R version 2.12.0 (2010-10-15)
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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+ ,0
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+ ,51.1313
+ ,46.459
+ ,49.5164
+ ,52.0729
+ ,0
+ ,49.7654
+ ,46.9331
+ ,51.1313
+ ,46.459
+ ,51.6425
+ ,0
+ ,52.0729
+ ,49.7654
+ ,46.9331
+ ,51.1313
+ ,53.9784
+ ,0
+ ,51.6425
+ ,52.0729
+ ,49.7654
+ ,46.9331
+ ,54.4891
+ ,0
+ ,53.9784
+ ,51.6425
+ ,52.0729
+ ,49.7654
+ ,59.0665
+ ,0
+ ,54.4891
+ ,53.9784
+ ,51.6425
+ ,52.0729
+ ,63.9929
+ ,0
+ ,59.0665
+ ,54.4891
+ ,53.9784
+ ,51.6425
+ ,61.6167
+ ,0
+ ,63.9929
+ ,59.0665
+ ,54.4891
+ ,53.9784
+ ,62.1816
+ ,0
+ ,61.6167
+ ,63.9929
+ ,59.0665
+ ,54.4891
+ ,58.9178
+ ,0
+ ,62.1816
+ ,61.6167
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+ ,59.0665
+ ,59.9151
+ ,0
+ ,58.9178
+ ,62.1816
+ ,61.6167
+ ,63.9929)
+ ,dim=c(6
+ ,292)
+ ,dimnames=list(c('Prijs'
+ ,'Crisis'
+ ,'1'
+ ,'2'
+ ,'3'
+ ,'4')
+ ,1:292))
> y <- array(NA,dim=c(6,292),dimnames=list(c('Prijs','Crisis','1','2','3','4'),1:292))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Prijs Crisis 1 2 3 4 M1 M2 M3 M4 M5 M6 M7 M8 M9
1 14.2754 0 12.5621 13.3181 16.8622 25.2609 1 0 0 0 0 0 0 0 0
2 12.2961 0 14.2754 12.5621 13.3181 16.8622 0 1 0 0 0 0 0 0 0
3 10.0871 0 12.2961 14.2754 12.5621 13.3181 0 0 1 0 0 0 0 0 0
4 13.5117 0 10.0871 12.2961 14.2754 12.5621 0 0 0 1 0 0 0 0 0
5 13.9921 0 13.5117 10.0871 12.2961 14.2754 0 0 0 0 1 0 0 0 0
6 13.6932 0 13.9921 13.5117 10.0871 12.2961 0 0 0 0 0 1 0 0 0
7 14.4211 0 13.6932 13.9921 13.5117 10.0871 0 0 0 0 0 0 1 0 0
8 15.3397 0 14.4211 13.6932 13.9921 13.5117 0 0 0 0 0 0 0 1 0
9 16.5182 0 15.3397 14.4211 13.6932 13.9921 0 0 0 0 0 0 0 0 1
10 15.2809 0 16.5182 15.3397 14.4211 13.6932 0 0 0 0 0 0 0 0 0
11 15.6204 0 15.2809 16.5182 15.3397 14.4211 0 0 0 0 0 0 0 0 0
12 15.5698 0 15.6204 15.2809 16.5182 15.3397 0 0 0 0 0 0 0 0 0
13 15.9458 0 15.5698 15.6204 15.2809 16.5182 1 0 0 0 0 0 0 0 0
14 16.4063 0 15.9458 15.5698 15.6204 15.2809 0 1 0 0 0 0 0 0 0
15 17.5500 0 16.4063 15.9458 15.5698 15.6204 0 0 1 0 0 0 0 0 0
16 17.0353 0 17.5500 16.4063 15.9458 15.5698 0 0 0 1 0 0 0 0 0
17 16.0591 0 17.0353 17.5500 16.4063 15.9458 0 0 0 0 1 0 0 0 0
18 16.3643 0 16.0591 17.0353 17.5500 16.4063 0 0 0 0 0 1 0 0 0
19 14.6527 0 16.3643 16.0591 17.0353 17.5500 0 0 0 0 0 0 1 0 0
20 13.4664 0 14.6527 16.3643 16.0591 17.0353 0 0 0 0 0 0 0 1 0
21 13.3266 0 13.4664 14.6527 16.3643 16.0591 0 0 0 0 0 0 0 0 1
22 13.1823 0 13.3266 13.4664 14.6527 16.3643 0 0 0 0 0 0 0 0 0
23 12.1130 0 13.1823 13.3266 13.4664 14.6527 0 0 0 0 0 0 0 0 0
24 13.3540 0 12.1130 13.1823 13.3266 13.4664 0 0 0 0 0 0 0 0 0
25 13.4537 0 13.3540 12.1130 13.1823 13.3266 1 0 0 0 0 0 0 0 0
26 13.2715 0 13.4537 13.3540 12.1130 13.1823 0 1 0 0 0 0 0 0 0
27 13.1959 0 13.2715 13.4537 13.3540 12.1130 0 0 1 0 0 0 0 0 0
28 13.5542 0 13.1959 13.2715 13.4537 13.3540 0 0 0 1 0 0 0 0 0
29 12.1240 0 13.5542 13.1959 13.2715 13.4537 0 0 0 0 1 0 0 0 0
30 10.9670 0 12.1240 13.5542 13.1959 13.2715 0 0 0 0 0 1 0 0 0
31 10.9201 0 10.9670 12.1240 13.5542 13.1959 0 0 0 0 0 0 1 0 0
32 12.5971 0 10.9201 10.9670 12.1240 13.5542 0 0 0 0 0 0 0 1 0
33 14.3177 0 12.5971 10.9201 10.9670 12.1240 0 0 0 0 0 0 0 0 1
34 14.2471 0 14.3177 12.5971 10.9201 10.9670 0 0 0 0 0 0 0 0 0
35 16.0926 0 14.2471 14.3177 12.5971 10.9201 0 0 0 0 0 0 0 0 0
36 17.1334 0 16.0926 14.2471 14.3177 12.5971 0 0 0 0 0 0 0 0 0
37 16.5866 0 17.1334 16.0926 14.2471 14.3177 1 0 0 0 0 0 0 0 0
38 16.3610 0 16.5866 17.1334 16.0926 14.2471 0 1 0 0 0 0 0 0 0
39 15.8494 0 16.3610 16.5866 17.1334 16.0926 0 0 1 0 0 0 0 0 0
40 15.5932 0 15.8494 16.3610 16.5866 17.1334 0 0 0 1 0 0 0 0 0
41 16.6387 0 15.5932 15.8494 16.3610 16.5866 0 0 0 0 1 0 0 0 0
42 16.8312 0 16.6387 15.5932 15.8494 16.3610 0 0 0 0 0 1 0 0 0
43 16.5044 0 16.8312 16.6387 15.5932 15.8494 0 0 0 0 0 0 1 0 0
44 16.5556 0 16.5044 16.8312 16.6387 15.5932 0 0 0 0 0 0 0 1 0
45 16.7469 0 16.5556 16.5044 16.8312 16.6387 0 0 0 0 0 0 0 0 1
46 15.9543 0 16.7469 16.5556 16.5044 16.8312 0 0 0 0 0 0 0 0 0
47 15.5888 0 15.9543 16.7469 16.5556 16.5044 0 0 0 0 0 0 0 0 0
48 14.3945 0 15.5888 15.9543 16.7469 16.5556 0 0 0 0 0 0 0 0 0
49 13.8889 0 14.3945 15.5888 15.9543 16.7469 1 0 0 0 0 0 0 0 0
50 12.9999 0 13.8889 14.3945 15.5888 15.9543 0 1 0 0 0 0 0 0 0
51 14.1022 0 12.9999 13.8889 14.3945 15.5888 0 0 1 0 0 0 0 0 0
52 19.6245 0 14.1022 12.9999 13.8889 14.3945 0 0 0 1 0 0 0 0 0
53 24.7658 0 19.6245 14.1022 12.9999 13.8889 0 0 0 0 1 0 0 0 0
54 25.9843 0 24.7658 19.6245 14.1022 12.9999 0 0 0 0 0 1 0 0 0
55 22.9635 0 25.9843 24.7658 19.6245 14.1022 0 0 0 0 0 0 1 0 0
56 19.6288 0 22.9635 25.9843 24.7658 19.6245 0 0 0 0 0 0 0 1 0
57 17.3363 0 19.6288 22.9635 25.9843 24.7658 0 0 0 0 0 0 0 0 1
58 13.3110 0 17.3363 19.6288 22.9635 25.9843 0 0 0 0 0 0 0 0 0
59 14.6359 0 13.3110 17.3363 19.6288 22.9635 0 0 0 0 0 0 0 0 0
60 15.8340 0 14.6359 13.3110 17.3363 19.6288 0 0 0 0 0 0 0 0 0
61 16.2415 0 15.8340 14.6359 13.3110 17.3363 1 0 0 0 0 0 0 0 0
62 15.9808 0 16.2415 15.8340 14.6359 13.3110 0 1 0 0 0 0 0 0 0
63 16.9726 0 15.9808 16.2415 15.8340 14.6359 0 0 1 0 0 0 0 0 0
64 16.8708 0 16.9726 15.9808 16.2415 15.8340 0 0 0 1 0 0 0 0 0
65 16.9230 0 16.8708 16.9726 15.9808 16.2415 0 0 0 0 1 0 0 0 0
66 18.1138 0 16.9230 16.8708 16.9726 15.9808 0 0 0 0 0 1 0 0 0
67 16.7716 0 18.1138 16.9230 16.8708 16.9726 0 0 0 0 0 0 1 0 0
68 14.0299 0 16.7716 18.1138 16.9230 16.8708 0 0 0 0 0 0 0 1 0
69 13.8220 0 14.0299 16.7716 18.1138 16.9230 0 0 0 0 0 0 0 0 1
70 14.2537 0 13.8220 14.0299 16.7716 18.1138 0 0 0 0 0 0 0 0 0
71 14.3985 0 14.2537 13.8220 14.0299 16.7716 0 0 0 0 0 0 0 0 0
72 15.2454 0 14.3985 14.2537 13.8220 14.0299 0 0 0 0 0 0 0 0 0
73 15.6683 0 15.2454 14.3985 14.2537 13.8220 1 0 0 0 0 0 0 0 0
74 16.1721 0 15.6683 15.2454 14.3985 14.2537 0 1 0 0 0 0 0 0 0
75 14.8679 0 16.1721 15.6683 15.2454 14.3985 0 0 1 0 0 0 0 0 0
76 14.1948 0 14.8679 16.1721 15.6683 15.2454 0 0 0 1 0 0 0 0 0
77 14.7056 0 14.1948 14.8679 16.1721 15.6683 0 0 0 0 1 0 0 0 0
78 15.3819 0 14.7056 14.1948 14.8679 16.1721 0 0 0 0 0 1 0 0 0
79 15.5001 0 15.3819 14.7056 14.1948 14.8679 0 0 0 0 0 0 1 0 0
80 14.7886 0 15.5001 15.3819 14.7056 14.1948 0 0 0 0 0 0 0 1 0
81 14.5630 0 14.7886 15.5001 15.3819 14.7056 0 0 0 0 0 0 0 0 1
82 15.5528 0 14.5630 14.7886 15.5001 15.3819 0 0 0 0 0 0 0 0 0
83 15.9781 0 15.5528 14.5630 14.7886 15.5001 0 0 0 0 0 0 0 0 0
84 15.5139 0 15.9781 15.5528 14.5630 14.7886 0 0 0 0 0 0 0 0 0
85 15.3603 0 15.5139 15.9781 15.5528 14.5630 1 0 0 0 0 0 0 0 0
86 15.0512 0 15.3603 15.5139 15.9781 15.5528 0 1 0 0 0 0 0 0 0
87 14.7874 0 15.0512 15.3603 15.5139 15.9781 0 0 1 0 0 0 0 0 0
88 14.9624 0 14.7874 15.0512 15.3603 15.5139 0 0 0 1 0 0 0 0 0
89 13.9188 0 14.9624 14.7874 15.0512 15.3603 0 0 0 0 1 0 0 0 0
90 14.5146 0 13.9188 14.9624 14.7874 15.0512 0 0 0 0 0 1 0 0 0
91 13.7115 0 14.5146 13.9188 14.9624 14.7874 0 0 0 0 0 0 1 0 0
92 12.0738 0 13.7115 14.5146 13.9188 14.9624 0 0 0 0 0 0 0 1 0
93 12.5688 0 12.0738 13.7115 14.5146 13.9188 0 0 0 0 0 0 0 0 1
94 12.2547 0 12.5688 12.0738 13.7115 14.5146 0 0 0 0 0 0 0 0 0
95 11.8741 0 12.2547 12.5688 12.0738 13.7115 0 0 0 0 0 0 0 0 0
96 13.0261 0 11.8741 12.2547 12.5688 12.0738 0 0 0 0 0 0 0 0 0
97 13.8681 0 13.0261 11.8741 12.2547 12.5688 1 0 0 0 0 0 0 0 0
98 14.2137 0 13.8681 13.0261 11.8741 12.2547 0 1 0 0 0 0 0 0 0
99 14.4743 0 14.2137 13.8681 13.0261 11.8741 0 0 1 0 0 0 0 0 0
100 13.9764 0 14.4743 14.2137 13.8681 13.0261 0 0 0 1 0 0 0 0 0
101 13.1558 0 13.9764 14.4743 14.2137 13.8681 0 0 0 0 1 0 0 0 0
102 13.0991 0 13.1558 13.9764 14.4743 14.2137 0 0 0 0 0 1 0 0 0
103 13.7831 0 13.0991 13.1558 13.9764 14.4743 0 0 0 0 0 0 1 0 0
104 13.2546 0 13.7831 13.0991 13.1558 13.9764 0 0 0 0 0 0 0 1 0
105 13.3426 0 13.2546 13.7831 13.0991 13.1558 0 0 0 0 0 0 0 0 1
106 13.5011 0 13.3426 13.2546 13.7831 13.0991 0 0 0 0 0 0 0 0 0
107 12.8245 0 13.5011 13.3426 13.2546 13.7831 0 0 0 0 0 0 0 0 0
108 13.6596 0 12.8245 13.5011 13.3426 13.2546 0 0 0 0 0 0 0 0 0
109 13.8754 0 13.6596 12.8245 13.5011 13.3426 1 0 0 0 0 0 0 0 0
110 12.9011 0 13.8754 13.6596 12.8245 13.5011 0 1 0 0 0 0 0 0 0
111 11.8710 0 12.9011 13.8754 13.6596 12.8245 0 0 1 0 0 0 0 0 0
112 12.3954 0 11.8710 12.9011 13.8754 13.6596 0 0 0 1 0 0 0 0 0
