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
+ ,0.7775
+ ,0.7461
+ ,0.7719
+ ,0.364
+ ,0.7905
+ ,0.7744
+ ,0.779
+ ,0.7775
+ ,0.7811
+ ,0.363
+ ,0.7719
+ ,0.7905
+ ,0.7744
+ ,0.779
+ ,0.7557
+ ,-0.155
+ ,0.7811
+ ,0.7719
+ ,0.7905
+ ,0.7744
+ ,0.7637
+ ,0.052
+ ,0.7557
+ ,0.7811
+ ,0.7719
+ ,0.7905
+ ,0.7595
+ ,0.568
+ ,0.7637
+ ,0.7557
+ ,0.7811
+ ,0.7719
+ ,0.7471
+ ,0.668
+ ,0.7595
+ ,0.7637
+ ,0.7557
+ ,0.7811
+ ,0.7615
+ ,1.378
+ ,0.7471
+ ,0.7595
+ ,0.7637
+ ,0.7557
+ ,0.7487
+ ,0.252
+ ,0.7615
+ ,0.7471
+ ,0.7595
+ ,0.7637
+ ,0.7389
+ ,-0.402
+ ,0.7487
+ ,0.7615
+ ,0.7471
+ ,0.7595
+ ,0.7337
+ ,-0.05
+ ,0.7389
+ ,0.7487
+ ,0.7615
+ ,0.7471
+ ,0.751
+ ,0.555
+ ,0.7337
+ ,0.7389
+ ,0.7487
+ ,0.7615
+ ,0.7382
+ ,0.05
+ ,0.751
+ ,0.7337
+ ,0.7389
+ ,0.7487
+ ,0.7159
+ ,0.15
+ ,0.7382
+ ,0.751
+ ,0.7337
+ ,0.7389
+ ,0.7542
+ ,0.45
+ ,0.7159
+ ,0.7382
+ ,0.751
+ ,0.7337
+ ,0.7636
+ ,0.299
+ ,0.7542
+ ,0.7159
+ ,0.7382
+ ,0.751
+ ,0.7433
+ ,0.199
+ ,0.7636
+ ,0.7542
+ ,0.7159
+ ,0.7382
+ ,0.7658
+ ,0.496
+ ,0.7433
+ ,0.7636
+ ,0.7542
+ ,0.7159
+ ,0.7627
+ ,0.444
+ ,0.7658
+ ,0.7433
+ ,0.7636
+ ,0.7542
+ ,0.748
+ ,-0.393
+ ,0.7627
+ ,0.7658
+ ,0.7433
+ ,0.7636
+ ,0.7692
+ ,-0.444
+ ,0.748
+ ,0.7627
+ ,0.7658
+ ,0.7433
+ ,0.785
+ ,0.198
+ ,0.7692
+ ,0.748
+ ,0.7627
+ ,0.7658
+ ,0.7913
+ ,0.494
+ ,0.785
+ ,0.7692
+ ,0.748
+ ,0.7627
+ ,0.772
+ ,0.133
+ ,0.7913
+ ,0.785
+ ,0.7692
+ ,0.748
+ ,0.788
+ ,0.388
+ ,0.772
+ ,0.7913
+ ,0.785
+ ,0.7692
+ ,0.807
+ ,0.484
+ ,0.788
+ ,0.772
+ ,0.7913
+ ,0.785
+ ,0.8268
+ ,0.278
+ ,0.807
+ ,0.788
+ ,0.772
+ ,0.7913
+ ,0.8244
+ ,0.369
+ ,0.8268
+ ,0.807
+ ,0.788
+ ,0.772
+ ,0.8487
+ ,0.165
+ ,0.8244
+ ,0.8268
+ ,0.807
+ ,0.788
+ ,0.8572
+ ,0.155
+ ,0.8487
+ ,0.8244
+ ,0.8268
+ ,0.807
+ ,0.8214
+ ,0.087
+ ,0.8572
+ ,0.8487
+ ,0.8244
+ ,0.8268
+ ,0.8827
+ ,0.414
+ ,0.8214
+ ,0.8572
+ ,0.8487
+ ,0.8244
+ ,0.9216
+ ,0.36
+ ,0.8827
+ ,0.8214
+ ,0.8572
+ ,0.8487
+ ,0.8865
+ ,0.975
+ ,0.9216
+ ,0.8827
+ ,0.8214
+ ,0.8572
+ ,0.8816
+ ,0.27
+ ,0.8865
+ ,0.9216
+ ,0.8827
+ ,0.8214
+ ,0.8884
+ ,0.359
+ ,0.8816
+ ,0.8865
