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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+ ,4
+ ,4)
+ ,dim=c(18
+ ,144)
+ ,dimnames=list(c('Pop'
+ ,'Happiness'
+ ,'Age'
+ ,'Age_p'
+ ,'Concern_over_mistakes'
+ ,'Concern_over_mistakes_p'
+ ,'Doubts_about_actions'
+ ,'Doubts_about_actions_p'
+ ,'Parental_expectations'
+ ,'Parental_expectations_p'
+ ,'Parental_criticism'
+ ,'Parental_criticism_p'
+ ,'Popularity'
+ ,'Popularity_p'
+ ,'Perceived_learning_competence'
+ ,'Perceived_learning_competence_p'
+ ,'Amotivation'
+ ,'Amotivation_p')
+ ,1:144))
> y <- array(NA,dim=c(18,144),dimnames=list(c('Pop','Happiness','Age','Age_p','Concern_over_mistakes','Concern_over_mistakes_p','Doubts_about_actions','Doubts_about_actions_p','Parental_expectations','Parental_expectations_p','Parental_criticism','Parental_criticism_p','Popularity','Popularity_p','Perceived_learning_competence','Perceived_learning_competence_p','Amotivation','Amotivation_p'),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 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'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
Happiness Pop Age Age_p Concern_over_mistakes Concern_over_mistakes_p
1 14 1 23 23 26 26
2 18 1 21 21 20 20
3 11 1 21 21 21 21
4 12 0 21 0 31 0
5 16 1 24 24 21 21
6 18 1 22 22 18 18
7 14 1 21 21 26 26
8 14 1 22 22 22 22
9 15 1 21 21 22 22
10 15 1 20 20 29 29
11 17 0 22 0 15 0
12 19 1 21 21 16 16
13 10 0 21 0 24 0
14 18 1 23 23 17 17
15 14 0 22 0 19 0
16 14 0 23 0 22 0
17 17 1 22 22 31 31
18 14 0 24 0 28 0
19 16 1 23 23 38 38
20 18 0 21 0 26 0
21 14 1 23 23 25 25
22 12 1 23 23 25 25
23 17 0 21 0 29 0
24 9 1 20 20 28 28
25 16 0 32 0 15 0
26 14 1 22 22 18 18
27 11 0 21 0 21 0
28 16 1 21 21 25 25
29 13 0 21 0 23 0
30 17 1 22 22 23 23
31 15 1 21 21 19 19
32 14 0 21 0 18 0
33 16 0 21 0 18 0
34 9 0 22 0 26 0
35 15 0 21 0 18 0
36 17 1 21 21 18 18
37 13 0 21 0 28 0
38 15 0 21 0 17 0
39 16 1 23 23 29 29
40 16 0 21 0 12 0
41 12 1 23 23 28 28
42 11 1 23 23 20 20
43 15 1 21 21 17 17
44 17 1 20 20 17 17
45 13 0 21 0 20 0
46 16 1 20 20 31 31
47 14 0 21 0 21 0
48 11 0 21 0 19 0
49 12 1 22 22 23 23
50 12 0 21 0 15 0
51 15 1 21 21 24 24
52 16 1 22 22 28 28
53 15 1 20 20 16 16
54 12 0 22 0 19 0
55 12 1 22 22 21 21
56 8 0 21 0 21 0
57 13 0 23 0 20 0
58 11 1 22 22 16 16
59 14 1 24 24 25 25
60 15 1 23 23 30 30
61 10 0 21 0 29 0
62 11 1 22 22 22 22
63 12 0 22 0 19 0
64 15 1 21 21 33 33
65 15 0 21 0 17 0
66 14 0 21 0 9 0
67 16 1 21 21 14 14
68 15 1 20 20 15 15
69 15 0 22 0 12 0
70 13 0 22 0 21 0
71 17 1 22 22 20 20
72 13 1 23 23 29 29
73 15 0 21 0 33 0
74 13 0 23 0 21 0
75 15 0 22 0 15 0
76 16 0 21 0 19 0
77 15 1 21 21 23 23
78 16 0 20 0 20 0
79 15 1 24 24 20 20
80 14 1 24 24 18 18
81 15 0 21 0 31 0
82 7 1 20 20 18 18
83 17 1 21 21 13 13
84 13 1 21 21 9 9
85 15 1 21 21 20 20
86 14 1 21 21 18 18
87 13 1 22 22 23 23
88 16 1 22 22 17 17
89 12 1 21 21 17 17
90 14 1 22 22 16 16
91 17 0 21 0 31 0
92 15 0 23 0 15 0
93 17 1 21 21 28 28
94 12 0 22 0 26 0
95 16 1 22 22 20 20
96 11 0 22 0 19 0
97 15 1 20 20 25 25
98 9 0 21 0 18 0
99 16 1 21 21 20 20
100 10 0 22 0 33 0
101 10 1 25 25 24 24
102 15 1 22 22 22 22
103 11 1 22 22 32 32
104 13 1 21 21 31 31
105 14 0 22 0 13 0
106 18 1 21 21 18 18
107 16 0 24 0 17 0
108 14 1 23 23 29 29
109 14 1 0 0 22 22
110 14 1 23 23 18 18
111 14 1 22 22 22 22
112 12 1 22 22 25 25
113 14 1 25 25 20 20
114 15 1 23 23 20 20
115 15 0 22 0 17 0
116 13 1 21 21 26 26
117 17 0 21 0 10 0
118 17 1 22 22 15 15
119 19 1 22 22 20 20
120 15 1 21 21 14 14
121 13 0 0 0 16 0
122 9 0 21 0 23 0
123 15 1 22 22 11 11
124 15 0 21 0 19 0
125 16 1 24 24 30 30
126 11 0 21 0 21 0
127 14 0 23 0 20 0
128 11 1 23 23 22 22
129 15 1 22 22 30 30
130 13 0 21 0 25 0
131 16 0 21 0 23 0
132 14 1 21 21 23 23
133 15 0 21 0 21 0
134 16 1 22 22 30 30
