R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
> x <- array(list(24
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+ ,dim=c(5
+ ,156)
+ ,dimnames=list(c('Concernovermistakes'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:156))
> y <- array(NA,dim=c(5,156),dimnames=list(c('Concernovermistakes','Y1','Y2','Y3','Y4'),1:156))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Concernovermistakes Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 24 18 17 25 24 1 0 0 0 0 0 0 0 0 0 0 1
2 25 18 18 17 25 0 1 0 0 0 0 0 0 0 0 0 2
3 17 16 18 18 17 0 0 1 0 0 0 0 0 0 0 0 3
4 18 20 16 18 18 0 0 0 1 0 0 0 0 0 0 0 4
5 18 16 20 16 18 0 0 0 0 1 0 0 0 0 0 0 5
6 16 18 16 20 16 0 0 0 0 0 1 0 0 0 0 0 6
7 20 17 18 16 20 0 0 0 0 0 0 1 0 0 0 0 7
8 16 23 17 18 16 0 0 0 0 0 0 0 1 0 0 0 8
9 18 30 23 17 18 0 0 0 0 0 0 0 0 1 0 0 9
10 17 23 30 23 17 0 0 0 0 0 0 0 0 0 1 0 10
11 23 18 23 30 23 0 0 0 0 0 0 0 0 0 0 1 11
12 30 15 18 23 30 0 0 0 0 0 0 0 0 0 0 0 12
13 23 12 15 18 23 1 0 0 0 0 0 0 0 0 0 0 13
14 18 21 12 15 18 0 1 0 0 0 0 0 0 0 0 0 14
15 15 15 21 12 15 0 0 1 0 0 0 0 0 0 0 0 15
16 12 20 15 21 12 0 0 0 1 0 0 0 0 0 0 0 16
17 21 31 20 15 21 0 0 0 0 1 0 0 0 0 0 0 17
18 15 27 31 20 15 0 0 0 0 0 1 0 0 0 0 0 18
19 20 34 27 31 20 0 0 0 0 0 0 1 0 0 0 0 19
20 31 21 34 27 31 0 0 0 0 0 0 0 1 0 0 0 20
21 27 31 21 34 27 0 0 0 0 0 0 0 0 1 0 0 21
22 34 19 31 21 34 0 0 0 0 0 0 0 0 0 1 0 22
23 21 16 19 31 21 0 0 0 0 0 0 0 0 0 0 1 23
24 31 20 16 19 31 0 0 0 0 0 0 0 0 0 0 0 24
25 19 21 20 16 19 1 0 0 0 0 0 0 0 0 0 0 25
26 16 22 21 20 16 0 1 0 0 0 0 0 0 0 0 0 26
27 20 17 22 21 20 0 0 1 0 0 0 0 0 0 0 0 27
28 21 24 17 22 21 0 0 0 1 0 0 0 0 0 0 0 28
29 22 25 24 17 22 0 0 0 0 1 0 0 0 0 0 0 29
30 17 26 25 24 17 0 0 0 0 0 1 0 0 0 0 0 30
31 24 25 26 25 24 0 0 0 0 0 0 1 0 0 0 0 31
32 25 17 25 26 25 0 0 0 0 0 0 0 1 0 0 0 32
33 26 32 17 25 26 0 0 0 0 0 0 0 0 1 0 0 33
34 25 33 32 17 25 0 0 0 0 0 0 0 0 0 1 0 34
35 17 13 33 32 17 0 0 0 0 0 0 0 0 0 0 1 35
36 32 32 13 33 32 0 0 0 0 0 0 0 0 0 0 0 36
37 33 25 32 13 33 1 0 0 0 0 0 0 0 0 0 0 37
38 13 29 25 32 13 0 1 0 0 0 0 0 0 0 0 0 38
39 32 22 29 25 32 0 0 1 0 0 0 0 0 0 0 0 39
40 25 18 22 29 25 0 0 0 1 0 0 0 0 0 0 0 40
41 29 17 18 22 29 0 0 0 0 1 0 0 0 0 0 0 41
42 22 20 17 18 22 0 0 0 0 0 1 0 0 0 0 0 42
43 18 15 20 17 18 0 0 0 0 0 0 1 0 0 0 0 43
44 17 20 15 20 17 0 0 0 0 0 0 0 1 0 0 0 44
45 20 33 20 15 20 0 0 0 0 0 0 0 0 1 0 0 45
46 15 29 33 20 15 0 0 0 0 0 0 0 0 0 1 0 46
47 20 23 29 33 20 0 0 0 0 0 0 0 0 0 0 1 47
48 33 26 23 29 33 0 0 0 0 0 0 0 0 0 0 0 48
49 29 18 26 23 29 1 0 0 0 0 0 0 0 0 0 0 49
50 23 20 18 26 23 0 1 0 0 0 0 0 0 0 0 0 50
51 26 11 20 18 26 0 0 1 0 0 0 0 0 0 0 0 51
52 18 28 11 20 18 0 0 0 1 0 0 0 0 0 0 0 52
53 20 26 28 11 20 0 0 0 0 1 0 0 0 0 0 0 53
54 11 22 26 28 11 0 0 0 0 0 1 0 0 0 0 0 54
55 28 17 22 26 28 0 0 0 0 0 0 1 0 0 0 0 55
56 26 12 17 22 26 0 0 0 0 0 0 0 1 0 0 0 56
57 22 14 12 17 22 0 0 0 0 0 0 0 0 1 0 0 57
58 17 17 14 12 17 0 0 0 0 0 0 0 0 0 1 0 58
59 12 21 17 14 12 0 0 0 0 0 0 0 0 0 0 1 59
60 14 19 21 17 14 0 0 0 0 0 0 0 0 0 0 0 60
61 17 18 19 21 17 1 0 0 0 0 0 0 0 0 0 0 61
62 21 10 18 19 21 0 1 0 0 0 0 0 0 0 0 0 62
63 19 29 10 18 19 0 0 1 0 0 0 0 0 0 0 0 63
64 18 31 29 10 18 0 0 0 1 0 0 0 0 0 0 0 64
65 10 19 31 29 10 0 0 0 0 1 0 0 0 0 0 0 65
66 29 9 19 31 29 0 0 0 0 0 1 0 0 0 0 0 66
67 31 20 9 19 31 0 0 0 0 0 0 1 0 0 0 0 67
68 19 28 20 9 19 0 0 0 0 0 0 0 1 0 0 0 68
69 9 19 28 20 9 0 0 0 0 0 0 0 0 1 0 0 69
70 20 30 19 28 20 0 0 0 0 0 0 0 0 0 1 0 70
71 28 29 30 19 28 0 0 0 0 0 0 0 0 0 0 1 71
72 19 26 29 30 19 0 0 0 0 0 0 0 0 0 0 0 72
73 30 23 26 29 30 1 0 0 0 0 0 0 0 0 0 0 73
74 29 13 23 26 29 0 1 0 0 0 0 0 0 0 0 0 74
75 26 21 13 23 26 0 0 1 0 0 0 0 0 0 0 0 75
76 23 19 21 13 23 0 0 0 1 0 0 0 0 0 0 0 76
77 13 28 19 21 13 0 0 0 0 1 0 0 0 0 0 0 77
78 21 23 28 19 21 0 0 0 0 0 1 0 0 0 0 0 78
79 19 18 23 28 19 0 0 0 0 0 0 1 0 0 0 0 79
80 28 21 18 23 28 0 0 0 0 0 0 0 1 0 0 0 80
81 23 20 21 18 23 0 0 0 0 0 0 0 0 1 0 0 81
82 18 23 20 21 18 0 0 0 0 0 0 0 0 0 1 0 82
83 21 21 23 20 21 0 0 0 0 0 0 0 0 0 0 1 83
84 20 21 21 23 20 0 0 0 0 0 0 0 0 0 0 0 84
85 23 15 21 21 23 1 0 0 0 0 0 0 0 0 0 0 85
86 21 28 15 21 21 0 1 0 0 0 0 0 0 0 0 0 86
