R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(0
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+ ,100009
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+ ,1
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+ ,112578
+ ,123663
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+ ,1
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+ ,1
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+ ,128390
+ ,1
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+ ,135963
+ ,134197
+ ,1
+ ,144
+ ,144
+ ,131510
+ ,143231
+ ,146803
+ ,135936
+ ,135963)
+ ,dim=c(8
+ ,140)
+ ,dimnames=list(c('crisis_10/8'
+ ,'t'
+ ,'t_crisis_10/8'
+ ,'Totale_goederenvervoer_ton'
+ ,'Totale_goederenvervoer_ton-1'
+ ,'Totale_goederenvervoer_ton-2'
+ ,'Totale_goederenvervoer_ton-3'
+ ,'Totale_goederenvervoer_ton-4')
+ ,1:140))
> y <- array(NA,dim=c(8,140),dimnames=list(c('crisis_10/8','t','t_crisis_10/8','Totale_goederenvervoer_ton','Totale_goederenvervoer_ton-1','Totale_goederenvervoer_ton-2','Totale_goederenvervoer_ton-3','Totale_goederenvervoer_ton-4'),1:140))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '4'
> 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
Totale_goederenvervoer_ton crisis_10/8 t t_crisis_10/8
1 100009 0 5 0
2 95558 0 6 0
3 98533 0 7 0
4 92694 0 8 0
5 97920 0 9 0
6 110933 0 10 0
7 110855 0 11 0
8 111716 0 12 0
9 96348 0 13 0
10 105425 0 14 0
11 114874 0 15 0
12 104199 0 16 0
13 101166 0 17 0
14 99010 0 18 0
15 101607 0 19 0
16 97492 0 20 0
17 106088 0 21 0
18 113536 0 22 0
19 112475 0 23 0
20 115491 0 24 0
21 97733 0 25 0
22 102591 0 26 0
23 114783 0 27 0
24 100397 0 28 0
25 97772 0 29 0
26 96128 0 30 0
27 91261 0 31 0
28 90686 0 32 0
29 97792 0 33 0
30 108848 0 34 0
31 109989 0 35 0
32 109453 0 36 0
33 93945 0 37 0
34 98750 0 38 0
35 119043 0 39 0
36 104776 0 40 0
37 103262 0 41 0
38 106735 0 42 0
39 101600 0 43 0
40 99358 0 44 0
41 105240 0 45 0
42 114079 0 46 0
43 121637 0 47 0
44 111747 0 48 0
45 99496 0 49 0
46 104992 0 50 0
47 124255 0 51 0
48 108258 0 52 0
49 106940 0 53 0
50 104939 0 54 0
51 105896 0 55 0
52 107287 0 56 0
53 110783 0 57 0
54 122139 0 58 0
55 125823 0 59 0
56 120480 0 60 0
57 103296 0 61 0
58 117121 0 62 0
59 129924 0 63 0
60 118589 0 64 0
61 118062 0 65 0
62 113597 0 66 0
63 117161 0 67 0
64 112893 0 68 0
65 119657 0 69 0
66 136562 0 70 0
67 140446 0 71 0
68 138744 0 72 0
69 120324 0 73 0
70 118113 0 74 0
71 130257 0 75 0
72 125510 0 76 0
73 117986 0 77 0
74 118316 0 78 0
75 122075 0 79 0
76 117573 0 80 0
77 122566 0 81 0
78 135934 0 82 0
79 138394 0 83 0
80 137999 0 84 0
81 118780 0 85 0
82 117907 0 86 0
83 142932 0 87 0
84 132200 0 88 0
85 125666 0 89 0
86 127958 0 90 0
87 127718 0 91 0
88 124368 0 92 0
89 135241 0 93 0
90 144734 0 94 0
91 142320 0 95 0
92 141481 0 96 0
93 120471 0 97 0
94 123422 0 98 0
95 145829 0 99 0
96 134572 0 100 0
97 132156 0 101 0
98 140265 0 102 0
99 137771 0 103 0
100 134035 0 104 0
101 144016 0 105 0
102 151905 0 106 0
103 155791 0 107 0
104 148440 0 108 0
105 129862 0 109 0
106 134264 0 110 0
107 151952 0 111 0
108 143191 0 112 0
109 137242 0 113 0
110 136993 0 114 0
111 134431 0 115 0
112 132523 0 116 0
113 133486 0 117 0
114 140120 0 118 0
115 137521 1 119 119
116 112193 1 120 120
117 94256 1 121 121
118 99047 1 122 122
119 109761 1 123 123
120 102160 1 124 124
121 104792 1 125 125
122 104341 1 126 126
123 112430 1 127 127
124 113034 1 128 128
125 114197 1 129 129
126 127876 1 130 130
127 135199 1 131 131
128 123663 1 132 132
129 112578 1 133 133
130 117104 1 134 134
131 139703 1 135 135
132 114961 1 136 136
133 134222 1 137 137
134 128390 1 138 138
135 134197 1 139 139
136 135963 1 140 140
137 135936 1 141 141
138 146803 1 142 142
139 143231 1 143 143
140 131510 1 144 144
Totale_goederenvervoer_ton-1 Totale_goederenvervoer_ton-2
1 100280 111940
2 100009 100280
3 95558 100009
4 98533 95558
5 92694 98533
6 97920 92694
7 110933 97920
8 110855 110933
9 111716 110855
10 96348 111716
11 105425 96348
12 114874 105425
13 104199 114874
14 101166 104199
15 99010 101166
16 101607 99010
17 97492 101607
18 106088 97492
19 113536 106088
20 112475 113536
21 115491 112475
22 97733 115491
23 102591 97733
24 114783 102591
25 100397 114783
26 97772 100397
27 96128 97772
28 91261 96128
29 90686 91261
30 97792 90686
31 108848 97792
32 109989 108848
