R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24
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+ ,20)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('Concernovermistakes'
+ ,'Doubtsaboutactions'
+ ,'Parentalexpectations'
+ ,'Parentalcritism'
+ ,'Personalstandards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Concernovermistakes','Doubtsaboutactions','Parentalexpectations','Parentalcritism','Personalstandards','Organization'),1:159))
> 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 = '1'
> #'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
Concernovermistakes Doubtsaboutactions Parentalexpectations Parentalcritism
1 24 14 11 12
2 25 11 7 8
3 17 6 17 8
4 18 12 10 8
5 18 8 12 9
6 16 10 12 7
7 20 10 11 4
8 16 11 11 11
9 18 16 12 7
10 17 11 13 7
11 23 13 14 12
12 30 12 16 10
13 23 8 11 10
14 18 12 10 8
15 15 11 11 8
16 12 4 15 4
17 21 9 9 9
18 15 8 11 8
19 20 8 17 7
20 31 14 17 11
21 27 15 11 9
22 34 16 18 11
23 21 9 14 13
24 31 14 10 8
25 19 11 11 8
26 16 8 15 9
27 20 9 15 6
28 21 9 13 9
29 22 9 16 9
30 17 9 13 6
31 24 10 9 6
32 25 16 18 16
33 26 11 18 5
34 25 8 12 7
35 17 9 17 9
36 32 16 9 6
37 33 11 9 6
38 13 16 12 5
39 32 12 18 12
40 25 12 12 7
41 29 14 18 10
42 22 9 14 9
43 18 10 15 8
44 17 9 16 5
45 20 10 10 8
46 15 12 11 8
47 20 14 14 10
48 33 14 9 6
49 29 10 12 8
50 23 14 17 7
51 26 16 5 4
52 18 9 12 8
53 20 10 12 8
54 11 6 6 4
55 28 8 24 20
56 26 13 12 8
57 22 10 12 8
58 17 8 14 6
59 12 7 7 4
60 14 15 13 8
61 17 9 12 9
62 21 10 13 6
63 19 12 14 7
64 18 13 8 9
65 10 10 11 5
66 29 11 9 5
67 31 8 11 8
68 19 9 13 8
69 9 13 10 6
70 20 11 11 8
71 28 8 12 7
72 19 9 9 7
73 30 9 15 9
74 29 15 18 11
75 26 9 15 6
76 23 10 12 8
77 13 14 13 6
78 21 12 14 9
79 19 12 10 8
80 28 11 13 6
81 23 14 13 10
82 18 6 11 8
83 21 12 13 8
84 20 8 16 10
85 23 14 8 5
86 21 11 16 7
87 21 10 11 5
88 15 14 9 8
89 28 12 16 14
90 19 10 12 7
91 26 14 14 8
92 10 5 8 6
93 16 11 9 5
94 22 10 15 6
95 19 9 11 10
96 31 10 21 12
97 31 16 14 9
98 29 13 18 12
99 19 9 12 7
100 22 10 13 8
101 23 10 15 10
102 15 7 12 6
103 20 9 19 10
104 18 8 15 10
105 23 14 11 10
106 25 14 11 5
107 21 8 10 7
108 24 9 13 10
109 25 14 15 11
110 17 14 12 6
111 13 8 12 7
112 28 8 16 12
113 21 8 9 11
114 25 7 18 11
115 9 6 8 11
116 16 8 13 5
117 19 6 17 8
118 17 11 9 6
119 25 14 15 9
120 20 11 8 4
121 29 11 7 4
122 14 11 12 7
123 22 14 14 11
124 15 8 6 6
125 19 20 8 7
126 20 11 17 8
127 15 8 10 4
128 20 11 11 8
129 18 10 14 9
130 33 14 11 8
131 22 11 13 11
132 16 9 12 8
133 17 9 11 5
134 16 8 9 4
135 21 10 12 8
136 26 13 20 10
137 18 13 12 6
138 18 12 13 9
139 17 8 12 9
140 22 13 12 13
141 30 14 9 9
142 30 12 15 10
143 24 14 24 20
144 21 15 7 5
145 21 13 17 11
146 29 16 11 6
147 31 9 17 9
148 20 9 11 7
149 16 9 12 9
150 22 8 14 10
151 20 7 11 9
152 28 16 16 8
153 38 11 21 7
154 22 9 14 6
155 20 11 20 13
156 17 9 13 6
157 28 14 11 8
158 22 13 15 10
159 31 16 19 16
Personalstandards Organization M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 24 26 1 0 0 0 0 0 0 0 0 0 0 1
2 25 23 0 1 0 0 0 0 0 0 0 0 0 2
3 30 25 0 0 1 0 0 0 0 0 0 0 0 3
4 19 23 0 0 0 1 0 0 0 0 0 0 0 4
5 22 19 0 0 0 0 1 0 0 0 0 0 0 5
6 22 29 0 0 0 0 0 1 0 0 0 0 0 6
