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
+ ,110855
+ ,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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+ ,20
+ ,0
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+ ,0
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+ ,0
+ ,106088
+ ,0
+ ,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
+ ,0
+ ,97772
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+ ,0
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+ ,33
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+ ,34
+ ,0
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+ ,0
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+ ,68
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+ ,137999
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+ ,0
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+ ,0
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+ ,1
+ ,121
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+ ,1
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+ ,1
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+ ,1
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+ ,126
+ ,104341
+ ,1
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+ ,127
+ ,112430
+ ,1
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+ ,113034
+ ,1
+ ,129
+ ,129
+ ,114197
+ ,1
+ ,130
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+ ,127876
+ ,1
+ ,131
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+ ,1
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+ ,132
+ ,123663
+ ,1
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+ ,133
+ ,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 1 119 119 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`
100340.8 -150216.1 401.0 944.7
M1 M2 M3 M4
-15696.2 -11373.9 4650.1 -8088.4
M5 M6 M7 M8
-9465.0 -10610.6 -10131.6 -12921.2
M9 M10 M11
-8061.7 2258.7 6534.2
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12402.0 -4086.1 121.7 3625.9 20718.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100340.78 1889.80 53.096 < 2e-16 ***
`crisis_10/8` -150216.14 19601.33 -7.664 3.81e-12 ***
t 401.04 15.26 26.284 < 2e-16 ***
`t_crisis_10/8` 944.71 149.31 6.327 3.78e-09 ***
M1 -15696.22 2305.71 -6.808 3.37e-10 ***
M2 -11373.88 2304.12 -4.936 2.41e-06 ***
M3 4650.13 2302.87 2.019 0.045530 *
M4 -8088.36 2301.95 -3.514 0.000610 ***
M5 -9465.01 2301.38 -4.113 6.92e-05 ***
M6 -10610.59 2301.15 -4.611 9.52e-06 ***
M7 -10131.58 2301.25 -4.403 2.22e-05 ***
M8 -12921.23 2301.70 -5.614 1.16e-07 ***
M9 -8061.72 2302.48 -3.501 0.000636 ***
M10 2258.70 2303.61 0.981 0.328671
M11 6534.22 2299.21 2.842 0.005214 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5631 on 129 degrees of freedom
Multiple R-squared: 0.889, Adjusted R-squared: 0.8769
F-statistic: 73.79 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,] 0.0801705421 0.160341084 0.9198295
[2,] 0.0287923716 0.057584743 0.9712076
[3,] 0.0092865025 0.018573005 0.9907135
[4,] 0.0086670115 0.017334023 0.9913330
[5,] 0.0037595065 0.007519013 0.9962405
[6,] 0.0019489100 0.003897820 0.9980511
[7,] 0.0007417712 0.001483542 0.9992582
[8,] 0.0003569135 0.000713827 0.9996431
[9,] 0.0007248421 0.001449684 0.9992752
[10,] 0.0006460807 0.001292161 0.9993539
[11,] 0.0020381075 0.004076215 0.9979619
[12,] 0.0045348778 0.009069756 0.9954651
