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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(14
+ ,13
+ ,41
+ ,12
+ ,53
+ ,18
+ ,16
+ ,39
+ ,11
+ ,86
+ ,11
+ ,19
+ ,30
+ ,14
+ ,66
+ ,12
+ ,15
+ ,31
+ ,12
+ ,67
+ ,16
+ ,14
+ ,34
+ ,21
+ ,76
+ ,18
+ ,13
+ ,35
+ ,12
+ ,78
+ ,14
+ ,19
+ ,39
+ ,22
+ ,53
+ ,14
+ ,15
+ ,34
+ ,11
+ ,80
+ ,15
+ ,14
+ ,36
+ ,10
+ ,74
+ ,15
+ ,15
+ ,37
+ ,13
+ ,76
+ ,17
+ ,16
+ ,38
+ ,10
+ ,79
+ ,19
+ ,16
+ ,36
+ ,8
+ ,54
+ ,10
+ ,16
+ ,38
+ ,15
+ ,67
+ ,16
+ ,16
+ ,39
+ ,14
+ ,54
+ ,18
+ ,17
+ ,33
+ ,10
+ ,87
+ ,14
+ ,15
+ ,32
+ ,14
+ ,58
+ ,14
+ ,15
+ ,36
+ ,14
+ ,75
+ ,17
+ ,20
+ ,38
+ ,11
+ ,88
+ ,14
+ ,18
+ ,39
+ ,10
+ ,64
+ ,16
+ ,16
+ ,32
+ ,13
+ ,57
+ ,18
+ ,16
+ ,32
+ ,7
+ ,66
+ ,11
+ ,16
+ ,31
+ ,14
+ ,68
+ ,14
+ ,19
+ ,39
+ ,12
+ ,54
+ ,12
+ ,16
+ ,37
+ ,14
+ ,56
+ ,17
+ ,17
+ ,39
+ ,11
+ ,86
+ ,9
+ ,17
+ ,41
+ ,9
+ ,80
+ ,16
+ ,16
+ ,36
+ ,11
+ ,76
+ ,14
+ ,15
+ ,33
+ ,15
+ ,69
+ ,15
+ ,16
+ ,33
+ ,14
+ ,78
+ ,11
+ ,14
+ ,34
+ ,13
+ ,67
+ ,16
+ ,15
+ ,31
+ ,9
+ ,80
+ ,13
+ ,12
+ ,27
+ ,15
+ ,54
+ ,17
+ ,14
+ ,37
+ ,10
+ ,71
+ ,15
+ ,16
+ ,34
+ ,11
+ ,84
+ ,14
+ ,14
+ ,34
+ ,13
+ ,74
+ ,16
+ ,7
+ ,32
+ ,8
+ ,71
+ ,9
+ ,10
+ ,29
+ ,20
+ ,63
+ ,15
+ ,14
+ ,36
+ ,12
+ ,71
+ ,17
+ ,16
+ ,29
+ ,10
+ ,76
+ ,13
+ ,16
+ ,35
+ ,10
+ ,69
+ ,15
+ ,16
+ ,37
+ ,9
+ ,74
+ ,16
+ ,14
+ ,34
+ ,14
+ ,75
+ ,16
+ ,20
+ ,38
+ ,8
+ ,54
+ ,12
+ ,14
+ ,35
+ ,14
+ ,52
+ ,12
+ ,14
+ ,38
+ ,11
+ ,69
+ ,11
+ ,11
+ ,37
+ ,13
+ ,68
+ ,15
+ ,14
+ ,38
+ ,9
+ ,65
+ ,15
+ ,15
+ ,33
+ ,11
+ ,75
+ ,17
+ ,16
+ ,36
+ ,15
+ ,74
+ ,13
+ ,14
+ ,38
+ ,11
+ ,75
+ ,16
+ ,16
+ ,32
+ ,10
+ ,72
+ ,14
+ ,14
+ ,32
+ ,14
+ ,67
+ ,11
+ ,12
+ ,32
+ ,18
+ ,63
+ ,12
+ ,16
+ ,34
+ ,14
+ ,62
+ ,12
+ ,9
+ ,32
+ ,11
+ ,63
+ ,15
+ ,14
+ ,37
+ ,12
+ ,76
+ ,16
+ ,16
+ ,39
+ ,13
+ ,74
+ ,15
+ ,16
+ ,29
+ ,9
+ ,67
+ ,12
+ ,15
+ ,37
+ ,10
+ ,73
+ ,12
+ ,16
+ ,35
+ ,15
+ ,70
+ ,8
+ ,12
+ ,30
+ ,20
+ ,53
+ ,13
+ ,16
+ ,38
+ ,12
+ ,77
+ ,11
+ ,16
+ ,34
+ ,12
+ ,77
+ ,14
+ ,14
+ ,31
+ ,14
+ ,52
+ ,15
+ ,16
+ ,34
+ ,13
+ ,54
+ ,10
+ ,17
+ ,35
+ ,11
+ ,80
+ ,11
+ ,18
+ ,36
+ ,17
+ ,66
+ ,12
+ ,18
+ ,30
+ ,12
+ ,73
+ ,15
+ ,12
+ ,39
+ ,13
+ ,63
+ ,15
+ ,16
+ ,35
+ ,14
+ ,69
+ ,14
+ ,10
+ ,38
+ ,13
+ ,67
+ ,16
+ ,14
+ ,31
+ ,15
+ ,54
+ ,15
+ ,18
+ ,34
+ ,13
+ ,81
+ ,15
+ ,18
+ ,38
+ ,10
+ ,69
+ ,13
+ ,16
+ ,34
+ ,11
+ ,84
+ ,12
+ ,17
+ ,39
+ ,19
+ ,80
+ ,17
+ ,16
+ ,37
+ ,13
+ ,70
+ ,13
+ ,16
+ ,34
+ ,17
+ ,69
+ ,15
+ ,13
+ ,28
+ ,13
+ ,77
+ ,13
+ ,16
+ ,37
+ ,9
+ ,54
+ ,15
+ ,16
+ ,33
+ ,11
+ ,79
+ ,16
+ ,20
+ ,37
+ ,10
+ ,30
+ ,15
+ ,16
+ ,35
+ ,9
+ ,71
+ ,16
+ ,15
+ ,37
+ ,12
+ ,73
+ ,15
+ ,15
+ ,32
+ ,12
+ ,72
+ ,14
+ ,16
+ ,33
+ ,13
+ ,77
+ ,15
+ ,14
+ ,38
+ ,13
+ ,75
+ ,14
+ ,16
+ ,33
+ ,12
+ ,69
+ ,13
+ ,16
+ ,29
+ ,15
+ ,54
+ ,7
+ ,15
+ ,33
+ ,22
+ ,70
+ ,17
+ ,12
+ ,31
+ ,13
+ ,73
+ ,13
+ ,17
+ ,36
+ ,15
+ ,54
+ ,15
+ ,16
+ ,35
+ ,13
+ ,77
+ ,14
+ ,15
+ ,32
+ ,15
+ ,82
+ ,13
+ ,13
+ ,29
+ ,10
+ ,80
+ ,16
+ ,16
+ ,39
+ ,11
+ ,80
+ ,12
+ ,16
+ ,37
+ ,16
+ ,69
+ ,14
+ ,16
+ ,35
+ ,11
+ ,78
+ ,17
+ ,16
+ ,37
+ ,11
+ ,81
+ ,15
+ ,14
+ ,32
+ ,10
+ ,76
+ ,17
+ ,16
+ ,38
+ ,10
+ ,76
+ ,12
+ ,16
+ ,37
+ ,16
+ ,73
+ ,16
+ ,20
+ ,36
+ ,12
+ ,85
+ ,11
+ ,15
+ ,32
+ ,11
+ ,66
+ ,15
+ ,16
+ ,33
+ ,16
+ ,79
+ ,9
+ ,13
+ ,40
+ ,19
+ ,68
+ ,16
+ ,17
+ ,38
+ ,11
+ ,76
+ ,15
+ ,16
+ ,41
+ ,16
+ ,71
+ ,10
+ ,16
+ ,36
+ ,15
+ ,54
+ ,10
+ ,12
+ ,43
+ ,24
+ ,46
+ ,15
+ ,16
+ ,30
+ ,14
+ ,82
+ ,11
+ ,16
+ ,31
+ ,15
+ ,74
+ ,13
+ ,17
+ ,32
+ ,11
+ ,88
+ ,14
+ ,13
+ ,32
+ ,15
+ ,38
+ ,18
+ ,12
+ ,37
+ ,12
+ ,76
+ ,16
+ ,18
+ ,37
+ ,10
+ ,86
+ ,14
+ ,14
+ ,33
+ ,14
+ ,54
+ ,14
+ ,14
+ ,34
+ ,13
+ ,70
+ ,14
+ ,13
+ ,33
+ ,9
+ ,69
+ ,14
+ ,16
+ ,38
+ ,15
+ ,90
+ ,12
+ ,13
+ ,33
+ ,15
+ ,54
+ ,14
+ ,16
+ ,31
+ ,14
+ ,76
+ ,15
+ ,13
+ ,38
+ ,11
+ ,89
+ ,15
+ ,16
+ ,37
+ ,8
+ ,76
+ ,15
+ ,15
+ ,33
+ ,11
+ ,73
+ ,13
+ ,16
+ ,31
+ ,11
+ ,79
+ ,17
+ ,15
+ ,39
+ ,8
+ ,90
+ ,17
+ ,17
+ ,44
+ ,10
+ ,74
+ ,19
+ ,15
+ ,33
+ ,11
+ ,81
+ ,15
+ ,12
+ ,35
+ ,13
+ ,72
+ ,13
+ ,16
+ ,32
+ ,11
+ ,71
+ ,9
+ ,10
+ ,28
+ ,20
+ ,66
+ ,15
+ ,16
+ ,40
+ ,10
+ ,77
+ ,15
+ ,12
+ ,27
+ ,15
+ ,65
+ ,15
+ ,14
+ ,37
+ ,12
+ ,74
+ ,16
+ ,15
+ ,32
+ ,14
+ ,82
+ ,11
+ ,13
+ ,28
+ ,23
+ ,54
+ ,14
+ ,15
+ ,34
+ ,14
+ ,63
+ ,11
+ ,11
+ ,30
+ ,16
+ ,54
+ ,15
+ ,12
+ ,35
+ ,11
+ ,64
+ ,13
+ ,8
+ ,31
+ ,12
+ ,69
+ ,15
+ ,16
+ ,32
+ ,10
+ ,54
