R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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(8.4
+ ,8.4
+ ,8.4
+ ,98.6
+ ,8.6
+ ,8.4
+ ,8.4
+ ,98.5
+ ,8.9
+ ,8.6
+ ,8.4
+ ,98.9
+ ,8.8
+ ,8.9
+ ,8.6
+ ,99.4
+ ,8.3
+ ,8.8
+ ,8.9
+ ,99.8
+ ,7.5
+ ,8.3
+ ,8.8
+ ,99.9
+ ,7.2
+ ,7.5
+ ,8.3
+ ,100
+ ,7.4
+ ,7.2
+ ,7.5
+ ,100.1
+ ,8.8
+ ,7.4
+ ,7.2
+ ,100.1
+ ,9.3
+ ,8.8
+ ,7.4
+ ,100.2
+ ,9.3
+ ,9.3
+ ,8.8
+ ,100.3
+ ,8.7
+ ,9.3
+ ,9.3
+ ,100
+ ,8.2
+ ,8.7
+ ,9.3
+ ,99.9
+ ,8.3
+ ,8.2
+ ,8.7
+ ,99.4
+ ,8.5
+ ,8.3
+ ,8.2
+ ,99.8
+ ,8.6
+ ,8.5
+ ,8.3
+ ,99.6
+ ,8.5
+ ,8.6
+ ,8.5
+ ,100
+ ,8.2
+ ,8.5
+ ,8.6
+ ,99.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,100.3
+ ,7.9
+ ,8.1
+ ,8.2
+ ,100.6
+ ,8.6
+ ,7.9
+ ,8.1
+ ,100.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,100.8
+ ,8.7
+ ,8.7
+ ,8.6
+ ,100.8
+ ,8.5
+ ,8.7
+ ,8.7
+ ,100.6
+ ,8.4
+ ,8.5
+ ,8.7
+ ,101.1
+ ,8.5
+ ,8.4
+ ,8.5
+ ,101.1
+ ,8.7
+ ,8.5
+ ,8.4
+ ,100.9
+ ,8.7
+ ,8.7
+ ,8.5
+ ,101.1
+ ,8.6
+ ,8.7
+ ,8.7
+ ,101.2
+ ,8.5
+ ,8.6
+ ,8.7
+ ,101.4
+ ,8.3
+ ,8.5
+ ,8.6
+ ,101.9
+ ,8
+ ,8.3
+ ,8.5
+ ,102.1
+ ,8.2
+ ,8
+ ,8.3
+ ,102.1
+ ,8.1
+ ,8.2
+ ,8
+ ,103
+ ,8.1
+ ,8.1
+ ,8.2
+ ,103.4
+ ,8
+ ,8.1
+ ,8.1
+ ,103.2
+ ,7.9
+ ,8
+ ,8.1
+ ,103.1
+ ,7.9
+ ,7.9
+ ,8
+ ,103
+ ,8
+ ,7.9
+ ,7.9
+ ,103.7
+ ,8
+ ,8
+ ,7.9
+ ,103.4
+ ,7.9
+ ,8
+ ,8
+ ,103.5
+ ,8
+ ,7.9
+ ,8
+ ,103.8
+ ,7.7
+ ,8
+ ,7.9
+ ,104
+ ,7.2
+ ,7.7
+ ,8
+ ,104.2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,104.4
+ ,7.3
+ ,7.5
+ ,7.2
+ ,104.4
+ ,7
+ ,7.3
+ ,7.5
+ ,104.9
+ ,7
+ ,7
+ ,7.3
+ ,105.3
+ ,7
+ ,7
+ ,7
+ ,105.2
+ ,7.2
+ ,7
+ ,7
+ ,105.4
+ ,7.3
+ ,7.2
+ ,7
+ ,105.4
+ ,7.1
+ ,7.3
+ ,7.2
+ ,105.5
+ ,6.8
+ ,7.1
+ ,7.3
+ ,105.7
+ ,6.4
+ ,6.8
+ ,7.1
+ ,105.6
+ ,6.1
+ ,6.4
+ ,6.8
+ ,105.8
+ ,6.5
+ ,6.1
+ ,6.4
+ ,105.4
+ ,7.7
+ ,6.5
+ ,6.1
+ ,105.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,105.8
+ ,7.5
+ ,7.9
+ ,7.7
+ ,106.1
+ ,6.9
+ ,7.5
+ ,7.9
+ ,106
+ ,6.6
+ ,6.9
+ ,7.5
+ ,105.5
+ ,6.9
+ ,6.6
+ ,6.9
+ ,105.4
+ ,7.7
+ ,6.9
+ ,6.6
+ ,106
+ ,8
+ ,7.7
+ ,6.9
+ ,106.1
+ ,8
+ ,8
+ ,7.7
+ ,106.4
+ ,7.7
+ ,8
+ ,8
+ ,106
+ ,7.3
+ ,7.7
+ ,8
+ ,106
+ ,7.4
+ ,7.3
+ ,7.7
+ ,106
+ ,8.1
+ ,7.4
+ ,7.3
+ ,106
+ ,8.3
+ ,8.1
+ ,7.4
+ ,106.1
+ ,8.2
+ ,8.3
+ ,8.1
+ ,106.1)
+ ,dim=c(4
+ ,71)
+ ,dimnames=list(c('werkl'
