R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(19,0,23,0,22,0,23,0,25,0,25,0,23,0,22,0,21,0,16,0,21,0,21,0,26,0,23,0,22,0,22,0,22,0,12,0,20,0,18,0,23,0,25,0,28,0,28,0,29,0,31,0,33,0,32,0,33,0,35,0,33,0,36,0,30,0,34,0,34,0,35,0,33,0,28,0,27,0,23,0,23,0,24,0,24,0,20,0,16,1,6,1,2,1,12,1,19,1,21,1,22,1,20,1,21,1,20,1,19,1,17,1,17,1,17,1,16,1,12,1,11,1,7,1,2,1,9,1,11,1,10,1,7,1,9,1,15,1,5,1,14,1,14,1,17,1,19,1,17,1,16,1,14,1,20,1,16,1,18,1,18,1,14,1,13,1,14,1,14,1,17,1,18,1,15,1,9,1,9,1,9,1,10,1,6,1,12,1,11,1,15,1,19,1,18,1,15,1,16,1,14,1,18,1,18,1,18,1,18,1,22,1,21,1,12,1,19,1,21,1,19,1,22,1,22,1,21,1,19,1,18,1,18,1,19,1,12,1,16,1),dim=c(2,120),dimnames=list(c('Vertrouwen','Aanslag'),1:120))
> y <- array(NA,dim=c(2,120),dimnames=list(c('Vertrouwen','Aanslag'),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
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
Vertrouwen Aanslag M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 19 0 1 0 0 0 0 0 0 0 0 0 0 1
2 23 0 0 1 0 0 0 0 0 0 0 0 0 2
3 22 0 0 0 1 0 0 0 0 0 0 0 0 3
4 23 0 0 0 0 1 0 0 0 0 0 0 0 4
5 25 0 0 0 0 0 1 0 0 0 0 0 0 5
6 25 0 0 0 0 0 0 1 0 0 0 0 0 6
7 23 0 0 0 0 0 0 0 1 0 0 0 0 7
8 22 0 0 0 0 0 0 0 0 1 0 0 0 8
9 21 0 0 0 0 0 0 0 0 0 1 0 0 9
10 16 0 0 0 0 0 0 0 0 0 0 1 0 10
11 21 0 0 0 0 0 0 0 0 0 0 0 1 11
12 21 0 0 0 0 0 0 0 0 0 0 0 0 12
13 26 0 1 0 0 0 0 0 0 0 0 0 0 13
14 23 0 0 1 0 0 0 0 0 0 0 0 0 14
15 22 0 0 0 1 0 0 0 0 0 0 0 0 15
16 22 0 0 0 0 1 0 0 0 0 0 0 0 16
17 22 0 0 0 0 0 1 0 0 0 0 0 0 17
18 12 0 0 0 0 0 0 1 0 0 0 0 0 18
19 20 0 0 0 0 0 0 0 1 0 0 0 0 19
20 18 0 0 0 0 0 0 0 0 1 0 0 0 20
21 23 0 0 0 0 0 0 0 0 0 1 0 0 21
22 25 0 0 0 0 0 0 0 0 0 0 1 0 22
23 28 0 0 0 0 0 0 0 0 0 0 0 1 23
24 28 0 0 0 0 0 0 0 0 0 0 0 0 24
25 29 0 1 0 0 0 0 0 0 0 0 0 0 25
26 31 0 0 1 0 0 0 0 0 0 0 0 0 26
27 33 0 0 0 1 0 0 0 0 0 0 0 0 27
28 32 0 0 0 0 1 0 0 0 0 0 0 0 28
29 33 0 0 0 0 0 1 0 0 0 0 0 0 29
30 35 0 0 0 0 0 0 1 0 0 0 0 0 30
31 33 0 0 0 0 0 0 0 1 0 0 0 0 31
32 36 0 0 0 0 0 0 0 0 1 0 0 0 32
33 30 0 0 0 0 0 0 0 0 0 1 0 0 33
34 34 0 0 0 0 0 0 0 0 0 0 1 0 34
35 34 0 0 0 0 0 0 0 0 0 0 0 1 35
36 35 0 0 0 0 0 0 0 0 0 0 0 0 36
37 33 0 1 0 0 0 0 0 0 0 0 0 0 37
38 28 0 0 1 0 0 0 0 0 0 0 0 0 38
39 27 0 0 0 1 0 0 0 0 0 0 0 0 39
40 23 0 0 0 0 1 0 0 0 0 0 0 0 40
41 23 0 0 0 0 0 1 0 0 0 0 0 0 41
42 24 0 0 0 0 0 0 1 0 0 0 0 0 42
43 24 0 0 0 0 0 0 0 1 0 0 0 0 43
44 20 0 0 0 0 0 0 0 0 1 0 0 0 44
45 16 1 0 0 0 0 0 0 0 0 1 0 0 45
46 6 1 0 0 0 0 0 0 0 0 0 1 0 46
47 2 1 0 0 0 0 0 0 0 0 0 0 1 47
48 12 1 0 0 0 0 0 0 0 0 0 0 0 48
49 19 1 1 0 0 0 0 0 0 0 0 0 0 49
50 21 1 0 1 0 0 0 0 0 0 0 0 0 50
51 22 1 0 0 1 0 0 0 0 0 0 0 0 51
52 20 1 0 0 0 1 0 0 0 0 0 0 0 52
53 21 1 0 0 0 0 1 0 0 0 0 0 0 53
54 20 1 0 0 0 0 0 1 0 0 0 0 0 54
55 19 1 0 0 0 0 0 0 1 0 0 0 0 55
56 17 1 0 0 0 0 0 0 0 1 0 0 0 56
