R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(15
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+ ,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 = 'No 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
1 15 10 12 16 6 2 0 0 9
2 12 9 7 12 6 1 1 2 9
3 9 12 11 11 4 1 2 1 9
4 10 12 11 12 6 0 0 0 9
5 13 9 14 14 6 0 0 0 9
6 16 11 16 16 7 1 0 0 9
7 14 12 13 13 6 0 0 0 9
8 16 11 13 14 7 1 1 0 9
9 10 12 5 13 6 0 0 0 9
10 8 12 8 13 4 2 0 1 10
11 12 11 14 13 5 1 0 0 10
12 15 11 15 15 8 0 0 0 10
13 14 12 8 14 4 0 1 0 10
14 14 6 13 12 6 1 1 2 10
15 12 13 12 12 6 1 2 1 10
16 12 11 11 12 5 0 0 0 10
17 10 12 8 11 4 0 0 0 10
18 4 10 4 10 2 0 0 0 10
19 14 11 15 15 8 0 1 0 10
20 15 12 12 16 7 0 0 0 10
21 16 12 14 14 6 0 0 0 10
22 12 12 9 13 4 0 1 0 10
23 12 11 16 13 4 0 0 0 10
24 12 12 10 13 4 0 0 1 10
25 12 12 8 13 5 1 0 1 9
26 12 12 14 14 4 0 0 0 9
27 11 6 6 9 4 3 2 1 9
28 11 5 16 14 6 1 0 0 9
29 11 12 11 12 6 1 1 0 9
30 11 14 7 13 6 1 1 0 9
31 11 12 13 11 4 3 1 1 9
32 11 9 7 13 2 0 0 0 9
33 15 11 14 15 7 0 0 0 9
34 15 11 17 16 6 0 0 0 9
35 9 11 15 15 7 0 0 0 9
36 16 12 8 14 4 0 0 0 9
37 13 10 8 8 4 0 2 1 9
38 9 12 11 11 4 1 0 0 9
39 16 11 16 15 6 0 0 0 9
40 12 12 10 15 6 0 0 0 9
41 15 9 5 11 3 0 0 2 9
42 5 15 8 12 3 0 0 0 9
43 11 11 8 12 6 2 2 0 9
44 17 11 15 14 5 2 2 0 9
45 9 15 6 8 4 0 1 1 9
46 13 12 16 16 6 0 0 0 9
47 16 9 16 16 6 0 0 0 10
48 16 12 16 14 6 0 0 0 10
49 14 9 19 12 6 2 0 2 10
50 16 11 14 15 6 1 0 0 10
51 11 12 15 12 6 0 0 0 10
52 11 11 11 14 5 0 0 0 10
53 11 6 14 17 6 0 0 0 10
54 12 10 12 13 6 0 0 0 10
55 12 12 15 13 6 1 1 1 10
56 12 13 14 12 5 0 0 0 10
57 14 11 13 16 6 0 0 0 10
58 10 10 11 12 5 2 0 0 10
59 9 11 8 10 4 0 2 0 10
60 12 7 11 15 5 0 0 1 10
61 10 11 9 12 4 0 0 0 10
62 14 11 10 16 6 0 0 0 10
63 8 7 4 13 6 0 0 0 10
64 16 12 15 15 7 1 0 0 10
65 14 14 17 18 6 1 0 0 10
66 14 11 12 12 4 0 0 0 10
67 12 12 12 13 4 0 0 0 10
68 14 11 15 14 6 1 0 0 10
69 7 12 13 12 3 1 1 1 10
70 19 12 15 15 6 0 0 0 10
71 15 12 14 16 4 0 0 0 10
72 8 12 8 14 5 0 0 0 10
73 10 15 15 15 6 0 0 0 10
74 13 11 12 13 7 0 0 0 10
75 13 13 14 13 3 0 0 0 9
76 10 10 10 11 5 0 0 0 9
77 12 12 7 12 3 0 0 0 9
78 15 13 16 18 8 0 1 1 9
79 7 14 12 12 4 1 0 0 9
80 14 11 15 16 6 0 0 0 9
81 10 11 7 9 4 0 0 0 9
82 6 7 9 11 4 0 3 0 9
83 11 11 15 10 5 2 0 0 9
84 12 12 7 11 4 0 0 0 9
85 14 12 15 13 6 0 0 2 9
86 12 10 14 13 7 0 0 0 9
87 14 12 14 15 7 0 0 0 9
88 11 8 8 13 4 2 2 0 9
89 10 7 8 9 5 1 0 1 9
90 13 11 14 13 6 0 0 1 9
91 8 11 10 12 4 0 0 0 9
92 9 11 12 13 5 0 0 0 9
93 6 9 15 11 6 0 0 0 10
94 12 12 12 14 5 1 0 2 10
95 14 13 13 13 5 0 0 0 10
96 11 9 12 12 4 0 0 0 10
97 8 11 10 15 2 1 0 1 10
98 7 12 8 12 3 0 0 0 10
99 9 9 6 12 5 0 2 1 10
100 14 12 13 13 5 2 1 0 10
101 13 12 7 12 5 0 0 0 10
102 15 12 13 13 6 0 0 0 10
103 5 14 4 5 2 0 0 0 10
104 15 11 14 13 5 3 1 0 10
105 13 12 13 13 5 0 1 0 10
106 12 8 13 13 5 0 0 0 10
107 6 12 6 11 2 1 0 0 10
108 7 12 7 12 4 0 0 0 10
109 13 12 5 12 3 0 0 0 10
110 16 11 14 15 8 1 1 0 10
111 10 11 13 15 6 0 0 0 10
112 16 12 16 16 7 0 0 0 10
113 15 10 16 13 6 0 0 0 10
114 8 13 7 10 3 0 0 0 10
115 11 8 14 15 5 0 0 0 10
116 13 12 11 13 6 0 3 1 10
117 16 11 17 16 7 1 0 0 10
118 11 10 5 13 3 0 0 0 10
119 14 13 10 16 8 0 0 0 10
120 9 10 11 13 3 2 1 0 10
121 8 10 10 14 3 0 0 0 10
122 8 7 9 15 4 1 0 1 10
123 11 10 12 14 5 2 0 0 10
124 12 8 15 13 7 0 0 0 10
125 11 12 7 13 6 4 0 0 10
126 14 12 13 15 6 0 1 2 10
127 11 12 8 16 6 2 1 0 10
128 14 11 16 12 5 0 0 0 10
129 13 13 15 14 6 2 1 2 10
130 12 12 6 14 5 0 0 0 10
131 4 8 6 4 4 0 0 0 10
132 15 11 12 13 6 2 1 1 10
133 10 12 8 16 4 0 0 0 10
134 13 13 11 15 6 1 2 1 10
135 15 12 13 14 6 1 1 2 10
136 12 10 14 14 5 1 2 1 10
