] 1.725538e-02 3.451077e-02 0.9827446 [13,] 1.057746e-02 2.115492e-02 0.9894225 [14,] 1.545719e-02 3.091439e-02 0.9845428 [15,] 5.830559e-01 8.338881e-01 0.4169441 [16,] 5.486459e-01 9.027082e-01 0.4513541 [17,] 5.173792e-01 9.652416e-01 0.4826208 [18,] 4.671752e-01 9.343503e-01 0.5328248 [19,] 4.027133e-01 8.054265e-01 0.5972867 [20,] 3.420972e-01 6.841943e-01 0.6579028 [21,] 2.943586e-01 5.887171e-01 0.7056414 [22,] 2.683614e-01 5.367229e-01 0.7316386 [23,] 2.285740e-01 4.571480e-01 0.7714260 [24,] 2.031878e-01 4.063755e-01 0.7968122 [25,] 1.606304e-01 3.212608e-01 0.8393696 [26,] 1.266528e-01 2.533055e-01 0.8733472 [27,] 9.995474e-02 1.999095e-01 0.9000453 [28,] 7.952383e-02 1.590477e-01 0.9204762 [29,] 6.916986e-02 1.383397e-01 0.9308301 [30,] 7.000462e-02 1.400092e-01 0.9299954 [31,] 5.228320e-02 1.045664e-01 0.9477168 [32,] 3.954097e-02 7.908195e-02 0.9604590 [33,] 3.331846e-02 6.663692e-02 0.9666815 [34,] 3.006697e-02 6.013395e-02 0.9699330 [35,] 2.235918e-02 4.471836e-02 0.9776408 [36,] 1.574755e-02 3.149509e-02 0.9842525 [37,] 1.173862e-02 2.347725e-02 0.9882614 [38,] 8.022050e-03 1.604410e-02 0.9919779 [39,] 5.688451e-03 1.137690e-02 0.9943115 [40,] 4.686488e-03 9.372976e-03 0.9953135 [41,] 3.299294e-03 6.598588e-03 0.9967007 [42,] 2.160833e-03 4.321666e-03 0.9978392 [43,] 1.914599e-03 3.829198e-03 0.9980854 [44,] 1.242195e-03 2.484390e-03 0.9987578 [45,] 1.183687e-03 2.367373e-03 0.9988163 [46,] 1.194166e-03 2.388332e-03 0.9988058 [47,] 7.891434e-04 1.578287e-03 0.9992109 [48,] 6.323386e-04 1.264677e-03 0.9993677 [49,] 3.904463e-04 7.808926e-04 0.9996096 [50,] 2.583622e-04 5.167244e-04 0.9997416 [51,] 1.719669e-04 3.439337e-04 0.9998280 [52,] 1.185823e-04 2.371646e-04 0.9998814 [53,] 7.713716e-05 1.542743e-04 0.9999229 [54,] 5.293547e-05 1.058709e-04 0.9999471 [55,] 3.597788e-05 7.195577e-05 0.9999640 [56,] 2.700600e-05 5.401199e-05 0.9999730 [57,] 1.725260e-05 3.450521e-05 0.9999827 [58,] 2.222809e-05 4.445619e-05 0.9999778 [59,] 1.979688e-05 3.959377e-05 0.9999802 [60,] 1.518866e-05 3.037732e-05 0.9999848 [61,] 1.061866e-05 2.123731e-05 0.9999894 [62,] 7.561798e-06 1.512360e-05 0.9999924 [63,] 1.182049e-04 2.364098e-04 0.9998818 [64,] 8.661629e-05 1.732326e-04 0.9999134 [65,] 6.014713e-05 1.202943e-04 0.9999399 [66,] 4.317648e-05 8.635296e-05 0.9999568 [67,] 2.552015e-05 5.104029e-05 0.9999745 [68,] 3.166888e-05 6.333776e-05 0.9999683 [69,] 8.578134e-03 1.715627e-02 0.9914219 [70,] 1.100646e-02 2.201293e-02 0.9889935 [71,] 1.664826e-02 3.329652e-02 0.9833517 [72,] 1.272837e-02 2.545673e-02 0.9872716 [73,] 9.752831e-03 1.950566e-02 0.9902472 [74,] 1.575984e-02 3.151968e-02 0.9842402 [75,] 2.745837e-02 5.491675e-02 0.9725416 [76,] 4.107393e-02 8.214785e-02 0.9589261 [77,] 3.082486e-02 6.164972e-02 0.9691751 [78,] 3.251399e-02 6.502799e-02 0.9674860 [79,] 2.443739e-02 4.887478e-02 0.9755626 [80,] 2.382493e-02 4.764985e-02 0.9761751 [81,] 2.252640e-02 