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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+ ,1
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+ ,0
+ ,0
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+ ,6
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+ ,1644
+ ,1
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+ ,0
+ ,0
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+ ,13
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+ ,6179
+ ,1
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+ ,0
+ ,0
+ ,1
+ ,1
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+ ,4
+ ,3
+ ,3
+ ,3926
+ ,3926
+ ,1
+ ,73567
+ ,0
+ ,0
+ ,23
+ ,23
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+ ,31
+ ,18
+ ,18
+ ,23238
+ ,23238
+ ,1
+ ,969
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,101060
+ ,2
+ ,2
+ ,16
+ ,16
+ ,29
+ ,29
+ ,48
+ ,48
+ ,49288
+ ,49288)
+ ,dim=c(12
+ ,164)
+ ,dimnames=list(c('Gender'
+ ,'Time_in_RFC'
+ ,'Shared_compendiums'
+ ,'Shared_compendiums_g'
+ ,'Reviewed_compendiums'
+ ,'Reviewed_compendiums_g'
+ ,'Long_feedback'
+ ,'Long_feedback_g'
+ ,'Blogs'
+ ,'Blogs_g'
+ ,'Characters'
+ ,'Characters_g')
+ ,1:164))
> y <- array(NA,dim=c(12,164),dimnames=list(c('Gender','Time_in_RFC','Shared_compendiums','Shared_compendiums_g','Reviewed_compendiums','Reviewed_compendiums_g','Long_feedback','Long_feedback_g','Blogs','Blogs_g','Characters','Characters_g'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> 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
Time_in_RFC Gender Shared_compendiums Shared_compendiums_g
1 146455 0 1 0
2 84944 0 4 0
3 113337 0 9 0
4 128655 0 2 0
5 74398 0 1 0
6 35523 0 2 0
7 293403 0 0 0
8 32750 0 0 0
9 106539 0 5 0
10 130539 0 0 0
11 154991 0 0 0
12 126683 0 7 0
13 100672 0 6 0
14 179562 0 3 0
15 125971 0 4 0
16 234509 0 0 0
17 158980 0 4 0
18 184217 0 3 0
19 107342 0 0 0
20 141371 0 5 0
21 154730 0 0 0
22 264020 0 1 0
23 90938 0 3 0
24 101324 0 5 0
25 130232 0 0 0
26 137793 0 0 0
27 161678 0 4 0
28 151503 0 0 0
29 105324 0 0 0
30 175914 0 0 0
31 181853 0 3 0
32 114928 0 4 0
33 190410 0 1 0
34 61499 0 4 0
35 223004 0 1 0
36 167131 0 0 0
37 233482 0 0 0
38 121185 0 2 0
39 78776 0 1 0
40 188967 0 2 0
41 199512 0 8 0
42 102531 0 5 0
43 118958 0 3 0
44 68948 0 4 0
45 93125 0 1 0
46 277108 0 2 0
47 78800 0 2 0
48 157250 0 0 0
49 210554 0 6 0
50 127324 0 3 0
51 114397 0 0 0
52 24188 0 0 0
53 246209 0 6 0
54 65029 0 5 0
55 98030 0 3 0
56 173587 0 1 0
57 172684 0 5 0
58 191381 0 5 0
59 191276 0 0 0
60 134043 0 9 0
61 233406 0 6 0
62 195304 0 6 0
63 127619 0 5 0
64 162810 0 6 0
65 129100 0 2 0
66 108715 0 0 0
67 106469 0 3 0
68 142069 0 8 0
69 143937 0 2 0
70 84256 0 5 0
71 118807 0 11 0
72 69471 0 6 0
73 122433 1 5 5
74 131122 1 1 1
75 94763 1 0 0
76 188780 1 3 3
77 191467 1 3 3
78 105615 1 6 6
79 89318 1 1 1
80 107335 1 0 0
81 98599 1 1 1
82 260646 1 0 0
83 131876 1 5 5
84 119291 1 2 2
85 80953 1 0 0
86 99768 1 0 0
87 84572 1 5 5
88 202373 1 1 1
89 166790 1 0 0
90 99946 1 1 1
91 116900 1 1 1
92 142146 1 2 2
93 99246 1 4 4
94 156833 1 1 1
95 175078 1 4 4
96 130533 1 0 0
97 142339 1 2 2
98 176789 1 0 0
99 181379 1 7 7
100 228548 1 7 7
101 142141 1 6 6
102 167845 1 0 0
103 103012 1 0 0
104 43287 1 4 4
105 125366 1 4 4
106 118372 1 0 0
107 135171 1 0 0
108 175568 1 0 0
109 74112 1 0 0
110 88817 1 0 0
111 164767 1 4 4
112 141933 1 0 0
113 22938 1 0 0
114 115199 1 0 0
115 61857 1 4 4
116 91185 1 0 0
117 213765 1 1 1
118 21054 1 0 0
119 167105 1 5 5
120 31414 1 0 0
121 178863 1 1 1
122 126681 1 7 7
123 64320 1 5 5
124 67746 1 2 2
125 38214 1 0 0
126 90961 1 1 1
127 181510 1 0 0
128 116775 1 0 0
129 223914 1 2 2
130 185139 1 0 0
131 242879 1 2 2
132 139144 1 0 0
133 75812 1 0 0
134 178218 1 4 4
135 246834 1 4 4
136 50999 1 8 8
137 223842 1 0 0
138 93577 1 4 4
139 155383 1 0 0
140 111664 1 1 1
141 75426 1 0 0
142 243551 1 9 9
143 136548 1 0 0
144 173260 1 3 3
145 185039 1 7 7
146 67507 1 5 5
147 139350 1 2 2
148 172964 1 1 1
149 0 1 9 9
150 14688 1 0 0
151 98 1 0 0
152 455 1 0 0
153 0 1 1 1
154 0 1 0 0
155 128066 1 2 2
156 176460 1 1 1
157 0 1 0 0
158 203 1 0 0
159 7199 1 0 0
160 46660 1 0 0
161 17547 1 0 0
162 73567 1 0 0
163 969 1 0 0
164 101060 1 2 2
Reviewed_compendiums Reviewed_compendiums_g Long_feedback Long_feedback_g