137 13 12 14 14 6 0 0 0 10
138 8 10 10 6 4 0 0 0 10
139 10 13 4 13 6 2 0 0 10
140 15 11 16 14 6 0 0 0 10
141 16 12 12 15 8 0 0 0 10
142 16 12 15 16 7 0 0 0 10
143 14 10 12 15 6 0 0 0 10
144 14 11 14 12 6 1 1 1 10
145 12 11 11 14 2 1 1 1 10
146 15 11 16 11 5 0 1 2 9
147 13 8 14 14 5 1 1 1 9
148 16 11 14 14 6 0 0 0 10
149 14 12 15 14 6 0 0 0 10
150 8 11 9 12 4 0 0 0 10
151 16 12 15 14 6 0 1 0 10
152 16 12 14 16 8 1 1 1 10
153 12 12 15 13 6 0 0 0 10
154 11 8 10 14 5 0 3 1 10
155 16 12 14 16 8 1 1 1 10
156 9 11 9 12 4 0 0 0 10
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
1.65208 0.11888 0.24088 0.37903 0.60780
B `2B` `3B` Month
-0.04689 0.16531 0.49935 -0.21449
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.00635 -1.21908 -0.05281 1.34553 6.01261
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.65208 3.68147 0.449 0.654270
FindingFriends 0.11888 0.09660 1.231 0.220453
KnowingPeople 0.24088 0.06174 3.901 0.000145 ***
Liked 0.37903 0.09778 3.876 0.000159 ***
Celebrity 0.60780 0.15677 3.877 0.000159 ***
B -0.04689 0.22369 -0.210 0.834249
`2B` 0.16531 0.26936 0.614 0.540366
`3B` 0.49935 0.31713 1.575 0.117504
Month -0.21449 0.36336 -0.590 0.555906
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.102 on 147 degrees of freedom
Multiple R-squared: 0.5141, Adjusted R-squared: 0.4876
F-statistic: 19.44 on 8 and 147 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,] 0.32896021 0.6579204273 0.6710397864
[2,] 0.43034706 0.8606941276 0.5696529362
[3,] 0.30232161 0.6046432208 0.6976783896
[4,] 0.19596787 0.3919357349 0.8040321326
[5,] 0.14943343 0.2988668506 0.8505665747
[6,] 0.10926450 0.2185290016 0.8907354992
[7,] 0.16913597 0.3382719445 0.8308640277
[8,] 0.28109488 0.5621897686 0.7189051157
[9,] 0.20706898 0.4141379597 0.7929310201
[10,] 0.23321765 0.4664353099 0.7667823451
[11,] 0.17436899 0.3487379800 0.8256310100
[12,] 0.13544819 0.2708963839 0.8645518081
[13,] 0.09517416 0.1903483154 0.9048258423
[14,] 0.07063288 0.1412657519 0.9293671241
[15,] 0.05663832 0.1132766461 0.9433616770
[16,] 0.08060007 0.1612001367 0.9193999317
[17,] 0.14841135 0.2968227003 0.8515886498
[18,] 0.11703534 0.2340706883 0.8829646559
[19,] 0.09033849 0.1806769757 0.9096615122
[20,] 0.06605632 0.1321126349 0.9339436826
[21,] 0.05830228 0.1166045542 0.9416977229
[22,] 0.04158942 0.0831788316 0.9584105842
[23,] 0.02952781 0.0590556176 0.9704721912
[24,] 0.20571181 0.4114236239 0.7942881880
[25,] 0.40780322 0.8156064476 0.5921967762
[26,] 0.54800860 0.9039827958 0.4519913979
[27,] 0.49407883 0.9881576507 0.5059211746
[28,] 0.47252039 0.9450407885 0.5274796057
[29,] 0.43757234 0.8751446792 0.5624276604
[30,] 0.68208196 0.6358360812 0.3179180406
[31,] 0.84322893 0.3135421465 0.1567710733
[32,] 0.80874999 0.3825000271 0.1912500136
[33,] 0.84905099 0.3018980136 0.1509490068
[34,] 0.82341105 0.3531779061 0.1765889530
[35,] 0.81853920 0.3629215939 0.1814607970
[36,] 0.79460512 0.4107897512 0.2053948756
[37,] 0.81453497 0.3709300612 0.1854650306
[38,] 0.77789255 0.4442148949 0.2221074475
[39,] 0.79435209 0.4112958221 0.2056479110
[40,] 0.77109940 0.4578011997 0.2289005999
[41,] 0.74658668 0.5068266411 0.2534133205
[42,] 0.84737431 0.3052513874 0.1526256937
[43,] 0.81543884 0.3691223254 0.1845611627
[44,] 0.80942395 0.3811520932 0.1905760466
[45,] 0.77754580 0.4449084011 0.2224542005
[46,] 0.73846433 0.5230713405 0.2615356703
[47,] 0.69856582 0.6028683600 0.3014341800
[48,] 0.66406023 0.6718795435 0.3359397717
[49,] 0.64211395 0.7157721037 0.3578860519
[50,] 0.59460436 0.8107912846 0.4053956423
[51,] 0.55508459 0.8898308155 0.4449154077
[52,] 0.53628681 0.9274263894 0.4637131947
