R version 2.12.1 (2010-12-16)
Copyright (C) 2010 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(14
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+ ,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 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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 Learning Connected Depression Belonging\r M1 M2 M3 M4 M5 M6 M7 M8
1 14 13 41 12 53 1 0 0 0 0 0 0 0
2 18 16 39 11 86 0 1 0 0 0 0 0 0
3 11 19 30 14 66 0 0 1 0 0 0 0 0
4 12 15 31 12 67 0 0 0 1 0 0 0 0
5 16 14 34 21 76 0 0 0 0 1 0 0 0
6 18 13 35 12 78 0 0 0 0 0 1 0 0
7 14 19 39 22 53 0 0 0 0 0 0 1 0
8 14 15 34 11 80 0 0 0 0 0 0 0 1
9 15 14 36 10 74 0 0 0 0 0 0 0 0
10 15 15 37 13 76 0 0 0 0 0 0 0 0
11 17 16 38 10 79 0 0 0 0 0 0 0 0
12 19 16 36 8 54 0 0 0 0 0 0 0 0
13 10 16 38 15 67 1 0 0 0 0 0 0 0
14 16 16 39 14 54 0 1 0 0 0 0 0 0
15 18 17 33 10 87 0 0 1 0 0 0 0 0
16 14 15 32 14 58 0 0 0 1 0 0 0 0
17 14 15 36 14 75 0 0 0 0 1 0 0 0
18 17 20 38 11 88 0 0 0 0 0 1 0 0
19 14 18 39 10 64 0 0 0 0 0 0 1 0
20 16 16 32 13 57 0 0 0 0 0 0 0 1
21 18 16 32 7 66 0 0 0 0 0 0 0 0
22 11 16 31 14 68 0 0 0 0 0 0 0 0
23 14 19 39 12 54 0 0 0 0 0 0 0 0
24 12 16 37 14 56 0 0 0 0 0 0 0 0
25 17 17 39 11 86 1 0 0 0 0 0 0 0
26 9 17 41 9 80 0 1 0 0 0 0 0 0
27 16 16 36 11 76 0 0 1 0 0 0 0 0
28 14 15 33 15 69 0 0 0 1 0 0 0 0
29 15 16 33 14 78 0 0 0 0 1 0 0 0
30 11 14 34 13 67 0 0 0 0 0 1 0 0
31 16 15 31 9 80 0 0 0 0 0 0 1 0
32 13 12 27 15 54 0 0 0 0 0 0 0 1
33 17 14 37 10 71 0 0 0 0 0 0 0 0
34 15 16 34 11 84 0 0 0 0 0 0 0 0
35 14 14 34 13 74 0 0 0 0 0 0 0 0
36 16 7 32 8 71 0 0 0 0 0 0 0 0
37 9 10 29 20 63 1 0 0 0 0 0 0 0
38 15 14 36 12 71 0 1 0 0 0 0 0 0
39 17 16 29 10 76 0 0 1 0 0 0 0 0
40 13 16 35 10 69 0 0 0 1 0 0 0 0
41 15 16 37 9 74 0 0 0 0 1 0 0 0
42 16 14 34 14 75 0 0 0 0 0 1 0 0
43 16 20 38 8 54 0 0 0 0 0 0 1 0
44 12 14 35 14 52 0 0 0 0 0 0 0 1
45 12 14 38 11 69 0 0 0 0 0 0 0 0
46 11 11 37 13 68 0 0 0 0 0 0 0 0
47 15 14 38 9 65 0 0 0 0 0 0 0 0
48 15 15 33 11 75 0 0 0 0 0 0 0 0
49 17 16 36 15 74 1 0 0 0 0 0 0 0
50 13 14 38 11 75 0 1 0 0 0 0 0 0
51 16 16 32 10 72 0 0 1 0 0 0 0 0
52 14 14 32 14 67 0 0 0 1 0 0 0 0
53 11 12 32 18 63 0 0 0 0 1 0 0 0
54 12 16 34 14 62 0 0 0 0 0 1 0 0
55 12 9 32 11 63 0 0 0 0 0 0 1 0
56 15 14 37 12 76 0 0 0 0 0 0 0 1
57 16 16 39 13 74 0 0 0 0 0 0 0 0
58 15 16 29 9 67 0 0 0 0 0 0 0 0
59 12 15 37 10 73 0 0 0 0 0 0 0 0
60 12 16 35 15 70 0 0 0 0 0 0 0 0
61 8 12 30 20 53 1 0 0 0 0 0 0 0
62 13 16 38 12 77 0 1 0 0 0 0 0 0
63 11 16 34 12 77 0 0 1 0 0 0 0 0
64 14 14 31 14 52 0 0 0 1 0 0 0 0
65 15 16 34 13 54 0 0 0 0 1 0 0 0
66 10 17 35 11 80 0 0 0 0 0 1 0 0
67 11 18 36 17 66 0 0 0 0 0 0 1 0
68 12 18 30 12 73 0 0 0 0 0 0 0 1
69 15 12 39 13 63 0 0 0 0 0 0 0 0
70 15 16 35 14 69 0 0 0 0 0 0 0 0
71 14 10 38 13 67 0 0 0 0 0 0 0 0
72 16 14 31 15 54 0 0 0 0 0 0 0 0
73 15 18 34 13 81 1 0 0 0 0 0 0 0
74 15 18 38 10 69 0 1 0 0 0 0 0 0
75 13 16 34 11 84 0 0 1 0 0 0 0 0
76 12 17 39 19 80 0 0 0 1 0 0 0 0
77 17 16 37 13 70 0 0 0 0 1 0 0 0
78 13 16 34 17 69 0 0 0 0 0 1 0 0
79 15 13 28 13 77 0 0 0 0 0 0 1 0
80 13 16 37 9 54 0 0 0 0 0 0 0 1
81 15 16 33 11 79 0 0 0 0 0 0 0 0
82 16 20 37 10 30 0 0 0 0 0 0 0 0
83 15 16 35 9 71 0 0 0 0 0 0 0 0
84 16 15 37 12 73 0 0 0 0 0 0 0 0
85 15 15 32 12 72 1 0 0 0 0 0 0 0
86 14 16 33 13 77 0 1 0 0 0 0 0 0
87 15 14 38 13 75 0 0 1 0 0 0 0 0
88 14 16 33 12 69 0 0 0 1 0 0 0 0
89 13 16 29 15 54 0 0 0 0 1 0 0 0
90 7 15 33 22 70 0 0 0 0 0 1 0 0
91 17 12 31 13 73 0 0 0 0 0 0 1 0
92 13 17 36 15 54 0 0 0 0 0 0 0 1
93 15 16 35 13 77 0 0 0 0 0 0 0 0
94 14 15 32 15 82 0 0 0 0 0 0 0 0
95 13 13 29 10 80 0 0 0 0 0 0 0 0
96 16 16 39 11 80 0 0 0 0 0 0 0 0
97 12 16 37 16 69 1 0 0 0 0 0 0 0
98 14 16 35 11 78 0 1 0 0 0 0 0 0
99 17 16 37 11 81 0 0 1 0 0 0 0 0
100 15 14 32 10 76 0 0 0 1 0 0 0 0
101 17 16 38 10 76 0 0 0 0 1 0 0 0
102 12 16 37 16 73 0 0 0 0 0 1 0 0
103 16 20 36 12 85 0 0 0 0 0 0 1 0
104 11 15 32 11 66 0 0 0 0 0 0 0 1
105 15 16 33 16 79 0 0 0 0 0 0 0 0
106 9 13 40 19 68 0 0 0 0 0 0 0 0
107 16 17 38 11 76 0 0 0 0 0 0 0 0
108 15 16 41 16 71 0 0 0 0 0 0 0 0
109 10 16 36 15 54 1 0 0 0 0 0 0 0
110 10 12 43 24 46 0 1 0 0 0 0 0 0
111 15 16 30 14 82 0 0 1 0 0 0 0 0
112 11 16 31 15 74 0 0 0 1 0 0 0 0
