R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(41
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '5'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Connected Separate Learning Software Depression Belonging
1 14 41 38 13 12 12 53
2 18 39 32 16 11 11 86
3 11 30 35 19 15 14 66
4 12 31 33 15 6 12 67
5 16 34 37 14 13 21 76
6 18 35 29 13 10 12 78
7 14 39 31 19 12 22 53
8 14 34 36 15 14 11 80
9 15 36 35 14 12 10 74
10 15 37 38 15 6 13 76
11 17 38 31 16 10 10 79
12 19 36 34 16 12 8 54
13 10 38 35 16 12 15 67
14 16 39 38 16 11 14 54
15 18 33 37 17 15 10 87
16 14 32 33 15 12 14 58
17 14 36 32 15 10 14 75
18 17 38 38 20 12 11 88
19 14 39 38 18 11 10 64
20 16 32 32 16 12 13 57
21 18 32 33 16 11 7 66
22 11 31 31 16 12 14 68
23 14 39 38 19 13 12 54
24 12 37 39 16 11 14 56
25 17 39 32 17 9 11 86
26 9 41 32 17 13 9 80
27 16 36 35 16 10 11 76
28 14 33 37 15 14 15 69
29 15 33 33 16 12 14 78
30 11 34 33 14 10 13 67
31 16 31 28 15 12 9 80
32 13 27 32 12 8 15 54
33 17 37 31 14 10 10 71
34 15 34 37 16 12 11 84
35 14 34 30 14 12 13 74
36 16 32 33 7 7 8 71
37 9 29 31 10 6 20 63
38 15 36 33 14 12 12 71
39 17 29 31 16 10 10 76
40 13 35 33 16 10 10 69
41 15 37 32 16 10 9 74
42 16 34 33 14 12 14 75
43 16 38 32 20 15 8 54
44 12 35 33 14 10 14 52
45 12 38 28 14 10 11 69
46 11 37 35 11 12 13 68
47 15 38 39 14 13 9 65
48 15 33 34 15 11 11 75
49 17 36 38 16 11 15 74
50 13 38 32 14 12 11 75
51 16 32 38 16 14 10 72
52 14 32 30 14 10 14 67
53 11 32 33 12 12 18 63
54 12 34 38 16 13 14 62
55 12 32 32 9 5 11 63
56 15 37 32 14 6 12 76
57 16 39 34 16 12 13 74
58 15 29 34 16 12 9 67
59 12 37 36 15 11 10 73
60 12 35 34 16 10 15 70
61 8 30 28 12 7 20 53
62 13 38 34 16 12 12 77
63 11 34 35 16 14 12 77
64 14 31 35 14 11 14 52
65 15 34 31 16 12 13 54
66 10 35 37 17 13 11 80
67 11 36 35 18 14 17 66
68 12 30 27 18 11 12 73
69 15 39 40 12 12 13 63
70 15 35 37 16 12 14 69
71 14 38 36 10 8 13 67
72 16 31 38 14 11 15 54
73 15 34 39 18 14 13 81
74 15 38 41 18 14 10 69
75 13 34 27 16 12 11 84
76 12 39 30 17 9 19 80
77 17 37 37 16 13 13 70
78 13 34 31 16 11 17 69
79 15 28 31 13 12 13 77
80 13 37 27 16 12 9 54
81 15 33 36 16 12 11 79
82 16 37 38 20 12 10 30
83 15 35 37 16 12 9 71
84 16 37 33 15 12 12 73
85 15 32 34 15 11 12 72
86 14 33 31 16 10 13 77
87 15 38 39 14 9 13 75
88 14 33 34 16 12 12 69
89 13 29 32 16 12 15 54
90 7 33 33 15 12 22 70
91 17 31 36 12 9 13 73
92 13 36 32 17 15 15 54
93 15 35 41 16 12 13 77
94 14 32 28 15 12 15 82
95 13 29 30 13 12 10 80
96 16 39 36 16 10 11 80
97 12 37 35 16 13 16 69
98 14 35 31 16 9 11 78
99 17 37 34 16 12 11 81
100 15 32 36 14 10 10 76
101 17 38 36 16 14 10 76
102 12 37 35 16 11 16 73
103 16 36 37 20 15 12 85
104 11 32 28 15 11 11 66
105 15 33 39 16 11 16 79
106 9 40 32 13 12 19 68
107 16 38 35 17 12 11 76
108 15 41 39 16 12 16 71
109 10 36 35 16 11 15 54
110 10 43 42 12 7 24 46
111 15 30 34 16 12 14 82
112 11 31 33 16 14 15 74
113 13 32 41 17 11 11 88
114 14 32 33 13 11 15 38
115 18 37 34 12 10 12 76
116 16 37 32 18 13 10 86
117 14 33 40 14 13 14 54
118 14 34 40 14 8 13 70
119 14 33 35 13 11 9 69
120 14 38 36 16 12 15 90
121 12 33 37 13 11 15 54
122 14 31 27 16 13 14 76
123 15 38 39 13 12 11 89
124 15 37 38 16 14 8 76
125 15 33 31 15 13 11 73
126 13 31 33 16 15 11 79
127 17 39 32 15 10 8 90
128 17 44 39 17 11 10 74
129 19 33 36 15 9 11 81
130 15 35 33 12 11 13 72
131 13 32 33 16 10 11 71
132 9 28 32 10 11 20 66
133 15 40 37 16 8 10 77
134 15 27 30 12 11 15 65
135 15 37 38 14 12 12 74
136 16 32 29 15 12 14 82
137 11 28 22 13 9 23 54
138 14 34 35 15 11 14 63
139 11 30 35 11 10 16 54
140 15 35 34 12 8 11 64
141 13 31 35 8 9 12 69
142 15 32 34 16 8 10 54
143 16 30 34 15 9 14 84
144 14 30 35 17 15 12 86
145 15 31 23 16 11 12 77
146 16 40 31 10 8 11 89
147 16 32 27 18 13 12 76
148 11 36 36 13 12 13 60
149 12 32 31 16 12 11 75
150 9 35 32 13 9 19 73
151 16 38 39 10 7 12 85
152 13 42 37 15 13 17 79
153 16 34 38 16 9 9 71
154 12 35 39 16 6 12 72
155 9 35 34 14 8 19 69
156 13 33 31 10 8 18 78
157 13 36 32 17 15 15 54
158 14 32 37 13 6 14 69
159 19 33 36 15 9 11 81
160 13 34 32 16 11 9 84
161 12 32 35 12 8 18 84
162 13 34 36 13 8 16 69
