R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(4.9,1,6.6,1,8,1,7.6,1,9.2,1,10.7,1,11.2,1,10.7,1,10.4,1,10.1,1,11.4,1,11.5,1,12.2,1,11.9,1,12.3,1,12.4,1,13,0,13.2,0,13,0,12.7,0,14.2,0,15.2,0,15,0,14.1,0,14,0,13.8,0,13.3,0,13.1,0,12.7,0,13.5,0,14.3,0,15,0,15,0,14.5,0,13.7,0,13.1,0,13.1,0,13.4,0,12.9,0,12.9,0,12.6,0,12.3,0,12.3,0,12.8,0,15.8,0,16.2,0,15.8,0,15.3,0,14.9,0,14.4,0,13.6,0,13.1,0,13.2,0,12.9,0,13,0,13,0,15.7,0,15.2,0,14.1,0,13.7,0,13.6,0),dim=c(2,61),dimnames=list(c('Koers/winst','kredietcrisis'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Koers/winst','kredietcrisis'),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 = '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
Koers/winst kredietcrisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 4.9 1 1 0 0 0 0 0 0 0 0 0 0 1
2 6.6 1 0 1 0 0 0 0 0 0 0 0 0 2
3 8.0 1 0 0 1 0 0 0 0 0 0 0 0 3
4 7.6 1 0 0 0 1 0 0 0 0 0 0 0 4
5 9.2 1 0 0 0 0 1 0 0 0 0 0 0 5
6 10.7 1 0 0 0 0 0 1 0 0 0 0 0 6
7 11.2 1 0 0 0 0 0 0 1 0 0 0 0 7
8 10.7 1 0 0 0 0 0 0 0 1 0 0 0 8
9 10.4 1 0 0 0 0 0 0 0 0 1 0 0 9
10 10.1 1 0 0 0 0 0 0 0 0 0 1 0 10
11 11.4 1 0 0 0 0 0 0 0 0 0 0 1 11
12 11.5 1 0 0 0 0 0 0 0 0 0 0 0 12
13 12.2 1 1 0 0 0 0 0 0 0 0 0 0 13
14 11.9 1 0 1 0 0 0 0 0 0 0 0 0 14
15 12.3 1 0 0 1 0 0 0 0 0 0 0 0 15
16 12.4 1 0 0 0 1 0 0 0 0 0 0 0 16
17 13.0 0 0 0 0 0 1 0 0 0 0 0 0 17
18 13.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 13.0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 12.7 0 0 0 0 0 0 0 0 1 0 0 0 20
21 14.2 0 0 0 0 0 0 0 0 0 1 0 0 21
22 15.2 0 0 0 0 0 0 0 0 0 0 1 0 22
23 15.0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 14.1 0 0 0 0 0 0 0 0 0 0 0 0 24
25 14.0 0 1 0 0 0 0 0 0 0 0 0 0 25
26 13.8 0 0 1 0 0 0 0 0 0 0 0 0 26
27 13.3 0 0 0 1 0 0 0 0 0 0 0 0 27
28 13.1 0 0 0 0 1 0 0 0 0 0 0 0 28
29 12.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 13.5 0 0 0 0 0 0 1 0 0 0 0 0 30
31 14.3 0 0 0 0 0 0 0 1 0 0 0 0 31
32 15.0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 15.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 14.5 0 0 0 0 0 0 0 0 0 0 1 0 34
35 13.7 0 0 0 0 0 0 0 0 0 0 0 1 35
36 13.1 0 0 0 0 0 0 0 0 0 0 0 0 36
37 13.1 0 1 0 0 0 0 0 0 0 0 0 0 37
38 13.4 0 0 1 0 0 0 0 0 0 0 0 0 38
39 12.9 0 0 0 1 0 0 0 0 0 0 0 0 39
40 12.9 0 0 0 0 1 0 0 0 0 0 0 0 40
41 12.6 0 0 0 0 0 1 0 0 0 0 0 0 41
42 12.3 0 0 0 0 0 0 1 0 0 0 0 0 42
43 12.3 0 0 0 0 0 0 0 1 0 0 0 0 43
44 12.8 0 0 0 0 0 0 0 0 1 0 0 0 44
45 15.8 0 0 0 0 0 0 0 0 0 1 0 0 45
46 16.2 0 0 0 0 0 0 0 0 0 0 1 0 46
47 15.8 0 0 0 0 0 0 0 0 0 0 0 1 47
48 15.3 0 0 0 0 0 0 0 0 0 0 0 0 48
49 14.9 0 1 0 0 0 0 0 0 0 0 0 0 49
