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(8.3
+ ,3.9
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,4
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,4.3
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,4.8
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,4.4
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,4.3
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,4.7
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,4.7
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,4.9
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,5
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,4.2
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,4.3
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,4.8
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,4.8
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,4.8
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,4.2
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,4.6
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,4.8
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,4.5
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,4.4
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,4.3
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,3.9
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,3.7
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,4
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,4.1
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,3.7
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,3.8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,3.8
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,3.8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,3.3
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,3.3
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,3.3
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,3.2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,3.4
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,4.2
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,4.9
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,5.1
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,5.5
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,5.6
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,6.4
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,6.1
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,7.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,7.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,7.9
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,7.4
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,6.8
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,5.2
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,4.7
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,4.1
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.5
+ ,8
+ ,3.9
+ ,7.7
+ ,6.9
+ ,6.6
+ ,6.9
+ ,8
+ ,2.6
+ ,8
+ ,7.7
+ ,6.9
+ ,6.6
+ ,7.7
+ ,2.7
+ ,8
+ ,8
+ ,7.7
+ ,6.9
+ ,7.3
+ ,1.8
+ ,7.7
+ ,8
+ ,8
+ ,7.7
+ ,7.4
+ ,1
+ ,7.3
+ ,7.7
+ ,8
+ ,8
+ ,8.1
+ ,0.3
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8)
+ ,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 8.3 3.9 8.2 8.7 9.3 9.3 1 0 0 0 0 0 0 0 0 0 0 1
2 8.5 4.0 8.3 8.2 8.7 9.3 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 4.3 8.5 8.3 8.2 8.7 0 0 1 0 0 0 0 0 0 0 0 3
4 8.5 4.8 8.6 8.5 8.3 8.2 0 0 0 1 0 0 0 0 0 0 0 4
5 8.2 4.4 8.5 8.6 8.5 8.3 0 0 0 0 1 0 0 0 0 0 0 5
6 8.1 4.3 8.2 8.5 8.6 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 7.9 4.7 8.1 8.2 8.5 8.6 0 0 0 0 0 0 1 0 0 0 0 7
8 8.6 4.7 7.9 8.1 8.2 8.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.7 4.9 8.6 7.9 8.1 8.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 5.0 8.7 8.6 7.9 8.1 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 4.2 8.7 8.7 8.6 7.9 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 4.3 8.5 8.7 8.7 8.6 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 4.8 8.4 8.5 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 4.8 8.5 8.4 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.7 4.8 8.7 8.5 8.4 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.6 4.2 8.7 8.7 8.5 8.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 4.6 8.6 8.7 8.7 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.3 4.8 8.5 8.6 8.7 8.7 0 0 0 0 0 1 0 0 0 0 0 18
