R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(0
+ ,1
+ ,0
+ ,90604
+ ,0
+ ,2
+ ,0
+ ,97527
+ ,0
+ ,3
+ ,0
+ ,111940
+ ,0
+ ,4
+ ,0
+ ,100280
+ ,0
+ ,5
+ ,0
+ ,100009
+ ,0
+ ,6
+ ,0
+ ,95558
+ ,0
+ ,7
+ ,0
+ ,98533
+ ,0
+ ,8
+ ,0
+ ,92694
+ ,0
+ ,9
+ ,0
+ ,97920
+ ,0
+ ,10
+ ,0
+ ,110933
+ ,0
+ ,11
+ ,0
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+ ,0
+ ,12
+ ,0
+ ,111716
+ ,0
+ ,13
+ ,0
+ ,96348
+ ,0
+ ,14
+ ,0
+ ,105425
+ ,0
+ ,15
+ ,0
+ ,114874
+ ,0
+ ,16
+ ,0
+ ,104199
+ ,0
+ ,17
+ ,0
+ ,101166
+ ,0
+ ,18
+ ,0
+ ,99010
+ ,0
+ ,19
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+ ,0
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+ ,0
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+ ,106088
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+ ,22
+ ,0
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+ ,0
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+ ,0
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+ ,0
+ ,25
+ ,0
+ ,97733
+ ,0
+ ,26
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+ ,102591
+ ,0
+ ,27
+ ,0
+ ,114783
+ ,0
+ ,28
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+ ,29
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+ ,0
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+ ,0
+ ,33
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+ ,34
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+ ,108848
+ ,0
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+ ,68
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,1
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+ ,1
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+ ,104341
+ ,1
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+ ,112430
+ ,1
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+ ,1
+ ,129
+ ,129
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+ ,1
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+ ,1
+ ,131
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+ ,1
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+ ,132
+ ,123663
+ ,1
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+ ,112578
+ ,1
+ ,134
+ ,134
+ ,117104
+ ,1
+ ,135
+ ,135
+ ,139703
+ ,1
+ ,136
+ ,136
+ ,114961
+ ,1
+ ,137
+ ,137
+ ,134222
+ ,1
+ ,138
+ ,138
+ ,128390
+ ,1
+ ,139
+ ,139
+ ,134197
+ ,1
+ ,140
+ ,140
+ ,135963
+ ,1
+ ,141
+ ,141
+ ,135936
+ ,1
+ ,142
+ ,142
+ ,146803
+ ,1
+ ,143
+ ,143
+ ,143231
+ ,1
+ ,144
+ ,144
+ ,131510)
+ ,dim=c(4
+ ,144)
+ ,dimnames=list(c('crisis_10/8'
+ ,'t'
+ ,'t_crisis_10/8'
+ ,'Totale_goederenvervoer_ton')
+ ,1:144))
> y <- array(NA,dim=c(4,144),dimnames=list(c('crisis_10/8','t','t_crisis_10/8','Totale_goederenvervoer_ton'),1:144))
> 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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Totale_goederenvervoer_ton crisis_10/8 t t_crisis_10/8 M1 M2 M3 M4 M5 M6
1 90604 0 1 0 1 0 0 0 0 0
2 97527 0 2 0 0 1 0 0 0 0
3 111940 0 3 0 0 0 1 0 0 0
4 100280 0 4 0 0 0 0 1 0 0
5 100009 0 5 0 0 0 0 0 1 0
6 95558 0 6 0 0 0 0 0 0 1
7 98533 0 7 0 0 0 0 0 0 0
8 92694 0 8 0 0 0 0 0 0 0
9 97920 0 9 0 0 0 0 0 0 0
10 110933 0 10 0 0 0 0 0 0 0
11 110855 0 11 0 0 0 0 0 0 0
12 111716 0 12 0 0 0 0 0 0 0
13 96348 0 13 0 1 0 0 0 0 0
14 105425 0 14 0 0 1 0 0 0 0
15 114874 0 15 0 0 0 1 0 0 0
16 104199 0 16 0 0 0 0 1 0 0
17 101166 0 17 0 0 0 0 0 1 0
18 99010 0 18 0 0 0 0 0 0 1
19 101607 0 19 0 0 0 0 0 0 0
20 97492 0 20 0 0 0 0 0 0 0
21 106088 0 21 0 0 0 0 0 0 0
22 113536 0 22 0 0 0 0 0 0 0
23 112475 0 23 0 0 0 0 0 0 0
24 115491 0 24 0 0 0 0 0 0 0
25 97733 0 25 0 1 0 0 0 0 0
26 102591 0 26 0 0 1 0 0 0 0
27 114783 0 27 0 0 0 1 0 0 0
