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(100.00
+ ,100.00
+ ,91.12
+ ,94.05
+ ,97.82
+ ,100.00
+ ,97.82
+ ,99.87
+ ,93.13
+ ,91.12
+ ,94.05
+ ,97.82
+ ,94.05
+ ,99.54
+ ,93.88
+ ,93.13
+ ,91.12
+ ,94.05
+ ,91.12
+ ,99.81
+ ,92.55
+ ,93.88
+ ,93.13
+ ,91.12
+ ,93.13
+ ,100.49
+ ,94.43
+ ,92.55
+ ,93.88
+ ,93.13
+ ,93.88
+ ,101.14
+ ,96.25
+ ,94.43
+ ,92.55
+ ,93.88
+ ,92.55
+ ,101.37
+ ,100.44
+ ,96.25
+ ,94.43
+ ,92.55
+ ,94.43
+ ,101.51
+ ,101.50
+ ,100.44
+ ,96.25
+ ,94.43
+ ,96.25
+ ,101.82
+ ,99.40
+ ,101.50
+ ,100.44
+ ,96.25
+ ,100.44
+ ,102.44
+ ,99.69
+ ,99.40
+ ,101.50
+ ,100.44
+ ,101.50
+ ,102.53
+ ,101.69
+ ,99.69
+ ,99.40
+ ,101.50
+ ,99.40
+ ,102.65
+ ,103.67
+ ,101.69
+ ,99.69
+ ,99.40
+ ,99.69
+ ,102.47
+ ,103.05
+ ,103.67
+ ,101.69
+ ,99.69
+ ,101.69
+ ,102.44
+ ,100.95
+ ,103.05
+ ,103.67
+ ,101.69
+ ,103.67
+ ,102.42
+ ,102.35
+ ,100.95
+ ,103.05
+ ,103.67
+ ,103.05
+ ,102.45
+ ,101.65
+ ,102.35
+ ,100.95
+ ,103.05
+ ,100.95
+ ,102.89
+ ,99.57
+ ,101.65
+ ,102.35
+ ,100.95
+ ,102.35
+ ,102.85
+ ,95.68
+ ,99.57
+ ,101.65
+ ,102.35
+ ,101.65
+ ,103.36
+ ,96.58
+ ,95.68
+ ,99.57
+ ,101.65
+ ,99.57
+ ,103.74
+ ,96.33
+ ,96.58
+ ,95.68
+ ,99.57
+ ,95.68
+ ,103.72
+ ,95.37
+ ,96.33
+ ,96.58
+ ,95.68
+ ,96.58
+ ,104.08
+ ,96.00
+ ,95.37
+ ,96.33
+ ,96.58
+ ,96.33
+ ,104.21
+ ,96.88
+ ,96.00
+ ,95.37
+ ,96.33
+ ,95.37
+ ,103.91
+ ,94.85
+ ,96.88
+ ,96.00
+ ,95.37
+ ,96.00
+ ,103.70
+ ,92.47
+ ,94.85
+ ,96.88
+ ,96.00
+ ,96.88
+ ,103.96
+ ,93.99
+ ,92.47
+ ,94.85
+ ,96.88
+ ,94.85
+ ,104.10
+ ,93.45
+ ,93.99
+ ,92.47
+ ,94.85
+ ,92.47
+ ,104.15
+ ,92.27
+ ,93.45
+ ,93.99
+ ,92.47
+ ,93.99
+ ,104.71
+ ,90.40
+ ,92.27
+ ,93.45
+ ,93.99
+ ,93.45
+ ,104.72
+ ,90.43
+ ,90.40
+ ,92.27
+ ,93.45
+ ,92.27
+ ,105.20
+ ,91.05
+ ,90.43
+ ,90.40
+ ,92.27
+ ,90.40
+ ,105.07
+ ,89.08
+ ,91.05
+ ,90.43
+ ,90.40
+ ,90.43
+ ,105.06
+ ,89.69
+ ,89.08
+ ,91.05
+ ,90.43
+ ,91.05
+ ,105.50
+ ,87.92
+ ,89.69
+ ,89.08
+ ,91.05
+ ,89.08
+ ,105.38
+ ,85.88
+ ,87.92
+ ,89.69
+ ,89.08
+ ,89.69
+ ,105.47
+ ,83.21
+ ,85.88
+ ,87.92
+ ,89.69
+ ,87.92
+ ,106.03
+ ,83.86
+ ,83.21
+ ,85.88
+ ,87.92
+ ,85.88
+ ,107.02
+ ,83.01
+ ,83.86
+ ,83.21
+ ,85.88
+ ,83.21
+ ,107.32
+ ,82.85
+ ,83.01
+ ,83.86
+ ,83.21
+ ,83.86
+ ,107.75
+ ,78.69
+ ,82.85
+ ,83.01
+ ,83.86
+ ,83.01
+ ,108.52
+ ,77.57
+ ,78.69
+ ,82.85
+ ,83.01
+ ,82.85
+ ,109.32
+ ,78.54
+ ,77.57
+ ,78.69
+ ,82.85
+ ,78.69
+ ,109.56
+ ,78.56
+ ,78.54
