R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(420.677
+ ,417.428
+ ,423.245
+ ,423.113
+ ,418.873
+ ,405.733
+ ,397.812
+ ,389.918
+ ,391.116
+ ,443.814
+ ,460.373
+ ,455.422
+ ,456.288
+ ,452.233
+ ,459.256
+ ,461.146
+ ,451.391
+ ,443.101
+ ,438.81
+ ,430.457
+ ,435.721
+ ,488.28
+ ,505.814
+ ,502.338
+ ,500.91
+ ,501.434
+ ,515.476
+ ,520.862
+ ,519.517
+ ,511.805
+ ,508.607
+ ,505.327
+ ,511.435
+ ,570.158
+ ,591.665
+ ,593.572
+ ,586.346
+ ,586.063
+ ,591.504
+ ,594.033
+ ,585.597
+ ,572.45
+ ,562.917
+ ,554.675
+ ,553.997
+ ,601.31
+ ,622.255
+ ,616.735
+ ,606.48
+ ,595.079
+ ,598.588
+ ,599.917
+ ,591.573
+ ,575.489
+ ,567.223
+ ,555.338
+ ,555.252
+ ,608.249
+ ,630.859
+ ,628.632
+ ,624.435
+ ,609.67
+ ,615.83
+ ,621.17
+ ,604.212
+ ,584.348
+ ,573.717
+ ,555.234
+ ,544.897
+ ,598.866
+ ,620.081
+ ,607.699
+ ,589.96
+ ,578.665
+ ,580.166
+ ,579.457
+ ,571.56
+ ,560.46
+ ,551.397
+ ,536.763
+ ,540.562
+ ,588.184
+ ,607.049
+ ,598.968
+ ,577.644
+ ,562.64
+ ,565.867
+ ,561.274
+ ,554.144
+ ,539.9
+ ,526.271
+ ,511.841
+ ,505.282
+ ,554.083
+ ,584.225
+ ,568.858
+ ,539.516
+ ,521.612
+ ,525.562
+ ,526.519
+ ,515.713
+ ,503.454
+ ,489.301
+ ,479.02
+ ,475.102
+ ,523.682
+ ,551.528
+ ,531.626
+ ,511.037
+ ,492.417
+ ,492.188
+ ,492.865
+ ,480.961
+ ,461.935
+ ,456.608
+ ,441.977
+ ,439.148
+ ,488.18
+ ,520.564
+ ,501.492
+ ,485.025
+ ,464.196
+ ,460.17
+ ,467.037
+ ,460.07
+ ,447.988
+ ,442.867
+ ,436.087
+ ,431.328
+ ,484.015
+ ,509.673
+ ,512.927
+ ,502.831
+ ,470.984
+ ,471.067
+ ,476.049
+ ,474.605
+ ,470.439
+ ,461.251
+ ,454.724
+ ,455.626
+ ,516.847
+ ,525.192
+ ,522.975
+ ,518.585
+ ,509.239
+ ,512.238
+ ,519.164
+ ,517.009
+ ,509.933
+ ,509.127
+ ,500.857
+ ,506.971
+ ,569.323
+ ,579.714
+ ,577.992
+ ,565.464
+ ,547.344
+ ,554.788
+ ,562.325
+ ,560.854
+ ,555.332
+ ,543.599
+ ,536.662
+ ,542.722
+ ,593.53
+ ,610.763
+ ,612.613
+ ,611.324
+ ,594.167
+ ,595.454
+ ,590.865
+ ,589.379
+ ,584.428
+ ,573.1
+ ,567.456
+ ,569.028
+ ,620.735
+ ,628.884
+ ,628.232
+ ,612.117
+ ,595.404
+ ,597.141
+ ,593.408
+ ,590.072
+ ,579.799
+ ,574.205
+ ,572.775
+ ,572.942
+ ,619.567
+ ,625.809
+ ,619.916
+ ,587.625
+ ,565.742
+ ,557.274
+ ,560.576
+ ,548.854
+ ,531.673
+ ,525.919
+ ,511.038
+ ,498.662
+ ,555.362
+ ,564.591
+ ,541.657
+ ,527.07
+ ,509.846
+ ,514.258
+ ,516.922
+ ,507.561
+ ,492.622
+ ,490.243
+ ,469.357
+ ,477.58
+ ,528.379
+ ,533.59
+ ,517.945
+ ,506.174
+ ,501.866
+ ,516.141
+ ,528.222
+ ,532.638
+ ,536.322
+ ,536.535
+ ,523.597
+ ,536.214
+ ,586.57
+ ,596.594
+ ,580.523
+ ,564.478
+ ,557.56
+ ,575.093
+ ,580.112
+ ,574.761
+ ,563.25
+ ,551.531
+ ,537.034
+ ,544.686
+ ,600.991
+ ,604.378
+ ,586.111
+ ,563.668
+ ,548.604
+ ,551.174
+ ,555.654
+ ,547.97
+ ,540.324
+ ,530.577
+ ,520.579
+ ,518.654
+ ,572.273
+ ,581.302
+ ,563.28
+ ,547.612)
+ ,dim=c(1
+ ,253)
+ ,dimnames=list(c('Unemployment')
+ ,1:253))
> y <- array(NA,dim=c(1,253),dimnames=list(c('Unemployment'),1:253))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> 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
Unemployment M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 420.677 1 0 0 0 0 0 0 0 0 0 0
2 417.428 0 1 0 0 0 0 0 0 0 0 0
3 423.245 0 0 1 0 0 0 0 0 0 0 0
4 423.113 0 0 0 1 0 0 0 0 0 0 0
5 418.873 0 0 0 0 1 0 0 0 0 0 0
6 405.733 0 0 0 0 0 1 0 0 0 0 0
7 397.812 0 0 0 0 0 0 1 0 0 0 0
8 389.918 0 0 0 0 0 0 0 1 0 0 0
9 391.116 0 0 0 0 0 0 0 0 1 0 0
10 443.814 0 0 0 0 0 0 0 0 0 1 0
11 460.373 0 0 0 0 0 0 0 0 0 0 1
12 455.422 0 0 0 0 0 0 0 0 0 0 0
13 456.288 1 0 0 0 0 0 0 0 0 0 0
14 452.233 0 1 0 0 0 0 0 0 0 0 0
15 459.256 0 0 1 0 0 0 0 0 0 0 0
16 461.146 0 0 0 1 0 0 0 0 0 0 0
17 451.391 0 0 0 0 1 0 0 0 0 0 0
18 443.101 0 0 0 0 0 1 0 0 0 0 0
19 438.810 0 0 0 0 0 0 1 0 0 0 0
20 430.457 0 0 0 0 0 0 0 1 0 0 0
21 435.721 0 0 0 0 0 0 0 0 1 0 0
22 488.280 0 0 0 0 0 0 0 0 0 1 0
23 505.814 0 0 0 0 0 0 0 0 0 0 1
24 502.338 0 0 0 0 0 0 0 0 0 0 0
25 500.910 1 0 0 0 0 0 0 0 0 0 0
26 501.434 0 1 0 0 0 0 0 0 0 0 0
27 515.476 0 0 1 0 0 0 0 0 0 0 0
28 520.862 0 0 0 1 0 0 0 0 0 0 0
29 519.517 0 0 0 0 1 0 0 0 0 0 0
30 511.805 0 0 0 0 0 1 0 0 0 0 0
31 508.607 0 0 0 0 0 0 1 0 0 0 0
32 505.327 0 0 0 0 0 0 0 1 0 0 0
33 511.435 0 0 0 0 0 0 0 0 1 0 0
34 570.158 0 0 0 0 0 0 0 0 0 1 0
35 591.665 0 0 0 0 0 0 0 0 0 0 1
36 593.572 0 0 0 0 0 0 0 0 0 0 0
37 586.346 1 0 0 0 0 0 0 0 0 0 0
38 586.063 0 1 0 0 0 0 0 0 0 0 0
39 591.504 0 0 1 0 0 0 0 0 0 0 0
40 594.033 0 0 0 1 0 0 0 0 0 0 0
41 585.597 0 0 0 0 1 0 0 0 0 0 0
42 572.450 0 0 0 0 0 1 0 0 0 0 0
43 562.917 0 0 0 0 0 0 1 0 0 0 0
44 554.675 0 0 0 0 0 0 0 1 0 0 0
45 553.997 0 0 0 0 0 0 0 0 1 0 0
46 601.310 0 0 0 0 0 0 0 0 0 1 0
47 622.255 0 0 0 0 0 0 0 0 0 0 1
48 616.735 0 0 0 0 0 0 0 0 0 0 0
49 606.480 1 0 0 0 0 0 0 0 0 0 0
50 595.079 0 1 0 0 0 0 0 0 0 0 0
51 598.588 0 0 1 0 0 0 0 0 0 0 0
52 599.917 0 0 0 1 0 0 0 0 0 0 0
53 591.573 0 0 0 0 1 0 0 0 0 0 0
54 575.489 0 0 0 0 0 1 0 0 0 0 0
55 567.223 0 0 0 0 0 0 1 0 0 0 0
56 555.338 0 0 0 0 0 0 0 1 0 0 0
57 555.252 0 0 0 0 0 0 0 0 1 0 0
58 608.249 0 0 0 0 0 0 0 0 0 1 0
59 630.859 0 0 0 0 0 0 0 0 0 0 1
60 628.632 0 0 0 0 0 0 0 0 0 0 0
61 624.435 1 0 0 0 0 0 0 0 0 0 0
62 609.670 0 1 0 0 0 0 0 0 0 0 0
63 615.830 0 0 1 0 0 0 0 0 0 0 0
64 621.170 0 0 0 1 0 0 0 0 0 0 0
65 604.212 0 0 0 0 1 0 0 0 0 0 0
66 584.348 0 0 0 0 0 1 0 0 0 0 0
67 573.717 0 0 0 0 0 0 1 0 0 0 0
68 555.234 0 0 0 0 0 0 0 1 0 0 0
69 544.897 0 0 0 0 0 0 0 0 1 0 0
70 598.866 0 0 0 0 0 0 0 0 0 1 0
71 620.081 0 0 0 0 0 0 0 0 0 0 1
72 607.699 0 0 0 0 0 0 0 0 0 0 0
73 589.960 1 0 0 0 0 0 0 0 0 0 0
74 578.665 0 1 0 0 0 0 0 0 0 0 0
75 580.166 0 0 1 0 0 0 0 0 0 0 0
76 579.457 0 0 0 1 0 0 0 0 0 0 0
77 571.560 0 0 0 0 1 0 0 0 0 0 0
78 560.460 0 0 0 0 0 1 0 0 0 0 0
79 551.397 0 0 0 0 0 0 1 0 0 0 0
80 536.763 0 0 0 0 0 0 0 1 0 0 0
81 540.562 0 0 0 0 0 0 0 0 1 0 0
82 588.184 0 0 0 0 0 0 0 0 0 1 0
83 607.049 0 0 0 0 0 0 0 0 0 0 1
84 598.968 0 0 0 0 0 0 0 0 0 0 0
85 577.644 1 0 0 0 0 0 0 0 0 0 0
86 562.640 0 1 0 0 0 0 0 0 0 0 0
87 565.867 0 0 1 0 0 0 0 0 0 0 0
88 561.274 0 0 0 1 0 0 0 0 0 0 0
89 554.144 0 0 0 0 1 0 0 0 0 0 0
90 539.900 0 0 0 0 0 1 0 0 0 0 0
91 526.271 0 0 0 0 0 0 1 0 0 0 0
92 511.841 0 0 0 0 0 0 0 1 0 0 0