7 25 25 0 0 0 0 0 0 1 0 0 0 0 7
8 23 21 0 0 0 0 0 0 0 1 0 0 0 8
9 17 22 0 0 0 0 0 0 0 0 1 0 0 9
10 21 25 0 0 0 0 0 0 0 0 0 1 0 10
11 19 24 0 0 0 0 0 0 0 0 0 0 1 11
12 19 18 0 0 0 0 0 0 0 0 0 0 0 12
13 15 22 1 0 0 0 0 0 0 0 0 0 0 13
14 16 15 0 1 0 0 0 0 0 0 0 0 0 14
15 23 22 0 0 1 0 0 0 0 0 0 0 0 15
16 27 28 0 0 0 1 0 0 0 0 0 0 0 16
17 22 20 0 0 0 0 1 0 0 0 0 0 0 17
18 14 12 0 0 0 0 0 1 0 0 0 0 0 18
19 22 24 0 0 0 0 0 0 1 0 0 0 0 19
20 23 20 0 0 0 0 0 0 0 1 0 0 0 20
21 23 21 0 0 0 0 0 0 0 0 1 0 0 21
22 21 20 0 0 0 0 0 0 0 0 0 1 0 22
23 19 21 0 0 0 0 0 0 0 0 0 0 1 23
24 18 23 0 0 0 0 0 0 0 0 0 0 0 24
25 20 28 1 0 0 0 0 0 0 0 0 0 0 25
26 23 24 0 1 0 0 0 0 0 0 0 0 0 26
27 25 24 0 0 1 0 0 0 0 0 0 0 0 27
28 19 24 0 0 0 1 0 0 0 0 0 0 0 28
29 24 23 0 0 0 0 1 0 0 0 0 0 0 29
30 22 23 0 0 0 0 0 1 0 0 0 0 0 30
31 25 29 0 0 0 0 0 0 1 0 0 0 0 31
32 26 24 0 0 0 0 0 0 0 1 0 0 0 32
33 29 18 0 0 0 0 0 0 0 0 1 0 0 33
34 32 25 0 0 0 0 0 0 0 0 0 1 0 34
35 25 21 0 0 0 0 0 0 0 0 0 0 1 35
36 29 26 0 0 0 0 0 0 0 0 0 0 0 36
37 28 22 1 0 0 0 0 0 0 0 0 0 0 37
38 17 22 0 1 0 0 0 0 0 0 0 0 0 38
39 28 22 0 0 1 0 0 0 0 0 0 0 0 39
40 29 23 0 0 0 1 0 0 0 0 0 0 0 40
41 26 30 0 0 0 0 1 0 0 0 0 0 0 41
42 25 23 0 0 0 0 0 1 0 0 0 0 0 42
43 14 17 0 0 0 0 0 0 1 0 0 0 0 43
44 25 23 0 0 0 0 0 0 0 1 0 0 0 44
45 26 23 0 0 0 0 0 0 0 0 1 0 0 45
46 20 25 0 0 0 0 0 0 0 0 0 1 0 46
47 18 24 0 0 0 0 0 0 0 0 0 0 1 47
48 32 24 0 0 0 0 0 0 0 0 0 0 0 48
49 25 23 1 0 0 0 0 0 0 0 0 0 0 49
50 25 21 0 1 0 0 0 0 0 0 0 0 0 50
51 23 24 0 0 1 0 0 0 0 0 0 0 0 51
52 21 24 0 0 0 1 0 0 0 0 0 0 0 52
53 20 28 0 0 0 0 1 0 0 0 0 0 0 53
54 15 16 0 0 0 0 0 1 0 0 0 0 0 54
55 30 20 0 0 0 0 0 0 1 0 0 0 0 55
56 24 29 0 0 0 0 0 0 0 1 0 0 0 56
57 26 27 0 0 0 0 0 0 0 0 1 0 0 57
58 24 22 0 0 0 0 0 0 0 0 0 1 0 58
59 22 28 0 0 0 0 0 0 0 0 0 0 1 59
60 14 16 0 0 0 0 0 0 0 0 0 0 0 60
61 24 25 1 0 0 0 0 0 0 0 0 0 0 61
62 24 24 0 1 0 0 0 0 0 0 0 0 0 62
63 24 28 0 0 1 0 0 0 0 0 0 0 0 63
64 24 24 0 0 0 1 0 0 0 0 0 0 0 64
65 19 23 0 0 0 0 1 0 0 0 0 0 0 65
66 31 30 0 0 0 0 0 1 0 0 0 0 0 66
67 22 24 0 0 0 0 0 0 1 0 0 0 0 67
68 27 21 0 0 0 0 0 0 0 1 0 0 0 68
69 19 25 0 0 0 0 0 0 0 0 1 0 0 69
70 25 25 0 0 0 0 0 0 0 0 0 1 0 70
71 20 22 0 0 0 0 0 0 0 0 0 0 1 71
72 21 23 0 0 0 0 0 0 0 0 0 0 0 72
73 27 26 1 0 0 0 0 0 0 0 0 0 0 73
74 23 23 0 1 0 0 0 0 0 0 0 0 0 74
75 25 25 0 0 1 0 0 0 0 0 0 0 0 75
76 20 21 0 0 0 1 0 0 0 0 0 0 0 76
77 21 25 0 0 0 0 1 0 0 0 0 0 0 77
78 22 24 0 0 0 0 0 1 0 0 0 0 0 78
79 23 29 0 0 0 0 0 0 1 0 0 0 0 79
80 25 22 0 0 0 0 0 0 0 1 0 0 0 80
81 25 27 0 0 0 0 0 0 0 0 1 0 0 81
82 17 26 0 0 0 0 0 0 0 0 0 1 0 82
83 19 22 0 0 0 0 0 0 0 0 0 0 1 83
84 25 24 0 0 0 0 0 0 0 0 0 0 0 84
85 19 27 1 0 0 0 0 0 0 0 0 0 0 85
86 20 24 0 1 0 0 0 0 0 0 0 0 0 86
87 26 24 0 0 1 0 0 0 0 0 0 0 0 87
88 23 29 0 0 0 1 0 0 0 0 0 0 0 88
89 27 22 0 0 0 0 1 0 0 0 0 0 0 89
90 17 21 0 0 0 0 0 1 0 0 0 0 0 90
91 17 24 0 0 0 0 0 0 1 0 0 0 0 91
92 19 24 0 0 0 0 0 0 0 1 0 0 0 92
93 17 23 0 0 0 0 0 0 0 0 1 0 0 93
94 22 20 0 0 0 0 0 0 0 0 0 1 0 94
95 21 27 0 0 0 0 0 0 0 0 0 0 1 95
96 32 26 0 0 0 0 0 0 0 0 0 0 0 96
97 21 25 1 0 0 0 0 0 0 0 0 0 0 97
98 21 21 0 1 0 0 0 0 0 0 0 0 0 98
99 18 21 0 0 1 0 0 0 0 0 0 0 0 99
