R version 2.8.0 (2008-10-20) Copyright (C) 2008 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. Natural language support but running in an English locale 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(15 + ,10 + ,12 + ,16 + ,6 + ,2 + ,0 + ,0 + ,9 + ,12 + ,9 + ,7 + ,12 + ,6 + ,1 + ,1 + ,2 + ,9 + ,9 + ,12 + ,11 + ,11 + ,4 + ,1 + ,2 + ,1 + ,9 + ,10 + ,12 + ,11 + ,12 + ,6 + ,0 + ,0 + ,0 + ,9 + ,13 + ,9 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,9 + ,16 + ,11 + ,16 + ,16 + ,7 + ,1 + ,0 + ,0 + ,9 + ,14 + ,12 + ,13 + ,13 + ,6 + ,0 + ,0 + ,0 + ,9 + ,16 + ,11 + ,13 + ,14 + ,7 + ,1 + ,1 + ,0 + ,9 + ,10 + ,12 + ,5 + ,13 + ,6 + ,0 + ,0 + ,0 + ,9 + ,8 + ,12 + ,8 + ,13 + ,4 + ,2 + ,0 + ,1 + ,10 + ,12 + ,11 + ,14 + ,13 + ,5 + ,1 + ,0 + ,0 + ,10 + ,15 + ,11 + ,15 + ,15 + ,8 + ,0 + ,0 + ,0 + ,10 + ,14 + ,12 + ,8 + ,14 + ,4 + ,0 + ,1 + ,0 + ,10 + ,14 + ,6 + ,13 + ,12 + ,6 + ,1 + ,1 + ,2 + ,10 + ,12 + ,13 + ,12 + ,12 + ,6 + ,1 + ,2 + ,1 + ,10 + ,12 + ,11 + ,11 + ,12 + ,5 + ,0 + ,0 + ,0 + ,10 + ,10 + ,12 + ,8 + ,11 + ,4 + ,0 + ,0 + ,0 + ,10 + ,4 + ,10 + ,4 + ,10 + ,2 + ,0 + ,0 + ,0 + ,10 + ,14 + ,11 + ,15 + ,15 + ,8 + ,0 + ,1 + ,0 + ,10 + ,15 + ,12 + ,12 + ,16 + ,7 + ,0 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,12 + ,15 + ,6 + ,0 + ,0 + ,0 + ,10 + ,14 + ,11 + ,14 + ,12 + ,6 + ,1 + ,1 + ,1 + ,10 + ,12 + ,11 + ,11 + ,14 + ,2 + ,1 + ,1 + ,1 + ,10 + ,15 + ,11 + ,16 + ,11 + ,5 + ,0 + ,1 + ,2 + ,9 + ,13 + ,8 + ,14 + ,14 + ,5 + ,1 + ,1 + ,1 + ,9 + ,16 + ,11 + ,14 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,14 + ,12 + ,15 + ,14 + ,6 + ,0 + ,0 + ,0 + ,10 + ,8 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10 + ,16 + ,12 + ,15 + ,14 + ,6 + ,0 + ,1 + ,0 + ,10 + ,16 + ,12 + ,14 + ,16 + ,8 + ,1 + ,1 + ,1 + ,10 + ,12 + ,12 + ,15 + ,13 + ,6 + ,0 + ,0 + ,0 + ,10 + ,11 + ,8 + ,10 + ,14 + ,5 + ,0 + ,3 + ,1 + ,10 + ,16 + ,12 + ,14 + ,16 + ,8 + ,1 + ,1 + ,1 + ,10 + ,9 + ,11 + ,9 + ,12 + ,4 + ,0 + ,0 + ,0 + ,10) + ,dim=c(9 + ,156) + ,dimnames=list(c('Popularity' + ,'FindingFriends' + ,'KnowingPeople' + ,'Liked' + ,'Celebrity' + ,'B' + ,'2B' + ,'3B' + ,'Month') + ,1:156)) > y <- array(NA,dim=c(9,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','B','2B','3B','Month'),1:156)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > ylab = '' > xlab = '' > main = '' > #'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 Attaching package: 'zoo' The following object(s) are masked from package:base : 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 Popularity FindingFriends KnowingPeople Liked Celebrity B 2B 3B Month t 1 15 10 12 16 6 2 0 0 9 1 2 12 9 7 12 6 1 1 2 9 2 3 9 12 11 11 4 1 2 1 9 3 4 10 12 11 12 6 0 0 0 9 4 5 13 9 14 14 6 0 0 0 9 5 6 16 11 16 16 7 1 0 0 9 6 7 14 12 13 13 6 0 0 0 9 7 8 16 11 13 14 7 1 1 0 9 8 9 10 12 5 13 6 0 0 0 9 9 10 8 12 8 13 4 2 0 1 10 10 11 12 11 14 13 5 1 0 0 10 11 12 15 11 15 15 8 0 0 0 10 12 13 14 12 8 14 4 0 1 0 10 13 14 14 6 13 12 6 1 1 2 10 14 15 12 13 12 12 6 1 2 1 10 15 16 12 11 11 12 5 0 0 0 10 16 17 10 12 8 11 4 0 0 0 10 17 18 4 10 4 10 2 0 0 0 10 18 19 14 11 15 15 8 0 1 0 10 19 20 15 12 12 16 7 0 0 0 10 20 21 16 12 14 14 6 0 0 0 10 21 22 12 12 9 13 4 0 1 0 10 22 23 12 11 16 13 4 0 0 0 10 23 24 12 12 10 13 4 0 0 1 10 24 25 12 12 8 13 5 1 0 1 9 25 26 12 12 14 14 4 0 0 0 9 26 27 11 6 6 9 4 3 2 1 9 27 28 11 5 16 14 6 1 0 0 9 28 29 11 12 11 12 6 1 1 0 9 29 30 11 14 7 13 6 1 1 0 9 30 31 11 12 13 11 4 3 1 1 9 31 32 11 9 7 13 2 0 0 0 9 32 33 15 11 14 15 7 0 0 0 9 33 34 15 11 17 16 6 0 0 0 9 34 35 9 11 15 15 7 0 0 0 9 35 36 16 12 8 14 4 0 0 0 9 36 37 13 10 8 8 4 0 2 1 9 37 38 9 12 11 11 4 1 0 0 9 38 39 16 11 16 15 6 0 0 0 9 39 40 12 