R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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
+ ,2.61
+ ,7.8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8
+ ,2.26
+ ,7.8
+ ,7.8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,2.41
+ ,8
+ ,7.8
+ ,7.8
+ ,8.3
+ ,8.9
+ ,2.26
+ ,8.6
+ ,8
+ ,7.8
+ ,7.8
+ ,8.9
+ ,2.03
+ ,8.9
+ ,8.6
+ ,8
+ ,7.8
+ ,8.6
+ ,2.86
+ ,8.9
+ ,8.9
+ ,8.6
+ ,8
+ ,8.3
+ ,2.55
+ ,8.6
+ ,8.9
+ ,8.9
+ ,8.6
+ ,8.3
+ ,2.27
+ ,8.3
+ ,8.6
+ ,8.9
+ ,8.9
+ ,8.3
+ ,2.26
+ ,8.3
+ ,8.3
+ ,8.6
+ ,8.9
+ ,8.4
+ ,2.57
+ ,8.3
+ ,8.3
+ ,8.3
+ ,8.6
+ ,8.5
+ ,3.07
+ ,8.4
+ ,8.3
+ ,8.3
+ ,8.3
+ ,8.4
+ ,2.76
+ ,8.5
+ ,8.4
+ ,8.3
+ ,8.3
+ ,8.6
+ ,2.51
+ ,8.4
+ ,8.5
+ ,8.4
+ ,8.3
+ ,8.5
+ ,2.87
+ ,8.6
+ ,8.4
+ ,8.5
+ ,8.4
+ ,8.5
+ ,3.14
+ ,8.5
+ ,8.6
+ ,8.4
+ ,8.5
+ ,8.5
+ ,3.11
+ ,8.5
+ ,8.5
+ ,8.6
+ ,8.4
+ ,8.5
+ ,3.16
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.6
+ ,8.5
+ ,2.47
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,2.57
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,2.89
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,2.63
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,2.38
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,1.69
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,1.96
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.6
+ ,2.19
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.4
+ ,1.87
+ ,8.6
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.1
+ ,1.6
+ ,8.4
+ ,8.6
+ ,8.5
+ ,8.5
+ ,8
+ ,1.63
+ ,8.1
+ ,8.4
+ ,8.6
+ ,8.5
+ ,8
+ ,1.22
+ ,8
+ ,8.1
+ ,8.4
+ ,8.6
+ ,8
+ ,1.21
+ ,8
+ ,8
+ ,8.1
+ ,8.4
+ ,8
+ ,1.49
+ ,8
+ ,8
+ ,8
+ ,8.1
+ ,7.9
+ ,1.64
+ ,8
+ ,8
+ ,8
+ ,8
+ ,7.8
+ ,1.66
+ ,7.9
+ ,8
+ ,8
+ ,8
+ ,7.8
+ ,1.77
+ ,7.8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,1.82
+ ,7.8
+ ,7.8
+ ,7.9
+ ,8
+ ,8.1
+ ,1.78
+ ,7.9
+ ,7.8
+ ,7.8
+ ,7.9
+ ,8
+ ,1.28
+ ,8.1
+ ,7.9
+ ,7.8
+ ,7.8
+ ,7.6
+ ,1.29
+ ,8
+ ,8.1
+ ,7.9
+ ,7.8
+ ,7.3
+ ,1.37
+ ,7.6
+ ,8
+ ,8.1
+ ,7.9
+ ,7
+ ,1.12
+ ,7.3
+ ,7.6
+ ,8
+ ,8.1
+ ,6.8
+ ,1.51
+ ,7
+ ,7.3
+ ,7.6
+ ,8
+ ,7
+ ,2.24
+ ,6.8
+ ,7
+ ,7.3
+ ,7.6
+ ,7.1
+ ,2.94
+ ,7
+ ,6.8
+ ,7
+ ,7.3
+ ,7.2
+ ,3.09
+ ,7.1
+ ,7
+ ,6.8
+ ,7
+ ,7.1
+ ,3.46
+ ,7.2
+ ,7.1
+ ,7
+ ,6.8
+ ,6.9
+ ,3.64
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.7
+ ,4.39
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.7
+ ,4.15
+ ,6.7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.6
+ ,5.21
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.1
+ ,6.9
+ ,5.8
+ ,6.6
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.3
+ ,5.91
+ ,6.9
+ ,6.6
+ ,6.7
+ ,6.7
+ ,7.5
+ ,5.39
+ ,7.3
+ ,6.9
+ ,6.6
+ ,6.7
+ ,7.3
+ ,5.46
+ ,7.5
+ ,7.3
+ ,6.9
+ ,6.6
+ ,7.1
+ ,4.72
+ ,7.3
+ ,7.5
+ ,7.3
+ ,6.9
+ ,6.9
+ ,3.14
+ ,7.1
+ ,7.3
+ ,7.5
+ ,7.3
+ ,7.1
+ ,2.63
+ ,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 2.61 7.8 8.3 8.5 8.6 1 0 0 0 0 0 0 0 0 0 0 1
2 8.0 2.26 7.8 7.8 8.3 8.5 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 2.41 8.0 7.8 7.8 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.9 2.26 8.6 8.0 7.8 7.8 0 0 0 1 0 0 0 0 0 0 0 4
5 8.9 2.03 8.9 8.6 8.0 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 8.6 2.86 8.9 8.9 8.6 8.0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.3 2.55 8.6 8.9 8.9 8.6 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 2.27 8.3 8.6 8.9 8.9 0 0 0 0 0 0 0 1 0 0 0 8
9 8.3 2.26 8.3 8.3 8.6 8.9 0 0 0 0 0 0 0 0 1 0 0 9
