R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(13
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+ ,dim=c(7
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'Sum_friends'
+ ,'Day')
+ ,1:156))
> y <- array(NA,dim=c(7,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','Sum_friends','Day'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Popularity FindingFriends KnowingPeople Liked Celebrity Sum_friends Day t
1 13 13 14 13 3 2 1 1
2 12 12 8 13 5 1 1 2
3 15 10 12 16 6 0 1 3
4 12 9 7 12 6 3 1 4
5 10 10 10 11 5 3 1 5
6 12 12 7 12 3 1 1 6
7 15 13 16 18 8 3 1 7
8 9 12 11 11 4 1 1 8
9 12 12 14 14 4 4 1 9
10 11 6 6 9 4 0 1 10
11 11 5 16 14 6 3 1 11
12 11 12 11 12 6 2 1 12
13 15 11 16 11 5 4 1 13
14 7 14 12 12 4 3 1 14
15 11 14 7 13 6 1 1 15
16 11 12 13 11 4 1 1 16
17 10 12 11 12 6 2 1 17
18 14 11 15 16 6 3 1 18
19 10 11 7 9 4 1 2 19
20 6 7 9 11 4 1 2 20
21 11 9 7 13 2 2 2 21
22 15 11 14 15 7 3 2 22
23 11 11 15 10 5 4 2 23
24 12 12 7 11 4 2 2 24
25 14 12 15 13 6 1 2 25
26 15 11 17 16 6 2 2 26
27 9 11 15 15 7 2 2 27
28 13 8 14 14 5 4 2 28
29 13 9 14 14 6 2 2 29
30 16 12 8 14 4 3 2 30
31 13 10 8 8 4 3 2 31
32 12 10 14 13 7 3 2 32
33 14 12 14 15 7 4 2 33
34 11 8 8 13 4 2 3 34
35 9 12 11 11 4 2 3 35
36 16 11 16 15 6 4 3 36
37 12 12 10 15 6 3 3 37
38 10 7 8 9 5 4 3 38
39 13 11 14 13 6 2 3 39
40 16 11 16 16 7 5 3 40
41 14 12 13 13 6 3 3 41
42 15 9 5 11 3 1 3 42
43 5 15 8 12 3 1 3 43
44 8 11 10 12 4 1 3 44
45 11 11 8 12 6 2 3 45
46 16 11 13 14 7 3 3 46
47 17 11 15 14 5 9 3 47
48 9 15 6 8 4 0 3 48
49 9 11 12 13 5 0 3 49
50 13 12 16 16 6 2 3 50
51 10 12 5 13 6 2 3 51
52 6 9 15 11 6 3 4 52
53 12 12 12 14 5 1 4 53
54 8 12 8 13 4 2 4 54
55 14 13 13 13 5 0 4 55
56 12 11 14 13 5 5 4 56
57 11 9 12 12 4 2 4 57
58 16 9 16 16 6 4 4 58
59 8 11 10 15 2 3 4 59
60 15 11 15 15 8 0 4 60
61 7 12 8 12 3 0 4 61
62 16 12 16 14 6 4 4 62
63 14 9 19 12 6 1 4 63
64 16 11 14 15 6 1 4 64
65 9 9 6 12 5 4 4 65
66 14 12 13 13 5 2 4 66
67 11 12 15 12 6 4 4 67
68 13 12 7 12 5 1 4 68
69 15 12 13 13 6 4 5 69
70 5 14 4 5 2 2 5 70
71 15 11 14 13 5 5 5 71
72 13 12 13 13 5 4 5 72
73 11 11 11 14 5 4 5 73
74 11 6 14 17 6 4 5 74
75 12 10 12 13 6 4 5 75
76 12 12 15 13 6 3 5 76
77 12 13 14 12 5 3 5 77
78 12 8 13 13 5 3 5 78
79 14 12 8 14 4 2 5 79
80 6 12 6 11 2 1 5 80
81 7 12 7 12 4 1 5 81
82 14 6 13 12 6 5 5 82
83 14 11 13 16 6 4 5 83
84 10 10 11 12 5 2 5 84
85 13 12 5 12 3 3 5 85
86 12 13 12 12 6 2 5 86
87 9 11 8 10 4 2 6 87
88 12 7 11 15 5 2 6 88
89 16 11 14 15 8 2 6 89
90 10 11 9 12 4 3 6 90
91 14 11 10 16 6 2 6 91
92 10 11 13 15 6 3 6 92
93 16 12 16 16 7 4 6 93
94 15 10 16 13 6 3 6 94
95 12 11 11 12 5 3 6 95
96 10 12 8 11 4 0 6 96
97 8 7 4 13 6 1 6 97
98 8 13 7 10 3 2 6 98
99 11 8 14 15 5 2 6 99
100 13 12 11 13 6 3 6 100
101 16 11 17 16 7 4 6 101
102 16 12 15 15 7 4 6 102
103 14 14 17 18 6 1 6 103
104 11 10 5 13 3 2 6 104
105 4 10 4 10 2 2 6 105
106 14 13 10 16 8 3 6 106
107 9 10 11 13 3 3 7 107
108 14 11 15 15 8 3 7 108
109 8 10 10 14 3 1 7 109
110 8 7 9 15 4 1 7 110
111 11 10 12 14 5 1 7 111
112 12 8 15 13 7 1 7 112
113 11 12 7 13 6 0 7 113
114 14 12 13 15 6 1 7 114
115 15 12 12 16 7 3 7 115
116 16 11 14 14 6 3 7 116
117 16 12 14 14 6 0 7 117
118 11 12 8 16 6 2 7 118
119 14 12 15 14 6 5 7 119
120 14 11 12 12 4 2 7 120
121 12 12 12 13 4 3 7 121
122 14 11 16 12 5 3 7 122
123 8 11 9 12 4 5 7 123
124 13 13 15 14 6 4 7 124
125 16 12 15 14 6 4 7 125
126 12 12 6 14 5 0 7 126
127 16 12 14 16 8 3 7 127
128 12 12 15 13 6 0 7 128
