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
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(3.2,27.6,2.7,2.6,2.8,24.9,3.2,2.4,2.8,23.8,2.8,2.5,3,24.3,2.8,2.7,3.1,23.6,3,3.2,3.1,24.2,3.1,2.8,3,28.1,3.1,2.8,2.4,30.1,3,3,2.7,31.1,2.4,3.1,3,32,2.7,3.1,2.7,32.4,3,3,2.7,34,2.7,2.4,2,35.1,2.7,2.7,2.4,37.1,2,3,2.6,37.3,2.4,2.7,2.4,38.1,2.6,2.7,2.3,39.5,2.4,2,2.4,38.3,2.3,2.4,2.5,37.3,2.4,2.6,2.6,38.7,2.5,2.4,2.6,37.5,2.6,2.3,2.6,38.7,2.6,2.4,2.7,37.9,2.6,2.5,2.8,36.6,2.7,2.6,2.6,35.5,2.8,2.6,2.6,37.6,2.6,2.6,2,38.6,2.6,2.7,2,40.3,2,2.8,2.1,39,2,2.6,1.9,36.8,2.1,2.6,2,36.5,1.9,2,2.5,34.1,2,2,2.9,34.2,2.5,2.1,3.3,31.9,2.9,1.9,3.5,33.7,3.3,2,3.8,33.5,3.5,2.5,4.6,33.8,3.8,2.9,4.4,29.9,4.6,3.3,5.3,32.3,4.4,3.5,5.8,30.5,5.3,3.8,5.9,28.5,5.8,4.6,5.6,29,5.9,4.4,5.8,23.8,5.6,5.3,5.5,17.9,5.8,5.8,4.6,9.9,5.5,5.9,4.2,3,4.6,5.6,4,4.2,4.2,5.8,3.5,0.4,4,5.5,2.3,0,3.5,4.6,2.2,2.4,2.3,4.2,1.4,4.2,2.2,4,0.6,8.2,1.4,3.5,0,9,0.6,2.3,0.5,13.6,0,2.2,0.1,14,0.5,1.4,0.1,17.6,0.1,0.6),dim=c(4,56),dimnames=list(c('Y','X','Y1','Y2'),1:56))
> y <- array(NA,dim=c(4,56),dimnames=list(c('Y','X','Y1','Y2'),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 = '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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 3.2 27.6 2.7 2.6 1 0 0 0 0 0 0 0 0 0 0 1
2 2.8 24.9 3.2 2.4 0 1 0 0 0 0 0 0 0 0 0 2
3 2.8 23.8 2.8 2.5 0 0 1 0 0 0 0 0 0 0 0 3
4 3.0 24.3 2.8 2.7 0 0 0 1 0 0 0 0 0 0 0 4
5 3.1 23.6 3.0 3.2 0 0 0 0 1 0 0 0 0 0 0 5
6 3.1 24.2 3.1 2.8 0 0 0 0 0 1 0 0 0 0 0 6
7 3.0 28.1 3.1 2.8 0 0 0 0 0 0 1 0 0 0 0 7
8 2.4 30.1 3.0 3.0 0 0 0 0 0 0 0 1 0 0 0 8
9 2.7 31.1 2.4 3.1 0 0 0 0 0 0 0 0 1 0 0 9
10 3.0 32.0 2.7 3.1 0 0 0 0 0 0 0 0 0 1 0 10
11 2.7 32.4 3.0 3.0 0 0 0 0 0 0 0 0 0 0 1 11
12 2.7 34.0 2.7 2.4 0 0 0 0 0 0 0 0 0 0 0 12
13 2.0 35.1 2.7 2.7 1 0 0 0 0 0 0 0 0 0 0 13
14 2.4 37.1 2.0 3.0 0 1 0 0 0 0 0 0 0 0 0 14
15 2.6 37.3 2.4 2.7 0 0 1 0 0 0 0 0 0 0 0 15
16 2.4 38.1 2.6 2.7 0 0 0 1 0 0 0 0 0 0 0 16
17 2.3 39.5 2.4 2.0 0 0 0 0 1 0 0 0 0 0 0 17
18 2.4 38.3 2.3 2.4 0 0 0 0 0 1 0 0 0 0 0 18
19 2.5 37.3 2.4 2.6 0 0 0 0 0 0 1 0 0 0 0 19
20 2.6 38.7 2.5 2.4 0 0 0 0 0 0 0 1 0 0 0 20
21 2.6 37.5 2.6 2.3 0 0 0 0 0 0 0 0 1 0 0 21
22 2.6 38.7 2.6 2.4 0 0 0 0 0 0 0 0 0 1 0 22
23 2.7 37.9 2.6 2.5 0 0 0 0 0 0 0 0 0 0 1 23
24 2.8 36.6 2.7 2.6 0 0 0 0 0 0 0 0 0 0 0 24
25 2.6 35.5 2.8 2.6 1 0 0 0 0 0 0 0 0 0 0 25
26 2.6 37.6 2.6 2.6 0 1 0 0 0 0 0 0 0 0 0 26
27 2.0 38.6 2.6 2.7 0 0 1 0 0 0 0 0 0 0 0 27
28 2.0 40.3 2.0 2.8 0 0 0 1 0 0 0 0 0 0 0 28
29 2.1 39.0 2.0 2.6 0 0 0 0 1 0 0 0 0 0 0 29
30 1.9 36.8 2.1 2.6 0 0 0 0 0 1 0 0 0 0 0 30
31 2.0 36.5 1.9 2.0 0 0 0 0 0 0 1 0 0 0 0 31
32 2.5 34.1 2.0 2.0 0 0 0 0 0 0 0 1 0 0 0 32
