R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(5
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+ ,2)
+ ,dim=c(5
+ ,164)
+ ,dimnames=list(c('Use_hands'
+ ,'Hand_on_hips'
+ ,'Quiet'
+ ,'Outgoing_individual'
+ ,'Cry')
+ ,1:164))
> y <- array(NA,dim=c(5,164),dimnames=list(c('Use_hands','Hand_on_hips','Quiet','Outgoing_individual','Cry'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = '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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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
Outgoing_individual Use_hands Hand_on_hips Quiet Cry M1 M2 M3 M4 M5 M6 M7
1 5 5 1 6 7 1 0 0 0 0 0 0
2 2 2 1 6 3 0 1 0 0 0 0 0
3 6 6 3 6 3 0 0 1 0 0 0 0
4 4 6 2 4 6 0 0 0 1 0 0 0
5 6 6 3 2 2 0 0 0 0 1 0 0
6 3 5 2 7 3 0 0 0 0 0 1 0
7 5 5 2 6 1 0 0 0 0 0 0 1
8 3 6 2 5 2 0 0 0 0 0 0 0
9 5 6 4 6 5 0 0 0 0 0 0 0
10 4 5 4 7 1 0 0 0 0 0 0 0
11 1 5 1 7 6 0 0 0 0 0 0 0
12 6 5 2 4 1 0 0 0 0 0 0 0
13 6 6 1 1 1 1 0 0 0 0 0 0
14 6 5 3 6 2 0 1 0 0 0 0 0
15 4 5 2 4 1 0 0 1 0 0 0 0
16 6 6 5 5 3 0 0 0 1 0 0 0
17 5 6 2 5 2 0 0 0 0 1 0 0
18 3 4 5 6 2 0 0 0 0 0 1 0
19 5 5 4 4 2 0 0 0 0 0 0 1
20 4 5 2 6 2 0 0 0 0 0 0 0
21 5 5 2 3 2 0 0 0 0 0 0 0
22 6 6 5 3 2 0 0 0 0 0 0 0
23 3 5 1 5 1 0 0 0 0 0 0 0
24 4 7 4 5 3 0 0 0 0 0 0 0
25 5 6 1 5 2 1 0 0 0 0 0 0
26 4 6 3 5 3 0 1 0 0 0 0 0
27 5 6 2 5 4 0 0 1 0 0 0 0
28 6 6 2 2 5 0 0 0 1 0 0 0
29 7 4 1 6 2 0 0 0 0 1 0 0
30 2 5 3 7 5 0 0 0 0 0 1 0
31 4 6 4 2 5 0 0 0 0 0 0 1
32 6 4 3 3 1 0 0 0 0 0 0 0
33 5 5 5 6 6 0 0 0 0 0 0 0
34 5 5 2 5 3 0 0 0 0 0 0 0
35 5 5 1 7 4 0 0 0 0 0 0 0
36 6 7 2 5 6 0 0 0 0 0 0 0
37 6 7 5 6 5 1 0 0 0 0 0 0
38 1 6 1 5 5 0 1 0 0 0 0 0
39 4 7 3 3 3 0 0 1 0 0 0 0
40 2 6 2 7 3 0 0 0 1 0 0 0
41 3 5 3 5 5 0 0 0 0 1 0 0
42 4 6 2 5 2 0 0 0 0 0 1 0
43 5 4 2 6 2 0 0 0 0 0 0 1
44 4 6 3 2 3 0 0 0 0 0 0 0
45 4 5 3 7 5 0 0 0 0 0 0 0
46 3 5 5 3 2 0 0 0 0 0 0 0
47 4 6 3 6 4 0 0 0 0 0 0 0
48 6 6 2 7 5 0 0 0 0 0 0 0
49 4 5 1 5 2 1 0 0 0 0 0 0
50 5 6 6 4 2 0 1 0 0 0 0 0
51 4 5 6 6 5 0 0 1 0 0 0 0
52 5 5 3 7 6 0 0 0 1 0 0 0
53 6 5 5 2 6 0 0 0 0 1 0 0
54 6 6 4 2 5 0 0 0 0 0 1 0
55 4 6 3 2 4 0 0 0 0 0 0 1
56 4 5 2 5 3 0 0 0 0 0 0 0
57 6 7 7 2 7 0 0 0 0 0 0 0
58 4 6 2 5 7 0 0 0 0 0 0 0
59 2 5 2 6 5 0 0 0 0 0 0 0
60 6 5 2 2 2 0 0 0 0 0 0 0
61 5 6 2 4 6 1 0 0 0 0 0 0
62 6 5 3 6 6 0 1 0 0 0 0 0
63 6 5 5 4 4 0 0 1 0 0 0 0
64 5 6 2 3 5 0 0 0 1 0 0 0
65 5 6 5 3 2 0 0 0 0 1 0 0
66 5 3 2 3 6 0 0 0 0 0 1 0
67 5 5 1 6 3 0 0 0 0 0 0 1
68 3 5 3 6 2 0 0 0 0 0 0 0
69 4 6 4 5 2 0 0 0 0 0 0 0
70 1 5 2 3 5 0 0 0 0 0 0 0
71 5 5 4 3 3 0 0 0 0 0 0 0
72 2 4 4 2 4 0 0 0 0 0 0 0
73 6 5 3 3 5 1 0 0 0 0 0 0
74 5 5 2 3 7 0 1 0 0 0 0 0
75 2 2 1 5 2 0 0 1 0 0 0 0
76 6 6 5 3 5 0 0 0 1 0 0 0
