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(1.2613
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+ ,1.3794
+ ,1.3458
+ ,1.6932
+ ,8.7994
+ ,1.3706
+ ,105.06
+ ,7.4442
+ ,9.1138
+ ,0.87036
+ ,7.7474
+ ,1.2295
+ ,1.3981
+ ,1.3525
+ ,1.7361
+ ,8.7308
+ ,1.3556
+ ,105.02
+ ,7.4412
+ ,9.1387
+ ,0.8574
+ ,7.7868
+ ,1.2307
+ ,1.3897
+ ,1.3414
+ ,1.7584
+ ,8.6154)
+ ,dim=c(11
+ ,95)
+ ,dimnames=list(c('EUR/USD'
+ ,'EUR/JPY'
+ ,'EUR/DAK'
+ ,'EUR/SWK'
+ ,'EUR/GBP'
+ ,'EUR/NOK'
+ ,'EUR/CHF'
+ ,'EUR/CAD'
+ ,'EUR/AUD'
+ ,'EUR/NZD'
+ ,'EUR/CHY')
+ ,1:95))
> y <- array(NA,dim=c(11,95),dimnames=list(c('EUR/USD','EUR/JPY','EUR/DAK','EUR/SWK','EUR/GBP','EUR/NOK','EUR/CHF','EUR/CAD','EUR/AUD','EUR/NZD','EUR/CHY'),1:95))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
EUR/USD EUR/JPY EUR/DAK EUR/SWK EUR/GBP EUR/NOK EUR/CHF EUR/CAD EUR/AUD
1 1.2613 134.13 7.4481 9.1368 0.69215 8.5925 1.5657 1.6346 1.6374
2 1.2646 134.78 7.4511 9.1763 0.67690 8.7752 1.5734 1.6817 1.6260
3 1.2262 133.13 7.4493 9.2346 0.67124 8.5407 1.5670 1.6314 1.6370
4 1.1985 129.08 7.4436 9.1653 0.66533 8.2976 1.5547 1.6068 1.6142
5 1.2007 134.48 7.4405 9.1277 0.67157 8.2074 1.5400 1.6541 1.7033
6 1.2138 132.86 7.4342 9.1430 0.66428 8.2856 1.5192 1.6492 1.7483
7 1.2266 134.08 7.4355 9.1962 0.66576 8.4751 1.5270 1.6220 1.7135
8 1.2176 134.54 7.4365 9.1861 0.66942 8.3315 1.5387 1.6007 1.7147
9 1.2218 134.51 7.4381 9.0920 0.68130 8.3604 1.5431 1.5767 1.7396
10 1.2490 135.97 7.4379 9.0620 0.69144 8.2349 1.5426 1.5600 1.7049
11 1.2991 136.09 7.4313 8.9980 0.69862 8.1412 1.5216 1.5540 1.6867
12 1.3408 139.14 7.4338 8.9819 0.69500 8.2207 1.5364 1.6333 1.7462
13 1.3119 135.63 7.4405 9.0476 0.69867 8.2125 1.5469 1.6060 1.7147
14 1.3014 136.55 7.4427 9.0852 0.68968 8.3199 1.5501 1.6128 1.6670
15 1.3201 138.83 7.4466 9.0884 0.69233 8.1880 1.5494 1.6064 1.6806
16 1.2938 138.84 7.4499 9.1670 0.68293 8.1763 1.5475 1.5991 1.6738
17 1.2694 135.37 7.4443 9.1931 0.68399 8.0814 1.5448 1.5942 1.6571
18 1.2165 132.22 7.4448 9.2628 0.66895 7.8932 1.5391 1.5111 1.5875
19 1.2037 134.75 7.4584 9.4276 0.68756 7.9200 1.5578 1.4730 1.6002
20 1.2292 135.98 7.4596 9.3398 0.68527 7.9165 1.5528 1.4819 1.6144
21 1.2256 136.06 7.4584 9.3342 0.67760 7.8087 1.5496 1.4452 1.6009
22 1.2015 138.05 7.4620 9.4223 0.68137 7.8347 1.5490 1.4149 1.5937
23 1.1786 139.59 7.4596 9.5614 0.67933 7.8295 1.5449 1.3944 1.6030
24 1.1856 140.58 7.4541 9.4316 0.67922 7.9737 1.5479 1.3778 1.5979
25 1.2103 139.82 7.4613 9.3111 0.68598 8.0366 1.5494 1.4025 1.6152
26 1.1938 140.77 7.4641 9.3414 0.68297 8.0593 1.5580 1.3723 1.6102
27 1.2020 140.96 7.4612 9.4017 0.68935 7.9775 1.5691 1.3919 1.6540
28 1.2271 143.59 7.4618 9.3346 0.69463 7.8413 1.5748 1.4052 1.6662
29 1.2770 142.70 7.4565 9.3310 0.68330 7.7988 1.5564 1.4173 1.6715
30 1.2650 145.11 7.4566 9.2349 0.68666 7.8559 1.5601 1.4089 1.7104
31 1.2684 146.70 7.4602 9.2170 0.68782 7.9386 1.5687 1.4303 1.6869
32 1.2811 148.53 7.4609 9.2098 0.67669 7.9920 1.5775 1.4338 1.6788
33 1.2727 148.99 7.4601 9.2665 0.67511 8.2572 1.5841 1.4203 1.6839
34 1.2611 149.65 7.4555 9.2533 0.67254 8.3960 1.5898 1.4235 1.6733
35 1.2881 151.11 7.4564 9.1008 0.67397 8.2446 1.5922 1.4635 1.6684
