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('Maand'
+ ,'Total_Time_spent_in_RFC'
+ ,'Number_of_Logins'
+ ,'Total_Number_of_Blogged_Computations'
+ ,'Total_Number_of_Reviewed_Compendiums'
+ ,'Total_Number_of_Feedback_Messages_PeerReviews'
+ ,'Total_number_of_characters')
+ ,1:164))
> y <- array(NA,dim=c(7,164),dimnames=list(c('Maand','Total_Time_spent_in_RFC','Number_of_Logins','Total_Number_of_Blogged_Computations','Total_Number_of_Reviewed_Compendiums','Total_Number_of_Feedback_Messages_PeerReviews','Total_number_of_characters'),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 = '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
Total_Time_spent_in_RFC Maand Number_of_Logins
1 236496 11 61
2 130631 10 58
3 198514 12 62
4 189326 11 94
5 137449 12 43
6 65295 10 27
7 439387 10 103
8 33186 11 19
9 174859 10 50
10 186657 10 38
11 261949 12 95
12 190794 10 94
13 138866 11 57
14 296878 11 65
15 192648 10 71
16 333348 12 161
17 242212 10 57
18 263451 10 130
19 150733 12 47
20 223226 10 67
21 240028 11 63
22 384138 10 86
23 156540 10 34
24 148421 12 43
25 176502 11 96
26 191441 12 105
27 249735 11 120
28 236812 12 76
29 142329 11 45
30 259667 10 53
31 228871 11 64
32 176054 12 66
33 286683 11 79
34 87485 10 33
35 322865 12 82
36 247013 12 50
37 340093 11 103
38 191653 12 72
39 114673 10 31
40 284210 11 160
41 284195 10 72
42 155363 11 59
43 174198 12 65
44 142986 10 48
45 140319 10 73
46 392666 10 132
47 78800 11 42
48 201970 12 69
49 302674 12 99
50 164733 11 50
51 194221 11 68
52 24188 10 24
53 340411 11 274
54 65029 12 17
55 101097 11 64
56 243889 12 45
57 273003 10 74
58 282220 12 160
59 273495 10 118
60 214872 10 74
61 333165 12 122
62 260981 12 105
63 184474 11 87
64 222366 12 76
65 205675 11 60
66 201345 10 60
67 163043 11 110
68 204250 10 128
69 197760 12 66
70 127260 10 57
71 216092 10 59
72 73566 12 32
73 213198 11 67
74 177949 11 48
75 148698 10 49
76 300103 11 69
77 251437 10 78
78 191971 10 99
79 154651 11 53
80 155473 12 56
81 132672 11 41
82 376465 10 99
83 145869 11 65
84 223666 11 85
85 80953 10 25
86 130789 11 46
87 135042 12 47
88 300074 10 152
89 271757 12 93
90 150949 12 95
91 216802 10 77
92 197389 10 67
93 156583 11 56
94 222599 12 65
95 261601 10 70
96 178489 10 35
97 200657 12 43
98 259084 12 67
99 302789 10 129
100 342025 12 98
101 246440 11 104
102 251306 12 56
103 159965 12 156
104 43287 11 14
105 172212 10 67
106 181781 10 117
107 227681 12 43
108 260464 12 80
109 106288 11 54
110 109632 11 76
111 268905 12 58
112 266568 10 77
113 23623 12 11
114 152474 11 65
115 61857 10 25
116 144889 11 43
117 330910 11 95
118 21054 11 16
119 223718 10 44
120 31414 10 19
121 259747 10 103
122 190495 12 55
123 154984 11 73
124 112933 11 45
125 38214 12 34
126 158671 11 33
127 299775 11 68
128 172783 10 54
129 348678 10 69
130 266701 11 89
131 358933 10 99
132 172464 11 31
133 94381 10 35
134 243875 11 274
135 382487 11 153
136 111853 12 39
137 334926 10 117
138 147979 10 72
139 216638 11 44
140 192853 10 71
141 173710 11 103
142 336678 10 103
143 212961 10 75
144 173260 12 63
145 271773 10 89
146 127096 12 51
147 203606 11 73
148 230177 12 87
149 1 10 0
150 14688 10 10
151 98 11 1
152 455 12 2
153 0 12 0
154 0 10 0
155 195765 11 75
156 306514 10 117
157 0 12 0
158 203 10 4
159 7199 10 5
160 46660 10 20
161 17547 11 5
162 105044 11 37
163 969 12 2
164 165838 10 56
Total_Number_of_Blogged_Computations Total_Number_of_Reviewed_Compendiums
1 85 34
2 58 30
3 62 38
4 108 34
5 55 25
6 8 31
7 134 29
8 1 18
9 63 30
10 77 29
11 86 38
12 93 49
13 44 33
14 106 46
15 63 38
16 160 52
17 104 32
18 86 35
19 92 25
20 119 42
21 107 40
22 86 35
23 50 25
24 92 46
25 123 36
26 81 35
27 93 38
28 113 35
29 52 28
30 113 37
31 109 40
32 44 42
33 123 44
34 38 33
35 111 35
36 77 37
37 92 37
38 74 32
39 33 17
40 105 34
41 108 33
42 66 35
43 69 32
44 62 35
45 50 45
46 91 34
47 20 26
48 101 45
49 129 44
50 93 40
51 89 33
52 8 4
53 79 41
54 21 18
55 30 14
56 86 33
57 116 49
58 106 32
59 127 37
60 75 32
61 138 41
62 114 25
63 55 38
64 67 34
65 43 33
66 88 28
67 67 31
68 75 40