57 17 1 0 0 0 0 0 0 0 0 1 0 0 57
58 17 1 0 0 0 0 0 0 0 0 0 1 0 58
59 16 1 0 0 0 0 0 0 0 0 0 0 1 59
60 12 1 0 0 0 0 0 0 0 0 0 0 0 60
61 11 1 1 0 0 0 0 0 0 0 0 0 0 61
62 7 1 0 1 0 0 0 0 0 0 0 0 0 62
63 2 1 0 0 1 0 0 0 0 0 0 0 0 63
64 9 1 0 0 0 1 0 0 0 0 0 0 0 64
65 11 1 0 0 0 0 1 0 0 0 0 0 0 65
66 10 1 0 0 0 0 0 1 0 0 0 0 0 66
67 7 1 0 0 0 0 0 0 1 0 0 0 0 67
68 9 1 0 0 0 0 0 0 0 1 0 0 0 68
69 15 1 0 0 0 0 0 0 0 0 1 0 0 69
70 5 1 0 0 0 0 0 0 0 0 0 1 0 70
71 14 1 0 0 0 0 0 0 0 0 0 0 1 71
72 14 1 0 0 0 0 0 0 0 0 0 0 0 72
73 17 1 1 0 0 0 0 0 0 0 0 0 0 73
74 19 1 0 1 0 0 0 0 0 0 0 0 0 74
75 17 1 0 0 1 0 0 0 0 0 0 0 0 75
76 16 1 0 0 0 1 0 0 0 0 0 0 0 76
77 14 1 0 0 0 0 1 0 0 0 0 0 0 77
78 20 1 0 0 0 0 0 1 0 0 0 0 0 78
79 16 1 0 0 0 0 0 0 1 0 0 0 0 79
80 18 1 0 0 0 0 0 0 0 1 0 0 0 80
81 18 1 0 0 0 0 0 0 0 0 1 0 0 81
82 14 1 0 0 0 0 0 0 0 0 0 1 0 82
83 13 1 0 0 0 0 0 0 0 0 0 0 1 83
84 14 1 0 0 0 0 0 0 0 0 0 0 0 84
85 14 1 1 0 0 0 0 0 0 0 0 0 0 85
86 17 1 0 1 0 0 0 0 0 0 0 0 0 86
87 18 1 0 0 1 0 0 0 0 0 0 0 0 87
88 15 1 0 0 0 1 0 0 0 0 0 0 0 88
89 9 1 0 0 0 0 1 0 0 0 0 0 0 89
90 9 1 0 0 0 0 0 1 0 0 0 0 0 90
91 9 1 0 0 0 0 0 0 1 0 0 0 0 91
92 10 1 0 0 0 0 0 0 0 1 0 0 0 92
93 6 1 0 0 0 0 0 0 0 0 1 0 0 93
94 12 1 0 0 0 0 0 0 0 0 0 1 0 94
95 11 1 0 0 0 0 0 0 0 0 0 0 1 95
96 15 1 0 0 0 0 0 0 0 0 0 0 0 96
97 19 1 1 0 0 0 0 0 0 0 0 0 0 97
98 18 1 0 1 0 0 0 0 0 0 0 0 0 98
99 15 1 0 0 1 0 0 0 0 0 0 0 0 99
100 16 1 0 0 0 1 0 0 0 0 0 0 0 100
101 14 1 0 0 0 0 1 0 0 0 0 0 0 101
102 18 1 0 0 0 0 0 1 0 0 0 0 0 102
103 18 1 0 0 0 0 0 0 1 0 0 0 0 103
104 18 1 0 0 0 0 0 0 0 1 0 0 0 104
105 18 1 0 0 0 0 0 0 0 0 1 0 0 105
106 22 1 0 0 0 0 0 0 0 0 0 1 0 106
107 21 1 0 0 0 0 0 0 0 0 0 0 1 107
108 12 1 0 0 0 0 0 0 0 0 0 0 0 108
109 19 1 1 0 0 0 0 0 0 0 0 0 0 109
110 21 1 0 1 0 0 0 0 0 0 0 0 0 110
111 19 1 0 0 1 0 0 0 0 0 0 0 0 111
112 22 1 0 0 0 1 0 0 0 0 0 0 0 112
113 22 1 0 0 0 0 1 0 0 0 0 0 0 113
114 21 1 0 0 0 0 0 1 0 0 0 0 0 114
115 19 1 0 0 0 0 0 0 1 0 0 0 0 115
116 18 1 0 0 0 0 0 0 0 1 0 0 0 116
117 18 1 0 0 0 0 0 0 0 0 1 0 0 117
118 19 1 0 0 0 0 0 0 0 0 0 1 0 118
119 12 1 0 0 0 0 0 0 0 0 0 0 1 119
120 16 1 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) Aanslag M1 M2 M3 M4
23.00007 -15.91558 2.11525 2.22372 1.03219 1.04066
M5 M6 M7 M8 M9 M10
0.54914 0.45761 -0.23392 -0.52545 0.57458 -0.71694
M11 t
-0.60847 0.09153
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.1052 -3.3260 -0.1779 3.3986 10.5965
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.00007 1.82991 12.569 < 2e-16 ***
Aanslag -15.91558 1.74764 -9.107 5.74e-15 ***
M1 2.11525 2.26561 0.934 0.352615
M2 2.22372 2.26443 0.982 0.328326
M3 1.03219 2.26352 0.456 0.649314
M4 1.04066 2.26287 0.460 0.646539
M5 0.54914 2.26247 0.243 0.808695
M6 0.45761 2.26234 0.202 0.840092
M7 -0.23392 2.26247 -0.103 0.917848