77 5 6 2 5 6 0 0 0 0 1 0 0
78 6 6 4 2 4 0 0 0 0 0 1 0
79 3 6 4 5 3 0 0 0 0 0 0 1
80 4 5 4 6 6 0 0 0 0 0 0 0
81 4 5 2 6 5 0 0 0 0 0 0 0
82 4 6 2 5 2 0 0 0 0 0 0 0
83 4 5 2 2 5 0 0 0 0 0 0 0
84 5 5 2 6 3 0 0 0 0 0 0 0
85 2 6 3 7 6 1 0 0 0 0 0 0
86 3 3 5 5 5 0 1 0 0 0 0 0
87 5 6 1 5 1 0 0 1 0 0 0 0
88 6 3 2 2 5 0 0 0 1 0 0 0
89 5 5 2 5 2 0 0 0 0 1 0 0
90 6 5 2 6 1 0 0 0 0 0 1 0
91 3 6 5 5 4 0 0 0 0 0 0 1
92 4 5 2 5 2 0 0 0 0 0 0 0
93 4 6 1 4 3 0 0 0 0 0 0 0
94 3 6 2 5 5 0 0 0 0 0 0 0
95 4 6 1 4 6 0 0 0 0 0 0 0
96 4 7 6 2 4 0 0 0 0 0 0 0
97 4 5 2 3 4 1 0 0 0 0 0 0
98 2 3 1 5 5 0 1 0 0 0 0 0
99 6 4 1 2 1 0 0 1 0 0 0 0
100 3 7 6 2 6 0 0 0 1 0 0 0
101 5 6 1 4 2 0 0 0 0 1 0 0
102 5 6 2 3 3 0 0 0 0 0 1 0
103 5 5 2 5 5 0 0 0 0 0 0 1
104 5 4 1 5 2 0 0 0 0 0 0 0
105 4 6 2 2 2 0 0 0 0 0 0 0
106 2 6 1 5 3 0 0 0 0 0 0 0
107 5 6 1 2 2 0 0 0 0 0 0 0
108 3 5 3 6 6 0 0 0 0 0 0 0
109 6 6 5 2 3 1 0 0 0 0 0 0
110 6 6 2 1 2 0 1 0 0 0 0 0
111 1 2 1 6 1 0 0 1 0 0 0 0
112 7 6 3 2 1 0 0 0 1 0 0 0
113 5 5 2 3 1 0 0 0 0 1 0 0
114 6 5 4 5 4 0 0 0 0 0 1 0
115 6 3 2 4 1 0 0 0 0 0 0 1
116 6 4 5 4 1 0 0 0 0 0 0 0
117 3 6 1 6 1 0 0 0 0 0 0 0
118 6 5 2 2 5 0 0 0 0 0 0 0
119 7 6 2 7 5 0 0 0 0 0 0 0
120 6 4 1 2 2 0 0 0 0 0 0 0
121 5 6 2 5 3 1 0 0 0 0 0 0
122 5 4 2 3 5 0 1 0 0 0 0 0
123 5 3 5 3 2 0 0 1 0 0 0 0
124 5 6 2 5 2 0 0 0 1 0 0 0
125 4 5 5 5 4 0 0 0 0 1 0 0
126 6 5 2 2 1 0 0 0 0 0 1 0
127 4 7 5 4 5 0 0 0 0 0 0 1
128 6 6 1 3 1 0 0 0 0 0 0 0
129 6 6 3 2 2 0 0 0 0 0 0 0
130 4 5 2 6 2 0 0 0 0 0 0 0
131 3 6 1 6 6 0 0 0 0 0 0 0
132 5 6 2 3 2 0 0 0 0 0 0 0
133 7 5 1 2 1 1 0 0 0 0 0 0
134 3 2 2 6 3 0 1 0 0 0 0 0
135 4 5 2 6 4 0 0 1 0 0 0 0
136 2 3 2 2 6 0 0 0 1 0 0 0
137 4 6 4 5 5 0 0 0 0 1 0 0
138 4 5 5 6 6 0 0 0 0 0 1 0
139 3 5 2 5 6 0 0 0 0 0 0 1
140 2 5 3 3 1 0 0 0 0 0 0 0
141 5 2 2 7 6 0 0 0 0 0 0 0
142 5 5 1 5 2 0 0 0 0 0 0 0
143 4 5 2 4 2 0 0 0 0 0 0 0
144 6 6 2 5 7 0 0 0 0 0 0 0
145 5 6 2 3 2 1 0 0 0 0 0 0
146 1 5 2 2 2 0 1 0 0 0 0 0
147 5 5 2 5 6 0 0 1 0 0 0 0
148 6 3 4 6 1 0 0 0 1 0 0 0
149 5 5 2 5 2 0 0 0 0 1 0 0
150 5 6 4 2 3 0 0 0 0 0 1 0
151 5 6 5 3 4 0 0 0 0 0 0 1
152 5 6 3 2 5 0 0 0 0 0 0 0
153 4 6 4 6 5 0 0 0 0 0 0 0
154 7 6 4 6 6 0 0 0 0 0 0 0
155 6 5 2 2 3 0 0 0 0 0 0 0
156 5 7 3 2 1 0 0 0 0 0 0 0
157 6 6 2 3 3 1 0 0 0 0 0 0
158 3 6 1 4 2 0 1 0 0 0 0 0
159 5 6 2 6 3 0 0 1 0 0 0 0
160 6 7 3 2 7 0 0 0 1 0 0 0
161 1 1 3 7 3 0 0 0 0 1 0 0
162 6 6 2 2 4 0 0 0 0 0 1 0
163 4 5 5 2 6 0 0 0 0 0 0 1
164 5 6 1 4 2 0 0 0 0 0 0 0
M8 M9 M10 M11 t
1 0 0 0 0 1
2 0 0 0 0 2
3 0 0 0 0 3
4 0 0 0 0 4
5 0 0 0 0 5
6 0 0 0 0 6
7 0 0 0 0 7
8 1 0 0 0 8
9 0 1 0 0 9
10 0 0 1 0 10
11 0 0 0 1 11
12 0 0 0 0 12
13 0 0 0 0 13
14 0 0 0 0 14
15 0 0 0 0 15
16 0 0 0 0 16
17 0 0 0 0 17
18 0 0 0 0 18
19 0 0 0 0 19
