R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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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(33,62,39,64,45,62,46,64,45,64,45,69,49,69,50,65,54,56,59,58,58,53,56,62,48,55,50,60,52,59,53,58,55,53,43,57,42,57,38,53,41,54,41,53,39,57,34,57,27,55,15,49,14,50,31,49,41,54,43,58,46,58,42,52,45,56,45,52,40,59,35,53,36,52,38,53,39,51,32,50,24,56,21,52,12,46,29,48,36,46,31,48,28,48,30,49,38,53,27,48,40,51,40,48,44,50,47,55,45,52,42,53,38,52,46,55,37,53,41,53,40,56,33,54,34,52,36,55,36,54,38,59,42,56,35,56,25,51,24,53,22,52,27,51,17,46,30,49,30,46,34,55,37,57,36,53,33,52,33,53,33,50,37,54,40,53,35,50,37,51,43,52,42,47,33,51,39,49,40,53,37,52,44,45,42,53,43,51,40,48,30,48,30,48,31,48,18,40,24,43,22,40,26,39,28,39,23,36,17,41,12,39,9,40,19,39,21,46,18,40,18,37,15,37,24,44,18,41,19,40,30,36,33,38,35,43,36,42,47,45,46,46),dim=c(2,121),dimnames=list(c('Alg_E','Spaar'),1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('Alg_E','Spaar'),1:121))
> 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 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Spaar Alg_E M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 62 33 1 0 0 0 0 0 0 0 0 0 0 1
2 64 39 0 1 0 0 0 0 0 0 0 0 0 2
3 62 45 0 0 1 0 0 0 0 0 0 0 0 3
4 64 46 0 0 0 1 0 0 0 0 0 0 0 4
5 64 45 0 0 0 0 1 0 0 0 0 0 0 5
6 69 45 0 0 0 0 0 1 0 0 0 0 0 6
7 69 49 0 0 0 0 0 0 1 0 0 0 0 7
8 65 50 0 0 0 0 0 0 0 1 0 0 0 8
9 56 54 0 0 0 0 0 0 0 0 1 0 0 9
10 58 59 0 0 0 0 0 0 0 0 0 1 0 10
11 53 58 0 0 0 0 0 0 0 0 0 0 1 11
12 62 56 0 0 0 0 0 0 0 0 0 0 0 12
13 55 48 1 0 0 0 0 0 0 0 0 0 0 13
14 60 50 0 1 0 0 0 0 0 0 0 0 0 14
15 59 52 0 0 1 0 0 0 0 0 0 0 0 15
16 58 53 0 0 0 1 0 0 0 0 0 0 0 16
17 53 55 0 0 0 0 1 0 0 0 0 0 0 17
18 57 43 0 0 0 0 0 1 0 0 0 0 0 18
19 57 42 0 0 0 0 0 0 1 0 0 0 0 19
20 53 38 0 0 0 0 0 0 0 1 0 0 0 20
21 54 41 0 0 0 0 0 0 0 0 1 0 0 21
22 53 41 0 0 0 0 0 0 0 0 0 1 0 22
23 57 39 0 0 0 0 0 0 0 0 0 0 1 23
24 57 34 0 0 0 0 0 0 0 0 0 0 0 24
25 55 27 1 0 0 0 0 0 0 0 0 0 0 25
26 49 15 0 1 0 0 0 0 0 0 0 0 0 26
27 50 14 0 0 1 0 0 0 0 0 0 0 0 27
28 49 31 0 0 0 1 0 0 0 0 0 0 0 28
29 54 41 0 0 0 0 1 0 0 0 0 0 0 29
30 58 43 0 0 0 0 0 1 0 0 0 0 0 30
31 58 46 0 0 0 0 0 0 1 0 0 0 0 31
32 52 42 0 0 0 0 0 0 0 1 0 0 0 32
33 56 45 0 0 0 0 0 0 0 0 1 0 0 33
34 52 45 0 0 0 0 0 0 0 0 0 1 0 34
35 59 40 0 0 0 0 0 0 0 0 0 0 1 35
36 53 35 0 0 0 0 0 0 0 0 0 0 0 36
37 52 36 1 0 0 0 0 0 0 0 0 0 0 37
38 53 38 0 1 0 0 0 0 0 0 0 0 0 38
39 51 39 0 0 1 0 0 0 0 0 0 0 0 39
40 50 32 0 0 0 1 0 0 0 0 0 0 0 40
41 56 24 0 0 0 0 1 0 0 0 0 0 0 41
42 52 21 0 0 0 0 0 1 0 0 0 0 0 42
43 46 12 0 0 0 0 0 0 1 0 0 0 0 43
44 48 29 0 0 0 0 0 0 0 1 0 0 0 44
45 46 36 0 0 0 0 0 0 0 0 1 0 0 45
46 48 31 0 0 0 0 0 0 0 0 0 1 0 46
47 48 28 0 0 0 0 0 0 0 0 0 0 1 47
48 49 30 0 0 0 0 0 0 0 0 0 0 0 48
49 53 38 1 0 0 0 0 0 0 0 0 0 0 49
50 48 27 0 1 0 0 0 0 0 0 0 0 0 50
51 51 40 0 0 1 0 0 0 0 0 0 0 0 51
52 48 40 0 0 0 1 0 0 0 0 0 0 0 52
53 50 44 0 0 0 0 1 0 0 0 0 0 0 53
54 55 47 0 0 0 0 0 1 0 0 0 0 0 54
55 52 45 0 0 0 0 0 0 1 0 0 0 0 55