129 11 8 10 14 5 2 7 129
130 4 8 6 4 4 0 7 130
131 16 12 14 16 8 6 7 131
132 15 11 12 13 6 3 7 132
133 10 12 8 16 4 1 7 133
134 13 13 11 15 6 6 7 134
135 15 12 13 14 6 2 7 135
136 12 12 9 13 4 1 7 136
137 14 11 15 14 6 3 7 137
138 7 12 13 12 3 1 8 138
139 19 12 15 15 6 2 8 139
140 12 10 14 14 5 4 8 140
141 12 11 16 13 4 1 8 141
142 13 12 14 14 6 2 8 142
143 15 12 14 16 4 0 8 143
144 8 10 10 6 4 5 8 144
145 12 12 10 13 4 2 8 145
146 10 13 4 13 6 1 8 146
147 8 12 8 14 5 1 8 147
148 10 15 15 15 6 4 8 148
149 15 11 16 14 6 3 8 149
150 16 12 12 15 8 0 9 150
151 13 11 12 13 7 3 10 151
152 16 12 15 16 7 3 10 152
153 9 11 9 12 4 0 14 153
154 14 10 12 15 6 2 14 154
155 14 11 14 12 6 5 14 155
156 12 11 11 14 2 2 14 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
-0.093121 0.114501 0.209268 0.358379 0.616587
Sum_friends Day t
0.213465 0.104201 -0.006679
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.4280 -1.2541 0.0269 1.3859 6.9788
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.093121 1.454348 -0.064 0.949033
FindingFriends 0.114501 0.097256 1.177 0.240957
KnowingPeople 0.209268 0.064112 3.264 0.001364 **
Liked 0.358379 0.097379 3.680 0.000326 ***
Celebrity 0.616587 0.157441 3.916 0.000137 ***
Sum_friends 0.213465 0.120726 1.768 0.079093 .
Day 0.104201 0.195341 0.533 0.594537
t -0.006679 0.011864 -0.563 0.574300
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.103 on 148 degrees of freedom
Multiple R-squared: 0.5105, Adjusted R-squared: 0.4874
F-statistic: 22.05 on 7 and 148 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,] 0.2319585 0.46391695 0.768041525
[2,] 0.1449269 0.28985376 0.855073122
[3,] 0.6768478 0.64630435 0.323152174
[4,] 0.8073182 0.38536360 0.192681801
[5,] 0.7427820 0.51443596 0.257217978
[6,] 0.6564144 0.68717126 0.343585631
[7,] 0.5691293 0.86174148 0.430870740
[8,] 0.5435395 0.91292095 0.456460477
[9,] 0.4536087 0.90721743 0.546391283
[10,] 0.5914349 0.81713022 0.408565110
[11,] 0.6103376 0.77932487 0.389662433
[12,] 0.6363470 0.72730591 0.363652953
[13,] 0.5644096 0.87118085 0.435590425
[14,] 0.5674443 0.86511150 0.432555749
[15,] 0.5218916 0.95621686 0.478108428
[16,] 0.4564734 0.91294689 0.543526557
[17,] 0.6938932 0.61221354 0.306106770
[18,] 0.6504963 0.69900733 0.349503666
[19,] 0.5966517 0.80669657 0.403348283
[20,] 0.7587721 0.48245573 0.241227867
[21,] 0.8366734 0.32665311 0.163326554
[22,] 0.8031135 0.39377298 0.196886491
[23,] 0.7590394 0.48192125 0.240960623
[24,] 0.7286911 0.54261786 0.271308928
[25,] 0.7213769 0.55724611 0.278623055
[26,] 0.7198323 0.56033537 0.280167684
[27,] 0.6935479 0.61290421 0.306452104
[28,] 0.6452524 0.70949512 0.354747560
[29,] 0.5968965 0.80620709 0.403103543
[30,] 0.5536627 0.89267456 0.446337281
[31,] 0.5138072 0.97238564 0.486192818
[32,] 0.7908096 0.41838074 0.209190369
[33,] 0.9575482 0.08490364 0.042451821
[34,] 0.9610055 0.07798902 0.038994511
[35,] 0.9487811 0.10243782 0.051218911
[36,] 0.9556607 0.08867868 0.044339342
[37,] 0.9552245 0.08955097 0.044775485
[38,] 0.9452516 0.10949674 0.054748368
[39,] 0.9427397 0.11452052 0.057260260
[40,] 0.9316304 0.13673915 0.068369577
[41,] 0.9235815 0.15283706 0.076418531
[42,] 0.9881598 0.02368030 0.011840152
[43,] 0.9846222 0.03075553 0.015377766
[44,] 0.9876017 0.02479654 0.012398270
[45,] 0.9921897 0.01562069 0.007810347
[46,] 0.9896349 0.02073020 0.010365101
[47,] 0.9859361 0.02812772 0.014063859
[48,] 0.9842134 0.03157316 0.015786578
