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(0.7905
+ ,0.313
+ ,0.7744
+ ,0.779
+ ,0.7719
+ ,0.364
+ ,0.7905
+ ,0.7744
+ ,0.7811
+ ,0.363
+ ,0.7719
+ ,0.7905
+ ,0.7557
+ ,-0.155
+ ,0.7811
+ ,0.7719
+ ,0.7637
+ ,0.052
+ ,0.7557
+ ,0.7811
+ ,0.7595
+ ,0.568
+ ,0.7637
+ ,0.7557
+ ,0.7471
+ ,0.668
+ ,0.7595
+ ,0.7637
+ ,0.7615
+ ,1.378
+ ,0.7471
+ ,0.7595
+ ,0.7487
+ ,0.252
+ ,0.7615
+ ,0.7471
+ ,0.7389
+ ,-0.402
+ ,0.7487
+ ,0.7615
+ ,0.7337
+ ,-0.05
+ ,0.7389
+ ,0.7487
+ ,0.751
+ ,0.555
+ ,0.7337
+ ,0.7389
+ ,0.7382
+ ,0.05
+ ,0.751
+ ,0.7337
+ ,0.7159
+ ,0.15
+ ,0.7382
+ ,0.751
+ ,0.7542
+ ,0.45
+ ,0.7159
+ ,0.7382
+ ,0.7636
+ ,0.299
+ ,0.7542
+ ,0.7159
+ ,0.7433
+ ,0.199
+ ,0.7636
+ ,0.7542
+ ,0.7658
+ ,0.496
+ ,0.7433
+ ,0.7636
+ ,0.7627
+ ,0.444
+ ,0.7658
+ ,0.7433
+ ,0.748
+ ,-0.393
+ ,0.7627
+ ,0.7658
+ ,0.7692
+ ,-0.444
+ ,0.748
+ ,0.7627
+ ,0.785
+ ,0.198
+ ,0.7692
+ ,0.748
+ ,0.7913
+ ,0.494
+ ,0.785
+ ,0.7692
+ ,0.772
+ ,0.133
+ ,0.7913
+ ,0.785
+ ,0.788
+ ,0.388
+ ,0.772
+ ,0.7913
+ ,0.807
+ ,0.484
+ ,0.788
+ ,0.772
+ ,0.8268
+ ,0.278
+ ,0.807
+ ,0.788
+ ,0.8244
+ ,0.369
+ ,0.8268
+ ,0.807
+ ,0.8487
+ ,0.165
+ ,0.8244
+ ,0.8268
+ ,0.8572
+ ,0.155
+ ,0.8487
+ ,0.8244
+ ,0.8214
+ ,0.087
+ ,0.8572
+ ,0.8487
+ ,0.8827
+ ,0.414
+ ,0.8214
+ ,0.8572
+ ,0.9216
+ ,0.36
+ ,0.8827
+ ,0.8214
+ ,0.8865
+ ,0.975
+ ,0.9216
+ ,0.8827
+ ,0.8816
+ ,0.27
+ ,0.8865
+ ,0.9216
+ ,0.8884
+ ,0.359
+ ,0.8816
+ ,0.8865
+ ,0.9466
+ ,0.169
+ ,0.8884
+ ,0.8816
+ ,0.918
+ ,0.381
+ ,0.9466
+ ,0.8884
+ ,0.9337
+ ,0.154
+ ,0.918
+ ,0.9466
+ ,0.9559
+ ,0.486
+ ,0.9337
+ ,0.918
+ ,0.9626
+ ,0.925
+ ,0.9559
+ ,0.9337
+ ,0.9434
+ ,0.728
+ ,0.9626
+ ,0.9559
+ ,0.8639
+ ,-0.014
+ ,0.9434
+ ,0.9626
+ ,0.7996
+ ,0.046
+ ,0.8639
+ ,0.9434
+ ,0.668
+ ,-0.819
+ ,0.7996
+ ,0.8639
+ ,0.6572
+ ,-1.674
+ ,0.668
+ ,0.7996
+ ,0.6928
+ ,-0.788
+ ,0.6572
+ ,0.668
+ ,0.6438
+ ,0.279
+ ,0.6928
+ ,0.6572
+ ,0.6454
+ ,0.396
+ ,0.6438
+ ,0.6928
+ ,0.6873
+ ,-0.141
+ ,0.6454
+ ,0.6438
+ ,0.7265
+ ,-0.019
+ ,0.6873
+ ,0.6454
