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 '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(7.8
+ ,0
+ ,7.8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8
+ ,0
+ ,7.8
+ ,7.8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,0
+ ,8
+ ,7.8
+ ,7.8
+ ,8.3
+ ,8.9
+ ,0
+ ,8.6
+ ,8
+ ,7.8
+ ,7.8
+ ,8.9
+ ,0
+ ,8.9
+ ,8.6
+ ,8
+ ,7.8
+ ,8.6
+ ,0
+ ,8.9
+ ,8.9
+ ,8.6
+ ,8
+ ,8.3
+ ,0
+ ,8.6
+ ,8.9
+ ,8.9
+ ,8.6
+ ,8.3
+ ,0
+ ,8.3
+ ,8.6
+ ,8.9
+ ,8.9
+ ,8.3
+ ,0
+ ,8.3
+ ,8.3
+ ,8.6
+ ,8.9
+ ,8.4
+ ,0
+ ,8.3
+ ,8.3
+ ,8.3
+ ,8.6
+ ,8.5
+ ,0
+ ,8.4
+ ,8.3
+ ,8.3
+ ,8.3
+ ,8.4
+ ,0
+ ,8.5
+ ,8.4
+ ,8.3
+ ,8.3
+ ,8.6
+ ,0
+ ,8.4
+ ,8.5
+ ,8.4
+ ,8.3
+ ,8.5
+ ,0
+ ,8.6
+ ,8.4
+ ,8.5
+ ,8.4
+ ,8.5
+ ,0
+ ,8.5
+ ,8.6
+ ,8.4
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.6
+ ,8.4
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.6
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.6
+ ,0
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.4
+ ,0
+ ,8.6
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.1
+ ,0
+ ,8.4
+ ,8.6
+ ,8.5
+ ,8.5
+ ,8
+ ,0
+ ,8.1
+ ,8.4
+ ,8.6
+ ,8.5
+ ,8
+ ,0
+ ,8
+ ,8.1
+ ,8.4
+ ,8.6
+ ,8
+ ,0
+ ,8
+ ,8
+ ,8.1
+ ,8.4
+ ,8
+ ,0
+ ,8
+ ,8
+ ,8
+ ,8.1
+ ,7.9
+ ,0
+ ,8
+ ,8
+ ,8
+ ,8
+ ,7.8
+ ,0
+ ,7.9
+ ,8
+ ,8
+ ,8
+ ,7.8
+ ,0
+ ,7.8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,0
+ ,7.8
+ ,7.8
+ ,7.9
+ ,8
+ ,8.1
+ ,0
+ ,7.9
+ ,7.8
+ ,7.8
+ ,7.9
+ ,8
+ ,0
+ ,8.1
+ ,7.9
+ ,7.8
+ ,7.8
+ ,7.6
+ ,0
+ ,8
+ ,8.1
+ ,7.9
+ ,7.8
+ ,7.3
+ ,0
+ ,7.6
+ ,8
+ ,8.1
+ ,7.9
+ ,7
+ ,0
+ ,7.3
+ ,7.6
+ ,8
+ ,8.1
+ ,6.8
+ ,0
+ ,7
+ ,7.3
+ ,7.6
+ ,8
+ ,7
+ ,0
+ ,6.8
+ ,7
+ ,7.3
+ ,7.6
+ ,7.1
+ ,0
+ ,7
+ ,6.8
+ ,7
+ ,7.3
+ ,7.2
+ ,0
+ ,7.1
+ ,7
+ ,6.8
+ ,7
+ ,7.1
+ ,1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.8
+ ,6.9
+ ,1
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.7
+ ,1
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.7
+ ,1
+ ,6.7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.6
+ ,1
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.1
+ ,6.9
+ ,1
+ ,6.6
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.3
+ ,1
+ ,6.9
+ ,6.6
+ ,6.7
+ ,6.7
+ ,7.5
+ ,1
+ ,7.3
+ ,6.9
+ ,6.6
+ ,6.7
+ ,7.3
+ ,1
+ ,7.5
+ ,7.3
+ ,6.9
+ ,6.6
+ ,7.1
+ ,1
+ ,7.3
+ ,7.5
+ ,7.3
+ ,6.9
+ ,6.9
+ ,1
+ ,7.1
+ ,7.3
+ ,7.5
+ ,7.3
+ ,7.1
+ ,1
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.5)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),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
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.8 0 7.8 8.3 8.5 8.6 1 0 0 0 0 0 0 0 0 0 0 1
2 8.0 0 7.8 7.8 8.3 8.5 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 0 8.0 7.8 7.8 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.9 0 8.6 8.0 7.8 7.8 0 0 0 1 0 0 0 0 0 0 0 4
5 8.9 0 8.9 8.6 8.0 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 8.6 0 8.9 8.9 8.6 8.0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.3 0 8.6 8.9 8.9 8.6 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 0 8.3 8.6 8.9 8.9 0 0 0 0 0 0 0 1 0 0 0 8
9 8.3 0 8.3 8.3 8.6 8.9 0 0 0 0 0 0 0 0 1 0 0 9
10 8.4 0 8.3 8.3 8.3 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 0 8.4 8.3 8.3 8.3 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 0 8.5 8.4 8.3 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.6 0 8.4 8.5 8.4 8.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 0 8.6 8.4 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 0 8.5 8.6 8.4 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 0 8.5 8.5 8.6 8.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 0 8.5 8.5 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 0 8.5 8.5 8.5 8.5 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 0 8.5 8.5 8.5 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 0 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 0 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 0 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.5 0 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 0 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.6 0 8.5 8.5 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 25