12 10 15 6 0 0 0 9 40 41 15 9 5 11 3 0 0 2 9 41 42 5 15 8 12 3 0 0 0 9 42 43 11 11 8 12 6 2 2 0 9 43 44 17 11 15 14 5 2 2 0 9 44 45 9 15 6 8 4 0 1 1 9 45 46 13 12 16 16 6 0 0 0 9 46 47 16 9 16 16 6 0 0 0 10 47 48 16 12 16 14 6 0 0 0 10 48 49 14 9 19 12 6 2 0 2 10 49 50 16 11 14 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11 13 3 2 1 0 10 120 121 8 10 10 14 3 0 0 0 10 121 122 8 7 9 15 4 1 0 1 10 122 123 11 10 12 14 5 2 0 0 10 123 124 12 8 15 13 7 0 0 0 10 124 125 11 12 7 13 6 4 0 0 10 125 126 14 12 13 15 6 0 1 2 10 126 127 11 12 8 16 6 2 1 0 10 127 128 14 11 16 12 5 0 0 0 10 128 129 13 13 15 14 6 2 1 2 10 129 130 12 12 6 14 5 0 0 0 10 130 131 4 8 6 4 4 0 0 0 10 131 132 15 11 12 13 6 2 1 1 10 132 133 10 12 8 16 4 0 0 0 10 133 134 13 13 11 15 6 1 2 1 10 134 135 15 12 13 14 6 1 1 2 10 135 136 12 10 14 14 5 1 2 1 10 136 137 13 12 14 14 6 0 0 0 10 137 138 8 10 10 6 4 0 0 0 10 138 139 10 13 4 13 6 2 0 0 10 139 140 15 11 16 14 6 0 0 0 10 140 141 16 12 12 15 8 0 0 0 10 141 142 16 12 15 16 7 0 0 0 10 142 143 14 10 12 15 6 0 0 0 10 143 144 14 11 14 12 6 1 1 1 10 144 145 12 11 11 14 2 1 1 1 10 145 146 15 11 16 11 5 0 1 2 9 146 147 13 8 14 14 5 1 1 1 9 147 148 16 11 14 14 6 0 0 0 10 148 149 14 12 15 14 6 0 0 0 10 149 150 8 11 9 12 4 0 0 0 10 150 151 16 12 15 14 6 0 1 0 10 151 152 16 12 14 16 8 1 1 1 10 152 153 12 12 15 13 6 0 0 0 10 153 154 11 8 10 14 5 0 3 1 10 154 155 16 12 14 16 8 1 1 1 10 155 156 9 11 9 12 4 0 0 0 10 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) FindingFriends KnowingPeople Liked Celebrity 1.044996 0.119499 0.241564 0.378019 0.607492 B `2B` `3B` Month t -0.048983 0.174147 0.508544 -0.136999 -0.001881 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -6.0022 -1.2083 -0.1294 1.2691 5.9840 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.044996 3.928053 0.266 0.790588 FindingFriends 0.119499 0.096875 1.234 0.219357 KnowingPeople 0.241564 0.061926 3.901 0.000146 *** Liked 0.378019 0.098076 3.854 0.000173 *** Celebrity 0.607492 0.157196 3.865 0.000167 *** B -0.048983 0.224347 -0.218 0.827473 `2B` 0.174147 0.270802 0.643 0.521181 `3B` 0.508544 0.318646 1.596 0.112661 Month -0.136999 0.402640 -0.340 0.734156 t -0.001881 0.004160 -0.452 0.651825 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.108 on 146 degrees of freedom Multiple R-squared: 0.5148, Adjusted R-squared: 0.4849 F-statistic: 17.21 on 9 and 146 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,] 0.56523502 0.8695299642 0.4347649821 [2,] 0.39584431 0.7916886176 0.6041556912 [3,] 0.26950997 0.5390199363 0.7304900319 [4,] 0.18663697 0.3732739349 0.8133630326 [5,] 0.12023914 0.2404782788 0.8797608606 [6,] 0.21701702 0.4340340483 0.7829829758 [7,] 0.36424329 0.7284865854 0.6357567073 [8,] 0.27500502 0.5500100374 0.7249949813 [9,] 0.28308810 0.5661762084 0.7169118958 [10,] 0.21088701 0.4217740211 0.7891129894 [11,] 0.17669448 0.3533889574 0.8233055213 [12,] 0.12565498 0.2513099658 0.8743450171 [13,] 0.08664178 0.1732835658 0.9133582171 [14,] 0.07658124 0.1531624731 0.9234187634 [15,] 0.06710154 0.1342030777 0.9328984611 [16,] 0.17241569 0.3448313737 0.8275843131 [17,] 0.14449392 0.2889878444 0.8555060778 [18,] 0.11483698 0.2296739696 0.8851630152 [19,] 0.08478095 0.1695619071 0.9152190464 [20,] 0.07287113 0.1457422520 0.9271288740 [21,] 0.05180764 0.1036152764 0.9481923618 [22,] 0.03792182 0.0758436460 0.9620781770 [23,] 0.22515741 0.4503148271 0.7748425864 [24,] 0.44613092 0.8922618326 0.5538690837 [25,] 0.57786169 0.8442766245 0.4221383122 [26,] 0.52475988 0.9504802400 0.4752401200 [27,] 0.49588021 0.9917604218 0.5041197891 [28,] 0.46781126 0.9356225264 0.5321887368 [29,] 0.69902307 