50 14.4 0 0 1 0 0 0 0 0 0 0 0 0 50
51 13.6 0 0 0 1 0 0 0 0 0 0 0 0 51
52 13.1 0 0 0 0 1 0 0 0 0 0 0 0 52
53 13.2 0 0 0 0 0 1 0 0 0 0 0 0 53
54 12.9 0 0 0 0 0 0 1 0 0 0 0 0 54
55 13.0 0 0 0 0 0 0 0 1 0 0 0 0 55
56 13.0 0 0 0 0 0 0 0 0 1 0 0 0 56
57 15.7 0 0 0 0 0 0 0 0 0 1 0 0 57
58 15.2 0 0 0 0 0 0 0 0 0 0 1 0 58
59 14.1 0 0 0 0 0 0 0 0 0 0 0 1 59
60 13.7 0 0 0 0 0 0 0 0 0 0 0 0 60
61 13.6 0 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) kredietcrisis M1 M2 M3
13.31348 -2.93394 -0.91918 -0.70729 -0.72988
M4 M5 M6 M7 M8
-0.95248 -1.24186 -0.88445 -0.66704 -0.60963
M9 M10 M11 t
0.74778 0.74518 0.48259 0.02259
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.58295 -0.74900 -0.05064 0.72211 2.61147
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.31348 0.90959 14.637 < 2e-16 ***
kredietcrisis -2.93394 0.63368 -4.630 2.90e-05 ***
M1 -0.91918 0.83707 -1.098 0.278
M2 -0.70729 0.87824 -0.805 0.425
M3 -0.72988 0.87730 -0.832 0.410
M4 -0.95248 0.87663 -1.087 0.283
M5 -1.24186 0.87923 -1.412 0.164
M6 -0.88445 0.87740 -1.008 0.319
M7 -0.66704 0.87585 -0.762 0.450
M8 -0.60963 0.87457 -0.697 0.489
M9 0.74778 0.87358 0.856 0.396
M10 0.74518 0.87287 0.854 0.398
M11 0.48259 0.87244 0.553 0.583
t 0.02259 0.01574 1.435 0.158
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.379 on 47 degrees of freedom
Multiple R-squared: 0.6907, Adjusted R-squared: 0.6051
F-statistic: 8.072 on 13 and 47 DF, p-value: 3.741e-08
> 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,] 0.7635041 0.47299177 0.236495884
[2,] 0.6950723 0.60985542 0.304927708
[3,] 0.6549353 0.69012936 0.345064678
[4,] 0.5676892 0.86462169 0.432310843
[5,] 0.5870203 0.82595947 0.412979733
[6,] 0.7332801 0.53343981 0.266719907
[7,] 0.6463808 0.70723845 0.353619226
[8,] 0.5569330 0.88613398 0.443066988
[9,] 0.4588083 0.91761654 0.541191730
[10,] 0.4373345 0.87466903 0.562665487
[11,] 0.6065417 0.78691655 0.393458276
[12,] 0.6679221 0.66415575 0.332077875
[13,] 0.9082528 0.18349435 0.091747173
[14,] 0.9525318 0.09493632 0.047468160
[15,] 0.9722786 0.05544277 0.027721385
[16,] 0.9923929 0.01521421 0.007607105
[17,] 0.9869192 0.02616157 0.013080786
[18,] 0.9840608 0.03187842 0.015939210
[19,] 0.9870222 0.02595558 0.012977791
[20,] 0.9907198 0.01856044 0.009280218
[21,] 0.9903261 0.01934779 0.009673893
[22,] 0.9877760 0.02444806 0.012224032
[23,] 0.9835437 0.03291255 0.016456275
[24,] 0.9694619 0.06107628 0.030538142
[25,] 0.9577679 0.08446425 0.042232126
[26,] 0.9508245 0.09835093 0.049175465
[27,] 0.9594437 0.08111251 0.040556254
[28,] 0.9547940 0.09041206 0.045206030
