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(106.1,97.89,106,98.69,105.9,99.01,105.8,99.18,105.7,98.45,105.6,98.13,105.4,98.29,105.4,99.1,105.5,99.26,105.6,98.85,105.7,98.05,105.9,98.53,106.1,99.34,106,100.14,105.8,100.3,105.8,100.22,105.7,99.9,105.5,99.58,105.3,99.9,105.2,100.78,105.2,100.78,105,100.46,105.1,100.06,105.1,100.28,105.2,100.78,104.9,101.58,104.8,102.06,104.5,102.02,104.5,101.68,104.4,101.32,104.4,101.81,104.2,102.3,104.1,102.12,103.9,102.1,103.8,101.75,103.9,101.5,104.2,102.16,104.1,103.47,103.8,104.05,103.6,104.09,103.7,103.55,103.5,102.77,103.4,102.89,103.1,103.6,103.1,103.76,103.1,103.92,103.2,103.35,103.3,103.32,103.5,104.2,103.6,105.44,103.5,105.81,103.3,106.25,103.2,105.94,103.1,105.82,103.2,105.96,103,106.49,103,106.32,103.1,105.88,103.4,105.07),dim=c(2,59),dimnames=list(c('Werkl','Infl'),1:59))
> y <- array(NA,dim=c(2,59),dimnames=list(c('Werkl','Infl'),1:59))
> 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
Werkl Infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 106.1 97.89 1 0 0 0 0 0 0 0 0 0 0
2 106.0 98.69 0 1 0 0 0 0 0 0 0 0 0
3 105.9 99.01 0 0 1 0 0 0 0 0 0 0 0
4 105.8 99.18 0 0 0 1 0 0 0 0 0 0 0
5 105.7 98.45 0 0 0 0 1 0 0 0 0 0 0
6 105.6 98.13 0 0 0 0 0 1 0 0 0 0 0
7 105.4 98.29 0 0 0 0 0 0 1 0 0 0 0
8 105.4 99.10 0 0 0 0 0 0 0 1 0 0 0
9 105.5 99.26 0 0 0 0 0 0 0 0 1 0 0
10 105.6 98.85 0 0 0 0 0 0 0 0 0 1 0
11 105.7 98.05 0 0 0 0 0 0 0 0 0 0 1
12 105.9 98.53 0 0 0 0 0 0 0 0 0 0 0
13 106.1 99.34 1 0 0 0 0 0 0 0 0 0 0
14 106.0 100.14 0 1 0 0 0 0 0 0 0 0 0
15 105.8 100.30 0 0 1 0 0 0 0 0 0 0 0
16 105.8 100.22 0 0 0 1 0 0 0 0 0 0 0
17 105.7 99.90 0 0 0 0 1 0 0 0 0 0 0
18 105.5 99.58 0 0 0 0 0 1 0 0 0 0 0
19 105.3 99.90 0 0 0 0 0 0 1 0 0 0 0
20 105.2 100.78 0 0 0 0 0 0 0 1 0 0 0
21 105.2 100.78 0 0 0 0 0 0 0 0 1 0 0
22 105.0 100.46 0 0 0 0 0 0 0 0 0 1 0
23 105.1 100.06 0 0 0 0 0 0 0 0 0 0 1
24 105.1 100.28 0 0 0 0 0 0 0 0 0 0 0
25 105.2 100.78 1 0 0 0 0 0 0 0 0 0 0
26 104.9 101.58 0 1 0 0 0 0 0 0 0 0 0
27 104.8 102.06 0 0 1 0 0 0 0 0 0 0 0
28 104.5 102.02 0 0 0 1 0 0 0 0 0 0 0
29 104.5 101.68 0 0 0 0 1 0 0 0 0 0 0
30 104.4 101.32 0 0 0 0 0 1 0 0 0 0 0
31 104.4 101.81 0 0 0 0 0 0 1 0 0 0 0
32 104.2 102.30 0 0 0 0 0 0 0 1 0 0 0
33 104.1 102.12 0 0 0 0 0 0 0 0 1 0 0
34 103.9 102.10 0 0 0 0 0 0 0 0 0 1 0
35 103.8 101.75 0 0 0 0 0 0 0 0 0 0 1
36 103.9 101.50 0 0 0 0 0 0 0 0 0 0 0
37 104.2 102.16 1 0 0 0 0 0 0 0 0 0 0
38 104.1 103.47 0 1 0 0 0 0 0 0 0 0 0
39 103.8 104.05 0 0 1 0 0 0 0 0 0 0 0
40 103.6 104.09 0 0 0 1 0 0 0 0 0 0 0
41 103.7 103.55 0 0 0 0 1 0 0 0 0 0 0
42 103.5 102.77 0 0 0 0 0 1 0 0 0 0 0
43 103.4 102.89 0 0 0 0 0 0 1 0 0 0 0
44 103.1 103.60 0 0 0 0 0 0 0 1 0 0 0
45 103.1 103.76 0 0 0 0 0 0 0 0 1 0 0
46 103.1 103.92 0 0 0 0 0 0 0 0 0 1 0
47 103.2 103.35 0 0 0 0 0 0 0 0 0 0 1
48 103.3 103.32 0 0 0 0 0 0 0 0 0 0 0
49 103.5 104.20 1 0 0 0 0 0 0 0 0 0 0
50 103.6 105.44 0 1 0 0 0 0 0 0 0 0 0
51 103.5 105.81 0 0 1 0 0 0 0 0 0 0 0
52 103.3 106.25 0 0 0 1 0 0 0 0 0 0 0
53 103.2 105.94 0 0 0 0 1 0 0 0 0 0 0
54 103.1 105.82 0 0 0 0 0 1 0 0 0 0 0
55 103.2 105.96 0 0 0 0 0 0 1 0 0 0 0
56 103.0 106.49 0 0 0 0 0 0 0 1 0 0 0
57 103.0 106.32 0 0 0 0 0 0 0 0 1 0 0
58 103.1 105.88 0 0 0 0 0 0 0 0 0 1 0
59 103.4 105.07 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl M1 M2 M3 M4