33 109453 109989
34 93945 109453
35 98750 93945
36 119043 98750
37 104776 119043
38 103262 104776
39 106735 103262
40 101600 106735
41 99358 101600
42 105240 99358
43 114079 105240
44 121637 114079
45 111747 121637
46 99496 111747
47 104992 99496
48 124255 104992
49 108258 124255
50 106940 108258
51 104939 106940
52 105896 104939
53 107287 105896
54 110783 107287
55 122139 110783
56 125823 122139
57 120480 125823
58 103296 120480
59 117121 103296
60 129924 117121
61 118589 129924
62 118062 118589
63 113597 118062
64 117161 113597
65 112893 117161
66 119657 112893
67 136562 119657
68 140446 136562
69 138744 140446
70 120324 138744
71 118113 120324
72 130257 118113
73 125510 130257
74 117986 125510
75 118316 117986
76 122075 118316
77 117573 122075
78 122566 117573
79 135934 122566
80 138394 135934
81 137999 138394
82 118780 137999
83 117907 118780
84 142932 117907
85 132200 142932
86 125666 132200
87 127958 125666
88 127718 127958
89 124368 127718
90 135241 124368
91 144734 135241
92 142320 144734
93 141481 142320
94 120471 141481
95 123422 120471
96 145829 123422
97 134572 145829
98 132156 134572
99 140265 132156
100 137771 140265
101 134035 137771
102 144016 134035
103 151905 144016
104 155791 151905
105 148440 155791
106 129862 148440
107 134264 129862
108 151952 134264
109 143191 151952
110 137242 143191
111 136993 137242
112 134431 136993
113 132523 134431
114 133486 132523
115 140120 133486
116 137521 140120
117 112193 137521
118 94256 112193
119 99047 94256
120 109761 99047
121 102160 109761
122 104792 102160
123 104341 104792
124 112430 104341
125 113034 112430
126 114197 113034
127 127876 114197
128 135199 127876
129 123663 135199
130 112578 123663
131 117104 112578
132 139703 117104
133 114961 139703
134 134222 114961
135 128390 134222
136 134197 128390
137 135963 134197
138 135936 135963
139 146803 135936
140 143231 146803
Totale_goederenvervoer_ton-3 Totale_goederenvervoer_ton-4 M1 M2 M3 M4 M5 M6
1 97527 90604 1 0 0 0 0 0
2 111940 97527 0 1 0 0 0 0
3 100280 111940 0 0 1 0 0 0
4 100009 100280 0 0 0 1 0 0
5 95558 100009 0 0 0 0 1 0
6 98533 95558 0 0 0 0 0 1
7 92694 98533 0 0 0 0 0 0
8 97920 92694 0 0 0 0 0 0
9 110933 97920 0 0 0 0 0 0
10 110855 110933 0 0 0 0 0 0
11 111716 110855 0 0 0 0 0 0
12 96348 111716 0 0 0 0 0 0
13 105425 96348 1 0 0 0 0 0
14 114874 105425 0 1 0 0 0 0
15 104199 114874 0 0 1 0 0 0
16 101166 104199 0 0 0 1 0 0
17 99010 101166 0 0 0 0 1 0
18 101607 99010 0 0 0 0 0 1
19 97492 101607 0 0 0 0 0 0
20 106088 97492 0 0 0 0 0 0
21 113536 106088 0 0 0 0 0 0
22 112475 113536 0 0 0 0 0 0
23 115491 112475 0 0 0 0 0 0
24 97733 115491 0 0 0 0 0 0
25 102591 97733 1 0 0 0 0 0
26 114783 102591 0 1 0 0 0 0
27 100397 114783 0 0 1 0 0 0
28 97772 100397 0 0 0 1 0 0
29 96128 97772 0 0 0 0 1 0
30 91261 96128 0 0 0 0 0 1
31 90686 91261 0 0 0 0 0 0
32 97792 90686 0 0 0 0 0 0
33 108848 97792 0 0 0 0 0 0
34 109989 108848 0 0 0 0 0 0
35 109453 109989 0 0 0 0 0 0
36 93945 109453 0 0 0 0 0 0
37 98750 93945 1 0 0 0 0 0
38 119043 98750 0 1 0 0 0 0
39 104776 119043 0 0 1 0 0 0
40 103262 104776 0 0 0 1 0 0
41 106735 103262 0 0 0 0 1 0
42 101600 106735 0 0 0 0 0 1
43 99358 101600 0 0 0 0 0 0
44 105240 99358 0 0 0 0 0 0
45 114079 105240 0 0 0 0 0 0
46 121637 114079 0 0 0 0 0 0
47 111747 121637 0 0 0 0 0 0
48 99496 111747 0 0 0 0 0 0
49 104992 99496 1 0 0 0 0 0
50 124255 104992 0 1 0 0 0 0
51 108258 124255 0 0 1 0 0 0
52 106940 108258 0 0 0 1 0 0
53 104939 106940 0 0 0 0 1 0
54 105896 104939 0 0 0 0 0 1
55 107287 105896 0 0 0 0 0 0
56 110783 107287 0 0 0 0 0 0
57 122139 110783 0 0 0 0 0 0
58 125823 122139 0 0 0 0 0 0
59 120480 125823 0 0 0 0 0 0
60 103296 120480 0 0 0 0 0 0
61 117121 103296 1 0 0 0 0 0
62 129924 117121 0 1 0 0 0 0
63 118589 129924 0 0 1 0 0 0
64 118062 118589 0 0 0 1 0 0
65 113597 118062 0 0 0 0 1 0
66 117161 113597 0 0 0 0 0 1
67 112893 117161 0 0 0 0 0 0
68 119657 112893 0 0 0 0 0 0
69 136562 119657 0 0 0 0 0 0
70 140446 136562 0 0 0 0 0 0
71 138744 140446 0 0 0 0 0 0
72 120324 138744 0 0 0 0 0 0
73 118113 120324 1 0 0 0 0 0
74 130257 118113 0 1 0 0 0 0
75 125510 130257 0 0 1 0 0 0
76 117986 125510 0 0 0 1 0 0
77 118316 117986 0 0 0 0 1 0
78 122075 118316 0 0 0 0 0 1
79 117573 122075 0 0 0 0 0 0
80 122566 117573 0 0 0 0 0 0
81 135934 122566 0 0 0 0 0 0
82 138394 135934 0 0 0 0 0 0
83 137999 138394 0 0 0 0 0 0
