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)
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> x <- array(list(617,614,647,580,614,636,388,356,639,753,611,639,630,586,695,552,619,681,421,307,754,690,644,643,608,651,691,627,634,731,475,337,803,722,590,724,627,696,825,677,656,785,412,352,839,729,696,641,695,638,762,635,721,854,418,367,824,687,601,676,740,691,683,594,729,731,386,331,706,715,657,653,642,643,718,654,632,731,392,344,792,852,649,629,685,617,715,715,629,916,531,357,917,828,708,858,775,785,1006,789,734,906,532,387,991,841,892,782,811,792,978,773,796,946,594,438,1023,868,791,760,779,852,1001,734,996,869,599,426,1138),dim=c(1,129),dimnames=list(c('faillissementen'),1:129))
> y <- array(NA,dim=c(1,129),dimnames=list(c('faillissementen'),1:129))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> 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
faillissementen M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 617 1 0 0 0 0 0 0 0 0 0 0 1
2 614 0 1 0 0 0 0 0 0 0 0 0 2
3 647 0 0 1 0 0 0 0 0 0 0 0 3
4 580 0 0 0 1 0 0 0 0 0 0 0 4
5 614 0 0 0 0 1 0 0 0 0 0 0 5
6 636 0 0 0 0 0 1 0 0 0 0 0 6
7 388 0 0 0 0 0 0 1 0 0 0 0 7
8 356 0 0 0 0 0 0 0 1 0 0 0 8
9 639 0 0 0 0 0 0 0 0 1 0 0 9
10 753 0 0 0 0 0 0 0 0 0 1 0 10
11 611 0 0 0 0 0 0 0 0 0 0 1 11
12 639 0 0 0 0 0 0 0 0 0 0 0 12
13 630 1 0 0 0 0 0 0 0 0 0 0 13
14 586 0 1 0 0 0 0 0 0 0 0 0 14
15 695 0 0 1 0 0 0 0 0 0 0 0 15
16 552 0 0 0 1 0 0 0 0 0 0 0 16
17 619 0 0 0 0 1 0 0 0 0 0 0 17
18 681 0 0 0 0 0 1 0 0 0 0 0 18
19 421 0 0 0 0 0 0 1 0 0 0 0 19
20 307 0 0 0 0 0 0 0 1 0 0 0 20
21 754 0 0 0 0 0 0 0 0 1 0 0 21
22 690 0 0 0 0 0 0 0 0 0 1 0 22
23 644 0 0 0 0 0 0 0 0 0 0 1 23
24 643 0 0 0 0 0 0 0 0 0 0 0 24
25 608 1 0 0 0 0 0 0 0 0 0 0 25
26 651 0 1 0 0 0 0 0 0 0 0 0 26
27 691 0 0 1 0 0 0 0 0 0 0 0 27
28 627 0 0 0 1 0 0 0 0 0 0 0 28
29 634 0 0 0 0 1 0 0 0 0 0 0 29
30 731 0 0 0 0 0 1 0 0 0 0 0 30
31 475 0 0 0 0 0 0 1 0 0 0 0 31
32 337 0 0 0 0 0 0 0 1 0 0 0 32
33 803 0 0 0 0 0 0 0 0 1 0 0 33
34 722 0 0 0 0 0 0 0 0 0 1 0 34
35 590 0 0 0 0 0 0 0 0 0 0 1 35
36 724 0 0 0 0 0 0 0 0 0 0 0 36
37 627 1 0 0 0 0 0 0 0 0 0 0 37
38 696 0 1 0 0 0 0 0 0 0 0 0 38
39 825 0 0 1 0 0 0 0 0 0 0 0 39
40 677 0 0 0 1 0 0 0 0 0 0 0 40
41 656 0 0 0 0 1 0 0 0 0 0 0 41
42 785 0 0 0 0 0 1 0 0 0 0 0 42
43 412 0 0 0 0 0 0 1 0 0 0 0 43
44 352 0 0 0 0 0 0 0 1 0 0 0 44
45 839 0 0 0 0 0 0 0 0 1 0 0 45
46 729 0 0 0 0 0 0 0 0 0 1 0 46
47 696 0 0 0 0 0 0 0 0 0 0 1 47
48 641 0 0 0 0 0 0 0 0 0 0 0 48
49 695 1 0 0 0 0 0 0 0 0 0 0 49
50 638 0 1 0 0 0 0 0 0 0 0 0 50
51 762 0 0 1 0 0 0 0 0 0 0 0 51
52 635 0 0 0 1 0 0 0 0 0 0 0 52
53 721 0 0 0 0 1 0 0 0 0 0 0 53
54 854 0 0 0 0 0 1 0 0 0 0 0 54
55 418 0 0 0 0 0 0 1 0 0 0 0 55
56 367 0 0 0 0 0 0 0 1 0 0 0 56
57 824 0 0 0 0 0 0 0 0 1 0 0 57
58 687 0 0 0 0 0 0 0 0 0 1 0 58
59 601 0 0 0 0 0 0 0 0 0 0 1 59
60 676 0 0 0 0 0 0 0 0 0 0 0 60
61 740 1 0 0 0 0 0 0 0 0 0 0 61
62 691 0 1 0 0 0 0 0 0 0 0 0 62
63 683 0 0 1 0 0 0 0 0 0 0 0 63
64 594 0 0 0 1 0 0 0 0 0 0 0 64
65 729 0 0 0 0 1 0 0 0 0 0 0 65
66 731 0 0 0 0 0 1 0 0 0 0 0 66
67 386 0 0 0 0 0 0 1 0 0 0 0 67
68 331 0 0 0 0 0 0 0 1 0 0 0 68
69 706 0 0 0 0 0 0 0 0 1 0 0 69
70 715 0 0 0 0 0 0 0 0 0 1 0 70
71 657 0 0 0 0 0 0 0 0 0 0 1 71