135 16 1 20 20 22 22
136 11 0 21 0 32 0
137 13 1 23 23 22 22
138 16 0 32 0 15 0
139 12 1 22 22 21 21
140 9 1 24 24 27 27
141 13 1 20 20 22 22
142 13 1 21 21 9 9
143 19 1 22 22 20 20
144 13 1 23 23 16 16
Doubts_about_actions Doubts_about_actions_p Parental_expectations
1 9 9 15
2 9 9 15
3 9 9 14
4 14 0 10
5 8 8 10
6 8 8 12
7 11 11 18
8 10 10 12
9 9 9 14
10 15 15 18
11 14 0 9
12 11 11 11
13 14 0 11
14 6 6 17
15 20 0 8
16 9 0 16
17 10 10 21
18 8 0 24
19 11 11 21
20 14 0 14
21 11 11 7
22 16 16 18
23 14 0 18
24 11 11 13
25 11 0 11
26 12 12 13
27 9 0 13
28 7 7 18
29 13 0 14
30 10 10 12
31 9 9 9
32 9 0 12
33 13 0 8
34 16 0 5
35 12 0 10
36 6 6 11
37 14 0 11
38 14 0 12
39 10 10 12
40 4 0 15
41 12 12 16
42 14 14 14
43 9 9 17
44 9 9 13
45 10 0 10
46 14 14 17
47 10 0 12
48 9 0 13
49 14 14 13
50 8 0 11
51 9 9 13
52 8 8 12
53 9 9 12
54 9 0 12
55 9 9 9
56 15 0 7
57 8 0 17
58 10 10 12
59 8 8 12
60 14 14 9
61 11 0 9
62 10 10 13
63 12 0 10
64 14 14 11
65 9 0 12
66 13 0 10
67 15 15 13
68 8 8 6
69 7 0 7
70 10 0 13
71 10 10 11
72 13 13 18
73 11 0 9
74 8 0 9
75 12 0 11
76 9 0 11
77 10 10 15
78 11 0 8
79 11 11 11
80 10 10 14
81 16 0 14
82 16 16 12
83 8 8 12
84 6 6 8
85 11 11 11
86 12 12 10
87 14 14 17
88 9 9 16
89 11 11 13
90 8 8 15
91 8 0 11
92 7 0 12
93 16 16 16
94 13 0 20
95 8 8 16
96 11 0 11
97 14 14 15
98 10 0 15
99 10 10 12
100 14 0 9
101 14 14 24
102 10 10 15
103 12 12 18
104 9 9 17
105 16 0 12
106 8 8 15
107 9 0 11
108 16 16 11
109 13 13 15
110 13 13 12
111 8 8 14
112 14 14 11
113 11 11 20
114 9 9 11
115 8 0 12
116 13 13 12
117 10 0 11
118 8 8 10
119 7 7 11
120 11 11 12
121 11 0 9
122 14 0 8
123 6 6 6
124 10 0 12
125 9 9 15
126 12 0 13
127 11 0 17
128 14 14 14
129 12 12 16
130 14 0 15
131 14 0 11
132 8 8 11
133 11 0 16
134 12 12 15
135 9 9 14
136 16 0 9
137 11 11 13
138 11 0 11
139 12 12 14
140 15 15 11
141 13 13 12
142 6 6 8
143 7 7 11
144 8 8 13
Parental_expectations_p Parental_criticism Parental_criticism_p Popularity
1 15 6 6 11
2 15 6 6 12
3 14 13 13 15
4 0 8 0 10
5 10 7 7 12
6 12 9 9 11
7 18 5 5 5
8 12 8 8 16
9 14 9 9 11
10 18 11 11 15
11 0 8 0 12
12 11 11 11 9
13 0 12 0 11
14 17 8 8 15
15 0 7 0 12
16 0 9 0 16
17 21 12 12 14
18 0 20 0 11
19 21 7 7 10
20 0 8 0 7
21 7 8 8 11
22 18 16 16 10
23 0 10 0 11
24 13 6 6 16
25 0 8 0 14
26 13 9 9 12
27 0 9 0 12
28 18 11 11 11
29 0 12 0 6
30 12 8 8 14
31 9 7 7 9
32 0 8 0 15
33 0 9 0 12
34 0 4 0 12
35 0 8 0 9
36 11 8 8 13
37 0 8 0 15
38 0 6 0 11
39 12 8 8 10
40 0 4 0 13
41 16 14 14 16
42 14 10 10 13
43 17 9 9 14
44 13 6 6 14
45 0 8 0 16
46 17 11 11 9
47 0 8 0 8
48 0 8 0 8
49 13 10 10 12
50 0 8 0 10
51 13 10 10 16
52 12 7 7 13
53 12 8 8 11
54 0 7 0 14
55 9 9 9 15
56 0 5 0 8
57 0 7 0 9
58 12 7 7 17
59 12 7 7 9
60 9 9 9 13
61 0 5 0 6
62 13 8 8 13
63 0 8 0 8
64 11 8 8 12
65 0 9 0 13
66 0 6 0 14
67 13 8 8 11
68 6 6 6 15
69 0 4 0 7
70 0 6 0 16
71 11 4 4 16
72 18 12 12 14
73 0 6 0 11
74 0 11 0 13
75 0 8 0 13
76 0 10 0 7
77 15 10 10 15
78 0 4 0 11
79 11 8 8 15
80 14 9 9 13
81 0 9 0 11
82 12 7 7 12
83 12 7 7 10
84 8 11 11 12
85 11 8 8 12
86 10 8 8 12
87 17 7 7 14
88 16 5 5 6
89 13 7 7 14
90 15 9 9 15
91 0 8 0 8
92 0 6 0 12
93 16 8 8 10
94 0 10 0 15
95 16 10 10 11
96 0 8 0 9
97 15 11 11 14
98 0 8 0 10
99 12 8 8 16
100 0 6 0 5
101 24 20 20 8
102 15 6 6 13
103 18 12 12 16
104 17 9 9 16
105 0 5 0 14
106 15 10 10 14
107 0 5 0 10
108 11 6 6 9
109 15 10 10 14
110 12 6 6 8
111 14 10 10 8
112 11 5 5 16
113 20 13 13 12
114 11 7 7 9
115 0 9 0 15
116 12 8 8 12
117 0 5 0 14
118 10 4 4 12
119 11 9 9 16
120 12 7 7 12
121 0 5 0 14
122 0 5 0 8
123 6 4 4 15
124 0 7 0 16
125 15 9 9 12
126 0 8 0 4
127 0 8 0 8
128 14 11 11 11
129 16 10 10 4
130 0 9 0 14
131 0 10 0 14
132 11 10 10 13
133 0 7 0 14
134 15 10 10 7
135 14 6 6 19
136 0 6 0 12
137 13 11 11 10
138 0 8 0 14
139 14 9 9 16
140 11 9 9 11
141 12 13 13 16