20 1 0 0 0 20
21 0 1 0 0 21
22 0 0 1 0 22
23 0 0 0 1 23
24 0 0 0 0 24
25 0 0 0 0 25
26 0 0 0 0 26
27 0 0 0 0 27
28 0 0 0 0 28
29 0 0 0 0 29
30 0 0 0 0 30
31 0 0 0 0 31
32 1 0 0 0 32
33 0 1 0 0 33
34 0 0 1 0 34
35 0 0 0 1 35
36 0 0 0 0 36
37 0 0 0 0 37
38 0 0 0 0 38
39 0 0 0 0 39
40 0 0 0 0 40
41 0 0 0 0 41
42 0 0 0 0 42
43 0 0 0 0 43
44 1 0 0 0 44
45 0 1 0 0 45
46 0 0 1 0 46
47 0 0 0 1 47
48 0 0 0 0 48
49 0 0 0 0 49
50 0 0 0 0 50
51 0 0 0 0 51
52 0 0 0 0 52
53 0 0 0 0 53
54 0 0 0 0 54
55 0 0 0 0 55
56 1 0 0 0 56
57 0 1 0 0 57
58 0 0 1 0 58
59 0 0 0 1 59
60 0 0 0 0 60
61 0 0 0 0 61
62 0 0 0 0 62
63 0 0 0 0 63
64 0 0 0 0 64
65 0 0 0 0 65
66 0 0 0 0 66
67 0 0 0 0 67
68 1 0 0 0 68
69 0 1 0 0 69
70 0 0 1 0 70
71 0 0 0 1 71
72 0 0 0 0 72
73 0 0 0 0 73
74 0 0 0 0 74
75 0 0 0 0 75
76 0 0 0 0 76
77 0 0 0 0 77
78 0 0 0 0 78
79 0 0 0 0 79
80 1 0 0 0 80
81 0 1 0 0 81
82 0 0 1 0 82
83 0 0 0 1 83
84 0 0 0 0 84
85 0 0 0 0 85
86 0 0 0 0 86
87 0 0 0 0 87
88 0 0 0 0 88
89 0 0 0 0 89
90 0 0 0 0 90
91 0 0 0 0 91
92 1 0 0 0 92
93 0 1 0 0 93
94 0 0 1 0 94
95 0 0 0 1 95
96 0 0 0 0 96
97 0 0 0 0 97
98 0 0 0 0 98
99 0 0 0 0 99
100 0 0 0 0 100
101 0 0 0 0 101
102 0 0 0 0 102
103 0 0 0 0 103
104 1 0 0 0 104
105 0 1 0 0 105
106 0 0 1 0 106
107 0 0 0 1 107
108 0 0 0 0 108
109 0 0 0 0 109
110 0 0 0 0 110
111 0 0 0 0 111
112 0 0 0 0 112
113 0 0 0 0 113
114 0 0 0 0 114
115 0 0 0 0 115
116 1 0 0 0 116
117 0 1 0 0 117
118 0 0 1 0 118
119 0 0 0 1 119
120 0 0 0 0 120
121 0 0 0 0 121
122 0 0 0 0 122
123 0 0 0 0 123
124 0 0 0 0 124
125 0 0 0 0 125
126 0 0 0 0 126
127 0 0 0 0 127
128 1 0 0 0 128
129 0 1 0 0 129
130 0 0 1 0 130
131 0 0 0 1 131
132 0 0 0 0 132
133 0 0 0 0 133
134 0 0 0 0 134
135 0 0 0 0 135
136 0 0 0 0 136
137 0 0 0 0 137
138 0 0 0 0 138
139 0 0 0 0 139
140 1 0 0 0 140
141 0 1 0 0 141
142 0 0 1 0 142
143 0 0 0 1 143
144 0 0 0 0 144
145 0 0 0 0 145
146 0 0 0 0 146
147 0 0 0 0 147
148 0 0 0 0 148
149 0 0 0 0 149
150 0 0 0 0 150
151 0 0 0 0 151
152 1 0 0 0 152
153 0 1 0 0 153
154 0 0 1 0 154
155 0 0 0 1 155
156 0 0 0 0 156
157 0 0 0 0 157
158 0 0 0 0 158
159 0 0 0 0 159
160 0 0 0 0 160
161 0 0 0 0 161
162 0 0 0 0 162
163 0 0 0 0 163
164 1 0 0 0 164
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Use_hands Hand_on_hips Quiet Cry
4.6380430 0.1982770 0.0495029 -0.1818638 -0.0854799
M1 M2 M3 M4 M5
0.2452268 -0.8776618 -0.2217177 0.0719584 -0.0352654
M6 M7 M8 M9 M10
-0.0246671 -0.4532720 -0.4964190 -0.1697430 -0.5994069
M11 t
-0.5596384 0.0004647
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.38393 -0.81614 0.09927 0.91287 3.27807
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6380430 0.8590930 5.399 2.63e-07 ***
Use_hands 0.1982770 0.1046593 1.894 0.06012 .
