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(216.234,627,213.586,696,209.465,825,204.045,677,200.237,656,203.666,785,241.476,412,260.307,352,243.324,839,244.460,729,233.575,696,237.217,641,235.243,695,230.354,638,227.184,762,221.678,635,217.142,721,219.452,854,256.446,418,265.845,367,248.624,824,241.114,687,229.245,601,231.805,676,219.277,740,219.313,691,212.610,683,214.771,594,211.142,729,211.457,731,240.048,386,240.636,331,230.580,707,208.795,715,197.922,657,194.596,653,194.581,642,185.686,643,178.106,718,172.608,654,167.302,632,168.053,731,202.300,392,202.388,344,182.516,792,173.476,852,166.444,649,171.297,629,169.701,685,164.182,617,161.914,715,159.612,715,151.001,629,158.114,916,186.530,531,187.069,357,174.330,917,169.362,828,166.827,708,178.037,858,186.413,775,189.226,785,191.563,1006,188.906,789,186.005,734,195.309,906,223.532,532,226.899,387,214.126,991,206.903,841,204.442,892,220.375,782),dim=c(2,72),dimnames=list(c('werlozen','faillissementen'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('werlozen','faillissementen'),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
faillissementen werlozen
1 627 216.234
2 696 213.586
3 825 209.465
4 677 204.045
5 656 200.237
6 785 203.666
7 412 241.476
8 352 260.307
9 839 243.324
10 729 244.460
11 696 233.575
12 641 237.217
13 695 235.243
14 638 230.354
15 762 227.184
16 635 221.678
17 721 217.142
18 854 219.452
19 418 256.446
20 367 265.845
21 824 248.624
22 687 241.114
23 601 229.245
24 676 231.805
25 740 219.277
26 691 219.313
27 683 212.610
28 594 214.771
29 729 211.142
30 731 211.457
31 386 240.048
32 331 240.636
33 707 230.580
34 715 208.795
35 657 197.922
36 653 194.596
37 642 194.581
38 643 185.686
39 718 178.106
40 654 172.608
41 632 167.302
42 731 168.053
43 392 202.300
44 344 202.388
45 792 182.516
46 852 173.476
47 649 166.444
48 629 171.297
49 685 169.701
50 617 164.182
51 715 161.914
52 715 159.612
53 629 151.001
54 916 158.114
55 531 186.530
56 357 187.069
57 917 174.330
58 828 169.362
59 708 166.827
60 858 178.037
61 775 186.413
62 785 189.226
63 1006 191.563
64 789 188.906
65 734 186.005
66 906 195.309
67 532 223.532
68 387 226.899
69 991 214.126
70 841 206.903
71 892 204.442
72 782 220.375
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) werlozen
1063.988 -1.870
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-357.10 -77.05 17.29 82.12 327.51
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1063.9877 130.0180 8.183 8.4e-12 ***
werlozen -1.8704 0.6285 -2.976 0.00401 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 148.5 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,] 0.198908572 0.39781714 0.8010914
[2,] 0.130098235 0.26019647 0.8699018
[3,] 0.100968622 0.20193724 0.8990314
[4,] 0.055531725 0.11106345 0.9444683
[5,] 0.531693669 0.93661266 0.4683063
[6,] 0.546250009 0.90749998 0.4537500
[7,] 0.461473938 0.92294788 0.5385261
[8,] 0.362805237 0.72561047 0.6371948
[9,] 0.291140316 0.58228063 0.7088597
[10,] 0.213859876 0.42771975 0.7861401
[11,] 0.183450656 0.36690131 0.8165493
[12,] 0.133870634 0.26774127 0.8661294
[13,] 0.093248066 0.18649613 0.9067519
[14,] 0.112528441 0.22505688 0.8874716
[15,] 0.110925460 0.22185092 0.8890745
[16,] 0.107325779 0.21465156 0.8926742
[17,] 0.217749320 0.43549864 0.7822507
[18,] 0.179197061 0.35839412 0.8208029
[19,] 0.139517112 0.27903422 0.8604829
[20,] 0.103409961 0.20681992 0.8965900
[21,] 0.077832665 0.15566533 0.9221673