33 2.9 34.2 2.5 2.1 0 0 0 0 0 0 0 0 1 0 0 33
34 3.3 31.9 2.9 1.9 0 0 0 0 0 0 0 0 0 1 0 34
35 3.5 33.7 3.3 2.0 0 0 0 0 0 0 0 0 0 0 1 35
36 3.8 33.5 3.5 2.5 0 0 0 0 0 0 0 0 0 0 0 36
37 4.6 33.8 3.8 2.9 1 0 0 0 0 0 0 0 0 0 0 37
38 4.4 29.9 4.6 3.3 0 1 0 0 0 0 0 0 0 0 0 38
39 5.3 32.3 4.4 3.5 0 0 1 0 0 0 0 0 0 0 0 39
40 5.8 30.5 5.3 3.8 0 0 0 1 0 0 0 0 0 0 0 40
41 5.9 28.5 5.8 4.6 0 0 0 0 1 0 0 0 0 0 0 41
42 5.6 29.0 5.9 4.4 0 0 0 0 0 1 0 0 0 0 0 42
43 5.8 23.8 5.6 5.3 0 0 0 0 0 0 1 0 0 0 0 43
44 5.5 17.9 5.8 5.8 0 0 0 0 0 0 0 1 0 0 0 44
45 4.6 9.9 5.5 5.9 0 0 0 0 0 0 0 0 1 0 0 45
46 4.2 3.0 4.6 5.6 0 0 0 0 0 0 0 0 0 1 0 46
47 4.0 4.2 4.2 5.8 0 0 0 0 0 0 0 0 0 0 1 47
48 3.5 0.4 4.0 5.5 0 0 0 0 0 0 0 0 0 0 0 48
49 2.3 0.0 3.5 4.6 1 0 0 0 0 0 0 0 0 0 0 49
50 2.2 2.4 2.3 4.2 0 1 0 0 0 0 0 0 0 0 0 50
51 1.4 4.2 2.2 4.0 0 0 1 0 0 0 0 0 0 0 0 51
52 0.6 8.2 1.4 3.5 0 0 0 1 0 0 0 0 0 0 0 52
53 0.0 9.0 0.6 2.3 0 0 0 0 1 0 0 0 0 0 0 53
54 0.5 13.6 0.0 2.2 0 0 0 0 0 1 0 0 0 0 0 54
55 0.1 14.0 0.5 1.4 0 0 0 0 0 0 1 0 0 0 0 55
56 0.1 17.6 0.1 0.6 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
-0.292849 0.013424 1.083502 -0.133310 -0.137400 -0.023808
M3 M4 M5 M6 M7 M8
-0.035713 -0.044701 -0.078891 0.010912 -0.035553 -0.081095
M9 M10 M11 t
-0.033167 0.103803 -0.028891 0.002703
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.71978 -0.26609 -0.02310 0.22740 0.78870
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.292849 0.453430 -0.646 0.522
X 0.013424 0.008012 1.676 0.102
Y1 1.083502 0.081233 13.338 2.60e-16 ***
Y2 -0.133310 0.112632 -1.184 0.244
M1 -0.137400 0.275986 -0.498 0.621
M2 -0.023808 0.275796 -0.086 0.932
M3 -0.035713 0.275814 -0.129 0.898
M4 -0.044701 0.276692 -0.162 0.872
M5 -0.078891 0.275954 -0.286 0.776
M6 0.010912 0.276434 0.039 0.969
M7 -0.035553 0.276285 -0.129 0.898
M8 -0.081095 0.276625 -0.293 0.771
M9 -0.033167 0.290904 -0.114 0.910
M10 0.103803 0.290052 0.358 0.722
M11 -0.028891 0.290153 -0.100 0.921
t 0.002703 0.004263 0.634 0.530
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.41 on 40 degrees of freedom
Multiple R-squared: 0.9353, Adjusted R-squared: 0.9111
F-statistic: 38.56 on 15 and 40 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,] 0.15972606 0.31945213 0.8402739
[2,] 0.46639485 0.93278971 0.5336051
[3,] 0.40455110 0.80910220 0.5954489
[4,] 0.27857747 0.55715494 0.7214225
[5,] 0.18188444 0.36376887 0.8181156
[6,] 0.11979632 0.23959264 0.8802037
[7,] 0.06785989 0.13571978 0.9321401
[8,] 0.03919446 0.07838891 0.9608055
[9,] 0.06076517 0.12153033 0.9392348
[10,] 0.07265874 0.14531749 0.9273413
[11,] 0.06273909 0.12547817 0.9372609
[12,] 0.10310984 0.20621969 0.8968902
[13,] 0.08481801 0.16963603 0.9151820
