R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(1418
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+ ,0)
+ ,dim=c(12
+ ,85)
+ ,dimnames=list(c('pageviews'
+ ,'logins'
+ ,'comp_views'
+ ,'comp_views_pr'
+ ,'comp_reviewed'
+ ,'FB_p1'
+ ,'feedback_p120'
+ ,'revisions'
+ ,'seconds'
+ ,'hyperlinks'
+ ,'blogs'
+ ,'testscores')
+ ,1:85))
> y <- array(NA,dim=c(12,85),dimnames=list(c('pageviews','logins','comp_views','comp_views_pr','comp_reviewed','FB_p1','feedback_p120','revisions','seconds','hyperlinks','blogs','testscores'),1:85))
> 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 = '12'
> #'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
> 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
testscores pageviews logins comp_views comp_views_pr comp_reviewed FB_p1
1 2 1418 56 396 81 30 115
2 4 2172 89 967 125 30 116
3 0 1583 44 656 66 26 100
4 0 1764 84 655 74 38 140
5 -4 1495 88 465 49 44 166
6 4 1373 55 525 52 30 99
7 4 2187 60 885 88 40 139
8 0 4041 154 1436 108 47 181
9 -1 1706 53 612 43 30 116
10 0 2152 119 865 75 31 116
11 1 2242 75 963 86 30 108
12 0 2515 92 966 135 34 129
13 3 2147 100 801 63 31 118
14 -1 1638 73 513 52 33 125
15 4 2452 77 992 59 33 127
16 3 2662 99 937 64 36 136
17 1 865 30 260 32 14 46
18 0 1793 76 503 129 17 54
19 -2 2527 146 927 37 32 124
20 -3 2747 67 1269 31 30 115
21 -4 1324 56 537 65 35 128
22 2 1383 58 532 74 28 97
23 2 4308 119 1635 715 34 125
24 -4 1831 66 557 66 39 149
25 3 3373 89 1178 106 39 149
26 2 2352 41 866 112 29 108
27 2 2144 68 574 66 44 166
28 0 4691 168 1276 190 21 80
29 5 2694 132 825 165 28 107
30 -2 1769 71 663 61 28 107
31 0 3148 112 1069 53 38 146
32 -2 1954 70 711 38 32 123
33 -3 1226 57 503 50 29 111
34 2 1496 103 464 42 27 105
35 2 1943 52 657 53 40 155
36 2 1762 62 577 50 40 155
37 0 1403 45 619 77 28 104
38 4 1425 46 479 57 34 132
39 4 1857 63 817 73 33 127
40 2 1420 53 537 63 33 122
41 2 1644 78 465 47 35 87
42 -4 1054 46 299 57 29 109
43 3 937 41 248 36 20 78
44 3 2547 91 905 63 37 141
45 2 1626 63 512 63 33 124
46 -1 1964 63 786 110 29 112
47 -3 1381 32 489 56 28 108
48 0 1290 34 351 71 21 78
49 1 1982 93 669 56 41 158
50 -3 1590 55 506 79 20 78
51 3 1281 72 407 67 30 119
52 0 1272 42 316 76 22 88
53 0 1944 71 603 65 42 155
54 0 1605 65 577 45 32 123
55 3 1386 41 411 97 36 136
56 -3 2395 86 975 53 31 117
57 0 2699 95 964 144 33 124
58 -4 1606 49 537 60 40 151
59 2 1204 64 369 89 38 145
60 -1 1138 38 417 42 24 87
61 3 1111 52 389 52 43 165
62 2 2186 247 719 128 31 120
63 5 3604 139 1277 142 40 150
64 2 3261 110 1402 128 37 136
65 -2 1641 67 564 50 31 116
66 0 2312 83 747 50 39 150
67 3 2201 70 861 46 32 118
68 -2 961 32 319 57 18 71
69 0 1900 83 612 52 39 144
70 6 1645 70 564 48 30 110
71 -3 2429 103 824 91 37 147
72 3 872 34 239 70 32 111
73 0 1018 40 459 37 17 68
74 -2 1403 46 454 72 12 48
75 1 616 18 225 24 13 51
76 0 1232 60 389 90 17 68
77 2 1255 39 339 45 17 64
78 2 995 31 333 26 20 76
79 -3 2048 54 636 132 17 66
80 -2 301 14 93 35 17 68
81 1 628 23 170 48 17 66
82 -4 1597 77 530 124 22 83
83 0 717 19 201 35 15 55
84 1 652 49 227 49 12 41
85 0 733 20 261 45 17 66
feedback_p120 revisions seconds hyperlinks blogs t