0.6019538576 0.3009769288 [30,] 0.85881549 0.2823690255 0.1411845128 [31,] 0.82715501 0.3456899712 0.1728449856 [32,] 0.86357999 0.2728400121 0.1364200060 [33,] 0.83718634 0.3256273283 0.1628136642 [34,] 0.83622840 0.3275431921 0.1637715961 [35,] 0.81430041 0.3713991704 0.1856995852 [36,] 0.82440990 0.3511801965 0.1755900982 [37,] 0.78935215 0.4212957030 0.2106478515 [38,] 0.79580307 0.4083938678 0.2041969339 [39,] 0.77224823 0.4555035334 0.2277517667 [40,] 0.75928906 0.4814218871 0.2407109436 [41,] 0.86594446 0.2681110733 0.1340555367 [42,] 0.83617098 0.3276580490 0.1638290245 [43,] 0.82719876 0.3456024764 0.1728012382 [44,] 0.79610782 0.4077843639 0.2038921820 [45,] 0.75948583 0.4810283441 0.2405141721 [46,] 0.71911743 0.5617651424 0.2808825712 [47,] 0.68538039 0.6292392238 0.3146196119 [48,] 0.66438645 0.6712270925 0.3356135462 [49,] 0.61852153 0.7629569426 0.3814784713 [50,] 0.58533496 0.8293300848 0.4146650424 [51,] 0.55854941 0.8829011776 0.4414505888 [52,] 0.55957112 0.8808577528 0.4404288764 [53,] 0.54755314 0.9048937205 0.4524468603 [54,] 0.63298177 0.7340364529 0.3670182265 [55,] 0.59598295 0.8080340909 0.4040170454 [56,] 0.56140676 0.8771864823 0.4385932411 [57,] 0.69713942 0.6057211601 0.3028605801 [58,] 0.88130880 0.2373824074 0.1186912037 [59,] 0.89281824 0.2143635108 0.1071817554 [60,] 0.90767337 0.1846532693 0.0923266346 [61,] 0.94842617 0.1031476662 0.0515738331 [62,] 0.93615660 0.1276868090 0.0638434045 [63,] 0.92902415 0.1419516932 0.0709758466 [64,] 0.91149969 0.1770006157 0.0885003079 [65,] 0.93284476 0.1343104858 0.0671552429 [66,] 0.93491259 0.1301748101 0.0650874051 [67,] 0.97112368 0.0577526374 0.0288763187 [68,] 0.96214295 0.0757141079 0.0378570539 [69,] 0.95943136 0.0811372853 0.0405686426 [70,] 0.97748062 0.0450387682 0.0225193841 [71,] 0.97121752 0.0575649580 0.0287824790 [72,] 0.97714227 0.0457154614 0.0228577307 [73,] 0.96979499 0.0604100238 0.0302050119 [74,] 0.96318478 0.0736304489 0.0368152244 [75,] 0.95307445 0.0938511002 0.0469255501 [76,] 0.94597000 0.1080599918 0.0540299959 [77,] 0.95046826 0.0990634802 0.0495317401 [78,] 0.93736290 0.1252741901 0.0626370950 [79,] 0.93486049 0.1302790176 0.0651395088 [80,] 0.95405605 0.0918879026 0.0459439513 [81,] 0.99606361 0.0078727705 0.0039363852 [82,] 0.99440253 0.0111949416 0.0055974708 [83,] 0.99358057 0.0128388670 0.0064194335 [84,] 0.99140582 0.0171883596 0.0085941798 [85,] 0.99143056 0.0171388726 0.0085694363 [86,] 0.99248705 0.0150258944 0.0075129472 [87,] 0.98946973 0.0210605415 0.0105302707 [88,] 0.98830581 0.0233883860 0.0116941930 [89,] 0.99232552 0.0153489596 0.0076744798 [90,] 0.99272701 0.0145459895 0.0072729948 [91,] 0.98989697 0.0202060569 0.0101030284 [92,] 0.99282516 0.0143496703 0.0071748351 [93,] 0.98994941 0.0201011835 0.0100505917 [94,] 0.98726662 0.0254667645 0.0127333823 [95,] 0.98528167 0.0294366677 0.0147183339 [96,] 0.98971774 0.0205645206 0.0102822603 [97,] 0.99898399 0.0020320123 0.0010160062 [98,] 0.99867157 0.0026568644 0.0013284322 [99,] 0.99957756 0.0008448893 0.0004224446 [100,] 0.99930483 0.0013903417 0.0006951709 [101,] 0.99929346 0.0014130834 0.0007065417 [102,] 0.99884336 0.0023132762 0.0011566381 [103,] 0.99826630 0.0034674011 0.0017337005 [104,] 0.99713469 0.0057306131 0.0028653066 [105,] 0.99557116 0.0088576887 0.0044288443 [106,] 0.99928575 0.0014284923 0.0007142462 [107,] 0.99875690 0.0024862032 0.0012431016 [108,] 0.99790663 0.0041867319 0.0020933660 [109,] 0.99737516 0.0052496836 0.0026248418 [110,] 0.99662832 0.0067433539 0.0033716770 [111,] 0.99472490 0.0105501983 0.0052750992 [112,] 0.99462270 0.0107545906 0.0053772953 [113,] 0.99111186 0.0177762827 0.0088881413 [114,] 0.98706737 0.0258652532 0.0129326266 [115,] 0.98365094 0.0326981156 0.0163490578 [116,] 0.97589200 0.0482160095 0.0241080047 [117,] 0.98769067 0.0246186569 0.0123093285 [118,] 0.98978922 0.0204215615 0.0102107808 [119,] 0.98489977 0.0302004683 0.0151002341 [120,] 0.98199611 0.0360077861 0.0180038931 [121,] 0.97488405 0.0502318909 0.0251159454 [122,] 0.96221397 0.0755720586 0.0377860293 [123,] 0.93787863 0.1242427430 0.0621213715 [124,] 0.95383948 0.0923210454 0.0461605227 [125,] 0.97214418 0.0557116402 0.0278558201 [126,] 0.94758417 0.1048316516 0.0524158258 [127,] 0.90425799 0.1914840149 0.0957420075 [128,] 0.85385804 0.2922839108 0.1461419554 [129,] 0.77312045 0.4537591045 0.2268795523 [130,] 0.70995135 0.5800973007 0.2900486503 [131,] 0.55442965 0.8911407001 0.4455703501 > postscript(file="/var/www/html/freestat/rcomp/tmp/1hf2c1293206190.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/2sp2x1293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3sp2x1293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/4sp2x1293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/53gjz1293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 156 Frequency = 1 1 2 3 4 5 6 1.50083206 0.10188999 -2.29358477 -2.07685053 -0.19720170 0.76800609 7 8 9 10 11 12 1.06764512 2.07835009 -0.99608036 -2.77748695 -0.25342245 -0.12060130 13 14 15 16 17 18 3.08656747 1.17057261 -1.08808087 0.80971002 0.40229445 -2.79756754 19 20 21 22 23 24 -1.28158275 0.72911169 2.61139354 1.23994976 -0.15547143 0.66775117 25 26 27 28 29 30 0.45725197 -0.30121789 2.53037466 -2.11009053 -1.15499381 -0.80387372 31 32 33 34 35 36 -0.45193602 2.35251593 0.63095324 0.13761486 -5.60684916 5.16697525 37 38 39 40 41 42 3.81912833 -1.37091641 1.76660194 -0.90163183 5.98403018 -4.81670812 43 44 45 46 47 48 -0.40963494 3.75275158 -0.10604537 -1.71775002 1.77962781 2.17904839 49 50 51 52 53 54 -0.34834958 2.45640163 -1.81770753 -0.87861667 -2.74547987 -0.22639291 55 56 57 58 59 60 -1.82191108 -0.07874661 0.28413019 -0.89382927 -0.36948662 -0.27213525 61 62 63 64 65 66 -0.01503158 1.01822676 -1.91845472 1.51417846 -1.73263169 3.26968039 67 68 69 70 71 72 0.77404330 0.62671160 -4.11195684 5.08397256 2.16438238 -3.23580652 73 74 75 76 77 78 -4.26888258 0.08423314 1.65695520 -0.47535601 2.84918356 -2.43077400 79 80 81 82 83 84 -4.15238269 -0.29273767 1.50277054 -3.77895959 -0.31352544 2.63287645 85 86 87 88 89 90 -0.28586377 -1.39382487 -0.38697990 0.87046604 0.68719759 -0.40685253 91 92 93 94 95 96 -2.33716935 -2.80392720 -6.00219528 -1.12878860 1.85815208 0.56510446 97 98 99 100 101 102 -2.56751831 -2.21588333 -1.44419865 1.91087377 2.81633975 2.38332549 103 104 105 106 107 108 -0.22559837 2.84531513 0.82231241 0.47633763 -1.68133414 -2.56300249 109 110 111 112 113 114 4.52949836 1.18012184 -3.23628501 0.93589381 1.91832104 -0.30768739 115 116 117 118 119 120 -1.50433621 -0.13820027 0.87221602 2.40740580 -0.32854663 -1.11439891 121 122 123 124 125 126 -2.17279086 -3.01591952 -0.76917531 -1.18791879 0.07190099 -0.51880613 127 128 129 130 131 132 -1.57207059 1.81254511 -1.63980667 1.35641119 -1.77603215 2.21608900 133 134 135 136 137 138 -1.26962008 -0.75675132 0.92512307 -1.13367372 -0.17042744 0.27584153 139 140 141 142 143 144 -0.39453961 1.47158615 1.72722189 1.23388352 1.18496564 1.08456694 145 146 147 148 149 150 1.48506984 1.89618521 0.16316228 2.96976118 0.61057870 -1.84763561 151 152 153 154 155 156 2.44019298 0.25305612 -1.00387918 -1.11769373 0.25869868 -0.83635049 > postscript(file="/var/www/html/freestat/rcomp/tmp/63gjz1293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 1.50083206 NA 1 0.10188999 1.50083206 2 -2.29358477 0.10188999 3 -2.07685053 -2.29358477 4 -0.19720170 -2.07685053 5 0.76800609 -0.19720170 6 1.06764512 0.76800609 7 2.07835009 1.06764512 8 -0.99608036 2.07835009 9 -2.77748695 -0.99608036 10 -0.25342245 -2.77748695 11 -0.12060130 -0.25342245 12 3.08656747 -0.12060130 13 1.17057261 3.08656747 14 -1.08808087 1.17057261 15 0.80971002 -1.08808087 16 0.40229445 0.80971002 17 -2.79756754 0.40229445 18 -1.28158275 -2.79756754 19 0.72911169 -1.28158275 20 2.61139354 0.72911169 21 1.23994976 2.61139354 22 -0.15547143 1.23994976 23 0.66775117 -0.15547143 24 0.45725197 0.66775117 25 -0.30121789 0.45725197 26 2.53037466 -0.30121789 27 -2.11009053 2.53037466 28 -1.15499381 -2.11009053 29 -0.80387372 -1.15499381 30 -0.45193602 -0.80387372 31 2.35251593 -0.45193602 32 0.63095324 2.35251593 33 0.13761486 0.63095324 34 -5.60684916 0.13761486 35 5.16697525 -5.60684916 36 3.81912833 5.16697525 37 -1.37091641 3.81912833 38 1.76660194 -1.37091641 39 -0.90163183 1.76660194 40 5.98403018 -0.90163183 41 -4.81670812 5.98403018 42 -0.40963494 -4.81670812 43 3.75275158 -0.40963494 44 -0.10604537 3.75275158 45 -1.71775002 -0.10604537 46 1.77962781 -1.71775002 47 2.17904839 1.77962781 48 -0.34834958 2.17904839 49 2.45640163 -0.34834958 50 -1.81770753 2.45640163 51 -0.87861667 -1.81770753 52 -2.74547987 -0.87861667 53 -0.22639291 -2.74547987 54 -1.82191108 -0.22639291 55 -0.07874661 -1.82191108 56 0.28413019 -0.07874661 57 -0.89382927 0.28413019 58 -0.36948662 -0.89382927 59 -0.27213525 -0.36948662 60 -0.01503158 -0.27213525 61 1.01822676 -0.01503158 62 -1.91845472 1.01822676 63 1.51417846 -1.91845472 64 -1.73263169 1.51417846 65 3.26968039 -1.73263169 66 0.77404330 3.26968039 67 0.62671160 0.77404330 68 -4.11195684 0.62671160 69 5.08397256 -4.11195684 70 2.16438238 5.08397256 71 -3.23580652 2.16438238 72 -4.26888258 -3.23580652 73 0.08423314 -4.26888258 74 1.65695520 0.08423314 75 -0.47535601 1.65695520 76 2.84918356 -0.47535601 77 -2.43077400 2.84918356 78 -4.15238269 -2.43077400 79 -0.29273767 -4.15238269 80 1.50277054 -0.29273767 81 -3.77895959 1.50277054 82 -0.31352544 -3.77895959 83 2.63287645 -0.31352544 84 -0.28586377 2.63287645 85 -1.39382487 -0.28586377 86 -0.38697990 -1.39382487 87 0.87046604 -0.38697990 88 0.68719759 0.87046604 89 -0.40685253 0.68719759 90 -2.33716935 -0.40685253 91 -2.80392720 -2.33716935 92 -6.00219528 -2.80392720 93 -1.12878860 -6.00219528 94 1.85815208 -1.12878860 95 0.56510446 1.85815208 96 -2.56751831 0.56510446 97 -2.21588333 -2.56751831 98 -1.44419865 -2.21588333 99 1.91087377 -1.44419865 100 2.81633975 1.91087377 101 2.38332549 2.81633975 102 -0.22559837 2.38332549 103 2.84531513 -0.22559837 104 0.82231241 2.84531513 105 0.47633763 0.82231241 106 -1.68133414 0.47633763 107 -2.56300249 -1.68133414 108 4.52949836 -2.56300249 109 1.18012184 4.52949836 110 -3.23628501 1.18012184 111 0.93589381 -3.23628501 112 1.91832104 0.93589381 113 -0.30768739 1.91832104 114 -1.50433621 -0.30768739 115 -0.13820027 -1.50433621 116 0.87221602 -0.13820027 117 2.40740580 0.87221602 118 -0.32854663 2.40740580 119 -1.11439891 -0.32854663 120 -2.17279086 -1.11439891 121 -3.01591952 -2.17279086 122 -0.76917531 -3.01591952 123 -1.18791879 -0.76917531 124 0.07190099 -1.18791879 125 -0.51880613 0.07190099 126 -1.57207059 -0.51880613 127 1.81254511 -1.57207059 128 -1.63980667 1.81254511 129 1.35641119 -1.63980667 130 -1.77603215 1.35641119 131 2.21608900 -1.77603215 132 -1.26962008 2.21608900 133 -0.75675132 -1.26962008 134 0.92512307 -0.75675132 135 -1.13367372 0.92512307 136 -0.17042744 -1.13367372 137 0.27584153 -0.17042744 138 -0.39453961 0.27584153 139 1.47158615 -0.39453961 140 1.72722189 