100 18 19 0 0 0 1 0 0 0 0 0 0 0 100
101 23 21 0 0 0 0 1 0 0 0 0 0 0 101
102 19 21 0 0 0 0 0 1 0 0 0 0 0 102
103 20 16 0 0 0 0 0 0 1 0 0 0 0 103
104 21 22 0 0 0 0 0 0 0 1 0 0 0 104
105 20 29 0 0 0 0 0 0 0 0 1 0 0 105
106 17 15 0 0 0 0 0 0 0 0 0 1 0 106
107 18 17 0 0 0 0 0 0 0 0 0 0 1 107
108 19 15 0 0 0 0 0 0 0 0 0 0 0 108
109 22 21 1 0 0 0 0 0 0 0 0 0 0 109
110 15 21 0 1 0 0 0 0 0 0 0 0 0 110
111 14 19 0 0 1 0 0 0 0 0 0 0 0 111
112 18 24 0 0 0 1 0 0 0 0 0 0 0 112
113 24 20 0 0 0 0 1 0 0 0 0 0 0 113
114 35 17 0 0 0 0 0 1 0 0 0 0 0 114
115 29 23 0 0 0 0 0 0 1 0 0 0 0 115
116 21 24 0 0 0 0 0 0 0 1 0 0 0 116
117 25 14 0 0 0 0 0 0 0 0 1 0 0 117
118 20 19 0 0 0 0 0 0 0 0 0 1 0 118
119 22 24 0 0 0 0 0 0 0 0 0 0 1 119
120 13 13 0 0 0 0 0 0 0 0 0 0 0 120
121 26 22 1 0 0 0 0 0 0 0 0 0 0 121
122 17 16 0 1 0 0 0 0 0 0 0 0 0 122
123 25 19 0 0 1 0 0 0 0 0 0 0 0 123
124 20 25 0 0 0 1 0 0 0 0 0 0 0 124
125 19 25 0 0 0 0 1 0 0 0 0 0 0 125
126 21 23 0 0 0 0 0 1 0 0 0 0 0 126
127 22 24 0 0 0 0 0 0 1 0 0 0 0 127
128 24 26 0 0 0 0 0 0 0 1 0 0 0 128
129 21 26 0 0 0 0 0 0 0 0 1 0 0 129
130 26 25 0 0 0 0 0 0 0 0 0 1 0 130
131 24 18 0 0 0 0 0 0 0 0 0 0 1 131
132 16 21 0 0 0 0 0 0 0 0 0 0 0 132
133 23 26 1 0 0 0 0 0 0 0 0 0 0 133
134 18 23 0 1 0 0 0 0 0 0 0 0 0 134
135 16 23 0 0 1 0 0 0 0 0 0 0 0 135
136 26 22 0 0 0 1 0 0 0 0 0 0 0 136
137 19 20 0 0 0 0 1 0 0 0 0 0 0 137
138 21 13 0 0 0 0 0 1 0 0 0 0 0 138
139 21 24 0 0 0 0 0 0 1 0 0 0 0 139
140 22 15 0 0 0 0 0 0 0 1 0 0 0 140
141 23 14 0 0 0 0 0 0 0 0 1 0 0 141
142 29 22 0 0 0 0 0 0 0 0 0 1 0 142
143 21 10 0 0 0 0 0 0 0 0 0 0 1 143
144 21 24 0 0 0 0 0 0 0 0 0 0 0 144
145 23 22 1 0 0 0 0 0 0 0 0 0 0 145
146 27 24 0 1 0 0 0 0 0 0 0 0 0 146
147 25 19 0 0 1 0 0 0 0 0 0 0 0 147
148 21 20 0 0 0 1 0 0 0 0 0 0 0 148
149 10 13 0 0 0 0 1 0 0 0 0 0 0 149
150 20 20 0 0 0 0 0 1 0 0 0 0 0 150
151 26 22 0 0 0 0 0 0 1 0 0 0 0 151
152 24 24 0 0 0 0 0 0 0 1 0 0 0 152
153 29 29 0 0 0 0 0 0 0 0 1 0 0 153
154 19 12 0 0 0 0 0 0 0 0 0 1 0 154
155 24 20 0 0 0 0 0 0 0 0 0 0 1 155
156 19 21 0 0 0 0 0 0 0 0 0 0 0 156
157 24 24 1 0 0 0 0 0 0 0 0 0 0 157
158 22 22 0 1 0 0 0 0 0 0 0 0 0 158
159 17 20 0 0 1 0 0 0 0 0 0 0 0 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Doubtsaboutactions Parentalexpectations
-0.12535 0.77092 0.29557
Parentalcritism Personalstandards Organization
0.20217 0.54302 -0.10601
M1 M2 M3
0.71098 -3.28910 -1.88964
M4 M5 M6
-2.50928 -3.67320 -3.05185
M7 M8 M9
-2.23348 -3.60826 -2.67398
M10 M11 t
-1.16907 -2.84271 0.00456
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.6891 -2.5539 -0.2753 2.8749 12.6149
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.125352 3.415074 -0.037 0.9708
Doubtsaboutactions 0.770919 0.139190 5.539 1.45e-07 ***
Parentalexpectations 0.295573 0.134921 2.191 0.0301 *
Parentalcritism 0.202170 0.177244 1.141 0.2560
Personalstandards 0.543024 0.096938 5.602 1.08e-07 ***
Organization -0.106006 0.108083 -0.981 0.3284
M1 0.710977 1.744070 0.408 0.6841
M2 -3.289105 1.725559 -1.906 0.0587 .
M3 -1.889638 1.731896 -1.091 0.2771
M4 -2.509281 1.779088 -1.410 0.1606
M5 -3.673197 1.758558 -2.089 0.0385 *
M6 -3.051846 1.773031 -1.721 0.0874 .