+ ,16
+ ,15
+ ,30
+ ,14
+ ,84
+ ,14
+ ,17
+ ,30
+ ,12
+ ,86
+ ,15
+ ,16
+ ,31
+ ,12
+ ,77
+ ,16
+ ,10
+ ,40
+ ,11
+ ,89
+ ,16
+ ,18
+ ,32
+ ,12
+ ,76
+ ,11
+ ,13
+ ,36
+ ,13
+ ,60
+ ,12
+ ,16
+ ,32
+ ,11
+ ,75
+ ,9
+ ,13
+ ,35
+ ,19
+ ,73
+ ,16
+ ,10
+ ,38
+ ,12
+ ,85
+ ,13
+ ,15
+ ,42
+ ,17
+ ,79
+ ,16
+ ,16
+ ,34
+ ,9
+ ,71
+ ,12
+ ,16
+ ,35
+ ,12
+ ,72
+ ,9
+ ,14
+ ,35
+ ,19
+ ,69
+ ,13
+ ,10
+ ,33
+ ,18
+ ,78
+ ,13
+ ,17
+ ,36
+ ,15
+ ,54
+ ,14
+ ,13
+ ,32
+ ,14
+ ,69
+ ,19
+ ,15
+ ,33
+ ,11
+ ,81
+ ,13
+ ,16
+ ,34
+ ,9
+ ,84
+ ,12
+ ,12
+ ,32
+ ,18
+ ,84
+ ,13
+ ,13
+ ,34
+ ,16
+ ,69)
+ ,dim=c(5
+ ,162)
+ ,dimnames=list(c('Happiness'
+ ,'Learning'
+ ,'Connected'
+ ,'Depression'
+ ,'Belonging
')
+ ,1:162))
> y <- array(NA,dim=c(5,162),dimnames=list(c('Happiness','Learning','Connected','Depression','Belonging
'),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 = 'First Differences'
> par2 = 'Do not include Seasonal 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
> 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
(1-B)Happiness (1-B)Learning (1-B)Connected (1-B)Depression
1 4 3 -2 -1
2 -7 3 -9 3
3 1 -4 1 -2
4 4 -1 3 9
5 2 -1 1 -9
6 -4 6 4 10
7 0 -4 -5 -11
8 1 -1 2 -1
9 0 1 1 3
10 2 1 1 -3
11 2 0 -2 -2
12 -9 0 2 7
13 6 0 1 -1
14 2 1 -6 -4
15 -4 -2 -1 4
16 0 0 4 0
17 3 5 2 -3
18 -3 -2 1 -1
19 2 -2 -7 3
20 2 0 0 -6
21 -7 0 -1 7
22 3 3 8 -2
23 -2 -3 -2 2
24 5 1 2 -3
25 -8 0 2 -2
26 7 -1 -5 2
27 -2 -1 -3 4
28 1 1 0 -1
29 -4 -2 1 -1
30 5 1 -3 -4
31 -3 -3 -4 6
32 4 2 10 -5
33 -2 2 -3 1
34 -1 -2 0 2
35 2 -7 -2 -5
36 -7 3 -3 12
37 6 4 7 -8
38 2 2 -7 -2
39 -4 0 6 0
40 2 0 2 -1
41 1 -2 -3 5
42 0 6 4 -6
43 -4 -6 -3 6
44 0 0 3 -3
45 -1 -3 -1 2
46 4 3 1 -4
47 0 1 -5 2
48 2 1 3 4
49 -4 -2 2 -4
50 3 2 -6 -1
51 -2 -2 0 4
52 -3 -2 0 4
53 1 4 2 -4
54 0 -7 -2 -3
55 3 5 5 1
56 1 2 2 1
57 -1 0 -10 -4
58 -3 -1 8 1
59 0 1 -2 5
60 -4 -4 -5 5
61 5 4 8 -8
62 -2 0 -4 0
63 3 -2 -3 2
64 1 2 3 -1
65 -5 1 1 -2
66 1 1 1 6
67 1 0 -6 -5
68 3 -6 9 1
69 0 4 -4 1
70 -1 -6 3 -1
71 2 4 -7 2
72 -1 4 3 -2
73 0 0 4 -3
74 -2 -2 -4 1
75 -1 1 5 8
76 5 -1 -2 -6
77 -4 0 -3 4
78 2 -3 -6 -4
79 -2 3 9 -4
80 2 0 -4 2
81 1 4 4 -1
82 -1 -4 -2 -1
83 1 -1 2 3
84 -1 0 -5 0
85 -1 1 1 1
86 1 -2 5 0
87 -1 2 -5 -1
88 -1 0 -4 3
89 -6 -1 4 7
90 10 -3 -2 -9
91 -4 5 5 2
92 2 -1 -1 -2
93 -1 -1 -3 2
94 -1 -2 -3 -5
95 3 3 10 1
96 -4 0 -2 5
97 2 0 -2 -5
98 3 0 2 0
99 -2 -2 -5 -1
100 2 2 6 0
101 -5 0 -1 6
102 4 4 -1 -4
103 -5 -5 -4 -1
104 4 1 1 5
105 -6 -3 7 3
106 7 4 -2 -8
107 -1 -1 3 5
108 -5 0 -5 -1
109 0 -4 7 9
110 5 4 -13 -10
111 -4 0 1 1
112 2 1 1 -4
113 1 -4 0 4
114 4 -1 5 -3
115 -2 6 0 -2
116 -2 -4 -4 4
117 0 0 1 -1
118 0 -1 -1 -4
119 0 3 5 6
120 -2 -3 -5 0
121 2 3 -2 -1
122 1 -3 7 -3
123 0 3 -1 -3
124 0 -1 -4 3
125 -2 1 -2 0
126 4 -1 8 -3
127 0 2 5 2
128 2 -2 -11 1
129 -4 -3 2 2
130 -2 4 -3 -2
131 -4 -6 -4 9
132 6 6 12 -10
133 0 -4 -13 5
134 0 2 10 -3
135 1 1 -5 2
136 -5 -2 -4 9
137 3 2 6 -9
138 -3 -4 -4 2
139 4 1 5 -5
140 -2 -4 -4 1
141 2 8 1 -2
142 1 -1 -2 4
143 -2 2 0 -2
144 1 -1 1 0
145 1 -6 9 -1
146 0 8 -8 1
147 -5 -5 4 1
148 1 3 -4 -2
149 -3 -3 3 8
150 7 -3 3 -7
151 -3 5 4 5
152 3 1 -8 -8
153 -4 0 1 3
154 -3 -2 0 7
155 4 -4 -2 -1
156 0 7 3 -3
157 1 -4 -4 -1
158 5 2 1 -3
159 -6 1 1 -2
160 -1 -4 -2 9
161 1 1 2 -2
(1-B)Belonging\r
1 33
2 -20
3 1
4 9
5 2
6 -25
7 27
8 -6
9 2
10 3
11 -25
12 13
13 -13
14 33
15 -29
16 17
17 13
18 -24
19 -7
20 9
21 2
22 -14
23 2
24 30
25 -6
26 -4
27 -7
28 9
29 -11
30 13
31 -26
32 17
33 13
34 -10
35 -3
36 -8
37 8
38 5
39 -7
40 5
41 1
42 -21
43 -2
44 17
45 -1
46 -3
47 10
48 -1
49 1
50 -3
51 -5
52 -4
53 -1
54 1
55 13
56 -2
57 -7
58 6
59 -3
60 -17
61 24
62 0
63 -25
64 2
65 26
66 -14
67 7
68 -10
69 6
70 -2
71 -13
72 27
73 -12
74 15
75 -4
76 -10
77 -1
78 8
79 -23
80 25
81 -49
82 41
83 2
84 -1
85 5
86 -2
87 -6
88 -15
89 16
90 3
91 -19
92 23
93 5
94 -2
95 0
96 -11
97 9
98 3
99 -5
100 0
101 -3
102 12
103 -19
104 13
105 -11
106 8
107 -5
108 -17
109 -8
110 36
111 -8
112 14
113 -50
114 38
115 10
116 -32
117 16
118 -1
119 21
120 -36
121 22
122 13
123 -13
124 -3
125 6
126 11
127 -16
128 7
129 -9
130 -1
131 -5
132 11
133 -12
134 9
135 8
136 -28
137 9
138 -9
139 10
140 5
141 -15
142 30
143 2
144 -9
145 12
146 -13
147 -16
148 15
149 -2
150 12
151 -6
152 -8
153 1
154 -3
155 9
156 -24
157 15
158 12
159 3
160 0
161 -15
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `(1-B)Learning` `(1-B)Connected` `(1-B)Depression`
