R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(235.1
+ ,0
+ ,280.7
+ ,0
+ ,264.6
+ ,0
+ ,240.7
+ ,0
+ ,201.4
+ ,0
+ ,240.8
+ ,0
+ ,241.1
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+ ,0
+ ,206.1
+ ,0
+ ,174.7
+ ,0
+ ,203.3
+ ,0
+ ,220.5
+ ,0
+ ,299.5
+ ,0
+ ,347.4
+ ,0
+ ,338.3
+ ,0
+ ,327.7
+ ,0
+ ,351.6
+ ,0
+ ,396.6
+ ,0
+ ,438.8
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+ ,0
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+ ,0
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+ ,0
+ ,357
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+ ,369
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+ ,464.8
+ ,0
+ ,479.1
+ ,0
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+ ,0
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+ ,0
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+ ,0
+ ,355.1
+ ,0
+ ,331.6
+ ,0
+ ,261.3
+ ,0
+ ,249
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+ ,0
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+ ,0
+ ,240.9
+ ,0
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+ ,0
+ ,253.8
+ ,0
+ ,232.3
+ ,0
+ ,193.8
+ ,0
+ ,177
+ ,0
+ ,213.2
+ ,0
+ ,207.2
+ ,0
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+ ,0
+ ,188.6
+ ,0
+ ,175.4
+ ,0
+ ,199
+ ,0
+ ,179.6
+ ,0
+ ,225.8
+ ,0
+ ,234
+ ,0
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+ ,0
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+ ,0
+ ,178.2
+ ,0
+ ,203.2
+ ,0
+ ,208.5
+ ,0
+ ,191.8
+ ,0
+ ,172.8
+ ,0
+ ,148
+ ,0
+ ,159.4
+ ,0
+ ,154.5
+ ,0
+ ,213.2
+ ,0
+ ,196.4
+ ,0
+ ,182.8
+ ,0
+ ,176.4
+ ,0
+ ,153.6
+ ,0
+ ,173.2
+ ,0
+ ,171
+ ,0
+ ,151.2
+ ,0
+ ,161.9
+ ,0
+ ,157.2
+ ,0
+ ,201.7
+ ,0
+ ,236.4
+ ,0
+ ,356.1
+ ,0
+ ,398.3
+ ,0
+ ,403.7
+ ,0
+ ,384.6
+ ,0
+ ,365.8
+ ,0
+ ,368.1
+ ,0
+ ,367.9
+ ,0
+ ,347
+ ,0
+ ,343.3
+ ,0
+ ,292.9
+ ,0
+ ,311.5
+ ,0
+ ,300.9
+ ,0
+ ,366.9
+ ,0
+ ,356.9
+ ,0
+ ,329.7
+ ,0
+ ,316.2
+ ,0
+ ,269
+ ,0
+ ,289.3
+ ,0
+ ,266.2
+ ,0
+ ,253.6
+ ,0
+ ,233.8
+ ,0
+ ,228.4
+ ,0
+ ,253.6
+ ,0
+ ,260.1
+ ,0
+ ,306.6
+ ,0
+ ,309.2
+ ,0
+ ,309.5
+ ,0
+ ,271
+ ,0
+ ,279.9
+ ,0
+ ,317.9
+ ,0
+ ,298.4
+ ,0
+ ,246.7
+ ,0
+ ,227.3
+ ,0
+ ,209.1
+ ,0
+ ,259.9
+ ,0
+ ,266
+ ,0
+ ,320.6
+ ,0
+ ,308.5
+ ,0
+ ,282.2
+ ,0
+ ,262.7
+ ,0
+ ,263.5
+ ,0
+ ,313.1
+ ,0
+ ,284.3
+ ,0
+ ,252.6
+ ,0
+ ,250.3
+ ,0
+ ,246.5
+ ,0
+ ,312.7
+ ,0
+ ,333.2
+ ,0
+ ,446.4
+ ,0
+ ,511.6
+ ,0
+ ,515.5
+ ,0
+ ,506.4
+ ,0
+ ,483.2
+ ,0
+ ,522.3
+ ,0
+ ,509.8
+ ,0
+ ,460.7
+ ,0
+ ,405.8
+ ,0
+ ,375
+ ,0
+ ,378.5
+ ,0
+ ,406.8
+ ,0
+ ,467.8
+ ,0
+ ,469.8
+ ,0
+ ,429.8
+ ,0
+ ,355.8
+ ,0
+ ,332.7
+ ,0
+ ,378
+ ,0
+ ,360.5
+ ,0
+ ,334.7
+ ,0
+ ,319.5
+ ,0
+ ,323.1
+ ,0
+ ,363.6
+ ,0
+ ,352.1
+ ,0
+ ,411.9
+ ,0
+ ,388.6
+ ,0
+ ,416.4
+ ,0
+ ,360.7
+ ,0
+ ,338
+ ,0
+ ,417.2
+ ,0
+ ,388.4
+ ,0
+ ,371.1
+ ,0
+ ,331.5
+ ,0
+ ,353.7
+ ,0
+ ,396.7
+ ,0
+ ,447
+ ,0
+ ,533.5
+ ,0
+ ,565.4
+ ,0
+ ,542.3
+ ,0
+ ,488.7
+ ,0
+ ,467.1
+ ,0
+ ,531.3
+ ,0
+ ,496.1
+ ,0
+ ,444
+ ,0
+ ,403.4
+ ,0
+ ,386.3
+ ,0
+ ,394.1
+ ,0
+ ,404.1
+ ,0
+ ,462.1
+ ,0
+ ,448.1
+ ,0
+ ,432.3
+ ,0
+ ,386.3
+ ,0
+ ,395.2
+ ,0
+ ,421.9
+ ,0
+ ,382.9
+ ,0
+ ,384.2
+ ,0
+ ,345.5
+ ,0
+ ,323.4
+ ,0
+ ,372.6
+ ,0
+ ,376
+ ,0
+ ,462.7
+ ,0
+ ,487
+ ,0
+ ,444.2
+ ,0
+ ,399.3
+ ,0
+ ,394.9
+ ,0
+ ,455.4
+ ,0
+ ,414
+ ,0
+ ,375.5
+ ,0
+ ,347
+ ,0
+ ,339.4
+ ,0
+ ,385.8
+ ,0
+ ,378.8
+ ,0
+ ,451.8
+ ,0
+ ,446.1
+ ,0
+ ,422.5
+ ,0
+ ,383.1
+ ,0
+ ,352.8
+ ,0
+ ,445.3
+ ,0
+ ,367.5
+ ,0
+ ,355.1
+ ,0
+ ,326.2
+ ,0
+ ,319.8
+ ,0
+ ,331.8
+ ,0
+ ,340.9
+ ,0
+ ,394.1
+ ,0
+ ,417.2
+ ,0
+ ,369.9
+ ,0
+ ,349.2
+ ,0
+ ,321.4
+ ,0
+ ,405.7
+ ,0
+ ,342.9
+ ,0
+ ,316.5
+ ,0
+ ,284.2
+ ,0
+ ,270.9
+ ,0
+ ,288.8
+ ,0
+ ,278.8
+ ,0
+ ,324.4
+ ,0
+ ,310.9
+ ,0
+ ,299
+ ,0
+ ,273
+ ,0
+ ,279.3
+ ,0
+ ,359.2
+ ,0
+ ,305
+ ,0
+ ,282.1
+ ,0
+ ,250.3
+ ,0
+ ,246.5
+ ,0
+ ,257.9
+ ,0
+ ,266.5
+ ,0
+ ,315.9
+ ,0
+ ,318.4
+ ,0
+ ,295.4
+ ,0
+ ,266.4
+ ,0
+ ,245.8
+ ,0
+ ,362.8
+ ,0
+ ,324.9
+ ,0
+ ,294.2
+ ,0
+ ,289.5
+ ,0
+ ,295.2
+ ,0
+ ,290.3
+ ,0
+ ,272
+ ,0
+ ,307.4
+ ,0
+ ,328.7
+ ,0
+ ,292.9
+ ,0
+ ,249.1
+ ,0
+ ,230.4
+ ,0
+ ,361.5
+ ,0
+ ,321.7
+ ,0
+ ,277.2
+ ,0
+ ,260.7
+ ,0
+ ,251
+ ,0
+ ,257.6
+ ,0
+ ,241.8
+ ,0
+ ,287.5
+ ,0
+ ,292.3
+ ,0
+ ,274.7
+ ,0
+ ,254.2
+ ,0
+ ,230
+ ,0
+ ,339
+ ,0
+ ,318.2
+ ,0
+ ,287
+ ,0
+ ,295.8
+ ,0
+ ,284
+ ,0
+ ,271
+ ,0
+ ,262.7
+ ,0
+ ,340.6
+ ,1
+ ,379.4
+ ,1
+ ,373.3
+ ,1
+ ,355.2
+ ,1
+ ,338.4
+ ,1
+ ,466.9
+ ,1
+ ,451
+ ,1
+ ,422
+ ,1
+ ,429.2
+ ,1
+ ,425.9
+ ,1
+ ,460.7
+ ,1
+ ,463.6
+ ,1
+ ,541.4
+ ,1
+ ,544.2
+ ,1
+ ,517.5
+ ,1
+ ,469.4
+ ,1
+ ,439.4
+ ,1
+ ,549
+ ,1
+ ,533
+ ,1
+ ,506.1
+ ,1
+ ,484
+ ,1
+ ,457
+ ,1
+ ,481.5
+ ,1
+ ,469.5
+ ,1
+ ,544.7
+ ,1
+ ,541.2
+ ,1
+ ,521.5
+ ,1
+ ,469.7
+ ,1
+ ,434.4
+ ,1
+ ,542.6
+ ,1
+ ,517.3
+ ,1
+ ,485.7
+ ,1
+ ,465.8
+ ,1
+ ,447
+ ,1
+ ,426.6
+ ,1
+ ,411.6
+ ,1
+ ,467.5
+ ,1
+ ,484.5
+ ,1
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+ ,1
+ ,417.4
+ ,1
+ ,379.9
+ ,1
+ ,484.7
+ ,1
+ ,455
+ ,1
+ ,420.8
+ ,1
+ ,416.5
+ ,1
+ ,376.3
+ ,1
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+ ,1
+ ,405.8
+ ,1
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+ ,1
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+ ,1
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+ ,538
+ ,1
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+ ,1
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+ ,1
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+ ,1
+ ,504.4
+ ,1
+ ,568.5
+ ,1
+ ,610.6
+ ,1
+ ,818
+ ,1
+ ,830.9
+ ,1
+ ,835.9
+ ,1
+ ,782
+ ,1
+ ,762.3
+ ,1
+ ,856.9
+ ,1
+ ,820.9
+ ,1
+ ,769.6
+ ,1
+ ,752.2
+ ,1
+ ,724.4
+ ,1
+ ,723.1
+ ,1
+ ,719.5
+ ,1
+ ,817.4
+ ,1
+ ,803.3
+ ,1
+ ,752.5
+ ,1
+ ,689
+ ,1
+ ,630.4
+ ,1
+ ,765.5
+ ,1
+ ,757.7
+ ,1
+ ,732.2
+ ,1
+ ,702.6
+ ,1
+ ,683.3
+ ,1
+ ,709.5
+ ,1
+ ,702.2
+ ,1
+ ,784.8
+ ,1
+ ,810.9
+ ,1
+ ,755.6
+ ,1
+ ,656.8
+ ,1
+ ,615.1
+ ,1
+ ,745.3
+ ,1
+ ,694.1
+ ,1
+ ,675.7
+ ,1
+ ,643.7
+ ,1
+ ,622.1
+ ,1
+ ,634.6
+ ,1
+ ,588
+ ,1
+ ,689.7
+ ,1
+ ,673.9
+ ,1
+ ,647.9
+ ,1
+ ,568.8
+ ,1
+ ,545.7
+ ,1
+ ,632.6
+ ,1
+ ,643.8
+ ,1
+ ,593.1
+ ,1
+ ,579.7
+ ,1
+ ,546
+ ,1
+ ,562.9
+ ,1
+ ,572.5
+ ,1)
+ ,dim=c(2
+ ,372)
+ ,dimnames=list(c('Maandelijkse_werkloosheid'
+ ,'Dummy_variabele')
+ ,1:372))
> y <- array(NA,dim=c(2,372),dimnames=list(c('Maandelijkse_werkloosheid','Dummy_variabele'),1:372))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Maandelijkse_werkloosheid Dummy_variabele M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 235.1 0 1 0 0 0 0 0 0 0 0 0
2 280.7 0 0 1 0 0 0 0 0 0 0 0
3 264.6 0 0 0 1 0 0 0 0 0 0 0
4 240.7 0 0 0 0 1 0 0 0 0 0 0
5 201.4 0 0 0 0 0 1 0 0 0 0 0
6 240.8 0 0 0 0 0 0 1 0 0 0 0
7 241.1 0 0 0 0 0 0 0 1 0 0 0
8 223.8 0 0 0 0 0 0 0 0 1 0 0
9 206.1 0 0 0 0 0 0 0 0 0 1 0
10 174.7 0 0 0 0 0 0 0 0 0 0 1
11 203.3 0 0 0 0 0 0 0 0 0 0 0
12 220.5 0 0 0 0 0 0 0 0 0 0 0
13 299.5 0 1 0 0 0 0 0 0 0 0 0
14 347.4 0 0 1 0 0 0 0 0 0 0 0
15 338.3 0 0 0 1 0 0 0 0 0 0 0
16 327.7 0 0 0 0 1 0 0 0 0 0 0
17 351.6 0 0 0 0 0 1 0 0 0 0 0
18 396.6 0 0 0 0 0 0 1 0 0 0 0
19 438.8 0 0 0 0 0 0 0 1 0 0 0
20 395.6 0 0 0 0 0 0 0 0 1 0 0
21 363.5 0 0 0 0 0 0 0 0 0 1 0
22 378.8 0 0 0 0 0 0 0 0 0 0 1
23 357.0 0 0 0 0 0 0 0 0 0 0 0
24 369.0 0 0 0 0 0 0 0 0 0 0 0
25 464.8 0 1 0 0 0 0 0 0 0 0 0
26 479.1 0 0 1 0 0 0 0 0 0 0 0
27 431.3 0 0 0 1 0 0 0 0 0 0 0
28 366.5 0 0 0 0 1 0 0 0 0 0 0
29 326.3 0 0 0 0 0 1 0 0 0 0 0
30 355.1 0 0 0 0 0 0 1 0 0 0 0
31 331.6 0 0 0 0 0 0 0 1 0 0 0
32 261.3 0 0 0 0 0 0 0 0 1 0 0
33 249.0 0 0 0 0 0 0 0 0 0 1 0
34 205.5 0 0 0 0 0 0 0 0 0 0 1
35 235.6 0 0 0 0 0 0 0 0 0 0 0
36 240.9 0 0 0 0 0 0 0 0 0 0 0
37 264.9 0 1 0 0 0 0 0 0 0 0 0
38 253.8 0 0 1 0 0 0 0 0 0 0 0
39 232.3 0 0 0 1 0 0 0 0 0 0 0
40 193.8 0 0 0 0 1 0 0 0 0 0 0
41 177.0 0 0 0 0 0 1 0 0 0 0 0
42 213.2 0 0 0 0 0 0 1 0 0 0 0
43 207.2 0 0 0 0 0 0 0 1 0 0 0
44 180.6 0 0 0 0 0 0 0 0 1 0 0
45 188.6 0 0 0 0 0 0 0 0 0 1 0
46 175.4 0 0 0 0 0 0 0 0 0 0 1
47 199.0 0 0 0 0 0 0 0 0 0 0 0
48 179.6 0 0 0 0 0 0 0 0 0 0 0
49 225.8 0 1 0 0 0 0 0 0 0 0 0
