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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+ ,0
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+ ,0
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+ ,0
+ ,6
+ ,0
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+ ,0
+ ,21
+ ,0
+ ,1
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+ ,14
+ ,14
+ ,11
+ ,11
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+ ,8
+ ,24
+ ,24
+ ,24
+ ,24
+ ,1
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+ ,13
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+ ,22
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+ ,0
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+ ,16
+ ,0
+ ,19
+ ,0
+ ,16
+ ,0
+ ,17
+ ,0
+ ,20
+ ,0)
+ ,dim=c(12
+ ,159)
+ ,dimnames=list(c('G'
+ ,'YT'
+ ,'X1'
+ ,'X1_G'
+ ,'X2'
+ ,'X2_G'
+ ,'X3'
+ ,'X3_G'
+ ,'X4'
+ ,'X4_G'
+ ,'X5'
+ ,'X5_G
')
+ ,1:159))
> y <- array(NA,dim=c(12,159),dimnames=list(c('G','YT','X1','X1_G','X2','X2_G','X3','X3_G','X4','X4_G','X5','X5_G
'),1:159))
> 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 = '2'
> #'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
YT G X1 X1_G X2 X2_G X3 X3_G X4 X4_G X5 X5_G\r t
1 24 0 14 0 11 0 12 0 24 0 26 0 1
2 25 0 11 0 7 0 8 0 25 0 23 0 2
3 17 0 6 0 17 0 8 0 30 0 25 0 3
4 18 1 12 12 10 10 8 8 19 19 23 23 4
5 18 1 8 8 12 12 9 9 22 22 19 19 5
6 16 1 10 10 12 12 7 7 22 22 29 29 6
7 20 1 10 10 11 11 4 4 25 25 25 25 7
8 16 1 11 11 11 11 11 11 23 23 21 21 8
9 18 1 16 16 12 12 7 7 17 17 22 22 9
10 17 1 11 11 13 13 7 7 21 21 25 25 10
11 23 0 13 0 14 0 12 0 19 0 24 0 11
12 30 0 12 0 16 0 10 0 19 0 18 0 12
13 23 1 8 8 11 11 10 10 15 15 22 22 13
14 18 1 12 12 10 10 8 8 16 16 15 15 14
15 15 1 11 11 11 11 8 8 23 23 22 22 15
16 12 1 4 4 15 15 4 4 27 27 28 28 16
17 21 0 9 0 9 0 9 0 22 0 20 0 17
18 15 1 8 8 11 11 8 8 14 14 12 12 18
19 20 1 8 8 17 17 7 7 22 22 24 24 19
20 31 0 14 0 17 0 11 0 23 0 20 0 20
21 27 0 15 0 11 0 9 0 23 0 21 0 21
22 34 1 16 16 18 18 11 11 21 21 20 20 22
23 21 1 9 9 14 14 13 13 19 19 21 21 23
24 31 1 14 14 10 10 8 8 18 18 23 23 24
25 19 1 11 11 11 11 8 8 20 20 28 28 25
26 16 0 8 0 15 0 9 0 23 0 24 0 26
27 20 1 9 9 15 15 6 6 25 25 24 24 27
28 21 1 9 9 13 13 9 9 19 19 24 24 28
29 22 1 9 9 16 16 9 9 24 24 23 23 29
30 17 1 9 9 13 13 6 6 22 22 23 23 30
31 24 1 10 10 9 9 6 6 25 25 29 29 31
32 25 0 16 0 18 0 16 0 26 0 24 0 32
33 26 0 11 0 18 0 5 0 29 0 18 0 33
34 25 1 8 8 12 12 7 7 32 32 25 25 34
35 17 1 9 9 17 17 9 9 25 25 21 21 35
36 32 1 16 16 9 9 6 6 29 29 26 26 36
37 33 1 11 11 9 9 6 6 28 28 22 22 37
38 13 1 16 16 12 12 5 5 17 17 22 22 38
39 32 1 12 12 18 18 12 12 28 28 22 22 39
40 25 1 12 12 12 12 7 7 29 29 23 23 40
41 29 1 14 14 18 18 10 10 26 26 30 30 41
42 22 1 9 9 14 14 9 9 25 25 23 23 42
43 18 1 10 10 15 15 8 8 14 14 17 17 43
44 17 1 9 9 16 16 5 5 25 25 23 23 44
45 20 0 10 0 10 0 8 0 26 0 23 0 45
46 15 1 12 12 11 11 8 8 20 20 25 25 46
47 20 1 14 14 14 14 10 10 18 18 24 24 47
48 33 1 14 14 9 9 6 6 32 32 24 24 48
49 29 0 10 0 12 0 8 0 25 0 23 0 49
50 23 1 14 14 17 17 7 7 25 25 21 21 50
51 26 0 16 0 5 0 4 0 23 0 24 0 51
52 18 1 9 9 12 12 8 8 21 21 24 24 52
53 20 0 10 0 12 0 8 0 20 0 28 0 53
54 6 11 0 6 0 4 0 15 0 16 0 1 54
55 8 28 8 24 24 20 20 30 30 20 20 1 55
56 13 26 13 12 12 8 8 24 24 29 29 0 56
57 10 22 0 12 0 8 0 26 0 27 0 1 57
58 8 17 8 14 14 6 6 24 24 22 22 0 58
59 7 12 0 7 0 4 0 22 0 28 0 1 59
60 15 14 15 13 13 8 8 14 14 16 16 1 60
61 9 17 9 12 12 9 9 24 24 25 25 1 61
62 10 21 10 13 13 6 6 24 24 24 24 1 62
63 12 19 12 14 14 7 7 24 24 28 28 1 63
64 13 18 13 8 8 9 9 24 24 24 24 0 64
65 10 10 0 11 0 5 0 19 0 23 0 0 65
66 11 29 0 9 0 5 0 31 0 30 0 1 66
67 8 31 8 11 11 8 8 22 22 24 24 0 67
68 9 19 0 13 0 8 0 27 0 21 0 1 68
69 13 9 13 10 10 6 6 19 19 25 25 1 69
70 11 20 11 11 11 8 8 25 25 25 25 1 70
71 8 28 8 12 12 7 7 20 20 22 22 0 71
72 9 19 0 9 0 7 0 21 0 23 0 0 72
73 9 30 0 15 0 9 0 27 0 26 0 0 73
