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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> x <- array(list(11
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+ ,dim=c(7
+ ,164)
+ ,dimnames=list(c('Month'
+ ,'BloggedComputations'
+ ,'TotalTime'
+ ,'Shared'
+ ,'Characters'
+ ,'Writing'
+ ,'Hyperlinks')
+ ,1:164))
> y <- array(NA,dim=c(7,164),dimnames=list(c('Month','BloggedComputations','TotalTime','Shared','Characters','Writing','Hyperlinks'),1:164))
> 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 = '4'
> #'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
Shared Month BloggedComputations TotalTime Characters Writing Hyperlinks
1 1 11 65 146455 95556 114468 127
2 4 11 54 84944 54565 88594 90
3 9 10 58 113337 63016 74151 68
4 2 9 75 128655 79774 77921 111
5 1 9 41 74398 31258 53212 51
6 2 10 0 35523 52491 34956 33
7 0 10 111 293403 91256 149703 123
8 0 10 1 32750 22807 6853 5
9 5 9 36 106539 77411 58907 63
10 0 9 60 130539 48821 67067 66
11 0 11 63 154991 52295 110563 99
12 7 11 71 126683 63262 58126 72
13 6 9 38 100672 50466 57113 55
14 3 9 76 179562 62932 77993 116
15 4 9 61 125971 38439 68091 71
16 0 9 125 234509 70817 124676 125
17 4 9 84 158980 105965 109522 123
18 3 9 69 184217 73795 75865 74
19 0 11 77 107342 82043 79746 116
20 5 9 95 141371 74349 77844 117
21 0 9 78 154730 82204 98681 98
22 1 9 76 264020 55709 105531 101
23 3 9 40 90938 37137 51428 43
24 5 9 81 101324 70780 65703 103
25 0 10 102 130232 55027 72562 107
26 0 9 70 137793 56699 81728 77
27 4 9 75 161678 65911 95580 87
28 0 10 93 151503 56316 98278 99
29 0 10 42 105324 26982 46629 46
30 0 10 95 175914 54628 115189 96
31 3 10 87 181853 96750 124865 92
32 4 11 44 114928 53009 59392 96
33 1 11 84 190410 64664 127818 96
34 4 11 28 61499 36990 17821 15
35 1 9 87 223004 85224 154076 147
36 0 9 71 167131 37048 64881 56
37 0 9 68 233482 59635 136506 81
38 2 10 50 121185 42051 66524 69
39 1 9 30 78776 26998 45988 34
40 2 10 86 188967 63717 107445 98
41 8 10 75 199512 55071 102772 82
42 5 9 46 102531 40001 46657 64
43 3 10 52 118958 54506 97563 61
44 4 10 31 68948 35838 36663 45
45 1 10 30 93125 50838 55369 37
46 2 11 70 277108 86997 77921 64
47 2 11 20 78800 33032 56968 21
48 0 10 84 157250 61704 77519 104
49 6 11 81 210554 117986 129805 126
50 3 11 79 127324 56733 72761 104
51 0 10 70 114397 55064 81278 87
52 0 9 8 24188 5950 15049 7
53 6 9 67 246209 84607 113935 130
54 5 10 21 65029 32551 25109 21
55 3 10 30 98030 31701 45824 35
56 1 9 70 173587 71170 89644 97
57 5 9 87 172684 101773 109011 103
58 5 9 87 191381 101653 134245 210
59 0 9 112 191276 81493 136692 151
60 9 11 54 134043 55901 50741 57
61 6 11 96 233406 109104 149510 117
62 6 11 93 195304 114425 147888 152
63 5 11 49 127619 36311 54987 52
64 6 9 49 162810 70027 74467 83
65 2 9 38 129100 73713 100033 87
66 0 9 64 108715 40671 85505 80
67 3 9 62 106469 89041 62426 88
68 8 9 66 142069 57231 82932 83
69 2 10 98 143937 78792 79169 140
70 5 10 97 84256 59155 65469 76
71 11 10 56 118807 55827 63572 70
72 6 10 22 69471 22618 23824 26
73 5 9 51 122433 58425 73831 66
74 1 10 56 131122 65724 63551 89
75 0 10 94 94763 56979 56756 100
76 3 10 98 188780 72369 81399 98
77 3 10 76 191467 79194 117881 109
78 6 10 57 105615 202316 70711 51
79 1 10 75 89318 44970 50495 82
80 0 11 48 107335 49319 53845 65
81 1 11 48 98599 36252 51390 46
82 0 11 109 260646 75741 104953 104
83 5 11 27 131876 38417 65983 36
84 2 11 83 119291 64102 76839 123
85 0 11 49 80953 56622 55792 59
86 0 11 24 99768 15430 25155 27
87 5 10 43 84572 72571 55291 84
88 1 10 44 202373 67271 84279 61
89 0 10 49 166790 43460 99692 46
90 1 10 106 99946 99501 59633 125
91 1 10 42 116900 28340 63249 58
92 2 9 108 142146 76013 82928 152
93 4 9 27 99246 37361 50000 52
94 1 11 79 156833 48204 69455 85
95 4 11 49 175078 76168 84068 95
96 0 10 64 130533 85168 76195 78
97 2 9 75 142339 125410 114634 144
98 0 9 115 176789 123328 139357 149
99 7 9 92 181379 83038 110044 101
