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(2.4
+ ,2
+ ,1.7
+ ,1
+ ,1.2
+ ,1.4
+ ,2
+ ,2
+ ,2.4
+ ,1.7
+ ,1
+ ,1.2
+ ,2.1
+ ,2
+ ,2
+ ,2.4
+ ,1.7
+ ,1
+ ,2
+ ,2
+ ,2.1
+ ,2
+ ,2.4
+ ,1.7
+ ,1.8
+ ,2
+ ,2
+ ,2.1
+ ,2
+ ,2.4
+ ,2.7
+ ,2
+ ,1.8
+ ,2
+ ,2.1
+ ,2
+ ,2.3
+ ,2
+ ,2.7
+ ,1.8
+ ,2
+ ,2.1
+ ,1.9
+ ,2
+ ,2.3
+ ,2.7
+ ,1.8
+ ,2
+ ,2
+ ,2
+ ,1.9
+ ,2.3
+ ,2.7
+ ,1.8
+ ,2.3
+ ,2
+ ,2
+ ,1.9
+ ,2.3
+ ,2.7
+ ,2.8
+ ,2
+ ,2.3
+ ,2
+ ,1.9
+ ,2.3
+ ,2.4
+ ,2
+ ,2.8
+ ,2.3
+ ,2
+ ,1.9
+ ,2.3
+ ,2
+ ,2.4
+ ,2.8
+ ,2.3
+ ,2
+ ,2.7
+ ,2
+ ,2.3
+ ,2.4
+ ,2.8
+ ,2.3
+ ,2.7
+ ,2
+ ,2.7
+ ,2.3
+ ,2.4
+ ,2.8
+ ,2.9
+ ,2
+ ,2.7
+ ,2.7
+ ,2.3
+ ,2.4
+ ,3
+ ,2
+ ,2.9
+ ,2.7
+ ,2.7
+ ,2.3
+ ,2.2
+ ,2
+ ,3
+ ,2.9
+ ,2.7
+ ,2.7
+ ,2.3
+ ,2
+ ,2.2
+ ,3
+ ,2.9
+ ,2.7
+ ,2.8
+ ,2.21
+ ,2.3
+ ,2.2
+ ,3
+ ,2.9
+ ,2.8
+ ,2.25
+ ,2.8
+ ,2.3
+ ,2.2
+ ,3
+ ,2.8
+ ,2.25
+ ,2.8
+ ,2.8
+ ,2.3
+ ,2.2
+ ,2.2
+ ,2.45
+ ,2.8
+ ,2.8
+ ,2.8
+ ,2.3
+ ,2.6
+ ,2.5
+ ,2.2
+ ,2.8
+ ,2.8
+ ,2.8
+ ,2.8
+ ,2.5
+ ,2.6
+ ,2.2
+ ,2.8
+ ,2.8
+ ,2.5
+ ,2.64
+ ,2.8
+ ,2.6
+ ,2.2
+ ,2.8
+ ,2.4
+ ,2.75
+ ,2.5
+ ,2.8
+ ,2.6
+ ,2.2
+ ,2.3
+ ,2.93
+ ,2.4
+ ,2.5
+ ,2.8
+ ,2.6
+ ,1.9
+ ,3
+ ,2.3
+ ,2.4
+ ,2.5
+ ,2.8
+ ,1.7
+ ,3.17
+ ,1.9
+ ,2.3
+ ,2.4
+ ,2.5
+ ,2
+ ,3.25
+ ,1.7
+ ,1.9
+ ,2.3
+ ,2.4
+ ,2.1
+ ,3.39
+ ,2
+ ,1.7
+ ,1.9
+ ,2.3
+ ,1.7
+ ,3.5
+ ,2.1
+ ,2
+ ,1.7
+ ,1.9
+ ,1.8
+ ,3.5
+ ,1.7
+ ,2.1
+ ,2
+ ,1.7
+ ,1.8
+ ,3.65
+ ,1.8
+ ,1.7
+ ,2.1
+ ,2
+ ,1.8
+ ,3.75
+ ,1.8
+ ,1.8
+ ,1.7
+ ,2.1
+ ,1.3
+ ,3.75
+ ,1.8
+ ,1.8
+ ,1.8
+ ,1.7
+ ,1.3
+ ,3.9
+ ,1.3
+ ,1.8
+ ,1.8
+ ,1.8
+ ,1.3
+ ,4
+ ,1.3
+ ,1.3
+ ,1.8
+ ,1.8
+ ,1.2
+ ,4
+ ,1.3
+ ,1.3
+ ,1.3
+ ,1.8
+ ,1.4
+ ,4
+ ,1.2
+ ,1.3
+ ,1.3
+ ,1.3
+ ,2.2
+ ,4
+ ,1.4
+ ,1.2
+ ,1.3
+ ,1.3
+ ,2.9
+ ,4
+ ,2.2
+ ,1.4
+ ,1.2
+ ,1.3
+ ,3.1
+ ,4
+ ,2.9
+ ,2.2
+ ,1.4
+ ,1.2
+ ,3.5
+ ,4
+ ,3.1
+ ,2.9
+ ,2.2
+ ,1.4
+ ,3.6
+ ,4
+ ,3.5
+ ,3.1
+ ,2.9
+ ,2.2
+ ,4.4
+ ,4
+ ,3.6
+ ,3.5
+ ,3.1
+ ,2.9
+ ,4.1
+ ,4
+ ,4.4
+ ,3.6
+ ,3.5
+ ,3.1
+ ,5.1
+ ,4
+ ,4.1
+ ,4.4
+ ,3.6
+ ,3.5
+ ,5.8
+ ,4
+ ,5.1
+ ,4.1
+ ,4.4
+ ,3.6
+ ,5.9
+ ,4.18
+ ,5.8
+ ,5.1
+ ,4.1
+ ,4.4
+ ,5.4
+ ,4.25
+ ,5.9
+ ,5.8
+ ,5.1
+ ,4.1
+ ,5.5
+ ,4.25
+ ,5.4
+ ,5.9
+ ,5.8
+ ,5.1
+ ,4.8
+ ,3.97
+ ,5.5
+ ,5.4
+ ,5.9
+ ,5.8
+ ,3.2
+ ,3.42
+ ,4.8
+ ,5.5
+ ,5.4
+ ,5.9
+ ,2.7
+ ,2.75
+ ,3.2
+ ,4.8
+ ,5.5
+ ,5.4)
+ ,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 2.4 2.00 1.7 1.0 1.2 1.4 1 0 0 0 0 0 0 0 0 0 0 1