[49,] 0.9892848 0.02143045 0.010715227
[50,] 0.9884596 0.02308070 0.011540351
[51,] 0.9883191 0.02336171 0.011680855
[52,] 0.9887439 0.02251215 0.011256073
[53,] 0.9873525 0.02529502 0.012647508
[54,] 0.9898229 0.02035421 0.010177105
[55,] 0.9886495 0.02270095 0.011350473
[56,] 0.9879518 0.02409639 0.012048195
[57,] 0.9883479 0.02330421 0.011652107
[58,] 0.9905749 0.01885018 0.009425088
[59,] 0.9900789 0.01984210 0.009921051
[60,] 0.9869746 0.02605089 0.013025447
[61,] 0.9873789 0.02524226 0.012621130
[62,] 0.9831883 0.03362342 0.016811710
[63,] 0.9800777 0.03984460 0.019922299
[64,] 0.9860153 0.02796933 0.013984666
[65,] 0.9815516 0.03689670 0.018448352
[66,] 0.9784707 0.04305858 0.021529292
[67,] 0.9719950 0.05600997 0.028004983
[68,] 0.9633422 0.07331565 0.036657825
[69,] 0.9761693 0.04766131 0.023830653
[70,] 0.9743586 0.05128280 0.025641401
[71,] 0.9774080 0.04518400 0.022591999
[72,] 0.9771643 0.04567142 0.022835710
[73,] 0.9697343 0.06053143 0.030265715
[74,] 0.9626228 0.07475437 0.037377185
[75,] 0.9879650 0.02406995 0.012034975
[76,] 0.9837860 0.03242790 0.016213951
[77,] 0.9782641 0.04347189 0.021735946
[78,] 0.9719501 0.05609980 0.028049900
[79,] 0.9669478 0.06610431 0.033052157
[80,] 0.9574460 0.08510807 0.042554036
[81,] 0.9503477 0.09930469 0.049652343
[82,] 0.9692206 0.06155883 0.030779416
[83,] 0.9606495 0.07870107 0.039350534
[84,] 0.9566776 0.08664472 0.043322360
[85,] 0.9465688 0.10686230 0.053431152
[86,] 0.9352304 0.12953929 0.064769645
[87,] 0.9279499 0.14410018 0.072050091
[88,] 0.9105085 0.17898307 0.089491533
[89,] 0.9003600 0.19928000 0.099640002
[90,] 0.8808113 0.23837735 0.119188673
[91,] 0.8549989 0.29000223 0.145001117
[92,] 0.8347836 0.33043287 0.165216435
[93,] 0.8365794 0.32684116 0.163420581
[94,] 0.8889877 0.22202466 0.111012332
[95,] 0.8861923 0.22761536 0.113807680
[96,] 0.8595331 0.28093379 0.140466896
[97,] 0.8325014 0.33499715 0.167498577
[98,] 0.8247189 0.35056224 0.175281118
[99,] 0.8169834 0.36603314 0.183016570
[100,] 0.8370563 0.32588746 0.162943732
[101,] 0.8261413 0.34771735 0.173858673
[102,] 0.8834129 0.23317422 0.116587110
[103,] 0.8545885 0.29082292 0.145411458
[104,] 0.8310828 0.33783448 0.168917242
[105,] 0.7969393 0.40612149 0.203060746
[106,] 0.7854262 0.42914762 0.214573808
[107,] 0.7868211 0.42635780 0.213178902
[108,] 0.7898540 0.42029194 0.210145968
[109,] 0.7485819 0.50283627 0.251418133
[110,] 0.7939435 0.41211299 0.206056493
[111,] 0.7543392 0.49132162 0.245660808
[112,] 0.7197070 0.56058599 0.280292997
[113,] 0.7173725 0.56525491 0.282627455
[114,] 0.6773158 0.64536842 0.322684209
[115,] 0.6601115 0.67977700 0.339888502
[116,] 0.6414794 0.71704114 0.358520569
[117,] 0.5775342 0.84493162 0.422465812
[118,] 0.5463987 0.90720251 0.453601257
[119,] 0.5488826 0.90223471 0.451117355
[120,] 0.5637784 0.87244316 0.436221580
[121,] 0.5052318 0.98953643 0.494768214
[122,] 0.4604197 0.92083949 0.539580256
[123,] 0.4448596 0.88971923 0.555140383
[124,] 0.3723995 0.74479891 0.627600545
[125,] 0.3196238 0.63924761 0.680376196
[126,] 0.3016831 0.60336625 0.698316873
[127,] 0.2345383 0.46907658 0.765461710
[128,] 0.3218850 0.64376992 0.678115042
[129,] 0.7053279 0.58934421 0.294672103
[130,] 0.6616926 0.67661484 0.338307418
[131,] 0.5946869 0.81062627 0.405313133
[132,] 0.4931567 0.98631334 0.506843328
[133,] 0.4230697 0.84613937 0.576930317
[134,] 0.2971135 0.59422699 0.702886506
[135,] 0.5276348 0.94473031 0.472365154
