R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-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(1232.473684 + ,12 + ,144 + ,0 + ,0 + ,0 + ,1237.294118 + ,13 + ,169 + ,0 + ,0 + ,0 + ,1223.466667 + ,14 + ,196 + ,0 + ,0 + ,0 + ,1221.323529 + ,15 + ,225 + ,0 + ,0 + ,0 + ,1216.75052 + ,16 + ,256 + ,0 + ,0 + ,0 + ,1219.537671 + ,17 + ,289 + ,0 + ,0 + ,0 + ,1208.551802 + ,18 + ,324 + ,0 + ,0 + ,0 + ,1204.034549 + ,19 + ,361 + ,0 + ,0 + ,0 + ,1210.345455 + ,20 + ,400 + ,0 + ,0 + ,0 + ,1197.856287 + ,21 + ,441 + ,0 + ,0 + ,0 + ,1212.115964 + ,22 + ,484 + ,0 + ,0 + ,0 + ,1207.23412 + ,23 + ,529 + ,0 + ,0 + ,0 + ,1206.348024 + ,24 + ,576 + ,0 + ,0 + ,0 + ,1203 + ,25 + ,625 + ,0 + ,0 + ,0 + ,1199.355809 + ,26 + ,676 + ,0 + ,0 + ,0 + ,1211.23913 + ,27 + ,729 + ,0 + ,0 + ,0 + ,1206.810916 + ,28 + ,784 + ,0 + ,0 + ,0 + ,1204.261745 + ,29 + ,841 + ,0 + ,0 + ,0 + ,1201.097039 + ,30 + ,900 + ,0 + ,0 + ,0 + ,1181.303383 + ,31 + ,961 + ,1 + ,0 + ,0 + ,1199.602151 + ,32 + ,1024 + ,0 + ,0 + ,0 + ,1200.824538 + ,33 + ,1089 + ,0 + ,0 + ,0 + ,1202.097331 + ,34 + ,1156 + ,0 + ,0 + ,0 + ,1193.003145 + ,35 + ,1225 + ,0 + ,0 + ,0 + ,1192.439252 + ,36 + ,1296 + ,0 + ,0 + ,0 + ,1190.44403 + ,37 + ,1369 + ,0 + ,0 + ,0 + ,1190.293651 + ,38 + ,1444 + ,0 + ,0 + ,0 + ,1187.373272 + ,39 + ,1521 + ,0 + ,0 + ,0 + ,1176.290598 + ,40 + ,1600 + ,0 + ,0 + ,0 + ,1178.640867 + ,41 + ,1681 + ,0 + ,0 + ,0 + ,1184.135802 + ,42 + ,1764 + ,0 + ,0 + ,0 + ,1183.482143 + ,43 + ,1849 + ,0 + ,0 + ,0 + ,1180.364486 + ,44 + ,1936 + ,0 + ,0 + ,0 + ,1225.571142 + ,11 + ,121 + ,0 + ,0 + ,1 + ,1214.600559 + ,12 + ,144 + ,0 + ,0 + ,1 + ,1206.073446 + ,13 + ,169 + ,0 + ,0 + ,1 + ,1194.742938 + ,14 + ,196 + ,0 + ,0 + ,1 + ,1209 + ,15 + ,225 + ,0 + ,0 + ,1 + ,1193 + ,16 + ,256 + ,0 + ,0 + ,1 + ,1194.937729 + ,17 + ,289 + ,0 + ,0 + ,1 + ,1174.09375 + ,18 + ,324 + ,1 + ,0 + ,1 + ,1182.644112 + ,19 + ,361 + ,0 + ,0 + ,1 + ,1210.255984 + ,20 + ,400 + ,0 + ,0 + ,1 + ,1206.651852 + ,21 + ,441 + ,0 + ,0 + ,1 + ,1217.050633 + ,22 + ,484 + ,0 + ,0 + ,1 + ,1221.727273 + ,23 + ,529 + ,0 + ,0 + ,1 + ,1214.094017 + ,24 + ,576 + ,0 + ,0 + ,1 + ,1204.811075 + ,25 + ,625 + ,0 + ,0 + ,1 + ,1203.929936 + ,26 + ,676 + ,0 + ,0 + ,1 + ,1216.153846 + ,27 + ,729 + ,0 + ,0 + ,1 + ,1202.124767 + ,28 + ,784 + ,0 + ,0 + ,1 + ,1190.448931 + ,29 + ,841 + ,0 + ,0 + ,1 + ,1169.838983 + ,30 + ,900 + ,1 + ,0 + ,1 + ,1183.221504 + ,31 + ,961 + ,1 + ,0 + ,1 + ,1196.886115 + ,32 + ,1024 + ,0 + ,0 + ,1 + ,1195.257576 + ,33 + ,1089 + ,0 + ,0 + ,1 + ,1189.007386 + ,34 + ,1156 + ,0 + ,0 + ,1 + ,1181.830334 + ,36 + ,1296 + ,0 + ,0 + ,1 + ,1192.382831 + ,37 + ,1369 + ,0 + ,0 + ,1 + ,1183.114286 + ,38 + ,1444 + ,0 + ,0 + ,1 + ,1174.167421 + ,39 + ,1521 + ,0 + ,0 + ,1 + ,1153.375 + ,40 + ,1600 + ,1 + ,0 + ,1 + ,1175.830228 + ,41 + ,1681 + ,0 + ,0 + ,1 + ,1163.878136 + ,42 + ,1764 + ,0 + ,0 + ,1 + ,1174.051788 + ,43 + ,1849 + ,0 + ,0 + ,1 + ,1178.93911 + ,44 + ,1936 + ,0 + ,0 + ,1 + ,1177.475904 + ,45 + ,2025 + ,0 + ,0 + ,1 + ,1174.25 + ,46 + ,2116 + ,0 + ,0 + ,1 + ,1228.840909 + ,8 + ,64 + ,0 + ,1 + ,0 + ,1205.849741 + ,9 + ,81 + ,0 + ,1 + ,0 + ,1213.511628 + ,10 + ,100 + ,1 + ,1 + ,0 + ,1213.254717 + ,12 + ,144 + ,0 + ,1 + ,0 + ,1213.8509 + ,13 + ,169 + ,0 + ,1 + ,0 + ,1206.565006 + ,14 + ,196 + ,0 + ,1 + ,0 + ,1209.912637 + ,15 + ,225 + ,0 + ,1 + ,0 + ,1212.326923 + ,16 + ,256 + ,0 + ,1 + ,0 + ,1220.332454 + ,18 + ,324 + ,0 + ,1 + ,0 + ,1212.054545 + ,19 + ,361 + ,0 + ,1 + ,0 + ,1203.460317 + ,20 + ,400 + ,0 + ,1 + ,0 + ,1197.084806 + ,21 + ,441 + ,0 + ,1 + ,0 + ,1203.432937 + ,22 + ,484 + ,0 + ,1 + ,0 + ,1198.666667 + ,23 + ,529 + ,0 + ,1 + ,0 + ,1199.354871 + ,24 + ,576 + ,0 + ,1 + ,0 + ,1179.174699 + ,26 + ,676 + ,1 + ,1 + ,0 + ,1193.416422 + ,27 + ,729 + ,0 + ,1 + ,0 + ,1195.810905 + ,29 + ,841 + ,0 + ,1 + ,0 + ,1190.699482 + ,30 + ,900 + ,0 + ,1 + ,0 + ,1187.140845 + ,31 + ,961 + ,0 + ,1 + ,0 + ,1192.640625 + ,32 + ,1024 + ,0 + ,1 + ,0 + ,1191.95 + ,33 + ,1089 + ,0 + ,1 + ,0 + ,1186.854002 + ,34 + ,1156 + ,0 + ,1 + ,0 + ,1185.809524 + ,35 + ,1225 + ,0 + ,1 + ,0 + ,1189.637681 + ,36 + ,1296 + ,0 + ,1 + ,0 + ,1192.894659 + ,37 + ,1369 + ,0 + ,1 + ,0 + ,1186.454404 + ,38 + ,1444 + ,0 + ,1 + ,0 + ,1181 + ,39 + ,1521 + ,0 + ,1 + ,0 + ,1188.41875 + ,40 + ,1600 + ,0 + ,1 + ,0 + ,1179.948468 + ,41 + ,1681 + ,0 + ,1 + ,0 + ,1178.643617 + ,42 + ,1764 + ,0 + ,1 + ,0 + ,1173.781421 + ,43 + ,1849 + ,0 + ,1 + ,0 + ,1175.359223 + ,44 + ,1936 + ,0 + ,1 + ,0 + ,1158.472906 + ,45 + ,2025 + ,0 + ,1 + ,0 + ,1151.759411 + ,46 + ,2116 + ,1 + ,1 + ,0 + ,1154.059777 + ,48 + ,2304 + ,1 + ,1 + ,0 + ,1165 + ,49 + ,2401 + ,0 + ,1 + ,0 + ,1158.298507 + ,50 + ,2500 + ,0 + ,1 + ,0 + ,1157.972292 + ,52 + ,2704 + ,0 + ,1 + ,0 + ,1152.881844 + ,55 + ,3025 + ,0 + ,1 + ,0 + ,1138.956585 + ,56 + ,3136 + ,1 + ,1 + ,0 + ,1147.403753 + ,57 + ,3249 + ,0 + ,1 + ,0 + ,1149.08561 + ,58 + ,3364 + ,0 + ,1 + ,0) + ,dim=c(6 + ,111) + ,dimnames=list(c('TIMIN' + ,'SEASDAY' + ,'SEASxSEAS' + ,'RAIN' + ,'2014' + ,'2011') + ,1:111)) > y <- array(NA,dim=c(6,111),dimnames=list(c('TIMIN','SEASDAY','SEASxSEAS','RAIN','2014','2011'),1:111)) > 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' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects 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 TIMIN SEASDAY SEASxSEAS RAIN 2014 2011 1 1232.474 12 144 0 0 0 2 1237.294 13 169 0 0 0 3 1223.467 14 196 0 0 0 4 1221.324 15 225 0 0 0 5 1216.751 16 256 0 0 0 6 1219.538 17 289 0 0 0 7 1208.552 18 324 0 0 0 8 1204.035 19 361 0 0 0 9 1210.345 20 400 0 0 0 10 1197.856 21 441 0 0 0 11 1212.116 22 484 0 0 0 12 1207.234 23 529 0 0 0 13 1206.348 24 576 0 0 0 14 1203.000 25 625 0 0 0 15 1199.356 26 676 0 0 0 16 1211.239 27 729 0 0 0 17 1206.811 28 784 0 0 0 18 1204.262 29 841 0 0 0 19 1201.097 30 900 0 0 0 20 1181.303 31 961 1 0 0 21 1199.602 32 1024 0 0 0 22 1200.825 33 1089 0 0 0 23 1202.097 34 1156 0 0 0 24 1193.003 35 1225 0 0 0 25 1192.439 36 1296 0 0 0 26 1190.444 37 1369 0 0 0 27 1190.294 38 1444 0 0 0 28 1187.373 39 1521 0 0 0 29 1176.291 40 1600 0 0 0 30 1178.641 41 1681 0 0 0 31 1184.136 42 1764 0 0 0 32 1183.482 43 1849 0 0 0 33 1180.364 44 1936 0 0 0 34 1225.571 11 121 0 0 1 35 1214.601 12 144 0 0 1 36 1206.073 13 169 0 0 1 37 1194.743 14 196 0 0 1 38 1209.000 15 225 0 0 1 39 1193.000 16 256 0 0 1 40 1194.938 17 289 0 0 1 41 1174.094 18 324 1 0 1 42 1182.644 19 361 0 0 1 43 1210.256 20 400 0 0 1 44 1206.652 21 441 0 0 1 45 1217.051 22 484 0 0 1 46 1221.727 23 529 0 0 1 47 1214.094 24 576 0 0 1 48 1204.811 25 625 0 0 1 49 1203.930 26 676 0 0 1 50 1216.154 27 729 0 0 1 51 1202.125 28 784 0 0 1 52 1190.449 29 841 0 0 1 53 1169.839 30 900 1 0 1 54 1183.222 