2 2.0 2.00 2.4 1.7 1.0 1.2 0 1 0 0 0 0 0 0 0 0 0 2
3 2.1 2.00 2.0 2.4 1.7 1.0 0 0 1 0 0 0 0 0 0 0 0 3
4 2.0 2.00 2.1 2.0 2.4 1.7 0 0 0 1 0 0 0 0 0 0 0 4
5 1.8 2.00 2.0 2.1 2.0 2.4 0 0 0 0 1 0 0 0 0 0 0 5
6 2.7 2.00 1.8 2.0 2.1 2.0 0 0 0 0 0 1 0 0 0 0 0 6
7 2.3 2.00 2.7 1.8 2.0 2.1 0 0 0 0 0 0 1 0 0 0 0 7
8 1.9 2.00 2.3 2.7 1.8 2.0 0 0 0 0 0 0 0 1 0 0 0 8
9 2.0 2.00 1.9 2.3 2.7 1.8 0 0 0 0 0 0 0 0 1 0 0 9
10 2.3 2.00 2.0 1.9 2.3 2.7 0 0 0 0 0 0 0 0 0 1 0 10
11 2.8 2.00 2.3 2.0 1.9 2.3 0 0 0 0 0 0 0 0 0 0 1 11
12 2.4 2.00 2.8 2.3 2.0 1.9 0 0 0 0 0 0 0 0 0 0 0 12
13 2.3 2.00 2.4 2.8 2.3 2.0 1 0 0 0 0 0 0 0 0 0 0 13
14 2.7 2.00 2.3 2.4 2.8 2.3 0 1 0 0 0 0 0 0 0 0 0 14
15 2.7 2.00 2.7 2.3 2.4 2.8 0 0 1 0 0 0 0 0 0 0 0 15
16 2.9 2.00 2.7 2.7 2.3 2.4 0 0 0 1 0 0 0 0 0 0 0 16
17 3.0 2.00 2.9 2.7 2.7 2.3 0 0 0 0 1 0 0 0 0 0 0 17
18 2.2 2.00 3.0 2.9 2.7 2.7 0 0 0 0 0 1 0 0 0 0 0 18
19 2.3 2.00 2.2 3.0 2.9 2.7 0 0 0 0 0 0 1 0 0 0 0 19
20 2.8 2.21 2.3 2.2 3.0 2.9 0 0 0 0 0 0 0 1 0 0 0 20
21 2.8 2.25 2.8 2.3 2.2 3.0 0 0 0 0 0 0 0 0 1 0 0 21
22 2.8 2.25 2.8 2.8 2.3 2.2 0 0 0 0 0 0 0 0 0 1 0 22
23 2.2 2.45 2.8 2.8 2.8 2.3 0 0 0 0 0 0 0 0 0 0 1 23
24 2.6 2.50 2.2 2.8 2.8 2.8 0 0 0 0 0 0 0 0 0 0 0 24
25 2.8 2.50 2.6 2.2 2.8 2.8 1 0 0 0 0 0 0 0 0 0 0 25
26 2.5 2.64 2.8 2.6 2.2 2.8 0 1 0 0 0 0 0 0 0 0 0 26
27 2.4 2.75 2.5 2.8 2.6 2.2 0 0 1 0 0 0 0 0 0 0 0 27
28 2.3 2.93 2.4 2.5 2.8 2.6 0 0 0 1 0 0 0 0 0 0 0 28
29 1.9 3.00 2.3 2.4 2.5 2.8 0 0 0 0 1 0 0 0 0 0 0 29
30 1.7 3.17 1.9 2.3 2.4 2.5 0 0 0 0 0 1 0 0 0 0 0 30
31 2.0 3.25 1.7 1.9 2.3 2.4 0 0 0 0 0 0 1 0 0 0 0 31
32 2.1 3.39 2.0 1.7 1.9 2.3 0 0 0 0 0 0 0 1 0 0 0 32
33 1.7 3.50 2.1 2.0 1.7 1.9 0 0 0 0 0 0 0 0 1 0 0 33
34 1.8 3.50 1.7 2.1 2.0 1.7 0 0 0 0 0 0 0 0 0 1 0 34
35 1.8 3.65 1.8 1.7 2.1 2.0 0 0 0 0 0 0 0 0 0 0 1 35
36 1.8 3.75 1.8 1.8 1.7 2.1 0 0 0 0 0 0 0 0 0 0 0 36
37 1.3 3.75 1.8 1.8 1.8 1.7 1 0 0 0 0 0 0 0 0 0 0 37
38 1.3 3.90 1.3 1.8 1.8 1.8 0 1 0 0 0 0 0 0 0 0 0 38
39 1.3 4.00 1.3 1.3 1.8 1.8 0 0 1 0 0 0 0 0 0 0 0 39
40 1.2 4.00 1.3 1.3 1.3 1.8 0 0 0 1 0 0 0 0 0 0 0 40
41 1.4 4.00 1.2 1.3 1.3 1.3 0 0 0 0 1 0 0 0 0 0 0 41
42 2.2 4.00 1.4 1.2 1.3 1.3 0 0 0 0 0 1 0 0 0 0 0 42
43 2.9 4.00 2.2 1.4 1.2 1.3 0 0 0 0 0 0 1 0 0 0 0 43
44 3.1 4.00 2.9 2.2 1.4 1.2 0 0 0 0 0 0 0 1 0 0 0 44
45 3.5 4.00 3.1 2.9 2.2 1.4 0 0 0 0 0 0 0 0 1 0 0 45
46 3.6 4.00 3.5 3.1 2.9 2.2 0 0 0 0 0 0 0 0 0 1 0 46
47 4.4 4.00 3.6 3.5 3.1 2.9 0 0 0 0 0 0 0 0 0 0 1 47