+ ,0.7912
+ ,0.099
+ ,0.7265
+ ,0.6873
+ ,0.8114
+ ,0.742
+ ,0.7912
+ ,0.7265
+ ,0.8281
+ ,0.005
+ ,0.8114
+ ,0.7912
+ ,0.8393
+ ,0.448
+ ,0.8281
+ ,0.8114)
+ ,dim=c(4
+ ,55)
+ ,dimnames=list(c('USDOLLAR'
+ ,'Amerikaanse_inflatie'
+ ,'Y[t-1]'
+ ,'Y[t-2]')
+ ,1:55))
> y <- array(NA,dim=c(4,55),dimnames=list(c('USDOLLAR','Amerikaanse_inflatie','Y[t-1]','Y[t-2]'),1:55))
> 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 = 'Include Monthly 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
USDOLLAR Amerikaanse_inflatie Y[t-1] Y[t-2] M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 0.7905 0.313 0.7744 0.7790 1 0 0 0 0 0 0 0 0 0
2 0.7719 0.364 0.7905 0.7744 0 1 0 0 0 0 0 0 0 0
3 0.7811 0.363 0.7719 0.7905 0 0 1 0 0 0 0 0 0 0
4 0.7557 -0.155 0.7811 0.7719 0 0 0 1 0 0 0 0 0 0
5 0.7637 0.052 0.7557 0.7811 0 0 0 0 1 0 0 0 0 0
6 0.7595 0.568 0.7637 0.7557 0 0 0 0 0 1 0 0 0 0
7 0.7471 0.668 0.7595 0.7637 0 0 0 0 0 0 1 0 0 0
8 0.7615 1.378 0.7471 0.7595 0 0 0 0 0 0 0 1 0 0
9 0.7487 0.252 0.7615 0.7471 0 0 0 0 0 0 0 0 1 0
10 0.7389 -0.402 0.7487 0.7615 0 0 0 0 0 0 0 0 0 1
11 0.7337 -0.050 0.7389 0.7487 0 0 0 0 0 0 0 0 0 0
12 0.7510 0.555 0.7337 0.7389 0 0 0 0 0 0 0 0 0 0
13 0.7382 0.050 0.7510 0.7337 1 0 0 0 0 0 0 0 0 0
14 0.7159 0.150 0.7382 0.7510 0 1 0 0 0 0 0 0 0 0
15 0.7542 0.450 0.7159 0.7382 0 0 1 0 0 0 0 0 0 0
16 0.7636 0.299 0.7542 0.7159 0 0 0 1 0 0 0 0 0 0
17 0.7433 0.199 0.7636 0.7542 0 0 0 0 1 0 0 0 0 0
18 0.7658 0.496 0.7433 0.7636 0 0 0 0 0 1 0 0 0 0
19 0.7627 0.444 0.7658 0.7433 0 0 0 0 0 0 1 0 0 0
20 0.7480 -0.393 0.7627 0.7658 0 0 0 0 0 0 0 1 0 0
21 0.7692 -0.444 0.7480 0.7627 0 0 0 0 0 0 0 0 1 0
22 0.7850 0.198 0.7692 0.7480 0 0 0 0 0 0 0 0 0 1
23 0.7913 0.494 0.7850 0.7692 0 0 0 0 0 0 0 0 0 0
24 0.7720 0.133 0.7913 0.7850 0 0 0 0 0 0 0 0 0 0
25 0.7880 0.388 0.7720 0.7913 1 0 0 0 0 0 0 0 0 0
26 0.8070 0.484 0.7880 0.7720 0 1 0 0 0 0 0 0 0 0
27 0.8268 0.278 0.8070 0.7880 0 0 1 0 0 0 0 0 0 0
28 0.8244 0.369 0.8268 0.8070 0 0 0 1 0 0 0 0 0 0
29 0.8487 0.165 0.8244 0.8268 0 0 0 0 1 0 0 0 0 0
