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 = '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 t
1 3.4 4.9 3.2 3.3 3.6 3.9 1 0 0 0 0 0 0 0 0 0 0 1
2 3.4 4.5 3.4 3.2 3.3 3.6 0 1 0 0 0 0 0 0 0 0 0 2
3 3.5 4.6 3.4 3.4 3.2 3.3 0 0 1 0 0 0 0 0 0 0 0 3
4 3.2 4.7 3.5 3.4 3.4 3.2 0 0 0 1 0 0 0 0 0 0 0 4
5 3.3 4.7 3.2 3.5 3.4 3.4 0 0 0 0 1 0 0 0 0 0 0 5
6 3.3 4.3 3.3 3.2 3.5 3.4 0 0 0 0 0 1 0 0 0 0 0 6
7 3.4 4.2 3.3 3.3 3.2 3.5 0 0 0 0 0 0 1 0 0 0 0 7
8 3.7 4.4 3.4 3.3 3.3 3.2 0 0 0 0 0 0 0 1 0 0 0 8
9 3.9 4.0 3.7 3.4 3.3 3.3 0 0 0 0 0 0 0 0 1 0 0 9
10 4.0 3.8 3.9 3.7 3.4 3.3 0 0 0 0 0 0 0 0 0 1 0 10
11 3.7 3.6 4.0 3.9 3.7 3.4 0 0 0 0 0 0 0 0 0 0 1 11
12 3.9 3.6 3.7 4.0 3.9 3.7 0 0 0 0 0 0 0 0 0 0 0 12
13 4.2 3.3 3.9 3.7 4.0 3.9 1 0 0 0 0 0 0 0 0 0 0 13
14 4.4 3.4 4.2 3.9 3.7 4.0 0 1 0 0 0 0 0 0 0 0 0 14
15 4.3 3.4 4.4 4.2 3.9 3.7 0 0 1 0 0 0 0 0 0 0 0 15
16 4.2 3.3 4.3 4.4 4.2 3.9 0 0 0 1 0 0 0 0 0 0 0 16
17 4.3 3.3 4.2 4.3 4.4 4.2 0 0 0 0 1 0 0 0 0 0 0 17
18 4.3 3.2 4.3 4.2 4.3 4.4 0 0 0 0 0 1 0 0 0 0 0 18
19 4.3 3.1 4.3 4.3 4.2 4.3 0 0 0 0 0 0 1 0 0 0 0 19
20 4.5 3.1 4.3 4.3 4.3 4.2 0 0 0 0 0 0 0 1 0 0 0 20
21 5.0 2.4 4.5 4.3 4.3 4.3 0 0 0 0 0 0 0 0 1 0 0 21
22 5.2 2.4 5.0 4.5 4.3 4.3 0 0 0 0 0 0 0 0 0 1 0 22
23 5.2 2.4 5.2 5.0 4.5 4.3 0 0 0 0 0 0 0 0 0 0 1 23
24 5.4 2.1 5.2 5.2 5.0 4.5 0 0 0 0 0 0 0 0 0 0 0 24
25 5.5 2.0 5.4 5.2 5.2 5.0 1 0 0 0 0 0 0 0 0 0 0 25
26 5.4 2.0 5.5 5.4 5.2 5.2 0 1 0 0 0 0 0 0 0 0 0 26
27 5.5 2.1 5.4 5.5 5.4 5.2 0 0 1 0 0 0 0 0 0 0 0 27
28 5.4 2.1 5.5 5.4 5.5 5.4 0 0 0 1 0 0 0 0 0 0 0 28
29 5.7 2.0 5.4 5.5 5.4 5.5 0 0 0 0 1 0 0 0 0 0 0 29
30 5.7 2.0 5.7 5.4 5.5 5.4 0 0 0 0 0 1 0 0 0 0 0 30
31 6.1 2.0 5.7 5.7 5.4 5.5 0 0 0 0 0 0 1 0 0 0 0 31
32 6.5 1.7 6.1 5.7 5.7 5.4 0 0 0 0 0 0 0 1 0 0 0 32
33 6.9 1.3 6.5 6.1 5.7 5.7 0 0 0 0 0 0 0 0 1 0 0 33
34 6.8 1.2 6.9 6.5 6.1 5.7 0 0 0 0 0 0 0 0 0 1 0 34
35 6.7 1.1 6.8 6.9 6.5 6.1 0 0 0 0 0 0 0 0 0 0 1 35
36 6.6 1.4 6.7 6.8 6.9 6.5 0 0 0 0 0 0 0 0 0 0 0 36
37 6.5 1.5 6.6 6.7 6.8 6.9 1 0 0 0 0 0 0 0 0 0 0 37
38 6.4 1.4 6.5 6.6 6.7 6.8 0 1 0 0 0 0 0 0 0 0 0 38
39 6.1 1.1 6.4 6.5 6.6 6.7 0 0 1 0 0 0 0 0 0 0 0 39
40 6.2 1.1 6.1 6.4 6.5 6.6 0 0 0 1 0 0 0 0 0 0 0 40
41 6.3 1.0 6.2 6.1 6.4 6.5 0 0 0 0 1 0 0 0 0 0 0 41
42 6.4 1.4 6.3 6.2 6.1 6.4 0 0 0 0 0 1 0 0 0 0 0 42
43 6.5 1.3 6.4 6.3 6.2 6.1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.7 1.2 6.5 6.4 6.3 6.2 0 0 0 0 0 0 0 1 0 0 0 44
45 7.0 1.5 6.7 6.5 6.4 6.3 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 1.6 7.0 6.7 6.5 6.4 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 1.8 7.0 7.0 6.7 6.5 0 0 0 0 0 0 0 0 0 0 1 47
48 6.7 1.5 6.8 7.0 7.0 6.7 0 0 0 0 0 0 0 0 0 0 0 48
49 6.7 1.3 6.7 6.8 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 1.6 6.7 6.7 6.8 7.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.4 1.6 6.5 6.7 6.7 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 6.1 1.8 6.4 6.5 6.7 6.7 0 0 0 1 0 0 0 0 0 0 0 52
53 6.2 1.8 6.1 6.4 6.5 6.7 0 0 0 0 1 0 0 0 0 0 0 53
54 6.0 1.6 6.2 6.1 6.4 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.1 1.8 6.0 6.2 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 55
56 6.1 2.0 6.1 6.0 6.2 6.1 0 0 0 0 0 0 0 1 0 0 0 56