4.505280e-02 0.9774736 [82,] 1.655020e-02 3.310041e-02 0.9834498 [83,] 6.191081e-02 1.238216e-01 0.9380892 [84,] 1.066527e-01 2.133055e-01 0.8933473 [85,] 9.681013e-02 1.936203e-01 0.9031899 [86,] 2.035600e-01 4.071199e-01 0.7964400 [87,] 1.724500e-01 3.449000e-01 0.8275500 [88,] 1.958325e-01 3.916649e-01 0.8041675 [89,] 1.617205e-01 3.234409e-01 0.8382795 [90,] 1.361598e-01 2.723196e-01 0.8638402 [91,] 1.384001e-01 2.768001e-01 0.8615999 [92,] 1.495929e-01 2.991858e-01 0.8504071 [93,] 1.280484e-01 2.560969e-01 0.8719516 [94,] 1.076238e-01 2.152476e-01 0.8923762 [95,] 8.544511e-02 1.708902e-01 0.9145549 [96,] 8.609716e-02 1.721943e-01 0.9139028 [97,] 6.471629e-02 1.294326e-01 0.9352837 [98,] 4.867776e-02 9.735551e-02 0.9513222 [99,] 1.491116e-01 2.982231e-01 0.8508884 [100,] 1.335279e-01 2.670558e-01 0.8664721 [101,] 1.536266e-01 3.072531e-01 0.8463734 [102,] 1.195249e-01 2.390498e-01 0.8804751 [103,] 1.050244e-01 2.100488e-01 0.8949756 [104,] 2.026462e-01 4.052924e-01 0.7973538 [105,] 5.035448e-01 9.929103e-01 0.4964552 [106,] 4.444086e-01 8.888172e-01 0.5555914 [107,] 5.169761e-01 9.660478e-01 0.4830239 [108,] 4.375856e-01 8.751711e-01 0.5624144 [109,] 3.782183e-01 7.564366e-01 0.6217817 [110,] 2.992484e-01 5.984968e-01 0.7007516 [111,] 3.470322e-01 6.940643e-01 0.6529678 [112,] 6.931517e-01 6.136966e-01 0.3068483 [113,] 6.130164e-01 7.739673e-01 0.3869836 [114,] 5.053379e-01 9.893242e-01 0.4946621 [115,] 6.653224e-01 6.693551e-01 0.3346776 [116,] 5.680107e-01 8.639787e-01 0.4319893 [117,] 4.962969e-01 9.925937e-01 0.5037031 > postscript(file="/var/wessaorg/rcomp/tmp/1o33z1352124478.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/24h951352124478.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3sjc31352124478.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/415a31352124478.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5neod1352124478.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 140 Frequency = 1 1 2 3 4 5 6 -11.07308322 8.60652534 -2.89332687 -15.32966229 1.85023409 -4.17198151 7 8 9 10 11 12 6.74871482 4.24508118 0.87280948 2.94073422 -14.42354899 -8.10297833 13 14 15 16 17 18 -5.60766801 -2.92383342 -1.46397507 3.07551603 4.65743800 3.38197180 19 20 21 22 23 24 -0.71871833 -1.85077310 0.83494418 -0.05418539 4.05174663 5.54576982 25 26 27 28 29 30 -14.01213199 -30.17294473 3.65355925 1.51134690 6.79825754 0.68290166 31 32 33 34 35 36 -0.17686232 -3.67741595 -7.48890680 2.99739585 -1.93492506 2.76760951 37 38 39 40 41 42 -1.11977504 -2.56907165 3.34053849 -4.19947856 -7.05274151 -0.06786440 43 44 45 46 47 48 -2.19480971 4.21647109 8.18153771 5.92096232 1.18291933 7.60764566 49 50 51 52 53 54 8.19924712 2.94169460 12.00319672 2.83942293 8.17466087 0.91067878 55 56 57 58 59 60 4.18014824 0.14026362 16.08006574 3.52836815 0.95700611 7.67820096 61 62 63 64 65 66 7.74198665 1.76270788 0.43036637 9.74234879 7.84016539 