113 13 17 32 11 88 0 0 0 0 1 0 0 0
114 14 13 32 15 38 0 0 0 0 0 1 0 0
115 18 12 37 12 76 0 0 0 0 0 0 1 0
116 16 18 37 10 86 0 0 0 0 0 0 0 1
117 14 14 33 14 54 0 0 0 0 0 0 0 0
118 14 14 34 13 70 0 0 0 0 0 0 0 0
119 14 13 33 9 69 0 0 0 0 0 0 0 0
120 14 16 38 15 90 0 0 0 0 0 0 0 0
121 12 13 33 15 54 1 0 0 0 0 0 0 0
122 14 16 31 14 76 0 1 0 0 0 0 0 0
123 15 13 38 11 89 0 0 1 0 0 0 0 0
124 15 16 37 8 76 0 0 0 1 0 0 0 0
125 15 15 33 11 73 0 0 0 0 1 0 0 0
126 13 16 31 11 79 0 0 0 0 0 1 0 0
127 17 15 39 8 90 0 0 0 0 0 0 1 0
128 17 17 44 10 74 0 0 0 0 0 0 0 1
129 19 15 33 11 81 0 0 0 0 0 0 0 0
130 15 12 35 13 72 0 0 0 0 0 0 0 0
131 13 16 32 11 71 0 0 0 0 0 0 0 0
132 9 10 28 20 66 0 0 0 0 0 0 0 0
133 15 16 40 10 77 1 0 0 0 0 0 0 0
134 15 12 27 15 65 0 1 0 0 0 0 0 0
135 15 14 37 12 74 0 0 1 0 0 0 0 0
136 16 15 32 14 82 0 0 0 1 0 0 0 0
137 11 13 28 23 54 0 0 0 0 1 0 0 0
138 14 15 34 14 63 0 0 0 0 0 1 0 0
139 11 11 30 16 54 0 0 0 0 0 0 1 0
140 15 12 35 11 64 0 0 0 0 0 0 0 1
141 13 8 31 12 69 0 0 0 0 0 0 0 0
142 15 16 32 10 54 0 0 0 0 0 0 0 0
143 16 15 30 14 84 0 0 0 0 0 0 0 0
144 14 17 30 12 86 0 0 0 0 0 0 0 0
145 15 16 31 12 77 1 0 0 0 0 0 0 0
146 16 10 40 11 89 0 1 0 0 0 0 0 0
147 16 18 32 12 76 0 0 1 0 0 0 0 0
148 11 13 36 13 60 0 0 0 1 0 0 0 0
149 12 16 32 11 75 0 0 0 0 1 0 0 0
150 9 13 35 19 73 0 0 0 0 0 1 0 0
151 16 10 38 12 85 0 0 0 0 0 0 1 0
152 13 15 42 17 79 0 0 0 0 0 0 0 1
153 16 16 34 9 71 0 0 0 0 0 0 0 0
154 12 16 35 12 72 0 0 0 0 0 0 0 0
155 9 14 35 19 69 0 0 0 0 0 0 0 0
156 13 10 33 18 78 0 0 0 0 0 0 0 0
157 13 17 36 15 54 1 0 0 0 0 0 0 0
158 14 13 32 14 69 0 1 0 0 0 0 0 0
159 19 15 33 11 81 0 0 1 0 0 0 0 0
160 13 16 34 9 84 0 0 0 1 0 0 0 0
161 12 12 32 18 84 0 0 0 0 1 0 0 0
162 13 13 34 16 69 0 0 0 0 0 1 0 0
M9 M10 M11 t
1 0 0 0 1
2 0 0 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 0 5
6 0 0 0 6
7 0 0 0 7
8 0 0 0 8
9 1 0 0 9
10 0 1 0 10
11 0 0 1 11
12 0 0 0 12
13 0 0 0 13
14 0 0 0 14
15 0 0 0 15
16 0 0 0 16
17 0 0 0 17
18 0 0 0 18
19 0 0 0 19
20 0 0 0 20
21 1 0 0 21
22 0 1 0 22
23 0 0 1 23
24 0 0 0 24
25 0 0 0 25
26 0 0 0 26
27 0 0 0 27
28 0 0 0 28
29 0 0 0 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 1 0 0 33
34 0 1 0 34
35 0 0 1 35
36 0 0 0 36
37 0 0 0 37
38 0 0 0 38
39 0 0 0 39
40 0 0 0 40
41 0 0 0 41
42 0 0 0 42
43 0 0 0 43
44 0 0 0 44
45 1 0 0 45
46 0 1 0 46
47 0 0 1 47
48 0 0 0 48
49 0 0 0 49
50 0 0 0 50
51 0 0 0 51
52 0 0 0 52
53 0 0 0 53
54 0 0 0 54
55 0 0 0 55
56 0 0 0 56
57 1 0 0 57
58 0 1 0 58
59 0 0 1 59
60 0 0 0 60
61 0 0 0 61
62 0 0 0 62
63 0 0 0 63
64 0 0 0 64
65 0 0 0 65
66 0 0 0 66
67 0 0 0 67
68 0 0 0 68
69 1 0 0 69
70 0 1 0 70
71 0 0 1 71
72 0 0 0 72
73 0 0 0 73
74 0 0 0 74
75 0 0 0 75
76 0 0 0 76
77 0 0 0 77
78 0 0 0 78
79 0 0 0 79
80 0 0 0 80
81 1 0 0 81
82 0 1 0 82
83 0 0 1 83
84 0 0 0 84
85 0 0 0 85
86 0 0 0 86
87 0 0 0 87
88 0 0 0 88
89 0 0 0 89
90 0 0 0 90
91 0 0 0 91
92 0 0 0 92
93 1 0 0 93
94 0 1 0 94
95 0 0 1 95
96 0 0 0 96
97 0 0 0 97
98 0 0 0 98
99 0 0 0 99
100 0 0 0 100
101 0 0 0 101
102 0 0 0 102
103 0 0 0 103
104 0 0 0 104
105 1 0 0 105
106 0 1 0 106
107 0 0 1 107
108 0 0 0 108
109 0 0 0 109
110 0 0 0 110
111 0 0 0 111
112 0 0 0 112
113 0 0 0 113
114 0 0 0 114
115 0 0 0 115
116 0 0 0 116
117 1 0 0 117
118 0 1 0 118
119 0 0 1 119
120 0 0 0 120
121 0 0 0 121
122 0 0 0 122
123 0 0 0 123
124 0 0 0 124
125 0 0 0 125
126 0 0 0 126
127 0 0 0 127
128 0 0 0 128
129 1 0 0 129
130 0 1 0 130
131 0 0 1 131
132 0 0 0 132
133 0 0 0 133
134 0 0 0 134
135 0 0 0 135
136 0 0 0 136
137 0 0 0 137
138 0 0 0 138
139 0 0 0 139
140 0 0 0 140
141 1 0 0 141
142 0 1 0 142
143 0 0 1 143
144 0 0 0 144
145 0 0 0 145
146 0 0 0 146
147 0 0 0 147
148 0 0 0 148
149 0 0 0 149
150 0 0 0 150
151 0 0 0 151
152 0 0 0 152
153 1 0 0 153
154 0 1 0 154
155 0 0 1 155
156 0 0 0 156
157 0 0 0 157
158 0 0 0 158
159 0 0 0 159
160 0 0 0 160
161 0 0 0 161
162 0 0 0 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Learning Connected Depression `Belonging\r`
14.814254 0.056293 0.047890 -0.341951 0.026415
M1 M2 M3 M4 M5
-1.049469 -0.724722 0.002541 -1.166967 -0.065593
M6 M7 M8 M9 M10
-1.209914 0.095210 -0.923234 0.306605 -0.964929
M11 t
-1.117100 -0.001886