50 234.0 0 0 1 0 0 0 0 0 0 0 0
51 200.2 0 0 0 1 0 0 0 0 0 0 0
52 183.6 0 0 0 0 1 0 0 0 0 0 0
53 178.2 0 0 0 0 0 1 0 0 0 0 0
54 203.2 0 0 0 0 0 0 1 0 0 0 0
55 208.5 0 0 0 0 0 0 0 1 0 0 0
56 191.8 0 0 0 0 0 0 0 0 1 0 0
57 172.8 0 0 0 0 0 0 0 0 0 1 0
58 148.0 0 0 0 0 0 0 0 0 0 0 1
59 159.4 0 0 0 0 0 0 0 0 0 0 0
60 154.5 0 0 0 0 0 0 0 0 0 0 0
61 213.2 0 1 0 0 0 0 0 0 0 0 0
62 196.4 0 0 1 0 0 0 0 0 0 0 0
63 182.8 0 0 0 1 0 0 0 0 0 0 0
64 176.4 0 0 0 0 1 0 0 0 0 0 0
65 153.6 0 0 0 0 0 1 0 0 0 0 0
66 173.2 0 0 0 0 0 0 1 0 0 0 0
67 171.0 0 0 0 0 0 0 0 1 0 0 0
68 151.2 0 0 0 0 0 0 0 0 1 0 0
69 161.9 0 0 0 0 0 0 0 0 0 1 0
70 157.2 0 0 0 0 0 0 0 0 0 0 1
71 201.7 0 0 0 0 0 0 0 0 0 0 0
72 236.4 0 0 0 0 0 0 0 0 0 0 0
73 356.1 0 1 0 0 0 0 0 0 0 0 0
74 398.3 0 0 1 0 0 0 0 0 0 0 0
75 403.7 0 0 0 1 0 0 0 0 0 0 0
76 384.6 0 0 0 0 1 0 0 0 0 0 0
77 365.8 0 0 0 0 0 1 0 0 0 0 0
78 368.1 0 0 0 0 0 0 1 0 0 0 0
79 367.9 0 0 0 0 0 0 0 1 0 0 0
80 347.0 0 0 0 0 0 0 0 0 1 0 0
81 343.3 0 0 0 0 0 0 0 0 0 1 0
82 292.9 0 0 0 0 0 0 0 0 0 0 1
83 311.5 0 0 0 0 0 0 0 0 0 0 0
84 300.9 0 0 0 0 0 0 0 0 0 0 0
85 366.9 0 1 0 0 0 0 0 0 0 0 0
86 356.9 0 0 1 0 0 0 0 0 0 0 0
87 329.7 0 0 0 1 0 0 0 0 0 0 0
88 316.2 0 0 0 0 1 0 0 0 0 0 0
89 269.0 0 0 0 0 0 1 0 0 0 0 0
90 289.3 0 0 0 0 0 0 1 0 0 0 0
91 266.2 0 0 0 0 0 0 0 1 0 0 0
92 253.6 0 0 0 0 0 0 0 0 1 0 0
93 233.8 0 0 0 0 0 0 0 0 0 1 0
94 228.4 0 0 0 0 0 0 0 0 0 0 1
95 253.6 0 0 0 0 0 0 0 0 0 0 0
96 260.1 0 0 0 0 0 0 0 0 0 0 0
97 306.6 0 1 0 0 0 0 0 0 0 0 0
98 309.2 0 0 1 0 0 0 0 0 0 0 0
99 309.5 0 0 0 1 0 0 0 0 0 0 0
100 271.0 0 0 0 0 1 0 0 0 0 0 0
101 279.9 0 0 0 0 0 1 0 0 0 0 0
102 317.9 0 0 0 0 0 0 1 0 0 0 0
103 298.4 0 0 0 0 0 0 0 1 0 0 0
104 246.7 0 0 0 0 0 0 0 0 1 0 0
105 227.3 0 0 0 0 0 0 0 0 0 1 0
106 209.1 0 0 0 0 0 0 0 0 0 0 1
107 259.9 0 0 0 0 0 0 0 0 0 0 0
108 266.0 0 0 0 0 0 0 0 0 0 0 0
109 320.6 0 1 0 0 0 0 0 0 0 0 0
110 308.5 0 0 1 0 0 0 0 0 0 0 0
111 282.2 0 0 0 1 0 0 0 0 0 0 0
112 262.7 0 0 0 0 1 0 0 0 0 0 0
113 263.5 0 0 0 0 0 1 0 0 0 0 0
114 313.1 0 0 0 0 0 0 1 0 0 0 0
115 284.3 0 0 0 0 0 0 0 1 0 0 0
116 252.6 0 0 0 0 0 0 0 0 1 0 0
117 250.3 0 0 0 0 0 0 0 0 0 1 0
118 246.5 0 0 0 0 0 0 0 0 0 0 1
119 312.7 0 0 0 0 0 0 0 0 0 0 0
120 333.2 0 0 0 0 0 0 0 0 0 0 0
121 446.4 0 1 0 0 0 0 0 0 0 0 0
122 511.6 0 0 1 0 0 0 0 0 0 0 0
123 515.5 0 0 0 1 0 0 0 0 0 0 0
124 506.4 0 0 0 0 1 0 0 0 0 0 0
125 483.2 0 0 0 0 0 1 0 0 0 0 0
126 522.3 0 0 0 0 0 0 1 0 0 0 0
127 509.8 0 0 0 0 0 0 0 1 0 0 0
128 460.7 0 0 0 0 0 0 0 0 1 0 0
129 405.8 0 0 0 0 0 0 0 0 0 1 0
130 375.0 0 0 0 0 0 0 0 0 0 0 1
131 378.5 0 0 0 0 0 0 0 0 0 0 0
132 406.8 0 0 0 0 0 0 0 0 0 0 0
133 467.8 0 1 0 0 0 0 0 0 0 0 0
134 469.8 0 0 1 0 0 0 0 0 0 0 0
135 429.8 0 0 0 1 0 0 0 0 0 0 0
136 355.8 0 0 0 0 1 0 0 0 0 0 0
137 332.7 0 0 0 0 0 1 0 0 0 0 0
138 378.0 0 0 0 0 0 0 1 0 0 0 0
139 360.5 0 0 0 0 0 0 0 1 0 0 0
140 334.7 0 0 0 0 0 0 0 0 1 0 0
141 319.5 0 0 0 0 0 0 0 0 0 1 0
142 323.1 0 0 0 0 0 0 0 0 0 0 1
143 363.6 0 0 0 0 0 0 0 0 0 0 0
144 352.1 0 0 0 0 0 0 0 0 0 0 0
145 411.9 0 1 0 0 0 0 0 0 0 0 0
146 388.6 0 0 1 0 0 0 0 0 0 0 0
147 416.4 0 0 0 1 0 0 0 0 0 0 0
148 360.7 0 0 0 0 1 0 0 0 0 0 0
149 338.0 0 0 0 0 0 1 0 0 0 0 0
150 417.2 0 0 0 0 0 0 1 0 0 0 0
151 388.4 0 0 0 0 0 0 0 1 0 0 0
152 371.1 0 0 0 0 0 0 0 0 1 0 0
153 331.5 0 0 0 0 0 0 0 0 0 1 0
154 353.7 0 0 0 0 0 0 0 0 0 0 1
155 396.7 0 0 0 0 0 0 0 0 0 0 0
156 447.0 0 0 0 0 0 0 0 0 0 0 0
157 533.5 0 1 0 0 0 0 0 0 0 0 0
158 565.4 0 0 1 0 0 0 0 0 0 0 0
159 542.3 0 0 0 1 0 0 0 0 0 0 0
160 488.7 0 0 0 0 1 0 0 0 0 0 0
161 467.1 0 0 0 0 0 1 0 0 0 0 0
162 531.3 0 0 0 0 0 0 1 0 0 0 0
163 496.1 0 0 0 0 0 0 0 1 0 0 0
164 444.0 0 0 0 0 0 0 0 0 1 0 0
165 403.4 0 0 0 0 0 0 0 0 0 1 0
166 386.3 0 0 0 0 0 0 0 0 0 0 1
167 394.1 0 0 0 0 0 0 0 0 0 0 0
168 404.1 0 0 0 0 0 0 0 0 0 0 0
169 462.1 0 1 0 0 0 0 0 0 0 0 0
170 448.1 0 0 1 0 0 0 0 0 0 0 0
171 432.3 0 0 0 1 0 0 0 0 0 0 0
172 386.3 0 0 0 0 1 0 0 0 0 0 0
173 395.2 0 0 0 0 0 1 0 0 0 0 0
174 421.9 0 0 0 0 0 0 1 0 0 0 0
175 382.9 0 0 0 0 0 0 0 1 0 0 0
176 384.2 0 0 0 0 0 0 0 0 1 0 0
177 345.5 0 0 0 0 0 0 0 0 0 1 0
178 323.4 0 0 0 0 0 0 0 0 0 0 1
179 372.6 0 0 0 0 0 0 0 0 0 0 0
180 376.0 0 0 0 0 0 0 0 0 0 0 0
181 462.7 0 1 0 0 0 0 0 0 0 0 0
182 487.0 0 0 1 0 0 0 0 0 0 0 0
183 444.2 0 0 0 1 0 0 0 0 0 0 0
184 399.3 0 0 0 0 1 0 0 0 0 0 0
185 394.9 0 0 0 0 0 1 0 0 0 0 0
186 455.4 0 0 0 0 0 0 1 0 0 0 0
187 414.0 0 0 0 0 0 0 0 1 0 0 0
188 375.5 0 0 0 0 0 0 0 0 1 0 0
189 347.0 0 0 0 0 0 0 0 0 0 1 0
190 339.4 0 0 0 0 0 0 0 0 0 0 1
191 385.8 0 0 0 0 0 0 0 0 0 0 0
192 378.8 0 0 0 0 0 0 0 0 0 0 0
193 451.8 0 1 0 0 0 0 0 0 0 0 0
194 446.1 0 0 1 0 0 0 0 0 0 0 0
195 422.5 0 0 0 1 0 0 0 0 0 0 0
196 383.1 0 0 0 0 1 0 0 0 0 0 0
197 352.8 0 0 0 0 0 1 0 0 0 0 0
198 445.3 0 0 0 0 0 0 1 0 0 0 0
199 367.5 0 0 0 0 0 0 0 1 0 0 0
200 355.1 0 0 0 0 0 0 0 0 1 0 0
201 326.2 0 0 0 0 0 0 0 0 0 1 0
202 319.8 0 0 0 0 0 0 0 0 0 0 1
203 331.8 0 0 0 0 0 0 0 0 0 0 0
204 340.9 0 0 0 0 0 0 0 0 0 0 0
205 394.1 0 1 0 0 0 0 0 0 0 0 0
206 417.2 0 0 1 0 0 0 0 0 0 0 0
207 369.9 0 0 0 1 0 0 0 0 0 0 0
208 349.2 0 0 0 0 1 0 0 0 0 0 0
209 321.4 0 0 0 0 0 1 0 0 0 0 0
210 405.7 0 0 0 0 0 0 1 0 0 0 0
211 342.9 0 0 0 0 0 0 0 1 0 0 0
212 316.5 0 0 0 0 0 0 0 0 1 0 0
213 284.2 0 0 0 0 0 0 0 0 0 1 0
214 270.9 0 0 0 0 0 0 0 0 0 0 1
215 288.8 0 0 0 0 0 0 0 0 0 0 0
216 278.8 0 0 0 0 0 0 0 0 0 0 0
217 324.4 0 1 0 0 0 0 0 0 0 0 0
218 310.9 0 0 1 0 0 0 0 0 0 0 0
219 299.0 0 0 0 1 0 0 0 0 0 0 0
220 273.0 0 0 0 0 1 0 0 0 0 0 0
221 279.3 0 0 0 0 0 1 0 0 0 0 0
222 359.2 0 0 0 0 0 0 1 0 0 0 0
223 305.0 0 0 0 0 0 0 0 1 0 0 0
224 282.1 0 0 0 0 0 0 0 0 1 0 0
225 250.3 0 0 0 0 0 0 0 0 0 1 0
226 246.5 0 0 0 0 0 0 0 0 0 0 1
227 257.9 0 0 0 0 0 0 0 0 0 0 0
228 266.5 0 0 0 0 0 0 0 0 0 0 0
229 315.9 0 1 0 0 0 0 0 0 0 0 0
230 318.4 0 0 1 0 0 0 0 0 0 0 0
231 295.4 0 0 0 1 0 0 0 0 0 0 0
232 266.4 0 0 0 0 1 0 0 0 0 0 0
233 245.8 0 0 0 0 0 1 0 0 0 0 0
234 362.8 0 0 0 0 0 0 1 0 0 0 0
235 324.9 0 0 0 0 0 0 0 1 0 0 0
236 294.2 0 0 0 0 0 0 0 0 1 0 0
237 289.5 0 0 0 0 0 0 0 0 0 1 0
238 295.2 0 0 0 0 0 0 0 0 0 0 1
239 290.3 0 0 0 0 0 0 0 0 0 0 0
240 272.0 0 0 0 0 0 0 0 0 0 0 0
241 307.4 0 1 0 0 0 0 0 0 0 0 0
242 328.7 0 0 1 0 0 0 0 0 0 0 0
243 292.9 0 0 0 1 0 0 0 0 0 0 0
244 249.1 0 0 0 0 1 0 0 0 0 0 0
245 230.4 0 0 0 0 0 1 0 0 0 0 0
246 361.5 0 0 0 0 0 0 1 0 0 0 0
247 321.7 0 0 0 0 0 0 0 1 0 0 0
248 277.2 0 0 0 0 0 0 0 0 1 0 0
249 260.7 0 0 0 0 0 0 0 0 0 1 0
250 251.0 0 0 0 0 0 0 0 0 0 0 1
251 257.6 0 0 0 0 0 0 0 0 0 0 0
252 241.8 0 0 0 0 0 0 0 0 0 0 0
253 287.5 0 1 0 0 0 0 0 0 0 0 0
254 292.3 0 0 1 0 0 0 0 0 0 0 0
255 274.7 0 0 0 1 0 0 0 0 0 0 0
256 254.2 0 0 0 0 1 0 0 0 0 0 0
257 230.0 0 0 0 0 0 1 0 0 0 0 0
258 339.0 0 0 0 0 0 0 1 0 0 0 0
259 318.2 0 0 0 0 0 0 0 1 0 0 0
260 287.0 0 0 0 0 0 0 0 0 1 0 0
261 295.8 0 0 0 0 0 0 0 0 0 1 0
262 284.0 0 0 0 0 0 0 0 0 0 0 1
263 271.0 0 0 0 0 0 0 0 0 0 0 0
264 262.7 0 0 0 0 0 0 0 0 0 0 0
265 340.6 1 1 0 0 0 0 0 0 0 0 0
266 379.4 1 0 1 0 0 0 0 0 0 0 0
267 373.3 1 0 0 1 0 0 0 0 0 0 0
268 355.2 1 0 0 0 1 0 0 0 0 0 0
269 338.4 1 0 0 0 0 1 0 0 0 0 0
270 466.9 1 0 0 0 0 0 1 0 0 0 0
271 451.0 1 0 0 0 0 0 0 1 0 0 0
272 422.0 1 0 0 0 0 0 0 0 1 0 0
273 429.2 1 0 0 0 0 0 0 0 0 1 0
274 425.9 1 0 0 0 0 0 0 0 0 0 1
275 460.7 1 0 0 0 0 0 0 0 0 0 0
276 463.6 1 0 0 0 0 0 0 0 0 0 0
277 541.4 1 1 0 0 0 0 0 0 0 0 0
278 544.2 1 0 1 0 0 0 0 0 0 0 0
279 517.5 1 0 0 1 0 0 0 0 0 0 0
280 469.4 1 0 0 0 1 0 0 0 0 0 0
281 439.4 1 0 0 0 0 1 0 0 0 0 0
282 549.0 1 0 0 0 0 0 1 0 0 0 0
283 533.0 1 0 0 0 0 0 0 1 0 0 0
284 506.1 1 0 0 0 0 0 0 0 1 0 0
285 484.0 1 0 0 0 0 0 0 0 0 1 0
286 457.0 1 0 0 0 0 0 0 0 0 0 1
287 481.5 1 0 0 0 0 0 0 0 0 0 0
288 469.5 1 0 0 0 0 0 0 0 0 0 0
289 544.7 1 1 0 0 0 0 0 0 0 0 0
290 541.2 1 0 1 0 0 0 0 0 0 0 0
291 521.5 1 0 0 1 0 0 0 0 0 0 0
292 469.7 1 0 0 0 1 0 0 0 0 0 0
293 434.4 1 0 0 0 0 1 0 0 0 0 0
294 542.6 1 0 0 0 0 0 1 0 0 0 0
295 517.3 1 0 0 0 0 0 0 1 0 0 0
296 485.7 1 0 0 0 0 0 0 0 1 0 0
297 465.8 1 0 0 0 0 0 0 0 0 1 0
298 447.0 1 0 0 0 0 0 0 0 0 0 1
299 426.6 1 0 0 0 0 0 0 0 0 0 0
300 411.6 1 0 0 0 0 0 0 0 0 0 0