36 1.3213 154.82 7.4549 9.0377 0.67286 8.1575 1.5969 1.5212 1.6814
37 1.2999 156.56 7.4539 9.0795 0.66341 8.2780 1.6155 1.5285 1.6602
38 1.3074 157.60 7.4541 9.1896 0.66800 8.0876 1.6212 1.5309 1.6708
39 1.3242 155.24 7.4494 9.2992 0.68021 8.1340 1.6124 1.5472 1.6704
40 1.3516 160.68 7.4530 9.2372 0.67934 8.1194 1.6375 1.5334 1.6336
41 1.3511 163.22 7.4519 9.2061 0.68136 8.1394 1.6506 1.4796 1.6378
42 1.3419 164.55 7.4452 9.3290 0.67562 8.0590 1.6543 1.4293 1.5930
43 1.3716 166.76 7.4410 9.1842 0.67440 7.9380 1.6567 1.4417 1.5809
44 1.3622 159.05 7.4429 9.3231 0.67766 7.9735 1.6383 1.4420 1.6442
45 1.3896 159.82 7.4506 9.2835 0.68887 7.8306 1.6475 1.4273 1.6445
46 1.4227 164.95 7.4534 9.1735 0.69614 7.6963 1.6706 1.3891 1.5837
47 1.4684 162.89 7.4543 9.2889 0.70896 7.9519 1.6485 1.4163 1.6373
48 1.4570 163.55 7.4599 9.4319 0.72064 8.0117 1.6592 1.4620 1.6703
49 1.4718 158.68 7.4505 9.4314 0.74725 7.9566 1.6203 1.4862 1.6694
50 1.4748 157.97 7.4540 9.3642 0.75094 7.9480 1.6080 1.4740 1.6156
51 1.5527 156.59 7.4561 9.4020 0.77494 7.9717 1.5720 1.5519 1.6763
52 1.5750 161.56 7.4603 9.3699 0.79487 7.9629 1.5964 1.5965 1.6933
53 1.5557 162.31 7.4609 9.3106 0.79209 7.8648 1.6247 1.5530 1.6382
54 1.5553 166.26 7.4586 9.3739 0.79152 7.9915 1.6139 1.5803 1.6343
55 1.5770 168.45 7.4599 9.4566 0.79308 8.0487 1.6193 1.5974 1.6386
56 1.4975 163.63 7.4595 9.3984 0.79279 7.9723 1.6212 1.5765 1.6961
57 1.4370 153.20 7.4583 9.5637 0.79924 8.1566 1.5942 1.5201 1.7543
58 1.3322 133.52 7.4545 9.8506 0.78668 8.5928 1.5194 1.5646 1.9345
59 1.2732 123.28 7.4485 10.1275 0.83063 8.8094 1.5162 1.5509 1.9381
60 1.3449 122.51 7.4503 10.7538 0.90448 9.4228 1.5393 1.6600 2.0105
61 1.3239 119.73 7.4519 10.7264 0.91819 9.2164 1.4935 1.6233 1.9633
62 1.2785 118.30 7.4514 10.9069 0.88691 8.7838 1.4904 1.5940 1.9723
63 1.3050 127.65 7.4509 11.1767 0.91966 8.8388 1.5083 1.6470 1.9594
64 1.3190 130.25 7.4491 10.8796 0.89756 8.7867 1.5147 1.6188 1.8504
65 1.3650 131.85 7.4468 10.5820 0.88444 8.7943 1.5118 1.5712 1.7830
66 1.4016 135.39 7.4457 10.8713 0.85670 8.9388 1.5148 1.5761 1.7463
67 1.4088 133.09 7.4458 10.8262 0.86092 8.9494 1.5202 1.5824 1.7504
68 1.4268 135.31 7.4440 10.2210 0.86265 8.6602 1.5236 1.5522 1.7081
69 1.4562 133.14 7.4428 10.1976 0.89135 8.5964 1.5148 1.5752 1.6903
70 1.4816 133.91 7.4438 10.3102 0.91557 8.3596 1.5138 1.5619 1.6341
71 1.4914 132.97 7.4415 10.3331 0.89892 8.4143 1.5105 1.5805 1.6223
72 1.4614 131.21 7.4419 10.4085 0.89972 8.4066 1.5021 1.5397 1.6185
73 1.4272 130.34 7.4424 10.1939 0.88305 8.1817 1.4765 1.4879 1.5624
74 1.3686 123.46 7.4440 9.9505 0.87604 8.0971 1.4671 1.4454 1.5434
75 1.3569 123.03 7.4416 9.7277 0.90160 8.0369 1.4482 1.3889 1.4882
76 1.3406 125.33 7.4428 9.6617 0.87456 7.9323 1.4337 1.3467 1.4463
77 1.2565 115.83 7.4413 9.6641 0.85714 7.8907 1.4181 1.3060 1.4436
78 1.2208 110.99 7.4409 9.5722 0.82771 7.9062 1.3767 1.2674 1.4315
79 1.2770 111.73 7.4522 9.4954 0.83566 8.0201 1.3460 1.3322 1.4586
80 1.2894 110.04 7.4495 9.4216 0.82363 7.9325 1.3413 1.3411 1.4337
81 1.3067 110.26 7.4476 9.2241 0.83987 7.9156 1.3089 1.3515 1.3943
82 1.3898 113.67 7.4567 9.2794 0.87638 8.1110 1.3452 1.4152 1.4164
83 1.3661 112.69 7.4547 9.3166 0.85510 8.1463 1.3442 1.3831 1.3813