84 118780 137999 0 0 0 0 0 0
85 117907 118780 1 0 0 0 0 0
86 142932 117907 0 1 0 0 0 0
87 132200 142932 0 0 1 0 0 0
88 125666 132200 0 0 0 1 0 0
89 127958 125666 0 0 0 0 1 0
90 127718 127958 0 0 0 0 0 1
91 124368 127718 0 0 0 0 0 0
92 135241 124368 0 0 0 0 0 0
93 144734 135241 0 0 0 0 0 0
94 142320 144734 0 0 0 0 0 0
95 141481 142320 0 0 0 0 0 0
96 120471 141481 0 0 0 0 0 0
97 123422 120471 1 0 0 0 0 0
98 145829 123422 0 1 0 0 0 0
99 134572 145829 0 0 1 0 0 0
100 132156 134572 0 0 0 1 0 0
101 140265 132156 0 0 0 0 1 0
102 137771 140265 0 0 0 0 0 1
103 134035 137771 0 0 0 0 0 0
104 144016 134035 0 0 0 0 0 0
105 151905 144016 0 0 0 0 0 0
106 155791 151905 0 0 0 0 0 0
107 148440 155791 0 0 0 0 0 0
108 129862 148440 0 0 0 0 0 0
109 134264 129862 1 0 0 0 0 0
110 151952 134264 0 1 0 0 0 0
111 143191 151952 0 0 1 0 0 0
112 137242 143191 0 0 0 1 0 0
113 136993 137242 0 0 0 0 1 0
114 134431 136993 0 0 0 0 0 1
115 132523 134431 0 0 0 0 0 0
116 133486 132523 0 0 0 0 0 0
117 140120 133486 0 0 0 0 0 0
118 137521 140120 0 0 0 0 0 0
119 112193 137521 0 0 0 0 0 0
120 94256 112193 0 0 0 0 0 0
121 99047 94256 1 0 0 0 0 0
122 109761 99047 0 1 0 0 0 0
123 102160 109761 0 0 1 0 0 0
124 104792 102160 0 0 0 1 0 0
125 104341 104792 0 0 0 0 1 0
126 112430 104341 0 0 0 0 0 1
127 113034 112430 0 0 0 0 0 0
128 114197 113034 0 0 0 0 0 0
129 127876 114197 0 0 0 0 0 0
130 135199 127876 0 0 0 0 0 0
131 123663 135199 0 0 0 0 0 0
132 112578 123663 0 0 0 0 0 0
133 117104 112578 1 0 0 0 0 0
134 139703 117104 0 1 0 0 0 0
135 114961 139703 0 0 1 0 0 0
136 134222 114961 0 0 0 1 0 0
137 128390 134222 0 0 0 0 1 0
138 134197 128390 0 0 0 0 0 1
139 135963 134197 0 0 0 0 0 0
140 135936 135963 0 0 0 0 0 0
M7 M8 M9 M10 M11
1 0 0 0 0 0
2 0 0 0 0 0
3 0 0 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
6 0 0 0 0 0
7 1 0 0 0 0
8 0 1 0 0 0
9 0 0 1 0 0
10 0 0 0 1 0
11 0 0 0 0 1
12 0 0 0 0 0
13 0 0 0 0 0
14 0 0 0 0 0
15 0 0 0 0 0
16 0 0 0 0 0
17 0 0 0 0 0
18 0 0 0 0 0
19 1 0 0 0 0
20 0 1 0 0 0
21 0 0 1 0 0
22 0 0 0 1 0
23 0 0 0 0 1
24 0 0 0 0 0
25 0 0 0 0 0
26 0 0 0 0 0
27 0 0 0 0 0
28 0 0 0 0 0
29 0 0 0 0 0
30 0 0 0 0 0
31 1 0 0 0 0
32 0 1 0 0 0
33 0 0 1 0 0
34 0 0 0 1 0
35 0 0 0 0 1
36 0 0 0 0 0
37 0 0 0 0 0
38 0 0 0 0 0
39 0 0 0 0 0
40 0 0 0 0 0
41 0 0 0 0 0
42 0 0 0 0 0
43 1 0 0 0 0
44 0 1 0 0 0
45 0 0 1 0 0
46 0 0 0 1 0
47 0 0 0 0 1
48 0 0 0 0 0
49 0 0 0 0 0
50 0 0 0 0 0
51 0 0 0 0 0
52 0 0 0 0 0
53 0 0 0 0 0
54 0 0 0 0 0
55 1 0 0 0 0
56 0 1 0 0 0
57 0 0 1 0 0
58 0 0 0 1 0
59 0 0 0 0 1
60 0 0 0 0 0
61 0 0 0 0 0
62 0 0 0 0 0
63 0 0 0 0 0
64 0 0 0 0 0
65 0 0 0 0 0
66 0 0 0 0 0
67 1 0 0 0 0
68 0 1 0 0 0
69 0 0 1 0 0
70 0 0 0 1 0
71 0 0 0 0 1
72 0 0 0 0 0
73 0 0 0 0 0
74 0 0 0 0 0
75 0 0 0 0 0
76 0 0 0 0 0
77 0 0 0 0 0
78 0 0 0 0 0
79 1 0 0 0 0
80 0 1 0 0 0
81 0 0 1 0 0
82 0 0 0 1 0
83 0 0 0 0 1
84 0 0 0 0 0
85 0 0 0 0 0
86 0 0 0 0 0
87 0 0 0 0 0
88 0 0 0 0 0
89 0 0 0 0 0
90 0 0 0 0 0
91 1 0 0 0 0
92 0 1 0 0 0
93 0 0 1 0 0
94 0 0 0 1 0
95 0 0 0 0 1
96 0 0 0 0 0
97 0 0 0 0 0
98 0 0 0 0 0
99 0 0 0 0 0
100 0 0 0 0 0
101 0 0 0 0 0
102 0 0 0 0 0
103 1 0 0 0 0
104 0 1 0 0 0
105 0 0 1 0 0
106 0 0 0 1 0
107 0 0 0 0 1
108 0 0 0 0 0
109 0 0 0 0 0
110 0 0 0 0 0
111 0 0 0 0 0
112 0 0 0 0 0
113 0 0 0 0 0
114 0 0 0 0 0
115 1 0 0 0 0
116 0 1 0 0 0
117 0 0 1 0 0
118 0 0 0 1 0
119 0 0 0 0 1
120 0 0 0 0 0
121 0 0 0 0 0
122 0 0 0 0 0
123 0 0 0 0 0
124 0 0 0 0 0
125 0 0 0 0 0
126 0 0 0 0 0
127 1 0 0 0 0
128 0 1 0 0 0
129 0 0 1 0 0
130 0 0 0 1 0
131 0 0 0 0 1
132 0 0 0 0 0
133 0 0 0 0 0
134 0 0 0 0 0
135 0 0 0 0 0
136 0 0 0 0 0
137 0 0 0 0 0
138 0 0 0 0 0
139 1 0 0 0 0
140 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `crisis_10/8`
3.290e+04 -5.105e+04
t `t_crisis_10/8`
1.505e+02 3.136e+02
`Totale_goederenvervoer_ton-1` `Totale_goederenvervoer_ton-2`
4.894e-01 2.982e-01
`Totale_goederenvervoer_ton-3` `Totale_goederenvervoer_ton-4`
1.534e-01 -3.109e-01
M1 M2
-5.763e+03 -3.547e+03
M3 M4
4.843e+03 -1.585e+03
M5 M6
4.249e+03 1.260e+04
M7 M8
8.948e+03 -2.771e+03
M9 M10
-1.696e+04 3.801e+02
M11
2.115e+04
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12116.4 -2193.8 -124.1 2126.7 13673.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.290e+04 6.735e+03 4.885 3.20e-06 ***
`crisis_10/8` -5.105e+04 1.677e+04 -3.045 0.002857 **
t 1.505e+02 2.913e+01 5.169 9.42e-07 ***