> postscript(file="/var/www/rcomp/tmp/1zjrh1322008504.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/rcomp/tmp/2w1q51322008504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3nndx1322008504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/46eh41322008504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/54omt1322008504.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.64171298 0.99879387 1.91914295 0.87978603 -0.88087510 2.82633291
7 8 9 10 11 12
-1.82504351 -1.25559119 -0.59224728 3.42133838 -2.21562790 -1.03389239
13 14 15 16 17 18
2.58898245 -4.43909438 -0.55069702 0.37930601 -2.00049608 -0.36336872
19 20 21 22 23 24
1.38201228 -3.28859807 2.21056876 0.51020734 -0.88077846 2.37068548
25 26 27 28 29 30
0.96674966 0.38079256 -5.45220010 0.27187518 -0.02560476 4.91289200
31 32 33 34 35 36
4.29884534 -1.59174164 -0.74428662 0.86525632 -1.49711705 1.48410225
37 38 39 40 41 42
-1.15464423 0.39647218 0.06636360 0.32238850 0.96102473 6.97880405
43 44 45 46 47 48
-4.68770750 -2.25814804 -0.27957150 2.13395609 2.67448517 0.79461748
49 50 51 52 53 54
-2.40479003 -1.46833846 -1.08457035 -6.42798022 -0.16861807 -2.56336361
55 56 57 58 59 60
2.09281420 -0.94809621 0.32148242 1.39746903 -2.53105012 0.37015624
61 62 63 64 65 66
-2.11471329 1.79743993 0.87696862 2.62585196 -1.41298964 1.85385782
67 68 69 70 71 72
-2.24313804 2.69467037 1.72617813 -0.85242054 2.04789210 0.36280337
73 74 75 76 77 78
-1.45585796 -3.19620171 -0.79547557 -1.43213924 -0.35572657 0.07434820
79 80 81 82 83 84
3.14103868 -1.91196964 -2.70611238 1.64492930 -0.14094697 -1.12419828
85 86 87 88 89 90
3.92879963 -0.28019908 -0.36171217 0.06668567 1.13779265 -0.48116480
91 92 93 94 95 96
0.86302126 -3.61319090 0.46275108 1.60362068 0.51710715 0.65245081
97 98 99 100 101 102
-2.09468732 -0.69138666 -1.60214894 0.46103623 0.42141787 1.09051152
103 104 105 106 107 108
-1.36850274 2.03559333 -3.05673560 -0.71243171 -1.51764491 -1.26223514
109 110 111 112 113 114
-2.22646717 -2.64198213 -0.86482046 -1.13174056 -0.07886609 0.74198050
115 116 117 118 119 120
0.55603273 2.59202095 3.12459317 -1.75680353 -0.13864025 3.20067178
121 122 123 124 125 126
0.52100657 1.54690435 -2.79187918 -1.00628038 2.11490002 1.47544159
127 128 129 130 131 132
0.60105951 -0.65282476 -0.31051927 -1.83946302 -0.01261754 2.47580467
133 134 135 136 137 138
-1.20997499 -0.88772200 2.02715894 1.67593025 0.52301700 -3.27701955
139 140 141 142 143 144
5.17275983 -0.83425349 0.25474793 -0.23955530 2.71047101 -0.70031081
145 146 147 148 149 150
1.20910984 -0.66281156 -3.12049613 -4.53755963 1.28969907 1.96359093
151 152 153 154 155 156
-0.32647866 0.86275821 -1.25358215 0.50455268 0.41293571 1.43740699
> postscript(file="/var/www/rcomp/tmp/60k9o1322008504.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 1.64171298 NA
1 0.99879387 1.64171298
2 1.91914295 0.99879387
3 0.87978603 1.91914295
4 -0.88087510 0.87978603
5 2.82633291 -0.88087510
6 -1.82504351 2.82633291
7 -1.25559119 -1.82504351
8 -0.59224728 -1.25559119
9 3.42133838 -0.59224728
10 -2.21562790 3.42133838
11 -1.03389239 -2.21562790
12 2.58898245 -1.03389239
13 -4.43909438 2.58898245
14 -0.55069702 -4.43909438
15 0.37930601 -0.55069702
16 -2.00049608 0.37930601
17 -0.36336872 -2.00049608
18 1.38201228 -0.36336872
19 -3.28859807 1.38201228
20 2.21056876 -3.28859807
21 0.51020734 2.21056876
22 -0.88077846 0.51020734
23 2.37068548 -0.88077846
24 0.96674966 2.37068548
25 0.38079256 0.96674966
26 -5.45220010 0.38079256