1 94 24188 146283 144 145 1
2 103 32287 96933 135 132 2
3 93 27101 95757 84 84 3
4 123 19716 143983 130 127 4
5 148 17753 75851 82 78 5
6 90 9028 59238 60 60 6
7 124 18653 93163 131 131 7
8 168 29498 151511 140 133 8
9 115 27563 136368 151 150 9
10 71 18293 112642 91 91 10
11 108 16116 127766 119 118 11
12 120 26569 85646 123 119 12
13 114 24785 98579 90 89 13
14 120 23825 131741 113 108 14
15 124 34461 171975 175 162 15
16 126 24919 159676 96 92 16
17 37 12558 58391 41 41 17
18 38 7784 31580 47 47 18
19 120 28522 136815 126 120 19
20 93 22265 120642 105 105 20
21 95 14459 69107 80 79 21
22 90 22240 108016 73 70 22
23 110 11912 79336 68 67 23
24 138 18220 93176 127 127 24
25 133 19199 161632 154 152 25
26 96 25239 102996 112 109 26
27 164 29801 160604 137 133 27
28 78 18450 158051 135 123 28
29 102 34861 162647 230 230 29
30 99 16688 60622 71 68 30
31 129 24683 179566 147 147 31
32 114 21436 96144 105 101 32
33 99 30546 129847 107 108 33
34 104 15977 71180 116 114 34
35 138 14251 86767 89 88 35
36 151 16851 93487 84 83 36
37 72 21113 82981 113 113 37
38 120 17401 73815 120 118 38
39 115 23958 94552 110 110 39
40 98 14587 67808 78 76 40
41 71 20537 106175 145 141 41
42 107 30495 76669 91 91 42
43 73 7117 57283 48 48 43
44 129 33473 72413 150 144 44
45 118 21115 96971 181 168 45
46 104 32902 120336 121 117 46
47 107 25131 93913 99 100 47
48 36 6943 32036 40 37 48
49 139 31808 102255 87 87 49
50 56 17014 63506 66 64 50
51 93 6440 68370 58 58 51
52 87 18647 50517 77 76 52
53 110 20556 103950 130 129 53
54 83 22392 84396 101 101 54
55 98 8388 55515 120 89 55
56 82 22120 209056 195 193 56
57 115 20923 142775 106 101 57
58 140 20237 68847 83 82 58
59 120 3769 20112 37 36 59
60 66 12252 61023 77 75 60
61 139 21721 112494 144 131 61
62 119 17939 78876 95 90 62
63 141 23436 170745 169 166 63
64 133 34538 122037 134 133 64
65 98 25515 112283 197 196 65
66 117 29402 120691 140 136 66
67 105 28732 122422 125 123 67
68 55 5250 25899 21 21 68
69 132 28608 139296 167 163 69
70 73 14817 89455 96 96 70
71 86 16714 147866 151 151 71
72 48 1669 14336 23 23 72
73 48 7768 30059 21 14 73
74 43 7936 41907 90 87 74
75 46 7294 35885 60 56 75
76 65 13275 55764 26 25 76
77 52 5401 35619 41 41 77
78 68 8702 40557 35 33 78
79 47 8030 44197 68 68 79
80 41 1278 4103 6 6 80
81 47 1574 4694 0 0 81
82 71 9653 62991 41 39 82
83 30 7067 24261 38 37 83
84 24 1514 21425 47 47 84
85 63 5432 27184 34 34 85
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews logins comp_views comp_views_pr
-4.992e-01 -6.989e-04 3.966e-03 2.248e-03 1.338e-03
comp_reviewed FB_p1 feedback_p120 revisions seconds
2.827e-01 -8.931e-02 2.626e-02 -6.293e-05 -7.276e-06
hyperlinks blogs t
7.820e-02 -6.573e-02 -1.027e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.0275 -1.4329 0.1254 1.5802 5.9229
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.992e-01 1.473e+00 -0.339 0.7356
pageviews -6.989e-04 1.359e-03 -0.514 0.6086
logins 3.966e-03 1.150e-02 0.345 0.7312
comp_views 2.248e-03 3.119e-03 0.721 0.4735
comp_views_pr 1.338e-03 4.867e-03 0.275 0.7841
comp_reviewed 2.827e-01 1.760e-01 1.607 0.1125
FB_p1 -8.931e-02 5.231e-02 -1.707 0.0921 .
