R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(26
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+ ,4)
+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('I1','I2','I3','E1','E2','E3','A'),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 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '6'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
E3 I1 I2 I3 E1 E2 A M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 23 26 21 21 23 17 4 1 0 0 0 0 0 0 0 0 0 0
2 20 20 16 15 24 17 4 0 1 0 0 0 0 0 0 0 0 0
3 20 19 19 18 22 18 6 0 0 1 0 0 0 0 0 0 0 0
4 21 19 18 11 20 21 8 0 0 0 1 0 0 0 0 0 0 0
5 24 20 16 8 24 20 8 0 0 0 0 1 0 0 0 0 0 0
6 22 25 23 19 27 28 4 0 0 0 0 0 1 0 0 0 0 0
7 23 25 17 4 28 19 4 0 0 0 0 0 0 1 0 0 0 0
8 20 22 12 20 27 22 8 0 0 0 0 0 0 0 1 0 0 0
9 25 26 19 16 24 16 5 0 0 0 0 0 0 0 0 1 0 0
10 23 22 16 14 23 18 4 0 0 0 0 0 0 0 0 0 1 0
11 27 17 19 10 24 25 4 0 0 0 0 0 0 0 0 0 0 1
12 27 22 20 13 27 17 4 0 0 0 0 0 0 0 0 0 0 0
13 22 19 13 14 27 14 4 1 0 0 0 0 0 0 0 0 0 0
14 24 24 20 8 28 11 4 0 1 0 0 0 0 0 0 0 0 0
15 25 26 27 23 27 27 4 0 0 1 0 0 0 0 0 0 0 0
16 22 21 17 11 23 20 8 0 0 0 1 0 0 0 0 0 0 0
17 28 13 8 9 24 22 4 0 0 0 0 1 0 0 0 0 0 0
18 28 26 25 24 28 22 4 0 0 0 0 0 1 0 0 0 0 0
19 27 20 26 5 27 21 4 0 0 0 0 0 0 1 0 0 0 0
20 25 22 13 15 25 23 8 0 0 0 0 0 0 0 1 0 0 0
21 16 14 19 5 19 17 4 0 0 0 0 0 0 0 0 1 0 0
22 28 21 15 19 24 24 7 0 0 0 0 0 0 0 0 0 1 0
23 21 7 5 6 20 14 4 0 0 0 0 0 0 0 0 0 0 1
24 24 23 16 13 28 17 4 0 0 0 0 0 0 0 0 0 0 0
25 27 17 14 11 26 23 5 1 0 0 0 0 0 0 0 0 0 0
26 14 25 24 17 23 24 4 0 1 0 0 0 0 0 0 0 0 0
27 14 25 24 17 23 24 4 0 0 1 0 0 0 0 0 0 0 0
28 27 19 9 5 20 8 4 0 0 0 1 0 0 0 0 0 0 0
29 20 20 19 9 11 22 4 0 0 0 0 1 0 0 0 0 0 0
30 21 23 19 15 24 23 4 0 0 0 0 0 1 0 0 0 0 0
31 22 22 25 17 25 25 4 0 0 0 0 0 0 1 0 0 0 0
32 21 22 19 17 23 21 4 0 0 0 0 0 0 0 1 0 0 0
33 12 21 18 20 18 24 15 0 0 0 0 0 0 0 0 1 0 0
34 20 15 15 12 20 15 10 0 0 0 0 0 0 0 0 0 1 0
35 24 20 12 7 20 22 4 0 0 0 0 0 0 0 0 0 0 1
36 19 22 21 16 24 21 8 0 0 0 0 0 0 0 0 0 0 0
37 28 18 12 7 23 25 4 1 0 0 0 0 0 0 0 0 0 0
38 23 20 15 14 25 16 4 0 1 0 0 0 0 0 0 0 0 0
39 27 28 28 24 28 28 4 0 0 1 0 0 0 0 0 0 0 0
40 22 22 25 15 26 23 4 0 0 0 1 0 0 0 0 0 0 0
41 27 18 19 15 26 21 7 0 0 0 0 1 0 0 0 0 0 0
42 26 23 20 10 23 21 4 0 0 0 0 0 1 0 0 0 0 0
43 22 20 24 14 22 26 6 0 0 0 0 0 0 1 0 0 0 0
44 21 25 26 18 24 22 5 0 0 0 0 0 0 0 1 0 0 0
45 19 26 25 12 21 21 4 0 0 0 0 0 0 0 0 1 0 0
46 24 15 12 9 20 18 16 0 0 0 0 0 0 0 0 0 1 0
47 19 17 12 9 22 12 5 0 0 0 0 0 0 0 0 0 0 1
48 26 23 15 8 20 25 12 0 0 0 0 0 0 0 0 0 0 0
49 22 21 17 18 25 17 6 1 0 0 0 0 0 0 0 0 0 0
50 28 13 14 10 20 24 9 0 1 0 0 0 0 0 0 0 0 0
51 21 18 16 17 22 15 9 0 0 1 0 0 0 0 0 0 0 0
52 23 19 11 14 23 13 4 0 0 0 1 0 0 0 0 0 0 0
53 28 22 20 16 25 26 5 0 0 0 0 1 0 0 0 0 0 0
54 10 16 11 10 23 16 4 0 0 0 0 0 1 0 0 0 0 0
55 24 24 22 19 23 24 4 0 0 0 0 0 0 1 0 0 0 0