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9966 -1.3045 0.2459 1.1115 4.0420
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.814254 2.401578 6.169 6.51e-09 ***
Learning 0.056293 0.073854 0.762 0.4472
Connected 0.047890 0.048585 0.986 0.3259
Depression -0.341951 0.055195 -6.195 5.69e-09 ***
`Belonging\r` 0.026415 0.015697 1.683 0.0946 .
M1 -1.049469 0.754703 -1.391 0.1665
M2 -0.724722 0.752213 -0.963 0.3369
M3 0.002541 0.768175 0.003 0.9974
M4 -1.166967 0.754424 -1.547 0.1241
M5 -0.065593 0.755473 -0.087 0.9309
M6 -1.209914 0.754338 -1.604 0.1109
M7 0.095210 0.763110 0.125 0.9009
M8 -0.923234 0.770852 -1.198 0.2330
M9 0.306605 0.767553 0.399 0.6901
M10 -0.964929 0.766731 -1.258 0.2102
M11 -1.117100 0.767795 -1.455 0.1478
t -0.001886 0.003412 -0.553 0.5812
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.94 on 145 degrees of freedom
Multiple R-squared: 0.38, Adjusted R-squared: 0.3116
F-statistic: 5.554 on 16 and 145 DF, p-value: 3.637e-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,] 0.8078531 0.3842937749 1.921469e-01
[2,] 0.9429387 0.1141226421 5.706132e-02
[3,] 0.9320190 0.1359620405 6.798102e-02
[4,] 0.9001485 0.1997030126 9.985151e-02
[5,] 0.9817914 0.0364171630 1.820858e-02
[6,] 0.9920334 0.0159331108 7.966555e-03
[7,] 0.9999476 0.0001048703 5.243513e-05
[8,] 0.9999163 0.0001673846 8.369228e-05
[9,] 0.9998666 0.0002668952 1.334476e-04
[10,] 0.9997446 0.0005107274 2.553637e-04
[11,] 0.9998863 0.0002273193 1.136597e-04
[12,] 0.9997885 0.0004229203 2.114601e-04
[13,] 0.9996338 0.0007324147 3.662074e-04
[14,] 0.9994325 0.0011350664 5.675332e-04
[15,] 0.9991616 0.0016767430 8.383715e-04
[16,] 0.9986537 0.0026925707 1.346285e-03
[17,] 0.9978075 0.0043849090 2.192455e-03
[18,] 0.9966269 0.0067461606 3.373080e-03
[19,] 0.9960734 0.0078532721 3.926636e-03
[20,] 0.9963012 0.0073976889 3.698844e-03
[21,] 0.9945555 0.0108889495 5.444475e-03
[22,] 0.9918943 0.0162114975 8.105749e-03
[23,] 0.9937470 0.0125059368 6.252968e-03
[24,] 0.9917253 0.0165493231 8.274662e-03
[25,] 0.9883904 0.0232191980 1.160960e-02
[26,] 0.9925017 0.0149965713 7.498286e-03
[27,] 0.9910472 0.0179056453 8.952823e-03
[28,] 0.9872646 0.0254708886 1.273544e-02
[29,] 0.9818210 0.0363580764 1.817904e-02
[30,] 0.9960318 0.0079364761 3.968238e-03
[31,] 0.9947854 0.0104292803 5.214640e-03
[32,] 0.9930878 0.0138244898 6.912245e-03
[33,] 0.9921219 0.0157561945 7.878097e-03
[34,] 0.9896753 0.0206493147 1.032466e-02
[35,] 0.9864735 0.0270529368 1.352647e-02
[36,] 0.9869625 0.0260750603 1.303753e-02
[37,] 0.9836808 0.0326383072 1.631915e-02
[38,] 0.9797101 0.0405797111 2.028986e-02
[39,] 0.9765124 0.0469751157 2.348756e-02
[40,] 0.9795025 0.0409949467 2.049747e-02
[41,] 0.9788519 0.0422961953 2.114810e-02
[42,] 0.9767743 0.0464513009 2.322565e-02
[43,] 0.9720826 0.0558348218 2.791741e-02
[44,] 0.9858931 0.0282137785 1.410689e-02
[45,] 0.9873175 0.0253649672 1.268248e-02
[46,] 0.9852821 0.0294357632 1.471788e-02
[47,] 0.9955397 0.0089206705 4.460335e-03
[48,] 0.9959523 0.0080954161 4.047708e-03
[49,] 0.9954150 0.0091699566 4.584978e-03
[50,] 0.9943756 0.0112487178 5.624359e-03
[51,] 0.9952383 0.0095234910 4.761746e-03
[52,] 0.9940065 0.0119870962 5.993548e-03
[53,] 0.9963385 0.0073230931 3.661547e-03
[54,] 0.9958770 0.0082459409 4.122970e-03
[55,] 0.9949402 0.0101195103 5.059755e-03
[56,] 0.9963313 0.0073374231 3.668712e-03
[57,] 0.9948676 0.0102647681 5.132384e-03
[58,] 0.9964770 0.0070459328 3.522966e-03
[59,] 0.9957299 0.0085402292 4.270115e-03
[60,] 0.9945811 0.0108377374 5.418869e-03
[61,] 0.9942766 0.0114468715 5.723436e-03
[62,] 0.9924586 0.0150828436 7.541422e-03
[63,] 0.9925118 0.0149764989 7.488249e-03
[64,] 0.9895229 0.0209541625 1.047708e-02
[65,] 0.9869026 0.0261947185 1.309736e-02
[66,] 0.9852259 0.0295482829 1.477414e-02
[67,] 0.9806590 0.0386819248 1.934096e-02
[68,] 0.9765459 0.0469082214 2.345411e-02
[69,] 0.9696300 0.0607399526 3.036998e-02
[70,] 0.9608613 0.0782773635 3.913868e-02
[71,] 0.9721148 0.0557703175 2.788516e-02
[72,] 0.9793483 0.0413034685 2.065173e-02
[73,] 0.9725841 0.0548317463 2.741587e-02
[74,] 0.9640665 0.0718670238 3.593351e-02
[75,] 0.9577654 0.0844692732 4.223464e-02
[76,] 0.9495696 0.1008608101 5.043041e-02