+ ,0.9216
+ ,0.8827
+ ,0.9466
+ ,0.169
+ ,0.8884
+ ,0.8816
+ ,0.8865
+ ,0.9216
+ ,0.918
+ ,0.381
+ ,0.9466
+ ,0.8884
+ ,0.8816
+ ,0.8865
+ ,0.9337
+ ,0.154
+ ,0.918
+ ,0.9466
+ ,0.8884
+ ,0.8816
+ ,0.9559
+ ,0.486
+ ,0.9337
+ ,0.918
+ ,0.9466
+ ,0.8884
+ ,0.9626
+ ,0.925
+ ,0.9559
+ ,0.9337
+ ,0.918
+ ,0.9466
+ ,0.9434
+ ,0.728
+ ,0.9626
+ ,0.9559
+ ,0.9337
+ ,0.918
+ ,0.8639
+ ,-0.014
+ ,0.9434
+ ,0.9626
+ ,0.9559
+ ,0.9337
+ ,0.7996
+ ,0.046
+ ,0.8639
+ ,0.9434
+ ,0.9626
+ ,0.9559
+ ,0.668
+ ,-0.819
+ ,0.7996
+ ,0.8639
+ ,0.9434
+ ,0.9626
+ ,0.6572
+ ,-1.674
+ ,0.668
+ ,0.7996
+ ,0.8639
+ ,0.9434
+ ,0.6928
+ ,-0.788
+ ,0.6572
+ ,0.668
+ ,0.7996
+ ,0.8639
+ ,0.6438
+ ,0.279
+ ,0.6928
+ ,0.6572
+ ,0.668
+ ,0.7996
+ ,0.6454
+ ,0.396
+ ,0.6438
+ ,0.6928
+ ,0.6572
+ ,0.668
+ ,0.6873
+ ,-0.141
+ ,0.6454
+ ,0.6438
+ ,0.6928
+ ,0.6572
+ ,0.7265
+ ,-0.019
+ ,0.6873
+ ,0.6454
+ ,0.6438
+ ,0.6928
+ ,0.7912
+ ,0.099
+ ,0.7265
+ ,0.6873
+ ,0.6454
+ ,0.6438
+ ,0.8114
+ ,0.742
+ ,0.7912
+ ,0.7265
+ ,0.6873
+ ,0.6454
+ ,0.8281
+ ,0.005
+ ,0.8114
+ ,0.7912
+ ,0.7265
+ ,0.6873
+ ,0.8393
+ ,0.448
+ ,0.8281
+ ,0.8114
+ ,0.7912
+ ,0.7265)
+ ,dim=c(6
+ ,55)
+ ,dimnames=list(c('USDOLLAR'
+ ,'Amerikaanse_inflatie'
+ ,'Y[t-1]'
+ ,'Y[t-2]'
+ ,'Y[t-3]'
+ ,'Y[t-4]')
+ ,1:55))
> y <- array(NA,dim=c(6,55),dimnames=list(c('USDOLLAR','Amerikaanse_inflatie','Y[t-1]','Y[t-2]','Y[t-3]','Y[t-4]'),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] Y[t-3] Y[t-4] M1 M2 M3 M4 M5 M6
1 0.7905 0.313 0.7744 0.7790 0.7775 0.7461 1 0 0 0 0 0
2 0.7719 0.364 0.7905 0.7744 0.7790 0.7775 0 1 0 0 0 0
3 0.7811 0.363 0.7719 0.7905 0.7744 0.7790 0 0 1 0 0 0
4 0.7557 -0.155 0.7811 0.7719 0.7905 0.7744 0 0 0 1 0 0
5 0.7637 0.052 0.7557 0.7811 0.7719 0.7905 0 0 0 0 1 0
6 0.7595 0.568 0.7637 0.7557 0.7811 0.7719 0 0 0 0 0 1
7 0.7471 0.668 0.7595 0.7637 0.7557 0.7811 0 0 0 0 0 0
8 0.7615 1.378 0.7471 0.7595 0.7637 0.7557 0 0 0 0 0 0
9 0.7487 0.252 0.7615 0.7471 0.7595 0.7637 0 0 0 0 0 0
10 0.7389 -0.402 0.7487 0.7615 0.7471 0.7595 0 0 0 0 0 0
11 0.7337 -0.050 0.7389 0.7487 0.7615 0.7471 0 0 0 0 0 0