Belonging_Final M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 32 1 0 0 0 0 0 0 0 0 0 0
2 51 0 1 0 0 0 0 0 0 0 0 0
3 42 0 0 1 0 0 0 0 0 0 0 0
4 41 0 0 0 1 0 0 0 0 0 0 0
5 46 0 0 0 0 1 0 0 0 0 0 0
6 47 0 0 0 0 0 1 0 0 0 0 0
7 37 0 0 0 0 0 0 1 0 0 0 0
8 49 0 0 0 0 0 0 0 1 0 0 0
9 45 0 0 0 0 0 0 0 0 1 0 0
10 47 0 0 0 0 0 0 0 0 0 1 0
11 49 0 0 0 0 0 0 0 0 0 0 1
12 33 0 0 0 0 0 0 0 0 0 0 0
13 42 1 0 0 0 0 0 0 0 0 0 0
14 33 0 1 0 0 0 0 0 0 0 0 0
15 53 0 0 1 0 0 0 0 0 0 0 0
16 36 0 0 0 1 0 0 0 0 0 0 0
17 45 0 0 0 0 1 0 0 0 0 0 0
18 54 0 0 0 0 0 1 0 0 0 0 0
19 41 0 0 0 0 0 0 1 0 0 0 0
20 36 0 0 0 0 0 0 0 1 0 0 0
21 41 0 0 0 0 0 0 0 0 1 0 0
22 44 0 0 0 0 0 0 0 0 0 1 0
23 33 0 0 0 0 0 0 0 0 0 0 1
24 37 0 0 0 0 0 0 0 0 0 0 0
25 52 1 0 0 0 0 0 0 0 0 0 0
26 47 0 1 0 0 0 0 0 0 0 0 0
27 43 0 0 1 0 0 0 0 0 0 0 0
28 44 0 0 0 1 0 0 0 0 0 0 0
29 45 0 0 0 0 1 0 0 0 0 0 0
30 44 0 0 0 0 0 1 0 0 0 0 0
31 49 0 0 0 0 0 0 1 0 0 0 0
32 33 0 0 0 0 0 0 0 1 0 0 0
33 43 0 0 0 0 0 0 0 0 1 0 0
34 54 0 0 0 0 0 0 0 0 0 1 0
35 42 0 0 0 0 0 0 0 0 0 0 1
36 44 0 0 0 0 0 0 0 0 0 0 0
37 37 1 0 0 0 0 0 0 0 0 0 0
38 43 0 1 0 0 0 0 0 0 0 0 0
39 46 0 0 1 0 0 0 0 0 0 0 0
40 42 0 0 0 1 0 0 0 0 0 0 0
41 45 0 0 0 0 1 0 0 0 0 0 0
42 44 0 0 0 0 0 1 0 0 0 0 0
43 33 0 0 0 0 0 0 1 0 0 0 0
44 31 0 0 0 0 0 0 0 1 0 0 0
45 42 0 0 0 0 0 0 0 0 1 0 0
46 40 0 0 0 0 0 0 0 0 0 1 0
47 43 0 0 0 0 0 0 0 0 0 0 1
48 46 0 0 0 0 0 0 0 0 0 0 0
49 42 1 0 0 0 0 0 0 0 0 0 0
50 45 0 1 0 0 0 0 0 0 0 0 0
51 44 0 0 1 0 0 0 0 0 0 0 0
52 40 0 0 0 1 0 0 0 0 0 0 0
53 37 0 0 0 0 1 0 0 0 0 0 0
54 46 0 0 0 0 0 1 0 0 0 0 0
55 36 0 0 0 0 0 0 1 0 0 0 0
56 47 0 0 0 0 0 0 0 1 0 0 0
57 45 0 0 0 0 0 0 0 0 1 0 0
58 42 0 0 0 0 0 0 0 0 0 1 0
59 43 0 0 0 0 0 0 0 0 0 0 1
60 43 0 0 0 0 0 0 0 0 0 0 0
61 32 1 0 0 0 0 0 0 0 0 0 0
62 45 0 1 0 0 0 0 0 0 0 0 0
63 45 0 0 1 0 0 0 0 0 0 0 0
64 31 0 0 0 1 0 0 0 0 0 0 0
65 33 0 0 0 0 1 0 0 0 0 0 0
66 49 0 0 0 0 0 1 0 0 0 0 0
67 42 0 0 0 0 0 0 1 0 0 0 0
68 41 0 0 0 0 0 0 0 1 0 0 0
69 38 0 0 0 0 0 0 0 0 1 0 0
70 42 0 0 0 0 0 0 0 0 0 1 0
71 44 0 0 0 0 0 0 0 0 0 0 1
72 33 0 0 0 0 0 0 0 0 0 0 0
73 48 1 0 0 0 0 0 0 0 0 0 0
74 40 0 1 0 0 0 0 0 0 0 0 0
75 50 0 0 1 0 0 0 0 0 0 0 0
76 49 0 0 0 1 0 0 0 0 0 0 0
77 43 0 0 0 0 1 0 0 0 0 0 0
78 44 0 0 0 0 0 1 0 0 0 0 0
79 47 0 0 0 0 0 0 1 0 0 0 0
80 33 0 0 0 0 0 0 0 1 0 0 0
81 46 0 0 0 0 0 0 0 0 1 0 0
82 0 0 0 0 0 0 0 0 0 0 1 0
83 45 0 0 0 0 0 0 0 0 0 0 1
84 43 0 0 0 0 0 0 0 0 0 0 0
85 44 1 0 0 0 0 0 0 0 0 0 0
86 47 0 1 0 0 0 0 0 0 0 0 0
87 45 0 0 1 0 0 0 0 0 0 0 0
88 42 0 0 0 1 0 0 0 0 0 0 0
89 33 0 0 0 0 1 0 0 0 0 0 0
90 43 0 0 0 0 0 1 0 0 0 0 0
91 46 0 0 0 0 0 0 1 0 0 0 0
92 33 0 0 0 0 0 0 0 1 0 0 0
93 46 0 0 0 0 0 0 0 0 1 0 0
94 48 0 0 0 0 0 0 0 0 0 1 0
95 47 0 0 0 0 0 0 0 0 0 0 1
96 47 0 0 0 0 0 0 0 0 0 0 0
97 43 1 0 0 0 0 0 0 0 0 0 0
98 46 0 1 0 0 0 0 0 0 0 0 0
99 48 0 0 1 0 0 0 0 0 0 0 0
100 46 0 0 0 1 0 0 0 0 0 0 0
101 45 0 0 0 0 1 0 0 0 0 0 0
102 45 0 0 0 0 0 1 0 0 0 0 0
103 52 0 0 0 0 0 0 1 0 0 0 0
104 42 0 0 0 0 0 0 0 1 0 0 0
105 47 0 0 0 0 0 0 0 0 1 0 0
106 41 0 0 0 0 0 0 0 0 0 1 0
107 47 0 0 0 0 0 0 0 0 0 0 1
108 43 0 0 0 0 0 0 0 0 0 0 0
109 33 1 0 0 0 0 0 0 0 0 0 0
110 30 0 1 0 0 0 0 0 0 0 0 0
111 49 0 0 1 0 0 0 0 0 0 0 0
112 44 0 0 0 1 0 0 0 0 0 0 0
113 55 0 0 0 0 1 0 0 0 0 0 0
114 11 0 0 0 0 0 1 0 0 0 0 0
115 47 0 0 0 0 0 0 1 0 0 0 0
116 53 0 0 0 0 0 0 0 1 0 0 0
117 33 0 0 0 0 0 0 0 0 1 0 0
118 44 0 0 0 0 0 0 0 0 0 1 0
119 42 0 0 0 0 0 0 0 0 0 0 1
120 55 0 0 0 0 0 0 0 0 0 0 0
121 33 1 0 0 0 0 0 0 0 0 0 0
122 46 0 1 0 0 0 0 0 0 0 0 0
123 54 0 0 1 0 0 0 0 0 0 0 0
124 47 0 0 0 1 0 0 0 0 0 0 0
125 45 0 0 0 0 1 0 0 0 0 0 0
126 47 0 0 0 0 0 1 0 0 0 0 0
127 55 0 0 0 0 0 0 1 0 0 0 0
128 44 0 0 0 0 0 0 0 1 0 0 0
129 53 0 0 0 0 0 0 0 0 1 0 0
130 44 0 0 0 0 0 0 0 0 0 1 0
131 42 0 0 0 0 0 0 0 0 0 0 1
132 40 0 0 0 0 0 0 0 0 0 0 0
133 46 1 0 0 0 0 0 0 0 0 0 0
134 40 0 1 0 0 0 0 0 0 0 0 0
135 46 0 0 1 0 0 0 0 0 0 0 0
136 53 0 0 0 1 0 0 0 0 0 0 0
137 33 0 0 0 0 1 0 0 0 0 0 0
138 42 0 0 0 0 0 1 0 0 0 0 0
139 35 0 0 0 0 0 0 1 0 0 0 0
140 40 0 0 0 0 0 0 0 1 0 0 0
141 41 0 0 0 0 0 0 0 0 1 0 0
142 33 0 0 0 0 0 0 0 0 0 1 0
143 51 0 0 0 0 0 0 0 0 0 0 1