19 8.0 4.5 8.3 8.5 8.6 8.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 4.4 8.0 8.3 8.5 8.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 4.3 8.2 8.0 8.3 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.1 3.9 8.1 8.2 8.0 8.3 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 3.7 8.1 8.1 8.2 8.0 0 0 0 0 0 0 0 0 0 0 1 23
24 7.9 4.0 8.0 8.1 8.1 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 4.1 7.9 8.0 8.1 8.1 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 3.7 7.9 7.9 8.0 8.1 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 3.8 8.0 7.9 7.9 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 3.8 8.0 8.0 7.9 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 3.8 7.9 8.0 8.0 7.9 0 0 0 0 1 0 0 0 0 0 0 29
30 7.7 3.3 8.0 7.9 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.2 3.3 7.7 8.0 7.9 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.5 3.3 7.2 7.7 8.0 7.9 0 0 0 0 0 0 0 1 0 0 0 32
33 7.3 3.2 7.5 7.2 7.7 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.0 3.4 7.3 7.5 7.2 7.7 0 0 0 0 0 0 0 0 0 1 0 34
35 7.0 4.2 7.0 7.3 7.5 7.2 0 0 0 0 0 0 0 0 0 0 1 35
36 7.0 4.9 7.0 7.0 7.3 7.5 0 0 0 0 0 0 0 0 0 0 0 36
37 7.2 5.1 7.0 7.0 7.0 7.3 1 0 0 0 0 0 0 0 0 0 0 37
38 7.3 5.5 7.2 7.0 7.0 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.1 5.6 7.3 7.2 7.0 7.0 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 6.4 7.1 7.3 7.2 7.0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 6.1 6.8 7.1 7.3 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 7.1 6.4 6.8 7.1 7.3 0 0 0 0 0 1 0 0 0 0 0 42
43 6.5 7.8 6.1 6.4 6.8 7.1 0 0 0 0 0 0 1 0 0 0 0 43
44 7.7 7.9 6.5 6.1 6.4 6.8 0 0 0 0 0 0 0 1 0 0 0 44
45 7.9 7.4 7.7 6.5 6.1 6.4 0 0 0 0 0 0 0 0 1 0 0 45
46 7.5 7.5 7.9 7.7 6.5 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 6.8 7.5 7.9 7.7 6.5 0 0 0 0 0 0 0 0 0 0 1 47
48 6.6 5.2 6.9 7.5 7.9 7.7 0 0 0 0 0 0 0 0 0 0 0 48
49 6.9 4.7 6.6 6.9 7.5 7.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.7 4.1 6.9 6.6 6.9 7.5 0 1 0 0 0 0 0 0 0 0 0 50
51 8.0 3.9 7.7 6.9 6.6 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 8.0 2.6 8.0 7.7 6.9 6.6 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 2.7 8.0 8.0 7.7 6.9 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 1.8 7.7 8.0 8.0 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 7.4 1.0 7.3 7.7 8.0 8.0 0 0 0 0 0 0 1 0 0 0 0 55
56 8.1 0.3 7.4 7.3 7.7 8.0 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
-0.055057 0.008933 1.583979 -0.915093 0.042693 0.279057
M1 M2 M3 M4 M5 M6
0.178003 0.104753 -0.080173 0.042356 0.017128 -0.116440
M7 M8 M9 M10 M11 t
0.005093 0.587598 -0.414900 0.027103 0.096308 0.001244
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.263712 -0.097832 -0.003794 0.083150 0.349489
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.055057 1.179969 -0.047 0.96303
X 0.008933 0.026548 0.336 0.73835
Y1 1.583979 0.159889 9.907 4.43e-12 ***
Y2 -0.915093 0.309550 -2.956 0.00533 **
Y3 0.042693 0.309987 0.138 0.89119
Y4 0.279057 0.189057 1.476 0.14817
M1 0.178003 0.117207 1.519 0.13711
M2 0.104753 0.126090 0.831 0.41129
M3 -0.080173 0.131226 -0.611 0.54487
M4 0.042356 0.127969 0.331 0.74248
M5 0.017128 0.122389 0.140 0.88944
M6 -0.116440 0.116067 -1.003 0.32211
M7 0.005093 0.117448 0.043 0.96564
M8 0.587598 0.119271 4.927 1.67e-05 ***
M9 -0.414900 0.165026 -2.514 0.01629 *
M10 0.027103 0.178597 0.152 0.88018
M11 0.096308 0.154263 0.624 0.53616
t 0.001244 0.004336 0.287 0.77569
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1725 on 38 degrees of freedom
Multiple R-squared: 0.9536, Adjusted R-squared: 0.9328
F-statistic: 45.89 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,] 0.60165136 0.79669727 0.3983486
[2,] 0.51220522 0.97558955 0.4877948
[3,] 0.36327201 0.72654403 0.6367280
[4,] 0.24077312 0.48154625 0.7592269
[5,] 0.14221282 0.28442565 0.8577872