113 12.8179 0 12.3954 11.8710 12.9011 13.8754 0 0 0 0 1 0 0 0 0
114 12.1219 0 12.8179 12.3954 11.8710 12.9011 0 0 0 0 0 1 0 0 0
115 12.6176 0 12.1219 12.8179 12.3954 11.8710 0 0 0 0 0 0 1 0 0
116 13.6362 0 12.6176 12.1219 12.8179 12.3954 0 0 0 0 0 0 0 1 0
117 13.5422 0 13.6362 12.6176 12.1219 12.8179 0 0 0 0 0 0 0 0 1
118 13.3620 0 13.5422 13.6362 12.6176 12.1219 0 0 0 0 0 0 0 0 0
119 14.5735 0 13.3620 13.5422 13.6362 12.6176 0 0 0 0 0 0 0 0 0
120 15.8357 0 14.5735 13.3620 13.5422 13.6362 0 0 0 0 0 0 0 0 0
121 14.9927 0 15.8357 14.5735 13.3620 13.5422 1 0 0 0 0 0 0 0 0
122 14.5078 0 14.9927 15.8357 14.5735 13.3620 0 1 0 0 0 0 0 0 0
123 15.2648 0 14.5078 14.9927 15.8357 14.5735 0 0 1 0 0 0 0 0 0
124 15.7163 0 15.2648 14.5078 14.9927 15.8357 0 0 0 1 0 0 0 0 0
125 17.7969 0 15.7163 15.2648 14.5078 14.9927 0 0 0 0 1 0 0 0 0
126 19.0408 0 17.7969 15.7163 15.2648 14.5078 0 0 0 0 0 1 0 0 0
127 17.8571 0 19.0408 17.7969 15.7163 15.2648 0 0 0 0 0 0 1 0 0
128 18.8150 0 17.8571 19.0408 17.7969 15.7163 0 0 0 0 0 0 0 1 0
129 19.0961 0 18.8150 17.8571 19.0408 17.7969 0 0 0 0 0 0 0 0 1
130 17.6215 0 19.0961 18.8150 17.8571 19.0408 0 0 0 0 0 0 0 0 0
131 17.0163 0 17.6215 19.0961 18.8150 17.8571 0 0 0 0 0 0 0 0 0
132 15.8286 0 17.0163 17.6215 19.0961 18.8150 0 0 0 0 0 0 0 0 0
133 16.7818 0 15.8286 17.0163 17.6215 19.0961 1 0 0 0 0 0 0 0 0
134 15.8726 0 16.7818 15.8286 17.0163 17.6215 0 1 0 0 0 0 0 0 0
135 16.6621 0 15.8726 16.7818 15.8286 17.0163 0 0 1 0 0 0 0 0 0
136 17.5709 0 16.6621 15.8726 16.7818 15.8286 0 0 0 1 0 0 0 0 0
137 16.9914 0 17.5709 16.6621 15.8726 16.7818 0 0 0 0 1 0 0 0 0
138 18.0412 0 16.9914 17.5709 16.6621 15.8726 0 0 0 0 0 1 0 0 0
139 16.9764 0 18.0412 16.9914 17.5709 16.6621 0 0 0 0 0 0 1 0 0
140 15.7649 0 16.9764 18.0412 16.9914 17.5709 0 0 0 0 0 0 0 1 0
141 14.3928 0 15.7649 16.9764 18.0412 16.9914 0 0 0 0 0 0 0 0 1
142 13.5061 0 14.3928 15.7649 16.9764 18.0412 0 0 0 0 0 0 0 0 0
143 12.7433 0 13.5061 14.3928 15.7649 16.9764 0 0 0 0 0 0 0 0 0
144 13.0170 0 12.7433 13.5061 14.3928 15.7649 0 0 0 0 0 0 0 0 0
145 13.0171 0 13.0170 12.7433 13.5061 14.3928 1 0 0 0 0 0 0 0 0
146 12.2412 0 13.0171 13.0170 12.7433 13.5061 0 1 0 0 0 0 0 0 0
147 11.8878 0 12.2412 13.0171 13.0170 12.7433 0 0 1 0 0 0 0 0 0
148 11.2511 0 11.8878 12.2412 13.0171 13.0170 0 0 0 1 0 0 0 0 0
149 11.8583 0 11.2511 11.8878 12.2412 13.0171 0 0 0 0 1 0 0 0 0
150 11.1202 0 11.8583 11.2511 11.8878 12.2412 0 0 0 0 0 1 0 0 0
151 10.1850 0 11.1202 11.8583 11.2511 11.8878 0 0 0 0 0 0 1 0 0
152 8.7563 0 10.1850 11.1202 11.8583 11.2511 0 0 0 0 0 0 0 1 0
153 9.5267 0 8.7563 10.1850 11.1202 11.8583 0 0 0 0 0 0 0 0 1
154 9.4106 0 9.5267 8.7563 10.1850 11.1202 0 0 0 0 0 0 0 0 0
155 11.8780 0 9.4106 9.5267 8.7563 10.1850 0 0 0 0 0 0 0 0 0
156 14.4228 0 11.8780 9.4106 9.5267 8.7563 0 0 0 0 0 0 0 0 0
157 14.8960 0 14.4228 11.8780 9.4106 9.5267 1 0 0 0 0 0 0 0 0
158 15.6664 0 14.8960 14.4228 11.8780 9.4106 0 1 0 0 0 0 0 0 0
159 18.1470 0 15.6664 14.8960 14.4228 11.8780 0 0 1 0 0 0 0 0 0
160 19.3069 0 18.1470 15.6664 14.8960 14.4228 0 0 0 1 0 0 0 0 0
161 21.6807 0 19.3069 18.1470 15.6664 14.8960 0 0 0 0 1 0 0 0 0
162 20.7934 0 21.6807 19.3069 18.1470 15.6664 0 0 0 0 0 1 0 0 0
163 23.4241 0 20.7934 21.6807 19.3069 18.1470 0 0 0 0 0 0 1 0 0
164 24.8273 0 23.4241 20.7934 21.6807 19.3069 0 0 0 0 0 0 0 1 0
165 24.9276 0 24.8273 23.4241 20.7934 21.6807 0 0 0 0 0 0 0 0 1
166 27.4256 0 24.9276 24.8273 23.4241 20.7934 0 0 0 0 0 0 0 0 0
167 28.1746 0 27.4256 24.9276 24.8273 23.4241 0 0 0 0 0 0 0 0 0
168 24.5615 0 28.1746 27.4256 24.9276 24.8273 0 0 0 0 0 0 0 0 0
169 30.2532 0 24.5615 28.1746 27.4256 24.9276 1 0 0 0 0 0 0 0 0
170 31.2514 0 30.2532 24.5615 28.1746 27.4256 0 1 0 0 0 0 0 0 0
171 30.4733 0 31.2514 30.2532 24.5615 28.1746 0 0 1 0 0 0 0 0 0
172 33.3047 0 30.4733 31.2514 30.2532 24.5615 0 0 0 1 0 0 0 0 0
173 37.2103 0 33.3047 30.4733 31.2514 30.2532 0 0 0 0 1 0 0 0 0
174 36.7711 0 37.2103 33.3047 30.4733 31.2514 0 0 0 0 0 1 0 0 0
175 37.7163 0 36.7711 37.2103 33.3047 30.4733 0 0 0 0 0 0 1 0 0
176 28.8488 0 37.7163 36.7711 37.2103 33.3047 0 0 0 0 0 0 0 1 0
177 27.4682 0 28.8488 37.7163 36.7711 37.2103 0 0 0 0 0 0 0 0 1
178 29.8793 0 27.4682 28.8488 37.7163 36.7711 0 0 0 0 0 0 0 0 0
179 28.0598 0 29.8793 27.4682 28.8488 37.7163 0 0 0 0 0 0 0 0 0
180 29.7733 0 28.0598 29.8793 27.4682 28.8488 0 0 0 0 0 0 0 0 0
181 32.6926 0 29.7733 28.0598 29.8793 27.4682 1 0 0 0 0 0 0 0 0
182 32.4803 0 32.6926 29.7733 28.0598 29.8793 0 1 0 0 0 0 0 0 0
183 29.4168 0 32.4803 32.6926 29.7733 28.0598 0 0 1 0 0 0 0 0 0
184 28.7054 0 29.4168 32.4803 32.6926 29.7733 0 0 0 1 0 0 0 0 0
185 28.7614 0 28.7054 29.4168 32.4803 32.6926 0 0 0 0 1 0 0 0 0
186 23.8075 0 28.7614 28.7054 29.4168 32.4803 0 0 0 0 0 1 0 0 0
187 21.6987 0 23.8075 28.7614 28.7054 29.4168 0 0 0 0 0 0 1 0 0
188 21.4691 0 21.6987 23.8075 28.7614 28.7054 0 0 0 0 0 0 0 1 0
189 22.5709 0 21.4691 21.6987 23.8075 28.7614 0 0 0 0 0 0 0 0 1
190 23.4546 0 22.5709 21.4691 21.6987 23.8075 0 0 0 0 0 0 0 0 0
191 27.8976 0 23.4546 22.5709 21.4691 21.6987 0 0 0 0 0 0 0 0 0
192 29.2965 0 27.8976 23.4546 22.5709 21.4691 0 0 0 0 0 0 0 0 0
193 28.1191 0 29.2965 27.8976 23.4546 22.5709 1 0 0 0 0 0 0 0 0
194 25.8120 0 28.1191 29.2965 27.8976 23.4546 0 1 0 0 0 0 0 0 0
195 25.9310 0 25.8120 28.1191 29.2965 27.8976 0 0 1 0 0 0 0 0 0
196 26.9925 0 25.9310 25.8120 28.1191 29.2965 0 0 0 1 0 0 0 0 0
197 28.9213 0 26.9925 25.9310 25.8120 28.1191 0 0 0 0 1 0 0 0 0
198 27.8898 0 28.9213 26.9925 25.9310 25.8120 0 0 0 0 0 1 0 0 0
199 24.2473 0 27.8898 28.9213 26.9925 25.9310 0 0 0 0 0 0 1 0 0
200 27.1056 0 24.2473 27.8898 28.9213 26.9925 0 0 0 0 0 0 0 1 0
201 28.2833 0 27.1056 24.2473 27.8898 28.9213 0 0 0 0 0 0 0 0 1
202 29.8076 0 28.2833 27.1056 24.2473 27.8898 0 0 0 0 0 0 0 0 0
203 27.1826 0 29.8076 28.2833 27.1056 24.2473 0 0 0 0 0 0 0 0 0
204 22.8764 0 27.1826 29.8076 28.2833 27.1056 0 0 0 0 0 0 0 0 0
205 21.9380 0 22.8764 27.1826 29.8076 28.2833 1 0 0 0 0 0 0 0 0
206 23.3076 0 21.9380 22.8764 27.1826 29.8076 0 1 0 0 0 0 0 0 0
207 24.9572 0 23.3076 21.9380 22.8764 27.1826 0 0 1 0 0 0 0 0 0
208 26.4694 0 24.9572 23.3076 21.9380 22.8764 0 0 0 1 0 0 0 0 0
209 23.9297 0 26.4694 24.9572 23.3076 21.9380 0 0 0 0 1 0 0 0 0
210 24.7033 0 23.9297 26.4694 24.9572 23.3076 0 0 0 0 0 1 0 0 0
211 24.6460 0 24.7033 23.9297 26.4694 24.9572 0 0 0 0 0 0 1 0 0
212 24.0496 0 24.6460 24.7033 23.9297 26.4694 0 0 0 0 0 0 0 1 0
213 24.2096 0 24.0496 24.6460 24.7033 23.9297 0 0 0 0 0 0 0 0 1
214 24.0717 0 24.2096 24.0496 24.6460 24.7033 0 0 0 0 0 0 0 0 0
215 26.6673 0 24.0717 24.2096 24.0496 24.6460 0 0 0 0 0 0 0 0 0
216 27.6457 0 26.6673 24.0717 24.2096 24.0496 0 0 0 0 0 0 0 0 0
217 30.8791 0 27.6457 26.6673 24.0717 24.2096 1 0 0 0 0 0 0 0 0
218 29.3278 0 30.8791 27.6457 26.6673 24.0717 0 1 0 0 0 0 0 0 0
219 30.7268 0 29.3278 30.8791 27.6457 26.6673 0 0 1 0 0 0 0 0 0
220 34.1204 0 30.7268 29.3278 30.8791 27.6457 0 0 0 1 0 0 0 0 0
221 35.0205 0 34.1204 30.7268 29.3278 30.8791 0 0 0 0 1 0 0 0 0
222 39.3565 0 35.0205 34.1204 30.7268 29.3278 0 0 0 0 0 1 0 0 0
223 34.4724 0 39.3565 35.0205 34.1204 30.7268 0 0 0 0 0 0 1 0 0
224 29.9762 0 34.4724 39.3565 35.0205 34.1204 0 0 0 0 0 0 0 1 0
225 33.6008 0 29.9762 34.4724 39.3565 35.0205 0 0 0 0 0 0 0 0 1
226 35.2464 0 33.6008 29.9762 34.4724 39.3565 0 0 0 0 0 0 0 0 0
227 40.4137 0 35.2464 33.6008 29.9762 34.4724 0 0 0 0 0 0 0 0 0
228 41.3922 0 40.4137 35.2464 33.6008 29.9762 0 0 0 0 0 0 0 0 0
229 39.4243 0 41.3922 40.4137 35.2464 33.6008 1 0 0 0 0 0 0 0 0
230 45.7259 0 39.4243 41.3922 40.4137 35.2464 0 1 0 0 0 0 0 0 0
231 48.2549 0 45.7259 39.4243 41.3922 40.4137 0 0 1 0 0 0 0 0 0
232 52.0461 0 48.2549 45.7259 39.4243 41.3922 0 0 0 1 0 0 0 0 0
233 52.1871 0 52.0461 48.2549 45.7259 39.4243 0 0 0 0 1 0 0 0 0
234 49.3474 0 52.1871 52.0461 48.2549 45.7259 0 0 0 0 0 1 0 0 0
235 47.8653 0 49.3474 52.1871 52.0461 48.2549 0 0 0 0 0 0 1 0 0
236 48.5179 0 47.8653 49.3474 52.1871 52.0461 0 0 0 0 0 0 0 1 0
237 52.4815 0 48.5179 47.8653 49.3474 52.1871 0 0 0 0 0 0 0 0 1
238 51.8171 0 52.4815 48.5179 47.8653 49.3474 0 0 0 0 0 0 0 0 0
239 52.5811 0 51.8171 52.4815 48.5179 47.8653 0 0 0 0 0 0 0 0 0
240 57.5617 0 52.5811 51.8171 52.4815 48.5179 0 0 0 0 0 0 0 0 0
241 55.7091 0 57.5617 52.5811 51.8171 52.4815 1 0 0 0 0 0 0 0 0
