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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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(3.4
+ ,4.9
+ ,3.2
+ ,3.3
+ ,3.6
+ ,3.9
+ ,3.4
+ ,4.5
+ ,3.4
+ ,3.2
+ ,3.3
+ ,3.6
+ ,3.5
+ ,4.6
+ ,3.4
+ ,3.4
+ ,3.2
+ ,3.3
+ ,3.2
+ ,4.7
+ ,3.5
+ ,3.4
+ ,3.4
+ ,3.2
+ ,3.3
+ ,4.7
+ ,3.2
+ ,3.5
+ ,3.4
+ ,3.4
+ ,3.3
+ ,4.3
+ ,3.3
+ ,3.2
+ ,3.5
+ ,3.4
+ ,3.4
+ ,4.2
+ ,3.3
+ ,3.3
+ ,3.2
+ ,3.5
+ ,3.7
+ ,4.4
+ ,3.4
+ ,3.3
+ ,3.3
+ ,3.2
+ ,3.9
+ ,4
+ ,3.7
+ ,3.4
+ ,3.3
+ ,3.3
+ ,4
+ ,3.8
+ ,3.9
+ ,3.7
+ ,3.4
+ ,3.3
+ ,3.7
+ ,3.6
+ ,4
+ ,3.9
+ ,3.7
+ ,3.4
+ ,3.9
+ ,3.6
+ ,3.7
+ ,4
+ ,3.9
+ ,3.7
+ ,4.2
+ ,3.3
+ ,3.9
+ ,3.7
+ ,4
+ ,3.9
+ ,4.4
+ ,3.4
+ ,4.2
+ ,3.9
+ ,3.7
+ ,4
+ ,4.3
+ ,3.4
+ ,4.4
+ ,4.2
+ ,3.9
+ ,3.7
+ ,4.2
+ ,3.3
+ ,4.3
+ ,4.4
+ ,4.2
+ ,3.9
+ ,4.3
+ ,3.3
+ ,4.2
+ ,4.3
+ ,4.4
+ ,4.2
+ ,4.3
+ ,3.2
+ ,4.3
+ ,4.2
+ ,4.3
+ ,4.4
+ ,4.3
+ ,3.1
+ ,4.3
+ ,4.3
+ ,4.2
+ ,4.3
+ ,4.5
+ ,3.1
+ ,4.3
+ ,4.3
+ ,4.3
+ ,4.2
+ ,5
+ ,2.4
+ ,4.5
+ ,4.3
+ ,4.3
+ ,4.3
+ ,5.2
+ ,2.4
+ ,5
+ ,4.5
+ ,4.3
+ ,4.3
+ ,5.2
+ ,2.4
+ ,5.2
+ ,5
+ ,4.5
+ ,4.3
+ ,5.4
+ ,2.1
+ ,5.2
+ ,5.2
+ ,5
+ ,4.5
+ ,5.5
+ ,2
+ ,5.4
+ ,5.2
+ ,5.2
+ ,5
+ ,5.4
+ ,2
+ ,5.5
+ ,5.4
+ ,5.2
+ ,5.2
+ ,5.5
+ ,2.1
+ ,5.4
+ ,5.5
+ ,5.4
+ ,5.2
+ ,5.4
+ ,2.1
+ ,5.5
+ ,5.4
+ ,5.5
+ ,5.4
+ ,5.7
+ ,2
+ ,5.4
+ ,5.5
+ ,5.4
+ ,5.5
+ ,5.7
+ ,2
+ ,5.7
+ ,5.4
+ ,5.5
+ ,5.4
+ ,6.1
+ ,2
+ ,5.7
+ ,5.7
+ ,5.4
+ ,5.5
+ ,6.5
+ ,1.7
+ ,6.1
+ ,5.7
+ ,5.7
+ ,5.4
+ ,6.9
+ ,1.3
+ ,6.5
+ ,6.1
+ ,5.7
+ ,5.7
+ ,6.8
+ ,1.2
+ ,6.9
+ ,6.5
+ ,6.1
+ ,5.7
+ ,6.7
+ ,1.1
+ ,6.8
+ ,6.9
+ ,6.5
+ ,6.1
+ ,6.6
+ ,1.4
+ ,6.7
+ ,6.8
+ ,6.9
+ ,6.5
+ ,6.5
+ ,1.5
+ ,6.6
+ ,6.7
+ ,6.8
+ ,6.9
+ ,6.4
+ ,1.4
+ ,6.5
+ ,6.6
+ ,6.7
+ ,6.8
+ ,6.1
+ ,1.1
+ ,6.4
+ ,6.5
+ ,6.6
+ ,6.7
+ ,6.2
+ ,1.1
+ ,6.1
+ ,6.4
+ ,6.5
+ ,6.6
+ ,6.3
+ ,1
+ ,6.2
+ ,6.1
+ ,6.4
+ ,6.5
+ ,6.4
+ ,1.4
+ ,6.3
+ ,6.2
+ ,6.1
+ ,6.4
+ ,6.5
+ ,1.3
+ ,6.4
+ ,6.3
+ ,6.2
+ ,6.1
+ ,6.7
+ ,1.2
+ ,6.5
+ ,6.4
+ ,6.3
+ ,6.2
+ ,7
+ ,1.5
+ ,6.7
+ ,6.5
+ ,6.4
+ ,6.3
+ ,7
+ ,1.6
+ ,7
+ ,6.7
+ ,6.5
+ ,6.4
+ ,6.8
+ ,1.8
+ ,7
+ ,7
+ ,6.7
+ ,6.5
+ ,6.7
+ ,1.5
+ ,6.8
+ ,7
+ ,7
+ ,6.7
+ ,6.7
+ ,1.3
+ ,6.7
+ ,6.8
+ ,7
+ ,7
+ ,6.5
+ ,1.6
+ ,6.7
+ ,6.7
+ ,6.8
+ ,7
+ ,6.4
+ ,1.6
+ ,6.5
+ ,6.7
+ ,6.7
+ ,6.8
+ ,6.1
+ ,1.8
+ ,6.4
+ ,6.5
+ ,6.7
+ ,6.7
+ ,6.2
+ ,1.8
+ ,6.1
+ ,6.4
+ ,6.5
+ ,6.7
+ ,6
+ ,1.6
+ ,6.2
+ ,6.1
+ ,6.4
+ ,6.5
+ ,6.1
+ ,1.8
+ ,6
+ ,6.2
+ ,6.1
+ ,6.4
+ ,6.1
+ ,2
+ ,6.1
+ ,6
+ ,6.2
+ ,6.1)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Werkl'
+ ,'Infl'
+ ,'M1(t)'
+ ,'M2(t)'
+ ,'M3(t)'
