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(20.7
+ ,7.8
+ ,21.3
+ ,22
+ ,23.7
+ ,25.6
+ ,20.4
+ ,7.8
+ ,20.7
+ ,21.3
+ ,22
+ ,23.7
+ ,20.3
+ ,7.8
+ ,20.4
+ ,20.7
+ ,21.3
+ ,22
+ ,20.4
+ ,7.5
+ ,20.3
+ ,20.4
+ ,20.7
+ ,21.3
+ ,19.8
+ ,7.5
+ ,20.4
+ ,20.3
+ ,20.4
+ ,20.7
+ ,19.5
+ ,7.1
+ ,19.8
+ ,20.4
+ ,20.3
+ ,20.4
+ ,23.1
+ ,7.5
+ ,19.5
+ ,19.8
+ ,20.4
+ ,20.3
+ ,23.5
+ ,7.5
+ ,23.1
+ ,19.5
+ ,19.8
+ ,20.4
+ ,23.5
+ ,7.6
+ ,23.5
+ ,23.1
+ ,19.5
+ ,19.8
+ ,22.9
+ ,7.7
+ ,23.5
+ ,23.5
+ ,23.1
+ ,19.5
+ ,21.9
+ ,7.7
+ ,22.9
+ ,23.5
+ ,23.5
+ ,23.1
+ ,21.5
+ ,7.9
+ ,21.9
+ ,22.9
+ ,23.5
+ ,23.5
+ ,20.5
+ ,8.1
+ ,21.5
+ ,21.9
+ ,22.9
+ ,23.5
+ ,20.2
+ ,8.2
+ ,20.5
+ ,21.5
+ ,21.9
+ ,22.9
+ ,19.4
+ ,8.2
+ ,20.2
+ ,20.5
+ ,21.5
+ ,21.9
+ ,19.2
+ ,8.2
+ ,19.4
+ ,20.2
+ ,20.5
+ ,21.5
+ ,18.8
+ ,7.9
+ ,19.2
+ ,19.4
+ ,20.2
+ ,20.5
+ ,18.8
+ ,7.3
+ ,18.8
+ ,19.2
+ ,19.4
+ ,20.2
+ ,22.6
+ ,6.9
+ ,18.8
+ ,18.8
+ ,19.2
+ ,19.4
+ ,23.3
+ ,6.6
+ ,22.6
+ ,18.8
+ ,18.8
+ ,19.2
+ ,23
+ ,6.7
+ ,23.3
+ ,22.6
+ ,18.8
+ ,18.8
+ ,21.4
+ ,6.9
+ ,23
+ ,23.3
+ ,22.6
+ ,18.8
+ ,19.9
+ ,7
+ ,21.4
+ ,23
+ ,23.3
+ ,22.6
+ ,18.8
+ ,7.1
+ ,19.9
+ ,21.4
+ ,23
+ ,23.3
+ ,18.6
+ ,7.2
+ ,18.8
+ ,19.9
+ ,21.4
+ ,23
+ ,18.4
+ ,7.1
+ ,18.6
+ ,18.8
+ ,19.9
+ ,21.4
+ ,18.6
+ ,6.9
+ ,18.4
+ ,18.6
+ ,18.8
+ ,19.9
+ ,19.9
+ ,7
+ ,18.6
+ ,18.4
+ ,18.6
+ ,18.8
+ ,19.2
+ ,6.8
+ ,19.9
+ ,18.6
+ ,18.4
+ ,18.6
+ ,18.4
+ ,6.4
+ ,19.2
+ ,19.9
+ ,18.6
+ ,18.4
+ ,21.1
+ ,6.7
+ ,18.4
+ ,19.2
+ ,19.9
+ ,18.6
+ ,20.5
+ ,6.6
+ ,21.1
+ ,18.4
+ ,19.2
+ ,19.9
+ ,19.1
+ ,6.4
+ ,20.5
+ ,21.1
+ ,18.4
+ ,19.2
+ ,18.1
+ ,6.3
+ ,19.1
+ ,20.5
+ ,21.1
+ ,18.4
+ ,17
+ ,6.2
+ ,18.1
+ ,19.1
+ ,20.5
+ ,21.1
+ ,17.1
+ ,6.5
+ ,17
+ ,18.1
+ ,19.1
+ ,20.5
+ ,17.4
+ ,6.8
+ ,17.1
+ ,17
+ ,18.1
+ ,19.1
+ ,16.8
+ ,6.8
+ ,17.4
+ ,17.1
+ ,17
+ ,18.1
+ ,15.3
+ ,6.4
+ ,16.8
+ ,17.4
+ ,17.1
+ ,17
+ ,14.3
+ ,6.1
+ ,15.3
+ ,16.8
+ ,17.4
+ ,17.1
+ ,13.4
+ ,5.8
+ ,14.3
+ ,15.3
+ ,16.8
+ ,17.4
+ ,15.3
+ ,6.1
+ ,13.4
+ ,14.3
+ ,15.3
+ ,16.8
+ ,22.1
+ ,7.2
+ ,15.3
+ ,13.4
+ ,14.3
+ ,15.3
+ ,23.7
+ ,7.3
+ ,22.1
+ ,15.3
+ ,13.4
+ ,14.3
+ ,22.2
+ ,6.9
+ ,23.7
+ ,22.1
+ ,15.3
+ ,13.4
+ ,19.5
+ ,6.1
+ ,22.2
+ ,23.7
+ ,22.1
+ ,15.3
+ ,16.6
+ ,5.8
+ ,19.5
+ ,22.2
+ ,23.7
+ ,22.1
+ ,17.3
+ ,6.2
+ ,16.6
+ ,19.5
+ ,22.2
+ ,23.7
+ ,19.8
+ ,7.1
+ ,17.3
+ ,16.6
+ ,19.5
+ ,22.2
+ ,21.2
+ ,7.7
+ ,19.8
+ ,17.3
+ ,16.6
+ ,19.5
+ ,21.5
+ ,7.9
+ ,21.2
+ ,19.8
+ ,17.3
+ ,16.6
+ ,20.6
+ ,7.7
+ ,21.5
+ ,21.2
+ ,19.8
+ ,17.3
+ ,19.1
+ ,7.4
+ ,20.6
+ ,21.5
+ ,21.2