242 55.4378 0 55.7091 57.5617 52.5811 51.8171 0 1 0 0 0 0 0 0 0
243 58.7493 0 55.4378 55.7091 57.5617 52.5811 0 0 1 0 0 0 0 0 0
244 57.7940 0 58.7493 55.4378 55.7091 57.5617 0 0 0 1 0 0 0 0 0
245 50.2820 0 57.7940 58.7493 55.4378 55.7091 0 0 0 0 1 0 0 0 0
246 47.6976 0 50.2820 57.7940 58.7493 55.4378 0 0 0 0 0 1 0 0 0
247 46.7381 0 47.6976 50.2820 57.7940 58.7493 0 0 0 0 0 0 1 0 0
248 47.4282 0 46.7381 47.6976 50.2820 57.7940 0 0 0 0 0 0 0 1 0
249 42.2269 0 47.4282 46.7381 47.6976 50.2820 0 0 0 0 0 0 0 0 1
250 44.9066 0 42.2269 47.4282 46.7381 47.6976 0 0 0 0 0 0 0 0 0
251 47.2648 0 44.9066 42.2269 47.4282 46.7381 0 0 0 0 0 0 0 0 0
252 50.2325 0 47.2648 44.9066 42.2269 47.4282 0 0 0 0 0 0 0 0 0
253 50.2504 0 50.2325 47.2648 44.9066 42.2269 1 0 0 0 0 0 0 0 0
254 52.5685 0 50.2504 50.2325 47.2648 44.9066 0 1 0 0 0 0 0 0 0
255 55.2325 0 52.5685 50.2504 50.2325 47.2648 0 0 1 0 0 0 0 0 0
256 52.3674 0 55.2325 52.5685 50.2504 50.2325 0 0 0 1 0 0 0 0 0
257 55.1692 0 52.3674 55.2325 52.5685 50.2504 0 0 0 0 1 0 0 0 0
258 57.7252 0 55.1692 52.3674 55.2325 52.5685 0 0 0 0 0 1 0 0 0
259 62.8232 0 57.7252 55.1692 52.3674 55.2325 0 0 0 0 0 0 1 0 0
260 62.7599 0 62.8232 57.7252 55.1692 52.3674 0 0 0 0 0 0 0 1 0
261 62.4387 0 62.7599 62.8232 57.7252 55.1692 0 0 0 0 0 0 0 0 1
262 64.0862 1 62.4387 62.7599 62.8232 57.7252 0 0 0 0 0 0 0 0 0
263 66.1209 1 64.0862 62.4387 62.7599 62.8232 0 0 0 0 0 0 0 0 0
264 69.8474 1 66.1209 64.0862 62.4387 62.7599 0 0 0 0 0 0 0 0 0
265 80.1039 1 69.8474 66.1209 64.0862 62.4387 1 0 0 0 0 0 0 0 0
266 85.9319 1 80.1039 69.8474 66.1209 64.0862 0 1 0 0 0 0 0 0 0
267 85.2843 1 85.9319 80.1039 69.8474 66.1209 0 0 1 0 0 0 0 0 0
268 77.0383 1 85.2843 85.9319 80.1039 69.8474 0 0 0 1 0 0 0 0 0
269 69.9981 1 77.0383 85.2843 85.9319 80.1039 0 0 0 0 1 0 0 0 0
270 55.2039 1 69.9981 77.0383 85.2843 85.9319 0 0 0 0 0 1 0 0 0
271 43.1188 1 55.2039 69.9981 77.0383 85.2843 0 0 0 0 0 0 1 0 0
272 32.0770 1 43.1188 55.2039 69.9981 77.0383 0 0 0 0 0 0 0 1 0
273 34.2974 1 32.0770 43.1188 55.2039 69.9981 0 0 0 0 0 0 0 0 1
274 34.5613 1 34.2974 32.0770 43.1188 55.2039 0 0 0 0 0 0 0 0 0
275 36.5235 1 34.5613 34.2974 32.0770 43.1188 0 0 0 0 0 0 0 0 0
276 39.0474 1 36.5235 34.5613 34.2974 32.0770 0 0 0 0 0 0 0 0 0
277 42.8033 1 39.0474 36.5235 34.5613 34.2974 1 0 0 0 0 0 0 0 0
278 49.5164 1 42.8033 39.0474 36.5235 34.5613 0 1 0 0 0 0 0 0 0
279 46.4590 0 49.5164 42.8033 39.0474 36.5235 0 0 1 0 0 0 0 0 0
280 51.1313 0 46.4590 49.5164 42.8033 39.0474 0 0 0 1 0 0 0 0 0
281 46.9331 0 51.1313 46.4590 49.5164 42.8033 0 0 0 0 1 0 0 0 0
282 49.7654 0 46.9331 51.1313 46.4590 49.5164 0 0 0 0 0 1 0 0 0
283 52.0729 0 49.7654 46.9331 51.1313 46.4590 0 0 0 0 0 0 1 0 0
284 51.6425 0 52.0729 49.7654 46.9331 51.1313 0 0 0 0 0 0 0 1 0
285 53.9784 0 51.6425 52.0729 49.7654 46.9331 0 0 0 0 0 0 0 0 1
286 54.4891 0 53.9784 51.6425 52.0729 49.7654 0 0 0 0 0 0 0 0 0
287 59.0665 0 54.4891 53.9784 51.6425 52.0729 0 0 0 0 0 0 0 0 0
288 63.9929 0 59.0665 54.4891 53.9784 51.6425 0 0 0 0 0 0 0 0 0
289 61.6167 0 63.9929 59.0665 54.4891 53.9784 1 0 0 0 0 0 0 0 0
290 62.1816 0 61.6167 63.9929 59.0665 54.4891 0 1 0 0 0 0 0 0 0
291 58.9178 0 62.1816 61.6167 63.9929 59.0665 0 0 1 0 0 0 0 0 0
292 59.9151 0 58.9178 62.1816 61.6167 63.9929 0 0 0 1 0 0 0 0 0
M10 M11 t
1 0 0 1
2 0 0 2
3 0 0 3
4 0 0 4
5 0 0 5
6 0 0 6
7 0 0 7
8 0 0 8
9 0 0 9
10 1 0 10
11 0 1 11
12 0 0 12
13 0 0 13
14 0 0 14
15 0 0 15
16 0 0 16
17 0 0 17
18 0 0 18
19 0 0 19
20 0 0 20
21 0 0 21
22 1 0 22
23 0 1 23
24 0 0 24
25 0 0 25
26 0 0 26
27 0 0 27
28 0 0 28
29 0 0 29
30 0 0 30
31 0 0 31
32 0 0 32
33 0 0 33
34 1 0 34
35 0 1 35
36 0 0 36
37 0 0 37
38 0 0 38
39 0 0 39
40 0 0 40
41 0 0 41
42 0 0 42
43 0 0 43
44 0 0 44
45 0 0 45
46 1 0 46
47 0 1 47
48 0 0 48
49 0 0 49
50 0 0 50
51 0 0 51
52 0 0 52
53 0 0 53
54 0 0 54
55 0 0 55
56 0 0 56
57 0 0 57
58 1 0 58
59 0 1 59
60 0 0 60
61 0 0 61
62 0 0 62
63 0 0 63
64 0 0 64
65 0 0 65
66 0 0 66
67 0 0 67
68 0 0 68
69 0 0 69
70 1 0 70
71 0 1 71
72 0 0 72
73 0 0 73
74 0 0 74
75 0 0 75
76 0 0 76
77 0 0 77
78 0 0 78
79 0 0 79
80 0 0 80
81 0 0 81
82 1 0 82
83 0 1 83
84 0 0 84
85 0 0 85
86 0 0 86
87 0 0 87
88 0 0 88
89 0 0 89
90 0 0 90
91 0 0 91
92 0 0 92
93 0 0 93
94 1 0 94
95 0 1 95
96 0 0 96
97 0 0 97
98 0 0 98
99 0 0 99
100 0 0 100
101 0 0 101
102 0 0 102
103 0 0 103
104 0 0 104
105 0 0 105
106 1 0 106
107 0 1 107
108 0 0 108
109 0 0 109
110 0 0 110
111 0 0 111
112 0 0 112
113 0 0 113
114 0 0 114
115 0 0 115
116 0 0 116
117 0 0 117
118 1 0 118
119 0 1 119
120 0 0 120
121 0 0 121
122 0 0 122
123 0 0 123
124 0 0 124
125 0 0 125
126 0 0 126
127 0 0 127
128 0 0 128
129 0 0 129
130 1 0 130
131 0 1 131
132 0 0 132
133 0 0 133
134 0 0 134
135 0 0 135
136 0 0 136
137 0 0 137
138 0 0 138
139 0 0 139
140 0 0 140
141 0 0 141
142 1 0 142
143 0 1 143
144 0 0 144
145 0 0 145
146 0 0 146
147 0 0 147
148 0 0 148
149 0 0 149
150 0 0 150
151 0 0 151
152 0 0 152
153 0 0 153
154 1 0 154
155 0 1 155
156 0 0 156
157 0 0 157
158 0 0 158
159 0 0 159
160 0 0 160
161 0 0 161
162 0 0 162
163 0 0 163
164 0 0 164
165 0 0 165
166 1 0 166
167 0 1 167
168 0 0 168
169 0 0 169
170 0 0 170
171 0 0 171
172 0 0 172
173 0 0 173
174 0 0 174
175 0 0 175
176 0 0 176
177 0 0 177
178 1 0 178
179 0 1 179
180 0 0 180
181 0 0 181
182 0 0 182
183 0 0 183
184 0 0 184
185 0 0 185
186 0 0 186
187 0 0 187
188 0 0 188
189 0 0 189
190 1 0 190
191 0 1 191
192 0 0 192
193 0 0 193
194 0 0 194
195 0 0 195
196 0 0 196
197 0 0 197
198 0 0 198
199 0 0 199
200 0 0 200
201 0 0 201
202 1 0 202
203 0 1 203
204 0 0 204
205 0 0 205
206 0 0 206
207 0 0 207
208 0 0 208
209 0 0 209
210 0 0 210
211 0 0 211
212 0 0 212
213 0 0 213
214 1 0 214
215 0 1 215
216 0 0 216
217 0 0 217
218 0 0 218
219 0 0 219
220 0 0 220
221 0 0 221
222 0 0 222
223 0 0 223
224 0 0 224
225 0 0 225
226 1 0 226
227 0 1 227
228 0 0 228
229 0 0 229
230 0 0 230
231 0 0 231
232 0 0 232
233 0 0 233
234 0 0 234
235 0 0 235
236 0 0 236
237 0 0 237
238 1 0 238
239 0 1 239
240 0 0 240
241 0 0 241
242 0 0 242
243 0 0 243
244 0 0 244
245 0 0 245
246 0 0 246
247 0 0 247
248 0 0 248
249 0 0 249
250 1 0 250
251 0 1 251
252 0 0 252
253 0 0 253
254 0 0 254
255 0 0 255
256 0 0 256
257 0 0 257
258 0 0 258
259 0 0 259
260 0 0 260
261 0 0 261
262 1 0 262
263 0 1 263
264 0 0 264
265 0 0 265
266 0 0 266
267 0 0 267
268 0 0 268
269 0 0 269
270 0 0 270
271 0 0 271
272 0 0 272
273 0 0 273
274 1 0 274
275 0 1 275
276 0 0 276
277 0 0 277
278 0 0 278
279 0 0 279
280 0 0 280
281 0 0 281
282 0 0 282
283 0 0 283
284 0 0 284
285 0 0 285
286 1 0 286
287 0 1 287
288 0 0 288
289 0 0 289
290 0 0 290
291 0 0 291
292 0 0 292
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Crisis `1` `2` `3` `4`
0.661626 0.018617 1.274912 -0.241332 -0.090013 0.006894
M1 M2 M3 M4 M5 M6
-0.111843 -0.301474 -0.680435 0.002097 -0.888811 -1.101792
M7 M8 M9 M10 M11 t
-1.321549 -1.459034 0.097913 -0.477650 0.203400 0.008501
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.2351 -1.0266 -0.1027 1.2569 9.5287
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.661626 0.557429 1.187 0.23629
Crisis 0.018617 0.737815 0.025 0.97989
`1` 1.274912 0.060492 21.076 < 2e-16 ***
`2` -0.241332 0.098076 -2.461 0.01449 *
`3` -0.090013 0.097819 -0.920 0.35828
`4` 0.006894 0.060472 0.114 0.90932
M1 -0.111843 0.689448 -0.162 0.87125
M2 -0.301474 0.689053 -0.438 0.66208
M3 -0.680435 0.690774 -0.985 0.32548
M4 0.002097 0.692586 0.003 0.99759
M5 -0.888811 0.696948 -1.275 0.20329
M6 -1.101792 0.701067 -1.572 0.11720
M7 -1.321549 0.702553 -1.881 0.06102 .
M8 -1.459034 0.705661 -2.068 0.03962 *
M9 0.097913 0.708556 0.138 0.89019
M10 -0.477650 0.700792 -0.682 0.49608
M11 0.203400 0.701598 0.290 0.77210
t 0.008501 0.002918 2.914 0.00387 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.404 on 274 degrees of freedom
Multiple R-squared: 0.9803, Adjusted R-squared: 0.979
F-statistic: 800.8 on 17 and 274 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 4.419845e-01 8.839689e-01 0.5580155
[2,] 3.249644e-01 6.499287e-01 0.6750356
[3,] 2.115693e-01 4.231386e-01 0.7884307
[4,] 1.706507e-01 3.413015e-01 0.8293493
[5,] 1.259987e-01 2.519974e-01 0.8740013
[6,] 8.789051e-02 1.757810e-01 0.9121095
[7,] 5.007602e-02 1.001520e-01 0.9499240
[8,] 2.737387e-02 5.474774e-02 0.9726261
[9,] 1.563975e-02 3.127951e-02 0.9843602