M8 -0.52545 2.26287 -0.232 0.816827
M9 0.57458 2.26147 0.254 0.799930
M10 -0.71694 2.26082 -0.317 0.751779
M11 -0.60847 2.26043 -0.269 0.788311
t 0.09153 0.02432 3.764 0.000275 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.054 on 106 degrees of freedom
Multiple R-squared: 0.5656, Adjusted R-squared: 0.5124
F-statistic: 10.62 on 13 and 106 DF, p-value: 4.425e-14
> 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,] 0.2100495 0.420099064 0.789950468
[2,] 0.6192179 0.761564289 0.380782144
[3,] 0.4911152 0.982230316 0.508884842
[4,] 0.3923648 0.784729534 0.607635233
[5,] 0.3238410 0.647681936 0.676159032
[6,] 0.4547942 0.909588363 0.545205818
[7,] 0.4570528 0.914105610 0.542947195
[8,] 0.4403574 0.880714735 0.559642633
[9,] 0.4048466 0.809693292 0.595153354
[10,] 0.3806363 0.761272501 0.619363749
[11,] 0.4132124 0.826424773 0.586787613
[12,] 0.3815252 0.763050386 0.618474807
[13,] 0.3480844 0.696168850 0.651915575
[14,] 0.4855689 0.971137843 0.514431078
[15,] 0.4619091 0.923818134 0.538090933
[16,] 0.5531143 0.893771348 0.446885674
[17,] 0.4810465 0.962092989 0.518953506
[18,] 0.4940963 0.988192595 0.505903703
[19,] 0.4847136 0.969427164 0.515286418
[20,] 0.5055325 0.988934998 0.494467499
[21,] 0.4858572 0.971714306 0.514142847
[22,] 0.5346292 0.930741532 0.465370766
[23,] 0.5862727 0.827454513 0.413727257
[24,] 0.6995486 0.600902726 0.300451363
[25,] 0.7824941 0.435011766 0.217505883
[26,] 0.7788547 0.442290579 0.221145289
[27,] 0.7889189 0.422162156 0.211081078
[28,] 0.8366053 0.326789353 0.163394677
[29,] 0.8016432 0.396713613 0.198356807
[30,] 0.8154930 0.369013904 0.184506952
[31,] 0.8804785 0.239043032 0.119521516
[32,] 0.8485757 0.302848633 0.151424317
[33,] 0.8550514 0.289897128 0.144948564
[34,] 0.8769337 0.246132588 0.123066294
[35,] 0.9177918 0.164416334 0.082208167
[36,] 0.9252940 0.149412011 0.074706005
[37,] 0.9471428 0.105714373 0.052857186
[38,] 0.9559183 0.088163350 0.044081675
[39,] 0.9653947 0.069210639 0.034605319
[40,] 0.9651016 0.069796767 0.034898384
[41,] 0.9645692 0.070861593 0.035430796
[42,] 0.9682655 0.063469082 0.031734541
[43,] 0.9707563 0.058487372 0.029243686
[44,] 0.9706583 0.058683383 0.029341692
[45,] 0.9729267 0.054146583 0.027073291
[46,] 0.9877494 0.024501179 0.012250590
[47,] 0.9986851 0.002629776 0.001314888
[48,] 0.9987458 0.002508393 0.001254196
[49,] 0.9982884 0.003423236 0.001711618
[50,] 0.9978628 0.004274484 0.002137242
[51,] 0.9981698 0.003660358 0.001830179
[52,] 0.9977683 0.004463440 0.002231720
[53,] 0.9968706 0.006258880 0.003129440
[54,] 0.9987200 0.002560093 0.001280047
[55,] 0.9980628 0.003874442 0.001937221
[56,] 0.9971939 0.005612270 0.002806135
[57,] 0.9957261 0.008547868 0.004273934
[58,] 0.9940560 0.011888012 0.005944006
[59,] 0.9913836 0.017232735 0.008616367