1 22 0 68 0
2 20 0 72 0
3 24 0 37 0
4 21 0 70 0
5 15 0 30 0
6 16 0 53 0
7 20 0 74 0
8 18 0 22 0
9 19 0 68 0
10 20 0 47 0
11 25 0 87 0
12 37 0 123 0
13 23 0 69 0
14 28 0 89 0
15 25 0 45 0
16 35 0 122 0
17 20 0 75 0
18 22 0 45 0
19 19 0 53 0
20 26 0 96 0
21 27 0 82 0
22 22 0 76 0
23 15 0 51 0
24 26 0 104 0
25 24 0 83 0
26 22 0 78 0
27 21 0 59 0
28 23 0 83 0
29 21 0 71 0
30 25 0 81 0
31 25 0 93 0
32 28 0 72 0
33 30 0 107 0
34 20 0 75 0
35 23 0 84 0
36 25 0 69 0
37 26 0 90 0
38 20 0 51 0
39 8 0 18 0
40 20 0 75 0
41 21 0 59 0
42 25 0 63 0
43 20 0 68 0
44 18 0 47 0
45 21 0 29 0
46 22 0 69 0
47 26 0 66 0
48 30 0 106 0
49 24 0 73 0
50 26 0 87 0
51 18 0 65 0
52 4 0 7 0
53 31 0 111 0
54 18 0 61 0
55 14 0 41 0
56 20 0 70 0
57 30 0 112 0
58 20 0 71 0
59 26 0 90 0
60 20 0 69 0
61 27 0 85 0
62 18 0 47 0
63 27 0 50 0
64 22 0 76 0
65 19 0 60 0
66 15 0 35 0
67 19 0 72 0
68 28 0 88 0
69 20 0 66 0
70 17 0 58 0
71 25 0 81 0
72 20 0 63 0
73 25 25 91 91
74 20 20 50 50
75 22 22 75 75
76 25 25 85 85
77 20 20 75 75
78 23 23 70 70
79 22 22 78 78
80 21 21 61 61
81 18 18 55 55
82 25 25 60 60
83 22 22 83 83
84 25 25 38 38
85 8 8 27 27
86 21 21 62 62
87 22 22 82 82
88 21 21 79 79
89 30 30 59 59
90 23 23 80 80
91 20 20 36 36
92 24 24 88 88
93 21 21 63 63
94 20 20 73 73
95 20 20 71 71
96 20 20 76 76
97 20 20 67 67
98 23 23 66 66
99 33 33 123 123
100 19 19 65 65
101 27 27 87 87
102 25 25 77 77
103 20 20 37 37
104 19 19 64 64
105 15 15 22 22
106 21 21 35 35
107 22 22 61 61
108 24 24 80 80
109 19 19 54 54
110 20 20 60 60
111 23 23 87 87
112 27 27 75 75
113 1 1 0 0
114 20 20 54 54
115 11 11 30 30
116 27 27 66 66
117 22 22 56 56
118 0 0 0 0
119 17 17 32 32
120 8 8 9 9
121 23 23 78 78
122 26 26 90 90
123 20 20 56 56
124 16 16 35 35
125 8 8 21 21
126 22 22 78 78
127 33 33 118 118
128 28 28 83 83
129 26 26 89 89
130 27 27 83 83
131 35 35 124 124
132 21 21 76 76
133 20 20 57 57
134 24 24 91 91
135 26 26 89 89
136 20 20 66 66
137 22 22 82 82
138 24 24 63 63
139 23 23 75 75
140 22 22 59 59
141 12 12 19 19
142 21 21 57 57
143 21 21 62 62
144 21 21 78 78
145 25 25 73 73
146 32 32 112 112
147 24 24 79 79
148 28 28 96 96
149 0 0 0 0
150 0 0 0 0
151 0 0 0 0
152 0 0 0 0
153 0 0 0 0
154 0 0 0 0
155 20 20 48 48
156 27 27 55 55
157 0 0 0 0
158 0 0 0 0
159 0 0 0 0
160 5 5 13 13
161 1 1 4 4
162 23 23 31 31
163 0 0 0 0
164 16 16 29 29
Blogs Blogs_g Characters Characters_g
1 128 0 95556 0
2 89 0 54565 0
3 68 0 63016 0
4 108 0 79774 0
5 51 0 31258 0
6 33 0 52491 0
7 119 0 91256 0
8 5 0 22807 0
9 63 0 77411 0
10 66 0 48821 0
11 98 0 52295 0
12 71 0 63262 0
13 55 0 50466 0
14 116 0 62932 0
15 71 0 38439 0
16 120 0 70817 0
17 122 0 105965 0
18 74 0 73795 0
19 111 0 82043 0
20 103 0 74349 0
21 98 0 82204 0
22 100 0 55709 0
23 42 0 37137 0
24 100 0 70780 0
25 105 0 55027 0
26 77 0 56699 0
27 83 0 65911 0
28 98 0 56316 0
29 46 0 26982 0
30 95 0 54628 0
31 91 0 96750 0
32 91 0 53009 0
33 94 0 64664 0
34 15 0 36990 0
35 137 0 85224 0
36 56 0 37048 0
37 78 0 59635 0
38 68 0 42051 0
39 34 0 26998 0
40 94 0 63717 0
41 82 0 55071 0
42 63 0 40001 0
43 58 0 54506 0
44 43 0 35838 0
45 36 0 50838 0
46 64 0 86997 0
47 21 0 33032 0
48 104 0 61704 0
49 124 0 117986 0
50 101 0 56733 0
51 85 0 55064 0
52 7 0 5950 0
53 124 0 84607 0
54 21 0 32551 0
55 35 0 31701 0
56 95 0 71170 0
57 102 0 101773 0
58 212 0 101653 0
59 141 0 81493 0
60 54 0 55901 0
61 117 0 109104 0
62 145 0 114425 0
63 50 0 36311 0
64 80 0 70027 0
65 87 0 73713 0
66 78 0 40671 0
67 86 0 89041 0
68 82 0 57231 0
69 139 0 78792 0
70 75 0 59155 0
71 70 0 55827 0
72 25 0 22618 0
73 66 66 58425 58425
74 89 89 65724 65724
75 99 99 56979 56979
76 98 98 72369 72369
77 104 104 79194 79194
78 48 48 202316 202316
79 81 81 44970 44970
80 64 64 49319 49319
81 44 44 36252 36252
82 104 104 75741 75741
83 36 36 38417 38417
84 120 120 64102 64102
85 58 58 56622 56622
86 27 27 15430 15430
87 84 84 72571 72571
88 56 56 67271 67271
89 46 46 43460 43460
90 119 119 99501 99501
91 57 57 28340 28340
92 139 139 76013 76013
93 51 51 37361 37361
94 85 85 48204 48204
95 91 91 76168 76168
96 79 79 85168 85168
97 142 142 125410 125410
98 149 149 123328 123328
99 96 96 83038 83038
100 198 198 120087 120087
101 61 61 91939 91939
102 145 145 103646 103646
103 26 26 29467 29467
104 49 49 43750 43750
105 68 68 34497 34497
106 145 145 66477 66477