M7 -2.233477 1.806722 -1.236 0.2184
M8 -3.608263 1.765463 -2.044 0.0428 *
M9 -2.673978 1.755091 -1.524 0.1299
M10 -1.169075 1.749617 -0.668 0.5051
M11 -2.842708 1.799076 -1.580 0.1163
t 0.004560 0.007937 0.574 0.5666
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.439 on 141 degrees of freedom
Multiple R-squared: 0.463, Adjusted R-squared: 0.3982
F-statistic: 7.15 on 17 and 141 DF, p-value: 2.601e-12
> 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,] 0.85354140 0.29291720 0.14645860
[2,] 0.78613518 0.42772963 0.21386482
[3,] 0.68698943 0.62602113 0.31301057
[4,] 0.59970665 0.80058670 0.40029335
[5,] 0.57898601 0.84202799 0.42101399
[6,] 0.62535750 0.74928500 0.37464250
[7,] 0.56242529 0.87514943 0.43757471
[8,] 0.49189605 0.98379210 0.50810395
[9,] 0.40827879 0.81655757 0.59172121
[10,] 0.33331825 0.66663651 0.66668175
[11,] 0.26729607 0.53459213 0.73270393
[12,] 0.34614665 0.69229330 0.65385335
[13,] 0.27530247 0.55060494 0.72469753
[14,] 0.21417854 0.42835708 0.78582146
[15,] 0.27065832 0.54131663 0.72934168
[16,] 0.23502698 0.47005395 0.76497302
[17,] 0.28003449 0.56006898 0.71996551
[18,] 0.36171791 0.72343583 0.63828209
[19,] 0.33790415 0.67580830 0.66209585
[20,] 0.28903466 0.57806932 0.71096534
[21,] 0.27394400 0.54788800 0.72605600
[22,] 0.22288572 0.44577145 0.77711428
[23,] 0.21845480 0.43690961 0.78154520
[24,] 0.18635323 0.37270646 0.81364677
[25,] 0.16094208 0.32188415 0.83905792
[26,] 0.19301362 0.38602724 0.80698638
[27,] 0.15371027 0.30742054 0.84628973
[28,] 0.14357252 0.28714503 0.85642748
[29,] 0.12551602 0.25103204 0.87448398
[30,] 0.10257791 0.20515581 0.89742209
[31,] 0.11483114 0.22966227 0.88516886
[32,] 0.08932354 0.17864708 0.91067646
[33,] 0.07414979 0.14829958 0.92585021
[34,] 0.05591927 0.11183854 0.94408073
[35,] 0.06669261 0.13338523 0.93330739
[36,] 0.08289106 0.16578213 0.91710894
[37,] 0.06490397 0.12980794 0.93509603
[38,] 0.05958254 0.11916508 0.94041746
[39,] 0.04755943 0.09511886 0.95244057
[40,] 0.19838204 0.39676409 0.80161796
[41,] 0.24928655 0.49857310 0.75071345
[42,] 0.22600143 0.45200286 0.77399857
[43,] 0.20856446 0.41712892 0.79143554
[44,] 0.21452276 0.42904553 0.78547724
[45,] 0.26240911 0.52481822 0.73759089
[46,] 0.28353671 0.56707342 0.71646329
[47,] 0.61143753 0.77712495 0.38856247
[48,] 0.57233631 0.85532738 0.42766369
[49,] 0.74775539 0.50448922 0.25224461
[50,] 0.72912953 0.54174095 0.27087047
[51,] 0.90592776 0.18814448 0.09407224
[52,] 0.88505281 0.22989439 0.11494719
[53,] 0.88281135 0.23437730 0.11718865
[54,] 0.87116488 0.25767025 0.12883512
[55,] 0.87814922 0.24370156 0.12185078
[56,] 0.87172858 0.25654285 0.12827142
[57,] 0.93351466 0.13297069 0.06648534
[58,] 0.91610689 0.16778623 0.08389311
[59,] 0.90505116 0.18989768 0.09494884
[60,] 0.92915129 0.14169741 0.07084871
[61,] 0.91670262 0.16659477 0.08329738
[62,] 0.91039198 0.17921604 0.08960802
[63,] 0.88873104 0.22253792 0.11126896
[64,] 0.88398904 0.23202192 0.11601096
[65,] 0.85830556 0.28338888 0.14169444
[66,] 0.83265871 0.33468259 0.16734129
[67,] 0.80216928 0.39566144 0.19783072
[68,] 0.87343504 0.25312991 0.12656496
[69,] 0.84957017 0.30085965 0.15042983
[70,] 0.82437385 0.35125230 0.17562615
[71,] 0.84820530 0.30358940 0.15179470
[72,] 0.82111775 0.35776450 0.17888225
[73,] 0.80253696 0.39492608 0.19746304
[74,] 0.77539462 0.44921076 0.22460538
[75,] 0.73509627 0.52980747 0.26490373
[76,] 0.69002185 0.61995631 0.30997815
[77,] 0.68224977 0.63550047 0.31775023
[78,] 0.71632279 0.56735443 0.28367721
[79,] 0.67162896 0.65674209 0.32837104
[80,] 0.63605221 0.72789558 0.36394779
[81,] 0.59673920 0.80652159 0.40326080
[82,] 0.54457566 0.91084867 0.45542434
[83,] 0.53996253 0.92007494 0.46003747
[84,] 0.48518590 0.97037180 0.51481410
[85,] 0.43951960 0.87903920 0.56048040
[86,] 0.40371042 0.80742085 0.59628958
[87,] 0.44475274 0.88950548 0.55524726
[88,] 0.47407631 0.94815263 0.52592369
[89,] 0.45233409 0.90466817 0.54766591
[90,] 0.40019719 0.80039438 0.59980281
[91,] 0.36954306 0.73908611 0.63045694
[92,] 0.73473187 0.53053626 0.26526813
[93,] 0.78985049 0.42029903 0.21014951
[94,] 0.78298520 0.43402960 0.21701480
[95,] 0.85028373 0.29943254 0.14971627
[96,] 0.80877092 0.38245816 0.19122908
[97,] 0.78671108 0.42657784 0.21328892
[98,] 0.79105182 0.41789636 0.20894818
[99,] 0.78097142 0.43805716 0.21902858
[100,] 0.85063273 0.29873454 0.14936727
[101,] 0.95428150 0.09143700 0.04571850
[102,] 0.93651090 0.12697820 0.06348910
[103,] 0.96087146 0.07825708 0.03912854
[104,] 0.94054477 0.11891045 0.05945523
[105,] 0.93737161 0.12525678 0.06262839
[106,] 0.90920052 0.18159897 0.09079948
[107,] 0.87008836 0.25982329 0.12991164
[108,] 0.81952712 0.36094575 0.18047288
[109,] 0.95592188 0.08815624 0.04407812
[110,] 0.95940861 0.08118278 0.04059139
[111,] 0.95378967 0.09242065 0.04621033