7.50403176 67 68 69 70 71 72 10.60269271 8.43070827 13.11309710 10.49380926 7.60950487 8.03164537 73 74 75 76 77 78 2.02724914 -12.39769001 5.02149128 -2.66159061 7.60586738 5.08505797 79 80 81 82 83 84 -2.00425280 -29.31944956 15.72478846 -10.83836753 6.29569122 2.31910694 85 86 87 88 89 90 -13.84984003 -14.87496660 -14.23321162 2.07275329 -10.25215089 -0.82954331 91 92 93 94 95 96 9.29663793 -5.19487283 4.07790156 -20.45865284 -16.01936336 6.47277583 97 98 99 100 101 102 20.44304821 -3.88449581 7.08982485 0.44013069 -1.08721792 -14.50428387 103 104 105 106 107 108 9.79100983 5.94144823 -6.83379228 1.62427745 6.94803496 1.92136557 109 110 111 112 113 114 -2.88062603 -6.62996558 5.78002524 -4.57792109 4.14675054 -1.80213207 115 116 117 118 119 120 1.72784392 15.54377658 -9.48023288 -26.26862935 -1.33564554 3.75572015 121 122 123 124 125 126 5.50564000 -18.43912231 1.52257140 -12.65525191 10.79783045 -14.22484491 127 128 129 130 131 132 -10.48985481 -4.68795594 -7.43003659 10.46177073 -0.74518789 5.45752392 133 134 135 136 137 138 10.77903051 1.93440618 1.56508584 4.04161888 -29.25124775 8.81773179 139 140 1.55814990 3.22047273 > postscript(file="/var/wessaorg/rcomp/tmp/65uh41352124478.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 = 140 Frequency = 1 lag(myerror, k = 1) myerror 0 -11.07308322 NA 1 8.60652534 -11.07308322 2 -2.89332687 8.60652534 3 -15.32966229 -2.89332687 4 1.85023409 -15.32966229 5 -4.17198151 1.85023409 6 6.74871482 -4.17198151 7 4.24508118 6.74871482 8 0.87280948 4.24508118 9 2.94073422 0.87280948 10 -14.42354899 2.94073422 11 -8.10297833 -14.42354899 12 -5.60766801 -8.10297833 13 -2.92383342 -5.60766801 14 -1.46397507 -2.92383342 15 3.07551603 -1.46397507 16 4.65743800 3.07551603 17 3.38197180 4.65743800 18 -0.71871833 3.38197180 19 -1.85077310 -0.71871833 20 0.83494418 -1.85077310 21 -0.05418539 0.83494418 22 4.05174663 -0.05418539 23 5.54576982 4.05174663 24 -14.01213199 5.54576982 25 -30.17294473 -14.01213199 26 3.65355925 -30.17294473 27 1.51134690 3.65355925 28 6.79825754 1.51134690 29 0.68290166 6.79825754 30 -0.17686232 0.68290166 31 -3.67741595 -0.17686232 32 -7.48890680 -3.67741595 33 2.99739585 -7.48890680 34 -1.93492506 2.99739585 35 2.76760951 -1.93492506 36 -1.11977504 2.76760951 37 -2.56907165 -1.11977504 38 3.34053849 -2.56907165 39 -4.19947856 3.34053849 40 -7.05274151 -4.19947856 41 -0.06786440 -7.05274151 42 -2.19480971 -0.06786440 43 4.21647109 -2.19480971 44 8.18153771 4.21647109 45 5.92096232 8.18153771 46 1.18291933 5.92096232 47 7.60764566 1.18291933 48 8.19924712 7.60764566 49 2.94169460 8.19924712 50 12.00319672 2.94169460 51 2.83942293 12.00319672 52 8.17466087 2.83942293 53 0.91067878 8.17466087 54 4.18014824 0.91067878 55 0.14026362 4.18014824 56 16.08006574 0.14026362 57 3.52836815 16.08006574 58 0.95700611 3.52836815 59 7.67820096 0.95700611 60 7.74198665 7.67820096 61 1.76270788 7.74198665 62 0.43036637 