84 1.3220 110.11 7.4528 9.0559 0.84813 7.9020 1.2811 1.3327 1.3304
85 1.3360 110.38 7.4518 8.9122 0.84712 7.8199 1.2779 1.3277 1.3417
86 1.3649 112.77 7.4555 8.7882 0.84635 7.8206 1.2974 1.3484 1.3543
87 1.3999 114.40 7.4574 8.8864 0.86653 7.8295 1.2867 1.3672 1.3854
88 1.4442 120.42 7.4574 8.9702 0.88291 7.8065 1.2977 1.3834 1.3662
89 1.4349 116.47 7.4566 8.9571 0.87788 7.8384 1.2537 1.3885 1.3437
90 1.4388 115.75 7.4579 9.1125 0.88745 7.8302 1.2092 1.4063 1.3567
91 1.4264 113.26 7.4560 9.1340 0.88476 7.7829 1.1766 1.3638 1.3249
92 1.4343 110.43 7.4498 9.1655 0.87668 7.7882 1.1203 1.4071 1.3651
93 1.3770 105.75 7.4462 9.1343 0.87172 7.7243 1.2005 1.3794 1.3458
94 1.3706 105.06 7.4442 9.1138 0.87036 7.7474 1.2295 1.3981 1.3525
95 1.3556 105.02 7.4412 9.1387 0.85740 7.7868 1.2307 1.3897 1.3414
EUR/NZD EUR/CHY
1 1.8751 10.4399
2 1.8262 10.4675
3 1.8566 10.1490
4 1.8727 9.9163
5 1.9484 9.9268
6 1.9301 10.0529
7 1.8961 10.1622
8 1.8604 10.0830
9 1.8538 10.1134
10 1.8280 10.3423
11 1.8540 10.7536
12 1.8737 11.0967
13 1.8620 10.8588
14 1.8192 10.7719
15 1.8081 10.9262
16 1.7967 10.7080
17 1.7665 10.5062
18 1.7175 10.0683
19 1.7732 9.8954
20 1.7675 9.9589
21 1.7515 9.9177
22 1.7212 9.7189
23 1.7088 9.5273
24 1.7072 9.5746
25 1.7616 9.7630
26 1.7741 9.6117
27 1.8956 9.6581
28 1.9733 9.8361
29 2.0240 10.2353
30 2.0462 10.1285
31 2.0551 10.1347
32 2.0220 10.2141
33 1.9453 10.0971
34 1.9066 9.9651
35 1.9263 10.1286
36 1.9094 10.3356
37 1.8699 10.1238
38 1.8859 10.1326
39 1.8952 10.2467
40 1.8394 10.4400
41 1.8441 10.3689
42 1.7738 10.2415
43 1.7446 10.3899
44 1.8786 10.3162
45 1.9358 10.4533
46 1.8739 10.6741
47 1.9231 10.8957
48 1.8930 10.7404
49 1.9054 10.6568
50 1.8513 10.5682
51 1.9344 10.9833
52 1.9960 11.0237
53 2.0011 10.8462
54 2.0424 10.7287
55 2.0900 10.7809
56 2.1097 10.2609
57 2.1293 9.8252
58 2.1891 9.1071
59 2.2554 8.6950
60 2.4119 9.2205
61 2.4132 9.0496
62 2.4851 8.7406
63 2.4527 8.9210
64 2.3123 9.0110
65 2.2663 9.3157
66 2.1967 9.5786
67 2.1873 9.6246
68 2.1097 9.7485
69 2.0691 9.9431
70 2.0065 10.1152
71 2.0450 10.1827
72 2.0383 9.9777
73 1.9646 9.7436
74 1.9615 9.3462
75 1.9301 9.2623
76 1.8814 9.1505
77 1.8010 8.5794
78 1.7667 8.3245
79 1.7925 8.6538
80 1.8059 8.7520
81 1.7955 8.8104
82 1.8498 9.2665
83 1.7703 9.0895
84 1.7587 8.7873
85 1.7435 8.8154
86 1.7925 8.9842
87 1.8877 9.1902
88 1.8331 9.4274
89 1.8024 9.3198
90 1.7666 9.3161
91 1.6877 9.2121
92 1.7108 9.1857
93 1.6932 8.7994
94 1.7361 8.7308
95 1.7584 8.6154
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `EUR/JPY` `EUR/DAK` `EUR/SWK` `EUR/GBP` `EUR/NOK`
0.309174 0.005471 -0.073215 -0.042729 1.304944 0.008822
`EUR/CHF` `EUR/CAD` `EUR/AUD` `EUR/NZD` `EUR/CHY`
-0.331036 0.057283 -0.054953 0.006063 0.065962
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.053362 -0.014010 -0.004037 0.011354 0.062499
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3091739 2.8226372 0.110 0.913040
`EUR/JPY` 0.0054711 0.0004581 11.943 < 2e-16 ***
`EUR/DAK` -0.0732152 0.3751360 -0.195 0.845731
`EUR/SWK` -0.0427295 0.0121919 -3.505 0.000736 ***
`EUR/GBP` 1.3049443 0.0881158 14.809 < 2e-16 ***
`EUR/NOK` 0.0088221 0.0156550 0.564 0.574574
`EUR/CHF` -0.3310362 0.0762154 -4.343 3.90e-05 ***