[112,] 0.93743349 0.12513303 0.06256651
[113,] 0.90038475 0.19923050 0.09961525
[114,] 0.85548742 0.28902517 0.14451258
[115,] 0.77750318 0.44499364 0.22249682
[116,] 0.66797969 0.66404062 0.33202031
[117,] 0.63004359 0.73991283 0.36995641
[118,] 0.70381529 0.59236941 0.29618471
> postscript(file="/var/www/html/freestat/rcomp/tmp/1u1fs1290850027.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2u1fs1290850027.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/35aev1290850027.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/45aev1290850027.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/55aev1290850027.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 = 159
Frequency = 1
1 2 3 4 5
-3.336836492 5.101376626 -5.906894939 -1.087060457 0.309561320
6 7 8 9 10
-2.393789978 -0.367729059 -4.521590469 -3.437771561 -4.242290770
11 12 13 14 15
1.558560711 5.659372383 5.101499337 0.428193313 -6.559615949
16 17 18 19 20
-5.457770842 3.476651582 0.728830748 0.262509854 6.231490435
21 22 23 24 25
2.805513053 6.031819697 2.067334297 8.313651334 -2.940719989
26 27 28 29 30
-3.069999216 -1.724482058 3.133381407 1.584892015 -1.461740039
31 32 33 34 35
4.133667917 -3.876539075 -0.002019100 0.283342182 -5.323564863
36 37 38 39 40
2.761761813 7.019822850 -7.550534559 3.967217374 -0.070426431
41 42 43 44 45
3.538254692 0.952386808 0.602368592 -3.282779819 -1.368639468
46 47 48 49 50
-6.245357564 -1.429141203 3.407800301 4.180044174 -2.395818451
51 52 53 54 55
3.215772460 -0.564355166 2.791130168 0.274068648 -1.367083303
56 57 58 59 60
4.333667563 0.409521377 -4.188889233 -2.553460147 -9.078079107
61 62 63 64 65
-6.550887009 0.878647994 -4.140937545 -4.351698604 -6.348505942
66 67 68 69 70
6.071559546 12.614915195 -2.410063323 -10.373307192 -3.298991158
71 72 73 74 75
10.986540757 -1.181944590 3.984610521 2.917635447 4.162660143
76 77 78 79 80
3.780300519 -8.154252940 -0.789440417 -2.240900028 6.589777256
81 82 83 84 85
-1.940477664 2.951089225 0.893428064 -4.207356221 0.403191967
86 87 88 89 90
1.081503620 -0.927541746 -7.252399287 2.252623792 2.090273434
91 92 93 94 95
4.708368820 -1.891399518 -0.569119796 -0.316411987 0.782256294
96 97 98 99 100
-0.275275821 2.926456161 5.021908716 1.114923963 3.249332335
101 102 103 104 105
1.910092685 -0.535562465 -1.851076553 -0.434627890 1.468369733
106 107 108 109 110
3.114750266 4.969558961 2.103105475 -3.253382630 -1.559119088
111 112 113 114 115
-2.208787303 9.571082629 2.319454369 -2.486980815 -11.689076767
116 117 118 119 120
-0.675333485 -2.093298143 -3.443281083 0.977063709 2.243573501
121 122 123 124 125
4.718347359 -4.119324893 -5.262113951 -0.294920919 -4.636888388
126 127 128 129 130
-1.484915506 -2.554409283 -0.475233343 -2.102975607 6.571647111
131 132 133 134 135
-1.300171883 -3.041304708 -5.125897359 1.830964638 3.275746536
136 137 138 139 140
-1.727103287 -1.805320433 -4.390486283 -1.668099659 -1.458228753
141 142 143 144 145
5.878379235 1.525045975 -5.957459103 -3.034262839 -7.674775883
146 147 148 149 150
2.832194470 7.000672124 1.071638104 2.762307082 3.425796318
151 152 153 154 155
-0.583455726 1.870860421 11.325825131 1.257527340 -5.670945595
156 157 158 159
-3.671041522 0.548526993 -1.397628618 3.993380892
> postscript(file="/var/www/html/freestat/rcomp/tmp/6gjvg1290850027.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.336836492 NA
1 5.101376626 -3.336836492
2 -5.906894939 5.101376626
3 -1.087060457 -5.906894939
4 0.309561320 -1.087060457
5 -2.393789978 0.309561320
6 -0.367729059 -2.393789978
7 -4.521590469 -0.367729059
8 -3.437771561 -4.521590469
9 -4.242290770 -3.437771561
10 1.558560711 -4.242290770
11 5.659372383 1.558560711
12 5.101499337 5.659372383
13 0.428193313 5.101499337
14 -6.559615949 0.428193313
15 -5.457770842 -6.559615949
16 3.476651582 -5.457770842
17 0.728830748 3.476651582
18 0.262509854 0.728830748
19 6.231490435 0.262509854
20 2.805513053 6.231490435
21 6.031819697 2.805513053
22 2.067334297 6.031819697
23 8.313651334 2.067334297
24 -2.940719989 8.313651334
25 -3.069999216 -2.940719989
26 -1.724482058 -3.069999216
27 3.133381407 -1.724482058
28 1.584892015 3.133381407
29 -1.461740039 1.584892015
30 4.133667917 -1.461740039
31 -3.876539075 4.133667917
32 -0.002019100 -3.876539075
33 0.283342182 -0.002019100
34 -5.323564863 0.283342182
35 2.761761813 -5.323564863
36 7.019822850 2.761761813
37 -7.550534559 7.019822850
38 3.967217374 -7.550534559
39 -0.070426431 3.967217374
40 3.538254692 -0.070426431
41 0.952386808 3.538254692
42 0.602368592 0.952386808
43 -3.282779819 0.602368592
44 -1.368639468 -3.282779819
45 -6.245357564 -1.368639468
46 -1.429141203 -6.245357564
47 3.407800301 -1.429141203
48 4.180044174 3.407800301
49 -2.395818451 4.180044174
50 3.215772460 -2.395818451
51 -0.564355166 3.215772460