72 653 0 0 0 0 0 0 0 0 0 0 0 72
73 642 1 0 0 0 0 0 0 0 0 0 0 73
74 643 0 1 0 0 0 0 0 0 0 0 0 74
75 718 0 0 1 0 0 0 0 0 0 0 0 75
76 654 0 0 0 1 0 0 0 0 0 0 0 76
77 632 0 0 0 0 1 0 0 0 0 0 0 77
78 731 0 0 0 0 0 1 0 0 0 0 0 78
79 392 0 0 0 0 0 0 1 0 0 0 0 79
80 344 0 0 0 0 0 0 0 1 0 0 0 80
81 792 0 0 0 0 0 0 0 0 1 0 0 81
82 852 0 0 0 0 0 0 0 0 0 1 0 82
83 649 0 0 0 0 0 0 0 0 0 0 1 83
84 629 0 0 0 0 0 0 0 0 0 0 0 84
85 685 1 0 0 0 0 0 0 0 0 0 0 85
86 617 0 1 0 0 0 0 0 0 0 0 0 86
87 715 0 0 1 0 0 0 0 0 0 0 0 87
88 715 0 0 0 1 0 0 0 0 0 0 0 88
89 629 0 0 0 0 1 0 0 0 0 0 0 89
90 916 0 0 0 0 0 1 0 0 0 0 0 90
91 531 0 0 0 0 0 0 1 0 0 0 0 91
92 357 0 0 0 0 0 0 0 1 0 0 0 92
93 917 0 0 0 0 0 0 0 0 1 0 0 93
94 828 0 0 0 0 0 0 0 0 0 1 0 94
95 708 0 0 0 0 0 0 0 0 0 0 1 95
96 858 0 0 0 0 0 0 0 0 0 0 0 96
97 775 1 0 0 0 0 0 0 0 0 0 0 97
98 785 0 1 0 0 0 0 0 0 0 0 0 98
99 1006 0 0 1 0 0 0 0 0 0 0 0 99
100 789 0 0 0 1 0 0 0 0 0 0 0 100
101 734 0 0 0 0 1 0 0 0 0 0 0 101
102 906 0 0 0 0 0 1 0 0 0 0 0 102
103 532 0 0 0 0 0 0 1 0 0 0 0 103
104 387 0 0 0 0 0 0 0 1 0 0 0 104
105 991 0 0 0 0 0 0 0 0 1 0 0 105
106 841 0 0 0 0 0 0 0 0 0 1 0 106
107 892 0 0 0 0 0 0 0 0 0 0 1 107
108 782 0 0 0 0 0 0 0 0 0 0 0 108
109 811 1 0 0 0 0 0 0 0 0 0 0 109
110 792 0 1 0 0 0 0 0 0 0 0 0 110
111 978 0 0 1 0 0 0 0 0 0 0 0 111
112 773 0 0 0 1 0 0 0 0 0 0 0 112
113 796 0 0 0 0 1 0 0 0 0 0 0 113
114 946 0 0 0 0 0 1 0 0 0 0 0 114
115 594 0 0 0 0 0 0 1 0 0 0 0 115
116 438 0 0 0 0 0 0 0 1 0 0 0 116
117 1023 0 0 0 0 0 0 0 0 1 0 0 117
118 868 0 0 0 0 0 0 0 0 0 1 0 118
119 791 0 0 0 0 0 0 0 0 0 0 1 119
120 760 0 0 0 0 0 0 0 0 0 0 0 120
121 779 1 0 0 0 0 0 0 0 0 0 0 121
122 852 0 1 0 0 0 0 0 0 0 0 0 122
123 1001 0 0 1 0 0 0 0 0 0 0 0 123
124 734 0 0 0 1 0 0 0 0 0 0 0 124
125 996 0 0 0 0 1 0 0 0 0 0 0 125
126 869 0 0 0 0 0 1 0 0 0 0 0 126
127 599 0 0 0 0 0 0 1 0 0 0 0 127
128 426 0 0 0 0 0 0 0 1 0 0 0 128
129 1138 0 0 0 0 0 0 0 0 1 0 0 129
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
575.9689 0.6614 -5.2254 97.9787 -30.3627 6.8414
M6 M7 M8 M9 M10 M11
98.2273 -234.3868 -340.4555 150.7486 71.7737 -14.7132
t
1.8868
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-150.909 -38.221 0.747 35.432 177.335
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 575.9689 22.3188 25.806 < 2e-16 ***
M1 0.6614 27.6538 0.024 0.980958
M2 -5.2254 27.6502 -0.189 0.850437
M3 97.9787 27.6473 3.544 0.000569 ***
M4 -30.3627 27.6453 -1.098 0.274350
M5 6.8414 27.6441 0.247 0.804974
M6 98.2273 27.6437 3.553 0.000551 ***
M7 -234.3868 27.6441 -8.479 8.39e-14 ***
M8 -340.4555 27.6453 -12.315 < 2e-16 ***
M9 150.7486 27.6473 5.453 2.84e-07 ***
M10 71.7737 28.2958 2.537 0.012524 *
M11 -14.7132 28.2946 -0.520 0.604055
t 1.8868 0.1499 12.589 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 63.27 on 116 degrees of freedom
Multiple R-squared: 0.8645, Adjusted R-squared: 0.8504
F-statistic: 61.66 on 12 and 116 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,] 0.0781493532 0.1562987063 0.9218506
[2,] 0.0251131768 0.0502263536 0.9748868
[3,] 0.0153070499 0.0306140997 0.9846930
[4,] 0.0061243147 0.0122486293 0.9938757
[5,] 0.0074549532 0.0149099064 0.9925450
[6,] 0.0333516582 0.0667033164 0.9666483
[7,] 0.0377626537 0.0755253075 0.9622373