[53,] 0.52809086 0.9438182874 0.4719091437
[54,] 0.52498156 0.9500368815 0.4750184408
[55,] 0.59410849 0.8117830124 0.4058915062
[56,] 0.55078843 0.8984231417 0.4492115708
[57,] 0.51038500 0.9792299962 0.4896149981
[58,] 0.67420160 0.6515968057 0.3257984029
[59,] 0.84438406 0.3112318883 0.1556159441
[60,] 0.84103738 0.3179252348 0.1589626174
[61,] 0.87445007 0.2510998565 0.1255499282
[62,] 0.94019263 0.1196147437 0.0598073719
[63,] 0.92518591 0.1496281852 0.0748140926
[64,] 0.91597966 0.1680406854 0.0840203427
[65,] 0.89654683 0.2069063382 0.1034531691
[66,] 0.91883278 0.1623344354 0.0811672177
[67,] 0.92611896 0.1477620781 0.0738810391
[68,] 0.96865962 0.0626807575 0.0313403788
[69,] 0.95957054 0.0808589240 0.0404294620
[70,] 0.95523284 0.0895343288 0.0447671644
[71,] 0.97844966 0.0431006764 0.0215503382
[72,] 0.97253841 0.0549231819 0.0274615910
[73,] 0.97671802 0.0465639577 0.0232819789
[74,] 0.96932449 0.0613510274 0.0306755137
[75,] 0.96422176 0.0715564824 0.0357782412
[76,] 0.95503714 0.0899257212 0.0449628606
[77,] 0.94648233 0.1070353383 0.0535176692
[78,] 0.94774201 0.1045159704 0.0522579852
[79,] 0.93351037 0.1329792573 0.0664896287
[80,] 0.93626909 0.1274618202 0.0637309101
[81,] 0.96230119 0.0753976178 0.0376988089
[82,] 0.99743386 0.0051322847 0.0025661423
[83,] 0.99646791 0.0070641896 0.0035320948
[84,] 0.99568077 0.0086384642 0.0043192321
[85,] 0.99414552 0.0117089515 0.0058544757
[86,] 0.99457717 0.0108456562 0.0054228281
[87,] 0.99530549 0.0093890148 0.0046945074
[88,] 0.99357055 0.0128588912 0.0064294456
[89,] 0.99229401 0.0154119764 0.0077059882
[90,] 0.99487215 0.0102557011 0.0051278505
[91,] 0.99485559 0.0102888249 0.0051444124
[92,] 0.99280637 0.0143872653 0.0071936326
[93,] 0.99455652 0.0108869611 0.0054434806
[94,] 0.99215145 0.0156970953 0.0078485477
[95,] 0.98963734 0.0207253190 0.0103626595
[96,] 0.98863297 0.0227340625 0.0113670312
[97,] 0.99271523 0.0145695492 0.0072847746
[98,] 0.99919691 0.0016061786 0.0008030893
[99,] 0.99882608 0.0023478423 0.0011739212
[100,] 0.99972454 0.0005509171 0.0002754586
[101,] 0.99953394 0.0009321131 0.0004660566
[102,] 0.99946413 0.0010717382 0.0005358691
[103,] 0.99916567 0.0016686653 0.0008343326
[104,] 0.99880939 0.0023812242 0.0011906121
[105,] 0.99802880 0.0039423906 0.0019711953
[106,] 0.99683504 0.0063299151 0.0031649575
[107,] 0.99935071 0.0012985704 0.0006492852
[108,] 0.99901788 0.0019642386 0.0009821193
[109,] 0.99840307 0.0031938564 0.0015969282
[110,] 0.99825395 0.0034920902 0.0017460451
[111,] 0.99782131 0.0043573804 0.0021786902
[112,] 0.99666166 0.0066766838 0.0033383419
[113,] 0.99680629 0.0063874222 0.0031937111
[114,] 0.99462830 0.0107434065 0.0053717032
[115,] 0.99186020 0.0162796098 0.0081398049
[116,] 0.98917380 0.0216523944 0.0108261972
[117,] 0.98437436 0.0312512781 0.0156256390
[118,] 0.99141921 0.0171615846 0.0085807923
[119,] 0.99410043 0.0117991444 0.0058995722
[120,] 0.99085939 0.0182812128 0.0091406064
[121,] 0.98962238 0.0207552447 0.0103776224
[122,] 0.98444955 0.0311009081 0.0155504540
[123,] 0.97396368 0.0520726305 0.0260363152
[124,] 0.95584815 0.0883037059 0.0441518529
[125,] 0.94854447 0.1029110648 0.0514555324
[126,] 0.93099843 0.1380031308 0.0690015654
[127,] 0.90122627 0.1975474638 0.0987737319
[128,] 0.87453952 0.2509209622 0.1254604811
[129,] 0.80523817 0.3895236595 0.1947618298
[130,] 0.83173674 0.3365265177 0.1682632588
[131,] 0.73332821 0.5333435814 0.2666717907
[132,] 0.61468672 0.7706265680 0.3853132840
[133,] 0.68537284 0.6292543131 0.3146271566