144 53 0 0 0 0 0 0 0 0 0 0 0
145 46 1 0 0 0 0 0 0 0 0 0 0
146 55 0 1 0 0 0 0 0 0 0 0 0
147 47 0 0 1 0 0 0 0 0 0 0 0
148 38 0 0 0 1 0 0 0 0 0 0 0
149 46 0 0 0 0 1 0 0 0 0 0 0
150 46 0 0 0 0 0 1 0 0 0 0 0
151 53 0 0 0 0 0 0 1 0 0 0 0
152 47 0 0 0 0 0 0 0 1 0 0 0
153 41 0 0 0 0 0 0 0 0 1 0 0
154 44 0 0 0 0 0 0 0 0 0 1 0
155 43 0 0 0 0 0 0 0 0 0 0 1
156 51 0 0 0 0 0 0 0 0 0 0 0
157 33 1 0 0 0 0 0 0 0 0 0 0
158 43 0 1 0 0 0 0 0 0 0 0 0
159 53 0 0 1 0 0 0 0 0 0 0 0
160 51 0 0 0 1 0 0 0 0 0 0 0
161 50 0 0 0 0 1 0 0 0 0 0 0
162 46 0 0 0 0 0 1 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning
13.541819 0.023688 0.059917 0.100159
Software Depression Belonging Belonging_Final
-0.079605 -0.340169 0.052338 -0.040249
M1 M2 M3 M4
-1.043080 -0.675853 -0.008638 -1.125401
M5 M6 M7 M8
-0.004747 -1.166177 0.174723 -0.796082
M9 M10 M11
0.228191 -1.107805 -1.112744
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.656 -1.215 0.151 1.215 4.443
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.541819 2.688148 5.038 1.40e-06 ***
Connected 0.023688 0.052577 0.451 0.653
Separate 0.059917 0.048850 1.227 0.222
Learning 0.100159 0.085977 1.165 0.246
Software -0.079605 0.086997 -0.915 0.362
Depression -0.340169 0.055440 -6.136 7.88e-09 ***
Belonging 0.052338 0.047941 1.092 0.277
Belonging_Final -0.040249 0.068906 -0.584 0.560
M1 -1.043080 0.760201 -1.372 0.172
M2 -0.675853 0.760575 -0.889 0.376
M3 -0.008638 0.769145 -0.011 0.991
M4 -1.125401 0.756017 -1.489 0.139
M5 -0.004747 0.759216 -0.006 0.995
M6 -1.166177 0.755106 -1.544 0.125
M7 0.174723 0.767709 0.228 0.820
M8 -0.796082 0.784562 -1.015 0.312
M9 0.228191 0.768882 0.297 0.767
M10 -1.107805 0.775446 -1.429 0.155
M11 -1.112744 0.768804 -1.447 0.150
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.939 on 143 degrees of freedom
Multiple R-squared: 0.3892, Adjusted R-squared: 0.3123
F-statistic: 5.062 on 18 and 143 DF, p-value: 8.589e-09
> 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,] 0.9846745 0.0306509925 1.532550e-02
[2,] 0.9801337 0.0397326186 1.986631e-02
[3,] 0.9847561 0.0304878435 1.524392e-02
[4,] 0.9823523 0.0352953419 1.764767e-02
[5,] 0.9999334 0.0001331180 6.655900e-05
[6,] 0.9999245 0.0001510030 7.550148e-05
[7,] 0.9999205 0.0001589550 7.947751e-05
[8,] 0.9998440 0.0003120417 1.560209e-04
[9,] 0.9999117 0.0001765544 8.827719e-05
[10,] 0.9998625 0.0002749133 1.374566e-04
[11,] 0.9998191 0.0003617606 1.808803e-04
[12,] 0.9996725 0.0006550970 3.275485e-04
[13,] 0.9995382 0.0009235168 4.617584e-04
[14,] 0.9995302 0.0009396192 4.698096e-04
[15,] 0.9991638 0.0016723267 8.361634e-04
[16,] 0.9990008 0.0019984909 9.992454e-04
[17,] 0.9986036 0.0027928084 1.396404e-03
[18,] 0.9983308 0.0033384763 1.669238e-03
[19,] 0.9978820 0.0042360767 2.118038e-03
[20,] 0.9968698 0.0062603357 3.130168e-03
[21,] 0.9968251 0.0063497217 3.174861e-03
[22,] 0.9950558 0.0098884690 4.944234e-03
[23,] 0.9942053 0.0115893733 5.794687e-03
[24,] 0.9970315 0.0059370145 2.968507e-03
[25,] 0.9968502 0.0062996281 3.149814e-03
[26,] 0.9951902 0.0096195876 4.809794e-03
[27,] 0.9934892 0.0130215658 6.510783e-03
[28,] 0.9960407 0.0079186645 3.959332e-03
[29,] 0.9950217 0.0099566619 4.978331e-03
[30,] 0.9926801 0.0146398363 7.319918e-03
[31,] 0.9905529 0.0188941562 9.447078e-03
[32,] 0.9889600 0.0220799249 1.103996e-02
[33,] 0.9847979 0.0304041780 1.520209e-02
[34,] 0.9897926 0.0204147571 1.020738e-02
[35,] 0.9857842 0.0284315114 1.421576e-02
[36,] 0.9805110 0.0389780624 1.948903e-02
[37,] 0.9750010 0.0499979163 2.499896e-02
[38,] 0.9838285 0.0323430740 1.617154e-02
[39,] 0.9876603 0.0246794609 1.233973e-02
[40,] 0.9880917 0.0238166434 1.190832e-02
[41,] 0.9866449 0.0267101300 1.335507e-02
[42,] 0.9954155 0.0091690404 4.584520e-03
[43,] 0.9944303 0.0111393270 5.569663e-03
[44,] 0.9927558 0.0144884951 7.244248e-03
[45,] 0.9989494 0.0021012178 1.050609e-03
[46,] 0.9991836 0.0016328683 8.164341e-04
[47,] 0.9992339 0.0015321683 7.660842e-04
[48,] 0.9988208 0.0023584705 1.179235e-03
[49,] 0.9986832 0.0026336616 1.316831e-03
[50,] 0.9981454 0.0037091452 1.854573e-03
[51,] 0.9984695 0.0030609287 1.530464e-03
[52,] 0.9979226 0.0041547082 2.077354e-03