10 8.4 2.57 8.3 8.3 8.3 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 3.07 8.4 8.3 8.3 8.3 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 2.76 8.5 8.4 8.3 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.6 2.51 8.4 8.5 8.4 8.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 2.87 8.6 8.4 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 3.14 8.5 8.6 8.4 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 3.11 8.5 8.5 8.6 8.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 3.16 8.5 8.5 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 2.47 8.5 8.5 8.5 8.5 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 2.57 8.5 8.5 8.5 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 2.89 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 2.63 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 2.38 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.5 1.69 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 1.96 8.5 8.5 8.5 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.6 2.19 8.5 8.5 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 25
26 8.4 1.87 8.6 8.5 8.5 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.1 1.60 8.4 8.6 8.5 8.5 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 1.63 8.1 8.4 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 1.22 8.0 8.1 8.4 8.6 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 1.21 8.0 8.0 8.1 8.4 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 1.49 8.0 8.0 8.0 8.1 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 1.64 8.0 8.0 8.0 8.0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.8 1.66 7.9 8.0 8.0 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.8 1.77 7.8 7.9 8.0 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1.82 7.8 7.8 7.9 8.0 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 1.78 7.9 7.8 7.8 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 1.28 8.1 7.9 7.8 7.8 1 0 0 0 0 0 0 0 0 0 0 37
38 7.6 1.29 8.0 8.1 7.9 7.8 0 1 0 0 0 0 0 0 0 0 0 38
39 7.3 1.37 7.6 8.0 8.1 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.0 1.12 7.3 7.6 8.0 8.1 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 1.51 7.0 7.3 7.6 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 2.24 6.8 7.0 7.3 7.6 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 2.94 7.0 6.8 7.0 7.3 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 3.09 7.1 7.0 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.1 3.46 7.2 7.1 7.0 6.8 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 3.64 7.1 7.2 7.1 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.7 4.39 6.9 7.1 7.2 7.1 0 0 0 0 0 0 0 0 0 0 1 47
48 6.7 4.15 6.7 6.9 7.1 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.6 5.21 6.7 6.7 6.9 7.1 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 5.80 6.6 6.7 6.7 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 7.3 5.91 6.9 6.6 6.7 6.7 0 0 1 0 0 0 0 0 0 0 0 51
52 7.5 5.39 7.3 6.9 6.6 6.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 5.46 7.5 7.3 6.9 6.6 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 4.72 7.3 7.5 7.3 6.9 0 0 0 0 0 1 0 0 0 0 0 54
55 6.9 3.14 7.1 7.3 7.5 7.3 0 0 0 0 0 0 1 0 0 0 0 55
56 7.1 2.63 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
0.640274 0.037707 1.368471 -0.520279 -0.356940 0.433839
M1 M2 M3 M4 M5 M6
0.005394 -0.097791 0.038504 -0.018084 -0.100888 0.008916
M7 M8 M9 M10 M11 t
-0.047141 0.040087 -0.069176 -0.031136 -0.002079 -0.004917
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.301455 -0.085359 0.005444 0.065937 0.248249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.640274 0.680519 0.941 0.35272
X 0.037707 0.021874 1.724 0.09287 .
