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.
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(627,216.234,696,213.586,825,209.465,677,204.045,656,200.237,785,203.666,412,241.476,352,260.307,839,243.324,729,244.460,696,233.575,641,237.217,695,235.243,638,230.354,762,227.184,635,221.678,721,217.142,854,219.452,418,256.446,367,265.845,824,248.624,687,241.114,601,229.245,676,231.805,740,219.277,691,219.313,683,212.610,594,214.771,729,211.142,731,211.457,386,240.048,331,240.636,707,230.580,715,208.795,657,197.922,653,194.596,642,194.581,643,185.686,718,178.106,654,172.608,632,167.302,731,168.053,392,202.300,344,202.388,792,182.516,852,173.476,649,166.444,629,171.297,685,169.701,617,164.182,715,161.914,715,159.612,629,151.001,916,158.114,531,186.530,357,187.069,917,174.330,828,169.362,708,166.827,858,178.037,775,186.413,785,189.226,1006,191.563,789,188.906,734,186.005,906,195.309,532,223.532,387,226.899,991,214.126,841,206.903,892,204.442,782,220.375),dim=c(2,72),dimnames=list(c('faillissementen','werklozen'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('faillissementen','werklozen'),1:72))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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
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
werklozen faillissementen
1 216.234 627
2 213.586 696
3 209.465 825
4 204.045 677
5 200.237 656
6 203.666 785
7 241.476 412
8 260.307 352
9 243.324 839
10 244.460 729
11 233.575 696
12 237.217 641
13 235.243 695
14 230.354 638
15 227.184 762
16 221.678 635
17 217.142 721
18 219.452 854
19 256.446 418
20 265.845 367
21 248.624 824
22 241.114 687
23 229.245 601
24 231.805 676
25 219.277 740
26 219.313 691
27 212.610 683
28 214.771 594
29 211.142 729
30 211.457 731
31 240.048 386
32 240.636 331
33 230.580 707
34 208.795 715
35 197.922 657
36 194.596 653
37 194.581 642
38 185.686 643
39 178.106 718
40 172.608 654
41 167.302 632
42 168.053 731
43 202.300 392
44 202.388 344
45 182.516 792
46 173.476 852
47 166.444 649
48 171.297 629
49 169.701 685
50 164.182 617
51 161.914 715
52 159.612 715
53 151.001 629
54 158.114 916
55 186.530 531
56 187.069 357
57 174.330 917
58 169.362 828
59 166.827 708
60 178.037 858
61 186.413 775
62 189.226 785
63 191.563 1006
64 188.906 789
65 186.005 734
66 195.309 906
67 223.532 532
68 226.899 387
69 214.126 991
70 206.903 841
71 204.442 892
72 220.375 782
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) faillissementen
245.83503 -0.06004
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-57.070 -21.291 5.424 19.714 52.261
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 245.83503 14.08555 17.453 < 2e-16 ***
faillissementen -0.06004 0.02018 -2.976 0.00401 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 26.61 on 70 degrees of freedom
Multiple R-squared: 0.1123, Adjusted R-squared: 0.09961
F-statistic: 8.855 on 1 and 70 DF, p-value: 0.00401
> 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,] 0.027725378 0.055450756 0.972274622
[2,] 0.007319096 0.014638193 0.992680904
[3,] 0.009269663 0.018539326 0.990730337
[4,] 0.008821153 0.017642306 0.991178847
[5,] 0.140847131 0.281694262 0.859152869
[6,] 0.180619599 0.361239197 0.819380401
[7,] 0.137167990 0.274335979 0.862832010
[8,] 0.102719038 0.205438075 0.897280962
[9,] 0.078802218 0.157604436 0.921197782
[10,] 0.051258099 0.102516198 0.948741901
[11,] 0.035105662 0.070211324 0.964894338
[12,] 0.022261646 0.044523291 0.977738354
[13,] 0.013637906 0.027275811 0.986362094