74 15 29 0 18 0 11 0 23 0 23 0 0 74
75 9 26 0 15 0 6 0 25 0 25 0 0 75
76 10 23 0 12 0 8 0 20 0 21 0 1 76
77 14 13 14 13 13 6 6 21 21 25 25 1 77
78 12 21 12 14 14 9 9 22 22 24 24 1 78
79 12 19 12 10 10 8 8 23 23 29 29 1 79
80 11 28 11 13 13 6 6 25 25 22 22 1 80
81 14 23 14 13 13 10 10 25 25 27 27 1 81
82 6 18 6 11 11 8 8 17 17 26 26 0 82
83 12 21 0 13 0 8 0 19 0 22 0 1 83
84 8 20 8 16 16 10 10 25 25 24 24 1 84
85 14 23 14 8 8 5 5 19 19 27 27 1 85
86 11 21 11 16 16 7 7 20 20 24 24 1 86
87 10 21 10 11 11 5 5 26 26 24 24 1 87
88 14 15 14 9 9 8 8 23 23 29 29 1 88
89 12 28 12 16 16 14 14 27 27 22 22 1 89
90 10 19 10 12 12 7 7 17 17 21 21 1 90
91 14 26 14 14 14 8 8 17 17 24 24 1 91
92 5 10 5 8 8 6 6 19 19 24 24 0 92
93 11 16 0 9 0 5 0 17 0 23 0 1 93
94 10 22 10 15 15 6 6 22 22 20 20 1 94
95 9 19 9 11 11 10 10 21 21 27 27 1 95
96 10 31 10 21 21 12 12 32 32 26 26 0 96
97 16 31 0 14 0 9 0 21 0 25 0 1 97
98 13 29 13 18 18 12 12 21 21 21 21 0 98
99 9 19 0 12 0 7 0 18 0 21 0 1 99
100 10 22 10 13 13 8 8 18 18 19 19 1 100
101 10 23 10 15 15 10 10 23 23 21 21 0 101
102 7 15 0 12 0 6 0 19 0 21 0 0 102
103 9 20 0 19 0 10 0 20 0 16 0 1 103
104 8 18 8 15 15 10 10 21 21 22 22 1 104
105 14 23 14 11 11 10 10 20 20 29 29 1 105
106 14 25 14 11 11 5 5 17 17 15 15 1 106
107 8 21 8 10 10 7 7 18 18 17 17 1 107
108 9 24 9 13 13 10 10 19 19 15 15 1 108
109 14 25 14 15 15 11 11 22 22 21 21 1 109
110 14 17 14 12 12 6 6 15 15 21 21 1 110
111 8 13 8 12 12 7 7 14 14 19 19 1 111
112 8 28 8 16 16 12 12 18 18 24 24 0 112
113 8 21 0 9 0 11 0 24 0 20 0 1 113
114 7 25 7 18 18 11 11 35 35 17 17 0 114
115 6 9 0 8 0 11 0 29 0 23 0 1 115
116 8 16 8 13 13 5 5 21 21 24 24 1 116
117 6 19 6 17 17 8 8 25 25 14 14 1 117
118 11 17 11 9 9 6 6 20 20 19 19 1 118
119 14 25 14 15 15 9 9 22 22 24 24 1 119
120 11 20 11 8 8 4 4 13 13 13 13 1 120
121 11 29 11 7 7 4 4 26 26 22 22 1 121
122 11 14 11 12 12 7 7 17 17 16 16 1 122
123 14 22 14 14 14 11 11 25 25 19 19 1 123
124 8 15 8 6 6 6 6 20 20 25 25 0 124
125 20 19 0 8 0 7 0 19 0 25 0 1 125
126 11 20 11 17 17 8 8 21 21 23 23 0 126
127 8 15 0 10 0 4 0 22 0 24 0 1 127
128 11 20 11 11 11 8 8 24 24 26 26 1 128
129 10 18 10 14 14 9 9 21 21 26 26 1 129
130 14 33 14 11 11 8 8 26 26 25 25 1 130
131 11 22 11 13 13 11 11 24 24 18 18 1 131
132 9 16 9 12 12 8 8 16 16 21 21 1 132
133 9 17 9 11 11 5 5 23 23 26 26 1 133
134 8 16 8 9 9 4 4 18 18 23 23 0 134
135 10 21 0 12 0 8 0 16 0 23 0 0 135
136 13 26 0 20 0 10 0 26 0 22 0 1 136
137 13 18 13 12 12 6 6 19 19 20 20 1 137
138 12 18 12 13 13 9 9 21 21 13 13 1 138
139 8 17 8 12 12 9 9 21 21 24 24 1 139
140 13 22 13 12 12 13 13 22 22 15 15 1 140
141 14 30 14 9 9 9 9 23 23 14 14 0 141
142 12 30 0 15 0 10 0 29 0 22 0 1 142
143 14 24 14 24 24 20 20 21 21 10 10 1 143
144 15 21 15 7 7 5 5 21 21 24 24 1 144
145 13 21 13 17 17 11 11 23 23 22 22 1 145
146 16 29 16 11 11 6 6 27 27 24 24 1 146
147 9 31 9 17 17 9 9 25 25 19 19 1 147
148 9 20 9 11 11 7 7 21 21 20 20 0 148
149 9 16 0 12 0 9 0 10 0 13 0 0 149
150 8 22 0 14 0 10 0 20 0 20 0 1 150
151 7 20 7 11 11 9 9 26 26 22 22 1 151
152 16 28 16 16 16 8 8 24 24 24 24 1 152
153 11 38 11 21 21 7 7 29 29 29 29 0 153
154 9 22 0 14 0 6 0 19 0 12 0 1 154
155 11 20 11 20 20 13 13 24 24 20 20 0 155
156 9 17 0 13 0 6 0 19 0 21 0 1 156
157 14 28 14 11 11 8 8 24 24 24 24 1 157
158 13 22 13 15 15 10 10 22 22 22 22 0 158
159 16 31 0 19 0 16 0 17 0 20 0 0 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G X1 X1_G X2 X2_G
18.54392 0.06221 0.73062 -0.04890 -0.23866 -0.10525
X3 X3_G X4 X4_G X5 `X5_G\r`
0.24723 -0.50101 0.33467 0.17208 -0.52673 0.13323
t
-0.02117
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.2715 -2.0332 -0.3682 1.3736 11.1619
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.54392 1.19709 15.491 < 2e-16 ***
G 0.06221 0.07451 0.835 0.405121
X1 0.73062 0.11739 6.224 4.86e-09 ***
X1_G -0.04890 0.12164 -0.402 0.688281
X2 -0.23866 0.13716 -1.740 0.083961 .