`EUR/CAD` 0.0572828 0.0553407 1.035 0.303596
`EUR/AUD` -0.0549529 0.0578136 -0.951 0.344576
`EUR/NZD` 0.0060626 0.0431056 0.141 0.888487
`EUR/CHY` 0.0659618 0.0091920 7.176 2.63e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02255 on 84 degrees of freedom
Multiple R-squared: 0.9539, Adjusted R-squared: 0.9484
F-statistic: 173.8 on 10 and 84 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,] 5.833590e-06 1.166718e-05 9.999942e-01
[2,] 9.443735e-08 1.888747e-07 9.999999e-01
[3,] 1.396458e-09 2.792916e-09 1.000000e+00
[4,] 4.648125e-11 9.296250e-11 1.000000e+00
[5,] 6.751330e-13 1.350266e-12 1.000000e+00
[6,] 1.750738e-10 3.501476e-10 1.000000e+00
[7,] 8.249135e-06 1.649827e-05 9.999918e-01
[8,] 3.278642e-06 6.557283e-06 9.999967e-01
[9,] 6.125305e-07 1.225061e-06 9.999994e-01
[10,] 1.466663e-07 2.933327e-07 9.999999e-01
[11,] 2.836128e-08 5.672255e-08 1.000000e+00
[12,] 8.610654e-09 1.722131e-08 1.000000e+00
[13,] 3.347036e-09 6.694072e-09 1.000000e+00
[14,] 6.464144e-10 1.292829e-09 1.000000e+00
[15,] 1.430059e-10 2.860118e-10 1.000000e+00
[16,] 2.588149e-11 5.176299e-11 1.000000e+00
[17,] 6.313191e-12 1.262638e-11 1.000000e+00
[18,] 1.826736e-12 3.653472e-12 1.000000e+00
[19,] 5.784219e-13 1.156844e-12 1.000000e+00
[20,] 9.604663e-13 1.920933e-12 1.000000e+00
[21,] 2.086019e-12 4.172038e-12 1.000000e+00
[22,] 6.068641e-12 1.213728e-11 1.000000e+00
[23,] 1.206455e-11 2.412910e-11 1.000000e+00
[24,] 4.786768e-12 9.573535e-12 1.000000e+00
[25,] 3.830966e-12 7.661932e-12 1.000000e+00
[26,] 2.480814e-10 4.961627e-10 1.000000e+00
[27,] 2.405230e-09 4.810460e-09 1.000000e+00
[28,] 2.382853e-08 4.765707e-08 1.000000e+00
[29,] 5.919571e-08 1.183914e-07 9.999999e-01
[30,] 6.478711e-07 1.295742e-06 9.999994e-01
[31,] 7.834480e-04 1.566896e-03 9.992166e-01
[32,] 2.285011e-02 4.570022e-02 9.771499e-01
[33,] 3.678799e-02 7.357598e-02 9.632120e-01
[34,] 4.443620e-01 8.887241e-01 5.556380e-01
[35,] 6.228850e-01 7.542301e-01 3.771150e-01
[36,] 8.993441e-01 2.013118e-01 1.006559e-01
[37,] 9.569032e-01 8.619362e-02 4.309681e-02
[38,] 9.765177e-01 4.696453e-02 2.348227e-02
[39,] 9.877157e-01 2.456857e-02 1.228428e-02
[40,] 9.850652e-01 2.986964e-02 1.493482e-02
[41,] 9.926139e-01 1.477212e-02 7.386059e-03
[42,] 9.917331e-01 1.653375e-02 8.266875e-03
[43,] 9.933248e-01 1.335049e-02 6.675244e-03
[44,] 9.921694e-01 1.566116e-02 7.830582e-03
[45,] 9.956993e-01 8.601307e-03 4.300653e-03
[46,] 9.971012e-01 5.797628e-03 2.898814e-03
[47,] 9.998746e-01 2.508976e-04 1.254488e-04
[48,] 9.998803e-01 2.394934e-04 1.197467e-04
[49,] 9.999174e-01 1.652354e-04 8.261772e-05
[50,] 9.999612e-01 7.767829e-05 3.883914e-05
[51,] 9.999814e-01 3.719779e-05 1.859889e-05
[52,] 9.999891e-01 2.182878e-05 1.091439e-05
[53,] 9.999919e-01 1.616715e-05 8.083575e-06
[54,] 9.999879e-01 2.417596e-05 1.208798e-05
[55,] 9.999743e-01 5.141100e-05 2.570550e-05
[56,] 9.999294e-01 1.412206e-04 7.061032e-05
[57,] 9.998475e-01 3.050930e-04 1.525465e-04
[58,] 9.996287e-01 7.426758e-04 3.713379e-04
[59,] 9.994550e-01 1.089944e-03 5.449722e-04
[60,] 9.987381e-01 2.523809e-03 1.261904e-03
[61,] 9.968631e-01 6.273758e-03 3.136879e-03
[62,] 9.945318e-01 1.093637e-02 5.468185e-03
[63,] 9.873335e-01 2.533295e-02 1.266647e-02