[14,] 0.008751927 0.017503855 0.991248073
[15,] 0.008352554 0.016705108 0.991647446
[16,] 0.010907971 0.021815942 0.989092029
[17,] 0.039908898 0.079817795 0.960091102
[18,] 0.045507402 0.091014804 0.954492598
[19,] 0.037543113 0.075086226 0.962456887
[20,] 0.034254769 0.068509537 0.965745231
[21,] 0.028272537 0.056545073 0.971727463
[22,] 0.024187495 0.048374991 0.975812505
[23,] 0.022761489 0.045522978 0.977238511
[24,] 0.023867677 0.047735354 0.976132323
[25,] 0.021216879 0.042433759 0.978783121
[26,] 0.018811142 0.037622284 0.981188858
[27,] 0.022295244 0.044590489 0.977704756
[28,] 0.033132477 0.066264953 0.966867523
[29,] 0.052728753 0.105457505 0.947271247
[30,] 0.057253402 0.114506803 0.942746598
[31,] 0.083199662 0.166399324 0.916800338
[32,] 0.119104190 0.238208380 0.880895810
[33,] 0.155180250 0.310360501 0.844819750
[34,] 0.226578888 0.453157777 0.773421112
[35,] 0.306306642 0.612613285 0.693693358
[36,] 0.446083373 0.892166746 0.553916627
[37,] 0.614511768 0.770976463 0.385488232
[38,] 0.689787682 0.620424637 0.310212318
[39,] 0.692671005 0.614657990 0.307328995
[40,] 0.692665184 0.614669632 0.307334816
[41,] 0.661573534 0.676852932 0.338426466
[42,] 0.647164319 0.705671362 0.352835681
[43,] 0.705091560 0.589816880 0.294908440
[44,] 0.725616890 0.548766220 0.274383110
[45,] 0.738074646 0.523850709 0.261925354
[46,] 0.788670244 0.422659512 0.211329756
[47,] 0.828034144 0.343931712 0.171965856
[48,] 0.874812474 0.250375052 0.125187526
[49,] 0.958584883 0.082830234 0.041415117
[50,] 0.972927060 0.054145881 0.027072940
[51,] 0.964910804 0.070178392 0.035089196
[52,] 0.968173519 0.063652962 0.031826481
[53,] 0.958395594 0.083208813 0.041604406
[54,] 0.965934011 0.068131978 0.034065989
[55,] 0.990036251 0.019927497 0.009963749
[56,] 0.990481252 0.019037495 0.009518748
[57,] 0.988889660 0.022220680 0.011110340
[58,] 0.985519347 0.028961306 0.014480653
[59,] 0.969562794 0.060874413 0.030437206
[60,] 0.967464224 0.065071551 0.032535776
[61,] 0.993669935 0.012660131 0.006330065
[62,] 0.995095232 0.009809536 0.004904768
[63,] 0.976931150 0.046137699 0.023068850
> postscript(file="/var/www/html/rcomp/tmp/12gu41291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22gu41291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3c7b71291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4c7b71291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c7b71291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 72
Frequency = 1
1 2 3 4 5 6 7
8.042975 9.537617 13.161556 -1.144110 -6.212915 4.961025 20.376743
8 9 10 11 12 13 14
35.605446 47.861092 42.392880 29.526617 29.866511 31.134579 22.823396
15 16 17 18 19 20 21
27.098144 13.967281 14.594574 24.889666 35.706973 42.044021 52.260518
22 23 24 25 26 27 28
36.525272 19.492980 26.555851 17.870302 14.964426 7.781119 4.598712
29 30 31 32 33 34 35
9.074880 9.509957 17.387748 14.673642 27.192038 5.887344 -8.467876
36 37 38 39 40 41 42
-12.034029 -12.709451 -21.544412 -24.621541 -33.961991 -40.588833 -33.894043
43 44 45 46 47 48 49
-20.000022 -22.793860 -15.768707 -21.206410 -40.426183 -36.773948 -35.007804
50 51 52 53 54 55 56
-44.609408 -40.993656 -43.295656 -57.069948 -32.725960 -27.424700 -37.332362
57 58 59 60 61 62 63
-16.449922 -26.761329 -36.500924 -16.285181 -12.892358 -9.478975 6.126486
64 65 66 67 68 69 70
-9.558822 -15.761928 3.868657 9.637338 4.298786 27.788912 11.560169
71 72
12.161121 21.489910