> postscript(file="/var/www/html/rcomp/tmp/19ru81293206094.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29ru81293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/320tt1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/420tt1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/520tt1293206094.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
1.581542850 0.210014404 -2.181437127 -1.992992911 -0.117053547 0.844475216
7 8 9 10 11 12
1.146225776 2.159856031 -0.926772649 -2.624881085 -0.106595233 0.024177424
13 14 15 16 17 18
3.236350627 1.335877357 -0.921330559 0.948169557 0.538749999 -2.665368040
19 20 21 22 23 24
-1.141129191 0.856696027 2.740807001 1.374506350 -0.027437817 0.799585082
25 26 27 28 29 30
0.505940325 -0.258081281 2.588037722 -2.076409830 -1.111407805 -0.764689132
31 32 33 34 35 36
-0.404097466 2.379301869 0.658364809 0.164507829 -5.582510388 5.187169901
37 38 39 40 41 42
3.869140697 -1.351471213 1.784413945 -0.889210472 6.012609201 -4.803595524
43 44 45 46 47 48
-0.388321512 3.775289802 -0.078180283 -1.713492572 1.857621562 2.259056607
49 50 51 52 53 54
-0.253802283 2.527543399 -1.742006357 -0.809892281 -2.683032170 -0.160660491
55 56 57 58 59 60
-1.738804856 -0.012207229 0.342495957 -0.839171405 -0.293956713 -0.212773495
61 62 63 64 65 66
0.037719481 1.065121548 -1.877032123 1.559993075 -1.688801742 3.315093890
67 68 69 70 71 72
0.817187373 0.665699122 -4.054624955 5.120900885 2.198344222 -3.206142288
73 74 75 76 77 78
-4.235725908 0.112664382 1.609873570 -0.527536070 2.793906465 -2.470688367
79 80 81 82 83 84
-4.209128524 -0.353741778 1.442075291 -3.818154393 -0.377973293 2.565137855
85 86 87 88 89 90
-0.334229988 -1.464697755 -0.460510789 0.804873421 0.616441990 -0.475126508
91 92 93 94 95 96
-2.417643057 -2.886223900 -6.006348645 -1.121456727 1.849637049 0.552845086
97 98 99 100 101 102
-2.577110380 -2.232481390 -1.439669177 1.896989472 2.792794747 2.360713117
103 104 105 106 107 108
-0.245715834 2.821881593 0.803206033 0.444015037 -1.717008828 -2.599405723
109 110 111 112 113 114
4.490144200 1.146637726 -3.278473124 0.893195239 1.875838722 -0.352419953
115 116 117 118 119 120
-1.554921998 -0.152809017 0.818087360 2.348864476 -0.388228707 -1.167909879
121 122 123 124 125 126
-2.234542427 -3.076331852 -0.838108440 -1.253334415 -0.006468822 -0.561360706
127 128 129 130 131 132
-1.643526831 1.743793573 -1.689172339 1.275608106 -1.850780782 2.149588052
133 134 135 136 137 138
-1.356404597 -0.817548119 0.864561933 -1.196716468 -0.259192999 0.189905397
139 140 141 142 143 144
-0.496502269 1.377932205 1.627927416 1.134070436 1.081277671 0.999976857
145 146 147 148 149 150
1.395738728 1.744325166 -0.008146000 2.859682599 0.499931804 -1.962280519
151 152 153 154 155 156
2.334625190 0.149378524 -1.121037277 -1.207662819 0.149378524 -0.962280519
> postscript(file="/var/www/html/rcomp/tmp/6uabe1293206094.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 1.581542850 NA
1 0.210014404 1.581542850
2 -2.181437127 0.210014404
3 -1.992992911 -2.181437127
4 -0.117053547 -1.992992911
5 0.844475216 -0.117053547
6 1.146225776 0.844475216
7 2.159856031 1.146225776
8 -0.926772649 2.159856031
9 -2.624881085 -0.926772649
10 -0.106595233 -2.624881085
11 0.024177424 -0.106595233
12 3.236350627 0.024177424
13 1.335877357 3.236350627
14 -0.921330559 1.335877357
15 0.948169557 -0.921330559
16 0.538749999 0.948169557
17 -2.665368040 0.538749999
18 -1.141129191 -2.665368040
19 0.856696027 -1.141129191
20 2.740807001 0.856696027
21 1.374506350 2.740807001
22 -0.027437817 1.374506350
23 0.799585082 -0.027437817
24 0.505940325 0.799585082
25 -0.258081281 0.505940325
26 2.588037722 -0.258081281
27 -2.076409830 2.588037722
28 -1.111407805 -2.076409830
29 -0.764689132 -1.111407805
30 -0.404097466 -0.764689132
