R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,19
+ ,18
+ ,21
+ ,4
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+ ,11
+ ,101
+ ,10
+ ,21
+ ,17
+ ,13
+ ,23
+ ,22
+ ,26
+ ,8
+ ,16
+ ,12
+ ,114
+ ,10
+ ,25
+ ,22
+ ,15
+ ,25
+ ,16
+ ,21
+ ,6
+ ,11
+ ,13
+ ,118
+ ,10
+ ,22
+ ,20
+ ,18
+ ,23
+ ,19
+ ,22
+ ,4
+ ,12
+ ,11
+ ,120
+ ,10
+ ,21
+ ,20
+ ,18
+ ,22
+ ,20
+ ,16
+ ,9
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+ ,108
+ ,10
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+ ,132
+ ,9
+ ,24
+ ,20
+ ,12
+ ,28
+ ,25
+ ,25
+ ,4
+ ,12
+ ,12
+ ,130
+ ,10
+ ,24
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+ ,16
+ ,28
+ ,21
+ ,23
+ ,4
+ ,9
+ ,19
+ ,130
+ ,10
+ ,21
+ ,18
+ ,16
+ ,20
+ ,21
+ ,21
+ ,5
+ ,13
+ ,18
+ ,112
+ ,10
+ ,18
+ ,16
+ ,18
+ ,25
+ ,23
+ ,20
+ ,6
+ ,13
+ ,15
+ ,114
+ ,10
+ ,16
+ ,16
+ ,16
+ ,19
+ ,27
+ ,25
+ ,16
+ ,14
+ ,14
+ ,103
+ ,10
+ ,22
+ ,16
+ ,13
+ ,25
+ ,23
+ ,22
+ ,6
+ ,19
+ ,11
+ ,115
+ ,10
+ ,20
+ ,16
+ ,17
+ ,22
+ ,18
+ ,21
+ ,6
+ ,13
+ ,9
+ ,108
+ ,10
+ ,18
+ ,17
+ ,13
+ ,18
+ ,16
+ ,16
+ ,4
+ ,12
+ ,18
+ ,94
+ ,11
+ ,20
+ ,18
+ ,17
+ ,20
+ ,16
+ ,18
+ ,4
+ ,13
+ ,16
+ ,105)
+ ,dim=c(11
+ ,162)
+ ,dimnames=list(c('Month'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A'
+ ,'Happiness'
+ ,'Depression'
+ ,'Motivation')
+ ,1:162))
> y <- array(NA,dim=c(11,162),dimnames=list(c('Month','I1','I2','I3','E1','E2','E3','A','Happiness','Depression','Motivation'),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 = 'Do not include Seasonal Dummies'
> par1 = '11'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Motivation Month I1 I2 I3 E1 E2 E3 A Happiness Depression t
1 127 9 26 21 21 23 17 23 4 14 12 1
2 108 9 20 16 15 24 17 20 4 18 11 2
3 110 9 19 19 18 22 18 20 6 11 14 3
4 102 9 19 18 11 20 21 21 8 12 12 4
5 104 9 20 16 8 24 20 24 8 16 21 5
6 140 9 25 23 19 27 28 22 4 18 12 6
7 112 9 25 17 4 28 19 23 4 14 22 7
8 115 9 22 12 20 27 22 20 8 14 11 8
9 121 9 26 19 16 24 16 25 5 15 10 9
10 112 9 22 16 14 23 18 23 4 15 13 10
11 118 9 17 19 10 24 25 27 4 17 10 11
12 122 9 22 20 13 27 17 27 4 19 8 12
13 105 9 19 13 14 27 14 22 4 10 15 13
14 111 9 24 20 8 28 11 24 4 16 14 14
15 151 9 26 27 23 27 27 25 4 18 10 15
16 106 9 21 17 11 23 20 22 8 14 14 16
17 100 9 13 8 9 24 22 28 4 14 14 17
18 149 9 26 25 24 28 22 28 4 17 11 18
19 122 9 20 26 5 27 21 27 4 14 10 19
20 115 9 22 13 15 25 23 25 8 16 13 20
21 86 9 14 19 5 19 17 16 4 18 7 21
22 124 9 21 15 19 24 24 28 7 11 14 22
23 69 9 7 5 6 20 14 21 4 14 12 23
24 117 9 23 16 13 28 17 24 4 12 14 24
25 113 9 17 14 11 26 23 27 5 17 11 25
26 123 9 25 24 17 23 24 14 4 9 9 26
27 123 9 25 24 17 23 24 14 4 16 11 27
28 84 9 19 9 5 20 8 27 4 14 15 28
29 97 9 20 19 9 11 22 20 4 15 14 29
30 121 9 23 19 15 24 23 21 4 11 13 30
31 132 9 22 25 17 25 25 22 4 16 9 31
32 119 9 22 19 17 23 21 21 4 13 15 32
33 98 9 21 18 20 18 24 12 15 17 10 33
34 87 9 15 15 12 20 15 20 10 15 11 34
35 101 9 20 12 7 20 22 24 4 14 13 35
36 115 9 22 21 16 24 21 19 8 16 8 36
37 109 9 18 12 7 23 25 28 4 9 20 37
38 109 9 20 15 14 25 16 23 4 15 12 38
39 159 9 28 28 24 28 28 27 4 17 10 39
40 129 9 22 25 15 26 23 22 4 13 10 40
41 119 9 18 19 15 26 21 27 7 15 9 41
42 119 9 23 20 10 23 21 26 4 16 14 42
43 122 9 20 24 14 22 26 22 6 16 8 43
44 131 9 25 26 18 24 22 21 5 12 14 44
45 120 9 26 25 12 21 21 19 4 12 11 45
46 82 9 15 12 9 20 18 24 16 11 13 46
47 86 9 17 12 9 22 12 19 5 15 9 47
48 105 9 23 15 8 20 25 26 12 15 11 48
49 114 9 21 17 18 25 17 22 6 17 15 49
50 100 9 13 14 10 20 24 28 9 13 11 50
51 100 9 18 16 17 22 15 21 9 16 10 51
52 99 9 19 11 14 23 13 23 4 14 14 52
53 132 9 22 20 16 25 26 28 5 11 18 53
54 82 9 16 11 10 23 16 10 4 12 14 54
55 132 9 24 22 19 23 24 24 4 12 11 55
56 107 9 18 20 10 22 21 21 5 15 12 56
57 114 9 20 19 14 24 20 21 4 16 13 57
58 110 9 24 17 10 25 14 24 4 15 9 58
59 105 9 14 21 4 21 25 24 4 12 10 59
60 121 9 22 23 19 12 25 25 5 12 15 60
61 109 9 24 18 9 17 20 25 4 8 20 61
62 106 9 18 17 12 20 22 23 6 13 12 62
63 124 9 21 27 16 23 20 21 4 11 12 63
64 120 9 23 25 11 23 26 16 4 14 14 64
65 91 9 17 19 18 20 18 17 18 15 13 65
66 126 10 22 22 11 28 22 25 4 10 11 66
67 138 10 24 24 24 24 24 24 6 11 17 67
68 118 10 21 20 17 24 17 23 4 12 12 68
