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(8.3
+ ,9.2
+ ,8.3
+ ,8.6
+ ,8.9
+ ,8.9
+ ,8.3
+ ,9.5
+ ,8.3
+ ,8.3
+ ,8.6
+ ,8.9
+ ,8.4
+ ,9.6
+ ,8.3
+ ,8.3
+ ,8.3
+ ,8.6
+ ,8.5
+ ,9.5
+ ,8.4
+ ,8.3
+ ,8.3
+ ,8.3
+ ,8.4
+ ,9.1
+ ,8.5
+ ,8.4
+ ,8.3
+ ,8.3
+ ,8.6
+ ,8.9
+ ,8.4
+ ,8.5
+ ,8.4
+ ,8.3
+ ,8.5
+ ,9
+ ,8.6
+ ,8.4
+ ,8.5
+ ,8.4
+ ,8.5
+ ,10.1
+ ,8.5
+ ,8.6
+ ,8.4
+ ,8.5
+ ,8.4
+ ,10.3
+ ,8.5
+ ,8.5
+ ,8.6
+ ,8.4
+ ,8.5
+ ,10.2
+ ,8.4
+ ,8.5
+ ,8.5
+ ,8.6
+ ,8.5
+ ,9.6
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.5
+ ,8.5
+ ,9.2
+ ,8.5
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.5
+ ,9.3
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.4
+ ,8.5
+ ,9.4
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,9.4
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,9.2
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,9
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.6
+ ,9
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.4
+ ,9
+ ,8.6
+ ,8.5
+ ,8.5
+ ,8.5
+ ,8.1
+ ,9.8
+ ,8.4
+ ,8.6
+ ,8.5
+ ,8.5
+ ,8.0
+ ,10
+ ,8.1
+ ,8.4
+ ,8.6
+ ,8.5
+ ,8.0
+ ,9.8
+ ,8.0
+ ,8.1
+ ,8.4
+ ,8.6
+ ,8.0
+ ,9.3
+ ,8.0
+ ,8.0
+ ,8.1
+ ,8.4
+ ,8.0
+ ,9
+ ,8.0
+ ,8.0
+ ,8.0
+ ,8.1
+ ,7.9
+ ,9
+ ,8.0
+ ,8.0
+ ,8.0
+ ,8.0
+ ,7.8
+ ,9.1
+ ,7.9
+ ,8.0
+ ,8.0
+ ,8.0
+ ,7.8
+ ,9.1
+ ,7.8
+ ,7.9
+ ,8.0
+ ,8.0
+ ,7.9
+ ,9.1
+ ,7.8
+ ,7.8
+ ,7.9
+ ,8.0
+ ,8.1
+ ,9.2
+ ,7.9
+ ,7.8
+ ,7.8
+ ,7.9
+ ,8.0
+ ,8.8
+ ,8.1
+ ,7.9
+ ,7.8
+ ,7.8
+ ,7.6
+ ,8.3
+ ,8.0
+ ,8.1
+ ,7.9
+ ,7.8
+ ,7.3
+ ,8.4
+ ,7.6
+ ,8.0
+ ,8.1
+ ,7.9
+ ,7.0
+ ,8.1
+ ,7.3
+ ,7.6
+ ,8.0
+ ,8.1
+ ,6.8
+ ,7.7
+ ,7.0
+ ,7.3
+ ,7.6
+ ,8.0
+ ,7.0
+ ,7.9
+ ,6.8
+ ,7.0
+ ,7.3
+ ,7.6
+ ,7.1
+ ,7.9
+ ,7.0
+ ,6.8
+ ,7.0
+ ,7.3
+ ,7.2
+ ,8
+ ,7.1
+ ,7.0
+ ,6.8
+ ,7.0
+ ,7.1
+ ,7.9
+ ,7.2
+ ,7.1
+ ,7.0
+ ,6.8
+ ,6.9
+ ,7.6
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7.0
+ ,6.7
+ ,7.1
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.7
+ ,6.8
+ ,6.7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.6
+ ,6.5
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.1
+ ,6.9
+ ,6.9
+ ,6.6
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.3
+ ,8.2
+ ,6.9
+ ,6.6
+ ,6.7
+ ,6.7
+ ,7.5
+ ,8.7
+ ,7.3
+ ,6.9
+ ,6.6
+ ,6.7
+ ,7.3
+ ,8.3
+ ,7.5
+ ,7.3
+ ,6.9
+ ,6.6
+ ,7.1
+ ,7.9
+ ,7.3
+ ,7.5
+ ,7.3
+ ,6.9
+ ,6.9
+ ,7.5
+ ,7.1
+ ,7.3
+ ,7.5
+ ,7.3
+ ,7.1
+ ,7.8
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.5
+ ,7.5
+ ,8.3
+ ,7.1
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.7
+ ,8.4
+ ,7.5
+ ,7.1
+ ,6.9
+ ,7.1
+ ,7.8
+ ,8.2
+ ,7.7
+ ,7.5
+ ,7.1
+ ,6.9
+ ,7.8
+ ,7.7
+ ,7.8
+ ,7.7
+ ,7.5
+ ,7.1
+ ,7.7
+ ,7.2
+ ,7.8
+ ,7.8
+ ,7.7
+ ,7.5
+ ,7.8
+ ,7.3
+ ,7.7
+ ,7.8
+ ,7.8
+ ,7.7
+ ,7.8
+ ,8.1
+ ,7.8
+ ,7.7
+ ,7.8
+ ,7.8
+ ,7.9
+ ,8.5
+ ,7.8
+ ,7.8
+ ,7.7
+ ,7.8)
+ ,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 8.3 9.2 8.3 8.6 8.9 8.9 1 0 0 0 0 0 0 0 0 0 0 1
2 8.3 9.5 8.3 8.3 8.6 8.9 0 1 0 0 0 0 0 0 0 0 0 2
3 8.4 9.6 8.3 8.3 8.3 8.6 0 0 1 0 0 0 0 0 0 0 0 3
4 8.5 9.5 8.4 8.3 8.3 8.3 0 0 0 1 0 0 0 0 0 0 0 4
5 8.4 9.1 8.5 8.4 8.3 8.3 0 0 0 0 1 0 0 0 0 0 0 5
6 8.6 8.9 8.4 8.5 8.4 8.3 0 0 0 0 0 1 0 0 0 0 0 6
7 8.5 9.0 8.6 8.4 8.5 8.4 0 0 0 0 0 0 1 0 0 0 0 7