30 0.8572 0.155 0.8487 0.8244 0 0 0 0 0 1 0 0 0 0
31 0.8214 0.087 0.8572 0.8487 0 0 0 0 0 0 1 0 0 0
32 0.8827 0.414 0.8214 0.8572 0 0 0 0 0 0 0 1 0 0
33 0.9216 0.360 0.8827 0.8214 0 0 0 0 0 0 0 0 1 0
34 0.8865 0.975 0.9216 0.8827 0 0 0 0 0 0 0 0 0 1
35 0.8816 0.270 0.8865 0.9216 0 0 0 0 0 0 0 0 0 0
36 0.8884 0.359 0.8816 0.8865 0 0 0 0 0 0 0 0 0 0
37 0.9466 0.169 0.8884 0.8816 1 0 0 0 0 0 0 0 0 0
38 0.9180 0.381 0.9466 0.8884 0 1 0 0 0 0 0 0 0 0
39 0.9337 0.154 0.9180 0.9466 0 0 1 0 0 0 0 0 0 0
40 0.9559 0.486 0.9337 0.9180 0 0 0 1 0 0 0 0 0 0
41 0.9626 0.925 0.9559 0.9337 0 0 0 0 1 0 0 0 0 0
42 0.9434 0.728 0.9626 0.9559 0 0 0 0 0 1 0 0 0 0
43 0.8639 -0.014 0.9434 0.9626 0 0 0 0 0 0 1 0 0 0
44 0.7996 0.046 0.8639 0.9434 0 0 0 0 0 0 0 1 0 0
45 0.6680 -0.819 0.7996 0.8639 0 0 0 0 0 0 0 0 1 0
46 0.6572 -1.674 0.6680 0.7996 0 0 0 0 0 0 0 0 0 1
47 0.6928 -0.788 0.6572 0.6680 0 0 0 0 0 0 0 0 0 0
48 0.6438 0.279 0.6928 0.6572 0 0 0 0 0 0 0 0 0 0
49 0.6454 0.396 0.6438 0.6928 1 0 0 0 0 0 0 0 0 0
50 0.6873 -0.141 0.6454 0.6438 0 1 0 0 0 0 0 0 0 0
51 0.7265 -0.019 0.6873 0.6454 0 0 1 0 0 0 0 0 0 0
52 0.7912 0.099 0.7265 0.6873 0 0 0 1 0 0 0 0 0 0
53 0.8114 0.742 0.7912 0.7265 0 0 0 0 1 0 0 0 0 0
54 0.8281 0.005 0.8114 0.7912 0 0 0 0 0 1 0 0 0 0
55 0.8393 0.448 0.8281 0.8114 0 0 0 0 0 0 1 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Amerikaanse_inflatie `Y[t-1]`
0.078111 0.013939 1.129797
`Y[t-2]` M1 M2
-0.264538 0.032724 0.010482
M3 M4 M5
0.040800 0.026505 0.022242
M6 M7 M8
0.022018 -0.004729 0.024923
M9 M10 M11
0.003150 0.017361 0.027833
t
0.000284
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.089476 -0.013709 0.001758 0.017045 0.063554
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0781107 0.0492626 1.586 0.1209
Amerikaanse_inflatie 0.0139386 0.0139591 0.999 0.3242
`Y[t-1]` 1.1297972 0.1907686 5.922 6.64e-07 ***
`Y[t-2]` -0.2645385 0.1773754 -1.491 0.1439
M1 0.0327237 0.0219185 1.493 0.1435
M2 0.0104819 0.0219161 0.478 0.6351
M3 0.0407996 0.0218003 1.872 0.0688 .