69 114 32
70 119 25
71 86 42
72 22 23
73 67 42
74 77 34
75 105 34
76 119 38
77 88 32
78 75 37
79 112 34
80 66 33
81 58 25
82 132 40
83 30 26
84 100 40
85 49 8
86 26 27
87 67 32
88 57 33
89 95 50
90 139 37
91 70 33
92 134 34
93 37 28
94 98 32
95 58 32
96 78 32
97 88 31
98 142 35
99 127 52
100 139 27
101 108 45
102 128 37
103 62 32
104 13 19
105 89 22
106 83 35
107 116 36
108 157 36
109 28 23
110 83 36
111 72 36
112 134 42
113 12 1
114 106 32
115 23 11
116 83 40
117 120 34
118 4 0
119 71 27
120 18 8
121 98 35
122 66 40
123 44 40
124 29 28
125 16 8
126 56 35
127 112 45
128 46 43
129 129 41
130 139 43
131 136 47
132 66 35
133 42 32
134 70 36
135 97 42
136 49 35
137 113 37
138 55 34
139 100 36
140 80 36
141 29 27
142 95 33
143 114 35
144 41 21
145 128 40
146 142 47
147 88 33
148 132 39
149 0 0
150 4 0
151 0 0
152 0 0
153 0 0
154 0 0
155 56 33
156 111 42
157 0 0
158 0 0
159 7 0
160 12 5
161 0 1
162 37 38
163 0 0
164 46 28
Total_Number_of_Feedback_Messages_PeerReviews Total_number_of_characters
1 131 124252
2 117 98956
3 146 98073
4 132 106816
5 80 41449
6 117 76173
7 112 177551
8 67 22807
9 116 126938
10 107 61680
11 140 72117
12 186 79738
13 109 57793
14 159 91677
15 146 64631
16 201 106385
17 124 161961
18 131 112669
19 96 114029
20 163 124550
21 151 105416
22 128 72875
23 89 81964
24 184 104880
25 136 76302
26 134 96740
27 146 93071
28 130 78912
29 105 35224
30 142 90694
31 155 125369
32 154 80849
33 169 104434
34 125 65702
35 135 108179
36 139 63583
37 139 95066
38 124 62486
39 55 31081
40 131 94584
41 125 87408
42 128 68966
43 107 88766
44 130 57139
45 73 90586
46 125 109249
47 82 33032
48 173 96056
49 169 146648
50 145 80613
51 134 87026
52 12 5950
53 151 131106
54 67 32551
55 52 31701
56 121 91072
57 186 159803
58 120 143950
59 135 112368
60 123 82124
61 158 144068
62 90 162627
63 149 55062
64 131 95329
65 125 105612
66 110 62853
67 121 125976
68 151 79146
69 123 108461
70 92 99971
71 162 77826
72 88 22618
73 163 84892
74 120 92059
75 132 77993
76 144 104155
77 124 109840
78 140 238712
79 132 67486
80 122 68007
81 97 48194
82 155 134796
83 99 38692
84 106 93587
85 28 56622
86 101 15986
87 120 113402
88 127 97967
89 178 74844
90 141 136051
91 122 50548
92 127 112215
93 102 59591
94 124 59938
95 124 137639
96 124 143372
97 111 138599
98 129 174110
99 199 135062
100 102 175681
101 174 130307
102 141 139141
103 122 44244
104 71 43750
105 81 48029
106 131 95216
107 139 92288
108 137 94588
109 91 197426
110 142 151244
111 133 139206
112 155 106271
113 0 1168
114 123 71764
115 32 25162
116 149 45635
117 128 101817
118 0 855
119 99 100174
120 25 14116
121 132 85008
122 151 124254
123 151 105793
124 103 117129
125 27 8773
126 131 94747
127 170 107549
128 165 97392
129 159 126893
130 167 118850
131 178 234853
132 135 74783
133 118 66089
134 140 95684
135 158 139537
136 132 144253
137 136 153824
138 123 63995
139 134 84891
140 129 61263
141 107 106221
142 128 113587
143 129 113864
144 79 37238
145 154 119906
146 180 135096
147 122 151611
148 144 144645
149 0 0
150 0 6023
151 0 0
152 0 0
153 0 0
154 0 0
155 120 77457
156 168 62464
157 0 0
158 0 0
159 0 1644
160 15 6179
161 4 3926
162 133 42087
163 0 0
164 101 87656
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)
1.098e+05
Maand
-8.640e+03
Number_of_Logins
7.302e+02
Total_Number_of_Blogged_Computations
1.033e+03
Total_Number_of_Reviewed_Compendiums
3.791e+02
Total_Number_of_Feedback_Messages_PeerReviews
1.689e+02
Total_number_of_characters
2.543e-01
t
-9.239e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-132230 -25148 -2183 21576 157722
Coefficients:
Estimate Std. Error t value
(Intercept) 1.098e+05 5.076e+04 2.164
Maand -8.640e+03 4.460e+03 -1.937
Number_of_Logins 7.302e+02 1.090e+02 6.701
Total_Number_of_Blogged_Computations 1.033e+03 1.490e+02 6.933
Total_Number_of_Reviewed_Compendiums 3.791e+02 1.499e+03 0.253
Total_Number_of_Feedback_Messages_PeerReviews 1.689e+02 3.985e+02 0.424
Total_number_of_characters 2.543e-01 1.141e-01 2.228
t -9.239e+01 8.125e+01 -1.137
Pr(>|t|)
(Intercept) 0.0320 *
Maand 0.0545 .
