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.
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'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(611,639,630,586,695,552,619,681,421,307,754,690,644,643,608,651,691,627,634,731,475,337,803,722,590,724,627,696,825,677,656,785,412,352,839,729,696,641,695,638,762,635,721,854,418,367,824,687,601,676,740,691,683,594,729,731,386,331,706,715,657,653,642,643,718,654,632,731,392,344,792,852,649,629,685,617,715,715,629,916,531,357,917,828,708,858,775,785,1006,789,734,906,532,387,991,841,892,782,811,792,978,773,796,946,594,438,1023,868,791,760,779,852,1001,734,996,869,599,426,1138,1091),dim=c(1,120),dimnames=list(c('AantalFaillissementen'),1:120))
> y <- array(NA,dim=c(1,120),dimnames=list(c('AantalFaillissementen'),1:120))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
AantalFaillissementen M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 611 1 0 0 0 0 0 0 0 0 0 0 1
2 639 0 1 0 0 0 0 0 0 0 0 0 2
3 630 0 0 1 0 0 0 0 0 0 0 0 3
4 586 0 0 0 1 0 0 0 0 0 0 0 4
5 695 0 0 0 0 1 0 0 0 0 0 0 5
6 552 0 0 0 0 0 1 0 0 0 0 0 6
7 619 0 0 0 0 0 0 1 0 0 0 0 7
8 681 0 0 0 0 0 0 0 1 0 0 0 8
9 421 0 0 0 0 0 0 0 0 1 0 0 9
10 307 0 0 0 0 0 0 0 0 0 1 0 10
11 754 0 0 0 0 0 0 0 0 0 0 1 11
12 690 0 0 0 0 0 0 0 0 0 0 0 12
13 644 1 0 0 0 0 0 0 0 0 0 0 13
14 643 0 1 0 0 0 0 0 0 0 0 0 14
15 608 0 0 1 0 0 0 0 0 0 0 0 15
16 651 0 0 0 1 0 0 0 0 0 0 0 16
17 691 0 0 0 0 1 0 0 0 0 0 0 17
18 627 0 0 0 0 0 1 0 0 0 0 0 18
19 634 0 0 0 0 0 0 1 0 0 0 0 19
20 731 0 0 0 0 0 0 0 1 0 0 0 20
21 475 0 0 0 0 0 0 0 0 1 0 0 21
22 337 0 0 0 0 0 0 0 0 0 1 0 22
23 803 0 0 0 0 0 0 0 0 0 0 1 23
24 722 0 0 0 0 0 0 0 0 0 0 0 24
25 590 1 0 0 0 0 0 0 0 0 0 0 25
26 724 0 1 0 0 0 0 0 0 0 0 0 26
27 627 0 0 1 0 0 0 0 0 0 0 0 27
28 696 0 0 0 1 0 0 0 0 0 0 0 28
29 825 0 0 0 0 1 0 0 0 0 0 0 29
30 677 0 0 0 0 0 1 0 0 0 0 0 30
31 656 0 0 0 0 0 0 1 0 0 0 0 31
32 785 0 0 0 0 0 0 0 1 0 0 0 32
33 412 0 0 0 0 0 0 0 0 1 0 0 33
34 352 0 0 0 0 0 0 0 0 0 1 0 34
35 839 0 0 0 0 0 0 0 0 0 0 1 35
36 729 0 0 0 0 0 0 0 0 0 0 0 36
37 696 1 0 0 0 0 0 0 0 0 0 0 37
38 641 0 1 0 0 0 0 0 0 0 0 0 38
39 695 0 0 1 0 0 0 0 0 0 0 0 39
40 638 0 0 0 1 0 0 0 0 0 0 0 40
41 762 0 0 0 0 1 0 0 0 0 0 0 41
42 635 0 0 0 0 0 1 0 0 0 0 0 42
43 721 0 0 0 0 0 0 1 0 0 0 0 43
44 854 0 0 0 0 0 0 0 1 0 0 0 44
45 418 0 0 0 0 0 0 0 0 1 0 0 45
46 367 0 0 0 0 0 0 0 0 0 1 0 46
47 824 0 0 0 0 0 0 0 0 0 0 1 47
48 687 0 0 0 0 0 0 0 0 0 0 0 48
49 601 1 0 0 0 0 0 0 0 0 0 0 49
50 676 0 1 0 0 0 0 0 0 0 0 0 50
51 740 0 0 1 0 0 0 0 0 0 0 0 51
52 691 0 0 0 1 0 0 0 0 0 0 0 52
53 683 0 0 0 0 1 0 0 0 0 0 0 53
54 594 0 0 0 0 0 1 0 0 0 0 0 54
55 729 0 0 0 0 0 0 1 0 0 0 0 55
56 731 0 0 0 0 0 0 0 1 0 0 0 56
57 386 0 0 0 0 0 0 0 0 1 0 0 57
58 331 0 0 0 0 0 0 0 0 0 1 0 58
59 706 0 0 0 0 0 0 0 0 0 0 1 59
60 715 0 0 0 0 0 0 0 0 0 0 0 60
61 657 1 0 0 0 0 0 0 0 0 0 0 61
62 653 0 1 0 0 0 0 0 0 0 0 0 62
63 642 0 0 1 0 0 0 0 0 0 0 0 63