feedback_p120 2.626e-02 2.450e-02 1.072 0.2874
revisions -6.294e-05 5.914e-05 -1.064 0.2908
seconds -7.276e-06 1.398e-05 -0.520 0.6044
hyperlinks 7.820e-02 6.604e-02 1.184 0.2403
blogs -6.573e-02 6.855e-02 -0.959 0.3409
t -1.027e-03 1.373e-02 -0.075 0.9406
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.5 on 72 degrees of freedom
Multiple R-squared: 0.1163, Adjusted R-squared: -0.03101
F-statistic: 0.7895 on 12 and 72 DF, p-value: 0.6595
> 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,] 0.4104710 0.82094199 0.58952900
[2,] 0.2591182 0.51823637 0.74088182
[3,] 0.1712565 0.34251298 0.82874351
[4,] 0.2842779 0.56855576 0.71572212
[5,] 0.1958271 0.39165418 0.80417291
[6,] 0.1498205 0.29964099 0.85017951
[7,] 0.1848937 0.36978737 0.81510631
[8,] 0.1212848 0.24256962 0.87871519
[9,] 0.1778364 0.35567279 0.82216361
[10,] 0.3346574 0.66931472 0.66534264
[11,] 0.2634991 0.52699811 0.73650095
[12,] 0.2030079 0.40601583 0.79699208
[13,] 0.2046242 0.40924834 0.79537583
[14,] 0.2711821 0.54236426 0.72881787
[15,] 0.3361104 0.67222080 0.66388960
[16,] 0.2730262 0.54605232 0.72697384
[17,] 0.3020825 0.60416498 0.69791751
[18,] 0.3653476 0.73069522 0.63465239
[19,] 0.5977904 0.80441929 0.40220964
[20,] 0.7766487 0.44670260 0.22335130
[21,] 0.8006698 0.39866047 0.19933023
[22,] 0.7484114 0.50317715 0.25158857
[23,] 0.8082212 0.38355763 0.19177881
[24,] 0.8429082 0.31418356 0.15709178
[25,] 0.8110573 0.37788531 0.18894266
[26,] 0.8511163 0.29776740 0.14888370
[27,] 0.8737593 0.25248133 0.12624066
[28,] 0.8657799 0.26844024 0.13422012
[29,] 0.8454504 0.30909926 0.15454963
[30,] 0.8067161 0.38656771 0.19328385
[31,] 0.8014292 0.39714153 0.19857077
[32,] 0.7918368 0.41632636 0.20816318
[33,] 0.7385515 0.52289698 0.26144849
[34,] 0.7075676 0.58486478 0.29243239
[35,] 0.6770789 0.64584214 0.32292107
[36,] 0.7280016 0.54399671 0.27199835
[37,] 0.7710994 0.45780128 0.22890064
[38,] 0.7094730 0.58105394 0.29052697
[39,] 0.6875160 0.62496806 0.31248403
[40,] 0.6559654 0.68806916 0.34403458
[41,] 0.7333065 0.53338700 0.26669350
[42,] 0.6596979 0.68060424 0.34030212
[43,] 0.8488113 0.30237747 0.15118874
[44,] 0.7947120 0.41057605 0.20528803
[45,] 0.8579452 0.28410959 0.14205479
[46,] 0.9380503 0.12389945 0.06194972
[47,] 0.8991996 0.20160088 0.10080044
[48,] 0.9160337 0.16793257 0.08396628
[49,] 0.8711340 0.25773204 0.12886602
[50,] 0.8099762 0.38004751 0.19002376
[51,] 0.7088795 0.58224109 0.29112054
[52,] 0.6558070 0.68838604 0.34419302
[53,] 0.9566915 0.08661703 0.04330851
[54,] 0.8826906 0.23461880 0.11730940
> postscript(file="/var/wessaorg/rcomp/tmp/1d1vt1323948800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xfvn1323948800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3bjcw1323948800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/47fsf1323948801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5j13r1323948801.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 = 85
Frequency = 1
1 2 3 4 5 6
2.44780471 3.35556193 0.36420517 -1.16811221 -5.02752877 2.24489211
7 8 9 10 11 12
1.75712662 -1.43286236 -1.30607050 0.06408498 -0.77770223 -1.19178258
13 14 15 16 17 18
2.60141697 -1.45085766 2.75383321 2.72120667 1.25745084 -0.68167448
19 20 21 22 23 24