Y1 1.368471 0.131860 10.378 1.20e-12 ***
Y2 -0.520279 0.242107 -2.149 0.03807 *
Y3 -0.356940 0.241719 -1.477 0.14800
Y4 0.433839 0.140082 3.097 0.00366 **
M1 0.005394 0.090308 0.060 0.95268
M2 -0.097791 0.090125 -1.085 0.28473
M3 0.038504 0.090617 0.425 0.67330
M4 -0.018084 0.091132 -0.198 0.84376
M5 -0.100888 0.090185 -1.119 0.27030
M6 0.008916 0.090649 0.098 0.92217
M7 -0.047141 0.090196 -0.523 0.60425
M8 0.040087 0.089741 0.447 0.65763
M9 -0.069176 0.094395 -0.733 0.46815
M10 -0.031136 0.094576 -0.329 0.74380
M11 -0.002079 0.094437 -0.022 0.98255
t -0.004917 0.002338 -2.103 0.04213 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1331 on 38 degrees of freedom
Multiple R-squared: 0.9725, Adjusted R-squared: 0.9602
F-statistic: 79.02 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,] 0.02803158 0.05606317 0.9719684
[2,] 0.15872814 0.31745628 0.8412719
[3,] 0.09305430 0.18610860 0.9069457
[4,] 0.05008954 0.10017907 0.9499105
[5,] 0.05233326 0.10466652 0.9476667
[6,] 0.04183114 0.08366227 0.9581689
[7,] 0.25906923 0.51813846 0.7409308
[8,] 0.25255395 0.50510789 0.7474461
[9,] 0.22472715 0.44945431 0.7752728
[10,] 0.39981389 0.79962779 0.6001861
[11,] 0.33459314 0.66918627 0.6654069
[12,] 0.27133216 0.54266431 0.7286678
[13,] 0.21581739 0.43163477 0.7841826
[14,] 0.12164915 0.24329831 0.8783508
[15,] 0.05802989 0.11605979 0.9419701
> postscript(file="/var/www/html/rcomp/tmp/1d2iz1258555715.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2a4n71258555715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30qs91258555715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/441kj1258555715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5j6u31258555715.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
0.008045305 0.041201861 0.138770581 0.005824085 0.075231846 -0.077472047
7 8 9 10 11 12
-0.047489152 0.005063684 -0.143545362 -0.065287946 -0.014976242 -0.185268903
13 14 15 16 17 18
0.248249332 -0.090634488 -0.070369067 0.055011057 0.018384941 -0.017100478
19 20 21 22 23 24
0.040102696 -0.054274522 0.069708943 0.046012477 0.047890752 0.040547319
25 26 27 28 29 30
0.131397160 -0.085280984 -0.180756642 0.121796649 0.090969044 -0.085883102
31 32 33 34 35 36
0.058990614 -0.085592495 0.064680183 0.112228465 0.098481697 0.173670176
37 38 39 40 41 42
-0.086236387 -0.101913677 -0.012944182 -0.162044680 -0.133963683 0.117687398
43 44 45 46 47 48
-0.102413941 -0.064408540 0.009156236 -0.092952995 -0.131396207 -0.028948592
49 50 51 52 53 54
-0.301455410 0.236627288 0.125299309 -0.020587111 -0.050622147 0.062768228
55 56
0.050809784 0.199211874
> postscript(file="/var/www/html/rcomp/tmp/6pd6j1258555715.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 0.008045305 NA
1 0.041201861 0.008045305
2 0.138770581 0.041201861
3 0.005824085 0.138770581
4 0.075231846 0.005824085
5 -0.077472047 0.075231846
6 -0.047489152 -0.077472047
7 0.005063684 -0.047489152
8 -0.143545362 0.005063684
9 -0.065287946 -0.143545362
10 -0.014976242 -0.065287946
11 -0.185268903 -0.014976242