[53,] 0.9970763 0.0058474600 2.923730e-03
[54,] 0.9979838 0.0040324698 2.016235e-03
[55,] 0.9970238 0.0059524018 2.976201e-03
[56,] 0.9977161 0.0045677399 2.283870e-03
[57,] 0.9970478 0.0059044799 2.952240e-03
[58,] 0.9958570 0.0082859349 4.142967e-03
[59,] 0.9958700 0.0082599660 4.129983e-03
[60,] 0.9948568 0.0102863466 5.143173e-03
[61,] 0.9931585 0.0136830601 6.841530e-03
[62,] 0.9903106 0.0193788011 9.689401e-03
[63,] 0.9871840 0.0256319198 1.281596e-02
[64,] 0.9850282 0.0299435487 1.497177e-02
[65,] 0.9803289 0.0393421244 1.967106e-02
[66,] 0.9741255 0.0517489547 2.587448e-02
[67,] 0.9659963 0.0680073598 3.400368e-02
[68,] 0.9563731 0.0872538754 4.362694e-02
[69,] 0.9707821 0.0584357342 2.921787e-02
[70,] 0.9738897 0.0522205142 2.611026e-02
[71,] 0.9655972 0.0688055508 3.440278e-02
[72,] 0.9556972 0.0886056378 4.430282e-02
[73,] 0.9485403 0.1029193261 5.145966e-02
[74,] 0.9385360 0.1229280573 6.146403e-02
[75,] 0.9209082 0.1581835334 7.909177e-02
[76,] 0.8998656 0.2002688883 1.001344e-01
[77,] 0.8991779 0.2016441200 1.008221e-01
[78,] 0.8814945 0.2370109558 1.185055e-01
[79,] 0.8603046 0.2793907967 1.396954e-01
[80,] 0.8581852 0.2836296559 1.418148e-01
[81,] 0.8309921 0.3380158725 1.690079e-01
[82,] 0.7938287 0.4123426309 2.061713e-01
[83,] 0.8855364 0.2289271073 1.144636e-01
[84,] 0.8629885 0.2740229321 1.370115e-01
[85,] 0.8709416 0.2581167994 1.290584e-01
[86,] 0.8578638 0.2842723023 1.421362e-01
[87,] 0.8607825 0.2784350722 1.392175e-01
[88,] 0.8781834 0.2436331447 1.218166e-01
[89,] 0.8458809 0.3082381558 1.541191e-01
[90,] 0.8094829 0.3810341711 1.905171e-01
[91,] 0.7844324 0.4311352888 2.155676e-01
[92,] 0.7907773 0.4184454178 2.092227e-01
[93,] 0.9692586 0.0614828163 3.074141e-02
[94,] 0.9868056 0.0263888535 1.319443e-02
[95,] 0.9890139 0.0219721803 1.098609e-02
[96,] 0.9838086 0.0323828766 1.619144e-02
[97,] 0.9758452 0.0483096222 2.415481e-02
[98,] 0.9645663 0.0708674546 3.543373e-02
[99,] 0.9565153 0.0869694973 4.348475e-02
[100,] 0.9375784 0.1248431378 6.242157e-02
[101,] 0.9266317 0.1467365710 7.336829e-02
[102,] 0.9011395 0.1977209767 9.886049e-02
[103,] 0.8750689 0.2498621563 1.249311e-01
[104,] 0.8354764 0.3290472262 1.645236e-01
[105,] 0.7862436 0.4275127981 2.137564e-01
[106,] 0.7262237 0.5475526158 2.737763e-01
[107,] 0.7556589 0.4886821609 2.443411e-01
[108,] 0.7111543 0.5776914544 2.888457e-01
[109,] 0.6800407 0.6399185353 3.199593e-01
[110,] 0.6048619 0.7902761351 3.951381e-01
[111,] 0.5265598 0.9468804000 4.734402e-01
[112,] 0.4422659 0.8845318783 5.577341e-01
[113,] 0.3762875 0.7525749189 6.237125e-01
[114,] 0.2896908 0.5793815340 7.103092e-01
[115,] 0.2537544 0.5075087780 7.462456e-01
[116,] 0.4532066 0.9064132190 5.467934e-01
[117,] 0.3889965 0.7779929195 6.110035e-01
[118,] 0.2683287 0.5366573997 7.316713e-01
[119,] 0.6604705 0.6790589196 3.395295e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1jg4y1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2hkkm1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/331cr1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4mt9j1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ootg1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5 6
0.50250070 2.85947937 -3.05133025 -1.92715497 4.09057356 4.44305257
7 8 9 10 11 12
2.75347320 -0.56901828 -0.82693728 0.72415905 2.24606984 3.14428717
13 14 15 16 17 18
-2.85689647 2.47081600 1.94105726 1.47692142 -0.36528126 1.70903107
19 20 21 22 23 24
-2.14213820 2.81952983 1.34492686 -1.69869903 0.08610344 -2.16125763
25 26 27 28 29 30
2.00758729 -6.65615003 0.20535687 1.45921945 0.54790084 -2.07794590
31 32 33 34 35 36
0.17102895 0.73678572 1.30634914 0.41531326 0.76071493 0.35535175
37 38 39 40 41 42
-1.57176986 0.95379371 1.39141770 -1.54840799 -1.13763214 3.00272665
43 44 45 46 47 48
-0.11979639 -0.86973006 -3.13299166 -2.08086553 0.35693581 -0.31945065
49 50 51 52 53 54
3.56475470 -1.50268691 0.34820977 1.28757497 -1.20402472 -0.65667469
55 56 57 58 59 60
-3.00176558 0.53192864 0.98210552 0.43992126 -2.77753163 -2.04497230
61 62 63 64 65 66
-2.21428359 -1.58734631 -4.06051876 1.51411060 1.07700293 -4.56244967