8 8.5 10.1 8.5 8.6 8.4 8.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.4 10.3 8.5 8.5 8.6 8.4 0 0 0 0 0 0 0 0 1 0 0 9
10 8.5 10.2 8.4 8.5 8.5 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 9.6 8.5 8.4 8.5 8.5 0 0 0 0 0 0 0 0 0 0 1 11
12 8.5 9.2 8.5 8.5 8.4 8.5 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 9.3 8.5 8.5 8.5 8.4 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 9.4 8.5 8.5 8.5 8.5 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 9.4 8.5 8.5 8.5 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 9.2 8.5 8.5 8.5 8.5 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 9.0 8.5 8.5 8.5 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.6 9.0 8.5 8.5 8.5 8.5 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 9.0 8.6 8.5 8.5 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.1 9.8 8.4 8.6 8.5 8.5 0 0 0 0 0 0 0 1 0 0 0 20
21 8.0 10.0 8.1 8.4 8.6 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.0 9.8 8.0 8.1 8.4 8.6 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 9.3 8.0 8.0 8.1 8.4 0 0 0 0 0 0 0 0 0 0 1 23
24 8.0 9.0 8.0 8.0 8.0 8.1 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 9.0 8.0 8.0 8.0 8.0 1 0 0 0 0 0 0 0 0 0 0 25
26 7.8 9.1 7.9 8.0 8.0 8.0 0 1 0 0 0 0 0 0 0 0 0 26
27 7.8 9.1 7.8 7.9 8.0 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 9.1 7.8 7.8 7.9 8.0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 9.2 7.9 7.8 7.8 7.9 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 8.8 8.1 7.9 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 30
31 7.6 8.3 8.0 8.1 7.9 7.8 0 0 0 0 0 0 1 0 0 0 0 31
32 7.3 8.4 7.6 8.0 8.1 7.9 0 0 0 0 0 0 0 1 0 0 0 32
33 7.0 8.1 7.3 7.6 8.0 8.1 0 0 0 0 0 0 0 0 1 0 0 33
34 6.8 7.7 7.0 7.3 7.6 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.0 7.9 6.8 7.0 7.3 7.6 0 0 0 0 0 0 0 0 0 0 1 35
36 7.1 7.9 7.0 6.8 7.0 7.3 0 0 0 0 0 0 0 0 0 0 0 36
37 7.2 8.0 7.1 7.0 6.8 7.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.1 7.9 7.2 7.1 7.0 6.8 0 1 0 0 0 0 0 0 0 0 0 38
39 6.9 7.6 7.1 7.2 7.1 7.0 0 0 1 0 0 0 0 0 0 0 0 39
40 6.7 7.1 6.9 7.1 7.2 7.1 0 0 0 1 0 0 0 0 0 0 0 40
41 6.7 6.8 6.7 6.9 7.1 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 6.6 6.5 6.7 6.7 6.9 7.1 0 0 0 0 0 1 0 0 0 0 0 42
43 6.9 6.9 6.6 6.7 6.7 6.9 0 0 0 0 0 0 1 0 0 0 0 43
44 7.3 8.2 6.9 6.6 6.7 6.7 0 0 0 0 0 0 0 1 0 0 0 44
45 7.5 8.7 7.3 6.9 6.6 6.7 0 0 0 0 0 0 0 0 1 0 0 45
46 7.3 8.3 7.5 7.3 6.9 6.6 0 0 0 0 0 0 0 0 0 1 0 46
47 7.1 7.9 7.3 7.5 7.3 6.9 0 0 0 0 0 0 0 0 0 0 1 47
48 6.9 7.5 7.1 7.3 7.5 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.1 7.8 6.9 7.1 7.3 7.5 1 0 0 0 0 0 0 0 0 0 0 49
50 7.5 8.3 7.1 6.9 7.1 7.3 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 8.4 7.5 7.1 6.9 7.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.8 8.2 7.7 7.5 7.1 6.9 0 0 0 1 0 0 0 0 0 0 0 52
53 7.8 7.7 7.8 7.7 7.5 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 7.2 7.8 7.8 7.7 7.5 0 0 0 0 0 1 0 0 0 0 0 54
55 7.8 7.3 7.7 7.8 7.8 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 7.8 8.1 7.8 7.7 7.8 7.8 0 0 0 0 0 0 0 1 0 0 0 56
57 7.9 8.5 7.8 7.8 7.7 7.8 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
