R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1260,2100,2,3,1080,1800,1,1,0660,1650,56,2,1324,2350,2,2,0859,1620,35,1,1008,1230,28,3,0847,0896,1,1,1057,1762,6,2,0919,1532,4,3,0865,1632,12,1,0769,2281,23,2,1292,2153,5,3,0741,1235,6,2,1008,1654,4,3,0893,1685,9,1,0635,0999,56,2,0661,1652,23,2,0874,1456,5,2,1008,1236,7,3,0847,1254,6,1,0772,1287,8,2,1068,1780,23,3,0846,1596,65,1,0947,1578,2,2,1008,1624,2,3,1008,1598,3,3,0742,1236,6,2,0925,1542,8,1,1008,1256,26,3,0952,1586,1,2,1324,2210,4,1,1033,2362,62,1,0937,1562,2,1,0941,1569,5,1,0819,1365,3,2,0582,1456,33,2,1111,1852,2,3,1008,1365,12,3,0847,1258,45,1,0592,1479,16,2,1207,2012,2,3,1299,2165,4,1,0819,1365,5,2,1008,1452,68,3,0674,1685,15,2,1008,1563,16,3,1008,1236,15,3,0581,1452,13,2,0946,1785,18,1,0958,1596,1,1,0938,1563,5,1,0847,1258,6,1,0950,1583,7,2,1008,1586,8,3,1054,1756,9,1,0745,1862,12,2,1011,1685,6,3,0769,2210,23,2,1324,3210,2,1,0756,1260,5,2),dim=c(4,60),dimnames=list(c('Werkloosheidsuitkering','Loon','Duur','Gezinslast'),1:60))
> y <- array(NA,dim=c(4,60),dimnames=list(c('Werkloosheidsuitkering','Loon','Duur','Gezinslast'),1:60))
> 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 = '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
> 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
Werkloosheidsuitkering Loon Duur Gezinslast t
1 1260 2100 2 3 1
2 1080 1800 1 1 2
3 660 1650 56 2 3
4 1324 2350 2 2 4
5 859 1620 35 1 5
6 1008 1230 28 3 6
7 847 896 1 1 7
8 1057 1762 6 2 8
9 919 1532 4 3 9
10 865 1632 12 1 10
11 769 2281 23 2 11
12 1292 2153 5 3 12
13 741 1235 6 2 13
14 1008 1654 4 3 14
15 893 1685 9 1 15
16 635 999 56 2 16
17 661 1652 23 2 17
18 874 1456 5 2 18
19 1008 1236 7 3 19
20 847 1254 6 1 20
21 772 1287 8 2 21
22 1068 1780 23 3 22
23 846 1596 65 1 23
24 947 1578 2 2 24
25 1008 1624 2 3 25
26 1008 1598 3 3 26
27 742 1236 6 2 27
28 925 1542 8 1 28
29 1008 1256 26 3 29
30 952 1586 1 2 30
31 1324 2210 4 1 31
32 1033 2362 62 1 32
33 937 1562 2 1 33
34 941 1569 5 1 34
35 819 1365 3 2 35
36 582 1456 33 2 36
37 1111 1852 2 3 37
38 1008 1365 12 3 38
39 847 1258 45 1 39
40 592 1479 16 2 40
41 1207 2012 2 3 41
42 1299 2165 4 1 42
43 819 1365 5 2 43
44 1008 1452 68 3 44
45 674 1685 15 2 45
46 1008 1563 16 3 46
47 1008 1236 15 3 47
48 581 1452 13 2 48
49 946 1785 18 1 49
50 958 1596 1 1 50
51 938 1563 5 1 51
52 847 1258 6 1 52
53 950 1583 7 2 53
54 1008 1586 8 3 54
55 1054 1756 9 1 55
56 745 1862 12 2 56
57 1011 1685 6 3 57
58 769 2210 23 2 58
59 1324 3210 2 1 59
60 756 1260 5 2 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Loon Duur Gezinslast t
469.3193 0.2791 -2.8944 43.2420 -1.1733
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-343.97 -59.10 33.90 85.21 252.14
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 469.31933 106.94657 4.388 5.22e-05 ***
Loon 0.27911 0.04954 5.634 6.24e-07 ***
Duur -2.89443 1.10317 -2.624 0.0112 *
Gezinslast 43.24200 23.67719 1.826 0.0732 .