+ ,'werkl-1'
+ ,'werkl-2'
+ ,'afzetp')
+ ,1:71))
> y <- array(NA,dim=c(4,71),dimnames=list(c('werkl','werkl-1','werkl-2','afzetp'),1:71))
> 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
werkl werkl-1 werkl-2 afzetp M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.4 8.4 8.4 98.6 1 0 0 0 0 0 0 0 0 0 0 1
2 8.6 8.4 8.4 98.5 0 1 0 0 0 0 0 0 0 0 0 2
3 8.9 8.6 8.4 98.9 0 0 1 0 0 0 0 0 0 0 0 3
4 8.8 8.9 8.6 99.4 0 0 0 1 0 0 0 0 0 0 0 4
5 8.3 8.8 8.9 99.8 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 8.3 8.8 99.9 0 0 0 0 0 1 0 0 0 0 0 6
7 7.2 7.5 8.3 100.0 0 0 0 0 0 0 1 0 0 0 0 7
8 7.4 7.2 7.5 100.1 0 0 0 0 0 0 0 1 0 0 0 8
9 8.8 7.4 7.2 100.1 0 0 0 0 0 0 0 0 1 0 0 9
10 9.3 8.8 7.4 100.2 0 0 0 0 0 0 0 0 0 1 0 10
11 9.3 9.3 8.8 100.3 0 0 0 0 0 0 0 0 0 0 1 11
12 8.7 9.3 9.3 100.0 0 0 0 0 0 0 0 0 0 0 0 12
13 8.2 8.7 9.3 99.9 1 0 0 0 0 0 0 0 0 0 0 13
14 8.3 8.2 8.7 99.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 8.3 8.2 99.8 0 0 1 0 0 0 0 0 0 0 0 15
16 8.6 8.5 8.3 99.6 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 8.6 8.5 100.0 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 8.5 8.6 99.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.1 8.2 8.5 100.3 0 0 0 0 0 0 1 0 0 0 0 19
20 7.9 8.1 8.2 100.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.6 7.9 8.1 100.7 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 8.6 7.9 100.8 0 0 0 0 0 0 0 0 0 1 0 22
23 8.7 8.7 8.6 100.8 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 8.7 8.7 100.6 0 0 0 0 0 0 0 0 0 0 0 24
25 8.4 8.5 8.7 101.1 1 0 0 0 0 0 0 0 0 0 0 25
26 8.5 8.4 8.5 101.1 0 1 0 0 0 0 0 0 0 0 0 26
27 8.7 8.5 8.4 100.9 0 0 1 0 0 0 0 0 0 0 0 27
28 8.7 8.7 8.5 101.1 0 0 0 1 0 0 0 0 0 0 0 28
29 8.6 8.7 8.7 101.2 0 0 0 0 1 0 0 0 0 0 0 29
30 8.5 8.6 8.7 101.4 0 0 0 0 0 1 0 0 0 0 0 30
31 8.3 8.5 8.6 101.9 0 0 0 0 0 0 1 0 0 0 0 31
32 8.0 8.3 8.5 102.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.2 8.0 8.3 102.1 0 0 0 0 0 0 0 0 1 0 0 33
34 8.1 8.2 8.0 103.0 0 0 0 0 0 0 0 0 0 1 0 34
35 8.1 8.1 8.2 103.4 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 8.1 8.1 103.2 0 0 0 0 0 0 0 0 0 0 0 36
37 7.9 8.0 8.1 103.1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 7.9 8.0 103.0 0 1 0 0 0 0 0 0 0 0 0 38
39 8.0 7.9 7.9 103.7 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 8.0 7.9 103.4 0 0 0 1 0 0 0 0 0 0 0 40