87 21 19 28 15 21 0 0 1 0 0 0 0 0 0 0 0 87
88 15 26 19 28 15 0 0 0 1 0 0 0 0 0 0 0 88
89 28 10 26 19 28 0 0 0 0 1 0 0 0 0 0 0 89
90 19 16 10 26 19 0 0 0 0 0 1 0 0 0 0 0 90
91 26 22 16 10 26 0 0 0 0 0 0 1 0 0 0 0 91
92 10 19 22 16 10 0 0 0 0 0 0 0 1 0 0 0 92
93 16 31 19 22 16 0 0 0 0 0 0 0 0 1 0 0 93
94 22 31 31 19 22 0 0 0 0 0 0 0 0 0 1 0 94
95 19 29 31 31 19 0 0 0 0 0 0 0 0 0 0 1 95
96 31 19 29 31 31 0 0 0 0 0 0 0 0 0 0 0 96
97 31 22 19 29 31 1 0 0 0 0 0 0 0 0 0 0 97
98 29 23 22 19 29 0 1 0 0 0 0 0 0 0 0 0 98
99 19 15 23 22 19 0 0 1 0 0 0 0 0 0 0 0 99
100 22 20 15 23 22 0 0 0 1 0 0 0 0 0 0 0 100
101 23 18 20 15 23 0 0 0 0 1 0 0 0 0 0 0 101
102 15 23 18 20 15 0 0 0 0 0 1 0 0 0 0 0 102
103 20 25 23 18 20 0 0 0 0 0 0 1 0 0 0 0 103
104 18 21 25 23 18 0 0 0 0 0 0 0 1 0 0 0 104
105 23 24 21 25 23 0 0 0 0 0 0 0 0 1 0 0 105
106 25 25 24 21 25 0 0 0 0 0 0 0 0 0 1 0 106
107 21 17 25 24 21 0 0 0 0 0 0 0 0 0 0 1 107
108 24 13 17 25 24 0 0 0 0 0 0 0 0 0 0 0 108
109 25 28 13 17 25 1 0 0 0 0 0 0 0 0 0 0 109
110 17 21 28 13 17 0 1 0 0 0 0 0 0 0 0 0 110
111 13 25 21 28 13 0 0 1 0 0 0 0 0 0 0 0 111
112 28 9 25 21 28 0 0 0 1 0 0 0 0 0 0 0 112
113 21 16 9 25 21 0 0 0 0 1 0 0 0 0 0 0 113
114 25 19 16 9 25 0 0 0 0 0 1 0 0 0 0 0 114
115 9 17 19 16 9 0 0 0 0 0 0 1 0 0 0 0 115
116 16 25 17 19 16 0 0 0 0 0 0 0 1 0 0 0 116
117 19 20 25 17 19 0 0 0 0 0 0 0 0 1 0 0 117
118 17 29 20 25 17 0 0 0 0 0 0 0 0 0 1 0 118
119 25 14 29 20 25 0 0 0 0 0 0 0 0 0 0 1 119
120 20 22 14 29 20 0 0 0 0 0 0 0 0 0 0 0 120
121 29 15 22 14 29 1 0 0 0 0 0 0 0 0 0 0 121
122 14 19 15 22 14 0 1 0 0 0 0 0 0 0 0 0 122
123 22 20 19 15 22 0 0 1 0 0 0 0 0 0 0 0 123
124 15 15 20 19 15 0 0 0 1 0 0 0 0 0 0 0 124
125 19 20 15 20 19 0 0 0 0 1 0 0 0 0 0 0 125
126 20 18 20 15 20 0 0 0 0 0 1 0 0 0 0 0 126
127 15 33 18 20 15 0 0 0 0 0 0 1 0 0 0 0 127
128 20 22 33 18 20 0 0 0 0 0 0 0 1 0 0 0 128
129 18 16 22 33 18 0 0 0 0 0 0 0 0 1 0 0 129
130 33 17 16 22 33 0 0 0 0 0 0 0 0 0 1 0 130
131 22 16 17 16 22 0 0 0 0 0 0 0 0 0 0 1 131
132 16 21 16 17 16 0 0 0 0 0 0 0 0 0 0 0 132
133 17 26 21 16 17 1 0 0 0 0 0 0 0 0 0 0 133
134 16 18 26 21 16 0 1 0 0 0 0 0 0 0 0 0 134
135 21 18 18 26 21 0 0 1 0 0 0 0 0 0 0 0 135
136 26 17 18 18 26 0 0 0 1 0 0 0 0 0 0 0 136
137 18 22 17 18 18 0 0 0 0 1 0 0 0 0 0 0 137
138 18 30 22 17 18 0 0 0 0 0 1 0 0 0 0 0 138
139 17 30 30 22 17 0 0 0 0 0 0 1 0 0 0 0 139
140 22 24 30 30 22 0 0 0 0 0 0 0 1 0 0 0 140
141 30 21 24 30 30 0 0 0 0 0 0 0 0 1 0 0 141
142 30 21 21 24 30 0 0 0 0 0 0 0 0 0 1 0 142
143 24 29 21 21 24 0 0 0 0 0 0 0 0 0 0 1 143
144 21 31 29 21 21 0 0 0 0 0 0 0 0 0 0 0 144
145 21 20 31 29 21 1 0 0 0 0 0 0 0 0 0 0 145
146 29 16 20 31 29 0 1 0 0 0 0 0 0 0 0 0 146
147 31 22 16 20 31 0 0 1 0 0 0 0 0 0 0 0 147
148 20 20 22 16 20 0 0 0 1 0 0 0 0 0 0 0 148
149 16 28 20 22 16 0 0 0 0 1 0 0 0 0 0 0 149
150 22 38 28 20 22 0 0 0 0 0 1 0 0 0 0 0 150
151 20 22 38 28 20 0 0 0 0 0 0 1 0 0 0 0 151
152 28 20 22 38 28 0 0 0 0 0 0 0 1 0 0 0 152
153 38 17 20 22 38 0 0 0 0 0 0 0 0 1 0 0 153
154 22 28 17 20 22 0 0 0 0 0 0 0 0 0 1 0 154
155 20 22 28 17 20 0 0 0 0 0 0 0 0 0 0 1 155
156 17 31 22 28 17 0 0 0 0 0 0 0 0 0 0 0 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y1 Y2 Y3 Y4 M1
-1.756e-14 1.532e-16 -6.311e-18 -1.773e-16 1.000e+00 5.517e-16
M2 M3 M4 M5 M6 M7
8.539e-16 4.089e-16 5.838e-16 -4.127e-15 3.665e-16 1.397e-16
M8 M9 M10 M11 t
1.970e-16 7.664e-17 2.833e-17 1.648e-16 1.068e-17
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.284e-14 -4.967e-16 -2.979e-17 5.477e-16 5.216e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.756e-14 3.385e-15 -5.188e+00 7.36e-07 ***
Y1 1.532e-16 7.186e-17 2.133e+00 0.0347 *
Y2 -6.311e-18 7.178e-17 -8.800e-02 0.9301
Y3 -1.773e-16 7.155e-17 -2.478e+00 0.0144 *
Y4 1.000e+00 7.205e-17 1.388e+16 < 2e-16 ***
M1 5.517e-16 1.912e-15 2.890e-01 0.7734
M2 8.539e-16 1.909e-15 4.470e-01 0.6553
M3 4.089e-16 1.924e-15 2.130e-01 0.8320
M4 5.838e-16 1.926e-15 3.030e-01 0.7623
M5 -4.127e-15 1.933e-15 -2.136e+00 0.0345 *
M6 3.665e-16 1.920e-15 1.910e-01 0.8489
M7 1.397e-16 1.905e-15 7.300e-02 0.9417
M8 1.970e-16 1.903e-15 1.040e-01 0.9177
M9 7.664e-17 1.881e-15 4.100e-02 0.9675
M10 2.833e-17 1.913e-15 1.500e-02 0.9882