107 82 82 71181 71181
108 102 102 74482 74482
109 52 52 174949 174949
110 56 56 46765 46765
111 80 80 90257 90257
112 99 99 51370 51370
113 11 11 1168 1168
114 87 87 51360 51360
115 28 28 25162 25162
116 67 67 21067 21067
117 150 150 58233 58233
118 4 4 855 855
119 71 71 85903 85903
120 39 39 14116 14116
121 87 87 57637 57637
122 66 66 94137 94137
123 23 23 62147 62147
124 56 56 62832 62832
125 16 16 8773 8773
126 49 49 63785 63785
127 108 108 65196 65196
128 112 112 73087 73087
129 110 110 72631 72631
130 126 126 86281 86281
131 155 155 162365 162365
132 75 75 56530 56530
133 30 30 35606 35606
134 78 78 70111 70111
135 135 135 92046 92046
136 8 8 63989 63989
137 114 114 104911 104911
138 60 60 43448 43448
139 99 99 60029 60029
140 98 98 38650 38650
141 33 33 47261 47261
142 93 93 73586 73586
143 157 157 83042 83042
144 15 15 37238 37238
145 98 98 63958 63958
146 49 49 78956 78956
147 88 88 99518 99518
148 151 151 111436 111436
149 0 0 0 0
150 5 5 6023 6023
151 0 0 0 0
152 0 0 0 0
153 0 0 0 0
154 0 0 0 0
155 80 80 42564 42564
156 122 122 38885 38885
157 0 0 0 0
158 0 0 0 0
159 6 6 1644 1644
160 13 13 6179 6179
161 3 3 3926 3926
162 18 18 23238 23238
163 0 0 0 0
164 48 48 49288 49288
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Shared_compendiums
-1486.6839 8004.3197 -3317.2469
Shared_compendiums_g Reviewed_compendiums Reviewed_compendiums_g
5615.6677 2738.5429 -511.9359
Long_feedback Long_feedback_g Blogs
57.8991 178.5758 405.5396
Blogs_g Characters Characters_g
329.3881 0.8864 -0.9069
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-82632 -25122 -3373 16705 117914
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1486.6839 20795.2130 -0.071 0.94310
Gender 8004.3197 22767.1398 0.352 0.72565
Shared_compendiums -3317.2469 1764.8637 -1.880 0.06208 .
Shared_compendiums_g 5615.6677 2491.5730 2.254 0.02563 *
Reviewed_compendiums 2738.5429 1501.8420 1.823 0.07020 .
Reviewed_compendiums_g -511.9359 1874.4856 -0.273 0.78514
Long_feedback 57.8991 364.6050 0.159 0.87404
Long_feedback_g 178.5758 482.9468 0.370 0.71207
Blogs 405.5396 214.8118 1.888 0.06095 .
Blogs_g 329.3881 251.5993 1.309 0.19245
Characters 0.8864 0.3269 2.711 0.00747 **
Characters_g -0.9069 0.3613 -2.510 0.01312 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 37570 on 152 degrees of freedom
Multiple R-squared: 0.6781, Adjusted R-squared: 0.6548
F-statistic: 29.1 on 11 and 152 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,] 0.9946699 0.010660199 0.005330100
[2,] 0.9875269 0.024946272 0.012473136
[3,] 0.9775911 0.044817838 0.022408919
[4,] 0.9712588 0.057482457 0.028741228
[5,] 0.9875530 0.024894026 0.012447013
[6,] 0.9780992 0.043801589 0.021900794
[7,] 0.9730764 0.053847141 0.026923570
[8,] 0.9986518 0.002696450 0.001348225
[9,] 0.9977947 0.004410661 0.002205330
[10,] 0.9981265 0.003746964 0.001873482
[11,] 0.9978350 0.004329992 0.002164996
[12,] 0.9962570 0.007485979 0.003742990
[13,] 0.9952023 0.009595454 0.004797727
[14,] 0.9922275 0.015544997 0.007772499
[15,] 0.9880019 0.023996293 0.011998146
[16,] 0.9824099 0.035180106 0.017590053
[17,] 0.9776691 0.044661807 0.022330903
[18,] 0.9745780 0.050844099 0.025422049
[19,] 0.9654157 0.069168511 0.034584256
[20,] 0.9553413 0.089317445 0.044658722
[21,] 0.9448261 0.110347726 0.055173863
[22,] 0.9446980 0.110603903 0.055301952
[23,] 0.9735575 0.052884901 0.026442451
[24,] 0.9636099 0.072780277 0.036390138
[25,] 0.9554790 0.089041955 0.044520977
[26,] 0.9586969 0.082606210 0.041303105
[27,] 0.9882407 0.023518637 0.011759319
[28,] 0.9836599 0.032680299 0.016340149
[29,] 0.9772557 0.045488514 0.022744257
[30,] 0.9702734 0.059453223 0.029726611
[31,] 0.9633162 0.073367575 0.036683787
[32,] 0.9982348 0.003530498 0.001765249
[33,] 0.9976591 0.004681770 0.002340885
[34,] 0.9969758 0.006048394 0.003024197
[35,] 0.9955584 0.008883159 0.004441580
[36,] 0.9945660 0.010868062 0.005434031
[37,] 0.9929841 0.014031754 0.007015877
[38,] 0.9903496 0.019300756 0.009650378
[39,] 0.9925172 0.014965637 0.007482819
[40,] 0.9895824 0.020835140 0.010417570
[41,] 0.9875614 0.024877115 0.012438558
[42,] 0.9852828 0.029434481 0.014717240
[43,] 0.9820797 0.035840583 0.017920291
[44,] 0.9796322 0.040735552 0.020367776
[45,] 0.9741416 0.051716839 0.025858420
[46,] 0.9726866 0.054626867 0.027313434