41 7.9 8.0 8.0 103.5 0 0 0 0 1 0 0 0 0 0 0 41
42 8.0 7.9 8.0 103.8 0 0 0 0 0 1 0 0 0 0 0 42
43 7.7 8.0 7.9 104.0 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 7.7 8.0 104.2 0 0 0 0 0 0 0 1 0 0 0 44
45 7.5 7.2 7.7 104.4 0 0 0 0 0 0 0 0 1 0 0 45
46 7.3 7.5 7.2 104.4 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 7.3 7.5 104.9 0 0 0 0 0 0 0 0 0 0 1 47
48 7.0 7.0 7.3 105.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.0 7.0 7.0 105.2 1 0 0 0 0 0 0 0 0 0 0 49
50 7.2 7.0 7.0 105.4 0 1 0 0 0 0 0 0 0 0 0 50
51 7.3 7.2 7.0 105.4 0 0 1 0 0 0 0 0 0 0 0 51
52 7.1 7.3 7.2 105.5 0 0 0 1 0 0 0 0 0 0 0 52
53 6.8 7.1 7.3 105.7 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 6.8 7.1 105.6 0 0 0 0 0 1 0 0 0 0 0 54
55 6.1 6.4 6.8 105.8 0 0 0 0 0 0 1 0 0 0 0 55
56 6.5 6.1 6.4 105.4 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 6.5 6.1 105.5 0 0 0 0 0 0 0 0 1 0 0 57
58 7.9 7.7 6.5 105.8 0 0 0 0 0 0 0 0 0 1 0 58
59 7.5 7.9 7.7 106.1 0 0 0 0 0 0 0 0 0 0 1 59
60 6.9 7.5 7.9 106.0 0 0 0 0 0 0 0 0 0 0 0 60
61 6.6 6.9 7.5 105.5 1 0 0 0 0 0 0 0 0 0 0 61
62 6.9 6.6 6.9 105.4 0 1 0 0 0 0 0 0 0 0 0 62
63 7.7 6.9 6.6 106.0 0 0 1 0 0 0 0 0 0 0 0 63
64 8.0 7.7 6.9 106.1 0 0 0 1 0 0 0 0 0 0 0 64
65 8.0 8.0 7.7 106.4 0 0 0 0 1 0 0 0 0 0 0 65
66 7.7 8.0 8.0 106.0 0 0 0 0 0 1 0 0 0 0 0 66
67 7.3 7.7 8.0 106.0 0 0 0 0 0 0 1 0 0 0 0 67
68 7.4 7.3 7.7 106.0 0 0 0 0 0 0 0 1 0 0 0 68
69 8.1 7.4 7.3 106.0 0 0 0 0 0 0 0 0 1 0 0 69
70 8.3 8.1 7.4 106.1 0 0 0 0 0 0 0 0 0 1 0 70
71 8.2 8.3 8.1 106.1 0 0 0 0 0 0 0 0 0 0 1 71
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `werkl-1` `werkl-2` afzetp M1 M2
26.310602 1.316700 -0.717070 -0.232618 0.045743 0.193077
M3 M4 M5 M6 M7 M8
0.213469 -0.039447 -0.002988 -0.069229 -0.037657 0.044263
M9 M10 M11 t
0.664800 -0.203344 0.078324 0.019587
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.25336 -0.09676 -0.02976 0.08903 0.41639
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.310602 5.272524 4.990 6.41e-06 ***
`werkl-1` 1.316700 0.090300 14.581 < 2e-16 ***
`werkl-2` -0.717070 0.087976 -8.151 4.96e-11 ***
afzetp -0.232618 0.049560 -4.694 1.82e-05 ***
M1 0.045743 0.104131 0.439 0.662175
M2 0.193077 0.109927 1.756 0.084585 .
M3 0.213469 0.108866 1.961 0.054968 .