M11 1.648e-16 1.924e-15 8.600e-02 0.9319
t 1.068e-17 8.520e-18 1.254e+00 0.2120
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.769e-15 on 139 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.386e+31 on 16 and 139 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,] 0.000000e+00 0.000000e+00 1.000000e+00
[2,] 7.198570e-01 5.602860e-01 2.801430e-01
[3,] 6.688704e-01 6.622592e-01 3.311296e-01
[4,] 1.000000e+00 1.200865e-09 6.004323e-10
[5,] 9.981270e-01 3.745952e-03 1.872976e-03
[6,] 6.245803e-01 7.508393e-01 3.754197e-01
[7,] 3.396990e-03 6.793980e-03 9.966030e-01
[8,] 9.976733e-06 1.995347e-05 9.999900e-01
[9,] 1.815941e-03 3.631882e-03 9.981841e-01
[10,] 1.000000e+00 3.520600e-42 1.760300e-42
[11,] 1.152225e-01 2.304451e-01 8.847775e-01
[12,] 8.945102e-01 2.109797e-01 1.054898e-01
[13,] 4.200935e-07 8.401869e-07 9.999996e-01
[14,] 9.991661e-01 1.667724e-03 8.338622e-04
[15,] 9.039315e-01 1.921370e-01 9.606848e-02
[16,] 3.506658e-14 7.013317e-14 1.000000e+00
[17,] 3.081442e-03 6.162884e-03 9.969186e-01
[18,] 9.999526e-01 9.479466e-05 4.739733e-05
[19,] 1.000000e+00 2.519787e-18 1.259893e-18
[20,] 9.999994e-01 1.261612e-06 6.308062e-07
[21,] 5.403276e-08 1.080655e-07 9.999999e-01
[22,] 1.004682e-50 2.009363e-50 1.000000e+00
[23,] 2.425017e-01 4.850035e-01 7.574983e-01
[24,] 1.000000e+00 2.026898e-31 1.013449e-31
[25,] 1.310803e-07 2.621607e-07 9.999999e-01
[26,] 1.680250e-36 3.360499e-36 1.000000e+00
[27,] 1.000000e+00 4.714018e-47 2.357009e-47
[28,] 1.329967e-12 2.659933e-12 1.000000e+00
[29,] 8.588513e-01 2.822974e-01 1.411487e-01
[30,] 1.000000e+00 1.617904e-15 8.089518e-16
[31,] 4.301769e-01 8.603537e-01 5.698231e-01
[32,] 1.910538e-11 3.821076e-11 1.000000e+00
[33,] 1.000000e+00 7.228454e-10 3.614227e-10
[34,] 7.129521e-01 5.740958e-01 2.870479e-01
[35,] 3.073553e-01 6.147106e-01 6.926447e-01
[36,] 1.000000e+00 2.738675e-16 1.369338e-16
[37,] 1.000000e+00 1.124344e-17 5.621718e-18
[38,] 1.000000e+00 1.526665e-66 7.633325e-67
[39,] 9.986379e-01 2.724189e-03 1.362095e-03
[40,] 2.830769e-04 5.661537e-04 9.997169e-01
[41,] 1.000000e+00 2.187635e-20 1.093818e-20
[42,] 1.027924e-29 2.055849e-29 1.000000e+00
[43,] 1.284376e-11 2.568753e-11 1.000000e+00
[44,] 1.000000e+00 2.371205e-57 1.185602e-57
[45,] 3.546642e-01 7.093284e-01 6.453358e-01
[46,] 1.369503e-02 2.739005e-02 9.863050e-01
[47,] 3.093467e-11 6.186934e-11 1.000000e+00
[48,] 1.000000e+00 1.503109e-08 7.515544e-09
[49,] 1.000000e+00 1.021806e-25 5.109032e-26
[50,] 1.221805e-02 2.443611e-02 9.877819e-01
[51,] 9.811043e-01 3.779137e-02 1.889568e-02
[52,] 3.708829e-12 7.417659e-12 1.000000e+00
[53,] 1.773887e-05 3.547775e-05 9.999823e-01
[54,] 3.422081e-23 6.844161e-23 1.000000e+00
[55,] 1.721987e-15 3.443974e-15 1.000000e+00
[56,] 1.000000e+00 3.772565e-18 1.886282e-18
[57,] 1.059939e-02 2.119879e-02 9.894006e-01
[58,] 3.622209e-03 7.244418e-03 9.963778e-01
[59,] 1.893461e-07 3.786922e-07 9.999998e-01
[60,] 3.840768e-01 7.681536e-01 6.159232e-01
[61,] 3.425756e-02 6.851512e-02 9.657424e-01
[62,] 1.641003e-07 3.282007e-07 9.999998e-01
[63,] 1.000000e+00 3.803630e-15 1.901815e-15
[64,] 9.842636e-01 3.147272e-02 1.573636e-02
[65,] 9.024767e-01 1.950466e-01 9.752331e-02
[66,] 1.528735e-02 3.057470e-02 9.847127e-01
[67,] 1.000000e+00 4.455404e-22 2.227702e-22
[68,] 6.387927e-01 7.224146e-01 3.612073e-01
[69,] 3.951118e-04 7.902235e-04 9.996049e-01
[70,] 4.198539e-01 8.397079e-01 5.801461e-01
[71,] 9.994970e-01 1.005945e-03 5.029723e-04
[72,] 1.000000e+00 4.807839e-21 2.403920e-21
[73,] 7.901207e-02 1.580241e-01 9.209879e-01
[74,] 2.233248e-23 4.466496e-23 1.000000e+00
[75,] 1.823382e-41 3.646764e-41 1.000000e+00
[76,] 1.000000e+00 4.381578e-20 2.190789e-20
[77,] 9.998862e-01 2.275663e-04 1.137831e-04
[78,] 1.000000e+00 2.131899e-36 1.065949e-36
[79,] 9.257684e-01 1.484633e-01 7.423163e-02
[80,] 1.000000e+00 1.497163e-20 7.485813e-21
[81,] 2.416851e-22 4.833702e-22 1.000000e+00
[82,] 1.236168e-06 2.472337e-06 9.999988e-01
[83,] 1.000000e+00 6.752308e-12 3.376154e-12
[84,] 1.000000e+00 1.602212e-19 8.011058e-20
[85,] 1.000000e+00 1.333557e-18 6.667786e-19
[86,] 6.341295e-03 1.268259e-02 9.936587e-01