31 2.379301869 -0.404097466
32 0.658364809 2.379301869
33 0.164507829 0.658364809
34 -5.582510388 0.164507829
35 5.187169901 -5.582510388
36 3.869140697 5.187169901
37 -1.351471213 3.869140697
38 1.784413945 -1.351471213
39 -0.889210472 1.784413945
40 6.012609201 -0.889210472
41 -4.803595524 6.012609201
42 -0.388321512 -4.803595524
43 3.775289802 -0.388321512
44 -0.078180283 3.775289802
45 -1.713492572 -0.078180283
46 1.857621562 -1.713492572
47 2.259056607 1.857621562
48 -0.253802283 2.259056607
49 2.527543399 -0.253802283
50 -1.742006357 2.527543399
51 -0.809892281 -1.742006357
52 -2.683032170 -0.809892281
53 -0.160660491 -2.683032170
54 -1.738804856 -0.160660491
55 -0.012207229 -1.738804856
56 0.342495957 -0.012207229
57 -0.839171405 0.342495957
58 -0.293956713 -0.839171405
59 -0.212773495 -0.293956713
60 0.037719481 -0.212773495
61 1.065121548 0.037719481
62 -1.877032123 1.065121548
63 1.559993075 -1.877032123
64 -1.688801742 1.559993075
65 3.315093890 -1.688801742
66 0.817187373 3.315093890
67 0.665699122 0.817187373
68 -4.054624955 0.665699122
69 5.120900885 -4.054624955
70 2.198344222 5.120900885
71 -3.206142288 2.198344222
72 -4.235725908 -3.206142288
73 0.112664382 -4.235725908
74 1.609873570 0.112664382
75 -0.527536070 1.609873570
76 2.793906465 -0.527536070
77 -2.470688367 2.793906465
78 -4.209128524 -2.470688367
79 -0.353741778 -4.209128524
80 1.442075291 -0.353741778
81 -3.818154393 1.442075291
82 -0.377973293 -3.818154393
83 2.565137855 -0.377973293
84 -0.334229988 2.565137855
85 -1.464697755 -0.334229988
86 -0.460510789 -1.464697755
87 0.804873421 -0.460510789
88 0.616441990 0.804873421
89 -0.475126508 0.616441990
90 -2.417643057 -0.475126508
91 -2.886223900 -2.417643057
92 -6.006348645 -2.886223900
93 -1.121456727 -6.006348645
94 1.849637049 -1.121456727
95 0.552845086 1.849637049
96 -2.577110380 0.552845086
97 -2.232481390 -2.577110380
98 -1.439669177 -2.232481390
99 1.896989472 -1.439669177
100 2.792794747 1.896989472
101 2.360713117 2.792794747
102 -0.245715834 2.360713117
103 2.821881593 -0.245715834
104 0.803206033 2.821881593
105 0.444015037 0.803206033
106 -1.717008828 0.444015037
107 -2.599405723 -1.717008828
108 4.490144200 -2.599405723
109 1.146637726 4.490144200
110 -3.278473124 1.146637726
111 0.893195239 -3.278473124
112 1.875838722 0.893195239
113 -0.352419953 1.875838722
114 -1.554921998 -0.352419953
115 -0.152809017 -1.554921998
116 0.818087360 -0.152809017
117 2.348864476 0.818087360
118 -0.388228707 2.348864476
119 -1.167909879 -0.388228707
120 -2.234542427 -1.167909879
121 -3.076331852 -2.234542427
122 -0.838108440 -3.076331852
123 -1.253334415 -0.838108440
124 -0.006468822 -1.253334415
125 -0.561360706 -0.006468822
126 -1.643526831 -0.561360706
127 1.743793573 -1.643526831
128 -1.689172339 1.743793573
129 1.275608106 -1.689172339
130 -1.850780782 1.275608106
131 2.149588052 -1.850780782
132 -1.356404597 2.149588052
133 -0.817548119 -1.356404597
134 0.864561933 -0.817548119
135 -1.196716468 0.864561933
136 -0.259192999 -1.196716468
137 0.189905397 -0.259192999
138 -0.496502269 0.189905397
139 1.377932205 -0.496502269
140 1.627927416 1.377932205
141 1.134070436 1.627927416
142 1.081277671 1.134070436
143 0.999976857 1.081277671
144 1.395738728 0.999976857
145 1.744325166 1.395738728
146 -0.008146000 1.744325166
147 2.859682599 -0.008146000
148 0.499931804 2.859682599
149 -1.962280519 0.499931804
150 2.334625190 -1.962280519
151 0.149378524 2.334625190
152 -1.121037277 0.149378524
153 -1.207662819 -1.121037277
154 0.149378524 -1.207662819
155 -0.962280519 0.149378524
156 NA -0.962280519
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.210014404 1.581542850