12 0.7510 0.555 0.7337 0.7389 0.7487 0.7615 0 0 0 0 0 0
13 0.7382 0.050 0.7510 0.7337 0.7389 0.7487 1 0 0 0 0 0
14 0.7159 0.150 0.7382 0.7510 0.7337 0.7389 0 1 0 0 0 0
15 0.7542 0.450 0.7159 0.7382 0.7510 0.7337 0 0 1 0 0 0
16 0.7636 0.299 0.7542 0.7159 0.7382 0.7510 0 0 0 1 0 0
17 0.7433 0.199 0.7636 0.7542 0.7159 0.7382 0 0 0 0 1 0
18 0.7658 0.496 0.7433 0.7636 0.7542 0.7159 0 0 0 0 0 1
19 0.7627 0.444 0.7658 0.7433 0.7636 0.7542 0 0 0 0 0 0
20 0.7480 -0.393 0.7627 0.7658 0.7433 0.7636 0 0 0 0 0 0
21 0.7692 -0.444 0.7480 0.7627 0.7658 0.7433 0 0 0 0 0 0
22 0.7850 0.198 0.7692 0.7480 0.7627 0.7658 0 0 0 0 0 0
23 0.7913 0.494 0.7850 0.7692 0.7480 0.7627 0 0 0 0 0 0
24 0.7720 0.133 0.7913 0.7850 0.7692 0.7480 0 0 0 0 0 0
25 0.7880 0.388 0.7720 0.7913 0.7850 0.7692 1 0 0 0 0 0
26 0.8070 0.484 0.7880 0.7720 0.7913 0.7850 0 1 0 0 0 0
27 0.8268 0.278 0.8070 0.7880 0.7720 0.7913 0 0 1 0 0 0
28 0.8244 0.369 0.8268 0.8070 0.7880 0.7720 0 0 0 1 0 0
29 0.8487 0.165 0.8244 0.8268 0.8070 0.7880 0 0 0 0 1 0
30 0.8572 0.155 0.8487 0.8244 0.8268 0.8070 0 0 0 0 0 1
31 0.8214 0.087 0.8572 0.8487 0.8244 0.8268 0 0 0 0 0 0
32 0.8827 0.414 0.8214 0.8572 0.8487 0.8244 0 0 0 0 0 0
33 0.9216 0.360 0.8827 0.8214 0.8572 0.8487 0 0 0 0 0 0
34 0.8865 0.975 0.9216 0.8827 0.8214 0.8572 0 0 0 0 0 0
35 0.8816 0.270 0.8865 0.9216 0.8827 0.8214 0 0 0 0 0 0
36 0.8884 0.359 0.8816 0.8865 0.9216 0.8827 0 0 0 0 0 0
37 0.9466 0.169 0.8884 0.8816 0.8865 0.9216 1 0 0 0 0 0
38 0.9180 0.381 0.9466 0.8884 0.8816 0.8865 0 1 0 0 0 0
39 0.9337 0.154 0.9180 0.9466 0.8884 0.8816 0 0 1 0 0 0
40 0.9559 0.486 0.9337 0.9180 0.9466 0.8884 0 0 0 1 0 0
41 0.9626 0.925 0.9559 0.9337 0.9180 0.9466 0 0 0 0 1 0
42 0.9434 0.728 0.9626 0.9559 0.9337 0.9180 0 0 0 0 0 1
43 0.8639 -0.014 0.9434 0.9626 0.9559 0.9337 0 0 0 0 0 0
44 0.7996 0.046 0.8639 0.9434 0.9626 0.9559 0 0 0 0 0 0
45 0.6680 -0.819 0.7996 0.8639 0.9434 0.9626 0 0 0 0 0 0
46 0.6572 -1.674 0.6680 0.7996 0.8639 0.9434 0 0 0 0 0 0
47 0.6928 -0.788 0.6572 0.6680 0.7996 0.8639 0 0 0 0 0 0
48 0.6438 0.279 0.6928 0.6572 0.6680 0.7996 0 0 0 0 0 0