[87,] 9.999995e-01 9.518043e-07 4.759021e-07
[88,] 3.539033e-03 7.078066e-03 9.964610e-01
[89,] 2.577752e-01 5.155503e-01 7.422248e-01
[90,] 2.370447e-09 4.740893e-09 1.000000e+00
[91,] 1.000000e+00 1.034984e-09 5.174918e-10
[92,] 9.997076e-01 5.847877e-04 2.923939e-04
[93,] 1.706233e-01 3.412465e-01 8.293767e-01
[94,] 1.619037e-01 3.238075e-01 8.380963e-01
[95,] 9.518983e-01 9.620340e-02 4.810170e-02
[96,] 9.999782e-01 4.366222e-05 2.183111e-05
[97,] 9.999999e-01 1.195888e-07 5.979440e-08
[98,] 9.999616e-01 7.671063e-05 3.835532e-05
[99,] 1.000000e+00 1.254490e-10 6.272449e-11
[100,] 9.999994e-01 1.164881e-06 5.824404e-07
[101,] 8.789369e-04 1.757874e-03 9.991211e-01
[102,] 9.495470e-01 1.009060e-01 5.045301e-02
[103,] 9.999997e-01 6.616181e-07 3.308090e-07
[104,] 9.184163e-03 1.836833e-02 9.908158e-01
[105,] 1.000000e+00 3.757663e-10 1.878831e-10
[106,] 9.969595e-01 6.081090e-03 3.040545e-03
[107,] 1.916310e-01 3.832619e-01 8.083690e-01
[108,] 8.982955e-01 2.034090e-01 1.017045e-01
[109,] 9.939199e-01 1.216024e-02 6.080122e-03
[110,] 9.999154e-01 1.692540e-04 8.462699e-05
[111,] 9.988780e-01 2.244003e-03 1.122001e-03
[112,] 9.985833e-01 2.833412e-03 1.416706e-03
[113,] 9.999999e-01 1.983700e-07 9.918501e-08
[114,] 9.982419e-01 3.516247e-03 1.758124e-03
[115,] 9.334150e-01 1.331700e-01 6.658502e-02
[116,] 9.710444e-01 5.791113e-02 2.895556e-02
[117,] 3.559132e-01 7.118264e-01 6.440868e-01
> postscript(file="/var/www/html/rcomp/tmp/1yaad1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2yaad1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3yaad1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rj9x1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rj9x1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5
3.883367e-15 4.459499e-15 9.242415e-16 4.611255e-15 -5.283882e-14
6 7 8 9 10
1.429031e-15 1.364954e-15 8.626107e-16 5.385162e-16 1.141347e-15
11 12 13 14 15
6.770594e-16 1.442698e-15 9.233770e-16 3.578725e-16 1.606257e-15
16 17 18 19 20
-3.137681e-16 4.581225e-15 1.108191e-15 -3.004122e-16 4.180791e-16
21 22 23 24 25
-6.598701e-16 1.508388e-15 5.748570e-16 1.917255e-16 3.973608e-16
26 27 28 29 30
1.828464e-16 7.586885e-16 -8.255873e-17 4.775565e-15 7.726812e-17
31 32 33 34 35
3.238727e-16 7.185826e-16 -2.656990e-16 2.395643e-16 6.779551e-18
36 37 38 39 40
-4.232750e-16 7.194647e-16 2.210029e-16 -1.083176e-15 -4.262043e-17
41 42 43 44 45
4.034304e-15 1.927339e-16 1.220937e-15 5.426416e-16 -3.174904e-17
46 47 48 49 50
4.496815e-16 6.178842e-17 -4.994473e-17 -2.306361e-16 -4.935578e-16
51 52 53 54 55
4.671314e-16 -5.513919e-16 4.820417e-15 5.628375e-16 4.436823e-16
56 57 58 59 60
4.535804e-16 8.163840e-16 1.040035e-15 1.215758e-15 1.055308e-15
61 62 63 64 65
2.582446e-16 4.111030e-16 -6.232138e-16 -2.495448e-16 5.086338e-15
66 67 68 69 70
2.182746e-16 -1.395376e-16 7.600500e-17 2.078287e-15 -4.248671e-16
71 72 73 74 75
-4.953272e-16 -2.565867e-16 -2.947347e-16 -2.249824e-16 -1.544374e-16
76 77 78 79 80
1.170181e-16 4.594280e-15 -2.783006e-17 4.543358e-16 -3.214323e-16
81 82 83 84 85
2.898931e-16 2.316577e-16 1.059848e-16 2.549468e-16 -2.453134e-17
86 87 88 89 90
-1.088987e-15 1.914534e-16 -1.260396e-15 5.216342e-15 -1.157876e-16
91 92 93 94 95
-7.805202e-16 7.452835e-17 -6.855424e-16 -4.171356e-16 -5.589477e-16
96 97 98 99 100
-2.489561e-16 -1.372216e-15 -1.147235e-15 -2.080576e-17 -6.768189e-16
101 102 103 104 105
4.477658e-15 1.738014e-16 -2.424043e-16 -1.745423e-16 -5.008839e-16
106 107 108 109 110
-3.803133e-16 -2.379044e-16 2.002757e-16 -1.914310e-15 -5.583427e-16
111 112 113 114 115
-1.407109e-15 1.759959e-16 4.030466e-15 -4.823549e-16 6.890653e-16
116 117 118 119 120
-2.400864e-16 1.478931e-17 -5.928851e-16 3.423230e-16 -5.808801e-16
121 122 123 124 125
-1.906785e-16 -4.237660e-16 -5.872895e-16 -3.194023e-16 3.867542e-15
126 127 128 129 130
-1.929656e-16 -1.195218e-15 -1.010998e-16 -3.709714e-16 -3.759114e-16
131 132 133 134 135
-1.033321e-16 6.103602e-16 -1.102749e-15 -1.362362e-16 -3.679598e-16
136 137 138 139 140
-6.259679e-16 3.985246e-15 -1.091127e-15 -9.732642e-16 -7.985964e-16
141 142 143 144 145
-9.528634e-16 -1.280142e-15 -1.084024e-15 -7.674786e-16 -1.051958e-15