301 467.5 1 1 0 0 0 0 0 0 0 0 0
302 484.5 1 0 1 0 0 0 0 0 0 0 0
303 451.2 1 0 0 1 0 0 0 0 0 0 0
304 417.4 1 0 0 0 1 0 0 0 0 0 0
305 379.9 1 0 0 0 0 1 0 0 0 0 0
306 484.7 1 0 0 0 0 0 1 0 0 0 0
307 455.0 1 0 0 0 0 0 0 1 0 0 0
308 420.8 1 0 0 0 0 0 0 0 1 0 0
309 416.5 1 0 0 0 0 0 0 0 0 1 0
310 376.3 1 0 0 0 0 0 0 0 0 0 1
311 405.6 1 0 0 0 0 0 0 0 0 0 0
312 405.8 1 0 0 0 0 0 0 0 0 0 0
313 500.8 1 1 0 0 0 0 0 0 0 0 0
314 514.0 1 0 1 0 0 0 0 0 0 0 0
315 475.5 1 0 0 1 0 0 0 0 0 0 0
316 430.1 1 0 0 0 1 0 0 0 0 0 0
317 414.4 1 0 0 0 0 1 0 0 0 0 0
318 538.0 1 0 0 0 0 0 1 0 0 0 0
319 526.0 1 0 0 0 0 0 0 1 0 0 0
320 488.5 1 0 0 0 0 0 0 0 1 0 0
321 520.2 1 0 0 0 0 0 0 0 0 1 0
322 504.4 1 0 0 0 0 0 0 0 0 0 1
323 568.5 1 0 0 0 0 0 0 0 0 0 0
324 610.6 1 0 0 0 0 0 0 0 0 0 0
325 818.0 1 1 0 0 0 0 0 0 0 0 0
326 830.9 1 0 1 0 0 0 0 0 0 0 0
327 835.9 1 0 0 1 0 0 0 0 0 0 0
328 782.0 1 0 0 0 1 0 0 0 0 0 0
329 762.3 1 0 0 0 0 1 0 0 0 0 0
330 856.9 1 0 0 0 0 0 1 0 0 0 0
331 820.9 1 0 0 0 0 0 0 1 0 0 0
332 769.6 1 0 0 0 0 0 0 0 1 0 0
333 752.2 1 0 0 0 0 0 0 0 0 1 0
334 724.4 1 0 0 0 0 0 0 0 0 0 1
335 723.1 1 0 0 0 0 0 0 0 0 0 0
336 719.5 1 0 0 0 0 0 0 0 0 0 0
337 817.4 1 1 0 0 0 0 0 0 0 0 0
338 803.3 1 0 1 0 0 0 0 0 0 0 0
339 752.5 1 0 0 1 0 0 0 0 0 0 0
340 689.0 1 0 0 0 1 0 0 0 0 0 0
341 630.4 1 0 0 0 0 1 0 0 0 0 0
342 765.5 1 0 0 0 0 0 1 0 0 0 0
343 757.7 1 0 0 0 0 0 0 1 0 0 0
344 732.2 1 0 0 0 0 0 0 0 1 0 0
345 702.6 1 0 0 0 0 0 0 0 0 1 0
346 683.3 1 0 0 0 0 0 0 0 0 0 1
347 709.5 1 0 0 0 0 0 0 0 0 0 0
348 702.2 1 0 0 0 0 0 0 0 0 0 0
349 784.8 1 1 0 0 0 0 0 0 0 0 0
350 810.9 1 0 1 0 0 0 0 0 0 0 0
351 755.6 1 0 0 1 0 0 0 0 0 0 0
352 656.8 1 0 0 0 1 0 0 0 0 0 0
353 615.1 1 0 0 0 0 1 0 0 0 0 0
354 745.3 1 0 0 0 0 0 1 0 0 0 0
355 694.1 1 0 0 0 0 0 0 1 0 0 0
356 675.7 1 0 0 0 0 0 0 0 1 0 0
357 643.7 1 0 0 0 0 0 0 0 0 1 0
358 622.1 1 0 0 0 0 0 0 0 0 0 1
359 634.6 1 0 0 0 0 0 0 0 0 0 0
360 588.0 1 0 0 0 0 0 0 0 0 0 0
361 689.7 1 1 0 0 0 0 0 0 0 0 0
362 673.9 1 0 1 0 0 0 0 0 0 0 0
363 647.9 1 0 0 1 0 0 0 0 0 0 0
364 568.8 1 0 0 0 1 0 0 0 0 0 0
365 545.7 1 0 0 0 0 1 0 0 0 0 0
366 632.6 1 0 0 0 0 0 1 0 0 0 0
367 643.8 1 0 0 0 0 0 0 1 0 0 0
368 593.1 1 0 0 0 0 0 0 0 1 0 0
369 579.7 1 0 0 0 0 0 0 0 0 1 0
370 546.0 1 0 0 0 0 0 0 0 0 0 1
371 562.9 1 0 0 0 0 0 0 0 0 0 0
372 572.5 1 0 0 0 0 0 0 0 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
61 0 61
62 0 62
63 0 63
64 0 64
65 0 65
66 0 66
67 0 67
68 0 68
69 0 69
70 0 70
71 1 71
72 0 72
73 0 73
74 0 74
75 0 75
76 0 76
77 0 77
78 0 78
79 0 79
80 0 80
81 0 81
82 0 82
83 1 83
84 0 84
85 0 85
86 0 86
87 0 87
88 0 88
89 0 89
90 0 90
91 0 91
92 0 92
93 0 93
94 0 94
95 1 95
96 0 96
97 0 97
98 0 98
99 0 99
100 0 100
101 0 101
102 0 102
103 0 103
104 0 104
105 0 105
106 0 106
107 1 107
108 0 108
109 0 109
110 0 110
111 0 111
112 0 112
113 0 113
114 0 114
115 0 115
116 0 116
117 0 117
118 0 118
119 1 119
120 0 120
121 0 121
122 0 122
123 0 123
124 0 124
125 0 125
126 0 126
127 0 127
128 0 128
129 0 129
130 0 130
131 1 131
132 0 132
133 0 133
134 0 134
135 0 135
136 0 136
137 0 137
138 0 138
139 0 139
140 0 140
141 0 141
142 0 142
143 1 143
144 0 144
145 0 145
146 0 146
147 0 147
148 0 148
149 0 149
150 0 150
151 0 151
152 0 152
153 0 153
154 0 154
155 1 155
156 0 156
157 0 157
158 0 158
159 0 159
160 0 160
161 0 161
162 0 162
163 0 163
164 0 164
165 0 165
166 0 166
167 1 167
168 0 168
169 0 169
170 0 170
171 0 171
172 0 172
173 0 173
174 0 174
175 0 175
176 0 176
177 0 177
178 0 178
179 1 179
180 0 180
181 0 181
182 0 182
183 0 183
184 0 184
185 0 185
186 0 186
187 0 187
188 0 188
189 0 189
190 0 190
191 1 191
192 0 192
193 0 193
194 0 194
195 0 195
196 0 196
197 0 197
198 0 198
199 0 199
200 0 200
201 0 201
202 0 202
203 1 203
204 0 204
205 0 205
206 0 206
207 0 207
208 0 208
209 0 209
210 0 210
211 0 211
212 0 212
213 0 213
214 0 214
215 1 215
216 0 216
217 0 217
218 0 218
219 0 219
220 0 220
221 0 221
222 0 222
223 0 223
224 0 224
225 0 225
226 0 226
227 1 227
228 0 228
229 0 229
230 0 230
231 0 231
232 0 232
233 0 233
234 0 234
235 0 235
236 0 236
237 0 237
238 0 238
239 1 239
240 0 240
241 0 241
242 0 242
243 0 243
244 0 244
245 0 245
246 0 246
247 0 247
248 0 248
249 0 249
250 0 250
251 1 251
252 0 252
253 0 253
254 0 254
255 0 255
256 0 256
257 0 257
258 0 258
259 0 259
260 0 260
261 0 261
262 0 262
263 1 263
264 0 264
265 0 265
266 0 266
267 0 267
268 0 268
269 0 269
270 0 270
271 0 271
272 0 272
273 0 273
274 0 274
275 1 275
276 0 276
277 0 277
278 0 278
279 0 279
280 0 280
281 0 281
282 0 282
283 0 283
284 0 284
285 0 285
286 0 286
287 1 287
288 0 288
289 0 289
290 0 290
291 0 291
292 0 292
293 0 293
294 0 294
295 0 295
296 0 296
297 0 297
298 0 298
299 1 299
300 0 300
301 0 301
302 0 302
303 0 303
304 0 304
305 0 305
306 0 306
307 0 307
308 0 308
309 0 309
310 0 310
311 1 311
312 0 312
313 0 313
314 0 314
315 0 315
316 0 316
317 0 317
318 0 318
319 0 319
320 0 320
321 0 321
322 0 322
323 1 323
324 0 324
325 0 325
326 0 326
327 0 327
328 0 328
329 0 329
330 0 330
331 0 331
332 0 332
333 0 333
334 0 334
335 1 335
336 0 336
337 0 337
338 0 338
339 0 339
340 0 340
341 0 341
342 0 342
343 0 343
344 0 344
345 0 345
346 0 346
347 1 347
348 0 348
349 0 349
350 0 350
351 0 351
352 0 352
353 0 353
354 0 354
355 0 355
356 0 356
357 0 357
358 0 358
359 1 359
360 0 360
361 0 361
362 0 362
363 0 363
364 0 364
365 0 365
366 0 366
367 0 367
368 0 368
369 0 369
370 0 370
371 1 371
372 0 372
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy_variabele M1 M2
231.3205 157.8055 66.4670 75.8697
M3 M4 M5 M6
53.1079 13.6913 -7.3415 64.8902
M7 M8 M9 M10
41.6252 9.9860 -7.6146 -23.5506
M11 t
-1.9737 0.4941
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-245.92 -72.98 -17.73 62.41 239.84
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 231.32051 20.56922 11.246 < 2e-16 ***
Dummy_variabele 157.80548 17.93484 8.799 < 2e-16 ***
M1 66.46705 24.63928 2.698 0.00731 **
M2 75.86975 24.63683 3.080 0.00223 **
M3 53.10794 24.63461 2.156 0.03176 *
M4 13.69128 24.63262 0.556 0.57868
M5 -7.34150 24.63087 -0.298 0.76583
M6 64.89024 24.62935 2.635 0.00879 **
M7 41.62520 24.62807 1.690 0.09187 .
M8 9.98596 24.62702 0.405 0.68536
M9 -7.61456 24.62620 -0.309 0.75735
M10 -23.55057 24.62562 -0.956 0.33954
M11 -1.97367 24.62527 -0.080 0.93616
t 0.49407 0.07585 6.514 2.48e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 96.95 on 358 degrees of freedom
Multiple R-squared: 0.6227, Adjusted R-squared: 0.609
F-statistic: 45.46 on 13 and 358 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,] 3.278876e-02 6.557752e-02 9.672112e-01
[2,] 2.230756e-02 4.461513e-02 9.776924e-01
[3,] 2.859747e-02 5.719494e-02 9.714025e-01
[4,] 1.601183e-02 3.202366e-02 9.839882e-01
[5,] 7.094994e-03 1.418999e-02 9.929050e-01
[6,] 5.904570e-03 1.180914e-02 9.940954e-01
[7,] 2.406062e-03 4.812125e-03 9.975939e-01
[8,] 9.214236e-04 1.842847e-03 9.990786e-01
[9,] 3.381665e-04 6.763331e-04 9.996618e-01
[10,] 1.612682e-04 3.225364e-04 9.998387e-01
[11,] 1.416304e-04 2.832607e-04 9.998584e-01
[12,] 3.493404e-04 6.986809e-04 9.996507e-01
[13,] 1.078470e-03 2.156940e-03 9.989215e-01
[14,] 2.307302e-03 4.614603e-03 9.976927e-01
[15,] 7.471753e-03 1.494351e-02 9.925282e-01
[16,] 2.514261e-02 5.028523e-02 9.748574e-01
[17,] 4.174110e-02 8.348220e-02 9.582589e-01
[18,] 7.769317e-02 1.553863e-01 9.223068e-01
[19,] 9.277443e-02 1.855489e-01 9.072256e-01
[20,] 1.081552e-01 2.163105e-01 8.918448e-01
[21,] 1.543233e-01 3.086466e-01 8.456767e-01
[22,] 2.429991e-01 4.859981e-01 7.570009e-01
[23,] 3.079893e-01 6.159786e-01 6.920107e-01
[24,] 3.596518e-01 7.193037e-01 6.403482e-01
[25,] 3.913233e-01 7.826467e-01 6.086767e-01
[26,] 4.133443e-01 8.266886e-01 5.866557e-01
[27,] 4.358996e-01 8.717993e-01 5.641004e-01
[28,] 4.324802e-01 8.649604e-01 5.675198e-01
[29,] 4.027450e-01 8.054901e-01 5.972550e-01
[30,] 3.682186e-01 7.364372e-01 6.317814e-01
[31,] 3.291044e-01 6.582087e-01 6.708956e-01
[32,] 3.054171e-01 6.108342e-01 6.945829e-01
[33,] 2.775984e-01 5.551967e-01 7.224016e-01
