R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0
+ ,1.3954
+ ,1.0685
+ ,0
+ ,1.4790
+ ,1.1010
+ ,0
+ ,1.4619
+ ,1.0996
+ ,0
+ ,1.4670
+ ,1.0978
+ ,0
+ ,1.4799
+ ,1.0893
+ ,0
+ ,1.4508
+ ,1.1018
+ ,0
+ ,1.4678
+ ,1.0931
+ ,0
+ ,1.4824
+ ,1.0842
+ ,0
+ ,1.5189
+ ,1.0409
+ ,0
+ ,1.5348
+ ,1.0245
+ ,0
+ ,1.5666
+ ,0.9994
+ ,0
+ ,1.5446
+ ,1.0090
+ ,0
+ ,1.5803
+ ,0.9947
+ ,0
+ ,1.5718
+ ,1.0080
+ ,0
+ ,1.5832
+ ,0.9986
+ ,0
+ ,1.5801
+ ,1.0184
+ ,0
+ ,1.5605
+ ,1.0357
+ ,0
+ ,1.5416
+ ,1.0556
+ ,0
+ ,1.5479
+ ,1.0409
+ ,0
+ ,1.5580
+ ,1.0474
+ ,0
+ ,1.5790
+ ,1.0219
+ ,0
+ ,1.5554
+ ,1.0427
+ ,0
+ ,1.5761
+ ,1.0205
+ ,0
+ ,1.5360
+ ,1.0490
+ ,0
+ ,1.5621
+ ,1.0344
+ ,0
+ ,1.5773
+ ,1.0193
+ ,0
+ ,1.5710
+ ,1.0238
+ ,0
+ ,1.5925
+ ,1.0165
+ ,0
+ ,1.5844
+ ,1.0218
+ ,0
+ ,1.5696
+ ,1.0370
+ ,0
+ ,1.5540
+ ,1.0508
+ ,0
+ ,1.5012
+ ,1.0813
+ ,0
+ ,1.4676
+ ,1.0970
+ ,0
+ ,1.4770
+ ,1.0989
+ ,0
+ ,1.4660
+ ,1.1018
+ ,0
+ ,1.4241
+ ,1.1166
+ ,0
+ ,1.4214
+ ,1.1319
+ ,1
+ ,1.4469
+ ,1.1020
+ ,1
+ ,1.4618
+ ,1.0884
+ ,1
+ ,1.3834
+ ,1.1263
+ ,1
+ ,1.3412
+ ,1.1345
+ ,1
+ ,1.3437
+ ,1.1337
+ ,1
+ ,1.2630
+ ,1.1660
+ ,1
+ ,1.2759
+ ,1.1550
+ ,1
+ ,1.2743
+ ,1.1782
+ ,1
+ ,1.2797
+ ,1.1856
+ ,1
+ ,1.2573
+ ,1.2219
+ ,1
+ ,1.2705
+ ,1.2130
+ ,1
+ ,1.2680
+ ,1.2230
+ ,1
+ ,1.3371
+ ,1.1767
+ ,1
+ ,1.3885
+ ,1.1077
+ ,1
+ ,1.4060
+ ,1.0672
+ ,1
+ ,1.3855
+ ,1.0840
+ ,1
+ ,1.3431
+ ,1.1154
+ ,1
+ ,1.3257
+ ,1.1184
+ ,1
+ ,1.2978
+ ,1.1570
+ ,1
+ ,1.2793
+ ,1.1625
+ ,1
+ ,1.2945
+ ,1.1627
+ ,1
+ ,1.2890
+ ,1.1578
+ ,1
+ ,1.2848
+ ,1.1533
+ ,1
+ ,1.2694
+ ,1.1684
+ ,1
+ ,1.2636
+ ,1.1597
+ ,1
+ ,1.2900
+ ,1.1888
+ ,1
+ ,1.3559
+ ,1.1296
+ ,1
+ ,1.3305
+ ,1.1424
+ ,1
+ ,1.3482
+ ,1.1317
+ ,1
+ ,1.3146
+ ,1.1581
+ ,1
+ ,1.3027
+ ,1.1672
+ ,1
+ ,1.3247
+ ,1.1391
+ ,1
+ ,1.3267
+ ,1.1357
+ ,1
+ ,1.3621
+ ,1.1065
+ ,1
+ ,1.3479
+ ,1.1232
+ ,1
+ ,1.4011
+ ,1.0845
+ ,1
+ ,1.4135
+ ,1.0676
+ ,1
+ ,1.3964
+ ,1.0863
+ ,1
+ ,1.4010
+ ,1.0792
+ ,1
+ ,1.3955
+ ,1.0799
+ ,1
+ ,1.4077
+ ,1.0817
+ ,1
+ ,1.3975
+ ,1.0869
+ ,1
+ ,1.3949
+ ,1.0843
+ ,1
+ ,1.4138
+ ,1.0747
+ ,1
+ ,1.4210
+ ,1.0711
+ ,1
+ ,1.4253
+ ,1.0688
+ ,1
+ ,1.4169
+ ,1.0828
+ ,1
+ ,1.4174
+ ,1.0746
+ ,1
+ ,1.4346
+ ,1.0568
+ ,1
+ ,1.4296
+ ,1.0600
+ ,1
+ ,1.4311
+ ,1.0593
+ ,1
+ ,1.4594
+ ,1.0370
+ ,1
+ ,1.4722
+ ,1.0288
+ ,1
+ ,1.4669
+ ,1.0295
+ ,1
+ ,1.4571
+ ,1.0352
+ ,1
+ ,1.4709
+ ,1.0324
+ ,1
+ ,1.4893
+ ,1.0186
+ ,1
+ ,1.4997
+ ,1.0094
+ ,1
+ ,1.4713
+ ,1.0258
+ ,1
+ ,1.4846
+ ,1.0170
+ ,1
+ ,1.4914
+ ,1.0117
+ ,1
+ ,1.4859
+ ,1.0175
+ ,1
+ ,1.4957
+ ,1.0064
+ ,1
+ ,1.4843
+ ,1.0168
+ ,1
+ ,1.4619
+ ,1.0340
+ ,1
+ ,1.4340
+ ,1.0423
+ ,1
+ ,1.4426
+ ,1.0356
+ ,1
+ ,1.4318
+ ,1.0348)
+ ,dim=c(3
+ ,105)
+ ,dimnames=list(c('Crisis'
+ ,'eu/us'
+ ,'us/ch')
+ ,1:105))
> y <- array(NA,dim=c(3,105),dimnames=list(c('Crisis','eu/us','us/ch'),1:105))
> 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 = 'Do not include Seasonal 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