100 7 9 106 228548 120087 155118 205
101 6 11 73 142141 91939 83061 61
102 0 10 105 167845 103646 127122 145
103 0 10 30 103012 29467 45653 28
104 4 10 13 43287 43750 19630 49
105 4 11 69 125366 34497 67229 68
106 0 10 72 118372 66477 86060 142
107 0 10 80 135171 71181 88003 82
108 0 10 106 175568 74482 95815 105
109 0 10 28 74112 174949 85499 52
110 0 11 70 88817 46765 27220 56
111 4 9 51 164767 90257 109882 81
112 0 9 90 141933 51370 72579 100
113 0 9 12 22938 1168 5841 11
114 0 9 84 115199 51360 68369 87
115 4 10 23 61857 25162 24610 31
116 0 10 57 91185 21067 30995 67
117 1 10 84 213765 58233 150662 150
118 0 11 4 21054 855 6622 4
119 5 10 56 167105 85903 93694 75
120 0 11 18 31414 14116 13155 39
121 1 11 86 178863 57637 111908 88
122 7 11 39 126681 94137 57550 67
123 5 10 16 64320 62147 16356 24
124 2 9 18 67746 62832 40174 58
125 0 9 16 38214 8773 13983 16
126 1 9 42 90961 63785 52316 49
127 0 9 75 181510 65196 99585 109
128 0 10 30 116775 73087 86271 124
129 2 10 104 223914 72631 131012 115
130 0 10 121 185139 86281 130274 128
131 2 10 106 242879 162365 159051 159
132 0 9 57 139144 56530 76506 75
133 0 10 28 75812 35606 49145 30
134 4 10 56 178218 70111 66398 83
135 4 10 81 246834 92046 127546 135
136 8 9 2 50999 63989 6802 8
137 0 11 88 223842 104911 99509 115
138 4 11 41 93577 43448 43106 60
139 0 11 83 155383 60029 108303 99
140 1 11 55 111664 38650 64167 98
141 0 11 3 75426 47261 8579 36
142 9 11 54 243551 73586 97811 93
143 0 10 89 136548 83042 84365 158
144 3 9 41 173260 37238 10901 16
145 7 9 94 185039 63958 91346 100
146 5 9 101 67507 78956 33660 49
147 2 10 70 139350 99518 93634 89
148 1 10 111 172964 111436 109348 153
149 9 11 0 0 0 0 0
150 0 11 4 14688 6023 7953 5
151 0 11 0 98 0 0 0
152 0 10 0 455 0 0 0
153 1 9 0 0 0 0 0
154 0 11 0 0 0 0 0
155 2 10 42 128066 42564 63538 80
156 1 9 97 176460 38885 108281 122
157 0 9 0 0 0 0 0
158 0 9 0 203 0 0 0
159 0 10 7 7199 1644 4245 6
160 0 9 12 46660 6179 21509 13
161 0 11 0 17547 3926 7670 3
162 0 11 37 73567 23238 10641 18
163 0 10 0 969 0 0 0
164 2 9 39 101060 49288 41243 49
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
118 118
119 119
120 120
121 121
122 122
123 123
124 124
125 125
126 126
127 127
128 128
129 129
130 130
131 131
132 132
133 133
134 134
135 135
136 136
137 137
138 138
139 139
140 140
141 141
142 142
143 143
144 144
145 145
146 146
147 147
148 148
149 149
150 150
151 151
152 152
153 153
154 154
155 155
156 156
157 157
158 158
159 159
160 160
161 161
162 162
163 163
164 164
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month BloggedComputations
2.048e+00 2.422e-02 -2.404e-02
TotalTime Characters Writing
1.597e-05 3.276e-05 -2.801e-05
Hyperlinks t
3.109e-03 -8.891e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.3294 -1.7007 -0.7669 1.4710 8.5245
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.048e+00 2.530e+00 0.809 0.419500
Month 2.422e-02 2.483e-01 0.098 0.922428
BloggedComputations -2.404e-02 1.264e-02 -1.901 0.059114 .
TotalTime 1.597e-05 6.546e-06 2.440 0.015826 *
Characters 3.276e-05 8.618e-06 3.802 0.000206 ***
Writing -2.801e-05 1.373e-05 -2.040 0.043015 *
Hyperlinks 3.109e-03 1.068e-02 0.291 0.771397
t -8.891e-03 4.322e-03 -2.057 0.041334 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.468 on 156 degrees of freedom
Multiple R-squared: 0.1424, Adjusted R-squared: 0.104
F-statistic: 3.702 on 7 and 156 DF, p-value: 0.0009894
> 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,] 0.2799629 5.599258e-01 7.200371e-01
[2,] 0.1997905 3.995810e-01 8.002095e-01
[3,] 0.3098249 6.196498e-01 6.901751e-01
[4,] 0.8301279 3.397441e-01 1.698721e-01
[5,] 0.7521655 4.956689e-01 2.478345e-01
[6,] 0.8888371 2.223259e-01 1.111629e-01
[7,] 0.8614995 2.770010e-01 1.385005e-01
[8,] 0.8063032 3.873935e-01 1.936968e-01
[9,] 0.8818603 2.362793e-01 1.181397e-01
[10,] 0.8532489 2.935023e-01 1.467511e-01
[11,] 0.8501684 2.996633e-01 1.498316e-01
[12,] 0.8337555 3.324891e-01 1.662445e-01
