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.
R is a collaborative project with many contributors.
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(0
+ ,6.5
+ ,6.3
+ ,6.1
+ ,6.2
+ ,6.3
+ ,0
+ ,6.6
+ ,6.5
+ ,6.3
+ ,6.1
+ ,6.2
+ ,0
+ ,6.5
+ ,6.6
+ ,6.5
+ ,6.3
+ ,6.1
+ ,0
+ ,6.2
+ ,6.5
+ ,6.6
+ ,6.5
+ ,6.3
+ ,0
+ ,6.2
+ ,6.2
+ ,6.5
+ ,6.6
+ ,6.5
+ ,0
+ ,5.9
+ ,6.2
+ ,6.2
+ ,6.5
+ ,6.6
+ ,0
+ ,6.1
+ ,5.9
+ ,6.2
+ ,6.2
+ ,6.5
+ ,0
+ ,6.1
+ ,6.1
+ ,5.9
+ ,6.2
+ ,6.2
+ ,0
+ ,6.1
+ ,6.1
+ ,6.1
+ ,5.9
+ ,6.2
+ ,0
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.1
+ ,5.9
+ ,0
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.1
+ ,0
+ ,6.4
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.1
+ ,0
+ ,6.7
+ ,6.4
+ ,6.1
+ ,6.1
+ ,6.1
+ ,0
+ ,6.9
+ ,6.7
+ ,6.4
+ ,6.1
+ ,6.1
+ ,0
+ ,7
+ ,6.9
+ ,6.7
+ ,6.4
+ ,6.1
+ ,0
+ ,7
+ ,7
+ ,6.9
+ ,6.7
+ ,6.4
+ ,0
+ ,6.8
+ ,7
+ ,7
+ ,6.9
+ ,6.7
+ ,0
+ ,6.4
+ ,6.8
+ ,7
+ ,7
+ ,6.9
+ ,0
+ ,5.9
+ ,6.4
+ ,6.8
+ ,7
+ ,7
+ ,0
+ ,5.5
+ ,5.9
+ ,6.4
+ ,6.8
+ ,7
+ ,0
+ ,5.5
+ ,5.5
+ ,5.9
+ ,6.4
+ ,6.8
+ ,0
+ ,5.6
+ ,5.5
+ ,5.5
+ ,5.9
+ ,6.4
+ ,0
+ ,5.8
+ ,5.6
+ ,5.5
+ ,5.5
+ ,5.9
+ ,0
+ ,5.9
+ ,5.8
+ ,5.6
+ ,5.5
+ ,5.5
+ ,0
+ ,6.1
+ ,5.9
+ ,5.8
+ ,5.6
+ ,5.5
+ ,0
+ ,6.1
+ ,6.1
+ ,5.9
+ ,5.8
+ ,5.6
+ ,0
+ ,6
+ ,6.1
+ ,6.1
+ ,5.9
+ ,5.8
+ ,0
+ ,6
+ ,6
+ ,6.1
+ ,6.1
+ ,5.9
+ ,0
+ ,5.9
+ ,6
+ ,6
+ ,6.1
+ ,6.1
+ ,0
+ ,5.5
+ ,5.9
+ ,6
+ ,6
+ ,6.1
+ ,0
+ ,5.6
+ ,5.5
+ ,5.9
+ ,6
+ ,6
+ ,0
+ ,5.4
+ ,5.6
+ ,5.5
+ ,5.9
+ ,6
+ ,0
+ ,5.2
+ ,5.4
+ ,5.6
+ ,5.5
+ ,5.9
+ ,0
+ ,5.2
+ ,5.2
+ ,5.4
+ ,5.6
+ ,5.5
+ ,0
+ ,5.2
+ ,5.2
+ ,5.2
+ ,5.4
+ ,5.6
+ ,0
+ ,5.5
+ ,5.2
+ ,5.2
+ ,5.2
+ ,5.4
+ ,1
+ ,5.8
+ ,5.5
+ ,5.2
+ ,5.2
+ ,5.2
+ ,1
+ ,5.8
+ ,5.8
+ ,5.5
+ ,5.2
+ ,5.2
+ ,1
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.5
+ ,5.2
+ ,1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.5
+ ,1
+ ,5.1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,5.8
+ ,1
+ ,5.2
+ ,5.1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,1
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.3
+ ,5.5
+ ,1
+ ,5.8
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.3
+ ,1
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.2
+ ,5.1
+ ,1
+ ,5