+ ,'M4(t)')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Werkl','Infl','M1(t)','M2(t)','M3(t)','M4(t)'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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 Infl M1(t) M2(t) M3(t) M4(t) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3.4 4.9 3.2 3.3 3.6 3.9 1 0 0 0 0 0 0 0 0 0 0
2 3.4 4.5 3.4 3.2 3.3 3.6 0 1 0 0 0 0 0 0 0 0 0
3 3.5 4.6 3.4 3.4 3.2 3.3 0 0 1 0 0 0 0 0 0 0 0
4 3.2 4.7 3.5 3.4 3.4 3.2 0 0 0 1 0 0 0 0 0 0 0
5 3.3 4.7 3.2 3.5 3.4 3.4 0 0 0 0 1 0 0 0 0 0 0
6 3.3 4.3 3.3 3.2 3.5 3.4 0 0 0 0 0 1 0 0 0 0 0
7 3.4 4.2 3.3 3.3 3.2 3.5 0 0 0 0 0 0 1 0 0 0 0
8 3.7 4.4 3.4 3.3 3.3 3.2 0 0 0 0 0 0 0 1 0 0 0
9 3.9 4.0 3.7 3.4 3.3 3.3 0 0 0 0 0 0 0 0 1 0 0
10 4.0 3.8 3.9 3.7 3.4 3.3 0 0 0 0 0 0 0 0 0 1 0
11 3.7 3.6 4.0 3.9 3.7 3.4 0 0 0 0 0 0 0 0 0 0 1
12 3.9 3.6 3.7 4.0 3.9 3.7 0 0 0 0 0 0 0 0 0 0 0
13 4.2 3.3 3.9 3.7 4.0 3.9 1 0 0 0 0 0 0 0 0 0 0
14 4.4 3.4 4.2 3.9 3.7 4.0 0 1 0 0 0 0 0 0 0 0 0
15 4.3 3.4 4.4 4.2 3.9 3.7 0 0 1 0 0 0 0 0 0 0 0
16 4.2 3.3 4.3 4.4 4.2 3.9 0 0 0 1 0 0 0 0 0 0 0
17 4.3 3.3 4.2 4.3 4.4 4.2 0 0 0 0 1 0 0 0 0 0 0
18 4.3 3.2 4.3 4.2 4.3 4.4 0 0 0 0 0 1 0 0 0 0 0
19 4.3 3.1 4.3 4.3 4.2 4.3 0 0 0 0 0 0 1 0 0 0 0
20 4.5 3.1 4.3 4.3 4.3 4.2 0 0 0 0 0 0 0 1 0 0 0
21 5.0 2.4 4.5 4.3 4.3 4.3 0 0 0 0 0 0 0 0 1 0 0
22 5.2 2.4 5.0 4.5 4.3 4.3 0 0 0 0 0 0 0 0 0 1 0
23 5.2 2.4 5.2 5.0 4.5 4.3 0 0 0 0 0 0 0 0 0 0 1
24 5.4 2.1 5.2 5.2 5.0 4.5 0 0 0 0 0 0 0 0 0 0 0
25 5.5 2.0 5.4 5.2 5.2 5.0 1 0 0 0 0 0 0 0 0 0 0
26 5.4 2.0 5.5 5.4 5.2 5.2 0 1 0 0 0 0 0 0 0 0 0
27 5.5 2.1 5.4 5.5 5.4 5.2 0 0 1 0 0 0 0 0 0 0 0
28 5.4 2.1 5.5 5.4 5.5 5.4 0 0 0 1 0 0 0 0 0 0 0
29 5.7 2.0 5.4 5.5 5.4 5.5 0 0 0 0 1 0 0 0 0 0 0
30 5.7 2.0 5.7 5.4 5.5 5.4 0 0 0 0 0 1 0 0 0 0 0
31 6.1 2.0 5.7 5.7 5.4 5.5 0 0 0 0 0 0 1 0 0 0 0
32 6.5 1.7 6.1 5.7 5.7 5.4 0 0 0 0 0 0 0 1 0 0 0
33 6.9 1.3 6.5 6.1 5.7 5.7 0 0 0 0 0 0 0 0 1 0 0
34 6.8 1.2 6.9 6.5 6.1 5.7 0 0 0 0 0 0 0 0 0 1 0
35 6.7 1.1 6.8 6.9 6.5 6.1 0 0 0 0 0 0 0 0 0 0 1
36 6.6 1.4 6.7 6.8 6.9 6.5 0 0 0 0 0 0 0 0 0 0 0
37 6.5 1.5 6.6 6.7 6.8 6.9 1 0 0 0 0 0 0 0 0 0 0
38 6.4 1.4 6.5 6.6 6.7 6.8 0 1 0 0 0 0 0 0 0 0 0
39 6.1 1.1 6.4 6.5 6.6 6.7 0 0 1 0 0 0 0 0 0 0 0
40 6.2 1.1 6.1 6.4 6.5 6.6 0 0 0 1 0 0 0 0 0 0 0
41 6.3 1.0 6.2 6.1 6.4 6.5 0 0 0 0 1 0 0 0 0 0 0
42 6.4 1.4 6.3 6.2 6.1 6.4 0 0 0 0 0 1 0 0 0 0 0
43 6.5 1.3 6.4 6.3 6.2 6.1 0 0 0 0 0 0 1 0 0 0 0
44 6.7 1.2 6.5 6.4 6.3 6.2 0 0 0 0 0 0 0 1 0 0 0
45 7.0 1.5 6.7 6.5 6.4 6.3 0 0 0 0 0 0 0 0 1 0 0
46 7.0 1.6 7.0 6.7 6.5 6.4 0 0 0 0 0 0 0 0 0 1 0