> 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)`
1.314951 -0.159168 0.936326 0.263924 -0.615614 0.282631
M1 M2 M3 M4 M5 M6
0.009028 -0.235331 -0.221374 -0.229469 0.001091 -0.145333
M7 M8 M9 M10 M11 t
-0.093074 0.129767 0.127508 -0.142574 -0.305950 -0.002894
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.244213 -0.067798 0.009298 0.056773 0.214577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.314951 0.407321 3.228 0.00257 **
Infl -0.159168 0.050855 -3.130 0.00336 **
`M1(t)` 0.936326 0.156065 6.000 5.7e-07 ***
`M2(t)` 0.263924 0.200089 1.319 0.19505
`M3(t)` -0.615614 0.200768 -3.066 0.00398 **
`M4(t)` 0.282631 0.138663 2.038 0.04853 *
M1 0.009028 0.104092 0.087 0.93134
M2 -0.235331 0.118892 -1.979 0.05505 .
M3 -0.221374 0.094345 -2.346 0.02427 *
M4 -0.229469 0.088150 -2.603 0.01310 *
M5 0.001091 0.094569 0.012 0.99085
M6 -0.145333 0.112216 -1.295 0.20309
M7 -0.093074 0.109827 -0.847 0.40205
M8 0.129767 0.099359 1.306 0.19939
M9 0.127508 0.119108 1.071 0.29114
M10 -0.142574 0.122040 -1.168 0.24998
M11 -0.305950 0.098625 -3.102 0.00361 **
t -0.002894 0.003718 -0.778 0.44128
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1175 on 38 degrees of freedom
Multiple R-squared: 0.9937, Adjusted R-squared: 0.9909
F-statistic: 354 on 17 and 38 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,] 0.5042316 0.9915367 0.4957684
[2,] 0.3649751 0.7299502 0.6350249
[3,] 0.3629610 0.7259220 0.6370390
[4,] 0.2741321 0.5482643 0.7258679
[5,] 0.3687985 0.7375969 0.6312015
[6,] 0.2855922 0.5711844 0.7144078
[7,] 0.3612077 0.7224155 0.6387923
[8,] 0.2647427 0.5294854 0.7352573
[9,] 0.1751911 0.3503823 0.8248089
[10,] 0.1293075 0.2586149 0.8706925
[11,] 0.3445954 0.6891907 0.6554046
[12,] 0.6464116 0.7071768 0.3535884
[13,] 0.5166953 0.9666093 0.4833047
[14,] 0.4779135 0.9558270 0.5220865
[15,] 0.6747820 0.6504360 0.3252180
> postscript(file="/var/www/html/rcomp/tmp/1q3ra1260302532.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wg3v1260302532.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3793j1260302532.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4skol1260302532.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5onky1260302532.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
0.105592310 0.028410070 0.103706779 -0.111635057 -0.041322724 0.091434240
7 8 9 10 11 12
-0.113188003 0.051416511 -0.142651075 -0.006390402 -0.161950430 0.027831852
13 14 15 16 17 18
0.170894676 0.087433793 -0.082159567 -0.018082671 0.012609114 -0.039317644
19 20 21 22 23 24
-0.164290765 -0.094413486 0.083793654 0.035821169 0.005986495 0.053675763
25 26 27 28 29 30
-0.073833211 -0.129524601 0.165691956 0.014474960 0.048307121 0.032944254
31 32 33 34 35 36
0.214576733 0.185296312 -0.038107439 -0.114903314 0.056705670 -0.045382317
37 38 39 40 41 42
-0.190188757 0.027873543 -0.244213183 0.140766660 -0.050570270 -0.014031530
43 44 45 46 47 48
0.047011905 -0.075578861 0.096964860 0.085472548 0.099258265 -0.036125298
49 50 51 52 53 54
-0.012465018 -0.014192804 0.056974015 -0.025523892 0.030976760 -0.071029319
55 56
0.015890129 -0.066720477
> postscript(file="/var/www/html/rcomp/tmp/6rxes1260302533.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 0.105592310 NA