+ ,19.8
+ ,19.6
+ ,7.5
+ ,19.1
+ ,20.6
+ ,21.5
+ ,21.2
+ ,23.5
+ ,8
+ ,19.6
+ ,19.1
+ ,20.6
+ ,21.5
+ ,24
+ ,8.1
+ ,23.5
+ ,19.6
+ ,19.1
+ ,20.6)
+ ,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 = 'No 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
1 20.7 7.8 21.3 22.0 23.7 25.6 1 0 0 0 0 0 0 0 0 0 0
2 20.4 7.8 20.7 21.3 22.0 23.7 0 1 0 0 0 0 0 0 0 0 0
3 20.3 7.8 20.4 20.7 21.3 22.0 0 0 1 0 0 0 0 0 0 0 0
4 20.4 7.5 20.3 20.4 20.7 21.3 0 0 0 1 0 0 0 0 0 0 0
5 19.8 7.5 20.4 20.3 20.4 20.7 0 0 0 0 1 0 0 0 0 0 0
6 19.5 7.1 19.8 20.4 20.3 20.4 0 0 0 0 0 1 0 0 0 0 0
7 23.1 7.5 19.5 19.8 20.4 20.3 0 0 0 0 0 0 1 0 0 0 0
8 23.5 7.5 23.1 19.5 19.8 20.4 0 0 0 0 0 0 0 1 0 0 0
9 23.5 7.6 23.5 23.1 19.5 19.8 0 0 0 0 0 0 0 0 1 0 0
10 22.9 7.7 23.5 23.5 23.1 19.5 0 0 0 0 0 0 0 0 0 1 0
11 21.9 7.7 22.9 23.5 23.5 23.1 0 0 0 0 0 0 0 0 0 0 1
12 21.5 7.9 21.9 22.9 23.5 23.5 0 0 0 0 0 0 0 0 0 0 0
13 20.5 8.1 21.5 21.9 22.9 23.5 1 0 0 0 0 0 0 0 0 0 0
14 20.2 8.2 20.5 21.5 21.9 22.9 0 1 0 0 0 0 0 0 0 0 0
15 19.4 8.2 20.2 20.5 21.5 21.9 0 0 1 0 0 0 0 0 0 0 0
16 19.2 8.2 19.4 20.2 20.5 21.5 0 0 0 1 0 0 0 0 0 0 0
17 18.8 7.9 19.2 19.4 20.2 20.5 0 0 0 0 1 0 0 0 0 0 0
18 18.8 7.3 18.8 19.2 19.4 20.2 0 0 0 0 0 1 0 0 0 0 0
19 22.6 6.9 18.8 18.8 19.2 19.4 0 0 0 0 0 0 1 0 0 0 0
20 23.3 6.6 22.6 18.8 18.8 19.2 0 0 0 0 0 0 0 1 0 0 0
21 23.0 6.7 23.3 22.6 18.8 18.8 0 0 0 0 0 0 0 0 1 0 0
22 21.4 6.9 23.0 23.3 22.6 18.8 0 0 0 0 0 0 0 0 0 1 0
23 19.9 7.0 21.4 23.0 23.3 22.6 0 0 0 0 0 0 0 0 0 0 1
24 18.8 7.1 19.9 21.4 23.0 23.3 0 0 0 0 0 0 0 0 0 0 0
25 18.6 7.2 18.8 19.9 21.4 23.0 1 0 0 0 0 0 0 0 0 0 0
26 18.4 7.1 18.6 18.8 19.9 21.4 0 1 0 0 0 0 0 0 0 0 0
27 18.6 6.9 18.4 18.6 18.8 19.9 0 0 1 0 0 0 0 0 0 0 0
28 19.9 7.0 18.6 18.4 18.6 18.8 0 0 0 1 0 0 0 0 0 0 0
29 19.2 6.8 19.9 18.6 18.4 18.6 0 0 0 0 1 0 0 0 0 0 0
30 18.4 6.4 19.2 19.9 18.6 18.4 0 0 0 0 0 1 0 0 0 0 0
31 21.1 6.7 18.4 19.2 19.9 18.6 0 0 0 0 0 0 1 0 0 0 0
32 20.5 6.6 21.1 18.4 19.2 19.9 0 0 0 0 0 0 0 1 0 0 0
33 19.1 6.4 20.5 21.1 18.4 19.2 0 0 0 0 0 0 0 0 1 0 0
34 18.1 6.3 19.1 20.5 21.1 18.4 0 0 0 0 0 0 0 0 0 1 0
35 17.0 6.2 18.1 19.1 20.5 21.1 0 0 0 0 0 0 0 0 0 0 1
36 17.1 6.5 17.0 18.1 19.1 20.5 0 0 0 0 0 0 0 0 0 0 0
37 17.4 6.8 17.1 17.0 18.1 19.1 1 0 0 0 0 0 0 0 0 0 0
38 16.8 6.8 17.4 17.1 17.0 18.1 0 1 0 0 0 0 0 0 0 0 0
39 15.3 6.4 16.8 17.4 17.1 17.0 0 0 1 0 0 0 0 0 0 0 0
40 14.3 6.1 15.3 16.8 17.4 17.1 0 0 0 1 0 0 0 0 0 0 0
41 13.4 5.8 14.3 15.3 16.8 17.4 0 0 0 0 1 0 0 0 0 0 0
42 15.3 6.1 13.4 14.3 15.3 16.8 0 0 0 0 0 1 0 0 0 0 0