> 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
eu/us Crisis us/ch t
1 1.3954 0 1.0685 1
2 1.4790 0 1.1010 2
3 1.4619 0 1.0996 3
4 1.4670 0 1.0978 4
5 1.4799 0 1.0893 5
6 1.4508 0 1.1018 6
7 1.4678 0 1.0931 7
8 1.4824 0 1.0842 8
9 1.5189 0 1.0409 9
10 1.5348 0 1.0245 10
11 1.5666 0 0.9994 11
12 1.5446 0 1.0090 12
13 1.5803 0 0.9947 13
14 1.5718 0 1.0080 14
15 1.5832 0 0.9986 15
16 1.5801 0 1.0184 16
17 1.5605 0 1.0357 17
18 1.5416 0 1.0556 18
19 1.5479 0 1.0409 19
20 1.5580 0 1.0474 20
21 1.5790 0 1.0219 21
22 1.5554 0 1.0427 22
23 1.5761 0 1.0205 23
24 1.5360 0 1.0490 24
25 1.5621 0 1.0344 25
26 1.5773 0 1.0193 26
27 1.5710 0 1.0238 27
28 1.5925 0 1.0165 28
29 1.5844 0 1.0218 29
30 1.5696 0 1.0370 30
31 1.5540 0 1.0508 31
32 1.5012 0 1.0813 32
33 1.4676 0 1.0970 33
34 1.4770 0 1.0989 34
35 1.4660 0 1.1018 35
36 1.4241 0 1.1166 36
37 1.4214 0 1.1319 37
38 1.4469 1 1.1020 38
39 1.4618 1 1.0884 39
40 1.3834 1 1.1263 40
41 1.3412 1 1.1345 41
42 1.3437 1 1.1337 42
43 1.2630 1 1.1660 43
44 1.2759 1 1.1550 44
45 1.2743 1 1.1782 45
46 1.2797 1 1.1856 46
47 1.2573 1 1.2219 47
48 1.2705 1 1.2130 48
49 1.2680 1 1.2230 49
50 1.3371 1 1.1767 50
51 1.3885 1 1.1077 51
52 1.4060 1 1.0672 52
53 1.3855 1 1.0840 53
54 1.3431 1 1.1154 54
55 1.3257 1 1.1184 55
56 1.2978 1 1.1570 56
57 1.2793 1 1.1625 57
58 1.2945 1 1.1627 58
59 1.2890 1 1.1578 59
60 1.2848 1 1.1533 60
61 1.2694 1 1.1684 61
62 1.2636 1 1.1597 62
63 1.2900 1 1.1888 63
64 1.3559 1 1.1296 64
65 1.3305 1 1.1424 65
66 1.3482 1 1.1317 66
67 1.3146 1 1.1581 67
68 1.3027 1 1.1672 68
69 1.3247 1 1.1391 69
70 1.3267 1 1.1357 70
71 1.3621 1 1.1065 71
72 1.3479 1 1.1232 72
73 1.4011 1 1.0845 73
74 1.4135 1 1.0676 74
75 1.3964 1 1.0863 75
76 1.4010 1 1.0792 76
77 1.3955 1 1.0799 77
78 1.4077 1 1.0817 78
79 1.3975 1 1.0869 79
80 1.3949 1 1.0843 80
81 1.4138 1 1.0747 81
82 1.4210 1 1.0711 82
83 1.4253 1 1.0688 83
84 1.4169 1 1.0828 84
85 1.4174 1 1.0746 85
86 1.4346 1 1.0568 86
87 1.4296 1 1.0600 87
88 1.4311 1 1.0593 88
89 1.4594 1 1.0370 89
90 1.4722 1 1.0288 90
91 1.4669 1 1.0295 91
92 1.4571 1 1.0352 92
93 1.4709 1 1.0324 93
94 1.4893 1 1.0186 94
95 1.4997 1 1.0094 95
96 1.4713 1 1.0258 96
97 1.4846 1 1.0170 97
98 1.4914 1 1.0117 98
99 1.4859 1 1.0175 99
100 1.4957 1 1.0064 100
101 1.4843 1 1.0168 101
102 1.4619 1 1.0340 102
103 1.4340 1 1.0423 103
104 1.4426 1 1.0356 104
105 1.4318 1 1.0348 105
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Crisis `us/ch` t
2.827e+00 -8.354e-02 -1.236e+00 -3.731e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.110513 -0.009947 0.001190 0.010808 0.067313
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.827e+00 6.246e-02 45.252 < 2e-16 ***
Crisis -8.354e-02 1.201e-02 -6.955 3.58e-10 ***
`us/ch` -1.236e+00 5.702e-02 -21.675 < 2e-16 ***
t -3.731e-05 1.743e-04 -0.214 0.831
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02238 on 101 degrees of freedom