Number_of_Logins 3.56e-10 ***
Total_Number_of_Blogged_Computations 1.03e-10 ***
Total_Number_of_Reviewed_Compendiums 0.8007
Total_Number_of_Feedback_Messages_PeerReviews 0.6722
Total_number_of_characters 0.0273 *
t 0.2572
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 46220 on 156 degrees of freedom
Multiple R-squared: 0.7784, Adjusted R-squared: 0.7685
F-statistic: 78.29 on 7 and 156 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,] 0.7831098 4.337805e-01 2.168902e-01
[2,] 0.6586632 6.826736e-01 3.413368e-01
[3,] 0.5739875 8.520250e-01 4.260125e-01
[4,] 0.4507410 9.014819e-01 5.492590e-01
[5,] 0.3684177 7.368353e-01 6.315823e-01
[6,] 0.4002980 8.005959e-01 5.997020e-01
[7,] 0.6391237 7.217526e-01 3.608763e-01
[8,] 0.5463135 9.073730e-01 4.536865e-01
[9,] 0.5690245 8.619509e-01 4.309755e-01
[10,] 0.4988622 9.977244e-01 5.011378e-01
[11,] 0.4367840 8.735679e-01 5.632160e-01
[12,] 0.9379754 1.240492e-01 6.202458e-02
[13,] 0.9144616 1.710769e-01 8.553843e-02
[14,] 0.8976924 2.046151e-01 1.023076e-01
[15,] 0.9497246 1.005508e-01 5.027541e-02
[16,] 0.9331552 1.336895e-01 6.684476e-02
[17,] 0.9094068 1.811863e-01 9.059315e-02
[18,] 0.8833995 2.332010e-01 1.166005e-01
[19,] 0.8661019 2.677963e-01 1.338981e-01
[20,] 0.8410964 3.178073e-01 1.589036e-01
[21,] 0.8014197 3.971607e-01 1.985803e-01
[22,] 0.7655360 4.689280e-01 2.344640e-01
[23,] 0.7302402 5.395197e-01 2.697598e-01
[24,] 0.7146495 5.707010e-01 2.853505e-01
[25,] 0.7983147 4.033706e-01 2.016853e-01
[26,] 0.8538462 2.923077e-01 1.461538e-01
[27,] 0.8946184 2.107631e-01 1.053816e-01
[28,] 0.8671799 2.656402e-01 1.328201e-01
[29,] 0.8524926 2.950147e-01 1.475074e-01
[30,] 0.8383756 3.232488e-01 1.616244e-01
[31,] 0.8181149 3.637702e-01 1.818851e-01
[32,] 0.8107410 3.785180e-01 1.892590e-01
[33,] 0.8295490 3.409021e-01 1.704510e-01
[34,] 0.8066141 3.867717e-01 1.933859e-01
[35,] 0.8793121 2.413758e-01 1.206879e-01
[36,] 0.9529029 9.419414e-02 4.709707e-02
[37,] 0.9417685 1.164631e-01 5.823154e-02
[38,] 0.9301469 1.397062e-01 6.985311e-02
[39,] 0.9132191 1.735618e-01 8.678091e-02
[40,] 0.9060313 1.879374e-01 9.396869e-02
[41,] 0.8899277 2.201446e-01 1.100723e-01
[42,] 0.8866292 2.267417e-01 1.133708e-01
[43,] 0.8814053 2.371895e-01 1.185947e-01
[44,] 0.8549592 2.900816e-01 1.450408e-01
[45,] 0.8283003 3.433994e-01 1.716997e-01
[46,] 0.8459088 3.081824e-01 1.540912e-01
[47,] 0.8172795 3.654410e-01 1.827205e-01
[48,] 0.7996175 4.007650e-01 2.003825e-01
[49,] 0.7875273 4.249455e-01 2.124727e-01
[50,] 0.7539788 4.920424e-01 2.460212e-01
[51,] 0.7241739 5.516522e-01 2.758261e-01
[52,] 0.6999319 6.001362e-01 3.000681e-01
[53,] 0.6602410 6.795180e-01 3.397590e-01
[54,] 0.6510997 6.978006e-01 3.489003e-01
[55,] 0.6596521 6.806957e-01 3.403479e-01
[56,] 0.6187665 7.624670e-01 3.812335e-01
[57,] 0.6422510 7.154980e-01 3.577490e-01
[58,] 0.6252298 7.495404e-01 3.747702e-01
[59,] 0.6057690 7.884621e-01 3.942310e-01
[60,] 0.7686111 4.627778e-01 2.313889e-01
[61,] 0.7348873 5.302254e-01 2.651127e-01
[62,] 0.6952779 6.094442e-01 3.047221e-01
[63,] 0.6679988 6.640025e-01 3.320012e-01
[64,] 0.6243741 7.512518e-01 3.756259e-01
[65,] 0.6554489 6.891022e-01 3.445511e-01
[66,] 0.6760760 6.478481e-01 3.239240e-01
[67,] 0.6511923 6.976153e-01 3.488077e-01
[68,] 0.6974616 6.050768e-01 3.025384e-01
[69,] 0.7135707 5.728586e-01 2.864293e-01
[70,] 0.6725843 6.548313e-01 3.274157e-01
[71,] 0.6297403 7.405194e-01 3.702597e-01
[72,] 0.7111103 5.777794e-01 2.888897e-01
[73,] 0.6860042 6.279915e-01 3.139958e-01
[74,] 0.6497527 7.004946e-01 3.502473e-01
[75,] 0.6157934 7.684132e-01 3.842066e-01
[76,] 0.5989693 8.020614e-01 4.010307e-01
[77,] 0.5642726 8.714549e-01 4.357274e-01
[78,] 0.5990895 8.018210e-01 4.009105e-01
[79,] 0.5876821 8.246358e-01 4.123179e-01
[80,] 0.8310715 3.378570e-01 1.689285e-01
[81,] 0.8160750 3.678501e-01 1.839250e-01
[82,] 0.8463829 3.072342e-01 1.536171e-01
[83,] 0.8340623 3.318753e-01 1.659377e-01
[84,] 0.8242780 3.514439e-01 1.757220e-01
[85,] 0.8714359 2.571283e-01 1.285641e-01
[86,] 0.8448759 3.102482e-01 1.551241e-01