M4 -0.039447 0.108836 -0.362 0.718408
M5 -0.002988 0.101962 -0.029 0.976729
M6 -0.069229 0.102329 -0.677 0.501541
M7 -0.037657 0.104668 -0.360 0.720389
M8 0.044263 0.109779 0.403 0.688366
M9 0.664800 0.113401 5.862 2.68e-07 ***
M10 -0.203344 0.127001 -1.601 0.115079
M11 0.078324 0.103561 0.756 0.452691
t 0.019587 0.005247 3.733 0.000451 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1666 on 55 degrees of freedom
Multiple R-squared: 0.9555, Adjusted R-squared: 0.9434
F-statistic: 78.72 on 15 and 55 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,] 0.070855877 0.14171175 0.9291441
[2,] 0.284203278 0.56840656 0.7157967
[3,] 0.321253293 0.64250659 0.6787467
[4,] 0.205007919 0.41001584 0.7949921
[5,] 0.129879370 0.25975874 0.8701206
[6,] 0.083825307 0.16765061 0.9161747
[7,] 0.045864323 0.09172865 0.9541357
[8,] 0.028565919 0.05713184 0.9714341
[9,] 0.016537486 0.03307497 0.9834625
[10,] 0.008295587 0.01659117 0.9917044
[11,] 0.007786005 0.01557201 0.9922140
[12,] 0.033842585 0.06768517 0.9661574
[13,] 0.021654427 0.04330885 0.9783456
[14,] 0.015404945 0.03080989 0.9845951
[15,] 0.121377780 0.24275556 0.8786222
[16,] 0.113450291 0.22690058 0.8865497
[17,] 0.164728130 0.32945626 0.8352719
[18,] 0.126474780 0.25294956 0.8735252
[19,] 0.123087044 0.24617409 0.8769130
[20,] 0.150386893 0.30077379 0.8496131
[21,] 0.102728784 0.20545757 0.8972712
[22,] 0.069740927 0.13948185 0.9302591
[23,] 0.055342857 0.11068571 0.9446571
[24,] 0.212577491 0.42515498 0.7874225
[25,] 0.240879625 0.48175925 0.7591204
[26,] 0.376424973 0.75284995 0.6235750
[27,] 0.330503536 0.66100707 0.6694965
[28,] 0.334930402 0.66986080 0.6650696
[29,] 0.351560571 0.70312114 0.6484394
[30,] 0.685083055 0.62983389 0.3149169
[31,] 0.691944717 0.61611057 0.3080553
[32,] 0.838060748 0.32387850 0.1619393
[33,] 0.799369260 0.40126148 0.2006307
[34,] 0.755898564 0.48820287 0.2441014
> postscript(file="/var/www/html/rcomp/tmp/1kp6e1263050491.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zsee1263050491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3261a1263050491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xtzs1263050491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kymy1263050491.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 = 71
Frequency = 1
1 2 3 4 5 6
-0.07666408 -0.06684649 0.02288174 0.02092481 -0.09528300 -0.23872447
7 8 9 10 11 12
0.12820404 0.07131290 0.35272777 0.02458114 0.09213641 -0.16037653
13 14 15 16 17 18
0.04105127 0.08592932 -0.15120756 -0.05603429 -0.10728904 -0.18051998
19 20 21 22 23 24
0.08467223 -0.23050013 0.04427045 -0.04901449 0.02000972 -0.09606945
25 26 27 28 29 30
0.11824942 0.03958477 -0.05029498 0.03792558 0.04855533 0.17340283
31 32 33 34 35 36
0.09851694 -0.06483325 -0.25336154 0.22609090 0.29296725 0.13347405
37 38 39 40 41 42
0.07655203 -0.05366743 0.09747918 0.12935358 0.06827632 0.41638564
43 44 45 46 47 48
-0.09162564 -0.17989184 -0.03026358 -0.13525152 -0.14173637 0.26164424
49 50 51 52 53 54
-0.04206879 0.03753427 -0.16578479 -0.09744908 -0.07192459 -0.19693665
55 56 57 58 59 60
-0.19001214 0.12361567 -0.03504757 -0.20991660 -0.24424183 -0.13867231
61 62 63 64 65 66
-0.11711985 -0.04253444 0.24692642 -0.03472060 0.15766498 0.02639264
67 68 69 70 71
-0.02975542 0.28029665 -0.07832553 0.14351058 -0.01913518
> postscript(file="/var/www/html/rcomp/tmp/6bnit1263050491.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 = 71
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.07666408 NA
1 -0.06684649 -0.07666408
2 0.02288174 -0.06684649
3 0.02092481 0.02288174
4 -0.09528300 0.02092481
5 -0.23872447 -0.09528300