5.305e-05 7.403e-02 2.623e-02 -3.506e-01
`(1-B)Belonging\r`
3.609e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5372 -1.7440 0.0203 1.6945 8.0506
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.305e-05 2.197e-01 0.000 0.9998
`(1-B)Learning` 7.403e-02 7.461e-02 0.992 0.3227
`(1-B)Connected` 2.623e-02 4.803e-02 0.546 0.5858
`(1-B)Depression` -3.506e-01 5.326e-02 -6.583 6.65e-10 ***
`(1-B)Belonging\r` 3.609e-02 1.492e-02 2.419 0.0167 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.788 on 156 degrees of freedom
Multiple R-squared: 0.3177, Adjusted R-squared: 0.3002
F-statistic: 18.16 on 4 and 156 DF, p-value: 2.906e-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,] 0.27702111 0.5540422241 0.7229788880
[2,] 0.15476169 0.3095233799 0.8452383101
[3,] 0.09644961 0.1928992256 0.9035503872
[4,] 0.47716752 0.9543350444 0.5228324778
[5,] 0.97933059 0.0413388127 0.0206694064
[6,] 0.99168621 0.0166275780 0.0083137890
[7,] 0.98841783 0.0231643376 0.0115821688
[8,] 0.98116726 0.0376654871 0.0188327435
[9,] 0.97619770 0.0476046019 0.0238023009
[10,] 0.96277819 0.0744436229 0.0372218114
[11,] 0.95805059 0.0838988260 0.0419494130
[12,] 0.97928969 0.0414206139 0.0207103070
[13,] 0.96849106 0.0630178817 0.0315089408
[14,] 0.98003354 0.0399329141 0.0199664571
[15,] 0.97166083 0.0566783329 0.0283391665
[16,] 0.95938448 0.0812310486 0.0406155243
[17,] 0.95293186 0.0941362840 0.0470681420
[18,] 0.99712440 0.0057511915 0.0028755957
[19,] 0.99988853 0.0002229445 0.0001114722
[20,] 0.99980264 0.0003947228 0.0001973614
[21,] 0.99965998 0.0006800458 0.0003400229
[22,] 0.99971663 0.0005667347 0.0002833673
[23,] 0.99971734 0.0005653196 0.0002826598
[24,] 0.99953873 0.0009225482 0.0004612741
[25,] 0.99928282 0.0014343543 0.0007171771
[26,] 0.99911988 0.0017602412 0.0008801206
[27,] 0.99861473 0.0027705463 0.0013852732
[28,] 0.99800797 0.0039840679 0.0019920340
[29,] 0.99775273 0.0044945302 0.0022472651
[30,] 0.99719655 0.0056068999 0.0028034499
[31,] 0.99612881 0.0077423779 0.0038711889
[32,] 0.99711417 0.0057716551 0.0028858275
[33,] 0.99606347 0.0078730636 0.0039365318
[34,] 0.99610683 0.0077863307 0.0038931653
[35,] 0.99501957 0.0099608623 0.0049804312
[36,] 0.99330181 0.0133963709 0.0066981855
[37,] 0.99171087 0.0165782517 0.0082891258
[38,] 0.98837895 0.0232420958 0.0116210479
[39,] 0.98712040 0.0257591935 0.0128795967
[40,] 0.98240457 0.0351908593 0.0175954296
[41,] 0.98354069 0.0329186275 0.0164593137
[42,] 0.99196759 0.0160648151 0.0080324075
[43,] 0.99150729 0.0169854129 0.0084927064
[44,] 0.98825216 0.0234956805 0.0117478403
[45,] 0.98476766 0.0304646800 0.0152323400
[46,] 0.97995116 0.0400976725 0.0200488363
[47,] 0.97358040 0.0528391983 0.0264195992
[48,] 0.97039925 0.0592014920 0.0296007460
[49,] 0.96300517 0.0739896630 0.0369948315
[50,] 0.95707742 0.0858451686 0.0429225843
[51,] 0.95750647 0.0849870519 0.0424935260
[52,] 0.95060170 0.0987966043 0.0493983021
[53,] 0.93928939 0.1214212274 0.0607106137
[54,] 0.92483138 0.1503372356 0.0751686178
[55,] 0.91521629 0.1695674244 0.0847837122
[56,] 0.94664868 0.1067026424 0.0533513212
[57,] 0.93285829 0.1342834125 0.0671417063
[58,] 0.97898678 0.0420264483 0.0210132241
[59,] 0.98192856 0.0361428719 0.0180714360
[60,] 0.97676040 0.0464791947 0.0232395973
[61,] 0.98226625 0.0354675049 0.0177337525
[62,] 0.97653259 0.0469348120 0.0234674060
[63,] 0.97026310 0.0594737934 0.0297368967
[64,] 0.97188542 0.0562291616 0.0281145808
[65,] 0.97285476 0.0542904772 0.0271452386
[66,] 0.96550725 0.0689854947 0.0344927474
[67,] 0.96040772 0.0791845627 0.0395922814
[68,] 0.95471513 0.0905697485 0.0452848742
[69,] 0.95852589 0.0829482228 0.0414741114
[70,] 0.95582460 0.0883507942 0.0441753971
[71,] 0.94500781 0.1099843882 0.0549921941
[72,] 0.94847384 0.1030523277 0.0515261638
[73,] 0.94202637 0.1159472614 0.0579736307
[74,] 0.93676757 0.1264648607 0.0632324304
[75,] 0.93815469 0.1236906190 0.0618453095
[76,] 0.93120801 0.1375839814 0.0687919907
[77,] 0.91612635 0.1677472965 0.0838736482
[78,] 0.89896477 0.2020704646 0.1010352323
[79,] 0.88033258 0.2393348407 0.1196674203
[80,] 0.85968536 0.2806292713 0.1403146356
[81,] 0.83516426 0.3296714825 0.1648357413
[82,] 0.87057520 0.2588495916 0.1294247958
[83,] 0.96093270 0.0781345927 0.0390672964
[84,] 0.96222306 0.0755538897 0.0377769449