28 100397 0 28 0 0 0 0 1 0 0
29 97772 0 29 0 0 0 0 0 1 0
30 96128 0 30 0 0 0 0 0 0 1
31 91261 0 31 0 0 0 0 0 0 0
32 90686 0 32 0 0 0 0 0 0 0
33 97792 0 33 0 0 0 0 0 0 0
34 108848 0 34 0 0 0 0 0 0 0
35 109989 0 35 0 0 0 0 0 0 0
36 109453 0 36 0 0 0 0 0 0 0
37 93945 0 37 0 1 0 0 0 0 0
38 98750 0 38 0 0 1 0 0 0 0
39 119043 0 39 0 0 0 1 0 0 0
40 104776 0 40 0 0 0 0 1 0 0
41 103262 0 41 0 0 0 0 0 1 0
42 106735 0 42 0 0 0 0 0 0 1
43 101600 0 43 0 0 0 0 0 0 0
44 99358 0 44 0 0 0 0 0 0 0
45 105240 0 45 0 0 0 0 0 0 0
46 114079 0 46 0 0 0 0 0 0 0
47 121637 0 47 0 0 0 0 0 0 0
48 111747 0 48 0 0 0 0 0 0 0
49 99496 0 49 0 1 0 0 0 0 0
50 104992 0 50 0 0 1 0 0 0 0
51 124255 0 51 0 0 0 1 0 0 0
52 108258 0 52 0 0 0 0 1 0 0
53 106940 0 53 0 0 0 0 0 1 0
54 104939 0 54 0 0 0 0 0 0 1
55 105896 0 55 0 0 0 0 0 0 0
56 107287 0 56 0 0 0 0 0 0 0
57 110783 0 57 0 0 0 0 0 0 0
58 122139 0 58 0 0 0 0 0 0 0
59 125823 0 59 0 0 0 0 0 0 0
60 120480 0 60 0 0 0 0 0 0 0
61 103296 0 61 0 1 0 0 0 0 0
62 117121 0 62 0 0 1 0 0 0 0
63 129924 0 63 0 0 0 1 0 0 0
64 118589 0 64 0 0 0 0 1 0 0
65 118062 0 65 0 0 0 0 0 1 0
66 113597 0 66 0 0 0 0 0 0 1
67 117161 0 67 0 0 0 0 0 0 0
68 112893 0 68 0 0 0 0 0 0 0
69 119657 0 69 0 0 0 0 0 0 0
70 136562 0 70 0 0 0 0 0 0 0
71 140446 0 71 0 0 0 0 0 0 0
72 138744 0 72 0 0 0 0 0 0 0
73 120324 0 73 0 1 0 0 0 0 0
74 118113 0 74 0 0 1 0 0 0 0
75 130257 0 75 0 0 0 1 0 0 0
76 125510 0 76 0 0 0 0 1 0 0
77 117986 0 77 0 0 0 0 0 1 0
78 118316 0 78 0 0 0 0 0 0 1
79 122075 0 79 0 0 0 0 0 0 0
80 117573 0 80 0 0 0 0 0 0 0
81 122566 0 81 0 0 0 0 0 0 0
82 135934 0 82 0 0 0 0 0 0 0
83 138394 0 83 0 0 0 0 0 0 0
84 137999 0 84 0 0 0 0 0 0 0
85 118780 0 85 0 1 0 0 0 0 0
86 117907 0 86 0 0 1 0 0 0 0
87 142932 0 87 0 0 0 1 0 0 0
88 132200 0 88 0 0 0 0 1 0 0
89 125666 0 89 0 0 0 0 0 1 0
90 127958 0 90 0 0 0 0 0 0 1
91 127718 0 91 0 0 0 0 0 0 0
92 124368 0 92 0 0 0 0 0 0 0
93 135241 0 93 0 0 0 0 0 0 0
94 144734 0 94 0 0 0 0 0 0 0
95 142320 0 95 0 0 0 0 0 0 0
96 141481 0 96 0 0 0 0 0 0 0
97 120471 0 97 0 1 0 0 0 0 0
98 123422 0 98 0 0 1 0 0 0 0
99 145829 0 99 0 0 0 1 0 0 0
100 134572 0 100 0 0 0 0 1 0 0
101 132156 0 101 0 0 0 0 0 1 0
102 140265 0 102 0 0 0 0 0 0 1
103 137771 0 103 0 0 0 0 0 0 0
104 134035 0 104 0 0 0 0 0 0 0
105 144016 0 105 0 0 0 0 0 0 0
106 151905 0 106 0 0 0 0 0 0 0
107 155791 0 107 0 0 0 0 0 0 0
108 148440 0 108 0 0 0 0 0 0 0
109 129862 0 109 0 1 0 0 0 0 0
110 134264 0 110 0 0 1 0 0 0 0
111 151952 0 111 0 0 0 1 0 0 0
112 143191 0 112 0 0 0 0 1 0 0
113 137242 0 113 0 0 0 0 0 1 0
114 136993 0 114 0 0 0 0 0 0 1
115 134431 0 115 0 0 0 0 0 0 0
116 132523 0 116 0 0 0 0 0 0 0
117 133486 0 117 0 0 0 0 0 0 0
118 140120 0 118 0 0 0 0 0 0 0
119 137521 0 119 0 0 0 0 0 0 0
120 112193 1 120 120 0 0 0 0 0 0
121 94256 1 121 121 1 0 0 0 0 0
122 99047 1 122 122 0 1 0 0 0 0
123 109761 1 123 123 0 0 1 0 0 0
124 102160 1 124 124 0 0 0 1 0 0
125 104792 1 125 125 0 0 0 0 1 0
126 104341 1 126 126 0 0 0 0 0 1
127 112430 1 127 127 0 0 0 0 0 0
128 113034 1 128 128 0 0 0 0 0 0
129 114197 1 129 129 0 0 0 0 0 0
130 127876 1 130 130 0 0 0 0 0 0
131 135199 1 131 131 0 0 0 0 0 0
132 123663 1 132 132 0 0 0 0 0 0
133 112578 1 133 133 1 0 0 0 0 0
134 117104 1 134 134 0 1 0 0 0 0
135 139703 1 135 135 0 0 1 0 0 0
136 114961 1 136 136 0 0 0 1 0 0
137 134222 1 137 137 0 0 0 0 1 0
138 128390 1 138 138 0 0 0 0 0 1
139 134197 1 139 139 0 0 0 0 0 0
140 135963 1 140 140 0 0 0 0 0 0
141 135936 1 141 141 0 0 0 0 0 0
142 146803 1 142 142 0 0 0 0 0 0
143 143231 1 143 143 0 0 0 0 0 0
144 131510 1 144 144 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
141 0 0 1 0 0
142 0 0 0 1 0
143 0 0 0 0 1