56 21 18 20 10 22 21 5 0 0 0 0 0 0 0 1 0 0 0
57 21 20 19 14 24 20 4 0 0 0 0 0 0 0 0 1 0 0
58 24 24 17 10 25 14 4 0 0 0 0 0 0 0 0 0 1 0
59 24 14 21 4 21 25 4 0 0 0 0 0 0 0 0 0 0 1
60 25 22 23 19 12 25 5 0 0 0 0 0 0 0 0 0 0 0
61 25 24 18 9 17 20 4 1 0 0 0 0 0 0 0 0 0 0
62 23 18 17 12 20 22 6 0 1 0 0 0 0 0 0 0 0 0
63 21 21 27 16 23 20 4 0 0 1 0 0 0 0 0 0 0 0
64 16 23 25 11 23 26 4 0 0 0 1 0 0 0 0 0 0 0
65 17 17 19 18 20 18 18 0 0 0 0 1 0 0 0 0 0 0
66 25 22 22 11 28 22 4 0 0 0 0 0 1 0 0 0 0 0
67 24 24 24 24 24 24 6 0 0 0 0 0 0 1 0 0 0 0
68 23 21 20 17 24 17 4 0 0 0 0 0 0 0 1 0 0 0
69 25 22 19 18 24 24 4 0 0 0 0 0 0 0 0 1 0 0
70 23 16 11 9 24 20 5 0 0 0 0 0 0 0 0 0 1 0
71 28 21 22 19 28 19 4 0 0 0 0 0 0 0 0 0 0 1
72 26 23 22 18 25 20 4 0 0 0 0 0 0 0 0 0 0 0
73 22 22 16 12 21 15 5 1 0 0 0 0 0 0 0 0 0 0
74 19 24 20 23 25 23 10 0 1 0 0 0 0 0 0 0 0 0
75 26 24 24 22 25 26 5 0 0 1 0 0 0 0 0 0 0 0
76 18 16 16 14 18 22 8 0 0 0 1 0 0 0 0 0 0 0
77 18 16 16 14 17 20 8 0 0 0 0 1 0 0 0 0 0 0
78 25 21 22 16 26 24 5 0 0 0 0 0 1 0 0 0 0 0
79 27 26 24 23 28 26 4 0 0 0 0 0 0 1 0 0 0 0
80 12 15 16 7 21 21 4 0 0 0 0 0 0 0 1 0 0 0
81 15 25 27 10 27 25 4 0 0 0 0 0 0 0 0 1 0 0
82 21 18 11 12 22 13 5 0 0 0 0 0 0 0 0 0 1 0
83 23 23 21 12 21 20 4 0 0 0 0 0 0 0 0 0 0 1
84 22 20 20 12 25 22 4 0 0 0 0 0 0 0 0 0 0 0
85 21 17 20 17 22 23 8 1 0 0 0 0 0 0 0 0 0 0
86 24 25 27 21 23 28 4 0 1 0 0 0 0 0 0 0 0 0
87 27 24 20 16 26 22 5 0 0 1 0 0 0 0 0 0 0 0
88 22 17 12 11 19 20 14 0 0 0 1 0 0 0 0 0 0 0
89 28 19 8 14 25 6 8 0 0 0 0 1 0 0 0 0 0 0
90 26 20 21 13 21 21 8 0 0 0 0 0 1 0 0 0 0 0
91 10 15 18 9 13 20 4 0 0 0 0 0 0 1 0 0 0 0
92 19 27 24 19 24 18 4 0 0 0 0 0 0 0 1 0 0 0
93 22 22 16 13 25 23 6 0 0 0 0 0 0 0 0 1 0 0
94 21 23 18 19 26 20 4 0 0 0 0 0 0 0 0 0 1 0
95 24 16 20 13 25 24 7 0 0 0 0 0 0 0 0 0 0 1
96 25 19 20 13 25 22 7 0 0 0 0 0 0 0 0 0 0 0
97 21 25 19 13 22 21 4 1 0 0 0 0 0 0 0 0 0 0
98 20 19 17 14 21 18 6 0 1 0 0 0 0 0 0 0 0 0
99 21 19 16 12 23 21 4 0 0 1 0 0 0 0 0 0 0 0
100 24 26 26 22 25 23 7 0 0 0 1 0 0 0 0 0 0 0
101 23 21 15 11 24 23 4 0 0 0 0 1 0 0 0 0 0 0
102 18 20 22 5 21 15 4 0 0 0 0 0 1 0 0 0 0 0
103 24 24 17 18 21 21 8 0 0 0 0 0 0 1 0 0 0 0
104 24 22 23 19 25 24 4 0 0 0 0 0 0 0 1 0 0 0
105 19 20 21 14 22 23 4 0 0 0 0 0 0 0 0 1 0 0
106 20 18 19 15 20 21 10 0 0 0 0 0 0 0 0 0 1 0
107 18 18 14 12 20 21 8 0 0 0 0 0 0 0 0 0 0 1
108 20 24 17 19 23 20 6 0 0 0 0 0 0 0 0 0 0 0
109 27 24 12 15 28 11 4 1 0 0 0 0 0 0 0 0 0 0
110 23 22 24 17 23 22 4 0 1 0 0 0 0 0 0 0 0 0
111 26 23 18 8 28 27 4 0 0 1 0 0 0 0 0 0 0 0
112 23 22 20 10 24 25 5 0 0 0 1 0 0 0 0 0 0 0
113 17 20 16 12 18 18 4 0 0 0 0 1 0 0 0 0 0 0
114 21 18 20 12 20 20 6 0 0 0 0 0 1 0 0 0 0 0
115 25 25 22 20 28 24 4 0 0 0 0 0 0 1 0 0 0 0
116 23 18 12 12 21 10 5 0 0 0 0 0 0 0 1 0 0 0
117 27 16 16 12 21 27 7 0 0 0 0 0 0 0 0 1 0 0
118 24 20 17 14 25 21 8 0 0 0 0 0 0 0 0 0 1 0
119 20 19 22 6 19 21 5 0 0 0 0 0 0 0 0 0 0 1
120 27 15 12 10 18 18 8 0 0 0 0 0 0 0 0 0 0 0