67 68 69 70 71 72
-2.33575642 -2.08976539 0.31721232 1.71421132 0.83554406 2.52494911
73 74 75 76 77 78
0.78550236 -0.51075164 -2.24568125 0.12437224 2.29112596 1.17717363
79 80 81 82 83 84
0.69984788 -1.32373067 -0.89738230 1.19632316 0.03437961 1.04941785
85 86 87 88 89 90
1.16400146 -0.02769992 -0.14779959 0.27859718 -0.18413928 -3.13095408
91 92 93 94 95 96
2.35964727 0.58003942 -0.45932893 1.32595430 -1.15397649 0.01738460
97 98 99 100 101 102
-0.47786237 -0.92760489 1.34035259 0.33785106 1.15292420 -0.64282925
103 104 105 106 107 108
0.13092235 -2.83013276 0.58435397 -2.09123339 1.48213730 0.96035758
109 110 111 112 113 114
-2.57097457 -0.08174419 0.51458231 -1.61558724 -3.22890500 2.01910600
115 116 117 118 119 120
2.96002731 0.72630612 -0.05140949 0.12803727 -0.59361213 -0.64043011
121 122 123 124 125 126
-0.31926999 0.85041566 -0.85965316 -0.42240577 0.08868413 -0.99682341
127 128 129 130 131 132
-0.03940364 1.34783633 3.14103137 1.85822492 -1.25451092 -2.28932090
133 134 135 136 137 138
-0.40575306 2.68123182 -0.07296086 3.14471296 1.22172846 1.25068891
139 140 141 142 143 144
-1.80479637 0.82514096 -1.56533201 0.70875908 2.45585472 -1.14400823
145 146 147 148 149 150
1.56542554 1.26182927 1.31910755 -1.96160857 -3.13181903 -2.21368173
151 152 153 154 155 156
0.36870964 0.11481015 -0.74259753 -2.64010568 -2.47810852 0.54769175
157 158 159 160 161 162
0.82703786 0.21641805 3.37785981 -2.14819533 -1.21813865 0.67957989
> postscript(file="/var/wessaorg/rcomp/tmp/68tye1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 0.50250070 NA
1 2.85947937 0.50250070
2 -3.05133025 2.85947937
3 -1.92715497 -3.05133025
4 4.09057356 -1.92715497
5 4.44305257 4.09057356
6 2.75347320 4.44305257
7 -0.56901828 2.75347320
8 -0.82693728 -0.56901828
9 0.72415905 -0.82693728
10 2.24606984 0.72415905
11 3.14428717 2.24606984
12 -2.85689647 3.14428717
13 2.47081600 -2.85689647
14 1.94105726 2.47081600
15 1.47692142 1.94105726
16 -0.36528126 1.47692142
17 1.70903107 -0.36528126
18 -2.14213820 1.70903107
19 2.81952983 -2.14213820
20 1.34492686 2.81952983
21 -1.69869903 1.34492686
22 0.08610344 -1.69869903
23 -2.16125763 0.08610344
24 2.00758729 -2.16125763
25 -6.65615003 2.00758729
26 0.20535687 -6.65615003
27 1.45921945 0.20535687
28 0.54790084 1.45921945
29 -2.07794590 0.54790084
30 0.17102895 -2.07794590
31 0.73678572 0.17102895
32 1.30634914 0.73678572
33 0.41531326 1.30634914
34 0.76071493 0.41531326
35 0.35535175 0.76071493
36 -1.57176986 0.35535175
37 0.95379371 -1.57176986
38 1.39141770 0.95379371
39 -1.54840799 1.39141770
40 -1.13763214 -1.54840799
41 3.00272665 -1.13763214
42 -0.11979639 3.00272665
43 -0.86973006 -0.11979639
44 -3.13299166 -0.86973006
45 -2.08086553 -3.13299166
46 0.35693581 -2.08086553
47 -0.31945065 0.35693581
48 3.56475470 -0.31945065
49 -1.50268691 3.56475470
50 0.34820977 -1.50268691
51 1.28757497 0.34820977
52 -1.20402472 1.28757497
53 -0.65667469 -1.20402472
54 -3.00176558 -0.65667469
55 0.53192864 -3.00176558
56 0.98210552 0.53192864
57 0.43992126 0.98210552
58 -2.77753163 0.43992126
59 -2.04497230 -2.77753163
60 -2.21428359 -2.04497230
61 -1.58734631 -2.21428359
62 -4.06051876 -1.58734631
63 1.51411060 -4.06051876
64 1.07700293 1.51411060
65 -4.56244967 1.07700293
66 -2.33575642 -4.56244967
67 -2.08976539 -2.33575642
68 0.31721232 -2.08976539
69 1.71421132 0.31721232
70 0.83554406 1.71421132
71 2.52494911 0.83554406
72 0.78550236 2.52494911
73 -0.51075164 0.78550236
74 -2.24568125 -0.51075164
75 0.12437224 -2.24568125
76 2.29112596 0.12437224
77 1.17717363 2.29112596
78 0.69984788 1.17717363
79 -1.32373067 0.69984788
80 -0.89738230 -1.32373067
81 1.19632316 -0.89738230
82 0.03437961 1.19632316
83 1.04941785 0.03437961
84 1.16400146 1.04941785
85 -0.02769992 1.16400146
86 -0.14779959 -0.02769992
87 0.27859718 -0.14779959
88 -0.18413928 0.27859718
89 -3.13095408 -0.18413928
90 2.35964727 -3.13095408
91 0.58003942 2.35964727
92 -0.45932893 0.58003942
93 1.32595430 -0.45932893
94 -1.15397649 1.32595430
95 0.01738460 -1.15397649
96 -0.47786237 0.01738460
97 -0.92760489 -0.47786237
98 1.34035259 -0.92760489