[10,] 9.818074e-03 1.963615e-02 0.9901819
[11,] 5.202254e-03 1.040451e-02 0.9947977
[12,] 4.248068e-03 8.496135e-03 0.9957519
[13,] 2.945005e-03 5.890010e-03 0.9970550
[14,] 1.701417e-03 3.402834e-03 0.9982986
[15,] 1.830421e-03 3.660841e-03 0.9981696
[16,] 9.596638e-04 1.919328e-03 0.9990403
[17,] 4.846800e-04 9.693601e-04 0.9995153
[18,] 2.630252e-04 5.260505e-04 0.9997370
[19,] 1.232969e-04 2.465939e-04 0.9998767
[20,] 5.681446e-05 1.136289e-04 0.9999432
[21,] 6.848897e-05 1.369779e-04 0.9999315
[22,] 3.423982e-05 6.847964e-05 0.9999658
[23,] 2.013931e-05 4.027863e-05 0.9999799
[24,] 9.358666e-06 1.871733e-05 0.9999906
[25,] 4.240198e-06 8.480396e-06 0.9999958
[26,] 1.836452e-06 3.672905e-06 0.9999982
[27,] 8.283156e-07 1.656631e-06 0.9999992
[28,] 6.263719e-07 1.252744e-06 0.9999994
[29,] 3.162196e-07 6.324392e-07 0.9999997
[30,] 1.458184e-07 2.916368e-07 0.9999999
[31,] 1.184883e-07 2.369766e-07 0.9999999
[32,] 4.061023e-06 8.122046e-06 0.9999959
[33,] 3.562177e-05 7.124354e-05 0.9999644
[34,] 1.947564e-05 3.895129e-05 0.9999805
[35,] 1.301390e-05 2.602780e-05 0.9999870
[36,] 7.156108e-06 1.431222e-05 0.9999928
[37,] 3.745765e-06 7.491530e-06 0.9999963
[38,] 2.908132e-06 5.816265e-06 0.9999971
[39,] 4.188775e-06 8.377549e-06 0.9999958
[40,] 2.322833e-06 4.645667e-06 0.9999977
[41,] 1.251866e-06 2.503731e-06 0.9999987
[42,] 6.325232e-07 1.265046e-06 0.9999994
[43,] 3.609257e-07 7.218513e-07 0.9999996
[44,] 4.411808e-07 8.823615e-07 0.9999996
[45,] 2.274692e-07 4.549383e-07 0.9999998
[46,] 1.413266e-07 2.826532e-07 0.9999999
[47,] 9.226095e-08 1.845219e-07 0.9999999
[48,] 9.821596e-08 1.964319e-07 0.9999999
[49,] 4.915075e-08 9.830151e-08 1.0000000
[50,] 2.651864e-08 5.303728e-08 1.0000000
[51,] 1.931396e-08 3.862793e-08 1.0000000
[52,] 9.714908e-09 1.942982e-08 1.0000000
[53,] 5.315338e-09 1.063068e-08 1.0000000
[54,] 2.787894e-09 5.575789e-09 1.0000000
[55,] 2.823105e-09 5.646211e-09 1.0000000
[56,] 1.971979e-09 3.943957e-09 1.0000000
[57,] 1.012615e-09 2.025230e-09 1.0000000
[58,] 5.335501e-10 1.067100e-09 1.0000000
[59,] 2.644824e-10 5.289649e-10 1.0000000
[60,] 1.464451e-10 2.928902e-10 1.0000000
[61,] 7.189342e-11 1.437868e-10 1.0000000
[62,] 5.721081e-11 1.144216e-10 1.0000000
[63,] 3.326436e-11 6.652873e-11 1.0000000
[64,] 1.625173e-11 3.250346e-11 1.0000000
[65,] 7.510054e-12 1.502011e-11 1.0000000
[66,] 3.352275e-12 6.704550e-12 1.0000000
[67,] 1.538002e-12 3.076004e-12 1.0000000
[68,] 8.456061e-13 1.691212e-12 1.0000000
[69,] 6.558152e-13 1.311630e-12 1.0000000
[70,] 3.366684e-13 6.733368e-13 1.0000000
[71,] 1.859805e-13 3.719610e-13 1.0000000
[72,] 1.134631e-13 2.269262e-13 1.0000000
[73,] 4.987171e-14 9.974342e-14 1.0000000
[74,] 2.378718e-14 4.757437e-14 1.0000000
[75,] 1.513744e-14 3.027488e-14 1.0000000
[76,] 7.070810e-15 1.414162e-14 1.0000000
[77,] 3.217406e-15 6.434812e-15 1.0000000
[78,] 1.362511e-15 2.725022e-15 1.0000000
[79,] 5.751079e-16 1.150216e-15 1.0000000
[80,] 4.816327e-16 9.632655e-16 1.0000000
[81,] 2.322639e-16 4.645278e-16 1.0000000
[82,] 1.101334e-16 2.202669e-16 1.0000000
[83,] 6.563563e-17 1.312713e-16 1.0000000
[84,] 3.110935e-17 6.221871e-17 1.0000000
[85,] 1.248948e-17 2.497895e-17 1.0000000
[86,] 5.242312e-18 1.048462e-17 1.0000000
[87,] 3.872177e-18 7.744353e-18 1.0000000
[88,] 1.833438e-18 3.666875e-18 1.0000000
[89,] 8.295922e-19 1.659184e-18 1.0000000
[90,] 3.700162e-19 7.400325e-19 1.0000000
[91,] 1.760522e-19 3.521045e-19 1.0000000
[92,] 7.146416e-20 1.429283e-19 1.0000000
[93,] 2.947356e-20 5.894711e-20 1.0000000
[94,] 1.954293e-20 3.908587e-20 1.0000000
[95,] 1.156805e-20 2.313610e-20 1.0000000
[96,] 5.982594e-21 1.196519e-20 1.0000000
[97,] 2.822624e-21 5.645247e-21 1.0000000
[98,] 1.180273e-21 2.360545e-21 1.0000000
[99,] 4.938015e-22 9.876031e-22 1.0000000
[100,] 2.029103e-22 4.058207e-22 1.0000000
[101,] 1.086016e-22 2.172032e-22 1.0000000
[102,] 4.580454e-23 9.160909e-23 1.0000000
[103,] 2.287055e-23 4.574110e-23 1.0000000
[104,] 8.644713e-24 1.728943e-23 1.0000000
[105,] 2.336761e-23 4.673522e-23 1.0000000
[106,] 1.119994e-23 2.239987e-23 1.0000000
[107,] 4.068545e-24 8.137089e-24 1.0000000
[108,] 1.327036e-23 2.654072e-23 1.0000000
[109,] 4.866848e-24 9.733695e-24 1.0000000
[110,] 1.903598e-24 3.807196e-24 1.0000000
[111,] 6.989730e-25 1.397946e-24 1.0000000
[112,] 3.360563e-25 6.721127e-25 1.0000000
[113,] 3.285702e-25 6.571403e-25 1.0000000
[114,] 1.456406e-25 2.912812e-25 1.0000000
[115,] 1.362474e-25 2.724949e-25 1.0000000
[116,] 5.012352e-26 1.002470e-25 1.0000000
[117,] 1.938451e-26 3.876901e-26 1.0000000
[118,] 1.633539e-26 3.267078e-26 1.0000000
[119,] 6.456237e-27 1.291247e-26 1.0000000
[120,] 2.322613e-27 4.645225e-27 1.0000000
[121,] 1.295950e-27 2.591901e-27 1.0000000
[122,] 4.695896e-28 9.391792e-28 1.0000000
[123,] 2.542162e-28 5.084325e-28 1.0000000
[124,] 8.719656e-29 1.743931e-28 1.0000000
[125,] 3.451475e-29 6.902950e-29 1.0000000
[126,] 1.488767e-29 2.977534e-29 1.0000000
[127,] 5.554786e-30 1.110957e-29 1.0000000
[128,] 4.938830e-30 9.877661e-30 1.0000000
[129,] 1.765905e-30 3.531810e-30 1.0000000
[130,] 1.809651e-30 3.619302e-30 1.0000000
[131,] 6.402291e-31 1.280458e-30 1.0000000
[132,] 4.671690e-31 9.343380e-31 1.0000000
[133,] 1.659016e-31 3.318031e-31 1.0000000
[134,] 7.048508e-32 1.409702e-31 1.0000000
[135,] 5.466475e-32 1.093295e-31 1.0000000
[136,] 2.200499e-32 4.400997e-32 1.0000000
[137,] 9.014774e-33 1.802955e-32 1.0000000
[138,] 5.223946e-33 1.044789e-32 1.0000000
[139,] 8.236148e-33 1.647230e-32 1.0000000
[140,] 2.703234e-33 5.406467e-33 1.0000000
[141,] 9.139797e-33 1.827959e-32 1.0000000
[142,] 4.583215e-33 9.166430e-33 1.0000000
[143,] 1.021542e-30 2.043083e-30 1.0000000
[144,] 8.976283e-31 1.795257e-30 1.0000000
[145,] 3.731995e-31 7.463990e-31 1.0000000
[146,] 7.213028e-30 1.442606e-29 1.0000000
[147,] 2.577818e-30 5.155637e-30 1.0000000
[148,] 1.695076e-29 3.390152e-29 1.0000000
[149,] 1.576401e-24 3.152803e-24 1.0000000
[150,] 6.388810e-25 1.277762e-24 1.0000000
[151,] 2.634253e-25 5.268507e-25 1.0000000
[152,] 4.492470e-25 8.984940e-25 1.0000000
[153,] 3.205877e-24 6.411753e-24 1.0000000
[154,] 1.461900e-24 2.923800e-24 1.0000000
[155,] 1.729560e-24 3.459120e-24 1.0000000
[156,] 2.341262e-19 4.682524e-19 1.0000000
[157,] 1.251219e-19 2.502437e-19 1.0000000
[158,] 3.013159e-19 6.026318e-19 1.0000000
[159,] 2.417731e-19 4.835462e-19 1.0000000
[160,] 2.805012e-19 5.610023e-19 1.0000000
[161,] 2.996684e-19 5.993367e-19 1.0000000
[162,] 1.598487e-19 3.196975e-19 1.0000000
[163,] 2.177788e-19 4.355575e-19 1.0000000
[164,] 1.089337e-19 2.178674e-19 1.0000000
[165,] 7.424214e-20 1.484843e-19 1.0000000
[166,] 5.433579e-19 1.086716e-18 1.0000000
[167,] 2.515497e-19 5.030994e-19 1.0000000
[168,] 1.449786e-19 2.899573e-19 1.0000000
[169,] 8.829713e-20 1.765943e-19 1.0000000
[170,] 4.617934e-20 9.235867e-20 1.0000000
[171,] 1.876939e-19 3.753879e-19 1.0000000
[172,] 8.794266e-20 1.758853e-19 1.0000000
[173,] 6.572851e-20 1.314570e-19 1.0000000
[174,] 8.698491e-20 1.739698e-19 1.0000000
[175,] 4.121204e-20 8.242407e-20 1.0000000
[176,] 1.942011e-20 3.884022e-20 1.0000000
[177,] 2.095565e-20 4.191131e-20 1.0000000
[178,] 1.060524e-20 2.121048e-20 1.0000000
[179,] 1.526109e-20 3.052218e-20 1.0000000
[180,] 2.101483e-19 4.202965e-19 1.0000000
[181,] 9.573669e-20 1.914734e-19 1.0000000
[182,] 7.849320e-20 1.569864e-19 1.0000000
[183,] 4.148834e-19 8.297668e-19 1.0000000
[184,] 4.522723e-18 9.045447e-18 1.0000000
[185,] 2.249766e-18 4.499532e-18 1.0000000
[186,] 1.378756e-18 2.757512e-18 1.0000000
[187,] 7.582228e-19 1.516446e-18 1.0000000
[188,] 3.467050e-19 6.934101e-19 1.0000000
[189,] 5.481314e-19 1.096263e-18 1.0000000
[190,] 3.137990e-19 6.275980e-19 1.0000000
[191,] 1.348952e-19 2.697903e-19 1.0000000
[192,] 5.839094e-20 1.167819e-19 1.0000000
[193,] 3.804830e-20 7.609660e-20 1.0000000
[194,] 2.215488e-20 4.430976e-20 1.0000000
[195,] 1.691217e-20 3.382433e-20 1.0000000
[196,] 1.497000e-20 2.994000e-20 1.0000000
[197,] 1.415342e-20 2.830683e-20 1.0000000
[198,] 1.375996e-19 2.751992e-19 1.0000000
[199,] 8.760559e-20 1.752112e-19 1.0000000
[200,] 6.786241e-20 1.357248e-19 1.0000000
[201,] 2.856999e-20 5.713999e-20 1.0000000
[202,] 2.185558e-19 4.371116e-19 1.0000000
[203,] 1.313748e-17 2.627496e-17 1.0000000