121 21 19 14 18 21 15 10 1 0 0 0 0 0 0 0 0 0 0
122 24 19 23 18 22 24 8 0 1 0 0 0 0 0 0 0 0 0
123 21 16 15 7 24 22 5 0 0 1 0 0 0 0 0 0 0 0
124 15 17 17 18 15 14 12 0 0 0 1 0 0 0 0 0 0 0
125 25 28 28 9 28 28 4 0 0 0 0 1 0 0 0 0 0 0
126 25 23 20 17 26 18 5 0 0 0 0 0 1 0 0 0 0 0
127 22 25 23 22 23 26 4 0 0 0 0 0 0 1 0 0 0 0
128 24 20 13 11 26 17 6 0 0 0 0 0 0 0 1 0 0 0
129 21 17 18 15 20 19 4 0 0 0 0 0 0 0 0 1 0 0
130 22 23 23 17 22 22 4 0 0 0 0 0 0 0 0 0 1 0
131 23 16 19 15 20 18 7 0 0 0 0 0 0 0 0 0 0 1
132 22 23 23 22 23 24 7 0 0 0 0 0 0 0 0 0 0 0
133 20 11 12 9 22 15 10 1 0 0 0 0 0 0 0 0 0 0
134 23 18 16 13 24 18 4 0 1 0 0 0 0 0 0 0 0 0
135 25 24 23 20 23 26 5 0 0 1 0 0 0 0 0 0 0 0
136 23 23 13 14 22 11 8 0 0 0 1 0 0 0 0 0 0 0
137 22 21 22 14 26 26 11 0 0 0 0 1 0 0 0 0 0 0
138 25 16 18 12 23 21 7 0 0 0 0 0 1 0 0 0 0 0
139 26 24 23 20 27 23 4 0 0 0 0 0 0 1 0 0 0 0
140 22 23 20 20 23 23 8 0 0 0 0 0 0 0 1 0 0 0
141 24 18 10 8 21 15 6 0 0 0 0 0 0 0 0 1 0 0
142 24 20 17 17 26 22 7 0 0 0 0 0 0 0 0 0 1 0
143 25 9 18 9 23 26 5 0 0 0 0 0 0 0 0 0 0 1
144 20 24 15 18 21 16 4 0 0 0 0 0 0 0 0 0 0 0
145 26 25 23 22 27 20 8 1 0 0 0 0 0 0 0 0 0 0
146 21 20 17 10 19 18 4 0 1 0 0 0 0 0 0 0 0 0
147 26 21 17 13 23 22 8 0 0 1 0 0 0 0 0 0 0 0
148 21 25 22 15 25 16 6 0 0 0 1 0 0 0 0 0 0 0
149 22 22 20 18 23 19 4 0 0 0 0 1 0 0 0 0 0 0
150 16 21 20 18 22 20 9 0 0 0 0 0 1 0 0 0 0 0
151 26 21 19 12 22 19 5 0 0 0 0 0 0 1 0 0 0 0
152 28 22 18 12 25 23 6 0 0 0 0 0 0 0 1 0 0 0
153 18 27 22 20 25 24 4 0 0 0 0 0 0 0 0 1 0 0
154 25 24 20 12 28 25 4 0 0 0 0 0 0 0 0 0 1 0
155 23 24 22 16 28 21 4 0 0 0 0 0 0 0 0 0 0 1
156 21 21 18 16 20 21 5 0 0 0 0 0 0 0 0 0 0 0
157 20 18 16 18 25 23 6 1 0 0 0 0 0 0 0 0 0 0
158 25 16 16 16 19 27 16 0 1 0 0 0 0 0 0 0 0 0
159 22 22 16 13 25 23 6 0 0 1 0 0 0 0 0 0 0 0
160 21 20 16 17 22 18 6 0 0 0 1 0 0 0 0 0 0 0
161 16 18 17 13 18 16 4 0 0 0 0 1 0 0 0 0 0 0
162 18 20 18 17 20 16 4 0 0 0 0 0 1 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) I1 I2 I3 E1 E2
11.175746 0.093347 -0.214259 -0.032504 0.484904 0.183317
A M1 M2 M3 M4 M5
0.006446 -0.745079 -1.103065 -1.362103 -1.613914 -0.849666
M6 M7 M8 M9 M10 M11
-1.806682 -0.697766 -2.306178 -3.098837 -1.098217 -0.342078
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.2924 -1.5728 0.2169 2.0729 6.8821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.175746 2.818611 3.965 0.000115 ***
I1 0.093347 0.112447 0.830 0.407834
I2 -0.214259 0.093031 -2.303 0.022706 *
I3 -0.032504 0.072750 -0.447 0.655702
E1 0.484904 0.097089 4.994 1.68e-06 ***
E2 0.183317 0.078918 2.323 0.021586 *
A 0.006446 0.117025 0.055 0.956149
M1 -0.745079 1.289154 -0.578 0.564194
M2 -1.103065 1.279738 -0.862 0.390150
M3 -1.362103 1.292050 -1.054 0.293549
M4 -1.613914 1.285242 -1.256 0.211248
M5 -0.849666 1.281527 -0.663 0.508384
M6 -1.806682 1.293269 -1.397 0.164566
M7 -0.697766 1.317342 -0.530 0.597151
M8 -2.306178 1.301698 -1.772 0.078565 .