X2_G -0.10525 0.16640 -0.632 0.528071
X3 0.24723 0.18621 1.328 0.186354
X3_G -0.50101 0.09752 -5.138 8.77e-07 ***
X4 0.33467 0.08933 3.747 0.000257 ***
X4_G 0.17208 0.08658 1.988 0.048720 *
X5 -0.52673 0.09381 -5.615 9.63e-08 ***
`X5_G\r` 0.13323 0.09185 1.450 0.149082
t -0.02117 0.01083 -1.955 0.052482 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.655 on 146 degrees of freedom
Multiple R-squared: 0.721, Adjusted R-squared: 0.6981
F-statistic: 31.45 on 12 and 146 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,] 0.4715656 9.431313e-01 5.284344e-01
[2,] 0.3027195 6.054390e-01 6.972805e-01
[3,] 0.3386961 6.773923e-01 6.613039e-01
[4,] 0.5176417 9.647166e-01 4.823583e-01
[5,] 0.4302533 8.605065e-01 5.697467e-01
[6,] 0.3754245 7.508491e-01 6.245755e-01
[7,] 0.8168956 3.662088e-01 1.831044e-01
[8,] 0.7580994 4.838012e-01 2.419006e-01
[9,] 0.9822876 3.542488e-02 1.771244e-02
[10,] 0.9768653 4.626945e-02 2.313472e-02
[11,] 0.9713814 5.723714e-02 2.861857e-02
[12,] 0.9583062 8.338769e-02 4.169385e-02
[13,] 0.9426136 1.147728e-01 5.738639e-02
[14,] 0.9203398 1.593204e-01 7.966018e-02
[15,] 0.9023035 1.953931e-01 9.769653e-02
[16,] 0.9308276 1.383448e-01 6.917239e-02
[17,] 0.9443501 1.112997e-01 5.564987e-02
[18,] 0.9492435 1.015131e-01 5.075654e-02
[19,] 0.9580613 8.387737e-02 4.193869e-02
[20,] 0.9709777 5.804469e-02 2.902235e-02
[21,] 0.9682909 6.341813e-02 3.170906e-02
[22,] 0.9909409 1.811823e-02 9.059117e-03
[23,] 0.9996703 6.593573e-04 3.296786e-04
[24,] 0.9999084 1.831901e-04 9.159504e-05
[25,] 0.9998626 2.747672e-04 1.373836e-04
[26,] 0.9999318 1.364244e-04 6.821221e-05
[27,] 0.9999150 1.700005e-04 8.500025e-05
[28,] 0.9998808 2.384607e-04 1.192304e-04
[29,] 0.9998471 3.057536e-04 1.528768e-04
[30,] 0.9998101 3.797751e-04 1.898876e-04
[31,] 0.9999471 1.057201e-04 5.286003e-05
[32,] 0.9999282 1.435347e-04 7.176736e-05
[33,] 0.9999352 1.295488e-04 6.477439e-05
[34,] 0.9999999 1.031128e-07 5.155639e-08
[35,] 0.9999999 1.799530e-07 8.997652e-08
[36,] 1.0000000 1.757582e-08 8.787911e-09
[37,] 1.0000000 3.081851e-08 1.540926e-08
[38,] 1.0000000 4.130422e-20 2.065211e-20
[39,] 1.0000000 1.614078e-21 8.070392e-22
[40,] 1.0000000 5.418811e-21 2.709406e-21
[41,] 1.0000000 9.034614e-21 4.517307e-21
[42,] 1.0000000 2.937374e-21 1.468687e-21
[43,] 1.0000000 9.416307e-22 4.708154e-22
[44,] 1.0000000 1.848838e-22 9.244189e-23
[45,] 1.0000000 3.734085e-22 1.867043e-22
[46,] 1.0000000 7.987580e-22 3.993790e-22
[47,] 1.0000000 2.391528e-21 1.195764e-21
[48,] 1.0000000 8.020434e-21 4.010217e-21
[49,] 1.0000000 8.081194e-21 4.040597e-21
[50,] 1.0000000 1.685833e-20 8.429167e-21
[51,] 1.0000000 2.377514e-20 1.188757e-20
[52,] 1.0000000 7.752012e-20 3.876006e-20
[53,] 1.0000000 1.064120e-19 5.320601e-20
[54,] 1.0000000 2.535805e-19 1.267902e-19
[55,] 1.0000000 6.340776e-19 3.170388e-19
[56,] 1.0000000 1.990864e-18 9.954319e-19
[57,] 1.0000000 5.851446e-18 2.925723e-18
[58,] 1.0000000 2.156854e-18 1.078427e-18
[59,] 1.0000000 1.116503e-19 5.582515e-20
[60,] 1.0000000 2.189138e-19 1.094569e-19
[61,] 1.0000000 6.406641e-19 3.203320e-19
[62,] 1.0000000 1.528808e-18 7.644038e-19
[63,] 1.0000000 4.721952e-18 2.360976e-18
[64,] 1.0000000 1.479657e-17 7.398287e-18
[65,] 1.0000000 3.479513e-17 1.739757e-17
[66,] 1.0000000 1.075782e-16 5.378908e-17
[67,] 1.0000000 3.288611e-16 1.644305e-16
[68,] 1.0000000 9.347091e-16 4.673545e-16
[69,] 1.0000000 2.704912e-15 1.352456e-15
[70,] 1.0000000 7.958873e-15 3.979436e-15
[71,] 1.0000000 2.322084e-14 1.161042e-14
[72,] 1.0000000 6.001928e-14 3.000964e-14
[73,] 1.0000000 1.368760e-13 6.843798e-14
[74,] 1.0000000 2.987120e-13 1.493560e-13
[75,] 1.0000000 6.751534e-13 3.375767e-13
[76,] 1.0000000 1.825556e-12 9.127778e-13
[77,] 1.0000000 3.381396e-12 1.690698e-12
[78,] 1.0000000 8.686221e-12 4.343110e-12
[79,] 1.0000000 2.089299e-11 1.044650e-11
[80,] 1.0000000 5.460634e-11 2.730317e-11
[81,] 1.0000000 1.319949e-10 6.599743e-11