[34,] 2.564248e-01 5.128497e-01 7.435752e-01
[35,] 2.397603e-01 4.795206e-01 7.602397e-01
[36,] 2.135163e-01 4.270325e-01 7.864837e-01
[37,] 1.844896e-01 3.689791e-01 8.155104e-01
[38,] 1.636813e-01 3.273625e-01 8.363187e-01
[39,] 1.418380e-01 2.836759e-01 8.581620e-01
[40,] 1.184482e-01 2.368963e-01 8.815518e-01
[41,] 9.853887e-02 1.970777e-01 9.014611e-01
[42,] 8.217633e-02 1.643527e-01 9.178237e-01
[43,] 6.873052e-02 1.374610e-01 9.312695e-01
[44,] 5.808438e-02 1.161688e-01 9.419156e-01
[45,] 4.786531e-02 9.573063e-02 9.521347e-01
[46,] 4.229542e-02 8.459084e-02 9.577046e-01
[47,] 3.610006e-02 7.220013e-02 9.638999e-01
[48,] 2.908716e-02 5.817432e-02 9.709128e-01
[49,] 2.344987e-02 4.689975e-02 9.765501e-01
[50,] 2.045328e-02 4.090656e-02 9.795467e-01
[51,] 1.748432e-02 3.496864e-02 9.825157e-01
[52,] 1.444103e-02 2.888206e-02 9.855590e-01
[53,] 1.147241e-02 2.294483e-02 9.885276e-01
[54,] 9.111106e-03 1.822221e-02 9.908889e-01
[55,] 7.553140e-03 1.510628e-02 9.924469e-01
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[337,] 9.980515e-01 3.896901e-03 1.948451e-03
[338,] 9.971812e-01 5.637654e-03 2.818827e-03
[339,] 9.855079e-01 2.898425e-02 1.449213e-02
> postscript(file="/var/www/html/rcomp/tmp/1j63z1291062748.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2j63z1291062748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3j63z1291062748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4uxkk1291062748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5uxkk1291062748.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 = 372
Frequency = 1
1 2 3 4 5 6
-63.1816311 -27.4784053 -21.3106633 -6.2880827 -25.0493730 -58.3751795
7 8 9 10 11 12
-35.3042117 -21.4590504 -22.0525988 -38.0106633 -31.4816311 -16.7493730
13 14 15 16 17 18
-4.7104915 33.2927343 46.4604763 74.7830569 119.2217666 91.4959601
19 20 21 22 23 24
156.4669279 144.4120892 129.4185408 160.1604763 116.2895085 125.8217666
25 26 27 28 29 30
154.6606481 159.0638739 133.5316159 107.6541965 87.9929062 44.0670997
31 32 33 34 35 36
43.3380675 4.1832288 8.9896804 -19.0683841 -11.0393519 -8.2070938
37 38 39 40 41 42
-51.1682123 -72.1649865 -71.3972445 -70.9746639 -67.2359542 -103.7617607
43 44 45 46 47 48
-86.9907929 -82.4456316 -57.3391800 -55.0972445 -53.5682123 -75.4359542
49 50 51 52 53 54
-96.1970727 -97.8938469 -109.4261049 -87.1035243 -71.9648146 -119.6906210
55 56 57 58 59 60
-91.6196533 -77.1744920 -79.0680404 -88.4261049 -99.0970727 -106.4648146
61 62 63 64 65 66
-114.7259330 -141.4227072 -132.7549653 -100.2323847 -102.4936750 -155.6194814
67 68 69 70 71 72
-135.0485137 -123.7033524 -95.8969008 -85.1549653 -62.7259330 -30.4936750
73 74 75 76 77 78
22.2452066 54.5484324 82.2161743 102.0387549 103.7774646 33.3516582
79 80 81 82 83 84
55.9226259 66.1677872 79.5742388 44.6161743 41.1452066 28.0774646
85 86 87 88 89 90
27.1163462 7.2195720 2.2873139 27.7098946 1.0486042 -51.3772022
91 92 93 94 95 96
-51.7062345 -33.1610732 -35.8546216 -25.8126861 -22.6836538 -18.6513958
97 98 99 100 101 102
-39.1125142 -46.4092884 -23.8415465 -23.4189658 6.0197438 -28.7060626
103 104 105 106 107 108
-25.4350949 -45.9899336 -48.2834820 -51.0415465 -22.3125142 -18.6802562
109 110 111 112 113 114
-31.0413746 -53.0381488 -57.0704069 -37.6478262 -16.3091165 -39.4349230
115 116 117 118 119 120
-45.4639553 -46.0187940 -31.2123424 -19.5704069 24.5586254 42.5908835
121 122 123 124 125 126
88.8297650 144.1329908 170.3007327 200.1233134 197.4620231 163.8362166
127 128 129 130 131 132
174.1071844 156.1523456 118.3587973 103.0007327 84.4297650 110.2620231
133 134 135 136 137 138
104.3009046 96.4041304 78.6718723 43.5944530 41.0331627 13.6073562
139 140 141 142 143 144
18.8783240 24.2234852 26.1299369 45.1718723 63.6009046 49.6331627
145 146 147 148 149 150
42.4720442 9.2752700 59.3430120 42.5655926 40.4043023 46.8784958
151 152 153 154 155 156
40.8494636 54.6946249 32.2010765 69.8430120 90.7720442 138.6043023
157 158 159 160 161 162
158.1431838 180.1464096 179.3141516 164.6367322 163.5754419 155.0496354
163 164 165 166 167 168
142.6206032 121.6657645 98.1722161 96.5141516 82.2431838 89.7754419
169 170 171 172 173 174
80.8143234 56.9175492 63.3852912 56.3078718 85.7465815 39.7207750
175 176 177 178 179 180
23.4917428 55.9369041 34.3433557 27.6852912 54.8143234 55.7465815
181 182 183 184 185 186
75.4854630 89.8886888 69.3564308 63.3790114 79.5177211 67.2919147
187 188 189 190 191 192
48.6628824 41.3080437 29.9144953 37.7564308 62.0854630 52.6177211
193 194 195 196 197 198
58.6566026 43.0598285 41.7275704 41.2501510 31.4888607 51.2630543
199 200 201 202 203 204
-3.7659780 14.9791833 3.1856349 12.2275704 2.1566026 8.7888607
205 206 207 208 209 210
-4.9722577 8.2309681 -16.8012900 1.4212906 -5.8399997 5.7341939
211 212 213 214 215 216
-34.2948384 -29.5496771 -44.7432255 -42.6012900 -46.7722577 -59.2399997
217 218 219 220 221 222
-80.6011181 -103.9978923 -93.6301504 -80.7075697 -53.8688601 -46.6946665
223 224 225 226 227 228
-78.1236988 -69.8785375 -84.5720859 -72.9301504 -83.6011181 -77.4688601
229 230 231 232 233 234
-95.0299785 -102.4267527 -103.1590108 -93.2364301 -93.2977205 -49.0235269
235 236 237 238 239 240
-64.1525592 -63.7073979 -51.3009463 -30.1590108 -57.1299785 -77.8977205
241 242 243 244 245 246
-109.4588389 -98.0556131 -111.5878712 -116.4652905 -114.6265809 -56.2523873
247 248 249 250 251 252
-73.2814196 -86.6362583 -86.0298067 -80.2878712 -95.7588389 -114.0265809
253 254 255 256 257 258
-135.2876993 -140.3844735 -135.7167316 -117.2941509 -120.9554412 -84.6812477
259 260 261 262 263 264
-82.7102800 -82.7651187 -56.8586671 -53.2167316 -88.2876993 -99.0554412
265 266 267 268 269 270
-245.9220435 -217.0188177 -200.8510758 -180.0284951 -176.2897854 -120.5155919
271 272 273 274 275 276
-113.6446241 -111.4994629 -87.1930112 -75.0510758 -62.3220435 -61.8897854
277 278 279 280 281 282
-51.0509039 -58.1476781 -62.5799361 -71.7573555 -81.2186458 -44.3444523
283 284 285 286 287 288
-37.5734845 -33.3283232 -38.3218716 -49.8799361 -47.4509039 -61.9186458
289 290 291 292 293 294
-53.6797643 -67.0765385 -64.5087965 -77.3862159 -92.1475062 -56.6733127
295 296 297 298 299 300
-59.2023449 -59.6571836 -62.4507320 -65.8087965 -108.2797643 -125.7475062
301 302 303 304 305 306
-136.8086247 -129.7053989 -140.7376569 -135.6150763 -152.5763666 -120.5021731
307 308 309 310 311 312
-127.4312053 -130.4860440 -117.6795924 -142.4376569 -135.2086247 -137.4763666
313 314 315 316 317 318
-109.4374851 -106.1342593 -122.3665173 -128.8439367 -124.0052270 -73.1310335
319 320 321 322 323 324
-62.3600657 -68.7149044 -19.9084528 -20.2665173 21.7625149 61.3947730
325 326 327 328 329 330
201.8336545 204.8368803 232.1046223 217.1272029 217.9659126 239.8401062
331 332 333 334 335 336
226.6110739 206.4562352 206.1626868 193.8046223 170.4336545 164.3659126
337 338 339 340 341 342
195.3047942 171.3080200 142.7757619 118.1983425 80.1370522 142.5112458
343 344 345 346 347 348
157.4822135 163.1273748 150.6338264 146.7757619 150.9047942 141.1370522
349 350 351 352 353 354
156.7759338 172.9791596 139.9469015 80.0694821 58.9081918 116.3823854
355 356 357 358 359 360
87.9533531 100.6985144 85.8049660 79.6469015 70.0759338 21.0081918
361 362 363 364 365 366
55.7470734 30.0502992 26.3180411 -13.8593782 -16.4206686 -2.2464750
367 368 369 370 371 372
31.7244927 12.1696540 15.8761056 -2.3819589 -7.5529266 -0.4206686
> postscript(file="/var/www/html/rcomp/tmp/6uxkk1291062748.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 = 372
Frequency = 1
lag(myerror, k = 1) myerror
0 -63.1816311 NA
1 -27.4784053 -63.1816311
2 -21.3106633 -27.4784053
3 -6.2880827 -21.3106633
4 -25.0493730 -6.2880827
5 -58.3751795 -25.0493730
6 -35.3042117 -58.3751795
7 -21.4590504 -35.3042117
8 -22.0525988 -21.4590504
9 -38.0106633 -22.0525988
10 -31.4816311 -38.0106633
11 -16.7493730 -31.4816311
12 -4.7104915 -16.7493730
13 33.2927343 -4.7104915
14 46.4604763 33.2927343
15 74.7830569 46.4604763
16 119.2217666 74.7830569
17 91.4959601 119.2217666
18 156.4669279 91.4959601