[60,] 0.9869037 0.026192608 0.013096304
[61,] 0.9814789 0.037042130 0.018521065
[62,] 0.9859768 0.028046352 0.014023176
[63,] 0.9832178 0.033564312 0.016782156
[64,] 0.9864438 0.027112348 0.013556174
[65,] 0.9935585 0.012882994 0.006441497
[66,] 0.9899738 0.020052460 0.010026230
[67,] 0.9880195 0.023960953 0.011980477
[68,] 0.9896807 0.020638641 0.010319320
[69,] 0.9839719 0.032056257 0.016028128
[70,] 0.9767666 0.046466815 0.023233408
[71,] 0.9796510 0.040698088 0.020349044
[72,] 0.9691002 0.061799646 0.030899823
[73,] 0.9633419 0.073316151 0.036658076
[74,] 0.9639157 0.072168551 0.036084276
[75,] 0.9607550 0.078489928 0.039244964
[76,] 0.9498624 0.100275250 0.050137625
[77,] 0.9785114 0.042977184 0.021488592
[78,] 0.9811964 0.037607231 0.018803616
[79,] 0.9746724 0.050655293 0.025327646
[80,] 0.9607385 0.078523004 0.039261502
[81,] 0.9324939 0.135012174 0.067506087
[82,] 0.8881508 0.223698437 0.111849218
[83,] 0.8319461 0.336107772 0.168053886
[84,] 0.7970339 0.405932228 0.202966114
[85,] 0.8494022 0.301195584 0.150597792
[86,] 0.7825592 0.434881523 0.217440761
[87,] 0.6480469 0.703906167 0.351953084
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ew3u1229608918.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/24gid1229608918.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3bklt1229608918.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4m03h1229608918.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5c2p21229608918.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 = 120
Frequency = 1
1 2 3 4 5 6
-6.2068478 -2.4068478 -2.3068478 -1.4068478 0.9931522 0.9931522
7 8 9 10 11 12
-0.4068478 -1.2068478 -3.3984058 -7.1984058 -2.3984058 -3.0984058
13 14 15 16 17 18
-0.3051812 -3.5051812 -3.4051812 -3.5051812 -3.1051812 -13.1051812
19 20 21 22 23 24
-4.5051812 -6.3051812 -2.4967391 0.7032609 3.5032609 2.8032609
25 26 27 28 29 30
1.5964855 3.3964855 6.4964855 5.3964855 6.7964855 8.7964855
31 32 33 34 35 36
7.3964855 10.5964855 3.4049275 8.6049275 8.4049275 8.7049275
37 38 39 40 41 42
4.4981522 -0.7018478 -0.6018478 -4.7018478 -4.3018478 -3.3018478
43 44 45 46 47 48
-2.7018478 -6.5018478 4.2221739 -4.5778261 -8.7778261 0.5221739
49 50 51 52 53 54
5.3153986 7.1153986 9.2153986 7.1153986 8.5153986 7.5153986
55 56 57 58 59 60
7.1153986 5.3153986 4.1238406 5.3238406 4.1238406 -0.5761594
61 62 63 64 65 66
-3.7829348 -7.9829348 -11.8829348 -4.9829348 -2.5829348 -3.5829348
67 68 69 70 71 72
-5.9829348 -3.7829348 1.0255072 -7.7744928 1.0255072 0.3255072
73 74 75 76 77 78
1.1187319 2.9187319 2.0187319 0.9187319 -0.6812681 5.3187319
79 80 81 82 83 84
1.9187319 4.1187319 2.9271739 0.1271739 -1.0728261 -0.7728261
85 86 87 88 89 90
-2.9796014 -0.1796014 1.9203986 -1.1796014 -6.7796014 -6.7796014
91 92 93 94 95 96
-6.1796014 -4.9796014 -10.1711594 -2.9711594 -4.1711594 -0.8711594
