R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(8.5
+ ,0
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.6
+ ,0
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.5
+ ,0
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,0
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.1
+ ,0
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,7.9
+ ,0
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,0
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,0
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.7
+ ,0
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.5
+ ,0
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.4
+ ,0
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,0
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,0
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,0
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.6
+ ,0
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.5
+ ,0
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.3
+ ,0
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8
+ ,0
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.2
+ ,0
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,0
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8.1
+ ,0
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,8
+ ,0
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,0
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,7.9
+ ,0
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,0
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,8
+ ,0
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,7.9
+ ,0
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,0
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.7
+ ,0
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.2
+ ,0
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.5
+ ,0
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7.3
+ ,0
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,0
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,0
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7
+ ,0
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.2
+ ,0
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,0
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,7.1
+ ,0
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.8
+ ,0
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.4
+ ,0
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.1
+ ,0
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,6.5
+ ,0
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.7
+ ,0
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.9
+ ,0
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,7.5
+ ,0
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.9
+ ,1
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.6
+ ,1
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9
+ ,6.9
+ ,1
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.7
+ ,1
+ ,7.7
+ ,6.9
+ ,6.6
+ ,6.9
+ ,8
+ ,1
+ ,8
+ ,7.7
+ ,6.9
+ ,6.6
+ ,8
+ ,1
+ ,8
+ ,8
+ ,7.7
+ ,6.9
+ ,7.7
+ ,1
+ ,7.7
+ ,8
+ ,8
+ ,7.7
+ ,7.3
+ ,1
+ ,7.3
+ ,7.7
+ ,8
+ ,8
+ ,7.4
+ ,1
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8
+ ,8.1
+ ,1
+ ,8.1
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8.3
+ ,1
+ ,8.3
+ ,8.1
+ ,7.4
+ ,7.3
+ ,8.2
+ ,1
+ ,8.2
+ ,8.3
+ ,8.1
+ ,7.4)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),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 Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.5 0 8.5 8.3 8.2 8.7 1 0 0 0 0 0 0 0 0 0 0 1
2 8.6 0 8.6 8.5 8.3 8.2 0 1 0 0 0 0 0 0 0 0 0 2
3 8.5 0 8.5 8.6 8.5 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.2 0 8.2 8.5 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 4
5 8.1 0 8.1 8.2 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 5
6 7.9 0 7.9 8.1 8.2 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 8.6 0 8.6 7.9 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 7