27 0.27187518 -5.45220010
28 -0.02560476 0.27187518
29 4.91289200 -0.02560476
30 4.29884534 4.91289200
31 -1.59174164 4.29884534
32 -0.74428662 -1.59174164
33 0.86525632 -0.74428662
34 -1.49711705 0.86525632
35 1.48410225 -1.49711705
36 -1.15464423 1.48410225
37 0.39647218 -1.15464423
38 0.06636360 0.39647218
39 0.32238850 0.06636360
40 0.96102473 0.32238850
41 6.97880405 0.96102473
42 -4.68770750 6.97880405
43 -2.25814804 -4.68770750
44 -0.27957150 -2.25814804
45 2.13395609 -0.27957150
46 2.67448517 2.13395609
47 0.79461748 2.67448517
48 -2.40479003 0.79461748
49 -1.46833846 -2.40479003
50 -1.08457035 -1.46833846
51 -6.42798022 -1.08457035
52 -0.16861807 -6.42798022
53 -2.56336361 -0.16861807
54 2.09281420 -2.56336361
55 -0.94809621 2.09281420
56 0.32148242 -0.94809621
57 1.39746903 0.32148242
58 -2.53105012 1.39746903
59 0.37015624 -2.53105012
60 -2.11471329 0.37015624
61 1.79743993 -2.11471329
62 0.87696862 1.79743993
63 2.62585196 0.87696862
64 -1.41298964 2.62585196
65 1.85385782 -1.41298964
66 -2.24313804 1.85385782
67 2.69467037 -2.24313804
68 1.72617813 2.69467037
69 -0.85242054 1.72617813
70 2.04789210 -0.85242054
71 0.36280337 2.04789210
72 -1.45585796 0.36280337
73 -3.19620171 -1.45585796
74 -0.79547557 -3.19620171
75 -1.43213924 -0.79547557
76 -0.35572657 -1.43213924
77 0.07434820 -0.35572657
78 3.14103868 0.07434820
79 -1.91196964 3.14103868
80 -2.70611238 -1.91196964
81 1.64492930 -2.70611238
82 -0.14094697 1.64492930
83 -1.12419828 -0.14094697
84 3.92879963 -1.12419828
85 -0.28019908 3.92879963
86 -0.36171217 -0.28019908
87 0.06668567 -0.36171217
88 1.13779265 0.06668567
89 -0.48116480 1.13779265
90 0.86302126 -0.48116480
91 -3.61319090 0.86302126
92 0.46275108 -3.61319090
93 1.60362068 0.46275108
94 0.51710715 1.60362068
95 0.65245081 0.51710715
96 -2.09468732 0.65245081
97 -0.69138666 -2.09468732
98 -1.60214894 -0.69138666
99 0.46103623 -1.60214894
100 0.42141787 0.46103623
101 1.09051152 0.42141787
102 -1.36850274 1.09051152
103 2.03559333 -1.36850274
104 -3.05673560 2.03559333
105 -0.71243171 -3.05673560
106 -1.51764491 -0.71243171
107 -1.26223514 -1.51764491
108 -2.22646717 -1.26223514
109 -2.64198213 -2.22646717
110 -0.86482046 -2.64198213
111 -1.13174056 -0.86482046
112 -0.07886609 -1.13174056
113 0.74198050 -0.07886609
114 0.55603273 0.74198050
115 2.59202095 0.55603273
116 3.12459317 2.59202095
117 -1.75680353 3.12459317
118 -0.13864025 -1.75680353
119 3.20067178 -0.13864025
120 0.52100657 3.20067178
121 1.54690435 0.52100657
122 -2.79187918 1.54690435
123 -1.00628038 -2.79187918
124 2.11490002 -1.00628038
125 1.47544159 2.11490002
126 0.60105951 1.47544159
127 -0.65282476 0.60105951
128 -0.31051927 -0.65282476
129 -1.83946302 -0.31051927
130 -0.01261754 -1.83946302
131 2.47580467 -0.01261754
132 -1.20997499 2.47580467
133 -0.88772200 -1.20997499
134 2.02715894 -0.88772200
135 1.67593025 2.02715894
136 0.52301700 1.67593025
137 -3.27701955 0.52301700
138 5.17275983 -3.27701955
139 -0.83425349 5.17275983
140 0.25474793 -0.83425349
141 -0.23955530 0.25474793
142 2.71047101 -0.23955530
143 -0.70031081 2.71047101
144 1.20910984 -0.70031081
145 -0.66281156 1.20910984
146 -3.12049613 -0.66281156
147 -4.53755963 -3.12049613
148 1.28969907 -4.53755963
149 1.96359093 1.28969907
150 -0.32647866 1.96359093
151 0.86275821 -0.32647866
152 -1.25358215 0.86275821
153 0.50455268 -1.25358215
154 0.41293571 0.50455268
155 1.43740699 0.41293571
156 NA 1.43740699