69 128 10 22 19 18 24 24 25 4 15 13 69
70 98 10 16 11 9 24 20 23 5 15 14 70
71 133 10 21 22 19 28 19 28 4 14 13 71
72 130 10 23 22 18 25 20 26 4 16 15 72
73 103 10 22 16 12 21 15 22 5 15 13 73
74 124 10 24 20 23 25 23 19 10 15 10 74
75 142 10 24 24 22 25 26 26 5 13 11 75
76 96 10 16 16 14 18 22 18 8 12 19 76
77 93 10 16 16 14 17 20 18 8 17 13 77
78 129 10 21 22 16 26 24 25 5 13 17 78
79 150 10 26 24 23 28 26 27 4 15 13 79
80 88 10 15 16 7 21 21 12 4 13 9 80
81 125 10 25 27 10 27 25 15 4 15 11 81
82 92 10 18 11 12 22 13 21 5 16 10 82
83 0 10 23 21 12 21 20 23 4 15 9 83
84 117 10 20 20 12 25 22 22 4 16 12 84
85 112 10 17 20 17 22 23 21 8 15 12 85
86 144 10 25 27 21 23 28 24 4 14 13 86
87 130 10 24 20 16 26 22 27 5 15 13 87
88 87 10 17 12 11 19 20 22 14 14 12 88
89 92 10 19 8 14 25 6 28 8 13 15 89
90 114 10 20 21 13 21 21 26 8 7 22 90
91 81 10 15 18 9 13 20 10 4 17 13 91
92 127 10 27 24 19 24 18 19 4 13 15 92
93 115 10 22 16 13 25 23 22 6 15 13 93
94 123 10 23 18 19 26 20 21 4 14 15 94
95 115 10 16 20 13 25 24 24 7 13 10 95
96 117 10 19 20 13 25 22 25 7 16 11 96
97 117 10 25 19 13 22 21 21 4 12 16 97
98 103 10 19 17 14 21 18 20 6 14 11 98
99 108 10 19 16 12 23 21 21 4 17 11 99
100 139 10 26 26 22 25 23 24 7 15 10 100
101 113 10 21 15 11 24 23 23 4 17 10 101
102 97 10 20 22 5 21 15 18 4 12 16 102
103 117 10 24 17 18 21 21 24 8 16 12 103
104 133 10 22 23 19 25 24 24 4 11 11 104
105 115 10 20 21 14 22 23 19 4 15 16 105
106 103 10 18 19 15 20 21 20 10 9 19 106
107 95 10 18 14 12 20 21 18 8 16 11 107
108 117 10 24 17 19 23 20 20 6 15 16 108
109 113 10 24 12 15 28 11 27 4 10 15 109
110 127 10 22 24 17 23 22 23 4 10 24 110
111 126 10 23 18 8 28 27 26 4 15 14 111
112 119 10 22 20 10 24 25 23 5 11 15 112
113 97 10 20 16 12 18 18 17 4 13 11 113
114 105 10 18 20 12 20 20 21 6 14 15 114
115 140 10 25 22 20 28 24 25 4 18 12 115
116 91 10 18 12 12 21 10 23 5 16 10 116
117 112 10 16 16 12 21 27 27 7 14 14 117
118 113 10 20 17 14 25 21 24 8 14 13 118
119 102 10 19 22 6 19 21 20 5 14 9 119
120 92 10 15 12 10 18 18 27 8 14 15 120
121 98 10 19 14 18 21 15 21 10 12 15 121
122 122 10 19 23 18 22 24 24 8 14 14 122
123 100 10 16 15 7 24 22 21 5 15 11 123
124 84 10 17 17 18 15 14 15 12 15 8 124
125 142 10 28 28 9 28 28 25 4 15 11 125
126 124 10 23 20 17 26 18 25 5 13 11 126
127 137 10 25 23 22 23 26 22 4 17 8 127
128 105 10 20 13 11 26 17 24 6 17 10 128
129 106 10 17 18 15 20 19 21 4 19 11 129
130 125 10 23 23 17 22 22 22 4 15 13 130
131 104 10 16 19 15 20 18 23 7 13 11 131
132 130 10 23 23 22 23 24 22 7 9 20 132
133 79 10 11 12 9 22 15 20 10 15 10 133
134 108 10 18 16 13 24 18 23 4 15 15 134
135 136 10 24 23 20 23 26 25 5 15 12 135
136 98 10 23 13 14 22 11 23 8 16 14 136
137 120 10 21 22 14 26 26 22 11 11 23 137
138 108 10 16 18 12 23 21 25 7 14 14 138
139 139 10 24 23 20 27 23 26 4 11 16 139
140 123 10 23 20 20 23 23 22 8 15 11 140
141 90 10 18 10 8 21 15 24 6 13 12 141
142 119 10 20 17 17 26 22 24 7 15 10 142
143 105 10 9 18 9 23 26 25 5 16 14 143
144 110 10 24 15 18 21 16 20 4 14 12 144
145 135 10 25 23 22 27 20 26 8 15 12 145
146 101 10 20 17 10 19 18 21 4 16 11 146
147 114 10 21 17 13 23 22 26 8 16 12 147
148 118 10 25 22 15 25 16 21 6 11 13 148
149 120 10 22 20 18 23 19 22 4 12 11 149
150 108 10 21 20 18 22 20 16 9 9 19 150
151 114 10 21 19 12 22 19 26 5 16 12 151
152 122 10 22 18 12 25 23 28 6 13 17 152
153 132 10 27 22 20 25 24 18 4 16 9 153
154 130 9 24 20 12 28 25 25 4 12 12 154
155 130 10 24 22 16 28 21 23 4 9 19 155
156 112 10 21 18 16 20 21 21 5 13 18 156
157 114 10 18 16 18 25 23 20 6 13 15 157
158 103 10 16 16 16 19 27 25 16 14 14 158
159 115 10 22 16 13 25 23 22 6 19 11 159
160 108 10 20 16 17 22 18 21 6 13 9 160
161 94 10 18 17 13 18 16 16 4 12 18 161
162 105 11 20 18 17 20 16 18 4 13 16 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month I1 I2 I3 E1
31.24930 -4.51155 0.64626 0.91648 1.23147 1.40678
E2 E3 A Happiness Depression t
1.04414 0.80032 -0.94476 0.16327 0.43772 0.03472
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-109.345 -0.657 0.893 2.152 5.740
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.24930 25.07178 1.246 0.214563
Month -4.51155 2.65656 -1.698 0.091531 .