Hand_on_hips 0.0495029 0.0829987 0.596 0.55181
Quiet -0.1818638 0.0693883 -2.621 0.00969 **
Cry -0.0854799 0.0641694 -1.332 0.18489
M1 0.2452268 0.5210226 0.471 0.63858
M2 -0.8776618 0.5319688 -1.650 0.10111
M3 -0.2217177 0.5300120 -0.418 0.67632
M4 0.0719584 0.5240041 0.137 0.89096
M5 -0.0352654 0.5248656 -0.067 0.94652
M6 -0.0246671 0.5230956 -0.047 0.96245
M7 -0.4532720 0.5241019 -0.865 0.38853
M8 -0.4964190 0.5270134 -0.942 0.34776
M9 -0.1697430 0.5333386 -0.318 0.75074
M10 -0.5994069 0.5309703 -1.129 0.26078
M11 -0.5596384 0.5369328 -1.042 0.29899
t 0.0004647 0.0023194 0.200 0.84147
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.348 on 147 degrees of freedom
Multiple R-squared: 0.1779, Adjusted R-squared: 0.08842
F-statistic: 1.988 on 16 and 147 DF, p-value: 0.01733
> 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,] 0.2674014 0.5348027 0.7325986
[2,] 0.2456511 0.4913022 0.7543489
[3,] 0.1676649 0.3353298 0.8323351
[4,] 0.1326959 0.2653917 0.8673041
[5,] 0.4264423 0.8528845 0.5735577
[6,] 0.3384713 0.6769426 0.6615287
[7,] 0.2607998 0.5215995 0.7392002
[8,] 0.2216062 0.4432124 0.7783938
[9,] 0.2127423 0.4254846 0.7872577
[10,] 0.3047993 0.6095987 0.6952007
[11,] 0.2667471 0.5334942 0.7332529
[12,] 0.2142878 0.4285756 0.7857122
[13,] 0.2335793 0.4671587 0.7664207
[14,] 0.1795332 0.3590665 0.8204668
[15,] 0.1411090 0.2822180 0.8588910
[16,] 0.2312182 0.4624365 0.7687818
[17,] 0.2225372 0.4450745 0.7774628
[18,] 0.1805989 0.3611978 0.8194011
[19,] 0.3297671 0.6595341 0.6702329
[20,] 0.3144672 0.6289345 0.6855328
[21,] 0.5366869 0.9266261 0.4633131
[22,] 0.6437522 0.7124956 0.3562478
[23,] 0.6348681 0.7302637 0.3651319
[24,] 0.5959233 0.8081534 0.4040767
[25,] 0.5436697 0.9126606 0.4563303
[26,] 0.4866258 0.9732516 0.5133742
[27,] 0.5435522 0.9128956 0.4564478
[28,] 0.4979349 0.9958698 0.5020651
[29,] 0.5009926 0.9980149 0.4990074
[30,] 0.4854165 0.9708329 0.5145835
[31,] 0.4425004 0.8850009 0.5574996
[32,] 0.3949470 0.7898940 0.6050530
[33,] 0.3711873 0.7423747 0.6288127
[34,] 0.3419568 0.6839137 0.6580432
[35,] 0.4068974 0.8137948 0.5931026
[36,] 0.3698235 0.7396470 0.6301765
[37,] 0.3197441 0.6394883 0.6802559
[38,] 0.2859274 0.5718548 0.7140726
[39,] 0.2481083 0.4962167 0.7518917
[40,] 0.2502100 0.5004201 0.7497900
[41,] 0.2202337 0.4404674 0.7797663
[42,] 0.1827461 0.3654921 0.8172539
[43,] 0.3044576 0.6089153 0.6955424
[44,] 0.3030538 0.6061076 0.6969462
[45,] 0.2593950 0.5187901 0.7406050
[46,] 0.2378988 0.4757977 0.7621012
[47,] 0.2164080 0.4328161 0.7835920
[48,] 0.2002663 0.4005325 0.7997337
[49,] 0.1846054 0.3692108 0.8153946
[50,] 0.1630378 0.3260756 0.8369622
[51,] 0.3223564 0.6447128 0.6776436
[52,] 0.2918979 0.5837957 0.7081021
[53,] 0.5272454 0.9455093 0.4727546
[54,] 0.5010678 0.9978644 0.4989322
[55,] 0.5066465 0.9867070 0.4933535
[56,] 0.5351196 0.9297609 0.4648804
[57,] 0.5113718 0.9772565 0.4886282
[58,] 0.4800388 0.9600776 0.5199612
[59,] 0.4622651 0.9245302 0.5377349
[60,] 0.4630329 0.9260658 0.5369671
[61,] 0.4180924 0.8361849 0.5819076
[62,] 0.3722217 0.7444435 0.6277783
[63,] 0.3315443 0.6630886 0.6684557
[64,] 0.2946552 0.5893103 0.7053448
[65,] 0.2642039 0.5284079 0.7357961
[66,] 0.3435700 0.6871400 0.6564300
[67,] 0.3082451 0.6164902 0.6917549
[68,] 0.2753237 0.5506475 0.7246763
[69,] 0.2797690 0.5595381 0.7202310
[70,] 0.2515559 0.5031118 0.7484441
[71,] 0.2667826 0.5335652 0.7332174
[72,] 0.2563919 0.5127839 0.7436081
[73,] 0.2185325 0.4370651 0.7814675
[74,] 0.1882446 0.3764892 0.8117554
[75,] 0.1735242 0.3470485 0.8264758
[76,] 0.1489527 0.2979054 0.8510473
[77,] 0.1516813 0.3033626 0.8483187
[78,] 0.1418067 0.2836133 0.8581933
[79,] 0.1279703 0.2559406 0.8720297
[80,] 0.1246914 0.2493827 0.8753086
[81,] 0.1963430 0.3926859 0.8036570
[82,] 0.1679323 0.3358646 0.8320677
[83,] 0.1409683 0.2819366 0.8590317
[84,] 0.1297827 0.2595653 0.8702173
[85,] 0.1187778 0.2375555 0.8812222
[86,] 0.1103887 0.2207775 0.8896113
[87,] 0.1962603 0.3925207 0.8037397
[88,] 0.1751474 0.3502947 0.8248526
[89,] 0.1922939 0.3845878 0.8077061
[90,] 0.1685962 0.3371924 0.8314038
[91,] 0.1696324 0.3392647 0.8303676
[92,] 0.3138691 0.6277382 0.6861309
[93,] 0.3095159 0.6190318 0.6904841
[94,] 0.2713042 0.5426085 0.7286958
[95,] 0.2543434 0.5086867 0.7456566
[96,] 0.3212857 0.6425714 0.6787143
[97,] 0.3230560 0.6461119 0.6769440
[98,] 0.3578832 0.7157663 0.6421168
[99,] 0.3390182 0.6780365 0.6609818
[100,] 0.5141702 0.9716596 0.4858298
[101,] 0.4779896 0.9559792 0.5220104
[102,] 0.4287369 0.8574739 0.5712631
[103,] 0.5307363 0.9385274 0.4692637
[104,] 0.4876492 0.9752985 0.5123508
[105,] 0.4235823 0.8471646 0.5764177
[106,] 0.3790818 0.7581636 0.6209182
[107,] 0.3509323 0.7018646 0.6490677
[108,] 0.2933214 0.5866428 0.7066786
[109,] 0.3501628 0.7003256 0.6498372
[110,] 0.3403470 0.6806940 0.6596530
[111,] 0.3057969 0.6115938 0.6942031
[112,] 0.3378097 0.6756195 0.6621903