47 6.8 1.8 7.0 7.0 6.7 6.5 0 0 0 0 0 0 0 0 0 0 1
48 6.7 1.5 6.8 7.0 7.0 6.7 0 0 0 0 0 0 0 0 0 0 0
49 6.7 1.3 6.7 6.8 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0
50 6.5 1.6 6.7 6.7 6.8 7.0 0 1 0 0 0 0 0 0 0 0 0
51 6.4 1.6 6.5 6.7 6.7 6.8 0 0 1 0 0 0 0 0 0 0 0
52 6.1 1.8 6.4 6.5 6.7 6.7 0 0 0 1 0 0 0 0 0 0 0
53 6.2 1.8 6.1 6.4 6.5 6.7 0 0 0 0 1 0 0 0 0 0 0
54 6.0 1.6 6.2 6.1 6.4 6.5 0 0 0 0 0 1 0 0 0 0 0
55 6.1 1.8 6.0 6.2 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0
56 6.1 2.0 6.1 6.0 6.2 6.1 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl `M1(t)` `M2(t)` `M3(t)` `M4(t)`
1.4298201 -0.1611938 0.9298717 0.2740126 -0.6248358 0.2514873
M1 M2 M3 M4 M5 M6
0.0239057 -0.2252263 -0.2208921 -0.2302378 -0.0002049 -0.1491274
M7 M8 M9 M10 M11
-0.1045034 0.1134800 0.1205270 -0.1509030 -0.3132241
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.224924 -0.074302 -0.007436 0.061575 0.225864
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4298201 0.3777073 3.786 0.000517 ***
Infl -0.1611938 0.0505311 -3.190 0.002807 **
`M1(t)` 0.9298717 0.1550546 5.997 5.23e-07 ***
`M2(t)` 0.2740126 0.1986558 1.379 0.175651
`M3(t)` -0.6248358 0.1994015 -3.134 0.003273 **
`M4(t)` 0.2514873 0.1320884 1.904 0.064316 .
M1 0.0239057 0.1018027 0.235 0.815574
M2 -0.2252263 0.1175819 -1.915 0.062784 .
M3 -0.2208921 0.0938643 -2.353 0.023743 *
M4 -0.2302378 0.0876979 -2.625 0.012300 *
M5 -0.0002049 0.0940748 -0.002 0.998273
M6 -0.1491274 0.1115414 -1.337 0.188979
M7 -0.1045034 0.1082883 -0.965 0.340467
M8 0.1134800 0.0966368 1.174 0.247398
M9 0.1205270 0.1181675 1.020 0.314029
M10 -0.1509030 0.1209533 -1.248 0.219612
M11 -0.3132241 0.0976836 -3.207 0.002683 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1169 on 39 degrees of freedom
Multiple R-squared: 0.9936, Adjusted R-squared: 0.991
F-statistic: 379.9 on 16 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,] 0.4446095 0.8892190 0.5553905
[2,] 0.3313637 0.6627273 0.6686363
[3,] 0.2198023 0.4396047 0.7801977
[4,] 0.3961476 0.7922951 0.6038524
[5,] 0.3378926 0.6757851 0.6621074
[6,] 0.2868419 0.5736837 0.7131581
[7,] 0.2785150 0.5570300 0.7214850
[8,] 0.3036921 0.6073842 0.6963079
[9,] 0.2323246 0.4646493 0.7676754
[10,] 0.1576373 0.3152745 0.8423627
[11,] 0.1131047 0.2262094 0.8868953
[12,] 0.3482883 0.6965766 0.6517117
[13,] 0.7082705 0.5834590 0.2917295
[14,] 0.5947402 0.8105196 0.4052598
[15,] 0.5478277 0.9043447 0.4521723
[16,] 0.4983669 0.9967338 0.5016331
[17,] 0.3693628 0.7387256 0.6306372