31 961 1 0 1 55 1196.886 32 1024 0 0 1 56 1195.258 33 1089 0 0 1 57 1189.007 34 1156 0 0 1 58 1181.830 36 1296 0 0 1 59 1192.383 37 1369 0 0 1 60 1183.114 38 1444 0 0 1 61 1174.167 39 1521 0 0 1 62 1153.375 40 1600 1 0 1 63 1175.830 41 1681 0 0 1 64 1163.878 42 1764 0 0 1 65 1174.052 43 1849 0 0 1 66 1178.939 44 1936 0 0 1 67 1177.476 45 2025 0 0 1 68 1174.250 46 2116 0 0 1 69 1228.841 8 64 0 1 0 70 1205.850 9 81 0 1 0 71 1213.512 10 100 1 1 0 72 1213.255 12 144 0 1 0 73 1213.851 13 169 0 1 0 74 1206.565 14 196 0 1 0 75 1209.913 15 225 0 1 0 76 1212.327 16 256 0 1 0 77 1220.332 18 324 0 1 0 78 1212.055 19 361 0 1 0 79 1203.460 20 400 0 1 0 80 1197.085 21 441 0 1 0 81 1203.433 22 484 0 1 0 82 1198.667 23 529 0 1 0 83 1199.355 24 576 0 1 0 84 1179.175 26 676 1 1 0 85 1193.416 27 729 0 1 0 86 1195.811 29 841 0 1 0 87 1190.699 30 900 0 1 0 88 1187.141 31 961 0 1 0 89 1192.641 32 1024 0 1 0 90 1191.950 33 1089 0 1 0 91 1186.854 34 1156 0 1 0 92 1185.810 35 1225 0 1 0 93 1189.638 36 1296 0 1 0 94 1192.895 37 1369 0 1 0 95 1186.454 38 1444 0 1 0 96 1181.000 39 1521 0 1 0 97 1188.419 40 1600 0 1 0 98 1179.948 41 1681 0 1 0 99 1178.644 42 1764 0 1 0 100 1173.781 43 1849 0 1 0 101 1175.359 44 1936 0 1 0 102 1158.473 45 2025 0 1 0 103 1151.759 46 2116 1 1 0 104 1154.060 48 2304 1 1 0 105 1165.000 49 2401 0 1 0 106 1158.299 50 2500 0 1 0 107 1157.972 52 2704 0 1 0 108 1152.882 55 3025 0 1 0 109 1138.957 56 3136 1 1 0 110 1147.404 57 3249 0 1 0 111 1149.086 58 3364 0 1 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) SEASDAY SEASxSEAS RAIN `2014` `2011` 1230.13299 -0.62620 -0.01159 -17.91617 -6.53123 -7.06693 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -24.3410 -3.3285 0.1143 4.3604 19.1938 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.230e+03 4.610e+00 266.860 < 2e-16 *** SEASDAY -6.262e-01 3.017e-01 -2.076 0.040366 * SEASxSEAS -1.159e-02 4.806e-03 -2.411 0.017636 * RAIN -1.792e+01 2.560e+00 -6.997 2.53e-10 *** `2014` -6.531e+00 1.867e+00 -3.498 0.000688 *** `2011` -7.067e+00 1.861e+00 -3.798 0.000244 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 7.611 on 105 degrees of freedom Multiple R-squared: 0.8601, Adjusted R-squared: 0.8535 F-statistic: 129.1 on 5 and 105 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,] 0.49844479 0.9968895816 5.015552e-01 [2,] 0.40248289 0.8049657862 5.975171e-01 [3,] 0.50314699 0.9937060227 4.968530e-01 [4,] 0.37203876 0.7440775196 6.279612e-01 [5,] 0.26274884 0.5254976708 7.372512e-01 [6,] 0.20446990 0.4089398099 7.955301e-01 [7,] 0.18541160 0.3708231952 8.145884e-01 [8,] 0.15926992 0.3185398309 8.407301e-01 [9,] 0.10950463 0.2190092584 8.904954e-01 [10,] 0.08909303 0.1781860555 9.109070e-01 [11,] 0.09222341 0.1844468203 9.077766e-01 [12,] 0.05966917 0.1193383314 9.403308e-01 [13,] 0.06908597 0.1381719406 9.309140e-01 [14,] 0.05527886 0.1105577155 9.447211e-01 [15,] 0.04038766 0.0807753203 9.596123e-01 [16,] 0.06501348 0.1300269636 9.349865e-01 [17,] 0.07021688 0.1404337624 9.297831e-01 [18,] 0.07080928 0.1416185637 9.291907e-01 [19,] 0.06005553 0.1201110590 9.399445e-01 [20,] 0.05401305 0.1080261020 9.459869e-01 [21,] 0.11959431 0.2391886267 8.804057e-01 [22,] 0.11817177 0.2363435334 8.818282e-01 [23,] 0.08731250 0.1746250057 9.126875e-01 [24,] 0.06330150 0.1266030059 9.366985e-01 [25,] 0.04520406 