93 505.282 0 0 0 0 0 0 0 0 1 0 0
94 554.083 0 0 0 0 0 0 0 0 0 1 0
95 584.225 0 0 0 0 0 0 0 0 0 0 1
96 568.858 0 0 0 0 0 0 0 0 0 0 0
97 539.516 1 0 0 0 0 0 0 0 0 0 0
98 521.612 0 1 0 0 0 0 0 0 0 0 0
99 525.562 0 0 1 0 0 0 0 0 0 0 0
100 526.519 0 0 0 1 0 0 0 0 0 0 0
101 515.713 0 0 0 0 1 0 0 0 0 0 0
102 503.454 0 0 0 0 0 1 0 0 0 0 0
103 489.301 0 0 0 0 0 0 1 0 0 0 0
104 479.020 0 0 0 0 0 0 0 1 0 0 0
105 475.102 0 0 0 0 0 0 0 0 1 0 0
106 523.682 0 0 0 0 0 0 0 0 0 1 0
107 551.528 0 0 0 0 0 0 0 0 0 0 1
108 531.626 0 0 0 0 0 0 0 0 0 0 0
109 511.037 1 0 0 0 0 0 0 0 0 0 0
110 492.417 0 1 0 0 0 0 0 0 0 0 0
111 492.188 0 0 1 0 0 0 0 0 0 0 0
112 492.865 0 0 0 1 0 0 0 0 0 0 0
113 480.961 0 0 0 0 1 0 0 0 0 0 0
114 461.935 0 0 0 0 0 1 0 0 0 0 0
115 456.608 0 0 0 0 0 0 1 0 0 0 0
116 441.977 0 0 0 0 0 0 0 1 0 0 0
117 439.148 0 0 0 0 0 0 0 0 1 0 0
118 488.180 0 0 0 0 0 0 0 0 0 1 0
119 520.564 0 0 0 0 0 0 0 0 0 0 1
120 501.492 0 0 0 0 0 0 0 0 0 0 0
121 485.025 1 0 0 0 0 0 0 0 0 0 0
122 464.196 0 1 0 0 0 0 0 0 0 0 0
123 460.170 0 0 1 0 0 0 0 0 0 0 0
124 467.037 0 0 0 1 0 0 0 0 0 0 0
125 460.070 0 0 0 0 1 0 0 0 0 0 0
126 447.988 0 0 0 0 0 1 0 0 0 0 0
127 442.867 0 0 0 0 0 0 1 0 0 0 0
128 436.087 0 0 0 0 0 0 0 1 0 0 0
129 431.328 0 0 0 0 0 0 0 0 1 0 0
130 484.015 0 0 0 0 0 0 0 0 0 1 0
131 509.673 0 0 0 0 0 0 0 0 0 0 1
132 512.927 0 0 0 0 0 0 0 0 0 0 0
133 502.831 1 0 0 0 0 0 0 0 0 0 0
134 470.984 0 1 0 0 0 0 0 0 0 0 0
135 471.067 0 0 1 0 0 0 0 0 0 0 0
136 476.049 0 0 0 1 0 0 0 0 0 0 0
137 474.605 0 0 0 0 1 0 0 0 0 0 0
138 470.439 0 0 0 0 0 1 0 0 0 0 0
139 461.251 0 0 0 0 0 0 1 0 0 0 0
140 454.724 0 0 0 0 0 0 0 1 0 0 0
141 455.626 0 0 0 0 0 0 0 0 1 0 0
142 516.847 0 0 0 0 0 0 0 0 0 1 0
143 525.192 0 0 0 0 0 0 0 0 0 0 1
144 522.975 0 0 0 0 0 0 0 0 0 0 0
145 518.585 1 0 0 0 0 0 0 0 0 0 0
146 509.239 0 1 0 0 0 0 0 0 0 0 0
147 512.238 0 0 1 0 0 0 0 0 0 0 0
148 519.164 0 0 0 1 0 0 0 0 0 0 0
149 517.009 0 0 0 0 1 0 0 0 0 0 0
150 509.933 0 0 0 0 0 1 0 0 0 0 0
151 509.127 0 0 0 0 0 0 1 0 0 0 0
152 500.857 0 0 0 0 0 0 0 1 0 0 0
153 506.971 0 0 0 0 0 0 0 0 1 0 0
154 569.323 0 0 0 0 0 0 0 0 0 1 0
155 579.714 0 0 0 0 0 0 0 0 0 0 1
156 577.992 0 0 0 0 0 0 0 0 0 0 0
157 565.464 1 0 0 0 0 0 0 0 0 0 0
158 547.344 0 1 0 0 0 0 0 0 0 0 0
159 554.788 0 0 1 0 0 0 0 0 0 0 0
160 562.325 0 0 0 1 0 0 0 0 0 0 0
161 560.854 0 0 0 0 1 0 0 0 0 0 0
162 555.332 0 0 0 0 0 1 0 0 0 0 0
163 543.599 0 0 0 0 0 0 1 0 0 0 0
164 536.662 0 0 0 0 0 0 0 1 0 0 0
165 542.722 0 0 0 0 0 0 0 0 1 0 0
166 593.530 0 0 0 0 0 0 0 0 0 1 0
167 610.763 0 0 0 0 0 0 0 0 0 0 1
168 612.613 0 0 0 0 0 0 0 0 0 0 0
169 611.324 1 0 0 0 0 0 0 0 0 0 0
170 594.167 0 1 0 0 0 0 0 0 0 0 0
171 595.454 0 0 1 0 0 0 0 0 0 0 0
172 590.865 0 0 0 1 0 0 0 0 0 0 0
173 589.379 0 0 0 0 1 0 0 0 0 0 0
174 584.428 0 0 0 0 0 1 0 0 0 0 0
175 573.100 0 0 0 0 0 0 1 0 0 0 0
176 567.456 0 0 0 0 0 0 0 1 0 0 0
177 569.028 0 0 0 0 0 0 0 0 1 0 0
178 620.735 0 0 0 0 0 0 0 0 0 1 0
179 628.884 0 0 0 0 0 0 0 0 0 0 1
180 628.232 0 0 0 0 0 0 0 0 0 0 0
181 612.117 1 0 0 0 0 0 0 0 0 0 0
182 595.404 0 1 0 0 0 0 0 0 0 0 0
183 597.141 0 0 1 0 0 0 0 0 0 0 0
184 593.408 0 0 0 1 0 0 0 0 0 0 0
185 590.072 0 0 0 0 1 0 0 0 0 0 0
186 579.799 0 0 0 0 0 1 0 0 0 0 0
187 574.205 0 0 0 0 0 0 1 0 0 0 0
188 572.775 0 0 0 0 0 0 0 1 0 0 0
189 572.942 0 0 0 0 0 0 0 0 1 0 0
190 619.567 0 0 0 0 0 0 0 0 0 1 0
191 625.809 0 0 0 0 0 0 0 0 0 0 1
192 619.916 0 0 0 0 0 0 0 0 0 0 0
193 587.625 1 0 0 0 0 0 0 0 0 0 0
194 565.742 0 1 0 0 0 0 0 0 0 0 0
195 557.274 0 0 1 0 0 0 0 0 0 0 0
196 560.576 0 0 0 1 0 0 0 0 0 0 0
197 548.854 0 0 0 0 1 0 0 0 0 0 0
198 531.673 0 0 0 0 0 1 0 0 0 0 0
199 525.919 0 0 0 0 0 0 1 0 0 0 0
200 511.038 0 0 0 0 0 0 0 1 0 0 0
201 498.662 0 0 0 0 0 0 0 0 1 0 0
202 555.362 0 0 0 0 0 0 0 0 0 1 0
203 564.591 0 0 0 0 0 0 0 0 0 0 1
204 541.657 0 0 0 0 0 0 0 0 0 0 0
205 527.070 1 0 0 0 0 0 0 0 0 0 0
206 509.846 0 1 0 0 0 0 0 0 0 0 0
207 514.258 0 0 1 0 0 0 0 0 0 0 0
208 516.922 0 0 0 1 0 0 0 0 0 0 0
209 507.561 0 0 0 0 1 0 0 0 0 0 0
210 492.622 0 0 0 0 0 1 0 0 0 0 0
211 490.243 0 0 0 0 0 0 1 0 0 0 0
212 469.357 0 0 0 0 0 0 0 1 0 0 0
213 477.580 0 0 0 0 0 0 0 0 1 0 0
214 528.379 0 0 0 0 0 0 0 0 0 1 0
215 533.590 0 0 0 0 0 0 0 0 0 0 1
216 517.945 0 0 0 0 0 0 0 0 0 0 0
217 506.174 1 0 0 0 0 0 0 0 0 0 0
218 501.866 0 1 0 0 0 0 0 0 0 0 0
219 516.141 0 0 1 0 0 0 0 0 0 0 0
220 528.222 0 0 0 1 0 0 0 0 0 0 0
221 532.638 0 0 0 0 1 0 0 0 0 0 0
222 536.322 0 0 0 0 0 1 0 0 0 0 0
223 536.535 0 0 0 0 0 0 1 0 0 0 0
224 523.597 0 0 0 0 0 0 0 1 0 0 0
225 536.214 0 0 0 0 0 0 0 0 1 0 0
226 586.570 0 0 0 0 0 0 0 0 0 1 0
227 596.594 0 0 0 0 0 0 0 0 0 0 1
228 580.523 0 0 0 0 0 0 0 0 0 0 0
229 564.478 1 0 0 0 0 0 0 0 0 0 0
230 557.560 0 1 0 0 0 0 0 0 0 0 0
231 575.093 0 0 1 0 0 0 0 0 0 0 0
232 580.112 0 0 0 1 0 0 0 0 0 0 0
233 574.761 0 0 0 0 1 0 0 0 0 0 0
234 563.250 0 0 0 0 0 1 0 0 0 0 0
235 551.531 0 0 0 0 0 0 1 0 0 0 0
236 537.034 0 0 0 0 0 0 0 1 0 0 0
237 544.686 0 0 0 0 0 0 0 0 1 0 0
238 600.991 0 0 0 0 0 0 0 0 0 1 0
239 604.378 0 0 0 0 0 0 0 0 0 0 1
240 586.111 0 0 0 0 0 0 0 0 0 0 0
241 563.668 1 0 0 0 0 0 0 0 0 0 0
242 548.604 0 1 0 0 0 0 0 0 0 0 0
243 551.174 0 0 1 0 0 0 0 0 0 0 0
244 555.654 0 0 0 1 0 0 0 0 0 0 0
245 547.970 0 0 0 0 1 0 0 0 0 0 0
246 540.324 0 0 0 0 0 1 0 0 0 0 0
247 530.577 0 0 0 0 0 0 1 0 0 0 0
248 520.579 0 0 0 0 0 0 0 1 0 0 0
249 518.654 0 0 0 0 0 0 0 0 1 0 0
250 572.273 0 0 0 0 0 0 0 0 0 1 0
251 581.302 0 0 0 0 0 0 0 0 0 0 1
252 563.280 0 0 0 0 0 0 0 0 0 0 0
253 547.612 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
565.215 -19.521 -32.730 -28.430 -25.658 -32.009
M6 M7 M8 M9 M10 M11
-42.797 -50.376 -60.895 -60.123 -7.482 8.828
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-125.02 -34.96 12.78 41.56 81.61
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 565.215 11.314 49.958 < 2e-16 ***
M1 -19.521 15.817 -1.234 0.218344
M2 -32.730 16.000 -2.046 0.041883 *
M3 -28.430 16.000 -1.777 0.076850 .
M4 -25.658 16.000 -1.604 0.110104
M5 -32.009 16.000 -2.001 0.046559 *
M6 -42.797 16.000 -2.675 0.007990 **
M7 -50.376 16.000 -3.148 0.001848 **
M8 -60.895 16.000 -3.806 0.000179 ***
M9 -60.123 16.000 -3.758 0.000215 ***
M10 -7.482 16.000 -0.468 0.640491
M11 8.828 16.000 0.552 0.581628