1.76270788 63 9.74234879 0.43036637 64 7.84016539 9.74234879 65 7.50403176 7.84016539 66 10.60269271 7.50403176 67 8.43070827 10.60269271 68 13.11309710 8.43070827 69 10.49380926 13.11309710 70 7.60950487 10.49380926 71 8.03164537 7.60950487 72 2.02724914 8.03164537 73 -12.39769001 2.02724914 74 5.02149128 -12.39769001 75 -2.66159061 5.02149128 76 7.60586738 -2.66159061 77 5.08505797 7.60586738 78 -2.00425280 5.08505797 79 -29.31944956 -2.00425280 80 15.72478846 -29.31944956 81 -10.83836753 15.72478846 82 6.29569122 -10.83836753 83 2.31910694 6.29569122 84 -13.84984003 2.31910694 85 -14.87496660 -13.84984003 86 -14.23321162 -14.87496660 87 2.07275329 -14.23321162 88 -10.25215089 2.07275329 89 -0.82954331 -10.25215089 90 9.29663793 -0.82954331 91 -5.19487283 9.29663793 92 4.07790156 -5.19487283 93 -20.45865284 4.07790156 94 -16.01936336 -20.45865284 95 6.47277583 -16.01936336 96 20.44304821 6.47277583 97 -3.88449581 20.44304821 98 7.08982485 -3.88449581 99 0.44013069 7.08982485 100 -1.08721792 0.44013069 101 -14.50428387 -1.08721792 102 9.79100983 -14.50428387 103 5.94144823 9.79100983 104 -6.83379228 5.94144823 105 1.62427745 -6.83379228 106 6.94803496 1.62427745 107 1.92136557 6.94803496 108 -2.88062603 1.92136557 109 -6.62996558 -2.88062603 110 5.78002524 -6.62996558 111 -4.57792109 5.78002524 112 4.14675054 -4.57792109 113 -1.80213207 4.14675054 114 1.72784392 -1.80213207 115 15.54377658 1.72784392 116 -9.48023288 15.54377658 117 -26.26862935 -9.48023288 118 -1.33564554 -26.26862935 119 3.75572015 -1.33564554 120 5.50564000 3.75572015 121 -18.43912231 5.50564000 122 1.52257140 -18.43912231 123 -12.65525191 1.52257140 124 10.79783045 -12.65525191 125 -14.22484491 10.79783045 126 -10.48985481 -14.22484491 127 -4.68795594 -10.48985481 128 -7.43003659 -4.68795594 129 10.46177073 -7.43003659 130 -0.74518789 10.46177073 131 5.45752392 -0.74518789 132 10.77903051 5.45752392 133 1.93440618 10.77903051 134 1.56508584 1.93440618 135 4.04161888 1.56508584 136 -29.25124775 4.04161888 137 8.81773179 -29.25124775 138 1.55814990 8.81773179 139 3.22047273 1.55814990 140 NA 3.22047273 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 8.60652534 -11.07308322 [2,] -2.89332687 8.60652534 [3,] -15.32966229 -2.89332687 [4,] 1.85023409 -15.32966229 [5,] -4.17198151 1.85023409 [6,] 6.74871482 -4.17198151 [7,] 4.24508118 6.74871482 [8,] 0.87280948 4.24508118 [9,] 2.94073422 0.87280948 [10,] -14.42354899 2.94073422 [11,] -8.10297833 -14.42354899 [12,] -5.60766801 -8.10297833 [13,] -2.92383342 -5.60766801 [14,] -1.46397507 -2.92383342 [15,] 3.07551603 -1.46397507 [16,] 4.65743800 3.07551603 [17,] 3.38197180 4.65743800 [18,] -0.71871833 3.38197180 [19,] -1.85077310 -0.71871833 [20,] 0.83494418 -1.85077310 [21,] -0.05418539 0.83494418 [22,] 4.05174663 -0.05418539 [23,] 5.54576982 4.05174663 [24,] -14.01213199 5.54576982 [25,] -30.17294473 -14.01213199 [26,] 3.65355925 -30.17294473 [27,] 1.51134690 3.65355925 [28,] 6.79825754 1.51134690 [29,] 0.68290166 6.79825754 [30,] -0.17686232 0.68290166 [31,] -3.67741595 -0.17686232 [32,] -7.48890680 -3.67741595 [33,] 2.99739585 -7.48890680 [34,] -1.93492506 2.99739585 [35,] 2.76760951 -1.93492506 [36,] -1.11977504 2.76760951 [37,] -2.56907165 -1.11977504 [38,] 3.34053849 -2.56907165 [39,] -4.19947856 3.34053849 [40,] -7.05274151 -4.19947856 [41,] -0.06786440 -7.05274151 [42,] -2.19480971 -0.06786440 [43,] 4.21647109 -2.19480971 [44,] 8.18153771 4.21647109 [45,] 5.92096232 8.18153771 [46,] 1.18291933 5.92096232 [47,] 7.60764566 1.18291933 [48,] 8.19924712 7.60764566 [49,] 2.94169460 8.19924712 [50,] 12.00319672 2.94169460 [51,] 2.83942293 12.00319672 [52,] 8.17466087 2.83942293 [53,] 0.91067878 8.17466087 [54,] 4.18014824 0.91067878 [55,] 0.14026362 4.18014824 [56,] 16.08006574 0.14026362 [57,] 3.52836815 16.08006574 [58,] 0.95700611 3.52836815 [59,] 7.67820096 0.95700611 [60,] 7.74198665 7.67820096 [61,] 1.76270788 7.74198665 [62,] 0.43036637 1.76270788 [63,] 9.74234879 0.43036637 [64,] 7.84016539 9.74234879 [65,] 7.50403176 7.84016539 [66,] 10.60269271 7.50403176 [67,] 8.43070827 10.60269271 [68,] 13.11309710 8.43070827 [69,] 10.49380926 13.11309710 [70,] 7.60950487 10.49380926 [71,] 8.03164537 7.60950487 [72,] 2.02724914 8.03164537 [73,] -12.39769001 2.02724914 [74,] 5.02149128 -12.39769001 [75,] -2.66159061 5.02149128 [76,] 7.60586738 -2.66159061 [77,] 5.08505797 7.60586738 [78,] -2.00425280 5.08505797 [79,] -29.31944956 -2.00425280 [80,] 15.72478846 -29.31944956 [81,] -10.83836753 15.72478846 [82,] 6.29569122 -10.83836753 [83,] 2.31910694 6.29569122 [84,] -13.84984003 2.31910694 [85,] -14.87496660 -13.84984003 [86,] -14.23321162 -14.87496660 [87,] 2.07275329 -14.23321162 [88,] -10.25215089 2.07275329 [89,] -0.82954331 -10.25215089 [90,] 9.29663793 -0.82954331 [91,] -5.19487283 9.29663793 [92,] 4.07790156 -5.19487283 [93,] -20.45865284 4.07790156 [94,] -16.01936336 -20.45865284 [95,] 6.47277583 -16.01936336 [96,] 20.44304821 6.47277583 [97,] -3.88449581 20.44304821 [98,] 7.08982485 -3.88449581 [99,] 0.44013069 7.08982485 [100,] -1.08721792 0.44013069 [101,] -14.50428387 -1.08721792 [102,] 9.79100983 -14.50428387 [103,] 5.94144823 9.79100983 [104,] -6.83379228 5.94144823 [105,] 1.62427745 -6.83379228 [106,] 6.94803496 1.62427745 [107,] 1.92136557 6.94803496 [108,] -2.88062603 1.92136557 [109,] -6.62996558 -2.88062603 [110,] 5.78002524 -6.62996558 [111,] -4.57792109 5.78002524 [112,] 4.14675054 -4.57792109 [113,] -1.80213207 4.14675054 [114,] 1.72784392 -1.80213207 [115,] 15.54377658 1.72784392 [116,] -9.48023288 15.54377658 [117,] -26.26862935 -9.48023288 [118,] -1.33564554 -26.26862935 [119,] 3.75572015 -1.33564554 [120,] 5.50564000 3.75572015 [121,] -18.43912231 5.50564000 [122,] 1.52257140 -18.43912231 [123,] -12.65525191 1.52257140 [124,] 10.79783045 -12.65525191 [125,] -14.22484491 