[22,] 0.055044282 0.11008856 0.9449557
[23,] 0.039141753 0.07828351 0.9608582
[24,] 0.034133669 0.06826734 0.9658663
[25,] 0.023281803 0.04656361 0.9767182
[26,] 0.015661937 0.03132387 0.9843381
[27,] 0.029473572 0.05894714 0.9705264
[28,] 0.079853363 0.15970673 0.9201466
[29,] 0.060948312 0.12189662 0.9390517
[30,] 0.043064764 0.08612953 0.9569352
[31,] 0.035888925 0.07177785 0.9641111
[32,] 0.030053764 0.06010753 0.9699462
[33,] 0.024868835 0.04973767 0.9751312
[34,] 0.021541197 0.04308239 0.9784588
[35,] 0.014954312 0.02990862 0.9850457
[36,] 0.012605831 0.02521166 0.9873942
[37,] 0.011498006 0.02299601 0.9885020
[38,] 0.007279032 0.01455806 0.9927210
[39,] 0.027401537 0.05480307 0.9725985
[40,] 0.128843708 0.25768742 0.8711563
[41,] 0.102009624 0.20401925 0.8979904
[42,] 0.089426672 0.17885334 0.9105733
[43,] 0.072919266 0.14583853 0.9270807
[44,] 0.061598910 0.12319782 0.9384011
[45,] 0.044889607 0.08977921 0.9551104
[46,] 0.040994056 0.08198811 0.9590059
[47,] 0.028497164 0.05699433 0.9715028
[48,] 0.019676202 0.03935240 0.9803238
[49,] 0.022348578 0.04469716 0.9776514
[50,] 0.019650524 0.03930105 0.9803495
[51,] 0.030449811 0.06089962 0.9695502
[52,] 0.290633379 0.58126676 0.7093666
[53,] 0.265381357 0.53076271 0.7346186
[54,] 0.211442873 0.42288575 0.7885571
[55,] 0.254250726 0.50850145 0.7457493
[56,] 0.206552692 0.41310538 0.7934473
[57,] 0.173928208 0.34785642 0.8260718
[58,] 0.141915097 0.28383019 0.8580849
[59,] 0.155385718 0.31077144 0.8446143
[60,] 0.117974993 0.23594999 0.8820250
[61,] 0.279320984 0.55864197 0.7206790
[62,] 0.311518978 0.62303796 0.6884810
[63,] 0.206361206 0.41272241 0.7936388
> postscript(file="/var/www/html/rcomp/tmp/14a571291991616.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ej5a1291991616.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ej5a1291991616.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ej5a1291991616.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pa4d1291991616.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
-32.547434 31.499793 152.791946 -5.345527 -33.467944 101.945598
7 8 9 10 11 12
-200.335237 -225.114062 230.121230 122.245985 68.886870 20.698804
13 14 15 16 17 18
71.006669 4.862368 122.933255 -14.365071 63.150873 200.471457
19 20 21 22 23 24
-166.335610 -199.755884 225.034258 73.987685 -34.211886 45.576293
25 26 27 28 29 30
86.144140 37.211474 16.674300 -68.283804 59.928578 62.517748
31 32 33 34 35 36
-229.006143 -282.906358 74.285075 41.538790 -36.797880 -47.018772
37 38 39 40 41 42
-58.046828 -73.683881 -12.861381 -87.144745 -119.068994 -18.664337
43 44 45 46 47 48
-293.609345 -341.444751 69.387006 112.478748 -103.673783 -114.596816
49 50 51 52 53 54
-61.581947 -139.904588 -46.146616 -50.452237 -152.558101 147.745930
55 56 57 58 59 60
-184.105278 -357.097142 179.076054 80.783994 -43.957426 127.009563
61 62 63 64 65 66
59.675887 74.937273 300.308357 78.338751 17.912771 207.314810
67 68 69 70 71 72
-113.897382 -252.599804 327.509800 164.000026 210.397015 130.197820
> postscript(file="/var/www/html/rcomp/tmp/6pa4d1291991616.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 -32.547434 NA
1 31.499793 -32.547434
2 152.791946 31.499793
3 -5.345527 152.791946
4 -33.467944 -5.345527
5 101.945598 -33.467944
6 -200.335237 101.945598
7 -225.114062 -200.335237
8 230.121230 -225.114062
9 122.245985 230.121230
10 68.886870 122.245985
11 20.698804 68.886870
12 71.006669 20.698804
13 4.862368 71.006669