144 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `crisis_10/8` t `t_crisis_10/8`
100862.8 -182585.8 395.3 1187.8
M1 M2 M3 M4
-15580.9 -11293.3 4695.9 -8077.3
M5 M6 M7 M8
-9488.7 -10669.0 -10224.7 -13049.1
M9 M10 M11
-8224.3 2061.4 3152.5
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14724.1 -3891.8 359.3 3404.5 10321.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100862.82 1805.22 55.873 < 2e-16 ***
`crisis_10/8` -182585.83 20060.26 -9.102 1.41e-15 ***
t 395.26 14.43 27.388 < 2e-16 ***
`t_crisis_10/8` 1187.78 152.14 7.807 1.76e-12 ***
M1 -15580.95 2207.08 -7.060 9.22e-11 ***
M2 -11293.33 2205.47 -5.121 1.08e-06 ***
M3 4695.95 2204.21 2.130 0.035033 *
M4 -8077.27 2203.31 -3.666 0.000359 ***
M5 -9488.66 2202.77 -4.308 3.24e-05 ***
M6 -10668.96 2202.59 -4.844 3.59e-06 ***
M7 -10224.68 2202.77 -4.642 8.38e-06 ***
M8 -13049.07 2203.31 -5.922 2.70e-08 ***
M9 -8224.29 2204.21 -3.731 0.000285 ***
M10 2061.41 2205.47 0.935 0.351699
M11 3152.52 2207.08 1.428 0.155603
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5390 on 129 degrees of freedom
Multiple R-squared: 0.8983, Adjusted R-squared: 0.8873
F-statistic: 81.39 on 14 and 129 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,] 0.0880736444 0.1761472887 0.9119264
[2,] 0.0325719106 0.0651438212 0.9674281
[3,] 0.0108105970 0.0216211939 0.9891894
[4,] 0.0102890165 0.0205780329 0.9897110
[5,] 0.0045691678 0.0091383357 0.9954308
[6,] 0.0024771073 0.0049542146 0.9975229
[7,] 0.0009632479 0.0019264958 0.9990368
[8,] 0.0004682517 0.0009365034 0.9995317
[9,] 0.0009448019 0.0018896039 0.9990552
[10,] 0.0008468539 0.0016937079 0.9991531
[11,] 0.0026563550 0.0053127100 0.9973436
[12,] 0.0059029132 0.0118058264 0.9940971
[13,] 0.0051007258 0.0102014516 0.9948993
[14,] 0.0504747784 0.1009495568 0.9495252
[15,] 0.0577192247 0.1154384493 0.9422808
[16,] 0.0567250491 0.1134500981 0.9432750
[17,] 0.0479327669 0.0958655337 0.9520672
[18,] 0.0349358142 0.0698716285 0.9650642
[19,] 0.0300497268 0.0600994536 0.9699503
[20,] 0.0197509094 0.0395018188 0.9802491
[21,] 0.0144548283 0.0289096567 0.9855452
[22,] 0.0158267440 0.0316534879 0.9841733
[23,] 0.0118173259 0.0236346518 0.9881827
[24,] 0.0089907723 0.0179815446 0.9910092
[25,] 0.0220809255 0.0441618511 0.9779191
[26,] 0.0176623416 0.0353246833 0.9823377
[27,] 0.0158716154 0.0317432308 0.9841284
[28,] 0.0121259845 0.0242519689 0.9878740
[29,] 0.0087983757 0.0175967513 0.9912016
[30,] 0.0158994071 0.0317988142 0.9841006
[31,] 0.0133666043 0.0267332085 0.9866334
[32,] 0.0096479639 0.0192959277 0.9903520
[33,] 0.0064260015 0.0128520029 0.9935740
[34,] 0.0071051461 0.0142102922 0.9928949
[35,] 0.0052500004 0.0105000009 0.9947500
[36,] 0.0040998986 0.0081997971 0.9959001
[37,] 0.0032458448 0.0064916897 0.9967542
[38,] 0.0033062664 0.0066125327 0.9966937
[39,] 0.0059110880 0.0118221759 0.9940889
[40,] 0.0058330152 0.0116660304 0.9941670
[41,] 0.0065586886 0.0131173773 0.9934413
[42,] 0.0084303768 0.0168607535 0.9915696
[43,] 0.0073211283 0.0146422566 0.9926789
[44,] 0.0064098936 0.0128197872 0.9935901
[45,] 0.0112369915 0.0224739830 0.9887630
[46,] 0.0112560002 0.0225120005 0.9887440
[47,] 0.0140274854 0.0280549708 0.9859725
[48,] 0.0190361023 0.0380722046 0.9809639
[49,] 0.0190315189 0.0380630379 0.9809685
[50,] 0.0277509309 0.0555018618 0.9722491
[51,] 0.0326581394 0.0653162788 0.9673419
[52,] 0.0362966103 0.0725932206 0.9637034
[53,] 0.0786243254 0.1572486507 0.9213757
[54,] 0.1711803127 0.3423606254 0.8288197
[55,] 0.3190385580 0.6380771161 0.6809614
[56,] 0.3837210372 0.7674420744 0.6162790
[57,] 0.3358771325 0.6717542649 0.6641229
[58,] 0.3131594792 0.6263189584 0.6868405