10.79783045 [126,] -10.48985481 -14.22484491 [127,] -4.68795594 -10.48985481 [128,] -7.43003659 -4.68795594 [129,] 10.46177073 -7.43003659 [130,] -0.74518789 10.46177073 [131,] 5.45752392 -0.74518789 [132,] 10.77903051 5.45752392 [133,] 1.93440618 10.77903051 [134,] 1.56508584 1.93440618 [135,] 4.04161888 1.56508584 [136,] -29.25124775 4.04161888 [137,] 8.81773179 -29.25124775 [138,] 1.55814990 8.81773179 [139,] 3.22047273 1.55814990 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 8.60652534 -11.07308322 2 -2.89332687 8.60652534 3 -15.32966229 -2.89332687 4 1.85023409 -15.32966229 5 -4.17198151 1.85023409 6 6.74871482 -4.17198151 7 4.24508118 6.74871482 8 0.87280948 4.24508118 9 2.94073422 0.87280948 10 -14.42354899 2.94073422 11 -8.10297833 -14.42354899 12 -5.60766801 -8.10297833 13 -2.92383342 -5.60766801 14 -1.46397507 -2.92383342 15 3.07551603 -1.46397507 16 4.65743800 3.07551603 17 3.38197180 4.65743800 18 -0.71871833 3.38197180 19 -1.85077310 -0.71871833 20 0.83494418 -1.85077310 21 -0.05418539 0.83494418 22 4.05174663 -0.05418539 23 5.54576982 4.05174663 24 -14.01213199 5.54576982 25 -30.17294473 -14.01213199 26 3.65355925 -30.17294473 27 1.51134690 3.65355925 28 6.79825754 1.51134690 29 0.68290166 6.79825754 30 -0.17686232 0.68290166 31 -3.67741595 -0.17686232 32 -7.48890680 -3.67741595 33 2.99739585 -7.48890680 34 -1.93492506 2.99739585 35 2.76760951 -1.93492506 36 -1.11977504 2.76760951 37 -2.56907165 -1.11977504 38 3.34053849 -2.56907165 39 -4.19947856 3.34053849 40 -7.05274151 -4.19947856 41 -0.06786440 -7.05274151 42 -2.19480971 -0.06786440 43 4.21647109 -2.19480971 44 8.18153771 4.21647109 45 5.92096232 8.18153771 46 1.18291933 5.92096232 47 7.60764566 1.18291933 48 8.19924712 7.60764566 49 2.94169460 8.19924712 50 12.00319672 2.94169460 51 2.83942293 12.00319672 52 8.17466087 2.83942293 53 0.91067878 8.17466087 54 4.18014824 0.91067878 55 0.14026362 4.18014824 56 16.08006574 0.14026362 57 3.52836815 16.08006574 58 0.95700611 3.52836815 59 7.67820096 0.95700611 60 7.74198665 7.67820096 61 1.76270788 7.74198665 62 0.43036637 1.76270788 63 9.74234879 0.43036637 64 7.84016539 9.74234879 65 7.50403176 7.84016539 66 10.60269271 7.50403176 67 8.43070827 10.60269271 68 13.11309710 8.43070827 69 10.49380926 13.11309710 70 7.60950487 10.49380926 71 8.03164537 7.60950487 72 2.02724914 8.03164537 73 -12.39769001 2.02724914 74 5.02149128 -12.39769001 75 -2.66159061 5.02149128 76 7.60586738 -2.66159061 77 5.08505797 7.60586738 78 -2.00425280 5.08505797 79 -29.31944956 -2.00425280 80 15.72478846 -29.31944956 81 -10.83836753 15.72478846 82 6.29569122 -10.83836753 83 2.31910694 6.29569122 84 -13.84984003 2.31910694 85 -14.87496660 -13.84984003 86 -14.23321162 -14.87496660 87 2.07275329 -14.23321162 88 -10.25215089 2.07275329 89 -0.82954331 -10.25215089 90 9.29663793 -0.82954331 91 -5.19487283 9.29663793 92 4.07790156 -5.19487283 93 -20.45865284 