97 98 99 100 101 102
0.9220652 -0.2779348 -2.1779348 -1.2779348 -2.8779348 1.1220652
103 104 105 106 107 108
1.7220652 1.9220652 0.7305072 5.9305072 4.7305072 -4.9694928
109 110 111 112 113 114
-0.1762681 1.6237319 0.7237319 3.6237319 4.0237319 3.0237319
115 116 117 118 119 120
1.6237319 0.8237319 -0.3678261 1.8321739 -5.3678261 -2.0678261
> postscript(file="/var/www/html/freestat/rcomp/tmp/6l4cz1229608918.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 -6.2068478 NA
1 -2.4068478 -6.2068478
2 -2.3068478 -2.4068478
3 -1.4068478 -2.3068478
4 0.9931522 -1.4068478
5 0.9931522 0.9931522
6 -0.4068478 0.9931522
7 -1.2068478 -0.4068478
8 -3.3984058 -1.2068478
9 -7.1984058 -3.3984058
10 -2.3984058 -7.1984058
11 -3.0984058 -2.3984058
12 -0.3051812 -3.0984058
13 -3.5051812 -0.3051812
14 -3.4051812 -3.5051812
15 -3.5051812 -3.4051812
16 -3.1051812 -3.5051812
17 -13.1051812 -3.1051812
18 -4.5051812 -13.1051812
19 -6.3051812 -4.5051812
20 -2.4967391 -6.3051812
21 0.7032609 -2.4967391
22 3.5032609 0.7032609
23 2.8032609 3.5032609
24 1.5964855 2.8032609
25 3.3964855 1.5964855
26 6.4964855 3.3964855
27 5.3964855 6.4964855
28 6.7964855 5.3964855
29 8.7964855 6.7964855
30 7.3964855 8.7964855
31 10.5964855 7.3964855
32 3.4049275 10.5964855
33 8.6049275 3.4049275
34 8.4049275 8.6049275
35 8.7049275 8.4049275
36 4.4981522 8.7049275
37 -0.7018478 4.4981522
38 -0.6018478 -0.7018478
39 -4.7018478 -0.6018478
40 -4.3018478 -4.7018478
41 -3.3018478 -4.3018478
42 -2.7018478 -3.3018478
43 -6.5018478 -2.7018478
44 4.2221739 -6.5018478
45 -4.5778261 4.2221739
46 -8.7778261 -4.5778261
47 0.5221739 -8.7778261
48 5.3153986 0.5221739
49 7.1153986 5.3153986
50 9.2153986 7.1153986
51 7.1153986 9.2153986
52 8.5153986 7.1153986
53 7.5153986 8.5153986
54 7.1153986 7.5153986
55 5.3153986 7.1153986
56 4.1238406 5.3153986
57 5.3238406 4.1238406
58 4.1238406 5.3238406
59 -0.5761594 4.1238406
60 -3.7829348 -0.5761594
61 -7.9829348 -3.7829348
62 -11.8829348 -7.9829348
63 -4.9829348 -11.8829348
64 -2.5829348 -4.9829348
65 -3.5829348 -2.5829348
66 -5.9829348 -3.5829348
67 -3.7829348 -5.9829348
68 1.0255072 -3.7829348
69 -7.7744928 1.0255072
70 1.0255072 -7.7744928
71 0.3255072 1.0255072
72 1.1187319 0.3255072
73 2.9187319 1.1187319
74 2.0187319 2.9187319
75 0.9187319 2.0187319
76 -0.6812681 0.9187319
77 5.3187319 -0.6812681
78 1.9187319 5.3187319
79 4.1187319 1.9187319
80 2.9271739 4.1187319
81 0.1271739 2.9271739
82 -1.0728261 0.1271739
83 -0.7728261 -1.0728261
84 -2.9796014 -0.7728261
85 -0.1796014 -2.9796014
86 1.9203986 -0.1796014
87 -1.1796014 1.9203986
88 -6.7796014 -1.1796014
89 -6.7796014 -6.7796014
90 -6.1796014 -6.7796014
91 -4.9796014 -6.1796014
92 -10.1711594 -4.9796014
93 -2.9711594 -10.1711594
94 -4.1711594 -2.9711594
95 -0.8711594 -4.1711594