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.2
+ ,1
+ ,4.9
+ ,5
+ ,5.5
+ ,5.8
+ ,5.8
+ ,1
+ ,5.3
+ ,4.9
+ ,5
+ ,5.5
+ ,5.8
+ ,1
+ ,6.1
+ ,5.3
+ ,4.9
+ ,5
+ ,5.5
+ ,1
+ ,6.5
+ ,6.1
+ ,5.3
+ ,4.9
+ ,5
+ ,1
+ ,6.8
+ ,6.5
+ ,6.1
+ ,5.3
+ ,4.9
+ ,1
+ ,6.6
+ ,6.8
+ ,6.5
+ ,6.1
+ ,5.3
+ ,1
+ ,6.4
+ ,6.6
+ ,6.8
+ ,6.5
+ ,6.1
+ ,1
+ ,6.4
+ ,6.4
+ ,6.6
+ ,6.8
+ ,6.5)
+ ,dim=c(6
+ ,54)
+ ,dimnames=list(c('x'
+ ,'y'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4')
+ ,1:54))
> y <- array(NA,dim=c(6,54),dimnames=list(c('x','y','y1','y2','y3','y4'),1:54))
> 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 = '2'
> #'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 6.5 0 6.3 6.1 6.2 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 6.6 0 6.5 6.3 6.1 6.2 0 1 0 0 0 0 0 0 0 0 0 2
3 6.5 0 6.6 6.5 6.3 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 6.2 0 6.5 6.6 6.5 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 6.2 0 6.2 6.5 6.6 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 5.9 0 6.2 6.2 6.5 6.6 0 0 0 0 0 1 0 0 0 0 0 6
7 6.1 0 5.9 6.2 6.2 6.5 0 0 0 0 0 0 1 0 0 0 0 7
8 6.1 0 6.1 5.9 6.2 6.2 0 0 0 0 0 0 0 1 0 0 0 8
9 6.1 0 6.1 6.1 5.9 6.2 0 0 0 0 0 0 0 0 1 0 0 9
10 6.1 0 6.1 6.1 6.1 5.9 0 0 0 0 0 0 0 0 0 1 0 10
11 6.1 0 6.1 6.1 6.1 6.1 0 0 0 0 0 0 0 0 0 0 1 11
12 6.4 0 6.1 6.1 6.1 6.1 0 0 0 0 0 0 0 0 0 0 0 12
13 6.7 0 6.4 6.1 6.1 6.1 1 0 0 0 0 0 0 0 0 0 0 13
14 6.9 0 6.7 6.4 6.1 6.1 0 1 0 0 0 0 0 0 0 0 0 14
15 7.0 0 6.9 6.7 6.4 6.1 0 0 1 0 0 0 0 0 0 0 0 15
16 7.0 0 7.0 6.9 6.7 6.4 0 0 0 1 0 0 0 0 0 0 0 16
17 6.8 0 7.0 7.0 6.9 6.7 0 0 0 0 1 0 0 0 0 0 0 17
18 6.4 0 6.8 7.0 7.0 6.9 0 0 0 0 0 1 0 0 0 0 0 18
19 5.9 0 6.4 6.8 7.0 7.0 0 0 0 0 0 0 1 0 0 0 0 19
20 5.5 0 5.9 6.4 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 20
21 5.5 0 5.5 5.9 6.4 6.8 0 0 0 0 0 0 0 0 1 0 0 21
22 5.6 0 5.5 5.5 5.9 6.4 0 0 0 0 0 0 0 0 0 1 0 22
23 5.8 0 5.6 5.5 5.5 5.9 0 0 0 0 0 0 0 0 0 0 1 23
24 5.9 0 5.8 5.6 5.5 5.5 0 0 0 0 0 0 0 0 0 0 0 24
25 6.1 0 5.9 5.8 5.6 5.5 1 0 0 0 0 0 0 0 0 0 0 25
26 6.1 0 6.1 5.9 5.8 5.6 0 1 0 0 0 0 0 0 0 0 0 26
27 6.0 0 6.1 6.1 5.9 5.8 0 0 1 0 0 0 0 0 0 0 0 27
28 6.0 0 6.0 6.1 6.1 5.9 0 0 0 1 0 0 0 0 0 0 0 28
29 5.9 0 6.0 6.0 6.1 6.1 0 0 0 0 1 0 0 0 0 0 0 29