[13,] 0.0038512683 0.007702537 0.9961487
[14,] 0.0389887929 0.077977586 0.9610112
[15,] 0.0435899354 0.087179871 0.9564101
[16,] 0.0419692673 0.083938535 0.9580307
[17,] 0.0344991717 0.068998343 0.9655008
[18,] 0.0272208296 0.054441659 0.9727792
[19,] 0.0230463747 0.046092749 0.9769536
[20,] 0.0147574391 0.029514878 0.9852426
[21,] 0.0106079180 0.021215836 0.9893921
[22,] 0.0117149229 0.023429846 0.9882851
[23,] 0.0086340014 0.017268003 0.9913660
[24,] 0.0064547817 0.012909563 0.9935452
[25,] 0.0165131572 0.033026314 0.9834868
[26,] 0.0127891516 0.025578303 0.9872108
[27,] 0.0111344670 0.022268934 0.9888655
[28,] 0.0082425070 0.016485014 0.9917575
[29,] 0.0056844571 0.011368914 0.9943155
[30,] 0.0108685915 0.021737183 0.9891314
[31,] 0.0086799741 0.017359948 0.9913200
[32,] 0.0060207097 0.012041419 0.9939793
[33,] 0.0038967727 0.007793545 0.9961032
[34,] 0.0043533543 0.008706709 0.9956466
[35,] 0.0030709625 0.006141925 0.9969290
[36,] 0.0022675097 0.004535019 0.9977325
[37,] 0.0016576585 0.003315317 0.9983423
[38,] 0.0015475108 0.003095022 0.9984525
[39,] 0.0027482815 0.005496563 0.9972517
[40,] 0.0025513349 0.005102670 0.9974487
[41,] 0.0027275626 0.005455125 0.9972724
[42,] 0.0043852366 0.008770473 0.9956148
[43,] 0.0035640973 0.007128195 0.9964359
[44,] 0.0027856094 0.005571219 0.9972144
[45,] 0.0053376629 0.010675326 0.9946623
[46,] 0.0053097348 0.010619470 0.9946903
[47,] 0.0067222314 0.013444463 0.9932778
[48,] 0.0093435335 0.018687067 0.9906565
[49,] 0.0087524213 0.017504843 0.9912476
[50,] 0.0127333645 0.025466729 0.9872666
[51,] 0.0141907669 0.028381534 0.9858092
[52,] 0.0152969284 0.030593857 0.9847031
[53,] 0.0380676086 0.076135217 0.9619324
[54,] 0.0822865678 0.164573136 0.9177134
[55,] 0.1848475128 0.369695026 0.8151525
[56,] 0.2440343940 0.488068788 0.7559656
[57,] 0.2092582994 0.418516599 0.7907417
[58,] 0.1827451497 0.365490299 0.8172549
[59,] 0.1760239887 0.352047977 0.8239760
[60,] 0.1517208319 0.303441664 0.8482792
[61,] 0.1337054359 0.267410872 0.8662946
[62,] 0.1229587475 0.245917495 0.8770413
[63,] 0.1154794097 0.230958819 0.8845206
[64,] 0.1049522095 0.209904419 0.8950478
[65,] 0.0897998260 0.179599652 0.9102002
[66,] 0.0936367045 0.187273409 0.9063633
[67,] 0.0851912844 0.170382569 0.9148087
[68,] 0.0671375143 0.134275029 0.9328625
[69,] 0.0627825241 0.125565048 0.9372175
[70,] 0.0546480514 0.109296103 0.9453519
[71,] 0.0531759079 0.106351816 0.9468241
[72,] 0.0447560699 0.089512140 0.9552439
[73,] 0.0397201889 0.079440378 0.9602798
[74,] 0.0366108299 0.073221660 0.9633892
[75,] 0.0392784567 0.078556913 0.9607215
[76,] 0.0393653767 0.078730753 0.9606346
[77,] 0.0339338422 0.067867684 0.9660662
[78,] 0.0562524995 0.112504999 0.9437475
[79,] 0.0429351624 0.085870325 0.9570648
[80,] 0.0431172617 0.086234523 0.9568827
[81,] 0.0550527433 0.110105487 0.9449473
[82,] 0.0470364670 0.094072934 0.9529635
[83,] 0.0361586088 0.072317218 0.9638414
[84,] 0.0464205042 0.092841008 0.9535795
[85,] 0.0526837216 0.105367443 0.9473163
[86,] 0.0563939998 0.112788000 0.9436060
[87,] 0.0835201042 0.167040208 0.9164799
[88,] 0.0834164412 0.166832882 0.9165836
[89,] 0.0746788654 0.149357731 0.9253211
[90,] 0.1123131583 0.224626317 0.8876868
[91,] 0.0864091204 0.172818241 0.9135909
[92,] 0.0786311794 0.157262359 0.9213688
[93,] 0.0702250640 0.140450128 0.9297749