56 53 42 0 0 0 0 0 0 0 1 0 0 0 56
57 52 38 0 0 0 0 0 0 0 0 1 0 0 57
58 55 46 0 0 0 0 0 0 0 0 0 1 0 58
59 53 37 0 0 0 0 0 0 0 0 0 0 1 59
60 53 41 0 0 0 0 0 0 0 0 0 0 0 60
61 56 40 1 0 0 0 0 0 0 0 0 0 0 61
62 54 33 0 1 0 0 0 0 0 0 0 0 0 62
63 52 34 0 0 1 0 0 0 0 0 0 0 0 63
64 55 36 0 0 0 1 0 0 0 0 0 0 0 64
65 54 36 0 0 0 0 1 0 0 0 0 0 0 65
66 59 38 0 0 0 0 0 1 0 0 0 0 0 66
67 56 42 0 0 0 0 0 0 1 0 0 0 0 67
68 56 35 0 0 0 0 0 0 0 1 0 0 0 68
69 51 25 0 0 0 0 0 0 0 0 1 0 0 69
70 53 24 0 0 0 0 0 0 0 0 0 1 0 70
71 52 22 0 0 0 0 0 0 0 0 0 0 1 71
72 51 27 0 0 0 0 0 0 0 0 0 0 0 72
73 46 17 1 0 0 0 0 0 0 0 0 0 0 73
74 49 30 0 1 0 0 0 0 0 0 0 0 0 74
75 46 30 0 0 1 0 0 0 0 0 0 0 0 75
76 55 34 0 0 0 1 0 0 0 0 0 0 0 76
77 57 37 0 0 0 0 1 0 0 0 0 0 0 77
78 53 36 0 0 0 0 0 1 0 0 0 0 0 78
79 52 33 0 0 0 0 0 0 1 0 0 0 0 79
80 53 33 0 0 0 0 0 0 0 1 0 0 0 80
81 50 33 0 0 0 0 0 0 0 0 1 0 0 81
82 54 37 0 0 0 0 0 0 0 0 0 1 0 82
83 53 40 0 0 0 0 0 0 0 0 0 0 1 83
84 50 35 0 0 0 0 0 0 0 0 0 0 0 84
85 51 37 1 0 0 0 0 0 0 0 0 0 0 85
86 52 43 0 1 0 0 0 0 0 0 0 0 0 86
87 47 42 0 0 1 0 0 0 0 0 0 0 0 87
88 51 33 0 0 0 1 0 0 0 0 0 0 0 88
89 49 39 0 0 0 0 1 0 0 0 0 0 0 89
90 53 40 0 0 0 0 0 1 0 0 0 0 0 90
91 52 37 0 0 0 0 0 0 1 0 0 0 0 91
92 45 44 0 0 0 0 0 0 0 1 0 0 0 92
93 53 42 0 0 0 0 0 0 0 0 1 0 0 93
94 51 43 0 0 0 0 0 0 0 0 0 1 0 94
95 48 40 0 0 0 0 0 0 0 0 0 0 1 95
96 48 30 0 0 0 0 0 0 0 0 0 0 0 96
97 48 30 1 0 0 0 0 0 0 0 0 0 0 97
98 48 31 0 1 0 0 0 0 0 0 0 0 0 98
99 40 18 0 0 1 0 0 0 0 0 0 0 0 99
100 43 24 0 0 0 1 0 0 0 0 0 0 0 100
101 40 22 0 0 0 0 1 0 0 0 0 0 0 101
102 39 26 0 0 0 0 0 1 0 0 0 0 0 102
103 39 28 0 0 0 0 0 0 1 0 0 0 0 103
104 36 23 0 0 0 0 0 0 0 1 0 0 0 104
105 41 17 0 0 0 0 0 0 0 0 1 0 0 105
106 39 12 0 0 0 0 0 0 0 0 0 1 0 106
107 40 9 0 0 0 0 0 0 0 0 0 0 1 107
108 39 19 0 0 0 0 0 0 0 0 0 0 0 108
109 46 21 1 0 0 0 0 0 0 0 0 0 0 109
110 40 18 0 1 0 0 0 0 0 0 0 0 0 110
111 37 18 0 0 1 0 0 0 0 0 0 0 0 111
112 37 15 0 0 0 1 0 0 0 0 0 0 0 112
113 44 24 0 0 0 0 1 0 0 0 0 0 0 113
114 41 18 0 0 0 0 0 1 0 0 0 0 0 114
115 40 19 0 0 0 0 0 0 1 0 0 0 0 115
116 36 30 0 0 0 0 0 0 0 1 0 0 0 116
117 38 33 0 0 0 0 0 0 0 0 1 0 0 117
118 43 35 0 0 0 0 0 0 0 0 0 1 0 118
119 42 36 0 0 0 0 0 0 0 0 0 0 1 119
120 45 47 0 0 0 0 0 0 0 0 0 0 0 120
121 46 46 1 0 0 0 0 0 0 0 0 0 0 121
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Alg_E M1 M2 M3 M4
50.6299 0.2248 0.8557 0.4792 -1.7811 -0.4314
M5 M6 M7 M8 M9 M10
0.2710 2.1154 0.8248 -1.7479 -1.5834 -0.7662
M11 t
-0.2071 -0.1195
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.14799 -2.61190 -0.09147 2.81241 7.37689
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.62988 2.22846 22.720 < 2e-16 ***
Alg_E 0.22483 0.03841 5.853 5.34e-08 ***
M1 0.85574 1.64807 0.519 0.605
M2 0.47921 1.69583 0.283 0.778
M3 -1.78112 1.69183 -1.053 0.295
M4 -0.43139 1.68782 -0.256 0.799
M5 0.27103 1.68511 0.161 0.873
M6 2.11539 1.68492 1.255 0.212
M7 0.82485 1.68476 0.490 0.625
M8 -1.74790 1.68428 -1.038 0.302
M9 -1.58341 1.68406 -0.940 0.349
M10 -0.76623 1.68489 -0.455 0.650