143.97372 -0.39069 0.45691 0.74370 0.73294 0.61435
M5 M6 M7 M8 M9 M10
0.39932 0.11086 0.12697 0.23420 0.23186 0.11138
M11
-0.01757
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.63227 -0.22513 -0.03927 0.29228 0.49700
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 143.97372 1.85253 77.717 < 2e-16 ***
Infl -0.39069 0.01828 -21.374 < 2e-16 ***
M1 0.45691 0.23101 1.978 0.05395 .
M2 0.74370 0.23167 3.210 0.00242 **
M3 0.73294 0.23230 3.155 0.00283 **
M4 0.61435 0.23251 2.642 0.01122 *
M5 0.39932 0.23173 1.723 0.09156 .
M6 0.11086 0.23128 0.479 0.63397
M7 0.12697 0.23155 0.548 0.58609
M8 0.23420 0.23273 1.006 0.31952
M9 0.23186 0.23272 0.996 0.32431
M10 0.11138 0.23229 0.479 0.63387
M11 -0.01757 0.23141 -0.076 0.93982
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3444 on 46 degrees of freedom
Multiple R-squared: 0.9154, Adjusted R-squared: 0.8933
F-statistic: 41.45 on 12 and 46 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,] 1.224131e-03 2.448262e-03 0.99877587
[2,] 1.162501e-04 2.325003e-04 0.99988375
[3,] 3.109137e-05 6.218274e-05 0.99996891
[4,] 5.111783e-06 1.022357e-05 0.99999489
[5,] 7.557763e-06 1.511553e-05 0.99999244
[6,] 4.005785e-05 8.011570e-05 0.99995994
[7,] 2.131631e-03 4.263261e-03 0.99786837
[8,] 5.142889e-03 1.028578e-02 0.99485711
[9,] 3.995516e-02 7.991032e-02 0.96004484
[10,] 1.334341e-01 2.668682e-01 0.86656588
[11,] 2.652389e-01 5.304779e-01 0.73476107
[12,] 3.120984e-01 6.241967e-01 0.68790165
[13,] 4.228364e-01 8.456728e-01 0.57716360
[14,] 4.260531e-01 8.521062e-01 0.57394688
[15,] 4.393589e-01 8.787178e-01 0.56064109
[16,] 4.832886e-01 9.665772e-01 0.51671142
[17,] 6.422771e-01 7.154457e-01 0.35772287
[18,] 8.305245e-01 3.389510e-01 0.16947552
[19,] 8.965069e-01 2.069861e-01 0.10349305
[20,] 8.976132e-01 2.047737e-01 0.10238684
[21,] 9.454822e-01 1.090356e-01 0.05451779
[22,] 9.780925e-01 4.381499e-02 0.02190750
[23,] 9.803688e-01 3.926237e-02 0.01963118
[24,] 9.653626e-01 6.927486e-02 0.03463743
[25,] 9.375666e-01 1.248668e-01 0.06243339
[26,] 9.602663e-01 7.946749e-02 0.03973374
[27,] 9.720421e-01 5.591582e-02 0.02795791
[28,] 9.450905e-01 1.098191e-01 0.05490953
> postscript(file="/var/www/html/rcomp/tmp/1x78g1258668001.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2h6yn1258668001.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3q0ks1258668001.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ycgz1258668001.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/518jb1258668001.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 = 59
Frequency = 1
1 2 3 4 5 6
-0.08582383 -0.16005524 -0.12427812 -0.03927386 -0.20944890 -0.14600740
7 8 9 10 11 12
-0.29960688 -0.09037973 0.07447508 0.13477399 0.05116598 0.42113065
13 14 15 16 17 18
0.48067904 0.40644763 0.27971409 0.36704544 0.35705397 0.32049547
19 20 21 22 23 24
0.22940665 0.36598221 0.36832636 0.16378751 0.23645616 0.30484100
25 26 27 28 29 30
0.14327499 -0.13095642 -0.03266864 -0.22970962 -0.14751493 -0.09970109
31 32 33 34 35 36
0.07562767 -0.04016651 -0.20814686 -0.29547821 -0.40327499 -0.41851521
37 38 39 40 41 42
-0.31757056 -0.19254924 -0.25519230 -0.32097794 -0.21692157 -0.43319823
43 44 45 46 47 48
-0.50242537 -0.63226739 -0.56741258 -0.38441944 -0.37816838 -0.30745644