[94,] 0.0551855310 0.110371062 0.9448145
[95,] 0.0595094489 0.119018898 0.9404906
[96,] 0.0430053532 0.086010706 0.9569946
[97,] 0.0323060849 0.064612170 0.9676939
[98,] 0.0211058269 0.042211654 0.9788942
[99,] 0.0137137203 0.027427441 0.9862863
[100,] 0.0091839629 0.018367926 0.9908160
[101,] 0.0071752123 0.014350425 0.9928248
[102,] 0.1524417256 0.304883451 0.8475583
[103,] 0.2843716469 0.568743294 0.7156284
[104,] 0.2232615296 0.446523059 0.7767385
[105,] 0.1668639794 0.333727959 0.8331360
[106,] 0.2139504960 0.427900992 0.7860495
[107,] 0.1845550207 0.369110041 0.8154450
[108,] 0.2639475768 0.527895154 0.7360524
[109,] 0.2221455833 0.444291167 0.7778544
> postscript(file="/var/wessaorg/rcomp/tmp/1y7wk1324323146.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2q26j1324323146.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ue8h1324323146.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/48dxq1324323146.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5urt31324323146.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
5558.40529 7758.02313 5745.97430 6423.42548 7128.04332 3421.57782
7 8 9 10 11 12
5516.52900 2066.14683 2031.59801 4323.13251 -431.41643 6562.76033
13 14 15 16 17 18
6489.94235 10843.56019 3867.51137 5529.96254 3472.58038 2061.11489
19 20 21 22 23 24
3778.06606 2051.68390 5387.13507 2113.66958 -3623.87937 5525.29739
25 26 27 28 29 30
3062.47942 3197.09726 -1035.95157 -3084.50039 -4733.88255 -5633.34805
31 32 33 34 35 36
-11380.39687 -9566.77903 -7721.32786 -7386.79335 -10922.34230 -5325.16554
37 38 39 40 41 42
-5537.98352 -5456.36568 -1588.41450 -3517.96333 -4056.34549 161.18902
43 44 45 46 47 48
-5853.85981 -5707.24197 -5085.79080 -6968.25629 -4086.80524 -7843.62848
49 50 51 52 53 54
-4799.44645 -4026.82861 -1188.87744 -4848.42626 -5190.80842 -6447.27392
55 56 57 58 59 60
-6370.32274 -2590.70490 -4355.25373 -3720.71922 -4713.26817 -3923.09141
61 62 63 64 65 66
-5811.90938 3289.70846 -332.34037 670.11080 1118.72864 -2601.73685
67 68 69 70 71 72
82.21432 -1797.16784 -293.71667 5889.81784 5097.26890 9528.44566
73 74 75 76 77 78
6403.62768 -530.75448 -4811.80331 2778.64787 -3769.73429 -2695.19979
79 80 81 82 83 84
183.75139 -1929.63077 -2197.17960 449.35491 -1767.19404 3970.98272
85 86 87 88 89 90
47.16475 -5549.21741 3050.73376 4656.18493 -902.19723 2134.33728
91 92 93 94 95 96
1014.28845 52.90629 5665.35746 4436.89197 -2653.65697 2640.51979
97 98 99 100 101 102
-3074.29819 -4846.68035 1135.27082 2215.72200 775.33984 9628.87434
103 104 105 106 107 108
6254.82552 4907.44336 9627.89453 6795.42904 6004.88009 4787.05685
109 110 111 112 113 114
1504.23888 1182.85672 2445.80789 6022.25906 1048.87690 1544.41141
115 116 117 118 119 120
-1897.63742 -1417.01958 -5714.56840 -9802.03390 20718.41858 578.88830
121 122 123 124 125 126
-3007.63671 -3884.72591 -10540.48178 -6748.73764 -4085.82684 -4736.99937
127 128 129 130 131 132
1527.24476 3575.15556 -1467.10031 545.72716 2247.47118 -4100.05910
133 134 135 136 137 138
-834.58412 -1976.67332 3252.57082 -10096.68505 9195.22575 3163.05322
139 140 141 142 143 144
7145.29735 10355.20815 4122.95229 3323.77975 -5869.47623 -12402.00651
> postscript(file="/var/wessaorg/rcomp/tmp/6jqm01324323146.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 5558.40529 NA
1 7758.02313 5558.40529
2 5745.97430 7758.02313