[204,] 2.576641e-17 5.153282e-17 1.0000000
[205,] 4.156769e-17 8.313537e-17 1.0000000
[206,] 2.159973e-17 4.319946e-17 1.0000000
[207,] 8.008813e-17 1.601763e-16 1.0000000
[208,] 1.762802e-16 3.525603e-16 1.0000000
[209,] 6.018241e-16 1.203648e-15 1.0000000
[210,] 1.082110e-14 2.164220e-14 1.0000000
[211,] 5.574210e-15 1.114842e-14 1.0000000
[212,] 6.535131e-15 1.307026e-14 1.0000000
[213,] 3.064888e-15 6.129776e-15 1.0000000
[214,] 3.136636e-15 6.273271e-15 1.0000000
[215,] 1.726248e-15 3.452495e-15 1.0000000
[216,] 1.278095e-15 2.556189e-15 1.0000000
[217,] 2.072269e-15 4.144538e-15 1.0000000
[218,] 1.785997e-15 3.571994e-15 1.0000000
[219,] 1.050748e-15 2.101495e-15 1.0000000
[220,] 2.033578e-15 4.067157e-15 1.0000000
[221,] 4.080592e-15 8.161184e-15 1.0000000
[222,] 3.717450e-15 7.434901e-15 1.0000000
[223,] 2.017214e-14 4.034428e-14 1.0000000
[224,] 1.146102e-14 2.292203e-14 1.0000000
[225,] 8.802246e-13 1.760449e-12 1.0000000
[226,] 4.325504e-13 8.651007e-13 1.0000000
[227,] 2.010237e-13 4.020474e-13 1.0000000
[228,] 1.549189e-13 3.098378e-13 1.0000000
[229,] 2.134001e-11 4.268002e-11 1.0000000
[230,] 1.827597e-11 3.655195e-11 1.0000000
[231,] 7.898551e-12 1.579710e-11 1.0000000
[232,] 9.913221e-12 1.982644e-11 1.0000000
[233,] 2.846857e-11 5.693715e-11 1.0000000
[234,] 6.619491e-11 1.323898e-10 1.0000000
[235,] 4.395149e-11 8.790298e-11 1.0000000
[236,] 1.654395e-09 3.308790e-09 1.0000000
[237,] 1.064266e-09 2.128531e-09 1.0000000
[238,] 6.924707e-10 1.384941e-09 1.0000000
[239,] 1.048303e-09 2.096606e-09 1.0000000
[240,] 4.172508e-10 8.345016e-10 1.0000000
[241,] 9.979510e-09 1.995902e-08 1.0000000
[242,] 4.788900e-09 9.577800e-09 1.0000000
[243,] 1.817391e-09 3.634782e-09 1.0000000
[244,] 7.102779e-10 1.420556e-09 1.0000000
[245,] 5.136484e-06 1.027297e-05 0.9999949
[246,] 1.620304e-05 3.240607e-05 0.9999838
[247,] 2.869047e-04 5.738094e-04 0.9997131
[248,] 3.365333e-04 6.730665e-04 0.9996635
[249,] 1.106777e-01 2.213555e-01 0.8893223
[250,] 9.714201e-02 1.942840e-01 0.9028580
[251,] 2.064201e-01 4.128402e-01 0.7935799
> postscript(file="/var/www/rcomp/tmp/1inl01292152132.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2tw331292152132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tw331292152132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4tw331292152132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5lnj61292152132.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 = 292
Frequency = 1
1 2 3 4 5 6
2.25929090 -2.16674399 -1.11199315 4.11961602 0.39328530 0.32767043
7 8 9 10 11 12
2.08732005 2.15439501 0.74176201 -1.14169116 0.44778398 -0.03960244
13 14 15 16 17 18
0.46668495 0.65582706 1.66673609 -0.85178673 0.02548836 1.75529626
19 20 21 22 23 24
-0.42395289 0.69020334 0.11852137 0.27705427 -1.42654659 1.33338617
25 26 27 28 29 30
-0.31581869 -0.23975912 0.43052835 0.15062657 -0.88929865 0.06248040
31 32 33 34 35 36
1.38952949 2.84488027 0.75640215 -0.53228054 1.28018817 0.28931399
37 38 39 40 41 42
-1.05391096 0.01652514 0.11200735 -0.29381968 1.82071811 0.77845330
43 44 45 46 47 48
0.65026750 1.38942421 -0.11874674 -0.60656240 -0.59808921 -1.30592308
49 50 51 52 53 54
-0.34642368 -0.72535539 1.65380170 4.82791238 4.00065970 0.30699279
55 56 57 58 59 60
-2.32579346 -0.96148145 -1.22276061 -2.84334356 2.09131547 0.64038618
61 62 63 64 65 66
-0.40302619 -0.56597352 1.32570920 -0.76607633 0.51139509 1.90662876
67 68 69 70 71 72
-0.74588306 -1.35463324 0.15035592 0.62348789 -0.75934916 0.20221357
73 74 75 76 77 78
-0.27603069 0.08418183 -1.31456574 -0.86214817 1.11689068 1.06313737
79 80 81 82 83 84
0.60204703 0.08266877 -0.71539989 0.96335157 -0.68210976 -1.27016197
85 86 87 88 89 90
-0.53531772 -0.54802790 -0.12907690 -0.39400976 -0.86873866 1.28265783
91 92 93 94 95 96
-0.30306136 -0.73925310 0.14523251 -0.70451276 -1.39663103 0.41554381
97 98 99 100 101 102
-0.23134909 -0.53217275 -0.03220208 -1.40212425 -0.61734301 0.47754497
103 104 105 106 107 108
1.20043800 -0.15523231 -0.79326471 -0.24547935 -1.84475317 0.09766701
109 110 111 112 113 114
-0.79749476 -1.72624863 -1.01182833 -0.08663710 0.21192409 -0.77769694
115 116 117 118 119 120
0.97286500 1.35492397 -1.54908233 -0.74714038 0.07013269 -0.07629527
121 122 123 124 125 126
-2.14834557 -0.96246212 0.68502181 -0.72122375 1.81101443 0.78725671
127 128 129 130 131 132
-1.23351250 1.84684566 -0.84678190 -1.99664920 -1.24919260 -1.80758621
133 134 135 136 137 138
0.48244380 -1.79181069 0.65460125 -0.25960565 -1.01321680 1.27652994
139 140 141 142 143 144
-0.97890728 -0.50897391 -2.06044606 -1.02623410 -1.78096120 -0.66900257
145 146 147 148 149 150
-1.16894686 -1.76033944 -0.72415576 -1.79046246 0.35575366 -1.13211033
151 152 153 154 155 156
-0.82337946 -1.04987929 -0.31977874 -1.27489103 0.71474433 0.35990279
157 158 159 160 161 162
-1.72824997 -0.54296810 1.65215214 -0.83055506 1.61161425 -1.59969640
163 164 165 166 167 168
3.03366724 1.20348324 -1.51198221 2.00675644 -0.98614836 -4.75705642
169 170 171 172 173 174
6.04928694 -0.84955573 -1.48661164 2.42389539 3.46494949 -1.14267928
175 176 177 178 179 180
1.77649044 -7.94103100 0.57984925 3.26625123 -3.45462498 1.29221321
181 182 183 184 185 186
1.91773773 -1.60215914 -3.15323438 -0.45024920 0.61657511 -4.65021372
187 188 189 190 191 192
-0.26137233 1.14095712 0.01480855 -0.15020321 2.73637777 -1.02023227
193 194 195 196 197 198
-2.73357830 -2.62703307 0.61492028 0.16127072 1.44832730 -1.55495203
199 200 201 202 203 204
-3.11091507 4.43760016 -0.57942494 0.37951279 -4.31177420 -4.62226679
205 206 207 208 209 210
-0.47170902 0.98938384 0.66734356 -0.33883649 -3.39619822 1.32375996
211 212 213 214 215 216
0.00327853 -0.44342022 -1.01519643 -0.94444165 1.12274298 -1.02788492
217 218 219 220 221 222
1.67436874 -3.34739332 1.25033443 2.07922313 -0.28911392 4.05942436
223 224 225 226 227 228
-5.62839174 -2.66477046 4.33204004 0.36905682 3.25249995 -1.40755983
229 230 231 232 233 234
-3.14944794 6.53210267 0.97511545 2.18792611 -0.43099253 -2.14683922
235 236 237 238 239 240
0.56053275 2.53290887 3.48479457 -1.62212117 0.32488313 4.71828372
241 242 243 244 245 246
-3.28355201 0.26350852 4.28731062 -1.84746018 -6.47160673 0.79501314
247 248 249 250 251 252
1.41994722 2.16902250 -5.88994046 4.08601321 1.15177454 1.48163367
253 254 255 256 257 258
-1.33450617 2.05189852 2.38617855 -4.02573627 4.16266462 2.88346904
259 260 261 262 263 264
5.33395384 -0.21106771 -0.57594691 2.45548703 1.58186005 3.27831492
265 266 267 268 269 270
9.52874640 3.53285905 -1.37784069 -7.18523460 -2.53250600 -10.23508625
271 272 273 274 275 276
-5.68443478 -5.33698538 5.19563760 -0.45477311 0.10669477 0.66353638
277 278 279 280 281 282
1.78701679 4.67670782 -5.43015157 4.38979452 -5.04224567 4.15295892
283 284 285 286 287 288
2.48926683 -0.48058506 1.67934795 -0.13664763 3.60918243 3.23117634
289 290 291 292
-4.18786863 1.18500846 -2.59010056 1.76570085
> postscript(file="/var/www/rcomp/tmp/6lnj61292152132.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 = 292
Frequency = 1
lag(myerror, k = 1) myerror
0 2.25929090 NA
1 -2.16674399 2.25929090
2 -1.11199315 -2.16674399
3 4.11961602 -1.11199315
4 0.39328530 4.11961602
5 0.32767043 0.39328530
6 2.08732005 0.32767043
7 2.15439501 2.08732005
8 0.74176201 2.15439501
9 -1.14169116 0.74176201
10 0.44778398 -1.14169116
11 -0.03960244 0.44778398
12 0.46668495 -0.03960244
13 0.65582706 0.46668495
14 1.66673609 0.65582706
15 -0.85178673 1.66673609
16 0.02548836 -0.85178673
17 1.75529626 0.02548836
18 -0.42395289 1.75529626
19 0.69020334 -0.42395289
20 0.11852137 0.69020334
21 0.27705427 0.11852137
22 -1.42654659 0.27705427
23 1.33338617 -1.42654659
24 -0.31581869 1.33338617
25 -0.23975912 -0.31581869
26 0.43052835 -0.23975912
27 0.15062657 0.43052835
28 -0.88929865 0.15062657
29 0.06248040 -0.88929865
30 1.38952949 0.06248040
31 2.84488027 1.38952949
32 0.75640215 2.84488027
33 -0.53228054 0.75640215
34 1.28018817 -0.53228054
35 0.28931399 1.28018817
36 -1.05391096 0.28931399
37 0.01652514 -1.05391096
38 0.11200735 0.01652514
39 -0.29381968 0.11200735
40 1.82071811 -0.29381968
41 0.77845330 1.82071811
42 0.65026750 0.77845330
43 1.38942421 0.65026750
44 -0.11874674 1.38942421
45 -0.60656240 -0.11874674
46 -0.59808921 -0.60656240