[59,] 0.2981531699 0.5963063397 0.7018468
[60,] 0.2770312687 0.5540625374 0.7229687
[61,] 0.2642504252 0.5285008503 0.7357496
[62,] 0.2557454130 0.5114908260 0.7442546
[63,] 0.2611701081 0.5223402161 0.7388299
[64,] 0.2608066470 0.5216132941 0.7391934
[65,] 0.2399486844 0.4798973688 0.7600513
[66,] 0.2110714270 0.4221428540 0.7889286
[67,] 0.1932522692 0.3865045384 0.8067477
[68,] 0.1632765153 0.3265530307 0.8367235
[69,] 0.1691730782 0.3383461564 0.8308269
[70,] 0.1510451359 0.3020902718 0.8489549
[71,] 0.1420799346 0.2841598692 0.8579201
[72,] 0.1357722259 0.2715444519 0.8642278
[73,] 0.1311128106 0.2622256211 0.8688872
[74,] 0.1370969322 0.2741938643 0.8629031
[75,] 0.1718473551 0.3436947102 0.8281526
[76,] 0.1750923109 0.3501846218 0.8249077
[77,] 0.1610801935 0.3221603870 0.8389198
[78,] 0.1507154677 0.3014309354 0.8492845
[79,] 0.1231416929 0.2462833859 0.8768583
[80,] 0.1483203768 0.2966407536 0.8516796
[81,] 0.2248568397 0.4497136794 0.7751432
[82,] 0.2277371244 0.4554742488 0.7722629
[83,] 0.2044924912 0.4089849823 0.7955075
[84,] 0.3143603353 0.6287206706 0.6856397
[85,] 0.3365202421 0.6730404841 0.6634798
[86,] 0.3954965374 0.7909930747 0.6045035
[87,] 0.5970076965 0.8059846071 0.4029923
[88,] 0.6102385130 0.7795229740 0.3897615
[89,] 0.6291131981 0.7417736037 0.3708868
[90,] 0.6068254958 0.7863490085 0.3931745
[91,] 0.5438225084 0.9123549831 0.4561775
[92,] 0.5508679543 0.8982640914 0.4491320
[93,] 0.5486735615 0.9026528769 0.4513264
[94,] 0.5033844224 0.9932311552 0.4966156
[95,] 0.5566213265 0.8867573471 0.4433787
[96,] 0.4958187151 0.9916374301 0.5041813
[97,] 0.4438172286 0.8876344572 0.5561828
[98,] 0.3693669765 0.7387339531 0.6306330
[99,] 0.3072552916 0.6145105833 0.6927447
[100,] 0.2489074726 0.4978149451 0.7510925
[101,] 0.2113760551 0.4227521102 0.7886239
[102,] 0.1963913964 0.3927827928 0.8036086
[103,] 0.3391587412 0.6783174823 0.6608413
[104,] 0.2537216023 0.5074432047 0.7462784
[105,] 0.1793662023 0.3587324046 0.8206338
[106,] 0.2257411430 0.4514822860 0.7742589
[107,] 0.1849648835 0.3699297671 0.8150351
[108,] 0.2494609868 0.4989219736 0.7505390
[109,] 0.2032405970 0.4064811940 0.7967594
> postscript(file="/var/wessaorg/rcomp/tmp/1d8j11324322400.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2v0101324322400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mr171324322400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4unn51324322400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/53qnm1324322400.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 = 144
Frequency = 1
1 2 3 4 5 6
4926.87056 7167.00035 5195.46346 5913.42658 6658.55636 2992.60281
7 8 9 10 11 12
5128.06593 1718.19572 1724.15883 4056.20528 2491.83507 6110.09971
13 14 15 16 17 18
5927.79013 10321.91992 3386.38304 5089.34615 3072.47594 1701.52239
19 20 21 22 23 24
3458.98550 1773.11529 5149.07840 1916.12486 -631.24536 5142.01928
25 26 27 28 29 30
2569.70971 2744.83949 -1447.69739 -3455.73428 -5064.60449 -5923.55804
31 32 33 34 35 36
-11630.09492 -9775.96514 -7890.00202 -7514.95557 -7860.32579 -5639.06114
37 38 39 40 41 42
-5961.37072 -5839.24094 -1930.77782 -3819.81470 -4317.68492 -59.63847
43 44 45 46 47 48
-6034.17535 -5847.04557 -5185.08245 -7027.03600 -955.40622 -8088.14157
49 50 51 52 53 54
-5153.45115 -4340.32136 -1461.85825 -5080.89513 -5382.76535 -6598.71890
55 56 57 58 59 60
-6481.25578 -2661.12599 -4385.16288 -3710.11643 -1512.48664 -4098.22200
61 62 63 64 65 66
-6096.53158 3045.59821 -535.93867 507.02444 996.15423 -2683.79932
67 68 69 70 71 72
40.66379 -1798.20642 -254.24330 5969.80315 8367.43293 9422.69758
73 74 75 76 77 78