Multiple R-squared: 0.9477, Adjusted R-squared: 0.9461
F-statistic: 609.8 on 3 and 101 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,] 0.7743834 4.512332e-01 2.256166e-01
[2,] 0.7579992 4.840015e-01 2.420008e-01
[3,] 0.9463346 1.073308e-01 5.366539e-02
[4,] 0.9387089 1.225822e-01 6.129110e-02
[5,] 0.9400037 1.199927e-01 5.999635e-02
[6,] 0.9339673 1.320655e-01 6.603274e-02
[7,] 0.9209803 1.580395e-01 7.901974e-02
[8,] 0.8932884 2.134232e-01 1.067116e-01
[9,] 0.8651373 2.697255e-01 1.348627e-01
[10,] 0.8183423 3.633154e-01 1.816577e-01
[11,] 0.8125168 3.749664e-01 1.874832e-01
[12,] 0.8256482 3.487035e-01 1.743518e-01
[13,] 0.8299120 3.401759e-01 1.700880e-01
[14,] 0.7875241 4.249517e-01 2.124759e-01
[15,] 0.7367594 5.264811e-01 2.632406e-01
[16,] 0.7150721 5.698559e-01 2.849279e-01
[17,] 0.6796747 6.406507e-01 3.203253e-01
[18,] 0.7362779 5.274443e-01 2.637221e-01
[19,] 0.7034149 5.931702e-01 2.965851e-01
[20,] 0.6653109 6.693782e-01 3.346891e-01
[21,] 0.6374504 7.250993e-01 3.625496e-01
[22,] 0.5739932 8.520135e-01 4.260068e-01
[23,] 0.5170343 9.659313e-01 4.829657e-01
[24,] 0.4722268 9.444535e-01 5.277732e-01
[25,] 0.4481024 8.962048e-01 5.518976e-01
[26,] 0.5320366 9.359267e-01 4.679634e-01
[27,] 0.6580660 6.838680e-01 3.419340e-01
[28,] 0.6583201 6.833599e-01 3.416799e-01
[29,] 0.6682808 6.634385e-01 3.317192e-01
[30,] 0.7656706 4.686589e-01 2.343294e-01
[31,] 0.7464380 5.071241e-01 2.535620e-01
[32,] 0.8278318 3.443364e-01 1.721682e-01
[33,] 0.9313940 1.372120e-01 6.860598e-02
[34,] 0.9640678 7.186446e-02 3.593223e-02
[35,] 0.9810772 3.784552e-02 1.892276e-02
[36,] 0.9855378 2.892441e-02 1.446221e-02
[37,] 0.9975567 4.886603e-03 2.443302e-03
[38,] 0.9994971 1.005767e-03 5.028835e-04
[39,] 0.9993225 1.354904e-03 6.774518e-04
[40,] 0.9988987 2.202600e-03 1.101300e-03
[41,] 0.9991032 1.793574e-03 8.967868e-04
[42,] 0.9993880 1.223987e-03 6.119936e-04
[43,] 0.9998743 2.514110e-04 1.257055e-04
[44,] 0.9999991 1.831881e-06 9.159407e-07
[45,] 0.9999996 7.209271e-07 3.604636e-07
[46,] 0.9999998 4.911790e-07 2.455895e-07
[47,] 0.9999998 4.814651e-07 2.407325e-07
[48,] 0.9999998 4.405329e-07 2.202664e-07
[49,] 1.0000000 6.361077e-08 3.180538e-08
[50,] 0.9999999 1.001756e-07 5.008780e-08
[51,] 1.0000000 6.250867e-08 3.125434e-08
[52,] 0.9999999 1.243125e-07 6.215627e-08
[53,] 0.9999999 1.149144e-07 5.745722e-08
[54,] 1.0000000 2.172678e-08 1.086339e-08
[55,] 1.0000000 6.507588e-09 3.253794e-09
[56,] 1.0000000 1.232907e-12 6.164537e-13
[57,] 1.0000000 2.082063e-13 1.041032e-13
[58,] 1.0000000 4.394774e-13 2.197387e-13
[59,] 1.0000000 1.563997e-12 7.819983e-13
[60,] 1.0000000 4.364771e-12 2.182386e-12
[61,] 1.0000000 6.641537e-12 3.320769e-12
[62,] 1.0000000 3.069310e-12 1.534655e-12
[63,] 1.0000000 1.092652e-11 5.463258e-12
[64,] 1.0000000 3.653641e-11 1.826820e-11
[65,] 1.0000000 5.554407e-11 2.777203e-11
[66,] 1.0000000 1.986375e-10 9.931874e-11
[67,] 1.0000000 6.649547e-10 3.324773e-10