47 -1.30592308 -0.59808921
48 -0.34642368 -1.30592308
49 -0.72535539 -0.34642368
50 1.65380170 -0.72535539
51 4.82791238 1.65380170
52 4.00065970 4.82791238
53 0.30699279 4.00065970
54 -2.32579346 0.30699279
55 -0.96148145 -2.32579346
56 -1.22276061 -0.96148145
57 -2.84334356 -1.22276061
58 2.09131547 -2.84334356
59 0.64038618 2.09131547
60 -0.40302619 0.64038618
61 -0.56597352 -0.40302619
62 1.32570920 -0.56597352
63 -0.76607633 1.32570920
64 0.51139509 -0.76607633
65 1.90662876 0.51139509
66 -0.74588306 1.90662876
67 -1.35463324 -0.74588306
68 0.15035592 -1.35463324
69 0.62348789 0.15035592
70 -0.75934916 0.62348789
71 0.20221357 -0.75934916
72 -0.27603069 0.20221357
73 0.08418183 -0.27603069
74 -1.31456574 0.08418183
75 -0.86214817 -1.31456574
76 1.11689068 -0.86214817
77 1.06313737 1.11689068
78 0.60204703 1.06313737
79 0.08266877 0.60204703
80 -0.71539989 0.08266877
81 0.96335157 -0.71539989
82 -0.68210976 0.96335157
83 -1.27016197 -0.68210976
84 -0.53531772 -1.27016197
85 -0.54802790 -0.53531772
86 -0.12907690 -0.54802790
87 -0.39400976 -0.12907690
88 -0.86873866 -0.39400976
89 1.28265783 -0.86873866
90 -0.30306136 1.28265783
91 -0.73925310 -0.30306136
92 0.14523251 -0.73925310
93 -0.70451276 0.14523251
94 -1.39663103 -0.70451276
95 0.41554381 -1.39663103
96 -0.23134909 0.41554381
97 -0.53217275 -0.23134909
98 -0.03220208 -0.53217275
99 -1.40212425 -0.03220208
100 -0.61734301 -1.40212425
101 0.47754497 -0.61734301
102 1.20043800 0.47754497
103 -0.15523231 1.20043800
104 -0.79326471 -0.15523231
105 -0.24547935 -0.79326471
106 -1.84475317 -0.24547935
107 0.09766701 -1.84475317
108 -0.79749476 0.09766701
109 -1.72624863 -0.79749476
110 -1.01182833 -1.72624863
111 -0.08663710 -1.01182833
112 0.21192409 -0.08663710
113 -0.77769694 0.21192409
114 0.97286500 -0.77769694
115 1.35492397 0.97286500
116 -1.54908233 1.35492397
117 -0.74714038 -1.54908233
118 0.07013269 -0.74714038
119 -0.07629527 0.07013269
120 -2.14834557 -0.07629527
121 -0.96246212 -2.14834557
122 0.68502181 -0.96246212
123 -0.72122375 0.68502181
124 1.81101443 -0.72122375
125 0.78725671 1.81101443
126 -1.23351250 0.78725671
127 1.84684566 -1.23351250
128 -0.84678190 1.84684566
129 -1.99664920 -0.84678190
130 -1.24919260 -1.99664920
131 -1.80758621 -1.24919260
132 0.48244380 -1.80758621
133 -1.79181069 0.48244380
134 0.65460125 -1.79181069
135 -0.25960565 0.65460125
136 -1.01321680 -0.25960565
137 1.27652994 -1.01321680
138 -0.97890728 1.27652994
139 -0.50897391 -0.97890728
140 -2.06044606 -0.50897391
141 -1.02623410 -2.06044606
142 -1.78096120 -1.02623410
143 -0.66900257 -1.78096120
144 -1.16894686 -0.66900257
145 -1.76033944 -1.16894686
146 -0.72415576 -1.76033944
147 -1.79046246 -0.72415576
148 0.35575366 -1.79046246
149 -1.13211033 0.35575366
150 -0.82337946 -1.13211033
151 -1.04987929 -0.82337946
152 -0.31977874 -1.04987929
153 -1.27489103 -0.31977874
154 0.71474433 -1.27489103
155 0.35990279 0.71474433
156 -1.72824997 0.35990279
157 -0.54296810 -1.72824997
158 1.65215214 -0.54296810
159 -0.83055506 1.65215214
160 1.61161425 -0.83055506
161 -1.59969640 1.61161425
162 3.03366724 -1.59969640
163 1.20348324 3.03366724
164 -1.51198221 1.20348324
165 2.00675644 -1.51198221
166 -0.98614836 2.00675644
167 -4.75705642 -0.98614836
168 6.04928694 -4.75705642
169 -0.84955573 6.04928694
170 -1.48661164 -0.84955573
171 2.42389539 -1.48661164
172 3.46494949 2.42389539
173 -1.14267928 3.46494949
174 1.77649044 -1.14267928
175 -7.94103100 1.77649044
176 0.57984925 -7.94103100
177 3.26625123 0.57984925
178 -3.45462498 3.26625123
179 1.29221321 -3.45462498
180 1.91773773 1.29221321
181 -1.60215914 1.91773773
182 -3.15323438 -1.60215914
183 -0.45024920 -3.15323438
184 0.61657511 -0.45024920
185 -4.65021372 0.61657511
186 -0.26137233 -4.65021372
187 1.14095712 -0.26137233
188 0.01480855 1.14095712
189 -0.15020321 0.01480855
190 2.73637777 -0.15020321
191 -1.02023227 2.73637777
192 -2.73357830 -1.02023227
193 -2.62703307 -2.73357830
194 0.61492028 -2.62703307
195 0.16127072 0.61492028
196 1.44832730 0.16127072
197 -1.55495203 1.44832730
198 -3.11091507 -1.55495203
199 4.43760016 -3.11091507
200 -0.57942494 4.43760016
201 0.37951279 -0.57942494
202 -4.31177420 0.37951279
203 -4.62226679 -4.31177420
204 -0.47170902 -4.62226679
205 0.98938384 -0.47170902
206 0.66734356 0.98938384
207 -0.33883649 0.66734356
208 -3.39619822 -0.33883649
209 1.32375996 -3.39619822
210 0.00327853 1.32375996
211 -0.44342022 0.00327853
212 -1.01519643 -0.44342022
213 -0.94444165 -1.01519643
214 1.12274298 -0.94444165
215 -1.02788492 1.12274298
216 1.67436874 -1.02788492
217 -3.34739332 1.67436874
218 1.25033443 -3.34739332
219 2.07922313 1.25033443
220 -0.28911392 2.07922313
221 4.05942436 -0.28911392
222 -5.62839174 4.05942436
223 -2.66477046 -5.62839174
224 4.33204004 -2.66477046
225 0.36905682 4.33204004
226 3.25249995 0.36905682
227 -1.40755983 3.25249995
228 -3.14944794 -1.40755983
229 6.53210267 -3.14944794
230 0.97511545 6.53210267
231 2.18792611 0.97511545
232 -0.43099253 2.18792611
233 -2.14683922 -0.43099253
234 0.56053275 -2.14683922
235 2.53290887 0.56053275
236 3.48479457 2.53290887
237 -1.62212117 3.48479457
238 0.32488313 -1.62212117
239 4.71828372 0.32488313
240 -3.28355201 4.71828372
241 0.26350852 -3.28355201
242 4.28731062 0.26350852
243 -1.84746018 4.28731062
244 -6.47160673 -1.84746018
245 0.79501314 -6.47160673
246 1.41994722 0.79501314
247 2.16902250 1.41994722
248 -5.88994046 2.16902250
249 4.08601321 -5.88994046
250 1.15177454 4.08601321
251 1.48163367 1.15177454
252 -1.33450617 1.48163367
253 2.05189852 -1.33450617
254 2.38617855 2.05189852
255 -4.02573627 2.38617855
256 4.16266462 -4.02573627
257 2.88346904 4.16266462
258 5.33395384 2.88346904
259 -0.21106771 5.33395384
260 -0.57594691 -0.21106771
261 2.45548703 -0.57594691
262 1.58186005 2.45548703
263 3.27831492 1.58186005
264 9.52874640 3.27831492
265 3.53285905 9.52874640
266 -1.37784069 3.53285905
267 -7.18523460 -1.37784069
268 -2.53250600 -7.18523460
269 -10.23508625 -2.53250600
270 -5.68443478 -10.23508625
271 -5.33698538 -5.68443478
272 5.19563760 -5.33698538
273 -0.45477311 5.19563760
274 0.10669477 -0.45477311
275 0.66353638 0.10669477
276 1.78701679 0.66353638
277 4.67670782 1.78701679
278 -5.43015157 4.67670782
279 4.38979452 -5.43015157
280 -5.04224567 4.38979452
281 4.15295892 -5.04224567
282 2.48926683 4.15295892
283 -0.48058506 2.48926683
284 1.67934795 -0.48058506
285 -0.13664763 1.67934795
286 3.60918243 -0.13664763
287 3.23117634 3.60918243
288 -4.18786863 3.23117634
289 1.18500846 -4.18786863
290 -2.59010056 1.18500846
291 1.76570085 -2.59010056
292 NA 1.76570085
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.16674399 2.25929090
[2,] -1.11199315 -2.16674399
[3,] 4.11961602 -1.11199315
[4,] 0.39328530 4.11961602
[5,] 0.32767043 0.39328530
[6,] 2.08732005 0.32767043
[7,] 2.15439501 2.08732005
[8,] 0.74176201 2.15439501
[9,] -1.14169116 0.74176201
[10,] 0.44778398 -1.14169116
[11,] -0.03960244 0.44778398
[12,] 0.46668495 -0.03960244
[13,] 0.65582706 0.46668495
[14,] 1.66673609 0.65582706
[15,] -0.85178673 1.66673609
[16,] 0.02548836 -0.85178673
[17,] 1.75529626 0.02548836
[18,] -0.42395289 1.75529626
[19,] 0.69020334 -0.42395289
[20,] 0.11852137 0.69020334
[21,] 0.27705427 0.11852137
[22,] -1.42654659 0.27705427
[23,] 1.33338617 -1.42654659
[24,] -0.31581869 1.33338617
[25,] -0.23975912 -0.31581869
[26,] 0.43052835 -0.23975912
[27,] 0.15062657 0.43052835
[28,] -0.88929865 0.15062657
[29,] 0.06248040 -0.88929865
[30,] 1.38952949 0.06248040
[31,] 2.84488027 1.38952949
[32,] 0.75640215 2.84488027
[33,] -0.53228054 0.75640215
[34,] 1.28018817 -0.53228054
[35,] 0.28931399 1.28018817
[36,] -1.05391096 0.28931399
[37,] 0.01652514 -1.05391096
[38,] 0.11200735 0.01652514
[39,] -0.29381968 0.11200735
[40,] 1.82071811 -0.29381968
[41,] 0.77845330 1.82071811
[42,] 0.65026750 0.77845330
[43,] 1.38942421 0.65026750
[44,] -0.11874674 1.38942421
[45,] -0.60656240 -0.11874674
[46,] -0.59808921 -0.60656240
[47,] -1.30592308 -0.59808921
[48,] -0.34642368 -1.30592308
[49,] -0.72535539 -0.34642368
[50,] 1.65380170 -0.72535539
[51,] 4.82791238 1.65380170
[52,] 4.00065970 4.82791238
[53,] 0.30699279 4.00065970
[54,] -2.32579346 0.30699279
[55,] -0.96148145 -2.32579346