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 51.85 on 241 degrees of freedom
Multiple R-squared: 0.1512, Adjusted R-squared: 0.1125
F-statistic: 3.904 on 11 and 241 DF, p-value: 3.256e-05
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.18349353 3.669871e-01 8.165065e-01
[2,] 0.14090336 2.818067e-01 8.590966e-01
[3,] 0.09919295 1.983859e-01 9.008071e-01
[4,] 0.07926080 1.585216e-01 9.207392e-01
[5,] 0.06964140 1.392828e-01 9.303586e-01
[6,] 0.06095708 1.219142e-01 9.390429e-01
[7,] 0.05832479 1.166496e-01 9.416752e-01
[8,] 0.05564950 1.112990e-01 9.443505e-01
[9,] 0.05413598 1.082720e-01 9.458640e-01
[10,] 0.05376684 1.075337e-01 9.462332e-01
[11,] 0.08692906 1.738581e-01 9.130709e-01
[12,] 0.13315488 2.663098e-01 8.668451e-01
[13,] 0.20631673 4.126335e-01 7.936833e-01
[14,] 0.29953941 5.990788e-01 7.004606e-01
[15,] 0.41347067 8.269413e-01 5.865293e-01
[16,] 0.52910340 9.417932e-01 4.708966e-01
[17,] 0.63749201 7.250160e-01 3.625080e-01
[18,] 0.73805729 5.238854e-01 2.619427e-01
[19,] 0.81923747 3.615251e-01 1.807625e-01
[20,] 0.88602163 2.279567e-01 1.139784e-01
[21,] 0.93338561 1.332288e-01 6.661439e-02
[22,] 0.96520431 6.959138e-02 3.479569e-02
[23,] 0.98778955 2.442091e-02 1.221045e-02
[24,] 0.99611123 7.777546e-03 3.888773e-03
[25,] 0.99868154 2.636924e-03 1.318462e-03
[26,] 0.99955410 8.918087e-04 4.459044e-04
[27,] 0.99983468 3.306421e-04 1.653210e-04
[28,] 0.99993226 1.354743e-04 6.773716e-05
[29,] 0.99996875 6.250468e-05 3.125234e-05
[30,] 0.99998497 3.005959e-05 1.502980e-05
[31,] 0.99999186 1.627423e-05 8.137115e-06
[32,] 0.99999475 1.049980e-05 5.249901e-06
[33,] 0.99999679 6.424604e-06 3.212302e-06
[34,] 0.99999794 4.126289e-06 2.063144e-06
[35,] 0.99999911 1.786552e-06 8.932761e-07
[36,] 0.99999954 9.282106e-07 4.641053e-07
[37,] 0.99999973 5.300239e-07 2.650119e-07
[38,] 0.99999984 3.148370e-07 1.574185e-07
[39,] 0.99999990 1.996414e-07 9.982071e-08
[40,] 0.99999993 1.456153e-07 7.280763e-08
[41,] 0.99999994 1.108007e-07 5.540036e-08
[42,] 0.99999995 9.178684e-08 4.589342e-08
[43,] 0.99999996 7.999663e-08 3.999832e-08
[44,] 0.99999997 6.960776e-08 3.480388e-08
[45,] 0.99999997 5.468858e-08 2.734429e-08
[46,] 0.99999998 3.941569e-08 1.970784e-08
[47,] 0.99999999 1.619897e-08 8.099484e-09
[48,] 1.00000000 7.961014e-09 3.980507e-09
[49,] 1.00000000 3.788709e-09 1.894355e-09
[50,] 1.00000000 1.626055e-09 8.130275e-10
[51,] 1.00000000 9.642911e-10 4.821455e-10
[52,] 1.00000000 7.290479e-10 3.645239e-10
[53,] 1.00000000 6.015699e-10 3.007849e-10
[54,] 1.00000000 6.118833e-10 3.059416e-10
[55,] 1.00000000 7.806018e-10 3.903009e-10
[56,] 1.00000000 9.719556e-10 4.859778e-10
[57,] 1.00000000 1.128148e-09 5.640740e-10
[58,] 1.00000000 1.437675e-09 7.188374e-10
[59,] 1.00000000 1.703179e-09 8.515897e-10
[60,] 1.00000000 2.019461e-09 1.009730e-09
[61,] 1.00000000 2.533623e-09 1.266811e-09
[62,] 1.00000000 3.353306e-09 1.676653e-09
[63,] 1.00000000 4.502067e-09 2.251034e-09
[64,] 1.00000000 6.004307e-09 3.002154e-09
[65,] 1.00000000 8.161298e-09 4.080649e-09
[66,] 0.99999999 1.170814e-08 5.854069e-09
[67,] 0.99999999 1.603771e-08 8.018855e-09
[68,] 0.99999999 2.333970e-08 1.166985e-08
[69,] 0.99999998 3.305503e-08 1.652752e-08
[70,] 0.99999998 4.648597e-08 2.324298e-08
[71,] 0.99999997 6.565108e-08 3.282554e-08
[72,] 0.99999995 9.494964e-08 4.747482e-08
[73,] 0.99999993 1.381808e-07 6.909040e-08
[74,] 0.99999989 2.136345e-07 1.068172e-07
[75,] 0.99999984 3.298036e-07 1.649018e-07
[76,] 0.99999974 5.168726e-07 2.584363e-07
[77,] 0.99999959 8.281541e-07 4.140771e-07
[78,] 0.99999933 1.331944e-06 6.659722e-07
[79,] 0.99999893 2.142832e-06 1.071416e-06
[80,] 0.99999830 3.408123e-06 1.704062e-06
[81,] 0.99999735 5.294121e-06 2.647060e-06
[82,] 0.99999588 8.243014e-06 4.121507e-06
[83,] 0.99999364 1.271172e-05 6.355861e-06
[84,] 0.99999043 1.914514e-05 9.572570e-06
[85,] 0.99998574 2.851711e-05 1.425855e-05
[86,] 0.99997903 4.193241e-05 2.096620e-05
[87,] 0.99996996 6.008598e-05 3.004299e-05
[88,] 0.99995753 8.493598e-05 4.246799e-05
[89,] 0.99994272 1.145583e-04 5.727913e-05
[90,] 0.99992303 1.539440e-04 7.697198e-05
[91,] 0.99990063 1.987403e-04 9.937013e-05
[92,] 0.99987661 2.467877e-04 1.233939e-04
[93,] 0.99983381 3.323814e-04 1.661907e-04
[94,] 0.99979475 4.104947e-04 2.052474e-04
[95,] 0.99974946 5.010742e-04 2.505371e-04
[96,] 0.99970955 5.808972e-04 2.904486e-04
[97,] 0.99967937 6.412505e-04 3.206252e-04
[98,] 0.99965500 6.899945e-04 3.449972e-04
[99,] 0.99965451 6.909834e-04 3.454917e-04
[100,] 0.99969418 6.116419e-04 3.058209e-04
[101,] 0.99972178 5.564414e-04 2.782207e-04
[102,] 0.99976409 4.718181e-04 2.359091e-04
[103,] 0.99981420 3.715984e-04 1.857992e-04
[104,] 0.99986547 2.690548e-04 1.345274e-04
[105,] 0.99986989 2.602244e-04 1.301122e-04
[106,] 0.99989380 2.123973e-04 1.061986e-04
[107,] 0.99991173 1.765351e-04 8.826754e-05
[108,] 0.99993633 1.273405e-04 6.367025e-05
[109,] 0.99996237 7.525336e-05 3.762668e-05
[110,] 0.99997640 4.720101e-05 2.360051e-05
[111,] 0.99998611 2.777282e-05 1.388641e-05
[112,] 0.99999261 1.478960e-05 7.394800e-06
[113,] 0.99999601 7.988105e-06 3.994052e-06
[114,] 0.99999772 4.569709e-06 2.284855e-06
[115,] 0.99999897 2.065143e-06 1.032572e-06
[116,] 0.99999957 8.537960e-07 4.268980e-07
[117,] 0.99999974 5.107222e-07 2.553611e-07
[118,] 0.99999979 4.200779e-07 2.100389e-07
[119,] 0.99999980 3.955165e-07 1.977583e-07
[120,] 0.99999989 2.240432e-07 1.120216e-07
[121,] 0.99999995 1.049682e-07 5.248412e-08
[122,] 0.99999998 4.923891e-08 2.461945e-08
[123,] 0.99999999 2.474672e-08 1.237336e-08
[124,] 0.99999999 1.404523e-08 7.022616e-09
[125,] 1.00000000 7.013333e-09 3.506666e-09
[126,] 1.00000000 3.864795e-09 1.932397e-09
[127,] 1.00000000 1.904021e-09 9.520104e-10
[128,] 1.00000000 1.203815e-09 6.019077e-10
[129,] 1.00000000 6.877920e-10 3.438960e-10
[130,] 1.00000000 5.131046e-10 2.565523e-10
[131,] 1.00000000 5.437902e-10 2.718951e-10
[132,] 1.00000000 6.244527e-10 3.122263e-10
[133,] 1.00000000 6.779433e-10 3.389717e-10
[134,] 1.00000000 8.007302e-10 4.003651e-10
[135,] 1.00000000 9.948830e-10 4.974415e-10
[136,] 1.00000000 1.269757e-09 6.348784e-10
[137,] 1.00000000 1.815519e-09 9.077597e-10
