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(7.2
+ ,-6
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,0
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,-4
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,-2
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,-2
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,-6
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,-7
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,-6
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,-6
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,-3
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,-2
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,-5
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,-11
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,-11
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,-11
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,-10
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,-14
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,-8
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,-9
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,-5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,-1
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,-2
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,-5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,-4
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,-6
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,-2
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,-2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,-2
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,-2
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,2
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,1
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,-8
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,-1
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,1
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,-1
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,2
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,2
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,1
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,-1
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,-2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,-2
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,-1
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,-8
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,-4
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,-6
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,-3
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,-3
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,-7
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,-9
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,-11
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,-13
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,-11
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,-9
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,-17
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,-22
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,-25
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('TW'
+ ,'CV'
+ ,'TW1'
+ ,'TW2'
+ ,'TW3'
+ ,'TW4
')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('TW','CV','TW1','TW2','TW3','TW4
'),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
TW CV TW1 TW2 TW3 TW4\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.2 -6 7.5 8.3 8.8 8.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.4 0 7.2 7.5 8.3 8.8 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 -4 7.4 7.2 7.5 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 -2 8.8 7.4 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 9.3 -2 9.3 8.8 7.4 7.2 0 0 0 0 1 0 0 0 0 0 0 5
6 8.7 -6 9.3 9.3 8.8 7.4 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 -7 8.7 9.3 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 -6 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 -6 8.3 8.2 8.7 9.3 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 -3 8.5 8.3 8.2 8.7 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 -2 8.6 8.5 8.3 8.2 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 -5 8.5 8.6 8.5 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 -11 8.2 8.5 8.6 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 -11 8.1 8.2 8.5 8.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 -11 7.9 8.1 8.2 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 -10 8.6 7.9 8.1 8.2 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 -14 8.7 8.6 7.9 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 -8 8.7 8.7 8.6 7.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 -9 8.5 8.7 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 -5 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 -1 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 -2 8.7 8.5 8.4 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 -5 8.7 8.7 8.5 8.4 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 -4 8.6 8.7 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 -6 8.5 8.6 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 -2 8.3 8.5 8.6 8.7 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 -2 8.0 8.3 8.5 8.6 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 -2 8.2 8.0 8.3 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 -2 8.1 8.2 8.0 8.3 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 2 8.1 8.1 8.2 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 1 8.0 8.1 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 -8 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 -1 7.9 7.9 8.0 8.1 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 1 8.0 7.9 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 -1 8.0 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 2 7.9 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 2 8.0 7.9 8.0 8.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 1 7.7 8.0 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.5 -1 7.2 7.7 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 -2 7.5 7.2 7.7 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 -2 7.3 7.5 7.2 7.7 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 -1 7.0 7.3 7.5 7.2 0 0 0 0 0 1 0 0 0 0 0 42