[85,] 0.95190976 0.0961804875 0.0480902438
[86,] 0.93922293 0.1215541362 0.0607770681
[87,] 0.93542040 0.1291591941 0.0645795971
[88,] 0.93736013 0.1252797446 0.0626398723
[89,] 0.92808244 0.1438351232 0.0719175616
[90,] 0.91015903 0.1796819480 0.0898409740
[91,] 0.91111984 0.1777603158 0.0888801579
[92,] 0.90035387 0.1992922517 0.0996461259
[93,] 0.88787140 0.2242571993 0.1121285997
[94,] 0.88951037 0.2209792585 0.1104896293
[95,] 0.87635320 0.2472936084 0.1236468042
[96,] 0.90546831 0.1890633785 0.0945316893
[97,] 0.94814032 0.1037193668 0.0518596834
[98,] 0.96751957 0.0649608685 0.0324804342
[99,] 0.97631770 0.0473645901 0.0236822951
[100,] 0.96933608 0.0613278320 0.0306639160
[101,] 0.98322500 0.0335499906 0.0167749953
[102,] 0.98737749 0.0252450222 0.0126225111
[103,] 0.98259889 0.0348022115 0.0174011057
[104,] 0.98569679 0.0286064205 0.0143032103
[105,] 0.98012659 0.0397468234 0.0198734117
[106,] 0.99211553 0.0157689447 0.0078844723
[107,] 0.98922174 0.0215565206 0.0107782603
[108,] 0.99210156 0.0157968702 0.0078984351
[109,] 0.99006425 0.0198714996 0.0099357498
[110,] 0.98721872 0.0255625690 0.0127812845
[111,] 0.98375198 0.0324960386 0.0162480193
[112,] 0.97822179 0.0435564167 0.0217782084
[113,] 0.96985955 0.0602809041 0.0301404520
[114,] 0.95890258 0.0821948375 0.0410974188
[115,] 0.94562705 0.1087458971 0.0543729485
[116,] 0.92832375 0.1433524952 0.0716762476
[117,] 0.91143509 0.1771298120 0.0885649060
[118,] 0.91048134 0.1790373269 0.0895186635
[119,] 0.90703164 0.1859367273 0.0929683637
[120,] 0.90065235 0.1986952945 0.0993476473
[121,] 0.88032133 0.2393573494 0.1196786747
[122,] 0.87253368 0.2549326411 0.1274663206
[123,] 0.88488590 0.2302281996 0.1151140998
[124,] 0.85030117 0.2993976632 0.1496988316
[125,] 0.83146685 0.3370662936 0.1685331468
[126,] 0.81332265 0.3733546981 0.1866773490
[127,] 0.77936040 0.4412791925 0.2206395963
[128,] 0.73268770 0.5346245937 0.2673122969
[129,] 0.67971400 0.6405720022 0.3202860011
[130,] 0.63296520 0.7340696067 0.3670348033
[131,] 0.57645181 0.8470963812 0.4235481906
[132,] 0.51766784 0.9646643242 0.4823321621
[133,] 0.48361301 0.9672260269 0.5163869866
[134,] 0.46719315 0.9343863059 0.5328068471
[135,] 0.39192802 0.7838560339 0.6080719831
[136,] 0.39462037 0.7892407483 0.6053796259
[137,] 0.34397615 0.6879522906 0.6560238547
[138,] 0.26864021 0.5372804150 0.7313597925
[139,] 0.21187341 0.4237468298 0.7881265851
[140,] 0.29009698 0.5801939516 0.7099030242
[141,] 0.21577133 0.4315426610 0.7842286695
[142,] 0.14970682 0.2994136350 0.8502931825
[143,] 0.12517775 0.2503554960 0.8748222520
[144,] 0.07672209 0.1534441872 0.9232779064
[145,] 0.04630413 0.0926082666 0.9536958667
[146,] 0.03068071 0.0613614188 0.9693192906
[147,] 0.01256824 0.0251364891 0.9874317554
> postscript(file="/var/wessaorg/rcomp/tmp/1cqax1322159749.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2a3vu1322159749.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vgf91322159749.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/49wg21322159749.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5jgx61322159749.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 = 161
Frequency = 1
1 2 3 4 5 6
2.288818165 -5.212582530 0.532567196 6.825845404 -1.179758216 -0.141009096
7 8 9 10 11 12
-4.403699471 0.887460591 0.879294749 0.739650857 2.253406617 -7.067495728
13 14 15 16 17 18
6.092270605 -0.510000505 -1.376851910 -0.718445775 1.056429387 -2.362704390
19 20 21 22 23 24
3.635974076 -0.428399330 -4.591854832 2.372101617 -1.096504370 2.739055265
25 26 27 28 29 30
-8.537161703 8.050638310 -0.192363007 0.250534470 -3.831845767 3.133080345
31 32 33 34 35 36
0.368774787 1.223181359 -2.187985855 0.210069520 0.925904292 -2.647652197
37 38 39 40 41 42
2.426809387 1.153838637 -3.904788693 1.416465199 2.943556902 -1.894843208
43 44 45 46 47 48
-1.301468573 -1.743998784 -0.014466014 2.457525740 0.397349911 3.285700750
49 50 51 52 53 54
-5.342902432 2.766908667 -0.269184106 -1.305271904 -0.714904507 -0.517261536
55 56 57 58 59 60
2.380125002 1.222205927 -1.887558810 -3.001757853 1.839594220 -1.206353429
61 62 63 64 65 66
0.823179577 -1.895152898 4.830061609 0.350444331 -6.739775821 3.508477569
67 68 69 70 71 72
-0.848280821 3.919569941 -0.057205453 -0.912967580 3.057730511 -3.050402532
73 74 75 76 77 78
-0.723677673 -1.937817965 1.743884798 3.383748526 -2.482919408 0.688312899