[6,] 0.08503960 0.17007920 0.9149604
[7,] 0.05793121 0.11586243 0.9420688
[8,] 0.02856585 0.05713169 0.9714342
[9,] 0.30011599 0.60023199 0.6998840
[10,] 0.31672243 0.63344486 0.6832776
[11,] 0.21741320 0.43482639 0.7825868
[12,] 0.26214984 0.52429968 0.7378502
[13,] 0.21879645 0.43759291 0.7812035
[14,] 0.21021143 0.42042287 0.7897886
[15,] 0.29662372 0.59324744 0.7033763
> postscript(file="/var/www/html/rcomp/tmp/16x1u1261060159.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25yfc1261060159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36tcx1261060159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hot51261060159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55spm1261060159.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
0.121380259 -0.197835070 0.046660508 -0.021699298 -0.080679756 0.276140257
7 8 9 10 11 12
-0.189976041 0.192274195 0.087923980 0.162394627 0.016527611 0.127884485
13 14 15 16 17 18
-0.008356157 0.022281729 0.040757330 0.028998910 0.071362838 0.012976515
19 20 21 22 23 24
-0.177564646 -0.236071085 0.111196596 0.081558084 -0.103435117 -0.004195738
25 26 27 28 29 30
-0.089542583 -0.001202939 0.055362295 -0.048995866 0.229116200 -0.211907131
31 32 33 34 35 36
-0.263711770 -0.006363599 -0.152054392 -0.200701278 0.140599001 -0.120296855
37 38 39 40 41 42
-0.032712062 -0.097357491 -0.089948689 -0.121101708 -0.262343909 -0.099257251
43 44 45 46 47 48
0.349488652 0.157520482 -0.047066183 -0.043251434 -0.053691495 -0.003391892
49 50 51 52 53 54
0.009230542 0.274113772 -0.052831444 0.162797961 0.042544626 0.022047611
55 56
0.281763805 -0.107359993
> postscript(file="/var/www/html/rcomp/tmp/654kq1261060159.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 0.121380259 NA
1 -0.197835070 0.121380259
2 0.046660508 -0.197835070
3 -0.021699298 0.046660508
4 -0.080679756 -0.021699298
5 0.276140257 -0.080679756
6 -0.189976041 0.276140257
7 0.192274195 -0.189976041
8 0.087923980 0.192274195
9 0.162394627 0.087923980
10 0.016527611 0.162394627
11 0.127884485 0.016527611
12 -0.008356157 0.127884485
13 0.022281729 -0.008356157
14 0.040757330 0.022281729
15 0.028998910 0.040757330
16 0.071362838 0.028998910
17 0.012976515 0.071362838
18 -0.177564646 0.012976515
19 -0.236071085 -0.177564646
20 0.111196596 -0.236071085
21 0.081558084 0.111196596
22 -0.103435117 0.081558084
23 -0.004195738 -0.103435117
24 -0.089542583 -0.004195738
25 -0.001202939 -0.089542583
26 0.055362295 -0.001202939
27 -0.048995866 0.055362295
28 0.229116200 -0.048995866
29 -0.211907131 0.229116200
30 -0.263711770 -0.211907131
31 -0.006363599 -0.263711770
32 -0.152054392 -0.006363599
33 -0.200701278 -0.152054392
34 0.140599001 -0.200701278
35 -0.120296855 0.140599001
36 -0.032712062 -0.120296855
37 -0.097357491 -0.032712062
38 -0.089948689 -0.097357491
39 -0.121101708 -0.089948689
40 -0.262343909 -0.121101708
41 -0.099257251 -0.262343909
42 0.349488652 -0.099257251
43 0.157520482 0.349488652
44 -0.047066183 0.157520482
45 -0.043251434 -0.047066183
46 -0.053691495 -0.043251434
47 -0.003391892 -0.053691495
48 0.009230542 -0.003391892
49 0.274113772 0.009230542
50 -0.052831444 0.274113772
51 0.162797961 -0.052831444
52 0.042544626 0.162797961
53 0.022047611 0.042544626
54 0.281763805 0.022047611
55 -0.107359993 0.281763805
56 NA -0.107359993
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.197835070 0.121380259
[2,] 0.046660508 -0.197835070
[3,] -0.021699298 0.046660508
[4,] -0.080679756 -0.021699298
[5,] 0.276140257 -0.080679756
[6,] -0.189976041 0.276140257
[7,] 0.192274195 -0.189976041
[8,] 0.087923980 0.192274195
[9,] 0.162394627 0.087923980
[10,] 0.016527611 0.162394627
[11,] 0.127884485 0.016527611
[12,] -0.008356157 0.127884485
[13,] 0.022281729 -0.008356157
[14,] 0.040757330 0.022281729
[15,] 0.028998910 0.040757330
[16,] 0.071362838 0.028998910
[17,] 0.012976515 0.071362838
[18,] -0.177564646 0.012976515
[19,] -0.236071085 -0.177564646
[20,] 0.111196596 -0.236071085
[21,] 0.081558084 0.111196596
[22,] -0.103435117 0.081558084
[23,] -0.004195738 -0.103435117
[24,] -0.089542583 -0.004195738
[25,] -0.001202939 -0.089542583
[26,] 0.055362295 -0.001202939