[2,] -2.181437127 0.210014404
[3,] -1.992992911 -2.181437127
[4,] -0.117053547 -1.992992911
[5,] 0.844475216 -0.117053547
[6,] 1.146225776 0.844475216
[7,] 2.159856031 1.146225776
[8,] -0.926772649 2.159856031
[9,] -2.624881085 -0.926772649
[10,] -0.106595233 -2.624881085
[11,] 0.024177424 -0.106595233
[12,] 3.236350627 0.024177424
[13,] 1.335877357 3.236350627
[14,] -0.921330559 1.335877357
[15,] 0.948169557 -0.921330559
[16,] 0.538749999 0.948169557
[17,] -2.665368040 0.538749999
[18,] -1.141129191 -2.665368040
[19,] 0.856696027 -1.141129191
[20,] 2.740807001 0.856696027
[21,] 1.374506350 2.740807001
[22,] -0.027437817 1.374506350
[23,] 0.799585082 -0.027437817
[24,] 0.505940325 0.799585082
[25,] -0.258081281 0.505940325
[26,] 2.588037722 -0.258081281
[27,] -2.076409830 2.588037722
[28,] -1.111407805 -2.076409830
[29,] -0.764689132 -1.111407805
[30,] -0.404097466 -0.764689132
[31,] 2.379301869 -0.404097466
[32,] 0.658364809 2.379301869
[33,] 0.164507829 0.658364809
[34,] -5.582510388 0.164507829
[35,] 5.187169901 -5.582510388
[36,] 3.869140697 5.187169901
[37,] -1.351471213 3.869140697
[38,] 1.784413945 -1.351471213
[39,] -0.889210472 1.784413945
[40,] 6.012609201 -0.889210472
[41,] -4.803595524 6.012609201
[42,] -0.388321512 -4.803595524
[43,] 3.775289802 -0.388321512
[44,] -0.078180283 3.775289802
[45,] -1.713492572 -0.078180283
[46,] 1.857621562 -1.713492572
[47,] 2.259056607 1.857621562
[48,] -0.253802283 2.259056607
[49,] 2.527543399 -0.253802283
[50,] -1.742006357 2.527543399
[51,] -0.809892281 -1.742006357
[52,] -2.683032170 -0.809892281
[53,] -0.160660491 -2.683032170
[54,] -1.738804856 -0.160660491
[55,] -0.012207229 -1.738804856
[56,] 0.342495957 -0.012207229
[57,] -0.839171405 0.342495957
[58,] -0.293956713 -0.839171405
[59,] -0.212773495 -0.293956713
[60,] 0.037719481 -0.212773495
[61,] 1.065121548 0.037719481
[62,] -1.877032123 1.065121548
[63,] 1.559993075 -1.877032123
[64,] -1.688801742 1.559993075
[65,] 3.315093890 -1.688801742
[66,] 0.817187373 3.315093890
[67,] 0.665699122 0.817187373
[68,] -4.054624955 0.665699122
[69,] 5.120900885 -4.054624955
[70,] 2.198344222 5.120900885
[71,] -3.206142288 2.198344222
[72,] -4.235725908 -3.206142288
[73,] 0.112664382 -4.235725908
[74,] 1.609873570 0.112664382
[75,] -0.527536070 1.609873570
[76,] 2.793906465 -0.527536070
[77,] -2.470688367 2.793906465
[78,] -4.209128524 -2.470688367
[79,] -0.353741778 -4.209128524
[80,] 1.442075291 -0.353741778
[81,] -3.818154393 1.442075291
[82,] -0.377973293 -3.818154393
[83,] 2.565137855 -0.377973293
[84,] -0.334229988 2.565137855
[85,] -1.464697755 -0.334229988
[86,] -0.460510789 -1.464697755
[87,] 0.804873421 -0.460510789
[88,] 0.616441990 0.804873421
[89,] -0.475126508 0.616441990
[90,] -2.417643057 -0.475126508
[91,] -2.886223900 -2.417643057
[92,] -6.006348645 -2.886223900
[93,] -1.121456727 -6.006348645
[94,] 1.849637049 -1.121456727
[95,] 0.552845086 1.849637049
[96,] -2.577110380 0.552845086
[97,] -2.232481390 -2.577110380
[98,] -1.439669177 -2.232481390
[99,] 1.896989472 -1.439669177
[100,] 2.792794747 1.896989472
[101,] 2.360713117 2.792794747
[102,] -0.245715834 2.360713117
[103,] 2.821881593 -0.245715834
[104,] 0.803206033 2.821881593
[105,] 0.444015037 0.803206033
[106,] -1.717008828 0.444015037
[107,] -2.599405723 -1.717008828
[108,] 4.490144200 -2.599405723
[109,] 1.146637726 4.490144200
[110,] -3.278473124 1.146637726
[111,] 0.893195239 -3.278473124
[112,] 1.875838722 0.893195239
[113,] -0.352419953 1.875838722
[114,] -1.554921998 -0.352419953
[115,] -0.152809017 -1.554921998
[116,] 0.818087360 -0.152809017
[117,] 2.348864476 0.818087360
[118,] -0.388228707 2.348864476