[8,] 0.0214708922 0.0429417844 0.9785291
[9,] 0.0109212675 0.0218425350 0.9890787
[10,] 0.0062439139 0.0124878279 0.9937561
[11,] 0.0041873826 0.0083747652 0.9958126
[12,] 0.0019729290 0.0039458579 0.9980271
[13,] 0.0013999984 0.0027999968 0.9986000
[14,] 0.0006350975 0.0012701949 0.9993649
[15,] 0.0005116636 0.0010233272 0.9994883
[16,] 0.0004269571 0.0008539141 0.9995730
[17,] 0.0002507317 0.0005014634 0.9997493
[18,] 0.0004365131 0.0008730263 0.9995635
[19,] 0.0002714825 0.0005429650 0.9997285
[20,] 0.0003414741 0.0006829482 0.9996585
[21,] 0.0003911451 0.0007822902 0.9996089
[22,] 0.0002387691 0.0004775382 0.9997612
[23,] 0.0001985783 0.0003971567 0.9998014
[24,] 0.0009081348 0.0018162695 0.9990919
[25,] 0.0007776857 0.0015553714 0.9992223
[26,] 0.0004613081 0.0009226162 0.9995387
[27,] 0.0003983763 0.0007967525 0.9996016
[28,] 0.0005165771 0.0010331542 0.9994834
[29,] 0.0004423599 0.0008847199 0.9995576
[30,] 0.0004366161 0.0008732323 0.9995634
[31,] 0.0003506854 0.0007013708 0.9996493
[32,] 0.0002963521 0.0005927042 0.9997036
[33,] 0.0004275080 0.0008550160 0.9995725
[34,] 0.0003028105 0.0006056210 0.9996972
[35,] 0.0002967505 0.0005935010 0.9997032
[36,] 0.0001769729 0.0003539458 0.9998230
[37,] 0.0001244974 0.0002489949 0.9998755
[38,] 0.0001200018 0.0002400036 0.9998800
[39,] 0.0004355534 0.0008711069 0.9995644
[40,] 0.0005121584 0.0010243168 0.9994878
[41,] 0.0006682799 0.0013365599 0.9993317
[42,] 0.0004408478 0.0008816955 0.9995592
[43,] 0.0007481335 0.0014962671 0.9992519
[44,] 0.0010339293 0.0020678585 0.9989661
[45,] 0.0008825659 0.0017651319 0.9991174
[46,] 0.0014983006 0.0029966012 0.9985017
[47,] 0.0013815189 0.0027630378 0.9986185
[48,] 0.0026471656 0.0052943312 0.9973528
[49,] 0.0027862011 0.0055724022 0.9972138
[50,] 0.0034722673 0.0069445346 0.9965277
[51,] 0.0030187214 0.0060374427 0.9969813
[52,] 0.0036020679 0.0072041357 0.9963979
[53,] 0.0044719071 0.0089438141 0.9955281
[54,] 0.0118921608 0.0237843216 0.9881078
[55,] 0.0090820764 0.0181641528 0.9909179
[56,] 0.0062992736 0.0125985472 0.9937007
[57,] 0.0050261317 0.0100522633 0.9949739
[58,] 0.0040095661 0.0080191322 0.9959904
[59,] 0.0029666799 0.0059333597 0.9970333
[60,] 0.0029362066 0.0058724131 0.9970638
[61,] 0.0019475939 0.0038951878 0.9980524
[62,] 0.0018056947 0.0036113893 0.9981943
[63,] 0.0015259371 0.0030518743 0.9984741
[64,] 0.0014333692 0.0028667385 0.9985666
[65,] 0.0011223226 0.0022446453 0.9988777
[66,] 0.0014398218 0.0028796436 0.9985602
[67,] 0.0031972518 0.0063945037 0.9968027
[68,] 0.0025008115 0.0050016229 0.9974992
[69,] 0.0031023568 0.0062047137 0.9968976
[70,] 0.0020176736 0.0040353472 0.9979823
[71,] 0.0034671933 0.0069343866 0.9965328
[72,] 0.0338237537 0.0676475073 0.9661762
[73,] 0.0293754108 0.0587508217 0.9706246
[74,] 0.0999631088 0.1999262176 0.9000369
[75,] 0.1682312608 0.3364625216 0.8317687
[76,] 0.1550442632 0.3100885263 0.8449557
[77,] 0.1216057348 0.2432114696 0.8783943
[78,] 0.1789591863 0.3579183726 0.8210408
[79,] 0.1472268256 0.2944536513 0.8527732
[80,] 0.1870884062 0.3741768124 0.8129116
[81,] 0.3682658859 0.7365317717 0.6317341
[82,] 0.3229715226 0.6459430452 0.6770285
[83,] 0.2881786394 0.5763572787 0.7118214
[84,] 0.4384268840 0.8768537680 0.5615731