[77,] 0.9352659 0.1294681619 6.473408e-02
[78,] 0.9171926 0.1656147197 8.280736e-02
[79,] 0.9109222 0.1781556346 8.907782e-02
[80,] 0.8986790 0.2026420104 1.013210e-01
[81,] 0.8814715 0.2370570335 1.185285e-01
[82,] 0.8770314 0.2459371464 1.229686e-01
[83,] 0.8498122 0.3003756724 1.501878e-01
[84,] 0.8184754 0.3630492221 1.815246e-01
[85,] 0.8848717 0.2302565444 1.151283e-01
[86,] 0.8612277 0.2775446092 1.387723e-01
[87,] 0.8736228 0.2527544816 1.263772e-01
[88,] 0.8629828 0.2740344720 1.370172e-01
[89,] 0.8667798 0.2664404788 1.332202e-01
[90,] 0.8857135 0.2285729893 1.142865e-01
[91,] 0.8605298 0.2789404238 1.394702e-01
[92,] 0.8350813 0.3298374092 1.649187e-01
[93,] 0.8324668 0.3350664116 1.675332e-01
[94,] 0.8481600 0.3036799033 1.518400e-01
[95,] 0.8870555 0.2258890456 1.129445e-01
[96,] 0.9216392 0.1567216287 7.836081e-02
[97,] 0.9112977 0.1774046134 8.870231e-02
[98,] 0.8833651 0.2332697986 1.166349e-01
[99,] 0.8505918 0.2988163120 1.494082e-01
[100,] 0.8107246 0.3785508535 1.892754e-01
[101,] 0.7668713 0.4662573444 2.331287e-01
[102,] 0.7179308 0.5641383785 2.820692e-01
[103,] 0.7110196 0.5779607703 2.889804e-01
[104,] 0.7295129 0.5409742111 2.704871e-01
[105,] 0.6693500 0.6613000199 3.306500e-01
[106,] 0.6183455 0.7633090623 3.816545e-01
[107,] 0.6363337 0.7273325901 3.636663e-01
[108,] 0.5706515 0.8586970454 4.293485e-01
[109,] 0.5320366 0.9359268605 4.679634e-01
[110,] 0.5819600 0.8360799252 4.180400e-01
[111,] 0.5324033 0.9351933291 4.675967e-01
[112,] 0.4663207 0.9326414526 5.336793e-01
[113,] 0.4832154 0.9664307010 5.167846e-01
[114,] 0.3992287 0.7984573895 6.007713e-01
[115,] 0.3364759 0.6729518986 6.635241e-01
[116,] 0.3043953 0.6087906587 6.956047e-01
[117,] 0.3889042 0.7778084905 6.110958e-01
[118,] 0.5639307 0.8721385455 4.360693e-01
[119,] 0.5725024 0.8549952756 4.274976e-01
[120,] 0.4657180 0.9314359391 5.342820e-01
[121,] 0.4185835 0.8371669957 5.814165e-01
[122,] 0.6519833 0.6960333257 3.480167e-01
[123,] 0.5112050 0.9775899677 4.887950e-01
> postscript(file="/var/www/rcomp/tmp/1f8v81322159630.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2fcla1322159630.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3gr8v1322159630.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/40jcc1322159630.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5q7fd1322159630.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
0.245207770 2.635593378 -3.273498186 -1.635136403 4.017821283 4.042043265
7 8 9 10 11 12
2.289376170 -0.700342509 -1.151240492 0.991018702 1.935794665 2.892839048
13 14 15 16 17 18
-3.101326271 2.529370657 1.795532658 1.261249830 -0.478856325 1.920853310
19 20 21 22 23 24
-2.025672375 2.653232984 1.135837980 -2.202025794 0.085944136 -2.133538351
25 26 27 28 29 30
1.947434322 -6.996616157 0.363312150 1.287380546 0.551911168 -2.288567687
31 32 33 34 35 36
-0.215630700 0.903640807 0.925389540 0.228445221 0.443144227 0.187254974
37 38 39 40 41 42
-1.471866895 0.697940531 1.379227215 -1.551810354 -1.221105052 2.864698560
43 44 45 46 47 48
-0.464844925 -0.858548323 -3.705081786 -2.504574314 0.144155113 -0.368153519
49 50 51 52 53 54
3.877458129 -1.822813943 0.363855681 1.147717546 -1.365718637 -0.881853319
55 56 57 58 59 60
-2.747524747 0.750441688 0.708904328 0.278325589 -2.710982260 -1.997709576
61 62 63 64 65 66
-2.322917205 -1.623643266 -4.157461118 1.614472904 0.863947107 -4.464726531
67 68 69 70 71 72
-2.450628120 -2.037620466 0.247282797 1.670547984 0.729576594 2.751717487
73 74 75 76 77 78
1.037116561 -0.186173216 -2.661681579 0.055239290 2.320271024 1.004367189
79 80 81 82 83 84
0.578223724 -1.761588948 -0.774460460 2.034621519 0.084657653 0.902979809
85 86 87 88 89 90
1.220199836 0.003031138 0.203622045 0.318419671 -0.167427522 -3.185472788
91 92 93 94 95 96
2.619145657 0.304350336 -0.110870667 0.914338202 -1.332271840 0.246686186
97 98 99 100 101 102
-0.605856219 -0.780428621 1.319168758 0.632725025 1.133311107 -0.541640033
103 104 105 106 107 108
0.293052084 -3.053652839 0.980568575 -2.595939492 1.481794639 1.121035474
109 110 111 112 113 114
-2.481051828 0.374913420 0.676472466 -1.646749671 -2.588037937 2.471908698
115 116 117 118 119 120
2.955884119 0.690399104 0.092271359 0.553206966 -0.529940499 -0.556498173