142 8 11 11 12
143 11 9 9 16
144 13 5 5 12
Popularity_p Perceived_learning_competence Perceived_learning_competence_p
1 11 13 13
2 12 16 16
3 15 19 19
4 0 15 0
5 12 14 14
6 11 13 13
7 5 19 19
8 16 15 15
9 11 14 14
10 15 15 15
11 0 16 0
12 9 16 16
13 0 16 0
14 15 17 17
15 0 15 0
16 0 15 0
17 14 20 20
18 0 18 0
19 10 16 16
20 0 16 0
21 11 19 19
22 10 16 16
23 0 17 0
24 16 17 17
25 0 16 0
26 12 15 15
27 0 14 0
28 11 15 15
29 0 12 0
30 14 14 14
31 9 16 16
32 0 14 0
33 0 7 0
34 0 10 0
35 0 14 0
36 13 16 16
37 0 16 0
38 0 16 0
39 10 14 14
40 0 20 0
41 16 14 14
42 13 11 11
43 14 15 15
44 14 16 16
45 0 14 0
46 9 16 16
47 0 14 0
48 0 12 0
49 12 16 16
50 0 9 0
51 16 14 14
52 13 16 16
53 11 16 16
54 0 15 0
55 15 16 16
56 0 12 0
57 0 16 0
58 17 16 16
59 9 14 14
60 13 16 16
61 0 17 0
62 13 18 18
63 0 18 0
64 12 12 12
65 0 16 0
66 0 10 0
67 11 14 14
68 15 18 18
69 0 18 0
70 0 16 0
71 16 16 16
72 14 16 16
73 0 13 0
74 0 16 0
75 0 16 0
76 0 20 0
77 15 16 16
78 0 15 0
79 15 15 15
80 13 16 16
81 0 14 0
82 12 15 15
83 10 12 12
84 12 17 17
85 12 16 16
86 12 15 15
87 14 13 13
88 6 16 16
89 14 16 16
90 15 16 16
91 0 16 0
92 0 14 0
93 10 16 16
94 0 16 0
95 11 20 20
96 0 15 0
97 14 16 16
98 0 13 0
99 16 17 17
100 0 16 0
101 8 12 12
102 13 16 16
103 16 16 16
104 16 17 17
105 0 13 0
106 14 12 12
107 0 18 0
108 9 14 14
109 14 14 14
110 8 13 13
111 8 16 16
112 16 13 13
113 12 16 16
114 9 13 13
115 0 16 0
116 12 16 16
117 0 15 0
118 12 17 17
119 16 15 15
120 12 12 12
121 0 16 0
122 0 10 0
123 15 16 16
124 0 14 0
125 12 15 15
126 0 13 0
127 0 15 0
128 11 11 11
129 4 12 12
130 0 8 0
131 0 15 0
132 13 17 17
133 0 16 0
134 7 10 10
135 19 18 18
136 0 13 0
137 10 15 15
138 0 16 0
139 16 16 16
140 11 14 14
141 16 10 10
142 12 17 17
143 16 15 15
144 12 16 16
Amotivation Amotivation_p
1 4 4
2 4 4
3 6 6
4 8 0
5 8 8
6 4 4
7 4 4
8 5 5
9 5 5
10 8 8
11 4 0
12 4 4
13 4 0
14 4 4
15 4 0
16 8 0
17 4 4
18 4 0
19 4 4
20 4 0
21 8 8
22 3 3
23 4 0
24 4 4
25 4 0
26 10 10
27 5 0
28 4 4
29 4 0
30 4 4
31 4 4
32 4 0
33 10 0
34 4 0
35 8 0
36 4 4
37 4 0
38 4 0
39 7 7
40 4 0
41 4 4
42 4 4
43 4 4
44 6 6
45 5 0
46 16 16
47 5 0
48 12 0
49 6 6
50 9 0
51 9 9
52 4 4
53 4 4
54 4 0
55 5 5
56 4 0
57 5 0
58 4 4
59 6 6
60 4 4
61 4 0
62 18 18
63 4 0
64 4 4
65 6 0
66 4 0
67 5 5
68 4 4
69 4 0
70 5 0
71 5 5
72 8 8
73 5 0
74 4 0
75 4 0
76 4 0
77 5 5
78 4 0
79 4 4
80 4 4
81 8 0
82 14 14
83 4 4
84 8 8
85 8 8
86 4 4
87 6 6
88 4 4
89 7 7
90 3 3
91 4 0
92 4 0
93 4 4
94 7 0
95 4 4
96 4 0
97 6 6
98 8 0
99 4 4
100 4 0
101 4 4
102 5 5
103 6 6
104 4 4
105 5 0
106 7 7
107 4 0
108 8 8
109 6 6
110 8 8
111 8 8
112 4 4
113 5 5
114 6 6
115 5 0
116 5 5
117 4 0
118 4 4
119 6 6
120 7 7
121 4 0
122 10 0
123 8 8
124 5 0
125 11 11
126 7 0
127 4 0
128 8 8
129 6 6
130 4 0
131 8 0
132 5 5
133 4 0
134 8 8
135 4 4
136 6 0
137 4 4
138 4 0
139 6 6
140 15 15
141 16 16
142 8 8
143 6 6
144 4 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop
7.803059 17.416371
Age Age_p
0.057917 -0.156072
Concern_over_mistakes Concern_over_mistakes_p
-0.092195 0.149225
Doubts_about_actions Doubts_about_actions_p
0.011622 -0.445948
Parental_expectations Parental_expectations_p
0.044157 -0.009354
Parental_criticism Parental_criticism_p
0.047104 -0.194861
Popularity Popularity_p
0.151966 -0.265854
Perceived_learning_competence Perceived_learning_competence_p
0.275841 -0.419494
Amotivation Amotivation_p
-0.059154 -0.092356
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.7711 -1.3616 -0.0353 1.1223 4.9794
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.803059 3.855283 2.024 0.045086 *
Pop 17.416371 5.139877 3.388 0.000938 ***
Age 0.057917 0.080771 0.717 0.474672
Age_p -0.156072 0.120191 -1.299 0.196475
Concern_over_mistakes -0.092195 0.057377 -1.607 0.110594
Concern_over_mistakes_p 0.149225 0.076320 1.955 0.052768 .