19 144.4120892 156.4669279
20 129.4185408 144.4120892
21 160.1604763 129.4185408
22 116.2895085 160.1604763
23 125.8217666 116.2895085
24 154.6606481 125.8217666
25 159.0638739 154.6606481
26 133.5316159 159.0638739
27 107.6541965 133.5316159
28 87.9929062 107.6541965
29 44.0670997 87.9929062
30 43.3380675 44.0670997
31 4.1832288 43.3380675
32 8.9896804 4.1832288
33 -19.0683841 8.9896804
34 -11.0393519 -19.0683841
35 -8.2070938 -11.0393519
36 -51.1682123 -8.2070938
37 -72.1649865 -51.1682123
38 -71.3972445 -72.1649865
39 -70.9746639 -71.3972445
40 -67.2359542 -70.9746639
41 -103.7617607 -67.2359542
42 -86.9907929 -103.7617607
43 -82.4456316 -86.9907929
44 -57.3391800 -82.4456316
45 -55.0972445 -57.3391800
46 -53.5682123 -55.0972445
47 -75.4359542 -53.5682123
48 -96.1970727 -75.4359542
49 -97.8938469 -96.1970727
50 -109.4261049 -97.8938469
51 -87.1035243 -109.4261049
52 -71.9648146 -87.1035243
53 -119.6906210 -71.9648146
54 -91.6196533 -119.6906210
55 -77.1744920 -91.6196533
56 -79.0680404 -77.1744920
57 -88.4261049 -79.0680404
58 -99.0970727 -88.4261049
59 -106.4648146 -99.0970727
60 -114.7259330 -106.4648146
61 -141.4227072 -114.7259330
62 -132.7549653 -141.4227072
63 -100.2323847 -132.7549653
64 -102.4936750 -100.2323847
65 -155.6194814 -102.4936750
66 -135.0485137 -155.6194814
67 -123.7033524 -135.0485137
68 -95.8969008 -123.7033524
69 -85.1549653 -95.8969008
70 -62.7259330 -85.1549653
71 -30.4936750 -62.7259330
72 22.2452066 -30.4936750
73 54.5484324 22.2452066
74 82.2161743 54.5484324
75 102.0387549 82.2161743
76 103.7774646 102.0387549
77 33.3516582 103.7774646
78 55.9226259 33.3516582
79 66.1677872 55.9226259
80 79.5742388 66.1677872
81 44.6161743 79.5742388
82 41.1452066 44.6161743
83 28.0774646 41.1452066
84 27.1163462 28.0774646
85 7.2195720 27.1163462
86 2.2873139 7.2195720
87 27.7098946 2.2873139
88 1.0486042 27.7098946
89 -51.3772022 1.0486042
90 -51.7062345 -51.3772022
91 -33.1610732 -51.7062345
92 -35.8546216 -33.1610732
93 -25.8126861 -35.8546216
94 -22.6836538 -25.8126861
95 -18.6513958 -22.6836538
96 -39.1125142 -18.6513958
97 -46.4092884 -39.1125142
98 -23.8415465 -46.4092884
99 -23.4189658 -23.8415465
100 6.0197438 -23.4189658
101 -28.7060626 6.0197438
102 -25.4350949 -28.7060626
103 -45.9899336 -25.4350949
104 -48.2834820 -45.9899336
105 -51.0415465 -48.2834820
106 -22.3125142 -51.0415465
107 -18.6802562 -22.3125142
108 -31.0413746 -18.6802562
109 -53.0381488 -31.0413746
110 -57.0704069 -53.0381488
111 -37.6478262 -57.0704069
112 -16.3091165 -37.6478262
113 -39.4349230 -16.3091165
114 -45.4639553 -39.4349230
115 -46.0187940 -45.4639553
116 -31.2123424 -46.0187940
117 -19.5704069 -31.2123424
118 24.5586254 -19.5704069
119 42.5908835 24.5586254
120 88.8297650 42.5908835
121 144.1329908 88.8297650
122 170.3007327 144.1329908
123 200.1233134 170.3007327
124 197.4620231 200.1233134
125 163.8362166 197.4620231
126 174.1071844 163.8362166
127 156.1523456 174.1071844
128 118.3587973 156.1523456
129 103.0007327 118.3587973
130 84.4297650 103.0007327
131 110.2620231 84.4297650
132 104.3009046 110.2620231
133 96.4041304 104.3009046
134 78.6718723 96.4041304
135 43.5944530 78.6718723
136 41.0331627 43.5944530
137 13.6073562 41.0331627
138 18.8783240 13.6073562
139 24.2234852 18.8783240
140 26.1299369 24.2234852
141 45.1718723 26.1299369
142 63.6009046 45.1718723
143 49.6331627 63.6009046
144 42.4720442 49.6331627
145 9.2752700 42.4720442
146 59.3430120 9.2752700
147 42.5655926 59.3430120
148 40.4043023 42.5655926
149 46.8784958 40.4043023
150 40.8494636 46.8784958
151 54.6946249 40.8494636
152 32.2010765 54.6946249
153 69.8430120 32.2010765
154 90.7720442 69.8430120
155 138.6043023 90.7720442
156 158.1431838 138.6043023
157 180.1464096 158.1431838
158 179.3141516 180.1464096
159 164.6367322 179.3141516
160 163.5754419 164.6367322
161 155.0496354 163.5754419
162 142.6206032 155.0496354
163 121.6657645 142.6206032
164 98.1722161 121.6657645
165 96.5141516 98.1722161
166 82.2431838 96.5141516
167 89.7754419 82.2431838
168 80.8143234 89.7754419
169 56.9175492 80.8143234
170 63.3852912 56.9175492
171 56.3078718 63.3852912
172 85.7465815 56.3078718
173 39.7207750 85.7465815
174 23.4917428 39.7207750
175 55.9369041 23.4917428
176 34.3433557 55.9369041
177 27.6852912 34.3433557
178 54.8143234 27.6852912
179 55.7465815 54.8143234
180 75.4854630 55.7465815
181 89.8886888 75.4854630
182 69.3564308 89.8886888
183 63.3790114 69.3564308
184 79.5177211 63.3790114
185 67.2919147 79.5177211
186 48.6628824 67.2919147
187 41.3080437 48.6628824
188 29.9144953 41.3080437
189 37.7564308 29.9144953
190 62.0854630 37.7564308
191 52.6177211 62.0854630
192 58.6566026 52.6177211
193 43.0598285 58.6566026
194 41.7275704 43.0598285
195 41.2501510 41.7275704
196 31.4888607 41.2501510
197 51.2630543 31.4888607
198 -3.7659780 51.2630543
199 14.9791833 -3.7659780
200 3.1856349 14.9791833
201 12.2275704 3.1856349
202 2.1566026 12.2275704
203 8.7888607 2.1566026
204 -4.9722577 8.7888607
205 8.2309681 -4.9722577
206 -16.8012900 8.2309681
207 1.4212906 -16.8012900
208 -5.8399997 1.4212906
209 5.7341939 -5.8399997
210 -34.2948384 5.7341939
211 -29.5496771 -34.2948384
212 -44.7432255 -29.5496771
213 -42.6012900 -44.7432255
214 -46.7722577 -42.6012900
215 -59.2399997 -46.7722577
216 -80.6011181 -59.2399997
217 -103.9978923 -80.6011181
218 -93.6301504 -103.9978923
219 -80.7075697 -93.6301504
220 -53.8688601 -80.7075697
221 -46.6946665 -53.8688601
222 -78.1236988 -46.6946665
223 -69.8785375 -78.1236988
224 -84.5720859 -69.8785375
225 -72.9301504 -84.5720859
226 -83.6011181 -72.9301504
227 -77.4688601 -83.6011181
228 -95.0299785 -77.4688601
229 -102.4267527 -95.0299785
230 -103.1590108 -102.4267527
231 -93.2364301 -103.1590108
232 -93.2977205 -93.2364301
233 -49.0235269 -93.2977205
234 -64.1525592 -49.0235269
235 -63.7073979 -64.1525592
236 -51.3009463 -63.7073979
237 -30.1590108 -51.3009463
238 -57.1299785 -30.1590108
239 -77.8977205 -57.1299785
240 -109.4588389 -77.8977205
241 -98.0556131 -109.4588389
242 -111.5878712 -98.0556131
243 -116.4652905 -111.5878712
244 -114.6265809 -116.4652905
245 -56.2523873 -114.6265809
246 -73.2814196 -56.2523873
247 -86.6362583 -73.2814196
248 -86.0298067 -86.6362583
249 -80.2878712 -86.0298067
250 -95.7588389 -80.2878712
251 -114.0265809 -95.7588389
252 -135.2876993 -114.0265809
253 -140.3844735 -135.2876993
254 -135.7167316 -140.3844735
255 -117.2941509 -135.7167316
256 -120.9554412 -117.2941509
257 -84.6812477 -120.9554412
258 -82.7102800 -84.6812477
259 -82.7651187 -82.7102800
260 -56.8586671 -82.7651187
261 -53.2167316 -56.8586671
262 -88.2876993 -53.2167316
263 -99.0554412 -88.2876993
264 -245.9220435 -99.0554412
265 -217.0188177 -245.9220435
266 -200.8510758 -217.0188177
267 -180.0284951 -200.8510758
268 -176.2897854 -180.0284951
269 -120.5155919 -176.2897854
270 -113.6446241 -120.5155919
271 -111.4994629 -113.6446241
272 -87.1930112 -111.4994629
273 -75.0510758 -87.1930112
274 -62.3220435 -75.0510758
275 -61.8897854 -62.3220435
276 -51.0509039 -61.8897854
277 -58.1476781 -51.0509039
278 -62.5799361 -58.1476781
279 -71.7573555 -62.5799361
280 -81.2186458 -71.7573555
281 -44.3444523 -81.2186458
282 -37.5734845 -44.3444523
283 -33.3283232 -37.5734845
284 -38.3218716 -33.3283232
285 -49.8799361 -38.3218716
286 -47.4509039 -49.8799361
287 -61.9186458 -47.4509039
288 -53.6797643 -61.9186458
289 -67.0765385 -53.6797643
290 -64.5087965 -67.0765385
291 -77.3862159 -64.5087965
292 -92.1475062 -77.3862159
293 -56.6733127 -92.1475062
294 -59.2023449 -56.6733127
295 -59.6571836 -59.2023449
296 -62.4507320 -59.6571836
297 -65.8087965 -62.4507320
298 -108.2797643 -65.8087965
299 -125.7475062 -108.2797643
300 -136.8086247 -125.7475062
301 -129.7053989 -136.8086247
302 -140.7376569 -129.7053989
303 -135.6150763 -140.7376569
304 -152.5763666 -135.6150763
305 -120.5021731 -152.5763666
306 -127.4312053 -120.5021731
307 -130.4860440 -127.4312053
308 -117.6795924 -130.4860440
309 -142.4376569 -117.6795924
310 -135.2086247 -142.4376569
311 -137.4763666 -135.2086247