48 4.1 4.00 4.4 3.6 3.5 3.1 0 0 0 0 0 0 0 0 0 0 0 48
49 5.1 4.00 4.1 4.4 3.6 3.5 1 0 0 0 0 0 0 0 0 0 0 49
50 5.8 4.00 5.1 4.1 4.4 3.6 0 1 0 0 0 0 0 0 0 0 0 50
51 5.9 4.18 5.8 5.1 4.1 4.4 0 0 1 0 0 0 0 0 0 0 0 51
52 5.4 4.25 5.9 5.8 5.1 4.1 0 0 0 1 0 0 0 0 0 0 0 52
53 5.5 4.25 5.4 5.9 5.8 5.1 0 0 0 0 1 0 0 0 0 0 0 53
54 4.8 3.97 5.5 5.4 5.9 5.8 0 0 0 0 0 1 0 0 0 0 0 54
55 3.2 3.42 4.8 5.5 5.4 5.9 0 0 0 0 0 0 1 0 0 0 0 55
56 2.7 2.75 3.2 4.8 5.5 5.4 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
-0.058306 0.137027 1.108668 -0.249101 0.287992 -0.279080
M1 M2 M3 M4 M5 M6
0.320790 0.116329 0.110448 -0.067334 0.082157 0.120059
M7 M8 M9 M10 M11 t
-0.013018 0.157208 0.039384 0.163884 0.218444 -0.003866
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.92447 -0.27734 -0.04174 0.31322 0.96935
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.058306 0.637778 -0.091 0.928
X 0.137027 0.293994 0.466 0.644
Y1 1.108668 0.160608 6.903 3.33e-08 ***
Y2 -0.249101 0.238615 -1.044 0.303
Y3 0.287992 0.239553 1.202 0.237
Y4 -0.279080 0.191233 -1.459 0.153
M1 0.320790 0.331669 0.967 0.340
M2 0.116329 0.331561 0.351 0.728
M3 0.110448 0.333414 0.331 0.742
M4 -0.067334 0.336104 -0.200 0.842
M5 0.082157 0.337721 0.243 0.809
M6 0.120059 0.335577 0.358 0.722
M7 -0.013018 0.334112 -0.039 0.969
M8 0.157208 0.339228 0.463 0.646
M9 0.039384 0.350849 0.112 0.911
M10 0.163884 0.350576 0.467 0.643
M11 0.218444 0.348888 0.626 0.535
t -0.003866 0.017938 -0.216 0.831
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4923 on 38 degrees of freedom
Multiple R-squared: 0.8738, Adjusted R-squared: 0.8173
F-statistic: 15.47 on 17 and 38 DF, p-value: 3.915e-12
> 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,] 0.3947262765 0.7894525530 0.6052737
[2,] 0.2315307891 0.4630615782 0.7684692
[3,] 0.1758459902 0.3516919803 0.8241540
[4,] 0.1379016305 0.2758032611 0.8620984
[5,] 0.0734290404 0.1468580808 0.9265710
[6,] 0.0351912755 0.0703825510 0.9648087
[7,] 0.0198384985 0.0396769971 0.9801615
[8,] 0.0101129585 0.0202259170 0.9898870
[9,] 0.0076826002 0.0153652003 0.9923174
[10,] 0.0053724437 0.0107448875 0.9946276
[11,] 0.0038640737 0.0077281475 0.9961359
[12,] 0.0026073367 0.0052146735 0.9973927
[13,] 0.0009718019 0.0019436038 0.9990282
[14,] 0.0008019828 0.0016039655 0.9991980
[15,] 0.0002870899 0.0005741797 0.9997129