3 6423.42548 5745.97430
4 7128.04332 6423.42548
5 3421.57782 7128.04332
6 5516.52900 3421.57782
7 2066.14683 5516.52900
8 2031.59801 2066.14683
9 4323.13251 2031.59801
10 -431.41643 4323.13251
11 6562.76033 -431.41643
12 6489.94235 6562.76033
13 10843.56019 6489.94235
14 3867.51137 10843.56019
15 5529.96254 3867.51137
16 3472.58038 5529.96254
17 2061.11489 3472.58038
18 3778.06606 2061.11489
19 2051.68390 3778.06606
20 5387.13507 2051.68390
21 2113.66958 5387.13507
22 -3623.87937 2113.66958
23 5525.29739 -3623.87937
24 3062.47942 5525.29739
25 3197.09726 3062.47942
26 -1035.95157 3197.09726
27 -3084.50039 -1035.95157
28 -4733.88255 -3084.50039
29 -5633.34805 -4733.88255
30 -11380.39687 -5633.34805
31 -9566.77903 -11380.39687
32 -7721.32786 -9566.77903
33 -7386.79335 -7721.32786
34 -10922.34230 -7386.79335
35 -5325.16554 -10922.34230
36 -5537.98352 -5325.16554
37 -5456.36568 -5537.98352
38 -1588.41450 -5456.36568
39 -3517.96333 -1588.41450
40 -4056.34549 -3517.96333
41 161.18902 -4056.34549
42 -5853.85981 161.18902
43 -5707.24197 -5853.85981
44 -5085.79080 -5707.24197
45 -6968.25629 -5085.79080
46 -4086.80524 -6968.25629
47 -7843.62848 -4086.80524
48 -4799.44645 -7843.62848
49 -4026.82861 -4799.44645
50 -1188.87744 -4026.82861
51 -4848.42626 -1188.87744
52 -5190.80842 -4848.42626
53 -6447.27392 -5190.80842
54 -6370.32274 -6447.27392
55 -2590.70490 -6370.32274
56 -4355.25373 -2590.70490
57 -3720.71922 -4355.25373
58 -4713.26817 -3720.71922
59 -3923.09141 -4713.26817
60 -5811.90938 -3923.09141
61 3289.70846 -5811.90938
62 -332.34037 3289.70846
63 670.11080 -332.34037
64 1118.72864 670.11080
65 -2601.73685 1118.72864
66 82.21432 -2601.73685
67 -1797.16784 82.21432
68 -293.71667 -1797.16784
69 5889.81784 -293.71667
70 5097.26890 5889.81784
71 9528.44566 5097.26890
72 6403.62768 9528.44566
73 -530.75448 6403.62768
74 -4811.80331 -530.75448
75 2778.64787 -4811.80331
76 -3769.73429 2778.64787
77 -2695.19979 -3769.73429
78 183.75139 -2695.19979
79 -1929.63077 183.75139
80 -2197.17960 -1929.63077
81 449.35491 -2197.17960
82 -1767.19404 449.35491
83 3970.98272 -1767.19404
84 47.16475 3970.98272
85 -5549.21741 47.16475
86 3050.73376 -5549.21741
87 4656.18493 3050.73376
88 -902.19723 4656.18493
89 2134.33728 -902.19723
90 1014.28845 2134.33728
91 52.90629 1014.28845
92 5665.35746 52.90629
93 4436.89197 5665.35746
94 -2653.65697 4436.89197
95 2640.51979 -2653.65697
96 -3074.29819 2640.51979
97 -4846.68035 -3074.29819
98 1135.27082 -4846.68035
99 2215.72200 1135.27082
100 775.33984 2215.72200
101 9628.87434 775.33984
102 6254.82552 9628.87434
103 4907.44336 6254.82552
104 9627.89453 4907.44336
105 6795.42904 9627.89453
106 6004.88009 6795.42904
107 4787.05685 6004.88009
108 1504.23888 4787.05685
109 1182.85672 1504.23888
110 2445.80789 1182.85672
111 6022.25906 2445.80789
112 1048.87690 6022.25906
113 1544.41141 1048.87690
114 -1897.63742 1544.41141
115 -1417.01958 -1897.63742
116 -5714.56840 -1417.01958
117 -9802.03390 -5714.56840
118 20718.41858 -9802.03390
119 578.88830 20718.41858
120 -3007.63671 578.88830
121 -3884.72591 -3007.63671
122 -10540.48178 -3884.72591
123 -6748.73764 -10540.48178
124 -4085.82684 -6748.73764
125 -4736.99937 -4085.82684
126 1527.24476 -4736.99937
127 3575.15556 1527.24476
128 -1467.10031 3575.15556
129 545.72716 -1467.10031
130 2247.47118 545.72716