[85,] 0.4718695487 0.9437390973 0.5281305
[86,] 0.5987460622 0.8025078756 0.4012539
[87,] 0.5537831824 0.8924336352 0.4462168
[88,] 0.4839159097 0.9678318194 0.5160841
[89,] 0.4023547807 0.8047095615 0.5976452
[90,] 0.4223013093 0.8446026185 0.5776987
[91,] 0.3369299150 0.6738598300 0.6630701
[92,] 0.4687239459 0.9374478917 0.5312761
[93,] 0.3944319867 0.7888639734 0.6055680
[94,] 0.3447145930 0.6894291861 0.6552854
[95,] 0.2557246819 0.5114493637 0.7442753
[96,] 0.1892884730 0.3785769461 0.8107115
[97,] 0.1542328875 0.3084657751 0.8457671
[98,] 0.4443523360 0.8887046720 0.5556477
> postscript(file="/var/wessaorg/rcomp/tmp/1amgr1323439096.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/28suy1323439096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/38y351323439096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hktc1323439096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ympf1323439096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 129
Frequency = 1
1 2 3 4 5
38.48282828 39.48282828 -32.60808081 26.84646465 21.75555556
6 7 8 9 10
-49.51717172 33.21010101 105.39191919 -104.69898990 86.38909091
11 12 13 14 15
28.98909091 40.38909091 28.84080808 -11.15919192 -7.25010101
16 17 18 19 20
-23.79555556 4.11353535 -27.15919192 43.56808081 33.74989899
21 22 23 24 25
-12.34101010 0.74707071 39.34707071 21.74707071 -15.80121212
26 27 28 29 30
31.19878788 -33.89212121 28.56242424 -3.52848485 0.19878788
31 32 33 34 35
74.92606061 41.10787879 14.01696970 10.10505051 -37.29494949
36 37 38 39 40
80.10505051 -19.44323232 53.55676768 77.46585859 55.92040404
41 42 43 44 45
-4.17050505 31.55676768 -10.71595960 33.46585859 27.37494949
46 47 48 49 50
-5.53696970 46.06303030 -25.53696970 25.91474747 -27.08525253
51 52 53 54 55
-8.17616162 -8.72161616 38.18747475 77.91474747 -27.35797980
56 57 58 59 60
25.82383838 -10.26707071 -70.17898990 -71.57898990 -13.17898990
61 62 63 64 65
48.27272727 3.27272727 -109.81818182 -72.36363636 23.54545455
66 67 68 69 70
-67.72727273 -82.00000000 -32.81818182 -150.90909091 -64.82101010
71 72 73 74 75
-38.22101010 -58.82101010 -72.36929293 -67.36929293 -97.46020202
76 77 78 79 80
-35.00565657 -96.09656566 -90.36929293 -98.64202020 -42.46020202
81 82 83 84 85
-87.55111111 49.53696970 -68.86303030 -105.46303030 -52.01131313
86 87 88 89 90
-116.01131313 -123.10222222 3.35232323 -121.73858586 71.98868687
91 92 93 94 95
17.71595960 -52.10222222 14.80686869 2.89494949 -32.50505051
96 97 98 99 100
100.89494949 15.34666667 29.34666667 145.25575758 54.71030303
101 102 103 104 105
-39.38060606 39.34666667 -3.92606061 -44.74424242 66.16484848
106 107 108 109 110
-6.74707071 128.85292929 2.25292929 28.70464646 13.70464646
111 112 113 114 115
94.61373737 16.06828283 -0.02262626 56.70464646 35.43191919
116 117 118 119 120
-16.38626263 75.52282828 -2.38909091 5.21090909 -42.38909091
121 122 123 124 125
-25.93737374 51.06262626 94.97171717 -45.57373737 177.33535354
126 127 128 129
-42.93737374 17.78989899 -51.02828283 167.88080808
> postscript(file="/var/wessaorg/rcomp/tmp/69txv1323439096.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 = 129
Frequency = 1
lag(myerror, k = 1) myerror
0 38.48282828 NA
1 39.48282828 38.48282828