79 80 81 82 83 84
-3.030518602 1.903837511 2.016637780 -2.481676937 2.001128935 -0.832840061
85 86 87 88 89 90
-0.930154010 1.089056578 -1.151052977 0.697942123 -4.154179587 7.010888325
91 92 93 94 95 96
-3.114472771 0.568996875 -0.326601951 -2.454106528 2.866200412 -1.797673782
97 98 99 100 101 102
-0.025356571 2.839233478 -1.891022327 1.694537495 -2.762008523 1.894629230
103 104 105 106 107 108
-4.189929353 5.183514333 -4.512795655 3.662834732 0.928703849 -4.606027972
109 110 111 112 113 114
3.556526657 0.239666437 -3.386983021 -0.007907607 4.502826040 1.519736988
115 116 117 118 119 120
-3.506294071 0.958145824 -0.954275544 -1.266081332 0.992445252 -0.347678286
121 122 123 124 125 126
0.685783945 -0.482458908 -0.778553519 1.338918156 -2.238159376 2.415432426
127 128 129 130 131 132
0.999352670 2.534459689 -2.804438742 -2.882593951 -0.115202093 1.338176212
133 134 135 136 137 138
2.823007886 -1.786930890 1.469525507 -0.581301181 -0.785586831 -1.573058901
139 140 141 142 143 144
1.680950751 -1.428880759 1.221616623 1.446163420 -2.921473237 1.372541707
145 146 147 148 149 150
0.424453015 0.437244419 -3.806807690 -0.359744073 0.020277726 4.256158314
151 152 153 154 155 156
-1.505611063 0.619678570 -3.010587840 -0.289581655 3.673132607 -0.782606339
157 158 159 160 161
0.509055894 3.340831060 -6.909756461 2.503849616 0.713598869
> postscript(file="/var/wessaorg/rcomp/tmp/60kyg1322159749.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 = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 2.288818165 NA
1 -5.212582530 2.288818165
2 0.532567196 -5.212582530
3 6.825845404 0.532567196
4 -1.179758216 6.825845404
5 -0.141009096 -1.179758216
6 -4.403699471 -0.141009096
7 0.887460591 -4.403699471
8 0.879294749 0.887460591
9 0.739650857 0.879294749
10 2.253406617 0.739650857
11 -7.067495728 2.253406617
12 6.092270605 -7.067495728
13 -0.510000505 6.092270605
14 -1.376851910 -0.510000505
15 -0.718445775 -1.376851910
16 1.056429387 -0.718445775
17 -2.362704390 1.056429387
18 3.635974076 -2.362704390
19 -0.428399330 3.635974076
20 -4.591854832 -0.428399330
21 2.372101617 -4.591854832
22 -1.096504370 2.372101617
23 2.739055265 -1.096504370
24 -8.537161703 2.739055265
25 8.050638310 -8.537161703
26 -0.192363007 8.050638310
27 0.250534470 -0.192363007
28 -3.831845767 0.250534470
29 3.133080345 -3.831845767
30 0.368774787 3.133080345
31 1.223181359 0.368774787
32 -2.187985855 1.223181359
33 0.210069520 -2.187985855
34 0.925904292 0.210069520
35 -2.647652197 0.925904292
36 2.426809387 -2.647652197
37 1.153838637 2.426809387
38 -3.904788693 1.153838637
39 1.416465199 -3.904788693
40 2.943556902 1.416465199
41 -1.894843208 2.943556902
42 -1.301468573 -1.894843208
43 -1.743998784 -1.301468573
44 -0.014466014 -1.743998784
45 2.457525740 -0.014466014
46 0.397349911 2.457525740
47 3.285700750 0.397349911
48 -5.342902432 3.285700750
49 2.766908667 -5.342902432
50 -0.269184106 2.766908667
51 -1.305271904 -0.269184106
52 -0.714904507 -1.305271904
53 -0.517261536 -0.714904507
54 2.380125002 -0.517261536
55 1.222205927 2.380125002
56 -1.887558810 1.222205927
57 -3.001757853 -1.887558810
58 1.839594220 -3.001757853
59 -1.206353429 1.839594220
60 0.823179577 -1.206353429
61 -1.895152898 0.823179577
62 4.830061609 -1.895152898
63 0.350444331 4.830061609
64 -6.739775821 0.350444331
65 3.508477569 -6.739775821
66 -0.848280821 3.508477569
67 3.919569941 -0.848280821
68 -0.057205453 3.919569941
69 -0.912967580 -0.057205453
70 3.057730511 -0.912967580
71 -3.050402532 3.057730511
72 -0.723677673 -3.050402532
73 -1.937817965 -0.723677673
74 1.743884798 -1.937817965
75 3.383748526 1.743884798
76 -2.482919408 3.383748526
77 0.688312899 -2.482919408
78 -3.030518602 0.688312899
79 1.903837511 -3.030518602
80 2.016637780 1.903837511
81 -2.481676937 2.016637780
82 2.001128935 -2.481676937
83 -0.832840061 2.001128935
84 -0.930154010 -0.832840061
85 1.089056578 -0.930154010
86 -1.151052977 1.089056578
87 0.697942123 -1.151052977
88 -4.154179587 0.697942123
89 7.010888325 -4.154179587
90 -3.114472771 7.010888325
91 0.568996875 -3.114472771
92 -0.326601951 0.568996875
93 -2.454106528 -0.326601951
94 2.866200412 -2.454106528
95 -1.797673782 2.866200412
96 -0.025356571 -1.797673782
97 2.839233478 -0.025356571
98 -1.891022327 2.839233478
99 1.694537495 -1.891022327
100 -2.762008523 1.694537495