> postscript(file="/var/www/html/rcomp/tmp/6nysa1291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 8.042975 NA
1 9.537617 8.042975
2 13.161556 9.537617
3 -1.144110 13.161556
4 -6.212915 -1.144110
5 4.961025 -6.212915
6 20.376743 4.961025
7 35.605446 20.376743
8 47.861092 35.605446
9 42.392880 47.861092
10 29.526617 42.392880
11 29.866511 29.526617
12 31.134579 29.866511
13 22.823396 31.134579
14 27.098144 22.823396
15 13.967281 27.098144
16 14.594574 13.967281
17 24.889666 14.594574
18 35.706973 24.889666
19 42.044021 35.706973
20 52.260518 42.044021
21 36.525272 52.260518
22 19.492980 36.525272
23 26.555851 19.492980
24 17.870302 26.555851
25 14.964426 17.870302
26 7.781119 14.964426
27 4.598712 7.781119
28 9.074880 4.598712
29 9.509957 9.074880
30 17.387748 9.509957
31 14.673642 17.387748
32 27.192038 14.673642
33 5.887344 27.192038
34 -8.467876 5.887344
35 -12.034029 -8.467876
36 -12.709451 -12.034029
37 -21.544412 -12.709451
38 -24.621541 -21.544412
39 -33.961991 -24.621541
40 -40.588833 -33.961991
41 -33.894043 -40.588833
42 -20.000022 -33.894043
43 -22.793860 -20.000022
44 -15.768707 -22.793860
45 -21.206410 -15.768707
46 -40.426183 -21.206410
47 -36.773948 -40.426183
48 -35.007804 -36.773948
49 -44.609408 -35.007804
50 -40.993656 -44.609408
51 -43.295656 -40.993656
52 -57.069948 -43.295656
53 -32.725960 -57.069948
54 -27.424700 -32.725960
55 -37.332362 -27.424700
56 -16.449922 -37.332362
57 -26.761329 -16.449922
58 -36.500924 -26.761329
59 -16.285181 -36.500924
60 -12.892358 -16.285181
61 -9.478975 -12.892358
62 6.126486 -9.478975
63 -9.558822 6.126486
64 -15.761928 -9.558822
65 3.868657 -15.761928
66 9.637338 3.868657
67 4.298786 9.637338
68 27.788912 4.298786
69 11.560169 27.788912
70 12.161121 11.560169
71 21.489910 12.161121
72 NA 21.489910
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.537617 8.042975
[2,] 13.161556 9.537617
[3,] -1.144110 13.161556
[4,] -6.212915 -1.144110
[5,] 4.961025 -6.212915
[6,] 20.376743 4.961025
[7,] 35.605446 20.376743
[8,] 47.861092 35.605446
[9,] 42.392880 47.861092
[10,] 29.526617 42.392880
[11,] 29.866511 29.526617
[12,] 31.134579 29.866511
[13,] 22.823396 31.134579
[14,] 27.098144 22.823396
[15,] 13.967281 27.098144
[16,] 14.594574 13.967281
[17,] 24.889666 14.594574
[18,] 35.706973 24.889666
[19,] 42.044021 35.706973
[20,] 52.260518 42.044021
[21,] 36.525272 52.260518
[22,] 19.492980 36.525272
[23,] 26.555851 19.492980
[24,] 17.870302 26.555851
[25,] 14.964426 17.870302
[26,] 7.781119 14.964426
[27,] 4.598712 7.781119
[28,] 9.074880 4.598712
[29,] 9.509957 9.074880
[30,] 17.387748 9.509957
[31,] 14.673642 17.387748
[32,] 27.192038 14.673642
[33,] 5.887344 27.192038
[34,] -8.467876 5.887344
[35,] -12.034029 -8.467876
[36,] -12.709451 -12.034029
[37,] -21.544412 -12.709451
[38,] -24.621541 -21.544412
[39,] -33.961991 -24.621541
[40,] -40.588833 -33.961991
[41,] -33.894043 -40.588833
[42,] -20.000022 -33.894043
[43,] -22.793860 -20.000022
[44,] -15.768707 -22.793860
[45,] -21.206410 -15.768707
[46,] -40.426183 -21.206410
[47,] -36.773948 -40.426183
[48,] -35.007804 -36.773948
[49,] -44.609408 -35.007804
[50,] -40.993656 -44.609408
[51,] -43.295656 -40.993656
[52,] -57.069948 -43.295656
[53,] -32.725960 -57.069948
[54,] -27.424700 -32.725960
[55,] -37.332362 -27.424700
[56,] -16.449922 -37.332362
[57,] -26.761329 -16.449922
[58,] -36.500924 -26.761329
[59,] -16.285181 -36.500924
[60,] -12.892358 -16.285181
[61,] -9.478975 -12.892358
[62,] 6.126486 -9.478975
[63,] -9.558822 6.126486
[64,] -15.761928 -9.558822