> postscript(file="/var/www/html/rcomp/tmp/1laeo1258722825.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dkkp1258722825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31wc81258722825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44q2w1258722825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z4l11258722825.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
0.27681415 -0.51477296 -0.04459844 -0.17969561 -0.07899108 0.84336532
7 8 9 10 11 12
-0.41060636 -0.27961766 -0.02910987 0.30611792 0.45129803 -0.34642753
13 14 15 16 17 18
-0.25382346 0.30545876 0.10156476 0.50002029 0.08955648 -0.69389371
19 20 21 22 23 24
0.39729562 0.41902888 0.26411470 0.01596785 -0.77821943 0.64198001
25 26 27 28 29 30
-0.06787252 -0.12802691 -0.13357755 0.01357585 -0.31347119 -0.20716939
31 32 33 34 35 36
0.34179557 -0.03888046 -0.42243420 -0.11690429 -0.34373477 0.03288691
37 38 39 40 41 42
-0.92446888 -0.15445317 -0.28295998 -0.05731610 -0.03161492 0.48770550
43 44 45 46 47 48
0.51633311 -0.11232016 0.18742937 -0.20518148 0.67065617 -0.32843939
49 50 51 52 53 54
0.96935071 0.49179428 0.35957122 -0.27658443 0.33452072 -0.43000772
55 56
-0.84481794 0.01178941
> postscript(file="/var/www/html/rcomp/tmp/6kmhx1258722825.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 0.27681415 NA
1 -0.51477296 0.27681415
2 -0.04459844 -0.51477296
3 -0.17969561 -0.04459844
4 -0.07899108 -0.17969561
5 0.84336532 -0.07899108
6 -0.41060636 0.84336532
7 -0.27961766 -0.41060636
8 -0.02910987 -0.27961766
9 0.30611792 -0.02910987
10 0.45129803 0.30611792
11 -0.34642753 0.45129803
12 -0.25382346 -0.34642753
13 0.30545876 -0.25382346
14 0.10156476 0.30545876
15 0.50002029 0.10156476
16 0.08955648 0.50002029
17 -0.69389371 0.08955648
18 0.39729562 -0.69389371
19 0.41902888 0.39729562
20 0.26411470 0.41902888
21 0.01596785 0.26411470
22 -0.77821943 0.01596785
23 0.64198001 -0.77821943
24 -0.06787252 0.64198001
25 -0.12802691 -0.06787252
26 -0.13357755 -0.12802691
27 0.01357585 -0.13357755
28 -0.31347119 0.01357585
29 -0.20716939 -0.31347119
30 0.34179557 -0.20716939
31 -0.03888046 0.34179557
32 -0.42243420 -0.03888046
33 -0.11690429 -0.42243420
34 -0.34373477 -0.11690429
35 0.03288691 -0.34373477
36 -0.92446888 0.03288691
37 -0.15445317 -0.92446888
38 -0.28295998 -0.15445317
39 -0.05731610 -0.28295998
40 -0.03161492 -0.05731610
41 0.48770550 -0.03161492
42 0.51633311 0.48770550
43 -0.11232016 0.51633311
44 0.18742937 -0.11232016
45 -0.20518148 0.18742937