-2.72633061 -3.40347651 -4.67877301 1.42513464 -0.49420037 -4.90044315
25 26 27 28 29 30
1.86050912 1.58023922 1.52902163 -0.98395600 4.28361814 -2.63805327
31 32 33 34 35 36
-0.25167521 -2.60814662 -2.32032124 1.03044109 1.40218827 1.35440691
37 38 39 40 41 42
-0.15045457 3.22828702 3.46986205 1.33102302 -1.72293529 -3.58350116
43 44 45 46 47 48
3.09092710 1.95811973 -0.23443871 -0.99752879 -2.77278991 0.48898883
49 50 51 52 53 54
1.51748409 -2.37988526 3.04041502 0.33298894 -0.36835727 0.58368062
55 56 57 58 59 60
0.10069982 -3.49147144 -0.86233198 -4.62604531 1.11079018 -1.41314696
61 62 63 64 65 66
1.65247455 0.34116780 3.26865914 0.60291422 -2.96514574 0.12541054
67 68 69 70 71 72
2.57473274 -1.61481434 -1.03974801 5.92288861 -2.45849653 2.94529157
73 74 75 76 77 78
0.03777315 -2.49368899 0.77795667 0.61671653 2.11036747 1.99079627
79 80 81 82 83 84
-2.97579835 -1.29403035 1.47096008 -4.21153533 0.47665468 0.57485285
85
-0.07791443
> postscript(file="/var/wessaorg/rcomp/tmp/6xojt1323948801.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 = 85
Frequency = 1
lag(myerror, k = 1) myerror
0 2.44780471 NA
1 3.35556193 2.44780471
2 0.36420517 3.35556193
3 -1.16811221 0.36420517
4 -5.02752877 -1.16811221
5 2.24489211 -5.02752877
6 1.75712662 2.24489211
7 -1.43286236 1.75712662
8 -1.30607050 -1.43286236
9 0.06408498 -1.30607050
10 -0.77770223 0.06408498
11 -1.19178258 -0.77770223
12 2.60141697 -1.19178258
13 -1.45085766 2.60141697
14 2.75383321 -1.45085766
15 2.72120667 2.75383321
16 1.25745084 2.72120667
17 -0.68167448 1.25745084
18 -2.72633061 -0.68167448
19 -3.40347651 -2.72633061
20 -4.67877301 -3.40347651
21 1.42513464 -4.67877301
22 -0.49420037 1.42513464
23 -4.90044315 -0.49420037
24 1.86050912 -4.90044315
25 1.58023922 1.86050912
26 1.52902163 1.58023922
27 -0.98395600 1.52902163
28 4.28361814 -0.98395600
29 -2.63805327 4.28361814
30 -0.25167521 -2.63805327
31 -2.60814662 -0.25167521
32 -2.32032124 -2.60814662
33 1.03044109 -2.32032124
34 1.40218827 1.03044109
35 1.35440691 1.40218827
36 -0.15045457 1.35440691
37 3.22828702 -0.15045457
38 3.46986205 3.22828702
39 1.33102302 3.46986205
40 -1.72293529 1.33102302
41 -3.58350116 -1.72293529
42 3.09092710 -3.58350116
43 1.95811973 3.09092710
44 -0.23443871 1.95811973
45 -0.99752879 -0.23443871
46 -2.77278991 -0.99752879
47 0.48898883 -2.77278991
48 1.51748409 0.48898883
49 -2.37988526 1.51748409
50 3.04041502 -2.37988526
51 0.33298894 3.04041502
52 -0.36835727 0.33298894
53 0.58368062 -0.36835727
54 0.10069982 0.58368062
55 -3.49147144 0.10069982
56 -0.86233198 -3.49147144
57 -4.62604531 -0.86233198
58 1.11079018 -4.62604531
59 -1.41314696 1.11079018
60 1.65247455 -1.41314696
61 0.34116780 1.65247455
62 3.26865914 0.34116780
63 0.60291422 3.26865914
64 -2.96514574 0.60291422
65 0.12541054 -2.96514574
66 2.57473274 0.12541054
67 -1.61481434 2.57473274
68 -1.03974801 -1.61481434
69 5.92288861 -1.03974801
70 -2.45849653 5.92288861
71 2.94529157 -2.45849653
72 0.03777315 2.94529157
73 -2.49368899 0.03777315
74 0.77795667 -2.49368899
75 0.61671653 0.77795667
76 2.11036747 0.61671653
77 1.99079627 2.11036747
78 -2.97579835 1.99079627
79 -1.29403035 -2.97579835
80 1.47096008 -1.29403035
81 -4.21153533 1.47096008