-0.272224 0.161944 1.208164 -0.467827 -0.193917 0.289843
M1 M2 M3 M4 M5 M6
0.094869 0.022036 0.013854 0.088052 0.127829 0.146967
M7 M8 M9 M10 M11 t
0.087481 -0.018427 -0.080041 -0.104472 0.019289 0.003685
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.278371 -0.067981 0.002914 0.068086 0.313001
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.272224 0.530892 -0.513 0.6110
X 0.161944 0.069133 2.343 0.0244 *
Y1 1.208164 0.173889 6.948 2.53e-08 ***
Y2 -0.467827 0.258642 -1.809 0.0782 .
Y3 -0.193917 0.256736 -0.755 0.4546
Y4 0.289843 0.147705 1.962 0.0569 .
M1 0.094869 0.096420 0.984 0.3312
M2 0.022036 0.096809 0.228 0.8211
M3 0.013854 0.097155 0.143 0.8873
M4 0.088052 0.096567 0.912 0.3675
M5 0.127829 0.097706 1.308 0.1984
M6 0.146967 0.102955 1.427 0.1614
M7 0.087481 0.102078 0.857 0.3967
M8 -0.018427 0.101686 -0.181 0.8571
M9 -0.080041 0.107434 -0.745 0.4607
M10 -0.104472 0.107614 -0.971 0.3376
M11 0.019289 0.103038 0.187 0.8525
t 0.003685 0.002479 1.487 0.1452
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1419 on 39 degrees of freedom
Multiple R-squared: 0.9624, Adjusted R-squared: 0.9461
F-statistic: 58.78 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,] 0.6437577 0.71248465 0.35624233
[2,] 0.5184215 0.96315706 0.48157853
[3,] 0.3629799 0.72595973 0.63702014
[4,] 0.3555694 0.71113881 0.64443060
[5,] 0.2776274 0.55525488 0.72237256
[6,] 0.2057583 0.41151666 0.79424167
[7,] 0.1961737 0.39234732 0.80382634
[8,] 0.1610815 0.32216305 0.83891847
[9,] 0.1177388 0.23547767 0.88226116
[10,] 0.1584096 0.31681924 0.84159038
[11,] 0.5216361 0.95672779 0.47836390
[12,] 0.8413240 0.31735190 0.15867595
[13,] 0.9774077 0.04518463 0.02259232
[14,] 0.9882506 0.02349889 0.01174944
[15,] 0.9785042 0.04299168 0.02149584
[16,] 0.9367210 0.12655795 0.06327897
> postscript(file="/var/www/html/rcomp/tmp/1nm2n1258723487.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2m5vd1258723487.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jljr1258723487.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u2ka1258723487.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5isti1258723487.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
0.125591643 -0.052367693 0.064712975 0.069161081 -0.083556630 0.313000696
7 8 9 10 11 12
-0.045400913 0.044689063 -0.008786012 0.171610736 0.002716977 0.110489654
13 14 15 16 17 18
0.044117815 0.068086244 0.072583636 0.027089735 0.016016838 0.093194493
19 20 21 22 23 24
-0.171821026 -0.210738001 0.003077426 -0.031086829 -0.124549487 0.007198910
25 26 27 28 29 30
-0.162370186 -0.088601086 -0.010069983 -0.054127165 -0.025007224 -0.248917590
31 32 33 34 35 36
-0.278371025 -0.046060297 -0.141589951 -0.082547379 0.116664805 -0.074151689
37 38 39 40 41 42
-0.067981120 -0.059921247 -0.077818300 -0.089471326 0.015341664 -0.162261815
43 44 45 46 47 48
0.268762795 0.209194841 0.023842166 -0.057976528 0.005167706 -0.043536875
49 50 51 52 53 54
0.060641848 0.132803782 -0.049408328 0.047347674 0.077205351 0.004984216
55 56 57
0.226830168 0.002914394 0.123456371
> postscript(file="/var/www/html/rcomp/tmp/6qqd71258723487.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 0.125591643 NA
1 -0.052367693 0.125591643