Doubts_about_actions 0.011622 0.116138 0.100 0.920451
Doubts_about_actions_p -0.445948 0.156561 -2.848 0.005133 **
Parental_expectations 0.044157 0.113091 0.390 0.696857
Parental_expectations_p -0.009354 0.144955 -0.065 0.948648
Parental_criticism 0.047104 0.144518 0.326 0.745012
Parental_criticism_p -0.194861 0.176933 -1.101 0.272855
Popularity 0.151966 0.097226 1.563 0.120557
Popularity_p -0.265854 0.128616 -2.067 0.040779 *
Perceived_learning_competence 0.275841 0.136637 2.019 0.045631 *
Perceived_learning_competence_p -0.419494 0.182803 -2.295 0.023399 *
Amotivation -0.059154 0.171926 -0.344 0.731369
Amotivation_p -0.092356 0.190163 -0.486 0.628048
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.119 on 126 degrees of freedom
Multiple R-squared: 0.3038, Adjusted R-squared: 0.2098
F-statistic: 3.234 on 17 and 126 DF, p-value: 7.94e-05
> 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,] 0.9433012 0.11339751 0.05669876
[2,] 0.9842330 0.03153408 0.01576704
[3,] 0.9712400 0.05752000 0.02876000
[4,] 0.9869534 0.02609322 0.01304661
[5,] 0.9754345 0.04913092 0.02456546
[6,] 0.9718670 0.05626594 0.02813297
[7,] 0.9602286 0.07954284 0.03977142
[8,] 0.9443344 0.11133119 0.05566559
[9,] 0.9249286 0.15014287 0.07507144
[10,] 0.9278866 0.14422681 0.07211340
[11,] 0.8961190 0.20776210 0.10388105
[12,] 0.9055081 0.18898390 0.09449195
[13,] 0.9633721 0.07325590 0.03662795
[14,] 0.9509469 0.09810628 0.04905314
[15,] 0.9475609 0.10487822 0.05243911
[16,] 0.9288689 0.14226220 0.07113110
[17,] 0.9278163 0.14436747 0.07218374
[18,] 0.9258873 0.14822536 0.07411268
[19,] 0.9030133 0.19397330 0.09698665
[20,] 0.8729609 0.25407830 0.12703915
[21,] 0.8584022 0.28319552 0.14159776
[22,] 0.8780599 0.24388013 0.12194006
[23,] 0.8470172 0.30596560 0.15298280
[24,] 0.8335102 0.33297956 0.16648978
[25,] 0.8162200 0.36756003 0.18378002
[26,] 0.8354563 0.32908747 0.16454373
[27,] 0.8021920 0.39561605 0.19780803
[28,] 0.8745200 0.25096009 0.12548004
[29,] 0.8444576 0.31108480 0.15554240
[30,] 0.8143699 0.37126020 0.18563010
[31,] 0.7743159 0.45136814 0.22568407
[32,] 0.7373908 0.52521830 0.26260915
[33,] 0.6928037 0.61439251 0.30719625
[34,] 0.6961574 0.60768523 0.30384262
[35,] 0.7060370 0.58792597 0.29396298
[36,] 0.8259081 0.34818376 0.17409188
[37,] 0.8075401 0.38491988 0.19245994
[38,] 0.8383195 0.32336102 0.16168051
[39,] 0.8551077 0.28978457 0.14489228
[40,] 0.8848998 0.23020043 0.11510021
[41,] 0.8878283 0.22434349 0.11217174
[42,] 0.8952591 0.20948172 0.10474086
[43,] 0.8876644 0.22467117 0.11233559
[44,] 0.8625903 0.27481945 0.13740972
[45,] 0.8374196 0.32516079 0.16258039
[46,] 0.8100126 0.37997472 0.18998736
[47,] 0.8616520 0.27669599 0.13834800
[48,] 0.8315273 0.33694548 0.16847274
[49,] 0.8222682 0.35546369 0.17773184
[50,] 0.8241561 0.35168771 0.17584386
[51,] 0.8418505 0.31629902 0.15814951
[52,] 0.8095861 0.38082784 0.19041392
[53,] 0.8750901 0.24981976 0.12490988
[54,] 0.8812716 0.23745687 0.11872843
[55,] 0.8533247 0.29335067 0.14667534
[56,] 0.8335720 0.33285592 0.16642796
[57,] 0.7991605 0.40167902 0.20083951
[58,] 0.8099838 0.38003247 0.19001623
[59,] 0.7804944 0.43901115 0.21950557
[60,] 0.7377268 0.52454645 0.26227323
[61,] 0.7821473 0.43570542 0.21785271
[62,] 0.8588670 0.28226597 0.14113299
[63,] 0.8271060 0.34578805 0.17289402
[64,] 0.8126411 0.37471775 0.18735888
[65,] 0.7860139 0.42797212 0.21398606
[66,] 0.7423971 0.51520587 0.25760294
[67,] 0.6965791 0.60684175 0.30342087
[68,] 0.6475641 0.70487171 0.35243586
[69,] 0.6281053 0.74378932 0.37189466
[70,] 0.5923810 0.81523810 0.40761905
[71,] 0.7450309 0.50993820 0.25496910
[72,] 0.7027863 0.59442736 0.29721368
[73,] 0.8584589 0.28308224 0.14154112
[74,] 0.8605099 0.27898015 0.13949008
[75,] 0.8564402 0.28711962 0.14355981
[76,] 0.8773990 0.24520198 0.12260099
[77,] 0.9287118 0.14257649 0.07128825
[78,] 0.9677276 0.06454473 0.03227237
[79,] 0.9716578 0.05668444 0.02834222
[80,] 0.9626806 0.07463884 0.03731942
[81,] 0.9556781 0.08864376 0.04432188
[82,] 0.9371793 0.12564150 0.06282075
[83,] 0.9385867 0.12282665 0.06141333
[84,] 0.9693595 0.06128095 0.03064047
[85,] 0.9625880 0.07482390 0.03741195
[86,] 0.9527227 0.09455466 0.04727733
[87,] 0.9533829 0.09323415 0.04661707
[88,] 0.9809816 0.03803671 0.01901836
[89,] 0.9722425 0.05551509 0.02775755
[90,] 0.9839387 0.03212251 0.01606125
[91,] 0.9779439 0.04411218 0.02205609