t -1.17333 1.08499 -1.081 0.2842
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 144.4 on 55 degrees of freedom
Multiple R-squared: 0.441, Adjusted R-squared: 0.4004
F-statistic: 10.85 on 4 and 55 DF, p-value: 1.483e-06
> 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,] 0.23288770 0.46577540 0.7671123
[2,] 0.35843398 0.71686796 0.6415660
[3,] 0.22552696 0.45105392 0.7744730
[4,] 0.27129467 0.54258933 0.7287053
[5,] 0.45094304 0.90188609 0.5490570
[6,] 0.39857618 0.79715237 0.6014238
[7,] 0.29743420 0.59486839 0.7025658
[8,] 0.24856874 0.49713747 0.7514313
[9,] 0.28280183 0.56560365 0.7171982
[10,] 0.33764181 0.67528363 0.6623582
[11,] 0.26450463 0.52900925 0.7354954
[12,] 0.24879130 0.49758260 0.7512087
[13,] 0.20986139 0.41972278 0.7901386
[14,] 0.16515824 0.33031649 0.8348418
[15,] 0.19589172 0.39178344 0.8041083
[16,] 0.33578142 0.67156283 0.6642186
[17,] 0.26491331 0.52982662 0.7350867
[18,] 0.20211576 0.40423152 0.7978842
[19,] 0.14957372 0.29914744 0.8504263
[20,] 0.13302671 0.26605342 0.8669733
[21,] 0.10478226 0.20956452 0.8952177
[22,] 0.11175732 0.22351465 0.8882427
[23,] 0.07918906 0.15837813 0.9208109
[24,] 0.13522639 0.27045279 0.8647736
[25,] 0.10032097 0.20064193 0.8996790
[26,] 0.06914195 0.13828389 0.9308581
[27,] 0.04626963 0.09253926 0.9537304
[28,] 0.03525551 0.07051102 0.9647445
[29,] 0.09107192 0.18214384 0.9089281
[30,] 0.06201856 0.12403712 0.9379814
[31,] 0.04671466 0.09342933 0.9532853
[32,] 0.03950400 0.07900801 0.9604960
[33,] 0.15726220 0.31452440 0.8427378
[34,] 0.11759722 0.23519445 0.8824028
[35,] 0.16085629 0.32171258 0.8391437
[36,] 0.11855613 0.23711227 0.8814439
[37,] 0.31420314 0.62840628 0.6857969
[38,] 0.53053320 0.93893359 0.4694668
[39,] 0.44054177 0.88108354 0.5594582
[40,] 0.55307483 0.89385034 0.4469252
[41,] 0.86343260 0.27313480 0.1365674
[42,] 0.81782349 0.36435302 0.1821765
[43,] 0.77062663 0.45874674 0.2293734
[44,] 0.66190006 0.67619989 0.3380999
[45,] 0.53567549 0.92864902 0.4643245
> postscript(file="/var/www/rcomp/tmp/1fu761321714783.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/29g751321714783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/30mho1321714783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/496ye1321714783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5li6h1321714783.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 = 60
Frequency = 1
1 2 3 4 5 6
81.788527 70.284054 -190.724786 122.773346 1.454163 153.734898
7 8 9 10 11 12
95.465003 36.160315 -85.502204 -56.600317 -343.971845 120.585703
13 14 15 16 17 18
-126.882725 -24.686806 -46.209700 -18.771686 -269.372463 -52.593519
19 20 21 22 23 24
106.530585 25.269539 -95.220864 64.526279 103.105585 -15.288072
25 26 27 28 29 30
-9.195744 2.128843 -109.735181 38.061740 167.675860 -8.375375
31 32 33 34 35 36
242.559399 78.185011 32.979662 44.882516 -68.036830 -242.429561
37 38 39 40 41 42
44.247455 107.291013 159.329098 -283.361003 100.283389 243.025948
43 44 45 46 47 48
-52.861316 252.136498 -255.885166 72.991855 162.539313 -286.121692
49 50 51 52 53 54
44.822577 61.542188 63.503819 61.699740 34.815161 52.803592
55 56 57 58 59 60
141.906875 -230.064037 25.902970 -269.008504 -9.484874 -66.608250