[68,] 1.0000000 2.923059e-10 1.461529e-10
[69,] 1.0000000 8.310722e-10 4.155361e-10
[70,] 1.0000000 8.899629e-10 4.449815e-10
[71,] 1.0000000 1.708075e-10 8.540377e-11
[72,] 1.0000000 6.712442e-10 3.356221e-10
[73,] 1.0000000 2.391363e-09 1.195682e-09
[74,] 1.0000000 3.227667e-09 1.613833e-09
[75,] 1.0000000 9.306036e-09 4.653018e-09
[76,] 1.0000000 3.367737e-08 1.683868e-08
[77,] 0.9999999 1.298024e-07 6.490118e-08
[78,] 1.0000000 3.197538e-08 1.598769e-08
[79,] 1.0000000 4.729814e-08 2.364907e-08
[80,] 0.9999999 2.139938e-07 1.069969e-07
[81,] 0.9999996 8.899940e-07 4.449970e-07
[82,] 0.9999987 2.546400e-06 1.273200e-06
[83,] 0.9999949 1.019757e-05 5.098784e-06
[84,] 0.9999809 3.823272e-05 1.911636e-05
[85,] 0.9999535 9.292987e-05 4.646494e-05
[86,] 0.9999184 1.631090e-04 8.155450e-05
[87,] 0.9996773 6.453565e-04 3.226782e-04
[88,] 0.9987891 2.421831e-03 1.210916e-03
[89,] 0.9960489 7.902281e-03 3.951141e-03
[90,] 0.9902768 1.944649e-02 9.723246e-03
[91,] 0.9801831 3.963389e-02 1.981694e-02
[92,] 0.9626864 7.462727e-02 3.731364e-02
> postscript(file="/var/www/rcomp/tmp/1kque1290506793.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2kque1290506793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3kque1290506793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4czby1290506793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5czby1290506793.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 = 105
Frequency = 1
1 2 3 4 5
-1.105131e-01 1.329367e-02 -5.499394e-03 -2.586855e-03 -1.554074e-04
6 7 8 9 10
-1.376830e-02 -7.484049e-03 -3.846995e-03 -2.082778e-02 -2.516061e-02
11 12 13 14 15
-2.434649e-02 -3.444374e-02 -1.638099e-02 -8.405101e-03 -8.586038e-03
16 17 18 19 20
1.282375e-02 1.464358e-02 2.037697e-02 8.545315e-03 2.671652e-02
21 22 23 24 25
1.623624e-02 1.838202e-02 1.168049e-02 6.843333e-03 1.493528e-02
26 27 28 29 30
1.150923e-02 1.080847e-02 2.332310e-02 2.181112e-02 2.583539e-02
31 32 33 34 35
2.732927e-02 1.226409e-02 -1.893656e-03 9.892023e-03 2.513686e-03
36 37 38 39 40
-2.105644e-02 -4.808583e-03 6.731337e-02 6.544131e-02 3.392240e-02
41 42 43 44 45
1.894778e-03 3.443300e-03 -3.729711e-02 -3.795563e-02 -1.084349e-02
46 47 48 49 50
3.740098e-03 2.624362e-02 2.848067e-02 3.837782e-02 5.028908e-02
51 52 53 54 55
1.644351e-02 -1.607652e-02 -1.577469e-02 -1.932748e-02 -3.298222e-02
56 57 58 59 60
-1.313594e-02 -2.480072e-02 -9.316212e-03 -2.083522e-02 -3.055984e-02
61 62 63 64 65
-2.725917e-02 -4.377492e-02 1.862951e-02 1.139658e-02 1.854486e-03
66 67 68 69 70
6.366769e-03 5.434051e-03 4.818814e-03 -7.875021e-03 -1.004006e-02
71 72 73 74 75
-1.069347e-02 -4.215234e-03 1.189504e-03 -7.261313e-03 -1.211106e-03
76 77 78 79 80
-5.349281e-03 -9.946783e-03 4.515298e-03 7.797237e-04 -4.996524e-03
81 82 83 84 85
2.075341e-03 4.863109e-03 6.357657e-03 1.529874e-02 5.700982e-03
86 87 88 89 90
9.377807e-04 -6.976135e-05 6.023599e-04 1.377231e-03 4.079474e-03
91 92 93 94 95