+ ,77.57
+ ,78.69
+ ,77.57
+ ,110.54
+ ,77.48
+ ,78.56
+ ,78.54
+ ,77.57
+ ,78.54
+ ,111.16
+ ,81.59
+ ,77.48
+ ,78.56
+ ,78.54
+ ,78.56
+ ,111.74
+ ,85.02
+ ,81.59
+ ,77.48
+ ,78.56
+ ,77.48
+ ,111.06
+ ,91.71
+ ,85.02
+ ,81.59
+ ,77.48
+ ,81.59
+ ,111.24
+ ,95.96
+ ,91.71
+ ,85.02
+ ,81.59
+ ,85.02
+ ,111.04
+ ,90.85
+ ,95.96
+ ,91.71
+ ,85.02
+ ,91.71
+ ,110.38
+ ,92.29
+ ,90.85
+ ,95.96
+ ,91.71
+ ,95.96
+ ,110.14
+ ,95.57
+ ,92.29
+ ,90.85
+ ,95.96
+ ,90.85
+ ,110.25
+ ,93.62
+ ,95.57
+ ,92.29
+ ,90.85
+ ,92.29
+ ,110.62
+ ,92.63
+ ,93.62
+ ,95.57
+ ,92.29
+ ,95.57
+ ,109.99
+ ,89.51
+ ,92.63
+ ,93.62
+ ,95.57
+ ,93.62
+ ,110.22
+ ,87.17
+ ,89.51
+ ,92.63
+ ,93.62
+ ,92.63
+ ,110.14
+ ,86.73
+ ,87.17
+ ,89.51
+ ,92.63
+ ,89.51
+ ,109.93
+ ,85.63
+ ,86.73
+ ,87.17
+ ,89.51)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('wisselkoers'
+ ,'consumptieprijzen'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3'
+ ,'Yt-4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('wisselkoers','consumptieprijzen','Yt-1','Yt-2','Yt-3','Yt-4'),1:57))
> 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
wisselkoers consumptieprijzen Yt-1 Yt-2 Yt-3 Yt-4 M1 M2 M3 M4 M5 M6
1 100.00 100.00 91.12 94.05 97.82 100.00 1 0 0 0 0 0
2 97.82 99.87 93.13 91.12 94.05 97.82 0 1 0 0 0 0
3 94.05 99.54 93.88 93.13 91.12 94.05 0 0 1 0 0 0
4 91.12 99.81 92.55 93.88 93.13 91.12 0 0 0 1 0 0
5 93.13 100.49 94.43 92.55 93.88 93.13 0 0 0 0 1 0
6 93.88 101.14 96.25 94.43 92.55 93.88 0 0 0 0 0 1
7 92.55 101.37 100.44 96.25 94.43 92.55 0 0 0 0 0 0
8 94.43 101.51 101.50 100.44 96.25 94.43 0 0 0 0 0 0
9 96.25 101.82 99.40 101.50 100.44 96.25 0 0 0 0 0 0
10 100.44 102.44 99.69 99.40 101.50 100.44 0 0 0 0 0 0
11 101.50 102.53 101.69 99.69 99.40 101.50 0 0 0 0 0 0
12 99.40 102.65 103.67 101.69 99.69 99.40 0 0 0 0 0 0
13 99.69 102.47 103.05 103.67 101.69 99.69 1 0 0 0 0 0
14 101.69 102.44 100.95 103.05 103.67 101.69 0 1 0 0 0 0
15 103.67 102.42 102.35 100.95 103.05 103.67 0 0 1 0 0 0
16 103.05 102.45 101.65 102.35 100.95 103.05 0 0 0 1 0 0
17 100.95 102.89 99.57 101.65 102.35 100.95 0 0 0 0 1 0
18 102.35 102.85 95.68 99.57 101.65 102.35 0 0 0 0 0 1
19 101.65 103.36 96.58 95.68 99.57 101.65 0 0 0 0 0 0
20 99.57 103.74 96.33 96.58 95.68 99.57 0 0 0 0 0 0
21 95.68 103.72 95.37 96.33 96.58 95.68 0 0 0 0 0 0
22 96.58 104.08 96.00 95.37 96.33 96.58 0 0 0 0 0 0
23 96.33 104.21 96.88 96.00 95.37 96.33 0 0 0 0 0 0
24 95.37 103.91 94.85 96.88 96.00 95.37 0 0 0 0 0 0