[87,] 0.8190451 3.619099e-01 1.809549e-01
[88,] 0.7920008 4.159984e-01 2.079992e-01
[89,] 0.7605704 4.788591e-01 2.394296e-01
[90,] 0.7777801 4.444399e-01 2.222199e-01
[91,] 0.7454144 5.091712e-01 2.545856e-01
[92,] 0.7080286 5.839428e-01 2.919714e-01
[93,] 0.7074874 5.850252e-01 2.925126e-01
[94,] 0.6653083 6.693833e-01 3.346917e-01
[95,] 0.6225727 7.548546e-01 3.774273e-01
[96,] 0.6494571 7.010858e-01 3.505429e-01
[97,] 0.6170910 7.658179e-01 3.829090e-01
[98,] 0.5729254 8.541491e-01 4.270746e-01
[99,] 0.5568453 8.863094e-01 4.431547e-01
[100,] 0.7997472 4.005055e-01 2.002528e-01
[101,] 0.8712647 2.574706e-01 1.287353e-01
[102,] 0.8457979 3.084042e-01 1.542021e-01
[103,] 0.8161643 3.676714e-01 1.838357e-01
[104,] 0.8485061 3.029879e-01 1.514939e-01
[105,] 0.8187888 3.624224e-01 1.812112e-01
[106,] 0.7996194 4.007612e-01 2.003806e-01
[107,] 0.8410894 3.178212e-01 1.589106e-01
[108,] 0.8062386 3.875228e-01 1.937614e-01
[109,] 0.8073981 3.852038e-01 1.926019e-01
[110,] 0.7849794 4.300413e-01 2.150206e-01
[111,] 0.7442891 5.114218e-01 2.557109e-01
[112,] 0.7042632 5.914736e-01 2.957368e-01
[113,] 0.6613141 6.773719e-01 3.386859e-01
[114,] 0.6160008 7.679984e-01 3.839992e-01
[115,] 0.5607166 8.785667e-01 4.392834e-01
[116,] 0.5053887 9.892226e-01 4.946113e-01
[117,] 0.5336167 9.327667e-01 4.663833e-01
[118,] 0.4788084 9.576168e-01 5.211916e-01
[119,] 0.5637109 8.725782e-01 4.362891e-01
[120,] 0.5056447 9.887105e-01 4.943553e-01
[121,] 0.4539752 9.079504e-01 5.460248e-01
[122,] 0.4182338 8.364676e-01 5.817662e-01
[123,] 0.3943596 7.887191e-01 6.056404e-01
[124,] 0.9944899 1.102018e-02 5.510091e-03
[125,] 0.9925518 1.489648e-02 7.448241e-03
[126,] 0.9912675 1.746500e-02 8.732498e-03
[127,] 0.9868676 2.626476e-02 1.313238e-02
[128,] 0.9891065 2.178700e-02 1.089350e-02
[129,] 0.9987700 2.459956e-03 1.229978e-03
[130,] 0.9979680 4.063905e-03 2.031952e-03
[131,] 0.9999962 7.598594e-06 3.799297e-06
[132,] 0.9999999 2.736840e-07 1.368420e-07
[133,] 0.9999995 1.083853e-06 5.419264e-07
[134,] 0.9999980 3.940607e-06 1.970303e-06
[135,] 1.0000000 6.207831e-09 3.103916e-09
[136,] 1.0000000 5.047765e-09 2.523882e-09
[137,] 1.0000000 3.772954e-08 1.886477e-08
[138,] 0.9999999 2.020254e-07 1.010127e-07
[139,] 0.9999993 1.425704e-06 7.128520e-07
[140,] 0.9999942 1.152924e-05 5.764618e-06
[141,] 0.9999437 1.126828e-04 5.634141e-05
[142,] 0.9994828 1.034473e-03 5.172367e-04
[143,] 0.9960042 7.991586e-03 3.995793e-03
> postscript(file="/var/wessaorg/rcomp/tmp/154kq1323344450.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/2au1u1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3vley1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tm6h1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ps2r1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
22875.2183 -51149.2657 19346.7595 -67601.8293 10044.9826 -36429.4585
7 8 9 10 11 12
127962.0911 -19698.5774 -12542.8866 12144.0270 42252.8332 -66467.5649
13 14 15 16 17 18
-7388.9607 58853.3963 -1791.3120 -34826.3041 -2919.2472 -6087.8135
19 20 21 22 23 24
-37667.3926 -45287.9328 3210.5463 157721.7572 13432.3509 -57097.9973
25 26 27 28 29 30
-89112.1675 -33117.3150 -8947.9340 5774.8551 6362.5402 22547.6598
31 32 33 34 35 36
-15567.3810 16746.2792 18460.0638 -46462.8932 81872.6094 74497.1905
37 38 39 40 41 42
96834.0825 11064.8511 14505.4214 -11205.9708 44606.1756 -19202.0784
43 44 45 46 47 48
536.2925 -25201.8971 -36304.9636 121352.4305 -15064.2820 -25129.7625
49 50 51 52 53 54
13035.4736 -38134.2233 -14684.5478 -25266.0475 -25511.8003 3362.3179
55 56 57 58 59 60
-8463.4879 65144.8732 -9608.5955 -13861.6253 -27193.3134 11719.1454
61 62 63 64 65 66
22198.1309 139.5256 1614.7827 38196.6868 48204.7348 4156.0515
67 68 69 70 71 72
-59280.9724 -44599.1008 -28413.6231 -104643.6056 4261.2625 -1340.5234
73 74 75 76 77 78
22006.9228 -1129.0320 -67024.9293 53854.5874 26292.2061 -72356.4846
79 80 81 82 83 84
-59540.7083 -2735.0919 -2574.8817 76392.5654 23898.4063 -5555.8748
85 86 87 88 89 90
-25628.3545 32156.3647 -27806.1510 56060.3709 39770.7816 -132229.6707