49 0.6454 0.396 0.6438 0.6928 0.6572 0.6680 1 0 0 0 0 0
50 0.6873 -0.141 0.6454 0.6438 0.6928 0.6572 0 1 0 0 0 0
51 0.7265 -0.019 0.6873 0.6454 0.6438 0.6928 0 0 1 0 0 0
52 0.7912 0.099 0.7265 0.6873 0.6454 0.6438 0 0 0 1 0 0
53 0.8114 0.742 0.7912 0.7265 0.6873 0.6454 0 0 0 0 1 0
54 0.8281 0.005 0.8114 0.7912 0.7265 0.6873 0 0 0 0 0 1
55 0.8393 0.448 0.8281 0.8114 0.7912 0.7265 0 0 0 0 0 0
M7 M8 M9 M10 M11 t
1 0 0 0 0 0 1
2 0 0 0 0 0 2
3 0 0 0 0 0 3
4 0 0 0 0 0 4
5 0 0 0 0 0 5
6 0 0 0 0 0 6
7 1 0 0 0 0 7
8 0 1 0 0 0 8
9 0 0 1 0 0 9
10 0 0 0 1 0 10
11 0 0 0 0 1 11
12 0 0 0 0 0 12
13 0 0 0 0 0 13
14 0 0 0 0 0 14
15 0 0 0 0 0 15
16 0 0 0 0 0 16
17 0 0 0 0 0 17
18 0 0 0 0 0 18
19 1 0 0 0 0 19
20 0 1 0 0 0 20
21 0 0 1 0 0 21
22 0 0 0 1 0 22
23 0 0 0 0 1 23
24 0 0 0 0 0 24
25 0 0 0 0 0 25
26 0 0 0 0 0 26
27 0 0 0 0 0 27
28 0 0 0 0 0 28
29 0 0 0 0 0 29
30 0 0 0 0 0 30
31 1 0 0 0 0 31
32 0 1 0 0 0 32
33 0 0 1 0 0 33
34 0 0 0 1 0 34
35 0 0 0 0 1 35
36 0 0 0 0 0 36
37 0 0 0 0 0 37
38 0 0 0 0 0 38
39 0 0 0 0 0 39
40 0 0 0 0 0 40
41 0 0 0 0 0 41
42 0 0 0 0 0 42
43 1 0 0 0 0 43
44 0 1 0 0 0 44
45 0 0 1 0 0 45
46 0 0 0 1 0 46
47 0 0 0 0 1 47
48 0 0 0 0 0 48
49 0 0 0 0 0 49
50 0 0 0 0 0 50
51 0 0 0 0 0 51
52 0 0 0 0 0 52
53 0 0 0 0 0 53
54 0 0 0 0 0 54
55 1 0 0 0 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]`
0.1055970 0.0094120 1.2059612
`Y[t-2]` `Y[t-3]` `Y[t-4]`
-0.5272015 0.5478133 -0.3889426
M1 M2 M3
0.0293146 -0.0011163 0.0413735
M4 M5 M6
0.0121472 0.0212072 0.0097723
M7 M8 M9
-0.0136302 0.0219708 -0.0099637
M10 M11 t
0.0239631 0.0182904 0.0003243
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.085767 -0.011184 -0.003386 0.014212 0.064317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1055970 0.0496539 2.127 0.0402 *
Amerikaanse_inflatie 0.0094120 0.0138101 0.682 0.4998
`Y[t-1]` 1.2059612 0.1833089 6.579 1.04e-07 ***
`Y[t-2]` -0.5272015 0.2476778 -2.129 0.0400 *
`Y[t-3]` 0.5478133 0.2389976 2.292 0.0277 *
`Y[t-4]` -0.3889426 0.1517093 -2.564 0.0146 *
M1 0.0293146 0.0210116 1.395 0.1713
M2 -0.0011163 0.0211641 -0.053 0.9582
M3 0.0413735 0.0210364 1.967 0.0567 .