8 8.7 0 8.7 8.6 7.9 8.1 0 0 0 0 0 0 0 1 0 0 0 8
9 8.7 0 8.7 8.7 8.6 7.9 0 0 0 0 0 0 0 0 1 0 0 9
10 8.5 0 8.5 8.7 8.7 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.4 0 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 0 0 0 1 11
12 8.5 0 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 0 0 0 12
13 8.7 0 8.7 8.5 8.4 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 0 8.7 8.7 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 0 8.6 8.7 8.7 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 0 8.5 8.6 8.7 8.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.3 0 8.3 8.5 8.6 8.7 0 0 0 0 1 0 0 0 0 0 0 17
18 8.0 0 8.0 8.3 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 0 8.2 8.0 8.3 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.1 0 8.1 8.2 8.0 8.3 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 0 8.1 8.1 8.2 8.0 0 0 0 0 0 0 0 0 1 0 0 21
22 8.0 0 8.0 8.1 8.1 8.2 0 0 0 0 0 0 0 0 0 1 0 22
23 7.9 0 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 7.9 0 7.9 7.9 8.0 8.1 0 0 0 0 0 0 0 0 0 0 0 24
25 8.0 0 8.0 7.9 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 0 8.0 8.0 7.9 7.9 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 0 7.9 8.0 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 0 8.0 7.9 8.0 8.0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.7 0 7.7 8.0 7.9 8.0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.2 0 7.2 7.7 8.0 7.9 0 0 0 0 0 1 0 0 0 0 0 30
31 7.5 0 7.5 7.2 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.3 0 7.3 7.5 7.2 7.7 0 0 0 0 0 0 0 1 0 0 0 32
33 7.0 0 7.0 7.3 7.5 7.2 0 0 0 0 0 0 0 0 1 0 0 33
34 7.0 0 7.0 7.0 7.3 7.5 0 0 0 0 0 0 0 0 0 1 0 34
35 7.0 0 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 0 0 0 1 35
36 7.2 0 7.2 7.0 7.0 7.0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.3 0 7.3 7.2 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.1 0 7.1 7.3 7.2 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 0 6.8 7.1 7.3 7.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.4 0 6.4 6.8 7.1 7.3 0 0 0 1 0 0 0 0 0 0 0 40
41 6.1 0 6.1 6.4 6.8 7.1 0 0 0 0 1 0 0 0 0 0 0 41
42 6.5 0 6.5 6.1 6.4 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 7.7 0 7.7 6.5 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 7.9 0 7.9 7.7 6.5 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 7.5 0 7.5 7.9 7.7 6.5 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 1 6.9 7.5 7.9 7.7 0 0 0 0 0 0 0 0 0 1 0 46
47 6.6 1 6.6 6.9 7.5 7.9 0 0 0 0 0 0 0 0 0 0 1 47
48 6.9 1 6.9 6.6 6.9 7.5 0 0 0 0 0 0 0 0 0 0 0 48
49 7.7 1 7.7 6.9 6.6 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 8.0 1 8.0 7.7 6.9 6.6 0 1 0 0 0 0 0 0 0 0 0 50
51 8.0 1 8.0 8.0 7.7 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 1 7.7 8.0 8.0 7.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 1 7.3 7.7 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 53
54 7.4 1 7.4 7.3 7.7 8.0 0 0 0 0 0 1 0 0 0 0 0 54
55 8.1 1 8.1 7.4 7.3 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.3 1 8.3 8.1 7.4 7.3 0 0 0 0 0 0 0 1 0 0 0 56
57 8.2 1 8.2 8.3 8.1 7.4 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 Y1 Y2 Y3 Y4
-3.038e-17 1.443e-16 1.000e+00 1.804e-16 -1.349e-16 1.252e-17
M1 M2 M3 M4 M5 M6
-1.187e-17 -4.295e-17 1.711e-16 -2.533e-17 -3.454e-17 -1.266e-17
M7 M8 M9 M10 M11 t
7.214e-17 -3.621e-17 -2.763e-17 -3.295e-17 -1.649e-17 -4.391e-18
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.861e-16 -4.728e-17 -2.499e-18 3.002e-17 6.576e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.038e-17 5.917e-16 -5.100e-02 0.9593
X 1.443e-16 8.528e-17 1.692e+00 0.0987 .
Y1 1.000e+00 1.247e-16 8.017e+15 <2e-16 ***
Y2 1.804e-16 2.301e-16 7.840e-01 0.4378
Y3 -1.349e-16 2.266e-16 -5.950e-01 0.5552
Y4 1.252e-17 1.235e-16 1.010e-01 0.9198
M1 -1.187e-17 9.284e-17 -1.280e-01 0.8989
M2 -4.295e-17 1.010e-16 -4.250e-01 0.6730
M3 1.711e-16 1.005e-16 1.703e+00 0.0966 .