146 147 148 149 150
-1.559217e-15 2.962198e-16 -7.818002e-16 3.369437e-15 -1.852071e-15
151 152 153 154 155
-8.654915e-16 -1.510271e-15 -2.702903e-16 -1.139419e-15 -5.050145e-16
156
-1.428194e-15
> postscript(file="/var/www/html/rcomp/tmp/6rj9x1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 3.883367e-15 NA
1 4.459499e-15 3.883367e-15
2 9.242415e-16 4.459499e-15
3 4.611255e-15 9.242415e-16
4 -5.283882e-14 4.611255e-15
5 1.429031e-15 -5.283882e-14
6 1.364954e-15 1.429031e-15
7 8.626107e-16 1.364954e-15
8 5.385162e-16 8.626107e-16
9 1.141347e-15 5.385162e-16
10 6.770594e-16 1.141347e-15
11 1.442698e-15 6.770594e-16
12 9.233770e-16 1.442698e-15
13 3.578725e-16 9.233770e-16
14 1.606257e-15 3.578725e-16
15 -3.137681e-16 1.606257e-15
16 4.581225e-15 -3.137681e-16
17 1.108191e-15 4.581225e-15
18 -3.004122e-16 1.108191e-15
19 4.180791e-16 -3.004122e-16
20 -6.598701e-16 4.180791e-16
21 1.508388e-15 -6.598701e-16
22 5.748570e-16 1.508388e-15
23 1.917255e-16 5.748570e-16
24 3.973608e-16 1.917255e-16
25 1.828464e-16 3.973608e-16
26 7.586885e-16 1.828464e-16
27 -8.255873e-17 7.586885e-16
28 4.775565e-15 -8.255873e-17
29 7.726812e-17 4.775565e-15
30 3.238727e-16 7.726812e-17
31 7.185826e-16 3.238727e-16
32 -2.656990e-16 7.185826e-16
33 2.395643e-16 -2.656990e-16
34 6.779551e-18 2.395643e-16
35 -4.232750e-16 6.779551e-18
36 7.194647e-16 -4.232750e-16
37 2.210029e-16 7.194647e-16
38 -1.083176e-15 2.210029e-16
39 -4.262043e-17 -1.083176e-15
40 4.034304e-15 -4.262043e-17
41 1.927339e-16 4.034304e-15
42 1.220937e-15 1.927339e-16
43 5.426416e-16 1.220937e-15
44 -3.174904e-17 5.426416e-16
45 4.496815e-16 -3.174904e-17
46 6.178842e-17 4.496815e-16
47 -4.994473e-17 6.178842e-17
48 -2.306361e-16 -4.994473e-17
49 -4.935578e-16 -2.306361e-16
50 4.671314e-16 -4.935578e-16
51 -5.513919e-16 4.671314e-16
52 4.820417e-15 -5.513919e-16
53 5.628375e-16 4.820417e-15
54 4.436823e-16 5.628375e-16
55 4.535804e-16 4.436823e-16
56 8.163840e-16 4.535804e-16
57 1.040035e-15 8.163840e-16
58 1.215758e-15 1.040035e-15
59 1.055308e-15 1.215758e-15
60 2.582446e-16 1.055308e-15
61 4.111030e-16 2.582446e-16
62 -6.232138e-16 4.111030e-16
63 -2.495448e-16 -6.232138e-16
64 5.086338e-15 -2.495448e-16
65 2.182746e-16 5.086338e-15
66 -1.395376e-16 2.182746e-16
67 7.600500e-17 -1.395376e-16
68 2.078287e-15 7.600500e-17
69 -4.248671e-16 2.078287e-15
70 -4.953272e-16 -4.248671e-16
71 -2.565867e-16 -4.953272e-16
72 -2.947347e-16 -2.565867e-16
73 -2.249824e-16 -2.947347e-16
74 -1.544374e-16 -2.249824e-16
75 1.170181e-16 -1.544374e-16
76 4.594280e-15 1.170181e-16
77 -2.783006e-17 4.594280e-15
78 4.543358e-16 -2.783006e-17
79 -3.214323e-16 4.543358e-16
80 2.898931e-16 -3.214323e-16
81 2.316577e-16 2.898931e-16
82 1.059848e-16 2.316577e-16
83 2.549468e-16 1.059848e-16
84 -2.453134e-17 2.549468e-16
85 -1.088987e-15 -2.453134e-17
86 1.914534e-16 -1.088987e-15
87 -1.260396e-15 1.914534e-16
88 5.216342e-15 -1.260396e-15
89 -1.157876e-16 5.216342e-15
90 -7.805202e-16 -1.157876e-16
91 7.452835e-17 -7.805202e-16
92 -6.855424e-16 7.452835e-17
93 -4.171356e-16 -6.855424e-16
94 -5.589477e-16 -4.171356e-16
95 -2.489561e-16 -5.589477e-16
96 -1.372216e-15 -2.489561e-16
97 -1.147235e-15 -1.372216e-15
98 -2.080576e-17 -1.147235e-15
99 -6.768189e-16 -2.080576e-17
100 4.477658e-15 -6.768189e-16
101 1.738014e-16 4.477658e-15
102 -2.424043e-16 1.738014e-16
103 -1.745423e-16 -2.424043e-16
104 -5.008839e-16 -1.745423e-16
105 -3.803133e-16 -5.008839e-16
106 -2.379044e-16 -3.803133e-16
107 2.002757e-16 -2.379044e-16
108 -1.914310e-15 2.002757e-16
109 -5.583427e-16 -1.914310e-15
110 -1.407109e-15 -5.583427e-16
111 1.759959e-16 -1.407109e-15
112 4.030466e-15 1.759959e-16
113 -4.823549e-16 4.030466e-15
114 6.890653e-16 -4.823549e-16
115 -2.400864e-16 6.890653e-16
116 1.478931e-17 -2.400864e-16
117 -5.928851e-16 1.478931e-17
118 3.423230e-16 -5.928851e-16
119 -5.808801e-16 3.423230e-16
120 -1.906785e-16 -5.808801e-16
121 -4.237660e-16 -1.906785e-16
122 -5.872895e-16 -4.237660e-16
123 -3.194023e-16 -5.872895e-16
124 3.867542e-15 -3.194023e-16
125 -1.929656e-16 3.867542e-15
126 -1.195218e-15 -1.929656e-16
127 -1.010998e-16 -1.195218e-15