M9 -3.098837 1.299025 -2.386 0.018356 *
M10 -1.098217 1.313657 -0.836 0.404540
M11 -0.342078 1.379769 -0.248 0.804547
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.292 on 144 degrees of freedom
Multiple R-squared: 0.2683, Adjusted R-squared: 0.1819
F-statistic: 3.106 on 17 and 144 DF, p-value: 0.0001121
> 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,] 0.6737801 0.6524397317 0.3262198659
[2,] 0.7420553 0.5158894369 0.2579447185
[3,] 0.6189728 0.7620543552 0.3810271776
[4,] 0.5248628 0.9502744159 0.4751372079
[5,] 0.5657672 0.8684655599 0.4342327799
[6,] 0.7110638 0.5778723685 0.2889361842
[7,] 0.7325867 0.5348266978 0.2674133489
[8,] 0.8796740 0.2406520701 0.1203260351
[9,] 0.8937769 0.2124461284 0.1062230642
[10,] 0.8518162 0.2963675677 0.1481837839
[11,] 0.8147962 0.3704075836 0.1852037918
[12,] 0.7687721 0.4624558762 0.2312279381
[13,] 0.7522529 0.4954941175 0.2477470587
[14,] 0.6959070 0.6081860134 0.3040930067
[15,] 0.6451438 0.7097123706 0.3548561853
[16,] 0.6084391 0.7831217006 0.3915608503
[17,] 0.7052980 0.5894039164 0.2947019582
[18,] 0.6902180 0.6195640414 0.3097820207
[19,] 0.6801893 0.6396214675 0.3198107337
[20,] 0.7213027 0.5573946545 0.2786973272
[21,] 0.6802260 0.6395479722 0.3197739861
[22,] 0.6907649 0.6184702762 0.3092351381
[23,] 0.6598574 0.6802852360 0.3401426180
[24,] 0.6009201 0.7981597031 0.3990798515
[25,] 0.5427763 0.9144474951 0.4572237475
[26,] 0.5502196 0.8995608963 0.4497804481
[27,] 0.5807798 0.8384404261 0.4192202130
[28,] 0.6220958 0.7558083177 0.3779041588
[29,] 0.5723218 0.8553564655 0.4276782328
[30,] 0.7875685 0.4248629697 0.2124314848
[31,] 0.7523666 0.4952667165 0.2476333582
[32,] 0.7107855 0.5784289875 0.2892144938
[33,] 0.7033318 0.5933363505 0.2966681752
[34,] 0.9806764 0.0386472642 0.0193236321
[35,] 0.9763636 0.0472728376 0.0236364188
[36,] 0.9680386 0.0639227962 0.0319613981
[37,] 0.9575023 0.0849954249 0.0424977125
[38,] 0.9469322 0.1061356426 0.0530678213
[39,] 0.9357264 0.1285472858 0.0642736429
[40,] 0.9735802 0.0528396987 0.0264198494
[41,] 0.9834878 0.0330244254 0.0165122127
[42,] 0.9790719 0.0418562320 0.0209281160
[43,] 0.9717909 0.0564181198 0.0282090599
[44,] 0.9881006 0.0237988257 0.0118994129
[45,] 0.9887573 0.0224853795 0.0112426897
[46,] 0.9855436 0.0289127852 0.0144563926
[47,] 0.9811453 0.0377093453 0.0188546727
[48,] 0.9769966 0.0460067142 0.0230033571
[49,] 0.9772197 0.0455605563 0.0227802781
[50,] 0.9727111 0.0545777868 0.0272888934
[51,] 0.9710552 0.0578896519 0.0289448260
[52,] 0.9651895 0.0696210456 0.0348105228
[53,] 0.9574691 0.0850617979 0.0425308990
[54,] 0.9766837 0.0466325152 0.0233162576
[55,] 0.9747458 0.0505083321 0.0252541660
[56,] 0.9678207 0.0643585937 0.0321792969
[57,] 0.9624187 0.0751625680 0.0375812840
[58,] 0.9553623 0.0892754153 0.0446377076
[59,] 0.9441499 0.1117001490 0.0558500745
[60,] 0.9894764 0.0210472329 0.0105236165
[61,] 0.9981123 0.0037754632 0.0018877316
[62,] 0.9973729 0.0052541789 0.0026270895
[63,] 0.9968948 0.0062103710 0.0031051855
[64,] 0.9965981 0.0068038511 0.0034019255
[65,] 0.9951251 0.0097497795 0.0048748897
[66,] 0.9940122 0.0119755214 0.0059877607
[67,] 0.9938629 0.0122741957 0.0061370978
[68,] 0.9918000 0.0164000113 0.0082000057
[69,] 0.9933502 0.0132996945 0.0066498472
[70,] 0.9970358 0.0059284203 0.0029642101
[71,] 0.9997253 0.0005494292 0.0002747146
[72,] 0.9996922 0.0006156388 0.0003078194
[73,] 0.9995693 0.0008614815 0.0004307407
[74,] 0.9994940 0.0010119939 0.0005059970
[75,] 0.9991883 0.0016234295 0.0008117147
[76,] 0.9987326 0.0025348898 0.0012674449
[77,] 0.9982026 0.0035948983 0.0017974492
[78,] 0.9977628 0.0044744210 0.0022372105
[79,] 0.9969563 0.0060873878 0.0030436939
[80,] 0.9964033 0.0071934408 0.0035967204
[81,] 0.9952513 0.0094974994 0.0047487497
[82,] 0.9950028 0.0099943480 0.0049971740
[83,] 0.9929646 0.0140708118 0.0070354059
[84,] 0.9904323 0.0191353946 0.0095676973
[85,] 0.9901307 0.0197385554 0.0098692777
[86,] 0.9868594 0.0262811788 0.0131405894
[87,] 0.9881874 0.0236251213 0.0118125607
[88,] 0.9889858 0.0220283147 0.0110141573
[89,] 0.9908953 0.0182094117 0.0091047059
[90,] 0.9867563 0.0264873861 0.0132436931
[91,] 0.9807199 0.0385601346 0.0192800673
[92,] 0.9723447 0.0553105109 0.0276552554
[93,] 0.9660282 0.0679435861 0.0339717931
[94,] 0.9533164 0.0933672262 0.0466836131
[95,] 0.9380509 0.1238981708 0.0619490854
[96,] 0.9250794 0.1498411878 0.0749205939
[97,] 0.9516211 0.0967578547 0.0483789273
[98,] 0.9331559 0.1336881630 0.0668440815
[99,] 0.9210961 0.1578077685 0.0789038842
[100,] 0.9529120 0.0941760214 0.0470880107
[101,] 0.9350452 0.1299096048 0.0649548024
[102,] 0.9133446 0.1733108146 0.0866554073
[103,] 0.9329598 0.1340803868 0.0670401934