46 0.67065617 -0.20518148
47 -0.32843939 0.67065617
48 0.96935071 -0.32843939
49 0.49179428 0.96935071
50 0.35957122 0.49179428
51 -0.27658443 0.35957122
52 0.33452072 -0.27658443
53 -0.43000772 0.33452072
54 -0.84481794 -0.43000772
55 0.01178941 -0.84481794
56 NA 0.01178941
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.51477296 0.27681415
[2,] -0.04459844 -0.51477296
[3,] -0.17969561 -0.04459844
[4,] -0.07899108 -0.17969561
[5,] 0.84336532 -0.07899108
[6,] -0.41060636 0.84336532
[7,] -0.27961766 -0.41060636
[8,] -0.02910987 -0.27961766
[9,] 0.30611792 -0.02910987
[10,] 0.45129803 0.30611792
[11,] -0.34642753 0.45129803
[12,] -0.25382346 -0.34642753
[13,] 0.30545876 -0.25382346
[14,] 0.10156476 0.30545876
[15,] 0.50002029 0.10156476
[16,] 0.08955648 0.50002029
[17,] -0.69389371 0.08955648
[18,] 0.39729562 -0.69389371
[19,] 0.41902888 0.39729562
[20,] 0.26411470 0.41902888
[21,] 0.01596785 0.26411470
[22,] -0.77821943 0.01596785
[23,] 0.64198001 -0.77821943
[24,] -0.06787252 0.64198001
[25,] -0.12802691 -0.06787252
[26,] -0.13357755 -0.12802691
[27,] 0.01357585 -0.13357755
[28,] -0.31347119 0.01357585
[29,] -0.20716939 -0.31347119
[30,] 0.34179557 -0.20716939
[31,] -0.03888046 0.34179557
[32,] -0.42243420 -0.03888046
[33,] -0.11690429 -0.42243420
[34,] -0.34373477 -0.11690429
[35,] 0.03288691 -0.34373477
[36,] -0.92446888 0.03288691
[37,] -0.15445317 -0.92446888
[38,] -0.28295998 -0.15445317
[39,] -0.05731610 -0.28295998
[40,] -0.03161492 -0.05731610
[41,] 0.48770550 -0.03161492
[42,] 0.51633311 0.48770550
[43,] -0.11232016 0.51633311
[44,] 0.18742937 -0.11232016
[45,] -0.20518148 0.18742937
[46,] 0.67065617 -0.20518148
[47,] -0.32843939 0.67065617
[48,] 0.96935071 -0.32843939
[49,] 0.49179428 0.96935071
[50,] 0.35957122 0.49179428
[51,] -0.27658443 0.35957122
[52,] 0.33452072 -0.27658443
[53,] -0.43000772 0.33452072
[54,] -0.84481794 -0.43000772
[55,] 0.01178941 -0.84481794
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.51477296 0.27681415
2 -0.04459844 -0.51477296
3 -0.17969561 -0.04459844
4 -0.07899108 -0.17969561
5 0.84336532 -0.07899108
6 -0.41060636 0.84336532
7 -0.27961766 -0.41060636
8 -0.02910987 -0.27961766
9 0.30611792 -0.02910987
10 0.45129803 0.30611792
11 -0.34642753 0.45129803
12 -0.25382346 -0.34642753
13 0.30545876 -0.25382346
14 0.10156476 0.30545876
15 0.50002029 0.10156476
16 0.08955648 0.50002029
17 -0.69389371 0.08955648
18 0.39729562 -0.69389371