`t_crisis_10/8` 3.136e+02 1.228e+02 2.554 0.011897 *
`Totale_goederenvervoer_ton-1` 4.894e-01 8.883e-02 5.510 2.06e-07 ***
`Totale_goederenvervoer_ton-2` 2.982e-01 9.818e-02 3.037 0.002929 **
`Totale_goederenvervoer_ton-3` 1.534e-01 9.660e-02 1.588 0.114923
`Totale_goederenvervoer_ton-4` -3.109e-01 8.304e-02 -3.744 0.000278 ***
M1 -5.763e+03 3.451e+03 -1.670 0.097503 .
M2 -3.547e+03 3.598e+03 -0.986 0.326174
M3 4.843e+03 2.374e+03 2.040 0.043524 *
M4 -1.585e+03 2.545e+03 -0.623 0.534621
M5 4.249e+03 2.707e+03 1.570 0.119123
M6 1.260e+04 2.462e+03 5.116 1.19e-06 ***
M7 8.948e+03 2.106e+03 4.249 4.25e-05 ***
M8 -2.771e+03 2.827e+03 -0.980 0.329002
M9 -1.696e+04 3.302e+03 -5.137 1.08e-06 ***
M10 3.801e+02 3.718e+03 0.102 0.918754
M11 2.115e+04 2.906e+03 7.277 3.73e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3994 on 121 degrees of freedom
Multiple R-squared: 0.9451, Adjusted R-squared: 0.937
F-statistic: 115.8 on 18 and 121 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,] 0.4369333946 0.8738667893 0.5630666
[2,] 0.2842347329 0.5684694657 0.7157653
[3,] 0.2424771859 0.4849543718 0.7575228
[4,] 0.1489968178 0.2979936357 0.8510032
[5,] 0.0890949734 0.1781899468 0.9109050
[6,] 0.1177307417 0.2354614835 0.8822693
[7,] 0.0850252722 0.1700505444 0.9149747
[8,] 0.0566613339 0.1133226678 0.9433387
[9,] 0.0510478861 0.1020957722 0.9489521
[10,] 0.0324541545 0.0649083090 0.9675458
[11,] 0.0206581437 0.0413162874 0.9793419
[12,] 0.0113752043 0.0227504086 0.9886248
[13,] 0.0073232544 0.0146465087 0.9926767
[14,] 0.0432057775 0.0864115549 0.9567942
[15,] 0.0342011693 0.0684023386 0.9657988
[16,] 0.0218730832 0.0437461665 0.9781269
[17,] 0.0269871943 0.0539743887 0.9730128
[18,] 0.0181083974 0.0362167949 0.9818916
[19,] 0.0116189844 0.0232379689 0.9883810
[20,] 0.0073460198 0.0146920397 0.9926540
[21,] 0.0044380606 0.0088761212 0.9955619
[22,] 0.0112807637 0.0225615275 0.9887192
[23,] 0.0151924033 0.0303848067 0.9848076
[24,] 0.0101736528 0.0203473055 0.9898263
[25,] 0.0064117208 0.0128234415 0.9935883
[26,] 0.0177728450 0.0355456900 0.9822272
[27,] 0.0135854196 0.0271708392 0.9864146
[28,] 0.0094988769 0.0189977537 0.9905011
[29,] 0.0066269633 0.0132539267 0.9933730
[30,] 0.0051180074 0.0102360148 0.9948820
[31,] 0.0077132909 0.0154265818 0.9922867
[32,] 0.0050505149 0.0101010298 0.9949495
[33,] 0.0033295817 0.0066591633 0.9966704
[34,] 0.0026032427 0.0052064853 0.9973968
[35,] 0.0016963605 0.0033927211 0.9983036
[36,] 0.0014861870 0.0029723739 0.9985138
[37,] 0.0034349944 0.0068699889 0.9965650
[38,] 0.0025893879 0.0051787759 0.9974106
[39,] 0.0019821294 0.0039642588 0.9980179
[40,] 0.0014200668 0.0028401336 0.9985799
[41,] 0.0009996353 0.0019992706 0.9990004
[42,] 0.0007162241 0.0014324482 0.9992838
[43,] 0.0004628652 0.0009257304 0.9995371
[44,] 0.0002727541 0.0005455082 0.9997272
[45,] 0.0005071583 0.0010143166 0.9994928
[46,] 0.0006549291 0.0013098581 0.9993451
[47,] 0.0006895890 0.0013791780 0.9993104
[48,] 0.0005954450 0.0011908899 0.9994046
[49,] 0.0042000365 0.0084000729 0.9958000
[50,] 0.0095875242 0.0191750484 0.9904125
[51,] 0.0179534437 0.0359068874 0.9820466
[52,] 0.0142874894 0.0285749787 0.9857125
[53,] 0.0106563854 0.0213127709 0.9893436
[54,] 0.0088407731 0.0176815461 0.9911592
[55,] 0.0061682539 0.0123365078 0.9938317
[56,] 0.0049481893 0.0098963786 0.9950518
[57,] 0.0032963422 0.0065926844 0.9967037
[58,] 0.0021996636 0.0043993272 0.9978003
[59,] 0.0025460876 0.0050921751 0.9974539
[60,] 0.0017699555 0.0035399110 0.9982300
[61,] 0.0045951127 0.0091902253 0.9954049
[62,] 0.0054669971 0.0109339941 0.9945330
[63,] 0.0066851104 0.0133702209 0.9933149
[64,] 0.0072273583 0.0144547166 0.9927726
[65,] 0.0075596543 0.0151193087 0.9924403
[66,] 0.0059444491 0.0118888983 0.9940556
[67,] 0.0045581362 0.0091162724 0.9954419
[68,] 0.0038593613 0.0077187226 0.9961406
[69,] 0.0025836504 0.0051673009 0.9974163
[70,] 0.0053101062 0.0106202124 0.9946899
[71,] 0.0039701796 0.0079403591 0.9960298
[72,] 0.0034611533 0.0069223066 0.9965388
[73,] 0.0052027148 0.0104054296 0.9947973
[74,] 0.0050077048 0.0100154096 0.9949923
[75,] 0.0040754393 0.0081508786 0.9959246
[76,] 0.0083866751 0.0167733502 0.9916133
[77,] 0.0105435250 0.0210870501 0.9894565
[78,] 0.0080933361 0.0161866722 0.9919067
[79,] 0.0189281606 0.0378563213 0.9810718