25 96.00 103.70 92.47 94.85 96.88 96.00 1 0 0 0 0 0
26 96.88 103.96 93.99 92.47 94.85 96.88 0 1 0 0 0 0
27 94.85 104.10 93.45 93.99 92.47 94.85 0 0 1 0 0 0
28 92.47 104.15 92.27 93.45 93.99 92.47 0 0 0 1 0 0
29 93.99 104.71 90.40 92.27 93.45 93.99 0 0 0 0 1 0
30 93.45 104.72 90.43 90.40 92.27 93.45 0 0 0 0 0 1
31 92.27 105.20 91.05 90.43 90.40 92.27 0 0 0 0 0 0
32 90.40 105.07 89.08 91.05 90.43 90.40 0 0 0 0 0 0
33 90.43 105.06 89.69 89.08 91.05 90.43 0 0 0 0 0 0
34 91.05 105.50 87.92 89.69 89.08 91.05 0 0 0 0 0 0
35 89.08 105.38 85.88 87.92 89.69 89.08 0 0 0 0 0 0
36 89.69 105.47 83.21 85.88 87.92 89.69 0 0 0 0 0 0
37 87.92 106.03 83.86 83.21 85.88 87.92 1 0 0 0 0 0
38 85.88 107.02 83.01 83.86 83.21 85.88 0 1 0 0 0 0
39 83.21 107.32 82.85 83.01 83.86 83.21 0 0 1 0 0 0
40 83.86 107.75 78.69 82.85 83.01 83.86 0 0 0 1 0 0
41 83.01 108.52 77.57 78.69 82.85 83.01 0 0 0 0 1 0
42 82.85 109.32 78.54 77.57 78.69 82.85 0 0 0 0 0 1
43 78.69 109.56 78.56 78.54 77.57 78.69 0 0 0 0 0 0
44 77.57 110.54 77.48 78.56 78.54 77.57 0 0 0 0 0 0
45 78.54 111.16 81.59 77.48 78.56 78.54 0 0 0 0 0 0
46 78.56 111.74 85.02 81.59 77.48 78.56 0 0 0 0 0 0
47 77.48 111.06 91.71 85.02 81.59 77.48 0 0 0 0 0 0
48 81.59 111.24 95.96 91.71 85.02 81.59 0 0 0 0 0 0
49 85.02 111.04 90.85 95.96 91.71 85.02 1 0 0 0 0 0
50 91.71 110.38 92.29 90.85 95.96 91.71 0 1 0 0 0 0
51 95.96 110.14 95.57 92.29 90.85 95.96 0 0 1 0 0 0
52 90.85 110.25 93.62 95.57 92.29 90.85 0 0 0 1 0 0
53 92.29 110.62 92.63 93.62 95.57 92.29 0 0 0 0 1 0
54 95.57 109.99 89.51 92.63 93.62 95.57 0 0 0 0 0 1
55 93.62 110.22 87.17 89.51 92.63 93.62 0 0 0 0 0 0
56 92.63 110.14 86.73 87.17 89.51 92.63 0 0 0 0 0 0
57 89.51 109.93 85.63 86.73 87.17 89.51 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
56 0 1 0 0 0 56
57 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) consumptieprijzen `Yt-1` `Yt-2`
8.468e-15 -2.461e-17 -4.297e-17 4.157e-17
`Yt-3` `Yt-4` M1 M2
-2.257e-16 1.000e+00 -8.475e-17 -8.206e-17
M3 M4 M5 M6
-7.192e-17 -2.749e-16 -4.201e-16 -1.303e-15
M7 M8 M9 M10
-2.210e-16 -1.990e-16 -1.224e-16 -1.636e-16
M11 t
-1.057e-16 -1.299e-17
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.817e-15 -1.239e-16 1.093e-18 1.528e-16 1.113e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.468e-15 1.226e-14 6.910e-01 0.49373
consumptieprijzen -2.461e-17 1.175e-16 -2.100e-01 0.83514
`Yt-1` -4.297e-17 4.776e-17 -9.000e-01 0.37380