[113,] 0.2730916 0.5461833 0.7269084
[114,] 0.3801707 0.7603413 0.6198293
[115,] 0.4903970 0.9807939 0.5096030
[116,] 0.4058560 0.8117121 0.5941440
[117,] 0.5109928 0.9780143 0.4890072
[118,] 0.4328373 0.8656746 0.5671627
[119,] 0.3581856 0.7163712 0.6418144
[120,] 0.4047950 0.8095900 0.5952050
[121,] 0.4564389 0.9128777 0.5435611
[122,] 0.5509769 0.8980462 0.4490231
[123,] 0.5033655 0.9932690 0.4966345
[124,] 0.7123824 0.5752353 0.2876176
[125,] 0.5605378 0.8789243 0.4394622
> postscript(file="/var/www/html/rcomp/tmp/1hjyo1290533524.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rsf91290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 164
Frequency = 1
1 2 3 4 5 6
0.764919603 -0.859745153 1.591732356 -0.760193436 0.591415614 -1.177068765
7 8 9 10 11 12
0.898247841 -1.353730706 0.658426180 0.125846523 -2.338478501 1.078924738
13 14 15 16 17 18
0.138867857 2.355361633 -0.700751689 1.011145294 0.180933224 -1.400220824
19 20 21 22 23 24
0.515417756 0.020833413 0.148101396 1.230514864 -1.135182098 -1.069388076
25 26 27 28 29 30
-0.053773794 0.055124191 0.533698185 0.779445926 2.803277222 -2.066765114
31 32 33 34 35 36
-0.795723669 1.532959657 0.881526899 1.020931396 1.479408502 1.280480828
37 38 39 40 41 42
0.982664407 -2.680486797 -1.168865724 -2.487771633 -1.425006274 -0.841282969
43 44 45 46 47 48
1.165274871 -0.880074853 0.071340004 -1.582361390 -0.005314667 1.751428822
49 50 51 52 53 54
-0.866650048 0.628118565 -0.209846084 0.911865476 1.010299876 0.764982963
55 56 57 58 59 60
-0.842853856 -0.092280283 0.732838756 0.153420788 -1.677631505 0.778370669
61 62 63 64 65 66
0.040049267 2.674974771 1.384872803 -0.055420129 -0.353609491 0.720586730
67 68 69 70 71 72
1.090827507 -1.050975933 -0.807760295 -3.188566174 0.501234970 -2.956975032
73 74 75 76 77 78
0.915903044 1.258789675 -1.816957268 0.790494510 0.494969755 0.668349857
79 80 81 82 83 84
-1.443398580 0.235864108 -0.077750665 -0.285131883 -0.416239784 0.580152412
85 86 87 88 89 90
-2.475015566 -0.305974018 0.298878394 1.346393771 0.345750543 1.431071438
91 92 93 94 95 96
-1.412998214 -0.194489995 -0.766788629 -1.034268816 -0.121383008 -1.661964962
97 98 99 100 101 102
-1.131227143 -1.113538951 1.144264408 -2.564822457 0.009536128 -0.147413645
103 104 105 106 107 108
1.013690785 1.047713276 -1.271075578 -2.161302287 0.167393287 -1.224064046
109 110 111 112 113 114
0.249066878 1.252655904 -2.737303214 1.348987194 -0.114610095 1.395488295
115 116 117 118 119 120
1.880884766 1.576781339 -1.585174093 1.607263632 3.278072267 0.998267513
121 122 123 124 125 126
-0.062409676 1.263800424 0.400720139 0.023984695 -0.648528250 0.686886552
127 128 129 130 131 132
-0.724388870 1.190798719 0.668268290 0.072702415 -0.774385296 -0.271502166
133 134 135 136 137 138
1.463242514 0.029409253 -0.136350551 -2.590432765 -0.717399007 -0.312344287
139 140 141 142 143 144
-0.917559144 -2.715506765 1.756540838 0.934764961 -0.336834960 1.514048217
145 146 147 148 149 150
-0.522770314 -3.383933190 0.847168859 1.605040399 0.317867508 -0.450589675
151 152 153 154 155 156
0.195391219 0.240695468 -0.408493103 3.106185971 1.379340792 -0.797778916
157 158 159 160 161 162
0.557132971 -1.174556306 0.568739384 0.641283155 -2.494896754 0.728319446
163 164
-0.622812412 0.441412556
> postscript(file="/var/www/html/rcomp/tmp/621ec1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 0.764919603 NA
1 -0.859745153 0.764919603
2 1.591732356 -0.859745153
3 -0.760193436 1.591732356
4 0.591415614 -0.760193436
5 -1.177068765 0.591415614
6 0.898247841 -1.177068765
7 -1.353730706 0.898247841
8 0.658426180 -1.353730706
9 0.125846523 0.658426180
10 -2.338478501 0.125846523
11 1.078924738 -2.338478501
12 0.138867857 1.078924738
13 2.355361633 0.138867857
14 -0.700751689 2.355361633
15 1.011145294 -0.700751689
16 0.180933224 1.011145294
17 -1.400220824 0.180933224
18 0.515417756 -1.400220824
19 0.020833413 0.515417756
20 0.148101396 0.020833413
21 1.230514864 0.148101396
22 -1.135182098 1.230514864
23 -1.069388076 -1.135182098
24 -0.053773794 -1.069388076
25 0.055124191 -0.053773794
26 0.533698185 0.055124191
27 0.779445926 0.533698185
28 2.803277222 0.779445926
29 -2.066765114 2.803277222
30 -0.795723669 -2.066765114
31 1.532959657 -0.795723669
32 0.881526899 1.532959657
33 1.020931396 0.881526899
34 1.479408502 1.020931396
35 1.280480828 1.479408502
36 0.982664407 1.280480828
37 -2.680486797 0.982664407
38 -1.168865724 -2.680486797
39 -2.487771633 -1.168865724
40 -1.425006274 -2.487771633
41 -0.841282969 -1.425006274
42 1.165274871 -0.841282969
43 -0.880074853 1.165274871
44 0.071340004 -0.880074853
45 -1.582361390 0.071340004
46 -0.005314667 -1.582361390
47 1.751428822 -0.005314667
48 -0.866650048 1.751428822
49 0.628118565 -0.866650048
50 -0.209846084 0.628118565
51 0.911865476 -0.209846084
52 1.010299876 0.911865476
53 0.764982963 1.010299876
54 -0.842853856 0.764982963