[47,] 0.9697163 0.060567366 0.030283683
[48,] 0.9609983 0.078003473 0.039001737
[49,] 0.9527545 0.094491053 0.047245526
[50,] 0.9498423 0.100315326 0.050157663
[51,] 0.9387459 0.122508252 0.061254126
[52,] 0.9226530 0.154694092 0.077347046
[53,] 0.9234443 0.153111417 0.076555708
[54,] 0.9044946 0.191010778 0.095505389
[55,] 0.8916576 0.216684705 0.108342353
[56,] 0.8753607 0.249278686 0.124639343
[57,] 0.8491036 0.301792748 0.150896374
[58,] 0.8190520 0.361896063 0.180948032
[59,] 0.7900970 0.419805962 0.209902981
[60,] 0.7536821 0.492635870 0.246317935
[61,] 0.7470234 0.505953190 0.252976595
[62,] 0.7171878 0.565624333 0.282812166
[63,] 0.6982299 0.603540133 0.301770066
[64,] 0.6579729 0.684054216 0.342027108
[65,] 0.6515571 0.696885805 0.348442902
[66,] 0.6508783 0.698243426 0.349121713
[67,] 0.6110208 0.777958425 0.388979212
[68,] 0.8074522 0.385095576 0.192547788
[69,] 0.7907031 0.418593851 0.209296926
[70,] 0.8782096 0.243580714 0.121790357
[71,] 0.8547566 0.290486747 0.145243373
[72,] 0.8282859 0.343428243 0.171714122
[73,] 0.8637032 0.272593611 0.136296806
[74,] 0.9368320 0.126336024 0.063168012
[75,] 0.9419975 0.116004985 0.058002492
[76,] 0.9667740 0.066451938 0.033225969
[77,] 0.9589767 0.082046538 0.041023269
[78,] 0.9621457 0.075708545 0.037854273
[79,] 0.9521354 0.095729296 0.047864648
[80,] 0.9441717 0.111656673 0.055828337
[81,] 0.9450436 0.109912894 0.054956447
[82,] 0.9303857 0.139228617 0.069614309
[83,] 0.9262513 0.147497380 0.073748690
[84,] 0.9083578 0.183284323 0.091642161
[85,] 0.8898701 0.220259715 0.110129857
[86,] 0.8921792 0.215641535 0.107820768
[87,] 0.8668913 0.266217458 0.133108729
[88,] 0.8480142 0.303971675 0.151985838
[89,] 0.8509478 0.298104484 0.149052242
[90,] 0.9074027 0.185194624 0.092597312
[91,] 0.8927322 0.214535628 0.107267814
[92,] 0.9140505 0.171899020 0.085949510
[93,] 0.8928942 0.214211562 0.107105781
[94,] 0.8752854 0.249429103 0.124714551
[95,] 0.8588206 0.282358750 0.141179375
[96,] 0.8319841 0.336031888 0.168015944
[97,] 0.8027484 0.394503145 0.197251572
[98,] 0.7666034 0.466793282 0.233396641
[99,] 0.7238747 0.552250698 0.276125349
[100,] 0.6814284 0.637143267 0.318571633
[101,] 0.6327253 0.734549433 0.367274716
[102,] 0.6138832 0.772233547 0.386116773
[103,] 0.5857935 0.828413013 0.414206507
[104,] 0.5343646 0.931270749 0.465635375
[105,] 0.5750436 0.849912726 0.424956363
[106,] 0.5380902 0.923819528 0.461909764
[107,] 0.5283129 0.943374128 0.471687064
[108,] 0.4928332 0.985666441 0.507166779
[109,] 0.4565156 0.913031262 0.543484369
[110,] 0.4266965 0.853393079 0.573303460
[111,] 0.3690943 0.738188699 0.630905650
[112,] 0.3256734 0.651346731 0.674326634
[113,] 0.2724160 0.544831926 0.727584037
[114,] 0.3179510 0.635901919 0.682049041
[115,] 0.3473187 0.694637360 0.652681320
[116,] 0.2903477 0.580695450 0.709652275
[117,] 0.2401644 0.480328820 0.759835590
[118,] 0.1963716 0.392743123 0.803628438
[119,] 0.1537922 0.307584447 0.846207776
[120,] 0.1351545 0.270308927 0.864845536
[121,] 0.1528493 0.305698560 0.847150720
[122,] 0.1672643 0.334528651 0.832735675
[123,] 0.2694824 0.538964718 0.730517641
[124,] 0.2607428 0.521485668 0.739257166
[125,] 0.2224135 0.444827091 0.777586454
[126,] 0.1830367 0.366073428 0.816963286
[127,] 0.1372178 0.274435618 0.862782191
[128,] 0.4038118 0.807623571 0.596188214
[129,] 0.3626666 0.725333167 0.637333417
[130,] 0.9768595 0.046281073 0.023140536
[131,] 0.9961152 0.007769581 0.003884791
[132,] 0.9907697 0.018460617 0.009230308
[133,] 0.9823110 0.035377993 0.017688996
[134,] 0.9904008 0.019198315 0.009599157
[135,] 0.9740128 0.051974390 0.025987195
> postscript(file="/var/wessaorg/rcomp/tmp/17yaf1321986445.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/2a7sf1321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3thq91321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4w44k1321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5wr4k1321986445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-49539.5158 -43701.3362 -6624.7442 -39298.9965 -12003.9271 -63153.8885
7 8 9 10 11 12
106682.4603 -38575.5155 -25526.4405 4491.2543 -3122.1713 -41928.3008
13 14 15 16 17 18
-11958.9249 6340.4082 6790.6081 21643.4893 -28784.6594 37377.5340
19 20 21 22 23 24
-64013.0622 -24992.7327 -35083.1805 114239.2222 8393.1837 -61122.6160