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.99879387 1.64171298
[2,] 1.91914295 0.99879387
[3,] 0.87978603 1.91914295
[4,] -0.88087510 0.87978603
[5,] 2.82633291 -0.88087510
[6,] -1.82504351 2.82633291
[7,] -1.25559119 -1.82504351
[8,] -0.59224728 -1.25559119
[9,] 3.42133838 -0.59224728
[10,] -2.21562790 3.42133838
[11,] -1.03389239 -2.21562790
[12,] 2.58898245 -1.03389239
[13,] -4.43909438 2.58898245
[14,] -0.55069702 -4.43909438
[15,] 0.37930601 -0.55069702
[16,] -2.00049608 0.37930601
[17,] -0.36336872 -2.00049608
[18,] 1.38201228 -0.36336872
[19,] -3.28859807 1.38201228
[20,] 2.21056876 -3.28859807
[21,] 0.51020734 2.21056876
[22,] -0.88077846 0.51020734
[23,] 2.37068548 -0.88077846
[24,] 0.96674966 2.37068548
[25,] 0.38079256 0.96674966
[26,] -5.45220010 0.38079256
[27,] 0.27187518 -5.45220010
[28,] -0.02560476 0.27187518
[29,] 4.91289200 -0.02560476
[30,] 4.29884534 4.91289200
[31,] -1.59174164 4.29884534
[32,] -0.74428662 -1.59174164
[33,] 0.86525632 -0.74428662
[34,] -1.49711705 0.86525632
[35,] 1.48410225 -1.49711705
[36,] -1.15464423 1.48410225
[37,] 0.39647218 -1.15464423
[38,] 0.06636360 0.39647218
[39,] 0.32238850 0.06636360
[40,] 0.96102473 0.32238850
[41,] 6.97880405 0.96102473
[42,] -4.68770750 6.97880405
[43,] -2.25814804 -4.68770750
[44,] -0.27957150 -2.25814804
[45,] 2.13395609 -0.27957150
[46,] 2.67448517 2.13395609
[47,] 0.79461748 2.67448517
[48,] -2.40479003 0.79461748
[49,] -1.46833846 -2.40479003
[50,] -1.08457035 -1.46833846
[51,] -6.42798022 -1.08457035
[52,] -0.16861807 -6.42798022
[53,] -2.56336361 -0.16861807
[54,] 2.09281420 -2.56336361
[55,] -0.94809621 2.09281420
[56,] 0.32148242 -0.94809621
[57,] 1.39746903 0.32148242
[58,] -2.53105012 1.39746903
[59,] 0.37015624 -2.53105012
[60,] -2.11471329 0.37015624
[61,] 1.79743993 -2.11471329
[62,] 0.87696862 1.79743993
[63,] 2.62585196 0.87696862
[64,] -1.41298964 2.62585196
[65,] 1.85385782 -1.41298964
[66,] -2.24313804 1.85385782
[67,] 2.69467037 -2.24313804
[68,] 1.72617813 2.69467037
[69,] -0.85242054 1.72617813
[70,] 2.04789210 -0.85242054
[71,] 0.36280337 2.04789210
[72,] -1.45585796 0.36280337
[73,] -3.19620171 -1.45585796
[74,] -0.79547557 -3.19620171
[75,] -1.43213924 -0.79547557
[76,] -0.35572657 -1.43213924
[77,] 0.07434820 -0.35572657
[78,] 3.14103868 0.07434820
[79,] -1.91196964 3.14103868
[80,] -2.70611238 -1.91196964
[81,] 1.64492930 -2.70611238
[82,] -0.14094697 1.64492930
[83,] -1.12419828 -0.14094697
[84,] 3.92879963 -1.12419828
[85,] -0.28019908 3.92879963
[86,] -0.36171217 -0.28019908
[87,] 0.06668567 -0.36171217
[88,] 1.13779265 0.06668567
[89,] -0.48116480 1.13779265
[90,] 0.86302126 -0.48116480
[91,] -3.61319090 0.86302126
[92,] 0.46275108 -3.61319090
[93,] 1.60362068 0.46275108
[94,] 0.51710715 1.60362068
[95,] 0.65245081 0.51710715
[96,] -2.09468732 0.65245081
[97,] -0.69138666 -2.09468732
[98,] -1.60214894 -0.69138666
[99,] 0.46103623 -1.60214894
[100,] 0.42141787 0.46103623
[101,] 1.09051152 0.42141787
[102,] -1.36850274 1.09051152
[103,] 2.03559333 -1.36850274
[104,] -3.05673560 2.03559333
[105,] -0.71243171 -3.05673560
[106,] -1.51764491 -0.71243171
[107,] -1.26223514 -1.51764491
[108,] -2.22646717 -1.26223514
[109,] -2.64198213 -2.22646717
[110,] -0.86482046 -2.64198213
[111,] -1.13174056 -0.86482046
[112,] -0.07886609 -1.13174056
[113,] 0.74198050 -0.07886609
[114,] 0.55603273 0.74198050
[115,] 2.59202095 0.55603273
[116,] 3.12459317 2.59202095
[117,] -1.75680353 3.12459317
[118,] -0.13864025 -1.75680353
