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(3,101.2,3.21,101.1,3.37,100.7,3.51,100.1,3.75,99.9,4.11,99.7,4.25,99.5,4.25,99.2,4.5,99,4.7,99,4.75,99.3,4.75,99.5,4.75,99.7,4.75,100,4.75,100.4,4.75,100.6,4.58,100.7,4.5,100.7,4.5,100.6,4.49,100.5,4.03,100.6,3.75,100.5,3.39,100.4,3.25,100.3,3.25,100.4,3.25,100.4,3.25,100.4,3.25,100.4,3.25,100.4,3.25,100.5,3.25,100.6,3.25,100.6,3.25,100.5,3.25,100.5,3.25,100.7,2.85,101.1,2.75,101.5,2.75,101.9,2.55,102.1,2.5,102.1,2.5,102.1,2.1,102.4,2,102.8,2,103.1,2,103.1,2,102.9,2,102.4,2,101.9,2,101.3,2,100.7,2,100.6,2,101,2,101.5,2,101.9,2,102.1,2,102.3,2,102.5,2,102.9,2,103.6,2,104.3),dim=c(2,60),dimnames=list(c('Rente','Tprod'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Rente','Tprod'),1:60))
> 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 = '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
Tprod Rente M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 101.2 3.00 1 0 0 0 0 0 0 0 0 0 0
2 101.1 3.21 0 1 0 0 0 0 0 0 0 0 0
3 100.7 3.37 0 0 1 0 0 0 0 0 0 0 0
4 100.1 3.51 0 0 0 1 0 0 0 0 0 0 0
5 99.9 3.75 0 0 0 0 1 0 0 0 0 0 0
6 99.7 4.11 0 0 0 0 0 1 0 0 0 0 0
7 99.5 4.25 0 0 0 0 0 0 1 0 0 0 0
8 99.2 4.25 0 0 0 0 0 0 0 1 0 0 0
9 99.0 4.50 0 0 0 0 0 0 0 0 1 0 0
10 99.0 4.70 0 0 0 0 0 0 0 0 0 1 0
11 99.3 4.75 0 0 0 0 0 0 0 0 0 0 1
12 99.5 4.75 0 0 0 0 0 0 0 0 0 0 0
13 99.7 4.75 1 0 0 0 0 0 0 0 0 0 0
14 100.0 4.75 0 1 0 0 0 0 0 0 0 0 0
15 100.4 4.75 0 0 1 0 0 0 0 0 0 0 0
16 100.6 4.75 0 0 0 1 0 0 0 0 0 0 0
17 100.7 4.58 0 0 0 0 1 0 0 0 0 0 0
18 100.7 4.50 0 0 0 0 0 1 0 0 0 0 0
19 100.6 4.50 0 0 0 0 0 0 1 0 0 0 0
20 100.5 4.49 0 0 0 0 0 0 0 1 0 0 0
21 100.6 4.03 0 0 0 0 0 0 0 0 1 0 0
22 100.5 3.75 0 0 0 0 0 0 0 0 0 1 0
23 100.4 3.39 0 0 0 0 0 0 0 0 0 0 1
24 100.3 3.25 0 0 0 0 0 0 0 0 0 0 0
25 100.4 3.25 1 0 0 0 0 0 0 0 0 0 0
26 100.4 3.25 0 1 0 0 0 0 0 0 0 0 0
27 100.4 3.25 0 0 1 0 0 0 0 0 0 0 0
28 100.4 3.25 0 0 0 1 0 0 0 0 0 0 0
29 100.4 3.25 0 0 0 0 1 0 0 0 0 0 0
30 100.5 3.25 0 0 0 0 0 1 0 0 0 0 0
31 100.6 3.25 0 0 0 0 0 0 1 0 0 0 0
32 100.6 3.25 0 0 0 0 0 0 0 1 0 0 0
33 100.5 3.25 0 0 0 0 0 0 0 0 1 0 0
34 100.5 3.25 0 0 0 0 0 0 0 0 0 1 0
35 100.7 3.25 0 0 0 0 0 0 0 0 0 0 1
36 101.1 2.85 0 0 0 0 0 0 0 0 0 0 0
37 101.5 2.75 1 0 0 0 0 0 0 0 0 0 0
38 101.9 2.75 0 1 0 0 0 0 0 0 0 0 0
39 102.1 2.55 0 0 1 0 0 0 0 0 0 0 0
40 102.1 2.50 0 0 0 1 0 0 0 0 0 0 0
41 102.1 2.50 0 0 0 0 1 0 0 0 0 0 0
42 102.4 2.10 0 0 0 0 0 1 0 0 0 0 0
43 102.8 2.00 0 0 0 0 0 0 1 0 0 0 0
44 103.1 2.00 0 0 0 0 0 0 0 1 0 0 0
45 103.1 2.00 0 0 0 0 0 0 0 0 1 0 0
46 102.9 2.00 0 0 0 0 0 0 0 0 0 1 0
47 102.4 2.00 0 0 0 0 0 0 0 0 0 0 1
48 101.9 2.00 0 0 0 0 0 0 0 0 0 0 0
49 101.3 2.00 1 0 0 0 0 0 0 0 0 0 0
50 100.7 2.00 0 1 0 0 0 0 0 0 0 0 0
51 100.6 2.00 0 0 1 0 0 0 0 0 0 0 0
52 101.0 2.00 0 0 0 1 0 0 0 0 0 0 0
53 101.5 2.00 0 0 0 0 1 0 0 0 0 0 0
54 101.9 2.00 0 0 0 0 0 1 0 0 0 0 0
55 102.1 2.00 0 0 0 0 0 0 1 0 0 0 0
56 102.3 2.00 0 0 0 0 0 0 0 1 0 0 0
57 102.5 2.00 0 0 0 0 0 0 0 0 1 0 0
58 102.9 2.00 0 0 0 0 0 0 0 0 0 1 0
59 103.6 2.00 0 0 0 0 0 0 0 0 0 0 1
60 104.3 2.00 0 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) Rente M1 M2 M3 M4
104.23901 -0.94916 -0.42915 -0.38929 -0.37688 -0.35979
M5 M6 M7 M8 M9 M10