82 0.47665468 -4.21153533
83 0.57485285 0.47665468
84 -0.07791443 0.57485285
85 NA -0.07791443
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.35556193 2.44780471
[2,] 0.36420517 3.35556193
[3,] -1.16811221 0.36420517
[4,] -5.02752877 -1.16811221
[5,] 2.24489211 -5.02752877
[6,] 1.75712662 2.24489211
[7,] -1.43286236 1.75712662
[8,] -1.30607050 -1.43286236
[9,] 0.06408498 -1.30607050
[10,] -0.77770223 0.06408498
[11,] -1.19178258 -0.77770223
[12,] 2.60141697 -1.19178258
[13,] -1.45085766 2.60141697
[14,] 2.75383321 -1.45085766
[15,] 2.72120667 2.75383321
[16,] 1.25745084 2.72120667
[17,] -0.68167448 1.25745084
[18,] -2.72633061 -0.68167448
[19,] -3.40347651 -2.72633061
[20,] -4.67877301 -3.40347651
[21,] 1.42513464 -4.67877301
[22,] -0.49420037 1.42513464
[23,] -4.90044315 -0.49420037
[24,] 1.86050912 -4.90044315
[25,] 1.58023922 1.86050912
[26,] 1.52902163 1.58023922
[27,] -0.98395600 1.52902163
[28,] 4.28361814 -0.98395600
[29,] -2.63805327 4.28361814
[30,] -0.25167521 -2.63805327
[31,] -2.60814662 -0.25167521
[32,] -2.32032124 -2.60814662
[33,] 1.03044109 -2.32032124
[34,] 1.40218827 1.03044109
[35,] 1.35440691 1.40218827
[36,] -0.15045457 1.35440691
[37,] 3.22828702 -0.15045457
[38,] 3.46986205 3.22828702
[39,] 1.33102302 3.46986205
[40,] -1.72293529 1.33102302
[41,] -3.58350116 -1.72293529
[42,] 3.09092710 -3.58350116
[43,] 1.95811973 3.09092710
[44,] -0.23443871 1.95811973
[45,] -0.99752879 -0.23443871
[46,] -2.77278991 -0.99752879
[47,] 0.48898883 -2.77278991
[48,] 1.51748409 0.48898883
[49,] -2.37988526 1.51748409
[50,] 3.04041502 -2.37988526
[51,] 0.33298894 3.04041502
[52,] -0.36835727 0.33298894
[53,] 0.58368062 -0.36835727
[54,] 0.10069982 0.58368062
[55,] -3.49147144 0.10069982
[56,] -0.86233198 -3.49147144
[57,] -4.62604531 -0.86233198
[58,] 1.11079018 -4.62604531
[59,] -1.41314696 1.11079018
[60,] 1.65247455 -1.41314696
[61,] 0.34116780 1.65247455
[62,] 3.26865914 0.34116780
[63,] 0.60291422 3.26865914
[64,] -2.96514574 0.60291422
[65,] 0.12541054 -2.96514574
[66,] 2.57473274 0.12541054
[67,] -1.61481434 2.57473274
[68,] -1.03974801 -1.61481434
[69,] 5.92288861 -1.03974801
[70,] -2.45849653 5.92288861
[71,] 2.94529157 -2.45849653
[72,] 0.03777315 2.94529157
[73,] -2.49368899 0.03777315
[74,] 0.77795667 -2.49368899
[75,] 0.61671653 0.77795667
[76,] 2.11036747 0.61671653
[77,] 1.99079627 2.11036747
[78,] -2.97579835 1.99079627
[79,] -1.29403035 -2.97579835
[80,] 1.47096008 -1.29403035
[81,] -4.21153533 1.47096008
[82,] 0.47665468 -4.21153533
[83,] 0.57485285 0.47665468
[84,] -0.07791443 0.57485285
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.35556193 2.44780471
2 0.36420517 3.35556193
3 -1.16811221 0.36420517
4 -5.02752877 -1.16811221
5 2.24489211 -5.02752877
6 1.75712662 2.24489211
7 -1.43286236 1.75712662
8 -1.30607050 -1.43286236
9 0.06408498 -1.30607050
10 -0.77770223 0.06408498
11 -1.19178258 -0.77770223
12 2.60141697 -1.19178258
13 -1.45085766 2.60141697
14 2.75383321 -1.45085766
15 2.72120667 2.75383321
16 1.25745084 2.72120667
17 -0.68167448 1.25745084
18 -2.72633061 -0.68167448
19 -3.40347651 -2.72633061
20 -4.67877301 -3.40347651
21 1.42513464 -4.67877301
22 -0.49420037 1.42513464
23 -4.90044315 -0.49420037
24 1.86050912 -4.90044315
