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
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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(7.6
+ ,0
+ ,7.5
+ ,7.7
+ ,8.1
+ ,8
+ ,7.8
+ ,0
+ ,7.6
+ ,7.5
+ ,7.7
+ ,8.1
+ ,7.8
+ ,0
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.7
+ ,7.8
+ ,0
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.5
+ ,0
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,0
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.1
+ ,0
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.5
+ ,0
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.5
+ ,0
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.6
+ ,0
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.7
+ ,0
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.7
+ ,0
+ ,7.7
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.9
+ ,0
+ ,7.7
+ ,7.7
+ ,7.6
+ ,7.5
+ ,8.1
+ ,0
+ ,7.9
+ ,7.7
+ ,7.7
+ ,7.6
+ ,8.2
+ ,0
+ ,8.1
+ ,7.9
+ ,7.7
+ ,7.7
+ ,8.2
+ ,0
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.7
+ ,8.2
+ ,0
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.9
+ ,0
+ ,8.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.3
+ ,0
+ ,7.9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,6.9
+ ,0
+ ,7.3
+ ,7.9
+ ,8.2
+ ,8.2
+ ,6.6
+ ,0
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,6.7
+ ,0
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7.9
+ ,6.9
+ ,0
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7
+ ,0
+ ,6.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.1
+ ,0
+ ,7
+ ,6.9
+ ,6.7
+ ,6.6
+ ,7.2
+ ,0
+ ,7.1
+ ,7
+ ,6.9
+ ,6.7
+ ,7.1
+ ,0
+ ,7.2
+ ,7.1
+ ,7
+ ,6.9
+ ,6.9
+ ,0
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,7
+ ,0
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.8
+ ,0
+ ,7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.4
+ ,0
+ ,6.8
+ ,7
+ ,6.9
+ ,7.1
+ ,6.7
+ ,0
+ ,6.4
+ ,6.8
+ ,7
+ ,6.9
+ ,6.6
+ ,0
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7
+ ,6.4
+ ,0
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,6.3
+ ,0
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.2
+ ,0
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.5
+ ,0
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.8
+ ,1
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.8
+ ,1
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,1
+ ,6.8
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.1
+ ,1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.5
+ ,5.8
+ ,1
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.1
+ ,1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.2
+ ,1
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,7.3
+ ,1
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.9
+ ,1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,1
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.2
+ ,1
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.1
+ ,1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.7
+ ,1
+ ,7.1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,7.9
+ ,1
+ ,7.7
+ ,7.1
+ ,6.2
+ ,5.8
+ ,7.7
+ ,1
+ ,7.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,7.4
+ ,1
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,7.5
+ ,1
+ ,7.4
+ ,7.7
+ ,7.9
+ ,7.7
+ ,8
+ ,1
+ ,7.5
+ ,7.4
+ ,7.7
+ ,7.9
+ ,8.1
+ ,1
+ ,8
+ ,7.5
+ ,7.4
+ ,7.7)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y-1'
+ ,'Y-2'
+ ,'Y-3'
+ ,'Y-4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Y','X','Y-1','Y-2','Y-3','Y-4'),1:57))
> 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 Y-1 Y-2 Y-3 Y-4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.6 0 7.5 7.7 8.1 8.0 1 0 0 0 0 0 0 0 0 0 0 1
2 7.8 0 7.6 7.5 7.7 8.1 0 1 0 0 0 0 0 0 0 0 0 2
3 7.8 0 7.8 7.6 7.5 7.7 0 0 1 0 0 0 0 0 0 0 0 3