64 643 0 0 0 1 0 0 0 0 0 0 0 64
65 718 0 0 0 0 1 0 0 0 0 0 0 65
66 654 0 0 0 0 0 1 0 0 0 0 0 66
67 632 0 0 0 0 0 0 1 0 0 0 0 67
68 731 0 0 0 0 0 0 0 1 0 0 0 68
69 392 0 0 0 0 0 0 0 0 1 0 0 69
70 344 0 0 0 0 0 0 0 0 0 1 0 70
71 792 0 0 0 0 0 0 0 0 0 0 1 71
72 852 0 0 0 0 0 0 0 0 0 0 0 72
73 649 1 0 0 0 0 0 0 0 0 0 0 73
74 629 0 1 0 0 0 0 0 0 0 0 0 74
75 685 0 0 1 0 0 0 0 0 0 0 0 75
76 617 0 0 0 1 0 0 0 0 0 0 0 76
77 715 0 0 0 0 1 0 0 0 0 0 0 77
78 715 0 0 0 0 0 1 0 0 0 0 0 78
79 629 0 0 0 0 0 0 1 0 0 0 0 79
80 916 0 0 0 0 0 0 0 1 0 0 0 80
81 531 0 0 0 0 0 0 0 0 1 0 0 81
82 357 0 0 0 0 0 0 0 0 0 1 0 82
83 917 0 0 0 0 0 0 0 0 0 0 1 83
84 828 0 0 0 0 0 0 0 0 0 0 0 84
85 708 1 0 0 0 0 0 0 0 0 0 0 85
86 858 0 1 0 0 0 0 0 0 0 0 0 86
87 775 0 0 1 0 0 0 0 0 0 0 0 87
88 785 0 0 0 1 0 0 0 0 0 0 0 88
89 1006 0 0 0 0 1 0 0 0 0 0 0 89
90 789 0 0 0 0 0 1 0 0 0 0 0 90
91 734 0 0 0 0 0 0 1 0 0 0 0 91
92 906 0 0 0 0 0 0 0 1 0 0 0 92
93 532 0 0 0 0 0 0 0 0 1 0 0 93
94 387 0 0 0 0 0 0 0 0 0 1 0 94
95 991 0 0 0 0 0 0 0 0 0 0 1 95
96 841 0 0 0 0 0 0 0 0 0 0 0 96
97 892 1 0 0 0 0 0 0 0 0 0 0 97
98 782 0 1 0 0 0 0 0 0 0 0 0 98
99 811 0 0 1 0 0 0 0 0 0 0 0 99
100 792 0 0 0 1 0 0 0 0 0 0 0 100
101 978 0 0 0 0 1 0 0 0 0 0 0 101
102 773 0 0 0 0 0 1 0 0 0 0 0 102
103 796 0 0 0 0 0 0 1 0 0 0 0 103
104 946 0 0 0 0 0 0 0 1 0 0 0 104
105 594 0 0 0 0 0 0 0 0 1 0 0 105
106 438 0 0 0 0 0 0 0 0 0 1 0 106
107 1023 0 0 0 0 0 0 0 0 0 0 1 107
108 868 0 0 0 0 0 0 0 0 0 0 0 108
109 791 1 0 0 0 0 0 0 0 0 0 0 109
110 760 0 1 0 0 0 0 0 0 0 0 0 110
111 779 0 0 1 0 0 0 0 0 0 0 0 111
112 852 0 0 0 1 0 0 0 0 0 0 0 112
113 1001 0 0 0 0 1 0 0 0 0 0 0 113
114 734 0 0 0 0 0 1 0 0 0 0 0 114
115 996 0 0 0 0 0 0 1 0 0 0 0 115
116 869 0 0 0 0 0 0 0 1 0 0 0 116
117 599 0 0 0 0 0 0 0 0 1 0 0 117
118 426 0 0 0 0 0 0 0 0 0 1 0 118
119 1138 0 0 0 0 0 0 0 0 0 0 1 119
120 1091 0 0 0 0 0 0 0 0 0 0 0 120
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
667.786 -95.981 -81.419 -84.757 -90.895 19.367
M6 M7 M8 M9 M10 M11
-115.071 -77.510 20.852 -320.186 -433.624 78.438
t
2.038
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-160.471 -37.468 -2.293 41.018 178.643
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 667.7861 23.6275 28.263 < 2e-16 ***
M1 -95.9810 29.3059 -3.275 0.00142 **
M2 -81.4191 29.2951 -2.779 0.00644 **
M3 -84.7572 29.2854 -2.894 0.00461 **
M4 -90.8953 29.2767 -3.105 0.00244 **
M5 19.3666 29.2690 0.662 0.50960
M6 -115.0715 29.2623 -3.932 0.00015 ***
M7 -77.5096 29.2567 -2.649 0.00929 **
M8 20.8524 29.2521 0.713 0.47749
M9 -320.1857 29.2485 -10.947 < 2e-16 ***
M10 -433.6238 29.2459 -14.827 < 2e-16 ***
M11 78.4381 29.2444 2.682 0.00848 **
t 2.0381 0.1732 11.768 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 65.39 on 107 degrees of freedom
Multiple R-squared: 0.8635, Adjusted R-squared: 0.8482
F-statistic: 56.42 on 12 and 107 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,] 6.876036e-02 1.375207e-01 0.9312396