25 26 27 28 29 30
-30171.5528 -6970.9836 23422.5424 -4465.8489 2617.8044 17296.7867
31 32 33 34 35 36
-3223.5673 -35057.4636 11421.2088 -21730.9866 28853.3575 40608.1687
37 38 39 40 41 42
74060.9394 6730.2453 22909.0537 43373.1108 84540.0377 -12514.6254
43 44 45 46 47 48
-148.9589 -17517.6605 -20923.6109 117914.3744 -25899.3689 -26429.6841
49 50 51 52 53 54
7118.5329 -28726.5959 -20455.1176 6202.1463 50991.9193 -7094.4140
55 56 57 58 59 60
26460.1620 17953.2112 -29464.3755 -25511.0686 -13069.7778 35167.2035
61 62 63 64 65 66
31772.2183 4445.4342 16391.9418 25034.2561 -18908.8982 -587.2627
67 68 69 70 71 72
-52099.1945 4333.4614 -32748.1049 -30437.0605 5755.1562 2254.7609
73 74 75 76 77 78
-20068.2334 1889.2970 -50064.9785 29062.7202 40977.8975 -13585.9963
79 80 81 82 83 84
-45535.3936 -6390.3372 5104.5013 109395.4374 19583.9137 -43351.4815
85 86 87 88 89 90
8773.0274 12303.5516 -62059.7455 88340.2579 46606.6983 -64415.9361
91 92 93 94 95 96
13728.9982 -43812.9854 -14837.1324 24741.8034 32728.3823 5198.3866
97 98 99 100 101 102
-30939.3843 -3523.0783 -12642.2187 5211.9579 -1804.0584 -16985.4237
103 104 105 106 107 108
24708.8146 -64978.5187 21785.4755 -48382.2763 6438.6811 23258.5842
109 110 111 112 113 114
-22109.3721 -16618.2086 20327.0916 -14143.0370 6133.5046 -11505.8888
115 116 117 118 119 120
-5503.2200 -39866.5001 33676.0255 11614.1868 53257.4920 -23417.4691
121 122 123 124 125 126
37633.1953 -21674.8609 -27093.3597 -27138.2647 -2661.3990 -19988.8676
127 128 129 130 131 132
-4424.9175 -52528.1526 54508.8748 8044.0357 13926.2202 13935.2110
133 134 135 136 137 138
-10034.5011 31662.3066 54856.9839 -38612.6810 67317.7233 -33675.4765
139 140 141 142 143 144
8390.9609 -31319.7466 14412.6253 89270.5152 -45070.5317 84383.0407
145 146 147 148 149 150
18793.2511 -82631.6538 -6517.3812 -29587.5044 -27203.4227 4619.2393
151 152 153 154 155 156
-6419.6358 -6062.6358 -8816.0566 -6517.6358 3147.2333 5655.6722
157 158 159 160 161 162
-6517.6358 -6314.6358 -3694.4886 16507.8087 5732.5851 -4245.4735
163 164
-5548.6358 13196.2594
> postscript(file="/var/wessaorg/rcomp/tmp/6e3ix1321986445.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 -49539.5158 NA
1 -43701.3362 -49539.5158
2 -6624.7442 -43701.3362
3 -39298.9965 -6624.7442
4 -12003.9271 -39298.9965
5 -63153.8885 -12003.9271
6 106682.4603 -63153.8885
7 -38575.5155 106682.4603
8 -25526.4405 -38575.5155
9 4491.2543 -25526.4405
10 -3122.1713 4491.2543
11 -41928.3008 -3122.1713
12 -11958.9249 -41928.3008
13 6340.4082 -11958.9249
14 6790.6081 6340.4082
15 21643.4893 6790.6081
16 -28784.6594 21643.4893
17 37377.5340 -28784.6594
18 -64013.0622 37377.5340
19 -24992.7327 -64013.0622
20 -35083.1805 -24992.7327
21 114239.2222 -35083.1805
22 8393.1837 114239.2222
23 -61122.6160 8393.1837
24 -30171.5528 -61122.6160
25 -6970.9836 -30171.5528
26 23422.5424 -6970.9836
27 -4465.8489 23422.5424
28 2617.8044 -4465.8489
29 17296.7867 2617.8044
30 -3223.5673 17296.7867
31 -35057.4636 -3223.5673
32 11421.2088 -35057.4636
33 -21730.9866 11421.2088
34 28853.3575 -21730.9866
35 40608.1687 28853.3575
36 74060.9394 40608.1687
37 6730.2453 74060.9394
38 22909.0537 6730.2453
39 43373.1108 22909.0537
40 84540.0377 43373.1108
41 -12514.6254 84540.0377
42 -148.9589 -12514.6254
43 -17517.6605 -148.9589
44 -20923.6109 -17517.6605
45 117914.3744 -20923.6109
46 -25899.3689 117914.3744
47 -26429.6841 -25899.3689
48 7118.5329 -26429.6841
49 -28726.5959 7118.5329
50 -20455.1176 -28726.5959
51 6202.1463 -20455.1176
52 50991.9193 6202.1463
53 -7094.4140 50991.9193
54 26460.1620 -7094.4140
55 17953.2112 26460.1620
56 -29464.3755 17953.2112
57 -25511.0686 -29464.3755
58 -13069.7778 -25511.0686
59 35167.2035 -13069.7778
60 31772.2183 35167.2035
61 4445.4342 31772.2183
62 16391.9418 4445.4342
63 25034.2561 16391.9418
64 -18908.8982 25034.2561
65 -587.2627 -18908.8982
66 -52099.1945 -587.2627
67 4333.4614 -52099.1945
68 -32748.1049 4333.4614
69 -30437.0605 -32748.1049
70 5755.1562 -30437.0605
71 2254.7609 5755.1562
72 -20068.2334 2254.7609
73 1889.2970 -20068.2334
74 -50064.9785 1889.2970
75 29062.7202 -50064.9785
76 40977.8975 29062.7202
77 -13585.9963 40977.8975
78 -45535.3936 -13585.9963
79 -6390.3372 -45535.3936
80 5104.5013 -6390.3372
81 109395.4374 5104.5013