14 122.933255 4.862368
15 -14.365071 122.933255
16 63.150873 -14.365071
17 200.471457 63.150873
18 -166.335610 200.471457
19 -199.755884 -166.335610
20 225.034258 -199.755884
21 73.987685 225.034258
22 -34.211886 73.987685
23 45.576293 -34.211886
24 86.144140 45.576293
25 37.211474 86.144140
26 16.674300 37.211474
27 -68.283804 16.674300
28 59.928578 -68.283804
29 62.517748 59.928578
30 -229.006143 62.517748
31 -282.906358 -229.006143
32 74.285075 -282.906358
33 41.538790 74.285075
34 -36.797880 41.538790
35 -47.018772 -36.797880
36 -58.046828 -47.018772
37 -73.683881 -58.046828
38 -12.861381 -73.683881
39 -87.144745 -12.861381
40 -119.068994 -87.144745
41 -18.664337 -119.068994
42 -293.609345 -18.664337
43 -341.444751 -293.609345
44 69.387006 -341.444751
45 112.478748 69.387006
46 -103.673783 112.478748
47 -114.596816 -103.673783
48 -61.581947 -114.596816
49 -139.904588 -61.581947
50 -46.146616 -139.904588
51 -50.452237 -46.146616
52 -152.558101 -50.452237
53 147.745930 -152.558101
54 -184.105278 147.745930
55 -357.097142 -184.105278
56 179.076054 -357.097142
57 80.783994 179.076054
58 -43.957426 80.783994
59 127.009563 -43.957426
60 59.675887 127.009563
61 74.937273 59.675887
62 300.308357 74.937273
63 78.338751 300.308357
64 17.912771 78.338751
65 207.314810 17.912771
66 -113.897382 207.314810
67 -252.599804 -113.897382
68 327.509800 -252.599804
69 164.000026 327.509800
70 210.397015 164.000026
71 130.197820 210.397015
72 NA 130.197820
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 31.499793 -32.547434
[2,] 152.791946 31.499793
[3,] -5.345527 152.791946
[4,] -33.467944 -5.345527
[5,] 101.945598 -33.467944
[6,] -200.335237 101.945598
[7,] -225.114062 -200.335237
[8,] 230.121230 -225.114062
[9,] 122.245985 230.121230
[10,] 68.886870 122.245985
[11,] 20.698804 68.886870
[12,] 71.006669 20.698804
[13,] 4.862368 71.006669
[14,] 122.933255 4.862368
[15,] -14.365071 122.933255
[16,] 63.150873 -14.365071
[17,] 200.471457 63.150873
[18,] -166.335610 200.471457
[19,] -199.755884 -166.335610
[20,] 225.034258 -199.755884
[21,] 73.987685 225.034258
[22,] -34.211886 73.987685
[23,] 45.576293 -34.211886
[24,] 86.144140 45.576293
[25,] 37.211474 86.144140
[26,] 16.674300 37.211474
[27,] -68.283804 16.674300
[28,] 59.928578 -68.283804
[29,] 62.517748 59.928578
[30,] -229.006143 62.517748
[31,] -282.906358 -229.006143
[32,] 74.285075 -282.906358
[33,] 41.538790 74.285075
[34,] -36.797880 41.538790
[35,] -47.018772 -36.797880
[36,] -58.046828 -47.018772
[37,] -73.683881 -58.046828
[38,] -12.861381 -73.683881
[39,] -87.144745 -12.861381
[40,] -119.068994 -87.144745
[41,] -18.664337 -119.068994
[42,] -293.609345 -18.664337
[43,] -341.444751 -293.609345
[44,] 69.387006 -341.444751
[45,] 112.478748 69.387006
[46,] -103.673783 112.478748
[47,] -114.596816 -103.673783
[48,] -61.581947 -114.596816
[49,] -139.904588 -61.581947
[50,] -46.146616 -139.904588
[51,] -50.452237 -46.146616
[52,] -152.558101 -50.452237
[53,] 147.745930 -152.558101
[54,] -184.105278 147.745930
[55,] -357.097142 -184.105278
[56,] 179.076054 -357.097142
[57,] 80.783994 179.076054
[58,] -43.957426 80.783994
[59,] 127.009563 -43.957426
[60,] 59.675887 127.009563
[61,] 74.937273 59.675887
[62,] 300.308357 74.937273
[63,] 78.338751 300.308357
[64,] 17.912771 78.338751
[65,] 207.314810 17.912771
[66,] -113.897382 207.314810
[67,] -252.599804 -113.897382
[68,] 327.509800 -252.599804
[69,] 164.000026 327.509800
[70,] 210.397015 164.000026