[64,] 9.849829e-01 3.003425e-02 1.501712e-02
[65,] 9.731163e-01 5.376749e-02 2.688375e-02
[66,] 9.821105e-01 3.577901e-02 1.788951e-02
[67,] 9.713552e-01 5.728966e-02 2.864483e-02
[68,] 9.748602e-01 5.027967e-02 2.513984e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1hvqo1324652129.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2wwxk1324652129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3szqz1324652129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4qqrc1324652129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5dscg1324652129.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 = 95
Frequency = 1
1 2 3 4 5
-0.0103674046 0.0072728275 0.0119060269 0.0241787159 -0.0161770525
6 7 8 9 10
0.0024630226 0.0025778313 -0.0022066812 -0.0152694962 -0.0255368486
11 12 13 14 15
-0.0227500912 -0.0133438459 -0.0053871612 -0.0043288513 -0.0092036010
16 17 18 19 20
-0.0057109710 0.0009964423 0.0178623767 -0.0051256303 0.0074615964
21 22 23 24 25
0.0171724367 -0.0044978034 -0.0143058637 -0.0212464159 -0.0191110989
26 27 28 29 30
-0.0213750233 -0.0182927074 -0.0265103449 0.0097447886 -0.0136741759
31 32 33 34 35
-0.0218511451 -0.0081359822 -0.0055453768 -0.0094652660 -0.0101041652
36 37 38 39 40
-0.0123778363 -0.0115317840 -0.0076581049 -0.0014013354 -0.0102207454
41 42 43 44 45
-0.0165287008 -0.0095707158 -0.0059896079 0.0297190477 0.0329241279
46 47 48 49 50
0.0174647327 0.0396038954 0.0285080208 0.0261306153 0.0255034191
51 52 53 54 55
0.0402662204 0.0117990133 0.0109084508 -0.0067932997 0.0013260532
56 57 58 59 60
-0.0141000850 0.0055081520 0.0624994334 0.0383573993 0.0091460586
61 62 63 64 65
-0.0181556950 0.0176722523 -0.0482356191 -0.0393296269 -0.0196711718
66 67 68 69 70
0.0265427170 0.0374809376 0.0104658876 -0.0040373402 -0.0211122721
71 72 73 74 75
0.0083933011 0.0032079701 -0.0043294292 -0.0011665240 -0.0533620139
76 77 78 79 80
-0.0457848658 -0.0196401170 0.0103147230 0.0143694869 0.0391431817
81 82 83 84 85
0.0083425659 0.0056156089 0.0279143356 -0.0028925661 0.0035457648
86 87 88 89 90
0.0098712581 -0.0031991043 -0.0230834943 -0.0139189564 -0.0263356301
91 92 93 94 95
-0.0231717368 -0.0057064170 0.0207010218 0.0317865957 0.0421594856
> postscript(file="/var/wessaorg/rcomp/tmp/6ksv71324652129.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 = 95
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0103674046 NA
1 0.0072728275 -0.0103674046
2 0.0119060269 0.0072728275
3 0.0241787159 0.0119060269
4 -0.0161770525 0.0241787159
5 0.0024630226 -0.0161770525
6 0.0025778313 0.0024630226
7 -0.0022066812 0.0025778313
8 -0.0152694962 -0.0022066812
9 -0.0255368486 -0.0152694962
10 -0.0227500912 -0.0255368486
11 -0.0133438459 -0.0227500912
12 -0.0053871612 -0.0133438459
13 -0.0043288513 -0.0053871612
14 -0.0092036010 -0.0043288513
15 -0.0057109710 -0.0092036010
16 0.0009964423 -0.0057109710
17 0.0178623767 0.0009964423
18 -0.0051256303 0.0178623767
19 0.0074615964 -0.0051256303
20 0.0171724367 0.0074615964
21 -0.0044978034 0.0171724367
22 -0.0143058637 -0.0044978034
23 -0.0212464159 -0.0143058637
24 -0.0191110989 -0.0212464159
25 -0.0213750233 -0.0191110989
26 -0.0182927074 -0.0213750233
27 -0.0265103449 -0.0182927074