M11 -0.20711 1.68391 -0.123 0.902
t -0.11953 0.01162 -10.284 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.765 on 107 degrees of freedom
Multiple R-squared: 0.7349, Adjusted R-squared: 0.7027
F-statistic: 22.82 on 13 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,] 0.3252349 0.650469762 0.674765119
[2,] 0.3225219 0.645043842 0.677478079
[3,] 0.1956579 0.391315790 0.804342105
[4,] 0.1137102 0.227420336 0.886289832
[5,] 0.3977902 0.795580305 0.602209848
[6,] 0.3584687 0.716937382 0.641531309
[7,] 0.6528763 0.694247395 0.347123697
[8,] 0.5621590 0.875682002 0.437841001
[9,] 0.5555035 0.888993028 0.444496514
[10,] 0.6127902 0.774419640 0.387209820
[11,] 0.5518923 0.896215301 0.448107651
[12,] 0.5166778 0.966644353 0.483322176
[13,] 0.5358004 0.928399229 0.464199614
[14,] 0.5075709 0.984858285 0.492429142
[15,] 0.4650794 0.930158777 0.534920612
[16,] 0.3999654 0.799930809 0.600034596
[17,] 0.5531318 0.893736377 0.446868189
[18,] 0.5599304 0.880139217 0.440069609
[19,] 0.7692044 0.461591286 0.230795643
[20,] 0.7155478 0.568904423 0.284452212
[21,] 0.6883072 0.623385616 0.311692808
[22,] 0.6500712 0.699857588 0.349928794
[23,] 0.5953943 0.809211376 0.404605688
[24,] 0.5547641 0.890471714 0.445235857
[25,] 0.6425815 0.714837052 0.357418526
[26,] 0.5856551 0.828689746 0.414344873
[27,] 0.6125359 0.774928171 0.387464086
[28,] 0.5615706 0.876858753 0.438429377
[29,] 0.6104867 0.779026640 0.389513320
[30,] 0.6324773 0.735045447 0.367522724
[31,] 0.6307209 0.738558193 0.369279097
[32,] 0.6134977 0.773004590 0.386502295
[33,] 0.6461262 0.707747558 0.353873779
[34,] 0.6553518 0.689296486 0.344648243
[35,] 0.6291246 0.741750757 0.370875378
[36,] 0.7471570 0.505686029 0.252843015
[37,] 0.8398244 0.320351242 0.160175621
[38,] 0.8541381 0.291723779 0.145861890
[39,] 0.8928285 0.214342999 0.107171500
[40,] 0.9017795 0.196441034 0.098220517
[41,] 0.9396675 0.120665034 0.060332517
[42,] 0.9762786 0.047442889 0.023721444
[43,] 0.9841834 0.031633113 0.015816557
[44,] 0.9883472 0.023305635 0.011652817
[45,] 0.9916840 0.016632098 0.008316049
[46,] 0.9915465 0.016907026 0.008453513
[47,] 0.9892132 0.021573689 0.010786844
[48,] 0.9917471 0.016505831 0.008252915
[49,] 0.9920408 0.015918385 0.007959193
[50,] 0.9924703 0.015059335 0.007529668
[51,] 0.9901075 0.019784929 0.009892464
[52,] 0.9950296 0.009940809 0.004970405
[53,] 0.9943690 0.011261967 0.005630983
[54,] 0.9946975 0.010604944 0.005302472
[55,] 0.9934871 0.013025899 0.006512949
[56,] 0.9902758 0.019448308 0.009724154
[57,] 0.9931481 0.013703853 0.006851926
[58,] 0.9935368 0.012926438 0.006463219
[59,] 0.9929418 0.014116382 0.007058191
[60,] 0.9923859 0.015228149 0.007614074
[61,] 0.9938893 0.012221368 0.006110684
[62,] 0.9903343 0.019331455 0.009665727
[63,] 0.9850207 0.029958590 0.014979295
[64,] 0.9960043 0.007991374 0.003995687
[65,] 0.9936917 0.012616550 0.006308275
[66,] 0.9920401 0.015919813 0.007959906
[67,] 0.9876072 0.024785645 0.012392823
[68,] 0.9807218 0.038556475 0.019278238
[69,] 0.9774092 0.045181675 0.022590838