M4 0.0121472 0.0218176 0.557 0.5810
M5 0.0212072 0.0211599 1.002 0.3227
M6 0.0097723 0.0215680 0.453 0.6531
M7 -0.0136302 0.0212309 -0.642 0.5248
M8 0.0219708 0.0228594 0.961 0.3427
M9 -0.0099637 0.0232191 -0.429 0.6703
M10 0.0239631 0.0229115 1.046 0.3024
M11 0.0182904 0.0222309 0.823 0.4159
t 0.0003243 0.0002816 1.151 0.2569
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03052 on 37 degrees of freedom
Multiple R-squared: 0.9002, Adjusted R-squared: 0.8544
F-statistic: 19.64 on 17 and 37 DF, p-value: 1.493e-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,] 0.1457302763 0.2914605525 0.8542697
[2,] 0.0666558641 0.1333117283 0.9333441
[3,] 0.0236722145 0.0473444291 0.9763278
[4,] 0.0232310826 0.0464621652 0.9767689
[5,] 0.0158342423 0.0316684846 0.9841658
[6,] 0.0074427846 0.0148855693 0.9925572
[7,] 0.0028250229 0.0056500458 0.9971750
[8,] 0.0012221235 0.0024442471 0.9987779
[9,] 0.0004035254 0.0008070509 0.9995965
[10,] 0.0002344835 0.0004689671 0.9997655
[11,] 0.0006971880 0.0013943760 0.9993028
[12,] 0.0004908591 0.0009817181 0.9995091
[13,] 0.0026897377 0.0053794755 0.9973103
[14,] 0.0012669921 0.0025339841 0.9987330
> postscript(file="/var/www/html/rcomp/tmp/1zfto1258654759.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2x8n21258654759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3q14p1258654759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49gsf1258654759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mezc1258654759.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
-0.006622992 -0.006046421 -0.005629018 -0.028761318 0.019839086 -0.013419652
7 8 9 10 11 12
0.023092675 -0.006637142 0.004280193 -0.005428177 -0.016233934 0.027055159
13 14 15 16 17 18
-0.033845128 -0.003385842 -0.002078536 -0.006559497 -0.019209036 0.011388305
19 20 21 22 23 24
0.003766614 -0.008603717 0.040558497 -0.006801930 0.001030162 -0.013504858
25 26 27 28 29 30
-0.003357221 0.018069536 -0.004460685 -0.008947966 0.017035313 0.002713162
31 32 33 34 35 36
0.002207463 0.057913706 0.040927847 -0.025888359 -0.003472228 0.010392919
37 38 39 40 41 42
0.064316624 -0.013741681 0.020823317 0.005551081 0.018543584 -0.003792090
43 44 45 46 47 48
-0.032598613 -0.042672847 -0.085766537 0.038118465 0.018675999 -0.023943220
49 50 51 52 53 54
-0.020491282 0.005104408 -0.008655078 0.038717700 -0.036208948 0.003110274
55
0.003531861
> postscript(file="/var/www/html/rcomp/tmp/6gr2s1258654759.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 -0.006622992 NA
1 -0.006046421 -0.006622992
2 -0.005629018 -0.006046421
3 -0.028761318 -0.005629018
4 0.019839086 -0.028761318
5 -0.013419652 0.019839086
6 0.023092675 -0.013419652
7 -0.006637142 0.023092675
8 0.004280193 -0.006637142
9 -0.005428177 0.004280193
10 -0.016233934 -0.005428177
11 0.027055159 -0.016233934
12 -0.033845128 0.027055159
13 -0.003385842 -0.033845128
14 -0.002078536 -0.003385842
15 -0.006559497 -0.002078536