4 7.8 0 7.8 7.8 7.6 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 7.5 0 7.8 7.8 7.8 7.6 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 0 7.5 7.8 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.1 0 7.5 7.5 7.8 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 0 7.1 7.5 7.5 7.8 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 0 7.5 7.1 7.5 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.6 0 7.5 7.5 7.1 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.7 0 7.6 7.5 7.5 7.1 0 0 0 0 0 0 0 0 0 0 1 11
12 7.7 0 7.7 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 7.9 0 7.7 7.7 7.6 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.1 0 7.9 7.7 7.7 7.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.2 0 8.1 7.9 7.7 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 0 8.2 8.1 7.9 7.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 0 8.2 8.2 8.1 7.9 0 0 0 0 1 0 0 0 0 0 0 17
18 7.9 0 8.2 8.2 8.2 8.1 0 0 0 0 0 1 0 0 0 0 0 18
19 7.3 0 7.9 8.2 8.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 6.9 0 7.3 7.9 8.2 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 6.6 0 6.9 7.3 7.9 8.2 0 0 0 0 0 0 0 0 1 0 0 21
22 6.7 0 6.6 6.9 7.3 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 6.9 0 6.7 6.6 6.9 7.3 0 0 0 0 0 0 0 0 0 0 1 23
24 7.0 0 6.9 6.7 6.6 6.9 0 0 0 0 0 0 0 0 0 0 0 24
25 7.1 0 7.0 6.9 6.7 6.6 1 0 0 0 0 0 0 0 0 0 0 25
26 7.2 0 7.1 7.0 6.9 6.7 0 1 0 0 0 0 0 0 0 0 0 26
27 7.1 0 7.2 7.1 7.0 6.9 0 0 1 0 0 0 0 0 0 0 0 27
28 6.9 0 7.1 7.2 7.1 7.0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.0 0 6.9 7.1 7.2 7.1 0 0 0 0 1 0 0 0 0 0 0 29
30 6.8 0 7.0 6.9 7.1 7.2 0 0 0 0 0 1 0 0 0 0 0 30
31 6.4 0 6.8 7.0 6.9 7.1 0 0 0 0 0 0 1 0 0 0 0 31
32 6.7 0 6.4 6.8 7.0 6.9 0 0 0 0 0 0 0 1 0 0 0 32
33 6.6 0 6.7 6.4 6.8 7.0 0 0 0 0 0 0 0 0 1 0 0 33
34 6.4 0 6.6 6.7 6.4 6.8 0 0 0 0 0 0 0 0 0 1 0 34
35 6.3 0 6.4 6.6 6.7 6.4 0 0 0 0 0 0 0 0 0 0 1 35
36 6.2 0 6.3 6.4 6.6 6.7 0 0 0 0 0 0 0 0 0 0 0 36
37 6.5 0 6.2 6.3 6.4 6.6 1 0 0 0 0 0 0 0 0 0 0 37
38 6.8 1 6.5 6.2 6.3 6.4 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 1 6.8 6.5 6.2 6.3 0 0 1 0 0 0 0 0 0 0 0 39
40 6.4 1 6.8 6.8 6.5 6.2 0 0 0 1 0 0 0 0 0 0 0 40
41 6.1 1 6.4 6.8 6.8 6.5 0 0 0 0 1 0 0 0 0 0 0 41
42 5.8 1 6.1 6.4 6.8 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.1 1 5.8 6.1 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 1 6.1 5.8 6.1 6.4 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 1 7.2 6.1 5.8 6.1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 1 7.3 7.2 6.1 5.8 0 0 0 0 0 0 0 0 0 1 0 46
47 6.1 1 6.9 7.3 7.2 6.1 0 0 0 0 0 0 0 0 0 0 1 47
48 5.8 1 6.1 6.9 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.2 1 5.8 6.1 6.9 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 1 6.2 5.8 6.1 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 1 7.1 6.2 5.8 6.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 1 7.7 7.1 6.2 5.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 1 7.9 7.7 7.1 6.2 0 0 0 0 1 0 0 0 0 0 0 53
54 7.4 1 7.7 7.9 7.7 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.5 1 7.4 7.7 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.0 1 7.5 7.4 7.7 7.9 0 0 0 0 0 0 0 1 0 0 0 56
57 8.1 1 8.0 7.5 7.4 7.7 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `Y-1` `Y-2` `Y-3` `Y-4`
-0.206619 0.083582 1.528903 -0.711924 -0.264801 0.461488
M1 M2 M3 M4 M5 M6
0.266355 0.155434 -0.023335 0.058274 0.113139 -0.076282
M7 M8 M9 M10 M11 t
-0.117538 0.410083 -0.348315 -0.068082 0.099621 0.001383
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.480744 -0.111492 0.008518 0.094274 0.370923
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.206619 0.544443 -0.380 0.70637
X 0.083582 0.105695 0.791 0.43385
`Y-1` 1.528903 0.145638 10.498 6.35e-13 ***
`Y-2` -0.711924 0.278027 -2.561 0.01443 *
`Y-3` -0.264801 0.277183 -0.955 0.34530
`Y-4` 0.461488 0.153193 3.012 0.00453 **
M1 0.266355 0.138643 1.921 0.06204 .