49 50 51 52 53 54
-0.22055963 0.07711327 0.13242497 0.22291598 0.21683142 0.35841125
55 56 57 58 59
0.49699794 0.39683142 0.33275800 0.38133616 0.49382123
> postscript(file="/var/www/html/rcomp/tmp/6j3vp1258668001.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.08582383 NA
1 -0.16005524 -0.08582383
2 -0.12427812 -0.16005524
3 -0.03927386 -0.12427812
4 -0.20944890 -0.03927386
5 -0.14600740 -0.20944890
6 -0.29960688 -0.14600740
7 -0.09037973 -0.29960688
8 0.07447508 -0.09037973
9 0.13477399 0.07447508
10 0.05116598 0.13477399
11 0.42113065 0.05116598
12 0.48067904 0.42113065
13 0.40644763 0.48067904
14 0.27971409 0.40644763
15 0.36704544 0.27971409
16 0.35705397 0.36704544
17 0.32049547 0.35705397
18 0.22940665 0.32049547
19 0.36598221 0.22940665
20 0.36832636 0.36598221
21 0.16378751 0.36832636
22 0.23645616 0.16378751
23 0.30484100 0.23645616
24 0.14327499 0.30484100
25 -0.13095642 0.14327499
26 -0.03266864 -0.13095642
27 -0.22970962 -0.03266864
28 -0.14751493 -0.22970962
29 -0.09970109 -0.14751493
30 0.07562767 -0.09970109
31 -0.04016651 0.07562767
32 -0.20814686 -0.04016651
33 -0.29547821 -0.20814686
34 -0.40327499 -0.29547821
35 -0.41851521 -0.40327499
36 -0.31757056 -0.41851521
37 -0.19254924 -0.31757056
38 -0.25519230 -0.19254924
39 -0.32097794 -0.25519230
40 -0.21692157 -0.32097794
41 -0.43319823 -0.21692157
42 -0.50242537 -0.43319823
43 -0.63226739 -0.50242537
44 -0.56741258 -0.63226739
45 -0.38441944 -0.56741258
46 -0.37816838 -0.38441944
47 -0.30745644 -0.37816838
48 -0.22055963 -0.30745644
49 0.07711327 -0.22055963
50 0.13242497 0.07711327
51 0.22291598 0.13242497
52 0.21683142 0.22291598
53 0.35841125 0.21683142
54 0.49699794 0.35841125
55 0.39683142 0.49699794
56 0.33275800 0.39683142
57 0.38133616 0.33275800
58 0.49382123 0.38133616
59 NA 0.49382123
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.16005524 -0.08582383
[2,] -0.12427812 -0.16005524
[3,] -0.03927386 -0.12427812
[4,] -0.20944890 -0.03927386
[5,] -0.14600740 -0.20944890
[6,] -0.29960688 -0.14600740
[7,] -0.09037973 -0.29960688
[8,] 0.07447508 -0.09037973
[9,] 0.13477399 0.07447508
[10,] 0.05116598 0.13477399
[11,] 0.42113065 0.05116598
[12,] 0.48067904 0.42113065
[13,] 0.40644763 0.48067904
[14,] 0.27971409 0.40644763
[15,] 0.36704544 0.27971409
[16,] 0.35705397 0.36704544
[17,] 0.32049547 0.35705397
[18,] 0.22940665 0.32049547
[19,] 0.36598221 0.22940665
[20,] 0.36832636 0.36598221
[21,] 0.16378751 0.36832636
[22,] 0.23645616 0.16378751
[23,] 0.30484100 0.23645616
[24,] 0.14327499 0.30484100
[25,] -0.13095642 0.14327499
[26,] -0.03266864 -0.13095642
[27,] -0.22970962 -0.03266864
[28,] -0.14751493 -0.22970962
[29,] -0.09970109 -0.14751493
[30,] 0.07562767 -0.09970109
[31,] -0.04016651 0.07562767
[32,] -0.20814686 -0.04016651
[33,] -0.29547821 -0.20814686
[34,] -0.40327499 -0.29547821
[35,] -0.41851521 -0.40327499
[36,] -0.31757056 -0.41851521
[37,] -0.19254924 -0.31757056
[38,] -0.25519230 -0.19254924
[39,] -0.32097794 -0.25519230
[40,] -0.21692157 -0.32097794
[41,] -0.43319823 -0.21692157
[42,] -0.50242537 -0.43319823
[43,] -0.63226739 -0.50242537
[44,] -0.56741258 -0.63226739
[45,] -0.38441944 -0.56741258
[46,] -0.37816838 -0.38441944
[47,] -0.30745644 -0.37816838
[48,] -0.22055963 -0.30745644