[2,] 2.544978e-02 5.089956e-02 0.9745502
[3,] 2.293043e-02 4.586087e-02 0.9770696
[4,] 8.053683e-03 1.610737e-02 0.9919463
[5,] 3.248410e-03 6.496820e-03 0.9967516
[6,] 1.456352e-03 2.912704e-03 0.9985436
[7,] 4.776590e-04 9.553180e-04 0.9995223
[8,] 1.657516e-04 3.315031e-04 0.9998342
[9,] 4.727046e-05 9.454092e-05 0.9999527
[10,] 3.310021e-04 6.620043e-04 0.9996690
[11,] 3.533027e-04 7.066055e-04 0.9996467
[12,] 1.807448e-04 3.614895e-04 0.9998193
[13,] 1.397769e-04 2.795539e-04 0.9998602
[14,] 6.477495e-04 1.295499e-03 0.9993523
[15,] 4.646633e-04 9.293266e-04 0.9995353
[16,] 2.405861e-04 4.811723e-04 0.9997594
[17,] 1.345481e-04 2.690962e-04 0.9998655
[18,] 3.855120e-04 7.710239e-04 0.9996145
[19,] 2.415175e-04 4.830351e-04 0.9997585
[20,] 1.273630e-04 2.547260e-04 0.9998726
[21,] 6.670051e-05 1.334010e-04 0.9999333
[22,] 4.272328e-05 8.544656e-05 0.9999573
[23,] 1.321334e-04 2.642668e-04 0.9998679
[24,] 8.234255e-05 1.646851e-04 0.9999177
[25,] 9.116897e-05 1.823379e-04 0.9999088
[26,] 5.294150e-05 1.058830e-04 0.9999471
[27,] 3.479830e-05 6.959659e-05 0.9999652
[28,] 3.200170e-05 6.400340e-05 0.9999680
[29,] 9.081454e-05 1.816291e-04 0.9999092
[30,] 1.320694e-04 2.641387e-04 0.9998679
[31,] 1.379144e-04 2.758288e-04 0.9998621
[32,] 8.796280e-05 1.759256e-04 0.9999120
[33,] 1.194833e-04 2.389665e-04 0.9998805
[34,] 2.273053e-04 4.546107e-04 0.9997727
[35,] 1.820104e-04 3.640209e-04 0.9998180
[36,] 3.416296e-04 6.832592e-04 0.9996584
[37,] 2.857794e-04 5.715589e-04 0.9997142
[38,] 8.984521e-04 1.796904e-03 0.9991015
[39,] 9.728675e-04 1.945735e-03 0.9990271
[40,] 1.198688e-03 2.397377e-03 0.9988013
[41,] 1.294181e-03 2.588363e-03 0.9987058
[42,] 1.603767e-03 3.207534e-03 0.9983962
[43,] 1.765073e-03 3.530145e-03 0.9982349
[44,] 7.670865e-03 1.534173e-02 0.9923291
[45,] 5.298377e-03 1.059675e-02 0.9947016
[46,] 3.513209e-03 7.026418e-03 0.9964868
[47,] 2.643844e-03 5.287687e-03 0.9973562
[48,] 1.986740e-03 3.973480e-03 0.9980133
[49,] 1.373329e-03 2.746658e-03 0.9986267
[50,] 1.241667e-03 2.483333e-03 0.9987583
[51,] 8.044604e-04 1.608921e-03 0.9991955
[52,] 7.138920e-04 1.427784e-03 0.9992861
[53,] 5.850324e-04 1.170065e-03 0.9994150
[54,] 4.828021e-04 9.656043e-04 0.9995172
[55,] 3.580669e-04 7.161337e-04 0.9996419
[56,] 3.769448e-04 7.538897e-04 0.9996231
[57,] 1.168843e-03 2.337685e-03 0.9988312
[58,] 8.067050e-04 1.613410e-03 0.9991933
[59,] 8.447711e-04 1.689542e-03 0.9991552
[60,] 5.074609e-04 1.014922e-03 0.9994925
[61,] 6.933326e-04 1.386665e-03 0.9993067
[62,] 4.754894e-03 9.509788e-03 0.9952451
[63,] 4.517309e-03 9.034618e-03 0.9954827
[64,] 1.221156e-02 2.442312e-02 0.9877884
[65,] 3.173224e-02 6.346447e-02 0.9682678
[66,] 3.214695e-02 6.429390e-02 0.9678531
[67,] 2.246752e-02 4.493505e-02 0.9775325
[68,] 2.995306e-02 5.990613e-02 0.9700469
[69,] 2.677080e-02 5.354159e-02 0.9732292