[56,] -1.22276061 -0.96148145
[57,] -2.84334356 -1.22276061
[58,] 2.09131547 -2.84334356
[59,] 0.64038618 2.09131547
[60,] -0.40302619 0.64038618
[61,] -0.56597352 -0.40302619
[62,] 1.32570920 -0.56597352
[63,] -0.76607633 1.32570920
[64,] 0.51139509 -0.76607633
[65,] 1.90662876 0.51139509
[66,] -0.74588306 1.90662876
[67,] -1.35463324 -0.74588306
[68,] 0.15035592 -1.35463324
[69,] 0.62348789 0.15035592
[70,] -0.75934916 0.62348789
[71,] 0.20221357 -0.75934916
[72,] -0.27603069 0.20221357
[73,] 0.08418183 -0.27603069
[74,] -1.31456574 0.08418183
[75,] -0.86214817 -1.31456574
[76,] 1.11689068 -0.86214817
[77,] 1.06313737 1.11689068
[78,] 0.60204703 1.06313737
[79,] 0.08266877 0.60204703
[80,] -0.71539989 0.08266877
[81,] 0.96335157 -0.71539989
[82,] -0.68210976 0.96335157
[83,] -1.27016197 -0.68210976
[84,] -0.53531772 -1.27016197
[85,] -0.54802790 -0.53531772
[86,] -0.12907690 -0.54802790
[87,] -0.39400976 -0.12907690
[88,] -0.86873866 -0.39400976
[89,] 1.28265783 -0.86873866
[90,] -0.30306136 1.28265783
[91,] -0.73925310 -0.30306136
[92,] 0.14523251 -0.73925310
[93,] -0.70451276 0.14523251
[94,] -1.39663103 -0.70451276
[95,] 0.41554381 -1.39663103
[96,] -0.23134909 0.41554381
[97,] -0.53217275 -0.23134909
[98,] -0.03220208 -0.53217275
[99,] -1.40212425 -0.03220208
[100,] -0.61734301 -1.40212425
[101,] 0.47754497 -0.61734301
[102,] 1.20043800 0.47754497
[103,] -0.15523231 1.20043800
[104,] -0.79326471 -0.15523231
[105,] -0.24547935 -0.79326471
[106,] -1.84475317 -0.24547935
[107,] 0.09766701 -1.84475317
[108,] -0.79749476 0.09766701
[109,] -1.72624863 -0.79749476
[110,] -1.01182833 -1.72624863
[111,] -0.08663710 -1.01182833
[112,] 0.21192409 -0.08663710
[113,] -0.77769694 0.21192409
[114,] 0.97286500 -0.77769694
[115,] 1.35492397 0.97286500
[116,] -1.54908233 1.35492397
[117,] -0.74714038 -1.54908233
[118,] 0.07013269 -0.74714038
[119,] -0.07629527 0.07013269
[120,] -2.14834557 -0.07629527
[121,] -0.96246212 -2.14834557
[122,] 0.68502181 -0.96246212
[123,] -0.72122375 0.68502181
[124,] 1.81101443 -0.72122375
[125,] 0.78725671 1.81101443
[126,] -1.23351250 0.78725671
[127,] 1.84684566 -1.23351250
[128,] -0.84678190 1.84684566
[129,] -1.99664920 -0.84678190
[130,] -1.24919260 -1.99664920
[131,] -1.80758621 -1.24919260
[132,] 0.48244380 -1.80758621
[133,] -1.79181069 0.48244380
[134,] 0.65460125 -1.79181069
[135,] -0.25960565 0.65460125
[136,] -1.01321680 -0.25960565
[137,] 1.27652994 -1.01321680
[138,] -0.97890728 1.27652994
[139,] -0.50897391 -0.97890728
[140,] -2.06044606 -0.50897391
[141,] -1.02623410 -2.06044606
[142,] -1.78096120 -1.02623410
[143,] -0.66900257 -1.78096120
[144,] -1.16894686 -0.66900257
[145,] -1.76033944 -1.16894686
[146,] -0.72415576 -1.76033944
[147,] -1.79046246 -0.72415576
[148,] 0.35575366 -1.79046246
[149,] -1.13211033 0.35575366
[150,] -0.82337946 -1.13211033
[151,] -1.04987929 -0.82337946
[152,] -0.31977874 -1.04987929
[153,] -1.27489103 -0.31977874
[154,] 0.71474433 -1.27489103
[155,] 0.35990279 0.71474433
[156,] -1.72824997 0.35990279
[157,] -0.54296810 -1.72824997
[158,] 1.65215214 -0.54296810
[159,] -0.83055506 1.65215214
[160,] 1.61161425 -0.83055506
[161,] -1.59969640 1.61161425
[162,] 3.03366724 -1.59969640
[163,] 1.20348324 3.03366724
[164,] -1.51198221 1.20348324
[165,] 2.00675644 -1.51198221
[166,] -0.98614836 2.00675644
[167,] -4.75705642 -0.98614836
[168,] 6.04928694 -4.75705642
[169,] -0.84955573 6.04928694
[170,] -1.48661164 -0.84955573
[171,] 2.42389539 -1.48661164
[172,] 3.46494949 2.42389539
[173,] -1.14267928 3.46494949
[174,] 1.77649044 -1.14267928
[175,] -7.94103100 1.77649044
[176,] 0.57984925 -7.94103100
[177,] 3.26625123 0.57984925
[178,] -3.45462498 3.26625123
[179,] 1.29221321 -3.45462498
[180,] 1.91773773 1.29221321
[181,] -1.60215914 1.91773773
[182,] -3.15323438 -1.60215914
[183,] -0.45024920 -3.15323438
[184,] 0.61657511 -0.45024920
[185,] -4.65021372 0.61657511
[186,] -0.26137233 -4.65021372
[187,] 1.14095712 -0.26137233
[188,] 0.01480855 1.14095712
[189,] -0.15020321 0.01480855
[190,] 2.73637777 -0.15020321
[191,] -1.02023227 2.73637777
[192,] -2.73357830 -1.02023227
[193,] -2.62703307 -2.73357830
[194,] 0.61492028 -2.62703307
[195,] 0.16127072 0.61492028
[196,] 1.44832730 0.16127072
[197,] -1.55495203 1.44832730
[198,] -3.11091507 -1.55495203
[199,] 4.43760016 -3.11091507
[200,] -0.57942494 4.43760016
[201,] 0.37951279 -0.57942494
[202,] -4.31177420 0.37951279
[203,] -4.62226679 -4.31177420
[204,] -0.47170902 -4.62226679
[205,] 0.98938384 -0.47170902
[206,] 0.66734356 0.98938384
[207,] -0.33883649 0.66734356
[208,] -3.39619822 -0.33883649
[209,] 1.32375996 -3.39619822
[210,] 0.00327853 1.32375996
[211,] -0.44342022 0.00327853
[212,] -1.01519643 -0.44342022
[213,] -0.94444165 -1.01519643
[214,] 1.12274298 -0.94444165
[215,] -1.02788492 1.12274298
[216,] 1.67436874 -1.02788492
[217,] -3.34739332 1.67436874
[218,] 1.25033443 -3.34739332
[219,] 2.07922313 1.25033443
[220,] -0.28911392 2.07922313
[221,] 4.05942436 -0.28911392
[222,] -5.62839174 4.05942436
[223,] -2.66477046 -5.62839174
[224,] 4.33204004 -2.66477046
[225,] 0.36905682 4.33204004
[226,] 3.25249995 0.36905682
[227,] -1.40755983 3.25249995
[228,] -3.14944794 -1.40755983
[229,] 6.53210267 -3.14944794
[230,] 0.97511545 6.53210267
[231,] 2.18792611 0.97511545
[232,] -0.43099253 2.18792611
[233,] -2.14683922 -0.43099253
[234,] 0.56053275 -2.14683922
[235,] 2.53290887 0.56053275
[236,] 3.48479457 2.53290887
[237,] -1.62212117 3.48479457
[238,] 0.32488313 -1.62212117
[239,] 4.71828372 0.32488313
[240,] -3.28355201 4.71828372
[241,] 0.26350852 -3.28355201
[242,] 4.28731062 0.26350852
[243,] -1.84746018 4.28731062
[244,] -6.47160673 -1.84746018
[245,] 0.79501314 -6.47160673
[246,] 1.41994722 0.79501314
[247,] 2.16902250 1.41994722
[248,] -5.88994046 2.16902250
[249,] 4.08601321 -5.88994046
[250,] 1.15177454 4.08601321
[251,] 1.48163367 1.15177454
[252,] -1.33450617 1.48163367
[253,] 2.05189852 -1.33450617
[254,] 2.38617855 2.05189852
[255,] -4.02573627 2.38617855
[256,] 4.16266462 -4.02573627
[257,] 2.88346904 4.16266462
[258,] 5.33395384 2.88346904
[259,] -0.21106771 5.33395384
[260,] -0.57594691 -0.21106771
[261,] 2.45548703 -0.57594691
[262,] 1.58186005 2.45548703
[263,] 3.27831492 1.58186005
[264,] 9.52874640 3.27831492
[265,] 3.53285905 9.52874640
[266,] -1.37784069 3.53285905
[267,] -7.18523460 -1.37784069
[268,] -2.53250600 -7.18523460
[269,] -10.23508625 -2.53250600
[270,] -5.68443478 -10.23508625
[271,] -5.33698538 -5.68443478
[272,] 5.19563760 -5.33698538
[273,] -0.45477311 5.19563760
[274,] 0.10669477 -0.45477311
[275,] 0.66353638 0.10669477
[276,] 1.78701679 0.66353638
[277,] 4.67670782 1.78701679
[278,] -5.43015157 4.67670782
[279,] 4.38979452 -5.43015157
[280,] -5.04224567 4.38979452
[281,] 4.15295892 -5.04224567
[282,] 2.48926683 4.15295892
[283,] -0.48058506 2.48926683
[284,] 1.67934795 -0.48058506
[285,] -0.13664763 1.67934795
[286,] 3.60918243 -0.13664763
[287,] 3.23117634 3.60918243
[288,] -4.18786863 3.23117634
[289,] 1.18500846 -4.18786863
[290,] -2.59010056 1.18500846
[291,] 1.76570085 -2.59010056
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.16674399 2.25929090
2 -1.11199315 -2.16674399
3 4.11961602 -1.11199315
4 0.39328530 4.11961602
5 0.32767043 0.39328530
6 2.08732005 0.32767043
7 2.15439501 2.08732005
8 0.74176201 2.15439501
9 -1.14169116 0.74176201
10 0.44778398 -1.14169116
11 -0.03960244 0.44778398
12 0.46668495 -0.03960244
13 0.65582706 0.46668495
14 1.66673609 0.65582706
15 -0.85178673 1.66673609
16 0.02548836 -0.85178673
17 1.75529626 0.02548836
18 -0.42395289 1.75529626
19 0.69020334 -0.42395289
20 0.11852137 0.69020334
21 0.27705427 0.11852137
22 -1.42654659 0.27705427
23 1.33338617 -1.42654659
24 -0.31581869 1.33338617
25 -0.23975912 -0.31581869
26 0.43052835 -0.23975912
27 0.15062657 0.43052835
28 -0.88929865 0.15062657
29 0.06248040 -0.88929865
30 1.38952949 0.06248040
31 2.84488027 1.38952949
32 0.75640215 2.84488027
33 -0.53228054 0.75640215
34 1.28018817 -0.53228054
35 0.28931399 1.28018817
36 -1.05391096 0.28931399
37 0.01652514 -1.05391096
38 0.11200735 0.01652514
39 -0.29381968 0.11200735
40 1.82071811 -0.29381968
41 0.77845330 1.82071811
42 0.65026750 0.77845330
43 1.38942421 0.65026750
44 -0.11874674 1.38942421
45 -0.60656240 -0.11874674