-3.180275e-04 -3.035610e-03 7.340945e-03 8.721679e-03 7.787938e-03
96 97 98 99 100
-3.046182e-04 2.156034e-03 2.442630e-03 4.148646e-03 2.665357e-04
101 102 103 104 105
1.758077e-03 6.543080e-04 -1.694972e-02 -1.659350e-02 -2.834498e-02
> postscript(file="/var/www/rcomp/tmp/6czby1290506793.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 = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.105131e-01 NA
1 1.329367e-02 -1.105131e-01
2 -5.499394e-03 1.329367e-02
3 -2.586855e-03 -5.499394e-03
4 -1.554074e-04 -2.586855e-03
5 -1.376830e-02 -1.554074e-04
6 -7.484049e-03 -1.376830e-02
7 -3.846995e-03 -7.484049e-03
8 -2.082778e-02 -3.846995e-03
9 -2.516061e-02 -2.082778e-02
10 -2.434649e-02 -2.516061e-02
11 -3.444374e-02 -2.434649e-02
12 -1.638099e-02 -3.444374e-02
13 -8.405101e-03 -1.638099e-02
14 -8.586038e-03 -8.405101e-03
15 1.282375e-02 -8.586038e-03
16 1.464358e-02 1.282375e-02
17 2.037697e-02 1.464358e-02
18 8.545315e-03 2.037697e-02
19 2.671652e-02 8.545315e-03
20 1.623624e-02 2.671652e-02
21 1.838202e-02 1.623624e-02
22 1.168049e-02 1.838202e-02
23 6.843333e-03 1.168049e-02
24 1.493528e-02 6.843333e-03
25 1.150923e-02 1.493528e-02
26 1.080847e-02 1.150923e-02
27 2.332310e-02 1.080847e-02
28 2.181112e-02 2.332310e-02
29 2.583539e-02 2.181112e-02
30 2.732927e-02 2.583539e-02
31 1.226409e-02 2.732927e-02
32 -1.893656e-03 1.226409e-02
33 9.892023e-03 -1.893656e-03
34 2.513686e-03 9.892023e-03
35 -2.105644e-02 2.513686e-03
36 -4.808583e-03 -2.105644e-02
37 6.731337e-02 -4.808583e-03
38 6.544131e-02 6.731337e-02
39 3.392240e-02 6.544131e-02
40 1.894778e-03 3.392240e-02
41 3.443300e-03 1.894778e-03
42 -3.729711e-02 3.443300e-03
43 -3.795563e-02 -3.729711e-02
44 -1.084349e-02 -3.795563e-02
45 3.740098e-03 -1.084349e-02
46 2.624362e-02 3.740098e-03
47 2.848067e-02 2.624362e-02
48 3.837782e-02 2.848067e-02
49 5.028908e-02 3.837782e-02
50 1.644351e-02 5.028908e-02
51 -1.607652e-02 1.644351e-02
52 -1.577469e-02 -1.607652e-02
53 -1.932748e-02 -1.577469e-02
54 -3.298222e-02 -1.932748e-02
55 -1.313594e-02 -3.298222e-02
56 -2.480072e-02 -1.313594e-02
57 -9.316212e-03 -2.480072e-02
58 -2.083522e-02 -9.316212e-03
59 -3.055984e-02 -2.083522e-02
60 -2.725917e-02 -3.055984e-02
61 -4.377492e-02 -2.725917e-02
62 1.862951e-02 -4.377492e-02
63 1.139658e-02 1.862951e-02
64 1.854486e-03 1.139658e-02
65 6.366769e-03 1.854486e-03
66 5.434051e-03 6.366769e-03
67 4.818814e-03 5.434051e-03
68 -7.875021e-03 4.818814e-03
69 -1.004006e-02 -7.875021e-03
70 -1.069347e-02 -1.004006e-02
71 -4.215234e-03 -1.069347e-02
72 1.189504e-03 -4.215234e-03
73 -7.261313e-03 1.189504e-03
74 -1.211106e-03 -7.261313e-03
75 -5.349281e-03 -1.211106e-03
76 -9.946783e-03 -5.349281e-03
77 4.515298e-03 -9.946783e-03
78 7.797237e-04 4.515298e-03
79 -4.996524e-03 7.797237e-04
80 2.075341e-03 -4.996524e-03
81 4.863109e-03 2.075341e-03
82 6.357657e-03 4.863109e-03
83 1.529874e-02 6.357657e-03
84 5.700982e-03 1.529874e-02
85 9.377807e-04 5.700982e-03
86 -6.976135e-05 9.377807e-04