91 92 93 94 95 96
27305.1049 -67712.8346 28299.6826 28158.2030 67874.2910 -11701.6317
97 98 99 100 101 102
15458.6219 -12900.7017 -24504.5053 57882.5319 -26066.1969 8287.3055
103 104 105 106 107 108
-58583.3724 -15850.0060 -16574.5760 -62599.0637 19639.7541 -17090.5532
109 110 111 112 113 114
-41055.6453 -112285.0509 84804.7966 -10239.4657 6826.4386 -59858.8351
115 116 117 118 119 120
-8915.1873 -28223.6029 73246.2149 1149.2382 53412.8589 -24222.9057
121 122 123 124 125 126
13910.8885 15042.6518 -14744.7032 -10991.5293 -7547.6213 14116.6052
127 128 129 130 131 132
58288.7974 5317.9528 78906.1154 -19334.0933 27275.8871 23997.4847
133 134 135 136 137 138
-34548.1933 -92508.7752 90196.9451 -33046.3622 45922.7268 -22000.8014
139 140 141 142 143 144
21432.5025 -3107.4422 11482.9131 90047.4888 -33751.8179 61309.3919
145 146 147 148 149 150
-7091.0313 -132015.9595 -13443.0448 -38022.6514 -9648.3483 -7833.1412
151 152 153 154 155 156
-1456.8496 6902.2576 8000.0014 -9187.3907 30235.4040 37260.4353
157 158 159 160 161 162
8369.5675 -11535.5289 -12824.4947 5029.8452 11942.2417 -7568.3338
163 164
8432.5645 19212.7666
> postscript(file="/var/wessaorg/rcomp/tmp/6lyqd1323344450.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 22875.2183 NA
1 -51149.2657 22875.2183
2 19346.7595 -51149.2657
3 -67601.8293 19346.7595
4 10044.9826 -67601.8293
5 -36429.4585 10044.9826
6 127962.0911 -36429.4585
7 -19698.5774 127962.0911
8 -12542.8866 -19698.5774
9 12144.0270 -12542.8866
10 42252.8332 12144.0270
11 -66467.5649 42252.8332
12 -7388.9607 -66467.5649
13 58853.3963 -7388.9607
14 -1791.3120 58853.3963
15 -34826.3041 -1791.3120
16 -2919.2472 -34826.3041
17 -6087.8135 -2919.2472
18 -37667.3926 -6087.8135
19 -45287.9328 -37667.3926
20 3210.5463 -45287.9328
21 157721.7572 3210.5463
22 13432.3509 157721.7572
23 -57097.9973 13432.3509
24 -89112.1675 -57097.9973
25 -33117.3150 -89112.1675
26 -8947.9340 -33117.3150
27 5774.8551 -8947.9340
28 6362.5402 5774.8551
29 22547.6598 6362.5402
30 -15567.3810 22547.6598
31 16746.2792 -15567.3810
32 18460.0638 16746.2792
33 -46462.8932 18460.0638
34 81872.6094 -46462.8932
35 74497.1905 81872.6094
36 96834.0825 74497.1905
37 11064.8511 96834.0825
38 14505.4214 11064.8511
39 -11205.9708 14505.4214
40 44606.1756 -11205.9708
41 -19202.0784 44606.1756
42 536.2925 -19202.0784
43 -25201.8971 536.2925
44 -36304.9636 -25201.8971
45 121352.4305 -36304.9636
46 -15064.2820 121352.4305
47 -25129.7625 -15064.2820
48 13035.4736 -25129.7625
49 -38134.2233 13035.4736
50 -14684.5478 -38134.2233
51 -25266.0475 -14684.5478
52 -25511.8003 -25266.0475
53 3362.3179 -25511.8003
54 -8463.4879 3362.3179
55 65144.8732 -8463.4879
56 -9608.5955 65144.8732
57 -13861.6253 -9608.5955
58 -27193.3134 -13861.6253
59 11719.1454 -27193.3134
60 22198.1309 11719.1454
61 139.5256 22198.1309
62 1614.7827 139.5256
63 38196.6868 1614.7827
64 48204.7348 38196.6868
65 4156.0515 48204.7348
66 -59280.9724 4156.0515
67 -44599.1008 -59280.9724
68 -28413.6231 -44599.1008
69 -104643.6056 -28413.6231
70 4261.2625 -104643.6056
71 -1340.5234 4261.2625
72 22006.9228 -1340.5234
73 -1129.0320 22006.9228
74 -67024.9293 -1129.0320
75 53854.5874 -67024.9293
76 26292.2061 53854.5874
77 -72356.4846 26292.2061
78 -59540.7083 -72356.4846
79 -2735.0919 -59540.7083
80 -2574.8817 -2735.0919
81 76392.5654 -2574.8817
82 23898.4063 76392.5654
83 -5555.8748 23898.4063
84 -25628.3545 -5555.8748
85 32156.3647 -25628.3545
86 -27806.1510 32156.3647
87 56060.3709 -27806.1510
88 39770.7816 56060.3709
89 -132229.6707 39770.7816
90 27305.1049 -132229.6707
91 -67712.8346 27305.1049
92 28299.6826 -67712.8346
93 28158.2030 28299.6826
94 67874.2910 28158.2030
95 -11701.6317 67874.2910
96 15458.6219 -11701.6317
97 -12900.7017 15458.6219
98 -24504.5053 -12900.7017
99 57882.5319 -24504.5053
100 -26066.1969 57882.5319
101 8287.3055 -26066.1969
102 -58583.3724 8287.3055
103 -15850.0060 -58583.3724
104 -16574.5760 -15850.0060
105 -62599.0637 -16574.5760
106 19639.7541 -62599.0637
107 -17090.5532 19639.7541
108 -41055.6453 -17090.5532