30 5.5 0 5.9 6.0 6.0 6.1 0 0 0 0 0 1 0 0 0 0 0 30
31 5.6 0 5.5 5.9 6.0 6.0 0 0 0 0 0 0 1 0 0 0 0 31
32 5.4 0 5.6 5.5 5.9 6.0 0 0 0 0 0 0 0 1 0 0 0 32
33 5.2 0 5.4 5.6 5.5 5.9 0 0 0 0 0 0 0 0 1 0 0 33
34 5.2 0 5.2 5.4 5.6 5.5 0 0 0 0 0 0 0 0 0 1 0 34
35 5.2 0 5.2 5.2 5.4 5.6 0 0 0 0 0 0 0 0 0 0 1 35
36 5.5 0 5.2 5.2 5.2 5.4 0 0 0 0 0 0 0 0 0 0 0 36
37 5.8 1 5.5 5.2 5.2 5.2 1 0 0 0 0 0 0 0 0 0 0 37
38 5.8 1 5.8 5.5 5.2 5.2 0 1 0 0 0 0 0 0 0 0 0 38
39 5.5 1 5.8 5.8 5.5 5.2 0 0 1 0 0 0 0 0 0 0 0 39
40 5.3 1 5.5 5.8 5.8 5.5 0 0 0 1 0 0 0 0 0 0 0 40
41 5.1 1 5.3 5.5 5.8 5.8 0 0 0 0 1 0 0 0 0 0 0 41
42 5.2 1 5.1 5.3 5.5 5.8 0 0 0 0 0 1 0 0 0 0 0 42
43 5.8 1 5.2 5.1 5.3 5.5 0 0 0 0 0 0 1 0 0 0 0 43
44 5.8 1 5.8 5.2 5.1 5.3 0 0 0 0 0 0 0 1 0 0 0 44
45 5.5 1 5.8 5.8 5.2 5.1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.0 1 5.5 5.8 5.8 5.2 0 0 0 0 0 0 0 0 0 1 0 46
47 4.9 1 5.0 5.5 5.8 5.8 0 0 0 0 0 0 0 0 0 0 1 47
48 5.3 1 4.9 5.0 5.5 5.8 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 1 5.3 4.9 5.0 5.5 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 1 6.1 5.3 4.9 5.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.8 1 6.5 6.1 5.3 4.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.6 1 6.8 6.5 6.1 5.3 0 0 0 1 0 0 0 0 0 0 0 52
53 6.4 1 6.6 6.8 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 1 6.4 6.6 6.8 6.5 0 0 0 0 0 1 0 0 0 0 0 54
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x y1 y2 y3 y4
-0.034662 0.058976 1.506829 -0.632806 -0.275700 0.431532
M1 M2 M3 M4 M5 M6
-0.012697 -0.209115 -0.142221 -0.178080 -0.238176 -0.383384
M7 M8 M9 M10 M11 t
-0.046980 -0.488028 -0.340349 -0.213105 -0.202674 0.001642
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.810e-01 -9.400e-02 -1.201e-05 1.204e-01 2.730e-01
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.034662 0.620921 -0.056 0.955791
x 0.058976 0.094445 0.624 0.536273
y1 1.506829 0.162330 9.283 4.37e-11 ***
y2 -0.632806 0.294958 -2.145 0.038738 *
y3 -0.275700 0.299869 -0.919 0.364007
y4 0.431532 0.186137 2.318 0.026223 *
M1 -0.012697 0.124226 -0.102 0.919158
M2 -0.209115 0.131705 -1.588 0.121089
M3 -0.142221 0.135794 -1.047 0.301929
M4 -0.178080 0.135795 -1.311 0.198030
M5 -0.238176 0.134072 -1.776 0.084106 .