87 6.023599e-04 -6.976135e-05
88 1.377231e-03 6.023599e-04
89 4.079474e-03 1.377231e-03
90 -3.180275e-04 4.079474e-03
91 -3.035610e-03 -3.180275e-04
92 7.340945e-03 -3.035610e-03
93 8.721679e-03 7.340945e-03
94 7.787938e-03 8.721679e-03
95 -3.046182e-04 7.787938e-03
96 2.156034e-03 -3.046182e-04
97 2.442630e-03 2.156034e-03
98 4.148646e-03 2.442630e-03
99 2.665357e-04 4.148646e-03
100 1.758077e-03 2.665357e-04
101 6.543080e-04 1.758077e-03
102 -1.694972e-02 6.543080e-04
103 -1.659350e-02 -1.694972e-02
104 -2.834498e-02 -1.659350e-02
105 NA -2.834498e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.329367e-02 -1.105131e-01
[2,] -5.499394e-03 1.329367e-02
[3,] -2.586855e-03 -5.499394e-03
[4,] -1.554074e-04 -2.586855e-03
[5,] -1.376830e-02 -1.554074e-04
[6,] -7.484049e-03 -1.376830e-02
[7,] -3.846995e-03 -7.484049e-03
[8,] -2.082778e-02 -3.846995e-03
[9,] -2.516061e-02 -2.082778e-02
[10,] -2.434649e-02 -2.516061e-02
[11,] -3.444374e-02 -2.434649e-02
[12,] -1.638099e-02 -3.444374e-02
[13,] -8.405101e-03 -1.638099e-02
[14,] -8.586038e-03 -8.405101e-03
[15,] 1.282375e-02 -8.586038e-03
[16,] 1.464358e-02 1.282375e-02
[17,] 2.037697e-02 1.464358e-02
[18,] 8.545315e-03 2.037697e-02
[19,] 2.671652e-02 8.545315e-03
[20,] 1.623624e-02 2.671652e-02
[21,] 1.838202e-02 1.623624e-02
[22,] 1.168049e-02 1.838202e-02
[23,] 6.843333e-03 1.168049e-02
[24,] 1.493528e-02 6.843333e-03
[25,] 1.150923e-02 1.493528e-02
[26,] 1.080847e-02 1.150923e-02
[27,] 2.332310e-02 1.080847e-02
[28,] 2.181112e-02 2.332310e-02
[29,] 2.583539e-02 2.181112e-02
[30,] 2.732927e-02 2.583539e-02
[31,] 1.226409e-02 2.732927e-02
[32,] -1.893656e-03 1.226409e-02
[33,] 9.892023e-03 -1.893656e-03
[34,] 2.513686e-03 9.892023e-03
[35,] -2.105644e-02 2.513686e-03
[36,] -4.808583e-03 -2.105644e-02
[37,] 6.731337e-02 -4.808583e-03
[38,] 6.544131e-02 6.731337e-02
[39,] 3.392240e-02 6.544131e-02
[40,] 1.894778e-03 3.392240e-02
[41,] 3.443300e-03 1.894778e-03
[42,] -3.729711e-02 3.443300e-03
[43,] -3.795563e-02 -3.729711e-02
[44,] -1.084349e-02 -3.795563e-02
[45,] 3.740098e-03 -1.084349e-02
[46,] 2.624362e-02 3.740098e-03
[47,] 2.848067e-02 2.624362e-02
[48,] 3.837782e-02 2.848067e-02
[49,] 5.028908e-02 3.837782e-02
[50,] 1.644351e-02 5.028908e-02
[51,] -1.607652e-02 1.644351e-02
[52,] -1.577469e-02 -1.607652e-02
[53,] -1.932748e-02 -1.577469e-02
[54,] -3.298222e-02 -1.932748e-02
[55,] -1.313594e-02 -3.298222e-02
[56,] -2.480072e-02 -1.313594e-02
[57,] -9.316212e-03 -2.480072e-02
[58,] -2.083522e-02 -9.316212e-03
[59,] -3.055984e-02 -2.083522e-02
[60,] -2.725917e-02 -3.055984e-02
[61,] -4.377492e-02 -2.725917e-02
[62,] 1.862951e-02 -4.377492e-02
[63,] 1.139658e-02 1.862951e-02
[64,] 1.854486e-03 1.139658e-02
[65,] 6.366769e-03 1.854486e-03
[66,] 5.434051e-03 6.366769e-03
[67,] 4.818814e-03 5.434051e-03
[68,] -7.875021e-03 4.818814e-03
[69,] -1.004006e-02 -7.875021e-03
[70,] -1.069347e-02 -1.004006e-02
[71,] -4.215234e-03 -1.069347e-02