0.0904081157 9.547959e-01 [26,] 0.03893338 0.0778667534 9.610666e-01 [27,] 0.03553986 0.0710797262 9.644601e-01 [28,] 0.04188963 0.0837792621 9.581104e-01 [29,] 0.11482440 0.2296488005 8.851756e-01 [30,] 0.09061173 0.1812234620 9.093883e-01 [31,] 0.15056606 0.3011321282 8.494339e-01 [32,] 0.17509220 0.3501844088 8.249078e-01 [33,] 0.24239849 0.4847969824 7.576015e-01 [34,] 0.61573639 0.7685272283 3.842636e-01 [35,] 0.76335915 0.4732817046 2.366409e-01 [36,] 0.80640814 0.3871837165 1.935919e-01 [37,] 0.93915943 0.1216811352 6.084057e-02 [38,] 0.99360115 0.0127977026 6.398851e-03 [39,] 0.99710340 0.0057932059 2.896603e-03 [40,] 0.99615348 0.0076930449 3.846522e-03 [41,] 0.99494373 0.0101125469 5.056273e-03 [42,] 0.99938919 0.0012216123 6.108061e-04 [43,] 0.99921090 0.0015782016 7.891008e-04 [44,] 0.99899494 0.0020101164 1.005058e-03 [45,] 0.99903075 0.0019385019 9.692510e-04 [46,] 0.99920456 0.0015908743 7.954371e-04 [47,] 0.99899387 0.0020122620 1.006131e-03 [48,] 0.99875154 0.0024969257 1.248463e-03 [49,] 0.99804301 0.0039139863 1.956993e-03 [50,] 0.99725197 0.0054960673 2.748034e-03 [51,] 0.99784191 0.0043161819 2.158091e-03 [52,] 0.99675767 0.0064846525 3.242326e-03 [53,] 0.99626114 0.0074777279 3.738864e-03 [54,] 0.99746299 0.0050740256 2.537013e-03 [55,] 0.99619846 0.0076030767 3.801538e-03 [56,] 0.99909392 0.0018121522 9.060761e-04 [57,] 0.99882527 0.0023494679 1.174734e-03 [58,] 0.99833095 0.0033380966 1.669048e-03 [59,] 0.99761748 0.0047650313 2.382516e-03 [60,] 0.99639172 0.0072165542 3.608277e-03 [61,] 0.99789194 0.0042161297 2.108065e-03 [62,] 0.99935446 0.0012910708 6.455354e-04 [63,] 0.99985094 0.0002981258 1.490629e-04 [64,] 0.99974704 0.0005059107 2.529554e-04 [65,] 0.99956476 0.0008704715 4.352357e-04 [66,] 0.99950882 0.0009823618 4.911809e-04 [67,] 0.99916206 0.0016758798 8.379399e-04 [68,] 0.99857091 0.0028581860 1.429093e-03 [69,] 0.99981127 0.0003774674 1.887337e-04 [70,] 0.99990304 0.0001939291 9.696457e-05 [71,] 0.99983244 0.0003351140 1.675570e-04 [72,] 0.99977739 0.0004452262 2.226131e-04 [73,] 0.99963587 0.0007282518 3.641259e-04 [74,] 0.99931350 0.0013729904 6.864952e-04 [75,] 0.99873185 0.0025362980 1.268149e-03 [76,] 0.99786302 0.0042739532 2.136977e-03 [77,] 0.99675851 0.0064829715 3.241486e-03 [78,] 0.99414289 0.0117142103 5.857105e-03 [79,] 0.99148832 0.0170233520 8.511676e-03 [80,] 0.99374781 0.0125043832 6.252192e-03 [81,] 0.98960040 0.0207991932 1.039960e-02 [82,] 0.98263097 0.0347380672 1.736903e-02 [83,] 0.98335458 0.0332908474 1.664542e-02 [84,] 0.98951389 0.0209722295 1.048611e-02 [85,] 0.98505985 0.0298802984 1.494015e-02 [86,] 0.97477932 0.0504413659 2.522068e-02 [87,] 0.95547716 0.0890456893 4.452284e-02 [88,] 0.94713390 0.1057321941 5.286610e-02 [89,] 0.93846337 0.1230732594 6.153663e-02 [90,] 0.89233222 0.2153355584 1.076678e-01 [91,] 0.84635797 0.3072840511 1.536420e-01 [92,] 0.76730691 0.4653861729 2.326931e-01 [93,] 0.93592614 0.1281477298 6.407386e-02 [94,] 0.91685290 0.1662942077 8.314710e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1jsgw1424129058.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2noai1424129058.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3271z1424129058.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/488ab1424129058.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5y08e1424129058.