128 -3.709714e-16 -1.010998e-16
129 -3.759114e-16 -3.709714e-16
130 -1.033321e-16 -3.759114e-16
131 6.103602e-16 -1.033321e-16
132 -1.102749e-15 6.103602e-16
133 -1.362362e-16 -1.102749e-15
134 -3.679598e-16 -1.362362e-16
135 -6.259679e-16 -3.679598e-16
136 3.985246e-15 -6.259679e-16
137 -1.091127e-15 3.985246e-15
138 -9.732642e-16 -1.091127e-15
139 -7.985964e-16 -9.732642e-16
140 -9.528634e-16 -7.985964e-16
141 -1.280142e-15 -9.528634e-16
142 -1.084024e-15 -1.280142e-15
143 -7.674786e-16 -1.084024e-15
144 -1.051958e-15 -7.674786e-16
145 -1.559217e-15 -1.051958e-15
146 2.962198e-16 -1.559217e-15
147 -7.818002e-16 2.962198e-16
148 3.369437e-15 -7.818002e-16
149 -1.852071e-15 3.369437e-15
150 -8.654915e-16 -1.852071e-15
151 -1.510271e-15 -8.654915e-16
152 -2.702903e-16 -1.510271e-15
153 -1.139419e-15 -2.702903e-16
154 -5.050145e-16 -1.139419e-15
155 -1.428194e-15 -5.050145e-16
156 NA -1.428194e-15
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.459499e-15 3.883367e-15
[2,] 9.242415e-16 4.459499e-15
[3,] 4.611255e-15 9.242415e-16
[4,] -5.283882e-14 4.611255e-15
[5,] 1.429031e-15 -5.283882e-14
[6,] 1.364954e-15 1.429031e-15
[7,] 8.626107e-16 1.364954e-15
[8,] 5.385162e-16 8.626107e-16
[9,] 1.141347e-15 5.385162e-16
[10,] 6.770594e-16 1.141347e-15
[11,] 1.442698e-15 6.770594e-16
[12,] 9.233770e-16 1.442698e-15
[13,] 3.578725e-16 9.233770e-16
[14,] 1.606257e-15 3.578725e-16
[15,] -3.137681e-16 1.606257e-15
[16,] 4.581225e-15 -3.137681e-16
[17,] 1.108191e-15 4.581225e-15
[18,] -3.004122e-16 1.108191e-15
[19,] 4.180791e-16 -3.004122e-16
[20,] -6.598701e-16 4.180791e-16
[21,] 1.508388e-15 -6.598701e-16
[22,] 5.748570e-16 1.508388e-15
[23,] 1.917255e-16 5.748570e-16
[24,] 3.973608e-16 1.917255e-16
[25,] 1.828464e-16 3.973608e-16
[26,] 7.586885e-16 1.828464e-16
[27,] -8.255873e-17 7.586885e-16
[28,] 4.775565e-15 -8.255873e-17
[29,] 7.726812e-17 4.775565e-15
[30,] 3.238727e-16 7.726812e-17
[31,] 7.185826e-16 3.238727e-16
[32,] -2.656990e-16 7.185826e-16
[33,] 2.395643e-16 -2.656990e-16
[34,] 6.779551e-18 2.395643e-16
[35,] -4.232750e-16 6.779551e-18
[36,] 7.194647e-16 -4.232750e-16
[37,] 2.210029e-16 7.194647e-16
[38,] -1.083176e-15 2.210029e-16
[39,] -4.262043e-17 -1.083176e-15
[40,] 4.034304e-15 -4.262043e-17
[41,] 1.927339e-16 4.034304e-15
[42,] 1.220937e-15 1.927339e-16
[43,] 5.426416e-16 1.220937e-15
[44,] -3.174904e-17 5.426416e-16
[45,] 4.496815e-16 -3.174904e-17
[46,] 6.178842e-17 4.496815e-16
[47,] -4.994473e-17 6.178842e-17
[48,] -2.306361e-16 -4.994473e-17
[49,] -4.935578e-16 -2.306361e-16
[50,] 4.671314e-16 -4.935578e-16
[51,] -5.513919e-16 4.671314e-16
[52,] 4.820417e-15 -5.513919e-16
[53,] 5.628375e-16 4.820417e-15
[54,] 4.436823e-16 5.628375e-16
[55,] 4.535804e-16 4.436823e-16
[56,] 8.163840e-16 4.535804e-16
[57,] 1.040035e-15 8.163840e-16
[58,] 1.215758e-15 1.040035e-15
[59,] 1.055308e-15 1.215758e-15
[60,] 2.582446e-16 1.055308e-15
[61,] 4.111030e-16 2.582446e-16
[62,] -6.232138e-16 4.111030e-16
[63,] -2.495448e-16 -6.232138e-16
[64,] 5.086338e-15 -2.495448e-16
[65,] 2.182746e-16 5.086338e-15
[66,] -1.395376e-16 2.182746e-16
[67,] 7.600500e-17 -1.395376e-16
[68,] 2.078287e-15 7.600500e-17
[69,] -4.248671e-16 2.078287e-15
[70,] -4.953272e-16 -4.248671e-16
[71,] -2.565867e-16 -4.953272e-16
[72,] -2.947347e-16 -2.565867e-16
[73,] -2.249824e-16 -2.947347e-16
[74,] -1.544374e-16 -2.249824e-16
[75,] 1.170181e-16 -1.544374e-16
[76,] 4.594280e-15 1.170181e-16
[77,] -2.783006e-17 4.594280e-15
[78,] 4.543358e-16 -2.783006e-17
[79,] -3.214323e-16 4.543358e-16
[80,] 2.898931e-16 -3.214323e-16
[81,] 2.316577e-16 2.898931e-16
[82,] 1.059848e-16 2.316577e-16
[83,] 2.549468e-16 1.059848e-16
[84,] -2.453134e-17 2.549468e-16
[85,] -1.088987e-15 -2.453134e-17
[86,] 1.914534e-16 -1.088987e-15
[87,] -1.260396e-15 1.914534e-16
[88,] 5.216342e-15 -1.260396e-15
[89,] -1.157876e-16 5.216342e-15
[90,] -7.805202e-16 -1.157876e-16
[91,] 7.452835e-17 -7.805202e-16
[92,] -6.855424e-16 7.452835e-17
[93,] -4.171356e-16 -6.855424e-16
[94,] -5.589477e-16 -4.171356e-16
[95,] -2.489561e-16 -5.589477e-16
[96,] -1.372216e-15 -2.489561e-16
[97,] -1.147235e-15 -1.372216e-15
[98,] -2.080576e-17 -1.147235e-15