26 8.4 0 8.6 8.5 8.5 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.1 0 8.4 8.6 8.5 8.5 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 0 8.1 8.4 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 0 8.0 8.1 8.4 8.6 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 0 8.0 8.0 8.1 8.4 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 0 8.0 8.0 8.0 8.1 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 0 8.0 8.0 8.0 8.0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.8 0 7.9 8.0 8.0 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.8 0 7.8 7.9 8.0 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 0 7.8 7.8 7.9 8.0 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 0 7.9 7.8 7.8 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 0 8.1 7.9 7.8 7.8 1 0 0 0 0 0 0 0 0 0 0 37
38 7.6 0 8.0 8.1 7.9 7.8 0 1 0 0 0 0 0 0 0 0 0 38
39 7.3 0 7.6 8.0 8.1 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.0 0 7.3 7.6 8.0 8.1 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 0 7.0 7.3 7.6 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 0 6.8 7.0 7.3 7.6 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 0 7.0 6.8 7.0 7.3 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 0 7.1 7.0 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.1 1 7.2 7.1 7.0 6.8 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 1 7.1 7.2 7.1 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.7 1 6.9 7.1 7.2 7.1 0 0 0 0 0 0 0 0 0 0 1 47
48 6.7 1 6.7 6.9 7.1 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.6 1 6.7 6.7 6.9 7.1 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 1 6.6 6.7 6.7 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 7.3 1 6.9 6.6 6.7 6.7 0 0 1 0 0 0 0 0 0 0 0 51
52 7.5 1 7.3 6.9 6.6 6.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 1 7.5 7.3 6.9 6.6 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 1 7.3 7.5 7.3 6.9 0 0 0 0 0 1 0 0 0 0 0 54
55 6.9 1 7.1 7.3 7.5 7.3 0 0 0 0 0 0 1 0 0 0 0 55
56 7.1 1 6.9 7.1 7.3 7.5 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) X Y1 Y2 Y3 Y4
0.828054 0.118092 1.392459 -0.532232 -0.386919 0.441899
M1 M2 M3 M4 M5 M6
0.011648 -0.090374 0.048897 -0.014492 -0.097871 0.019006
M7 M8 M9 M10 M11 t
-0.041433 0.044445 -0.077586 -0.035056 0.001446 -0.006206
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.284956 -0.074737 -0.002621 0.071985 0.273514
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.828054 0.661620 1.252 0.21838
X 0.118092 0.083523 1.414 0.16554
Y1 1.392459 0.133981 10.393 1.16e-12 ***
Y2 -0.532232 0.244710 -2.175 0.03593 *
Y3 -0.386919 0.249732 -1.549 0.12959
Y4 0.441899 0.149301 2.960 0.00528 **
M1 0.011648 0.091388 0.127 0.89925
M2 -0.090374 0.091278 -0.990 0.32839
M3 0.048897 0.091626 0.534 0.59668
M4 -0.014492 0.092508 -0.157 0.87634
M5 -0.097871 0.091328 -1.072 0.29064
M6 0.019006 0.092397 0.206 0.83813
M7 -0.041433 0.091657 -0.452 0.65381
M8 0.044445 0.090965 0.489 0.62794
M9 -0.077586 0.095506 -0.812 0.42164
M10 -0.035056 0.095754 -0.366 0.71632
M11 0.001446 0.095608 0.015 0.98802
t -0.006206 0.002260 -2.746 0.00916 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1347 on 38 degrees of freedom
Multiple R-squared: 0.9718, Adjusted R-squared: 0.9592
F-statistic: 77.09 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,] 0.27822159 0.55644318 0.7217784
[2,] 0.17158856 0.34317712 0.8284114
[3,] 0.08194420 0.16388840 0.9180558
[4,] 0.05332852 0.10665703 0.9466715
[5,] 0.05523213 0.11046426 0.9447679
[6,] 0.03460619 0.06921238 0.9653938
[7,] 0.16465649 0.32931297 0.8353435
[8,] 0.18359443 0.36718886 0.8164056
[9,] 0.18004851 0.36009702 0.8199515
[10,] 0.31824779 0.63649559 0.6817522
[11,] 0.23954755 0.47909511 0.7604524