M4 0.0265052 0.0222631 1.191 0.2410
M5 0.0222424 0.0218760 1.017 0.3155
M6 0.0220179 0.0219299 1.004 0.3216
M7 -0.0047295 0.0220026 -0.215 0.8309
M8 0.0249235 0.0240809 1.035 0.3070
M9 0.0031500 0.0240200 0.131 0.8963
M10 0.0173613 0.0238995 0.726 0.4719
M11 0.0278332 0.0232480 1.197 0.2384
t 0.0002840 0.0002982 0.952 0.3467
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03237 on 39 degrees of freedom
Multiple R-squared: 0.8817, Adjusted R-squared: 0.8362
F-statistic: 19.38 on 15 and 39 DF, p-value: 1.530e-13
> 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,] 1.265020e-01 0.2530039431 0.8734980
[2,] 8.494172e-02 0.1698834379 0.9150583
[3,] 5.409091e-02 0.1081818262 0.9459091
[4,] 2.204179e-02 0.0440835709 0.9779582
[5,] 9.270214e-03 0.0185404282 0.9907298
[6,] 6.692120e-03 0.0133842396 0.9933079
[7,] 2.758134e-03 0.0055162681 0.9972419
[8,] 1.984984e-03 0.0039699672 0.9980150
[9,] 6.881131e-04 0.0013762262 0.9993119
[10,] 3.755557e-04 0.0007511114 0.9996244
[11,] 1.965071e-04 0.0003930141 0.9998035
[12,] 8.023257e-05 0.0001604651 0.9999198
[13,] 2.985806e-04 0.0005971611 0.9997014
[14,] 4.323075e-04 0.0008646150 0.9995677
[15,] 4.724230e-03 0.0094484610 0.9952758
[16,] 1.039895e-02 0.0207978909 0.9896011
[17,] 4.239619e-03 0.0084792371 0.9957604
[18,] 1.118360e-02 0.0223671956 0.9888164
> postscript(file="/var/www/html/rcomp/tmp/19mh81258655957.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24hmm1258655957.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tbyw1258655957.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ebcx1258655957.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/503de1258655957.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 = 55
Frequency = 1
1 2 3 4 5 6
0.006179292 -0.010580343 -0.006694809 -0.026178747 0.014045372 -0.013164143
7 8 9 10 11 12
0.006366878 -0.006168064 -0.001333064 0.001758170 -0.011418099 0.028280688
13 14 15 16 17 18
-0.031409121 -0.014107213 0.011217892 -0.012437401 -0.027853008 0.015869230
19 20 21 22 23 24
0.009166891 -0.014348987 0.044839336 0.009354979 -0.011469229 -0.001126246
25 26 27 28 29 30
0.001783388 0.018220780 -0.006943104 -0.013944847 0.025126805 0.005617680
31 32 33 34 35 36
-0.005946064 0.063554368 0.045969508 -0.039930991 0.004186340 0.033545677
37 38 39 40 41 42
0.052407445 -0.021145027 0.014825672 0.021104864 0.004736382 -0.013474148
43 44 45 46 47 48
-0.032703776 -0.043037317 -0.089475780 0.028817842 0.018700988 -0.060700119
49 50 51 52 53 54
-0.028961004 0.027611803 -0.012405650 0.031456130 -0.016055552 0.005151381
55
0.023116071
> postscript(file="/var/www/html/rcomp/tmp/6o9j11258655957.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 = 55
Frequency = 1
lag(myerror, k = 1) myerror
0 0.006179292 NA
1 -0.010580343 0.006179292
2 -0.006694809 -0.010580343
3 -0.026178747 -0.006694809
4 0.014045372 -0.026178747
5 -0.013164143 0.014045372
6 0.006366878 -0.013164143
7 -0.006168064 0.006366878
8 -0.001333064 -0.006168064
9 0.001758170 -0.001333064