82 19583.9137 109395.4374
83 -43351.4815 19583.9137
84 8773.0274 -43351.4815
85 12303.5516 8773.0274
86 -62059.7455 12303.5516
87 88340.2579 -62059.7455
88 46606.6983 88340.2579
89 -64415.9361 46606.6983
90 13728.9982 -64415.9361
91 -43812.9854 13728.9982
92 -14837.1324 -43812.9854
93 24741.8034 -14837.1324
94 32728.3823 24741.8034
95 5198.3866 32728.3823
96 -30939.3843 5198.3866
97 -3523.0783 -30939.3843
98 -12642.2187 -3523.0783
99 5211.9579 -12642.2187
100 -1804.0584 5211.9579
101 -16985.4237 -1804.0584
102 24708.8146 -16985.4237
103 -64978.5187 24708.8146
104 21785.4755 -64978.5187
105 -48382.2763 21785.4755
106 6438.6811 -48382.2763
107 23258.5842 6438.6811
108 -22109.3721 23258.5842
109 -16618.2086 -22109.3721
110 20327.0916 -16618.2086
111 -14143.0370 20327.0916
112 6133.5046 -14143.0370
113 -11505.8888 6133.5046
114 -5503.2200 -11505.8888
115 -39866.5001 -5503.2200
116 33676.0255 -39866.5001
117 11614.1868 33676.0255
118 53257.4920 11614.1868
119 -23417.4691 53257.4920
120 37633.1953 -23417.4691
121 -21674.8609 37633.1953
122 -27093.3597 -21674.8609
123 -27138.2647 -27093.3597
124 -2661.3990 -27138.2647
125 -19988.8676 -2661.3990
126 -4424.9175 -19988.8676
127 -52528.1526 -4424.9175
128 54508.8748 -52528.1526
129 8044.0357 54508.8748
130 13926.2202 8044.0357
131 13935.2110 13926.2202
132 -10034.5011 13935.2110
133 31662.3066 -10034.5011
134 54856.9839 31662.3066
135 -38612.6810 54856.9839
136 67317.7233 -38612.6810
137 -33675.4765 67317.7233
138 8390.9609 -33675.4765
139 -31319.7466 8390.9609
140 14412.6253 -31319.7466
141 89270.5152 14412.6253
142 -45070.5317 89270.5152
143 84383.0407 -45070.5317
144 18793.2511 84383.0407
145 -82631.6538 18793.2511
146 -6517.3812 -82631.6538
147 -29587.5044 -6517.3812
148 -27203.4227 -29587.5044
149 4619.2393 -27203.4227
150 -6419.6358 4619.2393
151 -6062.6358 -6419.6358
152 -8816.0566 -6062.6358
153 -6517.6358 -8816.0566
154 3147.2333 -6517.6358
155 5655.6722 3147.2333
156 -6517.6358 5655.6722
157 -6314.6358 -6517.6358
158 -3694.4886 -6314.6358
159 16507.8087 -3694.4886
160 5732.5851 16507.8087
161 -4245.4735 5732.5851
162 -5548.6358 -4245.4735
163 13196.2594 -5548.6358
164 NA 13196.2594
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -43701.3362 -49539.5158
[2,] -6624.7442 -43701.3362
[3,] -39298.9965 -6624.7442
[4,] -12003.9271 -39298.9965
[5,] -63153.8885 -12003.9271
[6,] 106682.4603 -63153.8885
[7,] -38575.5155 106682.4603
[8,] -25526.4405 -38575.5155
[9,] 4491.2543 -25526.4405
[10,] -3122.1713 4491.2543
[11,] -41928.3008 -3122.1713
[12,] -11958.9249 -41928.3008
[13,] 6340.4082 -11958.9249
[14,] 6790.6081 6340.4082
[15,] 21643.4893 6790.6081
[16,] -28784.6594 21643.4893
[17,] 37377.5340 -28784.6594
[18,] -64013.0622 37377.5340
[19,] -24992.7327 -64013.0622
[20,] -35083.1805 -24992.7327
[21,] 114239.2222 -35083.1805
[22,] 8393.1837 114239.2222
[23,] -61122.6160 8393.1837
[24,] -30171.5528 -61122.6160
[25,] -6970.9836 -30171.5528
[26,] 23422.5424 -6970.9836
[27,] -4465.8489 23422.5424
[28,] 2617.8044 -4465.8489
[29,] 17296.7867 2617.8044
[30,] -3223.5673 17296.7867
[31,] -35057.4636 -3223.5673
[32,] 11421.2088 -35057.4636
[33,] -21730.9866 11421.2088
[34,] 28853.3575 -21730.9866
[35,] 40608.1687 28853.3575
[36,] 74060.9394 40608.1687
[37,] 6730.2453 74060.9394
[38,] 22909.0537 6730.2453
[39,] 43373.1108 22909.0537
[40,] 84540.0377 43373.1108
[41,] -12514.6254 84540.0377
[42,] -148.9589 -12514.6254
[43,] -17517.6605 -148.9589
[44,] -20923.6109 -17517.6605
[45,] 117914.3744 -20923.6109
[46,] -25899.3689 117914.3744
[47,] -26429.6841 -25899.3689
[48,] 7118.5329 -26429.6841
[49,] -28726.5959 7118.5329
[50,] -20455.1176 -28726.5959
[51,] 6202.1463 -20455.1176
[52,] 50991.9193 6202.1463
[53,] -7094.4140 50991.9193
[54,] 26460.1620 -7094.4140
[55,] 17953.2112 26460.1620
[56,] -29464.3755 17953.2112
[57,] -25511.0686 -29464.3755
[58,] -13069.7778 -25511.0686
[59,] 35167.2035 -13069.7778
[60,] 31772.2183 35167.2035
[61,] 4445.4342 31772.2183
[62,] 16391.9418 4445.4342
[63,] 25034.2561 16391.9418
[64,] -18908.8982 25034.2561
[65,] -587.2627 -18908.8982
[66,] -52099.1945 -587.2627
[67,] 4333.4614 -52099.1945
[68,] -32748.1049 4333.4614
[69,] -30437.0605 -32748.1049
[70,] 5755.1562 -30437.0605
[71,] 2254.7609 5755.1562
[72,] -20068.2334 2254.7609