-0.26651 -0.16929 -0.08169 -0.06359 -0.10346 -0.09864
M11
-0.03749
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.3638 -0.4885 -0.1947 0.5882 1.9593
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 104.23901 0.46018 226.520 < 2e-16 ***
Rente -0.94916 0.10156 -9.345 2.72e-12 ***
M1 -0.42915 0.49181 -0.873 0.387
M2 -0.38929 0.49199 -0.791 0.433
M3 -0.37688 0.49195 -0.766 0.447
M4 -0.35979 0.49203 -0.731 0.468
M5 -0.26651 0.49210 -0.542 0.591
M6 -0.16929 0.49199 -0.344 0.732
M7 -0.08169 0.49202 -0.166 0.869
M8 -0.06359 0.49202 -0.129 0.898
M9 -0.10346 0.49183 -0.210 0.834
M10 -0.09864 0.49177 -0.201 0.842
M11 -0.03749 0.49159 -0.076 0.940
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7771 on 47 degrees of freedom
Multiple R-squared: 0.6592, Adjusted R-squared: 0.5722
F-statistic: 7.577 on 12 and 47 DF, p-value: 1.544e-07
> 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,] 0.31053517 0.62107035 0.6894648
[2,] 0.36707072 0.73414144 0.6329293
[3,] 0.40803610 0.81607220 0.5919639
[4,] 0.45020552 0.90041103 0.5497945
[5,] 0.52540325 0.94919351 0.4745968
[6,] 0.58477690 0.83044620 0.4152231
[7,] 0.55963336 0.88073328 0.4403666
[8,] 0.47284827 0.94569654 0.5271517
[9,] 0.39398509 0.78797018 0.6060149
[10,] 0.30551848 0.61103697 0.6944815
[11,] 0.23671717 0.47343433 0.7632828
[12,] 0.18015403 0.36030806 0.8198460
[13,] 0.12525602 0.25051205 0.8747440
[14,] 0.08224957 0.16449915 0.9177504
[15,] 0.05042439 0.10084877 0.9495756
[16,] 0.02973465 0.05946929 0.9702654
[17,] 0.01835769 0.03671538 0.9816423
[18,] 0.01233617 0.02467235 0.9876638
[19,] 0.01090664 0.02181328 0.9890934
[20,] 0.02050226 0.04100452 0.9794977
[21,] 0.17752662 0.35505324 0.8224734
[22,] 0.18587392 0.37174785 0.8141261
[23,] 0.15958922 0.31917844 0.8404108
[24,] 0.13566312 0.27132625 0.8643369
[25,] 0.10736596 0.21473192 0.8926340
[26,] 0.07966888 0.15933776 0.9203311
[27,] 0.04994172 0.09988344 0.9500583
[28,] 0.03920183 0.07840366 0.9607982
[29,] 0.03424094 0.06848188 0.9657591
> postscript(file="/var/www/html/rcomp/tmp/1l7fy1258663129.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vrr71258663129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r00m1258663129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hsfl1258663129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fy9f1258663129.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 = 60
Frequency = 1
1 2 3 4 5 6
0.23762554 0.29708494 0.03654434 -0.44765777 -0.51314691 -0.46866828
7 8 9 10 11 12
-0.62337875 -0.94148043 -0.86432480 -0.67930557 -0.39299931 -0.23048969
13 14 15 16 17 18
0.39866095 0.65879610 1.04638940 1.22930447 1.07465846 0.90150533
19 20 21 22 23 24
0.71391202 0.58631872 0.28956854 -0.08101051 -0.58386111 -0.85423433
25 26 27 28 29 30
-0.32508369 -0.36494854 -0.37735524 -0.39444017 -0.48772845 -0.48494854
31 32 33 34 35 36
-0.47254185 -0.49064352 -0.55077867 -0.55559206 -0.41674395 -0.43389957
37 38 39 40 41 42
0.30033476 0.66046991 0.65823060 0.59368751 0.50039923 0.32351390
43 44 45 46 47 48
0.54100429 0.82290261 0.86276746 0.65795407 0.09680219 -0.44068820
49 50 51 52 53 54
-0.61153756 -1.25140241 -1.36380910 -0.98089404 -0.57418232 -0.27140241
55 56 57 58 59 60
-0.15899571 0.02290261 0.26276746 0.65795407 1.29680219 1.95931180