[92,] 0.9735933 0.05281337 0.02640669
[93,] 0.9789401 0.04211978 0.02105989
[94,] 0.9689623 0.06207537 0.03103769
[95,] 0.9464984 0.10700323 0.05350161
[96,] 0.9133336 0.17333287 0.08666644
[97,] 0.9037759 0.19244825 0.09622412
[98,] 0.9557250 0.08855001 0.04427500
[99,] 0.9437897 0.11242069 0.05621034
[100,] 0.9567454 0.08650913 0.04325456
[101,] 0.9114055 0.17718890 0.08859445
[102,] 0.8257382 0.34852365 0.17426183
[103,] 0.6797981 0.64040389 0.32020195
> postscript(file="/var/www/html/rcomp/tmp/1muex1290549744.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fmdi1290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fmdi1290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fmdi1290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fmdi1290549744.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 = 144
Frequency = 1
1 2 3 4 5 6
-2.444903811 2.245810250 -2.666478389 -0.326414369 0.689427695 2.026532812
7 8 9 10 11 12
-1.846137955 -0.472437219 -0.639859864 2.678766249 2.368315427 4.878914985
13 14 15 16 17 18
-3.868776962 2.021456110 0.034468925 -0.437739942 2.631071515 -1.106577532
19 20 21 22 23 24
-0.004601356 4.979420693 0.522673292 0.191127752 3.101462787 -5.771122771
25 26 27 28 29 30
0.431767830 1.039287254 -2.575386624 -0.531157980 0.781372810 1.947594771
31 32 33 34 35 36
-0.682247612 -0.275762771 4.548989286 -2.620688634 1.926100353 0.605504979
37 38 39 40 41 42
-0.919445247 0.724323651 0.702550216 -0.065997848 -1.395643570 -2.364794092
43 44 45 46 47 48
-0.125313674 1.919145534 -1.107491221 3.837589568 1.112117339 -1.139046771
49 50 51 52 53 54
-0.691840103 -0.061761182 0.604118119 -0.180545743 -0.338190944 -2.318255619
55 56 57 58 59 60
-2.567802335 -4.091363105 -0.949999629 -3.171983867 -2.252980959 1.809429857
61 62 63 64 65 66
-2.470905774 -1.448315807 -2.227637101 0.536166618 0.455496023 0.285849468
67 68 69 70 71 72
3.309382772 -0.059325649 0.657959335 -1.663159844 2.229051799 0.282103175
73 74 75 76 77 78
3.253459838 -1.359979092 0.151277653 1.327062264 0.593246352 2.640368992
79 80 81 82 83 84
1.041665852 -0.319375576 2.550484117 -4.074390282 0.660472666 -1.697741673
85 86 87 88 89 90
1.155227991 -0.011275471 -0.477505730 -0.350830017 -1.814779742 -1.228817355
91 92 93 94 95 96
4.490631695 0.905168084 3.862789074 -2.464292566 0.926587251 -2.584615008
97 98 99 100 101 102
2.303714329 -4.147568394 1.679262575 -1.814203311 -2.692851697 -0.070370017
103 104 105 106 107 108
-2.496713007 -2.408651336 -0.247732325 2.624264341 1.300226633 1.085419166
109 110 111 112 113 114
-1.357700406 0.117426045 -1.428101093 -1.753504865 0.518877006 -0.740510815
115 116 117 118 119 120
0.046114795 -0.807631977 2.036649052 1.216944356 3.672713803 0.588726342
121 122 123 124 125 126
-0.393081380 -2.032901141 -0.480326139 0.759102758 1.330165215 -0.953269376
127 128 129 130 131 132
0.336684949 -1.952832876 -0.549823784 0.938932632 2.189795689 -1.220316093
133 134 135 136 137 138
0.448336818 0.842356455 1.152917104 -0.989654958 -1.366324228 0.431767830
139 140 141 142 143 144
-1.173441719 -2.405076558 1.321357979 -1.697741673 3.672713803 -2.842235195
> postscript(file="/var/www/html/rcomp/tmp/6qvck1290549744.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.444903811 NA
1 2.245810250 -2.444903811
2 -2.666478389 2.245810250
3 -0.326414369 -2.666478389
4 0.689427695 -0.326414369
5 2.026532812 0.689427695
6 -1.846137955 2.026532812
7 -0.472437219 -1.846137955
8 -0.639859864 -0.472437219
9 2.678766249 -0.639859864
10 2.368315427 2.678766249
11 4.878914985 2.368315427
12 -3.868776962 4.878914985
13 2.021456110 -3.868776962
14 0.034468925 2.021456110
15 -0.437739942 0.034468925
16 2.631071515 -0.437739942
17 -1.106577532 2.631071515
18 -0.004601356 -1.106577532
19 4.979420693 -0.004601356
20 0.522673292 4.979420693
21 0.191127752 0.522673292
22 3.101462787 0.191127752
23 -5.771122771 3.101462787
24 0.431767830 -5.771122771
25 1.039287254 0.431767830
26 -2.575386624 1.039287254
27 -0.531157980 -2.575386624
28 0.781372810 -0.531157980
29 1.947594771 0.781372810
30 -0.682247612 1.947594771
31 -0.275762771 -0.682247612
32 4.548989286 -0.275762771
33 -2.620688634 4.548989286
34 1.926100353 -2.620688634
35 0.605504979 1.926100353
36 -0.919445247 0.605504979
37 0.724323651 -0.919445247
38 0.702550216 0.724323651
39 -0.065997848 0.702550216
40 -1.395643570 -0.065997848
41 -2.364794092 -1.395643570