[14,] 0.06042140 0.12084279 0.9395786
[15,] 0.05801516 0.11603032 0.9419848
[16,] 0.03581856 0.07163712 0.9641814
[17,] 0.02975516 0.05951033 0.9702448
[18,] 0.02397398 0.04794797 0.9760260
[19,] 0.03773676 0.07547352 0.9622632
> postscript(file="/var/www/html/rcomp/tmp/1pazo1261388735.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lg3y1261388735.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3z5if1261388735.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wjx71261388735.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5klc71261388735.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
0.678182839 -0.370278981 0.100420570 0.326655378 0.317493586 0.055258599
7 8 9 10 11 12
-0.053334698 -0.502333214 0.397042691 0.220237790 -0.293523168 -0.101531914
13 14 15 16 17 18
-0.641609253 0.413691049 0.146813693 -0.274341078 -0.238264984 -0.052988020
19 20 21 22 23 24
0.022510012 0.011541957 -0.144661546 -0.287112304 -0.033051555 -0.042213472
25 26 27 28 29 30
-0.201100177 -0.128886036 -0.719778399 -0.072882833 0.049393621 -0.321929192
31 32 33 34 35 36
-0.037425433 0.429281165 0.248887193 0.080028290 -0.034215076 0.086829842
37 38 39 40 41 42
0.745772485 -0.331644785 0.788699984 0.383990374 0.107222688 -0.427007935
43 44 45 46 47 48
0.331589987 0.003586634 -0.501268338 -0.013153776 0.360789799 0.056915544
49 50 51 52 53 54
-0.581245893 0.417118754 -0.316155848 -0.363421842 -0.235844911 0.746666548
55 56
-0.263339869 0.057923458
> postscript(file="/var/www/html/rcomp/tmp/6xclx1261388735.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 0.678182839 NA
1 -0.370278981 0.678182839
2 0.100420570 -0.370278981
3 0.326655378 0.100420570
4 0.317493586 0.326655378
5 0.055258599 0.317493586
6 -0.053334698 0.055258599
7 -0.502333214 -0.053334698
8 0.397042691 -0.502333214
9 0.220237790 0.397042691
10 -0.293523168 0.220237790
11 -0.101531914 -0.293523168
12 -0.641609253 -0.101531914
13 0.413691049 -0.641609253
14 0.146813693 0.413691049
15 -0.274341078 0.146813693
16 -0.238264984 -0.274341078
17 -0.052988020 -0.238264984
18 0.022510012 -0.052988020
19 0.011541957 0.022510012
20 -0.144661546 0.011541957
21 -0.287112304 -0.144661546
22 -0.033051555 -0.287112304
23 -0.042213472 -0.033051555
24 -0.201100177 -0.042213472
25 -0.128886036 -0.201100177
26 -0.719778399 -0.128886036
27 -0.072882833 -0.719778399
28 0.049393621 -0.072882833
29 -0.321929192 0.049393621
30 -0.037425433 -0.321929192
31 0.429281165 -0.037425433
32 0.248887193 0.429281165
33 0.080028290 0.248887193
34 -0.034215076 0.080028290
35 0.086829842 -0.034215076
36 0.745772485 0.086829842
37 -0.331644785 0.745772485
38 0.788699984 -0.331644785
39 0.383990374 0.788699984
40 0.107222688 0.383990374