M6 -0.383384 0.134458 -2.851 0.007165 **
M7 -0.046980 0.134530 -0.349 0.728962
M8 -0.488028 0.132214 -3.691 0.000734 ***
M9 -0.340349 0.144005 -2.363 0.023624 *
M10 -0.213105 0.128870 -1.654 0.106895
M11 -0.202674 0.124838 -1.623 0.113210
t 0.001642 0.003143 0.522 0.604525
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1743 on 36 degrees of freedom
Multiple R-squared: 0.9303, Adjusted R-squared: 0.8975
F-statistic: 28.28 on 17 and 36 DF, p-value: 7.824e-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,] 0.8607972 0.2784055 0.1392028
[2,] 0.8163992 0.3672016 0.1836008
[3,] 0.8138208 0.3723583 0.1861792
[4,] 0.8955298 0.2089405 0.1044702
[5,] 0.8315191 0.3369618 0.1684809
[6,] 0.8044172 0.3911656 0.1955828
[7,] 0.7039221 0.5921558 0.2960779
[8,] 0.6983656 0.6032687 0.3016344
[9,] 0.6915250 0.6169499 0.3084750
[10,] 0.7216490 0.5567020 0.2783510
[11,] 0.7301977 0.5396046 0.2698023
[12,] 0.5908924 0.8182152 0.4091076
[13,] 0.4674126 0.9348252 0.5325874
> postscript(file="/var/www/html/rcomp/tmp/1vza21258664024.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23cm21258664024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3w22w1258664024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40tr51258664024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5b19j1258664024.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 = 54
Frequency = 1
1 2 3 4 5 6
-0.096497939 0.039056313 -0.055308319 -0.138294325 0.250190851 -0.166808831
7 8 9 10 11 12
0.107636974 0.185294310 0.079824172 0.135538042 0.037157721 0.132841758
13 14 15 16 17 18
-0.008152151 0.124416645 0.127066556 0.090412228 -0.062173184 -0.075978557
19 20 21 22 23 24
-0.481007491 0.003549868 0.116583353 -0.130661815 0.012067246 -0.157721200
25 26 27 28 29 30
0.056781790 0.025459037 -0.075252191 0.121634367 -0.069499024 -0.202820883
31 32 33 34 35 36
0.141737106 -0.050232751 -0.102034568 0.144067025 -0.092861275 0.033989141
37 38 39 40 41 42
-0.079674251 -0.147105455 -0.243089815 -0.003573879 -0.163055900 0.172603965
43 44 45 46 47 48
0.231633411 -0.138611427 -0.094372957 -0.148943252 0.043636308 -0.009109700
49 50 51 52 53 54
0.127542552 -0.041826539 0.246583769 -0.070178391 0.044537256 0.273004306
> postscript(file="/var/www/html/rcomp/tmp/62cv31258664024.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 = 54
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.096497939 NA
1 0.039056313 -0.096497939
2 -0.055308319 0.039056313
3 -0.138294325 -0.055308319
4 0.250190851 -0.138294325
5 -0.166808831 0.250190851
6 0.107636974 -0.166808831
7 0.185294310 0.107636974
8 0.079824172 0.185294310
9 0.135538042 0.079824172
10 0.037157721 0.135538042
11 0.132841758 0.037157721
12 -0.008152151 0.132841758
13 0.124416645 -0.008152151
14 0.127066556 0.124416645
15 0.090412228 0.127066556
16 -0.062173184 0.090412228
17 -0.075978557 -0.062173184
18 -0.481007491 -0.075978557
19 0.003549868 -0.481007491
20 0.116583353 0.003549868
21 -0.130661815 0.116583353
22 0.012067246 -0.130661815
23 -0.157721200 0.012067246
24 0.056781790 -0.157721200
25 0.025459037 0.056781790
26 -0.075252191 0.025459037
27 0.121634367 -0.075252191
28 -0.069499024 0.121634367
29 -0.202820883 -0.069499024
30 0.141737106 -0.202820883
31 -0.050232751 0.141737106
32 -0.102034568 -0.050232751
33 0.144067025 -0.102034568
34 -0.092861275 0.144067025
35 0.033989141 -0.092861275
36 -0.079674251 0.033989141
37 -0.147105455 -0.079674251
38 -0.243089815 -0.147105455
39 -0.003573879 -0.243089815
40 -0.163055900 -0.003573879
41 0.172603965 -0.163055900
42 0.231633411 0.172603965
43 -0.138611427 0.231633411
44 -0.094372957 -0.138611427
45 -0.148943252 -0.094372957
46 0.043636308 -0.148943252
47 -0.009109700 0.043636308
48 0.127542552 -0.009109700
49 -0.041826539 0.127542552
50 0.246583769 -0.041826539
51 -0.070178391 0.246583769
52 0.044537256 -0.070178391
53 0.273004306 0.044537256
54 NA 0.273004306
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.039056313 -0.096497939
[2,] -0.055308319 0.039056313
[3,] -0.138294325 -0.055308319
[4,] 0.250190851 -0.138294325
[5,] -0.166808831 0.250190851
[6,] 0.107636974 -0.166808831
[7,] 0.185294310 0.107636974
[8,] 0.079824172 0.185294310
[9,] 0.135538042 0.079824172
[10,] 0.037157721 0.135538042
[11,] 0.132841758 0.037157721