I1 0.64626 0.29959 2.157 0.032588 *
I2 0.91648 0.25821 3.549 0.000516 ***
I3 1.23147 0.20360 6.048 1.12e-08 ***
E1 1.40678 0.28837 4.878 2.70e-06 ***
E2 1.04414 0.22060 4.733 5.07e-06 ***
E3 0.80032 0.22725 3.522 0.000568 ***
A -0.94476 0.31731 -2.977 0.003390 **
Happiness 0.16327 0.37762 0.432 0.666092
Depression 0.43772 0.28344 1.544 0.124619
t 0.03472 0.02831 1.227 0.221898
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.196 on 150 degrees of freedom
Multiple R-squared: 0.7674, Adjusted R-squared: 0.7503
F-statistic: 44.99 on 11 and 150 DF, p-value: < 2.2e-16
> 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,] 4.396144e-46 8.792289e-46 1.000000e+00
[2,] 1.122215e-60 2.244429e-60 1.000000e+00
[3,] 5.996715e-79 1.199343e-78 1.000000e+00
[4,] 5.326316e-95 1.065263e-94 1.000000e+00
[5,] 1.472022e-104 2.944045e-104 1.000000e+00
[6,] 2.445613e-119 4.891226e-119 1.000000e+00
[7,] 2.141552e-137 4.283103e-137 1.000000e+00
[8,] 9.941502e-156 1.988300e-155 1.000000e+00
[9,] 4.174297e-163 8.348593e-163 1.000000e+00
[10,] 3.701932e-179 7.403864e-179 1.000000e+00
[11,] 2.808202e-201 5.616403e-201 1.000000e+00
[12,] 1.717654e-214 3.435308e-214 1.000000e+00
[13,] 7.795881e-229 1.559176e-228 1.000000e+00
[14,] 5.152589e-246 1.030518e-245 1.000000e+00
[15,] 1.527975e-251 3.055950e-251 1.000000e+00
[16,] 5.503633e-279 1.100727e-278 1.000000e+00
[17,] 6.471021e-293 1.294204e-292 1.000000e+00
[18,] 2.398053e-308 4.796106e-308 1.000000e+00
[19,] 1.189957e-319 2.379914e-319 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 0.000000e+00 0.000000e+00 1.000000e+00
[56,] 0.000000e+00 0.000000e+00 1.000000e+00
[57,] 0.000000e+00 0.000000e+00 1.000000e+00
[58,] 0.000000e+00 0.000000e+00 1.000000e+00
[59,] 0.000000e+00 0.000000e+00 1.000000e+00
[60,] 0.000000e+00 0.000000e+00 1.000000e+00
[61,] 0.000000e+00 0.000000e+00 1.000000e+00
[62,] 0.000000e+00 0.000000e+00 1.000000e+00
[63,] 0.000000e+00 0.000000e+00 1.000000e+00
[64,] 0.000000e+00 0.000000e+00 1.000000e+00
[65,] 0.000000e+00 0.000000e+00 1.000000e+00
[66,] 0.000000e+00 0.000000e+00 1.000000e+00
[67,] 0.000000e+00 0.000000e+00 1.000000e+00
[68,] 0.000000e+00 0.000000e+00 1.000000e+00
[69,] 1.000000e+00 0.000000e+00 0.000000e+00
[70,] 1.000000e+00 0.000000e+00 0.000000e+00
[71,] 1.000000e+00 0.000000e+00 0.000000e+00
[72,] 1.000000e+00 0.000000e+00 0.000000e+00
[73,] 1.000000e+00 0.000000e+00 0.000000e+00
[74,] 1.000000e+00 0.000000e+00 0.000000e+00
[75,] 1.000000e+00 0.000000e+00 0.000000e+00
[76,] 1.000000e+00 0.000000e+00 0.000000e+00
[77,] 1.000000e+00 0.000000e+00 0.000000e+00
[78,] 1.000000e+00 0.000000e+00 0.000000e+00
[79,] 1.000000e+00 0.000000e+00 0.000000e+00
[80,] 1.000000e+00 0.000000e+00 0.000000e+00
[81,] 1.000000e+00 0.000000e+00 0.000000e+00
[82,] 1.000000e+00 0.000000e+00 0.000000e+00
[83,] 1.000000e+00 0.000000e+00 0.000000e+00
[84,] 1.000000e+00 0.000000e+00 0.000000e+00
[85,] 1.000000e+00 0.000000e+00 0.000000e+00
[86,] 1.000000e+00 0.000000e+00 0.000000e+00
[87,] 1.000000e+00 0.000000e+00 0.000000e+00
[88,] 1.000000e+00 0.000000e+00 0.000000e+00
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 0.000000e+00 0.000000e+00
[112,] 1.000000e+00 0.000000e+00 0.000000e+00
[113,] 1.000000e+00 0.000000e+00 0.000000e+00
[114,] 1.000000e+00 0.000000e+00 0.000000e+00
[115,] 1.000000e+00 4.940656e-323 2.470328e-323
[116,] 1.000000e+00 1.707322e-298 8.536612e-299
[117,] 1.000000e+00 5.262429e-288 2.631214e-288
[118,] 1.000000e+00 1.289553e-270 6.447767e-271
[119,] 1.000000e+00 9.933917e-251 4.966959e-251
[120,] 1.000000e+00 7.791171e-251 3.895585e-251
[121,] 1.000000e+00 4.778228e-233 2.389114e-233
[122,] 1.000000e+00 5.967372e-212 2.983686e-212
[123,] 1.000000e+00 2.740948e-194 1.370474e-194
[124,] 1.000000e+00 4.414847e-183 2.207423e-183
[125,] 1.000000e+00 5.142913e-168 2.571457e-168
[126,] 1.000000e+00 2.812378e-149 1.406189e-149
[127,] 1.000000e+00 3.031232e-134 1.515616e-134
[128,] 1.000000e+00 1.194261e-124 5.971305e-125
[129,] 1.000000e+00 6.186153e-109 3.093076e-109