[27,] -0.048995866 0.055362295
[28,] 0.229116200 -0.048995866
[29,] -0.211907131 0.229116200
[30,] -0.263711770 -0.211907131
[31,] -0.006363599 -0.263711770
[32,] -0.152054392 -0.006363599
[33,] -0.200701278 -0.152054392
[34,] 0.140599001 -0.200701278
[35,] -0.120296855 0.140599001
[36,] -0.032712062 -0.120296855
[37,] -0.097357491 -0.032712062
[38,] -0.089948689 -0.097357491
[39,] -0.121101708 -0.089948689
[40,] -0.262343909 -0.121101708
[41,] -0.099257251 -0.262343909
[42,] 0.349488652 -0.099257251
[43,] 0.157520482 0.349488652
[44,] -0.047066183 0.157520482
[45,] -0.043251434 -0.047066183
[46,] -0.053691495 -0.043251434
[47,] -0.003391892 -0.053691495
[48,] 0.009230542 -0.003391892
[49,] 0.274113772 0.009230542
[50,] -0.052831444 0.274113772
[51,] 0.162797961 -0.052831444
[52,] 0.042544626 0.162797961
[53,] 0.022047611 0.042544626
[54,] 0.281763805 0.022047611
[55,] -0.107359993 0.281763805
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.197835070 0.121380259
2 0.046660508 -0.197835070
3 -0.021699298 0.046660508
4 -0.080679756 -0.021699298
5 0.276140257 -0.080679756
6 -0.189976041 0.276140257
7 0.192274195 -0.189976041
8 0.087923980 0.192274195
9 0.162394627 0.087923980
10 0.016527611 0.162394627
11 0.127884485 0.016527611
12 -0.008356157 0.127884485
13 0.022281729 -0.008356157
14 0.040757330 0.022281729
15 0.028998910 0.040757330
16 0.071362838 0.028998910
17 0.012976515 0.071362838
18 -0.177564646 0.012976515
19 -0.236071085 -0.177564646
20 0.111196596 -0.236071085
21 0.081558084 0.111196596
22 -0.103435117 0.081558084
23 -0.004195738 -0.103435117
24 -0.089542583 -0.004195738
25 -0.001202939 -0.089542583
26 0.055362295 -0.001202939
27 -0.048995866 0.055362295
28 0.229116200 -0.048995866
29 -0.211907131 0.229116200
30 -0.263711770 -0.211907131
31 -0.006363599 -0.263711770
32 -0.152054392 -0.006363599
33 -0.200701278 -0.152054392
34 0.140599001 -0.200701278
35 -0.120296855 0.140599001
36 -0.032712062 -0.120296855
37 -0.097357491 -0.032712062
38 -0.089948689 -0.097357491
39 -0.121101708 -0.089948689
40 -0.262343909 -0.121101708
41 -0.099257251 -0.262343909
42 0.349488652 -0.099257251
43 0.157520482 0.349488652
44 -0.047066183 0.157520482
45 -0.043251434 -0.047066183
46 -0.053691495 -0.043251434
47 -0.003391892 -0.053691495
48 0.009230542 -0.003391892
49 0.274113772 0.009230542
50 -0.052831444 0.274113772
51 0.162797961 -0.052831444
52 0.042544626 0.162797961
53 0.022047611 0.042544626
54 0.281763805 0.022047611
55 -0.107359993 0.281763805
> 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/759pk1261060159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zqz31261060159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fnni1261060159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zmy11261060159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yac21261060159.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/1297js1261060159.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/130gsb1261060159.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/14oetv1261060159.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/15h0x91261060159.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/160s571261060159.tab")
+ }
>
> try(system("convert tmp/16x1u1261060159.ps tmp/16x1u1261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/25yfc1261060159.ps tmp/25yfc1261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/36tcx1261060159.ps tmp/36tcx1261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hot51261060159.ps tmp/4hot51261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/55spm1261060159.ps tmp/55spm1261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/654kq1261060159.ps tmp/654kq1261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/759pk1261060159.ps tmp/759pk1261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zqz31261060159.ps tmp/8zqz31261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fnni1261060159.ps tmp/9fnni1261060159.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zmy11261060159.ps tmp/10zmy11261060159.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.359 1.549 3.385