[70,] 2.917059e-02 5.834119e-02 0.9708294
[71,] 1.006922e-01 2.013845e-01 0.8993078
[72,] 8.647043e-02 1.729409e-01 0.9135296
[73,] 7.739998e-02 1.548000e-01 0.9226000
[74,] 1.834555e-01 3.669109e-01 0.8165445
[75,] 2.220485e-01 4.440970e-01 0.7779515
[76,] 2.442365e-01 4.884730e-01 0.7557635
[77,] 2.262314e-01 4.524629e-01 0.7737686
[78,] 1.737183e-01 3.474366e-01 0.8262817
[79,] 1.294674e-01 2.589349e-01 0.8705326
[80,] 1.154192e-01 2.308384e-01 0.8845808
[81,] 1.136714e-01 2.273428e-01 0.8863286
[82,] 2.056871e-01 4.113741e-01 0.7943129
[83,] 1.691700e-01 3.383401e-01 0.8308300
[84,] 1.530616e-01 3.061232e-01 0.8469384
[85,] 1.018245e-01 2.036490e-01 0.8981755
[86,] 7.764600e-02 1.552920e-01 0.9223540
[87,] 7.541397e-02 1.508279e-01 0.9245860
[88,] 1.061142e-01 2.122284e-01 0.8938858
[89,] 1.788019e-01 3.576039e-01 0.8211981
> postscript(file="/var/wessaorg/rcomp/tmp/1i5jh1324592746.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2uru81324592746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3j0k71324592746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4lgml1324592746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/563gh1324592746.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 = 120
Frequency = 1
1 2 3 4 5 6
37.1568182 48.5568182 40.8568182 0.9568182 -2.3431818 -12.9431818
7 8 9 10 11 12
14.4568182 -23.9431818 55.0568182 52.4568182 -14.6431818 -2.2431818
13 14 15 16 17 18
45.6997475 28.0997475 -5.6002525 41.4997475 -30.8002525 37.5997475
19 20 21 22 23 24
4.9997475 1.5997475 84.5997475 57.9997475 9.8997475 5.2997475
25 26 27 28 29 30
-32.7573232 84.6426768 -11.0573232 62.0426768 78.7426768 63.1426768
31 32 33 34 35 36
2.5426768 31.1426768 -2.8573232 48.5426768 21.4426768 -12.1573232
37 38 39 40 41 42
48.7856061 -22.8143939 32.4856061 -20.4143939 -8.7143939 -3.3143939
43 44 45 46 47 48
43.0856061 75.6856061 -21.3143939 39.0856061 -18.0143939 -78.6143939
49 50 51 52 53 54
-70.6714646 -12.2714646 53.0285354 8.1285354 -112.1714646 -68.7714646
55 56 57 58 59 60
26.6285354 -71.7714646 -77.7714646 -21.3714646 -160.4714646 -75.0714646
61 62 63 64 65 66
-39.1285354 -59.7285354 -69.4285354 -64.3285354 -101.6285354 -33.2285354
67 68 69 70 71 72
-94.8285354 -96.2285354 -96.2285354 -32.8285354 -98.9285354 37.4714646
73 74 75 76 77 78
-71.5856061 -108.1856061 -50.8856061 -114.7856061 -129.0856061 3.3143939
79 80 81 82 83 84
-122.2856061 64.3143939 18.3143939 -44.2856061 1.6143939 -10.9856061
85 86 87 88 89 90
-37.0426768 96.3573232 14.6573232 28.7573232 137.4573232 52.8573232
91 92 93 94 95 96
-41.7426768 29.8573232 -5.1426768 -38.7426768 51.1573232 -22.4426768
97 98 99 100 101 102
122.5002525 -4.0997475 26.2002525 11.3002525 85.0002525 12.4002525
103 104 105 106 107 108
-4.1997475 45.4002525 32.4002525 -12.1997475 58.7002525 -19.8997475
109 110 111 112 113 114
-2.9568182 -50.5568182 -30.2568182 46.8431818 83.5431818 -51.0568182