[104,] 0.9315140 0.1369720680 0.0684860340
[105,] 0.9080116 0.1839767331 0.0919883666
[106,] 0.9196644 0.1606711463 0.0803355731
[107,] 0.8951972 0.2096056645 0.1048028323
[108,] 0.8675689 0.2648622782 0.1324311391
[109,] 0.8222833 0.3554334992 0.1777167496
[110,] 0.7620553 0.4758894369 0.2379447185
[111,] 0.7000348 0.5999303628 0.2999651814
[112,] 0.6216419 0.7567161446 0.3783580723
[113,] 0.7297717 0.5404565575 0.2702282788
[114,] 0.6878244 0.6243511374 0.3121755687
[115,] 0.7581415 0.4837169867 0.2418584933
[116,] 0.6815141 0.6369717508 0.3184858754
[117,] 0.6692995 0.6614009593 0.3307004797
[118,] 0.5970744 0.8058512484 0.4029256242
[119,] 0.4746464 0.9492927766 0.5253536117
[120,] 0.4032616 0.8065232915 0.5967383542
[121,] 0.6093681 0.7812637906 0.3906318953
> postscript(file="/var/www/rcomp/tmp/1frk01321982222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2r7o61321982222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/32w9r1321982222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/46zct1321982222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5wvhg1321982222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5 6
1.02937257 -2.80378102 -0.93751156 0.27947856 0.13955815 -2.40827045
7 8 9 10 11 12
-3.12534366 -4.87895826 4.48406317 0.27376031 2.72899131 2.24377737
13 14 15 16 17 18
-2.64845978 0.61263000 1.22416981 -0.39286687 2.77056826 4.70441825
19 20 21 22 23 24
3.42048987 0.95927194 -1.50567214 3.71122196 -1.51107584 -2.19150739
25 26 27 28 29 30
1.48358388 -8.28975368 -8.03071583 6.56503586 2.77773475 -1.83733006
31 32 33 34 35 36
-1.35387882 -0.32794300 -6.75502398 -0.38609383 0.34119603 -4.74879483
37 38 39 40 41 42
2.92622779 -0.35212983 2.61601829 -0.62100761 3.04987475 4.06594802
43 44 45 46 47 48
-0.22045388 0.24966480 0.18417130 2.28499135 -3.45683852 1.79283873
49 50 51 52 53 54
-1.44113978 5.88277303 0.01116871 0.91478979 3.50446080 -12.29236820
55 56 57 58 59 60
1.03478399 0.51063345 0.25231235 0.93477249 1.69723686 6.88214652
61 62 63 64 65 66
4.54271546 1.50979615 0.68621125 -5.93960962 -3.37080078 1.01248027
67 68 69 70 71 72
1.12802412 2.22802725 3.46236541 -1.25795624 3.45120298 2.16132240
73 74 75 76 77 78
0.36892768 -4.68358582 2.88226161 -1.98499792 -1.89770808 1.86507221
79 80 81 82 83 84
1.61547057 -8.67252299 -7.00166271 -1.09411075 1.03373351 -2.54881186
85 86 87 88 89 90
-1.11556561 1.74976841 3.07856983 0.81016430 4.79539450 5.60178049
91 92 93 94 95 96
-7.72486389 -1.59332715 -0.65740760 -3.04989561 -0.18681696 0.55770026
97 98 99 100 101 102
-1.81419290 -1.27017791 -1.79727281 1.91295633 -1.59470327 -1.31830332
103 104 105 106 107 108
1.42496163 2.07434025 -0.89931462 -0.81149076 -4.72354205 -4.01389851
109 110 111 112 113 114
1.76810047 1.35692028 -0.39657764 -0.25809252 -3.42858677 1.22282409
115 116 117 118 119 120
-0.45057697 3.36296417 6.07006770 0.12916912 -0.79359988 5.24065412
121 122 123 124 125 126
-0.61675797 2.54769008 -2.56867721 -1.83860691 -0.38397437 2.38226856
127 128 129 130 131 132
-1.11342712 0.64384167 2.47352869 0.52937038 2.18835295 -1.27720368
133 134 135 136 137 138
-2.07593993 0.13458748 2.57280280 1.79567474 -2.56226264 3.33652579
139 140 141 142 143 144
1.52524902 0.49806173 3.67405999 -0.43509452 1.52414168 -2.75895151
145 146 147 148 149 150
2.06838995 0.48916005 3.05369514 -0.78859144 -0.17105831 -4.85133866
151 152 153 154 155 156
3.83956515 4.94594615 -3.78148755 -0.82864390 -2.29298209 -1.33927162
157 158 159 160 161 162
-4.47526183 3.11610278 -2.39414239 -0.45432669 -3.62849699 -1.48370699
> postscript(file="/var/www/rcomp/tmp/60gsp1321982223.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 1.02937257 NA
1 -2.80378102 1.02937257
2 -0.93751156 -2.80378102
3 0.27947856 -0.93751156
4 0.13955815 0.27947856
5 -2.40827045 0.13955815
6 -3.12534366 -2.40827045
7 -4.87895826 -3.12534366
8 4.48406317 -4.87895826
9 0.27376031 4.48406317
10 2.72899131 0.27376031
11 2.24377737 2.72899131
12 -2.64845978 2.24377737
13 0.61263000 -2.64845978
14 1.22416981 0.61263000
15 -0.39286687 1.22416981
16 2.77056826 -0.39286687
17 4.70441825 2.77056826
18 3.42048987 4.70441825
19 0.95927194 3.42048987
20 -1.50567214 0.95927194
21 3.71122196 -1.50567214
22 -1.51107584 3.71122196
23 -2.19150739 -1.51107584
24 1.48358388 -2.19150739
25 -8.28975368 1.48358388
26 -8.03071583 -8.28975368
27 6.56503586 -8.03071583
28 2.77773475 6.56503586
29 -1.83733006 2.77773475
30 -1.35387882 -1.83733006
31 -0.32794300 -1.35387882
32 -6.75502398 -0.32794300
33 -0.38609383 -6.75502398
34 0.34119603 -0.38609383
35 -4.74879483 0.34119603
36 2.92622779 -4.74879483
37 -0.35212983 2.92622779
38 2.61601829 -0.35212983