6188.38800 -705.48222 -4946.01910 2684.94402 -3822.92620 -2707.87975
79 80 81 82 83 84
211.58337 -1861.28685 -2088.32373 598.72272 1572.35250 3934.61715
85 86 87 88 89 90
-98.69243 -5654.56265 2985.90047 4631.86359 -886.00663 2191.03982
91 92 93 94 95 96
1111.50294 190.63272 5843.59584 4655.64229 755.27207 2673.53672
97 98 99 100 101 102
-3150.77286 -4882.64307 1139.82004 2260.78316 860.91294 9754.95939
103 104 105 106 107 108
6421.42251 5114.55230 9875.51541 7083.56186 9483.19165 4889.45629
109 110 111 112 113 114
1497.14671 1216.27650 2519.73962 6136.70273 1203.83252 1739.87897
115 116 117 118 119 120
-1661.65792 -1140.52813 -5397.56501 -9444.51856 -13529.88878 3951.75752
121 122 123 124 125 126
12.66924 -1066.97968 -7925.29526 -4336.11085 -1875.75977 -2729.49202
127 128 129 130 131 132
3332.19239 5177.54347 -67.27211 1742.99564 6391.84672 -3574.66734
133 134 135 136 137 138
-661.75562 -2006.40454 3020.27987 -10531.53571 8557.81537 2323.08311
139 140 141 142 143 144
6102.76753 9110.11861 2675.30302 1673.57077 -4572.57815 -14724.09221
> postscript(file="/var/wessaorg/rcomp/tmp/67lkn1324322400.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 4926.87056 NA
1 7167.00035 4926.87056
2 5195.46346 7167.00035
3 5913.42658 5195.46346
4 6658.55636 5913.42658
5 2992.60281 6658.55636
6 5128.06593 2992.60281
7 1718.19572 5128.06593
8 1724.15883 1718.19572
9 4056.20528 1724.15883
10 2491.83507 4056.20528
11 6110.09971 2491.83507
12 5927.79013 6110.09971
13 10321.91992 5927.79013
14 3386.38304 10321.91992
15 5089.34615 3386.38304
16 3072.47594 5089.34615
17 1701.52239 3072.47594
18 3458.98550 1701.52239
19 1773.11529 3458.98550
20 5149.07840 1773.11529
21 1916.12486 5149.07840
22 -631.24536 1916.12486
23 5142.01928 -631.24536
24 2569.70971 5142.01928
25 2744.83949 2569.70971
26 -1447.69739 2744.83949
27 -3455.73428 -1447.69739
28 -5064.60449 -3455.73428
29 -5923.55804 -5064.60449
30 -11630.09492 -5923.55804
31 -9775.96514 -11630.09492
32 -7890.00202 -9775.96514
33 -7514.95557 -7890.00202
34 -7860.32579 -7514.95557
35 -5639.06114 -7860.32579
36 -5961.37072 -5639.06114
37 -5839.24094 -5961.37072
38 -1930.77782 -5839.24094
39 -3819.81470 -1930.77782
40 -4317.68492 -3819.81470
41 -59.63847 -4317.68492
42 -6034.17535 -59.63847
43 -5847.04557 -6034.17535
44 -5185.08245 -5847.04557
45 -7027.03600 -5185.08245
46 -955.40622 -7027.03600
47 -8088.14157 -955.40622
48 -5153.45115 -8088.14157
49 -4340.32136 -5153.45115
50 -1461.85825 -4340.32136
51 -5080.89513 -1461.85825
52 -5382.76535 -5080.89513
53 -6598.71890 -5382.76535
54 -6481.25578 -6598.71890
55 -2661.12599 -6481.25578
56 -4385.16288 -2661.12599
57 -3710.11643 -4385.16288
58 -1512.48664 -3710.11643
59 -4098.22200 -1512.48664
60 -6096.53158 -4098.22200
61 3045.59821 -6096.53158
62 -535.93867 3045.59821
63 507.02444 -535.93867
64 996.15423 507.02444
65 -2683.79932 996.15423
66 40.66379 -2683.79932
67 -1798.20642 40.66379
68 -254.24330 -1798.20642
69 5969.80315 -254.24330
70 8367.43293 5969.80315
71 9422.69758 8367.43293
72 6188.38800 9422.69758
73 -705.48222 6188.38800
74 -4946.01910 -705.48222
75 2684.94402 -4946.01910
76 -3822.92620 2684.94402
77 -2707.87975 -3822.92620
78 211.58337 -2707.87975
79 -1861.28685 211.58337
80 -2088.32373 -1861.28685
81 598.72272 -2088.32373
82 1572.35250 598.72272
83 3934.61715 1572.35250
84 -98.69243 3934.61715
85 -5654.56265 -98.69243
86 2985.90047 -5654.56265
87 4631.86359 2985.90047
88 -886.00663 4631.86359
89 2191.03982 -886.00663
90 1111.50294 2191.03982
91 190.63272 1111.50294
92 5843.59584 190.63272
93 4655.64229 5843.59584
94 755.27207 4655.64229
95 2673.53672 755.27207