[138,] 1.00000000 2.694719e-09 1.347360e-09
[139,] 1.00000000 4.137166e-09 2.068583e-09
[140,] 1.00000000 6.893044e-09 3.446522e-09
[141,] 0.99999999 1.191939e-08 5.959694e-09
[142,] 0.99999999 2.114521e-08 1.057261e-08
[143,] 0.99999998 3.642804e-08 1.821402e-08
[144,] 0.99999997 6.284350e-08 3.142175e-08
[145,] 0.99999995 1.072018e-07 5.360090e-08
[146,] 0.99999991 1.791157e-07 8.955787e-08
[147,] 0.99999986 2.896545e-07 1.448272e-07
[148,] 0.99999978 4.490619e-07 2.245310e-07
[149,] 0.99999964 7.136657e-07 3.568329e-07
[150,] 0.99999945 1.101481e-06 5.507404e-07
[151,] 0.99999919 1.621689e-06 8.108447e-07
[152,] 0.99999879 2.419313e-06 1.209657e-06
[153,] 0.99999828 3.431504e-06 1.715752e-06
[154,] 0.99999805 3.909161e-06 1.954580e-06
[155,] 0.99999853 2.930191e-06 1.465095e-06
[156,] 0.99999873 2.540132e-06 1.270066e-06
[157,] 0.99999879 2.419101e-06 1.209550e-06
[158,] 0.99999862 2.768656e-06 1.384328e-06
[159,] 0.99999855 2.899476e-06 1.449738e-06
[160,] 0.99999865 2.700059e-06 1.350030e-06
[161,] 0.99999858 2.833902e-06 1.416951e-06
[162,] 0.99999872 2.566266e-06 1.283133e-06
[163,] 0.99999883 2.346687e-06 1.173343e-06
[164,] 0.99999890 2.191792e-06 1.095896e-06
[165,] 0.99999894 2.125335e-06 1.062668e-06
[166,] 0.99999933 1.342627e-06 6.713136e-07
[167,] 0.99999963 7.411768e-07 3.705884e-07
[168,] 0.99999977 4.699739e-07 2.349869e-07
[169,] 0.99999983 3.359393e-07 1.679696e-07
[170,] 0.99999984 3.163527e-07 1.581763e-07
[171,] 0.99999986 2.723000e-07 1.361500e-07
[172,] 0.99999988 2.367098e-07 1.183549e-07
[173,] 0.99999990 2.002826e-07 1.001413e-07
[174,] 0.99999995 1.006244e-07 5.031218e-08
[175,] 0.99999997 5.339755e-08 2.669877e-08
[176,] 0.99999998 3.682669e-08 1.841334e-08
[177,] 0.99999999 2.717369e-08 1.358685e-08
[178,] 0.99999999 1.085490e-08 5.427451e-09
[179,] 1.00000000 8.978442e-09 4.489221e-09
[180,] 0.99999999 1.091387e-08 5.456936e-09
[181,] 0.99999999 2.081424e-08 1.040712e-08
[182,] 0.99999998 4.097473e-08 2.048736e-08
[183,] 0.99999996 8.638705e-08 4.319353e-08
[184,] 0.99999991 1.855957e-07 9.279787e-08
[185,] 0.99999980 3.928791e-07 1.964395e-07
[186,] 0.99999959 8.228049e-07 4.114024e-07
[187,] 0.99999927 1.457210e-06 7.286051e-07
[188,] 0.99999865 2.705411e-06 1.352706e-06
[189,] 0.99999745 5.098635e-06 2.549317e-06
[190,] 0.99999562 8.757409e-06 4.378705e-06
[191,] 0.99999230 1.540895e-05 7.704473e-06
[192,] 0.99998786 2.428303e-05 1.214152e-05
[193,] 0.99998321 3.357077e-05 1.678539e-05
[194,] 0.99997902 4.195816e-05 2.097908e-05
[195,] 0.99997798 4.404963e-05 2.202481e-05
[196,] 0.99998355 3.290868e-05 1.645434e-05
[197,] 0.99998594 2.812993e-05 1.406496e-05
[198,] 0.99999199 1.602964e-05 8.014822e-06
[199,] 0.99999542 9.167265e-06 4.583632e-06
[200,] 0.99999802 3.957285e-06 1.978643e-06
[201,] 0.99999943 1.136591e-06 5.682954e-07
[202,] 0.99999987 2.524516e-07 1.262258e-07
[203,] 0.99999997 6.390444e-08 3.195222e-08
[204,] 0.99999999 1.576174e-08 7.880869e-09
[205,] 1.00000000 3.731654e-09 1.865827e-09
[206,] 1.00000000 1.320324e-09 6.601619e-10
[207,] 1.00000000 1.270953e-09 6.354764e-10
[208,] 1.00000000 3.510166e-09 1.755083e-09
[209,] 0.99999999 1.448065e-08 7.240326e-09
[210,] 0.99999997 5.779216e-08 2.889608e-08
[211,] 0.99999989 2.257453e-07 1.128727e-07
[212,] 0.99999956 8.886108e-07 4.443054e-07
[213,] 0.99999833 3.338427e-06 1.669213e-06
[214,] 0.99999406 1.188620e-05 5.943102e-06
[215,] 0.99998056 3.887352e-05 1.943676e-05
[216,] 0.99993681 1.263877e-04 6.319384e-05
[217,] 0.99986741 2.651719e-04 1.325860e-04
[218,] 0.99972727 5.454640e-04 2.727320e-04
[219,] 0.99949428 1.011432e-03 5.057162e-04
[220,] 0.99887896 2.242085e-03 1.121043e-03
[221,] 0.99728049 5.439029e-03 2.719514e-03
[222,] 0.99229675 1.540651e-02 7.703253e-03
[223,] 0.98440173 3.119655e-02 1.559827e-02
[224,] 0.97249791 5.500418e-02 2.750209e-02
> postscript(file="/var/wessaorg/rcomp/tmp/153sr1322553686.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/wessaorg/rcomp/tmp/20stm1322553686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/338m21322553686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4pa6k1322553686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/51n1m1322553686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 253
Frequency = 1
1 2 3 4 5 6
-125.0169091 -115.0573810 -113.5397619 -116.4436667 -114.3324286 -116.6853333
7 8 9 10 11 12
-117.0269048 -114.4018095 -113.9756667 -113.9192381 -113.6700000 -109.7929048
13 14 15 16 17 18
-89.4059091 -80.2523810 -77.5287619 -78.4106667 -81.8144286 -79.3173333
19 20 21 22 23 24
-76.0289048 -73.8628095 -69.3706667 -69.4532381 -68.2290000 -62.8769048
25 26 27 28 29 30
-44.7839091 -31.0513810 -21.3087619 -18.6946667 -13.6884286 -10.6133333
31 32 33 34 35 36
-6.2319048 1.0071905 6.3433333 12.4247619 17.6220000 28.3570952
37 38 39 40 41 42
40.6520909 53.5776190 54.7192381 54.4763333 52.3915714 50.0316667
43 44 45 46 47 48
48.0780952 50.3551905 48.9053333 43.5767619 48.2120000 51.5200952
49 50 51 52 53 54
60.7860909 62.5936190 61.8032381 60.3603333 58.3675714 53.0706667
55 56 57 58 59 60
52.3840952 51.0181905 50.1603333 50.5157619 56.8160000 63.4170952
61 62 63 64 65 66
78.7410909 77.1846190 79.0452381 81.6133333 71.0065714 61.9296667
67 68 69 70 71 72
58.8780952 50.9141905 39.8053333 41.1327619 46.0380000 42.4840952
73 74 75 76 77 78
44.2660909 46.1796190 43.3812381 39.9003333 38.3545714 38.0416667
79 80 81 82 83 84
36.5580952 32.4431905 35.4703333 30.4507619 33.0060000 33.7530952
85 86 87 88 89 90
31.9500909 30.1546190 29.0822381 21.7173333 20.9385714 17.4816667
91 92 93 94 95 96
11.4320952 7.5211905 0.1903333 -3.6502381 10.1820000 3.6430952
97 98 99 100 101 102
-6.1779091 -10.8733810 -11.2227619 -13.0376667 -17.4924286 -18.9643333
103 104 105 106 107 108
-25.5379048 -25.2998095 -29.9896667 -34.0512381 -22.5150000 -33.5889048
109 110 111 112 113 114
-34.6569091 -40.0683810 -44.5967619 -46.6916667 -52.2444286 -60.4833333
115 116 117 118 119 120
-58.2309048 -62.3428095 -65.9436667 -69.5532381 -53.4790000 -63.7229048
121 122 123 124 125 126
-60.6689091 -68.2893810 -76.6147619 -72.5196667 -73.1354286 -74.4303333
127 128 129 130 131 132