101 1.894629230 -2.762008523
102 -4.189929353 1.894629230
103 5.183514333 -4.189929353
104 -4.512795655 5.183514333
105 3.662834732 -4.512795655
106 0.928703849 3.662834732
107 -4.606027972 0.928703849
108 3.556526657 -4.606027972
109 0.239666437 3.556526657
110 -3.386983021 0.239666437
111 -0.007907607 -3.386983021
112 4.502826040 -0.007907607
113 1.519736988 4.502826040
114 -3.506294071 1.519736988
115 0.958145824 -3.506294071
116 -0.954275544 0.958145824
117 -1.266081332 -0.954275544
118 0.992445252 -1.266081332
119 -0.347678286 0.992445252
120 0.685783945 -0.347678286
121 -0.482458908 0.685783945
122 -0.778553519 -0.482458908
123 1.338918156 -0.778553519
124 -2.238159376 1.338918156
125 2.415432426 -2.238159376
126 0.999352670 2.415432426
127 2.534459689 0.999352670
128 -2.804438742 2.534459689
129 -2.882593951 -2.804438742
130 -0.115202093 -2.882593951
131 1.338176212 -0.115202093
132 2.823007886 1.338176212
133 -1.786930890 2.823007886
134 1.469525507 -1.786930890
135 -0.581301181 1.469525507
136 -0.785586831 -0.581301181
137 -1.573058901 -0.785586831
138 1.680950751 -1.573058901
139 -1.428880759 1.680950751
140 1.221616623 -1.428880759
141 1.446163420 1.221616623
142 -2.921473237 1.446163420
143 1.372541707 -2.921473237
144 0.424453015 1.372541707
145 0.437244419 0.424453015
146 -3.806807690 0.437244419
147 -0.359744073 -3.806807690
148 0.020277726 -0.359744073
149 4.256158314 0.020277726
150 -1.505611063 4.256158314
151 0.619678570 -1.505611063
152 -3.010587840 0.619678570
153 -0.289581655 -3.010587840
154 3.673132607 -0.289581655
155 -0.782606339 3.673132607
156 0.509055894 -0.782606339
157 3.340831060 0.509055894
158 -6.909756461 3.340831060
159 2.503849616 -6.909756461
160 0.713598869 2.503849616
161 NA 0.713598869
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.212582530 2.288818165
[2,] 0.532567196 -5.212582530
[3,] 6.825845404 0.532567196
[4,] -1.179758216 6.825845404
[5,] -0.141009096 -1.179758216
[6,] -4.403699471 -0.141009096
[7,] 0.887460591 -4.403699471
[8,] 0.879294749 0.887460591
[9,] 0.739650857 0.879294749
[10,] 2.253406617 0.739650857
[11,] -7.067495728 2.253406617
[12,] 6.092270605 -7.067495728
[13,] -0.510000505 6.092270605
[14,] -1.376851910 -0.510000505
[15,] -0.718445775 -1.376851910
[16,] 1.056429387 -0.718445775
[17,] -2.362704390 1.056429387
[18,] 3.635974076 -2.362704390
[19,] -0.428399330 3.635974076
[20,] -4.591854832 -0.428399330
[21,] 2.372101617 -4.591854832
[22,] -1.096504370 2.372101617
[23,] 2.739055265 -1.096504370
[24,] -8.537161703 2.739055265
[25,] 8.050638310 -8.537161703
[26,] -0.192363007 8.050638310
[27,] 0.250534470 -0.192363007
[28,] -3.831845767 0.250534470
[29,] 3.133080345 -3.831845767
[30,] 0.368774787 3.133080345
[31,] 1.223181359 0.368774787
[32,] -2.187985855 1.223181359
[33,] 0.210069520 -2.187985855
[34,] 0.925904292 0.210069520
[35,] -2.647652197 0.925904292
[36,] 2.426809387 -2.647652197
[37,] 1.153838637 2.426809387
[38,] -3.904788693 1.153838637
[39,] 1.416465199 -3.904788693
[40,] 2.943556902 1.416465199
[41,] -1.894843208 2.943556902
[42,] -1.301468573 -1.894843208
[43,] -1.743998784 -1.301468573
[44,] -0.014466014 -1.743998784
[45,] 2.457525740 -0.014466014
[46,] 0.397349911 2.457525740
[47,] 3.285700750 0.397349911
[48,] -5.342902432 3.285700750
[49,] 2.766908667 -5.342902432
[50,] -0.269184106 2.766908667
[51,] -1.305271904 -0.269184106
[52,] -0.714904507 -1.305271904
[53,] -0.517261536 -0.714904507
[54,] 2.380125002 -0.517261536
[55,] 1.222205927 2.380125002
[56,] -1.887558810 1.222205927
[57,] -3.001757853 -1.887558810
[58,] 1.839594220 -3.001757853
[59,] -1.206353429 1.839594220
[60,] 0.823179577 -1.206353429
[61,] -1.895152898 0.823179577
[62,] 4.830061609 -1.895152898
[63,] 0.350444331 4.830061609
[64,] -6.739775821 0.350444331
[65,] 3.508477569 -6.739775821
[66,] -0.848280821 3.508477569
[67,] 3.919569941 -0.848280821
[68,] -0.057205453 3.919569941
[69,] -0.912967580 -0.057205453
[70,] 3.057730511 -0.912967580
[71,] -3.050402532 3.057730511
[72,] -0.723677673 -3.050402532
[73,] -1.937817965 -0.723677673
[74,] 1.743884798 -1.937817965
[75,] 3.383748526 1.743884798
[76,] -2.482919408 3.383748526
[77,] 0.688312899 -2.482919408
[78,] -3.030518602 0.688312899
[79,] 1.903837511 -3.030518602
[80,] 2.016637780 1.903837511
[81,] -2.481676937 2.016637780
[82,] 2.001128935 -2.481676937