[82,] 1.0000000 1.063187e-10 5.315937e-11
[83,] 1.0000000 2.515599e-10 1.257799e-10
[84,] 1.0000000 4.013831e-10 2.006916e-10
[85,] 1.0000000 7.798859e-10 3.899429e-10
[86,] 1.0000000 1.662126e-09 8.310628e-10
[87,] 1.0000000 1.665572e-09 8.327862e-10
[88,] 1.0000000 3.785463e-09 1.892732e-09
[89,] 1.0000000 9.153973e-09 4.576987e-09
[90,] 1.0000000 2.215010e-08 1.107505e-08
[91,] 1.0000000 3.521207e-08 1.760604e-08
[92,] 1.0000000 5.751156e-08 2.875578e-08
[93,] 1.0000000 9.178787e-08 4.589393e-08
[94,] 0.9999999 2.149314e-07 1.074657e-07
[95,] 0.9999998 4.969086e-07 2.484543e-07
[96,] 0.9999995 1.069806e-06 5.349031e-07
[97,] 0.9999990 1.922854e-06 9.614271e-07
[98,] 0.9999985 3.097632e-06 1.548816e-06
[99,] 0.9999971 5.895364e-06 2.947682e-06
[100,] 0.9999996 7.763759e-07 3.881880e-07
[101,] 0.9999991 1.849913e-06 9.249566e-07
[102,] 0.9999979 4.121202e-06 2.060601e-06
[103,] 0.9999955 9.024238e-06 4.512119e-06
[104,] 0.9999900 1.999425e-05 9.997123e-06
[105,] 0.9999840 3.195930e-05 1.597965e-05
[106,] 0.9999662 6.762067e-05 3.381033e-05
[107,] 0.9999317 1.365108e-04 6.825538e-05
[108,] 0.9998550 2.899639e-04 1.449820e-04
[109,] 0.9997008 5.983528e-04 2.991764e-04
[110,] 1.0000000 1.962567e-12 9.812837e-13
[111,] 1.0000000 1.028725e-11 5.143624e-12
[112,] 1.0000000 2.837787e-11 1.418894e-11
[113,] 1.0000000 1.606937e-10 8.034687e-11
[114,] 1.0000000 8.786696e-10 4.393348e-10
[115,] 1.0000000 4.241457e-09 2.120729e-09
[116,] 1.0000000 2.173599e-08 1.086799e-08
[117,] 0.9999999 1.085750e-07 5.428749e-08
[118,] 0.9999997 5.083038e-07 2.541519e-07
[119,] 0.9999988 2.310621e-06 1.155311e-06
[120,] 0.9999967 6.542585e-06 3.271293e-06
[121,] 0.9999941 1.173683e-05 5.868415e-06
[122,] 0.9999737 5.262429e-05 2.631214e-05
[123,] 0.9999084 1.831643e-04 9.158216e-05
[124,] 0.9996231 7.538879e-04 3.769440e-04
[125,] 0.9985620 2.876078e-03 1.438039e-03
[126,] 0.9960098 7.980329e-03 3.990165e-03
[127,] 0.9996219 7.561918e-04 3.780959e-04
[128,] 0.9976523 4.695305e-03 2.347652e-03
> postscript(file="/var/www/html/rcomp/tmp/1r8vg1290690415.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2r8vg1290690415.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32hc11290690415.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42hc11290690415.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52hc11290690415.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 = 159
Frequency = 1
1 2 3 4 5 6
0.56972847 1.90219380 -0.65688000 -3.81078142 -3.21541411 -3.13025182
7 8 9 10 11 12
-3.30859682 -6.75318753 -5.37779958 -3.45064986 1.84789843 7.41109889
13 14 15 16 17 18
6.59159171 -5.22684874 -7.97286245 -5.48517430 -0.66514860 -5.23837700
19 20 21 22 23 24
2.26036358 6.82536644 1.70515414 11.16185677 3.49404112 8.75584826
25 26 27 28 29 30
0.12006811 -1.53985832 -0.71388918 3.42135289 2.54693877 -3.21142397
31 32 33 34 35 36
2.59313451 -0.27649420 2.95292270 1.18426067 -3.27592077 3.40109879
37 38 39 40 41 42
6.76363518 -10.27154718 9.74204053 -0.68236075 9.07488431 1.62753160
43 44 45 46 47 48
0.27043447 -3.65744547 -1.07575624 -5.29767113 0.51933067 3.71126114
49 50 51 52 53 54
8.82089608 -0.87460248 1.99389476 -0.68186347 4.21256574 -6.74233536
55 56 57 58 59 60
-0.95533275 1.11730245 0.96940236 -1.25079194 -4.20775712 -3.63213232
61 62 63 64 65 66
-0.98827726 -0.58770773 1.66078198 -1.28111580 -1.16488317 3.25078335
67 68 69 70 71 72
-2.70126001 1.97126399 -0.22458306 -0.42489535 -3.04238082 -1.46190369
73 74 75 76 77 78
0.86863493 5.82141421 0.01413723 -0.66420815 1.16061794 -0.18136200
79 80 81 82 83 84
0.89553639 -0.91576826 1.42986505 -2.23618032 0.98418025 -0.13754185
85 86 87 88 89 90
-0.21133406 0.24499889 -0.15902119 1.58607121 -0.39643597 -2.52845321
91 92 93 94 95 96
-0.36818531 -2.75153746 -1.17854220 -1.14874117 -0.61231434 3.13155756
97 98 99 100 101 102
5.29831165 -0.35911474 -2.03581535 -2.90080682 -0.97652351 -3.19448707
103 104 105 106 107 108