55 -0.092280283 -0.842853856
56 0.732838756 -0.092280283
57 0.153420788 0.732838756
58 -1.677631505 0.153420788
59 0.778370669 -1.677631505
60 0.040049267 0.778370669
61 2.674974771 0.040049267
62 1.384872803 2.674974771
63 -0.055420129 1.384872803
64 -0.353609491 -0.055420129
65 0.720586730 -0.353609491
66 1.090827507 0.720586730
67 -1.050975933 1.090827507
68 -0.807760295 -1.050975933
69 -3.188566174 -0.807760295
70 0.501234970 -3.188566174
71 -2.956975032 0.501234970
72 0.915903044 -2.956975032
73 1.258789675 0.915903044
74 -1.816957268 1.258789675
75 0.790494510 -1.816957268
76 0.494969755 0.790494510
77 0.668349857 0.494969755
78 -1.443398580 0.668349857
79 0.235864108 -1.443398580
80 -0.077750665 0.235864108
81 -0.285131883 -0.077750665
82 -0.416239784 -0.285131883
83 0.580152412 -0.416239784
84 -2.475015566 0.580152412
85 -0.305974018 -2.475015566
86 0.298878394 -0.305974018
87 1.346393771 0.298878394
88 0.345750543 1.346393771
89 1.431071438 0.345750543
90 -1.412998214 1.431071438
91 -0.194489995 -1.412998214
92 -0.766788629 -0.194489995
93 -1.034268816 -0.766788629
94 -0.121383008 -1.034268816
95 -1.661964962 -0.121383008
96 -1.131227143 -1.661964962
97 -1.113538951 -1.131227143
98 1.144264408 -1.113538951
99 -2.564822457 1.144264408
100 0.009536128 -2.564822457
101 -0.147413645 0.009536128
102 1.013690785 -0.147413645
103 1.047713276 1.013690785
104 -1.271075578 1.047713276
105 -2.161302287 -1.271075578
106 0.167393287 -2.161302287
107 -1.224064046 0.167393287
108 0.249066878 -1.224064046
109 1.252655904 0.249066878
110 -2.737303214 1.252655904
111 1.348987194 -2.737303214
112 -0.114610095 1.348987194
113 1.395488295 -0.114610095
114 1.880884766 1.395488295
115 1.576781339 1.880884766
116 -1.585174093 1.576781339
117 1.607263632 -1.585174093
118 3.278072267 1.607263632
119 0.998267513 3.278072267
120 -0.062409676 0.998267513
121 1.263800424 -0.062409676
122 0.400720139 1.263800424
123 0.023984695 0.400720139
124 -0.648528250 0.023984695
125 0.686886552 -0.648528250
126 -0.724388870 0.686886552
127 1.190798719 -0.724388870
128 0.668268290 1.190798719
129 0.072702415 0.668268290
130 -0.774385296 0.072702415
131 -0.271502166 -0.774385296
132 1.463242514 -0.271502166
133 0.029409253 1.463242514
134 -0.136350551 0.029409253
135 -2.590432765 -0.136350551
136 -0.717399007 -2.590432765
137 -0.312344287 -0.717399007
138 -0.917559144 -0.312344287
139 -2.715506765 -0.917559144
140 1.756540838 -2.715506765
141 0.934764961 1.756540838
142 -0.336834960 0.934764961
143 1.514048217 -0.336834960
144 -0.522770314 1.514048217
145 -3.383933190 -0.522770314
146 0.847168859 -3.383933190
147 1.605040399 0.847168859
148 0.317867508 1.605040399
149 -0.450589675 0.317867508
150 0.195391219 -0.450589675
151 0.240695468 0.195391219
152 -0.408493103 0.240695468
153 3.106185971 -0.408493103
154 1.379340792 3.106185971
155 -0.797778916 1.379340792
156 0.557132971 -0.797778916
157 -1.174556306 0.557132971
158 0.568739384 -1.174556306
159 0.641283155 0.568739384
160 -2.494896754 0.641283155
161 0.728319446 -2.494896754
162 -0.622812412 0.728319446
163 0.441412556 -0.622812412
164 NA 0.441412556
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.859745153 0.764919603
[2,] 1.591732356 -0.859745153
[3,] -0.760193436 1.591732356
[4,] 0.591415614 -0.760193436
[5,] -1.177068765 0.591415614
[6,] 0.898247841 -1.177068765
[7,] -1.353730706 0.898247841
[8,] 0.658426180 -1.353730706
[9,] 0.125846523 0.658426180
[10,] -2.338478501 0.125846523
[11,] 1.078924738 -2.338478501
[12,] 0.138867857 1.078924738
[13,] 2.355361633 0.138867857
[14,] -0.700751689 2.355361633
[15,] 1.011145294 -0.700751689
[16,] 0.180933224 1.011145294
[17,] -1.400220824 0.180933224
[18,] 0.515417756 -1.400220824
[19,] 0.020833413 0.515417756
[20,] 0.148101396 0.020833413
[21,] 1.230514864 0.148101396
[22,] -1.135182098 1.230514864
[23,] -1.069388076 -1.135182098
[24,] -0.053773794 -1.069388076
[25,] 0.055124191 -0.053773794
[26,] 0.533698185 0.055124191
[27,] 0.779445926 0.533698185
[28,] 2.803277222 0.779445926
[29,] -2.066765114 2.803277222
[30,] -0.795723669 -2.066765114
[31,] 1.532959657 -0.795723669
[32,] 0.881526899 1.532959657
[33,] 1.020931396 0.881526899
[34,] 1.479408502 1.020931396
[35,] 1.280480828 1.479408502
[36,] 0.982664407 1.280480828
[37,] -2.680486797 0.982664407
[38,] -1.168865724 -2.680486797
[39,] -2.487771633 -1.168865724
[40,] -1.425006274 -2.487771633
[41,] -0.841282969 -1.425006274
[42,] 1.165274871 -0.841282969
[43,] -0.880074853 1.165274871
[44,] 0.071340004 -0.880074853
[45,] -1.582361390 0.071340004
[46,] -0.005314667 -1.582361390
[47,] 1.751428822 -0.005314667
[48,] -0.866650048 1.751428822
[49,] 0.628118565 -0.866650048
[50,] -0.209846084 0.628118565
[51,] 0.911865476 -0.209846084
[52,] 1.010299876 0.911865476
[53,] 0.764982963 1.010299876
[54,] -0.842853856 0.764982963
[55,] -0.092280283 -0.842853856