[13,] 0.7879672 4.240657e-01 2.120328e-01
[14,] 0.7501030 4.997941e-01 2.498970e-01
[15,] 0.7569779 4.860442e-01 2.430221e-01
[16,] 0.7250642 5.498716e-01 2.749358e-01
[17,] 0.7205544 5.588911e-01 2.794456e-01
[18,] 0.6718811 6.562378e-01 3.281189e-01
[19,] 0.6257321 7.485357e-01 3.742679e-01
[20,] 0.5673724 8.652552e-01 4.326276e-01
[21,] 0.5085832 9.828336e-01 4.914168e-01
[22,] 0.5291100 9.417801e-01 4.708900e-01
[23,] 0.4768533 9.537066e-01 5.231467e-01
[24,] 0.4159958 8.319915e-01 5.840042e-01
[25,] 0.3734417 7.468834e-01 6.265583e-01
[26,] 0.3496923 6.993845e-01 6.503077e-01
[27,] 0.3192861 6.385722e-01 6.807139e-01
[28,] 0.2722541 5.445081e-01 7.277459e-01
[29,] 0.2340336 4.680672e-01 7.659664e-01
[30,] 0.1993811 3.987621e-01 8.006189e-01
[31,] 0.4988266 9.976533e-01 5.011734e-01
[32,] 0.4830294 9.660588e-01 5.169706e-01
[33,] 0.4342259 8.684518e-01 5.657741e-01
[34,] 0.3844719 7.689438e-01 6.155281e-01
[35,] 0.3926803 7.853605e-01 6.073197e-01
[36,] 0.4166489 8.332977e-01 5.833511e-01
[37,] 0.3678794 7.357588e-01 6.321206e-01
[38,] 0.3810712 7.621425e-01 6.189288e-01
[39,] 0.3682103 7.364206e-01 6.317897e-01
[40,] 0.3215838 6.431676e-01 6.784162e-01
[41,] 0.3191186 6.382372e-01 6.808814e-01
[42,] 0.3119622 6.239243e-01 6.880378e-01
[43,] 0.3159391 6.318782e-01 6.840609e-01
[44,] 0.2941489 5.882977e-01 7.058511e-01
[45,] 0.2540821 5.081641e-01 7.459179e-01
[46,] 0.2577657 5.155314e-01 7.422343e-01
[47,] 0.2250025 4.500050e-01 7.749975e-01
[48,] 0.1986419 3.972837e-01 8.013581e-01
[49,] 0.1876710 3.753420e-01 8.123290e-01
[50,] 0.3239184 6.478368e-01 6.760816e-01
[51,] 0.3165604 6.331208e-01 6.834396e-01
[52,] 0.3117383 6.234765e-01 6.882617e-01
[53,] 0.2964767 5.929535e-01 7.035233e-01
[54,] 0.2694917 5.389834e-01 7.305083e-01
[55,] 0.2507613 5.015226e-01 7.492387e-01
[56,] 0.2310628 4.621256e-01 7.689372e-01
[57,] 0.2226661 4.453321e-01 7.773339e-01
[58,] 0.3613255 7.226510e-01 6.386745e-01
[59,] 0.3421307 6.842614e-01 6.578693e-01
[60,] 0.3629099 7.258198e-01 6.370901e-01
[61,] 0.7589732 4.820537e-01 2.410268e-01
[62,] 0.7788964 4.422073e-01 2.211036e-01
[63,] 0.7704507 4.590986e-01 2.295493e-01
[64,] 0.7976116 4.047767e-01 2.023884e-01
[65,] 0.8057801 3.884397e-01 1.942199e-01
[66,] 0.7764173 4.471654e-01 2.235827e-01
[67,] 0.7449230 5.101539e-01 2.550770e-01
[68,] 0.7660250 4.679500e-01 2.339750e-01
[69,] 0.7457929 5.084141e-01 2.542071e-01
[70,] 0.7701787 4.596426e-01 2.298213e-01
[71,] 0.7492012 5.015975e-01 2.507988e-01
[72,] 0.7767910 4.464181e-01 2.232090e-01
[73,] 0.7703869 4.592262e-01 2.296131e-01
[74,] 0.7424235 5.151531e-01 2.575765e-01
[75,] 0.7449184 5.101632e-01 2.550816e-01
[76,] 0.7521242 4.957515e-01 2.478758e-01
[77,] 0.7485433 5.029134e-01 2.514567e-01
[78,] 0.7735893 4.528214e-01 2.264107e-01
[79,] 0.7700980 4.598039e-01 2.299020e-01
[80,] 0.7519986 4.960028e-01 2.480014e-01
[81,] 0.7212409 5.575183e-01 2.787591e-01
[82,] 0.6847773 6.304455e-01 3.152227e-01
[83,] 0.6531265 6.937471e-01 3.468735e-01
[84,] 0.6194729 7.610541e-01 3.805271e-01
[85,] 0.5738093 8.523814e-01 4.261907e-01
[86,] 0.5989783 8.020433e-01 4.010217e-01
[87,] 0.5693060 8.613879e-01 4.306940e-01
[88,] 0.5494070 9.011861e-01 4.505930e-01
[89,] 0.6686725 6.626550e-01 3.313275e-01
[90,] 0.7944507 4.110987e-01 2.055493e-01
[91,] 0.8140327 3.719346e-01 1.859673e-01
[92,] 0.7943217 4.113566e-01 2.056783e-01
[93,] 0.7946602 4.106796e-01 2.053398e-01
[94,] 0.7879832 4.240336e-01 2.120168e-01
[95,] 0.8000540 3.998921e-01 1.999460e-01
[96,] 0.7801688 4.396623e-01 2.198312e-01
[97,] 0.7561962 4.876076e-01 2.438038e-01
[98,] 0.7323993 5.352013e-01 2.676007e-01
[99,] 0.8469543 3.060913e-01 1.530457e-01
[100,] 0.8336875 3.326251e-01 1.663125e-01
[101,] 0.8032616 3.934769e-01 1.967384e-01
[102,] 0.7721658 4.556684e-01 2.278342e-01
[103,] 0.7396354 5.207292e-01 2.603646e-01
[104,] 0.7024053 5.951893e-01 2.975947e-01
[105,] 0.6908271 6.183458e-01 3.091729e-01
[106,] 0.6509696 6.980609e-01 3.490304e-01