96 0.9220652 -0.8711594
97 -0.2779348 0.9220652
98 -2.1779348 -0.2779348
99 -1.2779348 -2.1779348
100 -2.8779348 -1.2779348
101 1.1220652 -2.8779348
102 1.7220652 1.1220652
103 1.9220652 1.7220652
104 0.7305072 1.9220652
105 5.9305072 0.7305072
106 4.7305072 5.9305072
107 -4.9694928 4.7305072
108 -0.1762681 -4.9694928
109 1.6237319 -0.1762681
110 0.7237319 1.6237319
111 3.6237319 0.7237319
112 4.0237319 3.6237319
113 3.0237319 4.0237319
114 1.6237319 3.0237319
115 0.8237319 1.6237319
116 -0.3678261 0.8237319
117 1.8321739 -0.3678261
118 -5.3678261 1.8321739
119 -2.0678261 -5.3678261
120 NA -2.0678261
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.4068478 -6.2068478
[2,] -2.3068478 -2.4068478
[3,] -1.4068478 -2.3068478
[4,] 0.9931522 -1.4068478
[5,] 0.9931522 0.9931522
[6,] -0.4068478 0.9931522
[7,] -1.2068478 -0.4068478
[8,] -3.3984058 -1.2068478
[9,] -7.1984058 -3.3984058
[10,] -2.3984058 -7.1984058
[11,] -3.0984058 -2.3984058
[12,] -0.3051812 -3.0984058
[13,] -3.5051812 -0.3051812
[14,] -3.4051812 -3.5051812
[15,] -3.5051812 -3.4051812
[16,] -3.1051812 -3.5051812
[17,] -13.1051812 -3.1051812
[18,] -4.5051812 -13.1051812
[19,] -6.3051812 -4.5051812
[20,] -2.4967391 -6.3051812
[21,] 0.7032609 -2.4967391
[22,] 3.5032609 0.7032609
[23,] 2.8032609 3.5032609
[24,] 1.5964855 2.8032609
[25,] 3.3964855 1.5964855
[26,] 6.4964855 3.3964855
[27,] 5.3964855 6.4964855
[28,] 6.7964855 5.3964855
[29,] 8.7964855 6.7964855
[30,] 7.3964855 8.7964855
[31,] 10.5964855 7.3964855
[32,] 3.4049275 10.5964855
[33,] 8.6049275 3.4049275
[34,] 8.4049275 8.6049275
[35,] 8.7049275 8.4049275
[36,] 4.4981522 8.7049275
[37,] -0.7018478 4.4981522
[38,] -0.6018478 -0.7018478
[39,] -4.7018478 -0.6018478
[40,] -4.3018478 -4.7018478
[41,] -3.3018478 -4.3018478
[42,] -2.7018478 -3.3018478
[43,] -6.5018478 -2.7018478
[44,] 4.2221739 -6.5018478
[45,] -4.5778261 4.2221739
[46,] -8.7778261 -4.5778261
[47,] 0.5221739 -8.7778261
[48,] 5.3153986 0.5221739
[49,] 7.1153986 5.3153986
[50,] 9.2153986 7.1153986
[51,] 7.1153986 9.2153986
[52,] 8.5153986 7.1153986
[53,] 7.5153986 8.5153986
[54,] 7.1153986 7.5153986
[55,] 5.3153986 7.1153986
[56,] 4.1238406 5.3153986
[57,] 5.3238406 4.1238406
[58,] 4.1238406 5.3238406
[59,] -0.5761594 4.1238406
[60,] -3.7829348 -0.5761594
[61,] -7.9829348 -3.7829348
[62,] -11.8829348 -7.9829348
[63,] -4.9829348 -11.8829348
[64,] -2.5829348 -4.9829348
[65,] -3.5829348 -2.5829348
[66,] -5.9829348 -3.5829348
[67,] -3.7829348 -5.9829348
[68,] 1.0255072 -3.7829348
[69,] -7.7744928 1.0255072
[70,] 1.0255072 -7.7744928
[71,] 0.3255072 1.0255072
[72,] 1.1187319 0.3255072
[73,] 2.9187319 1.1187319
[74,] 2.0187319 2.9187319
[75,] 0.9187319 2.0187319
[76,] -0.6812681 0.9187319
[77,] 5.3187319 -0.6812681
[78,] 1.9187319 5.3187319
[79,] 4.1187319 1.9187319