[12,] 0.16992524 0.33985049 0.8300748
[13,] 0.10505180 0.21010361 0.8949482
[14,] 0.08261478 0.16522956 0.9173852
[15,] 0.04672100 0.09344199 0.9532790
> postscript(file="/var/www/html/rcomp/tmp/1acmd1258720338.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fsez1258720338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3j9d91258720338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/466le1258720338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mvzh1258720338.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
0.011324909 0.020243748 0.103606886 -0.034877211 0.033693120 -0.073536748
7 8 9 10 11 12
-0.038218112 0.007609173 -0.139898440 -0.059728923 0.003299931 -0.175071098
13 14 15 16 17 18
0.250648124 -0.078336357 -0.048591300 0.089354858 0.051868635 -0.014612299
19 20 21 22 23 24
0.052032203 -0.027639167 0.100598294 0.064273802 0.033978973 0.041630695
25 26 27 28 29 30
0.136188978 -0.094828423 -0.196178888 0.123400120 0.070988326 -0.120601492
31 32 33 34 35 36
0.039920745 -0.095560760 0.071922615 0.121620874 0.099411019 0.173314830
37 38 39 40 41 42
-0.113205684 -0.120593072 -0.016704176 -0.169334949 -0.132258909 0.136576506
43 44 45 46 47 48
-0.065223135 -0.022508221 -0.032622469 -0.126165753 -0.136689922 -0.039874427
49 50 51 52 53 54
-0.284956327 0.273514104 0.157867478 -0.008542819 -0.024291172 0.072174033
55 56
0.011488299 0.138098975
> postscript(file="/var/www/html/rcomp/tmp/6fozx1258720338.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 0.011324909 NA
1 0.020243748 0.011324909
2 0.103606886 0.020243748
3 -0.034877211 0.103606886
4 0.033693120 -0.034877211
5 -0.073536748 0.033693120
6 -0.038218112 -0.073536748
7 0.007609173 -0.038218112
8 -0.139898440 0.007609173
9 -0.059728923 -0.139898440
10 0.003299931 -0.059728923
11 -0.175071098 0.003299931
12 0.250648124 -0.175071098
13 -0.078336357 0.250648124
14 -0.048591300 -0.078336357
15 0.089354858 -0.048591300
16 0.051868635 0.089354858
17 -0.014612299 0.051868635
18 0.052032203 -0.014612299
19 -0.027639167 0.052032203
20 0.100598294 -0.027639167
21 0.064273802 0.100598294
22 0.033978973 0.064273802
23 0.041630695 0.033978973
24 0.136188978 0.041630695
25 -0.094828423 0.136188978
26 -0.196178888 -0.094828423
27 0.123400120 -0.196178888
28 0.070988326 0.123400120
29 -0.120601492 0.070988326
30 0.039920745 -0.120601492
31 -0.095560760 0.039920745
32 0.071922615 -0.095560760
33 0.121620874 0.071922615
34 0.099411019 0.121620874
35 0.173314830 0.099411019
36 -0.113205684 0.173314830
37 -0.120593072 -0.113205684
38 -0.016704176 -0.120593072
39 -0.169334949 -0.016704176
40 -0.132258909 -0.169334949
41 0.136576506 -0.132258909
42 -0.065223135 0.136576506
43 -0.022508221 -0.065223135
44 -0.032622469 -0.022508221
45 -0.126165753 -0.032622469
46 -0.136689922 -0.126165753
47 -0.039874427 -0.136689922
48 -0.284956327 -0.039874427
49 0.273514104 -0.284956327
50 0.157867478 0.273514104
51 -0.008542819 0.157867478
52 -0.024291172 -0.008542819
53 0.072174033 -0.024291172
54 0.011488299 0.072174033
55 0.138098975 0.011488299
56 NA 0.138098975
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.020243748 0.011324909
[2,] 0.103606886 0.020243748
[3,] -0.034877211 0.103606886
[4,] 0.033693120 -0.034877211
[5,] -0.073536748 0.033693120
[6,] -0.038218112 -0.073536748
[7,] 0.007609173 -0.038218112
[8,] -0.139898440 0.007609173
[9,] -0.059728923 -0.139898440
[10,] 0.003299931 -0.059728923
[11,] -0.175071098 0.003299931
[12,] 0.250648124 -0.175071098
[13,] -0.078336357 0.250648124
[14,] -0.048591300 -0.078336357
[15,] 0.089354858 -0.048591300
[16,] 0.051868635 0.089354858
[17,] -0.014612299 0.051868635
[18,] 0.052032203 -0.014612299
[19,] -0.027639167 0.052032203
[20,] 0.100598294 -0.027639167
[21,] 0.064273802 0.100598294
[22,] 0.033978973 0.064273802
[23,] 0.041630695 0.033978973
[24,] 0.136188978 0.041630695
[25,] -0.094828423 0.136188978
[26,] -0.196178888 -0.094828423