[130,] 1.000000e+00 3.916639e-94 1.958320e-94
[131,] 1.000000e+00 1.630485e-78 8.152426e-79
[132,] 1.000000e+00 1.983655e-61 9.918275e-62
[133,] 1.000000e+00 4.321016e-45 2.160508e-45
> postscript(file="/var/wessaorg/rcomp/tmp/1jask1353431149.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/29qxc1353431149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/37uae1353431149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42l0t1353431149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5tkdu1353431149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
2.137330e+00 -2.698367e-01 -6.134572e-01 2.371125e+00 -2.359011e+00
6 7 8 9 10
-7.254295e-01 -1.323217e+00 -2.271024e+00 3.543508e+00 9.674082e-01
11 12 13 14 15
1.410044e+00 2.214781e+00 -2.157900e+00 1.132650e+00 2.426367e-01
16 17 18 19 20
3.998977e-01 -1.829518e+00 -1.215519e-01 3.381519e+00 -8.809696e-01
21 22 23 24 25
2.499304e+00 -6.674196e-01 -2.258601e+00 -6.714890e-01 -9.434161e-01
26 27 28 29 30
2.115398e+00 6.231837e-02 2.528008e+00 4.676166e+00 2.720538e-01
31 32 33 34 35
5.609293e-01 -1.321017e+00 -1.455202e+00 -6.650307e-01 2.084813e+00
36 37 38 39 40
4.857275e-01 -1.493820e+00 -1.083418e+00 8.054248e-02 4.449520e-01
41 42 43 44 45
-4.737385e-01 1.335635e+00 1.551105e+00 -2.284423e-01 3.629402e+00
46 47 48 49 50
-5.253284e-01 3.057107e-01 3.250942e+00 -3.864481e+00 3.336834e-02
51 52 53 54 55
-1.552119e+00 -2.023575e+00 -2.413524e+00 -4.630106e+00 7.562651e-01
56 57 58 59 60
-5.273638e-01 -2.179189e+00 2.331142e+00 1.675638e+00 2.782697e+00
61 62 63 64 65
2.059061e+00 -9.113283e-03 -1.676807e-01 -1.133095e+00 -4.137525e+00
66 67 68 69 70
4.610743e+00 8.805080e-01 3.315846e+00 2.482750e+00 1.024846e+00
71 72 73 74 75
1.434531e+00 1.913618e+00 4.445390e+00 1.363762e+00 3.325004e+00
76 77 78 79 80
-4.337497e-01 1.836542e+00 2.368663e-01 1.494718e+00 2.754513e+00
81 82 83 84 85
3.261386e+00 2.931996e+00 -1.093454e+02 2.083657e+00 7.491813e-01
86 87 88 89 90
3.121281e+00 2.730520e+00 2.749923e+00 9.510438e-01 1.068641e+00
91 92 93 94 95
2.571698e+00 2.156791e+00 1.484010e+00 -4.945009e-01 1.565894e+00
96 97 98 99 100
1.952813e+00 3.052981e+00 2.588366e+00 1.807440e+00 3.065190e+00
101 102 103 104 105
2.935466e+00 3.285530e+00 3.049158e+00 2.292131e+00 -3.540414e-02
106 107 108 109 110
-7.395835e-01 1.572471e+00 -4.012200e-01 1.197996e+00 -2.194700e+00
111 112 113 114 115
1.611747e+00 2.203858e+00 3.695731e+00 1.159869e+00 5.511854e-02
116 117 118 119 120
2.773486e+00 8.789762e-01 3.009246e-01 5.740403e+00 1.674516e+00
121 122 123 124 125
-6.999321e-01 3.375209e-02 8.070801e-01 2.489451e+00 4.567436e+00
126 127 128 129 130
1.770269e+00 2.519765e+00 1.017605e+00 5.129573e-01 1.586727e+00
131 132 133 134 135
1.430709e+00 -2.385261e+00 -9.380332e-01 -1.292432e+00 1.470649e+00
136 137 138 139 140
1.100794e+00 -4.667402e+00 -6.312812e-01 -1.005831e+00 4.980789e-01
141 142 143 144 145
2.202392e+00 -4.726285e-01 -3.023427e+00 1.427919e+00 -3.141334e-01
146 147 148 149 150
2.998465e+00 1.159137e+00 1.436016e+00 1.182095e+00 -3.329981e+00
151 152 153 154 155
2.123670e+00 -3.923791e-01 9.053990e-01 -3.544146e+00 -2.623215e+00
156 157 158 159 160
-1.468988e+00 -4.258863e+00 -1.553528e+00 -5.853659e-01 8.429578e-01
161 162
-1.838604e+00 2.801321e+00
> postscript(file="/var/wessaorg/rcomp/tmp/622ae1353431149.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 2.137330e+00 NA
1 -2.698367e-01 2.137330e+00
2 -6.134572e-01 -2.698367e-01
3 2.371125e+00 -6.134572e-01
4 -2.359011e+00 2.371125e+00
5 -7.254295e-01 -2.359011e+00
6 -1.323217e+00 -7.254295e-01
7 -2.271024e+00 -1.323217e+00
8 3.543508e+00 -2.271024e+00
9 9.674082e-01 3.543508e+00
10 1.410044e+00 9.674082e-01
11 2.214781e+00 1.410044e+00
12 -2.157900e+00 2.214781e+00
13 1.132650e+00 -2.157900e+00
14 2.426367e-01 1.132650e+00
15 3.998977e-01 2.426367e-01
16 -1.829518e+00 3.998977e-01
17 -1.215519e-01 -1.829518e+00
18 3.381519e+00 -1.215519e-01
19 -8.809696e-01 3.381519e+00
20 2.499304e+00 -8.809696e-01
21 -6.674196e-01 2.499304e+00
22 -2.258601e+00 -6.674196e-01