0.50709911 -1.25198396 1.24035017 -3.61699462 -4.65916412 -4.66148832
109 110 111 112 113 114
-0.29564761 -1.09388461 -3.45775607 -1.35765992 0.58850574 -0.33580615
115 116 117 118 119 120
1.31725362 -0.02947628 -2.89052718 -2.47297919 1.26391044 -5.91306514
121 122 123 124 125 126
-1.38521225 -3.04393435 -0.31053627 -1.63127964 9.13157692 1.24433200
127 128 129 130 131 132
-1.12008081 0.99103325 1.08892237 1.01079312 -1.75787942 -2.03055430
133 134 135 136 137 138
1.00435479 -1.37714962 -1.50597626 6.85476104 -0.54330311 -3.07975020
139 140 141 142 143 144
-0.46036575 -2.99675307 -3.55370312 6.99144673 -2.27106736 0.41243126
145 146 147 148 149 150
1.54187463 2.23270422 -0.56270443 -1.47605957 -3.07857635 -0.55535352
151 152 153 154 155 156
-0.82755793 3.07671882 5.44676885 0.98398536 1.44595901 -0.26027996
157 158 159
1.20601026 1.28858567 6.58141586
> postscript(file="/var/www/html/rcomp/tmp/6dqt41290690415.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 0.56972847 NA
1 1.90219380 0.56972847
2 -0.65688000 1.90219380
3 -3.81078142 -0.65688000
4 -3.21541411 -3.81078142
5 -3.13025182 -3.21541411
6 -3.30859682 -3.13025182
7 -6.75318753 -3.30859682
8 -5.37779958 -6.75318753
9 -3.45064986 -5.37779958
10 1.84789843 -3.45064986
11 7.41109889 1.84789843
12 6.59159171 7.41109889
13 -5.22684874 6.59159171
14 -7.97286245 -5.22684874
15 -5.48517430 -7.97286245
16 -0.66514860 -5.48517430
17 -5.23837700 -0.66514860
18 2.26036358 -5.23837700
19 6.82536644 2.26036358
20 1.70515414 6.82536644
21 11.16185677 1.70515414
22 3.49404112 11.16185677
23 8.75584826 3.49404112
24 0.12006811 8.75584826
25 -1.53985832 0.12006811
26 -0.71388918 -1.53985832
27 3.42135289 -0.71388918
28 2.54693877 3.42135289
29 -3.21142397 2.54693877
30 2.59313451 -3.21142397
31 -0.27649420 2.59313451
32 2.95292270 -0.27649420
33 1.18426067 2.95292270
34 -3.27592077 1.18426067
35 3.40109879 -3.27592077
36 6.76363518 3.40109879
37 -10.27154718 6.76363518
38 9.74204053 -10.27154718
39 -0.68236075 9.74204053
40 9.07488431 -0.68236075
41 1.62753160 9.07488431
42 0.27043447 1.62753160
43 -3.65744547 0.27043447
44 -1.07575624 -3.65744547
45 -5.29767113 -1.07575624
46 0.51933067 -5.29767113
47 3.71126114 0.51933067
48 8.82089608 3.71126114
49 -0.87460248 8.82089608
50 1.99389476 -0.87460248
51 -0.68186347 1.99389476
52 4.21256574 -0.68186347
53 -6.74233536 4.21256574
54 -0.95533275 -6.74233536
55 1.11730245 -0.95533275
56 0.96940236 1.11730245
57 -1.25079194 0.96940236
58 -4.20775712 -1.25079194
59 -3.63213232 -4.20775712
60 -0.98827726 -3.63213232
61 -0.58770773 -0.98827726
62 1.66078198 -0.58770773
63 -1.28111580 1.66078198
64 -1.16488317 -1.28111580
65 3.25078335 -1.16488317
66 -2.70126001 3.25078335
67 1.97126399 -2.70126001
68 -0.22458306 1.97126399
69 -0.42489535 -0.22458306
70 -3.04238082 -0.42489535
71 -1.46190369 -3.04238082
72 0.86863493 -1.46190369
73 5.82141421 0.86863493
74 0.01413723 5.82141421
75 -0.66420815 0.01413723
76 1.16061794 -0.66420815
77 -0.18136200 1.16061794
78 0.89553639 -0.18136200
79 -0.91576826 0.89553639
80 1.42986505 -0.91576826
81 -2.23618032 1.42986505
82 0.98418025 -2.23618032
83 -0.13754185 0.98418025
84 -0.21133406 -0.13754185
85 0.24499889 -0.21133406
86 -0.15902119 0.24499889
87 1.58607121 -0.15902119
88 -0.39643597 1.58607121
89 -2.52845321 -0.39643597
90 -0.36818531 -2.52845321
91 -2.75153746 -0.36818531
92 -1.17854220 -2.75153746
93 -1.14874117 -1.17854220
94 -0.61231434 -1.14874117
95 3.13155756 -0.61231434
96 5.29831165 3.13155756
97 -0.35911474 5.29831165
98 -2.03581535 -0.35911474
99 -2.90080682 -2.03581535
100 -0.97652351 -2.90080682
101 -3.19448707 -0.97652351
102 0.50709911 -3.19448707
103 -1.25198396 0.50709911
104 1.24035017 -1.25198396
105 -3.61699462 1.24035017
106 -4.65916412 -3.61699462
107 -4.66148832 -4.65916412
108 -0.29564761 -4.66148832
109 -1.09388461 -0.29564761
110 -3.45775607 -1.09388461
111 -1.35765992 -3.45775607
112 0.58850574 -1.35765992
113 -0.33580615 0.58850574