99 0.33785106 1.34035259
100 1.15292420 0.33785106
101 -0.64282925 1.15292420
102 0.13092235 -0.64282925
103 -2.83013276 0.13092235
104 0.58435397 -2.83013276
105 -2.09123339 0.58435397
106 1.48213730 -2.09123339
107 0.96035758 1.48213730
108 -2.57097457 0.96035758
109 -0.08174419 -2.57097457
110 0.51458231 -0.08174419
111 -1.61558724 0.51458231
112 -3.22890500 -1.61558724
113 2.01910600 -3.22890500
114 2.96002731 2.01910600
115 0.72630612 2.96002731
116 -0.05140949 0.72630612
117 0.12803727 -0.05140949
118 -0.59361213 0.12803727
119 -0.64043011 -0.59361213
120 -0.31926999 -0.64043011
121 0.85041566 -0.31926999
122 -0.85965316 0.85041566
123 -0.42240577 -0.85965316
124 0.08868413 -0.42240577
125 -0.99682341 0.08868413
126 -0.03940364 -0.99682341
127 1.34783633 -0.03940364
128 3.14103137 1.34783633
129 1.85822492 3.14103137
130 -1.25451092 1.85822492
131 -2.28932090 -1.25451092
132 -0.40575306 -2.28932090
133 2.68123182 -0.40575306
134 -0.07296086 2.68123182
135 3.14471296 -0.07296086
136 1.22172846 3.14471296
137 1.25068891 1.22172846
138 -1.80479637 1.25068891
139 0.82514096 -1.80479637
140 -1.56533201 0.82514096
141 0.70875908 -1.56533201
142 2.45585472 0.70875908
143 -1.14400823 2.45585472
144 1.56542554 -1.14400823
145 1.26182927 1.56542554
146 1.31910755 1.26182927
147 -1.96160857 1.31910755
148 -3.13181903 -1.96160857
149 -2.21368173 -3.13181903
150 0.36870964 -2.21368173
151 0.11481015 0.36870964
152 -0.74259753 0.11481015
153 -2.64010568 -0.74259753
154 -2.47810852 -2.64010568
155 0.54769175 -2.47810852
156 0.82703786 0.54769175
157 0.21641805 0.82703786
158 3.37785981 0.21641805
159 -2.14819533 3.37785981
160 -1.21813865 -2.14819533
161 0.67957989 -1.21813865
162 NA 0.67957989
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.85947937 0.50250070
[2,] -3.05133025 2.85947937
[3,] -1.92715497 -3.05133025
[4,] 4.09057356 -1.92715497
[5,] 4.44305257 4.09057356
[6,] 2.75347320 4.44305257
[7,] -0.56901828 2.75347320
[8,] -0.82693728 -0.56901828
[9,] 0.72415905 -0.82693728
[10,] 2.24606984 0.72415905
[11,] 3.14428717 2.24606984
[12,] -2.85689647 3.14428717
[13,] 2.47081600 -2.85689647
[14,] 1.94105726 2.47081600
[15,] 1.47692142 1.94105726
[16,] -0.36528126 1.47692142
[17,] 1.70903107 -0.36528126
[18,] -2.14213820 1.70903107
[19,] 2.81952983 -2.14213820
[20,] 1.34492686 2.81952983
[21,] -1.69869903 1.34492686
[22,] 0.08610344 -1.69869903
[23,] -2.16125763 0.08610344
[24,] 2.00758729 -2.16125763
[25,] -6.65615003 2.00758729
[26,] 0.20535687 -6.65615003
[27,] 1.45921945 0.20535687
[28,] 0.54790084 1.45921945
[29,] -2.07794590 0.54790084
[30,] 0.17102895 -2.07794590
[31,] 0.73678572 0.17102895
[32,] 1.30634914 0.73678572
[33,] 0.41531326 1.30634914
[34,] 0.76071493 0.41531326
[35,] 0.35535175 0.76071493
[36,] -1.57176986 0.35535175
[37,] 0.95379371 -1.57176986
[38,] 1.39141770 0.95379371
[39,] -1.54840799 1.39141770
[40,] -1.13763214 -1.54840799
[41,] 3.00272665 -1.13763214
[42,] -0.11979639 3.00272665
[43,] -0.86973006 -0.11979639
[44,] -3.13299166 -0.86973006
[45,] -2.08086553 -3.13299166
[46,] 0.35693581 -2.08086553
[47,] -0.31945065 0.35693581
[48,] 3.56475470 -0.31945065
[49,] -1.50268691 3.56475470
[50,] 0.34820977 -1.50268691
[51,] 1.28757497 0.34820977
[52,] -1.20402472 1.28757497
[53,] -0.65667469 -1.20402472
[54,] -3.00176558 -0.65667469
[55,] 0.53192864 -3.00176558
[56,] 0.98210552 0.53192864
[57,] 0.43992126 0.98210552
[58,] -2.77753163 0.43992126
[59,] -2.04497230 -2.77753163
[60,] -2.21428359 -2.04497230
[61,] -1.58734631 -2.21428359
[62,] -4.06051876 -1.58734631
[63,] 1.51411060 -4.06051876
[64,] 1.07700293 1.51411060
[65,] -4.56244967 1.07700293
[66,] -2.33575642 -4.56244967
[67,] -2.08976539 -2.33575642
[68,] 0.31721232 -2.08976539
[69,] 1.71421132 0.31721232
[70,] 0.83554406 1.71421132
[71,] 2.52494911 0.83554406
[72,] 0.78550236 2.52494911
[73,] -0.51075164 0.78550236
[74,] -2.24568125 -0.51075164
[75,] 0.12437224 -2.24568125
[76,] 2.29112596 0.12437224
[77,] 1.17717363 2.29112596
[78,] 0.69984788 1.17717363
[79,] -1.32373067 0.69984788
[80,] -0.89738230 -1.32373067
[81,] 1.19632316 -0.89738230
[82,] 0.03437961 1.19632316