M2 0.155434 0.146075 1.064 0.29384
M3 -0.023335 0.148041 -0.158 0.87557
M4 0.058274 0.146148 0.399 0.69227
M5 0.113139 0.145224 0.779 0.44064
M6 -0.076282 0.141797 -0.538 0.59366
M7 -0.117538 0.140423 -0.837 0.40768
M8 0.410083 0.139288 2.944 0.00543 **
M9 -0.348315 0.159101 -2.189 0.03463 *
M10 -0.068082 0.168330 -0.404 0.68809
M11 0.099621 0.157438 0.633 0.53058
t 0.001383 0.003385 0.408 0.68516
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2032 on 39 degrees of freedom
Multiple R-squared: 0.934, Adjusted R-squared: 0.9052
F-statistic: 32.47 on 17 and 39 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,] 0.8669316 0.2661368 0.1330684
[2,] 0.8110531 0.3778938 0.1889469
[3,] 0.7814105 0.4371790 0.2185895
[4,] 0.7216307 0.5567386 0.2783693
[5,] 0.7002028 0.5995944 0.2997972
[6,] 0.5998214 0.8003572 0.4001786
[7,] 0.4891021 0.9782043 0.5108979
[8,] 0.3959257 0.7918514 0.6040743
[9,] 0.6373854 0.7252291 0.3626146
[10,] 0.6183185 0.7633630 0.3816815
[11,] 0.6407857 0.7184287 0.3592143
[12,] 0.6397947 0.7204107 0.3602053
[13,] 0.5184838 0.9630324 0.4815162
[14,] 0.4468549 0.8937098 0.5531451
[15,] 0.6879986 0.6240028 0.3120014
[16,] 0.8665833 0.2668334 0.1334167
> postscript(file="/var/www/html/rcomp/tmp/1agb91259314046.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xeio1259314046.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hp511259314047.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ixnp1259314047.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tq0q1259314047.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 = 57
Frequency = 1
1 2 3 4 5 6
0.006910807 -0.130895278 -0.056462288 0.121709319 -0.227727352 0.326683239
7 8 9 10 11 12
-0.247020542 0.156096098 0.155227524 0.152460229 0.221000128 0.052945019
13 14 15 16 17 18
0.082879287 0.066968313 0.134809803 0.094273602 0.069880537 -0.107899536
19 20 21 22 23 24
-0.255504082 -0.480743931 0.081237887 0.053088174 -0.111492372 -0.042687423
25 26 27 28 29 30
-0.056004496 0.078647317 0.008517902 -0.070059150 0.188612156 -0.191254410
31 32 33 34 35 36
-0.181219324 0.177730503 -0.007803322 -0.136575142 0.092962988 -0.063220184
37 38 39 40 41 42
0.043928001 -0.094161204 -0.142199983 -0.286024570 -0.089717776 -0.166225743
43 44 45 46 47 48
0.312820841 0.316723707 -0.235469920 -0.068973261 -0.202470744 0.052962588
49 50 51 52 53 54
-0.077713599 0.079440852 0.055334565 0.140100800 0.058952436 0.138696451
55 56 57
0.370923107 -0.169806377 0.006807831
> postscript(file="/var/www/html/rcomp/tmp/6j4hm1259314047.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 0.006910807 NA
1 -0.130895278 0.006910807
2 -0.056462288 -0.130895278
3 0.121709319 -0.056462288
4 -0.227727352 0.121709319
5 0.326683239 -0.227727352
6 -0.247020542 0.326683239
7 0.156096098 -0.247020542
8 0.155227524 0.156096098
9 0.152460229 0.155227524
10 0.221000128 0.152460229
11 0.052945019 0.221000128
12 0.082879287 0.052945019
13 0.066968313 0.082879287
14 0.134809803 0.066968313
15 0.094273602 0.134809803