`Yt-2` 4.157e-17 6.953e-17 5.980e-01 0.55339
`Yt-3` -2.257e-16 6.861e-17 -3.289e+00 0.00214 **
`Yt-4` 1.000e+00 4.495e-17 2.225e+16 < 2e-16 ***
M1 -8.475e-17 4.216e-16 -2.010e-01 0.84175
M2 -8.206e-17 4.294e-16 -1.910e-01 0.84945
M3 -7.192e-17 4.112e-16 -1.750e-01 0.86207
M4 -2.749e-16 4.076e-16 -6.740e-01 0.50403
M5 -4.201e-16 4.299e-16 -9.770e-01 0.33444
M6 -1.303e-15 4.207e-16 -3.097e+00 0.00362 **
M7 -2.209e-16 4.141e-16 -5.340e-01 0.59665
M8 -1.990e-16 4.052e-16 -4.910e-01 0.62616
M9 -1.224e-16 4.106e-16 -2.980e-01 0.76715
M10 -1.636e-16 4.322e-16 -3.790e-01 0.70705
M11 -1.057e-16 4.273e-16 -2.470e-01 0.80588
t -1.299e-17 2.460e-17 -5.280e-01 0.60040
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.9e-16 on 39 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 4.634e+32 on 17 and 39 DF, p-value: < 2.2e-16
> 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,] 8.733134e-02 1.746627e-01 0.912668656
[2,] 1.303675e-01 2.607350e-01 0.869632477
[3,] 8.197214e-08 1.639443e-07 0.999999918
[4,] 3.580822e-05 7.161644e-05 0.999964192
[5,] 1.051489e-02 2.102977e-02 0.989485113
[6,] 2.513594e-04 5.027187e-04 0.999748641
[7,] 1.276107e-04 2.552215e-04 0.999872389
[8,] 9.988208e-01 2.358453e-03 0.001179227
[9,] 4.662619e-03 9.325237e-03 0.995337381
[10,] 1.874876e-01 3.749752e-01 0.812512390
[11,] 3.433355e-01 6.866711e-01 0.656664455
[12,] 9.960839e-01 7.832104e-03 0.003916052
[13,] 5.952988e-01 8.094024e-01 0.404701223
[14,] 3.738974e-04 7.477948e-04 0.999626103
[15,] 6.778689e-01 6.442622e-01 0.322131110
[16,] 8.869589e-01 2.260822e-01 0.113041105
> postscript(file="/var/www/html/rcomp/tmp/19nvz1258737304.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gzyw1258737304.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3j83i1258737304.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41fbf1258737304.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xe5h1258737304.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 = 57
Frequency = 1
1 2 3 4 5
4.077638e-16 3.557458e-16 1.634655e-16 -4.188808e-16 -9.624887e-17
6 7 8 9 10
-2.816693e-15 4.685718e-16 7.338143e-16 1.291241e-17 -1.018296e-16
11 12 13 14 15
3.630475e-17 6.735534e-17 6.940912e-18 -1.351913e-16 -1.145539e-16
16 17 18 19 20
4.114285e-16 1.696734e-16 8.423835e-16 -1.208600e-16 -3.666196e-16
21 22 23 24 25
-2.681859e-17 2.051215e-17 1.092898e-18 -1.788636e-16 -1.657310e-16
26 27 28 29 30
-2.552044e-17 5.569415e-17 1.573915e-16 1.396880e-16 1.112529e-15