[70,] 0.9669677 0.066064656 0.033032328
[71,] 0.9595792 0.080841693 0.040420847
[72,] 0.9489690 0.102061909 0.051030954
[73,] 0.9327091 0.134581724 0.067290862
[74,] 0.9262141 0.147571894 0.073785947
[75,] 0.9366940 0.126612029 0.063306015
[76,] 0.9176622 0.164675642 0.082337821
[77,] 0.9557981 0.088403849 0.044201925
[78,] 0.9470026 0.105994878 0.052997439
[79,] 0.9190312 0.161937624 0.080968812
[80,] 0.9236114 0.152777285 0.076388643
[81,] 0.8801416 0.239716723 0.119858362
[82,] 0.9095211 0.180957819 0.090478910
[83,] 0.8844065 0.231186921 0.115593461
[84,] 0.9464242 0.107151618 0.053575809
[85,] 0.9309563 0.138087320 0.069043660
[86,] 0.9013474 0.197305137 0.098652568
[87,] 0.8869216 0.226156778 0.113078389
[88,] 0.8875898 0.224820407 0.112410204
> postscript(file="/var/www/html/rcomp/tmp/1fqiz1258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2itah1258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/30g871258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44bbq1258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50asu1258732455.ps",horizontal=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 = 121
Frequency = 1
1 2 3 4 5 6
3.214579376 4.361670765 3.392565636 3.937531217 3.579463319 6.854635409
7 8 9 10 11 12
7.365392608 5.832840980 -4.111436239 -3.933230668 -9.147989741 0.214080033
13 14 15 16 17 18
-5.723513703 -0.677110676 0.253095832 -2.201938586 -8.234490213 -3.261383209
19 20 21 22 23 24
-1.626486463 -2.034898544 -1.754347854 -3.452002735 0.558066101 1.594619603
25 26 27 28 29 30
0.432197958 -2.373808283 1.230881954 -4.821399015 -2.652573918 -0.827057646
31 32 33 34 35 36
-0.091472538 -2.499884618 0.780666072 -3.916988810 3.767563755 -1.195882744
37 38 39 40 41 42
-3.156927664 -2.110524637 -1.955490219 -2.611901362 4.603826105 -0.446518076
43 44 45 46 47 48
-3.012998055 -2.142796233 -5.761557181 -3.335072515 -3.100175769 -2.637417634
49 50 51 52 53 54
-1.172257920 -3.203092071 -0.745992566 -4.976199074 -4.458406520 -1.857718158
55 56 57 58 59 60
-2.997993503 1.368766508 1.223112563 1.726834407 1.310698609 0.323800926
61 62 63 64 65 66
2.812411824 2.882266036 3.037300454 4.357438126 2.774542318 5.600058590
67 68 69 70 71 72
3.110815788 7.376887437 4.580200949 6.107373977 5.117442813 2.905717221
73 74 75 76 77 78
-0.582220696 -0.008924673 -0.629062345 6.241419508 6.984039972 1.484039972
79 80 81 82 83 84
2.568592536 6.260868818 3.215903236 5.618936717 3.504866006 1.541419508
85 86 87 88 89 90
1.355546679 1.502638067 -0.892671696 3.900572981 -0.031290284 2.019053897
91 92 93 94 95 96
3.103606461 -2.777912622 5.626777615 2.704294824 -0.060808431 2.099884618
97 98 99 100 101 102
1.363667607 1.634898544 -1.062476306 -0.641650272 -3.774890261 -7.399029808
103 104 105 106 107 108
-6.438616791 -5.622200962 0.681800913 -0.891714421 0.343182324 -2.992682815
109 110 111 112 113 114
2.821444355 -2.008013071 -2.628150743 -3.183873524 1.209779483 -2.166080970
115 116 117 118 119 120
-1.980840043 -5.761670765 -4.481120075 -0.628430775 -2.292845667 -1.853538716
121
-1.364927817