[80,] 0.0128486097 0.0256972194 0.9871514
[81,] 0.0083155997 0.0166311994 0.9916844
[82,] 0.0078416977 0.0156833954 0.9921583
[83,] 0.0077322410 0.0154644820 0.9922678
[84,] 0.0048439570 0.0096879140 0.9951560
[85,] 0.0043607550 0.0087215100 0.9956392
[86,] 0.0026655558 0.0053311116 0.9973344
[87,] 0.0509750776 0.1019501552 0.9490249
[88,] 0.0351599275 0.0703198549 0.9648401
[89,] 0.0227481526 0.0454963052 0.9772518
[90,] 0.0150052152 0.0300104305 0.9849948
[91,] 0.0087996248 0.0175992496 0.9912004
[92,] 0.0060799793 0.0121599585 0.9939200
[93,] 0.0040550034 0.0081100067 0.9959450
[94,] 0.0053309171 0.0106618341 0.9946691
[95,] 0.0025036146 0.0050072292 0.9974964
[96,] 0.0048993699 0.0097987397 0.9951006
[97,] 0.0024284073 0.0048568145 0.9975716
> postscript(file="/var/www/rcomp/tmp/1686y1324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2tx441324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3squk1324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4gd9f1324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5maim1324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 140
Frequency = 1
1 2 3 4 5
2875.200611 -391.556179 2571.886743 -702.154822 1109.054112
6 7 8 9 10
2966.498750 280.371803 6250.704799 4151.867519 7060.995422
11 12 13 14 15
-4423.193122 1191.183608 7.184547 1524.742408 2115.933647
16 17 18 19 20
796.556696 4035.872178 -1063.559393 -3395.758022 6889.043266
21 22 23 24 25
3540.016675 1177.861932 -5421.996515 -2566.550682 -2440.039446
26 27 28 29 30
-1236.244289 -7059.183320 -2554.488889 -263.988708 -777.515950
31 32 33 34 35
-5093.087174 815.439041 -218.384771 -1891.670347 194.061600
36 37 38 39 40
-2230.147264 -2758.570113 1723.848892 -4702.257417 -3392.503131
41 42 43 44 45
-1869.693130 -1872.014979 1851.823694 -4403.891636 443.679562
46 47 48 49 50
-1017.357816 2159.114483 -5104.984671 -3376.898171 -3575.746472
51 52 53 54 55
-1343.997331 1681.281587 -1875.797349 -1913.016317 -1246.818660
56 57 58 59 60
-314.345711 -2598.123318 6705.336305 -1085.880030 -839.137515
61 62 63 64 65
-1487.094426 -2346.501888 738.682947 -1108.143056 1219.096836
66 67 68 69 70
5652.676431 4507.244241 5067.530147 -129.008437 -5647.575946
71 72 73 74 75
-6376.998375 6883.249698 -1714.081818 -1203.059705 601.044109
76 77 78 79 80
116.763730 -2181.666273 1112.437191 898.706333 4716.449009
81 82 83 84 85
-1502.288222 -6562.791997 4528.909035 5629.733375 -3342.598733
86 87 88 89 90
-1129.494027 343.224771 370.473137 4587.409258 2008.606105
91 92 93 94 95
-4355.835739 2015.009862 -1901.551534 -2586.271443 3102.501131
96 97 98 99 100
3956.183829 -1004.055800 6757.643381 1168.025022 -616.852456
101 102 103 104 105
3957.141413 2480.395912 2825.358597 95.819418 -111.243769
106 107 108 109 110
-58.759858 2433.437583 5262.350904 -2511.913265 -948.225350
111 112 113 114 115
-3311.856704 574.580493 -4560.254844 -6011.567452 4585.574435
116 117 118 119 120
-10934.655220 -2693.507603 3085.279524 -1350.344525 -65.433797
121 122 123 124 125
2079.001376 -227.857450 2939.991139 2916.506202 -4038.040476
126 127 128 129 130
-1301.392626 4587.120959 -3347.472685 1018.543898 -265.045778
131 132 133 134 135
6240.388734 -12116.447484 13673.865239 1052.450680 5938.506392
136 137 138 139 140
1917.980508 -119.133015 -1281.547672 -5444.700466 -6849.630290
> postscript(file="/var/www/rcomp/tmp/6anne1324338323.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 = 140
Frequency = 1
lag(myerror, k = 1) myerror
0 2875.200611 NA
1 -391.556179 2875.200611
2 2571.886743 -391.556179
3 -702.154822 2571.886743
4 1109.054112 -702.154822
5 2966.498750 1109.054112
6 280.371803 2966.498750
7 6250.704799 280.371803
8 4151.867519 6250.704799
9 7060.995422 4151.867519
10 -4423.193122 7060.995422
11 1191.183608 -4423.193122
12 7.184547 1191.183608
13 1524.742408 7.184547
14 2115.933647 1524.742408
15 796.556696 2115.933647
16 4035.872178 796.556696
17 -1063.559393 4035.872178
18 -3395.758022 -1063.559393
19 6889.043266 -3395.758022
20 3540.016675 6889.043266
21 1177.861932 3540.016675
22 -5421.996515 1177.861932
23 -2566.550682 -5421.996515
24 -2440.039446 -2566.550682
25 -1236.244289 -2440.039446
26 -7059.183320 -1236.244289
27 -2554.488889 -7059.183320
28 -263.988708 -2554.488889
29 -777.515950 -263.988708
30 -5093.087174 -777.515950
31 815.439041 -5093.087174