28 0.0097447886 -0.0265103449
29 -0.0136741759 0.0097447886
30 -0.0218511451 -0.0136741759
31 -0.0081359822 -0.0218511451
32 -0.0055453768 -0.0081359822
33 -0.0094652660 -0.0055453768
34 -0.0101041652 -0.0094652660
35 -0.0123778363 -0.0101041652
36 -0.0115317840 -0.0123778363
37 -0.0076581049 -0.0115317840
38 -0.0014013354 -0.0076581049
39 -0.0102207454 -0.0014013354
40 -0.0165287008 -0.0102207454
41 -0.0095707158 -0.0165287008
42 -0.0059896079 -0.0095707158
43 0.0297190477 -0.0059896079
44 0.0329241279 0.0297190477
45 0.0174647327 0.0329241279
46 0.0396038954 0.0174647327
47 0.0285080208 0.0396038954
48 0.0261306153 0.0285080208
49 0.0255034191 0.0261306153
50 0.0402662204 0.0255034191
51 0.0117990133 0.0402662204
52 0.0109084508 0.0117990133
53 -0.0067932997 0.0109084508
54 0.0013260532 -0.0067932997
55 -0.0141000850 0.0013260532
56 0.0055081520 -0.0141000850
57 0.0624994334 0.0055081520
58 0.0383573993 0.0624994334
59 0.0091460586 0.0383573993
60 -0.0181556950 0.0091460586
61 0.0176722523 -0.0181556950
62 -0.0482356191 0.0176722523
63 -0.0393296269 -0.0482356191
64 -0.0196711718 -0.0393296269
65 0.0265427170 -0.0196711718
66 0.0374809376 0.0265427170
67 0.0104658876 0.0374809376
68 -0.0040373402 0.0104658876
69 -0.0211122721 -0.0040373402
70 0.0083933011 -0.0211122721
71 0.0032079701 0.0083933011
72 -0.0043294292 0.0032079701
73 -0.0011665240 -0.0043294292
74 -0.0533620139 -0.0011665240
75 -0.0457848658 -0.0533620139
76 -0.0196401170 -0.0457848658
77 0.0103147230 -0.0196401170
78 0.0143694869 0.0103147230
79 0.0391431817 0.0143694869
80 0.0083425659 0.0391431817
81 0.0056156089 0.0083425659
82 0.0279143356 0.0056156089
83 -0.0028925661 0.0279143356
84 0.0035457648 -0.0028925661
85 0.0098712581 0.0035457648
86 -0.0031991043 0.0098712581
87 -0.0230834943 -0.0031991043
88 -0.0139189564 -0.0230834943
89 -0.0263356301 -0.0139189564
90 -0.0231717368 -0.0263356301
91 -0.0057064170 -0.0231717368
92 0.0207010218 -0.0057064170
93 0.0317865957 0.0207010218
94 0.0421594856 0.0317865957
95 NA 0.0421594856
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0072728275 -0.0103674046
[2,] 0.0119060269 0.0072728275
[3,] 0.0241787159 0.0119060269
[4,] -0.0161770525 0.0241787159
[5,] 0.0024630226 -0.0161770525
[6,] 0.0025778313 0.0024630226
[7,] -0.0022066812 0.0025778313
[8,] -0.0152694962 -0.0022066812
[9,] -0.0255368486 -0.0152694962
[10,] -0.0227500912 -0.0255368486
[11,] -0.0133438459 -0.0227500912
[12,] -0.0053871612 -0.0133438459
[13,] -0.0043288513 -0.0053871612
[14,] -0.0092036010 -0.0043288513
[15,] -0.0057109710 -0.0092036010
[16,] 0.0009964423 -0.0057109710
[17,] 0.0178623767 0.0009964423
[18,] -0.0051256303 0.0178623767
[19,] 0.0074615964 -0.0051256303
[20,] 0.0171724367 0.0074615964
[21,] -0.0044978034 0.0171724367
[22,] -0.0143058637 -0.0044978034
[23,] -0.0212464159 -0.0143058637
[24,] -0.0191110989 -0.0212464159
[25,] -0.0213750233 -0.0191110989
[26,] -0.0182927074 -0.0213750233
[27,] -0.0265103449 -0.0182927074
[28,] 0.0097447886 -0.0265103449
[29,] -0.0136741759 0.0097447886
[30,] -0.0218511451 -0.0136741759
[31,] -0.0081359822 -0.0218511451
[32,] -0.0055453768 -0.0081359822
[33,] -0.0094652660 -0.0055453768
[34,] -0.0101041652 -0.0094652660
[35,] -0.0123778363 -0.0101041652