6 0.12820404 -0.23872447
7 0.07131290 0.12820404
8 0.35272777 0.07131290
9 0.02458114 0.35272777
10 0.09213641 0.02458114
11 -0.16037653 0.09213641
12 0.04105127 -0.16037653
13 0.08592932 0.04105127
14 -0.15120756 0.08592932
15 -0.05603429 -0.15120756
16 -0.10728904 -0.05603429
17 -0.18051998 -0.10728904
18 0.08467223 -0.18051998
19 -0.23050013 0.08467223
20 0.04427045 -0.23050013
21 -0.04901449 0.04427045
22 0.02000972 -0.04901449
23 -0.09606945 0.02000972
24 0.11824942 -0.09606945
25 0.03958477 0.11824942
26 -0.05029498 0.03958477
27 0.03792558 -0.05029498
28 0.04855533 0.03792558
29 0.17340283 0.04855533
30 0.09851694 0.17340283
31 -0.06483325 0.09851694
32 -0.25336154 -0.06483325
33 0.22609090 -0.25336154
34 0.29296725 0.22609090
35 0.13347405 0.29296725
36 0.07655203 0.13347405
37 -0.05366743 0.07655203
38 0.09747918 -0.05366743
39 0.12935358 0.09747918
40 0.06827632 0.12935358
41 0.41638564 0.06827632
42 -0.09162564 0.41638564
43 -0.17989184 -0.09162564
44 -0.03026358 -0.17989184
45 -0.13525152 -0.03026358
46 -0.14173637 -0.13525152
47 0.26164424 -0.14173637
48 -0.04206879 0.26164424
49 0.03753427 -0.04206879
50 -0.16578479 0.03753427
51 -0.09744908 -0.16578479
52 -0.07192459 -0.09744908
53 -0.19693665 -0.07192459
54 -0.19001214 -0.19693665
55 0.12361567 -0.19001214
56 -0.03504757 0.12361567
57 -0.20991660 -0.03504757
58 -0.24424183 -0.20991660
59 -0.13867231 -0.24424183
60 -0.11711985 -0.13867231
61 -0.04253444 -0.11711985
62 0.24692642 -0.04253444
63 -0.03472060 0.24692642
64 0.15766498 -0.03472060
65 0.02639264 0.15766498
66 -0.02975542 0.02639264
67 0.28029665 -0.02975542
68 -0.07832553 0.28029665
69 0.14351058 -0.07832553
70 -0.01913518 0.14351058
71 NA -0.01913518
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.06684649 -0.07666408
[2,] 0.02288174 -0.06684649
[3,] 0.02092481 0.02288174
[4,] -0.09528300 0.02092481
[5,] -0.23872447 -0.09528300
[6,] 0.12820404 -0.23872447
[7,] 0.07131290 0.12820404
[8,] 0.35272777 0.07131290
[9,] 0.02458114 0.35272777
[10,] 0.09213641 0.02458114
[11,] -0.16037653 0.09213641
[12,] 0.04105127 -0.16037653
[13,] 0.08592932 0.04105127
[14,] -0.15120756 0.08592932
[15,] -0.05603429 -0.15120756
[16,] -0.10728904 -0.05603429
[17,] -0.18051998 -0.10728904
[18,] 0.08467223 -0.18051998
[19,] -0.23050013 0.08467223
[20,] 0.04427045 -0.23050013
[21,] -0.04901449 0.04427045
[22,] 0.02000972 -0.04901449
[23,] -0.09606945 0.02000972
[24,] 0.11824942 -0.09606945
[25,] 0.03958477 0.11824942
[26,] -0.05029498 0.03958477
[27,] 0.03792558 -0.05029498
[28,] 0.04855533 0.03792558
[29,] 0.17340283 0.04855533
[30,] 0.09851694 0.17340283
[31,] -0.06483325 0.09851694
[32,] -0.25336154 -0.06483325
[33,] 0.22609090 -0.25336154
[34,] 0.29296725 0.22609090
[35,] 0.13347405 0.29296725
[36,] 0.07655203 0.13347405
[37,] -0.05366743 0.07655203
[38,] 0.09747918 -0.05366743
[39,] 0.12935358 0.09747918
[40,] 0.06827632 0.12935358
[41,] 0.41638564 0.06827632
[42,] -0.09162564 0.41638564
[43,] -0.17989184 -0.09162564
[44,] -0.03026358 -0.17989184
[45,] -0.13525152 -0.03026358
[46,] -0.14173637 -0.13525152
[47,] 0.26164424 -0.14173637
[48,] -0.04206879 0.26164424
[49,] 0.03753427 -0.04206879
[50,] -0.16578479 0.03753427
[51,] -0.09744908 -0.16578479
[52,] -0.07192459 -0.09744908
[53,] -0.19693665 -0.07192459
[54,] -0.19001214 -0.19693665
[55,] 0.12361567 -0.19001214
[56,] -0.03504757 0.12361567
[57,] -0.20991660 -0.03504757
[58,] -0.24424183 -0.20991660
[59,] -0.13867231 -0.24424183
[60,] -0.11711985 -0.13867231
[61,] -0.04253444 -0.11711985
[62,] 0.24692642 -0.04253444
[63,] -0.03472060 0.24692642
[64,] 0.15766498 -0.03472060