-71.9719048 -68.2328095 -73.7636667 -73.7182381 -64.3700000 -52.2879048
133 134 135 136 137 138
-42.8629091 -61.5013810 -65.7177619 -63.5076667 -58.6004286 -51.9793333
139 140 141 142 143 144
-53.5879048 -49.5958095 -49.4656667 -40.8862381 -48.8510000 -42.2399048
145 146 147 148 149 150
-27.1089091 -23.2463810 -24.5467619 -20.3926667 -16.1964286 -12.4853333
151 152 153 154 155 156
-5.7119048 -3.4628095 1.8793333 11.5897619 5.6710000 12.7770952
157 158 159 160 161 162
19.7700909 14.8586190 18.0032381 22.7683333 27.6485714 32.9136667
163 164 165 166 167 168
28.7600952 32.3421905 37.6303333 35.7967619 36.7200000 47.3980952
169 170 171 172 173 174
65.6300909 61.6816190 58.6692381 51.3083333 56.1735714 62.0096667
175 176 177 178 179 180
58.2610952 63.1361905 63.9363333 63.0017619 54.8410000 63.0170952
181 182 183 184 185 186
66.4230909 62.9186190 60.3562381 53.8513333 56.8665714 57.3806667
187 188 189 190 191 192
59.3660952 68.4551905 67.8503333 61.8337619 51.7660000 54.7010952
193 194 195 196 197 198
41.9310909 33.2566190 20.4892381 21.0193333 15.6485714 9.2546667
199 200 201 202 203 204
11.0800952 6.7181905 -6.4296667 -2.3712381 -9.4520000 -23.5579048
205 206 207 208 209 210
-18.6239091 -22.6393810 -22.5267619 -22.6346667 -25.6444286 -29.7963333
211 212 213 214 215 216
-24.5959048 -34.9628095 -27.5116667 -29.3542381 -40.4530000 -47.2699048
217 218 219 220 221 222
-39.5199091 -30.6193810 -20.6437619 -11.3346667 -0.5674286 13.9036667
223 224 225 226 227 228
21.6960952 19.2771905 31.1223333 28.8367619 22.5510000 15.3080952
229 230 231 232 233 234
18.7840909 25.0746190 38.3082381 40.5553333 41.5555714 40.8316667
235 236 237 238 239 240
36.6920952 32.7141905 39.5943333 43.2577619 30.3350000 20.8960952
241 242 243 244 245 246
17.9740909 16.1186190 14.3892381 16.0973333 14.7645714 17.9056667
247 248 249 250 251 252
15.7380952 16.2591905 13.5623333 14.5397619 7.2590000 -1.9349048
253
1.9180909
> postscript(file="/var/wessaorg/rcomp/tmp/6y9e51322553686.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 = 253
Frequency = 1
lag(myerror, k = 1) myerror
0 -125.0169091 NA
1 -115.0573810 -125.0169091
2 -113.5397619 -115.0573810
3 -116.4436667 -113.5397619
4 -114.3324286 -116.4436667
5 -116.6853333 -114.3324286
6 -117.0269048 -116.6853333
7 -114.4018095 -117.0269048
8 -113.9756667 -114.4018095
9 -113.9192381 -113.9756667
10 -113.6700000 -113.9192381
11 -109.7929048 -113.6700000
12 -89.4059091 -109.7929048
13 -80.2523810 -89.4059091
14 -77.5287619 -80.2523810
15 -78.4106667 -77.5287619
16 -81.8144286 -78.4106667
17 -79.3173333 -81.8144286
18 -76.0289048 -79.3173333
19 -73.8628095 -76.0289048
20 -69.3706667 -73.8628095
21 -69.4532381 -69.3706667
22 -68.2290000 -69.4532381
23 -62.8769048 -68.2290000
24 -44.7839091 -62.8769048
25 -31.0513810 -44.7839091
26 -21.3087619 -31.0513810
27 -18.6946667 -21.3087619
28 -13.6884286 -18.6946667
29 -10.6133333 -13.6884286
30 -6.2319048 -10.6133333
31 1.0071905 -6.2319048
32 6.3433333 1.0071905
33 12.4247619 6.3433333
34 17.6220000 12.4247619
35 28.3570952 17.6220000
36 40.6520909 28.3570952
37 53.5776190 40.6520909
38 54.7192381 53.5776190
39 54.4763333 54.7192381
40 52.3915714 54.4763333
41 50.0316667 52.3915714
42 48.0780952 50.0316667
43 50.3551905 48.0780952
44 48.9053333 50.3551905
45 43.5767619 48.9053333
46 48.2120000 43.5767619
47 51.5200952 48.2120000
48 60.7860909 51.5200952
49 62.5936190 60.7860909
50 61.8032381 62.5936190
51 60.3603333 61.8032381
52 58.3675714 60.3603333
53 53.0706667 58.3675714
54 52.3840952 53.0706667
55 51.0181905 52.3840952
56 50.1603333 51.0181905
57 50.5157619 50.1603333
58 56.8160000 50.5157619
59 63.4170952 56.8160000
60 78.7410909 63.4170952
61 77.1846190 78.7410909
62 79.0452381 77.1846190
63 81.6133333 79.0452381
64 71.0065714 81.6133333
65 61.9296667 71.0065714
66 58.8780952 61.9296667
67 50.9141905 58.8780952
68 39.8053333 50.9141905
69 41.1327619 39.8053333
70 46.0380000 41.1327619
71 42.4840952 46.0380000
72 44.2660909 42.4840952
73 46.1796190 44.2660909
74 43.3812381 46.1796190
75 39.9003333 43.3812381
76 38.3545714 39.9003333
77 38.0416667 38.3545714
78 36.5580952 38.0416667
79 32.4431905 36.5580952
80 35.4703333 32.4431905
81 30.4507619 35.4703333
82 33.0060000 30.4507619
83 33.7530952 33.0060000
84 31.9500909 33.7530952
85 30.1546190 31.9500909
86 29.0822381 30.1546190
87 21.7173333 29.0822381
88 20.9385714 21.7173333
89 17.4816667 20.9385714
90 11.4320952 17.4816667
91 7.5211905 11.4320952
92 0.1903333 7.5211905
93 -3.6502381 0.1903333
94 10.1820000 -3.6502381
95 3.6430952 10.1820000
96 -6.1779091 3.6430952
97 -10.8733810 -6.1779091
98 -11.2227619 -10.8733810
99 -13.0376667 -11.2227619
100 -17.4924286 -13.0376667
101 -18.9643333 -17.4924286
102 -25.5379048 -18.9643333
103 -25.2998095 -25.5379048
104 -29.9896667 -25.2998095
105 -34.0512381 -29.9896667
106 -22.5150000 -34.0512381
107 -33.5889048 -22.5150000
108 -34.6569091 -33.5889048
109 -40.0683810 -34.6569091
110 -44.5967619 -40.0683810
111 -46.6916667 -44.5967619
112 -52.2444286 -46.6916667
113 -60.4833333 -52.2444286
114 -58.2309048 -60.4833333
115 -62.3428095 -58.2309048
116 -65.9436667 -62.3428095
117 -69.5532381 -65.9436667
118 -53.4790000 -69.5532381
119 -63.7229048 -53.4790000
120 -60.6689091 -63.7229048
121 -68.2893810 -60.6689091
122 -76.6147619 -68.2893810
123 -72.5196667 -76.6147619
124 -73.1354286 -72.5196667
125 -74.4303333 -73.1354286
126 -71.9719048 -74.4303333
127 -68.2328095 -71.9719048
128 -73.7636667 -68.2328095
129 -73.7182381 -73.7636667
130 -64.3700000 -73.7182381
131 -52.2879048 -64.3700000
132 -42.8629091 -52.2879048
133 -61.5013810 -42.8629091
134 -65.7177619 -61.5013810
135 -63.5076667 -65.7177619
136 -58.6004286 -63.5076667
137 -51.9793333 -58.6004286
138 -53.5879048 -51.9793333
139 -49.5958095 -53.5879048
140 -49.4656667 -49.5958095
141 -40.8862381 -49.4656667
142 -48.8510000 -40.8862381
143 -42.2399048 -48.8510000
144 -27.1089091 -42.2399048
145 -23.2463810 -27.1089091
146 -24.5467619 -23.2463810
147 -20.3926667 -24.5467619
148 -16.1964286 -20.3926667
149 -12.4853333 -16.1964286
150 -5.7119048 -12.4853333
151 -3.4628095 -5.7119048
152 1.8793333 -3.4628095
153 11.5897619 1.8793333
154 5.6710000 11.5897619