32 -218.384771 815.439041
33 -1891.670347 -218.384771
34 194.061600 -1891.670347
35 -2230.147264 194.061600
36 -2758.570113 -2230.147264
37 1723.848892 -2758.570113
38 -4702.257417 1723.848892
39 -3392.503131 -4702.257417
40 -1869.693130 -3392.503131
41 -1872.014979 -1869.693130
42 1851.823694 -1872.014979
43 -4403.891636 1851.823694
44 443.679562 -4403.891636
45 -1017.357816 443.679562
46 2159.114483 -1017.357816
47 -5104.984671 2159.114483
48 -3376.898171 -5104.984671
49 -3575.746472 -3376.898171
50 -1343.997331 -3575.746472
51 1681.281587 -1343.997331
52 -1875.797349 1681.281587
53 -1913.016317 -1875.797349
54 -1246.818660 -1913.016317
55 -314.345711 -1246.818660
56 -2598.123318 -314.345711
57 6705.336305 -2598.123318
58 -1085.880030 6705.336305
59 -839.137515 -1085.880030
60 -1487.094426 -839.137515
61 -2346.501888 -1487.094426
62 738.682947 -2346.501888
63 -1108.143056 738.682947
64 1219.096836 -1108.143056
65 5652.676431 1219.096836
66 4507.244241 5652.676431
67 5067.530147 4507.244241
68 -129.008437 5067.530147
69 -5647.575946 -129.008437
70 -6376.998375 -5647.575946
71 6883.249698 -6376.998375
72 -1714.081818 6883.249698
73 -1203.059705 -1714.081818
74 601.044109 -1203.059705
75 116.763730 601.044109
76 -2181.666273 116.763730
77 1112.437191 -2181.666273
78 898.706333 1112.437191
79 4716.449009 898.706333
80 -1502.288222 4716.449009
81 -6562.791997 -1502.288222
82 4528.909035 -6562.791997
83 5629.733375 4528.909035
84 -3342.598733 5629.733375
85 -1129.494027 -3342.598733
86 343.224771 -1129.494027
87 370.473137 343.224771
88 4587.409258 370.473137
89 2008.606105 4587.409258
90 -4355.835739 2008.606105
91 2015.009862 -4355.835739
92 -1901.551534 2015.009862
93 -2586.271443 -1901.551534
94 3102.501131 -2586.271443
95 3956.183829 3102.501131
96 -1004.055800 3956.183829
97 6757.643381 -1004.055800
98 1168.025022 6757.643381
99 -616.852456 1168.025022
100 3957.141413 -616.852456
101 2480.395912 3957.141413
102 2825.358597 2480.395912
103 95.819418 2825.358597
104 -111.243769 95.819418
105 -58.759858 -111.243769
106 2433.437583 -58.759858
107 5262.350904 2433.437583
108 -2511.913265 5262.350904
109 -948.225350 -2511.913265
110 -3311.856704 -948.225350
111 574.580493 -3311.856704
112 -4560.254844 574.580493
113 -6011.567452 -4560.254844
114 4585.574435 -6011.567452
115 -10934.655220 4585.574435
116 -2693.507603 -10934.655220
117 3085.279524 -2693.507603
118 -1350.344525 3085.279524
119 -65.433797 -1350.344525
120 2079.001376 -65.433797
121 -227.857450 2079.001376
122 2939.991139 -227.857450
123 2916.506202 2939.991139
124 -4038.040476 2916.506202
125 -1301.392626 -4038.040476
126 4587.120959 -1301.392626
127 -3347.472685 4587.120959
128 1018.543898 -3347.472685
129 -265.045778 1018.543898
130 6240.388734 -265.045778
131 -12116.447484 6240.388734
132 13673.865239 -12116.447484
133 1052.450680 13673.865239
134 5938.506392 1052.450680
135 1917.980508 5938.506392
136 -119.133015 1917.980508
137 -1281.547672 -119.133015
138 -5444.700466 -1281.547672
139 -6849.630290 -5444.700466
140 NA -6849.630290
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -391.556179 2875.200611
[2,] 2571.886743 -391.556179
[3,] -702.154822 2571.886743
[4,] 1109.054112 -702.154822
[5,] 2966.498750 1109.054112
[6,] 280.371803 2966.498750
[7,] 6250.704799 280.371803
[8,] 4151.867519 6250.704799
[9,] 7060.995422 4151.867519
[10,] -4423.193122 7060.995422
[11,] 1191.183608 -4423.193122
[12,] 7.184547 1191.183608
[13,] 1524.742408 7.184547
[14,] 2115.933647 1524.742408
[15,] 796.556696 2115.933647
[16,] 4035.872178 796.556696
[17,] -1063.559393 4035.872178
[18,] -3395.758022 -1063.559393
[19,] 6889.043266 -3395.758022
[20,] 3540.016675 6889.043266
[21,] 1177.861932 3540.016675
[22,] -5421.996515 1177.861932
[23,] -2566.550682 -5421.996515
[24,] -2440.039446 -2566.550682
[25,] -1236.244289 -2440.039446
[26,] -7059.183320 -1236.244289
[27,] -2554.488889 -7059.183320
[28,] -263.988708 -2554.488889
[29,] -777.515950 -263.988708
[30,] -5093.087174 -777.515950
[31,] 815.439041 -5093.087174
[32,] -218.384771 815.439041
[33,] -1891.670347 -218.384771
[34,] 194.061600 -1891.670347
[35,] -2230.147264 194.061600
[36,] -2758.570113 -2230.147264
[37,] 1723.848892 -2758.570113
[38,] -4702.257417 1723.848892
[39,] -3392.503131 -4702.257417
[40,] -1869.693130 -3392.503131