> postscript(file="/var/www/html/rcomp/tmp/6qpcj1258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 3.214579376 NA
1 4.361670765 3.214579376
2 3.392565636 4.361670765
3 3.937531217 3.392565636
4 3.579463319 3.937531217
5 6.854635409 3.579463319
6 7.365392608 6.854635409
7 5.832840980 7.365392608
8 -4.111436239 5.832840980
9 -3.933230668 -4.111436239
10 -9.147989741 -3.933230668
11 0.214080033 -9.147989741
12 -5.723513703 0.214080033
13 -0.677110676 -5.723513703
14 0.253095832 -0.677110676
15 -2.201938586 0.253095832
16 -8.234490213 -2.201938586
17 -3.261383209 -8.234490213
18 -1.626486463 -3.261383209
19 -2.034898544 -1.626486463
20 -1.754347854 -2.034898544
21 -3.452002735 -1.754347854
22 0.558066101 -3.452002735
23 1.594619603 0.558066101
24 0.432197958 1.594619603
25 -2.373808283 0.432197958
26 1.230881954 -2.373808283
27 -4.821399015 1.230881954
28 -2.652573918 -4.821399015
29 -0.827057646 -2.652573918
30 -0.091472538 -0.827057646
31 -2.499884618 -0.091472538
32 0.780666072 -2.499884618
33 -3.916988810 0.780666072
34 3.767563755 -3.916988810
35 -1.195882744 3.767563755
36 -3.156927664 -1.195882744
37 -2.110524637 -3.156927664
38 -1.955490219 -2.110524637
39 -2.611901362 -1.955490219
40 4.603826105 -2.611901362
41 -0.446518076 4.603826105
42 -3.012998055 -0.446518076
43 -2.142796233 -3.012998055
44 -5.761557181 -2.142796233
45 -3.335072515 -5.761557181
46 -3.100175769 -3.335072515
47 -2.637417634 -3.100175769
48 -1.172257920 -2.637417634
49 -3.203092071 -1.172257920
50 -0.745992566 -3.203092071
51 -4.976199074 -0.745992566
52 -4.458406520 -4.976199074
53 -1.857718158 -4.458406520
54 -2.997993503 -1.857718158
55 1.368766508 -2.997993503
56 1.223112563 1.368766508
57 1.726834407 1.223112563
58 1.310698609 1.726834407
59 0.323800926 1.310698609
60 2.812411824 0.323800926
61 2.882266036 2.812411824
62 3.037300454 2.882266036
63 4.357438126 3.037300454
64 2.774542318 4.357438126
65 5.600058590 2.774542318
66 3.110815788 5.600058590
67 7.376887437 3.110815788
68 4.580200949 7.376887437
69 6.107373977 4.580200949
70 5.117442813 6.107373977
71 2.905717221 5.117442813
72 -0.582220696 2.905717221
73 -0.008924673 -0.582220696
74 -0.629062345 -0.008924673
75 6.241419508 -0.629062345
76 6.984039972 6.241419508
77 1.484039972 6.984039972
78 2.568592536 1.484039972
79 6.260868818 2.568592536
80 3.215903236 6.260868818
81 5.618936717 3.215903236
82 3.504866006 5.618936717
83 1.541419508 3.504866006
84 1.355546679 1.541419508
85 1.502638067 1.355546679
86 -0.892671696 1.502638067
87 3.900572981 -0.892671696
88 -0.031290284 3.900572981
89 2.019053897 -0.031290284
90 3.103606461 2.019053897
91 -2.777912622 3.103606461
92 5.626777615 -2.777912622
93 2.704294824 5.626777615
94 -0.060808431 2.704294824
95 2.099884618 -0.060808431
96 1.363667607 2.099884618
97 1.634898544 1.363667607
98 -1.062476306 1.634898544
99 -0.641650272 -1.062476306
100 -3.774890261 -0.641650272
101 -7.399029808 -3.774890261
102 -6.438616791 -7.399029808
103 -5.622200962 -6.438616791
104 0.681800913 -5.622200962