109 -112285.0509 -41055.6453
110 84804.7966 -112285.0509
111 -10239.4657 84804.7966
112 6826.4386 -10239.4657
113 -59858.8351 6826.4386
114 -8915.1873 -59858.8351
115 -28223.6029 -8915.1873
116 73246.2149 -28223.6029
117 1149.2382 73246.2149
118 53412.8589 1149.2382
119 -24222.9057 53412.8589
120 13910.8885 -24222.9057
121 15042.6518 13910.8885
122 -14744.7032 15042.6518
123 -10991.5293 -14744.7032
124 -7547.6213 -10991.5293
125 14116.6052 -7547.6213
126 58288.7974 14116.6052
127 5317.9528 58288.7974
128 78906.1154 5317.9528
129 -19334.0933 78906.1154
130 27275.8871 -19334.0933
131 23997.4847 27275.8871
132 -34548.1933 23997.4847
133 -92508.7752 -34548.1933
134 90196.9451 -92508.7752
135 -33046.3622 90196.9451
136 45922.7268 -33046.3622
137 -22000.8014 45922.7268
138 21432.5025 -22000.8014
139 -3107.4422 21432.5025
140 11482.9131 -3107.4422
141 90047.4888 11482.9131
142 -33751.8179 90047.4888
143 61309.3919 -33751.8179
144 -7091.0313 61309.3919
145 -132015.9595 -7091.0313
146 -13443.0448 -132015.9595
147 -38022.6514 -13443.0448
148 -9648.3483 -38022.6514
149 -7833.1412 -9648.3483
150 -1456.8496 -7833.1412
151 6902.2576 -1456.8496
152 8000.0014 6902.2576
153 -9187.3907 8000.0014
154 30235.4040 -9187.3907
155 37260.4353 30235.4040
156 8369.5675 37260.4353
157 -11535.5289 8369.5675
158 -12824.4947 -11535.5289
159 5029.8452 -12824.4947
160 11942.2417 5029.8452
161 -7568.3338 11942.2417
162 8432.5645 -7568.3338
163 19212.7666 8432.5645
164 NA 19212.7666
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -51149.2657 22875.2183
[2,] 19346.7595 -51149.2657
[3,] -67601.8293 19346.7595
[4,] 10044.9826 -67601.8293
[5,] -36429.4585 10044.9826
[6,] 127962.0911 -36429.4585
[7,] -19698.5774 127962.0911
[8,] -12542.8866 -19698.5774
[9,] 12144.0270 -12542.8866
[10,] 42252.8332 12144.0270
[11,] -66467.5649 42252.8332
[12,] -7388.9607 -66467.5649
[13,] 58853.3963 -7388.9607
[14,] -1791.3120 58853.3963
[15,] -34826.3041 -1791.3120
[16,] -2919.2472 -34826.3041
[17,] -6087.8135 -2919.2472
[18,] -37667.3926 -6087.8135
[19,] -45287.9328 -37667.3926
[20,] 3210.5463 -45287.9328
[21,] 157721.7572 3210.5463
[22,] 13432.3509 157721.7572
[23,] -57097.9973 13432.3509
[24,] -89112.1675 -57097.9973
[25,] -33117.3150 -89112.1675
[26,] -8947.9340 -33117.3150
[27,] 5774.8551 -8947.9340
[28,] 6362.5402 5774.8551
[29,] 22547.6598 6362.5402
[30,] -15567.3810 22547.6598
[31,] 16746.2792 -15567.3810
[32,] 18460.0638 16746.2792
[33,] -46462.8932 18460.0638
[34,] 81872.6094 -46462.8932
[35,] 74497.1905 81872.6094
[36,] 96834.0825 74497.1905
[37,] 11064.8511 96834.0825
[38,] 14505.4214 11064.8511
[39,] -11205.9708 14505.4214
[40,] 44606.1756 -11205.9708
[41,] -19202.0784 44606.1756
[42,] 536.2925 -19202.0784
[43,] -25201.8971 536.2925
[44,] -36304.9636 -25201.8971
[45,] 121352.4305 -36304.9636
[46,] -15064.2820 121352.4305
[47,] -25129.7625 -15064.2820
[48,] 13035.4736 -25129.7625
[49,] -38134.2233 13035.4736
[50,] -14684.5478 -38134.2233
[51,] -25266.0475 -14684.5478
[52,] -25511.8003 -25266.0475
[53,] 3362.3179 -25511.8003
[54,] -8463.4879 3362.3179
[55,] 65144.8732 -8463.4879
[56,] -9608.5955 65144.8732
[57,] -13861.6253 -9608.5955
[58,] -27193.3134 -13861.6253
[59,] 11719.1454 -27193.3134
[60,] 22198.1309 11719.1454
[61,] 139.5256 22198.1309
[62,] 1614.7827 139.5256
[63,] 38196.6868 1614.7827
[64,] 48204.7348 38196.6868
[65,] 4156.0515 48204.7348
[66,] -59280.9724 4156.0515
[67,] -44599.1008 -59280.9724
[68,] -28413.6231 -44599.1008
[69,] -104643.6056 -28413.6231
[70,] 4261.2625 -104643.6056
[71,] -1340.5234 4261.2625
[72,] 22006.9228 -1340.5234
[73,] -1129.0320 22006.9228
[74,] -67024.9293 -1129.0320
[75,] 53854.5874 -67024.9293
[76,] 26292.2061 53854.5874
[77,] -72356.4846 26292.2061
[78,] -59540.7083 -72356.4846
[79,] -2735.0919 -59540.7083
[80,] -2574.8817 -2735.0919
[81,] 76392.5654 -2574.8817
[82,] 23898.4063 76392.5654
[83,] -5555.8748 23898.4063
[84,] -25628.3545 -5555.8748
[85,] 32156.3647 -25628.3545
[86,] -27806.1510 32156.3647
[87,] 56060.3709 -27806.1510
[88,] 39770.7816 56060.3709