121 122 123 124 125 126
-0.145864785 0.535088324 -0.725888310 -0.357938885 -0.104475340 -1.077272952
127 128 129 130 131 132
-0.023755933 1.751083466 3.319550948 1.587710697 -0.997222473 -2.373481500
133 134 135 136 137 138
0.055357911 2.606977152 0.026524548 2.853655360 0.875498862 1.306484613
139 140 141 142 143 144
-1.658380409 1.092299771 -1.509044292 0.978464662 2.859942734 -1.104590654
145 146 147 148 149 150
1.192905573 1.117863837 1.010606562 -1.963495962 -3.120435216 -2.160578639
151 152 153 154 155 156
0.850755455 0.266304928 -0.159107830 -1.934139942 -2.194592690 0.431458796
157 158 159 160 161 162
0.553203100 0.908896767 3.680207110 -2.015728897 -0.716704524 0.989756315
> postscript(file="/var/www/rcomp/tmp/6z6ap1322159630.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 0.245207770 NA
1 2.635593378 0.245207770
2 -3.273498186 2.635593378
3 -1.635136403 -3.273498186
4 4.017821283 -1.635136403
5 4.042043265 4.017821283
6 2.289376170 4.042043265
7 -0.700342509 2.289376170
8 -1.151240492 -0.700342509
9 0.991018702 -1.151240492
10 1.935794665 0.991018702
11 2.892839048 1.935794665
12 -3.101326271 2.892839048
13 2.529370657 -3.101326271
14 1.795532658 2.529370657
15 1.261249830 1.795532658
16 -0.478856325 1.261249830
17 1.920853310 -0.478856325
18 -2.025672375 1.920853310
19 2.653232984 -2.025672375
20 1.135837980 2.653232984
21 -2.202025794 1.135837980
22 0.085944136 -2.202025794
23 -2.133538351 0.085944136
24 1.947434322 -2.133538351
25 -6.996616157 1.947434322
26 0.363312150 -6.996616157
27 1.287380546 0.363312150
28 0.551911168 1.287380546
29 -2.288567687 0.551911168
30 -0.215630700 -2.288567687
31 0.903640807 -0.215630700
32 0.925389540 0.903640807
33 0.228445221 0.925389540
34 0.443144227 0.228445221
35 0.187254974 0.443144227
36 -1.471866895 0.187254974
37 0.697940531 -1.471866895
38 1.379227215 0.697940531
39 -1.551810354 1.379227215
40 -1.221105052 -1.551810354
41 2.864698560 -1.221105052
42 -0.464844925 2.864698560
43 -0.858548323 -0.464844925
44 -3.705081786 -0.858548323
45 -2.504574314 -3.705081786
46 0.144155113 -2.504574314
47 -0.368153519 0.144155113
48 3.877458129 -0.368153519
49 -1.822813943 3.877458129
50 0.363855681 -1.822813943
51 1.147717546 0.363855681
52 -1.365718637 1.147717546
53 -0.881853319 -1.365718637
54 -2.747524747 -0.881853319
55 0.750441688 -2.747524747
56 0.708904328 0.750441688
57 0.278325589 0.708904328
58 -2.710982260 0.278325589
59 -1.997709576 -2.710982260
60 -2.322917205 -1.997709576
61 -1.623643266 -2.322917205
62 -4.157461118 -1.623643266
63 1.614472904 -4.157461118
64 0.863947107 1.614472904
65 -4.464726531 0.863947107
66 -2.450628120 -4.464726531
67 -2.037620466 -2.450628120
68 0.247282797 -2.037620466
69 1.670547984 0.247282797
70 0.729576594 1.670547984
71 2.751717487 0.729576594
72 1.037116561 2.751717487
73 -0.186173216 1.037116561
74 -2.661681579 -0.186173216
75 0.055239290 -2.661681579
76 2.320271024 0.055239290
77 1.004367189 2.320271024
78 0.578223724 1.004367189
79 -1.761588948 0.578223724
80 -0.774460460 -1.761588948
81 2.034621519 -0.774460460
82 0.084657653 2.034621519
83 0.902979809 0.084657653
84 1.220199836 0.902979809
85 0.003031138 1.220199836
86 0.203622045 0.003031138
87 0.318419671 0.203622045
88 -0.167427522 0.318419671
89 -3.185472788 -0.167427522
90 2.619145657 -3.185472788
91 0.304350336 2.619145657
92 -0.110870667 0.304350336
93 0.914338202 -0.110870667
94 -1.332271840 0.914338202
95 0.246686186 -1.332271840
96 -0.605856219 0.246686186
97 -0.780428621 -0.605856219
98 1.319168758 -0.780428621
99 0.632725025 1.319168758
100 1.133311107 0.632725025
101 -0.541640033 1.133311107
102 0.293052084 -0.541640033
103 -3.053652839 0.293052084
104 0.980568575 -3.053652839
105 -2.595939492 0.980568575
106 1.481794639 -2.595939492
107 1.121035474 1.481794639
108 -2.481051828 1.121035474
109 0.374913420 -2.481051828
110 0.676472466 0.374913420
111 -1.646749671 0.676472466
112 -2.588037937 -1.646749671
113 2.471908698 -2.588037937
114 2.955884119 2.471908698
115 0.690399104 2.955884119
116 0.092271359 0.690399104
117 0.553206966 0.092271359
118 -0.529940499 0.553206966
119 -0.556498173 -0.529940499