312 -109.4374851 -137.4763666
313 -106.1342593 -109.4374851
314 -122.3665173 -106.1342593
315 -128.8439367 -122.3665173
316 -124.0052270 -128.8439367
317 -73.1310335 -124.0052270
318 -62.3600657 -73.1310335
319 -68.7149044 -62.3600657
320 -19.9084528 -68.7149044
321 -20.2665173 -19.9084528
322 21.7625149 -20.2665173
323 61.3947730 21.7625149
324 201.8336545 61.3947730
325 204.8368803 201.8336545
326 232.1046223 204.8368803
327 217.1272029 232.1046223
328 217.9659126 217.1272029
329 239.8401062 217.9659126
330 226.6110739 239.8401062
331 206.4562352 226.6110739
332 206.1626868 206.4562352
333 193.8046223 206.1626868
334 170.4336545 193.8046223
335 164.3659126 170.4336545
336 195.3047942 164.3659126
337 171.3080200 195.3047942
338 142.7757619 171.3080200
339 118.1983425 142.7757619
340 80.1370522 118.1983425
341 142.5112458 80.1370522
342 157.4822135 142.5112458
343 163.1273748 157.4822135
344 150.6338264 163.1273748
345 146.7757619 150.6338264
346 150.9047942 146.7757619
347 141.1370522 150.9047942
348 156.7759338 141.1370522
349 172.9791596 156.7759338
350 139.9469015 172.9791596
351 80.0694821 139.9469015
352 58.9081918 80.0694821
353 116.3823854 58.9081918
354 87.9533531 116.3823854
355 100.6985144 87.9533531
356 85.8049660 100.6985144
357 79.6469015 85.8049660
358 70.0759338 79.6469015
359 21.0081918 70.0759338
360 55.7470734 21.0081918
361 30.0502992 55.7470734
362 26.3180411 30.0502992
363 -13.8593782 26.3180411
364 -16.4206686 -13.8593782
365 -2.2464750 -16.4206686
366 31.7244927 -2.2464750
367 12.1696540 31.7244927
368 15.8761056 12.1696540
369 -2.3819589 15.8761056
370 -7.5529266 -2.3819589
371 -0.4206686 -7.5529266
372 NA -0.4206686
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -27.4784053 -63.181631
[2,] -21.3106633 -27.478405
[3,] -6.2880827 -21.310663
[4,] -25.0493730 -6.288083
[5,] -58.3751795 -25.049373
[6,] -35.3042117 -58.375179
[7,] -21.4590504 -35.304212
[8,] -22.0525988 -21.459050
[9,] -38.0106633 -22.052599
[10,] -31.4816311 -38.010663
[11,] -16.7493730 -31.481631
[12,] -4.7104915 -16.749373
[13,] 33.2927343 -4.710491
[14,] 46.4604763 33.292734
[15,] 74.7830569 46.460476
[16,] 119.2217666 74.783057
[17,] 91.4959601 119.221767
[18,] 156.4669279 91.495960
[19,] 144.4120892 156.466928
[20,] 129.4185408 144.412089
[21,] 160.1604763 129.418541
[22,] 116.2895085 160.160476
[23,] 125.8217666 116.289509
[24,] 154.6606481 125.821767
[25,] 159.0638739 154.660648
[26,] 133.5316159 159.063874
[27,] 107.6541965 133.531616
[28,] 87.9929062 107.654197
[29,] 44.0670997 87.992906
[30,] 43.3380675 44.067100
[31,] 4.1832288 43.338067
[32,] 8.9896804 4.183229
[33,] -19.0683841 8.989680
[34,] -11.0393519 -19.068384
[35,] -8.2070938 -11.039352
[36,] -51.1682123 -8.207094
[37,] -72.1649865 -51.168212
[38,] -71.3972445 -72.164986
[39,] -70.9746639 -71.397245
[40,] -67.2359542 -70.974664
[41,] -103.7617607 -67.235954
[42,] -86.9907929 -103.761761
[43,] -82.4456316 -86.990793
[44,] -57.3391800 -82.445632
[45,] -55.0972445 -57.339180
[46,] -53.5682123 -55.097245
[47,] -75.4359542 -53.568212
[48,] -96.1970727 -75.435954
[49,] -97.8938469 -96.197073
[50,] -109.4261049 -97.893847
[51,] -87.1035243 -109.426105
[52,] -71.9648146 -87.103524
[53,] -119.6906210 -71.964815
[54,] -91.6196533 -119.690621
[55,] -77.1744920 -91.619653
[56,] -79.0680404 -77.174492
[57,] -88.4261049 -79.068040
[58,] -99.0970727 -88.426105
[59,] -106.4648146 -99.097073
[60,] -114.7259330 -106.464815
[61,] -141.4227072 -114.725933
[62,] -132.7549653 -141.422707
[63,] -100.2323847 -132.754965
[64,] -102.4936750 -100.232385
[65,] -155.6194814 -102.493675
[66,] -135.0485137 -155.619481
[67,] -123.7033524 -135.048514
[68,] -95.8969008 -123.703352
[69,] -85.1549653 -95.896901
[70,] -62.7259330 -85.154965
[71,] -30.4936750 -62.725933
[72,] 22.2452066 -30.493675
[73,] 54.5484324 22.245207
[74,] 82.2161743 54.548432
[75,] 102.0387549 82.216174
[76,] 103.7774646 102.038755
[77,] 33.3516582 103.777465
[78,] 55.9226259 33.351658
[79,] 66.1677872 55.922626
[80,] 79.5742388 66.167787
[81,] 44.6161743 79.574239
[82,] 41.1452066 44.616174
[83,] 28.0774646 41.145207
[84,] 27.1163462 28.077465
[85,] 7.2195720 27.116346
[86,] 2.2873139 7.219572
[87,] 27.7098946 2.287314
[88,] 1.0486042 27.709895
[89,] -51.3772022 1.048604
[90,] -51.7062345 -51.377202
[91,] -33.1610732 -51.706234
[92,] -35.8546216 -33.161073
[93,] -25.8126861 -35.854622
[94,] -22.6836538 -25.812686
[95,] -18.6513958 -22.683654
[96,] -39.1125142 -18.651396
[97,] -46.4092884 -39.112514
[98,] -23.8415465 -46.409288
[99,] -23.4189658 -23.841546
[100,] 6.0197438 -23.418966
[101,] -28.7060626 6.019744
[102,] -25.4350949 -28.706063
[103,] -45.9899336 -25.435095
[104,] -48.2834820 -45.989934
[105,] -51.0415465 -48.283482
[106,] -22.3125142 -51.041546
[107,] -18.6802562 -22.312514
[108,] -31.0413746 -18.680256
[109,] -53.0381488 -31.041375
[110,] -57.0704069 -53.038149
[111,] -37.6478262 -57.070407
[112,] -16.3091165 -37.647826
[113,] -39.4349230 -16.309117
[114,] -45.4639553 -39.434923
[115,] -46.0187940 -45.463955
[116,] -31.2123424 -46.018794
[117,] -19.5704069 -31.212342
[118,] 24.5586254 -19.570407
[119,] 42.5908835 24.558625
[120,] 88.8297650 42.590883
[121,] 144.1329908 88.829765
[122,] 170.3007327 144.132991
[123,] 200.1233134 170.300733
[124,] 197.4620231 200.123313
[125,] 163.8362166 197.462023
[126,] 174.1071844 163.836217
[127,] 156.1523456 174.107184
[128,] 118.3587973 156.152346
[129,] 103.0007327 118.358797
[130,] 84.4297650 103.000733
[131,] 110.2620231 84.429765
[132,] 104.3009046 110.262023
[133,] 96.4041304 104.300905
[134,] 78.6718723 96.404130
[135,] 43.5944530 78.671872
[136,] 41.0331627 43.594453
[137,] 13.6073562 41.033163
[138,] 18.8783240 13.607356
[139,] 24.2234852 18.878324
[140,] 26.1299369 24.223485
[141,] 45.1718723 26.129937
[142,] 63.6009046 45.171872
[143,] 49.6331627 63.600905
[144,] 42.4720442 49.633163
[145,] 9.2752700 42.472044
[146,] 59.3430120 9.275270
[147,] 42.5655926 59.343012
[148,] 40.4043023 42.565593
[149,] 46.8784958 40.404302
[150,] 40.8494636 46.878496
[151,] 54.6946249 40.849464
[152,] 32.2010765 54.694625
[153,] 69.8430120 32.201076
[154,] 90.7720442 69.843012
[155,] 138.6043023 90.772044
[156,] 158.1431838 138.604302
[157,] 180.1464096 158.143184
[158,] 179.3141516 180.146410
[159,] 164.6367322 179.314152
[160,] 163.5754419 164.636732
[161,] 155.0496354 163.575442
[162,] 142.6206032 155.049635
[163,] 121.6657645 142.620603
[164,] 98.1722161 121.665764
[165,] 96.5141516 98.172216
[166,] 82.2431838 96.514152
[167,] 89.7754419 82.243184
[168,] 80.8143234 89.775442
[169,] 56.9175492 80.814323
[170,] 63.3852912 56.917549
[171,] 56.3078718 63.385291
[172,] 85.7465815 56.307872
[173,] 39.7207750 85.746581
[174,] 23.4917428 39.720775
[175,] 55.9369041 23.491743
[176,] 34.3433557 55.936904
[177,] 27.6852912 34.343356
[178,] 54.8143234 27.685291
[179,] 55.7465815 54.814323
[180,] 75.4854630 55.746581
[181,] 89.8886888 75.485463
[182,] 69.3564308 89.888689
[183,] 63.3790114 69.356431
[184,] 79.5177211 63.379011
[185,] 67.2919147 79.517721
[186,] 48.6628824 67.291915
[187,] 41.3080437 48.662882
[188,] 29.9144953 41.308044
[189,] 37.7564308 29.914495
[190,] 62.0854630 37.756431
[191,] 52.6177211 62.085463
[192,] 58.6566026 52.617721
[193,] 43.0598285 58.656603
[194,] 41.7275704 43.059828
[195,] 41.2501510 41.727570
[196,] 31.4888607 41.250151
[197,] 51.2630543 31.488861
[198,] -3.7659780 51.263054
[199,] 14.9791833 -3.765978
[200,] 3.1856349 14.979183
[201,] 12.2275704 3.185635
[202,] 2.1566026 12.227570
[203,] 8.7888607 2.156603
[204,] -4.9722577 8.788861
[205,] 8.2309681 -4.972258
[206,] -16.8012900 8.230968
[207,] 1.4212906 -16.801290
[208,] -5.8399997 1.421291
[209,] 5.7341939 -5.840000
[210,] -34.2948384 5.734194
[211,] -29.5496771 -34.294838
[212,] -44.7432255 -29.549677
[213,] -42.6012900 -44.743225
[214,] -46.7722577 -42.601290
[215,] -59.2399997 -46.772258
[216,] -80.6011181 -59.240000
[217,] -103.9978923 -80.601118
[218,] -93.6301504 -103.997892
[219,] -80.7075697 -93.630150