23 -6.714890e-01 -2.258601e+00
24 -9.434161e-01 -6.714890e-01
25 2.115398e+00 -9.434161e-01
26 6.231837e-02 2.115398e+00
27 2.528008e+00 6.231837e-02
28 4.676166e+00 2.528008e+00
29 2.720538e-01 4.676166e+00
30 5.609293e-01 2.720538e-01
31 -1.321017e+00 5.609293e-01
32 -1.455202e+00 -1.321017e+00
33 -6.650307e-01 -1.455202e+00
34 2.084813e+00 -6.650307e-01
35 4.857275e-01 2.084813e+00
36 -1.493820e+00 4.857275e-01
37 -1.083418e+00 -1.493820e+00
38 8.054248e-02 -1.083418e+00
39 4.449520e-01 8.054248e-02
40 -4.737385e-01 4.449520e-01
41 1.335635e+00 -4.737385e-01
42 1.551105e+00 1.335635e+00
43 -2.284423e-01 1.551105e+00
44 3.629402e+00 -2.284423e-01
45 -5.253284e-01 3.629402e+00
46 3.057107e-01 -5.253284e-01
47 3.250942e+00 3.057107e-01
48 -3.864481e+00 3.250942e+00
49 3.336834e-02 -3.864481e+00
50 -1.552119e+00 3.336834e-02
51 -2.023575e+00 -1.552119e+00
52 -2.413524e+00 -2.023575e+00
53 -4.630106e+00 -2.413524e+00
54 7.562651e-01 -4.630106e+00
55 -5.273638e-01 7.562651e-01
56 -2.179189e+00 -5.273638e-01
57 2.331142e+00 -2.179189e+00
58 1.675638e+00 2.331142e+00
59 2.782697e+00 1.675638e+00
60 2.059061e+00 2.782697e+00
61 -9.113283e-03 2.059061e+00
62 -1.676807e-01 -9.113283e-03
63 -1.133095e+00 -1.676807e-01
64 -4.137525e+00 -1.133095e+00
65 4.610743e+00 -4.137525e+00
66 8.805080e-01 4.610743e+00
67 3.315846e+00 8.805080e-01
68 2.482750e+00 3.315846e+00
69 1.024846e+00 2.482750e+00
70 1.434531e+00 1.024846e+00
71 1.913618e+00 1.434531e+00
72 4.445390e+00 1.913618e+00
73 1.363762e+00 4.445390e+00
74 3.325004e+00 1.363762e+00
75 -4.337497e-01 3.325004e+00
76 1.836542e+00 -4.337497e-01
77 2.368663e-01 1.836542e+00
78 1.494718e+00 2.368663e-01
79 2.754513e+00 1.494718e+00
80 3.261386e+00 2.754513e+00
81 2.931996e+00 3.261386e+00
82 -1.093454e+02 2.931996e+00
83 2.083657e+00 -1.093454e+02
84 7.491813e-01 2.083657e+00
85 3.121281e+00 7.491813e-01
86 2.730520e+00 3.121281e+00
87 2.749923e+00 2.730520e+00
88 9.510438e-01 2.749923e+00
89 1.068641e+00 9.510438e-01
90 2.571698e+00 1.068641e+00
91 2.156791e+00 2.571698e+00
92 1.484010e+00 2.156791e+00
93 -4.945009e-01 1.484010e+00
94 1.565894e+00 -4.945009e-01
95 1.952813e+00 1.565894e+00
96 3.052981e+00 1.952813e+00
97 2.588366e+00 3.052981e+00
98 1.807440e+00 2.588366e+00
99 3.065190e+00 1.807440e+00
100 2.935466e+00 3.065190e+00
101 3.285530e+00 2.935466e+00
102 3.049158e+00 3.285530e+00
103 2.292131e+00 3.049158e+00
104 -3.540414e-02 2.292131e+00
105 -7.395835e-01 -3.540414e-02
106 1.572471e+00 -7.395835e-01
107 -4.012200e-01 1.572471e+00
108 1.197996e+00 -4.012200e-01
109 -2.194700e+00 1.197996e+00
110 1.611747e+00 -2.194700e+00
111 2.203858e+00 1.611747e+00
112 3.695731e+00 2.203858e+00
113 1.159869e+00 3.695731e+00
114 5.511854e-02 1.159869e+00
115 2.773486e+00 5.511854e-02
116 8.789762e-01 2.773486e+00
117 3.009246e-01 8.789762e-01
118 5.740403e+00 3.009246e-01
119 1.674516e+00 5.740403e+00
120 -6.999321e-01 1.674516e+00
121 3.375209e-02 -6.999321e-01
122 8.070801e-01 3.375209e-02
123 2.489451e+00 8.070801e-01
124 4.567436e+00 2.489451e+00
125 1.770269e+00 4.567436e+00
126 2.519765e+00 1.770269e+00
127 1.017605e+00 2.519765e+00
128 5.129573e-01 1.017605e+00
129 1.586727e+00 5.129573e-01
130 1.430709e+00 1.586727e+00
131 -2.385261e+00 1.430709e+00
132 -9.380332e-01 -2.385261e+00
133 -1.292432e+00 -9.380332e-01
134 1.470649e+00 -1.292432e+00
135 1.100794e+00 1.470649e+00
136 -4.667402e+00 1.100794e+00
137 -6.312812e-01 -4.667402e+00
138 -1.005831e+00 -6.312812e-01
139 4.980789e-01 -1.005831e+00
140 2.202392e+00 4.980789e-01
141 -4.726285e-01 2.202392e+00
142 -3.023427e+00 -4.726285e-01
143 1.427919e+00 -3.023427e+00
144 -3.141334e-01 1.427919e+00
145 2.998465e+00 -3.141334e-01
146 1.159137e+00 2.998465e+00
147 1.436016e+00 1.159137e+00
148 1.182095e+00 1.436016e+00
149 -3.329981e+00 1.182095e+00
150 2.123670e+00 -3.329981e+00
151 -3.923791e-01 2.123670e+00
152 9.053990e-01 -3.923791e-01
153 -3.544146e+00 9.053990e-01
154 -2.623215e+00 -3.544146e+00
155 -1.468988e+00 -2.623215e+00