120 -0.145864785 -0.556498173
121 0.535088324 -0.145864785
122 -0.725888310 0.535088324
123 -0.357938885 -0.725888310
124 -0.104475340 -0.357938885
125 -1.077272952 -0.104475340
126 -0.023755933 -1.077272952
127 1.751083466 -0.023755933
128 3.319550948 1.751083466
129 1.587710697 3.319550948
130 -0.997222473 1.587710697
131 -2.373481500 -0.997222473
132 0.055357911 -2.373481500
133 2.606977152 0.055357911
134 0.026524548 2.606977152
135 2.853655360 0.026524548
136 0.875498862 2.853655360
137 1.306484613 0.875498862
138 -1.658380409 1.306484613
139 1.092299771 -1.658380409
140 -1.509044292 1.092299771
141 0.978464662 -1.509044292
142 2.859942734 0.978464662
143 -1.104590654 2.859942734
144 1.192905573 -1.104590654
145 1.117863837 1.192905573
146 1.010606562 1.117863837
147 -1.963495962 1.010606562
148 -3.120435216 -1.963495962
149 -2.160578639 -3.120435216
150 0.850755455 -2.160578639
151 0.266304928 0.850755455
152 -0.159107830 0.266304928
153 -1.934139942 -0.159107830
154 -2.194592690 -1.934139942
155 0.431458796 -2.194592690
156 0.553203100 0.431458796
157 0.908896767 0.553203100
158 3.680207110 0.908896767
159 -2.015728897 3.680207110
160 -0.716704524 -2.015728897
161 0.989756315 -0.716704524
162 NA 0.989756315
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.635593378 0.245207770
[2,] -3.273498186 2.635593378
[3,] -1.635136403 -3.273498186
[4,] 4.017821283 -1.635136403
[5,] 4.042043265 4.017821283
[6,] 2.289376170 4.042043265
[7,] -0.700342509 2.289376170
[8,] -1.151240492 -0.700342509
[9,] 0.991018702 -1.151240492
[10,] 1.935794665 0.991018702
[11,] 2.892839048 1.935794665
[12,] -3.101326271 2.892839048
[13,] 2.529370657 -3.101326271
[14,] 1.795532658 2.529370657
[15,] 1.261249830 1.795532658
[16,] -0.478856325 1.261249830
[17,] 1.920853310 -0.478856325
[18,] -2.025672375 1.920853310
[19,] 2.653232984 -2.025672375
[20,] 1.135837980 2.653232984
[21,] -2.202025794 1.135837980
[22,] 0.085944136 -2.202025794
[23,] -2.133538351 0.085944136
[24,] 1.947434322 -2.133538351
[25,] -6.996616157 1.947434322
[26,] 0.363312150 -6.996616157
[27,] 1.287380546 0.363312150
[28,] 0.551911168 1.287380546
[29,] -2.288567687 0.551911168
[30,] -0.215630700 -2.288567687
[31,] 0.903640807 -0.215630700
[32,] 0.925389540 0.903640807
[33,] 0.228445221 0.925389540
[34,] 0.443144227 0.228445221
[35,] 0.187254974 0.443144227
[36,] -1.471866895 0.187254974
[37,] 0.697940531 -1.471866895
[38,] 1.379227215 0.697940531
[39,] -1.551810354 1.379227215
[40,] -1.221105052 -1.551810354
[41,] 2.864698560 -1.221105052
[42,] -0.464844925 2.864698560
[43,] -0.858548323 -0.464844925
[44,] -3.705081786 -0.858548323
[45,] -2.504574314 -3.705081786
[46,] 0.144155113 -2.504574314
[47,] -0.368153519 0.144155113
[48,] 3.877458129 -0.368153519
[49,] -1.822813943 3.877458129
[50,] 0.363855681 -1.822813943
[51,] 1.147717546 0.363855681
[52,] -1.365718637 1.147717546
[53,] -0.881853319 -1.365718637
[54,] -2.747524747 -0.881853319
[55,] 0.750441688 -2.747524747
[56,] 0.708904328 0.750441688
[57,] 0.278325589 0.708904328
[58,] -2.710982260 0.278325589
[59,] -1.997709576 -2.710982260
[60,] -2.322917205 -1.997709576
[61,] -1.623643266 -2.322917205
[62,] -4.157461118 -1.623643266
[63,] 1.614472904 -4.157461118
[64,] 0.863947107 1.614472904
[65,] -4.464726531 0.863947107
[66,] -2.450628120 -4.464726531
[67,] -2.037620466 -2.450628120
[68,] 0.247282797 -2.037620466
[69,] 1.670547984 0.247282797
[70,] 0.729576594 1.670547984
[71,] 2.751717487 0.729576594
[72,] 1.037116561 2.751717487
[73,] -0.186173216 1.037116561
[74,] -2.661681579 -0.186173216
[75,] 0.055239290 -2.661681579
[76,] 2.320271024 0.055239290
[77,] 1.004367189 2.320271024
[78,] 0.578223724 1.004367189
[79,] -1.761588948 0.578223724
[80,] -0.774460460 -1.761588948
[81,] 2.034621519 -0.774460460
[82,] 0.084657653 2.034621519
[83,] 0.902979809 0.084657653
[84,] 1.220199836 0.902979809
[85,] 0.003031138 1.220199836
[86,] 0.203622045 0.003031138
[87,] 0.318419671 0.203622045
[88,] -0.167427522 0.318419671
[89,] -3.185472788 -0.167427522
[90,] 2.619145657 -3.185472788
[91,] 0.304350336 2.619145657
[92,] -0.110870667 0.304350336
[93,] 0.914338202 -0.110870667
[94,] -1.332271840 0.914338202
[95,] 0.246686186 -1.332271840