[73,] 1889.2970 -20068.2334
[74,] -50064.9785 1889.2970
[75,] 29062.7202 -50064.9785
[76,] 40977.8975 29062.7202
[77,] -13585.9963 40977.8975
[78,] -45535.3936 -13585.9963
[79,] -6390.3372 -45535.3936
[80,] 5104.5013 -6390.3372
[81,] 109395.4374 5104.5013
[82,] 19583.9137 109395.4374
[83,] -43351.4815 19583.9137
[84,] 8773.0274 -43351.4815
[85,] 12303.5516 8773.0274
[86,] -62059.7455 12303.5516
[87,] 88340.2579 -62059.7455
[88,] 46606.6983 88340.2579
[89,] -64415.9361 46606.6983
[90,] 13728.9982 -64415.9361
[91,] -43812.9854 13728.9982
[92,] -14837.1324 -43812.9854
[93,] 24741.8034 -14837.1324
[94,] 32728.3823 24741.8034
[95,] 5198.3866 32728.3823
[96,] -30939.3843 5198.3866
[97,] -3523.0783 -30939.3843
[98,] -12642.2187 -3523.0783
[99,] 5211.9579 -12642.2187
[100,] -1804.0584 5211.9579
[101,] -16985.4237 -1804.0584
[102,] 24708.8146 -16985.4237
[103,] -64978.5187 24708.8146
[104,] 21785.4755 -64978.5187
[105,] -48382.2763 21785.4755
[106,] 6438.6811 -48382.2763
[107,] 23258.5842 6438.6811
[108,] -22109.3721 23258.5842
[109,] -16618.2086 -22109.3721
[110,] 20327.0916 -16618.2086
[111,] -14143.0370 20327.0916
[112,] 6133.5046 -14143.0370
[113,] -11505.8888 6133.5046
[114,] -5503.2200 -11505.8888
[115,] -39866.5001 -5503.2200
[116,] 33676.0255 -39866.5001
[117,] 11614.1868 33676.0255
[118,] 53257.4920 11614.1868
[119,] -23417.4691 53257.4920
[120,] 37633.1953 -23417.4691
[121,] -21674.8609 37633.1953
[122,] -27093.3597 -21674.8609
[123,] -27138.2647 -27093.3597
[124,] -2661.3990 -27138.2647
[125,] -19988.8676 -2661.3990
[126,] -4424.9175 -19988.8676
[127,] -52528.1526 -4424.9175
[128,] 54508.8748 -52528.1526
[129,] 8044.0357 54508.8748
[130,] 13926.2202 8044.0357
[131,] 13935.2110 13926.2202
[132,] -10034.5011 13935.2110
[133,] 31662.3066 -10034.5011
[134,] 54856.9839 31662.3066
[135,] -38612.6810 54856.9839
[136,] 67317.7233 -38612.6810
[137,] -33675.4765 67317.7233
[138,] 8390.9609 -33675.4765
[139,] -31319.7466 8390.9609
[140,] 14412.6253 -31319.7466
[141,] 89270.5152 14412.6253
[142,] -45070.5317 89270.5152
[143,] 84383.0407 -45070.5317
[144,] 18793.2511 84383.0407
[145,] -82631.6538 18793.2511
[146,] -6517.3812 -82631.6538
[147,] -29587.5044 -6517.3812
[148,] -27203.4227 -29587.5044
[149,] 4619.2393 -27203.4227
[150,] -6419.6358 4619.2393
[151,] -6062.6358 -6419.6358
[152,] -8816.0566 -6062.6358
[153,] -6517.6358 -8816.0566
[154,] 3147.2333 -6517.6358
[155,] 5655.6722 3147.2333
[156,] -6517.6358 5655.6722
[157,] -6314.6358 -6517.6358
[158,] -3694.4886 -6314.6358
[159,] 16507.8087 -3694.4886
[160,] 5732.5851 16507.8087
[161,] -4245.4735 5732.5851
[162,] -5548.6358 -4245.4735
[163,] 13196.2594 -5548.6358
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -43701.3362 -49539.5158
2 -6624.7442 -43701.3362
3 -39298.9965 -6624.7442
4 -12003.9271 -39298.9965
5 -63153.8885 -12003.9271
6 106682.4603 -63153.8885
7 -38575.5155 106682.4603
8 -25526.4405 -38575.5155
9 4491.2543 -25526.4405
10 -3122.1713 4491.2543
11 -41928.3008 -3122.1713
12 -11958.9249 -41928.3008
13 6340.4082 -11958.9249
14 6790.6081 6340.4082
15 21643.4893 6790.6081
16 -28784.6594 21643.4893
17 37377.5340 -28784.6594
18 -64013.0622 37377.5340
19 -24992.7327 -64013.0622
20 -35083.1805 -24992.7327
21 114239.2222 -35083.1805
22 8393.1837 114239.2222
23 -61122.6160 8393.1837
24 -30171.5528 -61122.6160
25 -6970.9836 -30171.5528
26 23422.5424 -6970.9836
27 -4465.8489 23422.5424
28 2617.8044 -4465.8489
29 17296.7867 2617.8044
30 -3223.5673 17296.7867
31 -35057.4636 -3223.5673
32 11421.2088 -35057.4636
33 -21730.9866 11421.2088
34 28853.3575 -21730.9866
35 40608.1687 28853.3575
36 74060.9394 40608.1687
37 6730.2453 74060.9394
38 22909.0537 6730.2453
39 43373.1108 22909.0537
40 84540.0377 43373.1108
41 -12514.6254 84540.0377
42 -148.9589 -12514.6254
43 -17517.6605 -148.9589
44 -20923.6109 -17517.6605
45 117914.3744 -20923.6109
46 -25899.3689 117914.3744
47 -26429.6841 -25899.3689
48 7118.5329 -26429.6841
49 -28726.5959 7118.5329
50 -20455.1176 -28726.5959
51 6202.1463 -20455.1176
52 50991.9193 6202.1463
53 -7094.4140 50991.9193
54 26460.1620 -7094.4140
55 17953.2112 26460.1620
56 -29464.3755 17953.2112
57 -25511.0686 -29464.3755
58 -13069.7778 -25511.0686
59 35167.2035 -13069.7778
60 31772.2183 35167.2035
61 4445.4342 31772.2183
62 16391.9418 4445.4342