42 -0.125313674 -2.364794092
43 1.919145534 -0.125313674
44 -1.107491221 1.919145534
45 3.837589568 -1.107491221
46 1.112117339 3.837589568
47 -1.139046771 1.112117339
48 -0.691840103 -1.139046771
49 -0.061761182 -0.691840103
50 0.604118119 -0.061761182
51 -0.180545743 0.604118119
52 -0.338190944 -0.180545743
53 -2.318255619 -0.338190944
54 -2.567802335 -2.318255619
55 -4.091363105 -2.567802335
56 -0.949999629 -4.091363105
57 -3.171983867 -0.949999629
58 -2.252980959 -3.171983867
59 1.809429857 -2.252980959
60 -2.470905774 1.809429857
61 -1.448315807 -2.470905774
62 -2.227637101 -1.448315807
63 0.536166618 -2.227637101
64 0.455496023 0.536166618
65 0.285849468 0.455496023
66 3.309382772 0.285849468
67 -0.059325649 3.309382772
68 0.657959335 -0.059325649
69 -1.663159844 0.657959335
70 2.229051799 -1.663159844
71 0.282103175 2.229051799
72 3.253459838 0.282103175
73 -1.359979092 3.253459838
74 0.151277653 -1.359979092
75 1.327062264 0.151277653
76 0.593246352 1.327062264
77 2.640368992 0.593246352
78 1.041665852 2.640368992
79 -0.319375576 1.041665852
80 2.550484117 -0.319375576
81 -4.074390282 2.550484117
82 0.660472666 -4.074390282
83 -1.697741673 0.660472666
84 1.155227991 -1.697741673
85 -0.011275471 1.155227991
86 -0.477505730 -0.011275471
87 -0.350830017 -0.477505730
88 -1.814779742 -0.350830017
89 -1.228817355 -1.814779742
90 4.490631695 -1.228817355
91 0.905168084 4.490631695
92 3.862789074 0.905168084
93 -2.464292566 3.862789074
94 0.926587251 -2.464292566
95 -2.584615008 0.926587251
96 2.303714329 -2.584615008
97 -4.147568394 2.303714329
98 1.679262575 -4.147568394
99 -1.814203311 1.679262575
100 -2.692851697 -1.814203311
101 -0.070370017 -2.692851697
102 -2.496713007 -0.070370017
103 -2.408651336 -2.496713007
104 -0.247732325 -2.408651336
105 2.624264341 -0.247732325
106 1.300226633 2.624264341
107 1.085419166 1.300226633
108 -1.357700406 1.085419166
109 0.117426045 -1.357700406
110 -1.428101093 0.117426045
111 -1.753504865 -1.428101093
112 0.518877006 -1.753504865
113 -0.740510815 0.518877006
114 0.046114795 -0.740510815
115 -0.807631977 0.046114795
116 2.036649052 -0.807631977
117 1.216944356 2.036649052
118 3.672713803 1.216944356
119 0.588726342 3.672713803
120 -0.393081380 0.588726342
121 -2.032901141 -0.393081380
122 -0.480326139 -2.032901141
123 0.759102758 -0.480326139
124 1.330165215 0.759102758
125 -0.953269376 1.330165215
126 0.336684949 -0.953269376
127 -1.952832876 0.336684949
128 -0.549823784 -1.952832876
129 0.938932632 -0.549823784
130 2.189795689 0.938932632
131 -1.220316093 2.189795689
132 0.448336818 -1.220316093
133 0.842356455 0.448336818
134 1.152917104 0.842356455
135 -0.989654958 1.152917104
136 -1.366324228 -0.989654958
137 0.431767830 -1.366324228
138 -1.173441719 0.431767830
139 -2.405076558 -1.173441719
140 1.321357979 -2.405076558
141 -1.697741673 1.321357979
142 3.672713803 -1.697741673
143 -2.842235195 3.672713803
144 NA -2.842235195
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.245810250 -2.444903811
[2,] -2.666478389 2.245810250
[3,] -0.326414369 -2.666478389
[4,] 0.689427695 -0.326414369
[5,] 2.026532812 0.689427695
[6,] -1.846137955 2.026532812
[7,] -0.472437219 -1.846137955
[8,] -0.639859864 -0.472437219
[9,] 2.678766249 -0.639859864
[10,] 2.368315427 2.678766249
[11,] 4.878914985 2.368315427
[12,] -3.868776962 4.878914985
[13,] 2.021456110 -3.868776962
[14,] 0.034468925 2.021456110
[15,] -0.437739942 0.034468925
[16,] 2.631071515 -0.437739942
[17,] -1.106577532 2.631071515
[18,] -0.004601356 -1.106577532
[19,] 4.979420693 -0.004601356
[20,] 0.522673292 4.979420693
[21,] 0.191127752 0.522673292
[22,] 3.101462787 0.191127752
[23,] -5.771122771 3.101462787
[24,] 0.431767830 -5.771122771
[25,] 1.039287254 0.431767830
[26,] -2.575386624 1.039287254
[27,] -0.531157980 -2.575386624
[28,] 0.781372810 -0.531157980
[29,] 1.947594771 0.781372810
[30,] -0.682247612 1.947594771
[31,] -0.275762771 -0.682247612
[32,] 4.548989286 -0.275762771
[33,] -2.620688634 4.548989286
[34,] 1.926100353 -2.620688634
[35,] 0.605504979 1.926100353
[36,] -0.919445247 0.605504979
[37,] 0.724323651 -0.919445247
[38,] 0.702550216 0.724323651
[39,] -0.065997848 0.702550216
[40,] -1.395643570 -0.065997848
[41,] -2.364794092 -1.395643570
[42,] -0.125313674 -2.364794092
[43,] 1.919145534 -0.125313674
[44,] -1.107491221 1.919145534
[45,] 3.837589568 -1.107491221
[46,] 1.112117339 3.837589568