114 1.31725362 -0.33580615
115 -0.02947628 1.31725362
116 -2.89052718 -0.02947628
117 -2.47297919 -2.89052718
118 1.26391044 -2.47297919
119 -5.91306514 1.26391044
120 -1.38521225 -5.91306514
121 -3.04393435 -1.38521225
122 -0.31053627 -3.04393435
123 -1.63127964 -0.31053627
124 9.13157692 -1.63127964
125 1.24433200 9.13157692
126 -1.12008081 1.24433200
127 0.99103325 -1.12008081
128 1.08892237 0.99103325
129 1.01079312 1.08892237
130 -1.75787942 1.01079312
131 -2.03055430 -1.75787942
132 1.00435479 -2.03055430
133 -1.37714962 1.00435479
134 -1.50597626 -1.37714962
135 6.85476104 -1.50597626
136 -0.54330311 6.85476104
137 -3.07975020 -0.54330311
138 -0.46036575 -3.07975020
139 -2.99675307 -0.46036575
140 -3.55370312 -2.99675307
141 6.99144673 -3.55370312
142 -2.27106736 6.99144673
143 0.41243126 -2.27106736
144 1.54187463 0.41243126
145 2.23270422 1.54187463
146 -0.56270443 2.23270422
147 -1.47605957 -0.56270443
148 -3.07857635 -1.47605957
149 -0.55535352 -3.07857635
150 -0.82755793 -0.55535352
151 3.07671882 -0.82755793
152 5.44676885 3.07671882
153 0.98398536 5.44676885
154 1.44595901 0.98398536
155 -0.26027996 1.44595901
156 1.20601026 -0.26027996
157 1.28858567 1.20601026
158 6.58141586 1.28858567
159 NA 6.58141586
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.90219380 0.56972847
[2,] -0.65688000 1.90219380
[3,] -3.81078142 -0.65688000
[4,] -3.21541411 -3.81078142
[5,] -3.13025182 -3.21541411
[6,] -3.30859682 -3.13025182
[7,] -6.75318753 -3.30859682
[8,] -5.37779958 -6.75318753
[9,] -3.45064986 -5.37779958
[10,] 1.84789843 -3.45064986
[11,] 7.41109889 1.84789843
[12,] 6.59159171 7.41109889
[13,] -5.22684874 6.59159171
[14,] -7.97286245 -5.22684874
[15,] -5.48517430 -7.97286245
[16,] -0.66514860 -5.48517430
[17,] -5.23837700 -0.66514860
[18,] 2.26036358 -5.23837700
[19,] 6.82536644 2.26036358
[20,] 1.70515414 6.82536644
[21,] 11.16185677 1.70515414
[22,] 3.49404112 11.16185677
[23,] 8.75584826 3.49404112
[24,] 0.12006811 8.75584826
[25,] -1.53985832 0.12006811
[26,] -0.71388918 -1.53985832
[27,] 3.42135289 -0.71388918
[28,] 2.54693877 3.42135289
[29,] -3.21142397 2.54693877
[30,] 2.59313451 -3.21142397
[31,] -0.27649420 2.59313451
[32,] 2.95292270 -0.27649420
[33,] 1.18426067 2.95292270
[34,] -3.27592077 1.18426067
[35,] 3.40109879 -3.27592077
[36,] 6.76363518 3.40109879
[37,] -10.27154718 6.76363518
[38,] 9.74204053 -10.27154718
[39,] -0.68236075 9.74204053
[40,] 9.07488431 -0.68236075
[41,] 1.62753160 9.07488431
[42,] 0.27043447 1.62753160
[43,] -3.65744547 0.27043447
[44,] -1.07575624 -3.65744547
[45,] -5.29767113 -1.07575624
[46,] 0.51933067 -5.29767113
[47,] 3.71126114 0.51933067
[48,] 8.82089608 3.71126114
[49,] -0.87460248 8.82089608
[50,] 1.99389476 -0.87460248
[51,] -0.68186347 1.99389476
[52,] 4.21256574 -0.68186347
[53,] -6.74233536 4.21256574
[54,] -0.95533275 -6.74233536
[55,] 1.11730245 -0.95533275
[56,] 0.96940236 1.11730245
[57,] -1.25079194 0.96940236
[58,] -4.20775712 -1.25079194
[59,] -3.63213232 -4.20775712
[60,] -0.98827726 -3.63213232
[61,] -0.58770773 -0.98827726
[62,] 1.66078198 -0.58770773
[63,] -1.28111580 1.66078198
[64,] -1.16488317 -1.28111580
[65,] 3.25078335 -1.16488317
[66,] -2.70126001 3.25078335
[67,] 1.97126399 -2.70126001
[68,] -0.22458306 1.97126399
[69,] -0.42489535 -0.22458306
[70,] -3.04238082 -0.42489535
[71,] -1.46190369 -3.04238082
[72,] 0.86863493 -1.46190369
[73,] 5.82141421 0.86863493
[74,] 0.01413723 5.82141421
[75,] -0.66420815 0.01413723
[76,] 1.16061794 -0.66420815
[77,] -0.18136200 1.16061794
[78,] 0.89553639 -0.18136200
[79,] -0.91576826 0.89553639
[80,] 1.42986505 -0.91576826
[81,] -2.23618032 1.42986505
[82,] 0.98418025 -2.23618032
[83,] -0.13754185 0.98418025
[84,] -0.21133406 -0.13754185
[85,] 0.24499889 -0.21133406
[86,] -0.15902119 0.24499889
[87,] 1.58607121 -0.15902119
[88,] -0.39643597 1.58607121
[89,] -2.52845321 -0.39643597
[90,] -0.36818531 -2.52845321
[91,] -2.75153746 -0.36818531
[92,] -1.17854220 -2.75153746