[27,] 0.123400120 -0.196178888
[28,] 0.070988326 0.123400120
[29,] -0.120601492 0.070988326
[30,] 0.039920745 -0.120601492
[31,] -0.095560760 0.039920745
[32,] 0.071922615 -0.095560760
[33,] 0.121620874 0.071922615
[34,] 0.099411019 0.121620874
[35,] 0.173314830 0.099411019
[36,] -0.113205684 0.173314830
[37,] -0.120593072 -0.113205684
[38,] -0.016704176 -0.120593072
[39,] -0.169334949 -0.016704176
[40,] -0.132258909 -0.169334949
[41,] 0.136576506 -0.132258909
[42,] -0.065223135 0.136576506
[43,] -0.022508221 -0.065223135
[44,] -0.032622469 -0.022508221
[45,] -0.126165753 -0.032622469
[46,] -0.136689922 -0.126165753
[47,] -0.039874427 -0.136689922
[48,] -0.284956327 -0.039874427
[49,] 0.273514104 -0.284956327
[50,] 0.157867478 0.273514104
[51,] -0.008542819 0.157867478
[52,] -0.024291172 -0.008542819
[53,] 0.072174033 -0.024291172
[54,] 0.011488299 0.072174033
[55,] 0.138098975 0.011488299
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.020243748 0.011324909
2 0.103606886 0.020243748
3 -0.034877211 0.103606886
4 0.033693120 -0.034877211
5 -0.073536748 0.033693120
6 -0.038218112 -0.073536748
7 0.007609173 -0.038218112
8 -0.139898440 0.007609173
9 -0.059728923 -0.139898440
10 0.003299931 -0.059728923
11 -0.175071098 0.003299931
12 0.250648124 -0.175071098
13 -0.078336357 0.250648124
14 -0.048591300 -0.078336357
15 0.089354858 -0.048591300
16 0.051868635 0.089354858
17 -0.014612299 0.051868635
18 0.052032203 -0.014612299
19 -0.027639167 0.052032203
20 0.100598294 -0.027639167
21 0.064273802 0.100598294
22 0.033978973 0.064273802
23 0.041630695 0.033978973
24 0.136188978 0.041630695
25 -0.094828423 0.136188978
26 -0.196178888 -0.094828423
27 0.123400120 -0.196178888
28 0.070988326 0.123400120
29 -0.120601492 0.070988326
30 0.039920745 -0.120601492
31 -0.095560760 0.039920745
32 0.071922615 -0.095560760
33 0.121620874 0.071922615
34 0.099411019 0.121620874
35 0.173314830 0.099411019
36 -0.113205684 0.173314830
37 -0.120593072 -0.113205684
38 -0.016704176 -0.120593072
39 -0.169334949 -0.016704176
40 -0.132258909 -0.169334949
41 0.136576506 -0.132258909
42 -0.065223135 0.136576506
43 -0.022508221 -0.065223135
44 -0.032622469 -0.022508221
45 -0.126165753 -0.032622469
46 -0.136689922 -0.126165753
47 -0.039874427 -0.136689922
48 -0.284956327 -0.039874427
49 0.273514104 -0.284956327
50 0.157867478 0.273514104
51 -0.008542819 0.157867478
52 -0.024291172 -0.008542819
53 0.072174033 -0.024291172
54 0.011488299 0.072174033
55 0.138098975 0.011488299
> 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/7ixw01258720338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dis61258720338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/971uy1258720338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10m9971258720338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yiqo1258720338.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/129azm1258720338.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/13h4my1258720338.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/14ins61258720338.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/15f7521258720338.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/169wym1258720338.tab")
+ }
>
> system("convert tmp/1acmd1258720338.ps tmp/1acmd1258720338.png")
> system("convert tmp/2fsez1258720338.ps tmp/2fsez1258720338.png")
> system("convert tmp/3j9d91258720338.ps tmp/3j9d91258720338.png")
> system("convert tmp/466le1258720338.ps tmp/466le1258720338.png")
> system("convert tmp/5mvzh1258720338.ps tmp/5mvzh1258720338.png")
> system("convert tmp/6fozx1258720338.ps tmp/6fozx1258720338.png")
> system("convert tmp/7ixw01258720338.ps tmp/7ixw01258720338.png")
> system("convert tmp/8dis61258720338.ps tmp/8dis61258720338.png")
> system("convert tmp/971uy1258720338.ps tmp/971uy1258720338.png")
> system("convert tmp/10m9971258720338.ps tmp/10m9971258720338.png")
>
>
> proc.time()
user system elapsed
2.335 1.572 4.221