31 32 33 34 35
1.528287e-16 2.125541e-17 3.521270e-17 -9.377965e-17 -1.985132e-17
36 37 38 39 40
-3.486551e-16 -6.073065e-17 1.259945e-17 2.783517e-16 -1.238545e-16
41 42 43 44 45
-1.755576e-16 8.422122e-16 1.019170e-16 -2.076617e-16 -1.036108e-16
46 47 48 49 50
1.750971e-16 -1.754633e-17 4.601634e-16 -1.882431e-16 -2.076335e-16
51 52 53 54 55
-3.829574e-16 -2.608467e-17 -3.755500e-17 1.956893e-17 -6.024574e-16
56 57
-1.807884e-16 8.230429e-17
> postscript(file="/var/www/html/rcomp/tmp/68dzq1258737304.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 4.077638e-16 NA
1 3.557458e-16 4.077638e-16
2 1.634655e-16 3.557458e-16
3 -4.188808e-16 1.634655e-16
4 -9.624887e-17 -4.188808e-16
5 -2.816693e-15 -9.624887e-17
6 4.685718e-16 -2.816693e-15
7 7.338143e-16 4.685718e-16
8 1.291241e-17 7.338143e-16
9 -1.018296e-16 1.291241e-17
10 3.630475e-17 -1.018296e-16
11 6.735534e-17 3.630475e-17
12 6.940912e-18 6.735534e-17
13 -1.351913e-16 6.940912e-18
14 -1.145539e-16 -1.351913e-16
15 4.114285e-16 -1.145539e-16
16 1.696734e-16 4.114285e-16
17 8.423835e-16 1.696734e-16
18 -1.208600e-16 8.423835e-16
19 -3.666196e-16 -1.208600e-16
20 -2.681859e-17 -3.666196e-16
21 2.051215e-17 -2.681859e-17
22 1.092898e-18 2.051215e-17
23 -1.788636e-16 1.092898e-18
24 -1.657310e-16 -1.788636e-16
25 -2.552044e-17 -1.657310e-16
26 5.569415e-17 -2.552044e-17
27 1.573915e-16 5.569415e-17
28 1.396880e-16 1.573915e-16
29 1.112529e-15 1.396880e-16
30 1.528287e-16 1.112529e-15
31 2.125541e-17 1.528287e-16
32 3.521270e-17 2.125541e-17
33 -9.377965e-17 3.521270e-17
34 -1.985132e-17 -9.377965e-17
35 -3.486551e-16 -1.985132e-17
36 -6.073065e-17 -3.486551e-16
37 1.259945e-17 -6.073065e-17
38 2.783517e-16 1.259945e-17
39 -1.238545e-16 2.783517e-16
40 -1.755576e-16 -1.238545e-16
41 8.422122e-16 -1.755576e-16
42 1.019170e-16 8.422122e-16
43 -2.076617e-16 1.019170e-16
44 -1.036108e-16 -2.076617e-16
45 1.750971e-16 -1.036108e-16
46 -1.754633e-17 1.750971e-16
47 4.601634e-16 -1.754633e-17
48 -1.882431e-16 4.601634e-16
49 -2.076335e-16 -1.882431e-16
50 -3.829574e-16 -2.076335e-16
51 -2.608467e-17 -3.829574e-16
52 -3.755500e-17 -2.608467e-17
53 1.956893e-17 -3.755500e-17
54 -6.024574e-16 1.956893e-17
55 -1.807884e-16 -6.024574e-16
56 8.230429e-17 -1.807884e-16
57 NA 8.230429e-17
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.557458e-16 4.077638e-16
[2,] 1.634655e-16 3.557458e-16
[3,] -4.188808e-16 1.634655e-16
[4,] -9.624887e-17 -4.188808e-16