52 2.791130168 -0.564355166
53 0.274068648 2.791130168
54 -1.367083303 0.274068648
55 4.333667563 -1.367083303
56 0.409521377 4.333667563
57 -4.188889233 0.409521377
58 -2.553460147 -4.188889233
59 -9.078079107 -2.553460147
60 -6.550887009 -9.078079107
61 0.878647994 -6.550887009
62 -4.140937545 0.878647994
63 -4.351698604 -4.140937545
64 -6.348505942 -4.351698604
65 6.071559546 -6.348505942
66 12.614915195 6.071559546
67 -2.410063323 12.614915195
68 -10.373307192 -2.410063323
69 -3.298991158 -10.373307192
70 10.986540757 -3.298991158
71 -1.181944590 10.986540757
72 3.984610521 -1.181944590
73 2.917635447 3.984610521
74 4.162660143 2.917635447
75 3.780300519 4.162660143
76 -8.154252940 3.780300519
77 -0.789440417 -8.154252940
78 -2.240900028 -0.789440417
79 6.589777256 -2.240900028
80 -1.940477664 6.589777256
81 2.951089225 -1.940477664
82 0.893428064 2.951089225
83 -4.207356221 0.893428064
84 0.403191967 -4.207356221
85 1.081503620 0.403191967
86 -0.927541746 1.081503620
87 -7.252399287 -0.927541746
88 2.252623792 -7.252399287
89 2.090273434 2.252623792
90 4.708368820 2.090273434
91 -1.891399518 4.708368820
92 -0.569119796 -1.891399518
93 -0.316411987 -0.569119796
94 0.782256294 -0.316411987
95 -0.275275821 0.782256294
96 2.926456161 -0.275275821
97 5.021908716 2.926456161
98 1.114923963 5.021908716
99 3.249332335 1.114923963
100 1.910092685 3.249332335
101 -0.535562465 1.910092685
102 -1.851076553 -0.535562465
103 -0.434627890 -1.851076553
104 1.468369733 -0.434627890
105 3.114750266 1.468369733
106 4.969558961 3.114750266
107 2.103105475 4.969558961
108 -3.253382630 2.103105475
109 -1.559119088 -3.253382630
110 -2.208787303 -1.559119088
111 9.571082629 -2.208787303
112 2.319454369 9.571082629
113 -2.486980815 2.319454369
114 -11.689076767 -2.486980815
115 -0.675333485 -11.689076767
116 -2.093298143 -0.675333485
117 -3.443281083 -2.093298143
118 0.977063709 -3.443281083
119 2.243573501 0.977063709
120 4.718347359 2.243573501
121 -4.119324893 4.718347359
122 -5.262113951 -4.119324893
123 -0.294920919 -5.262113951
124 -4.636888388 -0.294920919
125 -1.484915506 -4.636888388
126 -2.554409283 -1.484915506
127 -0.475233343 -2.554409283
128 -2.102975607 -0.475233343
129 6.571647111 -2.102975607
130 -1.300171883 6.571647111
131 -3.041304708 -1.300171883
132 -5.125897359 -3.041304708
133 1.830964638 -5.125897359
134 3.275746536 1.830964638
135 -1.727103287 3.275746536
136 -1.805320433 -1.727103287
137 -4.390486283 -1.805320433
138 -1.668099659 -4.390486283
139 -1.458228753 -1.668099659
140 5.878379235 -1.458228753
141 1.525045975 5.878379235
142 -5.957459103 1.525045975
143 -3.034262839 -5.957459103
144 -7.674775883 -3.034262839
145 2.832194470 -7.674775883
146 7.000672124 2.832194470
147 1.071638104 7.000672124
148 2.762307082 1.071638104
149 3.425796318 2.762307082
150 -0.583455726 3.425796318
151 1.870860421 -0.583455726
152 11.325825131 1.870860421
153 1.257527340 11.325825131
154 -5.670945595 1.257527340
155 -3.671041522 -5.670945595
156 0.548526993 -3.671041522
157 -1.397628618 0.548526993
158 3.993380892 -1.397628618
159 NA 3.993380892
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.101376626 -3.336836492
[2,] -5.906894939 5.101376626
[3,] -1.087060457 -5.906894939
[4,] 0.309561320 -1.087060457
[5,] -2.393789978 0.309561320
[6,] -0.367729059 -2.393789978
[7,] -4.521590469 -0.367729059
[8,] -3.437771561 -4.521590469
[9,] -4.242290770 -3.437771561
[10,] 1.558560711 -4.242290770
[11,] 5.659372383 1.558560711
[12,] 5.101499337 5.659372383
[13,] 0.428193313 5.101499337
[14,] -6.559615949 0.428193313
[15,] -5.457770842 -6.559615949
[16,] 3.476651582 -5.457770842
[17,] 0.728830748 3.476651582
[18,] 0.262509854 0.728830748
[19,] 6.231490435 0.262509854
[20,] 2.805513053 6.231490435
[21,] 6.031819697 2.805513053
[22,] 2.067334297 6.031819697
[23,] 8.313651334 2.067334297
[24,] -2.940719989 8.313651334
[25,] -3.069999216 -2.940719989
[26,] -1.724482058 -3.069999216
[27,] 3.133381407 -1.724482058
[28,] 1.584892015 3.133381407
[29,] -1.461740039 1.584892015
[30,] 4.133667917 -1.461740039
[31,] -3.876539075 4.133667917
[32,] -0.002019100 -3.876539075
[33,] 0.283342182 -0.002019100
[34,] -5.323564863 0.283342182
[35,] 2.761761813 -5.323564863
[36,] 7.019822850 2.761761813
[37,] -7.550534559 7.019822850
[38,] 3.967217374 -7.550534559
[39,] -0.070426431 3.967217374
[40,] 3.538254692 -0.070426431
[41,] 0.952386808 3.538254692
[42,] 0.602368592 0.952386808
[43,] -3.282779819 0.602368592
[44,] -1.368639468 -3.282779819
[45,] -6.245357564 -1.368639468
[46,] -1.429141203 -6.245357564
[47,] 3.407800301 -1.429141203
[48,] 4.180044174 3.407800301
[49,] -2.395818451 4.180044174
[50,] 3.215772460 -2.395818451
[51,] -0.564355166 3.215772460
[52,] 2.791130168 -0.564355166
[53,] 0.274068648 2.791130168
[54,] -1.367083303 0.274068648