[83,] -0.832840061 2.001128935
[84,] -0.930154010 -0.832840061
[85,] 1.089056578 -0.930154010
[86,] -1.151052977 1.089056578
[87,] 0.697942123 -1.151052977
[88,] -4.154179587 0.697942123
[89,] 7.010888325 -4.154179587
[90,] -3.114472771 7.010888325
[91,] 0.568996875 -3.114472771
[92,] -0.326601951 0.568996875
[93,] -2.454106528 -0.326601951
[94,] 2.866200412 -2.454106528
[95,] -1.797673782 2.866200412
[96,] -0.025356571 -1.797673782
[97,] 2.839233478 -0.025356571
[98,] -1.891022327 2.839233478
[99,] 1.694537495 -1.891022327
[100,] -2.762008523 1.694537495
[101,] 1.894629230 -2.762008523
[102,] -4.189929353 1.894629230
[103,] 5.183514333 -4.189929353
[104,] -4.512795655 5.183514333
[105,] 3.662834732 -4.512795655
[106,] 0.928703849 3.662834732
[107,] -4.606027972 0.928703849
[108,] 3.556526657 -4.606027972
[109,] 0.239666437 3.556526657
[110,] -3.386983021 0.239666437
[111,] -0.007907607 -3.386983021
[112,] 4.502826040 -0.007907607
[113,] 1.519736988 4.502826040
[114,] -3.506294071 1.519736988
[115,] 0.958145824 -3.506294071
[116,] -0.954275544 0.958145824
[117,] -1.266081332 -0.954275544
[118,] 0.992445252 -1.266081332
[119,] -0.347678286 0.992445252
[120,] 0.685783945 -0.347678286
[121,] -0.482458908 0.685783945
[122,] -0.778553519 -0.482458908
[123,] 1.338918156 -0.778553519
[124,] -2.238159376 1.338918156
[125,] 2.415432426 -2.238159376
[126,] 0.999352670 2.415432426
[127,] 2.534459689 0.999352670
[128,] -2.804438742 2.534459689
[129,] -2.882593951 -2.804438742
[130,] -0.115202093 -2.882593951
[131,] 1.338176212 -0.115202093
[132,] 2.823007886 1.338176212
[133,] -1.786930890 2.823007886
[134,] 1.469525507 -1.786930890
[135,] -0.581301181 1.469525507
[136,] -0.785586831 -0.581301181
[137,] -1.573058901 -0.785586831
[138,] 1.680950751 -1.573058901
[139,] -1.428880759 1.680950751
[140,] 1.221616623 -1.428880759
[141,] 1.446163420 1.221616623
[142,] -2.921473237 1.446163420
[143,] 1.372541707 -2.921473237
[144,] 0.424453015 1.372541707
[145,] 0.437244419 0.424453015
[146,] -3.806807690 0.437244419
[147,] -0.359744073 -3.806807690
[148,] 0.020277726 -0.359744073
[149,] 4.256158314 0.020277726
[150,] -1.505611063 4.256158314
[151,] 0.619678570 -1.505611063
[152,] -3.010587840 0.619678570
[153,] -0.289581655 -3.010587840
[154,] 3.673132607 -0.289581655
[155,] -0.782606339 3.673132607
[156,] 0.509055894 -0.782606339
[157,] 3.340831060 0.509055894
[158,] -6.909756461 3.340831060
[159,] 2.503849616 -6.909756461
[160,] 0.713598869 2.503849616
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.212582530 2.288818165
2 0.532567196 -5.212582530
3 6.825845404 0.532567196
4 -1.179758216 6.825845404
5 -0.141009096 -1.179758216
6 -4.403699471 -0.141009096
7 0.887460591 -4.403699471
8 0.879294749 0.887460591
9 0.739650857 0.879294749
10 2.253406617 0.739650857
11 -7.067495728 2.253406617
12 6.092270605 -7.067495728
13 -0.510000505 6.092270605
14 -1.376851910 -0.510000505
15 -0.718445775 -1.376851910
16 1.056429387 -0.718445775
17 -2.362704390 1.056429387
18 3.635974076 -2.362704390
19 -0.428399330 3.635974076
20 -4.591854832 -0.428399330
21 2.372101617 -4.591854832
22 -1.096504370 2.372101617
23 2.739055265 -1.096504370
24 -8.537161703 2.739055265
25 8.050638310 -8.537161703
26 -0.192363007 8.050638310
27 0.250534470 -0.192363007
28 -3.831845767 0.250534470
29 3.133080345 -3.831845767
30 0.368774787 3.133080345
31 1.223181359 0.368774787
32 -2.187985855 1.223181359
33 0.210069520 -2.187985855
34 0.925904292 0.210069520
35 -2.647652197 0.925904292
36 2.426809387 -2.647652197
37 1.153838637 2.426809387
38 -3.904788693 1.153838637
39 1.416465199 -3.904788693
40 2.943556902 1.416465199
41 -1.894843208 2.943556902
42 -1.301468573 -1.894843208
43 -1.743998784 -1.301468573
44 -0.014466014 -1.743998784
45 2.457525740 -0.014466014
46 0.397349911 2.457525740
47 3.285700750 0.397349911
48 -5.342902432 3.285700750
49 2.766908667 -5.342902432
50 -0.269184106 2.766908667
51 -1.305271904 -0.269184106
52 -0.714904507 -1.305271904
53 -0.517261536 -0.714904507
54 2.380125002 -0.517261536
55 1.222205927 2.380125002
56 -1.887558810 1.222205927
57 -3.001757853 -1.887558810
58 1.839594220 -3.001757853
59 -1.206353429 1.839594220
60 0.823179577 -1.206353429
61 -1.895152898 0.823179577
62 4.830061609 -1.895152898
63 0.350444331 4.830061609
64 -6.739775821 0.350444331
65 3.508477569 -6.739775821