[96,] -0.605856219 0.246686186
[97,] -0.780428621 -0.605856219
[98,] 1.319168758 -0.780428621
[99,] 0.632725025 1.319168758
[100,] 1.133311107 0.632725025
[101,] -0.541640033 1.133311107
[102,] 0.293052084 -0.541640033
[103,] -3.053652839 0.293052084
[104,] 0.980568575 -3.053652839
[105,] -2.595939492 0.980568575
[106,] 1.481794639 -2.595939492
[107,] 1.121035474 1.481794639
[108,] -2.481051828 1.121035474
[109,] 0.374913420 -2.481051828
[110,] 0.676472466 0.374913420
[111,] -1.646749671 0.676472466
[112,] -2.588037937 -1.646749671
[113,] 2.471908698 -2.588037937
[114,] 2.955884119 2.471908698
[115,] 0.690399104 2.955884119
[116,] 0.092271359 0.690399104
[117,] 0.553206966 0.092271359
[118,] -0.529940499 0.553206966
[119,] -0.556498173 -0.529940499
[120,] -0.145864785 -0.556498173
[121,] 0.535088324 -0.145864785
[122,] -0.725888310 0.535088324
[123,] -0.357938885 -0.725888310
[124,] -0.104475340 -0.357938885
[125,] -1.077272952 -0.104475340
[126,] -0.023755933 -1.077272952
[127,] 1.751083466 -0.023755933
[128,] 3.319550948 1.751083466
[129,] 1.587710697 3.319550948
[130,] -0.997222473 1.587710697
[131,] -2.373481500 -0.997222473
[132,] 0.055357911 -2.373481500
[133,] 2.606977152 0.055357911
[134,] 0.026524548 2.606977152
[135,] 2.853655360 0.026524548
[136,] 0.875498862 2.853655360
[137,] 1.306484613 0.875498862
[138,] -1.658380409 1.306484613
[139,] 1.092299771 -1.658380409
[140,] -1.509044292 1.092299771
[141,] 0.978464662 -1.509044292
[142,] 2.859942734 0.978464662
[143,] -1.104590654 2.859942734
[144,] 1.192905573 -1.104590654
[145,] 1.117863837 1.192905573
[146,] 1.010606562 1.117863837
[147,] -1.963495962 1.010606562
[148,] -3.120435216 -1.963495962
[149,] -2.160578639 -3.120435216
[150,] 0.850755455 -2.160578639
[151,] 0.266304928 0.850755455
[152,] -0.159107830 0.266304928
[153,] -1.934139942 -0.159107830
[154,] -2.194592690 -1.934139942
[155,] 0.431458796 -2.194592690
[156,] 0.553203100 0.431458796
[157,] 0.908896767 0.553203100
[158,] 3.680207110 0.908896767
[159,] -2.015728897 3.680207110
[160,] -0.716704524 -2.015728897
[161,] 0.989756315 -0.716704524
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.635593378 0.245207770
2 -3.273498186 2.635593378
3 -1.635136403 -3.273498186
4 4.017821283 -1.635136403
5 4.042043265 4.017821283
6 2.289376170 4.042043265
7 -0.700342509 2.289376170
8 -1.151240492 -0.700342509
9 0.991018702 -1.151240492
10 1.935794665 0.991018702
11 2.892839048 1.935794665
12 -3.101326271 2.892839048
13 2.529370657 -3.101326271
14 1.795532658 2.529370657
15 1.261249830 1.795532658
16 -0.478856325 1.261249830
17 1.920853310 -0.478856325
18 -2.025672375 1.920853310
19 2.653232984 -2.025672375
20 1.135837980 2.653232984
21 -2.202025794 1.135837980
22 0.085944136 -2.202025794
23 -2.133538351 0.085944136
24 1.947434322 -2.133538351
25 -6.996616157 1.947434322
26 0.363312150 -6.996616157
27 1.287380546 0.363312150
28 0.551911168 1.287380546
29 -2.288567687 0.551911168
30 -0.215630700 -2.288567687
31 0.903640807 -0.215630700
32 0.925389540 0.903640807
33 0.228445221 0.925389540
34 0.443144227 0.228445221
35 0.187254974 0.443144227
36 -1.471866895 0.187254974
37 0.697940531 -1.471866895
38 1.379227215 0.697940531
39 -1.551810354 1.379227215
40 -1.221105052 -1.551810354
41 2.864698560 -1.221105052
42 -0.464844925 2.864698560
43 -0.858548323 -0.464844925
44 -3.705081786 -0.858548323
45 -2.504574314 -3.705081786
46 0.144155113 -2.504574314
47 -0.368153519 0.144155113
48 3.877458129 -0.368153519
49 -1.822813943 3.877458129
50 0.363855681 -1.822813943
51 1.147717546 0.363855681
52 -1.365718637 1.147717546
53 -0.881853319 -1.365718637
54 -2.747524747 -0.881853319
55 0.750441688 -2.747524747
56 0.708904328 0.750441688
57 0.278325589 0.708904328
58 -2.710982260 0.278325589
59 -1.997709576 -2.710982260
60 -2.322917205 -1.997709576
61 -1.623643266 -2.322917205
62 -4.157461118 -1.623643266
63 1.614472904 -4.157461118
64 0.863947107 1.614472904
65 -4.464726531 0.863947107
66 -2.450628120 -4.464726531
67 -2.037620466 -2.450628120
68 0.247282797 -2.037620466
69 1.670547984 0.247282797
70 0.729576594 1.670547984
71 2.751717487 0.729576594
72 1.037116561 2.751717487
73 -0.186173216 1.037116561