12 0.248249332 -0.185268903
13 -0.090634488 0.248249332
14 -0.070369067 -0.090634488
15 0.055011057 -0.070369067
16 0.018384941 0.055011057
17 -0.017100478 0.018384941
18 0.040102696 -0.017100478
19 -0.054274522 0.040102696
20 0.069708943 -0.054274522
21 0.046012477 0.069708943
22 0.047890752 0.046012477
23 0.040547319 0.047890752
24 0.131397160 0.040547319
25 -0.085280984 0.131397160
26 -0.180756642 -0.085280984
27 0.121796649 -0.180756642
28 0.090969044 0.121796649
29 -0.085883102 0.090969044
30 0.058990614 -0.085883102
31 -0.085592495 0.058990614
32 0.064680183 -0.085592495
33 0.112228465 0.064680183
34 0.098481697 0.112228465
35 0.173670176 0.098481697
36 -0.086236387 0.173670176
37 -0.101913677 -0.086236387
38 -0.012944182 -0.101913677
39 -0.162044680 -0.012944182
40 -0.133963683 -0.162044680
41 0.117687398 -0.133963683
42 -0.102413941 0.117687398
43 -0.064408540 -0.102413941
44 0.009156236 -0.064408540
45 -0.092952995 0.009156236
46 -0.131396207 -0.092952995
47 -0.028948592 -0.131396207
48 -0.301455410 -0.028948592
49 0.236627288 -0.301455410
50 0.125299309 0.236627288
51 -0.020587111 0.125299309
52 -0.050622147 -0.020587111
53 0.062768228 -0.050622147
54 0.050809784 0.062768228
55 0.199211874 0.050809784
56 NA 0.199211874
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.041201861 0.008045305
[2,] 0.138770581 0.041201861
[3,] 0.005824085 0.138770581
[4,] 0.075231846 0.005824085
[5,] -0.077472047 0.075231846
[6,] -0.047489152 -0.077472047
[7,] 0.005063684 -0.047489152
[8,] -0.143545362 0.005063684
[9,] -0.065287946 -0.143545362
[10,] -0.014976242 -0.065287946
[11,] -0.185268903 -0.014976242
[12,] 0.248249332 -0.185268903
[13,] -0.090634488 0.248249332
[14,] -0.070369067 -0.090634488
[15,] 0.055011057 -0.070369067
[16,] 0.018384941 0.055011057
[17,] -0.017100478 0.018384941
[18,] 0.040102696 -0.017100478
[19,] -0.054274522 0.040102696
[20,] 0.069708943 -0.054274522
[21,] 0.046012477 0.069708943
[22,] 0.047890752 0.046012477
[23,] 0.040547319 0.047890752
[24,] 0.131397160 0.040547319
[25,] -0.085280984 0.131397160
[26,] -0.180756642 -0.085280984
[27,] 0.121796649 -0.180756642
[28,] 0.090969044 0.121796649
[29,] -0.085883102 0.090969044
[30,] 0.058990614 -0.085883102
[31,] -0.085592495 0.058990614
[32,] 0.064680183 -0.085592495
[33,] 0.112228465 0.064680183
[34,] 0.098481697 0.112228465
[35,] 0.173670176 0.098481697
[36,] -0.086236387 0.173670176
[37,] -0.101913677 -0.086236387
[38,] -0.012944182 -0.101913677
[39,] -0.162044680 -0.012944182
[40,] -0.133963683 -0.162044680
[41,] 0.117687398 -0.133963683
[42,] -0.102413941 0.117687398
[43,] -0.064408540 -0.102413941
[44,] 0.009156236 -0.064408540
[45,] -0.092952995 0.009156236
[46,] -0.131396207 -0.092952995
[47,] -0.028948592 -0.131396207
[48,] -0.301455410 -0.028948592
[49,] 0.236627288 -0.301455410
[50,] 0.125299309 0.236627288
[51,] -0.020587111 0.125299309
[52,] -0.050622147 -0.020587111
[53,] 0.062768228 -0.050622147
[54,] 0.050809784 0.062768228
[55,] 0.199211874 0.050809784