[55,] 4.333667563 -1.367083303
[56,] 0.409521377 4.333667563
[57,] -4.188889233 0.409521377
[58,] -2.553460147 -4.188889233
[59,] -9.078079107 -2.553460147
[60,] -6.550887009 -9.078079107
[61,] 0.878647994 -6.550887009
[62,] -4.140937545 0.878647994
[63,] -4.351698604 -4.140937545
[64,] -6.348505942 -4.351698604
[65,] 6.071559546 -6.348505942
[66,] 12.614915195 6.071559546
[67,] -2.410063323 12.614915195
[68,] -10.373307192 -2.410063323
[69,] -3.298991158 -10.373307192
[70,] 10.986540757 -3.298991158
[71,] -1.181944590 10.986540757
[72,] 3.984610521 -1.181944590
[73,] 2.917635447 3.984610521
[74,] 4.162660143 2.917635447
[75,] 3.780300519 4.162660143
[76,] -8.154252940 3.780300519
[77,] -0.789440417 -8.154252940
[78,] -2.240900028 -0.789440417
[79,] 6.589777256 -2.240900028
[80,] -1.940477664 6.589777256
[81,] 2.951089225 -1.940477664
[82,] 0.893428064 2.951089225
[83,] -4.207356221 0.893428064
[84,] 0.403191967 -4.207356221
[85,] 1.081503620 0.403191967
[86,] -0.927541746 1.081503620
[87,] -7.252399287 -0.927541746
[88,] 2.252623792 -7.252399287
[89,] 2.090273434 2.252623792
[90,] 4.708368820 2.090273434
[91,] -1.891399518 4.708368820
[92,] -0.569119796 -1.891399518
[93,] -0.316411987 -0.569119796
[94,] 0.782256294 -0.316411987
[95,] -0.275275821 0.782256294
[96,] 2.926456161 -0.275275821
[97,] 5.021908716 2.926456161
[98,] 1.114923963 5.021908716
[99,] 3.249332335 1.114923963
[100,] 1.910092685 3.249332335
[101,] -0.535562465 1.910092685
[102,] -1.851076553 -0.535562465
[103,] -0.434627890 -1.851076553
[104,] 1.468369733 -0.434627890
[105,] 3.114750266 1.468369733
[106,] 4.969558961 3.114750266
[107,] 2.103105475 4.969558961
[108,] -3.253382630 2.103105475
[109,] -1.559119088 -3.253382630
[110,] -2.208787303 -1.559119088
[111,] 9.571082629 -2.208787303
[112,] 2.319454369 9.571082629
[113,] -2.486980815 2.319454369
[114,] -11.689076767 -2.486980815
[115,] -0.675333485 -11.689076767
[116,] -2.093298143 -0.675333485
[117,] -3.443281083 -2.093298143
[118,] 0.977063709 -3.443281083
[119,] 2.243573501 0.977063709
[120,] 4.718347359 2.243573501
[121,] -4.119324893 4.718347359
[122,] -5.262113951 -4.119324893
[123,] -0.294920919 -5.262113951
[124,] -4.636888388 -0.294920919
[125,] -1.484915506 -4.636888388
[126,] -2.554409283 -1.484915506
[127,] -0.475233343 -2.554409283
[128,] -2.102975607 -0.475233343
[129,] 6.571647111 -2.102975607
[130,] -1.300171883 6.571647111
[131,] -3.041304708 -1.300171883
[132,] -5.125897359 -3.041304708
[133,] 1.830964638 -5.125897359
[134,] 3.275746536 1.830964638
[135,] -1.727103287 3.275746536
[136,] -1.805320433 -1.727103287
[137,] -4.390486283 -1.805320433
[138,] -1.668099659 -4.390486283
[139,] -1.458228753 -1.668099659
[140,] 5.878379235 -1.458228753
[141,] 1.525045975 5.878379235
[142,] -5.957459103 1.525045975
[143,] -3.034262839 -5.957459103
[144,] -7.674775883 -3.034262839
[145,] 2.832194470 -7.674775883
[146,] 7.000672124 2.832194470
[147,] 1.071638104 7.000672124
[148,] 2.762307082 1.071638104
[149,] 3.425796318 2.762307082
[150,] -0.583455726 3.425796318
[151,] 1.870860421 -0.583455726
[152,] 11.325825131 1.870860421
[153,] 1.257527340 11.325825131
[154,] -5.670945595 1.257527340
[155,] -3.671041522 -5.670945595
[156,] 0.548526993 -3.671041522
[157,] -1.397628618 0.548526993
[158,] 3.993380892 -1.397628618
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.101376626 -3.336836492
2 -5.906894939 5.101376626
3 -1.087060457 -5.906894939
4 0.309561320 -1.087060457
5 -2.393789978 0.309561320
6 -0.367729059 -2.393789978
7 -4.521590469 -0.367729059
8 -3.437771561 -4.521590469
9 -4.242290770 -3.437771561
10 1.558560711 -4.242290770
11 5.659372383 1.558560711
12 5.101499337 5.659372383
13 0.428193313 5.101499337
14 -6.559615949 0.428193313
15 -5.457770842 -6.559615949
16 3.476651582 -5.457770842
17 0.728830748 3.476651582
18 0.262509854 0.728830748
19 6.231490435 0.262509854
20 2.805513053 6.231490435
21 6.031819697 2.805513053
22 2.067334297 6.031819697
23 8.313651334 2.067334297
24 -2.940719989 8.313651334
25 -3.069999216 -2.940719989
26 -1.724482058 -3.069999216
27 3.133381407 -1.724482058
28 1.584892015 3.133381407
29 -1.461740039 1.584892015
30 4.133667917 -1.461740039
31 -3.876539075 4.133667917
32 -0.002019100 -3.876539075
33 0.283342182 -0.002019100
34 -5.323564863 0.283342182
35 2.761761813 -5.323564863
36 7.019822850 2.761761813
37 -7.550534559 7.019822850
38 3.967217374 -7.550534559
39 -0.070426431 3.967217374
40 3.538254692 -0.070426431
41 0.952386808 3.538254692
42 0.602368592 0.952386808
43 -3.282779819 0.602368592
44 -1.368639468 -3.282779819
45 -6.245357564 -1.368639468
46 -1.429141203 -6.245357564
47 3.407800301 -1.429141203
48 4.180044174 3.407800301
49 -2.395818451 4.180044174
50 3.215772460 -2.395818451
51 -0.564355166 3.215772460
52 2.791130168 -0.564355166