10 -0.011418099 0.001758170
11 0.028280688 -0.011418099
12 -0.031409121 0.028280688
13 -0.014107213 -0.031409121
14 0.011217892 -0.014107213
15 -0.012437401 0.011217892
16 -0.027853008 -0.012437401
17 0.015869230 -0.027853008
18 0.009166891 0.015869230
19 -0.014348987 0.009166891
20 0.044839336 -0.014348987
21 0.009354979 0.044839336
22 -0.011469229 0.009354979
23 -0.001126246 -0.011469229
24 0.001783388 -0.001126246
25 0.018220780 0.001783388
26 -0.006943104 0.018220780
27 -0.013944847 -0.006943104
28 0.025126805 -0.013944847
29 0.005617680 0.025126805
30 -0.005946064 0.005617680
31 0.063554368 -0.005946064
32 0.045969508 0.063554368
33 -0.039930991 0.045969508
34 0.004186340 -0.039930991
35 0.033545677 0.004186340
36 0.052407445 0.033545677
37 -0.021145027 0.052407445
38 0.014825672 -0.021145027
39 0.021104864 0.014825672
40 0.004736382 0.021104864
41 -0.013474148 0.004736382
42 -0.032703776 -0.013474148
43 -0.043037317 -0.032703776
44 -0.089475780 -0.043037317
45 0.028817842 -0.089475780
46 0.018700988 0.028817842
47 -0.060700119 0.018700988
48 -0.028961004 -0.060700119
49 0.027611803 -0.028961004
50 -0.012405650 0.027611803
51 0.031456130 -0.012405650
52 -0.016055552 0.031456130
53 0.005151381 -0.016055552
54 0.023116071 0.005151381
55 NA 0.023116071
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.010580343 0.006179292
[2,] -0.006694809 -0.010580343
[3,] -0.026178747 -0.006694809
[4,] 0.014045372 -0.026178747
[5,] -0.013164143 0.014045372
[6,] 0.006366878 -0.013164143
[7,] -0.006168064 0.006366878
[8,] -0.001333064 -0.006168064
[9,] 0.001758170 -0.001333064
[10,] -0.011418099 0.001758170
[11,] 0.028280688 -0.011418099
[12,] -0.031409121 0.028280688
[13,] -0.014107213 -0.031409121
[14,] 0.011217892 -0.014107213
[15,] -0.012437401 0.011217892
[16,] -0.027853008 -0.012437401
[17,] 0.015869230 -0.027853008
[18,] 0.009166891 0.015869230
[19,] -0.014348987 0.009166891
[20,] 0.044839336 -0.014348987
[21,] 0.009354979 0.044839336
[22,] -0.011469229 0.009354979
[23,] -0.001126246 -0.011469229
[24,] 0.001783388 -0.001126246
[25,] 0.018220780 0.001783388
[26,] -0.006943104 0.018220780
[27,] -0.013944847 -0.006943104
[28,] 0.025126805 -0.013944847
[29,] 0.005617680 0.025126805
[30,] -0.005946064 0.005617680
[31,] 0.063554368 -0.005946064
[32,] 0.045969508 0.063554368
[33,] -0.039930991 0.045969508
[34,] 0.004186340 -0.039930991
[35,] 0.033545677 0.004186340
[36,] 0.052407445 0.033545677
[37,] -0.021145027 0.052407445
[38,] 0.014825672 -0.021145027
[39,] 0.021104864 0.014825672
[40,] 0.004736382 0.021104864
[41,] -0.013474148 0.004736382
[42,] -0.032703776 -0.013474148
[43,] -0.043037317 -0.032703776
[44,] -0.089475780 -0.043037317
[45,] 0.028817842 -0.089475780
[46,] 0.018700988 0.028817842
[47,] -0.060700119 0.018700988
[48,] -0.028961004 -0.060700119
[49,] 0.027611803 -0.028961004
[50,] -0.012405650 0.027611803
[51,] 0.031456130 -0.012405650
[52,] -0.016055552 0.031456130
[53,] 0.005151381 -0.016055552
[54,] 0.023116071 0.005151381