105 -0.891714421 0.681800913
106 0.343182324 -0.891714421
107 -2.992682815 0.343182324
108 2.821444355 -2.992682815
109 -2.008013071 2.821444355
110 -2.628150743 -2.008013071
111 -3.183873524 -2.628150743
112 1.209779483 -3.183873524
113 -2.166080970 1.209779483
114 -1.980840043 -2.166080970
115 -5.761670765 -1.980840043
116 -4.481120075 -5.761670765
117 -0.628430775 -4.481120075
118 -2.292845667 -0.628430775
119 -1.853538716 -2.292845667
120 -1.364927817 -1.853538716
121 NA -1.364927817
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.361670765 3.214579376
[2,] 3.392565636 4.361670765
[3,] 3.937531217 3.392565636
[4,] 3.579463319 3.937531217
[5,] 6.854635409 3.579463319
[6,] 7.365392608 6.854635409
[7,] 5.832840980 7.365392608
[8,] -4.111436239 5.832840980
[9,] -3.933230668 -4.111436239
[10,] -9.147989741 -3.933230668
[11,] 0.214080033 -9.147989741
[12,] -5.723513703 0.214080033
[13,] -0.677110676 -5.723513703
[14,] 0.253095832 -0.677110676
[15,] -2.201938586 0.253095832
[16,] -8.234490213 -2.201938586
[17,] -3.261383209 -8.234490213
[18,] -1.626486463 -3.261383209
[19,] -2.034898544 -1.626486463
[20,] -1.754347854 -2.034898544
[21,] -3.452002735 -1.754347854
[22,] 0.558066101 -3.452002735
[23,] 1.594619603 0.558066101
[24,] 0.432197958 1.594619603
[25,] -2.373808283 0.432197958
[26,] 1.230881954 -2.373808283
[27,] -4.821399015 1.230881954
[28,] -2.652573918 -4.821399015
[29,] -0.827057646 -2.652573918
[30,] -0.091472538 -0.827057646
[31,] -2.499884618 -0.091472538
[32,] 0.780666072 -2.499884618
[33,] -3.916988810 0.780666072
[34,] 3.767563755 -3.916988810
[35,] -1.195882744 3.767563755
[36,] -3.156927664 -1.195882744
[37,] -2.110524637 -3.156927664
[38,] -1.955490219 -2.110524637
[39,] -2.611901362 -1.955490219
[40,] 4.603826105 -2.611901362
[41,] -0.446518076 4.603826105
[42,] -3.012998055 -0.446518076
[43,] -2.142796233 -3.012998055
[44,] -5.761557181 -2.142796233
[45,] -3.335072515 -5.761557181
[46,] -3.100175769 -3.335072515
[47,] -2.637417634 -3.100175769
[48,] -1.172257920 -2.637417634
[49,] -3.203092071 -1.172257920
[50,] -0.745992566 -3.203092071
[51,] -4.976199074 -0.745992566
[52,] -4.458406520 -4.976199074
[53,] -1.857718158 -4.458406520
[54,] -2.997993503 -1.857718158
[55,] 1.368766508 -2.997993503
[56,] 1.223112563 1.368766508
[57,] 1.726834407 1.223112563
[58,] 1.310698609 1.726834407
[59,] 0.323800926 1.310698609
[60,] 2.812411824 0.323800926
[61,] 2.882266036 2.812411824
[62,] 3.037300454 2.882266036
[63,] 4.357438126 3.037300454
[64,] 2.774542318 4.357438126
[65,] 5.600058590 2.774542318
[66,] 3.110815788 5.600058590
[67,] 7.376887437 3.110815788
[68,] 4.580200949 7.376887437
[69,] 6.107373977 4.580200949
[70,] 5.117442813 6.107373977
[71,] 2.905717221 5.117442813
[72,] -0.582220696 2.905717221
[73,] -0.008924673 -0.582220696
[74,] -0.629062345 -0.008924673
[75,] 6.241419508 -0.629062345
[76,] 6.984039972 6.241419508
[77,] 1.484039972 6.984039972
[78,] 2.568592536 1.484039972
[79,] 6.260868818 2.568592536
[80,] 3.215903236 6.260868818
[81,] 5.618936717 3.215903236