[72,] 1.189504e-03 -4.215234e-03
[73,] -7.261313e-03 1.189504e-03
[74,] -1.211106e-03 -7.261313e-03
[75,] -5.349281e-03 -1.211106e-03
[76,] -9.946783e-03 -5.349281e-03
[77,] 4.515298e-03 -9.946783e-03
[78,] 7.797237e-04 4.515298e-03
[79,] -4.996524e-03 7.797237e-04
[80,] 2.075341e-03 -4.996524e-03
[81,] 4.863109e-03 2.075341e-03
[82,] 6.357657e-03 4.863109e-03
[83,] 1.529874e-02 6.357657e-03
[84,] 5.700982e-03 1.529874e-02
[85,] 9.377807e-04 5.700982e-03
[86,] -6.976135e-05 9.377807e-04
[87,] 6.023599e-04 -6.976135e-05
[88,] 1.377231e-03 6.023599e-04
[89,] 4.079474e-03 1.377231e-03
[90,] -3.180275e-04 4.079474e-03
[91,] -3.035610e-03 -3.180275e-04
[92,] 7.340945e-03 -3.035610e-03
[93,] 8.721679e-03 7.340945e-03
[94,] 7.787938e-03 8.721679e-03
[95,] -3.046182e-04 7.787938e-03
[96,] 2.156034e-03 -3.046182e-04
[97,] 2.442630e-03 2.156034e-03
[98,] 4.148646e-03 2.442630e-03
[99,] 2.665357e-04 4.148646e-03
[100,] 1.758077e-03 2.665357e-04
[101,] 6.543080e-04 1.758077e-03
[102,] -1.694972e-02 6.543080e-04
[103,] -1.659350e-02 -1.694972e-02
[104,] -2.834498e-02 -1.659350e-02
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.329367e-02 -1.105131e-01
2 -5.499394e-03 1.329367e-02
3 -2.586855e-03 -5.499394e-03
4 -1.554074e-04 -2.586855e-03
5 -1.376830e-02 -1.554074e-04
6 -7.484049e-03 -1.376830e-02
7 -3.846995e-03 -7.484049e-03
8 -2.082778e-02 -3.846995e-03
9 -2.516061e-02 -2.082778e-02
10 -2.434649e-02 -2.516061e-02
11 -3.444374e-02 -2.434649e-02
12 -1.638099e-02 -3.444374e-02
13 -8.405101e-03 -1.638099e-02
14 -8.586038e-03 -8.405101e-03
15 1.282375e-02 -8.586038e-03
16 1.464358e-02 1.282375e-02
17 2.037697e-02 1.464358e-02
18 8.545315e-03 2.037697e-02
19 2.671652e-02 8.545315e-03
20 1.623624e-02 2.671652e-02
21 1.838202e-02 1.623624e-02
22 1.168049e-02 1.838202e-02
23 6.843333e-03 1.168049e-02
24 1.493528e-02 6.843333e-03
25 1.150923e-02 1.493528e-02
26 1.080847e-02 1.150923e-02
27 2.332310e-02 1.080847e-02
28 2.181112e-02 2.332310e-02
29 2.583539e-02 2.181112e-02
30 2.732927e-02 2.583539e-02
31 1.226409e-02 2.732927e-02
32 -1.893656e-03 1.226409e-02
33 9.892023e-03 -1.893656e-03
34 2.513686e-03 9.892023e-03
35 -2.105644e-02 2.513686e-03
36 -4.808583e-03 -2.105644e-02
37 6.731337e-02 -4.808583e-03
38 6.544131e-02 6.731337e-02
39 3.392240e-02 6.544131e-02
40 1.894778e-03 3.392240e-02
41 3.443300e-03 1.894778e-03
42 -3.729711e-02 3.443300e-03
43 -3.795563e-02 -3.729711e-02
44 -1.084349e-02 -3.795563e-02
45 3.740098e-03 -1.084349e-02
46 2.624362e-02 3.740098e-03
47 2.848067e-02 2.624362e-02
48 3.837782e-02 2.848067e-02
49 5.028908e-02 3.837782e-02
50 1.644351e-02 5.028908e-02
51 -1.607652e-02 1.644351e-02
52 -1.577469e-02 -1.607652e-02
53 -1.932748e-02 -1.577469e-02
54 -3.298222e-02 -1.932748e-02
55 -1.313594e-02 -3.298222e-02
56 -2.480072e-02 -1.313594e-02
57 -9.316212e-03 -2.480072e-02
58 -2.083522e-02 -9.316212e-03
59 -3.055984e-02 -2.083522e-02
60 -2.725917e-02 -3.055984e-02
61 -4.377492e-02 -2.725917e-02