39 -0.62100761 2.61601829
40 3.04987475 -0.62100761
41 4.06594802 3.04987475
42 -0.22045388 4.06594802
43 0.24966480 -0.22045388
44 0.18417130 0.24966480
45 2.28499135 0.18417130
46 -3.45683852 2.28499135
47 1.79283873 -3.45683852
48 -1.44113978 1.79283873
49 5.88277303 -1.44113978
50 0.01116871 5.88277303
51 0.91478979 0.01116871
52 3.50446080 0.91478979
53 -12.29236820 3.50446080
54 1.03478399 -12.29236820
55 0.51063345 1.03478399
56 0.25231235 0.51063345
57 0.93477249 0.25231235
58 1.69723686 0.93477249
59 6.88214652 1.69723686
60 4.54271546 6.88214652
61 1.50979615 4.54271546
62 0.68621125 1.50979615
63 -5.93960962 0.68621125
64 -3.37080078 -5.93960962
65 1.01248027 -3.37080078
66 1.12802412 1.01248027
67 2.22802725 1.12802412
68 3.46236541 2.22802725
69 -1.25795624 3.46236541
70 3.45120298 -1.25795624
71 2.16132240 3.45120298
72 0.36892768 2.16132240
73 -4.68358582 0.36892768
74 2.88226161 -4.68358582
75 -1.98499792 2.88226161
76 -1.89770808 -1.98499792
77 1.86507221 -1.89770808
78 1.61547057 1.86507221
79 -8.67252299 1.61547057
80 -7.00166271 -8.67252299
81 -1.09411075 -7.00166271
82 1.03373351 -1.09411075
83 -2.54881186 1.03373351
84 -1.11556561 -2.54881186
85 1.74976841 -1.11556561
86 3.07856983 1.74976841
87 0.81016430 3.07856983
88 4.79539450 0.81016430
89 5.60178049 4.79539450
90 -7.72486389 5.60178049
91 -1.59332715 -7.72486389
92 -0.65740760 -1.59332715
93 -3.04989561 -0.65740760
94 -0.18681696 -3.04989561
95 0.55770026 -0.18681696
96 -1.81419290 0.55770026
97 -1.27017791 -1.81419290
98 -1.79727281 -1.27017791
99 1.91295633 -1.79727281
100 -1.59470327 1.91295633
101 -1.31830332 -1.59470327
102 1.42496163 -1.31830332
103 2.07434025 1.42496163
104 -0.89931462 2.07434025
105 -0.81149076 -0.89931462
106 -4.72354205 -0.81149076
107 -4.01389851 -4.72354205
108 1.76810047 -4.01389851
109 1.35692028 1.76810047
110 -0.39657764 1.35692028
111 -0.25809252 -0.39657764
112 -3.42858677 -0.25809252
113 1.22282409 -3.42858677
114 -0.45057697 1.22282409
115 3.36296417 -0.45057697
116 6.07006770 3.36296417
117 0.12916912 6.07006770
118 -0.79359988 0.12916912
119 5.24065412 -0.79359988
120 -0.61675797 5.24065412
121 2.54769008 -0.61675797
122 -2.56867721 2.54769008
123 -1.83860691 -2.56867721
124 -0.38397437 -1.83860691
125 2.38226856 -0.38397437
126 -1.11342712 2.38226856
127 0.64384167 -1.11342712
128 2.47352869 0.64384167
129 0.52937038 2.47352869
130 2.18835295 0.52937038
131 -1.27720368 2.18835295
132 -2.07593993 -1.27720368
133 0.13458748 -2.07593993
134 2.57280280 0.13458748
135 1.79567474 2.57280280
136 -2.56226264 1.79567474
137 3.33652579 -2.56226264
138 1.52524902 3.33652579
139 0.49806173 1.52524902
140 3.67405999 0.49806173
141 -0.43509452 3.67405999
142 1.52414168 -0.43509452
143 -2.75895151 1.52414168
144 2.06838995 -2.75895151
145 0.48916005 2.06838995
146 3.05369514 0.48916005
147 -0.78859144 3.05369514
148 -0.17105831 -0.78859144
149 -4.85133866 -0.17105831
150 3.83956515 -4.85133866
151 4.94594615 3.83956515
152 -3.78148755 4.94594615
153 -0.82864390 -3.78148755
154 -2.29298209 -0.82864390
155 -1.33927162 -2.29298209
156 -4.47526183 -1.33927162
157 3.11610278 -4.47526183
158 -2.39414239 3.11610278
159 -0.45432669 -2.39414239
160 -3.62849699 -0.45432669
161 -1.48370699 -3.62849699
162 NA -1.48370699
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.80378102 1.02937257
[2,] -0.93751156 -2.80378102
[3,] 0.27947856 -0.93751156
[4,] 0.13955815 0.27947856
[5,] -2.40827045 0.13955815
[6,] -3.12534366 -2.40827045
[7,] -4.87895826 -3.12534366
[8,] 4.48406317 -4.87895826
[9,] 0.27376031 4.48406317
[10,] 2.72899131 0.27376031
[11,] 2.24377737 2.72899131
[12,] -2.64845978 2.24377737
[13,] 0.61263000 -2.64845978
[14,] 1.22416981 0.61263000
[15,] -0.39286687 1.22416981
[16,] 2.77056826 -0.39286687
[17,] 4.70441825 2.77056826
[18,] 3.42048987 4.70441825
[19,] 0.95927194 3.42048987
[20,] -1.50567214 0.95927194
[21,] 3.71122196 -1.50567214
[22,] -1.51107584 3.71122196
[23,] -2.19150739 -1.51107584
[24,] 1.48358388 -2.19150739
[25,] -8.28975368 1.48358388
[26,] -8.03071583 -8.28975368
[27,] 6.56503586 -8.03071583
[28,] 2.77773475 6.56503586
[29,] -1.83733006 2.77773475
[30,] -1.35387882 -1.83733006
[31,] -0.32794300 -1.35387882
[32,] -6.75502398 -0.32794300
[33,] -0.38609383 -6.75502398
[34,] 0.34119603 -0.38609383
[35,] -4.74879483 0.34119603
[36,] 2.92622779 -4.74879483
[37,] -0.35212983 2.92622779
[38,] 2.61601829 -0.35212983
[39,] -0.62100761 2.61601829
[40,] 3.04987475 -0.62100761
[41,] 4.06594802 3.04987475
[42,] -0.22045388 4.06594802
[43,] 0.24966480 -0.22045388
[44,] 0.18417130 0.24966480
[45,] 2.28499135 0.18417130
[46,] -3.45683852 2.28499135
[47,] 1.79283873 -3.45683852