[119,] -1.167909879 -0.388228707
[120,] -2.234542427 -1.167909879
[121,] -3.076331852 -2.234542427
[122,] -0.838108440 -3.076331852
[123,] -1.253334415 -0.838108440
[124,] -0.006468822 -1.253334415
[125,] -0.561360706 -0.006468822
[126,] -1.643526831 -0.561360706
[127,] 1.743793573 -1.643526831
[128,] -1.689172339 1.743793573
[129,] 1.275608106 -1.689172339
[130,] -1.850780782 1.275608106
[131,] 2.149588052 -1.850780782
[132,] -1.356404597 2.149588052
[133,] -0.817548119 -1.356404597
[134,] 0.864561933 -0.817548119
[135,] -1.196716468 0.864561933
[136,] -0.259192999 -1.196716468
[137,] 0.189905397 -0.259192999
[138,] -0.496502269 0.189905397
[139,] 1.377932205 -0.496502269
[140,] 1.627927416 1.377932205
[141,] 1.134070436 1.627927416
[142,] 1.081277671 1.134070436
[143,] 0.999976857 1.081277671
[144,] 1.395738728 0.999976857
[145,] 1.744325166 1.395738728
[146,] -0.008146000 1.744325166
[147,] 2.859682599 -0.008146000
[148,] 0.499931804 2.859682599
[149,] -1.962280519 0.499931804
[150,] 2.334625190 -1.962280519
[151,] 0.149378524 2.334625190
[152,] -1.121037277 0.149378524
[153,] -1.207662819 -1.121037277
[154,] 0.149378524 -1.207662819
[155,] -0.962280519 0.149378524
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.210014404 1.581542850
2 -2.181437127 0.210014404
3 -1.992992911 -2.181437127
4 -0.117053547 -1.992992911
5 0.844475216 -0.117053547
6 1.146225776 0.844475216
7 2.159856031 1.146225776
8 -0.926772649 2.159856031
9 -2.624881085 -0.926772649
10 -0.106595233 -2.624881085
11 0.024177424 -0.106595233
12 3.236350627 0.024177424
13 1.335877357 3.236350627
14 -0.921330559 1.335877357
15 0.948169557 -0.921330559
16 0.538749999 0.948169557
17 -2.665368040 0.538749999
18 -1.141129191 -2.665368040
19 0.856696027 -1.141129191
20 2.740807001 0.856696027
21 1.374506350 2.740807001
22 -0.027437817 1.374506350
23 0.799585082 -0.027437817
24 0.505940325 0.799585082
25 -0.258081281 0.505940325
26 2.588037722 -0.258081281
27 -2.076409830 2.588037722
28 -1.111407805 -2.076409830
29 -0.764689132 -1.111407805
30 -0.404097466 -0.764689132
31 2.379301869 -0.404097466
32 0.658364809 2.379301869
33 0.164507829 0.658364809
34 -5.582510388 0.164507829
35 5.187169901 -5.582510388
36 3.869140697 5.187169901
37 -1.351471213 3.869140697
38 1.784413945 -1.351471213
39 -0.889210472 1.784413945
40 6.012609201 -0.889210472
41 -4.803595524 6.012609201
42 -0.388321512 -4.803595524
43 3.775289802 -0.388321512
44 -0.078180283 3.775289802
45 -1.713492572 -0.078180283
46 1.857621562 -1.713492572
47 2.259056607 1.857621562
48 -0.253802283 2.259056607
49 2.527543399 -0.253802283
50 -1.742006357 2.527543399
51 -0.809892281 -1.742006357
52 -2.683032170 -0.809892281
53 -0.160660491 -2.683032170
54 -1.738804856 -0.160660491
55 -0.012207229 -1.738804856
56 0.342495957 -0.012207229
57 -0.839171405 0.342495957
58 -0.293956713 -0.839171405
59 -0.212773495 -0.293956713
60 0.037719481 -0.212773495
61 1.065121548 0.037719481
62 -1.877032123 1.065121548
63 1.559993075 -1.877032123
64 -1.688801742 1.559993075
65 3.315093890 -1.688801742
66 0.817187373 3.315093890
67 0.665699122 0.817187373
68 -4.054624955 0.665699122
69 5.120900885 -4.054624955
70 2.198344222 5.120900885
71 -3.206142288 2.198344222
72 -4.235725908 -3.206142288
73 0.112664382 -4.235725908
74 1.609873570 0.112664382
75 -0.527536070 1.609873570
76 2.793906465 -0.527536070
77 -2.470688367 2.793906465
78 -4.209128524 -2.470688367
79 -0.353741778 -4.209128524
80 1.442075291 -0.353741778
81 -3.818154393 1.442075291
82 -0.377973293 -3.818154393
83 2.565137855 -0.377973293
84 -0.334229988 2.565137855
85 -1.464697755 -0.334229988
86 -0.460510789 -1.464697755