[82,] 3.504866006 5.618936717
[83,] 1.541419508 3.504866006
[84,] 1.355546679 1.541419508
[85,] 1.502638067 1.355546679
[86,] -0.892671696 1.502638067
[87,] 3.900572981 -0.892671696
[88,] -0.031290284 3.900572981
[89,] 2.019053897 -0.031290284
[90,] 3.103606461 2.019053897
[91,] -2.777912622 3.103606461
[92,] 5.626777615 -2.777912622
[93,] 2.704294824 5.626777615
[94,] -0.060808431 2.704294824
[95,] 2.099884618 -0.060808431
[96,] 1.363667607 2.099884618
[97,] 1.634898544 1.363667607
[98,] -1.062476306 1.634898544
[99,] -0.641650272 -1.062476306
[100,] -3.774890261 -0.641650272
[101,] -7.399029808 -3.774890261
[102,] -6.438616791 -7.399029808
[103,] -5.622200962 -6.438616791
[104,] 0.681800913 -5.622200962
[105,] -0.891714421 0.681800913
[106,] 0.343182324 -0.891714421
[107,] -2.992682815 0.343182324
[108,] 2.821444355 -2.992682815
[109,] -2.008013071 2.821444355
[110,] -2.628150743 -2.008013071
[111,] -3.183873524 -2.628150743
[112,] 1.209779483 -3.183873524
[113,] -2.166080970 1.209779483
[114,] -1.980840043 -2.166080970
[115,] -5.761670765 -1.980840043
[116,] -4.481120075 -5.761670765
[117,] -0.628430775 -4.481120075
[118,] -2.292845667 -0.628430775
[119,] -1.853538716 -2.292845667
[120,] -1.364927817 -1.853538716
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.361670765 3.214579376
2 3.392565636 4.361670765
3 3.937531217 3.392565636
4 3.579463319 3.937531217
5 6.854635409 3.579463319
6 7.365392608 6.854635409
7 5.832840980 7.365392608
8 -4.111436239 5.832840980
9 -3.933230668 -4.111436239
10 -9.147989741 -3.933230668
11 0.214080033 -9.147989741
12 -5.723513703 0.214080033
13 -0.677110676 -5.723513703
14 0.253095832 -0.677110676
15 -2.201938586 0.253095832
16 -8.234490213 -2.201938586
17 -3.261383209 -8.234490213
18 -1.626486463 -3.261383209
19 -2.034898544 -1.626486463
20 -1.754347854 -2.034898544
21 -3.452002735 -1.754347854
22 0.558066101 -3.452002735
23 1.594619603 0.558066101
24 0.432197958 1.594619603
25 -2.373808283 0.432197958
26 1.230881954 -2.373808283
27 -4.821399015 1.230881954
28 -2.652573918 -4.821399015
29 -0.827057646 -2.652573918
30 -0.091472538 -0.827057646
31 -2.499884618 -0.091472538
32 0.780666072 -2.499884618
33 -3.916988810 0.780666072
34 3.767563755 -3.916988810
35 -1.195882744 3.767563755
36 -3.156927664 -1.195882744
37 -2.110524637 -3.156927664
38 -1.955490219 -2.110524637
39 -2.611901362 -1.955490219
40 4.603826105 -2.611901362
41 -0.446518076 4.603826105
42 -3.012998055 -0.446518076
43 -2.142796233 -3.012998055
44 -5.761557181 -2.142796233
45 -3.335072515 -5.761557181
46 -3.100175769 -3.335072515
47 -2.637417634 -3.100175769
48 -1.172257920 -2.637417634
49 -3.203092071 -1.172257920
50 -0.745992566 -3.203092071
51 -4.976199074 -0.745992566
52 -4.458406520 -4.976199074
53 -1.857718158 -4.458406520
54 -2.997993503 -1.857718158
55 1.368766508 -2.997993503
56 1.223112563 1.368766508
57 1.726834407 1.223112563
58 1.310698609 1.726834407
59 0.323800926 1.310698609
60 2.812411824 0.323800926
61 2.882266036 2.812411824
62 3.037300454 2.882266036
63 4.357438126 3.037300454