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.041201861 0.008045305
2 0.138770581 0.041201861
3 0.005824085 0.138770581
4 0.075231846 0.005824085
5 -0.077472047 0.075231846
6 -0.047489152 -0.077472047
7 0.005063684 -0.047489152
8 -0.143545362 0.005063684
9 -0.065287946 -0.143545362
10 -0.014976242 -0.065287946
11 -0.185268903 -0.014976242
12 0.248249332 -0.185268903
13 -0.090634488 0.248249332
14 -0.070369067 -0.090634488
15 0.055011057 -0.070369067
16 0.018384941 0.055011057
17 -0.017100478 0.018384941
18 0.040102696 -0.017100478
19 -0.054274522 0.040102696
20 0.069708943 -0.054274522
21 0.046012477 0.069708943
22 0.047890752 0.046012477
23 0.040547319 0.047890752
24 0.131397160 0.040547319
25 -0.085280984 0.131397160
26 -0.180756642 -0.085280984
27 0.121796649 -0.180756642
28 0.090969044 0.121796649
29 -0.085883102 0.090969044
30 0.058990614 -0.085883102
31 -0.085592495 0.058990614
32 0.064680183 -0.085592495
33 0.112228465 0.064680183
34 0.098481697 0.112228465
35 0.173670176 0.098481697
36 -0.086236387 0.173670176
37 -0.101913677 -0.086236387
38 -0.012944182 -0.101913677
39 -0.162044680 -0.012944182
40 -0.133963683 -0.162044680
41 0.117687398 -0.133963683
42 -0.102413941 0.117687398
43 -0.064408540 -0.102413941
44 0.009156236 -0.064408540
45 -0.092952995 0.009156236
46 -0.131396207 -0.092952995
47 -0.028948592 -0.131396207
48 -0.301455410 -0.028948592
49 0.236627288 -0.301455410
50 0.125299309 0.236627288
51 -0.020587111 0.125299309
52 -0.050622147 -0.020587111
53 0.062768228 -0.050622147
54 0.050809784 0.062768228
55 0.199211874 0.050809784
> 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/7zj7y1258555715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8g1db1258555715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ryd91258555715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10erer1258555715.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rj291258555715.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/12nys91258555715.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/13y3dx1258555716.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/14ekqa1258555716.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/1537bo1258555716.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/16s9mm1258555716.tab")
+ }
>
> system("convert tmp/1d2iz1258555715.ps tmp/1d2iz1258555715.png")
> system("convert tmp/2a4n71258555715.ps tmp/2a4n71258555715.png")
> system("convert tmp/30qs91258555715.ps tmp/30qs91258555715.png")
> system("convert tmp/441kj1258555715.ps tmp/441kj1258555715.png")
> system("convert tmp/5j6u31258555715.ps tmp/5j6u31258555715.png")
> system("convert tmp/6pd6j1258555715.ps tmp/6pd6j1258555715.png")
> system("convert tmp/7zj7y1258555715.ps tmp/7zj7y1258555715.png")
> system("convert tmp/8g1db1258555715.ps tmp/8g1db1258555715.png")
> system("convert tmp/9ryd91258555715.ps tmp/9ryd91258555715.png")
> system("convert tmp/10erer1258555715.ps tmp/10erer1258555715.png")
>
>
> proc.time()
user system elapsed
2.349 1.563 2.815