[48,] -1.44113978 1.79283873
[49,] 5.88277303 -1.44113978
[50,] 0.01116871 5.88277303
[51,] 0.91478979 0.01116871
[52,] 3.50446080 0.91478979
[53,] -12.29236820 3.50446080
[54,] 1.03478399 -12.29236820
[55,] 0.51063345 1.03478399
[56,] 0.25231235 0.51063345
[57,] 0.93477249 0.25231235
[58,] 1.69723686 0.93477249
[59,] 6.88214652 1.69723686
[60,] 4.54271546 6.88214652
[61,] 1.50979615 4.54271546
[62,] 0.68621125 1.50979615
[63,] -5.93960962 0.68621125
[64,] -3.37080078 -5.93960962
[65,] 1.01248027 -3.37080078
[66,] 1.12802412 1.01248027
[67,] 2.22802725 1.12802412
[68,] 3.46236541 2.22802725
[69,] -1.25795624 3.46236541
[70,] 3.45120298 -1.25795624
[71,] 2.16132240 3.45120298
[72,] 0.36892768 2.16132240
[73,] -4.68358582 0.36892768
[74,] 2.88226161 -4.68358582
[75,] -1.98499792 2.88226161
[76,] -1.89770808 -1.98499792
[77,] 1.86507221 -1.89770808
[78,] 1.61547057 1.86507221
[79,] -8.67252299 1.61547057
[80,] -7.00166271 -8.67252299
[81,] -1.09411075 -7.00166271
[82,] 1.03373351 -1.09411075
[83,] -2.54881186 1.03373351
[84,] -1.11556561 -2.54881186
[85,] 1.74976841 -1.11556561
[86,] 3.07856983 1.74976841
[87,] 0.81016430 3.07856983
[88,] 4.79539450 0.81016430
[89,] 5.60178049 4.79539450
[90,] -7.72486389 5.60178049
[91,] -1.59332715 -7.72486389
[92,] -0.65740760 -1.59332715
[93,] -3.04989561 -0.65740760
[94,] -0.18681696 -3.04989561
[95,] 0.55770026 -0.18681696
[96,] -1.81419290 0.55770026
[97,] -1.27017791 -1.81419290
[98,] -1.79727281 -1.27017791
[99,] 1.91295633 -1.79727281
[100,] -1.59470327 1.91295633
[101,] -1.31830332 -1.59470327
[102,] 1.42496163 -1.31830332
[103,] 2.07434025 1.42496163
[104,] -0.89931462 2.07434025
[105,] -0.81149076 -0.89931462
[106,] -4.72354205 -0.81149076
[107,] -4.01389851 -4.72354205
[108,] 1.76810047 -4.01389851
[109,] 1.35692028 1.76810047
[110,] -0.39657764 1.35692028
[111,] -0.25809252 -0.39657764
[112,] -3.42858677 -0.25809252
[113,] 1.22282409 -3.42858677
[114,] -0.45057697 1.22282409
[115,] 3.36296417 -0.45057697
[116,] 6.07006770 3.36296417
[117,] 0.12916912 6.07006770
[118,] -0.79359988 0.12916912
[119,] 5.24065412 -0.79359988
[120,] -0.61675797 5.24065412
[121,] 2.54769008 -0.61675797
[122,] -2.56867721 2.54769008
[123,] -1.83860691 -2.56867721
[124,] -0.38397437 -1.83860691
[125,] 2.38226856 -0.38397437
[126,] -1.11342712 2.38226856
[127,] 0.64384167 -1.11342712
[128,] 2.47352869 0.64384167
[129,] 0.52937038 2.47352869
[130,] 2.18835295 0.52937038
[131,] -1.27720368 2.18835295
[132,] -2.07593993 -1.27720368
[133,] 0.13458748 -2.07593993
[134,] 2.57280280 0.13458748
[135,] 1.79567474 2.57280280
[136,] -2.56226264 1.79567474
[137,] 3.33652579 -2.56226264
[138,] 1.52524902 3.33652579
[139,] 0.49806173 1.52524902
[140,] 3.67405999 0.49806173
[141,] -0.43509452 3.67405999
[142,] 1.52414168 -0.43509452
[143,] -2.75895151 1.52414168
[144,] 2.06838995 -2.75895151
[145,] 0.48916005 2.06838995
[146,] 3.05369514 0.48916005
[147,] -0.78859144 3.05369514
[148,] -0.17105831 -0.78859144
[149,] -4.85133866 -0.17105831
[150,] 3.83956515 -4.85133866
[151,] 4.94594615 3.83956515
[152,] -3.78148755 4.94594615
[153,] -0.82864390 -3.78148755
[154,] -2.29298209 -0.82864390
[155,] -1.33927162 -2.29298209
[156,] -4.47526183 -1.33927162
[157,] 3.11610278 -4.47526183
[158,] -2.39414239 3.11610278
[159,] -0.45432669 -2.39414239
[160,] -3.62849699 -0.45432669
[161,] -1.48370699 -3.62849699
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.80378102 1.02937257
2 -0.93751156 -2.80378102
3 0.27947856 -0.93751156
4 0.13955815 0.27947856
5 -2.40827045 0.13955815
6 -3.12534366 -2.40827045
7 -4.87895826 -3.12534366
8 4.48406317 -4.87895826
9 0.27376031 4.48406317
10 2.72899131 0.27376031
11 2.24377737 2.72899131
12 -2.64845978 2.24377737
13 0.61263000 -2.64845978
14 1.22416981 0.61263000
15 -0.39286687 1.22416981
16 2.77056826 -0.39286687
17 4.70441825 2.77056826
18 3.42048987 4.70441825
19 0.95927194 3.42048987
20 -1.50567214 0.95927194
21 3.71122196 -1.50567214
22 -1.51107584 3.71122196
23 -2.19150739 -1.51107584
24 1.48358388 -2.19150739
25 -8.28975368 1.48358388
26 -8.03071583 -8.28975368
27 6.56503586 -8.03071583
28 2.77773475 6.56503586
29 -1.83733006 2.77773475
30 -1.35387882 -1.83733006
31 -0.32794300 -1.35387882
32 -6.75502398 -0.32794300
33 -0.38609383 -6.75502398
34 0.34119603 -0.38609383
35 -4.74879483 0.34119603
36 2.92622779 -4.74879483
37 -0.35212983 2.92622779
38 2.61601829 -0.35212983
39 -0.62100761 2.61601829
40 3.04987475 -0.62100761
41 4.06594802 3.04987475
42 -0.22045388 4.06594802
43 0.24966480 -0.22045388
44 0.18417130 0.24966480
45 2.28499135 0.18417130
46 -3.45683852 2.28499135
47 1.79283873 -3.45683852
48 -1.44113978 1.79283873
49 5.88277303 -1.44113978