2 -32.60808081 39.48282828
3 26.84646465 -32.60808081
4 21.75555556 26.84646465
5 -49.51717172 21.75555556
6 33.21010101 -49.51717172
7 105.39191919 33.21010101
8 -104.69898990 105.39191919
9 86.38909091 -104.69898990
10 28.98909091 86.38909091
11 40.38909091 28.98909091
12 28.84080808 40.38909091
13 -11.15919192 28.84080808
14 -7.25010101 -11.15919192
15 -23.79555556 -7.25010101
16 4.11353535 -23.79555556
17 -27.15919192 4.11353535
18 43.56808081 -27.15919192
19 33.74989899 43.56808081
20 -12.34101010 33.74989899
21 0.74707071 -12.34101010
22 39.34707071 0.74707071
23 21.74707071 39.34707071
24 -15.80121212 21.74707071
25 31.19878788 -15.80121212
26 -33.89212121 31.19878788
27 28.56242424 -33.89212121
28 -3.52848485 28.56242424
29 0.19878788 -3.52848485
30 74.92606061 0.19878788
31 41.10787879 74.92606061
32 14.01696970 41.10787879
33 10.10505051 14.01696970
34 -37.29494949 10.10505051
35 80.10505051 -37.29494949
36 -19.44323232 80.10505051
37 53.55676768 -19.44323232
38 77.46585859 53.55676768
39 55.92040404 77.46585859
40 -4.17050505 55.92040404
41 31.55676768 -4.17050505
42 -10.71595960 31.55676768
43 33.46585859 -10.71595960
44 27.37494949 33.46585859
45 -5.53696970 27.37494949
46 46.06303030 -5.53696970
47 -25.53696970 46.06303030
48 25.91474747 -25.53696970
49 -27.08525253 25.91474747
50 -8.17616162 -27.08525253
51 -8.72161616 -8.17616162
52 38.18747475 -8.72161616
53 77.91474747 38.18747475
54 -27.35797980 77.91474747
55 25.82383838 -27.35797980
56 -10.26707071 25.82383838
57 -70.17898990 -10.26707071
58 -71.57898990 -70.17898990
59 -13.17898990 -71.57898990
60 48.27272727 -13.17898990
61 3.27272727 48.27272727
62 -109.81818182 3.27272727
63 -72.36363636 -109.81818182
64 23.54545455 -72.36363636
65 -67.72727273 23.54545455
66 -82.00000000 -67.72727273
67 -32.81818182 -82.00000000
68 -150.90909091 -32.81818182
69 -64.82101010 -150.90909091
70 -38.22101010 -64.82101010
71 -58.82101010 -38.22101010
72 -72.36929293 -58.82101010
73 -67.36929293 -72.36929293
74 -97.46020202 -67.36929293
75 -35.00565657 -97.46020202
76 -96.09656566 -35.00565657
77 -90.36929293 -96.09656566
78 -98.64202020 -90.36929293
79 -42.46020202 -98.64202020
80 -87.55111111 -42.46020202
81 49.53696970 -87.55111111
82 -68.86303030 49.53696970
83 -105.46303030 -68.86303030
84 -52.01131313 -105.46303030
85 -116.01131313 -52.01131313
86 -123.10222222 -116.01131313
87 3.35232323 -123.10222222
88 -121.73858586 3.35232323
89 71.98868687 -121.73858586
90 17.71595960 71.98868687
91 -52.10222222 17.71595960
92 14.80686869 -52.10222222
93 2.89494949 14.80686869
94 -32.50505051 2.89494949
95 100.89494949 -32.50505051
96 15.34666667 100.89494949
97 29.34666667 15.34666667
98 145.25575758 29.34666667
99 54.71030303 145.25575758
100 -39.38060606 54.71030303
101 39.34666667 -39.38060606
102 -3.92606061 39.34666667
103 -44.74424242 -3.92606061
104 66.16484848 -44.74424242
105 -6.74707071 66.16484848
106 128.85292929 -6.74707071
107 2.25292929 128.85292929
108 28.70464646 2.25292929
109 13.70464646 28.70464646
110 94.61373737 13.70464646
111 16.06828283 94.61373737
112 -0.02262626 16.06828283
113 56.70464646 -0.02262626
114 35.43191919 56.70464646
115 -16.38626263 35.43191919
116 75.52282828 -16.38626263