[5,] -2.816693e-15 -9.624887e-17
[6,] 4.685718e-16 -2.816693e-15
[7,] 7.338143e-16 4.685718e-16
[8,] 1.291241e-17 7.338143e-16
[9,] -1.018296e-16 1.291241e-17
[10,] 3.630475e-17 -1.018296e-16
[11,] 6.735534e-17 3.630475e-17
[12,] 6.940912e-18 6.735534e-17
[13,] -1.351913e-16 6.940912e-18
[14,] -1.145539e-16 -1.351913e-16
[15,] 4.114285e-16 -1.145539e-16
[16,] 1.696734e-16 4.114285e-16
[17,] 8.423835e-16 1.696734e-16
[18,] -1.208600e-16 8.423835e-16
[19,] -3.666196e-16 -1.208600e-16
[20,] -2.681859e-17 -3.666196e-16
[21,] 2.051215e-17 -2.681859e-17
[22,] 1.092898e-18 2.051215e-17
[23,] -1.788636e-16 1.092898e-18
[24,] -1.657310e-16 -1.788636e-16
[25,] -2.552044e-17 -1.657310e-16
[26,] 5.569415e-17 -2.552044e-17
[27,] 1.573915e-16 5.569415e-17
[28,] 1.396880e-16 1.573915e-16
[29,] 1.112529e-15 1.396880e-16
[30,] 1.528287e-16 1.112529e-15
[31,] 2.125541e-17 1.528287e-16
[32,] 3.521270e-17 2.125541e-17
[33,] -9.377965e-17 3.521270e-17
[34,] -1.985132e-17 -9.377965e-17
[35,] -3.486551e-16 -1.985132e-17
[36,] -6.073065e-17 -3.486551e-16
[37,] 1.259945e-17 -6.073065e-17
[38,] 2.783517e-16 1.259945e-17
[39,] -1.238545e-16 2.783517e-16
[40,] -1.755576e-16 -1.238545e-16
[41,] 8.422122e-16 -1.755576e-16
[42,] 1.019170e-16 8.422122e-16
[43,] -2.076617e-16 1.019170e-16
[44,] -1.036108e-16 -2.076617e-16
[45,] 1.750971e-16 -1.036108e-16
[46,] -1.754633e-17 1.750971e-16
[47,] 4.601634e-16 -1.754633e-17
[48,] -1.882431e-16 4.601634e-16
[49,] -2.076335e-16 -1.882431e-16
[50,] -3.829574e-16 -2.076335e-16
[51,] -2.608467e-17 -3.829574e-16
[52,] -3.755500e-17 -2.608467e-17
[53,] 1.956893e-17 -3.755500e-17
[54,] -6.024574e-16 1.956893e-17
[55,] -1.807884e-16 -6.024574e-16
[56,] 8.230429e-17 -1.807884e-16
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.557458e-16 4.077638e-16
2 1.634655e-16 3.557458e-16
3 -4.188808e-16 1.634655e-16
4 -9.624887e-17 -4.188808e-16
5 -2.816693e-15 -9.624887e-17
6 4.685718e-16 -2.816693e-15
7 7.338143e-16 4.685718e-16
8 1.291241e-17 7.338143e-16
9 -1.018296e-16 1.291241e-17
10 3.630475e-17 -1.018296e-16
11 6.735534e-17 3.630475e-17
12 6.940912e-18 6.735534e-17
13 -1.351913e-16 6.940912e-18
14 -1.145539e-16 -1.351913e-16
15 4.114285e-16 -1.145539e-16
16 1.696734e-16 4.114285e-16
17 8.423835e-16 1.696734e-16
18 -1.208600e-16 8.423835e-16
19 -3.666196e-16 -1.208600e-16
20 -2.681859e-17 -3.666196e-16
21 2.051215e-17 -2.681859e-17
22 1.092898e-18 2.051215e-17
23 -1.788636e-16 1.092898e-18