[56,] 0.732838756 -0.092280283
[57,] 0.153420788 0.732838756
[58,] -1.677631505 0.153420788
[59,] 0.778370669 -1.677631505
[60,] 0.040049267 0.778370669
[61,] 2.674974771 0.040049267
[62,] 1.384872803 2.674974771
[63,] -0.055420129 1.384872803
[64,] -0.353609491 -0.055420129
[65,] 0.720586730 -0.353609491
[66,] 1.090827507 0.720586730
[67,] -1.050975933 1.090827507
[68,] -0.807760295 -1.050975933
[69,] -3.188566174 -0.807760295
[70,] 0.501234970 -3.188566174
[71,] -2.956975032 0.501234970
[72,] 0.915903044 -2.956975032
[73,] 1.258789675 0.915903044
[74,] -1.816957268 1.258789675
[75,] 0.790494510 -1.816957268
[76,] 0.494969755 0.790494510
[77,] 0.668349857 0.494969755
[78,] -1.443398580 0.668349857
[79,] 0.235864108 -1.443398580
[80,] -0.077750665 0.235864108
[81,] -0.285131883 -0.077750665
[82,] -0.416239784 -0.285131883
[83,] 0.580152412 -0.416239784
[84,] -2.475015566 0.580152412
[85,] -0.305974018 -2.475015566
[86,] 0.298878394 -0.305974018
[87,] 1.346393771 0.298878394
[88,] 0.345750543 1.346393771
[89,] 1.431071438 0.345750543
[90,] -1.412998214 1.431071438
[91,] -0.194489995 -1.412998214
[92,] -0.766788629 -0.194489995
[93,] -1.034268816 -0.766788629
[94,] -0.121383008 -1.034268816
[95,] -1.661964962 -0.121383008
[96,] -1.131227143 -1.661964962
[97,] -1.113538951 -1.131227143
[98,] 1.144264408 -1.113538951
[99,] -2.564822457 1.144264408
[100,] 0.009536128 -2.564822457
[101,] -0.147413645 0.009536128
[102,] 1.013690785 -0.147413645
[103,] 1.047713276 1.013690785
[104,] -1.271075578 1.047713276
[105,] -2.161302287 -1.271075578
[106,] 0.167393287 -2.161302287
[107,] -1.224064046 0.167393287
[108,] 0.249066878 -1.224064046
[109,] 1.252655904 0.249066878
[110,] -2.737303214 1.252655904
[111,] 1.348987194 -2.737303214
[112,] -0.114610095 1.348987194
[113,] 1.395488295 -0.114610095
[114,] 1.880884766 1.395488295
[115,] 1.576781339 1.880884766
[116,] -1.585174093 1.576781339
[117,] 1.607263632 -1.585174093
[118,] 3.278072267 1.607263632
[119,] 0.998267513 3.278072267
[120,] -0.062409676 0.998267513
[121,] 1.263800424 -0.062409676
[122,] 0.400720139 1.263800424
[123,] 0.023984695 0.400720139
[124,] -0.648528250 0.023984695
[125,] 0.686886552 -0.648528250
[126,] -0.724388870 0.686886552
[127,] 1.190798719 -0.724388870
[128,] 0.668268290 1.190798719
[129,] 0.072702415 0.668268290
[130,] -0.774385296 0.072702415
[131,] -0.271502166 -0.774385296
[132,] 1.463242514 -0.271502166
[133,] 0.029409253 1.463242514
[134,] -0.136350551 0.029409253
[135,] -2.590432765 -0.136350551
[136,] -0.717399007 -2.590432765
[137,] -0.312344287 -0.717399007
[138,] -0.917559144 -0.312344287
[139,] -2.715506765 -0.917559144
[140,] 1.756540838 -2.715506765
[141,] 0.934764961 1.756540838
[142,] -0.336834960 0.934764961
[143,] 1.514048217 -0.336834960
[144,] -0.522770314 1.514048217
[145,] -3.383933190 -0.522770314
[146,] 0.847168859 -3.383933190
[147,] 1.605040399 0.847168859
[148,] 0.317867508 1.605040399
[149,] -0.450589675 0.317867508
[150,] 0.195391219 -0.450589675
[151,] 0.240695468 0.195391219
[152,] -0.408493103 0.240695468
[153,] 3.106185971 -0.408493103
[154,] 1.379340792 3.106185971
[155,] -0.797778916 1.379340792
[156,] 0.557132971 -0.797778916
[157,] -1.174556306 0.557132971
[158,] 0.568739384 -1.174556306
[159,] 0.641283155 0.568739384
[160,] -2.494896754 0.641283155
[161,] 0.728319446 -2.494896754
[162,] -0.622812412 0.728319446
[163,] 0.441412556 -0.622812412
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.859745153 0.764919603
2 1.591732356 -0.859745153
3 -0.760193436 1.591732356
4 0.591415614 -0.760193436
5 -1.177068765 0.591415614
6 0.898247841 -1.177068765
7 -1.353730706 0.898247841
8 0.658426180 -1.353730706
9 0.125846523 0.658426180
10 -2.338478501 0.125846523
11 1.078924738 -2.338478501
12 0.138867857 1.078924738
13 2.355361633 0.138867857
14 -0.700751689 2.355361633
15 1.011145294 -0.700751689
16 0.180933224 1.011145294
17 -1.400220824 0.180933224
18 0.515417756 -1.400220824
19 0.020833413 0.515417756
20 0.148101396 0.020833413
21 1.230514864 0.148101396
22 -1.135182098 1.230514864
23 -1.069388076 -1.135182098
24 -0.053773794 -1.069388076
25 0.055124191 -0.053773794
26 0.533698185 0.055124191
27 0.779445926 0.533698185
28 2.803277222 0.779445926
29 -2.066765114 2.803277222
30 -0.795723669 -2.066765114
31 1.532959657 -0.795723669
32 0.881526899 1.532959657
33 1.020931396 0.881526899
34 1.479408502 1.020931396
35 1.280480828 1.479408502
36 0.982664407 1.280480828
37 -2.680486797 0.982664407
38 -1.168865724 -2.680486797
39 -2.487771633 -1.168865724
40 -1.425006274 -2.487771633
41 -0.841282969 -1.425006274
42 1.165274871 -0.841282969
43 -0.880074853 1.165274871
44 0.071340004 -0.880074853
45 -1.582361390 0.071340004
46 -0.005314667 -1.582361390
47 1.751428822 -0.005314667
48 -0.866650048 1.751428822
49 0.628118565 -0.866650048
50 -0.209846084 0.628118565
51 0.911865476 -0.209846084