[36,] -0.0115317840 -0.0123778363
[37,] -0.0076581049 -0.0115317840
[38,] -0.0014013354 -0.0076581049
[39,] -0.0102207454 -0.0014013354
[40,] -0.0165287008 -0.0102207454
[41,] -0.0095707158 -0.0165287008
[42,] -0.0059896079 -0.0095707158
[43,] 0.0297190477 -0.0059896079
[44,] 0.0329241279 0.0297190477
[45,] 0.0174647327 0.0329241279
[46,] 0.0396038954 0.0174647327
[47,] 0.0285080208 0.0396038954
[48,] 0.0261306153 0.0285080208
[49,] 0.0255034191 0.0261306153
[50,] 0.0402662204 0.0255034191
[51,] 0.0117990133 0.0402662204
[52,] 0.0109084508 0.0117990133
[53,] -0.0067932997 0.0109084508
[54,] 0.0013260532 -0.0067932997
[55,] -0.0141000850 0.0013260532
[56,] 0.0055081520 -0.0141000850
[57,] 0.0624994334 0.0055081520
[58,] 0.0383573993 0.0624994334
[59,] 0.0091460586 0.0383573993
[60,] -0.0181556950 0.0091460586
[61,] 0.0176722523 -0.0181556950
[62,] -0.0482356191 0.0176722523
[63,] -0.0393296269 -0.0482356191
[64,] -0.0196711718 -0.0393296269
[65,] 0.0265427170 -0.0196711718
[66,] 0.0374809376 0.0265427170
[67,] 0.0104658876 0.0374809376
[68,] -0.0040373402 0.0104658876
[69,] -0.0211122721 -0.0040373402
[70,] 0.0083933011 -0.0211122721
[71,] 0.0032079701 0.0083933011
[72,] -0.0043294292 0.0032079701
[73,] -0.0011665240 -0.0043294292
[74,] -0.0533620139 -0.0011665240
[75,] -0.0457848658 -0.0533620139
[76,] -0.0196401170 -0.0457848658
[77,] 0.0103147230 -0.0196401170
[78,] 0.0143694869 0.0103147230
[79,] 0.0391431817 0.0143694869
[80,] 0.0083425659 0.0391431817
[81,] 0.0056156089 0.0083425659
[82,] 0.0279143356 0.0056156089
[83,] -0.0028925661 0.0279143356
[84,] 0.0035457648 -0.0028925661
[85,] 0.0098712581 0.0035457648
[86,] -0.0031991043 0.0098712581
[87,] -0.0230834943 -0.0031991043
[88,] -0.0139189564 -0.0230834943
[89,] -0.0263356301 -0.0139189564
[90,] -0.0231717368 -0.0263356301
[91,] -0.0057064170 -0.0231717368
[92,] 0.0207010218 -0.0057064170
[93,] 0.0317865957 0.0207010218
[94,] 0.0421594856 0.0317865957
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0072728275 -0.0103674046
2 0.0119060269 0.0072728275
3 0.0241787159 0.0119060269
4 -0.0161770525 0.0241787159
5 0.0024630226 -0.0161770525
6 0.0025778313 0.0024630226
7 -0.0022066812 0.0025778313
8 -0.0152694962 -0.0022066812
9 -0.0255368486 -0.0152694962
10 -0.0227500912 -0.0255368486
11 -0.0133438459 -0.0227500912
12 -0.0053871612 -0.0133438459
13 -0.0043288513 -0.0053871612
14 -0.0092036010 -0.0043288513
15 -0.0057109710 -0.0092036010
16 0.0009964423 -0.0057109710
17 0.0178623767 0.0009964423
18 -0.0051256303 0.0178623767
19 0.0074615964 -0.0051256303
20 0.0171724367 0.0074615964
21 -0.0044978034 0.0171724367
22 -0.0143058637 -0.0044978034
23 -0.0212464159 -0.0143058637
24 -0.0191110989 -0.0212464159
25 -0.0213750233 -0.0191110989
26 -0.0182927074 -0.0213750233
27 -0.0265103449 -0.0182927074
28 0.0097447886 -0.0265103449
29 -0.0136741759 0.0097447886
30 -0.0218511451 -0.0136741759
31 -0.0081359822 -0.0218511451
32 -0.0055453768 -0.0081359822
33 -0.0094652660 -0.0055453768
34 -0.0101041652 -0.0094652660
35 -0.0123778363 -0.0101041652
36 -0.0115317840 -0.0123778363
37 -0.0076581049 -0.0115317840
38 -0.0014013354 -0.0076581049
39 -0.0102207454 -0.0014013354
40 -0.0165287008 -0.0102207454