43 22.1 7.2 15.3 13.4 14.3 15.3 0 0 0 0 0 0 1 0 0 0 0
44 23.7 7.3 22.1 15.3 13.4 14.3 0 0 0 0 0 0 0 1 0 0 0
45 22.2 6.9 23.7 22.1 15.3 13.4 0 0 0 0 0 0 0 0 1 0 0
46 19.5 6.1 22.2 23.7 22.1 15.3 0 0 0 0 0 0 0 0 0 1 0
47 16.6 5.8 19.5 22.2 23.7 22.1 0 0 0 0 0 0 0 0 0 0 1
48 17.3 6.2 16.6 19.5 22.2 23.7 0 0 0 0 0 0 0 0 0 0 0
49 19.8 7.1 17.3 16.6 19.5 22.2 1 0 0 0 0 0 0 0 0 0 0
50 21.2 7.7 19.8 17.3 16.6 19.5 0 1 0 0 0 0 0 0 0 0 0
51 21.5 7.9 21.2 19.8 17.3 16.6 0 0 1 0 0 0 0 0 0 0 0
52 20.6 7.7 21.5 21.2 19.8 17.3 0 0 0 1 0 0 0 0 0 0 0
53 19.1 7.4 20.6 21.5 21.2 19.8 0 0 0 0 1 0 0 0 0 0 0
54 19.6 7.5 19.1 20.6 21.5 21.2 0 0 0 0 0 1 0 0 0 0 0
55 23.5 8.0 19.6 19.1 20.6 21.5 0 0 0 0 0 0 1 0 0 0 0
56 24.0 8.1 23.5 19.6 19.1 20.6 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
1.2179 0.4841 1.4774 -1.0025 0.1429 0.1602
M1 M2 M3 M4 M5 M6
-0.8897 -1.0398 -0.8778 -0.3723 -1.2920 0.1903
M7 M8 M9 M10 M11
3.0410 -2.1655 0.6368 0.3070 -0.6563
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.82984 -0.34234 -0.07999 0.38874 1.13490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2179 1.0702 1.138 0.26208
X 0.4841 0.2492 1.943 0.05925 .
Y1 1.4774 0.1787 8.269 4.16e-10 ***
Y2 -1.0025 0.2982 -3.361 0.00175 **
Y3 0.1429 0.2946 0.485 0.63040
Y4 0.1602 0.1512 1.059 0.29598
M1 -0.8897 0.4165 -2.136 0.03899 *
M2 -1.0398 0.4332 -2.400 0.02124 *
M3 -0.8778 0.4360 -2.014 0.05099 .
M4 -0.3723 0.4397 -0.847 0.40228
M5 -1.2920 0.4300 -3.004 0.00463 **
M6 0.1903 0.4228 0.450 0.65516
M7 3.0410 0.4818 6.311 1.91e-07 ***
M8 -2.1655 0.7911 -2.737 0.00928 **
M9 0.6368 0.7901 0.806 0.42518
M10 0.3070 0.6900 0.445 0.65882
M11 -0.6563 0.4399 -1.492 0.14373
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5711 on 39 degrees of freedom
Multiple R-squared: 0.9613, Adjusted R-squared: 0.9454
F-statistic: 60.49 on 16 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,] 0.09806775 0.19613550 0.9019323
[2,] 0.07783176 0.15566352 0.9221682
[3,] 0.11066494 0.22132987 0.8893351
[4,] 0.06128819 0.12257639 0.9387118
[5,] 0.06278865 0.12557730 0.9372114
[6,] 0.06321065 0.12642130 0.9367894
[7,] 0.03168406 0.06336813 0.9683159
[8,] 0.01691372 0.03382744 0.9830863
[9,] 0.12510181 0.25020362 0.8748982
[10,] 0.15295961 0.30591923 0.8470404
[11,] 0.11022116 0.22044232 0.8897788
[12,] 0.07331312 0.14662624 0.9266869
[13,] 0.06566723 0.13133447 0.9343328
[14,] 0.05252420 0.10504839 0.9474758
[15,] 0.03985626 0.07971253 0.9601437
[16,] 0.04684591 0.09369183 0.9531541