[47,] -1.139046771 1.112117339
[48,] -0.691840103 -1.139046771
[49,] -0.061761182 -0.691840103
[50,] 0.604118119 -0.061761182
[51,] -0.180545743 0.604118119
[52,] -0.338190944 -0.180545743
[53,] -2.318255619 -0.338190944
[54,] -2.567802335 -2.318255619
[55,] -4.091363105 -2.567802335
[56,] -0.949999629 -4.091363105
[57,] -3.171983867 -0.949999629
[58,] -2.252980959 -3.171983867
[59,] 1.809429857 -2.252980959
[60,] -2.470905774 1.809429857
[61,] -1.448315807 -2.470905774
[62,] -2.227637101 -1.448315807
[63,] 0.536166618 -2.227637101
[64,] 0.455496023 0.536166618
[65,] 0.285849468 0.455496023
[66,] 3.309382772 0.285849468
[67,] -0.059325649 3.309382772
[68,] 0.657959335 -0.059325649
[69,] -1.663159844 0.657959335
[70,] 2.229051799 -1.663159844
[71,] 0.282103175 2.229051799
[72,] 3.253459838 0.282103175
[73,] -1.359979092 3.253459838
[74,] 0.151277653 -1.359979092
[75,] 1.327062264 0.151277653
[76,] 0.593246352 1.327062264
[77,] 2.640368992 0.593246352
[78,] 1.041665852 2.640368992
[79,] -0.319375576 1.041665852
[80,] 2.550484117 -0.319375576
[81,] -4.074390282 2.550484117
[82,] 0.660472666 -4.074390282
[83,] -1.697741673 0.660472666
[84,] 1.155227991 -1.697741673
[85,] -0.011275471 1.155227991
[86,] -0.477505730 -0.011275471
[87,] -0.350830017 -0.477505730
[88,] -1.814779742 -0.350830017
[89,] -1.228817355 -1.814779742
[90,] 4.490631695 -1.228817355
[91,] 0.905168084 4.490631695
[92,] 3.862789074 0.905168084
[93,] -2.464292566 3.862789074
[94,] 0.926587251 -2.464292566
[95,] -2.584615008 0.926587251
[96,] 2.303714329 -2.584615008
[97,] -4.147568394 2.303714329
[98,] 1.679262575 -4.147568394
[99,] -1.814203311 1.679262575
[100,] -2.692851697 -1.814203311
[101,] -0.070370017 -2.692851697
[102,] -2.496713007 -0.070370017
[103,] -2.408651336 -2.496713007
[104,] -0.247732325 -2.408651336
[105,] 2.624264341 -0.247732325
[106,] 1.300226633 2.624264341
[107,] 1.085419166 1.300226633
[108,] -1.357700406 1.085419166
[109,] 0.117426045 -1.357700406
[110,] -1.428101093 0.117426045
[111,] -1.753504865 -1.428101093
[112,] 0.518877006 -1.753504865
[113,] -0.740510815 0.518877006
[114,] 0.046114795 -0.740510815
[115,] -0.807631977 0.046114795
[116,] 2.036649052 -0.807631977
[117,] 1.216944356 2.036649052
[118,] 3.672713803 1.216944356
[119,] 0.588726342 3.672713803
[120,] -0.393081380 0.588726342
[121,] -2.032901141 -0.393081380
[122,] -0.480326139 -2.032901141
[123,] 0.759102758 -0.480326139
[124,] 1.330165215 0.759102758
[125,] -0.953269376 1.330165215
[126,] 0.336684949 -0.953269376
[127,] -1.952832876 0.336684949
[128,] -0.549823784 -1.952832876
[129,] 0.938932632 -0.549823784
[130,] 2.189795689 0.938932632
[131,] -1.220316093 2.189795689
[132,] 0.448336818 -1.220316093
[133,] 0.842356455 0.448336818
[134,] 1.152917104 0.842356455
[135,] -0.989654958 1.152917104
[136,] -1.366324228 -0.989654958
[137,] 0.431767830 -1.366324228
[138,] -1.173441719 0.431767830
[139,] -2.405076558 -1.173441719
[140,] 1.321357979 -2.405076558
[141,] -1.697741673 1.321357979
[142,] 3.672713803 -1.697741673
[143,] -2.842235195 3.672713803
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.245810250 -2.444903811
2 -2.666478389 2.245810250
3 -0.326414369 -2.666478389
4 0.689427695 -0.326414369
5 2.026532812 0.689427695
6 -1.846137955 2.026532812
7 -0.472437219 -1.846137955
8 -0.639859864 -0.472437219
9 2.678766249 -0.639859864
10 2.368315427 2.678766249
11 4.878914985 2.368315427
12 -3.868776962 4.878914985
13 2.021456110 -3.868776962
14 0.034468925 2.021456110
15 -0.437739942 0.034468925
16 2.631071515 -0.437739942
17 -1.106577532 2.631071515
18 -0.004601356 -1.106577532
19 4.979420693 -0.004601356
20 0.522673292 4.979420693
21 0.191127752 0.522673292
22 3.101462787 0.191127752
23 -5.771122771 3.101462787
24 0.431767830 -5.771122771
25 1.039287254 0.431767830
26 -2.575386624 1.039287254
27 -0.531157980 -2.575386624
28 0.781372810 -0.531157980
29 1.947594771 0.781372810
30 -0.682247612 1.947594771
31 -0.275762771 -0.682247612
32 4.548989286 -0.275762771
33 -2.620688634 4.548989286
34 1.926100353 -2.620688634
35 0.605504979 1.926100353
36 -0.919445247 0.605504979
37 0.724323651 -0.919445247
38 0.702550216 0.724323651
39 -0.065997848 0.702550216
40 -1.395643570 -0.065997848
41 -2.364794092 -1.395643570
42 -0.125313674 -2.364794092
43 1.919145534 -0.125313674
44 -1.107491221 1.919145534
45 3.837589568 -1.107491221
46 1.112117339 3.837589568