115 116 117 118 119 120
171.3431818 -56.0568182 12.9431818 -48.6568182 149.2431818 178.6431818
> postscript(file="/var/wessaorg/rcomp/tmp/69ob41324592746.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 37.1568182 NA
1 48.5568182 37.1568182
2 40.8568182 48.5568182
3 0.9568182 40.8568182
4 -2.3431818 0.9568182
5 -12.9431818 -2.3431818
6 14.4568182 -12.9431818
7 -23.9431818 14.4568182
8 55.0568182 -23.9431818
9 52.4568182 55.0568182
10 -14.6431818 52.4568182
11 -2.2431818 -14.6431818
12 45.6997475 -2.2431818
13 28.0997475 45.6997475
14 -5.6002525 28.0997475
15 41.4997475 -5.6002525
16 -30.8002525 41.4997475
17 37.5997475 -30.8002525
18 4.9997475 37.5997475
19 1.5997475 4.9997475
20 84.5997475 1.5997475
21 57.9997475 84.5997475
22 9.8997475 57.9997475
23 5.2997475 9.8997475
24 -32.7573232 5.2997475
25 84.6426768 -32.7573232
26 -11.0573232 84.6426768
27 62.0426768 -11.0573232
28 78.7426768 62.0426768
29 63.1426768 78.7426768
30 2.5426768 63.1426768
31 31.1426768 2.5426768
32 -2.8573232 31.1426768
33 48.5426768 -2.8573232
34 21.4426768 48.5426768
35 -12.1573232 21.4426768
36 48.7856061 -12.1573232
37 -22.8143939 48.7856061
38 32.4856061 -22.8143939
39 -20.4143939 32.4856061
40 -8.7143939 -20.4143939
41 -3.3143939 -8.7143939
42 43.0856061 -3.3143939
43 75.6856061 43.0856061
44 -21.3143939 75.6856061
45 39.0856061 -21.3143939
46 -18.0143939 39.0856061
47 -78.6143939 -18.0143939
48 -70.6714646 -78.6143939
49 -12.2714646 -70.6714646
50 53.0285354 -12.2714646
51 8.1285354 53.0285354
52 -112.1714646 8.1285354
53 -68.7714646 -112.1714646
54 26.6285354 -68.7714646
55 -71.7714646 26.6285354
56 -77.7714646 -71.7714646
57 -21.3714646 -77.7714646
58 -160.4714646 -21.3714646
59 -75.0714646 -160.4714646
60 -39.1285354 -75.0714646
61 -59.7285354 -39.1285354
62 -69.4285354 -59.7285354
63 -64.3285354 -69.4285354
64 -101.6285354 -64.3285354
65 -33.2285354 -101.6285354
66 -94.8285354 -33.2285354
67 -96.2285354 -94.8285354
68 -96.2285354 -96.2285354
69 -32.8285354 -96.2285354
70 -98.9285354 -32.8285354
71 37.4714646 -98.9285354
72 -71.5856061 37.4714646
73 -108.1856061 -71.5856061
74 -50.8856061 -108.1856061
75 -114.7856061 -50.8856061
76 -129.0856061 -114.7856061
77 3.3143939 -129.0856061
78 -122.2856061 3.3143939
79 64.3143939 -122.2856061
80 18.3143939 64.3143939
81 -44.2856061 18.3143939
82 1.6143939 -44.2856061
83 -10.9856061 1.6143939
84 -37.0426768 -10.9856061
85 96.3573232 -37.0426768
86 14.6573232 96.3573232
87 28.7573232 14.6573232
88 137.4573232 28.7573232
89 52.8573232 137.4573232
90 -41.7426768 52.8573232
91 29.8573232 -41.7426768
92 -5.1426768 29.8573232
93 -38.7426768 -5.1426768
94 51.1573232 -38.7426768
95 -22.4426768 51.1573232
96 122.5002525 -22.4426768
97 -4.0997475 122.5002525
98 26.2002525 -4.0997475
99 11.3002525 26.2002525
100 85.0002525 11.3002525
101 12.4002525 85.0002525
102 -4.1997475 12.4002525
103 45.4002525 -4.1997475
104 32.4002525 45.4002525