[107,] 0.6120779 7.758441e-01 3.879221e-01
[108,] 0.5753393 8.493214e-01 4.246607e-01
[109,] 0.5458504 9.082992e-01 4.541496e-01
[110,] 0.5048448 9.903105e-01 4.951552e-01
[111,] 0.4549849 9.099698e-01 5.450151e-01
[112,] 0.4747625 9.495249e-01 5.252375e-01
[113,] 0.4437107 8.874214e-01 5.562893e-01
[114,] 0.3939018 7.878035e-01 6.060982e-01
[115,] 0.3522203 7.044406e-01 6.477797e-01
[116,] 0.3152524 6.305049e-01 6.847476e-01
[117,] 0.2997143 5.994287e-01 7.002857e-01
[118,] 0.2717117 5.434234e-01 7.282883e-01
[119,] 0.2278721 4.557442e-01 7.721279e-01
[120,] 0.2049284 4.098567e-01 7.950716e-01
[121,] 0.1820994 3.641988e-01 8.179006e-01
[122,] 0.2082481 4.164961e-01 7.917519e-01
[123,] 0.2775166 5.550333e-01 7.224834e-01
[124,] 0.2306863 4.613727e-01 7.693137e-01
[125,] 0.1893494 3.786987e-01 8.106506e-01
[126,] 0.2271182 4.542365e-01 7.728818e-01
[127,] 0.3280151 6.560303e-01 6.719849e-01
[128,] 0.2823998 5.647995e-01 7.176002e-01
[129,] 0.4962913 9.925825e-01 5.037087e-01
[130,] 0.4585541 9.171082e-01 5.414459e-01
[131,] 0.4425420 8.850841e-01 5.574580e-01
[132,] 0.5123926 9.752148e-01 4.876074e-01
[133,] 0.4426686 8.853371e-01 5.573314e-01
[134,] 0.5169824 9.660352e-01 4.830176e-01
[135,] 0.4803398 9.606797e-01 5.196602e-01
[136,] 0.4435605 8.871210e-01 5.564395e-01
[137,] 0.3511624 7.023247e-01 6.488376e-01
[138,] 0.3069374 6.138747e-01 6.930626e-01
[139,] 0.9999978 4.417631e-06 2.208815e-06
[140,] 0.9999850 3.005902e-05 1.502951e-05
[141,] 0.9998788 2.424011e-04 1.212006e-04
[142,] 0.9994045 1.191052e-03 5.955259e-04
[143,] 0.9999759 4.819921e-05 2.409960e-05
> postscript(file="/var/wessaorg/rcomp/tmp/1k93k1321895968.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/24hzl1321895968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3p7061321895968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4choq1321895968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5l2cj1321895968.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 = 164
Frequency = 1
1 2 3 4 5 6
-2.40031287 2.05961366 6.12235745 -1.25760795 -1.11570625 -1.64684645
7 8 9 10 11 12
-3.42335121 -3.28846632 0.89655714 -2.74499116 -2.10081744 3.80809214
13 14 15 16 17 18
2.93127994 -0.41936277 1.74973253 -2.07977753 0.57992549 -0.91134734
19 20 21 22 23 24
-2.82300164 2.31924983 -2.90838290 -2.64226886 0.53844486 2.47815922
25 26 27 28 29 30
-1.79822258 -2.35978663 1.44301551 -1.62452442 -2.42515521 -1.41012757
31 32 33 34 35 36
0.21510952 0.82141365 -0.87858857 0.91988061 -1.35730178 -2.47821241
37 38 39 40 41 42
-2.41223981 -0.41398366 -1.15768613 -0.26693176 5.51124379 2.37370072
43 44 45 46 47 48
1.20045044 1.45849293 -1.88528472 -3.51406717 -0.22543266 -2.52841709
49 50 51 52 53 54
2.08532816 0.85243545 -1.77822768 -1.79238249 1.89997836 2.22824083
55 56 57 58 59 60
0.49109249 -1.97917744 1.97403427 2.06256794 -1.41349958 5.78961524
61 62 63 64 65 66
3.05862846 3.27531499 2.57499539 2.41507826 -0.71909471 -1.06239971
67 68 69 70 71 72
-0.32182899 5.84686011 -0.41810656 3.97833183 8.52448570 3.61527064
73 74 75 76 77 78
2.60311099 -2.02940376 -1.46451422 0.33147150 0.53284906 -0.71882841
79 80 81 82 83 84
-0.52469748 -2.47253893 -0.90574564 -2.99194511 2.44480532 0.19292268
85 86 87 88 89 90
-2.14880619 -2.45055367 2.07690731 -2.71418602 -1.75835631 -1.51570646
91 92 93 94 95 96
-0.67496264 0.23863185 1.64035696 -0.98171772 0.47681919 -2.88072200
97 98 99 100 101 102
-1.21849287 -2.05301533 4.97775292 4.29550134 3.19382789 -1.82469839
103 104 105 106 107 108
-2.07152797 1.22028485 2.81778776 -1.71553592 -1.69579262 -1.66786044
109 110 111 112 113 114
-5.32941632 -2.01548532 1.18533931 -1.33372007 -1.24762457 -1.11043042
115 116 117 118 119 120
2.06635214 -1.37473009 0.20231291 -1.35992451 2.02290502 -1.53131226
121 122 123 124 125 126
-0.05429896 3.00484891 1.50870974 -0.92571148 -1.32534229 -1.36487457
127 128 129 130 131 132
-1.91733450 -2.65867219 0.71428452 -0.75726743 -1.81390242 -1.88590558
133 134 135 136 137 138