96 -3150.77286 2673.53672
97 -4882.64307 -3150.77286
98 1139.82004 -4882.64307
99 2260.78316 1139.82004
100 860.91294 2260.78316
101 9754.95939 860.91294
102 6421.42251 9754.95939
103 5114.55230 6421.42251
104 9875.51541 5114.55230
105 7083.56186 9875.51541
106 9483.19165 7083.56186
107 4889.45629 9483.19165
108 1497.14671 4889.45629
109 1216.27650 1497.14671
110 2519.73962 1216.27650
111 6136.70273 2519.73962
112 1203.83252 6136.70273
113 1739.87897 1203.83252
114 -1661.65792 1739.87897
115 -1140.52813 -1661.65792
116 -5397.56501 -1140.52813
117 -9444.51856 -5397.56501
118 -13529.88878 -9444.51856
119 3951.75752 -13529.88878
120 12.66924 3951.75752
121 -1066.97968 12.66924
122 -7925.29526 -1066.97968
123 -4336.11085 -7925.29526
124 -1875.75977 -4336.11085
125 -2729.49202 -1875.75977
126 3332.19239 -2729.49202
127 5177.54347 3332.19239
128 -67.27211 5177.54347
129 1742.99564 -67.27211
130 6391.84672 1742.99564
131 -3574.66734 6391.84672
132 -661.75562 -3574.66734
133 -2006.40454 -661.75562
134 3020.27987 -2006.40454
135 -10531.53571 3020.27987
136 8557.81537 -10531.53571
137 2323.08311 8557.81537
138 6102.76753 2323.08311
139 9110.11861 6102.76753
140 2675.30302 9110.11861
141 1673.57077 2675.30302
142 -4572.57815 1673.57077
143 -14724.09221 -4572.57815
144 NA -14724.09221
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7167.00035 4926.87056
[2,] 5195.46346 7167.00035
[3,] 5913.42658 5195.46346
[4,] 6658.55636 5913.42658
[5,] 2992.60281 6658.55636
[6,] 5128.06593 2992.60281
[7,] 1718.19572 5128.06593
[8,] 1724.15883 1718.19572
[9,] 4056.20528 1724.15883
[10,] 2491.83507 4056.20528
[11,] 6110.09971 2491.83507
[12,] 5927.79013 6110.09971
[13,] 10321.91992 5927.79013
[14,] 3386.38304 10321.91992
[15,] 5089.34615 3386.38304
[16,] 3072.47594 5089.34615
[17,] 1701.52239 3072.47594
[18,] 3458.98550 1701.52239
[19,] 1773.11529 3458.98550
[20,] 5149.07840 1773.11529
[21,] 1916.12486 5149.07840
[22,] -631.24536 1916.12486
[23,] 5142.01928 -631.24536
[24,] 2569.70971 5142.01928
[25,] 2744.83949 2569.70971
[26,] -1447.69739 2744.83949
[27,] -3455.73428 -1447.69739
[28,] -5064.60449 -3455.73428
[29,] -5923.55804 -5064.60449
[30,] -11630.09492 -5923.55804
[31,] -9775.96514 -11630.09492
[32,] -7890.00202 -9775.96514
[33,] -7514.95557 -7890.00202
[34,] -7860.32579 -7514.95557
[35,] -5639.06114 -7860.32579
[36,] -5961.37072 -5639.06114
[37,] -5839.24094 -5961.37072
[38,] -1930.77782 -5839.24094
[39,] -3819.81470 -1930.77782
[40,] -4317.68492 -3819.81470
[41,] -59.63847 -4317.68492
[42,] -6034.17535 -59.63847
[43,] -5847.04557 -6034.17535
[44,] -5185.08245 -5847.04557
[45,] -7027.03600 -5185.08245
[46,] -955.40622 -7027.03600
[47,] -8088.14157 -955.40622
[48,] -5153.45115 -8088.14157
[49,] -4340.32136 -5153.45115
[50,] -1461.85825 -4340.32136
[51,] -5080.89513 -1461.85825
[52,] -5382.76535 -5080.89513
[53,] -6598.71890 -5382.76535
[54,] -6481.25578 -6598.71890
[55,] -2661.12599 -6481.25578
[56,] -4385.16288 -2661.12599
[57,] -3710.11643 -4385.16288
[58,] -1512.48664 -3710.11643
[59,] -4098.22200 -1512.48664
[60,] -6096.53158 -4098.22200
[61,] 3045.59821 -6096.53158
[62,] -535.93867 3045.59821
[63,] 507.02444 -535.93867
[64,] 996.15423 507.02444
[65,] -2683.79932 996.15423
[66,] 40.66379 -2683.79932
[67,] -1798.20642 40.66379
[68,] -254.24330 -1798.20642
[69,] 5969.80315 -254.24330
[70,] 8367.43293 5969.80315
[71,] 9422.69758 8367.43293
[72,] 6188.38800 9422.69758
[73,] -705.48222 6188.38800
[74,] -4946.01910 -705.48222
[75,] 2684.94402 -4946.01910
[76,] -3822.92620 2684.94402
[77,] -2707.87975 -3822.92620
[78,] 211.58337 -2707.87975
[79,] -1861.28685 211.58337