105 -12.1997475 32.4002525
106 58.7002525 -12.1997475
107 -19.8997475 58.7002525
108 -2.9568182 -19.8997475
109 -50.5568182 -2.9568182
110 -30.2568182 -50.5568182
111 46.8431818 -30.2568182
112 83.5431818 46.8431818
113 -51.0568182 83.5431818
114 171.3431818 -51.0568182
115 -56.0568182 171.3431818
116 12.9431818 -56.0568182
117 -48.6568182 12.9431818
118 149.2431818 -48.6568182
119 178.6431818 149.2431818
120 NA 178.6431818
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 48.5568182 37.1568182
[2,] 40.8568182 48.5568182
[3,] 0.9568182 40.8568182
[4,] -2.3431818 0.9568182
[5,] -12.9431818 -2.3431818
[6,] 14.4568182 -12.9431818
[7,] -23.9431818 14.4568182
[8,] 55.0568182 -23.9431818
[9,] 52.4568182 55.0568182
[10,] -14.6431818 52.4568182
[11,] -2.2431818 -14.6431818
[12,] 45.6997475 -2.2431818
[13,] 28.0997475 45.6997475
[14,] -5.6002525 28.0997475
[15,] 41.4997475 -5.6002525
[16,] -30.8002525 41.4997475
[17,] 37.5997475 -30.8002525
[18,] 4.9997475 37.5997475
[19,] 1.5997475 4.9997475
[20,] 84.5997475 1.5997475
[21,] 57.9997475 84.5997475
[22,] 9.8997475 57.9997475
[23,] 5.2997475 9.8997475
[24,] -32.7573232 5.2997475
[25,] 84.6426768 -32.7573232
[26,] -11.0573232 84.6426768
[27,] 62.0426768 -11.0573232
[28,] 78.7426768 62.0426768
[29,] 63.1426768 78.7426768
[30,] 2.5426768 63.1426768
[31,] 31.1426768 2.5426768
[32,] -2.8573232 31.1426768
[33,] 48.5426768 -2.8573232
[34,] 21.4426768 48.5426768
[35,] -12.1573232 21.4426768
[36,] 48.7856061 -12.1573232
[37,] -22.8143939 48.7856061
[38,] 32.4856061 -22.8143939
[39,] -20.4143939 32.4856061
[40,] -8.7143939 -20.4143939
[41,] -3.3143939 -8.7143939
[42,] 43.0856061 -3.3143939
[43,] 75.6856061 43.0856061
[44,] -21.3143939 75.6856061
[45,] 39.0856061 -21.3143939
[46,] -18.0143939 39.0856061
[47,] -78.6143939 -18.0143939
[48,] -70.6714646 -78.6143939
[49,] -12.2714646 -70.6714646
[50,] 53.0285354 -12.2714646
[51,] 8.1285354 53.0285354
[52,] -112.1714646 8.1285354
[53,] -68.7714646 -112.1714646
[54,] 26.6285354 -68.7714646
[55,] -71.7714646 26.6285354
[56,] -77.7714646 -71.7714646
[57,] -21.3714646 -77.7714646
[58,] -160.4714646 -21.3714646
[59,] -75.0714646 -160.4714646
[60,] -39.1285354 -75.0714646
[61,] -59.7285354 -39.1285354
[62,] -69.4285354 -59.7285354
[63,] -64.3285354 -69.4285354
[64,] -101.6285354 -64.3285354
[65,] -33.2285354 -101.6285354
[66,] -94.8285354 -33.2285354
[67,] -96.2285354 -94.8285354
[68,] -96.2285354 -96.2285354
[69,] -32.8285354 -96.2285354
[70,] -98.9285354 -32.8285354
[71,] 37.4714646 -98.9285354
[72,] -71.5856061 37.4714646
[73,] -108.1856061 -71.5856061
[74,] -50.8856061 -108.1856061
[75,] -114.7856061 -50.8856061
[76,] -129.0856061 -114.7856061
[77,] 3.3143939 -129.0856061
[78,] -122.2856061 3.3143939
[79,] 64.3143939 -122.2856061
[80,] 18.3143939 64.3143939
[81,] -44.2856061 18.3143939
[82,] 1.6143939 -44.2856061
[83,] -10.9856061 1.6143939
[84,] -37.0426768 -10.9856061