63 25034.2561 16391.9418
64 -18908.8982 25034.2561
65 -587.2627 -18908.8982
66 -52099.1945 -587.2627
67 4333.4614 -52099.1945
68 -32748.1049 4333.4614
69 -30437.0605 -32748.1049
70 5755.1562 -30437.0605
71 2254.7609 5755.1562
72 -20068.2334 2254.7609
73 1889.2970 -20068.2334
74 -50064.9785 1889.2970
75 29062.7202 -50064.9785
76 40977.8975 29062.7202
77 -13585.9963 40977.8975
78 -45535.3936 -13585.9963
79 -6390.3372 -45535.3936
80 5104.5013 -6390.3372
81 109395.4374 5104.5013
82 19583.9137 109395.4374
83 -43351.4815 19583.9137
84 8773.0274 -43351.4815
85 12303.5516 8773.0274
86 -62059.7455 12303.5516
87 88340.2579 -62059.7455
88 46606.6983 88340.2579
89 -64415.9361 46606.6983
90 13728.9982 -64415.9361
91 -43812.9854 13728.9982
92 -14837.1324 -43812.9854
93 24741.8034 -14837.1324
94 32728.3823 24741.8034
95 5198.3866 32728.3823
96 -30939.3843 5198.3866
97 -3523.0783 -30939.3843
98 -12642.2187 -3523.0783
99 5211.9579 -12642.2187
100 -1804.0584 5211.9579
101 -16985.4237 -1804.0584
102 24708.8146 -16985.4237
103 -64978.5187 24708.8146
104 21785.4755 -64978.5187
105 -48382.2763 21785.4755
106 6438.6811 -48382.2763
107 23258.5842 6438.6811
108 -22109.3721 23258.5842
109 -16618.2086 -22109.3721
110 20327.0916 -16618.2086
111 -14143.0370 20327.0916
112 6133.5046 -14143.0370
113 -11505.8888 6133.5046
114 -5503.2200 -11505.8888
115 -39866.5001 -5503.2200
116 33676.0255 -39866.5001
117 11614.1868 33676.0255
118 53257.4920 11614.1868
119 -23417.4691 53257.4920
120 37633.1953 -23417.4691
121 -21674.8609 37633.1953
122 -27093.3597 -21674.8609
123 -27138.2647 -27093.3597
124 -2661.3990 -27138.2647
125 -19988.8676 -2661.3990
126 -4424.9175 -19988.8676
127 -52528.1526 -4424.9175
128 54508.8748 -52528.1526
129 8044.0357 54508.8748
130 13926.2202 8044.0357
131 13935.2110 13926.2202
132 -10034.5011 13935.2110
133 31662.3066 -10034.5011
134 54856.9839 31662.3066
135 -38612.6810 54856.9839
136 67317.7233 -38612.6810
137 -33675.4765 67317.7233
138 8390.9609 -33675.4765
139 -31319.7466 8390.9609
140 14412.6253 -31319.7466
141 89270.5152 14412.6253
142 -45070.5317 89270.5152
143 84383.0407 -45070.5317
144 18793.2511 84383.0407
145 -82631.6538 18793.2511
146 -6517.3812 -82631.6538
147 -29587.5044 -6517.3812
148 -27203.4227 -29587.5044
149 4619.2393 -27203.4227
150 -6419.6358 4619.2393
151 -6062.6358 -6419.6358
152 -8816.0566 -6062.6358
153 -6517.6358 -8816.0566
154 3147.2333 -6517.6358
155 5655.6722 3147.2333
156 -6517.6358 5655.6722
157 -6314.6358 -6517.6358
158 -3694.4886 -6314.6358
159 16507.8087 -3694.4886
160 5732.5851 16507.8087
161 -4245.4735 5732.5851
162 -5548.6358 -4245.4735
163 13196.2594 -5548.6358
> 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/7cp131321986445.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/8pkox1321986445.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/9izdt1321986445.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/10659z1321986445.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11mz8i1321986445.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/123c201321986445.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1347kj1321986445.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14a3gn1321986445.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/15siqi1321986446.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/162fgl1321986446.tab")
+ }
>
> try(system("convert tmp/17yaf1321986445.ps tmp/17yaf1321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a7sf1321986445.ps tmp/2a7sf1321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/3thq91321986445.ps tmp/3thq91321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/4w44k1321986445.ps tmp/4w44k1321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wr4k1321986445.ps tmp/5wr4k1321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e3ix1321986445.ps tmp/6e3ix1321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cp131321986445.ps tmp/7cp131321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pkox1321986445.ps tmp/8pkox1321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/9izdt1321986445.ps tmp/9izdt1321986445.png",intern=TRUE))
character(0)
> try(system("convert tmp/10659z1321986445.ps tmp/10659z1321986445.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.661 0.536 6.295