[80,] 2.9271739 4.1187319
[81,] 0.1271739 2.9271739
[82,] -1.0728261 0.1271739
[83,] -0.7728261 -1.0728261
[84,] -2.9796014 -0.7728261
[85,] -0.1796014 -2.9796014
[86,] 1.9203986 -0.1796014
[87,] -1.1796014 1.9203986
[88,] -6.7796014 -1.1796014
[89,] -6.7796014 -6.7796014
[90,] -6.1796014 -6.7796014
[91,] -4.9796014 -6.1796014
[92,] -10.1711594 -4.9796014
[93,] -2.9711594 -10.1711594
[94,] -4.1711594 -2.9711594
[95,] -0.8711594 -4.1711594
[96,] 0.9220652 -0.8711594
[97,] -0.2779348 0.9220652
[98,] -2.1779348 -0.2779348
[99,] -1.2779348 -2.1779348
[100,] -2.8779348 -1.2779348
[101,] 1.1220652 -2.8779348
[102,] 1.7220652 1.1220652
[103,] 1.9220652 1.7220652
[104,] 0.7305072 1.9220652
[105,] 5.9305072 0.7305072
[106,] 4.7305072 5.9305072
[107,] -4.9694928 4.7305072
[108,] -0.1762681 -4.9694928
[109,] 1.6237319 -0.1762681
[110,] 0.7237319 1.6237319
[111,] 3.6237319 0.7237319
[112,] 4.0237319 3.6237319
[113,] 3.0237319 4.0237319
[114,] 1.6237319 3.0237319
[115,] 0.8237319 1.6237319
[116,] -0.3678261 0.8237319
[117,] 1.8321739 -0.3678261
[118,] -5.3678261 1.8321739
[119,] -2.0678261 -5.3678261
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.4068478 -6.2068478
2 -2.3068478 -2.4068478
3 -1.4068478 -2.3068478
4 0.9931522 -1.4068478
5 0.9931522 0.9931522
6 -0.4068478 0.9931522
7 -1.2068478 -0.4068478
8 -3.3984058 -1.2068478
9 -7.1984058 -3.3984058
10 -2.3984058 -7.1984058
11 -3.0984058 -2.3984058
12 -0.3051812 -3.0984058
13 -3.5051812 -0.3051812
14 -3.4051812 -3.5051812
15 -3.5051812 -3.4051812
16 -3.1051812 -3.5051812
17 -13.1051812 -3.1051812
18 -4.5051812 -13.1051812
19 -6.3051812 -4.5051812
20 -2.4967391 -6.3051812
21 0.7032609 -2.4967391
22 3.5032609 0.7032609
23 2.8032609 3.5032609
24 1.5964855 2.8032609
25 3.3964855 1.5964855
26 6.4964855 3.3964855
27 5.3964855 6.4964855
28 6.7964855 5.3964855
29 8.7964855 6.7964855
30 7.3964855 8.7964855
31 10.5964855 7.3964855
32 3.4049275 10.5964855
33 8.6049275 3.4049275
34 8.4049275 8.6049275
35 8.7049275 8.4049275
36 4.4981522 8.7049275
37 -0.7018478 4.4981522
38 -0.6018478 -0.7018478
39 -4.7018478 -0.6018478
40 -4.3018478 -4.7018478
41 -3.3018478 -4.3018478
42 -2.7018478 -3.3018478
43 -6.5018478 -2.7018478
44 4.2221739 -6.5018478
45 -4.5778261 4.2221739
46 -8.7778261 -4.5778261
47 0.5221739 -8.7778261
48 5.3153986 0.5221739
49 7.1153986 5.3153986
50 9.2153986 7.1153986
51 7.1153986 9.2153986
52 8.5153986 7.1153986
53 7.5153986 8.5153986
54 7.1153986 7.5153986
55 5.3153986 7.1153986
56 4.1238406 5.3153986
57 5.3238406 4.1238406
58 4.1238406 5.3238406
59 -0.5761594 4.1238406
60 -3.7829348 -0.5761594
61 -7.9829348 -3.7829348
62 -11.8829348 -7.9829348
63 -4.9829348 -11.8829348
64 -2.5829348 -4.9829348
65 -3.5829348 -2.5829348
66 -5.9829348 -3.5829348
67 -3.7829348 -5.9829348
68 1.0255072 -3.7829348