4.07790156 94 -16.01936336 -20.45865284 95 6.47277583 -16.01936336 96 20.44304821 6.47277583 97 -3.88449581 20.44304821 98 7.08982485 -3.88449581 99 0.44013069 7.08982485 100 -1.08721792 0.44013069 101 -14.50428387 -1.08721792 102 9.79100983 -14.50428387 103 5.94144823 9.79100983 104 -6.83379228 5.94144823 105 1.62427745 -6.83379228 106 6.94803496 1.62427745 107 1.92136557 6.94803496 108 -2.88062603 1.92136557 109 -6.62996558 -2.88062603 110 5.78002524 -6.62996558 111 -4.57792109 5.78002524 112 4.14675054 -4.57792109 113 -1.80213207 4.14675054 114 1.72784392 -1.80213207 115 15.54377658 1.72784392 116 -9.48023288 15.54377658 117 -26.26862935 -9.48023288 118 -1.33564554 -26.26862935 119 3.75572015 -1.33564554 120 5.50564000 3.75572015 121 -18.43912231 5.50564000 122 1.52257140 -18.43912231 123 -12.65525191 1.52257140 124 10.79783045 -12.65525191 125 -14.22484491 10.79783045 126 -10.48985481 -14.22484491 127 -4.68795594 -10.48985481 128 -7.43003659 -4.68795594 129 10.46177073 -7.43003659 130 -0.74518789 10.46177073 131 5.45752392 -0.74518789 132 10.77903051 5.45752392 133 1.93440618 10.77903051 134 1.56508584 1.93440618 135 4.04161888 1.56508584 136 -29.25124775 4.04161888 137 8.81773179 -29.25124775 138 1.55814990 8.81773179 139 3.22047273 1.55814990 > 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/763h01352124478.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/8hdzj1352124478.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/9f3fh1352124478.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/106q171352124478.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/1133t81352124478.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12hgg71352124478.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/130nr01352124478.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14c7431352124478.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/15pr221352124478.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/164qo71352124479.tab") + } > > try(system("convert tmp/1o33z1352124478.ps tmp/1o33z1352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/24h951352124478.ps tmp/24h951352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/3sjc31352124478.ps tmp/3sjc31352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/415a31352124478.ps tmp/415a31352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/5neod1352124478.ps tmp/5neod1352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/65uh41352124478.ps tmp/65uh41352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/763h01352124478.ps tmp/763h01352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/8hdzj1352124478.ps tmp/8hdzj1352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/9f3fh1352124478.ps tmp/9f3fh1352124478.png",intern=TRUE)) character(0) > try(system("convert tmp/106q171352124478.ps tmp/106q171352124478.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.342 1.292 9.625