M4 -2.533e-17 9.587e-17 -2.640e-01 0.7930
M5 -3.454e-17 9.666e-17 -3.570e-01 0.7227
M6 -1.266e-17 9.145e-17 -1.380e-01 0.8906
M7 7.214e-17 1.049e-16 6.880e-01 0.4958
M8 -3.621e-17 1.329e-16 -2.720e-01 0.7867
M9 -2.763e-17 1.168e-16 -2.360e-01 0.8143
M10 -3.295e-17 1.005e-16 -3.280e-01 0.7447
M11 -1.649e-17 9.683e-17 -1.700e-01 0.8656
t -4.391e-18 3.251e-18 -1.351e+00 0.1846
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.325e-16 on 39 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 8.207e+31 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,] 1.582948e-01 3.165897e-01 0.84170516
[2,] 4.725455e-01 9.450909e-01 0.52745454
[3,] 1.133390e-02 2.266781e-02 0.98866610
[4,] 3.272510e-03 6.545020e-03 0.99672749
[5,] 3.154557e-05 6.309113e-05 0.99996845
[6,] 8.006614e-01 3.986772e-01 0.19933862
[7,] 4.174464e-13 8.348928e-13 1.00000000
[8,] 8.487692e-06 1.697538e-05 0.99999151
[9,] 1.033626e-04 2.067252e-04 0.99989664
[10,] 0.000000e+00 0.000000e+00 1.00000000
[11,] 3.042155e-20 6.084309e-20 1.00000000
[12,] 8.870695e-01 2.258610e-01 0.11293048
[13,] 3.595124e-05 7.190249e-05 0.99996405
[14,] 9.801764e-01 3.964712e-02 0.01982356
[15,] 1.926021e-02 3.852043e-02 0.98073979
[16,] 1.061076e-04 2.122152e-04 0.99989389
> postscript(file="/var/www/html/rcomp/tmp/1aafu1260889681.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2klpx1260889681.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37z491260889681.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ji391260889681.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v0ds1260889681.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
-7.699138e-17 -6.437094e-17 6.575649e-16 -5.678660e-17 -6.348766e-18
6 7 8 9 10
-6.399367e-17 -3.079385e-17 -1.507796e-16 -7.608964e-17 -1.125442e-17
11 12 13 14 15
-1.879732e-17 -9.525700e-18 -7.269123e-17 -5.856142e-17 -1.617567e-16
16 17 18 19 20
2.425877e-17 3.731548e-17 1.316871e-18 -3.864503e-17 -2.499322e-18
21 22 23 24 25
4.208873e-17 2.631376e-17 2.405165e-17 1.650278e-17 3.002352e-17
26 27 28 29 30
4.870689e-17 -1.569287e-16 7.013777e-17 2.374426e-17 -2.091571e-17
31 32 33 34 35
3.809176e-17 8.343390e-17 -3.989059e-17 -6.791972e-18 -5.681290e-17
36 37 38 39 40
-4.542771e-18 1.169702e-16 1.007602e-16 -1.527308e-16 -4.727876e-17
41 42 43 44 45
-9.038814e-17 1.727867e-17 2.641712e-17 -6.478859e-19 9.183654e-17
46 47 48 49 50
-8.267366e-18 5.155857e-17 -2.434305e-18 2.688850e-18 -2.653474e-17
51 52 53 54 55
-1.861487e-16 9.668819e-18 3.567716e-17 6.631384e-17 4.929986e-18
56 57
7.049293e-17 -1.794505e-17
> postscript(file="/var/www/html/rcomp/tmp/69giy1260889681.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 -7.699138e-17 NA
1 -6.437094e-17 -7.699138e-17
2 6.575649e-16 -6.437094e-17
3 -5.678660e-17 6.575649e-16
4 -6.348766e-18 -5.678660e-17
5 -6.399367e-17 -6.348766e-18
6 -3.079385e-17 -6.399367e-17
7 -1.507796e-16 -3.079385e-17
8 -7.608964e-17 -1.507796e-16
9 -1.125442e-17 -7.608964e-17
10 -1.879732e-17 -1.125442e-17
11 -9.525700e-18 -1.879732e-17
12 -7.269123e-17 -9.525700e-18
13 -5.856142e-17 -7.269123e-17
14 -1.617567e-16 -5.856142e-17
15 2.425877e-17 -1.617567e-16
16 3.731548e-17 2.425877e-17
17 1.316871e-18 3.731548e-17