131 -4100.05910 2247.47118
132 -834.58412 -4100.05910
133 -1976.67332 -834.58412
134 3252.57082 -1976.67332
135 -10096.68505 3252.57082
136 9195.22575 -10096.68505
137 3163.05322 9195.22575
138 7145.29735 3163.05322
139 10355.20815 7145.29735
140 4122.95229 10355.20815
141 3323.77975 4122.95229
142 -5869.47623 3323.77975
143 -12402.00651 -5869.47623
144 NA -12402.00651
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7758.02313 5558.40529
[2,] 5745.97430 7758.02313
[3,] 6423.42548 5745.97430
[4,] 7128.04332 6423.42548
[5,] 3421.57782 7128.04332
[6,] 5516.52900 3421.57782
[7,] 2066.14683 5516.52900
[8,] 2031.59801 2066.14683
[9,] 4323.13251 2031.59801
[10,] -431.41643 4323.13251
[11,] 6562.76033 -431.41643
[12,] 6489.94235 6562.76033
[13,] 10843.56019 6489.94235
[14,] 3867.51137 10843.56019
[15,] 5529.96254 3867.51137
[16,] 3472.58038 5529.96254
[17,] 2061.11489 3472.58038
[18,] 3778.06606 2061.11489
[19,] 2051.68390 3778.06606
[20,] 5387.13507 2051.68390
[21,] 2113.66958 5387.13507
[22,] -3623.87937 2113.66958
[23,] 5525.29739 -3623.87937
[24,] 3062.47942 5525.29739
[25,] 3197.09726 3062.47942
[26,] -1035.95157 3197.09726
[27,] -3084.50039 -1035.95157
[28,] -4733.88255 -3084.50039
[29,] -5633.34805 -4733.88255
[30,] -11380.39687 -5633.34805
[31,] -9566.77903 -11380.39687
[32,] -7721.32786 -9566.77903
[33,] -7386.79335 -7721.32786
[34,] -10922.34230 -7386.79335
[35,] -5325.16554 -10922.34230
[36,] -5537.98352 -5325.16554
[37,] -5456.36568 -5537.98352
[38,] -1588.41450 -5456.36568
[39,] -3517.96333 -1588.41450
[40,] -4056.34549 -3517.96333
[41,] 161.18902 -4056.34549
[42,] -5853.85981 161.18902
[43,] -5707.24197 -5853.85981
[44,] -5085.79080 -5707.24197
[45,] -6968.25629 -5085.79080
[46,] -4086.80524 -6968.25629
[47,] -7843.62848 -4086.80524
[48,] -4799.44645 -7843.62848
[49,] -4026.82861 -4799.44645
[50,] -1188.87744 -4026.82861
[51,] -4848.42626 -1188.87744
[52,] -5190.80842 -4848.42626
[53,] -6447.27392 -5190.80842
[54,] -6370.32274 -6447.27392
[55,] -2590.70490 -6370.32274
[56,] -4355.25373 -2590.70490
[57,] -3720.71922 -4355.25373
[58,] -4713.26817 -3720.71922
[59,] -3923.09141 -4713.26817
[60,] -5811.90938 -3923.09141
[61,] 3289.70846 -5811.90938
[62,] -332.34037 3289.70846
[63,] 670.11080 -332.34037
[64,] 1118.72864 670.11080
[65,] -2601.73685 1118.72864
[66,] 82.21432 -2601.73685
[67,] -1797.16784 82.21432
[68,] -293.71667 -1797.16784
[69,] 5889.81784 -293.71667
[70,] 5097.26890 5889.81784
[71,] 9528.44566 5097.26890
[72,] 6403.62768 9528.44566
[73,] -530.75448 6403.62768
[74,] -4811.80331 -530.75448
[75,] 2778.64787 -4811.80331
[76,] -3769.73429 2778.64787
[77,] -2695.19979 -3769.73429
[78,] 183.75139 -2695.19979
[79,] -1929.63077 183.75139
[80,] -2197.17960 -1929.63077
[81,] 449.35491 -2197.17960
[82,] -1767.19404 449.35491
[83,] 3970.98272 -1767.19404
[84,] 47.16475 3970.98272
[85,] -5549.21741 47.16475
[86,] 3050.73376 -5549.21741
[87,] 4656.18493 3050.73376
[88,] -902.19723 4656.18493
[89,] 2134.33728 -902.19723
[90,] 1014.28845 2134.33728
[91,] 52.90629 1014.28845
[92,] 5665.35746 52.90629
[93,] 4436.89197 5665.35746
[94,] -2653.65697 4436.89197
[95,] 2640.51979 -2653.65697
[96,] -3074.29819 2640.51979
[97,] -4846.68035 -3074.29819
[98,] 1135.27082 -4846.68035
[99,] 2215.72200 1135.27082
[100,] 775.33984 2215.72200
[101,] 9628.87434 775.33984