[220,] -53.8688601 -80.707570
[221,] -46.6946665 -53.868860
[222,] -78.1236988 -46.694667
[223,] -69.8785375 -78.123699
[224,] -84.5720859 -69.878537
[225,] -72.9301504 -84.572086
[226,] -83.6011181 -72.930150
[227,] -77.4688601 -83.601118
[228,] -95.0299785 -77.468860
[229,] -102.4267527 -95.029979
[230,] -103.1590108 -102.426753
[231,] -93.2364301 -103.159011
[232,] -93.2977205 -93.236430
[233,] -49.0235269 -93.297720
[234,] -64.1525592 -49.023527
[235,] -63.7073979 -64.152559
[236,] -51.3009463 -63.707398
[237,] -30.1590108 -51.300946
[238,] -57.1299785 -30.159011
[239,] -77.8977205 -57.129979
[240,] -109.4588389 -77.897720
[241,] -98.0556131 -109.458839
[242,] -111.5878712 -98.055613
[243,] -116.4652905 -111.587871
[244,] -114.6265809 -116.465291
[245,] -56.2523873 -114.626581
[246,] -73.2814196 -56.252387
[247,] -86.6362583 -73.281420
[248,] -86.0298067 -86.636258
[249,] -80.2878712 -86.029807
[250,] -95.7588389 -80.287871
[251,] -114.0265809 -95.758839
[252,] -135.2876993 -114.026581
[253,] -140.3844735 -135.287699
[254,] -135.7167316 -140.384474
[255,] -117.2941509 -135.716732
[256,] -120.9554412 -117.294151
[257,] -84.6812477 -120.955441
[258,] -82.7102800 -84.681248
[259,] -82.7651187 -82.710280
[260,] -56.8586671 -82.765119
[261,] -53.2167316 -56.858667
[262,] -88.2876993 -53.216732
[263,] -99.0554412 -88.287699
[264,] -245.9220435 -99.055441
[265,] -217.0188177 -245.922044
[266,] -200.8510758 -217.018818
[267,] -180.0284951 -200.851076
[268,] -176.2897854 -180.028495
[269,] -120.5155919 -176.289785
[270,] -113.6446241 -120.515592
[271,] -111.4994629 -113.644624
[272,] -87.1930112 -111.499463
[273,] -75.0510758 -87.193011
[274,] -62.3220435 -75.051076
[275,] -61.8897854 -62.322044
[276,] -51.0509039 -61.889785
[277,] -58.1476781 -51.050904
[278,] -62.5799361 -58.147678
[279,] -71.7573555 -62.579936
[280,] -81.2186458 -71.757356
[281,] -44.3444523 -81.218646
[282,] -37.5734845 -44.344452
[283,] -33.3283232 -37.573485
[284,] -38.3218716 -33.328323
[285,] -49.8799361 -38.321872
[286,] -47.4509039 -49.879936
[287,] -61.9186458 -47.450904
[288,] -53.6797643 -61.918646
[289,] -67.0765385 -53.679764
[290,] -64.5087965 -67.076538
[291,] -77.3862159 -64.508797
[292,] -92.1475062 -77.386216
[293,] -56.6733127 -92.147506
[294,] -59.2023449 -56.673313
[295,] -59.6571836 -59.202345
[296,] -62.4507320 -59.657184
[297,] -65.8087965 -62.450732
[298,] -108.2797643 -65.808797
[299,] -125.7475062 -108.279764
[300,] -136.8086247 -125.747506
[301,] -129.7053989 -136.808625
[302,] -140.7376569 -129.705399
[303,] -135.6150763 -140.737657
[304,] -152.5763666 -135.615076
[305,] -120.5021731 -152.576367
[306,] -127.4312053 -120.502173
[307,] -130.4860440 -127.431205
[308,] -117.6795924 -130.486044
[309,] -142.4376569 -117.679592
[310,] -135.2086247 -142.437657
[311,] -137.4763666 -135.208625
[312,] -109.4374851 -137.476367
[313,] -106.1342593 -109.437485
[314,] -122.3665173 -106.134259
[315,] -128.8439367 -122.366517
[316,] -124.0052270 -128.843937
[317,] -73.1310335 -124.005227
[318,] -62.3600657 -73.131033
[319,] -68.7149044 -62.360066
[320,] -19.9084528 -68.714904
[321,] -20.2665173 -19.908453
[322,] 21.7625149 -20.266517
[323,] 61.3947730 21.762515
[324,] 201.8336545 61.394773
[325,] 204.8368803 201.833655
[326,] 232.1046223 204.836880
[327,] 217.1272029 232.104622
[328,] 217.9659126 217.127203
[329,] 239.8401062 217.965913
[330,] 226.6110739 239.840106
[331,] 206.4562352 226.611074
[332,] 206.1626868 206.456235
[333,] 193.8046223 206.162687
[334,] 170.4336545 193.804622
[335,] 164.3659126 170.433655
[336,] 195.3047942 164.365913
[337,] 171.3080200 195.304794
[338,] 142.7757619 171.308020
[339,] 118.1983425 142.775762
[340,] 80.1370522 118.198343
[341,] 142.5112458 80.137052
[342,] 157.4822135 142.511246
[343,] 163.1273748 157.482214
[344,] 150.6338264 163.127375
[345,] 146.7757619 150.633826
[346,] 150.9047942 146.775762
[347,] 141.1370522 150.904794
[348,] 156.7759338 141.137052
[349,] 172.9791596 156.775934
[350,] 139.9469015 172.979160
[351,] 80.0694821 139.946902
[352,] 58.9081918 80.069482
[353,] 116.3823854 58.908192
[354,] 87.9533531 116.382385
[355,] 100.6985144 87.953353
[356,] 85.8049660 100.698514
[357,] 79.6469015 85.804966
[358,] 70.0759338 79.646902
[359,] 21.0081918 70.075934
[360,] 55.7470734 21.008192
[361,] 30.0502992 55.747073
[362,] 26.3180411 30.050299
[363,] -13.8593782 26.318041
[364,] -16.4206686 -13.859378
[365,] -2.2464750 -16.420669
[366,] 31.7244927 -2.246475
[367,] 12.1696540 31.724493
[368,] 15.8761056 12.169654
[369,] -2.3819589 15.876106
[370,] -7.5529266 -2.381959
[371,] -0.4206686 -7.552927
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -27.4784053 -63.181631
2 -21.3106633 -27.478405
3 -6.2880827 -21.310663
4 -25.0493730 -6.288083
5 -58.3751795 -25.049373
6 -35.3042117 -58.375179
7 -21.4590504 -35.304212
8 -22.0525988 -21.459050
9 -38.0106633 -22.052599
10 -31.4816311 -38.010663
11 -16.7493730 -31.481631
12 -4.7104915 -16.749373
13 33.2927343 -4.710491
14 46.4604763 33.292734
15 74.7830569 46.460476
16 119.2217666 74.783057
17 91.4959601 119.221767
18 156.4669279 91.495960
19 144.4120892 156.466928
20 129.4185408 144.412089
21 160.1604763 129.418541
22 116.2895085 160.160476
23 125.8217666 116.289509
24 154.6606481 125.821767
25 159.0638739 154.660648
26 133.5316159 159.063874
27 107.6541965 133.531616
28 87.9929062 107.654197
29 44.0670997 87.992906
30 43.3380675 44.067100
31 4.1832288 43.338067
32 8.9896804 4.183229
33 -19.0683841 8.989680
34 -11.0393519 -19.068384
35 -8.2070938 -11.039352
36 -51.1682123 -8.207094
37 -72.1649865 -51.168212
38 -71.3972445 -72.164986
39 -70.9746639 -71.397245
40 -67.2359542 -70.974664
41 -103.7617607 -67.235954
42 -86.9907929 -103.761761
43 -82.4456316 -86.990793
44 -57.3391800 -82.445632
45 -55.0972445 -57.339180
46 -53.5682123 -55.097245
47 -75.4359542 -53.568212
48 -96.1970727 -75.435954
49 -97.8938469 -96.197073
50 -109.4261049 -97.893847
51 -87.1035243 -109.426105
52 -71.9648146 -87.103524
53 -119.6906210 -71.964815
54 -91.6196533 -119.690621
55 -77.1744920 -91.619653
56 -79.0680404 -77.174492
57 -88.4261049 -79.068040
58 -99.0970727 -88.426105
59 -106.4648146 -99.097073
60 -114.7259330 -106.464815
61 -141.4227072 -114.725933
62 -132.7549653 -141.422707
63 -100.2323847 -132.754965
64 -102.4936750 -100.232385
65 -155.6194814 -102.493675
66 -135.0485137 -155.619481
67 -123.7033524 -135.048514
68 -95.8969008 -123.703352
69 -85.1549653 -95.896901
70 -62.7259330 -85.154965
71 -30.4936750 -62.725933
72 22.2452066 -30.493675
73 54.5484324 22.245207
74 82.2161743 54.548432
75 102.0387549 82.216174
76 103.7774646 102.038755
77 33.3516582 103.777465
78 55.9226259 33.351658
79 66.1677872 55.922626
80 79.5742388 66.167787
81 44.6161743 79.574239
82 41.1452066 44.616174
83 28.0774646 41.145207
84 27.1163462 28.077465
85 7.2195720 27.116346
86 2.2873139 7.219572
87 27.7098946 2.287314
88 1.0486042 27.709895
89 -51.3772022 1.048604
90 -51.7062345 -51.377202
91 -33.1610732 -51.706234
92 -35.8546216 -33.161073
93 -25.8126861 -35.854622
94 -22.6836538 -25.812686
95 -18.6513958 -22.683654
96 -39.1125142 -18.651396
97 -46.4092884 -39.112514
98 -23.8415465 -46.409288
99 -23.4189658 -23.841546
100 6.0197438 -23.418966
101 -28.7060626 6.019744
102 -25.4350949 -28.706063
103 -45.9899336 -25.435095
104 -48.2834820 -45.989934
105 -51.0415465 -48.283482
106 -22.3125142 -51.041546
107 -18.6802562 -22.312514
108 -31.0413746 -18.680256
109 -53.0381488 -31.041375
110 -57.0704069 -53.038149
111 -37.6478262 -57.070407
112 -16.3091165 -37.647826
113 -39.4349230 -16.309117
114 -45.4639553 -39.434923
115 -46.0187940 -45.463955
116 -31.2123424 -46.018794
117 -19.5704069 -31.212342
118 24.5586254 -19.570407
119 42.5908835 24.558625
120 88.8297650 42.590883
121 144.1329908 88.829765
122 170.3007327 144.132991
123 200.1233134 170.300733
124 197.4620231 200.123313
125 163.8362166 197.462023
126 174.1071844 163.836217
127 156.1523456 174.107184
128 118.3587973 156.152346
129 103.0007327 118.358797
130 84.4297650 103.000733