18 -3.864503e-17 1.316871e-18
19 -2.499322e-18 -3.864503e-17
20 4.208873e-17 -2.499322e-18
21 2.631376e-17 4.208873e-17
22 2.405165e-17 2.631376e-17
23 1.650278e-17 2.405165e-17
24 3.002352e-17 1.650278e-17
25 4.870689e-17 3.002352e-17
26 -1.569287e-16 4.870689e-17
27 7.013777e-17 -1.569287e-16
28 2.374426e-17 7.013777e-17
29 -2.091571e-17 2.374426e-17
30 3.809176e-17 -2.091571e-17
31 8.343390e-17 3.809176e-17
32 -3.989059e-17 8.343390e-17
33 -6.791972e-18 -3.989059e-17
34 -5.681290e-17 -6.791972e-18
35 -4.542771e-18 -5.681290e-17
36 1.169702e-16 -4.542771e-18
37 1.007602e-16 1.169702e-16
38 -1.527308e-16 1.007602e-16
39 -4.727876e-17 -1.527308e-16
40 -9.038814e-17 -4.727876e-17
41 1.727867e-17 -9.038814e-17
42 2.641712e-17 1.727867e-17
43 -6.478859e-19 2.641712e-17
44 9.183654e-17 -6.478859e-19
45 -8.267366e-18 9.183654e-17
46 5.155857e-17 -8.267366e-18
47 -2.434305e-18 5.155857e-17
48 2.688850e-18 -2.434305e-18
49 -2.653474e-17 2.688850e-18
50 -1.861487e-16 -2.653474e-17
51 9.668819e-18 -1.861487e-16
52 3.567716e-17 9.668819e-18
53 6.631384e-17 3.567716e-17
54 4.929986e-18 6.631384e-17
55 7.049293e-17 4.929986e-18
56 -1.794505e-17 7.049293e-17
57 NA -1.794505e-17
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.437094e-17 -7.699138e-17
[2,] 6.575649e-16 -6.437094e-17
[3,] -5.678660e-17 6.575649e-16
[4,] -6.348766e-18 -5.678660e-17
[5,] -6.399367e-17 -6.348766e-18
[6,] -3.079385e-17 -6.399367e-17
[7,] -1.507796e-16 -3.079385e-17
[8,] -7.608964e-17 -1.507796e-16
[9,] -1.125442e-17 -7.608964e-17
[10,] -1.879732e-17 -1.125442e-17
[11,] -9.525700e-18 -1.879732e-17
[12,] -7.269123e-17 -9.525700e-18
[13,] -5.856142e-17 -7.269123e-17
[14,] -1.617567e-16 -5.856142e-17
[15,] 2.425877e-17 -1.617567e-16
[16,] 3.731548e-17 2.425877e-17
[17,] 1.316871e-18 3.731548e-17
[18,] -3.864503e-17 1.316871e-18
[19,] -2.499322e-18 -3.864503e-17
[20,] 4.208873e-17 -2.499322e-18
[21,] 2.631376e-17 4.208873e-17
[22,] 2.405165e-17 2.631376e-17
[23,] 1.650278e-17 2.405165e-17
[24,] 3.002352e-17 1.650278e-17
[25,] 4.870689e-17 3.002352e-17
[26,] -1.569287e-16 4.870689e-17
[27,] 7.013777e-17 -1.569287e-16
[28,] 2.374426e-17 7.013777e-17
[29,] -2.091571e-17 2.374426e-17
[30,] 3.809176e-17 -2.091571e-17
[31,] 8.343390e-17 3.809176e-17
[32,] -3.989059e-17 8.343390e-17
[33,] -6.791972e-18 -3.989059e-17
[34,] -5.681290e-17 -6.791972e-18
[35,] -4.542771e-18 -5.681290e-17
[36,] 1.169702e-16 -4.542771e-18
[37,] 1.007602e-16 1.169702e-16
[38,] -1.527308e-16 1.007602e-16
[39,] -4.727876e-17 -1.527308e-16
[40,] -9.038814e-17 -4.727876e-17
[41,] 1.727867e-17 -9.038814e-17
[42,] 2.641712e-17 1.727867e-17
[43,] -6.478859e-19 2.641712e-17
[44,] 9.183654e-17 -6.478859e-19
[45,] -8.267366e-18 9.183654e-17
[46,] 5.155857e-17 -8.267366e-18
[47,] -2.434305e-18 5.155857e-17
[48,] 2.688850e-18 -2.434305e-18
[49,] -2.653474e-17 2.688850e-18
[50,] -1.861487e-16 -2.653474e-17
[51,] 9.668819e-18 -1.861487e-16
[52,] 3.567716e-17 9.668819e-18
[53,] 6.631384e-17 3.567716e-17
[54,] 4.929986e-18 6.631384e-17
[55,] 7.049293e-17 4.929986e-18
[56,] -1.794505e-17 7.049293e-17
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.437094e-17 -7.699138e-17
2 6.575649e-16 -6.437094e-17
3 -5.678660e-17 6.575649e-16