[89,] -132229.6707 39770.7816
[90,] 27305.1049 -132229.6707
[91,] -67712.8346 27305.1049
[92,] 28299.6826 -67712.8346
[93,] 28158.2030 28299.6826
[94,] 67874.2910 28158.2030
[95,] -11701.6317 67874.2910
[96,] 15458.6219 -11701.6317
[97,] -12900.7017 15458.6219
[98,] -24504.5053 -12900.7017
[99,] 57882.5319 -24504.5053
[100,] -26066.1969 57882.5319
[101,] 8287.3055 -26066.1969
[102,] -58583.3724 8287.3055
[103,] -15850.0060 -58583.3724
[104,] -16574.5760 -15850.0060
[105,] -62599.0637 -16574.5760
[106,] 19639.7541 -62599.0637
[107,] -17090.5532 19639.7541
[108,] -41055.6453 -17090.5532
[109,] -112285.0509 -41055.6453
[110,] 84804.7966 -112285.0509
[111,] -10239.4657 84804.7966
[112,] 6826.4386 -10239.4657
[113,] -59858.8351 6826.4386
[114,] -8915.1873 -59858.8351
[115,] -28223.6029 -8915.1873
[116,] 73246.2149 -28223.6029
[117,] 1149.2382 73246.2149
[118,] 53412.8589 1149.2382
[119,] -24222.9057 53412.8589
[120,] 13910.8885 -24222.9057
[121,] 15042.6518 13910.8885
[122,] -14744.7032 15042.6518
[123,] -10991.5293 -14744.7032
[124,] -7547.6213 -10991.5293
[125,] 14116.6052 -7547.6213
[126,] 58288.7974 14116.6052
[127,] 5317.9528 58288.7974
[128,] 78906.1154 5317.9528
[129,] -19334.0933 78906.1154
[130,] 27275.8871 -19334.0933
[131,] 23997.4847 27275.8871
[132,] -34548.1933 23997.4847
[133,] -92508.7752 -34548.1933
[134,] 90196.9451 -92508.7752
[135,] -33046.3622 90196.9451
[136,] 45922.7268 -33046.3622
[137,] -22000.8014 45922.7268
[138,] 21432.5025 -22000.8014
[139,] -3107.4422 21432.5025
[140,] 11482.9131 -3107.4422
[141,] 90047.4888 11482.9131
[142,] -33751.8179 90047.4888
[143,] 61309.3919 -33751.8179
[144,] -7091.0313 61309.3919
[145,] -132015.9595 -7091.0313
[146,] -13443.0448 -132015.9595
[147,] -38022.6514 -13443.0448
[148,] -9648.3483 -38022.6514
[149,] -7833.1412 -9648.3483
[150,] -1456.8496 -7833.1412
[151,] 6902.2576 -1456.8496
[152,] 8000.0014 6902.2576
[153,] -9187.3907 8000.0014
[154,] 30235.4040 -9187.3907
[155,] 37260.4353 30235.4040
[156,] 8369.5675 37260.4353
[157,] -11535.5289 8369.5675
[158,] -12824.4947 -11535.5289
[159,] 5029.8452 -12824.4947
[160,] 11942.2417 5029.8452
[161,] -7568.3338 11942.2417
[162,] 8432.5645 -7568.3338
[163,] 19212.7666 8432.5645
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -51149.2657 22875.2183
2 19346.7595 -51149.2657
3 -67601.8293 19346.7595
4 10044.9826 -67601.8293
5 -36429.4585 10044.9826
6 127962.0911 -36429.4585
7 -19698.5774 127962.0911
8 -12542.8866 -19698.5774
9 12144.0270 -12542.8866
10 42252.8332 12144.0270
11 -66467.5649 42252.8332
12 -7388.9607 -66467.5649
13 58853.3963 -7388.9607
14 -1791.3120 58853.3963
15 -34826.3041 -1791.3120
16 -2919.2472 -34826.3041
17 -6087.8135 -2919.2472
18 -37667.3926 -6087.8135
19 -45287.9328 -37667.3926
20 3210.5463 -45287.9328
21 157721.7572 3210.5463
22 13432.3509 157721.7572
23 -57097.9973 13432.3509
24 -89112.1675 -57097.9973
25 -33117.3150 -89112.1675
26 -8947.9340 -33117.3150
27 5774.8551 -8947.9340
28 6362.5402 5774.8551
29 22547.6598 6362.5402
30 -15567.3810 22547.6598
31 16746.2792 -15567.3810
32 18460.0638 16746.2792
33 -46462.8932 18460.0638
34 81872.6094 -46462.8932
35 74497.1905 81872.6094
36 96834.0825 74497.1905
37 11064.8511 96834.0825
38 14505.4214 11064.8511
39 -11205.9708 14505.4214
40 44606.1756 -11205.9708
41 -19202.0784 44606.1756
42 536.2925 -19202.0784
43 -25201.8971 536.2925
44 -36304.9636 -25201.8971
45 121352.4305 -36304.9636
46 -15064.2820 121352.4305
47 -25129.7625 -15064.2820
48 13035.4736 -25129.7625
49 -38134.2233 13035.4736
50 -14684.5478 -38134.2233
51 -25266.0475 -14684.5478
52 -25511.8003 -25266.0475
53 3362.3179 -25511.8003
54 -8463.4879 3362.3179
55 65144.8732 -8463.4879
56 -9608.5955 65144.8732
57 -13861.6253 -9608.5955
58 -27193.3134 -13861.6253
59 11719.1454 -27193.3134
60 22198.1309 11719.1454
61 139.5256 22198.1309
62 1614.7827 139.5256
63 38196.6868 1614.7827
64 48204.7348 38196.6868
65 4156.0515 48204.7348
66 -59280.9724 4156.0515
67 -44599.1008 -59280.9724
68 -28413.6231 -44599.1008
69 -104643.6056 -28413.6231
70 4261.2625 -104643.6056