[80,] -2088.32373 -1861.28685
[81,] 598.72272 -2088.32373
[82,] 1572.35250 598.72272
[83,] 3934.61715 1572.35250
[84,] -98.69243 3934.61715
[85,] -5654.56265 -98.69243
[86,] 2985.90047 -5654.56265
[87,] 4631.86359 2985.90047
[88,] -886.00663 4631.86359
[89,] 2191.03982 -886.00663
[90,] 1111.50294 2191.03982
[91,] 190.63272 1111.50294
[92,] 5843.59584 190.63272
[93,] 4655.64229 5843.59584
[94,] 755.27207 4655.64229
[95,] 2673.53672 755.27207
[96,] -3150.77286 2673.53672
[97,] -4882.64307 -3150.77286
[98,] 1139.82004 -4882.64307
[99,] 2260.78316 1139.82004
[100,] 860.91294 2260.78316
[101,] 9754.95939 860.91294
[102,] 6421.42251 9754.95939
[103,] 5114.55230 6421.42251
[104,] 9875.51541 5114.55230
[105,] 7083.56186 9875.51541
[106,] 9483.19165 7083.56186
[107,] 4889.45629 9483.19165
[108,] 1497.14671 4889.45629
[109,] 1216.27650 1497.14671
[110,] 2519.73962 1216.27650
[111,] 6136.70273 2519.73962
[112,] 1203.83252 6136.70273
[113,] 1739.87897 1203.83252
[114,] -1661.65792 1739.87897
[115,] -1140.52813 -1661.65792
[116,] -5397.56501 -1140.52813
[117,] -9444.51856 -5397.56501
[118,] -13529.88878 -9444.51856
[119,] 3951.75752 -13529.88878
[120,] 12.66924 3951.75752
[121,] -1066.97968 12.66924
[122,] -7925.29526 -1066.97968
[123,] -4336.11085 -7925.29526
[124,] -1875.75977 -4336.11085
[125,] -2729.49202 -1875.75977
[126,] 3332.19239 -2729.49202
[127,] 5177.54347 3332.19239
[128,] -67.27211 5177.54347
[129,] 1742.99564 -67.27211
[130,] 6391.84672 1742.99564
[131,] -3574.66734 6391.84672
[132,] -661.75562 -3574.66734
[133,] -2006.40454 -661.75562
[134,] 3020.27987 -2006.40454
[135,] -10531.53571 3020.27987
[136,] 8557.81537 -10531.53571
[137,] 2323.08311 8557.81537
[138,] 6102.76753 2323.08311
[139,] 9110.11861 6102.76753
[140,] 2675.30302 9110.11861
[141,] 1673.57077 2675.30302
[142,] -4572.57815 1673.57077
[143,] -14724.09221 -4572.57815
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7167.00035 4926.87056
2 5195.46346 7167.00035
3 5913.42658 5195.46346
4 6658.55636 5913.42658
5 2992.60281 6658.55636
6 5128.06593 2992.60281
7 1718.19572 5128.06593
8 1724.15883 1718.19572
9 4056.20528 1724.15883
10 2491.83507 4056.20528
11 6110.09971 2491.83507
12 5927.79013 6110.09971
13 10321.91992 5927.79013
14 3386.38304 10321.91992
15 5089.34615 3386.38304
16 3072.47594 5089.34615
17 1701.52239 3072.47594
18 3458.98550 1701.52239
19 1773.11529 3458.98550
20 5149.07840 1773.11529
21 1916.12486 5149.07840
22 -631.24536 1916.12486
23 5142.01928 -631.24536
24 2569.70971 5142.01928
25 2744.83949 2569.70971
26 -1447.69739 2744.83949
27 -3455.73428 -1447.69739
28 -5064.60449 -3455.73428
29 -5923.55804 -5064.60449
30 -11630.09492 -5923.55804
31 -9775.96514 -11630.09492
32 -7890.00202 -9775.96514
33 -7514.95557 -7890.00202
34 -7860.32579 -7514.95557
35 -5639.06114 -7860.32579
36 -5961.37072 -5639.06114
37 -5839.24094 -5961.37072
38 -1930.77782 -5839.24094
39 -3819.81470 -1930.77782
40 -4317.68492 -3819.81470
41 -59.63847 -4317.68492
42 -6034.17535 -59.63847
43 -5847.04557 -6034.17535
44 -5185.08245 -5847.04557
45 -7027.03600 -5185.08245
46 -955.40622 -7027.03600
47 -8088.14157 -955.40622
48 -5153.45115 -8088.14157
49 -4340.32136 -5153.45115
50 -1461.85825 -4340.32136
51 -5080.89513 -1461.85825
52 -5382.76535 -5080.89513
53 -6598.71890 -5382.76535
54 -6481.25578 -6598.71890
55 -2661.12599 -6481.25578
56 -4385.16288 -2661.12599
57 -3710.11643 -4385.16288
58 -1512.48664 -3710.11643
59 -4098.22200 -1512.48664
60 -6096.53158 -4098.22200
61 3045.59821 -6096.53158
62 -535.93867 3045.59821
63 507.02444 -535.93867
64 996.15423 507.02444
65 -2683.79932 996.15423
66 40.66379 -2683.79932
67 -1798.20642 40.66379