25 1.58023922 1.86050912
26 1.52902163 1.58023922
27 -0.98395600 1.52902163
28 4.28361814 -0.98395600
29 -2.63805327 4.28361814
30 -0.25167521 -2.63805327
31 -2.60814662 -0.25167521
32 -2.32032124 -2.60814662
33 1.03044109 -2.32032124
34 1.40218827 1.03044109
35 1.35440691 1.40218827
36 -0.15045457 1.35440691
37 3.22828702 -0.15045457
38 3.46986205 3.22828702
39 1.33102302 3.46986205
40 -1.72293529 1.33102302
41 -3.58350116 -1.72293529
42 3.09092710 -3.58350116
43 1.95811973 3.09092710
44 -0.23443871 1.95811973
45 -0.99752879 -0.23443871
46 -2.77278991 -0.99752879
47 0.48898883 -2.77278991
48 1.51748409 0.48898883
49 -2.37988526 1.51748409
50 3.04041502 -2.37988526
51 0.33298894 3.04041502
52 -0.36835727 0.33298894
53 0.58368062 -0.36835727
54 0.10069982 0.58368062
55 -3.49147144 0.10069982
56 -0.86233198 -3.49147144
57 -4.62604531 -0.86233198
58 1.11079018 -4.62604531
59 -1.41314696 1.11079018
60 1.65247455 -1.41314696
61 0.34116780 1.65247455
62 3.26865914 0.34116780
63 0.60291422 3.26865914
64 -2.96514574 0.60291422
65 0.12541054 -2.96514574
66 2.57473274 0.12541054
67 -1.61481434 2.57473274
68 -1.03974801 -1.61481434
69 5.92288861 -1.03974801
70 -2.45849653 5.92288861
71 2.94529157 -2.45849653
72 0.03777315 2.94529157
73 -2.49368899 0.03777315
74 0.77795667 -2.49368899
75 0.61671653 0.77795667
76 2.11036747 0.61671653
77 1.99079627 2.11036747
78 -2.97579835 1.99079627
79 -1.29403035 -2.97579835
80 1.47096008 -1.29403035
81 -4.21153533 1.47096008
82 0.47665468 -4.21153533
83 0.57485285 0.47665468
84 -0.07791443 0.57485285
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7mfla1323948801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8hqov1323948801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9skpp1323948801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10zlda1323948801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/114q6b1323948801.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/125zrp1323948801.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/130uhy1323948801.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14j0r51323948801.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15gdgy1323948801.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1634uv1323948801.tab")
+ }
>
> try(system("convert tmp/1d1vt1323948800.ps tmp/1d1vt1323948800.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xfvn1323948800.ps tmp/2xfvn1323948800.png",intern=TRUE))
character(0)
> try(system("convert tmp/3bjcw1323948800.ps tmp/3bjcw1323948800.png",intern=TRUE))
character(0)
> try(system("convert tmp/47fsf1323948801.ps tmp/47fsf1323948801.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j13r1323948801.ps tmp/5j13r1323948801.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xojt1323948801.ps tmp/6xojt1323948801.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mfla1323948801.ps tmp/7mfla1323948801.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hqov1323948801.ps tmp/8hqov1323948801.png",intern=TRUE))
character(0)
> try(system("convert tmp/9skpp1323948801.ps tmp/9skpp1323948801.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zlda1323948801.ps tmp/10zlda1323948801.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.733 0.685 4.614