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.010580343 0.006179292
2 -0.006694809 -0.010580343
3 -0.026178747 -0.006694809
4 0.014045372 -0.026178747
5 -0.013164143 0.014045372
6 0.006366878 -0.013164143
7 -0.006168064 0.006366878
8 -0.001333064 -0.006168064
9 0.001758170 -0.001333064
10 -0.011418099 0.001758170
11 0.028280688 -0.011418099
12 -0.031409121 0.028280688
13 -0.014107213 -0.031409121
14 0.011217892 -0.014107213
15 -0.012437401 0.011217892
16 -0.027853008 -0.012437401
17 0.015869230 -0.027853008
18 0.009166891 0.015869230
19 -0.014348987 0.009166891
20 0.044839336 -0.014348987
21 0.009354979 0.044839336
22 -0.011469229 0.009354979
23 -0.001126246 -0.011469229
24 0.001783388 -0.001126246
25 0.018220780 0.001783388
26 -0.006943104 0.018220780
27 -0.013944847 -0.006943104
28 0.025126805 -0.013944847
29 0.005617680 0.025126805
30 -0.005946064 0.005617680
31 0.063554368 -0.005946064
32 0.045969508 0.063554368
33 -0.039930991 0.045969508
34 0.004186340 -0.039930991
35 0.033545677 0.004186340
36 0.052407445 0.033545677
37 -0.021145027 0.052407445
38 0.014825672 -0.021145027
39 0.021104864 0.014825672
40 0.004736382 0.021104864
41 -0.013474148 0.004736382
42 -0.032703776 -0.013474148
43 -0.043037317 -0.032703776
44 -0.089475780 -0.043037317
45 0.028817842 -0.089475780
46 0.018700988 0.028817842
47 -0.060700119 0.018700988
48 -0.028961004 -0.060700119
49 0.027611803 -0.028961004
50 -0.012405650 0.027611803
51 0.031456130 -0.012405650
52 -0.016055552 0.031456130
53 0.005151381 -0.016055552
54 0.023116071 0.005151381
> 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/7f8hu1258655957.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8spz91258655957.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hw7w1258655957.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rwgd1258655957.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11916p1258655957.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/1286yd1258655958.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/134r9j1258655958.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/14gt3q1258655958.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/15jg4q1258655958.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/16caqc1258655958.tab")
+ }
>
> system("convert tmp/19mh81258655957.ps tmp/19mh81258655957.png")
> system("convert tmp/24hmm1258655957.ps tmp/24hmm1258655957.png")
> system("convert tmp/3tbyw1258655957.ps tmp/3tbyw1258655957.png")
> system("convert tmp/4ebcx1258655957.ps tmp/4ebcx1258655957.png")
> system("convert tmp/503de1258655957.ps tmp/503de1258655957.png")
> system("convert tmp/6o9j11258655957.ps tmp/6o9j11258655957.png")
> system("convert tmp/7f8hu1258655957.ps tmp/7f8hu1258655957.png")
> system("convert tmp/8spz91258655957.ps tmp/8spz91258655957.png")
> system("convert tmp/9hw7w1258655957.ps tmp/9hw7w1258655957.png")
> system("convert tmp/10rwgd1258655957.ps tmp/10rwgd1258655957.png")
>
>
> proc.time()
user system elapsed
2.308 1.530 3.115