41 -0.427007935 0.107222688
42 0.331589987 -0.427007935
43 0.003586634 0.331589987
44 -0.501268338 0.003586634
45 -0.013153776 -0.501268338
46 0.360789799 -0.013153776
47 0.056915544 0.360789799
48 -0.581245893 0.056915544
49 0.417118754 -0.581245893
50 -0.316155848 0.417118754
51 -0.363421842 -0.316155848
52 -0.235844911 -0.363421842
53 0.746666548 -0.235844911
54 -0.263339869 0.746666548
55 0.057923458 -0.263339869
56 NA 0.057923458
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.370278981 0.678182839
[2,] 0.100420570 -0.370278981
[3,] 0.326655378 0.100420570
[4,] 0.317493586 0.326655378
[5,] 0.055258599 0.317493586
[6,] -0.053334698 0.055258599
[7,] -0.502333214 -0.053334698
[8,] 0.397042691 -0.502333214
[9,] 0.220237790 0.397042691
[10,] -0.293523168 0.220237790
[11,] -0.101531914 -0.293523168
[12,] -0.641609253 -0.101531914
[13,] 0.413691049 -0.641609253
[14,] 0.146813693 0.413691049
[15,] -0.274341078 0.146813693
[16,] -0.238264984 -0.274341078
[17,] -0.052988020 -0.238264984
[18,] 0.022510012 -0.052988020
[19,] 0.011541957 0.022510012
[20,] -0.144661546 0.011541957
[21,] -0.287112304 -0.144661546
[22,] -0.033051555 -0.287112304
[23,] -0.042213472 -0.033051555
[24,] -0.201100177 -0.042213472
[25,] -0.128886036 -0.201100177
[26,] -0.719778399 -0.128886036
[27,] -0.072882833 -0.719778399
[28,] 0.049393621 -0.072882833
[29,] -0.321929192 0.049393621
[30,] -0.037425433 -0.321929192
[31,] 0.429281165 -0.037425433
[32,] 0.248887193 0.429281165
[33,] 0.080028290 0.248887193
[34,] -0.034215076 0.080028290
[35,] 0.086829842 -0.034215076
[36,] 0.745772485 0.086829842
[37,] -0.331644785 0.745772485
[38,] 0.788699984 -0.331644785
[39,] 0.383990374 0.788699984
[40,] 0.107222688 0.383990374
[41,] -0.427007935 0.107222688
[42,] 0.331589987 -0.427007935
[43,] 0.003586634 0.331589987
[44,] -0.501268338 0.003586634
[45,] -0.013153776 -0.501268338
[46,] 0.360789799 -0.013153776
[47,] 0.056915544 0.360789799
[48,] -0.581245893 0.056915544
[49,] 0.417118754 -0.581245893
[50,] -0.316155848 0.417118754
[51,] -0.363421842 -0.316155848
[52,] -0.235844911 -0.363421842
[53,] 0.746666548 -0.235844911
[54,] -0.263339869 0.746666548
[55,] 0.057923458 -0.263339869
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.370278981 0.678182839
2 0.100420570 -0.370278981
3 0.326655378 0.100420570
4 0.317493586 0.326655378
5 0.055258599 0.317493586
6 -0.053334698 0.055258599
7 -0.502333214 -0.053334698
8 0.397042691 -0.502333214
9 0.220237790 0.397042691
10 -0.293523168 0.220237790
11 -0.101531914 -0.293523168
12 -0.641609253 -0.101531914
13 0.413691049 -0.641609253
14 0.146813693 0.413691049
15 -0.274341078 0.146813693
16 -0.238264984 -0.274341078
17 -0.052988020 -0.238264984