87 0.804873421 -0.460510789
88 0.616441990 0.804873421
89 -0.475126508 0.616441990
90 -2.417643057 -0.475126508
91 -2.886223900 -2.417643057
92 -6.006348645 -2.886223900
93 -1.121456727 -6.006348645
94 1.849637049 -1.121456727
95 0.552845086 1.849637049
96 -2.577110380 0.552845086
97 -2.232481390 -2.577110380
98 -1.439669177 -2.232481390
99 1.896989472 -1.439669177
100 2.792794747 1.896989472
101 2.360713117 2.792794747
102 -0.245715834 2.360713117
103 2.821881593 -0.245715834
104 0.803206033 2.821881593
105 0.444015037 0.803206033
106 -1.717008828 0.444015037
107 -2.599405723 -1.717008828
108 4.490144200 -2.599405723
109 1.146637726 4.490144200
110 -3.278473124 1.146637726
111 0.893195239 -3.278473124
112 1.875838722 0.893195239
113 -0.352419953 1.875838722
114 -1.554921998 -0.352419953
115 -0.152809017 -1.554921998
116 0.818087360 -0.152809017
117 2.348864476 0.818087360
118 -0.388228707 2.348864476
119 -1.167909879 -0.388228707
120 -2.234542427 -1.167909879
121 -3.076331852 -2.234542427
122 -0.838108440 -3.076331852
123 -1.253334415 -0.838108440
124 -0.006468822 -1.253334415
125 -0.561360706 -0.006468822
126 -1.643526831 -0.561360706
127 1.743793573 -1.643526831
128 -1.689172339 1.743793573
129 1.275608106 -1.689172339
130 -1.850780782 1.275608106
131 2.149588052 -1.850780782
132 -1.356404597 2.149588052
133 -0.817548119 -1.356404597
134 0.864561933 -0.817548119
135 -1.196716468 0.864561933
136 -0.259192999 -1.196716468
137 0.189905397 -0.259192999
138 -0.496502269 0.189905397
139 1.377932205 -0.496502269
140 1.627927416 1.377932205
141 1.134070436 1.627927416
142 1.081277671 1.134070436
143 0.999976857 1.081277671
144 1.395738728 0.999976857
145 1.744325166 1.395738728
146 -0.008146000 1.744325166
147 2.859682599 -0.008146000
148 0.499931804 2.859682599
149 -1.962280519 0.499931804
150 2.334625190 -1.962280519
151 0.149378524 2.334625190
152 -1.121037277 0.149378524
153 -1.207662819 -1.121037277
154 0.149378524 -1.207662819
155 -0.962280519 0.149378524
> 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/rcomp/tmp/7uabe1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8njsz1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9njsz1293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gar21293206094.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11jb8p1293206094.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/rcomp/tmp/12u27a1293206094.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/rcomp/tmp/131lmm1293206094.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/rcomp/tmp/14tc3p1293206094.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/rcomp/tmp/15fdkv1293206094.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/rcomp/tmp/16tmz41293206094.tab")
+ }
>
> try(system("convert tmp/19ru81293206094.ps tmp/19ru81293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/29ru81293206094.ps tmp/29ru81293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/320tt1293206094.ps tmp/320tt1293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/420tt1293206094.ps tmp/420tt1293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/520tt1293206094.ps tmp/520tt1293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uabe1293206094.ps tmp/6uabe1293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uabe1293206094.ps tmp/7uabe1293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/8njsz1293206094.ps tmp/8njsz1293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/9njsz1293206094.ps tmp/9njsz1293206094.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gar21293206094.ps tmp/10gar21293206094.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.374 1.868 9.881