2 0.064712975 -0.052367693
3 0.069161081 0.064712975
4 -0.083556630 0.069161081
5 0.313000696 -0.083556630
6 -0.045400913 0.313000696
7 0.044689063 -0.045400913
8 -0.008786012 0.044689063
9 0.171610736 -0.008786012
10 0.002716977 0.171610736
11 0.110489654 0.002716977
12 0.044117815 0.110489654
13 0.068086244 0.044117815
14 0.072583636 0.068086244
15 0.027089735 0.072583636
16 0.016016838 0.027089735
17 0.093194493 0.016016838
18 -0.171821026 0.093194493
19 -0.210738001 -0.171821026
20 0.003077426 -0.210738001
21 -0.031086829 0.003077426
22 -0.124549487 -0.031086829
23 0.007198910 -0.124549487
24 -0.162370186 0.007198910
25 -0.088601086 -0.162370186
26 -0.010069983 -0.088601086
27 -0.054127165 -0.010069983
28 -0.025007224 -0.054127165
29 -0.248917590 -0.025007224
30 -0.278371025 -0.248917590
31 -0.046060297 -0.278371025
32 -0.141589951 -0.046060297
33 -0.082547379 -0.141589951
34 0.116664805 -0.082547379
35 -0.074151689 0.116664805
36 -0.067981120 -0.074151689
37 -0.059921247 -0.067981120
38 -0.077818300 -0.059921247
39 -0.089471326 -0.077818300
40 0.015341664 -0.089471326
41 -0.162261815 0.015341664
42 0.268762795 -0.162261815
43 0.209194841 0.268762795
44 0.023842166 0.209194841
45 -0.057976528 0.023842166
46 0.005167706 -0.057976528
47 -0.043536875 0.005167706
48 0.060641848 -0.043536875
49 0.132803782 0.060641848
50 -0.049408328 0.132803782
51 0.047347674 -0.049408328
52 0.077205351 0.047347674
53 0.004984216 0.077205351
54 0.226830168 0.004984216
55 0.002914394 0.226830168
56 0.123456371 0.002914394
57 NA 0.123456371
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.052367693 0.125591643
[2,] 0.064712975 -0.052367693
[3,] 0.069161081 0.064712975
[4,] -0.083556630 0.069161081
[5,] 0.313000696 -0.083556630
[6,] -0.045400913 0.313000696
[7,] 0.044689063 -0.045400913
[8,] -0.008786012 0.044689063
[9,] 0.171610736 -0.008786012
[10,] 0.002716977 0.171610736
[11,] 0.110489654 0.002716977
[12,] 0.044117815 0.110489654
[13,] 0.068086244 0.044117815
[14,] 0.072583636 0.068086244
[15,] 0.027089735 0.072583636
[16,] 0.016016838 0.027089735
[17,] 0.093194493 0.016016838
[18,] -0.171821026 0.093194493
[19,] -0.210738001 -0.171821026
[20,] 0.003077426 -0.210738001
[21,] -0.031086829 0.003077426
[22,] -0.124549487 -0.031086829
[23,] 0.007198910 -0.124549487
[24,] -0.162370186 0.007198910
[25,] -0.088601086 -0.162370186
[26,] -0.010069983 -0.088601086
[27,] -0.054127165 -0.010069983
[28,] -0.025007224 -0.054127165
[29,] -0.248917590 -0.025007224
[30,] -0.278371025 -0.248917590
[31,] -0.046060297 -0.278371025
[32,] -0.141589951 -0.046060297
[33,] -0.082547379 -0.141589951
[34,] 0.116664805 -0.082547379
[35,] -0.074151689 0.116664805
[36,] -0.067981120 -0.074151689
[37,] -0.059921247 -0.067981120
[38,] -0.077818300 -0.059921247
[39,] -0.089471326 -0.077818300
[40,] 0.015341664 -0.089471326
[41,] -0.162261815 0.015341664
[42,] 0.268762795 -0.162261815
[43,] 0.209194841 0.268762795
[44,] 0.023842166 0.209194841
[45,] -0.057976528 0.023842166
[46,] 0.005167706 -0.057976528
[47,] -0.043536875 0.005167706
[48,] 0.060641848 -0.043536875
[49,] 0.132803782 0.060641848
[50,] -0.049408328 0.132803782
[51,] 0.047347674 -0.049408328