[41,] -1872.014979 -1869.693130
[42,] 1851.823694 -1872.014979
[43,] -4403.891636 1851.823694
[44,] 443.679562 -4403.891636
[45,] -1017.357816 443.679562
[46,] 2159.114483 -1017.357816
[47,] -5104.984671 2159.114483
[48,] -3376.898171 -5104.984671
[49,] -3575.746472 -3376.898171
[50,] -1343.997331 -3575.746472
[51,] 1681.281587 -1343.997331
[52,] -1875.797349 1681.281587
[53,] -1913.016317 -1875.797349
[54,] -1246.818660 -1913.016317
[55,] -314.345711 -1246.818660
[56,] -2598.123318 -314.345711
[57,] 6705.336305 -2598.123318
[58,] -1085.880030 6705.336305
[59,] -839.137515 -1085.880030
[60,] -1487.094426 -839.137515
[61,] -2346.501888 -1487.094426
[62,] 738.682947 -2346.501888
[63,] -1108.143056 738.682947
[64,] 1219.096836 -1108.143056
[65,] 5652.676431 1219.096836
[66,] 4507.244241 5652.676431
[67,] 5067.530147 4507.244241
[68,] -129.008437 5067.530147
[69,] -5647.575946 -129.008437
[70,] -6376.998375 -5647.575946
[71,] 6883.249698 -6376.998375
[72,] -1714.081818 6883.249698
[73,] -1203.059705 -1714.081818
[74,] 601.044109 -1203.059705
[75,] 116.763730 601.044109
[76,] -2181.666273 116.763730
[77,] 1112.437191 -2181.666273
[78,] 898.706333 1112.437191
[79,] 4716.449009 898.706333
[80,] -1502.288222 4716.449009
[81,] -6562.791997 -1502.288222
[82,] 4528.909035 -6562.791997
[83,] 5629.733375 4528.909035
[84,] -3342.598733 5629.733375
[85,] -1129.494027 -3342.598733
[86,] 343.224771 -1129.494027
[87,] 370.473137 343.224771
[88,] 4587.409258 370.473137
[89,] 2008.606105 4587.409258
[90,] -4355.835739 2008.606105
[91,] 2015.009862 -4355.835739
[92,] -1901.551534 2015.009862
[93,] -2586.271443 -1901.551534
[94,] 3102.501131 -2586.271443
[95,] 3956.183829 3102.501131
[96,] -1004.055800 3956.183829
[97,] 6757.643381 -1004.055800
[98,] 1168.025022 6757.643381
[99,] -616.852456 1168.025022
[100,] 3957.141413 -616.852456
[101,] 2480.395912 3957.141413
[102,] 2825.358597 2480.395912
[103,] 95.819418 2825.358597
[104,] -111.243769 95.819418
[105,] -58.759858 -111.243769
[106,] 2433.437583 -58.759858
[107,] 5262.350904 2433.437583
[108,] -2511.913265 5262.350904
[109,] -948.225350 -2511.913265
[110,] -3311.856704 -948.225350
[111,] 574.580493 -3311.856704
[112,] -4560.254844 574.580493
[113,] -6011.567452 -4560.254844
[114,] 4585.574435 -6011.567452
[115,] -10934.655220 4585.574435
[116,] -2693.507603 -10934.655220
[117,] 3085.279524 -2693.507603
[118,] -1350.344525 3085.279524
[119,] -65.433797 -1350.344525
[120,] 2079.001376 -65.433797
[121,] -227.857450 2079.001376
[122,] 2939.991139 -227.857450
[123,] 2916.506202 2939.991139
[124,] -4038.040476 2916.506202
[125,] -1301.392626 -4038.040476
[126,] 4587.120959 -1301.392626
[127,] -3347.472685 4587.120959
[128,] 1018.543898 -3347.472685
[129,] -265.045778 1018.543898
[130,] 6240.388734 -265.045778
[131,] -12116.447484 6240.388734
[132,] 13673.865239 -12116.447484
[133,] 1052.450680 13673.865239
[134,] 5938.506392 1052.450680
[135,] 1917.980508 5938.506392
[136,] -119.133015 1917.980508
[137,] -1281.547672 -119.133015
[138,] -5444.700466 -1281.547672
[139,] -6849.630290 -5444.700466
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -391.556179 2875.200611
2 2571.886743 -391.556179
3 -702.154822 2571.886743
4 1109.054112 -702.154822
5 2966.498750 1109.054112
6 280.371803 2966.498750
7 6250.704799 280.371803
8 4151.867519 6250.704799
9 7060.995422 4151.867519
10 -4423.193122 7060.995422
11 1191.183608 -4423.193122
12 7.184547 1191.183608
13 1524.742408 7.184547
14 2115.933647 1524.742408
15 796.556696 2115.933647
16 4035.872178 796.556696
17 -1063.559393 4035.872178
18 -3395.758022 -1063.559393
19 6889.043266 -3395.758022
20 3540.016675 6889.043266
21 1177.861932 3540.016675
22 -5421.996515 1177.861932
23 -2566.550682 -5421.996515
24 -2440.039446 -2566.550682
25 -1236.244289 -2440.039446
26 -7059.183320 -1236.244289
27 -2554.488889 -7059.183320
28 -263.988708 -2554.488889
29 -777.515950 -263.988708
30 -5093.087174 -777.515950
31 815.439041 -5093.087174
32 -218.384771 815.439041
33 -1891.670347 -218.384771
34 194.061600 -1891.670347
35 -2230.147264 194.061600
36 -2758.570113 -2230.147264
37 1723.848892 -2758.570113
38 -4702.257417 1723.848892
39 -3392.503131 -4702.257417
40 -1869.693130 -3392.503131
41 -1872.014979 -1869.693130
42 1851.823694 -1872.014979
43 -4403.891636 1851.823694