[71,] 130.197820 210.397015
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 31.499793 -32.547434
2 152.791946 31.499793
3 -5.345527 152.791946
4 -33.467944 -5.345527
5 101.945598 -33.467944
6 -200.335237 101.945598
7 -225.114062 -200.335237
8 230.121230 -225.114062
9 122.245985 230.121230
10 68.886870 122.245985
11 20.698804 68.886870
12 71.006669 20.698804
13 4.862368 71.006669
14 122.933255 4.862368
15 -14.365071 122.933255
16 63.150873 -14.365071
17 200.471457 63.150873
18 -166.335610 200.471457
19 -199.755884 -166.335610
20 225.034258 -199.755884
21 73.987685 225.034258
22 -34.211886 73.987685
23 45.576293 -34.211886
24 86.144140 45.576293
25 37.211474 86.144140
26 16.674300 37.211474
27 -68.283804 16.674300
28 59.928578 -68.283804
29 62.517748 59.928578
30 -229.006143 62.517748
31 -282.906358 -229.006143
32 74.285075 -282.906358
33 41.538790 74.285075
34 -36.797880 41.538790
35 -47.018772 -36.797880
36 -58.046828 -47.018772
37 -73.683881 -58.046828
38 -12.861381 -73.683881
39 -87.144745 -12.861381
40 -119.068994 -87.144745
41 -18.664337 -119.068994
42 -293.609345 -18.664337
43 -341.444751 -293.609345
44 69.387006 -341.444751
45 112.478748 69.387006
46 -103.673783 112.478748
47 -114.596816 -103.673783
48 -61.581947 -114.596816
49 -139.904588 -61.581947
50 -46.146616 -139.904588
51 -50.452237 -46.146616
52 -152.558101 -50.452237
53 147.745930 -152.558101
54 -184.105278 147.745930
55 -357.097142 -184.105278
56 179.076054 -357.097142
57 80.783994 179.076054
58 -43.957426 80.783994
59 127.009563 -43.957426
60 59.675887 127.009563
61 74.937273 59.675887
62 300.308357 74.937273
63 78.338751 300.308357
64 17.912771 78.338751
65 207.314810 17.912771
66 -113.897382 207.314810
67 -252.599804 -113.897382
68 327.509800 -252.599804
69 164.000026 327.509800
70 210.397015 164.000026
71 130.197820 210.397015
> 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/702ly1291991616.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/802ly1291991616.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tb2j1291991616.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tb2j1291991616.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wb171291991616.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/12iciv1291991616.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/13dmxm1291991616.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/14rwg41291991617.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/15vffs1291991617.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/1697v11291991617.tab")
+ }
> try(system("convert tmp/14a571291991616.ps tmp/14a571291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ej5a1291991616.ps tmp/2ej5a1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ej5a1291991616.ps tmp/3ej5a1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ej5a1291991616.ps tmp/4ej5a1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pa4d1291991616.ps tmp/5pa4d1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pa4d1291991616.ps tmp/6pa4d1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/702ly1291991616.ps tmp/702ly1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/802ly1291991616.ps tmp/802ly1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tb2j1291991616.ps tmp/9tb2j1291991616.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tb2j1291991616.ps tmp/10tb2j1291991616.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.671 1.673 6.227