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 = 111 Frequency = 1 1 2 3 4 5 6 11.52371441 17.26004431 4.37166519 3.19077504 -0.39681013 3.39894067 7 8 9 10 11 12 -6.55515256 -10.01745382 -2.62842010 -14.01628441 1.36787225 -2.36631611 13 14 15 16 17 18 -2.08158051 -4.23559692 -6.66260437 6.46107616 3.29639765 2.03393813 19 20 21 22 23 24 0.17911957 -0.36530099 1.37353438 3.97533674 6.65072108 -1.01769761 25 26 27 28 29 30 -0.13264733 -0.65575007 0.68916616 -0.71274164 -10.25376847 -6.33867632 31 32 33 34 35 36 0.74425779 1.70177389 0.21846795 10.79538664 0.71752357 -6.89369352 37 38 39 40 41 42 -17.28512964 -2.06581979 -17.08039596 -14.13406716 -16.03009838 -24.34095665 43 44 45 46 47 48 4.34904307 1.84621476 13.36947542 19.19377105 12.73134666 4.64241224 49 50 51 52 53 54 4.97845680 18.44272632 5.67718282 -4.71194170 -6.09583024 8.61975418 55 56 57 58 59 60 5.72443255 5.47530891 0.62771025 -3.67463116 8.34998510 0.57673532 61 62 63 64 65 66 -6.85165848 -8.18626029 -2.08238116 -12.44647404 -0.66164695 5.86002612 67 68 69 70 71 72 6.05434716 4.50914617 10.99034430 -11.17763169 15.24679532 -1.16402381 73 74 75 76 77 78 0.34805510 -5.99876702 -1.68888817 1.71082166 11.75672823 4.53377097 79 80 81 82 83 84 -2.98232931 -8.25653662 -0.78392596 -4.40254032 -2.54350472 -2.39631356 85 86 87 88 89 90 -4.83040306 0.11432692 -3.68720864 -5.91278222 0.94323717 1.63202753 91 92 93 94 95 96 -2.06137913 -1.68008982 3.59701046 8.32610772 3.38114794 -0.55478486 97 98 99 100 101 102 8.40561232 1.50015346 1.78330158 -1.46771932 1.74443374 -13.48435622 103 104 105 106 107 108 -0.60097619 5.13032375 -0.09539434 -5.02348044 -1.73335372 -1.22546934 109 110 111 4.67790642 -2.85545889 0.78521280 > postscript(file="/var/wessaorg/rcomp/tmp/60kjc1424129058.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 = 111 Frequency = 1 lag(myerror, k = 1) myerror 0 11.52371441 NA 1 17.26004431 11.52371441 2 4.37166519 17.26004431 3 3.19077504 4.37166519 4 -0.39681013 3.19077504 5 3.39894067 -0.39681013 6 -6.55515256 3.39894067 7 -10.01745382 -6.55515256 8 -2.62842010 -10.01745382 9 -14.01628441 -2.62842010 10 1.36787225 -14.01628441 11 -2.36631611 1.36787225 12 -2.08158051 -2.36631611 13 -4.23559692 -2.08158051 14 -6.66260437 -4.23559692 15 6.46107616 -6.66260437 16 3.29639765 6.46107616 17 2.03393813 3.29639765 18 0.17911957 2.03393813 19 -0.36530099 0.17911957 20 1.37353438 -0.36530099 21 3.97533674 1.37353438 22 6.65072108 3.97533674 23 -1.01769761 6.65072108 24 -0.13264733 -1.01769761 25 -0.65575007 -0.13264733 26 0.68916616 -0.65575007 27 -0.71274164 0.68916616 28 -10.25376847 -0.71274164 29 -6.33867632 -10.25376847 30 0.74425779 -6.33867632 31 1.70177389 0.74425779 32 0.21846795 1.70177389 33 10.79538664 0.21846795 34 0.71752357 10.79538664 35 -6.89369352 0.71752357 36 -17.28512964 -6.89369352 37 -2.06581979 -17.28512964 38 -17.08039596 -2.06581979 39 -14.13406716 -17.08039596 40 -16.03009838 -14.13406716 41 -24.34095665 -16.03009838 42 4.34904307 -24.34095665 43 1.84621476 4.34904307 44 13.36947542 1.84621476 45 19.19377105 13.36947542 46 12.73134666 19.19377105 47 4.64241224 12.73134666 48 4.97845680 4.64241224 49 18.44272632 4.97845680 50 5.67718282 18.44272632 51 -4.71194170 5.67718282 52 -6.09583024 -4.71194170 53 8.61975418 -6.09583024 54 5.72443255 8.61975418 