131 110.2620231 84.429765
132 104.3009046 110.262023
133 96.4041304 104.300905
134 78.6718723 96.404130
135 43.5944530 78.671872
136 41.0331627 43.594453
137 13.6073562 41.033163
138 18.8783240 13.607356
139 24.2234852 18.878324
140 26.1299369 24.223485
141 45.1718723 26.129937
142 63.6009046 45.171872
143 49.6331627 63.600905
144 42.4720442 49.633163
145 9.2752700 42.472044
146 59.3430120 9.275270
147 42.5655926 59.343012
148 40.4043023 42.565593
149 46.8784958 40.404302
150 40.8494636 46.878496
151 54.6946249 40.849464
152 32.2010765 54.694625
153 69.8430120 32.201076
154 90.7720442 69.843012
155 138.6043023 90.772044
156 158.1431838 138.604302
157 180.1464096 158.143184
158 179.3141516 180.146410
159 164.6367322 179.314152
160 163.5754419 164.636732
161 155.0496354 163.575442
162 142.6206032 155.049635
163 121.6657645 142.620603
164 98.1722161 121.665764
165 96.5141516 98.172216
166 82.2431838 96.514152
167 89.7754419 82.243184
168 80.8143234 89.775442
169 56.9175492 80.814323
170 63.3852912 56.917549
171 56.3078718 63.385291
172 85.7465815 56.307872
173 39.7207750 85.746581
174 23.4917428 39.720775
175 55.9369041 23.491743
176 34.3433557 55.936904
177 27.6852912 34.343356
178 54.8143234 27.685291
179 55.7465815 54.814323
180 75.4854630 55.746581
181 89.8886888 75.485463
182 69.3564308 89.888689
183 63.3790114 69.356431
184 79.5177211 63.379011
185 67.2919147 79.517721
186 48.6628824 67.291915
187 41.3080437 48.662882
188 29.9144953 41.308044
189 37.7564308 29.914495
190 62.0854630 37.756431
191 52.6177211 62.085463
192 58.6566026 52.617721
193 43.0598285 58.656603
194 41.7275704 43.059828
195 41.2501510 41.727570
196 31.4888607 41.250151
197 51.2630543 31.488861
198 -3.7659780 51.263054
199 14.9791833 -3.765978
200 3.1856349 14.979183
201 12.2275704 3.185635
202 2.1566026 12.227570
203 8.7888607 2.156603
204 -4.9722577 8.788861
205 8.2309681 -4.972258
206 -16.8012900 8.230968
207 1.4212906 -16.801290
208 -5.8399997 1.421291
209 5.7341939 -5.840000
210 -34.2948384 5.734194
211 -29.5496771 -34.294838
212 -44.7432255 -29.549677
213 -42.6012900 -44.743225
214 -46.7722577 -42.601290
215 -59.2399997 -46.772258
216 -80.6011181 -59.240000
217 -103.9978923 -80.601118
218 -93.6301504 -103.997892
219 -80.7075697 -93.630150
220 -53.8688601 -80.707570
221 -46.6946665 -53.868860
222 -78.1236988 -46.694667
223 -69.8785375 -78.123699
224 -84.5720859 -69.878537
225 -72.9301504 -84.572086
226 -83.6011181 -72.930150
227 -77.4688601 -83.601118
228 -95.0299785 -77.468860
229 -102.4267527 -95.029979
230 -103.1590108 -102.426753
231 -93.2364301 -103.159011
232 -93.2977205 -93.236430
233 -49.0235269 -93.297720
234 -64.1525592 -49.023527
235 -63.7073979 -64.152559
236 -51.3009463 -63.707398
237 -30.1590108 -51.300946
238 -57.1299785 -30.159011
239 -77.8977205 -57.129979
240 -109.4588389 -77.897720
241 -98.0556131 -109.458839
242 -111.5878712 -98.055613
243 -116.4652905 -111.587871
244 -114.6265809 -116.465291
245 -56.2523873 -114.626581
246 -73.2814196 -56.252387
247 -86.6362583 -73.281420
248 -86.0298067 -86.636258
249 -80.2878712 -86.029807
250 -95.7588389 -80.287871
251 -114.0265809 -95.758839
252 -135.2876993 -114.026581
253 -140.3844735 -135.287699
254 -135.7167316 -140.384474
255 -117.2941509 -135.716732
256 -120.9554412 -117.294151
257 -84.6812477 -120.955441
258 -82.7102800 -84.681248
259 -82.7651187 -82.710280
260 -56.8586671 -82.765119
261 -53.2167316 -56.858667
262 -88.2876993 -53.216732
263 -99.0554412 -88.287699
264 -245.9220435 -99.055441
265 -217.0188177 -245.922044
266 -200.8510758 -217.018818
267 -180.0284951 -200.851076
268 -176.2897854 -180.028495
269 -120.5155919 -176.289785
270 -113.6446241 -120.515592
271 -111.4994629 -113.644624
272 -87.1930112 -111.499463
273 -75.0510758 -87.193011
274 -62.3220435 -75.051076
275 -61.8897854 -62.322044
276 -51.0509039 -61.889785
277 -58.1476781 -51.050904
278 -62.5799361 -58.147678
279 -71.7573555 -62.579936
280 -81.2186458 -71.757356
281 -44.3444523 -81.218646
282 -37.5734845 -44.344452
283 -33.3283232 -37.573485
284 -38.3218716 -33.328323
285 -49.8799361 -38.321872
286 -47.4509039 -49.879936
287 -61.9186458 -47.450904
288 -53.6797643 -61.918646
289 -67.0765385 -53.679764
290 -64.5087965 -67.076538
291 -77.3862159 -64.508797
292 -92.1475062 -77.386216
293 -56.6733127 -92.147506
294 -59.2023449 -56.673313
295 -59.6571836 -59.202345
296 -62.4507320 -59.657184
297 -65.8087965 -62.450732
298 -108.2797643 -65.808797
299 -125.7475062 -108.279764
300 -136.8086247 -125.747506
301 -129.7053989 -136.808625
302 -140.7376569 -129.705399
303 -135.6150763 -140.737657
304 -152.5763666 -135.615076
305 -120.5021731 -152.576367
306 -127.4312053 -120.502173
307 -130.4860440 -127.431205
308 -117.6795924 -130.486044
309 -142.4376569 -117.679592
310 -135.2086247 -142.437657
311 -137.4763666 -135.208625
312 -109.4374851 -137.476367
313 -106.1342593 -109.437485
314 -122.3665173 -106.134259
315 -128.8439367 -122.366517
316 -124.0052270 -128.843937
317 -73.1310335 -124.005227
318 -62.3600657 -73.131033
319 -68.7149044 -62.360066
320 -19.9084528 -68.714904
321 -20.2665173 -19.908453
322 21.7625149 -20.266517
323 61.3947730 21.762515
324 201.8336545 61.394773
325 204.8368803 201.833655
326 232.1046223 204.836880
327 217.1272029 232.104622
328 217.9659126 217.127203
329 239.8401062 217.965913
330 226.6110739 239.840106
331 206.4562352 226.611074
332 206.1626868 206.456235
333 193.8046223 206.162687
334 170.4336545 193.804622
335 164.3659126 170.433655
336 195.3047942 164.365913
337 171.3080200 195.304794
338 142.7757619 171.308020
339 118.1983425 142.775762
340 80.1370522 118.198343
341 142.5112458 80.137052
342 157.4822135 142.511246
343 163.1273748 157.482214
344 150.6338264 163.127375
345 146.7757619 150.633826
346 150.9047942 146.775762
347 141.1370522 150.904794
348 156.7759338 141.137052
349 172.9791596 156.775934
350 139.9469015 172.979160
351 80.0694821 139.946902
352 58.9081918 80.069482
353 116.3823854 58.908192
354 87.9533531 116.382385
355 100.6985144 87.953353
356 85.8049660 100.698514
357 79.6469015 85.804966
358 70.0759338 79.646902
359 21.0081918 70.075934
360 55.7470734 21.008192
361 30.0502992 55.747073
362 26.3180411 30.050299
363 -13.8593782 26.318041
364 -16.4206686 -13.859378
365 -2.2464750 -16.420669
366 31.7244927 -2.246475
367 12.1696540 31.724493
368 15.8761056 12.169654
369 -2.3819589 15.876106
370 -7.5529266 -2.381959
371 -0.4206686 -7.552927
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7s99c1291062748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xf1q1291062748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xf1q1291062748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xf1q1291062748.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/110ghe1291062748.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12mgyk1291062748.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13bhdv1291062748.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14lrcy1291062748.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/157rsm1291062748.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16l1qv1291062748.tab")
+ }
>
> try(system("convert tmp/1j63z1291062748.ps tmp/1j63z1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j63z1291062748.ps tmp/2j63z1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j63z1291062748.ps tmp/3j63z1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uxkk1291062748.ps tmp/4uxkk1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uxkk1291062748.ps tmp/5uxkk1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uxkk1291062748.ps tmp/6uxkk1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/7s99c1291062748.ps tmp/7s99c1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xf1q1291062748.ps tmp/8xf1q1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xf1q1291062748.ps tmp/9xf1q1291062748.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xf1q1291062748.ps tmp/10xf1q1291062748.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.204 2.025 21.556