66 -0.848280821 3.508477569
67 3.919569941 -0.848280821
68 -0.057205453 3.919569941
69 -0.912967580 -0.057205453
70 3.057730511 -0.912967580
71 -3.050402532 3.057730511
72 -0.723677673 -3.050402532
73 -1.937817965 -0.723677673
74 1.743884798 -1.937817965
75 3.383748526 1.743884798
76 -2.482919408 3.383748526
77 0.688312899 -2.482919408
78 -3.030518602 0.688312899
79 1.903837511 -3.030518602
80 2.016637780 1.903837511
81 -2.481676937 2.016637780
82 2.001128935 -2.481676937
83 -0.832840061 2.001128935
84 -0.930154010 -0.832840061
85 1.089056578 -0.930154010
86 -1.151052977 1.089056578
87 0.697942123 -1.151052977
88 -4.154179587 0.697942123
89 7.010888325 -4.154179587
90 -3.114472771 7.010888325
91 0.568996875 -3.114472771
92 -0.326601951 0.568996875
93 -2.454106528 -0.326601951
94 2.866200412 -2.454106528
95 -1.797673782 2.866200412
96 -0.025356571 -1.797673782
97 2.839233478 -0.025356571
98 -1.891022327 2.839233478
99 1.694537495 -1.891022327
100 -2.762008523 1.694537495
101 1.894629230 -2.762008523
102 -4.189929353 1.894629230
103 5.183514333 -4.189929353
104 -4.512795655 5.183514333
105 3.662834732 -4.512795655
106 0.928703849 3.662834732
107 -4.606027972 0.928703849
108 3.556526657 -4.606027972
109 0.239666437 3.556526657
110 -3.386983021 0.239666437
111 -0.007907607 -3.386983021
112 4.502826040 -0.007907607
113 1.519736988 4.502826040
114 -3.506294071 1.519736988
115 0.958145824 -3.506294071
116 -0.954275544 0.958145824
117 -1.266081332 -0.954275544
118 0.992445252 -1.266081332
119 -0.347678286 0.992445252
120 0.685783945 -0.347678286
121 -0.482458908 0.685783945
122 -0.778553519 -0.482458908
123 1.338918156 -0.778553519
124 -2.238159376 1.338918156
125 2.415432426 -2.238159376
126 0.999352670 2.415432426
127 2.534459689 0.999352670
128 -2.804438742 2.534459689
129 -2.882593951 -2.804438742
130 -0.115202093 -2.882593951
131 1.338176212 -0.115202093
132 2.823007886 1.338176212
133 -1.786930890 2.823007886
134 1.469525507 -1.786930890
135 -0.581301181 1.469525507
136 -0.785586831 -0.581301181
137 -1.573058901 -0.785586831
138 1.680950751 -1.573058901
139 -1.428880759 1.680950751
140 1.221616623 -1.428880759
141 1.446163420 1.221616623
142 -2.921473237 1.446163420
143 1.372541707 -2.921473237
144 0.424453015 1.372541707
145 0.437244419 0.424453015
146 -3.806807690 0.437244419
147 -0.359744073 -3.806807690
148 0.020277726 -0.359744073
149 4.256158314 0.020277726
150 -1.505611063 4.256158314
151 0.619678570 -1.505611063
152 -3.010587840 0.619678570
153 -0.289581655 -3.010587840
154 3.673132607 -0.289581655
155 -0.782606339 3.673132607
156 0.509055894 -0.782606339
157 3.340831060 0.509055894
158 -6.909756461 3.340831060
159 2.503849616 -6.909756461
160 0.713598869 2.503849616
> 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/7jznx1322159749.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ybza1322159749.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9197c1322159749.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10d3681322159749.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jlit1322159749.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/12hzv11322159749.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/13fs7g1322159749.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/14d6ww1322159749.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/155ju41322159749.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/16vuex1322159749.tab")
+ }
>
> try(system("convert tmp/1cqax1322159749.ps tmp/1cqax1322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a3vu1322159749.ps tmp/2a3vu1322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vgf91322159749.ps tmp/3vgf91322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/49wg21322159749.ps tmp/49wg21322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jgx61322159749.ps tmp/5jgx61322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/60kyg1322159749.ps tmp/60kyg1322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jznx1322159749.ps tmp/7jznx1322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ybza1322159749.ps tmp/8ybza1322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/9197c1322159749.ps tmp/9197c1322159749.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d3681322159749.ps tmp/10d3681322159749.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.694 0.498 5.221