50 0.01116871 5.88277303
51 0.91478979 0.01116871
52 3.50446080 0.91478979
53 -12.29236820 3.50446080
54 1.03478399 -12.29236820
55 0.51063345 1.03478399
56 0.25231235 0.51063345
57 0.93477249 0.25231235
58 1.69723686 0.93477249
59 6.88214652 1.69723686
60 4.54271546 6.88214652
61 1.50979615 4.54271546
62 0.68621125 1.50979615
63 -5.93960962 0.68621125
64 -3.37080078 -5.93960962
65 1.01248027 -3.37080078
66 1.12802412 1.01248027
67 2.22802725 1.12802412
68 3.46236541 2.22802725
69 -1.25795624 3.46236541
70 3.45120298 -1.25795624
71 2.16132240 3.45120298
72 0.36892768 2.16132240
73 -4.68358582 0.36892768
74 2.88226161 -4.68358582
75 -1.98499792 2.88226161
76 -1.89770808 -1.98499792
77 1.86507221 -1.89770808
78 1.61547057 1.86507221
79 -8.67252299 1.61547057
80 -7.00166271 -8.67252299
81 -1.09411075 -7.00166271
82 1.03373351 -1.09411075
83 -2.54881186 1.03373351
84 -1.11556561 -2.54881186
85 1.74976841 -1.11556561
86 3.07856983 1.74976841
87 0.81016430 3.07856983
88 4.79539450 0.81016430
89 5.60178049 4.79539450
90 -7.72486389 5.60178049
91 -1.59332715 -7.72486389
92 -0.65740760 -1.59332715
93 -3.04989561 -0.65740760
94 -0.18681696 -3.04989561
95 0.55770026 -0.18681696
96 -1.81419290 0.55770026
97 -1.27017791 -1.81419290
98 -1.79727281 -1.27017791
99 1.91295633 -1.79727281
100 -1.59470327 1.91295633
101 -1.31830332 -1.59470327
102 1.42496163 -1.31830332
103 2.07434025 1.42496163
104 -0.89931462 2.07434025
105 -0.81149076 -0.89931462
106 -4.72354205 -0.81149076
107 -4.01389851 -4.72354205
108 1.76810047 -4.01389851
109 1.35692028 1.76810047
110 -0.39657764 1.35692028
111 -0.25809252 -0.39657764
112 -3.42858677 -0.25809252
113 1.22282409 -3.42858677
114 -0.45057697 1.22282409
115 3.36296417 -0.45057697
116 6.07006770 3.36296417
117 0.12916912 6.07006770
118 -0.79359988 0.12916912
119 5.24065412 -0.79359988
120 -0.61675797 5.24065412
121 2.54769008 -0.61675797
122 -2.56867721 2.54769008
123 -1.83860691 -2.56867721
124 -0.38397437 -1.83860691
125 2.38226856 -0.38397437
126 -1.11342712 2.38226856
127 0.64384167 -1.11342712
128 2.47352869 0.64384167
129 0.52937038 2.47352869
130 2.18835295 0.52937038
131 -1.27720368 2.18835295
132 -2.07593993 -1.27720368
133 0.13458748 -2.07593993
134 2.57280280 0.13458748
135 1.79567474 2.57280280
136 -2.56226264 1.79567474
137 3.33652579 -2.56226264
138 1.52524902 3.33652579
139 0.49806173 1.52524902
140 3.67405999 0.49806173
141 -0.43509452 3.67405999
142 1.52414168 -0.43509452
143 -2.75895151 1.52414168
144 2.06838995 -2.75895151
145 0.48916005 2.06838995
146 3.05369514 0.48916005
147 -0.78859144 3.05369514
148 -0.17105831 -0.78859144
149 -4.85133866 -0.17105831
150 3.83956515 -4.85133866
151 4.94594615 3.83956515
152 -3.78148755 4.94594615
153 -0.82864390 -3.78148755
154 -2.29298209 -0.82864390
155 -1.33927162 -2.29298209
156 -4.47526183 -1.33927162
157 3.11610278 -4.47526183
158 -2.39414239 3.11610278
159 -0.45432669 -2.39414239
160 -3.62849699 -0.45432669
161 -1.48370699 -3.62849699
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7d9z61321982223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8d08t1321982223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9rtuj1321982223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/100iq21321982223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11f0um1321982223.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12z6fv1321982223.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/132wc21321982223.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14d4rq1321982223.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15drxr1321982223.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16r5lj1321982223.tab")
+ }
>
> try(system("convert tmp/1frk01321982222.ps tmp/1frk01321982222.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r7o61321982222.ps tmp/2r7o61321982222.png",intern=TRUE))
character(0)
> try(system("convert tmp/32w9r1321982222.ps tmp/32w9r1321982222.png",intern=TRUE))
character(0)
> try(system("convert tmp/46zct1321982222.ps tmp/46zct1321982222.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wvhg1321982222.ps tmp/5wvhg1321982222.png",intern=TRUE))
character(0)
> try(system("convert tmp/60gsp1321982223.ps tmp/60gsp1321982223.png",intern=TRUE))
character(0)
> try(system("convert tmp/7d9z61321982223.ps tmp/7d9z61321982223.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d08t1321982223.ps tmp/8d08t1321982223.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rtuj1321982223.ps tmp/9rtuj1321982223.png",intern=TRUE))
character(0)
> try(system("convert tmp/100iq21321982223.ps tmp/100iq21321982223.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.848 0.664 7.499