> postscript(file="/var/www/html/rcomp/tmp/1cgar1260103638.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nt3d1260103638.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3869a1260103638.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u5721260103638.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hee11260103638.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 = 56
Frequency = 1
1 2 3 4 5 6
0.124901433 0.038978279 0.108923592 -0.108482700 -0.037252789 0.098892430
7 8 9 10 11 12
-0.101851688 0.057346217 -0.145689766 -0.012193005 -0.167598360 0.020258750
13 14 15 16 17 18
0.156410492 0.075298390 -0.096800536 -0.028236370 0.011640146 -0.033923616
19 20 21 22 23 24
-0.159403129 -0.089754269 0.079240034 0.030931734 -0.004760646 0.040975028
25 26 27 28 29 30
-0.085800844 -0.134755992 0.167582291 0.023528119 0.055329470 0.040324087
31 32 33 34 35 36
0.225863988 0.200173200 -0.028351171 -0.104659891 0.074263349 -0.020874801
37 38 39 40 41 42
-0.171351163 0.044715015 -0.224923706 0.153449814 -0.040820689 -0.010111041
43 44 45 46 47 48
0.046686886 -0.070469508 0.094800902 0.085921162 0.098095657 -0.040358977
49 50 51 52 53 54
-0.024159917 -0.024235693 0.045218359 -0.040258863 0.011103861 -0.095181860
55 56
-0.011296057 -0.097295640
> postscript(file="/var/www/html/rcomp/tmp/639x21260103638.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 0.124901433 NA
1 0.038978279 0.124901433
2 0.108923592 0.038978279
3 -0.108482700 0.108923592
4 -0.037252789 -0.108482700
5 0.098892430 -0.037252789
6 -0.101851688 0.098892430
7 0.057346217 -0.101851688
8 -0.145689766 0.057346217
9 -0.012193005 -0.145689766
10 -0.167598360 -0.012193005
11 0.020258750 -0.167598360
12 0.156410492 0.020258750
13 0.075298390 0.156410492
14 -0.096800536 0.075298390
15 -0.028236370 -0.096800536
16 0.011640146 -0.028236370
17 -0.033923616 0.011640146
18 -0.159403129 -0.033923616
19 -0.089754269 -0.159403129
20 0.079240034 -0.089754269
21 0.030931734 0.079240034
22 -0.004760646 0.030931734
23 0.040975028 -0.004760646
24 -0.085800844 0.040975028
25 -0.134755992 -0.085800844
26 0.167582291 -0.134755992
27 0.023528119 0.167582291
28 0.055329470 0.023528119
29 0.040324087 0.055329470
30 0.225863988 0.040324087
31 0.200173200 0.225863988
32 -0.028351171 0.200173200
33 -0.104659891 -0.028351171
34 0.074263349 -0.104659891
35 -0.020874801 0.074263349
36 -0.171351163 -0.020874801
37 0.044715015 -0.171351163
38 -0.224923706 0.044715015
39 0.153449814 -0.224923706
40 -0.040820689 0.153449814
41 -0.010111041 -0.040820689
42 0.046686886 -0.010111041
43 -0.070469508 0.046686886
44 0.094800902 -0.070469508