[99,] -6.768189e-16 -2.080576e-17
[100,] 4.477658e-15 -6.768189e-16
[101,] 1.738014e-16 4.477658e-15
[102,] -2.424043e-16 1.738014e-16
[103,] -1.745423e-16 -2.424043e-16
[104,] -5.008839e-16 -1.745423e-16
[105,] -3.803133e-16 -5.008839e-16
[106,] -2.379044e-16 -3.803133e-16
[107,] 2.002757e-16 -2.379044e-16
[108,] -1.914310e-15 2.002757e-16
[109,] -5.583427e-16 -1.914310e-15
[110,] -1.407109e-15 -5.583427e-16
[111,] 1.759959e-16 -1.407109e-15
[112,] 4.030466e-15 1.759959e-16
[113,] -4.823549e-16 4.030466e-15
[114,] 6.890653e-16 -4.823549e-16
[115,] -2.400864e-16 6.890653e-16
[116,] 1.478931e-17 -2.400864e-16
[117,] -5.928851e-16 1.478931e-17
[118,] 3.423230e-16 -5.928851e-16
[119,] -5.808801e-16 3.423230e-16
[120,] -1.906785e-16 -5.808801e-16
[121,] -4.237660e-16 -1.906785e-16
[122,] -5.872895e-16 -4.237660e-16
[123,] -3.194023e-16 -5.872895e-16
[124,] 3.867542e-15 -3.194023e-16
[125,] -1.929656e-16 3.867542e-15
[126,] -1.195218e-15 -1.929656e-16
[127,] -1.010998e-16 -1.195218e-15
[128,] -3.709714e-16 -1.010998e-16
[129,] -3.759114e-16 -3.709714e-16
[130,] -1.033321e-16 -3.759114e-16
[131,] 6.103602e-16 -1.033321e-16
[132,] -1.102749e-15 6.103602e-16
[133,] -1.362362e-16 -1.102749e-15
[134,] -3.679598e-16 -1.362362e-16
[135,] -6.259679e-16 -3.679598e-16
[136,] 3.985246e-15 -6.259679e-16
[137,] -1.091127e-15 3.985246e-15
[138,] -9.732642e-16 -1.091127e-15
[139,] -7.985964e-16 -9.732642e-16
[140,] -9.528634e-16 -7.985964e-16
[141,] -1.280142e-15 -9.528634e-16
[142,] -1.084024e-15 -1.280142e-15
[143,] -7.674786e-16 -1.084024e-15
[144,] -1.051958e-15 -7.674786e-16
[145,] -1.559217e-15 -1.051958e-15
[146,] 2.962198e-16 -1.559217e-15
[147,] -7.818002e-16 2.962198e-16
[148,] 3.369437e-15 -7.818002e-16
[149,] -1.852071e-15 3.369437e-15
[150,] -8.654915e-16 -1.852071e-15
[151,] -1.510271e-15 -8.654915e-16
[152,] -2.702903e-16 -1.510271e-15
[153,] -1.139419e-15 -2.702903e-16
[154,] -5.050145e-16 -1.139419e-15
[155,] -1.428194e-15 -5.050145e-16
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.459499e-15 3.883367e-15
2 9.242415e-16 4.459499e-15
3 4.611255e-15 9.242415e-16
4 -5.283882e-14 4.611255e-15
5 1.429031e-15 -5.283882e-14
6 1.364954e-15 1.429031e-15
7 8.626107e-16 1.364954e-15
8 5.385162e-16 8.626107e-16
9 1.141347e-15 5.385162e-16
10 6.770594e-16 1.141347e-15
11 1.442698e-15 6.770594e-16
12 9.233770e-16 1.442698e-15
13 3.578725e-16 9.233770e-16
14 1.606257e-15 3.578725e-16
15 -3.137681e-16 1.606257e-15
16 4.581225e-15 -3.137681e-16
17 1.108191e-15 4.581225e-15
18 -3.004122e-16 1.108191e-15
19 4.180791e-16 -3.004122e-16
20 -6.598701e-16 4.180791e-16
21 1.508388e-15 -6.598701e-16
22 5.748570e-16 1.508388e-15
23 1.917255e-16 5.748570e-16
24 3.973608e-16 1.917255e-16
25 1.828464e-16 3.973608e-16
26 7.586885e-16 1.828464e-16
27 -8.255873e-17 7.586885e-16
28 4.775565e-15 -8.255873e-17
29 7.726812e-17 4.775565e-15
30 3.238727e-16 7.726812e-17
31 7.185826e-16 3.238727e-16
32 -2.656990e-16 7.185826e-16
33 2.395643e-16 -2.656990e-16
34 6.779551e-18 2.395643e-16
35 -4.232750e-16 6.779551e-18
36 7.194647e-16 -4.232750e-16
37 2.210029e-16 7.194647e-16
38 -1.083176e-15 2.210029e-16
39 -4.262043e-17 -1.083176e-15
40 4.034304e-15 -4.262043e-17
41 1.927339e-16 4.034304e-15
42 1.220937e-15 1.927339e-16
43 5.426416e-16 1.220937e-15
44 -3.174904e-17 5.426416e-16
45 4.496815e-16 -3.174904e-17
46 6.178842e-17 4.496815e-16
47 -4.994473e-17 6.178842e-17
48 -2.306361e-16 -4.994473e-17
49 -4.935578e-16 -2.306361e-16
50 4.671314e-16 -4.935578e-16
51 -5.513919e-16 4.671314e-16
52 4.820417e-15 -5.513919e-16
53 5.628375e-16 4.820417e-15
54 4.436823e-16 5.628375e-16
55 4.535804e-16 4.436823e-16
56 8.163840e-16 4.535804e-16
57 1.040035e-15 8.163840e-16
58 1.215758e-15 1.040035e-15
59 1.055308e-15 1.215758e-15
60 2.582446e-16 1.055308e-15
61 4.111030e-16 2.582446e-16
62 -6.232138e-16 4.111030e-16
63 -2.495448e-16 -6.232138e-16
64 5.086338e-15 -2.495448e-16
65 2.182746e-16 5.086338e-15
66 -1.395376e-16 2.182746e-16
67 7.600500e-17 -1.395376e-16
68 2.078287e-15 7.600500e-17
69 -4.248671e-16 2.078287e-15
70 -4.953272e-16 -4.248671e-16
71 -2.565867e-16 -4.953272e-16
72 -2.947347e-16 -2.565867e-16
73 -2.249824e-16 -2.947347e-16