1 0.028410070 0.105592310
2 0.103706779 0.028410070
3 -0.111635057 0.103706779
4 -0.041322724 -0.111635057
5 0.091434240 -0.041322724
6 -0.113188003 0.091434240
7 0.051416511 -0.113188003
8 -0.142651075 0.051416511
9 -0.006390402 -0.142651075
10 -0.161950430 -0.006390402
11 0.027831852 -0.161950430
12 0.170894676 0.027831852
13 0.087433793 0.170894676
14 -0.082159567 0.087433793
15 -0.018082671 -0.082159567
16 0.012609114 -0.018082671
17 -0.039317644 0.012609114
18 -0.164290765 -0.039317644
19 -0.094413486 -0.164290765
20 0.083793654 -0.094413486
21 0.035821169 0.083793654
22 0.005986495 0.035821169
23 0.053675763 0.005986495
24 -0.073833211 0.053675763
25 -0.129524601 -0.073833211
26 0.165691956 -0.129524601
27 0.014474960 0.165691956
28 0.048307121 0.014474960
29 0.032944254 0.048307121
30 0.214576733 0.032944254
31 0.185296312 0.214576733
32 -0.038107439 0.185296312
33 -0.114903314 -0.038107439
34 0.056705670 -0.114903314
35 -0.045382317 0.056705670
36 -0.190188757 -0.045382317
37 0.027873543 -0.190188757
38 -0.244213183 0.027873543
39 0.140766660 -0.244213183
40 -0.050570270 0.140766660
41 -0.014031530 -0.050570270
42 0.047011905 -0.014031530
43 -0.075578861 0.047011905
44 0.096964860 -0.075578861
45 0.085472548 0.096964860
46 0.099258265 0.085472548
47 -0.036125298 0.099258265
48 -0.012465018 -0.036125298
49 -0.014192804 -0.012465018
50 0.056974015 -0.014192804
51 -0.025523892 0.056974015
52 0.030976760 -0.025523892
53 -0.071029319 0.030976760
54 0.015890129 -0.071029319
55 -0.066720477 0.015890129
56 NA -0.066720477
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.028410070 0.105592310
[2,] 0.103706779 0.028410070
[3,] -0.111635057 0.103706779
[4,] -0.041322724 -0.111635057
[5,] 0.091434240 -0.041322724
[6,] -0.113188003 0.091434240
[7,] 0.051416511 -0.113188003
[8,] -0.142651075 0.051416511
[9,] -0.006390402 -0.142651075
[10,] -0.161950430 -0.006390402
[11,] 0.027831852 -0.161950430
[12,] 0.170894676 0.027831852
[13,] 0.087433793 0.170894676
[14,] -0.082159567 0.087433793
[15,] -0.018082671 -0.082159567
[16,] 0.012609114 -0.018082671
[17,] -0.039317644 0.012609114
[18,] -0.164290765 -0.039317644
[19,] -0.094413486 -0.164290765
[20,] 0.083793654 -0.094413486
[21,] 0.035821169 0.083793654
[22,] 0.005986495 0.035821169
[23,] 0.053675763 0.005986495
[24,] -0.073833211 0.053675763
[25,] -0.129524601 -0.073833211
[26,] 0.165691956 -0.129524601
[27,] 0.014474960 0.165691956
[28,] 0.048307121 0.014474960
[29,] 0.032944254 0.048307121
[30,] 0.214576733 0.032944254
[31,] 0.185296312 0.214576733
[32,] -0.038107439 0.185296312
[33,] -0.114903314 -0.038107439
[34,] 0.056705670 -0.114903314
[35,] -0.045382317 0.056705670
[36,] -0.190188757 -0.045382317
[37,] 0.027873543 -0.190188757
[38,] -0.244213183 0.027873543
[39,] 0.140766660 -0.244213183
[40,] -0.050570270 0.140766660
[41,] -0.014031530 -0.050570270
[42,] 0.047011905 -0.014031530
[43,] -0.075578861 0.047011905
[44,] 0.096964860 -0.075578861
[45,] 0.085472548 0.096964860
[46,] 0.099258265 0.085472548
[47,] -0.036125298 0.099258265
[48,] -0.012465018 -0.036125298
[49,] -0.014192804 -0.012465018
[50,] 0.056974015 -0.014192804