55 5.47530891 5.72443255 56 0.62771025 5.47530891 57 -3.67463116 0.62771025 58 8.34998510 -3.67463116 59 0.57673532 8.34998510 60 -6.85165848 0.57673532 61 -8.18626029 -6.85165848 62 -2.08238116 -8.18626029 63 -12.44647404 -2.08238116 64 -0.66164695 -12.44647404 65 5.86002612 -0.66164695 66 6.05434716 5.86002612 67 4.50914617 6.05434716 68 10.99034430 4.50914617 69 -11.17763169 10.99034430 70 15.24679532 -11.17763169 71 -1.16402381 15.24679532 72 0.34805510 -1.16402381 73 -5.99876702 0.34805510 74 -1.68888817 -5.99876702 75 1.71082166 -1.68888817 76 11.75672823 1.71082166 77 4.53377097 11.75672823 78 -2.98232931 4.53377097 79 -8.25653662 -2.98232931 80 -0.78392596 -8.25653662 81 -4.40254032 -0.78392596 82 -2.54350472 -4.40254032 83 -2.39631356 -2.54350472 84 -4.83040306 -2.39631356 85 0.11432692 -4.83040306 86 -3.68720864 0.11432692 87 -5.91278222 -3.68720864 88 0.94323717 -5.91278222 89 1.63202753 0.94323717 90 -2.06137913 1.63202753 91 -1.68008982 -2.06137913 92 3.59701046 -1.68008982 93 8.32610772 3.59701046 94 3.38114794 8.32610772 95 -0.55478486 3.38114794 96 8.40561232 -0.55478486 97 1.50015346 8.40561232 98 1.78330158 1.50015346 99 -1.46771932 1.78330158 100 1.74443374 -1.46771932 101 -13.48435622 1.74443374 102 -0.60097619 -13.48435622 103 5.13032375 -0.60097619 104 -0.09539434 5.13032375 105 -5.02348044 -0.09539434 106 -1.73335372 -5.02348044 107 -1.22546934 -1.73335372 108 4.67790642 -1.22546934 109 -2.85545889 4.67790642 110 0.78521280 -2.85545889 111 NA 0.78521280 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 17.26004431 11.52371441 [2,] 4.37166519 17.26004431 [3,] 3.19077504 4.37166519 [4,] -0.39681013 3.19077504 [5,] 3.39894067 -0.39681013 [6,] -6.55515256 3.39894067 [7,] -10.01745382 -6.55515256 [8,] -2.62842010 -10.01745382 [9,] -14.01628441 -2.62842010 [10,] 1.36787225 -14.01628441 [11,] -2.36631611 1.36787225 [12,] -2.08158051 -2.36631611 [13,] -4.23559692 -2.08158051 [14,] -6.66260437 -4.23559692 [15,] 6.46107616 -6.66260437 [16,] 3.29639765 6.46107616 [17,] 2.03393813 3.29639765 [18,] 0.17911957 2.03393813 [19,] -0.36530099 0.17911957 [20,] 1.37353438 -0.36530099 [21,] 3.97533674 1.37353438 [22,] 6.65072108 3.97533674 [23,] -1.01769761 6.65072108 [24,] -0.13264733 -1.01769761 [25,] -0.65575007 -0.13264733 [26,] 0.68916616 -0.65575007 [27,] -0.71274164 0.68916616 [28,] -10.25376847 -0.71274164 [29,] -6.33867632 -10.25376847 [30,] 0.74425779 -6.33867632 [31,] 1.70177389 0.74425779 [32,] 0.21846795 1.70177389 [33,] 10.79538664 0.21846795 [34,] 0.71752357 10.79538664 [35,] -6.89369352 0.71752357 [36,] -17.28512964 -6.89369352 [37,] -2.06581979 -17.28512964 [38,] -17.08039596 -2.06581979 [39,] -14.13406716 -17.08039596 [40,] -16.03009838 -14.13406716 [41,] -24.34095665 -16.03009838 [42,] 4.34904307 -24.34095665 [43,] 1.84621476 4.34904307 [44,] 13.36947542 1.84621476 [45,] 19.19377105 13.36947542 [46,] 12.73134666 19.19377105 [47,] 4.64241224 12.73134666 [48,] 4.97845680 4.64241224 [49,] 18.44272632 4.97845680 [50,] 5.67718282 18.44272632 [51,] -4.71194170 5.67718282 [52,] -6.09583024 -4.71194170 [53,] 8.61975418 -6.09583024 [54,] 5.72443255 8.61975418 [55,] 5.47530891 5.72443255 [56,] 0.62771025 5.47530891 [57,] -3.67463116 0.62771025 [58,] 8.34998510 -3.67463116 [59,] 0.57673532 8.34998510 [60,] -6.85165848 0.57673532 [61,] -8.18626029 -6.85165848 [62,] -2.08238116 -8.18626029 [63,] -12.44647404 -2.08238116 [64,] -0.66164695 -12.44647404 [65,] 5.86002612 -0.66164695 [66,] 6.05434716 5.86002612 [67,] 4.50914617 6.05434716 [68,] 10.99034430 4.50914617 [69,] -11.17763169 10.99034430 [70,] 15.24679532 -11.17763169 [71,] -1.16402381 15.24679532 [72,] 0.34805510 -1.16402381 [73,] -5.99876702 0.34805510 [74,] -1.68888817 -5.99876702 [75,] 1.71082166 -1.68888817 [76,] 11.75672823 1.71082166 [77,] 4.53377097 11.75672823 [78,] -2.98232931 4.53377097 [79,] -8.25653662 -2.98232931 [80,] -0.78392596 -8.25653662 [81,] -4.40254032 -0.78392596 [82,] -2.54350472 -4.40254032 [83,] -2.39631356 -2.54350472 [84,] -4.83040306 -2.39631356 [85,] 0.11432692 -4.83040306 [86,] -3.68720864 0.11432692 [87,] -5.91278222 -3.68720864 [88,] 0.94323717 -5.91278222 [89,] 1.63202753 0.94323717 [90,] -2.06137913 1.63202753 [91,] -1.68008982 -2.06137913 [92,] 3.59701046 -1.68008982 [93,] 8.32610772 3.59701046 [94,] 3.38114794 8.32610772 [95,] -0.55478486 3.38114794 [96,] 8.40561232 -0.55478486 [97,] 1.50015346 8.40561232 [98,] 1.78330158 1.50015346 [99,] -1.46771932 1.78330158 [100,] 1.74443374 -1.46771932 [101,] -13.48435622 1.74443374 [102,] -0.60097619 -13.48435622 [103,] 5.13032375 -0.60097619 [104,] -0.09539434 5.13032375 [105,] -5.02348044 -0.09539434 [106,] -1.73335372 -5.02348044 [107,] -1.22546934 -1.73335372 [108,] 4.67790642 -1.22546934 [109,] -2.85545889 4.67790642 [110,] 0.78521280 -2.85545889 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 17.26004431 11.52371441 2 4.37166519 17.26004431 3 3.19077504 4.37166519 4 -0.39681013 3.19077504 5 3.39894067 -0.39681013 6 -6.55515256 3.39894067 7 -10.01745382 -6.55515256 8 -2.62842010 -10.01745382 9 -14.01628441 -2.62842010 10 1.36787225 -14.01628441 11 -2.36631611 1.36787225 12 -2.08158051 -2.36631611 13 -4.23559692 -2.08158051 14 -6.66260437 -4.23559692 15 6.46107616 -6.66260437 16 3.29639765 6.46107616 17 2.03393813 3.29639765 18 0.17911957 2.03393813 19 -0.36530099 0.17911957 20 1.37353438 -0.36530099 21 3.97533674 1.37353438 22 6.65072108 3.97533674 23 -1.01769761 6.65072108 24 -0.13264733 -1.01769761 25 -0.65575007 -0.13264733 26 0.68916616 -0.65575007 27 -0.71274164 0.68916616 28 -10.25376847 -0.71274164 29 -6.33867632 -10.25376847 30 0.74425779 -6.33867632 31 1.70177389 0.74425779 32 0.21846795 1.70177389 33 10.79538664 0.21846795 34 0.71752357 10.79538664 35 -6.89369352 0.71752357 36 -17.28512964 -6.89369352 37 -2.06581979 -17.28512964 38 -17.08039596 -2.06581979 39 -14.13406716 -17.08039596 40 -16.03009838 -14.13406716 41 -24.34095665 -16.03009838 42 4.34904307 -24.34095665 43 1.84621476 4.34904307 44 13.36947542 1.84621476 45 19.19377105 13.36947542 46 12.73134666 19.19377105 47 4.64241224 12.73134666 48 4.97845680 4.64241224 49 18.44272632 4.97845680 50 5.67718282 18.44272632 51 -4.71194170 5.67718282 52 -6.09583024 -4.71194170 53 8.61975418 -6.09583024 54 5.72443255 8.61975418 55 5.47530891 5.72443255 56 0.62771025 5.47530891 57 -3.67463116 0.62771025 58 8.34998510 -3.67463116 59 0.57673532 8.34998510 60 -6.85165848 0.57673532 61 -8.18626029 -6.85165848 62 -2.08238116 -8.18626029 63 -12.44647404 -2.08238116 64 -0.66164695 -12.44647404 65 5.86002612 -0.66164695 66 6.05434716 5.86002612 67 4.50914617 6.05434716 68 10.99034430 4.50914617 69 -11.17763169 