[85,] 96.3573232 -37.0426768
[86,] 14.6573232 96.3573232
[87,] 28.7573232 14.6573232
[88,] 137.4573232 28.7573232
[89,] 52.8573232 137.4573232
[90,] -41.7426768 52.8573232
[91,] 29.8573232 -41.7426768
[92,] -5.1426768 29.8573232
[93,] -38.7426768 -5.1426768
[94,] 51.1573232 -38.7426768
[95,] -22.4426768 51.1573232
[96,] 122.5002525 -22.4426768
[97,] -4.0997475 122.5002525
[98,] 26.2002525 -4.0997475
[99,] 11.3002525 26.2002525
[100,] 85.0002525 11.3002525
[101,] 12.4002525 85.0002525
[102,] -4.1997475 12.4002525
[103,] 45.4002525 -4.1997475
[104,] 32.4002525 45.4002525
[105,] -12.1997475 32.4002525
[106,] 58.7002525 -12.1997475
[107,] -19.8997475 58.7002525
[108,] -2.9568182 -19.8997475
[109,] -50.5568182 -2.9568182
[110,] -30.2568182 -50.5568182
[111,] 46.8431818 -30.2568182
[112,] 83.5431818 46.8431818
[113,] -51.0568182 83.5431818
[114,] 171.3431818 -51.0568182
[115,] -56.0568182 171.3431818
[116,] 12.9431818 -56.0568182
[117,] -48.6568182 12.9431818
[118,] 149.2431818 -48.6568182
[119,] 178.6431818 149.2431818
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 48.5568182 37.1568182
2 40.8568182 48.5568182
3 0.9568182 40.8568182
4 -2.3431818 0.9568182
5 -12.9431818 -2.3431818
6 14.4568182 -12.9431818
7 -23.9431818 14.4568182
8 55.0568182 -23.9431818
9 52.4568182 55.0568182
10 -14.6431818 52.4568182
11 -2.2431818 -14.6431818
12 45.6997475 -2.2431818
13 28.0997475 45.6997475
14 -5.6002525 28.0997475
15 41.4997475 -5.6002525
16 -30.8002525 41.4997475
17 37.5997475 -30.8002525
18 4.9997475 37.5997475
19 1.5997475 4.9997475
20 84.5997475 1.5997475
21 57.9997475 84.5997475
22 9.8997475 57.9997475
23 5.2997475 9.8997475
24 -32.7573232 5.2997475
25 84.6426768 -32.7573232
26 -11.0573232 84.6426768
27 62.0426768 -11.0573232
28 78.7426768 62.0426768
29 63.1426768 78.7426768
30 2.5426768 63.1426768
31 31.1426768 2.5426768
32 -2.8573232 31.1426768
33 48.5426768 -2.8573232
34 21.4426768 48.5426768
35 -12.1573232 21.4426768
36 48.7856061 -12.1573232
37 -22.8143939 48.7856061
38 32.4856061 -22.8143939
39 -20.4143939 32.4856061
40 -8.7143939 -20.4143939
41 -3.3143939 -8.7143939
42 43.0856061 -3.3143939
43 75.6856061 43.0856061
44 -21.3143939 75.6856061
45 39.0856061 -21.3143939
46 -18.0143939 39.0856061
47 -78.6143939 -18.0143939
48 -70.6714646 -78.6143939
49 -12.2714646 -70.6714646
50 53.0285354 -12.2714646
51 8.1285354 53.0285354
52 -112.1714646 8.1285354
53 -68.7714646 -112.1714646
54 26.6285354 -68.7714646
55 -71.7714646 26.6285354
56 -77.7714646 -71.7714646
57 -21.3714646 -77.7714646
58 -160.4714646 -21.3714646
59 -75.0714646 -160.4714646
60 -39.1285354 -75.0714646
61 -59.7285354 -39.1285354
62 -69.4285354 -59.7285354
63 -64.3285354 -69.4285354
64 -101.6285354 -64.3285354
65 -33.2285354 -101.6285354
66 -94.8285354 -33.2285354
67 -96.2285354 -94.8285354
68 -96.2285354 -96.2285354
69 -32.8285354 -96.2285354
70 -98.9285354 -32.8285354
71 37.4714646 -98.9285354