-1.52801328 0.70664846 1.05341367 4.24647120 -3.56233796 2.00165093
139 140 141 142 143 144
-0.80495438 -0.30381788 -3.61277380 5.39721599 -1.90812092 -0.73102320
145 146 147 148 149 150
5.48078290 3.58601527 -0.43964691 -1.13123427 8.01071587 -1.10888013
151 152 153 154 155 156
-0.97306667 -0.94565647 0.09472099 -0.94482793 0.18945900 1.01517798
157 158 159 160 161 162
-0.86971404 -0.86406462 -0.77645050 -0.94003127 -1.08588965 -1.67834720
163 164
-0.85606115 -0.09563862
> postscript(file="/var/wessaorg/rcomp/tmp/6uz7f1321895968.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.40031287 NA
1 2.05961366 -2.40031287
2 6.12235745 2.05961366
3 -1.25760795 6.12235745
4 -1.11570625 -1.25760795
5 -1.64684645 -1.11570625
6 -3.42335121 -1.64684645
7 -3.28846632 -3.42335121
8 0.89655714 -3.28846632
9 -2.74499116 0.89655714
10 -2.10081744 -2.74499116
11 3.80809214 -2.10081744
12 2.93127994 3.80809214
13 -0.41936277 2.93127994
14 1.74973253 -0.41936277
15 -2.07977753 1.74973253
16 0.57992549 -2.07977753
17 -0.91134734 0.57992549
18 -2.82300164 -0.91134734
19 2.31924983 -2.82300164
20 -2.90838290 2.31924983
21 -2.64226886 -2.90838290
22 0.53844486 -2.64226886
23 2.47815922 0.53844486
24 -1.79822258 2.47815922
25 -2.35978663 -1.79822258
26 1.44301551 -2.35978663
27 -1.62452442 1.44301551
28 -2.42515521 -1.62452442
29 -1.41012757 -2.42515521
30 0.21510952 -1.41012757
31 0.82141365 0.21510952
32 -0.87858857 0.82141365
33 0.91988061 -0.87858857
34 -1.35730178 0.91988061
35 -2.47821241 -1.35730178
36 -2.41223981 -2.47821241
37 -0.41398366 -2.41223981
38 -1.15768613 -0.41398366
39 -0.26693176 -1.15768613
40 5.51124379 -0.26693176
41 2.37370072 5.51124379
42 1.20045044 2.37370072
43 1.45849293 1.20045044
44 -1.88528472 1.45849293
45 -3.51406717 -1.88528472
46 -0.22543266 -3.51406717
47 -2.52841709 -0.22543266
48 2.08532816 -2.52841709
49 0.85243545 2.08532816
50 -1.77822768 0.85243545
51 -1.79238249 -1.77822768
52 1.89997836 -1.79238249
53 2.22824083 1.89997836
54 0.49109249 2.22824083
55 -1.97917744 0.49109249
56 1.97403427 -1.97917744
57 2.06256794 1.97403427
58 -1.41349958 2.06256794
59 5.78961524 -1.41349958
60 3.05862846 5.78961524
61 3.27531499 3.05862846
62 2.57499539 3.27531499
63 2.41507826 2.57499539
64 -0.71909471 2.41507826
65 -1.06239971 -0.71909471
66 -0.32182899 -1.06239971
67 5.84686011 -0.32182899
68 -0.41810656 5.84686011
69 3.97833183 -0.41810656
70 8.52448570 3.97833183
71 3.61527064 8.52448570
72 2.60311099 3.61527064
73 -2.02940376 2.60311099
74 -1.46451422 -2.02940376
75 0.33147150 -1.46451422
76 0.53284906 0.33147150
77 -0.71882841 0.53284906
78 -0.52469748 -0.71882841
79 -2.47253893 -0.52469748
80 -0.90574564 -2.47253893
81 -2.99194511 -0.90574564
82 2.44480532 -2.99194511
83 0.19292268 2.44480532
84 -2.14880619 0.19292268
85 -2.45055367 -2.14880619
86 2.07690731 -2.45055367
87 -2.71418602 2.07690731
88 -1.75835631 -2.71418602
89 -1.51570646 -1.75835631
90 -0.67496264 -1.51570646
91 0.23863185 -0.67496264
92 1.64035696 0.23863185
93 -0.98171772 1.64035696
94 0.47681919 -0.98171772
95 -2.88072200 0.47681919
96 -1.21849287 -2.88072200
97 -2.05301533 -1.21849287
98 4.97775292 -2.05301533
99 4.29550134 4.97775292
100 3.19382789 4.29550134
101 -1.82469839 3.19382789
102 -2.07152797 -1.82469839
103 1.22028485 -2.07152797
104 2.81778776 1.22028485
105 -1.71553592 2.81778776
106 -1.69579262 -1.71553592
107 -1.66786044 -1.69579262
108 -5.32941632 -1.66786044
109 -2.01548532 -5.32941632
110 1.18533931 -2.01548532
111 -1.33372007 1.18533931
112 -1.24762457 -1.33372007
113 -1.11043042 -1.24762457
114 2.06635214 -1.11043042
115 -1.37473009 2.06635214
116 0.20231291 -1.37473009
117 -1.35992451 0.20231291
118 2.02290502 -1.35992451
119 -1.53131226 2.02290502
120 -0.05429896 -1.53131226
121 3.00484891 -0.05429896
122 1.50870974 3.00484891
123 -0.92571148 1.50870974
124 -1.32534229 -0.92571148
125 -1.36487457 -1.32534229
126 -1.91733450 -1.36487457
127 -2.65867219 -1.91733450