10.99034430 70 15.24679532 -11.17763169 71 -1.16402381 15.24679532 72 0.34805510 -1.16402381 73 -5.99876702 0.34805510 74 -1.68888817 -5.99876702 75 1.71082166 -1.68888817 76 11.75672823 1.71082166 77 4.53377097 11.75672823 78 -2.98232931 4.53377097 79 -8.25653662 -2.98232931 80 -0.78392596 -8.25653662 81 -4.40254032 -0.78392596 82 -2.54350472 -4.40254032 83 -2.39631356 -2.54350472 84 -4.83040306 -2.39631356 85 0.11432692 -4.83040306 86 -3.68720864 0.11432692 87 -5.91278222 -3.68720864 88 0.94323717 -5.91278222 89 1.63202753 0.94323717 90 -2.06137913 1.63202753 91 -1.68008982 -2.06137913 92 3.59701046 -1.68008982 93 8.32610772 3.59701046 94 3.38114794 8.32610772 95 -0.55478486 3.38114794 96 8.40561232 -0.55478486 97 1.50015346 8.40561232 98 1.78330158 1.50015346 99 -1.46771932 1.78330158 100 1.74443374 -1.46771932 101 -13.48435622 1.74443374 102 -0.60097619 -13.48435622 103 5.13032375 -0.60097619 104 -0.09539434 5.13032375 105 -5.02348044 -0.09539434 106 -1.73335372 -5.02348044 107 -1.22546934 -1.73335372 108 4.67790642 -1.22546934 109 -2.85545889 4.67790642 110 0.78521280 -2.85545889 > 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/7xux81424129058.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8hf9r1424129058.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/929oo1424129058.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10ubp51424129058.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$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/11egwy1424129058.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$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12h9c31424129058.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, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13gx1i1424129058.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,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14qs2g1424129058.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,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/155z561424129058.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,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + 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,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + 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,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + 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/16bge51424129058.tab") + } > > try(system("convert tmp/1jsgw1424129058.ps tmp/1jsgw1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/2noai1424129058.ps tmp/2noai1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/3271z1424129058.ps tmp/3271z1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/488ab1424129058.ps tmp/488ab1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/5y08e1424129058.ps tmp/5y08e1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/60kjc1424129058.ps tmp/60kjc1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/7xux81424129058.ps tmp/7xux81424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/8hf9r1424129058.ps tmp/8hf9r1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/929oo1424129058.ps tmp/929oo1424129058.png",intern=TRUE)) character(0) > try(system("convert tmp/10ubp51424129058.ps tmp/10ubp51424129058.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.912 0.729 5.679