24 -1.657310e-16 -1.788636e-16
25 -2.552044e-17 -1.657310e-16
26 5.569415e-17 -2.552044e-17
27 1.573915e-16 5.569415e-17
28 1.396880e-16 1.573915e-16
29 1.112529e-15 1.396880e-16
30 1.528287e-16 1.112529e-15
31 2.125541e-17 1.528287e-16
32 3.521270e-17 2.125541e-17
33 -9.377965e-17 3.521270e-17
34 -1.985132e-17 -9.377965e-17
35 -3.486551e-16 -1.985132e-17
36 -6.073065e-17 -3.486551e-16
37 1.259945e-17 -6.073065e-17
38 2.783517e-16 1.259945e-17
39 -1.238545e-16 2.783517e-16
40 -1.755576e-16 -1.238545e-16
41 8.422122e-16 -1.755576e-16
42 1.019170e-16 8.422122e-16
43 -2.076617e-16 1.019170e-16
44 -1.036108e-16 -2.076617e-16
45 1.750971e-16 -1.036108e-16
46 -1.754633e-17 1.750971e-16
47 4.601634e-16 -1.754633e-17
48 -1.882431e-16 4.601634e-16
49 -2.076335e-16 -1.882431e-16
50 -3.829574e-16 -2.076335e-16
51 -2.608467e-17 -3.829574e-16
52 -3.755500e-17 -2.608467e-17
53 1.956893e-17 -3.755500e-17
54 -6.024574e-16 1.956893e-17
55 -1.807884e-16 -6.024574e-16
56 8.230429e-17 -1.807884e-16
> 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/7d3b31258737304.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dlps1258737304.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9d1fc1258737304.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ikbm1258737304.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ihji1258737304.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/12kzis1258737304.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/13rtj51258737304.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/14s6zz1258737304.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/15ufzl1258737304.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/16rn9x1258737304.tab")
+ }
>
> system("convert tmp/19nvz1258737304.ps tmp/19nvz1258737304.png")
> system("convert tmp/2gzyw1258737304.ps tmp/2gzyw1258737304.png")
> system("convert tmp/3j83i1258737304.ps tmp/3j83i1258737304.png")
> system("convert tmp/41fbf1258737304.ps tmp/41fbf1258737304.png")
> system("convert tmp/5xe5h1258737304.ps tmp/5xe5h1258737304.png")
> system("convert tmp/68dzq1258737304.ps tmp/68dzq1258737304.png")
> system("convert tmp/7d3b31258737304.ps tmp/7d3b31258737304.png")
> system("convert tmp/8dlps1258737304.ps tmp/8dlps1258737304.png")
> system("convert tmp/9d1fc1258737304.ps tmp/9d1fc1258737304.png")
> system("convert tmp/10ikbm1258737304.ps tmp/10ikbm1258737304.png")
>
>
> proc.time()
user system elapsed
2.421 1.576 5.341