[17,] 0.58828024 0.82343952 0.4117198
> postscript(file="/var/www/html/rcomp/tmp/1bmwj1261769006.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d99m1261769006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3knyh1261769006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42n9w1261769006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/578ow1261769006.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
-0.30641472 0.27563014 0.22776322 0.01237855 0.22300202 -0.31655373
7 8 9 10 11 12
0.08252789 0.13925831 0.44548935 0.06159129 0.27749423 -0.06379560
13 14 15 16 17 18
-0.59666792 0.52040935 -0.78345237 -0.40078628 -0.03934583 -0.67829838
19 20 21 22 23 24
0.22035882 0.74707383 0.43566892 -0.32930263 0.43997790 -0.82186088
25 26 27 28 29 30
0.21757446 -0.12056812 0.50674633 0.96165957 -0.38143769 -0.12915512
31 32 33 34 35 36
-0.16269240 -0.40705382 -0.69292387 -0.10538141 -0.46651081 -0.24924370
37 38 39 40 41 42
-0.08806291 -0.56363087 -0.68279816 -0.48726034 -0.31101152 0.59911792
43 44 45 46 47 48
0.68964239 -0.40529619 -0.18823440 0.37309275 -0.25096132 1.13490018
49 50 51 52 53 54
0.77357110 -0.11184049 0.73174098 -0.08599150 0.50879301 0.52488931
55 56
-0.82983670 -0.07398213
> postscript(file="/var/www/html/rcomp/tmp/6lg0w1261769006.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 -0.30641472 NA
1 0.27563014 -0.30641472
2 0.22776322 0.27563014
3 0.01237855 0.22776322
4 0.22300202 0.01237855
5 -0.31655373 0.22300202
6 0.08252789 -0.31655373
7 0.13925831 0.08252789
8 0.44548935 0.13925831
9 0.06159129 0.44548935
10 0.27749423 0.06159129
11 -0.06379560 0.27749423
12 -0.59666792 -0.06379560
13 0.52040935 -0.59666792
14 -0.78345237 0.52040935
15 -0.40078628 -0.78345237
16 -0.03934583 -0.40078628
17 -0.67829838 -0.03934583
18 0.22035882 -0.67829838
19 0.74707383 0.22035882
20 0.43566892 0.74707383
21 -0.32930263 0.43566892
22 0.43997790 -0.32930263
23 -0.82186088 0.43997790
24 0.21757446 -0.82186088
25 -0.12056812 0.21757446
26 0.50674633 -0.12056812
27 0.96165957 0.50674633
28 -0.38143769 0.96165957
29 -0.12915512 -0.38143769
30 -0.16269240 -0.12915512
31 -0.40705382 -0.16269240
32 -0.69292387 -0.40705382
33 -0.10538141 -0.69292387
34 -0.46651081 -0.10538141
35 -0.24924370 -0.46651081
36 -0.08806291 -0.24924370
37 -0.56363087 -0.08806291
38 -0.68279816 -0.56363087
39 -0.48726034 -0.68279816
40 -0.31101152 -0.48726034
41 0.59911792 -0.31101152
42 0.68964239 0.59911792
43 -0.40529619 0.68964239
44 -0.18823440 -0.40529619
45 0.37309275 -0.18823440