74 -2.661681579 -0.186173216
75 0.055239290 -2.661681579
76 2.320271024 0.055239290
77 1.004367189 2.320271024
78 0.578223724 1.004367189
79 -1.761588948 0.578223724
80 -0.774460460 -1.761588948
81 2.034621519 -0.774460460
82 0.084657653 2.034621519
83 0.902979809 0.084657653
84 1.220199836 0.902979809
85 0.003031138 1.220199836
86 0.203622045 0.003031138
87 0.318419671 0.203622045
88 -0.167427522 0.318419671
89 -3.185472788 -0.167427522
90 2.619145657 -3.185472788
91 0.304350336 2.619145657
92 -0.110870667 0.304350336
93 0.914338202 -0.110870667
94 -1.332271840 0.914338202
95 0.246686186 -1.332271840
96 -0.605856219 0.246686186
97 -0.780428621 -0.605856219
98 1.319168758 -0.780428621
99 0.632725025 1.319168758
100 1.133311107 0.632725025
101 -0.541640033 1.133311107
102 0.293052084 -0.541640033
103 -3.053652839 0.293052084
104 0.980568575 -3.053652839
105 -2.595939492 0.980568575
106 1.481794639 -2.595939492
107 1.121035474 1.481794639
108 -2.481051828 1.121035474
109 0.374913420 -2.481051828
110 0.676472466 0.374913420
111 -1.646749671 0.676472466
112 -2.588037937 -1.646749671
113 2.471908698 -2.588037937
114 2.955884119 2.471908698
115 0.690399104 2.955884119
116 0.092271359 0.690399104
117 0.553206966 0.092271359
118 -0.529940499 0.553206966
119 -0.556498173 -0.529940499
120 -0.145864785 -0.556498173
121 0.535088324 -0.145864785
122 -0.725888310 0.535088324
123 -0.357938885 -0.725888310
124 -0.104475340 -0.357938885
125 -1.077272952 -0.104475340
126 -0.023755933 -1.077272952
127 1.751083466 -0.023755933
128 3.319550948 1.751083466
129 1.587710697 3.319550948
130 -0.997222473 1.587710697
131 -2.373481500 -0.997222473
132 0.055357911 -2.373481500
133 2.606977152 0.055357911
134 0.026524548 2.606977152
135 2.853655360 0.026524548
136 0.875498862 2.853655360
137 1.306484613 0.875498862
138 -1.658380409 1.306484613
139 1.092299771 -1.658380409
140 -1.509044292 1.092299771
141 0.978464662 -1.509044292
142 2.859942734 0.978464662
143 -1.104590654 2.859942734
144 1.192905573 -1.104590654
145 1.117863837 1.192905573
146 1.010606562 1.117863837
147 -1.963495962 1.010606562
148 -3.120435216 -1.963495962
149 -2.160578639 -3.120435216
150 0.850755455 -2.160578639
151 0.266304928 0.850755455
152 -0.159107830 0.266304928
153 -1.934139942 -0.159107830
154 -2.194592690 -1.934139942
155 0.431458796 -2.194592690
156 0.553203100 0.431458796
157 0.908896767 0.553203100
158 3.680207110 0.908896767
159 -2.015728897 3.680207110
160 -0.716704524 -2.015728897
161 0.989756315 -0.716704524
> 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/rcomp/tmp/7ny6t1322159630.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8118h1322159630.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/994qo1322159630.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10hczy1322159630.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/1137t41322159630.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/rcomp/tmp/12r6hh1322159630.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/rcomp/tmp/13ewvd1322159630.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/rcomp/tmp/14353q1322159630.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/rcomp/tmp/1539k51322159630.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/rcomp/tmp/16atmk1322159630.tab")
+ }
>
> try(system("convert tmp/1f8v81322159630.ps tmp/1f8v81322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fcla1322159630.ps tmp/2fcla1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gr8v1322159630.ps tmp/3gr8v1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/40jcc1322159630.ps tmp/40jcc1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q7fd1322159630.ps tmp/5q7fd1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z6ap1322159630.ps tmp/6z6ap1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ny6t1322159630.ps tmp/7ny6t1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/8118h1322159630.ps tmp/8118h1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/994qo1322159630.ps tmp/994qo1322159630.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hczy1322159630.ps tmp/10hczy1322159630.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.756 0.692 7.477