16 0.069880537 0.094273602
17 -0.107899536 0.069880537
18 -0.255504082 -0.107899536
19 -0.480743931 -0.255504082
20 0.081237887 -0.480743931
21 0.053088174 0.081237887
22 -0.111492372 0.053088174
23 -0.042687423 -0.111492372
24 -0.056004496 -0.042687423
25 0.078647317 -0.056004496
26 0.008517902 0.078647317
27 -0.070059150 0.008517902
28 0.188612156 -0.070059150
29 -0.191254410 0.188612156
30 -0.181219324 -0.191254410
31 0.177730503 -0.181219324
32 -0.007803322 0.177730503
33 -0.136575142 -0.007803322
34 0.092962988 -0.136575142
35 -0.063220184 0.092962988
36 0.043928001 -0.063220184
37 -0.094161204 0.043928001
38 -0.142199983 -0.094161204
39 -0.286024570 -0.142199983
40 -0.089717776 -0.286024570
41 -0.166225743 -0.089717776
42 0.312820841 -0.166225743
43 0.316723707 0.312820841
44 -0.235469920 0.316723707
45 -0.068973261 -0.235469920
46 -0.202470744 -0.068973261
47 0.052962588 -0.202470744
48 -0.077713599 0.052962588
49 0.079440852 -0.077713599
50 0.055334565 0.079440852
51 0.140100800 0.055334565
52 0.058952436 0.140100800
53 0.138696451 0.058952436
54 0.370923107 0.138696451
55 -0.169806377 0.370923107
56 0.006807831 -0.169806377
57 NA 0.006807831
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.130895278 0.006910807
[2,] -0.056462288 -0.130895278
[3,] 0.121709319 -0.056462288
[4,] -0.227727352 0.121709319
[5,] 0.326683239 -0.227727352
[6,] -0.247020542 0.326683239
[7,] 0.156096098 -0.247020542
[8,] 0.155227524 0.156096098
[9,] 0.152460229 0.155227524
[10,] 0.221000128 0.152460229
[11,] 0.052945019 0.221000128
[12,] 0.082879287 0.052945019
[13,] 0.066968313 0.082879287
[14,] 0.134809803 0.066968313
[15,] 0.094273602 0.134809803
[16,] 0.069880537 0.094273602
[17,] -0.107899536 0.069880537
[18,] -0.255504082 -0.107899536
[19,] -0.480743931 -0.255504082
[20,] 0.081237887 -0.480743931
[21,] 0.053088174 0.081237887
[22,] -0.111492372 0.053088174
[23,] -0.042687423 -0.111492372
[24,] -0.056004496 -0.042687423
[25,] 0.078647317 -0.056004496
[26,] 0.008517902 0.078647317
[27,] -0.070059150 0.008517902
[28,] 0.188612156 -0.070059150
[29,] -0.191254410 0.188612156
[30,] -0.181219324 -0.191254410
[31,] 0.177730503 -0.181219324
[32,] -0.007803322 0.177730503
[33,] -0.136575142 -0.007803322
[34,] 0.092962988 -0.136575142
[35,] -0.063220184 0.092962988
[36,] 0.043928001 -0.063220184
[37,] -0.094161204 0.043928001
[38,] -0.142199983 -0.094161204
[39,] -0.286024570 -0.142199983
[40,] -0.089717776 -0.286024570
[41,] -0.166225743 -0.089717776
[42,] 0.312820841 -0.166225743
[43,] 0.316723707 0.312820841
[44,] -0.235469920 0.316723707
[45,] -0.068973261 -0.235469920
[46,] -0.202470744 -0.068973261
[47,] 0.052962588 -0.202470744
[48,] -0.077713599 0.052962588
[49,] 0.079440852 -0.077713599
[50,] 0.055334565 0.079440852
[51,] 0.140100800 0.055334565
[52,] 0.058952436 0.140100800
[53,] 0.138696451 0.058952436
[54,] 0.370923107 0.138696451
[55,] -0.169806377 0.370923107
[56,] 0.006807831 -0.169806377
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.130895278 0.006910807