> postscript(file="/var/www/rcomp/tmp/6hzxh1321714783.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 81.788527 NA
1 70.284054 81.788527
2 -190.724786 70.284054
3 122.773346 -190.724786
4 1.454163 122.773346
5 153.734898 1.454163
6 95.465003 153.734898
7 36.160315 95.465003
8 -85.502204 36.160315
9 -56.600317 -85.502204
10 -343.971845 -56.600317
11 120.585703 -343.971845
12 -126.882725 120.585703
13 -24.686806 -126.882725
14 -46.209700 -24.686806
15 -18.771686 -46.209700
16 -269.372463 -18.771686
17 -52.593519 -269.372463
18 106.530585 -52.593519
19 25.269539 106.530585
20 -95.220864 25.269539
21 64.526279 -95.220864
22 103.105585 64.526279
23 -15.288072 103.105585
24 -9.195744 -15.288072
25 2.128843 -9.195744
26 -109.735181 2.128843
27 38.061740 -109.735181
28 167.675860 38.061740
29 -8.375375 167.675860
30 242.559399 -8.375375
31 78.185011 242.559399
32 32.979662 78.185011
33 44.882516 32.979662
34 -68.036830 44.882516
35 -242.429561 -68.036830
36 44.247455 -242.429561
37 107.291013 44.247455
38 159.329098 107.291013
39 -283.361003 159.329098
40 100.283389 -283.361003
41 243.025948 100.283389
42 -52.861316 243.025948
43 252.136498 -52.861316
44 -255.885166 252.136498
45 72.991855 -255.885166
46 162.539313 72.991855
47 -286.121692 162.539313
48 44.822577 -286.121692
49 61.542188 44.822577
50 63.503819 61.542188
51 61.699740 63.503819
52 34.815161 61.699740
53 52.803592 34.815161
54 141.906875 52.803592
55 -230.064037 141.906875
56 25.902970 -230.064037
57 -269.008504 25.902970
58 -9.484874 -269.008504
59 -66.608250 -9.484874
60 NA -66.608250
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 70.284054 81.788527
[2,] -190.724786 70.284054
[3,] 122.773346 -190.724786
[4,] 1.454163 122.773346
[5,] 153.734898 1.454163
[6,] 95.465003 153.734898
[7,] 36.160315 95.465003
[8,] -85.502204 36.160315
[9,] -56.600317 -85.502204
[10,] -343.971845 -56.600317
[11,] 120.585703 -343.971845
[12,] -126.882725 120.585703
[13,] -24.686806 -126.882725
[14,] -46.209700 -24.686806
[15,] -18.771686 -46.209700
[16,] -269.372463 -18.771686
[17,] -52.593519 -269.372463
[18,] 106.530585 -52.593519
[19,] 25.269539 106.530585
[20,] -95.220864 25.269539
[21,] 64.526279 -95.220864
[22,] 103.105585 64.526279
[23,] -15.288072 103.105585
[24,] -9.195744 -15.288072
[25,] 2.128843 -9.195744
[26,] -109.735181 2.128843
[27,] 38.061740 -109.735181
[28,] 167.675860 38.061740
[29,] -8.375375 167.675860
[30,] 242.559399 -8.375375
[31,] 78.185011 242.559399
[32,] 32.979662 78.185011
[33,] 44.882516 32.979662
[34,] -68.036830 44.882516
[35,] -242.429561 -68.036830
[36,] 44.247455 -242.429561
[37,] 107.291013 44.247455
[38,] 159.329098 107.291013
[39,] -283.361003 159.329098
[40,] 100.283389 -283.361003
[41,] 243.025948 100.283389
[42,] -52.861316 243.025948
[43,] 252.136498 -52.861316
[44,] -255.885166 252.136498
[45,] 72.991855 -255.885166
[46,] 162.539313 72.991855
[47,] -286.121692 162.539313
[48,] 44.822577 -286.121692
[49,] 61.542188 44.822577
[50,] 63.503819 61.542188
[51,] 61.699740 63.503819
[52,] 34.815161 61.699740
[53,] 52.803592 34.815161
[54,] 141.906875 52.803592
[55,] -230.064037 141.906875
[56,] 25.902970 -230.064037