71 -1340.5234 4261.2625
72 22006.9228 -1340.5234
73 -1129.0320 22006.9228
74 -67024.9293 -1129.0320
75 53854.5874 -67024.9293
76 26292.2061 53854.5874
77 -72356.4846 26292.2061
78 -59540.7083 -72356.4846
79 -2735.0919 -59540.7083
80 -2574.8817 -2735.0919
81 76392.5654 -2574.8817
82 23898.4063 76392.5654
83 -5555.8748 23898.4063
84 -25628.3545 -5555.8748
85 32156.3647 -25628.3545
86 -27806.1510 32156.3647
87 56060.3709 -27806.1510
88 39770.7816 56060.3709
89 -132229.6707 39770.7816
90 27305.1049 -132229.6707
91 -67712.8346 27305.1049
92 28299.6826 -67712.8346
93 28158.2030 28299.6826
94 67874.2910 28158.2030
95 -11701.6317 67874.2910
96 15458.6219 -11701.6317
97 -12900.7017 15458.6219
98 -24504.5053 -12900.7017
99 57882.5319 -24504.5053
100 -26066.1969 57882.5319
101 8287.3055 -26066.1969
102 -58583.3724 8287.3055
103 -15850.0060 -58583.3724
104 -16574.5760 -15850.0060
105 -62599.0637 -16574.5760
106 19639.7541 -62599.0637
107 -17090.5532 19639.7541
108 -41055.6453 -17090.5532
109 -112285.0509 -41055.6453
110 84804.7966 -112285.0509
111 -10239.4657 84804.7966
112 6826.4386 -10239.4657
113 -59858.8351 6826.4386
114 -8915.1873 -59858.8351
115 -28223.6029 -8915.1873
116 73246.2149 -28223.6029
117 1149.2382 73246.2149
118 53412.8589 1149.2382
119 -24222.9057 53412.8589
120 13910.8885 -24222.9057
121 15042.6518 13910.8885
122 -14744.7032 15042.6518
123 -10991.5293 -14744.7032
124 -7547.6213 -10991.5293
125 14116.6052 -7547.6213
126 58288.7974 14116.6052
127 5317.9528 58288.7974
128 78906.1154 5317.9528
129 -19334.0933 78906.1154
130 27275.8871 -19334.0933
131 23997.4847 27275.8871
132 -34548.1933 23997.4847
133 -92508.7752 -34548.1933
134 90196.9451 -92508.7752
135 -33046.3622 90196.9451
136 45922.7268 -33046.3622
137 -22000.8014 45922.7268
138 21432.5025 -22000.8014
139 -3107.4422 21432.5025
140 11482.9131 -3107.4422
141 90047.4888 11482.9131
142 -33751.8179 90047.4888
143 61309.3919 -33751.8179
144 -7091.0313 61309.3919
145 -132015.9595 -7091.0313
146 -13443.0448 -132015.9595
147 -38022.6514 -13443.0448
148 -9648.3483 -38022.6514
149 -7833.1412 -9648.3483
150 -1456.8496 -7833.1412
151 6902.2576 -1456.8496
152 8000.0014 6902.2576
153 -9187.3907 8000.0014
154 30235.4040 -9187.3907
155 37260.4353 30235.4040
156 8369.5675 37260.4353
157 -11535.5289 8369.5675
158 -12824.4947 -11535.5289
159 5029.8452 -12824.4947
160 11942.2417 5029.8452
161 -7568.3338 11942.2417
162 8432.5645 -7568.3338
163 19212.7666 8432.5645
> 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/7sn7p1323344450.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/819u71323344450.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/9eexo1323344450.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/109zj41323344450.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11ky3e1323344450.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12kav91323344450.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/132x0n1323344450.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14wr0o1323344450.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/15e8dg1323344450.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/16i8nw1323344451.tab")
+ }
>
> try(system("convert tmp/154kq1323344450.ps tmp/154kq1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/2au1u1323344450.ps tmp/2au1u1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vley1323344450.ps tmp/3vley1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tm6h1323344450.ps tmp/4tm6h1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ps2r1323344450.ps tmp/5ps2r1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lyqd1323344450.ps tmp/6lyqd1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sn7p1323344450.ps tmp/7sn7p1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/819u71323344450.ps tmp/819u71323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/9eexo1323344450.ps tmp/9eexo1323344450.png",intern=TRUE))
character(0)
> try(system("convert tmp/109zj41323344450.ps tmp/109zj41323344450.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.082 0.507 5.600