156 -4.258863e+00 -1.468988e+00
157 -1.553528e+00 -4.258863e+00
158 -5.853659e-01 -1.553528e+00
159 8.429578e-01 -5.853659e-01
160 -1.838604e+00 8.429578e-01
161 2.801321e+00 -1.838604e+00
162 NA 2.801321e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.698367e-01 2.137330e+00
[2,] -6.134572e-01 -2.698367e-01
[3,] 2.371125e+00 -6.134572e-01
[4,] -2.359011e+00 2.371125e+00
[5,] -7.254295e-01 -2.359011e+00
[6,] -1.323217e+00 -7.254295e-01
[7,] -2.271024e+00 -1.323217e+00
[8,] 3.543508e+00 -2.271024e+00
[9,] 9.674082e-01 3.543508e+00
[10,] 1.410044e+00 9.674082e-01
[11,] 2.214781e+00 1.410044e+00
[12,] -2.157900e+00 2.214781e+00
[13,] 1.132650e+00 -2.157900e+00
[14,] 2.426367e-01 1.132650e+00
[15,] 3.998977e-01 2.426367e-01
[16,] -1.829518e+00 3.998977e-01
[17,] -1.215519e-01 -1.829518e+00
[18,] 3.381519e+00 -1.215519e-01
[19,] -8.809696e-01 3.381519e+00
[20,] 2.499304e+00 -8.809696e-01
[21,] -6.674196e-01 2.499304e+00
[22,] -2.258601e+00 -6.674196e-01
[23,] -6.714890e-01 -2.258601e+00
[24,] -9.434161e-01 -6.714890e-01
[25,] 2.115398e+00 -9.434161e-01
[26,] 6.231837e-02 2.115398e+00
[27,] 2.528008e+00 6.231837e-02
[28,] 4.676166e+00 2.528008e+00
[29,] 2.720538e-01 4.676166e+00
[30,] 5.609293e-01 2.720538e-01
[31,] -1.321017e+00 5.609293e-01
[32,] -1.455202e+00 -1.321017e+00
[33,] -6.650307e-01 -1.455202e+00
[34,] 2.084813e+00 -6.650307e-01
[35,] 4.857275e-01 2.084813e+00
[36,] -1.493820e+00 4.857275e-01
[37,] -1.083418e+00 -1.493820e+00
[38,] 8.054248e-02 -1.083418e+00
[39,] 4.449520e-01 8.054248e-02
[40,] -4.737385e-01 4.449520e-01
[41,] 1.335635e+00 -4.737385e-01
[42,] 1.551105e+00 1.335635e+00
[43,] -2.284423e-01 1.551105e+00
[44,] 3.629402e+00 -2.284423e-01
[45,] -5.253284e-01 3.629402e+00
[46,] 3.057107e-01 -5.253284e-01
[47,] 3.250942e+00 3.057107e-01
[48,] -3.864481e+00 3.250942e+00
[49,] 3.336834e-02 -3.864481e+00
[50,] -1.552119e+00 3.336834e-02
[51,] -2.023575e+00 -1.552119e+00
[52,] -2.413524e+00 -2.023575e+00
[53,] -4.630106e+00 -2.413524e+00
[54,] 7.562651e-01 -4.630106e+00
[55,] -5.273638e-01 7.562651e-01
[56,] -2.179189e+00 -5.273638e-01
[57,] 2.331142e+00 -2.179189e+00
[58,] 1.675638e+00 2.331142e+00
[59,] 2.782697e+00 1.675638e+00
[60,] 2.059061e+00 2.782697e+00
[61,] -9.113283e-03 2.059061e+00
[62,] -1.676807e-01 -9.113283e-03
[63,] -1.133095e+00 -1.676807e-01
[64,] -4.137525e+00 -1.133095e+00
[65,] 4.610743e+00 -4.137525e+00
[66,] 8.805080e-01 4.610743e+00
[67,] 3.315846e+00 8.805080e-01
[68,] 2.482750e+00 3.315846e+00
[69,] 1.024846e+00 2.482750e+00
[70,] 1.434531e+00 1.024846e+00
[71,] 1.913618e+00 1.434531e+00
[72,] 4.445390e+00 1.913618e+00
[73,] 1.363762e+00 4.445390e+00
[74,] 3.325004e+00 1.363762e+00
[75,] -4.337497e-01 3.325004e+00
[76,] 1.836542e+00 -4.337497e-01
[77,] 2.368663e-01 1.836542e+00
[78,] 1.494718e+00 2.368663e-01
[79,] 2.754513e+00 1.494718e+00
[80,] 3.261386e+00 2.754513e+00
[81,] 2.931996e+00 3.261386e+00
[82,] -1.093454e+02 2.931996e+00
[83,] 2.083657e+00 -1.093454e+02
[84,] 7.491813e-01 2.083657e+00
[85,] 3.121281e+00 7.491813e-01
[86,] 2.730520e+00 3.121281e+00
[87,] 2.749923e+00 2.730520e+00
[88,] 9.510438e-01 2.749923e+00
[89,] 1.068641e+00 9.510438e-01
[90,] 2.571698e+00 1.068641e+00
[91,] 2.156791e+00 2.571698e+00
[92,] 1.484010e+00 2.156791e+00
[93,] -4.945009e-01 1.484010e+00
[94,] 1.565894e+00 -4.945009e-01
[95,] 1.952813e+00 1.565894e+00
[96,] 3.052981e+00 1.952813e+00
[97,] 2.588366e+00 3.052981e+00
[98,] 1.807440e+00 2.588366e+00
[99,] 3.065190e+00 1.807440e+00
[100,] 2.935466e+00 3.065190e+00
[101,] 3.285530e+00 2.935466e+00
[102,] 3.049158e+00 3.285530e+00
[103,] 2.292131e+00 3.049158e+00
[104,] -3.540414e-02 2.292131e+00
[105,] -7.395835e-01 -3.540414e-02
[106,] 1.572471e+00 -7.395835e-01
[107,] -4.012200e-01 1.572471e+00
[108,] 1.197996e+00 -4.012200e-01
[109,] -2.194700e+00 1.197996e+00
[110,] 1.611747e+00 -2.194700e+00
[111,] 2.203858e+00 1.611747e+00
[112,] 3.695731e+00 2.203858e+00
[113,] 1.159869e+00 3.695731e+00
[114,] 5.511854e-02 1.159869e+00
[115,] 2.773486e+00 5.511854e-02
