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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> x <- array(list(8.9,11.1,8.9,10.9,8.6,10,8.3,9.2,8.3,9.2,8.3,9.5,8.4,9.6,8.5,9.5,8.4,9.1,8.6,8.9,8.5,9,8.5,10.1,8.4,10.3,8.5,10.2,8.5,9.6,8.5,9.2,8.5,9.3,8.5,9.4,8.5,9.4,8.5,9.2,8.5,9,8.6,9,8.4,9,8.1,9.8,8.0,10,8.0,9.8,8.0,9.3,8.0,9,7.9,9,7.8,9.1,7.8,9.1,7.9,9.1,8.1,9.2,8.0,8.8,7.6,8.3,7.3,8.4,7.0,8.1,6.8,7.7,7.0,7.9,7.1,7.9,7.2,8,7.1,7.9,6.9,7.6,6.7,7.1,6.7,6.8,6.6,6.5,6.9,6.9,7.3,8.2,7.5,8.7,7.3,8.3,7.1,7.9,6.9,7.5,7.1,7.8,7.5,8.3,7.7,8.4,7.8,8.2,7.8,7.7,7.7,7.2,7.8,7.3,7.8,8.1,7.9,8.5),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.9 11.1 1 0 0 0 0 0 0 0 0 0 0
2 8.9 10.9 0 1 0 0 0 0 0 0 0 0 0
3 8.6 10.0 0 0 1 0 0 0 0 0 0 0 0
4 8.3 9.2 0 0 0 1 0 0 0 0 0 0 0
5 8.3 9.2 0 0 0 0 1 0 0 0 0 0 0
6 8.3 9.5 0 0 0 0 0 1 0 0 0 0 0
7 8.4 9.6 0 0 0 0 0 0 1 0 0 0 0
8 8.5 9.5 0 0 0 0 0 0 0 1 0 0 0
9 8.4 9.1 0 0 0 0 0 0 0 0 1 0 0
10 8.6 8.9 0 0 0 0 0 0 0 0 0 1 0
11 8.5 9.0 0 0 0 0 0 0 0 0 0 0 1
12 8.5 10.1 0 0 0 0 0 0 0 0 0 0 0
13 8.4 10.3 1 0 0 0 0 0 0 0 0 0 0
14 8.5 10.2 0 1 0 0 0 0 0 0 0 0 0
15 8.5 9.6 0 0 1 0 0 0 0 0 0 0 0
16 8.5 9.2 0 0 0 1 0 0 0 0 0 0 0
17 8.5 9.3 0 0 0 0 1 0 0 0 0 0 0
18 8.5 9.4 0 0 0 0 0 1 0 0 0 0 0
19 8.5 9.4 0 0 0 0 0 0 1 0 0 0 0
20 8.5 9.2 0 0 0 0 0 0 0 1 0 0 0
21 8.5 9.0 0 0 0 0 0 0 0 0 1 0 0
22 8.6 9.0 0 0 0 0 0 0 0 0 0 1 0
23 8.4 9.0 0 0 0 0 0 0 0 0 0 0 1
24 8.1 9.8 0 0 0 0 0 0 0 0 0 0 0
25 8.0 10.0 1 0 0 0 0 0 0 0 0 0 0
26 8.0 9.8 0 1 0 0 0 0 0 0 0 0 0
27 8.0 9.3 0 0 1 0 0 0 0 0 0 0 0
28 8.0 9.0 0 0 0 1 0 0 0 0 0 0 0
29 7.9 9.0 0 0 0 0 1 0 0 0 0 0 0
30 7.8 9.1 0 0 0 0 0 1 0 0 0 0 0
31 7.8 9.1 0 0 0 0 0 0 1 0 0 0 0