72 -71.5856061 37.4714646
73 -108.1856061 -71.5856061
74 -50.8856061 -108.1856061
75 -114.7856061 -50.8856061
76 -129.0856061 -114.7856061
77 3.3143939 -129.0856061
78 -122.2856061 3.3143939
79 64.3143939 -122.2856061
80 18.3143939 64.3143939
81 -44.2856061 18.3143939
82 1.6143939 -44.2856061
83 -10.9856061 1.6143939
84 -37.0426768 -10.9856061
85 96.3573232 -37.0426768
86 14.6573232 96.3573232
87 28.7573232 14.6573232
88 137.4573232 28.7573232
89 52.8573232 137.4573232
90 -41.7426768 52.8573232
91 29.8573232 -41.7426768
92 -5.1426768 29.8573232
93 -38.7426768 -5.1426768
94 51.1573232 -38.7426768
95 -22.4426768 51.1573232
96 122.5002525 -22.4426768
97 -4.0997475 122.5002525
98 26.2002525 -4.0997475
99 11.3002525 26.2002525
100 85.0002525 11.3002525
101 12.4002525 85.0002525
102 -4.1997475 12.4002525
103 45.4002525 -4.1997475
104 32.4002525 45.4002525
105 -12.1997475 32.4002525
106 58.7002525 -12.1997475
107 -19.8997475 58.7002525
108 -2.9568182 -19.8997475
109 -50.5568182 -2.9568182
110 -30.2568182 -50.5568182
111 46.8431818 -30.2568182
112 83.5431818 46.8431818
113 -51.0568182 83.5431818
114 171.3431818 -51.0568182
115 -56.0568182 171.3431818
116 12.9431818 -56.0568182
117 -48.6568182 12.9431818
118 149.2431818 -48.6568182
119 178.6431818 149.2431818
> 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/745n21324592746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/840pm1324592746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/969411324592746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/100y9j1324592746.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zurk1324592746.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/12ae5h1324592746.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/13imzs1324592746.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/14aawf1324592746.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/15gkhk1324592746.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/16l67r1324592746.tab")
+ }
>
> try(system("convert tmp/1i5jh1324592746.ps tmp/1i5jh1324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uru81324592746.ps tmp/2uru81324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j0k71324592746.ps tmp/3j0k71324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lgml1324592746.ps tmp/4lgml1324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/563gh1324592746.ps tmp/563gh1324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/69ob41324592746.ps tmp/69ob41324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/745n21324592746.ps tmp/745n21324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/840pm1324592746.ps tmp/840pm1324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/969411324592746.ps tmp/969411324592746.png",intern=TRUE))
character(0)
> try(system("convert tmp/100y9j1324592746.ps tmp/100y9j1324592746.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.020 0.601 4.645