16 -0.019209036 -0.006559497
17 0.011388305 -0.019209036
18 0.003766614 0.011388305
19 -0.008603717 0.003766614
20 0.040558497 -0.008603717
21 -0.006801930 0.040558497
22 0.001030162 -0.006801930
23 -0.013504858 0.001030162
24 -0.003357221 -0.013504858
25 0.018069536 -0.003357221
26 -0.004460685 0.018069536
27 -0.008947966 -0.004460685
28 0.017035313 -0.008947966
29 0.002713162 0.017035313
30 0.002207463 0.002713162
31 0.057913706 0.002207463
32 0.040927847 0.057913706
33 -0.025888359 0.040927847
34 -0.003472228 -0.025888359
35 0.010392919 -0.003472228
36 0.064316624 0.010392919
37 -0.013741681 0.064316624
38 0.020823317 -0.013741681
39 0.005551081 0.020823317
40 0.018543584 0.005551081
41 -0.003792090 0.018543584
42 -0.032598613 -0.003792090
43 -0.042672847 -0.032598613
44 -0.085766537 -0.042672847
45 0.038118465 -0.085766537
46 0.018675999 0.038118465
47 -0.023943220 0.018675999
48 -0.020491282 -0.023943220
49 0.005104408 -0.020491282
50 -0.008655078 0.005104408
51 0.038717700 -0.008655078
52 -0.036208948 0.038717700
53 0.003110274 -0.036208948
54 0.003531861 0.003110274
55 NA 0.003531861
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.006046421 -0.006622992
[2,] -0.005629018 -0.006046421
[3,] -0.028761318 -0.005629018
[4,] 0.019839086 -0.028761318
[5,] -0.013419652 0.019839086
[6,] 0.023092675 -0.013419652
[7,] -0.006637142 0.023092675
[8,] 0.004280193 -0.006637142
[9,] -0.005428177 0.004280193
[10,] -0.016233934 -0.005428177
[11,] 0.027055159 -0.016233934
[12,] -0.033845128 0.027055159
[13,] -0.003385842 -0.033845128
[14,] -0.002078536 -0.003385842
[15,] -0.006559497 -0.002078536
[16,] -0.019209036 -0.006559497
[17,] 0.011388305 -0.019209036
[18,] 0.003766614 0.011388305
[19,] -0.008603717 0.003766614
[20,] 0.040558497 -0.008603717
[21,] -0.006801930 0.040558497
[22,] 0.001030162 -0.006801930
[23,] -0.013504858 0.001030162
[24,] -0.003357221 -0.013504858
[25,] 0.018069536 -0.003357221
[26,] -0.004460685 0.018069536
[27,] -0.008947966 -0.004460685
[28,] 0.017035313 -0.008947966
[29,] 0.002713162 0.017035313
[30,] 0.002207463 0.002713162
[31,] 0.057913706 0.002207463
[32,] 0.040927847 0.057913706
[33,] -0.025888359 0.040927847
[34,] -0.003472228 -0.025888359
[35,] 0.010392919 -0.003472228
[36,] 0.064316624 0.010392919
[37,] -0.013741681 0.064316624
[38,] 0.020823317 -0.013741681
[39,] 0.005551081 0.020823317
[40,] 0.018543584 0.005551081
[41,] -0.003792090 0.018543584
[42,] -0.032598613 -0.003792090
[43,] -0.042672847 -0.032598613
[44,] -0.085766537 -0.042672847
[45,] 0.038118465 -0.085766537
[46,] 0.018675999 0.038118465
[47,] -0.023943220 0.018675999
[48,] -0.020491282 -0.023943220
[49,] 0.005104408 -0.020491282
[50,] -0.008655078 0.005104408
[51,] 0.038717700 -0.008655078
[52,] -0.036208948 0.038717700
[53,] 0.003110274 -0.036208948
[54,] 0.003531861 0.003110274
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.006046421 -0.006622992
