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.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(7.2,102.9,7.4,97.4,8.8,111.4,9.3,87.4,9.3,96.8,8.7,114.1,8.2,110.3,8.3,103.9,8.5,101.6,8.6,94.6,8.5,95.9,8.2,104.7,8.1,102.8,7.9,98.1,8.6,113.9,8.7,80.9,8.7,95.7,8.5,113.2,8.4,105.9,8.5,108.8,8.7,102.3,8.7,99,8.6,100.7,8.5,115.5,8.3,100.7,8,109.9,8.2,114.6,8.1,85.4,8.1,100.5,8,114.8,7.9,116.5,7.9,112.9,8,102,8,106,7.9,105.3,8,118.8,7.7,106.1,7.2,109.3,7.5,117.2,7.3,92.5,7,104.2,7,112.5,7,122.4,7.2,113.3,7.3,100,7.1,110.7,6.8,112.8,6.4,109.8,6.1,117.3,6.5,109.1,7.7,115.9,7.9,96,7.5,99.8,6.9,116.8,6.6,115.7,6.9,99.4,7.7,94.3,8,91,8,93.2,7.7,103.1,7.3,94.1,7.4,91.8,8.1,102.7,8.3,82.6,8.2,89.1),dim=c(2,65),dimnames=list(c('Werkl.graad','Industr.prod.'),1:65))
> y <- array(NA,dim=c(2,65),dimnames=list(c('Werkl.graad','Industr.prod.'),1:65))
> 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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Werkl.graad Industr.prod.
1 7.2 102.9
2 7.4 97.4
3 8.8 111.4
4 9.3 87.4
5 9.3 96.8
6 8.7 114.1
7 8.2 110.3
8 8.3 103.9
9 8.5 101.6
10 8.6 94.6
11 8.5 95.9
12 8.2 104.7
13 8.1 102.8
14 7.9 98.1
15 8.6 113.9
16 8.7 80.9
17 8.7 95.7
18 8.5 113.2
19 8.4 105.9
20 8.5 108.8
21 8.7 102.3
22 8.7 99.0
23 8.6 100.7
24 8.5 115.5
25 8.3 100.7
26 8.0 109.9
27 8.2 114.6
28 8.1 85.4
29 8.1 100.5
30 8.0 114.8
31 7.9 116.5
32 7.9 112.9
33 8.0 102.0
34 8.0 106.0
35 7.9 105.3
36 8.0 118.8
37 7.7 106.1
38 7.2 109.3
39 7.5 117.2
40 7.3 92.5
41 7.0 104.2
42 7.0 112.5
43 7.0 122.4
44 7.2 113.3
45 7.3 100.0
46 7.1 110.7
47 6.8 112.8
48 6.4 109.8
49 6.1 117.3
50 6.5 109.1
51 7.7 115.9
52 7.9 96.0
53 7.5 99.8
54 6.9 116.8
55 6.6 115.7
56 6.9 99.4
57 7.7 94.3
58 8.0 91.0
59 8.0 93.2
60 7.7 103.1
61 7.3 94.1
62 7.4 91.8
63 8.1 102.7
64 8.3 82.6
65 8.2 89.1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Industr.prod.