128 0.71428452 -2.65867219
129 -0.75726743 0.71428452
130 -1.81390242 -0.75726743
131 -1.88590558 -1.81390242
132 -1.52801328 -1.88590558
133 0.70664846 -1.52801328
134 1.05341367 0.70664846
135 4.24647120 1.05341367
136 -3.56233796 4.24647120
137 2.00165093 -3.56233796
138 -0.80495438 2.00165093
139 -0.30381788 -0.80495438
140 -3.61277380 -0.30381788
141 5.39721599 -3.61277380
142 -1.90812092 5.39721599
143 -0.73102320 -1.90812092
144 5.48078290 -0.73102320
145 3.58601527 5.48078290
146 -0.43964691 3.58601527
147 -1.13123427 -0.43964691
148 8.01071587 -1.13123427
149 -1.10888013 8.01071587
150 -0.97306667 -1.10888013
151 -0.94565647 -0.97306667
152 0.09472099 -0.94565647
153 -0.94482793 0.09472099
154 0.18945900 -0.94482793
155 1.01517798 0.18945900
156 -0.86971404 1.01517798
157 -0.86406462 -0.86971404
158 -0.77645050 -0.86406462
159 -0.94003127 -0.77645050
160 -1.08588965 -0.94003127
161 -1.67834720 -1.08588965
162 -0.85606115 -1.67834720
163 -0.09563862 -0.85606115
164 NA -0.09563862
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.05961366 -2.40031287
[2,] 6.12235745 2.05961366
[3,] -1.25760795 6.12235745
[4,] -1.11570625 -1.25760795
[5,] -1.64684645 -1.11570625
[6,] -3.42335121 -1.64684645
[7,] -3.28846632 -3.42335121
[8,] 0.89655714 -3.28846632
[9,] -2.74499116 0.89655714
[10,] -2.10081744 -2.74499116
[11,] 3.80809214 -2.10081744
[12,] 2.93127994 3.80809214
[13,] -0.41936277 2.93127994
[14,] 1.74973253 -0.41936277
[15,] -2.07977753 1.74973253
[16,] 0.57992549 -2.07977753
[17,] -0.91134734 0.57992549
[18,] -2.82300164 -0.91134734
[19,] 2.31924983 -2.82300164
[20,] -2.90838290 2.31924983
[21,] -2.64226886 -2.90838290
[22,] 0.53844486 -2.64226886
[23,] 2.47815922 0.53844486
[24,] -1.79822258 2.47815922
[25,] -2.35978663 -1.79822258
[26,] 1.44301551 -2.35978663
[27,] -1.62452442 1.44301551
[28,] -2.42515521 -1.62452442
[29,] -1.41012757 -2.42515521
[30,] 0.21510952 -1.41012757
[31,] 0.82141365 0.21510952
[32,] -0.87858857 0.82141365
[33,] 0.91988061 -0.87858857
[34,] -1.35730178 0.91988061
[35,] -2.47821241 -1.35730178
[36,] -2.41223981 -2.47821241
[37,] -0.41398366 -2.41223981
[38,] -1.15768613 -0.41398366
[39,] -0.26693176 -1.15768613
[40,] 5.51124379 -0.26693176
[41,] 2.37370072 5.51124379
[42,] 1.20045044 2.37370072
[43,] 1.45849293 1.20045044
[44,] -1.88528472 1.45849293
[45,] -3.51406717 -1.88528472
[46,] -0.22543266 -3.51406717
[47,] -2.52841709 -0.22543266
[48,] 2.08532816 -2.52841709
[49,] 0.85243545 2.08532816
[50,] -1.77822768 0.85243545
[51,] -1.79238249 -1.77822768
[52,] 1.89997836 -1.79238249
[53,] 2.22824083 1.89997836
[54,] 0.49109249 2.22824083
[55,] -1.97917744 0.49109249
[56,] 1.97403427 -1.97917744
[57,] 2.06256794 1.97403427
[58,] -1.41349958 2.06256794
[59,] 5.78961524 -1.41349958
[60,] 3.05862846 5.78961524
[61,] 3.27531499 3.05862846
[62,] 2.57499539 3.27531499
[63,] 2.41507826 2.57499539
[64,] -0.71909471 2.41507826
[65,] -1.06239971 -0.71909471
[66,] -0.32182899 -1.06239971
[67,] 5.84686011 -0.32182899
[68,] -0.41810656 5.84686011
[69,] 3.97833183 -0.41810656
[70,] 8.52448570 3.97833183
[71,] 3.61527064 8.52448570
[72,] 2.60311099 3.61527064
[73,] -2.02940376 2.60311099
[74,] -1.46451422 -2.02940376
[75,] 0.33147150 -1.46451422
[76,] 0.53284906 0.33147150
[77,] -0.71882841 0.53284906
[78,] -0.52469748 -0.71882841
[79,] -2.47253893 -0.52469748
[80,] -0.90574564 -2.47253893
[81,] -2.99194511 -0.90574564
[82,] 2.44480532 -2.99194511
[83,] 0.19292268 2.44480532
[84,] -2.14880619 0.19292268
[85,] -2.45055367 -2.14880619
[86,] 2.07690731 -2.45055367
[87,] -2.71418602 2.07690731
[88,] -1.75835631 -2.71418602
[89,] -1.51570646 -1.75835631
[90,] -0.67496264 -1.51570646
[91,] 0.23863185 -0.67496264
[92,] 1.64035696 0.23863185
[93,] -0.98171772 1.64035696
[94,] 0.47681919 -0.98171772
[95,] -2.88072200 0.47681919
[96,] -1.21849287 -2.88072200
[97,] -2.05301533 -1.21849287
[98,] 4.97775292 -2.05301533
[99,] 4.29550134 4.97775292
[100,] 3.19382789 4.29550134