41 -0.0095707158 -0.0165287008
42 -0.0059896079 -0.0095707158
43 0.0297190477 -0.0059896079
44 0.0329241279 0.0297190477
45 0.0174647327 0.0329241279
46 0.0396038954 0.0174647327
47 0.0285080208 0.0396038954
48 0.0261306153 0.0285080208
49 0.0255034191 0.0261306153
50 0.0402662204 0.0255034191
51 0.0117990133 0.0402662204
52 0.0109084508 0.0117990133
53 -0.0067932997 0.0109084508
54 0.0013260532 -0.0067932997
55 -0.0141000850 0.0013260532
56 0.0055081520 -0.0141000850
57 0.0624994334 0.0055081520
58 0.0383573993 0.0624994334
59 0.0091460586 0.0383573993
60 -0.0181556950 0.0091460586
61 0.0176722523 -0.0181556950
62 -0.0482356191 0.0176722523
63 -0.0393296269 -0.0482356191
64 -0.0196711718 -0.0393296269
65 0.0265427170 -0.0196711718
66 0.0374809376 0.0265427170
67 0.0104658876 0.0374809376
68 -0.0040373402 0.0104658876
69 -0.0211122721 -0.0040373402
70 0.0083933011 -0.0211122721
71 0.0032079701 0.0083933011
72 -0.0043294292 0.0032079701
73 -0.0011665240 -0.0043294292
74 -0.0533620139 -0.0011665240
75 -0.0457848658 -0.0533620139
76 -0.0196401170 -0.0457848658
77 0.0103147230 -0.0196401170
78 0.0143694869 0.0103147230
79 0.0391431817 0.0143694869
80 0.0083425659 0.0391431817
81 0.0056156089 0.0083425659
82 0.0279143356 0.0056156089
83 -0.0028925661 0.0279143356
84 0.0035457648 -0.0028925661
85 0.0098712581 0.0035457648
86 -0.0031991043 0.0098712581
87 -0.0230834943 -0.0031991043
88 -0.0139189564 -0.0230834943
89 -0.0263356301 -0.0139189564
90 -0.0231717368 -0.0263356301
91 -0.0057064170 -0.0231717368
92 0.0207010218 -0.0057064170
93 0.0317865957 0.0207010218
94 0.0421594856 0.0317865957
> 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/7598v1324652129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/87s2p1324652129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9nsej1324652129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ob4g1324652129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11avg11324652129.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/12l7dz1324652129.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/130qfl1324652129.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/14iasn1324652129.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/15j0ir1324652130.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/16tkgh1324652130.tab")
+ }
>
> try(system("convert tmp/1hvqo1324652129.ps tmp/1hvqo1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wwxk1324652129.ps tmp/2wwxk1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/3szqz1324652129.ps tmp/3szqz1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qqrc1324652129.ps tmp/4qqrc1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dscg1324652129.ps tmp/5dscg1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ksv71324652129.ps tmp/6ksv71324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/7598v1324652129.ps tmp/7598v1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/87s2p1324652129.ps tmp/87s2p1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nsej1324652129.ps tmp/9nsej1324652129.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ob4g1324652129.ps tmp/10ob4g1324652129.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.775 0.549 4.335