[93,] -1.14874117 -1.17854220
[94,] -0.61231434 -1.14874117
[95,] 3.13155756 -0.61231434
[96,] 5.29831165 3.13155756
[97,] -0.35911474 5.29831165
[98,] -2.03581535 -0.35911474
[99,] -2.90080682 -2.03581535
[100,] -0.97652351 -2.90080682
[101,] -3.19448707 -0.97652351
[102,] 0.50709911 -3.19448707
[103,] -1.25198396 0.50709911
[104,] 1.24035017 -1.25198396
[105,] -3.61699462 1.24035017
[106,] -4.65916412 -3.61699462
[107,] -4.66148832 -4.65916412
[108,] -0.29564761 -4.66148832
[109,] -1.09388461 -0.29564761
[110,] -3.45775607 -1.09388461
[111,] -1.35765992 -3.45775607
[112,] 0.58850574 -1.35765992
[113,] -0.33580615 0.58850574
[114,] 1.31725362 -0.33580615
[115,] -0.02947628 1.31725362
[116,] -2.89052718 -0.02947628
[117,] -2.47297919 -2.89052718
[118,] 1.26391044 -2.47297919
[119,] -5.91306514 1.26391044
[120,] -1.38521225 -5.91306514
[121,] -3.04393435 -1.38521225
[122,] -0.31053627 -3.04393435
[123,] -1.63127964 -0.31053627
[124,] 9.13157692 -1.63127964
[125,] 1.24433200 9.13157692
[126,] -1.12008081 1.24433200
[127,] 0.99103325 -1.12008081
[128,] 1.08892237 0.99103325
[129,] 1.01079312 1.08892237
[130,] -1.75787942 1.01079312
[131,] -2.03055430 -1.75787942
[132,] 1.00435479 -2.03055430
[133,] -1.37714962 1.00435479
[134,] -1.50597626 -1.37714962
[135,] 6.85476104 -1.50597626
[136,] -0.54330311 6.85476104
[137,] -3.07975020 -0.54330311
[138,] -0.46036575 -3.07975020
[139,] -2.99675307 -0.46036575
[140,] -3.55370312 -2.99675307
[141,] 6.99144673 -3.55370312
[142,] -2.27106736 6.99144673
[143,] 0.41243126 -2.27106736
[144,] 1.54187463 0.41243126
[145,] 2.23270422 1.54187463
[146,] -0.56270443 2.23270422
[147,] -1.47605957 -0.56270443
[148,] -3.07857635 -1.47605957
[149,] -0.55535352 -3.07857635
[150,] -0.82755793 -0.55535352
[151,] 3.07671882 -0.82755793
[152,] 5.44676885 3.07671882
[153,] 0.98398536 5.44676885
[154,] 1.44595901 0.98398536
[155,] -0.26027996 1.44595901
[156,] 1.20601026 -0.26027996
[157,] 1.28858567 1.20601026
[158,] 6.58141586 1.28858567
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.90219380 0.56972847
2 -0.65688000 1.90219380
3 -3.81078142 -0.65688000
4 -3.21541411 -3.81078142
5 -3.13025182 -3.21541411
6 -3.30859682 -3.13025182
7 -6.75318753 -3.30859682
8 -5.37779958 -6.75318753
9 -3.45064986 -5.37779958
10 1.84789843 -3.45064986
11 7.41109889 1.84789843
12 6.59159171 7.41109889
13 -5.22684874 6.59159171
14 -7.97286245 -5.22684874
15 -5.48517430 -7.97286245
16 -0.66514860 -5.48517430
17 -5.23837700 -0.66514860
18 2.26036358 -5.23837700
19 6.82536644 2.26036358
20 1.70515414 6.82536644
21 11.16185677 1.70515414
22 3.49404112 11.16185677
23 8.75584826 3.49404112
24 0.12006811 8.75584826
25 -1.53985832 0.12006811
26 -0.71388918 -1.53985832
27 3.42135289 -0.71388918
28 2.54693877 3.42135289
29 -3.21142397 2.54693877
30 2.59313451 -3.21142397
31 -0.27649420 2.59313451
32 2.95292270 -0.27649420
33 1.18426067 2.95292270
34 -3.27592077 1.18426067
35 3.40109879 -3.27592077
36 6.76363518 3.40109879
37 -10.27154718 6.76363518
38 9.74204053 -10.27154718
39 -0.68236075 9.74204053
40 9.07488431 -0.68236075
41 1.62753160 9.07488431
42 0.27043447 1.62753160
43 -3.65744547 0.27043447
44 -1.07575624 -3.65744547
45 -5.29767113 -1.07575624
46 0.51933067 -5.29767113
47 3.71126114 0.51933067
48 8.82089608 3.71126114
49 -0.87460248 8.82089608
50 1.99389476 -0.87460248
51 -0.68186347 1.99389476
52 4.21256574 -0.68186347
53 -6.74233536 4.21256574
54 -0.95533275 -6.74233536
55 1.11730245 -0.95533275
56 0.96940236 1.11730245
57 -1.25079194 0.96940236
58 -4.20775712 -1.25079194
59 -3.63213232 -4.20775712
60 -0.98827726 -3.63213232
61 -0.58770773 -0.98827726
62 1.66078198 -0.58770773
63 -1.28111580 1.66078198
64 -1.16488317 -1.28111580
65 3.25078335 -1.16488317
66 -2.70126001 3.25078335
67 1.97126399 -2.70126001
68 -0.22458306 1.97126399
69 -0.42489535 -0.22458306
70 -3.04238082 -0.42489535
71 -1.46190369 -3.04238082
72 0.86863493 -1.46190369