32 7.9 9.1 0 0 0 0 0 0 0 1 0 0 0
33 8.1 9.2 0 0 0 0 0 0 0 0 1 0 0
34 8.0 8.8 0 0 0 0 0 0 0 0 0 1 0
35 7.6 8.3 0 0 0 0 0 0 0 0 0 0 1
36 7.3 8.4 0 0 0 0 0 0 0 0 0 0 0
37 7.0 8.1 1 0 0 0 0 0 0 0 0 0 0
38 6.8 7.7 0 1 0 0 0 0 0 0 0 0 0
39 7.0 7.9 0 0 1 0 0 0 0 0 0 0 0
40 7.1 7.9 0 0 0 1 0 0 0 0 0 0 0
41 7.2 8.0 0 0 0 0 1 0 0 0 0 0 0
42 7.1 7.9 0 0 0 0 0 1 0 0 0 0 0
43 6.9 7.6 0 0 0 0 0 0 1 0 0 0 0
44 6.7 7.1 0 0 0 0 0 0 0 1 0 0 0
45 6.7 6.8 0 0 0 0 0 0 0 0 1 0 0
46 6.6 6.5 0 0 0 0 0 0 0 0 0 1 0
47 6.9 6.9 0 0 0 0 0 0 0 0 0 0 1
48 7.3 8.2 0 0 0 0 0 0 0 0 0 0 0
49 7.5 8.7 1 0 0 0 0 0 0 0 0 0 0
50 7.3 8.3 0 1 0 0 0 0 0 0 0 0 0
51 7.1 7.9 0 0 1 0 0 0 0 0 0 0 0
52 6.9 7.5 0 0 0 1 0 0 0 0 0 0 0
53 7.1 7.8 0 0 0 0 1 0 0 0 0 0 0
54 7.5 8.3 0 0 0 0 0 1 0 0 0 0 0
55 7.7 8.4 0 0 0 0 0 0 1 0 0 0 0
56 7.8 8.2 0 0 0 0 0 0 0 1 0 0 0
57 7.8 7.7 0 0 0 0 0 0 0 0 1 0 0
58 7.7 7.2 0 0 0 0 0 0 0 0 0 1 0
59 7.8 7.3 0 0 0 0 0 0 0 0 0 0 1
60 7.8 8.1 0 0 0 0 0 0 0 0 0 0 0
61 7.9 8.5 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
1.93697 0.65729 -0.19836 -0.20235 0.02685 0.19662
M5 M6 M7 M8 M9 M10
0.17090 0.09258 0.12573 0.27719 0.46808 0.65212
M11
0.57898
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.37325 -0.15821 -0.02675 0.11933 0.57443
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.93697 0.33801 5.731 6.45e-07 ***
X 0.65729 0.03578 18.369 < 2e-16 ***
M1 -0.19836 0.15180 -1.307 0.197520
M2 -0.20235 0.15816 -1.279 0.206911
M3 0.02685 0.15731 0.171 0.865168
M4 0.19662 0.15783 1.246 0.218892
M5 0.17090 0.15758 1.084 0.283563
M6 0.09258 0.15733 0.588 0.558984
M7 0.12573 0.15735 0.799 0.428193
M8 0.27719 0.15767 1.758 0.085123 .