53 0.274068648 2.791130168
54 -1.367083303 0.274068648
55 4.333667563 -1.367083303
56 0.409521377 4.333667563
57 -4.188889233 0.409521377
58 -2.553460147 -4.188889233
59 -9.078079107 -2.553460147
60 -6.550887009 -9.078079107
61 0.878647994 -6.550887009
62 -4.140937545 0.878647994
63 -4.351698604 -4.140937545
64 -6.348505942 -4.351698604
65 6.071559546 -6.348505942
66 12.614915195 6.071559546
67 -2.410063323 12.614915195
68 -10.373307192 -2.410063323
69 -3.298991158 -10.373307192
70 10.986540757 -3.298991158
71 -1.181944590 10.986540757
72 3.984610521 -1.181944590
73 2.917635447 3.984610521
74 4.162660143 2.917635447
75 3.780300519 4.162660143
76 -8.154252940 3.780300519
77 -0.789440417 -8.154252940
78 -2.240900028 -0.789440417
79 6.589777256 -2.240900028
80 -1.940477664 6.589777256
81 2.951089225 -1.940477664
82 0.893428064 2.951089225
83 -4.207356221 0.893428064
84 0.403191967 -4.207356221
85 1.081503620 0.403191967
86 -0.927541746 1.081503620
87 -7.252399287 -0.927541746
88 2.252623792 -7.252399287
89 2.090273434 2.252623792
90 4.708368820 2.090273434
91 -1.891399518 4.708368820
92 -0.569119796 -1.891399518
93 -0.316411987 -0.569119796
94 0.782256294 -0.316411987
95 -0.275275821 0.782256294
96 2.926456161 -0.275275821
97 5.021908716 2.926456161
98 1.114923963 5.021908716
99 3.249332335 1.114923963
100 1.910092685 3.249332335
101 -0.535562465 1.910092685
102 -1.851076553 -0.535562465
103 -0.434627890 -1.851076553
104 1.468369733 -0.434627890
105 3.114750266 1.468369733
106 4.969558961 3.114750266
107 2.103105475 4.969558961
108 -3.253382630 2.103105475
109 -1.559119088 -3.253382630
110 -2.208787303 -1.559119088
111 9.571082629 -2.208787303
112 2.319454369 9.571082629
113 -2.486980815 2.319454369
114 -11.689076767 -2.486980815
115 -0.675333485 -11.689076767
116 -2.093298143 -0.675333485
117 -3.443281083 -2.093298143
118 0.977063709 -3.443281083
119 2.243573501 0.977063709
120 4.718347359 2.243573501
121 -4.119324893 4.718347359
122 -5.262113951 -4.119324893
123 -0.294920919 -5.262113951
124 -4.636888388 -0.294920919
125 -1.484915506 -4.636888388
126 -2.554409283 -1.484915506
127 -0.475233343 -2.554409283
128 -2.102975607 -0.475233343
129 6.571647111 -2.102975607
130 -1.300171883 6.571647111
131 -3.041304708 -1.300171883
132 -5.125897359 -3.041304708
133 1.830964638 -5.125897359
134 3.275746536 1.830964638
135 -1.727103287 3.275746536
136 -1.805320433 -1.727103287
137 -4.390486283 -1.805320433
138 -1.668099659 -4.390486283
139 -1.458228753 -1.668099659
140 5.878379235 -1.458228753
141 1.525045975 5.878379235
142 -5.957459103 1.525045975
143 -3.034262839 -5.957459103
144 -7.674775883 -3.034262839
145 2.832194470 -7.674775883
146 7.000672124 2.832194470
147 1.071638104 7.000672124
148 2.762307082 1.071638104
149 3.425796318 2.762307082
150 -0.583455726 3.425796318
151 1.870860421 -0.583455726
152 11.325825131 1.870860421
153 1.257527340 11.325825131
154 -5.670945595 1.257527340
155 -3.671041522 -5.670945595
156 0.548526993 -3.671041522
157 -1.397628618 0.548526993
158 3.993380892 -1.397628618
> 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/freestat/rcomp/tmp/7rbv11290850027.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8rbv11290850027.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9rbv11290850027.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10jku41290850027.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11n2ba1290850027.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/freestat/rcomp/tmp/128l9x1290850027.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/freestat/rcomp/tmp/13mv7o1290850027.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/freestat/rcomp/tmp/147dnu1290850027.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/freestat/rcomp/tmp/15bwm01290850027.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/freestat/rcomp/tmp/16wwk61290850027.tab")
+ }
>
> try(system("convert tmp/1u1fs1290850027.ps tmp/1u1fs1290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/2u1fs1290850027.ps tmp/2u1fs1290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/35aev1290850027.ps tmp/35aev1290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/45aev1290850027.ps tmp/45aev1290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/55aev1290850027.ps tmp/55aev1290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gjvg1290850027.ps tmp/6gjvg1290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rbv11290850027.ps tmp/7rbv11290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rbv11290850027.ps tmp/8rbv11290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rbv11290850027.ps tmp/9rbv11290850027.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jku41290850027.ps tmp/10jku41290850027.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.007 2.684 6.611