2 -0.056462288 -0.130895278
3 0.121709319 -0.056462288
4 -0.227727352 0.121709319
5 0.326683239 -0.227727352
6 -0.247020542 0.326683239
7 0.156096098 -0.247020542
8 0.155227524 0.156096098
9 0.152460229 0.155227524
10 0.221000128 0.152460229
11 0.052945019 0.221000128
12 0.082879287 0.052945019
13 0.066968313 0.082879287
14 0.134809803 0.066968313
15 0.094273602 0.134809803
16 0.069880537 0.094273602
17 -0.107899536 0.069880537
18 -0.255504082 -0.107899536
19 -0.480743931 -0.255504082
20 0.081237887 -0.480743931
21 0.053088174 0.081237887
22 -0.111492372 0.053088174
23 -0.042687423 -0.111492372
24 -0.056004496 -0.042687423
25 0.078647317 -0.056004496
26 0.008517902 0.078647317
27 -0.070059150 0.008517902
28 0.188612156 -0.070059150
29 -0.191254410 0.188612156
30 -0.181219324 -0.191254410
31 0.177730503 -0.181219324
32 -0.007803322 0.177730503
33 -0.136575142 -0.007803322
34 0.092962988 -0.136575142
35 -0.063220184 0.092962988
36 0.043928001 -0.063220184
37 -0.094161204 0.043928001
38 -0.142199983 -0.094161204
39 -0.286024570 -0.142199983
40 -0.089717776 -0.286024570
41 -0.166225743 -0.089717776
42 0.312820841 -0.166225743
43 0.316723707 0.312820841
44 -0.235469920 0.316723707
45 -0.068973261 -0.235469920
46 -0.202470744 -0.068973261
47 0.052962588 -0.202470744
48 -0.077713599 0.052962588
49 0.079440852 -0.077713599
50 0.055334565 0.079440852
51 0.140100800 0.055334565
52 0.058952436 0.140100800
53 0.138696451 0.058952436
54 0.370923107 0.138696451
55 -0.169806377 0.370923107
56 0.006807831 -0.169806377
> 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/7u5ma1259314047.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8chvp1259314047.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97jp71259314047.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106eh61259314047.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11csx11259314047.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/12bgnv1259314047.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/13azjg1259314047.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/14yhvm1259314047.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/15qw6i1259314047.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/16394y1259314047.tab")
+ }
>
> system("convert tmp/1agb91259314046.ps tmp/1agb91259314046.png")
> system("convert tmp/2xeio1259314046.ps tmp/2xeio1259314046.png")
> system("convert tmp/3hp511259314047.ps tmp/3hp511259314047.png")
> system("convert tmp/4ixnp1259314047.ps tmp/4ixnp1259314047.png")
> system("convert tmp/5tq0q1259314047.ps tmp/5tq0q1259314047.png")
> system("convert tmp/6j4hm1259314047.ps tmp/6j4hm1259314047.png")
> system("convert tmp/7u5ma1259314047.ps tmp/7u5ma1259314047.png")
> system("convert tmp/8chvp1259314047.ps tmp/8chvp1259314047.png")
> system("convert tmp/97jp71259314047.ps tmp/97jp71259314047.png")
> system("convert tmp/106eh61259314047.ps tmp/106eh61259314047.png")
>
>
> proc.time()
user system elapsed
2.326 1.537 2.820