10.43984 -0.02467
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.44561 -0.54300 0.07688 0.49140 1.24858
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.439843 0.903781 11.551 < 2e-16 ***
Industr.prod. -0.024674 0.008644 -2.854 0.00583 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6645 on 63 degrees of freedom
Multiple R-squared: 0.1145, Adjusted R-squared: 0.1005
F-statistic: 8.148 on 1 and 63 DF, p-value: 0.005828
> 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,] 0.9717786 0.05644287 0.02822143
[2,] 0.9632511 0.07349776 0.03674888
[3,] 0.9326284 0.13474314 0.06737157
[4,] 0.8883490 0.22330200 0.11165100
[5,] 0.8344082 0.33118368 0.16559184
[6,] 0.7682084 0.46358318 0.23179159
[7,] 0.6913335 0.61733307 0.30866654
[8,] 0.6108320 0.77833605 0.38916803
[9,] 0.5342502 0.93149963 0.46574982
[10,] 0.4927608 0.98552168 0.50723916
[11,] 0.4792912 0.95858232 0.52070884
[12,] 0.4017360 0.80347199 0.59826400
[13,] 0.3589173 0.71783465 0.64108267
[14,] 0.3363567 0.67271339 0.66364330
[15,] 0.2906839 0.58136785 0.70931608
[16,] 0.2696807 0.53936139 0.73031930
[17,] 0.2707505 0.54150110 0.72924945
[18,] 0.2692304 0.53846076 0.73076962
[19,] 0.2637185 0.52743693 0.73628154
[20,] 0.3038667 0.60773345 0.69613327
[21,] 0.2801380 0.56027606 0.71986197
[22,] 0.2699860 0.53997200 0.73001400
[23,] 0.2881177 0.57623549 0.71188226
[24,] 0.2627734 0.52554685 0.73722657
[25,] 0.2444979 0.48899577 0.75550211
[26,] 0.2614364 0.52287274 0.73856363
[27,] 0.2884706 0.57694129 0.71152936
[28,] 0.3119796 0.62395919 0.68802041
[29,] 0.3062193 0.61243867 0.69378066
[30,] 0.3195528 0.63910551 0.68044724
[31,] 0.3285542 0.65710835 0.67144583
[32,] 0.5013111 0.99737786 0.49868893
[33,] 0.5284645 0.94307100 0.47153550
[34,] 0.6071043 0.78579147 0.39289574
[35,] 0.6798274 0.64034511 0.32017256
[36,] 0.7805197 0.43896057 0.21948029
[37,] 0.8435632 0.31287351 0.15643675
[38,] 0.8562157 0.28756857 0.14378428
[39,] 0.8744118 0.25117634 0.12558817
[40,] 0.8697000 0.26059995 0.13029997
[41,] 0.8593166 0.28136689 0.14068345
[42,] 0.8413227 0.31735470 0.15867735
[43,] 0.8318885 0.33622302 0.16811151
[44,] 0.9005852 0.19882957 0.09941479
[45,] 0.9510978 0.09780432 0.04890216
[46,] 0.9771811 0.04563771 0.02281886
[47,] 0.9866174 0.02676525 0.01338262
[48,] 0.9775673 0.04486531 0.02243266
[49,] 0.9602816 0.07943690 0.03971845
[50,] 0.9341468 0.13170635 0.06585318
[51,] 0.9148810 0.17023810 0.08511905
[52,] 0.9654936 0.06901284 0.03450642
[53,] 0.9336078 0.13278445 0.06639223
[54,] 0.8714574 0.25708511 0.12854256
[55,] 0.7718360 0.45632801 0.22816401
[56,] 0.6117276 0.77654476 0.38827238
> postscript(file="/var/www/html/rcomp/tmp/177iz1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ra5x1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/33hhl1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vzal1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5itnx1258654875.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 = 65
Frequency = 1
1 2 3 4 5 6
-0.70090923 -0.63661513 1.10881807 1.01664687 1.24858059 1.07543733
7 8 9 10 11 12
0.48167689 0.42376457 0.56701483 0.49429823 0.42637417 0.34350361
13 14 15 16 17 18
0.19662339 -0.11934347 0.97050257 0.25626717 0.62143941 0.85323091
19 20 21 22 23 24
0.57311217 0.74466619 0.78428649 0.70286295 0.64480841 0.90998065
25 26 27 28 29 30
0.34480841 0.27180737 0.58777423 -0.23270073 0.13987365 0.39270899
31 32 33 34 35 36
0.33465445 0.24582877 0.07688435 0.17557955 0.05830789 0.49140419
37 38 39 40 41 42
-0.12195307 -0.54299691 -0.04807389 -0.85751675 -0.86883329 -0.66404075
43 44 45 46 47 48