[102,] 6254.82552 9628.87434
[103,] 4907.44336 6254.82552
[104,] 9627.89453 4907.44336
[105,] 6795.42904 9627.89453
[106,] 6004.88009 6795.42904
[107,] 4787.05685 6004.88009
[108,] 1504.23888 4787.05685
[109,] 1182.85672 1504.23888
[110,] 2445.80789 1182.85672
[111,] 6022.25906 2445.80789
[112,] 1048.87690 6022.25906
[113,] 1544.41141 1048.87690
[114,] -1897.63742 1544.41141
[115,] -1417.01958 -1897.63742
[116,] -5714.56840 -1417.01958
[117,] -9802.03390 -5714.56840
[118,] 20718.41858 -9802.03390
[119,] 578.88830 20718.41858
[120,] -3007.63671 578.88830
[121,] -3884.72591 -3007.63671
[122,] -10540.48178 -3884.72591
[123,] -6748.73764 -10540.48178
[124,] -4085.82684 -6748.73764
[125,] -4736.99937 -4085.82684
[126,] 1527.24476 -4736.99937
[127,] 3575.15556 1527.24476
[128,] -1467.10031 3575.15556
[129,] 545.72716 -1467.10031
[130,] 2247.47118 545.72716
[131,] -4100.05910 2247.47118
[132,] -834.58412 -4100.05910
[133,] -1976.67332 -834.58412
[134,] 3252.57082 -1976.67332
[135,] -10096.68505 3252.57082
[136,] 9195.22575 -10096.68505
[137,] 3163.05322 9195.22575
[138,] 7145.29735 3163.05322
[139,] 10355.20815 7145.29735
[140,] 4122.95229 10355.20815
[141,] 3323.77975 4122.95229
[142,] -5869.47623 3323.77975
[143,] -12402.00651 -5869.47623
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7758.02313 5558.40529
2 5745.97430 7758.02313
3 6423.42548 5745.97430
4 7128.04332 6423.42548
5 3421.57782 7128.04332
6 5516.52900 3421.57782
7 2066.14683 5516.52900
8 2031.59801 2066.14683
9 4323.13251 2031.59801
10 -431.41643 4323.13251
11 6562.76033 -431.41643
12 6489.94235 6562.76033
13 10843.56019 6489.94235
14 3867.51137 10843.56019
15 5529.96254 3867.51137
16 3472.58038 5529.96254
17 2061.11489 3472.58038
18 3778.06606 2061.11489
19 2051.68390 3778.06606
20 5387.13507 2051.68390
21 2113.66958 5387.13507
22 -3623.87937 2113.66958
23 5525.29739 -3623.87937
24 3062.47942 5525.29739
25 3197.09726 3062.47942
26 -1035.95157 3197.09726
27 -3084.50039 -1035.95157
28 -4733.88255 -3084.50039
29 -5633.34805 -4733.88255
30 -11380.39687 -5633.34805
31 -9566.77903 -11380.39687
32 -7721.32786 -9566.77903
33 -7386.79335 -7721.32786
34 -10922.34230 -7386.79335
35 -5325.16554 -10922.34230
36 -5537.98352 -5325.16554
37 -5456.36568 -5537.98352
38 -1588.41450 -5456.36568
39 -3517.96333 -1588.41450
40 -4056.34549 -3517.96333
41 161.18902 -4056.34549
42 -5853.85981 161.18902
43 -5707.24197 -5853.85981
44 -5085.79080 -5707.24197
45 -6968.25629 -5085.79080
46 -4086.80524 -6968.25629
47 -7843.62848 -4086.80524
48 -4799.44645 -7843.62848
49 -4026.82861 -4799.44645
50 -1188.87744 -4026.82861
51 -4848.42626 -1188.87744
52 -5190.80842 -4848.42626
53 -6447.27392 -5190.80842
54 -6370.32274 -6447.27392
55 -2590.70490 -6370.32274
56 -4355.25373 -2590.70490
57 -3720.71922 -4355.25373
58 -4713.26817 -3720.71922
59 -3923.09141 -4713.26817
60 -5811.90938 -3923.09141
61 3289.70846 -5811.90938
62 -332.34037 3289.70846
63 670.11080 -332.34037
64 1118.72864 670.11080
65 -2601.73685 1118.72864
66 82.21432 -2601.73685
67 -1797.16784 82.21432
68 -293.71667 -1797.16784
69 5889.81784 -293.71667
70 5097.26890 5889.81784
71 9528.44566 5097.26890
72 6403.62768 9528.44566
73 -530.75448 6403.62768
74 -4811.80331 -530.75448
75 2778.64787 -4811.80331
76 -3769.73429 2778.64787
77 -2695.19979 -3769.73429
78 183.75139 -2695.19979