[12,] -0.008152151 0.132841758
[13,] 0.124416645 -0.008152151
[14,] 0.127066556 0.124416645
[15,] 0.090412228 0.127066556
[16,] -0.062173184 0.090412228
[17,] -0.075978557 -0.062173184
[18,] -0.481007491 -0.075978557
[19,] 0.003549868 -0.481007491
[20,] 0.116583353 0.003549868
[21,] -0.130661815 0.116583353
[22,] 0.012067246 -0.130661815
[23,] -0.157721200 0.012067246
[24,] 0.056781790 -0.157721200
[25,] 0.025459037 0.056781790
[26,] -0.075252191 0.025459037
[27,] 0.121634367 -0.075252191
[28,] -0.069499024 0.121634367
[29,] -0.202820883 -0.069499024
[30,] 0.141737106 -0.202820883
[31,] -0.050232751 0.141737106
[32,] -0.102034568 -0.050232751
[33,] 0.144067025 -0.102034568
[34,] -0.092861275 0.144067025
[35,] 0.033989141 -0.092861275
[36,] -0.079674251 0.033989141
[37,] -0.147105455 -0.079674251
[38,] -0.243089815 -0.147105455
[39,] -0.003573879 -0.243089815
[40,] -0.163055900 -0.003573879
[41,] 0.172603965 -0.163055900
[42,] 0.231633411 0.172603965
[43,] -0.138611427 0.231633411
[44,] -0.094372957 -0.138611427
[45,] -0.148943252 -0.094372957
[46,] 0.043636308 -0.148943252
[47,] -0.009109700 0.043636308
[48,] 0.127542552 -0.009109700
[49,] -0.041826539 0.127542552
[50,] 0.246583769 -0.041826539
[51,] -0.070178391 0.246583769
[52,] 0.044537256 -0.070178391
[53,] 0.273004306 0.044537256
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.039056313 -0.096497939
2 -0.055308319 0.039056313
3 -0.138294325 -0.055308319
4 0.250190851 -0.138294325
5 -0.166808831 0.250190851
6 0.107636974 -0.166808831
7 0.185294310 0.107636974
8 0.079824172 0.185294310
9 0.135538042 0.079824172
10 0.037157721 0.135538042
11 0.132841758 0.037157721
12 -0.008152151 0.132841758
13 0.124416645 -0.008152151
14 0.127066556 0.124416645
15 0.090412228 0.127066556
16 -0.062173184 0.090412228
17 -0.075978557 -0.062173184
18 -0.481007491 -0.075978557
19 0.003549868 -0.481007491
20 0.116583353 0.003549868
21 -0.130661815 0.116583353
22 0.012067246 -0.130661815
23 -0.157721200 0.012067246
24 0.056781790 -0.157721200
25 0.025459037 0.056781790
26 -0.075252191 0.025459037
27 0.121634367 -0.075252191
28 -0.069499024 0.121634367
29 -0.202820883 -0.069499024
30 0.141737106 -0.202820883
31 -0.050232751 0.141737106
32 -0.102034568 -0.050232751
33 0.144067025 -0.102034568
34 -0.092861275 0.144067025
35 0.033989141 -0.092861275
36 -0.079674251 0.033989141
37 -0.147105455 -0.079674251
38 -0.243089815 -0.147105455
39 -0.003573879 -0.243089815
40 -0.163055900 -0.003573879
41 0.172603965 -0.163055900
42 0.231633411 0.172603965
43 -0.138611427 0.231633411
44 -0.094372957 -0.138611427
45 -0.148943252 -0.094372957
46 0.043636308 -0.148943252
47 -0.009109700 0.043636308
48 0.127542552 -0.009109700
49 -0.041826539 0.127542552
50 0.246583769 -0.041826539
51 -0.070178391 0.246583769
52 0.044537256 -0.070178391
53 0.273004306 0.044537256
> 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/7c8b31258664024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8347m1258664024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zx3b1258664024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zlah1258664024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ugzt1258664024.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/122y3f1258664024.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/13p97h1258664024.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/14nmte1258664024.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/15v90q1258664024.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/16yxdg1258664024.tab")
+ }
>
> system("convert tmp/1vza21258664024.ps tmp/1vza21258664024.png")
> system("convert tmp/23cm21258664024.ps tmp/23cm21258664024.png")
> system("convert tmp/3w22w1258664024.ps tmp/3w22w1258664024.png")
> system("convert tmp/40tr51258664024.ps tmp/40tr51258664024.png")
> system("convert tmp/5b19j1258664024.ps tmp/5b19j1258664024.png")
> system("convert tmp/62cv31258664024.ps tmp/62cv31258664024.png")
> system("convert tmp/7c8b31258664024.ps tmp/7c8b31258664024.png")
> system("convert tmp/8347m1258664024.ps tmp/8347m1258664024.png")
> system("convert tmp/9zx3b1258664024.ps tmp/9zx3b1258664024.png")
> system("convert tmp/10zlah1258664024.ps tmp/10zlah1258664024.png")
>
>
> proc.time()
user system elapsed
2.284 1.549 2.689