18 0.022510012 -0.052988020
19 0.011541957 0.022510012
20 -0.144661546 0.011541957
21 -0.287112304 -0.144661546
22 -0.033051555 -0.287112304
23 -0.042213472 -0.033051555
24 -0.201100177 -0.042213472
25 -0.128886036 -0.201100177
26 -0.719778399 -0.128886036
27 -0.072882833 -0.719778399
28 0.049393621 -0.072882833
29 -0.321929192 0.049393621
30 -0.037425433 -0.321929192
31 0.429281165 -0.037425433
32 0.248887193 0.429281165
33 0.080028290 0.248887193
34 -0.034215076 0.080028290
35 0.086829842 -0.034215076
36 0.745772485 0.086829842
37 -0.331644785 0.745772485
38 0.788699984 -0.331644785
39 0.383990374 0.788699984
40 0.107222688 0.383990374
41 -0.427007935 0.107222688
42 0.331589987 -0.427007935
43 0.003586634 0.331589987
44 -0.501268338 0.003586634
45 -0.013153776 -0.501268338
46 0.360789799 -0.013153776
47 0.056915544 0.360789799
48 -0.581245893 0.056915544
49 0.417118754 -0.581245893
50 -0.316155848 0.417118754
51 -0.363421842 -0.316155848
52 -0.235844911 -0.363421842
53 0.746666548 -0.235844911
54 -0.263339869 0.746666548
55 0.057923458 -0.263339869
> 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/7afpa1261388735.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kmyu1261388735.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9beyl1261388735.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wo581261388735.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11j2ev1261388735.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/12x8ip1261388735.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/13nrm91261388736.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/14y6ic1261388736.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/15ivyl1261388736.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/16hf1e1261388736.tab")
+ }
>
> try(system("convert tmp/1pazo1261388735.ps tmp/1pazo1261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lg3y1261388735.ps tmp/2lg3y1261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z5if1261388735.ps tmp/3z5if1261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wjx71261388735.ps tmp/4wjx71261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/5klc71261388735.ps tmp/5klc71261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xclx1261388735.ps tmp/6xclx1261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/7afpa1261388735.ps tmp/7afpa1261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kmyu1261388735.ps tmp/8kmyu1261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/9beyl1261388735.ps tmp/9beyl1261388735.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wo581261388735.ps tmp/10wo581261388735.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.285 1.520 3.136