[116,] 8.789762e-01 2.773486e+00
[117,] 3.009246e-01 8.789762e-01
[118,] 5.740403e+00 3.009246e-01
[119,] 1.674516e+00 5.740403e+00
[120,] -6.999321e-01 1.674516e+00
[121,] 3.375209e-02 -6.999321e-01
[122,] 8.070801e-01 3.375209e-02
[123,] 2.489451e+00 8.070801e-01
[124,] 4.567436e+00 2.489451e+00
[125,] 1.770269e+00 4.567436e+00
[126,] 2.519765e+00 1.770269e+00
[127,] 1.017605e+00 2.519765e+00
[128,] 5.129573e-01 1.017605e+00
[129,] 1.586727e+00 5.129573e-01
[130,] 1.430709e+00 1.586727e+00
[131,] -2.385261e+00 1.430709e+00
[132,] -9.380332e-01 -2.385261e+00
[133,] -1.292432e+00 -9.380332e-01
[134,] 1.470649e+00 -1.292432e+00
[135,] 1.100794e+00 1.470649e+00
[136,] -4.667402e+00 1.100794e+00
[137,] -6.312812e-01 -4.667402e+00
[138,] -1.005831e+00 -6.312812e-01
[139,] 4.980789e-01 -1.005831e+00
[140,] 2.202392e+00 4.980789e-01
[141,] -4.726285e-01 2.202392e+00
[142,] -3.023427e+00 -4.726285e-01
[143,] 1.427919e+00 -3.023427e+00
[144,] -3.141334e-01 1.427919e+00
[145,] 2.998465e+00 -3.141334e-01
[146,] 1.159137e+00 2.998465e+00
[147,] 1.436016e+00 1.159137e+00
[148,] 1.182095e+00 1.436016e+00
[149,] -3.329981e+00 1.182095e+00
[150,] 2.123670e+00 -3.329981e+00
[151,] -3.923791e-01 2.123670e+00
[152,] 9.053990e-01 -3.923791e-01
[153,] -3.544146e+00 9.053990e-01
[154,] -2.623215e+00 -3.544146e+00
[155,] -1.468988e+00 -2.623215e+00
[156,] -4.258863e+00 -1.468988e+00
[157,] -1.553528e+00 -4.258863e+00
[158,] -5.853659e-01 -1.553528e+00
[159,] 8.429578e-01 -5.853659e-01
[160,] -1.838604e+00 8.429578e-01
[161,] 2.801321e+00 -1.838604e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.698367e-01 2.137330e+00
2 -6.134572e-01 -2.698367e-01
3 2.371125e+00 -6.134572e-01
4 -2.359011e+00 2.371125e+00
5 -7.254295e-01 -2.359011e+00
6 -1.323217e+00 -7.254295e-01
7 -2.271024e+00 -1.323217e+00
8 3.543508e+00 -2.271024e+00
9 9.674082e-01 3.543508e+00
10 1.410044e+00 9.674082e-01
11 2.214781e+00 1.410044e+00
12 -2.157900e+00 2.214781e+00
13 1.132650e+00 -2.157900e+00
14 2.426367e-01 1.132650e+00
15 3.998977e-01 2.426367e-01
16 -1.829518e+00 3.998977e-01
17 -1.215519e-01 -1.829518e+00
18 3.381519e+00 -1.215519e-01
19 -8.809696e-01 3.381519e+00
20 2.499304e+00 -8.809696e-01
21 -6.674196e-01 2.499304e+00
22 -2.258601e+00 -6.674196e-01
23 -6.714890e-01 -2.258601e+00
24 -9.434161e-01 -6.714890e-01
25 2.115398e+00 -9.434161e-01
26 6.231837e-02 2.115398e+00
27 2.528008e+00 6.231837e-02
28 4.676166e+00 2.528008e+00
29 2.720538e-01 4.676166e+00
30 5.609293e-01 2.720538e-01
31 -1.321017e+00 5.609293e-01
32 -1.455202e+00 -1.321017e+00
33 -6.650307e-01 -1.455202e+00
34 2.084813e+00 -6.650307e-01
35 4.857275e-01 2.084813e+00
36 -1.493820e+00 4.857275e-01
37 -1.083418e+00 -1.493820e+00
38 8.054248e-02 -1.083418e+00
39 4.449520e-01 8.054248e-02
40 -4.737385e-01 4.449520e-01
41 1.335635e+00 -4.737385e-01
42 1.551105e+00 1.335635e+00
43 -2.284423e-01 1.551105e+00
44 3.629402e+00 -2.284423e-01
45 -5.253284e-01 3.629402e+00
46 3.057107e-01 -5.253284e-01
47 3.250942e+00 3.057107e-01
48 -3.864481e+00 3.250942e+00
49 3.336834e-02 -3.864481e+00
50 -1.552119e+00 3.336834e-02
51 -2.023575e+00 -1.552119e+00
52 -2.413524e+00 -2.023575e+00
53 -4.630106e+00 -2.413524e+00
54 7.562651e-01 -4.630106e+00
55 -5.273638e-01 7.562651e-01
56 -2.179189e+00 -5.273638e-01
57 2.331142e+00 -2.179189e+00
58 1.675638e+00 2.331142e+00
59 2.782697e+00 1.675638e+00
60 2.059061e+00 2.782697e+00
61 -9.113283e-03 2.059061e+00
62 -1.676807e-01 -9.113283e-03
63 -1.133095e+00 -1.676807e-01
64 -4.137525e+00 -1.133095e+00
65 4.610743e+00 -4.137525e+00
66 8.805080e-01 4.610743e+00
67 3.315846e+00 8.805080e-01
68 2.482750e+00 3.315846e+00
69 1.024846e+00 2.482750e+00
70 1.434531e+00 1.024846e+00
71 1.913618e+00 1.434531e+00
72 4.445390e+00 1.913618e+00
73 1.363762e+00 4.445390e+00
74 3.325004e+00 1.363762e+00
75 -4.337497e-01 3.325004e+00
76 1.836542e+00 -4.337497e-01
77 2.368663e-01 1.836542e+00
78 1.494718e+00 2.368663e-01
79 2.754513e+00 1.494718e+00
80 3.261386e+00 2.754513e+00
81 2.931996e+00 3.261386e+00
82 -1.093454e+02 2.931996e+00