79 -1929.63077 183.75139
80 -2197.17960 -1929.63077
81 449.35491 -2197.17960
82 -1767.19404 449.35491
83 3970.98272 -1767.19404
84 47.16475 3970.98272
85 -5549.21741 47.16475
86 3050.73376 -5549.21741
87 4656.18493 3050.73376
88 -902.19723 4656.18493
89 2134.33728 -902.19723
90 1014.28845 2134.33728
91 52.90629 1014.28845
92 5665.35746 52.90629
93 4436.89197 5665.35746
94 -2653.65697 4436.89197
95 2640.51979 -2653.65697
96 -3074.29819 2640.51979
97 -4846.68035 -3074.29819
98 1135.27082 -4846.68035
99 2215.72200 1135.27082
100 775.33984 2215.72200
101 9628.87434 775.33984
102 6254.82552 9628.87434
103 4907.44336 6254.82552
104 9627.89453 4907.44336
105 6795.42904 9627.89453
106 6004.88009 6795.42904
107 4787.05685 6004.88009
108 1504.23888 4787.05685
109 1182.85672 1504.23888
110 2445.80789 1182.85672
111 6022.25906 2445.80789
112 1048.87690 6022.25906
113 1544.41141 1048.87690
114 -1897.63742 1544.41141
115 -1417.01958 -1897.63742
116 -5714.56840 -1417.01958
117 -9802.03390 -5714.56840
118 20718.41858 -9802.03390
119 578.88830 20718.41858
120 -3007.63671 578.88830
121 -3884.72591 -3007.63671
122 -10540.48178 -3884.72591
123 -6748.73764 -10540.48178
124 -4085.82684 -6748.73764
125 -4736.99937 -4085.82684
126 1527.24476 -4736.99937
127 3575.15556 1527.24476
128 -1467.10031 3575.15556
129 545.72716 -1467.10031
130 2247.47118 545.72716
131 -4100.05910 2247.47118
132 -834.58412 -4100.05910
133 -1976.67332 -834.58412
134 3252.57082 -1976.67332
135 -10096.68505 3252.57082
136 9195.22575 -10096.68505
137 3163.05322 9195.22575
138 7145.29735 3163.05322
139 10355.20815 7145.29735
140 4122.95229 10355.20815
141 3323.77975 4122.95229
142 -5869.47623 3323.77975
143 -12402.00651 -5869.47623
> 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/7ghgi1324323146.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/84ev21324323146.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9lsmc1324323146.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10utwo1324323146.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11746g1324323146.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/12h46b1324323146.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/13v0fe1324323146.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/14q2f71324323146.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/15jvqo1324323146.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/16amqv1324323146.tab")
+ }
>
> try(system("convert tmp/1y7wk1324323146.ps tmp/1y7wk1324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/2q26j1324323146.ps tmp/2q26j1324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ue8h1324323146.ps tmp/3ue8h1324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/48dxq1324323146.ps tmp/48dxq1324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/5urt31324323146.ps tmp/5urt31324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jqm01324323146.ps tmp/6jqm01324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ghgi1324323146.ps tmp/7ghgi1324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/84ev21324323146.ps tmp/84ev21324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lsmc1324323146.ps tmp/9lsmc1324323146.png",intern=TRUE))
character(0)
> try(system("convert tmp/10utwo1324323146.ps tmp/10utwo1324323146.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.778 0.862 6.020