44 443.679562 -4403.891636
45 -1017.357816 443.679562
46 2159.114483 -1017.357816
47 -5104.984671 2159.114483
48 -3376.898171 -5104.984671
49 -3575.746472 -3376.898171
50 -1343.997331 -3575.746472
51 1681.281587 -1343.997331
52 -1875.797349 1681.281587
53 -1913.016317 -1875.797349
54 -1246.818660 -1913.016317
55 -314.345711 -1246.818660
56 -2598.123318 -314.345711
57 6705.336305 -2598.123318
58 -1085.880030 6705.336305
59 -839.137515 -1085.880030
60 -1487.094426 -839.137515
61 -2346.501888 -1487.094426
62 738.682947 -2346.501888
63 -1108.143056 738.682947
64 1219.096836 -1108.143056
65 5652.676431 1219.096836
66 4507.244241 5652.676431
67 5067.530147 4507.244241
68 -129.008437 5067.530147
69 -5647.575946 -129.008437
70 -6376.998375 -5647.575946
71 6883.249698 -6376.998375
72 -1714.081818 6883.249698
73 -1203.059705 -1714.081818
74 601.044109 -1203.059705
75 116.763730 601.044109
76 -2181.666273 116.763730
77 1112.437191 -2181.666273
78 898.706333 1112.437191
79 4716.449009 898.706333
80 -1502.288222 4716.449009
81 -6562.791997 -1502.288222
82 4528.909035 -6562.791997
83 5629.733375 4528.909035
84 -3342.598733 5629.733375
85 -1129.494027 -3342.598733
86 343.224771 -1129.494027
87 370.473137 343.224771
88 4587.409258 370.473137
89 2008.606105 4587.409258
90 -4355.835739 2008.606105
91 2015.009862 -4355.835739
92 -1901.551534 2015.009862
93 -2586.271443 -1901.551534
94 3102.501131 -2586.271443
95 3956.183829 3102.501131
96 -1004.055800 3956.183829
97 6757.643381 -1004.055800
98 1168.025022 6757.643381
99 -616.852456 1168.025022
100 3957.141413 -616.852456
101 2480.395912 3957.141413
102 2825.358597 2480.395912
103 95.819418 2825.358597
104 -111.243769 95.819418
105 -58.759858 -111.243769
106 2433.437583 -58.759858
107 5262.350904 2433.437583
108 -2511.913265 5262.350904
109 -948.225350 -2511.913265
110 -3311.856704 -948.225350
111 574.580493 -3311.856704
112 -4560.254844 574.580493
113 -6011.567452 -4560.254844
114 4585.574435 -6011.567452
115 -10934.655220 4585.574435
116 -2693.507603 -10934.655220
117 3085.279524 -2693.507603
118 -1350.344525 3085.279524
119 -65.433797 -1350.344525
120 2079.001376 -65.433797
121 -227.857450 2079.001376
122 2939.991139 -227.857450
123 2916.506202 2939.991139
124 -4038.040476 2916.506202
125 -1301.392626 -4038.040476
126 4587.120959 -1301.392626
127 -3347.472685 4587.120959
128 1018.543898 -3347.472685
129 -265.045778 1018.543898
130 6240.388734 -265.045778
131 -12116.447484 6240.388734
132 13673.865239 -12116.447484
133 1052.450680 13673.865239
134 5938.506392 1052.450680
135 1917.980508 5938.506392
136 -119.133015 1917.980508
137 -1281.547672 -119.133015
138 -5444.700466 -1281.547672
139 -6849.630290 -5444.700466
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/762pi1324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8zkb51324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9ti3m1324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10f8r81324338323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11ymsf1324338323.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1202um1324338323.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13cd8m1324338323.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/140v061324338323.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15yppx1324338323.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16a1341324338324.tab")
+ }
>
> try(system("convert tmp/1686y1324338323.ps tmp/1686y1324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tx441324338323.ps tmp/2tx441324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/3squk1324338323.ps tmp/3squk1324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gd9f1324338323.ps tmp/4gd9f1324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/5maim1324338323.ps tmp/5maim1324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/6anne1324338323.ps tmp/6anne1324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/762pi1324338323.ps tmp/762pi1324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zkb51324338323.ps tmp/8zkb51324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ti3m1324338323.ps tmp/9ti3m1324338323.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f8r81324338323.ps tmp/10f8r81324338323.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.960 0.260 5.231