46 -0.25096132 0.37309275
47 1.13490018 -0.25096132
48 0.77357110 1.13490018
49 -0.11184049 0.77357110
50 0.73174098 -0.11184049
51 -0.08599150 0.73174098
52 0.50879301 -0.08599150
53 0.52488931 0.50879301
54 -0.82983670 0.52488931
55 -0.07398213 -0.82983670
56 NA -0.07398213
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.27563014 -0.30641472
[2,] 0.22776322 0.27563014
[3,] 0.01237855 0.22776322
[4,] 0.22300202 0.01237855
[5,] -0.31655373 0.22300202
[6,] 0.08252789 -0.31655373
[7,] 0.13925831 0.08252789
[8,] 0.44548935 0.13925831
[9,] 0.06159129 0.44548935
[10,] 0.27749423 0.06159129
[11,] -0.06379560 0.27749423
[12,] -0.59666792 -0.06379560
[13,] 0.52040935 -0.59666792
[14,] -0.78345237 0.52040935
[15,] -0.40078628 -0.78345237
[16,] -0.03934583 -0.40078628
[17,] -0.67829838 -0.03934583
[18,] 0.22035882 -0.67829838
[19,] 0.74707383 0.22035882
[20,] 0.43566892 0.74707383
[21,] -0.32930263 0.43566892
[22,] 0.43997790 -0.32930263
[23,] -0.82186088 0.43997790
[24,] 0.21757446 -0.82186088
[25,] -0.12056812 0.21757446
[26,] 0.50674633 -0.12056812
[27,] 0.96165957 0.50674633
[28,] -0.38143769 0.96165957
[29,] -0.12915512 -0.38143769
[30,] -0.16269240 -0.12915512
[31,] -0.40705382 -0.16269240
[32,] -0.69292387 -0.40705382
[33,] -0.10538141 -0.69292387
[34,] -0.46651081 -0.10538141
[35,] -0.24924370 -0.46651081
[36,] -0.08806291 -0.24924370
[37,] -0.56363087 -0.08806291
[38,] -0.68279816 -0.56363087
[39,] -0.48726034 -0.68279816
[40,] -0.31101152 -0.48726034
[41,] 0.59911792 -0.31101152
[42,] 0.68964239 0.59911792
[43,] -0.40529619 0.68964239
[44,] -0.18823440 -0.40529619
[45,] 0.37309275 -0.18823440
[46,] -0.25096132 0.37309275
[47,] 1.13490018 -0.25096132
[48,] 0.77357110 1.13490018
[49,] -0.11184049 0.77357110
[50,] 0.73174098 -0.11184049
[51,] -0.08599150 0.73174098
[52,] 0.50879301 -0.08599150
[53,] 0.52488931 0.50879301
[54,] -0.82983670 0.52488931
[55,] -0.07398213 -0.82983670
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.27563014 -0.30641472
2 0.22776322 0.27563014
3 0.01237855 0.22776322
4 0.22300202 0.01237855
5 -0.31655373 0.22300202
6 0.08252789 -0.31655373
7 0.13925831 0.08252789
8 0.44548935 0.13925831
9 0.06159129 0.44548935
10 0.27749423 0.06159129
11 -0.06379560 0.27749423
12 -0.59666792 -0.06379560
13 0.52040935 -0.59666792
14 -0.78345237 0.52040935
15 -0.40078628 -0.78345237
16 -0.03934583 -0.40078628
17 -0.67829838 -0.03934583
18 0.22035882 -0.67829838
19 0.74707383 0.22035882
20 0.43566892 0.74707383
21 -0.32930263 0.43566892