4 -6.348766e-18 -5.678660e-17
5 -6.399367e-17 -6.348766e-18
6 -3.079385e-17 -6.399367e-17
7 -1.507796e-16 -3.079385e-17
8 -7.608964e-17 -1.507796e-16
9 -1.125442e-17 -7.608964e-17
10 -1.879732e-17 -1.125442e-17
11 -9.525700e-18 -1.879732e-17
12 -7.269123e-17 -9.525700e-18
13 -5.856142e-17 -7.269123e-17
14 -1.617567e-16 -5.856142e-17
15 2.425877e-17 -1.617567e-16
16 3.731548e-17 2.425877e-17
17 1.316871e-18 3.731548e-17
18 -3.864503e-17 1.316871e-18
19 -2.499322e-18 -3.864503e-17
20 4.208873e-17 -2.499322e-18
21 2.631376e-17 4.208873e-17
22 2.405165e-17 2.631376e-17
23 1.650278e-17 2.405165e-17
24 3.002352e-17 1.650278e-17
25 4.870689e-17 3.002352e-17
26 -1.569287e-16 4.870689e-17
27 7.013777e-17 -1.569287e-16
28 2.374426e-17 7.013777e-17
29 -2.091571e-17 2.374426e-17
30 3.809176e-17 -2.091571e-17
31 8.343390e-17 3.809176e-17
32 -3.989059e-17 8.343390e-17
33 -6.791972e-18 -3.989059e-17
34 -5.681290e-17 -6.791972e-18
35 -4.542771e-18 -5.681290e-17
36 1.169702e-16 -4.542771e-18
37 1.007602e-16 1.169702e-16
38 -1.527308e-16 1.007602e-16
39 -4.727876e-17 -1.527308e-16
40 -9.038814e-17 -4.727876e-17
41 1.727867e-17 -9.038814e-17
42 2.641712e-17 1.727867e-17
43 -6.478859e-19 2.641712e-17
44 9.183654e-17 -6.478859e-19
45 -8.267366e-18 9.183654e-17
46 5.155857e-17 -8.267366e-18
47 -2.434305e-18 5.155857e-17
48 2.688850e-18 -2.434305e-18
49 -2.653474e-17 2.688850e-18
50 -1.861487e-16 -2.653474e-17
51 9.668819e-18 -1.861487e-16
52 3.567716e-17 9.668819e-18
53 6.631384e-17 3.567716e-17
54 4.929986e-18 6.631384e-17
55 7.049293e-17 4.929986e-18
56 -1.794505e-17 7.049293e-17
> 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/72sqz1260889681.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jwcv1260889681.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xos81260889681.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gzee1260889681.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qhlx1260889681.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/12sfpo1260889681.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/1383ht1260889681.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/1498k61260889681.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/15nsb41260889681.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/16mkpy1260889681.tab")
+ }
>
> try(system("convert tmp/1aafu1260889681.ps tmp/1aafu1260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/2klpx1260889681.ps tmp/2klpx1260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/37z491260889681.ps tmp/37z491260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ji391260889681.ps tmp/4ji391260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v0ds1260889681.ps tmp/5v0ds1260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/69giy1260889681.ps tmp/69giy1260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/72sqz1260889681.ps tmp/72sqz1260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jwcv1260889681.ps tmp/8jwcv1260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xos81260889681.ps tmp/9xos81260889681.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gzee1260889681.ps tmp/10gzee1260889681.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.382 1.557 10.891