[52,] 0.077205351 0.047347674
[53,] 0.004984216 0.077205351
[54,] 0.226830168 0.004984216
[55,] 0.002914394 0.226830168
[56,] 0.123456371 0.002914394
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.052367693 0.125591643
2 0.064712975 -0.052367693
3 0.069161081 0.064712975
4 -0.083556630 0.069161081
5 0.313000696 -0.083556630
6 -0.045400913 0.313000696
7 0.044689063 -0.045400913
8 -0.008786012 0.044689063
9 0.171610736 -0.008786012
10 0.002716977 0.171610736
11 0.110489654 0.002716977
12 0.044117815 0.110489654
13 0.068086244 0.044117815
14 0.072583636 0.068086244
15 0.027089735 0.072583636
16 0.016016838 0.027089735
17 0.093194493 0.016016838
18 -0.171821026 0.093194493
19 -0.210738001 -0.171821026
20 0.003077426 -0.210738001
21 -0.031086829 0.003077426
22 -0.124549487 -0.031086829
23 0.007198910 -0.124549487
24 -0.162370186 0.007198910
25 -0.088601086 -0.162370186
26 -0.010069983 -0.088601086
27 -0.054127165 -0.010069983
28 -0.025007224 -0.054127165
29 -0.248917590 -0.025007224
30 -0.278371025 -0.248917590
31 -0.046060297 -0.278371025
32 -0.141589951 -0.046060297
33 -0.082547379 -0.141589951
34 0.116664805 -0.082547379
35 -0.074151689 0.116664805
36 -0.067981120 -0.074151689
37 -0.059921247 -0.067981120
38 -0.077818300 -0.059921247
39 -0.089471326 -0.077818300
40 0.015341664 -0.089471326
41 -0.162261815 0.015341664
42 0.268762795 -0.162261815
43 0.209194841 0.268762795
44 0.023842166 0.209194841
45 -0.057976528 0.023842166
46 0.005167706 -0.057976528
47 -0.043536875 0.005167706
48 0.060641848 -0.043536875
49 0.132803782 0.060641848
50 -0.049408328 0.132803782
51 0.047347674 -0.049408328
52 0.077205351 0.047347674
53 0.004984216 0.077205351
54 0.226830168 0.004984216
55 0.002914394 0.226830168
56 0.123456371 0.002914394
> 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/7qaey1258723487.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gqo91258723487.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z34u1258723487.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1061lr1258723487.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yp601258723487.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/12g5h61258723487.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/13hq2l1258723487.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/14v48m1258723487.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/15qtcg1258723487.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/16dyea1258723487.tab")
+ }
>
> system("convert tmp/1nm2n1258723487.ps tmp/1nm2n1258723487.png")
> system("convert tmp/2m5vd1258723487.ps tmp/2m5vd1258723487.png")
> system("convert tmp/3jljr1258723487.ps tmp/3jljr1258723487.png")
> system("convert tmp/4u2ka1258723487.ps tmp/4u2ka1258723487.png")
> system("convert tmp/5isti1258723487.ps tmp/5isti1258723487.png")
> system("convert tmp/6qqd71258723487.ps tmp/6qqd71258723487.png")
> system("convert tmp/7qaey1258723487.ps tmp/7qaey1258723487.png")
> system("convert tmp/8gqo91258723487.ps tmp/8gqo91258723487.png")
> system("convert tmp/9z34u1258723487.ps tmp/9z34u1258723487.png")
> system("convert tmp/1061lr1258723487.ps tmp/1061lr1258723487.png")
>
>
> proc.time()
user system elapsed
2.336 1.616 2.764