155 12.7770952 5.6710000
156 19.7700909 12.7770952
157 14.8586190 19.7700909
158 18.0032381 14.8586190
159 22.7683333 18.0032381
160 27.6485714 22.7683333
161 32.9136667 27.6485714
162 28.7600952 32.9136667
163 32.3421905 28.7600952
164 37.6303333 32.3421905
165 35.7967619 37.6303333
166 36.7200000 35.7967619
167 47.3980952 36.7200000
168 65.6300909 47.3980952
169 61.6816190 65.6300909
170 58.6692381 61.6816190
171 51.3083333 58.6692381
172 56.1735714 51.3083333
173 62.0096667 56.1735714
174 58.2610952 62.0096667
175 63.1361905 58.2610952
176 63.9363333 63.1361905
177 63.0017619 63.9363333
178 54.8410000 63.0017619
179 63.0170952 54.8410000
180 66.4230909 63.0170952
181 62.9186190 66.4230909
182 60.3562381 62.9186190
183 53.8513333 60.3562381
184 56.8665714 53.8513333
185 57.3806667 56.8665714
186 59.3660952 57.3806667
187 68.4551905 59.3660952
188 67.8503333 68.4551905
189 61.8337619 67.8503333
190 51.7660000 61.8337619
191 54.7010952 51.7660000
192 41.9310909 54.7010952
193 33.2566190 41.9310909
194 20.4892381 33.2566190
195 21.0193333 20.4892381
196 15.6485714 21.0193333
197 9.2546667 15.6485714
198 11.0800952 9.2546667
199 6.7181905 11.0800952
200 -6.4296667 6.7181905
201 -2.3712381 -6.4296667
202 -9.4520000 -2.3712381
203 -23.5579048 -9.4520000
204 -18.6239091 -23.5579048
205 -22.6393810 -18.6239091
206 -22.5267619 -22.6393810
207 -22.6346667 -22.5267619
208 -25.6444286 -22.6346667
209 -29.7963333 -25.6444286
210 -24.5959048 -29.7963333
211 -34.9628095 -24.5959048
212 -27.5116667 -34.9628095
213 -29.3542381 -27.5116667
214 -40.4530000 -29.3542381
215 -47.2699048 -40.4530000
216 -39.5199091 -47.2699048
217 -30.6193810 -39.5199091
218 -20.6437619 -30.6193810
219 -11.3346667 -20.6437619
220 -0.5674286 -11.3346667
221 13.9036667 -0.5674286
222 21.6960952 13.9036667
223 19.2771905 21.6960952
224 31.1223333 19.2771905
225 28.8367619 31.1223333
226 22.5510000 28.8367619
227 15.3080952 22.5510000
228 18.7840909 15.3080952
229 25.0746190 18.7840909
230 38.3082381 25.0746190
231 40.5553333 38.3082381
232 41.5555714 40.5553333
233 40.8316667 41.5555714
234 36.6920952 40.8316667
235 32.7141905 36.6920952
236 39.5943333 32.7141905
237 43.2577619 39.5943333
238 30.3350000 43.2577619
239 20.8960952 30.3350000
240 17.9740909 20.8960952
241 16.1186190 17.9740909
242 14.3892381 16.1186190
243 16.0973333 14.3892381
244 14.7645714 16.0973333
245 17.9056667 14.7645714
246 15.7380952 17.9056667
247 16.2591905 15.7380952
248 13.5623333 16.2591905
249 14.5397619 13.5623333
250 7.2590000 14.5397619
251 -1.9349048 7.2590000
252 1.9180909 -1.9349048
253 NA 1.9180909
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -115.0573810 -125.0169091
[2,] -113.5397619 -115.0573810
[3,] -116.4436667 -113.5397619
[4,] -114.3324286 -116.4436667
[5,] -116.6853333 -114.3324286
[6,] -117.0269048 -116.6853333
[7,] -114.4018095 -117.0269048
[8,] -113.9756667 -114.4018095
[9,] -113.9192381 -113.9756667
[10,] -113.6700000 -113.9192381
[11,] -109.7929048 -113.6700000
[12,] -89.4059091 -109.7929048
[13,] -80.2523810 -89.4059091
[14,] -77.5287619 -80.2523810
[15,] -78.4106667 -77.5287619
[16,] -81.8144286 -78.4106667
[17,] -79.3173333 -81.8144286
[18,] -76.0289048 -79.3173333
[19,] -73.8628095 -76.0289048
[20,] -69.3706667 -73.8628095
[21,] -69.4532381 -69.3706667
[22,] -68.2290000 -69.4532381
[23,] -62.8769048 -68.2290000
[24,] -44.7839091 -62.8769048
[25,] -31.0513810 -44.7839091
[26,] -21.3087619 -31.0513810
[27,] -18.6946667 -21.3087619
[28,] -13.6884286 -18.6946667
[29,] -10.6133333 -13.6884286
[30,] -6.2319048 -10.6133333
[31,] 1.0071905 -6.2319048
[32,] 6.3433333 1.0071905
[33,] 12.4247619 6.3433333
[34,] 17.6220000 12.4247619
[35,] 28.3570952 17.6220000
[36,] 40.6520909 28.3570952
[37,] 53.5776190 40.6520909
[38,] 54.7192381 53.5776190
[39,] 54.4763333 54.7192381
[40,] 52.3915714 54.4763333
[41,] 50.0316667 52.3915714
[42,] 48.0780952 50.0316667
[43,] 50.3551905 48.0780952
[44,] 48.9053333 50.3551905
[45,] 43.5767619 48.9053333
[46,] 48.2120000 43.5767619
[47,] 51.5200952 48.2120000
[48,] 60.7860909 51.5200952
[49,] 62.5936190 60.7860909
[50,] 61.8032381 62.5936190
[51,] 60.3603333 61.8032381
[52,] 58.3675714 60.3603333
[53,] 53.0706667 58.3675714
[54,] 52.3840952 53.0706667
[55,] 51.0181905 52.3840952
[56,] 50.1603333 51.0181905
[57,] 50.5157619 50.1603333
[58,] 56.8160000 50.5157619
[59,] 63.4170952 56.8160000
[60,] 78.7410909 63.4170952
[61,] 77.1846190 78.7410909
[62,] 79.0452381 77.1846190
[63,] 81.6133333 79.0452381
[64,] 71.0065714 81.6133333
[65,] 61.9296667 71.0065714
[66,] 58.8780952 61.9296667
[67,] 50.9141905 58.8780952
[68,] 39.8053333 50.9141905
[69,] 41.1327619 39.8053333
[70,] 46.0380000 41.1327619
[71,] 42.4840952 46.0380000
[72,] 44.2660909 42.4840952
[73,] 46.1796190 44.2660909
[74,] 43.3812381 46.1796190
[75,] 39.9003333 43.3812381
[76,] 38.3545714 39.9003333
[77,] 38.0416667 38.3545714
[78,] 36.5580952 38.0416667
[79,] 32.4431905 36.5580952
[80,] 35.4703333 32.4431905
[81,] 30.4507619 35.4703333
[82,] 33.0060000 30.4507619
[83,] 33.7530952 33.0060000
[84,] 31.9500909 33.7530952
[85,] 30.1546190 31.9500909
[86,] 29.0822381 30.1546190
[87,] 21.7173333 29.0822381
[88,] 20.9385714 21.7173333
[89,] 17.4816667 20.9385714
[90,] 11.4320952 17.4816667
[91,] 7.5211905 11.4320952
[92,] 0.1903333 7.5211905
[93,] -3.6502381 0.1903333
[94,] 10.1820000 -3.6502381
[95,] 3.6430952 10.1820000
[96,] -6.1779091 3.6430952
[97,] -10.8733810 -6.1779091
[98,] -11.2227619 -10.8733810
[99,] -13.0376667 -11.2227619
[100,] -17.4924286 -13.0376667
[101,] -18.9643333 -17.4924286
[102,] -25.5379048 -18.9643333
[103,] -25.2998095 -25.5379048
[104,] -29.9896667 -25.2998095
[105,] -34.0512381 -29.9896667
[106,] -22.5150000 -34.0512381
[107,] -33.5889048 -22.5150000
[108,] -34.6569091 -33.5889048
[109,] -40.0683810 -34.6569091
[110,] -44.5967619 -40.0683810
[111,] -46.6916667 -44.5967619
[112,] -52.2444286 -46.6916667
[113,] -60.4833333 -52.2444286
[114,] -58.2309048 -60.4833333
[115,] -62.3428095 -58.2309048
[116,] -65.9436667 -62.3428095
[117,] -69.5532381 -65.9436667
[118,] -53.4790000 -69.5532381
[119,] -63.7229048 -53.4790000
[120,] -60.6689091 -63.7229048
[121,] -68.2893810 -60.6689091