[101,] -1.82469839 3.19382789
[102,] -2.07152797 -1.82469839
[103,] 1.22028485 -2.07152797
[104,] 2.81778776 1.22028485
[105,] -1.71553592 2.81778776
[106,] -1.69579262 -1.71553592
[107,] -1.66786044 -1.69579262
[108,] -5.32941632 -1.66786044
[109,] -2.01548532 -5.32941632
[110,] 1.18533931 -2.01548532
[111,] -1.33372007 1.18533931
[112,] -1.24762457 -1.33372007
[113,] -1.11043042 -1.24762457
[114,] 2.06635214 -1.11043042
[115,] -1.37473009 2.06635214
[116,] 0.20231291 -1.37473009
[117,] -1.35992451 0.20231291
[118,] 2.02290502 -1.35992451
[119,] -1.53131226 2.02290502
[120,] -0.05429896 -1.53131226
[121,] 3.00484891 -0.05429896
[122,] 1.50870974 3.00484891
[123,] -0.92571148 1.50870974
[124,] -1.32534229 -0.92571148
[125,] -1.36487457 -1.32534229
[126,] -1.91733450 -1.36487457
[127,] -2.65867219 -1.91733450
[128,] 0.71428452 -2.65867219
[129,] -0.75726743 0.71428452
[130,] -1.81390242 -0.75726743
[131,] -1.88590558 -1.81390242
[132,] -1.52801328 -1.88590558
[133,] 0.70664846 -1.52801328
[134,] 1.05341367 0.70664846
[135,] 4.24647120 1.05341367
[136,] -3.56233796 4.24647120
[137,] 2.00165093 -3.56233796
[138,] -0.80495438 2.00165093
[139,] -0.30381788 -0.80495438
[140,] -3.61277380 -0.30381788
[141,] 5.39721599 -3.61277380
[142,] -1.90812092 5.39721599
[143,] -0.73102320 -1.90812092
[144,] 5.48078290 -0.73102320
[145,] 3.58601527 5.48078290
[146,] -0.43964691 3.58601527
[147,] -1.13123427 -0.43964691
[148,] 8.01071587 -1.13123427
[149,] -1.10888013 8.01071587
[150,] -0.97306667 -1.10888013
[151,] -0.94565647 -0.97306667
[152,] 0.09472099 -0.94565647
[153,] -0.94482793 0.09472099
[154,] 0.18945900 -0.94482793
[155,] 1.01517798 0.18945900
[156,] -0.86971404 1.01517798
[157,] -0.86406462 -0.86971404
[158,] -0.77645050 -0.86406462
[159,] -0.94003127 -0.77645050
[160,] -1.08588965 -0.94003127
[161,] -1.67834720 -1.08588965
[162,] -0.85606115 -1.67834720
[163,] -0.09563862 -0.85606115
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.05961366 -2.40031287
2 6.12235745 2.05961366
3 -1.25760795 6.12235745
4 -1.11570625 -1.25760795
5 -1.64684645 -1.11570625
6 -3.42335121 -1.64684645
7 -3.28846632 -3.42335121
8 0.89655714 -3.28846632
9 -2.74499116 0.89655714
10 -2.10081744 -2.74499116
11 3.80809214 -2.10081744
12 2.93127994 3.80809214
13 -0.41936277 2.93127994
14 1.74973253 -0.41936277
15 -2.07977753 1.74973253
16 0.57992549 -2.07977753
17 -0.91134734 0.57992549
18 -2.82300164 -0.91134734
19 2.31924983 -2.82300164
20 -2.90838290 2.31924983
21 -2.64226886 -2.90838290
22 0.53844486 -2.64226886
23 2.47815922 0.53844486
24 -1.79822258 2.47815922
25 -2.35978663 -1.79822258
26 1.44301551 -2.35978663
27 -1.62452442 1.44301551
28 -2.42515521 -1.62452442
29 -1.41012757 -2.42515521
30 0.21510952 -1.41012757
31 0.82141365 0.21510952
32 -0.87858857 0.82141365
33 0.91988061 -0.87858857
34 -1.35730178 0.91988061
35 -2.47821241 -1.35730178
36 -2.41223981 -2.47821241
37 -0.41398366 -2.41223981
38 -1.15768613 -0.41398366
39 -0.26693176 -1.15768613
40 5.51124379 -0.26693176
41 2.37370072 5.51124379
42 1.20045044 2.37370072
43 1.45849293 1.20045044
44 -1.88528472 1.45849293
45 -3.51406717 -1.88528472
46 -0.22543266 -3.51406717
47 -2.52841709 -0.22543266
48 2.08532816 -2.52841709
49 0.85243545 2.08532816
50 -1.77822768 0.85243545
51 -1.79238249 -1.77822768
52 1.89997836 -1.79238249
53 2.22824083 1.89997836
54 0.49109249 2.22824083
55 -1.97917744 0.49109249
56 1.97403427 -1.97917744
57 2.06256794 1.97403427
58 -1.41349958 2.06256794
59 5.78961524 -1.41349958
60 3.05862846 5.78961524
61 3.27531499 3.05862846
62 2.57499539 3.27531499
63 2.41507826 2.57499539
64 -0.71909471 2.41507826
65 -1.06239971 -0.71909471
66 -0.32182899 -1.06239971
67 5.84686011 -0.32182899
68 -0.41810656 5.84686011
69 3.97833183 -0.41810656
70 8.52448570 3.97833183
71 3.61527064 8.52448570
72 2.60311099 3.61527064
73 -2.02940376 2.60311099
74 -1.46451422 -2.02940376
75 0.33147150 -1.46451422
76 0.53284906 0.33147150