83 2.083657e+00 -1.093454e+02
84 7.491813e-01 2.083657e+00
85 3.121281e+00 7.491813e-01
86 2.730520e+00 3.121281e+00
87 2.749923e+00 2.730520e+00
88 9.510438e-01 2.749923e+00
89 1.068641e+00 9.510438e-01
90 2.571698e+00 1.068641e+00
91 2.156791e+00 2.571698e+00
92 1.484010e+00 2.156791e+00
93 -4.945009e-01 1.484010e+00
94 1.565894e+00 -4.945009e-01
95 1.952813e+00 1.565894e+00
96 3.052981e+00 1.952813e+00
97 2.588366e+00 3.052981e+00
98 1.807440e+00 2.588366e+00
99 3.065190e+00 1.807440e+00
100 2.935466e+00 3.065190e+00
101 3.285530e+00 2.935466e+00
102 3.049158e+00 3.285530e+00
103 2.292131e+00 3.049158e+00
104 -3.540414e-02 2.292131e+00
105 -7.395835e-01 -3.540414e-02
106 1.572471e+00 -7.395835e-01
107 -4.012200e-01 1.572471e+00
108 1.197996e+00 -4.012200e-01
109 -2.194700e+00 1.197996e+00
110 1.611747e+00 -2.194700e+00
111 2.203858e+00 1.611747e+00
112 3.695731e+00 2.203858e+00
113 1.159869e+00 3.695731e+00
114 5.511854e-02 1.159869e+00
115 2.773486e+00 5.511854e-02
116 8.789762e-01 2.773486e+00
117 3.009246e-01 8.789762e-01
118 5.740403e+00 3.009246e-01
119 1.674516e+00 5.740403e+00
120 -6.999321e-01 1.674516e+00
121 3.375209e-02 -6.999321e-01
122 8.070801e-01 3.375209e-02
123 2.489451e+00 8.070801e-01
124 4.567436e+00 2.489451e+00
125 1.770269e+00 4.567436e+00
126 2.519765e+00 1.770269e+00
127 1.017605e+00 2.519765e+00
128 5.129573e-01 1.017605e+00
129 1.586727e+00 5.129573e-01
130 1.430709e+00 1.586727e+00
131 -2.385261e+00 1.430709e+00
132 -9.380332e-01 -2.385261e+00
133 -1.292432e+00 -9.380332e-01
134 1.470649e+00 -1.292432e+00
135 1.100794e+00 1.470649e+00
136 -4.667402e+00 1.100794e+00
137 -6.312812e-01 -4.667402e+00
138 -1.005831e+00 -6.312812e-01
139 4.980789e-01 -1.005831e+00
140 2.202392e+00 4.980789e-01
141 -4.726285e-01 2.202392e+00
142 -3.023427e+00 -4.726285e-01
143 1.427919e+00 -3.023427e+00
144 -3.141334e-01 1.427919e+00
145 2.998465e+00 -3.141334e-01
146 1.159137e+00 2.998465e+00
147 1.436016e+00 1.159137e+00
148 1.182095e+00 1.436016e+00
149 -3.329981e+00 1.182095e+00
150 2.123670e+00 -3.329981e+00
151 -3.923791e-01 2.123670e+00
152 9.053990e-01 -3.923791e-01
153 -3.544146e+00 9.053990e-01
154 -2.623215e+00 -3.544146e+00
155 -1.468988e+00 -2.623215e+00
156 -4.258863e+00 -1.468988e+00
157 -1.553528e+00 -4.258863e+00
158 -5.853659e-01 -1.553528e+00
159 8.429578e-01 -5.853659e-01
160 -1.838604e+00 8.429578e-01
161 2.801321e+00 -1.838604e+00
> 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/7n6c31353431149.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/8zghr1353431149.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/9fwlt1353431149.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/104kkq1353431149.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11i3041353431149.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12fbyr1353431150.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/139nj61353431150.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14k10d1353431150.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/15w05o1353431150.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/16ya541353431150.tab")
+ }
>
> try(system("convert tmp/1jask1353431149.ps tmp/1jask1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/29qxc1353431149.ps tmp/29qxc1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/37uae1353431149.ps tmp/37uae1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/42l0t1353431149.ps tmp/42l0t1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tkdu1353431149.ps tmp/5tkdu1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/622ae1353431149.ps tmp/622ae1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/7n6c31353431149.ps tmp/7n6c31353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zghr1353431149.ps tmp/8zghr1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fwlt1353431149.ps tmp/9fwlt1353431149.png",intern=TRUE))
character(0)
> try(system("convert tmp/104kkq1353431149.ps tmp/104kkq1353431149.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
17.517 2.529 20.121