-0.41977013 -0.44430171 -0.67246325 -0.60845359 -0.85663861 -1.33066001
49 50 51 52 53 54
-1.44560651 -1.24793167 0.11985017 -0.17115845 -0.47739801 -0.65794341
55 56 57 58 59 60
-0.98508459 -1.08726753 -0.41310391 -0.19452745 -0.14024509 -0.19597447
61 62 63 64 65
-0.81803867 -0.77478841 0.19415601 -0.10178737 -0.04140767
> postscript(file="/var/www/html/rcomp/tmp/6vmmr1258654875.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 = 65
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.70090923 NA
1 -0.63661513 -0.70090923
2 1.10881807 -0.63661513
3 1.01664687 1.10881807
4 1.24858059 1.01664687
5 1.07543733 1.24858059
6 0.48167689 1.07543733
7 0.42376457 0.48167689
8 0.56701483 0.42376457
9 0.49429823 0.56701483
10 0.42637417 0.49429823
11 0.34350361 0.42637417
12 0.19662339 0.34350361
13 -0.11934347 0.19662339
14 0.97050257 -0.11934347
15 0.25626717 0.97050257
16 0.62143941 0.25626717
17 0.85323091 0.62143941
18 0.57311217 0.85323091
19 0.74466619 0.57311217
20 0.78428649 0.74466619
21 0.70286295 0.78428649
22 0.64480841 0.70286295
23 0.90998065 0.64480841
24 0.34480841 0.90998065
25 0.27180737 0.34480841
26 0.58777423 0.27180737
27 -0.23270073 0.58777423
28 0.13987365 -0.23270073
29 0.39270899 0.13987365
30 0.33465445 0.39270899
31 0.24582877 0.33465445
32 0.07688435 0.24582877
33 0.17557955 0.07688435
34 0.05830789 0.17557955
35 0.49140419 0.05830789
36 -0.12195307 0.49140419
37 -0.54299691 -0.12195307
38 -0.04807389 -0.54299691
39 -0.85751675 -0.04807389
40 -0.86883329 -0.85751675
41 -0.66404075 -0.86883329
42 -0.41977013 -0.66404075
43 -0.44430171 -0.41977013
44 -0.67246325 -0.44430171
45 -0.60845359 -0.67246325
46 -0.85663861 -0.60845359
47 -1.33066001 -0.85663861
48 -1.44560651 -1.33066001
49 -1.24793167 -1.44560651
50 0.11985017 -1.24793167
51 -0.17115845 0.11985017
52 -0.47739801 -0.17115845
53 -0.65794341 -0.47739801
54 -0.98508459 -0.65794341
55 -1.08726753 -0.98508459
56 -0.41310391 -1.08726753
57 -0.19452745 -0.41310391
58 -0.14024509 -0.19452745
59 -0.19597447 -0.14024509
60 -0.81803867 -0.19597447
61 -0.77478841 -0.81803867
62 0.19415601 -0.77478841
63 -0.10178737 0.19415601
64 -0.04140767 -0.10178737
65 NA -0.04140767
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.63661513 -0.70090923
[2,] 1.10881807 -0.63661513
[3,] 1.01664687 1.10881807
[4,] 1.24858059 1.01664687
[5,] 1.07543733 1.24858059
[6,] 0.48167689 1.07543733
[7,] 0.42376457 0.48167689
[8,] 0.56701483 0.42376457
[9,] 0.49429823 0.56701483
[10,] 0.42637417 0.49429823
[11,] 0.34350361 0.42637417
[12,] 0.19662339 0.34350361
[13,] -0.11934347 0.19662339
[14,] 0.97050257 -0.11934347
[15,] 0.25626717 0.97050257
[16,] 0.62143941 0.25626717
[17,] 0.85323091 0.62143941
[18,] 0.57311217 0.85323091
[19,] 0.74466619 0.57311217
[20,] 0.78428649 0.74466619
[21,] 0.70286295 0.78428649
[22,] 0.64480841 0.70286295
[23,] 0.90998065 0.64480841
[24,] 0.34480841 0.90998065
[25,] 0.27180737 0.34480841
[26,] 0.58777423 0.27180737
[27,] -0.23270073 0.58777423
[28,] 0.13987365 -0.23270073
[29,] 0.39270899 0.13987365
[30,] 0.33465445 0.39270899
[31,] 0.24582877 0.33465445
[32,] 0.07688435 0.24582877
[33,] 0.17557955 0.07688435
[34,] 0.05830789 0.17557955
[35,] 0.49140419 0.05830789
[36,] -0.12195307 0.49140419
[37,] -0.54299691 -0.12195307
[38,] -0.04807389 -0.54299691
[39,] -0.85751675 -0.04807389
[40,] -0.86883329 -0.85751675
[41,] -0.66404075 -0.86883329
[42,] -0.41977013 -0.66404075
[43,] -0.44430171 -0.41977013
[44,] -0.67246325 -0.44430171
[45,] -0.60845359 -0.67246325
[46,] -0.85663861 -0.60845359
[47,] -1.33066001 -0.85663861
[48,] -1.44560651 -1.33066001