[119,] 3.20067178 -0.13864025
[120,] 0.52100657 3.20067178
[121,] 1.54690435 0.52100657
[122,] -2.79187918 1.54690435
[123,] -1.00628038 -2.79187918
[124,] 2.11490002 -1.00628038
[125,] 1.47544159 2.11490002
[126,] 0.60105951 1.47544159
[127,] -0.65282476 0.60105951
[128,] -0.31051927 -0.65282476
[129,] -1.83946302 -0.31051927
[130,] -0.01261754 -1.83946302
[131,] 2.47580467 -0.01261754
[132,] -1.20997499 2.47580467
[133,] -0.88772200 -1.20997499
[134,] 2.02715894 -0.88772200
[135,] 1.67593025 2.02715894
[136,] 0.52301700 1.67593025
[137,] -3.27701955 0.52301700
[138,] 5.17275983 -3.27701955
[139,] -0.83425349 5.17275983
[140,] 0.25474793 -0.83425349
[141,] -0.23955530 0.25474793
[142,] 2.71047101 -0.23955530
[143,] -0.70031081 2.71047101
[144,] 1.20910984 -0.70031081
[145,] -0.66281156 1.20910984
[146,] -3.12049613 -0.66281156
[147,] -4.53755963 -3.12049613
[148,] 1.28969907 -4.53755963
[149,] 1.96359093 1.28969907
[150,] -0.32647866 1.96359093
[151,] 0.86275821 -0.32647866
[152,] -1.25358215 0.86275821
[153,] 0.50455268 -1.25358215
[154,] 0.41293571 0.50455268
[155,] 1.43740699 0.41293571
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.99879387 1.64171298
2 1.91914295 0.99879387
3 0.87978603 1.91914295
4 -0.88087510 0.87978603
5 2.82633291 -0.88087510
6 -1.82504351 2.82633291
7 -1.25559119 -1.82504351
8 -0.59224728 -1.25559119
9 3.42133838 -0.59224728
10 -2.21562790 3.42133838
11 -1.03389239 -2.21562790
12 2.58898245 -1.03389239
13 -4.43909438 2.58898245
14 -0.55069702 -4.43909438
15 0.37930601 -0.55069702
16 -2.00049608 0.37930601
17 -0.36336872 -2.00049608
18 1.38201228 -0.36336872
19 -3.28859807 1.38201228
20 2.21056876 -3.28859807
21 0.51020734 2.21056876
22 -0.88077846 0.51020734
23 2.37068548 -0.88077846
24 0.96674966 2.37068548
25 0.38079256 0.96674966
26 -5.45220010 0.38079256
27 0.27187518 -5.45220010
28 -0.02560476 0.27187518
29 4.91289200 -0.02560476
30 4.29884534 4.91289200
31 -1.59174164 4.29884534
32 -0.74428662 -1.59174164
33 0.86525632 -0.74428662
34 -1.49711705 0.86525632
35 1.48410225 -1.49711705
36 -1.15464423 1.48410225
37 0.39647218 -1.15464423
38 0.06636360 0.39647218
39 0.32238850 0.06636360
40 0.96102473 0.32238850
41 6.97880405 0.96102473
42 -4.68770750 6.97880405
43 -2.25814804 -4.68770750
44 -0.27957150 -2.25814804
45 2.13395609 -0.27957150
46 2.67448517 2.13395609
47 0.79461748 2.67448517
48 -2.40479003 0.79461748
49 -1.46833846 -2.40479003
50 -1.08457035 -1.46833846
51 -6.42798022 -1.08457035
52 -0.16861807 -6.42798022
53 -2.56336361 -0.16861807
54 2.09281420 -2.56336361
55 -0.94809621 2.09281420
56 0.32148242 -0.94809621
57 1.39746903 0.32148242
58 -2.53105012 1.39746903
59 0.37015624 -2.53105012
60 -2.11471329 0.37015624
61 1.79743993 -2.11471329
62 0.87696862 1.79743993
63 2.62585196 0.87696862
64 -1.41298964 2.62585196
65 1.85385782 -1.41298964
66 -2.24313804 1.85385782
67 2.69467037 -2.24313804
68 1.72617813 2.69467037
69 -0.85242054 1.72617813
70 2.04789210 -0.85242054
71 0.36280337 2.04789210
72 -1.45585796 0.36280337
73 -3.19620171 -1.45585796
74 -0.79547557 -3.19620171
75 -1.43213924 -0.79547557
76 -0.35572657 -1.43213924
77 0.07434820 -0.35572657
78 3.14103868 0.07434820
79 -1.91196964 3.14103868
80 -2.70611238 -1.91196964
81 1.64492930 -2.70611238
82 -0.14094697 1.64492930
83 -1.12419828 -0.14094697
84 3.92879963 -1.12419828
85 -0.28019908 3.92879963
86 -0.36171217 -0.28019908
87 0.06668567 -0.36171217
88 1.13779265 0.06668567