74 -1.544374e-16 -2.249824e-16
75 1.170181e-16 -1.544374e-16
76 4.594280e-15 1.170181e-16
77 -2.783006e-17 4.594280e-15
78 4.543358e-16 -2.783006e-17
79 -3.214323e-16 4.543358e-16
80 2.898931e-16 -3.214323e-16
81 2.316577e-16 2.898931e-16
82 1.059848e-16 2.316577e-16
83 2.549468e-16 1.059848e-16
84 -2.453134e-17 2.549468e-16
85 -1.088987e-15 -2.453134e-17
86 1.914534e-16 -1.088987e-15
87 -1.260396e-15 1.914534e-16
88 5.216342e-15 -1.260396e-15
89 -1.157876e-16 5.216342e-15
90 -7.805202e-16 -1.157876e-16
91 7.452835e-17 -7.805202e-16
92 -6.855424e-16 7.452835e-17
93 -4.171356e-16 -6.855424e-16
94 -5.589477e-16 -4.171356e-16
95 -2.489561e-16 -5.589477e-16
96 -1.372216e-15 -2.489561e-16
97 -1.147235e-15 -1.372216e-15
98 -2.080576e-17 -1.147235e-15
99 -6.768189e-16 -2.080576e-17
100 4.477658e-15 -6.768189e-16
101 1.738014e-16 4.477658e-15
102 -2.424043e-16 1.738014e-16
103 -1.745423e-16 -2.424043e-16
104 -5.008839e-16 -1.745423e-16
105 -3.803133e-16 -5.008839e-16
106 -2.379044e-16 -3.803133e-16
107 2.002757e-16 -2.379044e-16
108 -1.914310e-15 2.002757e-16
109 -5.583427e-16 -1.914310e-15
110 -1.407109e-15 -5.583427e-16
111 1.759959e-16 -1.407109e-15
112 4.030466e-15 1.759959e-16
113 -4.823549e-16 4.030466e-15
114 6.890653e-16 -4.823549e-16
115 -2.400864e-16 6.890653e-16
116 1.478931e-17 -2.400864e-16
117 -5.928851e-16 1.478931e-17
118 3.423230e-16 -5.928851e-16
119 -5.808801e-16 3.423230e-16
120 -1.906785e-16 -5.808801e-16
121 -4.237660e-16 -1.906785e-16
122 -5.872895e-16 -4.237660e-16
123 -3.194023e-16 -5.872895e-16
124 3.867542e-15 -3.194023e-16
125 -1.929656e-16 3.867542e-15
126 -1.195218e-15 -1.929656e-16
127 -1.010998e-16 -1.195218e-15
128 -3.709714e-16 -1.010998e-16
129 -3.759114e-16 -3.709714e-16
130 -1.033321e-16 -3.759114e-16
131 6.103602e-16 -1.033321e-16
132 -1.102749e-15 6.103602e-16
133 -1.362362e-16 -1.102749e-15
134 -3.679598e-16 -1.362362e-16
135 -6.259679e-16 -3.679598e-16
136 3.985246e-15 -6.259679e-16
137 -1.091127e-15 3.985246e-15
138 -9.732642e-16 -1.091127e-15
139 -7.985964e-16 -9.732642e-16
140 -9.528634e-16 -7.985964e-16
141 -1.280142e-15 -9.528634e-16
142 -1.084024e-15 -1.280142e-15
143 -7.674786e-16 -1.084024e-15
144 -1.051958e-15 -7.674786e-16
145 -1.559217e-15 -1.051958e-15
146 2.962198e-16 -1.559217e-15
147 -7.818002e-16 2.962198e-16
148 3.369437e-15 -7.818002e-16
149 -1.852071e-15 3.369437e-15
150 -8.654915e-16 -1.852071e-15
151 -1.510271e-15 -8.654915e-16
152 -2.702903e-16 -1.510271e-15
153 -1.139419e-15 -2.702903e-16
154 -5.050145e-16 -1.139419e-15
155 -1.428194e-15 -5.050145e-16
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/71s801290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8c2p31290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c2p31290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10c2p31290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/119b5c1290857931.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12j3mf1290857931.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/138mjr1290857931.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/141d1c1290857931.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/154dhi1290857931.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16inx91290857931.tab")
+ }
>
> try(system("convert tmp/1yaad1290857931.ps tmp/1yaad1290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yaad1290857931.ps tmp/2yaad1290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yaad1290857931.ps tmp/3yaad1290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rj9x1290857931.ps tmp/4rj9x1290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rj9x1290857931.ps tmp/5rj9x1290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rj9x1290857931.ps tmp/6rj9x1290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/71s801290857931.ps tmp/71s801290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/8c2p31290857931.ps tmp/8c2p31290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/9c2p31290857931.ps tmp/9c2p31290857931.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c2p31290857931.ps tmp/10c2p31290857931.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.212 1.855 11.992