[122,] -76.6147619 -68.2893810
[123,] -72.5196667 -76.6147619
[124,] -73.1354286 -72.5196667
[125,] -74.4303333 -73.1354286
[126,] -71.9719048 -74.4303333
[127,] -68.2328095 -71.9719048
[128,] -73.7636667 -68.2328095
[129,] -73.7182381 -73.7636667
[130,] -64.3700000 -73.7182381
[131,] -52.2879048 -64.3700000
[132,] -42.8629091 -52.2879048
[133,] -61.5013810 -42.8629091
[134,] -65.7177619 -61.5013810
[135,] -63.5076667 -65.7177619
[136,] -58.6004286 -63.5076667
[137,] -51.9793333 -58.6004286
[138,] -53.5879048 -51.9793333
[139,] -49.5958095 -53.5879048
[140,] -49.4656667 -49.5958095
[141,] -40.8862381 -49.4656667
[142,] -48.8510000 -40.8862381
[143,] -42.2399048 -48.8510000
[144,] -27.1089091 -42.2399048
[145,] -23.2463810 -27.1089091
[146,] -24.5467619 -23.2463810
[147,] -20.3926667 -24.5467619
[148,] -16.1964286 -20.3926667
[149,] -12.4853333 -16.1964286
[150,] -5.7119048 -12.4853333
[151,] -3.4628095 -5.7119048
[152,] 1.8793333 -3.4628095
[153,] 11.5897619 1.8793333
[154,] 5.6710000 11.5897619
[155,] 12.7770952 5.6710000
[156,] 19.7700909 12.7770952
[157,] 14.8586190 19.7700909
[158,] 18.0032381 14.8586190
[159,] 22.7683333 18.0032381
[160,] 27.6485714 22.7683333
[161,] 32.9136667 27.6485714
[162,] 28.7600952 32.9136667
[163,] 32.3421905 28.7600952
[164,] 37.6303333 32.3421905
[165,] 35.7967619 37.6303333
[166,] 36.7200000 35.7967619
[167,] 47.3980952 36.7200000
[168,] 65.6300909 47.3980952
[169,] 61.6816190 65.6300909
[170,] 58.6692381 61.6816190
[171,] 51.3083333 58.6692381
[172,] 56.1735714 51.3083333
[173,] 62.0096667 56.1735714
[174,] 58.2610952 62.0096667
[175,] 63.1361905 58.2610952
[176,] 63.9363333 63.1361905
[177,] 63.0017619 63.9363333
[178,] 54.8410000 63.0017619
[179,] 63.0170952 54.8410000
[180,] 66.4230909 63.0170952
[181,] 62.9186190 66.4230909
[182,] 60.3562381 62.9186190
[183,] 53.8513333 60.3562381
[184,] 56.8665714 53.8513333
[185,] 57.3806667 56.8665714
[186,] 59.3660952 57.3806667
[187,] 68.4551905 59.3660952
[188,] 67.8503333 68.4551905
[189,] 61.8337619 67.8503333
[190,] 51.7660000 61.8337619
[191,] 54.7010952 51.7660000
[192,] 41.9310909 54.7010952
[193,] 33.2566190 41.9310909
[194,] 20.4892381 33.2566190
[195,] 21.0193333 20.4892381
[196,] 15.6485714 21.0193333
[197,] 9.2546667 15.6485714
[198,] 11.0800952 9.2546667
[199,] 6.7181905 11.0800952
[200,] -6.4296667 6.7181905
[201,] -2.3712381 -6.4296667
[202,] -9.4520000 -2.3712381
[203,] -23.5579048 -9.4520000
[204,] -18.6239091 -23.5579048
[205,] -22.6393810 -18.6239091
[206,] -22.5267619 -22.6393810
[207,] -22.6346667 -22.5267619
[208,] -25.6444286 -22.6346667
[209,] -29.7963333 -25.6444286
[210,] -24.5959048 -29.7963333
[211,] -34.9628095 -24.5959048
[212,] -27.5116667 -34.9628095
[213,] -29.3542381 -27.5116667
[214,] -40.4530000 -29.3542381
[215,] -47.2699048 -40.4530000
[216,] -39.5199091 -47.2699048
[217,] -30.6193810 -39.5199091
[218,] -20.6437619 -30.6193810
[219,] -11.3346667 -20.6437619
[220,] -0.5674286 -11.3346667
[221,] 13.9036667 -0.5674286
[222,] 21.6960952 13.9036667
[223,] 19.2771905 21.6960952
[224,] 31.1223333 19.2771905
[225,] 28.8367619 31.1223333
[226,] 22.5510000 28.8367619
[227,] 15.3080952 22.5510000
[228,] 18.7840909 15.3080952
[229,] 25.0746190 18.7840909
[230,] 38.3082381 25.0746190
[231,] 40.5553333 38.3082381
[232,] 41.5555714 40.5553333
[233,] 40.8316667 41.5555714
[234,] 36.6920952 40.8316667
[235,] 32.7141905 36.6920952
[236,] 39.5943333 32.7141905
[237,] 43.2577619 39.5943333
[238,] 30.3350000 43.2577619
[239,] 20.8960952 30.3350000
[240,] 17.9740909 20.8960952
[241,] 16.1186190 17.9740909
[242,] 14.3892381 16.1186190
[243,] 16.0973333 14.3892381
[244,] 14.7645714 16.0973333
[245,] 17.9056667 14.7645714
[246,] 15.7380952 17.9056667
[247,] 16.2591905 15.7380952
[248,] 13.5623333 16.2591905
[249,] 14.5397619 13.5623333
[250,] 7.2590000 14.5397619
[251,] -1.9349048 7.2590000
[252,] 1.9180909 -1.9349048
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -115.0573810 -125.0169091
2 -113.5397619 -115.0573810
3 -116.4436667 -113.5397619
4 -114.3324286 -116.4436667
5 -116.6853333 -114.3324286
6 -117.0269048 -116.6853333
7 -114.4018095 -117.0269048
8 -113.9756667 -114.4018095
9 -113.9192381 -113.9756667
10 -113.6700000 -113.9192381
11 -109.7929048 -113.6700000
12 -89.4059091 -109.7929048
13 -80.2523810 -89.4059091
14 -77.5287619 -80.2523810
15 -78.4106667 -77.5287619
16 -81.8144286 -78.4106667
17 -79.3173333 -81.8144286
18 -76.0289048 -79.3173333
19 -73.8628095 -76.0289048
20 -69.3706667 -73.8628095
21 -69.4532381 -69.3706667
22 -68.2290000 -69.4532381
23 -62.8769048 -68.2290000
24 -44.7839091 -62.8769048
25 -31.0513810 -44.7839091
26 -21.3087619 -31.0513810
27 -18.6946667 -21.3087619
28 -13.6884286 -18.6946667
29 -10.6133333 -13.6884286
30 -6.2319048 -10.6133333
31 1.0071905 -6.2319048
32 6.3433333 1.0071905
33 12.4247619 6.3433333
34 17.6220000 12.4247619
35 28.3570952 17.6220000
36 40.6520909 28.3570952
37 53.5776190 40.6520909
38 54.7192381 53.5776190
39 54.4763333 54.7192381
40 52.3915714 54.4763333
41 50.0316667 52.3915714
42 48.0780952 50.0316667
43 50.3551905 48.0780952
44 48.9053333 50.3551905
45 43.5767619 48.9053333
46 48.2120000 43.5767619
47 51.5200952 48.2120000
48 60.7860909 51.5200952
49 62.5936190 60.7860909
50 61.8032381 62.5936190
51 60.3603333 61.8032381
52 58.3675714 60.3603333
53 53.0706667 58.3675714
54 52.3840952 53.0706667
55 51.0181905 52.3840952
56 50.1603333 51.0181905
57 50.5157619 50.1603333
58 56.8160000 50.5157619
59 63.4170952 56.8160000
60 78.7410909 63.4170952
61 77.1846190 78.7410909
62 79.0452381 77.1846190
63 81.6133333 79.0452381
64 71.0065714 81.6133333
65 61.9296667 71.0065714
66 58.8780952 61.9296667
67 50.9141905 58.8780952
68 39.8053333 50.9141905
69 41.1327619 39.8053333
70 46.0380000 41.1327619
71 42.4840952 46.0380000
72 44.2660909 42.4840952
73 46.1796190 44.2660909
74 43.3812381 46.1796190
75 39.9003333 43.3812381
76 38.3545714 39.9003333
77 38.0416667 38.3545714
78 36.5580952 38.0416667
79 32.4431905 36.5580952
80 35.4703333 32.4431905
81 30.4507619 35.4703333
82 33.0060000 30.4507619
83 33.7530952 33.0060000
84 31.9500909 33.7530952
85 30.1546190 31.9500909
86 29.0822381 30.1546190
87 21.7173333 29.0822381