[49,] -1.24793167 -1.44560651
[50,] 0.11985017 -1.24793167
[51,] -0.17115845 0.11985017
[52,] -0.47739801 -0.17115845
[53,] -0.65794341 -0.47739801
[54,] -0.98508459 -0.65794341
[55,] -1.08726753 -0.98508459
[56,] -0.41310391 -1.08726753
[57,] -0.19452745 -0.41310391
[58,] -0.14024509 -0.19452745
[59,] -0.19597447 -0.14024509
[60,] -0.81803867 -0.19597447
[61,] -0.77478841 -0.81803867
[62,] 0.19415601 -0.77478841
[63,] -0.10178737 0.19415601
[64,] -0.04140767 -0.10178737
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.63661513 -0.70090923
2 1.10881807 -0.63661513
3 1.01664687 1.10881807
4 1.24858059 1.01664687
5 1.07543733 1.24858059
6 0.48167689 1.07543733
7 0.42376457 0.48167689
8 0.56701483 0.42376457
9 0.49429823 0.56701483
10 0.42637417 0.49429823
11 0.34350361 0.42637417
12 0.19662339 0.34350361
13 -0.11934347 0.19662339
14 0.97050257 -0.11934347
15 0.25626717 0.97050257
16 0.62143941 0.25626717
17 0.85323091 0.62143941
18 0.57311217 0.85323091
19 0.74466619 0.57311217
20 0.78428649 0.74466619
21 0.70286295 0.78428649
22 0.64480841 0.70286295
23 0.90998065 0.64480841
24 0.34480841 0.90998065
25 0.27180737 0.34480841
26 0.58777423 0.27180737
27 -0.23270073 0.58777423
28 0.13987365 -0.23270073
29 0.39270899 0.13987365
30 0.33465445 0.39270899
31 0.24582877 0.33465445
32 0.07688435 0.24582877
33 0.17557955 0.07688435
34 0.05830789 0.17557955
35 0.49140419 0.05830789
36 -0.12195307 0.49140419
37 -0.54299691 -0.12195307
38 -0.04807389 -0.54299691
39 -0.85751675 -0.04807389
40 -0.86883329 -0.85751675
41 -0.66404075 -0.86883329
42 -0.41977013 -0.66404075
43 -0.44430171 -0.41977013
44 -0.67246325 -0.44430171
45 -0.60845359 -0.67246325
46 -0.85663861 -0.60845359
47 -1.33066001 -0.85663861
48 -1.44560651 -1.33066001
49 -1.24793167 -1.44560651
50 0.11985017 -1.24793167
51 -0.17115845 0.11985017
52 -0.47739801 -0.17115845
53 -0.65794341 -0.47739801
54 -0.98508459 -0.65794341
55 -1.08726753 -0.98508459
56 -0.41310391 -1.08726753
57 -0.19452745 -0.41310391
58 -0.14024509 -0.19452745
59 -0.19597447 -0.14024509
60 -0.81803867 -0.19597447
61 -0.77478841 -0.81803867
62 0.19415601 -0.77478841
63 -0.10178737 0.19415601
64 -0.04140767 -0.10178737
> 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/7pq6l1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8frvm1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jnnl1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lx5k1258654875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110zpq1258654875.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/12e3np1258654875.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/138nrl1258654875.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/149oy71258654875.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/15kme21258654875.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/16bpsf1258654875.tab")
+ }
>
> system("convert tmp/177iz1258654875.ps tmp/177iz1258654875.png")
> system("convert tmp/2ra5x1258654875.ps tmp/2ra5x1258654875.png")
> system("convert tmp/33hhl1258654875.ps tmp/33hhl1258654875.png")
> system("convert tmp/4vzal1258654875.ps tmp/4vzal1258654875.png")
> system("convert tmp/5itnx1258654875.ps tmp/5itnx1258654875.png")
> system("convert tmp/6vmmr1258654875.ps tmp/6vmmr1258654875.png")
> system("convert tmp/7pq6l1258654875.ps tmp/7pq6l1258654875.png")
> system("convert tmp/8frvm1258654875.ps tmp/8frvm1258654875.png")
> system("convert tmp/9jnnl1258654875.ps tmp/9jnnl1258654875.png")
> system("convert tmp/10lx5k1258654875.ps tmp/10lx5k1258654875.png")
>
>
> proc.time()
user system elapsed
2.476 1.556 2.896