M9 0.46808 0.15858 2.952 0.004877 **
M10 0.65212 0.16015 4.072 0.000174 ***
M11 0.57898 0.16002 3.618 0.000712 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2487 on 48 degrees of freedom
Multiple R-squared: 0.8763, Adjusted R-squared: 0.8454
F-statistic: 28.34 on 12 and 48 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,] 0.053117001 0.106234002 0.9468830
[2,] 0.028560067 0.057120134 0.9714399
[3,] 0.036866819 0.073733637 0.9631332
[4,] 0.030372805 0.060745610 0.9696272
[5,] 0.019846889 0.039693778 0.9801531
[6,] 0.012564675 0.025129349 0.9874353
[7,] 0.006050425 0.012100851 0.9939496
[8,] 0.002833860 0.005667721 0.9971661
[9,] 0.003163671 0.006327343 0.9968363
[10,] 0.004431007 0.008862014 0.9955690
[11,] 0.003328692 0.006657385 0.9966713
[12,] 0.002400812 0.004801624 0.9975992
[13,] 0.002750371 0.005500741 0.9972496
[14,] 0.003727236 0.007454471 0.9962728
[15,] 0.004482139 0.008964279 0.9955179
[16,] 0.004770813 0.009541626 0.9952292
[17,] 0.006852952 0.013705904 0.9931470
[18,] 0.017035320 0.034070641 0.9829647
[19,] 0.043752398 0.087504796 0.9562476
[20,] 0.178794881 0.357589762 0.8212051
[21,] 0.326236447 0.652472895 0.6737636
[22,] 0.323405886 0.646811772 0.6765941
[23,] 0.256542822 0.513085644 0.7434572
[24,] 0.191154784 0.382309568 0.8088452
[25,] 0.171412732 0.342825464 0.8285873
[26,] 0.121168400 0.242336801 0.8788316
[27,] 0.072023255 0.144046510 0.9279767
[28,] 0.040294524 0.080589048 0.9597055
[29,] 0.024439665 0.048879330 0.9755603
[30,] 0.010625947 0.021251895 0.9893741
> postscript(file="/var/www/html/rcomp/tmp/1muca1258722860.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2u8811258722860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jam21258722860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4p4rp1258722860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pemq1258722860.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 = 61
Frequency = 1
1 2 3 4 5
-0.1345296492 0.0009181414 0.0632718617 0.1193339543 0.1450630239
6 7 8 9 10
0.0261881403 0.0273132568 0.0415841871 0.0136048846 0.1610216289
11 12 13 14 15
0.0684383732 -0.0756030218 -0.1086970920 0.0610216289 0.2261881403
16 17 18 19 20
0.3193339543 0.2793339543 0.2919172100 0.2587713960 0.2387713960
21 22 23 24 25
0.1793339543 0.0952925592 -0.0315616268 -0.2784158129 -0.3115098831
26 27 28 29 30
-0.1760620925 -0.0766246507 -0.0492079064 -0.1234788368 -0.2108955811
31 32 33 34 35
-0.2440413950 -0.2954995343 -0.3521241850 -0.3732493015 -0.3714581393
36 37 38 39 40
-0.1582088378 -0.0626575598 0.0042483701 -0.1564176757 -0.2261881403
41 42 43 44 45
-0.1661881403 -0.1221467453 -0.1581053503 -0.1809181414 -0.1746265135
46 47 48 49 50
-0.2614806996 -0.1512511642 -0.0267506985 0.0429680224 0.1098739522
51 52 53 54 55
-0.0564176757 -0.1632718617 -0.1347300010 0.0149369761 0.1160620925
56 57 58 59 60
0.1960620925 0.3338118597 0.3784158129 0.4858325572 0.5389783711
61
0.5744261616
> postscript(file="/var/www/html/rcomp/tmp/67yf51258722860.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.1345296492 NA
1 0.0009181414 -0.1345296492
2 0.0632718617 0.0009181414
3 0.1193339543 0.0632718617
4 0.1450630239 0.1193339543
5 0.0261881403 0.1450630239
6 0.0273132568 0.0261881403
7 0.0415841871 0.0273132568
8 0.0136048846 0.0415841871
9 0.1610216289 0.0136048846
10 0.0684383732 0.1610216289
11 -0.0756030218 0.0684383732
12 -0.1086970920 -0.0756030218