68 -254.24330 -1798.20642
69 5969.80315 -254.24330
70 8367.43293 5969.80315
71 9422.69758 8367.43293
72 6188.38800 9422.69758
73 -705.48222 6188.38800
74 -4946.01910 -705.48222
75 2684.94402 -4946.01910
76 -3822.92620 2684.94402
77 -2707.87975 -3822.92620
78 211.58337 -2707.87975
79 -1861.28685 211.58337
80 -2088.32373 -1861.28685
81 598.72272 -2088.32373
82 1572.35250 598.72272
83 3934.61715 1572.35250
84 -98.69243 3934.61715
85 -5654.56265 -98.69243
86 2985.90047 -5654.56265
87 4631.86359 2985.90047
88 -886.00663 4631.86359
89 2191.03982 -886.00663
90 1111.50294 2191.03982
91 190.63272 1111.50294
92 5843.59584 190.63272
93 4655.64229 5843.59584
94 755.27207 4655.64229
95 2673.53672 755.27207
96 -3150.77286 2673.53672
97 -4882.64307 -3150.77286
98 1139.82004 -4882.64307
99 2260.78316 1139.82004
100 860.91294 2260.78316
101 9754.95939 860.91294
102 6421.42251 9754.95939
103 5114.55230 6421.42251
104 9875.51541 5114.55230
105 7083.56186 9875.51541
106 9483.19165 7083.56186
107 4889.45629 9483.19165
108 1497.14671 4889.45629
109 1216.27650 1497.14671
110 2519.73962 1216.27650
111 6136.70273 2519.73962
112 1203.83252 6136.70273
113 1739.87897 1203.83252
114 -1661.65792 1739.87897
115 -1140.52813 -1661.65792
116 -5397.56501 -1140.52813
117 -9444.51856 -5397.56501
118 -13529.88878 -9444.51856
119 3951.75752 -13529.88878
120 12.66924 3951.75752
121 -1066.97968 12.66924
122 -7925.29526 -1066.97968
123 -4336.11085 -7925.29526
124 -1875.75977 -4336.11085
125 -2729.49202 -1875.75977
126 3332.19239 -2729.49202
127 5177.54347 3332.19239
128 -67.27211 5177.54347
129 1742.99564 -67.27211
130 6391.84672 1742.99564
131 -3574.66734 6391.84672
132 -661.75562 -3574.66734
133 -2006.40454 -661.75562
134 3020.27987 -2006.40454
135 -10531.53571 3020.27987
136 8557.81537 -10531.53571
137 2323.08311 8557.81537
138 6102.76753 2323.08311
139 9110.11861 6102.76753
140 2675.30302 9110.11861
141 1673.57077 2675.30302
142 -4572.57815 1673.57077
143 -14724.09221 -4572.57815
> 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/7d0n31324322400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8dhkq1324322400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9q3sl1324322400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/104tmq1324322400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11knyf1324322400.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/12bmke1324322400.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/13c80k1324322400.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/14uk871324322400.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/15lvby1324322400.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/16dvp31324322400.tab")
+ }
>
> try(system("convert tmp/1d8j11324322400.ps tmp/1d8j11324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v0101324322400.ps tmp/2v0101324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mr171324322400.ps tmp/3mr171324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/4unn51324322400.ps tmp/4unn51324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/53qnm1324322400.ps tmp/53qnm1324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/67lkn1324322400.ps tmp/67lkn1324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/7d0n31324322400.ps tmp/7d0n31324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dhkq1324322400.ps tmp/8dhkq1324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/9q3sl1324322400.ps tmp/9q3sl1324322400.png",intern=TRUE))
character(0)
> try(system("convert tmp/104tmq1324322400.ps tmp/104tmq1324322400.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.504 0.748 6.269