77 -0.71882841 0.53284906
78 -0.52469748 -0.71882841
79 -2.47253893 -0.52469748
80 -0.90574564 -2.47253893
81 -2.99194511 -0.90574564
82 2.44480532 -2.99194511
83 0.19292268 2.44480532
84 -2.14880619 0.19292268
85 -2.45055367 -2.14880619
86 2.07690731 -2.45055367
87 -2.71418602 2.07690731
88 -1.75835631 -2.71418602
89 -1.51570646 -1.75835631
90 -0.67496264 -1.51570646
91 0.23863185 -0.67496264
92 1.64035696 0.23863185
93 -0.98171772 1.64035696
94 0.47681919 -0.98171772
95 -2.88072200 0.47681919
96 -1.21849287 -2.88072200
97 -2.05301533 -1.21849287
98 4.97775292 -2.05301533
99 4.29550134 4.97775292
100 3.19382789 4.29550134
101 -1.82469839 3.19382789
102 -2.07152797 -1.82469839
103 1.22028485 -2.07152797
104 2.81778776 1.22028485
105 -1.71553592 2.81778776
106 -1.69579262 -1.71553592
107 -1.66786044 -1.69579262
108 -5.32941632 -1.66786044
109 -2.01548532 -5.32941632
110 1.18533931 -2.01548532
111 -1.33372007 1.18533931
112 -1.24762457 -1.33372007
113 -1.11043042 -1.24762457
114 2.06635214 -1.11043042
115 -1.37473009 2.06635214
116 0.20231291 -1.37473009
117 -1.35992451 0.20231291
118 2.02290502 -1.35992451
119 -1.53131226 2.02290502
120 -0.05429896 -1.53131226
121 3.00484891 -0.05429896
122 1.50870974 3.00484891
123 -0.92571148 1.50870974
124 -1.32534229 -0.92571148
125 -1.36487457 -1.32534229
126 -1.91733450 -1.36487457
127 -2.65867219 -1.91733450
128 0.71428452 -2.65867219
129 -0.75726743 0.71428452
130 -1.81390242 -0.75726743
131 -1.88590558 -1.81390242
132 -1.52801328 -1.88590558
133 0.70664846 -1.52801328
134 1.05341367 0.70664846
135 4.24647120 1.05341367
136 -3.56233796 4.24647120
137 2.00165093 -3.56233796
138 -0.80495438 2.00165093
139 -0.30381788 -0.80495438
140 -3.61277380 -0.30381788
141 5.39721599 -3.61277380
142 -1.90812092 5.39721599
143 -0.73102320 -1.90812092
144 5.48078290 -0.73102320
145 3.58601527 5.48078290
146 -0.43964691 3.58601527
147 -1.13123427 -0.43964691
148 8.01071587 -1.13123427
149 -1.10888013 8.01071587
150 -0.97306667 -1.10888013
151 -0.94565647 -0.97306667
152 0.09472099 -0.94565647
153 -0.94482793 0.09472099
154 0.18945900 -0.94482793
155 1.01517798 0.18945900
156 -0.86971404 1.01517798
157 -0.86406462 -0.86971404
158 -0.77645050 -0.86406462
159 -0.94003127 -0.77645050
160 -1.08588965 -0.94003127
161 -1.67834720 -1.08588965
162 -0.85606115 -1.67834720
163 -0.09563862 -0.85606115
> 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/73sjq1321895968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cgsw1321895968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9qvhu1321895968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10chg11321895968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ph811321895968.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/12yxsj1321895968.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/13z42u1321895968.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/1439a71321895968.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/152uxe1321895968.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/16aqb01321895968.tab")
+ }
>
> try(system("convert tmp/1k93k1321895968.ps tmp/1k93k1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/24hzl1321895968.ps tmp/24hzl1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/3p7061321895968.ps tmp/3p7061321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/4choq1321895968.ps tmp/4choq1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/5l2cj1321895968.ps tmp/5l2cj1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uz7f1321895968.ps tmp/6uz7f1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/73sjq1321895968.ps tmp/73sjq1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cgsw1321895968.ps tmp/8cgsw1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qvhu1321895968.ps tmp/9qvhu1321895968.png",intern=TRUE))
character(0)
> try(system("convert tmp/10chg11321895968.ps tmp/10chg11321895968.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.208 0.557 5.866