> postscript(file="/var/www/html/rcomp/tmp/68e4l1258663129.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.23762554 NA
1 0.29708494 0.23762554
2 0.03654434 0.29708494
3 -0.44765777 0.03654434
4 -0.51314691 -0.44765777
5 -0.46866828 -0.51314691
6 -0.62337875 -0.46866828
7 -0.94148043 -0.62337875
8 -0.86432480 -0.94148043
9 -0.67930557 -0.86432480
10 -0.39299931 -0.67930557
11 -0.23048969 -0.39299931
12 0.39866095 -0.23048969
13 0.65879610 0.39866095
14 1.04638940 0.65879610
15 1.22930447 1.04638940
16 1.07465846 1.22930447
17 0.90150533 1.07465846
18 0.71391202 0.90150533
19 0.58631872 0.71391202
20 0.28956854 0.58631872
21 -0.08101051 0.28956854
22 -0.58386111 -0.08101051
23 -0.85423433 -0.58386111
24 -0.32508369 -0.85423433
25 -0.36494854 -0.32508369
26 -0.37735524 -0.36494854
27 -0.39444017 -0.37735524
28 -0.48772845 -0.39444017
29 -0.48494854 -0.48772845
30 -0.47254185 -0.48494854
31 -0.49064352 -0.47254185
32 -0.55077867 -0.49064352
33 -0.55559206 -0.55077867
34 -0.41674395 -0.55559206
35 -0.43389957 -0.41674395
36 0.30033476 -0.43389957
37 0.66046991 0.30033476
38 0.65823060 0.66046991
39 0.59368751 0.65823060
40 0.50039923 0.59368751
41 0.32351390 0.50039923
42 0.54100429 0.32351390
43 0.82290261 0.54100429
44 0.86276746 0.82290261
45 0.65795407 0.86276746
46 0.09680219 0.65795407
47 -0.44068820 0.09680219
48 -0.61153756 -0.44068820
49 -1.25140241 -0.61153756
50 -1.36380910 -1.25140241
51 -0.98089404 -1.36380910
52 -0.57418232 -0.98089404
53 -0.27140241 -0.57418232
54 -0.15899571 -0.27140241
55 0.02290261 -0.15899571
56 0.26276746 0.02290261
57 0.65795407 0.26276746
58 1.29680219 0.65795407
59 1.95931180 1.29680219
60 NA 1.95931180
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.29708494 0.23762554
[2,] 0.03654434 0.29708494
[3,] -0.44765777 0.03654434
[4,] -0.51314691 -0.44765777
[5,] -0.46866828 -0.51314691
[6,] -0.62337875 -0.46866828
[7,] -0.94148043 -0.62337875
[8,] -0.86432480 -0.94148043
[9,] -0.67930557 -0.86432480
[10,] -0.39299931 -0.67930557
[11,] -0.23048969 -0.39299931
[12,] 0.39866095 -0.23048969
[13,] 0.65879610 0.39866095
[14,] 1.04638940 0.65879610
[15,] 1.22930447 1.04638940
[16,] 1.07465846 1.22930447
[17,] 0.90150533 1.07465846
[18,] 0.71391202 0.90150533
[19,] 0.58631872 0.71391202
[20,] 0.28956854 0.58631872
[21,] -0.08101051 0.28956854
[22,] -0.58386111 -0.08101051
[23,] -0.85423433 -0.58386111
[24,] -0.32508369 -0.85423433
[25,] -0.36494854 -0.32508369
[26,] -0.37735524 -0.36494854
[27,] -0.39444017 -0.37735524
[28,] -0.48772845 -0.39444017
[29,] -0.48494854 -0.48772845
[30,] -0.47254185 -0.48494854
[31,] -0.49064352 -0.47254185
[32,] -0.55077867 -0.49064352
[33,] -0.55559206 -0.55077867
[34,] -0.41674395 -0.55559206
[35,] -0.43389957 -0.41674395
[36,] 0.30033476 -0.43389957
[37,] 0.66046991 0.30033476
[38,] 0.65823060 0.66046991
[39,] 0.59368751 0.65823060
[40,] 0.50039923 0.59368751
[41,] 0.32351390 0.50039923
[42,] 0.54100429 0.32351390
[43,] 0.82290261 0.54100429
[44,] 0.86276746 0.82290261
[45,] 0.65795407 0.86276746
[46,] 0.09680219 0.65795407
[47,] -0.44068820 0.09680219
[48,] -0.61153756 -0.44068820
[49,] -1.25140241 -0.61153756
[50,] -1.36380910 -1.25140241
[51,] -0.98089404 -1.36380910
[52,] -0.57418232 -0.98089404