45 0.085921162 0.094800902
46 0.098095657 0.085921162
47 -0.040358977 0.098095657
48 -0.024159917 -0.040358977
49 -0.024235693 -0.024159917
50 0.045218359 -0.024235693
51 -0.040258863 0.045218359
52 0.011103861 -0.040258863
53 -0.095181860 0.011103861
54 -0.011296057 -0.095181860
55 -0.097295640 -0.011296057
56 NA -0.097295640
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.038978279 0.124901433
[2,] 0.108923592 0.038978279
[3,] -0.108482700 0.108923592
[4,] -0.037252789 -0.108482700
[5,] 0.098892430 -0.037252789
[6,] -0.101851688 0.098892430
[7,] 0.057346217 -0.101851688
[8,] -0.145689766 0.057346217
[9,] -0.012193005 -0.145689766
[10,] -0.167598360 -0.012193005
[11,] 0.020258750 -0.167598360
[12,] 0.156410492 0.020258750
[13,] 0.075298390 0.156410492
[14,] -0.096800536 0.075298390
[15,] -0.028236370 -0.096800536
[16,] 0.011640146 -0.028236370
[17,] -0.033923616 0.011640146
[18,] -0.159403129 -0.033923616
[19,] -0.089754269 -0.159403129
[20,] 0.079240034 -0.089754269
[21,] 0.030931734 0.079240034
[22,] -0.004760646 0.030931734
[23,] 0.040975028 -0.004760646
[24,] -0.085800844 0.040975028
[25,] -0.134755992 -0.085800844
[26,] 0.167582291 -0.134755992
[27,] 0.023528119 0.167582291
[28,] 0.055329470 0.023528119
[29,] 0.040324087 0.055329470
[30,] 0.225863988 0.040324087
[31,] 0.200173200 0.225863988
[32,] -0.028351171 0.200173200
[33,] -0.104659891 -0.028351171
[34,] 0.074263349 -0.104659891
[35,] -0.020874801 0.074263349
[36,] -0.171351163 -0.020874801
[37,] 0.044715015 -0.171351163
[38,] -0.224923706 0.044715015
[39,] 0.153449814 -0.224923706
[40,] -0.040820689 0.153449814
[41,] -0.010111041 -0.040820689
[42,] 0.046686886 -0.010111041
[43,] -0.070469508 0.046686886
[44,] 0.094800902 -0.070469508
[45,] 0.085921162 0.094800902
[46,] 0.098095657 0.085921162
[47,] -0.040358977 0.098095657
[48,] -0.024159917 -0.040358977
[49,] -0.024235693 -0.024159917
[50,] 0.045218359 -0.024235693
[51,] -0.040258863 0.045218359
[52,] 0.011103861 -0.040258863
[53,] -0.095181860 0.011103861
[54,] -0.011296057 -0.095181860
[55,] -0.097295640 -0.011296057
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.038978279 0.124901433
2 0.108923592 0.038978279
3 -0.108482700 0.108923592
4 -0.037252789 -0.108482700
5 0.098892430 -0.037252789
6 -0.101851688 0.098892430
7 0.057346217 -0.101851688
8 -0.145689766 0.057346217
9 -0.012193005 -0.145689766
10 -0.167598360 -0.012193005
11 0.020258750 -0.167598360
12 0.156410492 0.020258750
13 0.075298390 0.156410492
14 -0.096800536 0.075298390
15 -0.028236370 -0.096800536