52 1.010299876 0.911865476
53 0.764982963 1.010299876
54 -0.842853856 0.764982963
55 -0.092280283 -0.842853856
56 0.732838756 -0.092280283
57 0.153420788 0.732838756
58 -1.677631505 0.153420788
59 0.778370669 -1.677631505
60 0.040049267 0.778370669
61 2.674974771 0.040049267
62 1.384872803 2.674974771
63 -0.055420129 1.384872803
64 -0.353609491 -0.055420129
65 0.720586730 -0.353609491
66 1.090827507 0.720586730
67 -1.050975933 1.090827507
68 -0.807760295 -1.050975933
69 -3.188566174 -0.807760295
70 0.501234970 -3.188566174
71 -2.956975032 0.501234970
72 0.915903044 -2.956975032
73 1.258789675 0.915903044
74 -1.816957268 1.258789675
75 0.790494510 -1.816957268
76 0.494969755 0.790494510
77 0.668349857 0.494969755
78 -1.443398580 0.668349857
79 0.235864108 -1.443398580
80 -0.077750665 0.235864108
81 -0.285131883 -0.077750665
82 -0.416239784 -0.285131883
83 0.580152412 -0.416239784
84 -2.475015566 0.580152412
85 -0.305974018 -2.475015566
86 0.298878394 -0.305974018
87 1.346393771 0.298878394
88 0.345750543 1.346393771
89 1.431071438 0.345750543
90 -1.412998214 1.431071438
91 -0.194489995 -1.412998214
92 -0.766788629 -0.194489995
93 -1.034268816 -0.766788629
94 -0.121383008 -1.034268816
95 -1.661964962 -0.121383008
96 -1.131227143 -1.661964962
97 -1.113538951 -1.131227143
98 1.144264408 -1.113538951
99 -2.564822457 1.144264408
100 0.009536128 -2.564822457
101 -0.147413645 0.009536128
102 1.013690785 -0.147413645
103 1.047713276 1.013690785
104 -1.271075578 1.047713276
105 -2.161302287 -1.271075578
106 0.167393287 -2.161302287
107 -1.224064046 0.167393287
108 0.249066878 -1.224064046
109 1.252655904 0.249066878
110 -2.737303214 1.252655904
111 1.348987194 -2.737303214
112 -0.114610095 1.348987194
113 1.395488295 -0.114610095
114 1.880884766 1.395488295
115 1.576781339 1.880884766
116 -1.585174093 1.576781339
117 1.607263632 -1.585174093
118 3.278072267 1.607263632
119 0.998267513 3.278072267
120 -0.062409676 0.998267513
121 1.263800424 -0.062409676
122 0.400720139 1.263800424
123 0.023984695 0.400720139
124 -0.648528250 0.023984695
125 0.686886552 -0.648528250
126 -0.724388870 0.686886552
127 1.190798719 -0.724388870
128 0.668268290 1.190798719
129 0.072702415 0.668268290
130 -0.774385296 0.072702415
131 -0.271502166 -0.774385296
132 1.463242514 -0.271502166
133 0.029409253 1.463242514
134 -0.136350551 0.029409253
135 -2.590432765 -0.136350551
136 -0.717399007 -2.590432765
137 -0.312344287 -0.717399007
138 -0.917559144 -0.312344287
139 -2.715506765 -0.917559144
140 1.756540838 -2.715506765
141 0.934764961 1.756540838
142 -0.336834960 0.934764961
143 1.514048217 -0.336834960
144 -0.522770314 1.514048217
145 -3.383933190 -0.522770314
146 0.847168859 -3.383933190
147 1.605040399 0.847168859
148 0.317867508 1.605040399
149 -0.450589675 0.317867508
150 0.195391219 -0.450589675
151 0.240695468 0.195391219
152 -0.408493103 0.240695468
153 3.106185971 -0.408493103
154 1.379340792 3.106185971
155 -0.797778916 1.379340792
156 0.557132971 -0.797778916
157 -1.174556306 0.557132971
158 0.568739384 -1.174556306
159 0.641283155 0.568739384
160 -2.494896754 0.641283155
161 0.728319446 -2.494896754
162 -0.622812412 0.728319446
163 0.441412556 -0.622812412
> 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/html/rcomp/tmp/7dtdx1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dtdx1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/962ci1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1062ci1290533524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/1192b51290533524.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/html/rcomp/tmp/12ulat1290533524.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/html/rcomp/tmp/13jm751290533524.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/html/rcomp/tmp/14cdoq1290533524.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/html/rcomp/tmp/15fe4w1290533524.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/html/rcomp/tmp/16bnkn1290533524.tab")
+ }
> try(system("convert tmp/1hjyo1290533524.ps tmp/1hjyo1290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rsf91290533524.ps tmp/2rsf91290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rsf91290533524.ps tmp/3rsf91290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rsf91290533524.ps tmp/4rsf91290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rsf91290533524.ps tmp/5rsf91290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/621ec1290533524.ps tmp/621ec1290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dtdx1290533524.ps tmp/7dtdx1290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dtdx1290533524.ps tmp/8dtdx1290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/962ci1290533524.ps tmp/962ci1290533524.png",intern=TRUE))
character(0)
> try(system("convert tmp/1062ci1290533524.ps tmp/1062ci1290533524.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.230 1.737 10.086