62 1.862951e-02 -4.377492e-02
63 1.139658e-02 1.862951e-02
64 1.854486e-03 1.139658e-02
65 6.366769e-03 1.854486e-03
66 5.434051e-03 6.366769e-03
67 4.818814e-03 5.434051e-03
68 -7.875021e-03 4.818814e-03
69 -1.004006e-02 -7.875021e-03
70 -1.069347e-02 -1.004006e-02
71 -4.215234e-03 -1.069347e-02
72 1.189504e-03 -4.215234e-03
73 -7.261313e-03 1.189504e-03
74 -1.211106e-03 -7.261313e-03
75 -5.349281e-03 -1.211106e-03
76 -9.946783e-03 -5.349281e-03
77 4.515298e-03 -9.946783e-03
78 7.797237e-04 4.515298e-03
79 -4.996524e-03 7.797237e-04
80 2.075341e-03 -4.996524e-03
81 4.863109e-03 2.075341e-03
82 6.357657e-03 4.863109e-03
83 1.529874e-02 6.357657e-03
84 5.700982e-03 1.529874e-02
85 9.377807e-04 5.700982e-03
86 -6.976135e-05 9.377807e-04
87 6.023599e-04 -6.976135e-05
88 1.377231e-03 6.023599e-04
89 4.079474e-03 1.377231e-03
90 -3.180275e-04 4.079474e-03
91 -3.035610e-03 -3.180275e-04
92 7.340945e-03 -3.035610e-03
93 8.721679e-03 7.340945e-03
94 7.787938e-03 8.721679e-03
95 -3.046182e-04 7.787938e-03
96 2.156034e-03 -3.046182e-04
97 2.442630e-03 2.156034e-03
98 4.148646e-03 2.442630e-03
99 2.665357e-04 4.148646e-03
100 1.758077e-03 2.665357e-04
101 6.543080e-04 1.758077e-03
102 -1.694972e-02 6.543080e-04
103 -1.659350e-02 -1.694972e-02
104 -2.834498e-02 -1.659350e-02
> 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/rcomp/tmp/7nqs11290506793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8yh9m1290506793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9yh9m1290506793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10yh9m1290506793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11ur7v1290506793.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/rcomp/tmp/12mi6y1290506793.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/rcomp/tmp/13b13a1290506793.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/rcomp/tmp/14xkky1290506793.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/rcomp/tmp/150k0l1290506793.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/rcomp/tmp/163lzr1290506793.tab")
+ }
>
> try(system("convert tmp/1kque1290506793.ps tmp/1kque1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kque1290506793.ps tmp/2kque1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kque1290506793.ps tmp/3kque1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/4czby1290506793.ps tmp/4czby1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/5czby1290506793.ps tmp/5czby1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/6czby1290506793.ps tmp/6czby1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nqs11290506793.ps tmp/7nqs11290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yh9m1290506793.ps tmp/8yh9m1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yh9m1290506793.ps tmp/9yh9m1290506793.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yh9m1290506793.ps tmp/10yh9m1290506793.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.370 2.010 6.417