[53,] -0.27140241 -0.57418232
[54,] -0.15899571 -0.27140241
[55,] 0.02290261 -0.15899571
[56,] 0.26276746 0.02290261
[57,] 0.65795407 0.26276746
[58,] 1.29680219 0.65795407
[59,] 1.95931180 1.29680219
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.29708494 0.23762554
2 0.03654434 0.29708494
3 -0.44765777 0.03654434
4 -0.51314691 -0.44765777
5 -0.46866828 -0.51314691
6 -0.62337875 -0.46866828
7 -0.94148043 -0.62337875
8 -0.86432480 -0.94148043
9 -0.67930557 -0.86432480
10 -0.39299931 -0.67930557
11 -0.23048969 -0.39299931
12 0.39866095 -0.23048969
13 0.65879610 0.39866095
14 1.04638940 0.65879610
15 1.22930447 1.04638940
16 1.07465846 1.22930447
17 0.90150533 1.07465846
18 0.71391202 0.90150533
19 0.58631872 0.71391202
20 0.28956854 0.58631872
21 -0.08101051 0.28956854
22 -0.58386111 -0.08101051
23 -0.85423433 -0.58386111
24 -0.32508369 -0.85423433
25 -0.36494854 -0.32508369
26 -0.37735524 -0.36494854
27 -0.39444017 -0.37735524
28 -0.48772845 -0.39444017
29 -0.48494854 -0.48772845
30 -0.47254185 -0.48494854
31 -0.49064352 -0.47254185
32 -0.55077867 -0.49064352
33 -0.55559206 -0.55077867
34 -0.41674395 -0.55559206
35 -0.43389957 -0.41674395
36 0.30033476 -0.43389957
37 0.66046991 0.30033476
38 0.65823060 0.66046991
39 0.59368751 0.65823060
40 0.50039923 0.59368751
41 0.32351390 0.50039923
42 0.54100429 0.32351390
43 0.82290261 0.54100429
44 0.86276746 0.82290261
45 0.65795407 0.86276746
46 0.09680219 0.65795407
47 -0.44068820 0.09680219
48 -0.61153756 -0.44068820
49 -1.25140241 -0.61153756
50 -1.36380910 -1.25140241
51 -0.98089404 -1.36380910
52 -0.57418232 -0.98089404
53 -0.27140241 -0.57418232
54 -0.15899571 -0.27140241
55 0.02290261 -0.15899571
56 0.26276746 0.02290261
57 0.65795407 0.26276746
58 1.29680219 0.65795407
59 1.95931180 1.29680219
> 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/7ap7j1258663129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89qx61258663129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/912fw1258663129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hwzw1258663129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vvi41258663129.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/12f3br1258663129.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/13vnex1258663129.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/143gsh1258663129.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/15ccha1258663129.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/16zqhz1258663129.tab")
+ }
> system("convert tmp/1l7fy1258663129.ps tmp/1l7fy1258663129.png")
> system("convert tmp/2vrr71258663129.ps tmp/2vrr71258663129.png")
> system("convert tmp/3r00m1258663129.ps tmp/3r00m1258663129.png")
> system("convert tmp/4hsfl1258663129.ps tmp/4hsfl1258663129.png")
> system("convert tmp/5fy9f1258663129.ps tmp/5fy9f1258663129.png")
> system("convert tmp/68e4l1258663129.ps tmp/68e4l1258663129.png")
> system("convert tmp/7ap7j1258663129.ps tmp/7ap7j1258663129.png")
> system("convert tmp/89qx61258663129.ps tmp/89qx61258663129.png")
> system("convert tmp/912fw1258663129.ps tmp/912fw1258663129.png")
> system("convert tmp/10hwzw1258663129.ps tmp/10hwzw1258663129.png")
>
>
> proc.time()
user system elapsed
2.440 1.588 3.908