73 5.82141421 0.86863493
74 0.01413723 5.82141421
75 -0.66420815 0.01413723
76 1.16061794 -0.66420815
77 -0.18136200 1.16061794
78 0.89553639 -0.18136200
79 -0.91576826 0.89553639
80 1.42986505 -0.91576826
81 -2.23618032 1.42986505
82 0.98418025 -2.23618032
83 -0.13754185 0.98418025
84 -0.21133406 -0.13754185
85 0.24499889 -0.21133406
86 -0.15902119 0.24499889
87 1.58607121 -0.15902119
88 -0.39643597 1.58607121
89 -2.52845321 -0.39643597
90 -0.36818531 -2.52845321
91 -2.75153746 -0.36818531
92 -1.17854220 -2.75153746
93 -1.14874117 -1.17854220
94 -0.61231434 -1.14874117
95 3.13155756 -0.61231434
96 5.29831165 3.13155756
97 -0.35911474 5.29831165
98 -2.03581535 -0.35911474
99 -2.90080682 -2.03581535
100 -0.97652351 -2.90080682
101 -3.19448707 -0.97652351
102 0.50709911 -3.19448707
103 -1.25198396 0.50709911
104 1.24035017 -1.25198396
105 -3.61699462 1.24035017
106 -4.65916412 -3.61699462
107 -4.66148832 -4.65916412
108 -0.29564761 -4.66148832
109 -1.09388461 -0.29564761
110 -3.45775607 -1.09388461
111 -1.35765992 -3.45775607
112 0.58850574 -1.35765992
113 -0.33580615 0.58850574
114 1.31725362 -0.33580615
115 -0.02947628 1.31725362
116 -2.89052718 -0.02947628
117 -2.47297919 -2.89052718
118 1.26391044 -2.47297919
119 -5.91306514 1.26391044
120 -1.38521225 -5.91306514
121 -3.04393435 -1.38521225
122 -0.31053627 -3.04393435
123 -1.63127964 -0.31053627
124 9.13157692 -1.63127964
125 1.24433200 9.13157692
126 -1.12008081 1.24433200
127 0.99103325 -1.12008081
128 1.08892237 0.99103325
129 1.01079312 1.08892237
130 -1.75787942 1.01079312
131 -2.03055430 -1.75787942
132 1.00435479 -2.03055430
133 -1.37714962 1.00435479
134 -1.50597626 -1.37714962
135 6.85476104 -1.50597626
136 -0.54330311 6.85476104
137 -3.07975020 -0.54330311
138 -0.46036575 -3.07975020
139 -2.99675307 -0.46036575
140 -3.55370312 -2.99675307
141 6.99144673 -3.55370312
142 -2.27106736 6.99144673
143 0.41243126 -2.27106736
144 1.54187463 0.41243126
145 2.23270422 1.54187463
146 -0.56270443 2.23270422
147 -1.47605957 -0.56270443
148 -3.07857635 -1.47605957
149 -0.55535352 -3.07857635
150 -0.82755793 -0.55535352
151 3.07671882 -0.82755793
152 5.44676885 3.07671882
153 0.98398536 5.44676885
154 1.44595901 0.98398536
155 -0.26027996 1.44595901
156 1.20601026 -0.26027996
157 1.28858567 1.20601026
158 6.58141586 1.28858567
> 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/7dqt41290690415.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/860b71290690415.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/960b71290690415.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10g9as1290690415.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1119qy1290690415.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/12nap41290690415.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/13jk5c1290690415.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/1442301290690415.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/158lk61290690415.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/16muzx1290690415.tab")
+ }
>
> try(system("convert tmp/1r8vg1290690415.ps tmp/1r8vg1290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r8vg1290690415.ps tmp/2r8vg1290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/32hc11290690415.ps tmp/32hc11290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/42hc11290690415.ps tmp/42hc11290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/52hc11290690415.ps tmp/52hc11290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dqt41290690415.ps tmp/6dqt41290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dqt41290690415.ps tmp/7dqt41290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/860b71290690415.ps tmp/860b71290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/960b71290690415.ps tmp/960b71290690415.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g9as1290690415.ps tmp/10g9as1290690415.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.733 1.752 13.585