13 0.0610216289 -0.1086970920
14 0.2261881403 0.0610216289
15 0.3193339543 0.2261881403
16 0.2793339543 0.3193339543
17 0.2919172100 0.2793339543
18 0.2587713960 0.2919172100
19 0.2387713960 0.2587713960
20 0.1793339543 0.2387713960
21 0.0952925592 0.1793339543
22 -0.0315616268 0.0952925592
23 -0.2784158129 -0.0315616268
24 -0.3115098831 -0.2784158129
25 -0.1760620925 -0.3115098831
26 -0.0766246507 -0.1760620925
27 -0.0492079064 -0.0766246507
28 -0.1234788368 -0.0492079064
29 -0.2108955811 -0.1234788368
30 -0.2440413950 -0.2108955811
31 -0.2954995343 -0.2440413950
32 -0.3521241850 -0.2954995343
33 -0.3732493015 -0.3521241850
34 -0.3714581393 -0.3732493015
35 -0.1582088378 -0.3714581393
36 -0.0626575598 -0.1582088378
37 0.0042483701 -0.0626575598
38 -0.1564176757 0.0042483701
39 -0.2261881403 -0.1564176757
40 -0.1661881403 -0.2261881403
41 -0.1221467453 -0.1661881403
42 -0.1581053503 -0.1221467453
43 -0.1809181414 -0.1581053503
44 -0.1746265135 -0.1809181414
45 -0.2614806996 -0.1746265135
46 -0.1512511642 -0.2614806996
47 -0.0267506985 -0.1512511642
48 0.0429680224 -0.0267506985
49 0.1098739522 0.0429680224
50 -0.0564176757 0.1098739522
51 -0.1632718617 -0.0564176757
52 -0.1347300010 -0.1632718617
53 0.0149369761 -0.1347300010
54 0.1160620925 0.0149369761
55 0.1960620925 0.1160620925
56 0.3338118597 0.1960620925
57 0.3784158129 0.3338118597
58 0.4858325572 0.3784158129
59 0.5389783711 0.4858325572
60 0.5744261616 0.5389783711
61 NA 0.5744261616
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0009181414 -0.1345296492
[2,] 0.0632718617 0.0009181414
[3,] 0.1193339543 0.0632718617
[4,] 0.1450630239 0.1193339543
[5,] 0.0261881403 0.1450630239
[6,] 0.0273132568 0.0261881403
[7,] 0.0415841871 0.0273132568
[8,] 0.0136048846 0.0415841871
[9,] 0.1610216289 0.0136048846
[10,] 0.0684383732 0.1610216289
[11,] -0.0756030218 0.0684383732
[12,] -0.1086970920 -0.0756030218
[13,] 0.0610216289 -0.1086970920
[14,] 0.2261881403 0.0610216289
[15,] 0.3193339543 0.2261881403
[16,] 0.2793339543 0.3193339543
[17,] 0.2919172100 0.2793339543
[18,] 0.2587713960 0.2919172100
[19,] 0.2387713960 0.2587713960
[20,] 0.1793339543 0.2387713960
[21,] 0.0952925592 0.1793339543
[22,] -0.0315616268 0.0952925592
[23,] -0.2784158129 -0.0315616268
[24,] -0.3115098831 -0.2784158129
[25,] -0.1760620925 -0.3115098831
[26,] -0.0766246507 -0.1760620925
[27,] -0.0492079064 -0.0766246507
[28,] -0.1234788368 -0.0492079064
[29,] -0.2108955811 -0.1234788368
[30,] -0.2440413950 -0.2108955811
[31,] -0.2954995343 -0.2440413950
[32,] -0.3521241850 -0.2954995343
[33,] -0.3732493015 -0.3521241850
[34,] -0.3714581393 -0.3732493015
[35,] -0.1582088378 -0.3714581393
[36,] -0.0626575598 -0.1582088378
[37,] 0.0042483701 -0.0626575598
[38,] -0.1564176757 0.0042483701
[39,] -0.2261881403 -0.1564176757
[40,] -0.1661881403 -0.2261881403
[41,] -0.1221467453 -0.1661881403
[42,] -0.1581053503 -0.1221467453
[43,] -0.1809181414 -0.1581053503
[44,] -0.1746265135 -0.1809181414
[45,] -0.2614806996 -0.1746265135
[46,] -0.1512511642 -0.2614806996
[47,] -0.0267506985 -0.1512511642
[48,] 0.0429680224 -0.0267506985
[49,] 0.1098739522 0.0429680224
[50,] -0.0564176757 0.1098739522
[51,] -0.1632718617 -0.0564176757
[52,] -0.1347300010 -0.1632718617
[53,] 0.0149369761 -0.1347300010
[54,] 0.1160620925 0.0149369761
[55,] 0.1960620925 0.1160620925
[56,] 0.3338118597 0.1960620925
[57,] 0.3784158129 0.3338118597