1.47158615 141 1.23388352 1.72722189 142 1.18496564 1.23388352 143 1.08456694 1.18496564 144 1.48506984 1.08456694 145 1.89618521 1.48506984 146 0.16316228 1.89618521 147 2.96976118 0.16316228 148 0.61057870 2.96976118 149 -1.84763561 0.61057870 150 2.44019298 -1.84763561 151 0.25305612 2.44019298 152 -1.00387918 0.25305612 153 -1.11769373 -1.00387918 154 0.25869868 -1.11769373 155 -0.83635049 0.25869868 156 NA -0.83635049 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.10188999 1.50083206 [2,] -2.29358477 0.10188999 [3,] -2.07685053 -2.29358477 [4,] -0.19720170 -2.07685053 [5,] 0.76800609 -0.19720170 [6,] 1.06764512 0.76800609 [7,] 2.07835009 1.06764512 [8,] -0.99608036 2.07835009 [9,] -2.77748695 -0.99608036 [10,] -0.25342245 -2.77748695 [11,] -0.12060130 -0.25342245 [12,] 3.08656747 -0.12060130 [13,] 1.17057261 3.08656747 [14,] -1.08808087 1.17057261 [15,] 0.80971002 -1.08808087 [16,] 0.40229445 0.80971002 [17,] -2.79756754 0.40229445 [18,] -1.28158275 -2.79756754 [19,] 0.72911169 -1.28158275 [20,] 2.61139354 0.72911169 [21,] 1.23994976 2.61139354 [22,] -0.15547143 1.23994976 [23,] 0.66775117 -0.15547143 [24,] 0.45725197 0.66775117 [25,] -0.30121789 0.45725197 [26,] 2.53037466 -0.30121789 [27,] -2.11009053 2.53037466 [28,] -1.15499381 -2.11009053 [29,] -0.80387372 -1.15499381 [30,] -0.45193602 -0.80387372 [31,] 2.35251593 -0.45193602 [32,] 0.63095324 2.35251593 [33,] 0.13761486 0.63095324 [34,] -5.60684916 0.13761486 [35,] 5.16697525 -5.60684916 [36,] 3.81912833 5.16697525 [37,] -1.37091641 3.81912833 [38,] 1.76660194 -1.37091641 [39,] -0.90163183 1.76660194 [40,] 5.98403018 -0.90163183 [41,] -4.81670812 5.98403018 [42,] -0.40963494 -4.81670812 [43,] 3.75275158 -0.40963494 [44,] -0.10604537 3.75275158 [45,] -1.71775002 -0.10604537 [46,] 1.77962781 -1.71775002 [47,] 2.17904839 1.77962781 [48,] -0.34834958 2.17904839 [49,] 2.45640163 -0.34834958 [50,] -1.81770753 2.45640163 [51,] -0.87861667 -1.81770753 [52,] -2.74547987 -0.87861667 [53,] -0.22639291 -2.74547987 [54,] -1.82191108 -0.22639291 [55,] -0.07874661 -1.82191108 [56,] 0.28413019 -0.07874661 [57,] -0.89382927 0.28413019 [58,] -0.36948662 -0.89382927 [59,] -0.27213525 -0.36948662 [60,] -0.01503158 -0.27213525 [61,] 1.01822676 -0.01503158 [62,] -1.91845472 1.01822676 [63,] 1.51417846 -1.91845472 [64,] -1.73263169 1.51417846 [65,] 3.26968039 -1.73263169 [66,] 0.77404330 3.26968039 [67,] 0.62671160 0.77404330 [68,] -4.11195684 0.62671160 [69,] 5.08397256 -4.11195684 [70,] 2.16438238 5.08397256 [71,] -3.23580652 2.16438238 [72,] -4.26888258 -3.23580652 [73,] 0.08423314 -4.26888258 [74,] 1.65695520 0.08423314 [75,] -0.47535601 1.65695520 [76,] 2.84918356 -0.47535601 [77,] -2.43077400 2.84918356 [78,] -4.15238269 -2.43077400 [79,] -0.29273767 -4.15238269 [80,] 1.50277054 -0.29273767 [81,] -3.77895959 1.50277054 [82,] -0.31352544 -3.77895959 [83,] 2.63287645 -0.31352544 [84,] -0.28586377 2.63287645 [85,] -1.39382487 -0.28586377 [86,] -0.38697990 -1.39382487 [87,] 0.87046604 -0.38697990 [88,] 0.68719759 0.87046604 [89,] -0.40685253 0.68719759 [90,] -2.33716935 -0.40685253 [91,] -2.80392720 -2.33716935 [92,] -6.00219528 -2.80392720 [93,] -1.12878860 -6.00219528 [94,] 1.85815208 -1.12878860 [95,] 0.56510446 1.85815208 [96,] -2.56751831 0.56510446 [97,] -2.21588333 -2.56751831 [98,] -1.44419865 -2.21588333 [99,] 1.91087377 -1.44419865 [100,] 2.81633975 1.91087377 [101,] 2.38332549 2.81633975 [102,] -0.22559837 2.38332549 [103,] 2.84531513 -0.22559837 [104,] 0.82231241 2.84531513 [105,] 0.47633763 0.82231241 [106,] -1.68133414 0.47633763 [107,] -2.56300249 -1.68133414 [108,] 4.52949836 -2.56300249 [109,] 1.18012184 4.52949836 [110,] -3.23628501 1.18012184 [111,] 0.93589381 -3.23628501 [112,] 1.91832104 0.93589381 [113,] -0.30768739 1.91832104 [114,] -1.50433621 -0.30768739 [115,] -0.13820027 -1.50433621 [116,] 0.87221602 -0.13820027 [117,] 2.40740580 0.87221602 [118,] -0.32854663 2.40740580 [119,] -1.11439891 -0.32854663 [120,] -2.17279086 -1.11439891 [121,] -3.01591952 -2.17279086 [122,] -0.76917531 -3.01591952 [123,] -1.18791879 -0.76917531 [124,] 0.07190099 -1.18791879 [125,] -0.51880613 0.07190099 [126,] -1.57207059 -0.51880613 [127,] 1.81254511 -1.57207059 [128,] -1.63980667 1.81254511 [129,] 1.35641119 -1.63980667 [130,] -1.77603215 1.35641119 [131,] 2.21608900 -1.77603215 [132,] -1.26962008 2.21608900 [133,] -0.75675132 -1.26962008 [134,] 0.92512307 -0.75675132 [135,] -1.13367372 0.92512307 [136,] -0.17042744 -1.13367372 [137,] 0.27584153 -0.17042744 [138,] -0.39453961 0.27584153 [139,] 1.47158615 -0.39453961 [140,] 1.72722189 1.47158615 [141,] 1.23388352 1.72722189 [142,] 1.18496564 1.23388352 [143,] 1.08456694 1.18496564 [144,] 1.48506984 1.08456694 [145,] 1.89618521 1.48506984 [146,] 0.16316228 1.89618521 [147,] 2.96976118 0.16316228 [148,] 0.61057870 2.96976118 [149,] -1.84763561 0.61057870 [150,] 2.44019298 -1.84763561 [151,] 0.25305612 2.44019298 [152,] -1.00387918 0.25305612 [153,] -1.11769373 -1.00387918 [154,] 0.25869868 -1.11769373 [155,] -0.83635049 0.25869868 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.10188999 1.50083206 2 -2.29358477 0.10188999 3 -2.07685053 -2.29358477 4 -0.19720170 -2.07685053 5 0.76800609 -0.19720170 6 1.06764512 0.76800609 7 2.07835009 1.06764512 8 -0.99608036 2.07835009 9 -2.77748695 -0.99608036 10 -0.25342245 -2.77748695 11 -0.12060130 -0.25342245 12 3.08656747 -0.12060130 13 1.17057261 3.08656747 14 -1.08808087 1.17057261 15 0.80971002 -1.08808087 16 0.40229445 0.80971002 17 -2.79756754 0.40229445 18 -1.28158275 -2.79756754 19 0.72911169 -1.28158275 20 2.61139354 0.72911169 21 1.23994976 2.61139354 22 -0.15547143 1.23994976 23 0.66775117 -0.15547143 24 0.45725197 0.66775117 25 -0.30121789 0.45725197 26 2.53037466 -0.30121789 27 -2.11009053 2.53037466 28 -1.15499381 -2.11009053 29 -0.80387372 -1.15499381 30 -0.45193602 -0.80387372 31 2.35251593 -0.45193602 32 0.63095324 2.35251593 33 0.13761486 0.63095324 34 -5.60684916 0.13761486 35 5.16697525 -5.60684916 36 3.81912833 5.16697525 37 -1.37091641 3.81912833 38 1.76660194 -1.37091641 39 -0.90163183 1.76660194 40 5.98403018 -0.90163183 41 -4.81670812 5.98403018 42 -0.40963494 -4.81670812 43 3.75275158 -0.40963494 44 -0.10604537 3.75275158 45 -1.71775002 -0.10604537 46 1.77962781 -1.71775002 47 2.17904839 1.77962781 48 -0.34834958 2.17904839 49 2.45640163 -0.34834958 50 -1.81770753 2.45640163 51 -0.87861667 -1.81770753 52 -2.74547987 -0.87861667 53 -0.22639291 -2.74547987 54 -1.82191108 -0.22639291 55 -0.07874661 -1.82191108 56 0.28413019 -0.07874661 57 -0.89382927 0.28413019 58 -0.36948662 -0.89382927 59 -0.27213525 -0.36948662 60 -0.01503158 -0.27213525 61 1.01822676 -0.01503158 62 -1.91845472 1.01822676 63 1.51417846 -1.91845472 64 -1.73263169 1.51417846 65 3.26968039 -1.73263169 66 0.77404330 3.26968039 67 0.62671160 0.77404330 68 -4.11195684 0.62671160 69 5.08397256 -4.11195684 70 2.16438238 5.08397256 71 -3.23580652 2.16438238 72 -4.26888258 -3.23580652 73 0.08423314 -4.26888258 74 1.65695520 0.08423314 75 -0.47535601 1.65695520 76 2.84918356 -0.47535601 77 -2.43077400 2.84918356 78 -4.15238269 -2.43077400 79 -0.29273767 -4.15238269 80 1.50277054 -0.29273767 81 -3.77895959 1.50277054 82 -0.31352544 -3.77895959 83 2.63287645 -0.31352544 84 -0.28586377 2.63287645 85 -1.39382487 -0.28586377 86 -0.38697990 -1.39382487 87 0.87046604 -0.38697990 88 0.68719759 0.87046604 89 -0.40685253 0.68719759 90 -2.33716935 -0.40685253 91 -2.80392720 -2.33716935 92 -6.00219528 -2.80392720 93 -1.12878860 -6.00219528 94 1.85815208 -1.12878860 95 0.56510446 1.85815208 96 -2.56751831 0.56510446 97 -2.21588333 -2.56751831 98 -1.44419865 -2.21588333 99 1.91087377 -1.44419865 100 2.81633975 1.91087377 101 2.38332549 2.81633975 102 -0.22559837 2.38332549 103 2.84531513 -0.22559837 104 0.82231241 2.84531513 105 0.47633763 0.82231241 106 -1.68133414 0.47633763 107 -2.56300249 -1.68133414 108 4.52949836 -2.56300249 109 1.18012184 4.52949836 110 -3.23628501 1.18012184 111 0.93589381 -3.23628501 112 1.91832104 0.93589381 113 -0.30768739 1.91832104 114 -1.50433621 -0.30768739 115 -0.13820027 -1.50433621 116 0.87221602 -0.13820027 117 2.40740580 0.87221602 118 -0.32854663 2.40740580 119 -1.11439891 -0.32854663 120 -2.17279086 -1.11439891 121 -3.01591952 -2.17279086 122 -0.76917531 -3.01591952 123 -1.18791879 -0.76917531 124 0.07190099 -1.18791879 125 -0.51880613 0.07190099 126 -1.57207059 -0.51880613 127 1.81254511 -1.57207059 128 -1.63980667 1.81254511 129 1.35641119 -1.63980667 130 -1.77603215 1.35641119 131 2.21608900 -1.77603215 132 -1.26962008 2.21608900 133 -0.75675132 -1.26962008 134 0.92512307 -0.75675132 135 -1.13367372 0.92512307 136 -0.17042744 -1.13367372 137 0.27584153 -0.17042744 138 -0.39453961 0.27584153 139 1.47158615 -0.39453961 140 1.72722189 1.47158615 141 1.23388352 1.72722189 142 1.18496564 1.23388352 143 1.08456694 1.18496564 144 1.48506984 1.08456694 145 1.89618521 1.48506984 146 0.16316228 1.89618521 147 2.96976118 0.16316228 148 0.61057870 2.96976118 149 -1.84763561 0.61057870 150 2.44019298 -1.84763561 151 0.25305612 2.44019298 152 -1.00387918 0.25305612 153 -1.11769373 -1.00387918 154 0.25869868 -1.11769373 155 -0.83635049 0.25869868 > 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/html/freestat/rcomp/tmp/7v7ik1293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/8v7ik1293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/96yz51293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/freestat/rcomp/tmp/106yz51293206190.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/html/freestat/rcomp/tmp/11azgb1293206190.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/html/freestat/rcomp/tmp/12vzwz1293206190.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/html/freestat/rcomp/tmp/139ru81293206190.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/html/freestat/rcomp/tmp/14uabe1293206190.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/html/freestat/rcomp/tmp/15gar21293206190.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/html/freestat/rcomp/tmp/16jb8p1293206190.tab") + } > > try(system("convert tmp/1hf2c1293206190.ps tmp/1hf2c1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/2sp2x1293206190.ps tmp/2sp2x1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/3sp2x1293206190.ps tmp/3sp2x1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/4sp2x1293206190.ps tmp/4sp2x1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/53gjz1293206190.ps tmp/53gjz1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/63gjz1293206190.ps tmp/63gjz1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/7v7ik1293206190.ps tmp/7v7ik1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/8v7ik1293206190.ps tmp/8v7ik1293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/96yz51293206190.ps tmp/96yz51293206190.png",intern=TRUE)) character(0) > try(system("convert tmp/106yz51293206190.ps tmp/106yz51293206190.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.073 2.681 6.477