117 -2.38909091 75.52282828
118 5.21090909 -2.38909091
119 -42.38909091 5.21090909
120 -25.93737374 -42.38909091
121 51.06262626 -25.93737374
122 94.97171717 51.06262626
123 -45.57373737 94.97171717
124 177.33535354 -45.57373737
125 -42.93737374 177.33535354
126 17.78989899 -42.93737374
127 -51.02828283 17.78989899
128 167.88080808 -51.02828283
129 NA 167.88080808
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 39.48282828 38.48282828
[2,] -32.60808081 39.48282828
[3,] 26.84646465 -32.60808081
[4,] 21.75555556 26.84646465
[5,] -49.51717172 21.75555556
[6,] 33.21010101 -49.51717172
[7,] 105.39191919 33.21010101
[8,] -104.69898990 105.39191919
[9,] 86.38909091 -104.69898990
[10,] 28.98909091 86.38909091
[11,] 40.38909091 28.98909091
[12,] 28.84080808 40.38909091
[13,] -11.15919192 28.84080808
[14,] -7.25010101 -11.15919192
[15,] -23.79555556 -7.25010101
[16,] 4.11353535 -23.79555556
[17,] -27.15919192 4.11353535
[18,] 43.56808081 -27.15919192
[19,] 33.74989899 43.56808081
[20,] -12.34101010 33.74989899
[21,] 0.74707071 -12.34101010
[22,] 39.34707071 0.74707071
[23,] 21.74707071 39.34707071
[24,] -15.80121212 21.74707071
[25,] 31.19878788 -15.80121212
[26,] -33.89212121 31.19878788
[27,] 28.56242424 -33.89212121
[28,] -3.52848485 28.56242424
[29,] 0.19878788 -3.52848485
[30,] 74.92606061 0.19878788
[31,] 41.10787879 74.92606061
[32,] 14.01696970 41.10787879
[33,] 10.10505051 14.01696970
[34,] -37.29494949 10.10505051
[35,] 80.10505051 -37.29494949
[36,] -19.44323232 80.10505051
[37,] 53.55676768 -19.44323232
[38,] 77.46585859 53.55676768
[39,] 55.92040404 77.46585859
[40,] -4.17050505 55.92040404
[41,] 31.55676768 -4.17050505
[42,] -10.71595960 31.55676768
[43,] 33.46585859 -10.71595960
[44,] 27.37494949 33.46585859
[45,] -5.53696970 27.37494949
[46,] 46.06303030 -5.53696970
[47,] -25.53696970 46.06303030
[48,] 25.91474747 -25.53696970
[49,] -27.08525253 25.91474747
[50,] -8.17616162 -27.08525253
[51,] -8.72161616 -8.17616162
[52,] 38.18747475 -8.72161616
[53,] 77.91474747 38.18747475
[54,] -27.35797980 77.91474747
[55,] 25.82383838 -27.35797980
[56,] -10.26707071 25.82383838
[57,] -70.17898990 -10.26707071
[58,] -71.57898990 -70.17898990
[59,] -13.17898990 -71.57898990
[60,] 48.27272727 -13.17898990
[61,] 3.27272727 48.27272727
[62,] -109.81818182 3.27272727
[63,] -72.36363636 -109.81818182
[64,] 23.54545455 -72.36363636
[65,] -67.72727273 23.54545455
[66,] -82.00000000 -67.72727273
[67,] -32.81818182 -82.00000000
[68,] -150.90909091 -32.81818182
[69,] -64.82101010 -150.90909091
[70,] -38.22101010 -64.82101010
[71,] -58.82101010 -38.22101010
[72,] -72.36929293 -58.82101010
[73,] -67.36929293 -72.36929293
[74,] -97.46020202 -67.36929293
[75,] -35.00565657 -97.46020202
[76,] -96.09656566 -35.00565657
[77,] -90.36929293 -96.09656566
[78,] -98.64202020 -90.36929293
[79,] -42.46020202 -98.64202020
[80,] -87.55111111 -42.46020202
[81,] 49.53696970 -87.55111111
[82,] -68.86303030 49.53696970
[83,] -105.46303030 -68.86303030
[84,] -52.01131313 -105.46303030
[85,] -116.01131313 -52.01131313
[86,] -123.10222222 -116.01131313
[87,] 3.35232323 -123.10222222
[88,] -121.73858586 3.35232323
[89,] 71.98868687 -121.73858586
[90,] 17.71595960 71.98868687
[91,] -52.10222222 17.71595960