[49,] 0.07711327 -0.22055963
[50,] 0.13242497 0.07711327
[51,] 0.22291598 0.13242497
[52,] 0.21683142 0.22291598
[53,] 0.35841125 0.21683142
[54,] 0.49699794 0.35841125
[55,] 0.39683142 0.49699794
[56,] 0.33275800 0.39683142
[57,] 0.38133616 0.33275800
[58,] 0.49382123 0.38133616
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.16005524 -0.08582383
2 -0.12427812 -0.16005524
3 -0.03927386 -0.12427812
4 -0.20944890 -0.03927386
5 -0.14600740 -0.20944890
6 -0.29960688 -0.14600740
7 -0.09037973 -0.29960688
8 0.07447508 -0.09037973
9 0.13477399 0.07447508
10 0.05116598 0.13477399
11 0.42113065 0.05116598
12 0.48067904 0.42113065
13 0.40644763 0.48067904
14 0.27971409 0.40644763
15 0.36704544 0.27971409
16 0.35705397 0.36704544
17 0.32049547 0.35705397
18 0.22940665 0.32049547
19 0.36598221 0.22940665
20 0.36832636 0.36598221
21 0.16378751 0.36832636
22 0.23645616 0.16378751
23 0.30484100 0.23645616
24 0.14327499 0.30484100
25 -0.13095642 0.14327499
26 -0.03266864 -0.13095642
27 -0.22970962 -0.03266864
28 -0.14751493 -0.22970962
29 -0.09970109 -0.14751493
30 0.07562767 -0.09970109
31 -0.04016651 0.07562767
32 -0.20814686 -0.04016651
33 -0.29547821 -0.20814686
34 -0.40327499 -0.29547821
35 -0.41851521 -0.40327499
36 -0.31757056 -0.41851521
37 -0.19254924 -0.31757056
38 -0.25519230 -0.19254924
39 -0.32097794 -0.25519230
40 -0.21692157 -0.32097794
41 -0.43319823 -0.21692157
42 -0.50242537 -0.43319823
43 -0.63226739 -0.50242537
44 -0.56741258 -0.63226739
45 -0.38441944 -0.56741258
46 -0.37816838 -0.38441944
47 -0.30745644 -0.37816838
48 -0.22055963 -0.30745644
49 0.07711327 -0.22055963
50 0.13242497 0.07711327
51 0.22291598 0.13242497
52 0.21683142 0.22291598
53 0.35841125 0.21683142
54 0.49699794 0.35841125
55 0.39683142 0.49699794
56 0.33275800 0.39683142
57 0.38133616 0.33275800
58 0.49382123 0.38133616
> 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/7k0nd1258668001.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8imqw1258668001.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qoh41258668001.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1000sp1258668002.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sadw1258668002.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/12osf41258668002.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/13n9fm1258668002.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/14zqyy1258668002.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/15dot81258668002.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/16z8m61258668002.tab")
+ }
>
> system("convert tmp/1x78g1258668001.ps tmp/1x78g1258668001.png")
> system("convert tmp/2h6yn1258668001.ps tmp/2h6yn1258668001.png")
> system("convert tmp/3q0ks1258668001.ps tmp/3q0ks1258668001.png")
> system("convert tmp/4ycgz1258668001.ps tmp/4ycgz1258668001.png")
> system("convert tmp/518jb1258668001.ps tmp/518jb1258668001.png")
> system("convert tmp/6j3vp1258668001.ps tmp/6j3vp1258668001.png")
> system("convert tmp/7k0nd1258668001.ps tmp/7k0nd1258668001.png")
> system("convert tmp/8imqw1258668001.ps tmp/8imqw1258668001.png")
> system("convert tmp/9qoh41258668001.ps tmp/9qoh41258668001.png")
> system("convert tmp/1000sp1258668002.ps tmp/1000sp1258668002.png")
>
>
> proc.time()
user system elapsed
2.515 1.635 3.321