69 -7.7744928 1.0255072
70 1.0255072 -7.7744928
71 0.3255072 1.0255072
72 1.1187319 0.3255072
73 2.9187319 1.1187319
74 2.0187319 2.9187319
75 0.9187319 2.0187319
76 -0.6812681 0.9187319
77 5.3187319 -0.6812681
78 1.9187319 5.3187319
79 4.1187319 1.9187319
80 2.9271739 4.1187319
81 0.1271739 2.9271739
82 -1.0728261 0.1271739
83 -0.7728261 -1.0728261
84 -2.9796014 -0.7728261
85 -0.1796014 -2.9796014
86 1.9203986 -0.1796014
87 -1.1796014 1.9203986
88 -6.7796014 -1.1796014
89 -6.7796014 -6.7796014
90 -6.1796014 -6.7796014
91 -4.9796014 -6.1796014
92 -10.1711594 -4.9796014
93 -2.9711594 -10.1711594
94 -4.1711594 -2.9711594
95 -0.8711594 -4.1711594
96 0.9220652 -0.8711594
97 -0.2779348 0.9220652
98 -2.1779348 -0.2779348
99 -1.2779348 -2.1779348
100 -2.8779348 -1.2779348
101 1.1220652 -2.8779348
102 1.7220652 1.1220652
103 1.9220652 1.7220652
104 0.7305072 1.9220652
105 5.9305072 0.7305072
106 4.7305072 5.9305072
107 -4.9694928 4.7305072
108 -0.1762681 -4.9694928
109 1.6237319 -0.1762681
110 0.7237319 1.6237319
111 3.6237319 0.7237319
112 4.0237319 3.6237319
113 3.0237319 4.0237319
114 1.6237319 3.0237319
115 0.8237319 1.6237319
116 -0.3678261 0.8237319
117 1.8321739 -0.3678261
118 -5.3678261 1.8321739
119 -2.0678261 -5.3678261
> 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/freestat/rcomp/tmp/7a7ni1229608918.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8mv281229608918.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9h8ee1229608918.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/1058it1229608918.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11ib4n1229608918.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/freestat/rcomp/tmp/12xga01229608918.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/freestat/rcomp/tmp/13br7m1229608918.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/freestat/rcomp/tmp/14fsu71229608918.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/freestat/rcomp/tmp/15u2z81229608918.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/freestat/rcomp/tmp/16g8a51229608918.tab")
+ }
>
> system("convert tmp/1ew3u1229608918.ps tmp/1ew3u1229608918.png")
> system("convert tmp/24gid1229608918.ps tmp/24gid1229608918.png")
> system("convert tmp/3bklt1229608918.ps tmp/3bklt1229608918.png")
> system("convert tmp/4m03h1229608918.ps tmp/4m03h1229608918.png")
> system("convert tmp/5c2p21229608918.ps tmp/5c2p21229608918.png")
> system("convert tmp/6l4cz1229608918.ps tmp/6l4cz1229608918.png")
> system("convert tmp/7a7ni1229608918.ps tmp/7a7ni1229608918.png")
> system("convert tmp/8mv281229608918.ps tmp/8mv281229608918.png")
> system("convert tmp/9h8ee1229608918.ps tmp/9h8ee1229608918.png")
> system("convert tmp/1058it1229608918.ps tmp/1058it1229608918.png")
>
>
> proc.time()
user system elapsed
4.656 2.598 5.159