[92,] 14.80686869 -52.10222222
[93,] 2.89494949 14.80686869
[94,] -32.50505051 2.89494949
[95,] 100.89494949 -32.50505051
[96,] 15.34666667 100.89494949
[97,] 29.34666667 15.34666667
[98,] 145.25575758 29.34666667
[99,] 54.71030303 145.25575758
[100,] -39.38060606 54.71030303
[101,] 39.34666667 -39.38060606
[102,] -3.92606061 39.34666667
[103,] -44.74424242 -3.92606061
[104,] 66.16484848 -44.74424242
[105,] -6.74707071 66.16484848
[106,] 128.85292929 -6.74707071
[107,] 2.25292929 128.85292929
[108,] 28.70464646 2.25292929
[109,] 13.70464646 28.70464646
[110,] 94.61373737 13.70464646
[111,] 16.06828283 94.61373737
[112,] -0.02262626 16.06828283
[113,] 56.70464646 -0.02262626
[114,] 35.43191919 56.70464646
[115,] -16.38626263 35.43191919
[116,] 75.52282828 -16.38626263
[117,] -2.38909091 75.52282828
[118,] 5.21090909 -2.38909091
[119,] -42.38909091 5.21090909
[120,] -25.93737374 -42.38909091
[121,] 51.06262626 -25.93737374
[122,] 94.97171717 51.06262626
[123,] -45.57373737 94.97171717
[124,] 177.33535354 -45.57373737
[125,] -42.93737374 177.33535354
[126,] 17.78989899 -42.93737374
[127,] -51.02828283 17.78989899
[128,] 167.88080808 -51.02828283
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 39.48282828 38.48282828
2 -32.60808081 39.48282828
3 26.84646465 -32.60808081
4 21.75555556 26.84646465
5 -49.51717172 21.75555556
6 33.21010101 -49.51717172
7 105.39191919 33.21010101
8 -104.69898990 105.39191919
9 86.38909091 -104.69898990
10 28.98909091 86.38909091
11 40.38909091 28.98909091
12 28.84080808 40.38909091
13 -11.15919192 28.84080808
14 -7.25010101 -11.15919192
15 -23.79555556 -7.25010101
16 4.11353535 -23.79555556
17 -27.15919192 4.11353535
18 43.56808081 -27.15919192
19 33.74989899 43.56808081
20 -12.34101010 33.74989899
21 0.74707071 -12.34101010
22 39.34707071 0.74707071
23 21.74707071 39.34707071
24 -15.80121212 21.74707071
25 31.19878788 -15.80121212
26 -33.89212121 31.19878788
27 28.56242424 -33.89212121
28 -3.52848485 28.56242424
29 0.19878788 -3.52848485
30 74.92606061 0.19878788
31 41.10787879 74.92606061
32 14.01696970 41.10787879
33 10.10505051 14.01696970
34 -37.29494949 10.10505051
35 80.10505051 -37.29494949
36 -19.44323232 80.10505051
37 53.55676768 -19.44323232
38 77.46585859 53.55676768
39 55.92040404 77.46585859
40 -4.17050505 55.92040404
41 31.55676768 -4.17050505
42 -10.71595960 31.55676768
43 33.46585859 -10.71595960
44 27.37494949 33.46585859
45 -5.53696970 27.37494949
46 46.06303030 -5.53696970
47 -25.53696970 46.06303030
48 25.91474747 -25.53696970
49 -27.08525253 25.91474747
50 -8.17616162 -27.08525253
51 -8.72161616 -8.17616162
52 38.18747475 -8.72161616
53 77.91474747 38.18747475
54 -27.35797980 77.91474747
55 25.82383838 -27.35797980
56 -10.26707071 25.82383838
57 -70.17898990 -10.26707071
58 -71.57898990 -70.17898990
59 -13.17898990 -71.57898990
60 48.27272727 -13.17898990
61 3.27272727 48.27272727
62 -109.81818182 3.27272727
63 -72.36363636 -109.81818182
64 23.54545455 -72.36363636
65 -67.72727273 23.54545455
66 -82.00000000 -67.72727273
67 -32.81818182 -82.00000000
68 -150.90909091 -32.81818182
69 -64.82101010 -150.90909091
70 -38.22101010 -64.82101010
71 -58.82101010 -38.22101010
72 -72.36929293 -58.82101010