2 -0.005629018 -0.006046421
3 -0.028761318 -0.005629018
4 0.019839086 -0.028761318
5 -0.013419652 0.019839086
6 0.023092675 -0.013419652
7 -0.006637142 0.023092675
8 0.004280193 -0.006637142
9 -0.005428177 0.004280193
10 -0.016233934 -0.005428177
11 0.027055159 -0.016233934
12 -0.033845128 0.027055159
13 -0.003385842 -0.033845128
14 -0.002078536 -0.003385842
15 -0.006559497 -0.002078536
16 -0.019209036 -0.006559497
17 0.011388305 -0.019209036
18 0.003766614 0.011388305
19 -0.008603717 0.003766614
20 0.040558497 -0.008603717
21 -0.006801930 0.040558497
22 0.001030162 -0.006801930
23 -0.013504858 0.001030162
24 -0.003357221 -0.013504858
25 0.018069536 -0.003357221
26 -0.004460685 0.018069536
27 -0.008947966 -0.004460685
28 0.017035313 -0.008947966
29 0.002713162 0.017035313
30 0.002207463 0.002713162
31 0.057913706 0.002207463
32 0.040927847 0.057913706
33 -0.025888359 0.040927847
34 -0.003472228 -0.025888359
35 0.010392919 -0.003472228
36 0.064316624 0.010392919
37 -0.013741681 0.064316624
38 0.020823317 -0.013741681
39 0.005551081 0.020823317
40 0.018543584 0.005551081
41 -0.003792090 0.018543584
42 -0.032598613 -0.003792090
43 -0.042672847 -0.032598613
44 -0.085766537 -0.042672847
45 0.038118465 -0.085766537
46 0.018675999 0.038118465
47 -0.023943220 0.018675999
48 -0.020491282 -0.023943220
49 0.005104408 -0.020491282
50 -0.008655078 0.005104408
51 0.038717700 -0.008655078
52 -0.036208948 0.038717700
53 0.003110274 -0.036208948
54 0.003531861 0.003110274
> 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/7n8a91258654759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89ylu1258654759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97vbi1258654759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tnpv1258654759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kj8t1258654759.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/12lluy1258654759.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/131ueo1258654759.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/14wqwm1258654759.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/158zuw1258654759.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/16u6q81258654759.tab")
+ }
>
> system("convert tmp/1zfto1258654759.ps tmp/1zfto1258654759.png")
> system("convert tmp/2x8n21258654759.ps tmp/2x8n21258654759.png")
> system("convert tmp/3q14p1258654759.ps tmp/3q14p1258654759.png")
> system("convert tmp/49gsf1258654759.ps tmp/49gsf1258654759.png")
> system("convert tmp/5mezc1258654759.ps tmp/5mezc1258654759.png")
> system("convert tmp/6gr2s1258654759.ps tmp/6gr2s1258654759.png")
> system("convert tmp/7n8a91258654759.ps tmp/7n8a91258654759.png")
> system("convert tmp/89ylu1258654759.ps tmp/89ylu1258654759.png")
> system("convert tmp/97vbi1258654759.ps tmp/97vbi1258654759.png")
> system("convert tmp/10tnpv1258654759.ps tmp/10tnpv1258654759.png")
>
>
> proc.time()
user system elapsed
2.322 1.545 2.690