89 -0.48116480 1.13779265
90 0.86302126 -0.48116480
91 -3.61319090 0.86302126
92 0.46275108 -3.61319090
93 1.60362068 0.46275108
94 0.51710715 1.60362068
95 0.65245081 0.51710715
96 -2.09468732 0.65245081
97 -0.69138666 -2.09468732
98 -1.60214894 -0.69138666
99 0.46103623 -1.60214894
100 0.42141787 0.46103623
101 1.09051152 0.42141787
102 -1.36850274 1.09051152
103 2.03559333 -1.36850274
104 -3.05673560 2.03559333
105 -0.71243171 -3.05673560
106 -1.51764491 -0.71243171
107 -1.26223514 -1.51764491
108 -2.22646717 -1.26223514
109 -2.64198213 -2.22646717
110 -0.86482046 -2.64198213
111 -1.13174056 -0.86482046
112 -0.07886609 -1.13174056
113 0.74198050 -0.07886609
114 0.55603273 0.74198050
115 2.59202095 0.55603273
116 3.12459317 2.59202095
117 -1.75680353 3.12459317
118 -0.13864025 -1.75680353
119 3.20067178 -0.13864025
120 0.52100657 3.20067178
121 1.54690435 0.52100657
122 -2.79187918 1.54690435
123 -1.00628038 -2.79187918
124 2.11490002 -1.00628038
125 1.47544159 2.11490002
126 0.60105951 1.47544159
127 -0.65282476 0.60105951
128 -0.31051927 -0.65282476
129 -1.83946302 -0.31051927
130 -0.01261754 -1.83946302
131 2.47580467 -0.01261754
132 -1.20997499 2.47580467
133 -0.88772200 -1.20997499
134 2.02715894 -0.88772200
135 1.67593025 2.02715894
136 0.52301700 1.67593025
137 -3.27701955 0.52301700
138 5.17275983 -3.27701955
139 -0.83425349 5.17275983
140 0.25474793 -0.83425349
141 -0.23955530 0.25474793
142 2.71047101 -0.23955530
143 -0.70031081 2.71047101
144 1.20910984 -0.70031081
145 -0.66281156 1.20910984
146 -3.12049613 -0.66281156
147 -4.53755963 -3.12049613
148 1.28969907 -4.53755963
149 1.96359093 1.28969907
150 -0.32647866 1.96359093
151 0.86275821 -0.32647866
152 -1.25358215 0.86275821
153 0.50455268 -1.25358215
154 0.41293571 0.50455268
155 1.43740699 0.41293571
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7fjb01322008504.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/rcomp/tmp/8ce2b1322008504.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/rcomp/tmp/9yutu1322008504.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/rcomp/tmp/10wj0j1322008504.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/117dgd1322008504.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12syiw1322008504.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13m5qm1322008504.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14p0b51322008504.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15kn5w1322008504.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/1618801322008504.tab")
+ }
>
> try(system("convert tmp/1zjrh1322008504.ps tmp/1zjrh1322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w1q51322008504.ps tmp/2w1q51322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nndx1322008504.ps tmp/3nndx1322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/46eh41322008504.ps tmp/46eh41322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/54omt1322008504.ps tmp/54omt1322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/60k9o1322008504.ps tmp/60k9o1322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fjb01322008504.ps tmp/7fjb01322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ce2b1322008504.ps tmp/8ce2b1322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yutu1322008504.ps tmp/9yutu1322008504.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wj0j1322008504.ps tmp/10wj0j1322008504.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.040 0.270 5.284