73 -67.36929293 -72.36929293
74 -97.46020202 -67.36929293
75 -35.00565657 -97.46020202
76 -96.09656566 -35.00565657
77 -90.36929293 -96.09656566
78 -98.64202020 -90.36929293
79 -42.46020202 -98.64202020
80 -87.55111111 -42.46020202
81 49.53696970 -87.55111111
82 -68.86303030 49.53696970
83 -105.46303030 -68.86303030
84 -52.01131313 -105.46303030
85 -116.01131313 -52.01131313
86 -123.10222222 -116.01131313
87 3.35232323 -123.10222222
88 -121.73858586 3.35232323
89 71.98868687 -121.73858586
90 17.71595960 71.98868687
91 -52.10222222 17.71595960
92 14.80686869 -52.10222222
93 2.89494949 14.80686869
94 -32.50505051 2.89494949
95 100.89494949 -32.50505051
96 15.34666667 100.89494949
97 29.34666667 15.34666667
98 145.25575758 29.34666667
99 54.71030303 145.25575758
100 -39.38060606 54.71030303
101 39.34666667 -39.38060606
102 -3.92606061 39.34666667
103 -44.74424242 -3.92606061
104 66.16484848 -44.74424242
105 -6.74707071 66.16484848
106 128.85292929 -6.74707071
107 2.25292929 128.85292929
108 28.70464646 2.25292929
109 13.70464646 28.70464646
110 94.61373737 13.70464646
111 16.06828283 94.61373737
112 -0.02262626 16.06828283
113 56.70464646 -0.02262626
114 35.43191919 56.70464646
115 -16.38626263 35.43191919
116 75.52282828 -16.38626263
117 -2.38909091 75.52282828
118 5.21090909 -2.38909091
119 -42.38909091 5.21090909
120 -25.93737374 -42.38909091
121 51.06262626 -25.93737374
122 94.97171717 51.06262626
123 -45.57373737 94.97171717
124 177.33535354 -45.57373737
125 -42.93737374 177.33535354
126 17.78989899 -42.93737374
127 -51.02828283 17.78989899
128 167.88080808 -51.02828283
> 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/7urj31323439096.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/81k7c1323439096.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/97n3f1323439096.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/10gyeo1323439096.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11whud1323439096.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/122nq01323439096.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13affj1323439096.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/148de31323439096.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/155y761323439096.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/16jejq1323439096.tab")
+ }
>
> try(system("convert tmp/1amgr1323439096.ps tmp/1amgr1323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/28suy1323439096.ps tmp/28suy1323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/38y351323439096.ps tmp/38y351323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hktc1323439096.ps tmp/4hktc1323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ympf1323439096.ps tmp/5ympf1323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/69txv1323439096.ps tmp/69txv1323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/7urj31323439096.ps tmp/7urj31323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/81k7c1323439096.ps tmp/81k7c1323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/97n3f1323439096.ps tmp/97n3f1323439096.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gyeo1323439096.ps tmp/10gyeo1323439096.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.300 0.663 4.976