[65,] 3.868657 -15.761928
[66,] 9.637338 3.868657
[67,] 4.298786 9.637338
[68,] 27.788912 4.298786
[69,] 11.560169 27.788912
[70,] 12.161121 11.560169
[71,] 21.489910 12.161121
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.537617 8.042975
2 13.161556 9.537617
3 -1.144110 13.161556
4 -6.212915 -1.144110
5 4.961025 -6.212915
6 20.376743 4.961025
7 35.605446 20.376743
8 47.861092 35.605446
9 42.392880 47.861092
10 29.526617 42.392880
11 29.866511 29.526617
12 31.134579 29.866511
13 22.823396 31.134579
14 27.098144 22.823396
15 13.967281 27.098144
16 14.594574 13.967281
17 24.889666 14.594574
18 35.706973 24.889666
19 42.044021 35.706973
20 52.260518 42.044021
21 36.525272 52.260518
22 19.492980 36.525272
23 26.555851 19.492980
24 17.870302 26.555851
25 14.964426 17.870302
26 7.781119 14.964426
27 4.598712 7.781119
28 9.074880 4.598712
29 9.509957 9.074880
30 17.387748 9.509957
31 14.673642 17.387748
32 27.192038 14.673642
33 5.887344 27.192038
34 -8.467876 5.887344
35 -12.034029 -8.467876
36 -12.709451 -12.034029
37 -21.544412 -12.709451
38 -24.621541 -21.544412
39 -33.961991 -24.621541
40 -40.588833 -33.961991
41 -33.894043 -40.588833
42 -20.000022 -33.894043
43 -22.793860 -20.000022
44 -15.768707 -22.793860
45 -21.206410 -15.768707
46 -40.426183 -21.206410
47 -36.773948 -40.426183
48 -35.007804 -36.773948
49 -44.609408 -35.007804
50 -40.993656 -44.609408
51 -43.295656 -40.993656
52 -57.069948 -43.295656
53 -32.725960 -57.069948
54 -27.424700 -32.725960
55 -37.332362 -27.424700
56 -16.449922 -37.332362
57 -26.761329 -16.449922
58 -36.500924 -26.761329
59 -16.285181 -36.500924
60 -12.892358 -16.285181
61 -9.478975 -12.892358
62 6.126486 -9.478975
63 -9.558822 6.126486
64 -15.761928 -9.558822
65 3.868657 -15.761928
66 9.637338 3.868657
67 4.298786 9.637338
68 27.788912 4.298786
69 11.560169 27.788912
70 12.161121 11.560169
71 21.489910 12.161121
> 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/7yqav1291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8yqav1291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yqav1291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qh9y1291992853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ch7m1291992853.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/12x0691291992853.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/13ta3i1291992853.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/14wa261291992853.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/157j1r1291992853.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/16lbh01291992853.tab")
+ }
> try(system("convert tmp/12gu41291992853.ps tmp/12gu41291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/22gu41291992853.ps tmp/22gu41291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/3c7b71291992853.ps tmp/3c7b71291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c7b71291992853.ps tmp/4c7b71291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/5c7b71291992853.ps tmp/5c7b71291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nysa1291992853.ps tmp/6nysa1291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yqav1291992853.ps tmp/7yqav1291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yqav1291992853.ps tmp/8yqav1291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yqav1291992853.ps tmp/9yqav1291992853.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qh9y1291992853.ps tmp/10qh9y1291992853.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.659 1.630 7.243