[83,] 1.04941785 0.03437961
[84,] 1.16400146 1.04941785
[85,] -0.02769992 1.16400146
[86,] -0.14779959 -0.02769992
[87,] 0.27859718 -0.14779959
[88,] -0.18413928 0.27859718
[89,] -3.13095408 -0.18413928
[90,] 2.35964727 -3.13095408
[91,] 0.58003942 2.35964727
[92,] -0.45932893 0.58003942
[93,] 1.32595430 -0.45932893
[94,] -1.15397649 1.32595430
[95,] 0.01738460 -1.15397649
[96,] -0.47786237 0.01738460
[97,] -0.92760489 -0.47786237
[98,] 1.34035259 -0.92760489
[99,] 0.33785106 1.34035259
[100,] 1.15292420 0.33785106
[101,] -0.64282925 1.15292420
[102,] 0.13092235 -0.64282925
[103,] -2.83013276 0.13092235
[104,] 0.58435397 -2.83013276
[105,] -2.09123339 0.58435397
[106,] 1.48213730 -2.09123339
[107,] 0.96035758 1.48213730
[108,] -2.57097457 0.96035758
[109,] -0.08174419 -2.57097457
[110,] 0.51458231 -0.08174419
[111,] -1.61558724 0.51458231
[112,] -3.22890500 -1.61558724
[113,] 2.01910600 -3.22890500
[114,] 2.96002731 2.01910600
[115,] 0.72630612 2.96002731
[116,] -0.05140949 0.72630612
[117,] 0.12803727 -0.05140949
[118,] -0.59361213 0.12803727
[119,] -0.64043011 -0.59361213
[120,] -0.31926999 -0.64043011
[121,] 0.85041566 -0.31926999
[122,] -0.85965316 0.85041566
[123,] -0.42240577 -0.85965316
[124,] 0.08868413 -0.42240577
[125,] -0.99682341 0.08868413
[126,] -0.03940364 -0.99682341
[127,] 1.34783633 -0.03940364
[128,] 3.14103137 1.34783633
[129,] 1.85822492 3.14103137
[130,] -1.25451092 1.85822492
[131,] -2.28932090 -1.25451092
[132,] -0.40575306 -2.28932090
[133,] 2.68123182 -0.40575306
[134,] -0.07296086 2.68123182
[135,] 3.14471296 -0.07296086
[136,] 1.22172846 3.14471296
[137,] 1.25068891 1.22172846
[138,] -1.80479637 1.25068891
[139,] 0.82514096 -1.80479637
[140,] -1.56533201 0.82514096
[141,] 0.70875908 -1.56533201
[142,] 2.45585472 0.70875908
[143,] -1.14400823 2.45585472
[144,] 1.56542554 -1.14400823
[145,] 1.26182927 1.56542554
[146,] 1.31910755 1.26182927
[147,] -1.96160857 1.31910755
[148,] -3.13181903 -1.96160857
[149,] -2.21368173 -3.13181903
[150,] 0.36870964 -2.21368173
[151,] 0.11481015 0.36870964
[152,] -0.74259753 0.11481015
[153,] -2.64010568 -0.74259753
[154,] -2.47810852 -2.64010568
[155,] 0.54769175 -2.47810852
[156,] 0.82703786 0.54769175
[157,] 0.21641805 0.82703786
[158,] 3.37785981 0.21641805
[159,] -2.14819533 3.37785981
[160,] -1.21813865 -2.14819533
[161,] 0.67957989 -1.21813865
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.85947937 0.50250070
2 -3.05133025 2.85947937
3 -1.92715497 -3.05133025
4 4.09057356 -1.92715497
5 4.44305257 4.09057356
6 2.75347320 4.44305257
7 -0.56901828 2.75347320
8 -0.82693728 -0.56901828
9 0.72415905 -0.82693728
10 2.24606984 0.72415905
11 3.14428717 2.24606984
12 -2.85689647 3.14428717
13 2.47081600 -2.85689647
14 1.94105726 2.47081600
15 1.47692142 1.94105726
16 -0.36528126 1.47692142
17 1.70903107 -0.36528126
18 -2.14213820 1.70903107
19 2.81952983 -2.14213820
20 1.34492686 2.81952983
21 -1.69869903 1.34492686
22 0.08610344 -1.69869903
23 -2.16125763 0.08610344
24 2.00758729 -2.16125763
25 -6.65615003 2.00758729
26 0.20535687 -6.65615003
27 1.45921945 0.20535687
28 0.54790084 1.45921945
29 -2.07794590 0.54790084
30 0.17102895 -2.07794590
31 0.73678572 0.17102895
32 1.30634914 0.73678572
33 0.41531326 1.30634914
34 0.76071493 0.41531326
35 0.35535175 0.76071493
36 -1.57176986 0.35535175
37 0.95379371 -1.57176986
38 1.39141770 0.95379371
39 -1.54840799 1.39141770
40 -1.13763214 -1.54840799
41 3.00272665 -1.13763214
42 -0.11979639 3.00272665
43 -0.86973006 -0.11979639
44 -3.13299166 -0.86973006
45 -2.08086553 -3.13299166
46 0.35693581 -2.08086553
47 -0.31945065 0.35693581
48 3.56475470 -0.31945065
49 -1.50268691 3.56475470
50 0.34820977 -1.50268691
51 1.28757497 0.34820977
52 -1.20402472 1.28757497
53 -0.65667469 -1.20402472
54 -3.00176558 -0.65667469
55 0.53192864 -3.00176558
56 0.98210552 0.53192864
57 0.43992126 0.98210552
58 -2.77753163 0.43992126
59 -2.04497230 -2.77753163
60 -2.21428359 -2.04497230
61 -1.58734631 -2.21428359
62 -4.06051876 -1.58734631
63 1.51411060 -4.06051876
64 1.07700293 1.51411060
65 -4.56244967 1.07700293
66 -2.33575642 -4.56244967