[58,] 0.4858325572 0.3784158129
[59,] 0.5389783711 0.4858325572
[60,] 0.5744261616 0.5389783711
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0009181414 -0.1345296492
2 0.0632718617 0.0009181414
3 0.1193339543 0.0632718617
4 0.1450630239 0.1193339543
5 0.0261881403 0.1450630239
6 0.0273132568 0.0261881403
7 0.0415841871 0.0273132568
8 0.0136048846 0.0415841871
9 0.1610216289 0.0136048846
10 0.0684383732 0.1610216289
11 -0.0756030218 0.0684383732
12 -0.1086970920 -0.0756030218
13 0.0610216289 -0.1086970920
14 0.2261881403 0.0610216289
15 0.3193339543 0.2261881403
16 0.2793339543 0.3193339543
17 0.2919172100 0.2793339543
18 0.2587713960 0.2919172100
19 0.2387713960 0.2587713960
20 0.1793339543 0.2387713960
21 0.0952925592 0.1793339543
22 -0.0315616268 0.0952925592
23 -0.2784158129 -0.0315616268
24 -0.3115098831 -0.2784158129
25 -0.1760620925 -0.3115098831
26 -0.0766246507 -0.1760620925
27 -0.0492079064 -0.0766246507
28 -0.1234788368 -0.0492079064
29 -0.2108955811 -0.1234788368
30 -0.2440413950 -0.2108955811
31 -0.2954995343 -0.2440413950
32 -0.3521241850 -0.2954995343
33 -0.3732493015 -0.3521241850
34 -0.3714581393 -0.3732493015
35 -0.1582088378 -0.3714581393
36 -0.0626575598 -0.1582088378
37 0.0042483701 -0.0626575598
38 -0.1564176757 0.0042483701
39 -0.2261881403 -0.1564176757
40 -0.1661881403 -0.2261881403
41 -0.1221467453 -0.1661881403
42 -0.1581053503 -0.1221467453
43 -0.1809181414 -0.1581053503
44 -0.1746265135 -0.1809181414
45 -0.2614806996 -0.1746265135
46 -0.1512511642 -0.2614806996
47 -0.0267506985 -0.1512511642
48 0.0429680224 -0.0267506985
49 0.1098739522 0.0429680224
50 -0.0564176757 0.1098739522
51 -0.1632718617 -0.0564176757
52 -0.1347300010 -0.1632718617
53 0.0149369761 -0.1347300010
54 0.1160620925 0.0149369761
55 0.1960620925 0.1160620925
56 0.3338118597 0.1960620925
57 0.3784158129 0.3338118597
58 0.4858325572 0.3784158129
59 0.5389783711 0.4858325572
60 0.5744261616 0.5389783711
> 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/7h5da1258722860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kee01258722860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9go5x1258722860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109p6y1258722860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112kt51258722860.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/122c7f1258722860.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/13s94i1258722860.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/149atq1258722860.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/15pflf1258722861.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/16sxya1258722861.tab")
+ }
>
> system("convert tmp/1muca1258722860.ps tmp/1muca1258722860.png")
> system("convert tmp/2u8811258722860.ps tmp/2u8811258722860.png")
> system("convert tmp/3jam21258722860.ps tmp/3jam21258722860.png")
> system("convert tmp/4p4rp1258722860.ps tmp/4p4rp1258722860.png")
> system("convert tmp/5pemq1258722860.ps tmp/5pemq1258722860.png")
> system("convert tmp/67yf51258722860.ps tmp/67yf51258722860.png")
> system("convert tmp/7h5da1258722860.ps tmp/7h5da1258722860.png")
> system("convert tmp/8kee01258722860.ps tmp/8kee01258722860.png")
> system("convert tmp/9go5x1258722860.ps tmp/9go5x1258722860.png")
> system("convert tmp/109p6y1258722860.ps tmp/109p6y1258722860.png")
>
>
> proc.time()
user system elapsed
2.432 1.556 2.829