22 0.43997790 -0.32930263
23 -0.82186088 0.43997790
24 0.21757446 -0.82186088
25 -0.12056812 0.21757446
26 0.50674633 -0.12056812
27 0.96165957 0.50674633
28 -0.38143769 0.96165957
29 -0.12915512 -0.38143769
30 -0.16269240 -0.12915512
31 -0.40705382 -0.16269240
32 -0.69292387 -0.40705382
33 -0.10538141 -0.69292387
34 -0.46651081 -0.10538141
35 -0.24924370 -0.46651081
36 -0.08806291 -0.24924370
37 -0.56363087 -0.08806291
38 -0.68279816 -0.56363087
39 -0.48726034 -0.68279816
40 -0.31101152 -0.48726034
41 0.59911792 -0.31101152
42 0.68964239 0.59911792
43 -0.40529619 0.68964239
44 -0.18823440 -0.40529619
45 0.37309275 -0.18823440
46 -0.25096132 0.37309275
47 1.13490018 -0.25096132
48 0.77357110 1.13490018
49 -0.11184049 0.77357110
50 0.73174098 -0.11184049
51 -0.08599150 0.73174098
52 0.50879301 -0.08599150
53 0.52488931 0.50879301
54 -0.82983670 0.52488931
55 -0.07398213 -0.82983670
> 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/7p3t71261769006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89n5u1261769006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/975v11261769006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ggmt1261769006.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11erat1261769006.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/122wjk1261769007.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/13nk661261769007.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/14p3v01261769007.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/1505pf1261769007.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/16uhw51261769007.tab")
+ }
>
> try(system("convert tmp/1bmwj1261769006.ps tmp/1bmwj1261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d99m1261769006.ps tmp/2d99m1261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/3knyh1261769006.ps tmp/3knyh1261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/42n9w1261769006.ps tmp/42n9w1261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/578ow1261769006.ps tmp/578ow1261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lg0w1261769006.ps tmp/6lg0w1261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p3t71261769006.ps tmp/7p3t71261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/89n5u1261769006.ps tmp/89n5u1261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/975v11261769006.ps tmp/975v11261769006.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ggmt1261769006.ps tmp/10ggmt1261769006.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.360 1.544 3.040