47 -1.139046771 1.112117339
48 -0.691840103 -1.139046771
49 -0.061761182 -0.691840103
50 0.604118119 -0.061761182
51 -0.180545743 0.604118119
52 -0.338190944 -0.180545743
53 -2.318255619 -0.338190944
54 -2.567802335 -2.318255619
55 -4.091363105 -2.567802335
56 -0.949999629 -4.091363105
57 -3.171983867 -0.949999629
58 -2.252980959 -3.171983867
59 1.809429857 -2.252980959
60 -2.470905774 1.809429857
61 -1.448315807 -2.470905774
62 -2.227637101 -1.448315807
63 0.536166618 -2.227637101
64 0.455496023 0.536166618
65 0.285849468 0.455496023
66 3.309382772 0.285849468
67 -0.059325649 3.309382772
68 0.657959335 -0.059325649
69 -1.663159844 0.657959335
70 2.229051799 -1.663159844
71 0.282103175 2.229051799
72 3.253459838 0.282103175
73 -1.359979092 3.253459838
74 0.151277653 -1.359979092
75 1.327062264 0.151277653
76 0.593246352 1.327062264
77 2.640368992 0.593246352
78 1.041665852 2.640368992
79 -0.319375576 1.041665852
80 2.550484117 -0.319375576
81 -4.074390282 2.550484117
82 0.660472666 -4.074390282
83 -1.697741673 0.660472666
84 1.155227991 -1.697741673
85 -0.011275471 1.155227991
86 -0.477505730 -0.011275471
87 -0.350830017 -0.477505730
88 -1.814779742 -0.350830017
89 -1.228817355 -1.814779742
90 4.490631695 -1.228817355
91 0.905168084 4.490631695
92 3.862789074 0.905168084
93 -2.464292566 3.862789074
94 0.926587251 -2.464292566
95 -2.584615008 0.926587251
96 2.303714329 -2.584615008
97 -4.147568394 2.303714329
98 1.679262575 -4.147568394
99 -1.814203311 1.679262575
100 -2.692851697 -1.814203311
101 -0.070370017 -2.692851697
102 -2.496713007 -0.070370017
103 -2.408651336 -2.496713007
104 -0.247732325 -2.408651336
105 2.624264341 -0.247732325
106 1.300226633 2.624264341
107 1.085419166 1.300226633
108 -1.357700406 1.085419166
109 0.117426045 -1.357700406
110 -1.428101093 0.117426045
111 -1.753504865 -1.428101093
112 0.518877006 -1.753504865
113 -0.740510815 0.518877006
114 0.046114795 -0.740510815
115 -0.807631977 0.046114795
116 2.036649052 -0.807631977
117 1.216944356 2.036649052
118 3.672713803 1.216944356
119 0.588726342 3.672713803
120 -0.393081380 0.588726342
121 -2.032901141 -0.393081380
122 -0.480326139 -2.032901141
123 0.759102758 -0.480326139
124 1.330165215 0.759102758
125 -0.953269376 1.330165215
126 0.336684949 -0.953269376
127 -1.952832876 0.336684949
128 -0.549823784 -1.952832876
129 0.938932632 -0.549823784
130 2.189795689 0.938932632
131 -1.220316093 2.189795689
132 0.448336818 -1.220316093
133 0.842356455 0.448336818
134 1.152917104 0.842356455
135 -0.989654958 1.152917104
136 -1.366324228 -0.989654958
137 0.431767830 -1.366324228
138 -1.173441719 0.431767830
139 -2.405076558 -1.173441719
140 1.321357979 -2.405076558
141 -1.697741673 1.321357979
142 3.672713803 -1.697741673
143 -2.842235195 3.672713803
> 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/7i4u51290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8i4u51290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tet91290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tet91290549744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ew9e1290549744.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/12ixqk1290549744.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/13wo6t1290549744.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/140pnz1290549744.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/15l8l51290549744.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/166q2t1290549744.tab")
+ }
>
> try(system("convert tmp/1muex1290549744.ps tmp/1muex1290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fmdi1290549744.ps tmp/2fmdi1290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fmdi1290549744.ps tmp/3fmdi1290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fmdi1290549744.ps tmp/4fmdi1290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fmdi1290549744.ps tmp/5fmdi1290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qvck1290549744.ps tmp/6qvck1290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/7i4u51290549744.ps tmp/7i4u51290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/8i4u51290549744.ps tmp/8i4u51290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tet91290549744.ps tmp/9tet91290549744.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tet91290549744.ps tmp/10tet91290549744.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.838 1.713 10.948