46 -0.59808921 -0.60656240
47 -1.30592308 -0.59808921
48 -0.34642368 -1.30592308
49 -0.72535539 -0.34642368
50 1.65380170 -0.72535539
51 4.82791238 1.65380170
52 4.00065970 4.82791238
53 0.30699279 4.00065970
54 -2.32579346 0.30699279
55 -0.96148145 -2.32579346
56 -1.22276061 -0.96148145
57 -2.84334356 -1.22276061
58 2.09131547 -2.84334356
59 0.64038618 2.09131547
60 -0.40302619 0.64038618
61 -0.56597352 -0.40302619
62 1.32570920 -0.56597352
63 -0.76607633 1.32570920
64 0.51139509 -0.76607633
65 1.90662876 0.51139509
66 -0.74588306 1.90662876
67 -1.35463324 -0.74588306
68 0.15035592 -1.35463324
69 0.62348789 0.15035592
70 -0.75934916 0.62348789
71 0.20221357 -0.75934916
72 -0.27603069 0.20221357
73 0.08418183 -0.27603069
74 -1.31456574 0.08418183
75 -0.86214817 -1.31456574
76 1.11689068 -0.86214817
77 1.06313737 1.11689068
78 0.60204703 1.06313737
79 0.08266877 0.60204703
80 -0.71539989 0.08266877
81 0.96335157 -0.71539989
82 -0.68210976 0.96335157
83 -1.27016197 -0.68210976
84 -0.53531772 -1.27016197
85 -0.54802790 -0.53531772
86 -0.12907690 -0.54802790
87 -0.39400976 -0.12907690
88 -0.86873866 -0.39400976
89 1.28265783 -0.86873866
90 -0.30306136 1.28265783
91 -0.73925310 -0.30306136
92 0.14523251 -0.73925310
93 -0.70451276 0.14523251
94 -1.39663103 -0.70451276
95 0.41554381 -1.39663103
96 -0.23134909 0.41554381
97 -0.53217275 -0.23134909
98 -0.03220208 -0.53217275
99 -1.40212425 -0.03220208
100 -0.61734301 -1.40212425
101 0.47754497 -0.61734301
102 1.20043800 0.47754497
103 -0.15523231 1.20043800
104 -0.79326471 -0.15523231
105 -0.24547935 -0.79326471
106 -1.84475317 -0.24547935
107 0.09766701 -1.84475317
108 -0.79749476 0.09766701
109 -1.72624863 -0.79749476
110 -1.01182833 -1.72624863
111 -0.08663710 -1.01182833
112 0.21192409 -0.08663710
113 -0.77769694 0.21192409
114 0.97286500 -0.77769694
115 1.35492397 0.97286500
116 -1.54908233 1.35492397
117 -0.74714038 -1.54908233
118 0.07013269 -0.74714038
119 -0.07629527 0.07013269
120 -2.14834557 -0.07629527
121 -0.96246212 -2.14834557
122 0.68502181 -0.96246212
123 -0.72122375 0.68502181
124 1.81101443 -0.72122375
125 0.78725671 1.81101443
126 -1.23351250 0.78725671
127 1.84684566 -1.23351250
128 -0.84678190 1.84684566
129 -1.99664920 -0.84678190
130 -1.24919260 -1.99664920
131 -1.80758621 -1.24919260
132 0.48244380 -1.80758621
133 -1.79181069 0.48244380
134 0.65460125 -1.79181069
135 -0.25960565 0.65460125
136 -1.01321680 -0.25960565
137 1.27652994 -1.01321680
138 -0.97890728 1.27652994
139 -0.50897391 -0.97890728
140 -2.06044606 -0.50897391
141 -1.02623410 -2.06044606
142 -1.78096120 -1.02623410
143 -0.66900257 -1.78096120
144 -1.16894686 -0.66900257
145 -1.76033944 -1.16894686
146 -0.72415576 -1.76033944
147 -1.79046246 -0.72415576
148 0.35575366 -1.79046246
149 -1.13211033 0.35575366
150 -0.82337946 -1.13211033
151 -1.04987929 -0.82337946
152 -0.31977874 -1.04987929
153 -1.27489103 -0.31977874
154 0.71474433 -1.27489103
155 0.35990279 0.71474433
156 -1.72824997 0.35990279
157 -0.54296810 -1.72824997
158 1.65215214 -0.54296810
159 -0.83055506 1.65215214
160 1.61161425 -0.83055506
161 -1.59969640 1.61161425
162 3.03366724 -1.59969640
163 1.20348324 3.03366724
164 -1.51198221 1.20348324
165 2.00675644 -1.51198221
166 -0.98614836 2.00675644
167 -4.75705642 -0.98614836
168 6.04928694 -4.75705642
169 -0.84955573 6.04928694
170 -1.48661164 -0.84955573
171 2.42389539 -1.48661164
172 3.46494949 2.42389539
173 -1.14267928 3.46494949
174 1.77649044 -1.14267928
175 -7.94103100 1.77649044
176 0.57984925 -7.94103100
177 3.26625123 0.57984925
178 -3.45462498 3.26625123
179 1.29221321 -3.45462498
180 1.91773773 1.29221321
181 -1.60215914 1.91773773
182 -3.15323438 -1.60215914
183 -0.45024920 -3.15323438
184 0.61657511 -0.45024920
185 -4.65021372 0.61657511
186 -0.26137233 -4.65021372
187 1.14095712 -0.26137233
188 0.01480855 1.14095712
189 -0.15020321 0.01480855
190 2.73637777 -0.15020321
191 -1.02023227 2.73637777
192 -2.73357830 -1.02023227
193 -2.62703307 -2.73357830
194 0.61492028 -2.62703307
195 0.16127072 0.61492028
196 1.44832730 0.16127072
197 -1.55495203 1.44832730
198 -3.11091507 -1.55495203
199 4.43760016 -3.11091507
200 -0.57942494 4.43760016
201 0.37951279 -0.57942494
202 -4.31177420 0.37951279
203 -4.62226679 -4.31177420
204 -0.47170902 -4.62226679
205 0.98938384 -0.47170902
206 0.66734356 0.98938384
207 -0.33883649 0.66734356
208 -3.39619822 -0.33883649
209 1.32375996 -3.39619822
210 0.00327853 1.32375996
211 -0.44342022 0.00327853
212 -1.01519643 -0.44342022
213 -0.94444165 -1.01519643
214 1.12274298 -0.94444165
215 -1.02788492 1.12274298
216 1.67436874 -1.02788492
217 -3.34739332 1.67436874
218 1.25033443 -3.34739332
219 2.07922313 1.25033443
220 -0.28911392 2.07922313
221 4.05942436 -0.28911392
222 -5.62839174 4.05942436
223 -2.66477046 -5.62839174
224 4.33204004 -2.66477046
225 0.36905682 4.33204004
226 3.25249995 0.36905682
227 -1.40755983 3.25249995
228 -3.14944794 -1.40755983
229 6.53210267 -3.14944794
230 0.97511545 6.53210267
231 2.18792611 0.97511545
232 -0.43099253 2.18792611
233 -2.14683922 -0.43099253
234 0.56053275 -2.14683922
235 2.53290887 0.56053275
236 3.48479457 2.53290887
237 -1.62212117 3.48479457
238 0.32488313 -1.62212117
239 4.71828372 0.32488313
240 -3.28355201 4.71828372
241 0.26350852 -3.28355201
242 4.28731062 0.26350852
243 -1.84746018 4.28731062
244 -6.47160673 -1.84746018
245 0.79501314 -6.47160673
246 1.41994722 0.79501314
247 2.16902250 1.41994722
248 -5.88994046 2.16902250
249 4.08601321 -5.88994046
250 1.15177454 4.08601321
251 1.48163367 1.15177454
252 -1.33450617 1.48163367
253 2.05189852 -1.33450617
254 2.38617855 2.05189852
255 -4.02573627 2.38617855
256 4.16266462 -4.02573627
257 2.88346904 4.16266462
258 5.33395384 2.88346904
259 -0.21106771 5.33395384
260 -0.57594691 -0.21106771
261 2.45548703 -0.57594691
262 1.58186005 2.45548703
263 3.27831492 1.58186005
264 9.52874640 3.27831492
265 3.53285905 9.52874640
266 -1.37784069 3.53285905
267 -7.18523460 -1.37784069
268 -2.53250600 -7.18523460
269 -10.23508625 -2.53250600
270 -5.68443478 -10.23508625
271 -5.33698538 -5.68443478
272 5.19563760 -5.33698538
273 -0.45477311 5.19563760
274 0.10669477 -0.45477311
275 0.66353638 0.10669477
276 1.78701679 0.66353638
277 4.67670782 1.78701679
278 -5.43015157 4.67670782
279 4.38979452 -5.43015157
280 -5.04224567 4.38979452
281 4.15295892 -5.04224567
282 2.48926683 4.15295892
283 -0.48058506 2.48926683
284 1.67934795 -0.48058506
285 -0.13664763 1.67934795
286 3.60918243 -0.13664763
287 3.23117634 3.60918243
288 -4.18786863 3.23117634
289 1.18500846 -4.18786863
290 -2.59010056 1.18500846
291 1.76570085 -2.59010056
> 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/7ef1r1292152132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8ef1r1292152132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9p60u1292152132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10p60u1292152132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sphi1292152132.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/12dpfo1292152132.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/13k8ui1292152132.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/14vzb21292152132.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/15gis81292152132.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/16caqh1292152132.tab")
+ }
>
> try(system("convert tmp/1inl01292152132.ps tmp/1inl01292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tw331292152132.ps tmp/2tw331292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tw331292152132.ps tmp/3tw331292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tw331292152132.ps tmp/4tw331292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lnj61292152132.ps tmp/5lnj61292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lnj61292152132.ps tmp/6lnj61292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ef1r1292152132.ps tmp/7ef1r1292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ef1r1292152132.ps tmp/8ef1r1292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p60u1292152132.ps tmp/9p60u1292152132.png",intern=TRUE))
character(0)
> try(system("convert tmp/10p60u1292152132.ps tmp/10p60u1292152132.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.71 1.81 10.61