[65,] 0.02639264 0.15766498
[66,] -0.02975542 0.02639264
[67,] 0.28029665 -0.02975542
[68,] -0.07832553 0.28029665
[69,] 0.14351058 -0.07832553
[70,] -0.01913518 0.14351058
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.06684649 -0.07666408
2 0.02288174 -0.06684649
3 0.02092481 0.02288174
4 -0.09528300 0.02092481
5 -0.23872447 -0.09528300
6 0.12820404 -0.23872447
7 0.07131290 0.12820404
8 0.35272777 0.07131290
9 0.02458114 0.35272777
10 0.09213641 0.02458114
11 -0.16037653 0.09213641
12 0.04105127 -0.16037653
13 0.08592932 0.04105127
14 -0.15120756 0.08592932
15 -0.05603429 -0.15120756
16 -0.10728904 -0.05603429
17 -0.18051998 -0.10728904
18 0.08467223 -0.18051998
19 -0.23050013 0.08467223
20 0.04427045 -0.23050013
21 -0.04901449 0.04427045
22 0.02000972 -0.04901449
23 -0.09606945 0.02000972
24 0.11824942 -0.09606945
25 0.03958477 0.11824942
26 -0.05029498 0.03958477
27 0.03792558 -0.05029498
28 0.04855533 0.03792558
29 0.17340283 0.04855533
30 0.09851694 0.17340283
31 -0.06483325 0.09851694
32 -0.25336154 -0.06483325
33 0.22609090 -0.25336154
34 0.29296725 0.22609090
35 0.13347405 0.29296725
36 0.07655203 0.13347405
37 -0.05366743 0.07655203
38 0.09747918 -0.05366743
39 0.12935358 0.09747918
40 0.06827632 0.12935358
41 0.41638564 0.06827632
42 -0.09162564 0.41638564
43 -0.17989184 -0.09162564
44 -0.03026358 -0.17989184
45 -0.13525152 -0.03026358
46 -0.14173637 -0.13525152
47 0.26164424 -0.14173637
48 -0.04206879 0.26164424
49 0.03753427 -0.04206879
50 -0.16578479 0.03753427
51 -0.09744908 -0.16578479
52 -0.07192459 -0.09744908
53 -0.19693665 -0.07192459
54 -0.19001214 -0.19693665
55 0.12361567 -0.19001214
56 -0.03504757 0.12361567
57 -0.20991660 -0.03504757
58 -0.24424183 -0.20991660
59 -0.13867231 -0.24424183
60 -0.11711985 -0.13867231
61 -0.04253444 -0.11711985
62 0.24692642 -0.04253444
63 -0.03472060 0.24692642
64 0.15766498 -0.03472060
65 0.02639264 0.15766498
66 -0.02975542 0.02639264
67 0.28029665 -0.02975542
68 -0.07832553 0.28029665
69 0.14351058 -0.07832553
70 -0.01913518 0.14351058
> 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/717k51263050491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87ouj1263050491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9s79d1263050491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ffkq1263050491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gcfh1263050491.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/12h2hq1263050491.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/13c4h21263050491.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/14205c1263050491.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/15aozv1263050491.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/16fao91263050491.tab")
+ }
> try(system("convert tmp/1kp6e1263050491.ps tmp/1kp6e1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zsee1263050491.ps tmp/2zsee1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/3261a1263050491.ps tmp/3261a1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xtzs1263050491.ps tmp/4xtzs1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kymy1263050491.ps tmp/5kymy1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bnit1263050491.ps tmp/6bnit1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/717k51263050491.ps tmp/717k51263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/87ouj1263050491.ps tmp/87ouj1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/9s79d1263050491.ps tmp/9s79d1263050491.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ffkq1263050491.ps tmp/10ffkq1263050491.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.630 1.642 3.655