67 -2.08976539 -2.33575642
68 0.31721232 -2.08976539
69 1.71421132 0.31721232
70 0.83554406 1.71421132
71 2.52494911 0.83554406
72 0.78550236 2.52494911
73 -0.51075164 0.78550236
74 -2.24568125 -0.51075164
75 0.12437224 -2.24568125
76 2.29112596 0.12437224
77 1.17717363 2.29112596
78 0.69984788 1.17717363
79 -1.32373067 0.69984788
80 -0.89738230 -1.32373067
81 1.19632316 -0.89738230
82 0.03437961 1.19632316
83 1.04941785 0.03437961
84 1.16400146 1.04941785
85 -0.02769992 1.16400146
86 -0.14779959 -0.02769992
87 0.27859718 -0.14779959
88 -0.18413928 0.27859718
89 -3.13095408 -0.18413928
90 2.35964727 -3.13095408
91 0.58003942 2.35964727
92 -0.45932893 0.58003942
93 1.32595430 -0.45932893
94 -1.15397649 1.32595430
95 0.01738460 -1.15397649
96 -0.47786237 0.01738460
97 -0.92760489 -0.47786237
98 1.34035259 -0.92760489
99 0.33785106 1.34035259
100 1.15292420 0.33785106
101 -0.64282925 1.15292420
102 0.13092235 -0.64282925
103 -2.83013276 0.13092235
104 0.58435397 -2.83013276
105 -2.09123339 0.58435397
106 1.48213730 -2.09123339
107 0.96035758 1.48213730
108 -2.57097457 0.96035758
109 -0.08174419 -2.57097457
110 0.51458231 -0.08174419
111 -1.61558724 0.51458231
112 -3.22890500 -1.61558724
113 2.01910600 -3.22890500
114 2.96002731 2.01910600
115 0.72630612 2.96002731
116 -0.05140949 0.72630612
117 0.12803727 -0.05140949
118 -0.59361213 0.12803727
119 -0.64043011 -0.59361213
120 -0.31926999 -0.64043011
121 0.85041566 -0.31926999
122 -0.85965316 0.85041566
123 -0.42240577 -0.85965316
124 0.08868413 -0.42240577
125 -0.99682341 0.08868413
126 -0.03940364 -0.99682341
127 1.34783633 -0.03940364
128 3.14103137 1.34783633
129 1.85822492 3.14103137
130 -1.25451092 1.85822492
131 -2.28932090 -1.25451092
132 -0.40575306 -2.28932090
133 2.68123182 -0.40575306
134 -0.07296086 2.68123182
135 3.14471296 -0.07296086
136 1.22172846 3.14471296
137 1.25068891 1.22172846
138 -1.80479637 1.25068891
139 0.82514096 -1.80479637
140 -1.56533201 0.82514096
141 0.70875908 -1.56533201
142 2.45585472 0.70875908
143 -1.14400823 2.45585472
144 1.56542554 -1.14400823
145 1.26182927 1.56542554
146 1.31910755 1.26182927
147 -1.96160857 1.31910755
148 -3.13181903 -1.96160857
149 -2.21368173 -3.13181903
150 0.36870964 -2.21368173
151 0.11481015 0.36870964
152 -0.74259753 0.11481015
153 -2.64010568 -0.74259753
154 -2.47810852 -2.64010568
155 0.54769175 -2.47810852
156 0.82703786 0.54769175
157 0.21641805 0.82703786
158 3.37785981 0.21641805
159 -2.14819533 3.37785981
160 -1.21813865 -2.14819533
161 0.67957989 -1.21813865
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/74s721322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8nxz71322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9d35v1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ophr1322168068.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11rtjd1322168068.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12ntxc1322168068.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13hqsp1322168069.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14jq3o1322168069.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15jbq61322168069.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/168qdl1322168069.tab")
+ }
>
> try(system("convert tmp/1jg4y1322168068.ps tmp/1jg4y1322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hkkm1322168068.ps tmp/2hkkm1322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/331cr1322168068.ps tmp/331cr1322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mt9j1322168068.ps tmp/4mt9j1322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ootg1322168068.ps tmp/5ootg1322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/68tye1322168068.ps tmp/68tye1322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/74s721322168068.ps tmp/74s721322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nxz71322168068.ps tmp/8nxz71322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/9d35v1322168068.ps tmp/9d35v1322168068.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ophr1322168068.ps tmp/10ophr1322168068.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.307 0.538 5.859