64 2.774542318 4.357438126
65 5.600058590 2.774542318
66 3.110815788 5.600058590
67 7.376887437 3.110815788
68 4.580200949 7.376887437
69 6.107373977 4.580200949
70 5.117442813 6.107373977
71 2.905717221 5.117442813
72 -0.582220696 2.905717221
73 -0.008924673 -0.582220696
74 -0.629062345 -0.008924673
75 6.241419508 -0.629062345
76 6.984039972 6.241419508
77 1.484039972 6.984039972
78 2.568592536 1.484039972
79 6.260868818 2.568592536
80 3.215903236 6.260868818
81 5.618936717 3.215903236
82 3.504866006 5.618936717
83 1.541419508 3.504866006
84 1.355546679 1.541419508
85 1.502638067 1.355546679
86 -0.892671696 1.502638067
87 3.900572981 -0.892671696
88 -0.031290284 3.900572981
89 2.019053897 -0.031290284
90 3.103606461 2.019053897
91 -2.777912622 3.103606461
92 5.626777615 -2.777912622
93 2.704294824 5.626777615
94 -0.060808431 2.704294824
95 2.099884618 -0.060808431
96 1.363667607 2.099884618
97 1.634898544 1.363667607
98 -1.062476306 1.634898544
99 -0.641650272 -1.062476306
100 -3.774890261 -0.641650272
101 -7.399029808 -3.774890261
102 -6.438616791 -7.399029808
103 -5.622200962 -6.438616791
104 0.681800913 -5.622200962
105 -0.891714421 0.681800913
106 0.343182324 -0.891714421
107 -2.992682815 0.343182324
108 2.821444355 -2.992682815
109 -2.008013071 2.821444355
110 -2.628150743 -2.008013071
111 -3.183873524 -2.628150743
112 1.209779483 -3.183873524
113 -2.166080970 1.209779483
114 -1.980840043 -2.166080970
115 -5.761670765 -1.980840043
116 -4.481120075 -5.761670765
117 -0.628430775 -4.481120075
118 -2.292845667 -0.628430775
119 -1.853538716 -2.292845667
120 -1.364927817 -1.853538716
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/79vqa1258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kyw51258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94v5n1258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10euu91258732455.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11kpcy1258732455.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12br4s1258732455.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13ueft1258732455.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/146dvv1258732455.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/156rih1258732455.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16hlsp1258732455.tab")
+ }
>
> system("convert tmp/1fqiz1258732455.ps tmp/1fqiz1258732455.png")
> system("convert tmp/2itah1258732455.ps tmp/2itah1258732455.png")
> system("convert tmp/30g871258732455.ps tmp/30g871258732455.png")
> system("convert tmp/44bbq1258732455.ps tmp/44bbq1258732455.png")
> system("convert tmp/50asu1258732455.ps tmp/50asu1258732455.png")
> system("convert tmp/6qpcj1258732455.ps tmp/6qpcj1258732455.png")
> system("convert tmp/79vqa1258732455.ps tmp/79vqa1258732455.png")
> system("convert tmp/8kyw51258732455.ps tmp/8kyw51258732455.png")
> system("convert tmp/94v5n1258732455.ps tmp/94v5n1258732455.png")
> system("convert tmp/10euu91258732455.ps tmp/10euu91258732455.png")
>
>
> proc.time()
user system elapsed
3.325 1.637 3.702