16 0.011640146 -0.028236370
17 -0.033923616 0.011640146
18 -0.159403129 -0.033923616
19 -0.089754269 -0.159403129
20 0.079240034 -0.089754269
21 0.030931734 0.079240034
22 -0.004760646 0.030931734
23 0.040975028 -0.004760646
24 -0.085800844 0.040975028
25 -0.134755992 -0.085800844
26 0.167582291 -0.134755992
27 0.023528119 0.167582291
28 0.055329470 0.023528119
29 0.040324087 0.055329470
30 0.225863988 0.040324087
31 0.200173200 0.225863988
32 -0.028351171 0.200173200
33 -0.104659891 -0.028351171
34 0.074263349 -0.104659891
35 -0.020874801 0.074263349
36 -0.171351163 -0.020874801
37 0.044715015 -0.171351163
38 -0.224923706 0.044715015
39 0.153449814 -0.224923706
40 -0.040820689 0.153449814
41 -0.010111041 -0.040820689
42 0.046686886 -0.010111041
43 -0.070469508 0.046686886
44 0.094800902 -0.070469508
45 0.085921162 0.094800902
46 0.098095657 0.085921162
47 -0.040358977 0.098095657
48 -0.024159917 -0.040358977
49 -0.024235693 -0.024159917
50 0.045218359 -0.024235693
51 -0.040258863 0.045218359
52 0.011103861 -0.040258863
53 -0.095181860 0.011103861
54 -0.011296057 -0.095181860
55 -0.097295640 -0.011296057
> 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/7ijqp1260103638.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8e73c1260103638.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c6it1260103638.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ciwf1260103638.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bl4q1260103638.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/12vtc61260103638.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/138dh41260103639.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/14pvlb1260103639.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/15ho8k1260103639.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/16j8jb1260103639.tab")
+ }
>
> system("convert tmp/1cgar1260103638.ps tmp/1cgar1260103638.png")
> system("convert tmp/2nt3d1260103638.ps tmp/2nt3d1260103638.png")
> system("convert tmp/3869a1260103638.ps tmp/3869a1260103638.png")
> system("convert tmp/4u5721260103638.ps tmp/4u5721260103638.png")
> system("convert tmp/5hee11260103638.ps tmp/5hee11260103638.png")
> system("convert tmp/639x21260103638.ps tmp/639x21260103638.png")
> system("convert tmp/7ijqp1260103638.ps tmp/7ijqp1260103638.png")
> system("convert tmp/8e73c1260103638.ps tmp/8e73c1260103638.png")
> system("convert tmp/9c6it1260103638.ps tmp/9c6it1260103638.png")
> system("convert tmp/10ciwf1260103638.ps tmp/10ciwf1260103638.png")
>
>
> proc.time()
user system elapsed
2.345 1.536 11.329