19 0.41902888 0.39729562
20 0.26411470 0.41902888
21 0.01596785 0.26411470
22 -0.77821943 0.01596785
23 0.64198001 -0.77821943
24 -0.06787252 0.64198001
25 -0.12802691 -0.06787252
26 -0.13357755 -0.12802691
27 0.01357585 -0.13357755
28 -0.31347119 0.01357585
29 -0.20716939 -0.31347119
30 0.34179557 -0.20716939
31 -0.03888046 0.34179557
32 -0.42243420 -0.03888046
33 -0.11690429 -0.42243420
34 -0.34373477 -0.11690429
35 0.03288691 -0.34373477
36 -0.92446888 0.03288691
37 -0.15445317 -0.92446888
38 -0.28295998 -0.15445317
39 -0.05731610 -0.28295998
40 -0.03161492 -0.05731610
41 0.48770550 -0.03161492
42 0.51633311 0.48770550
43 -0.11232016 0.51633311
44 0.18742937 -0.11232016
45 -0.20518148 0.18742937
46 0.67065617 -0.20518148
47 -0.32843939 0.67065617
48 0.96935071 -0.32843939
49 0.49179428 0.96935071
50 0.35957122 0.49179428
51 -0.27658443 0.35957122
52 0.33452072 -0.27658443
53 -0.43000772 0.33452072
54 -0.84481794 -0.43000772
55 0.01178941 -0.84481794
> 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/7mys61258722825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8a7qa1258722825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wihe1258722825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dcpg1258722825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11928g1258722825.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/12ys1q1258722825.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/13rr791258722825.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/1453tf1258722825.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/15enol1258722825.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/16gjzg1258722825.tab")
+ }
>
> system("convert tmp/1laeo1258722825.ps tmp/1laeo1258722825.png")
> system("convert tmp/2dkkp1258722825.ps tmp/2dkkp1258722825.png")
> system("convert tmp/31wc81258722825.ps tmp/31wc81258722825.png")
> system("convert tmp/44q2w1258722825.ps tmp/44q2w1258722825.png")
> system("convert tmp/5z4l11258722825.ps tmp/5z4l11258722825.png")
> system("convert tmp/6kmhx1258722825.ps tmp/6kmhx1258722825.png")
> system("convert tmp/7mys61258722825.ps tmp/7mys61258722825.png")
> system("convert tmp/8a7qa1258722825.ps tmp/8a7qa1258722825.png")
> system("convert tmp/9wihe1258722825.ps tmp/9wihe1258722825.png")
> system("convert tmp/10dcpg1258722825.ps tmp/10dcpg1258722825.png")
>
>
> proc.time()
user system elapsed
2.332 1.573 2.757