[51,] -0.025523892 0.056974015
[52,] 0.030976760 -0.025523892
[53,] -0.071029319 0.030976760
[54,] 0.015890129 -0.071029319
[55,] -0.066720477 0.015890129
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.028410070 0.105592310
2 0.103706779 0.028410070
3 -0.111635057 0.103706779
4 -0.041322724 -0.111635057
5 0.091434240 -0.041322724
6 -0.113188003 0.091434240
7 0.051416511 -0.113188003
8 -0.142651075 0.051416511
9 -0.006390402 -0.142651075
10 -0.161950430 -0.006390402
11 0.027831852 -0.161950430
12 0.170894676 0.027831852
13 0.087433793 0.170894676
14 -0.082159567 0.087433793
15 -0.018082671 -0.082159567
16 0.012609114 -0.018082671
17 -0.039317644 0.012609114
18 -0.164290765 -0.039317644
19 -0.094413486 -0.164290765
20 0.083793654 -0.094413486
21 0.035821169 0.083793654
22 0.005986495 0.035821169
23 0.053675763 0.005986495
24 -0.073833211 0.053675763
25 -0.129524601 -0.073833211
26 0.165691956 -0.129524601
27 0.014474960 0.165691956
28 0.048307121 0.014474960
29 0.032944254 0.048307121
30 0.214576733 0.032944254
31 0.185296312 0.214576733
32 -0.038107439 0.185296312
33 -0.114903314 -0.038107439
34 0.056705670 -0.114903314
35 -0.045382317 0.056705670
36 -0.190188757 -0.045382317
37 0.027873543 -0.190188757
38 -0.244213183 0.027873543
39 0.140766660 -0.244213183
40 -0.050570270 0.140766660
41 -0.014031530 -0.050570270
42 0.047011905 -0.014031530
43 -0.075578861 0.047011905
44 0.096964860 -0.075578861
45 0.085472548 0.096964860
46 0.099258265 0.085472548
47 -0.036125298 0.099258265
48 -0.012465018 -0.036125298
49 -0.014192804 -0.012465018
50 0.056974015 -0.014192804
51 -0.025523892 0.056974015
52 0.030976760 -0.025523892
53 -0.071029319 0.030976760
54 0.015890129 -0.071029319
55 -0.066720477 0.015890129
> 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/7ntbf1260302533.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x3gq1260302533.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fd041260302533.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pjyr1260302533.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11a4ql1260302533.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/125tfn1260302533.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/139l6g1260302533.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/14a4rj1260302533.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/15ywo51260302533.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/16suew1260302533.tab")
+ }
>
> system("convert tmp/1q3ra1260302532.ps tmp/1q3ra1260302532.png")
> system("convert tmp/2wg3v1260302532.ps tmp/2wg3v1260302532.png")
> system("convert tmp/3793j1260302532.ps tmp/3793j1260302532.png")
> system("convert tmp/4skol1260302532.ps tmp/4skol1260302532.png")
> system("convert tmp/5onky1260302532.ps tmp/5onky1260302532.png")
> system("convert tmp/6rxes1260302533.ps tmp/6rxes1260302533.png")
> system("convert tmp/7ntbf1260302533.ps tmp/7ntbf1260302533.png")
> system("convert tmp/8x3gq1260302533.ps tmp/8x3gq1260302533.png")
> system("convert tmp/9fd041260302533.ps tmp/9fd041260302533.png")
> system("convert tmp/10pjyr1260302533.ps tmp/10pjyr1260302533.png")
>
>
> proc.time()
user system elapsed
2.329 1.558 4.226