43 7.0 -8 7.0 7.0 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 -4 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 -6 7.2 7.0 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.1 -3 7.3 7.2 7.0 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 -3 7.1 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 -7 6.8 7.1 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 -9 6.4 6.8 7.1 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 -11 6.1 6.4 6.8 7.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 -13 6.5 6.1 6.4 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 -11 7.7 6.5 6.1 6.4 0 0 0 1 0 0 0 0 0 0 0 52
53 7.5 -9 7.9 7.7 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.9 -17 7.5 7.9 7.7 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 -22 6.9 7.5 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 -25 6.6 6.9 7.5 7.9 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) CV TW1 TW2 TW3 `TW4\r`
1.352700 -0.006576 1.467602 -0.781350 -0.150147 0.312242
M1 M2 M3 M4 M5 M6
-0.149267 -0.115804 0.594189 -0.413819 -0.039477 0.088879
M7 M8 M9 M10 M11 t
-0.015266 0.134640 0.011632 -0.092907 -0.018953 -0.006980
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.2478593 -0.0748245 0.0001939 0.0813364 0.3921302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.352700 0.585528 2.310 0.02640 *
CV -0.006576 0.004255 -1.545 0.13057
TW1 1.467602 0.137899 10.643 5.88e-13 ***
TW2 -0.781350 0.265134 -2.947 0.00546 **
TW3 -0.150147 0.267526 -0.561 0.57793
`TW4\r` 0.312242 0.139906 2.232 0.03160 *
M1 -0.149267 0.103935 -1.436 0.15914
M2 -0.115804 0.106599 -1.086 0.28416
M3 0.594189 0.109430 5.430 3.44e-06 ***
M4 -0.413819 0.141402 -2.927 0.00576 **
M5 -0.039477 0.159351 -0.248 0.80567
M6 0.088879 0.121514 0.731 0.46900
M7 -0.015266 0.103899 -0.147 0.88396
M8 0.134640 0.107486 1.253 0.21800
M9 0.011632 0.112751 0.103 0.91838
M10 -0.092907 0.113715 -0.817 0.41901
M11 -0.018953 0.107729 -0.176 0.86128
t -0.006980 0.002418 -2.886 0.00640 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1491 on 38 degrees of freedom
Multiple R-squared: 0.9722, Adjusted R-squared: 0.9598
F-statistic: 78.25 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,] 0.09500313 0.19000626 0.9049969
[2,] 0.16519189 0.33038378 0.8348081
[3,] 0.08341106 0.16682212 0.9165889
[4,] 0.03549652 0.07099305 0.9645035
[5,] 0.06663170 0.13326341 0.9333683
[6,] 0.03712312 0.07424623 0.9628769
[7,] 0.22853878 0.45707757 0.7714612
[8,] 0.15965431 0.31930861 0.8403457
[9,] 0.10017823 0.20035646 0.8998218
[10,] 0.08154460 0.16308921 0.9184554
[11,] 0.06447475 0.12894949 0.9355253
[12,] 0.05719825 0.11439650 0.9428017
[13,] 0.03243827 0.06487653 0.9675617
[14,] 0.01425199 0.02850398 0.9857480
[15,] 0.00506901 0.01013802 0.9949310
> postscript(file="/var/www/html/rcomp/tmp/1wb2w1260819880.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rhxd1260819880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wc351260819880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ot0w1260819880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58f3e1260819880.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
-0.015385630 -0.031062645 0.147700413 -0.017783994 0.098644799 -0.110601184
7 8 9 10 11 12
0.012443577 0.084964168 -0.212570315 -0.084437682 -0.064190330 -0.172189740
13 14 15 16 17 18
0.159315708 -0.201050901 -0.002497614 0.014132278 0.021847200 -0.014387472
19 20 21 22 23 24
0.080127200 0.022770758 0.124137726 0.061129372 0.076937207 0.117105668
25 26 27 28 29 30
0.066378796 -0.033430590 -0.236222654 0.152034577 0.105107518 -0.044397722
31 32 33 34 35 36
0.029448480 -0.072808754 0.110060263 0.104180075 0.033413964 0.302943380
37 38 39 40 41 42
-0.096928310 -0.126586080 0.002885321 -0.095926464 -0.216763480 0.153613034
43 44 45 46 47 48
-0.139399058 -0.038617191 -0.021627675 -0.080871766 -0.046160842 -0.247859308
49 50 51 52 53 54
-0.113380564 0.392130216 0.088134533 -0.052456398 -0.008836037 0.015773344
55 56
0.017379801 0.003691018
> postscript(file="/var/www/html/rcomp/tmp/6h9041260819880.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 -0.015385630 NA
1 -0.031062645 -0.015385630
2 0.147700413 -0.031062645
3 -0.017783994 0.147700413
4 0.098644799 -0.017783994
5 -0.110601184 0.098644799
6 0.012443577 -0.110601184
7 0.084964168 0.012443577
8 -0.212570315 0.084964168
9 -0.084437682 -0.212570315
10 -0.064190330 -0.084437682
11 -0.172189740 -0.064190330
12 0.159315708 -0.172189740
13 -0.201050901 0.159315708
14 -0.002497614 -0.201050901
15 0.014132278 -0.002497614
16 0.021847200 0.014132278
17 -0.014387472 0.021847200
18 0.080127200 -0.014387472
19 0.022770758 0.080127200
20 0.124137726 0.022770758
21 0.061129372 0.124137726
22 0.076937207 0.061129372
23 0.117105668 0.076937207
24 0.066378796 0.117105668
25 -0.033430590 0.066378796
26 -0.236222654 -0.033430590
27 0.152034577 -0.236222654
28 0.105107518 0.152034577
29 -0.044397722 0.105107518
30 0.029448480 -0.044397722
31 -0.072808754 0.029448480