[57,] -269.008504 25.902970
[58,] -9.484874 -269.008504
[59,] -66.608250 -9.484874
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 70.284054 81.788527
2 -190.724786 70.284054
3 122.773346 -190.724786
4 1.454163 122.773346
5 153.734898 1.454163
6 95.465003 153.734898
7 36.160315 95.465003
8 -85.502204 36.160315
9 -56.600317 -85.502204
10 -343.971845 -56.600317
11 120.585703 -343.971845
12 -126.882725 120.585703
13 -24.686806 -126.882725
14 -46.209700 -24.686806
15 -18.771686 -46.209700
16 -269.372463 -18.771686
17 -52.593519 -269.372463
18 106.530585 -52.593519
19 25.269539 106.530585
20 -95.220864 25.269539
21 64.526279 -95.220864
22 103.105585 64.526279
23 -15.288072 103.105585
24 -9.195744 -15.288072
25 2.128843 -9.195744
26 -109.735181 2.128843
27 38.061740 -109.735181
28 167.675860 38.061740
29 -8.375375 167.675860
30 242.559399 -8.375375
31 78.185011 242.559399
32 32.979662 78.185011
33 44.882516 32.979662
34 -68.036830 44.882516
35 -242.429561 -68.036830
36 44.247455 -242.429561
37 107.291013 44.247455
38 159.329098 107.291013
39 -283.361003 159.329098
40 100.283389 -283.361003
41 243.025948 100.283389
42 -52.861316 243.025948
43 252.136498 -52.861316
44 -255.885166 252.136498
45 72.991855 -255.885166
46 162.539313 72.991855
47 -286.121692 162.539313
48 44.822577 -286.121692
49 61.542188 44.822577
50 63.503819 61.542188
51 61.699740 63.503819
52 34.815161 61.699740
53 52.803592 34.815161
54 141.906875 52.803592
55 -230.064037 141.906875
56 25.902970 -230.064037
57 -269.008504 25.902970
58 -9.484874 -269.008504
59 -66.608250 -9.484874
> 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/7lbsa1321714783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8f2rv1321714783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9wy161321714783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10f8bf1321714783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vs351321714783.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/12z2jk1321714783.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/13kq211321714783.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/14qzib1321714783.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/15bbmq1321714783.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/16ddla1321714783.tab")
+ }
>
> try(system("convert tmp/1fu761321714783.ps tmp/1fu761321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/29g751321714783.ps tmp/29g751321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/30mho1321714783.ps tmp/30mho1321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/496ye1321714783.ps tmp/496ye1321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/5li6h1321714783.ps tmp/5li6h1321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hzxh1321714783.ps tmp/6hzxh1321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lbsa1321714783.ps tmp/7lbsa1321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/8f2rv1321714783.ps tmp/8f2rv1321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wy161321714783.ps tmp/9wy161321714783.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f8bf1321714783.ps tmp/10f8bf1321714783.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.176 0.668 4.899