88 20.9385714 21.7173333
89 17.4816667 20.9385714
90 11.4320952 17.4816667
91 7.5211905 11.4320952
92 0.1903333 7.5211905
93 -3.6502381 0.1903333
94 10.1820000 -3.6502381
95 3.6430952 10.1820000
96 -6.1779091 3.6430952
97 -10.8733810 -6.1779091
98 -11.2227619 -10.8733810
99 -13.0376667 -11.2227619
100 -17.4924286 -13.0376667
101 -18.9643333 -17.4924286
102 -25.5379048 -18.9643333
103 -25.2998095 -25.5379048
104 -29.9896667 -25.2998095
105 -34.0512381 -29.9896667
106 -22.5150000 -34.0512381
107 -33.5889048 -22.5150000
108 -34.6569091 -33.5889048
109 -40.0683810 -34.6569091
110 -44.5967619 -40.0683810
111 -46.6916667 -44.5967619
112 -52.2444286 -46.6916667
113 -60.4833333 -52.2444286
114 -58.2309048 -60.4833333
115 -62.3428095 -58.2309048
116 -65.9436667 -62.3428095
117 -69.5532381 -65.9436667
118 -53.4790000 -69.5532381
119 -63.7229048 -53.4790000
120 -60.6689091 -63.7229048
121 -68.2893810 -60.6689091
122 -76.6147619 -68.2893810
123 -72.5196667 -76.6147619
124 -73.1354286 -72.5196667
125 -74.4303333 -73.1354286
126 -71.9719048 -74.4303333
127 -68.2328095 -71.9719048
128 -73.7636667 -68.2328095
129 -73.7182381 -73.7636667
130 -64.3700000 -73.7182381
131 -52.2879048 -64.3700000
132 -42.8629091 -52.2879048
133 -61.5013810 -42.8629091
134 -65.7177619 -61.5013810
135 -63.5076667 -65.7177619
136 -58.6004286 -63.5076667
137 -51.9793333 -58.6004286
138 -53.5879048 -51.9793333
139 -49.5958095 -53.5879048
140 -49.4656667 -49.5958095
141 -40.8862381 -49.4656667
142 -48.8510000 -40.8862381
143 -42.2399048 -48.8510000
144 -27.1089091 -42.2399048
145 -23.2463810 -27.1089091
146 -24.5467619 -23.2463810
147 -20.3926667 -24.5467619
148 -16.1964286 -20.3926667
149 -12.4853333 -16.1964286
150 -5.7119048 -12.4853333
151 -3.4628095 -5.7119048
152 1.8793333 -3.4628095
153 11.5897619 1.8793333
154 5.6710000 11.5897619
155 12.7770952 5.6710000
156 19.7700909 12.7770952
157 14.8586190 19.7700909
158 18.0032381 14.8586190
159 22.7683333 18.0032381
160 27.6485714 22.7683333
161 32.9136667 27.6485714
162 28.7600952 32.9136667
163 32.3421905 28.7600952
164 37.6303333 32.3421905
165 35.7967619 37.6303333
166 36.7200000 35.7967619
167 47.3980952 36.7200000
168 65.6300909 47.3980952
169 61.6816190 65.6300909
170 58.6692381 61.6816190
171 51.3083333 58.6692381
172 56.1735714 51.3083333
173 62.0096667 56.1735714
174 58.2610952 62.0096667
175 63.1361905 58.2610952
176 63.9363333 63.1361905
177 63.0017619 63.9363333
178 54.8410000 63.0017619
179 63.0170952 54.8410000
180 66.4230909 63.0170952
181 62.9186190 66.4230909
182 60.3562381 62.9186190
183 53.8513333 60.3562381
184 56.8665714 53.8513333
185 57.3806667 56.8665714
186 59.3660952 57.3806667
187 68.4551905 59.3660952
188 67.8503333 68.4551905
189 61.8337619 67.8503333
190 51.7660000 61.8337619
191 54.7010952 51.7660000
192 41.9310909 54.7010952
193 33.2566190 41.9310909
194 20.4892381 33.2566190
195 21.0193333 20.4892381
196 15.6485714 21.0193333
197 9.2546667 15.6485714
198 11.0800952 9.2546667
199 6.7181905 11.0800952
200 -6.4296667 6.7181905
201 -2.3712381 -6.4296667
202 -9.4520000 -2.3712381
203 -23.5579048 -9.4520000
204 -18.6239091 -23.5579048
205 -22.6393810 -18.6239091
206 -22.5267619 -22.6393810
207 -22.6346667 -22.5267619
208 -25.6444286 -22.6346667
209 -29.7963333 -25.6444286
210 -24.5959048 -29.7963333
211 -34.9628095 -24.5959048
212 -27.5116667 -34.9628095
213 -29.3542381 -27.5116667
214 -40.4530000 -29.3542381
215 -47.2699048 -40.4530000
216 -39.5199091 -47.2699048
217 -30.6193810 -39.5199091
218 -20.6437619 -30.6193810
219 -11.3346667 -20.6437619
220 -0.5674286 -11.3346667
221 13.9036667 -0.5674286
222 21.6960952 13.9036667
223 19.2771905 21.6960952
224 31.1223333 19.2771905
225 28.8367619 31.1223333
226 22.5510000 28.8367619
227 15.3080952 22.5510000
228 18.7840909 15.3080952
229 25.0746190 18.7840909
230 38.3082381 25.0746190
231 40.5553333 38.3082381
232 41.5555714 40.5553333
233 40.8316667 41.5555714
234 36.6920952 40.8316667
235 32.7141905 36.6920952
236 39.5943333 32.7141905
237 43.2577619 39.5943333
238 30.3350000 43.2577619
239 20.8960952 30.3350000
240 17.9740909 20.8960952
241 16.1186190 17.9740909
242 14.3892381 16.1186190
243 16.0973333 14.3892381
244 14.7645714 16.0973333
245 17.9056667 14.7645714
246 15.7380952 17.9056667
247 16.2591905 15.7380952
248 13.5623333 16.2591905
249 14.5397619 13.5623333
250 7.2590000 14.5397619
251 -1.9349048 7.2590000
252 1.9180909 -1.9349048
> 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/wessaorg/rcomp/tmp/71jax1322553686.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/wessaorg/rcomp/tmp/848g91322553686.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/wessaorg/rcomp/tmp/9b91b1322553686.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/wessaorg/rcomp/tmp/101vy51322553686.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/113l8s1322553686.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12vedw1322553686.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/132ej01322553686.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14nx5p1322553686.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/wessaorg/rcomp/tmp/15ggml1322553686.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/wessaorg/rcomp/tmp/16o3uz1322553686.tab")
+ }
>
> try(system("convert tmp/153sr1322553686.ps tmp/153sr1322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/20stm1322553686.ps tmp/20stm1322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/338m21322553686.ps tmp/338m21322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pa6k1322553686.ps tmp/4pa6k1322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/51n1m1322553686.ps tmp/51n1m1322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y9e51322553686.ps tmp/6y9e51322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/71jax1322553686.ps tmp/71jax1322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/848g91322553686.ps tmp/848g91322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b91b1322553686.ps tmp/9b91b1322553686.png",intern=TRUE))
character(0)
> try(system("convert tmp/101vy51322553686.ps tmp/101vy51322553686.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.418 0.706 7.148