32 0.110060263 -0.072808754
33 0.104180075 0.110060263
34 0.033413964 0.104180075
35 0.302943380 0.033413964
36 -0.096928310 0.302943380
37 -0.126586080 -0.096928310
38 0.002885321 -0.126586080
39 -0.095926464 0.002885321
40 -0.216763480 -0.095926464
41 0.153613034 -0.216763480
42 -0.139399058 0.153613034
43 -0.038617191 -0.139399058
44 -0.021627675 -0.038617191
45 -0.080871766 -0.021627675
46 -0.046160842 -0.080871766
47 -0.247859308 -0.046160842
48 -0.113380564 -0.247859308
49 0.392130216 -0.113380564
50 0.088134533 0.392130216
51 -0.052456398 0.088134533
52 -0.008836037 -0.052456398
53 0.015773344 -0.008836037
54 0.017379801 0.015773344
55 0.003691018 0.017379801
56 NA 0.003691018
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.031062645 -0.015385630
[2,] 0.147700413 -0.031062645
[3,] -0.017783994 0.147700413
[4,] 0.098644799 -0.017783994
[5,] -0.110601184 0.098644799
[6,] 0.012443577 -0.110601184
[7,] 0.084964168 0.012443577
[8,] -0.212570315 0.084964168
[9,] -0.084437682 -0.212570315
[10,] -0.064190330 -0.084437682
[11,] -0.172189740 -0.064190330
[12,] 0.159315708 -0.172189740
[13,] -0.201050901 0.159315708
[14,] -0.002497614 -0.201050901
[15,] 0.014132278 -0.002497614
[16,] 0.021847200 0.014132278
[17,] -0.014387472 0.021847200
[18,] 0.080127200 -0.014387472
[19,] 0.022770758 0.080127200
[20,] 0.124137726 0.022770758
[21,] 0.061129372 0.124137726
[22,] 0.076937207 0.061129372
[23,] 0.117105668 0.076937207
[24,] 0.066378796 0.117105668
[25,] -0.033430590 0.066378796
[26,] -0.236222654 -0.033430590
[27,] 0.152034577 -0.236222654
[28,] 0.105107518 0.152034577
[29,] -0.044397722 0.105107518
[30,] 0.029448480 -0.044397722
[31,] -0.072808754 0.029448480
[32,] 0.110060263 -0.072808754
[33,] 0.104180075 0.110060263
[34,] 0.033413964 0.104180075
[35,] 0.302943380 0.033413964
[36,] -0.096928310 0.302943380
[37,] -0.126586080 -0.096928310
[38,] 0.002885321 -0.126586080
[39,] -0.095926464 0.002885321
[40,] -0.216763480 -0.095926464
[41,] 0.153613034 -0.216763480
[42,] -0.139399058 0.153613034
[43,] -0.038617191 -0.139399058
[44,] -0.021627675 -0.038617191
[45,] -0.080871766 -0.021627675
[46,] -0.046160842 -0.080871766
[47,] -0.247859308 -0.046160842
[48,] -0.113380564 -0.247859308
[49,] 0.392130216 -0.113380564
[50,] 0.088134533 0.392130216
[51,] -0.052456398 0.088134533
[52,] -0.008836037 -0.052456398
[53,] 0.015773344 -0.008836037
[54,] 0.017379801 0.015773344
[55,] 0.003691018 0.017379801
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.031062645 -0.015385630
2 0.147700413 -0.031062645
3 -0.017783994 0.147700413
4 0.098644799 -0.017783994
5 -0.110601184 0.098644799
6 0.012443577 -0.110601184
7 0.084964168 0.012443577
8 -0.212570315 0.084964168
9 -0.084437682 -0.212570315
10 -0.064190330 -0.084437682
11 -0.172189740 -0.064190330
12 0.159315708 -0.172189740
13 -0.201050901 0.159315708
14 -0.002497614 -0.201050901
15 0.014132278 -0.002497614
16 0.021847200 0.014132278
17 -0.014387472 0.021847200
18 0.080127200 -0.014387472
19 0.022770758 0.080127200
20 0.124137726 0.022770758
21 0.061129372 0.124137726
22 0.076937207 0.061129372
23 0.117105668 0.076937207
24 0.066378796 0.117105668
25 -0.033430590 0.066378796
26 -0.236222654 -0.033430590
27 0.152034577 -0.236222654
28 0.105107518 0.152034577
29 -0.044397722 0.105107518
30 0.029448480 -0.044397722
31 -0.072808754 0.029448480
32 0.110060263 -0.072808754
33 0.104180075 0.110060263
34 0.033413964 0.104180075
35 0.302943380 0.033413964
36 -0.096928310 0.302943380
37 -0.126586080 -0.096928310
38 0.002885321 -0.126586080
39 -0.095926464 0.002885321
40 -0.216763480 -0.095926464
41 0.153613034 -0.216763480
42 -0.139399058 0.153613034
43 -0.038617191 -0.139399058
44 -0.021627675 -0.038617191
45 -0.080871766 -0.021627675
46 -0.046160842 -0.080871766
47 -0.247859308 -0.046160842
48 -0.113380564 -0.247859308
49 0.392130216 -0.113380564
50 0.088134533 0.392130216
51 -0.052456398 0.088134533
52 -0.008836037 -0.052456398
53 0.015773344 -0.008836037
54 0.017379801 0.015773344
55 0.003691018 0.017379801
> 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/7bj4j1260819880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8f9in1260819880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9spy71260819880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10uooh1260819880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118h9c1260819880.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/1241jd1260819880.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/13cwfu1260819880.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/14g21p1260819880.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/15oyo91260819880.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/16f4o21260819880.tab")
+ }
>
> try(system("convert tmp/1wb2w1260819880.ps tmp/1wb2w1260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rhxd1260819880.ps tmp/2rhxd1260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wc351260819880.ps tmp/3wc351260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ot0w1260819880.ps tmp/4ot0w1260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/58f3e1260819880.ps tmp/58f3e1260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/6h9041260819880.ps tmp/6h9041260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bj4j1260819880.ps tmp/7bj4j1260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/8f9in1260819880.ps tmp/8f9in1260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/9spy71260819880.ps tmp/9spy71260819880.png",intern=TRUE))
character(0)
> try(system("convert tmp/10uooh1260819880.ps tmp/10uooh1260819880.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.342 1.559 3.716