> postscript(file="/var/www/html/rcomp/tmp/1dwnw1227804168.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/233lu1227804168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3o3a71227804168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4dhse1227804168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53pk21227804168.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 6
-4.58294702 -3.11742826 -1.71742826 -1.91742826 -0.05064018 1.06935982
7 8 9 10 11 12
1.32935982 0.74935982 -0.93064018 -1.25064018 0.28935982 0.84935982
13 14 15 16 17 18
2.44594923 1.91146799 2.31146799 2.61146799 0.54431567 0.36431567
19 20 21 22 23 24
-0.07568433 -0.45568433 -0.33568433 0.64431567 0.68431567 0.24431567
25 26 27 28 29 30
1.04090508 0.60642384 0.10642384 0.10642384 -0.02678808 0.39321192
31 32 33 34 35 36
0.95321192 1.57321192 0.19321192 -0.32678808 -0.88678808 -1.02678808
37 38 39 40 41 42
-0.13019868 -0.06467991 -0.56467991 -0.36467991 -0.39789183 -1.07789183
43 44 45 46 47 48
-1.31789183 -0.89789183 0.72210817 1.10210817 0.94210817 0.90210817
49 50 51 52 53 54
1.39869757 0.66421634 -0.13578366 -0.43578366 -0.06899558 -0.74899558
55 56 57 58 59 60
-0.88899558 -0.96899558 0.35100442 -0.16899558 -1.02899558 -0.96899558
61
-0.17240618
> postscript(file="/var/www/html/rcomp/tmp/6v53c1227804168.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 -4.58294702 NA
1 -3.11742826 -4.58294702
2 -1.71742826 -3.11742826
3 -1.91742826 -1.71742826
4 -0.05064018 -1.91742826
5 1.06935982 -0.05064018
6 1.32935982 1.06935982
7 0.74935982 1.32935982
8 -0.93064018 0.74935982
9 -1.25064018 -0.93064018
10 0.28935982 -1.25064018
11 0.84935982 0.28935982
12 2.44594923 0.84935982
13 1.91146799 2.44594923
14 2.31146799 1.91146799
15 2.61146799 2.31146799
16 0.54431567 2.61146799
17 0.36431567 0.54431567
18 -0.07568433 0.36431567
19 -0.45568433 -0.07568433
20 -0.33568433 -0.45568433
21 0.64431567 -0.33568433
22 0.68431567 0.64431567
23 0.24431567 0.68431567
24 1.04090508 0.24431567
25 0.60642384 1.04090508
26 0.10642384 0.60642384
27 0.10642384 0.10642384
28 -0.02678808 0.10642384
29 0.39321192 -0.02678808
30 0.95321192 0.39321192
31 1.57321192 0.95321192
32 0.19321192 1.57321192
33 -0.32678808 0.19321192
34 -0.88678808 -0.32678808
35 -1.02678808 -0.88678808
36 -0.13019868 -1.02678808
37 -0.06467991 -0.13019868
38 -0.56467991 -0.06467991
39 -0.36467991 -0.56467991
40 -0.39789183 -0.36467991
41 -1.07789183 -0.39789183
42 -1.31789183 -1.07789183
43 -0.89789183 -1.31789183
44 0.72210817 -0.89789183
45 1.10210817 0.72210817
46 0.94210817 1.10210817
47 0.90210817 0.94210817
48 1.39869757 0.90210817
49 0.66421634 1.39869757
50 -0.13578366 0.66421634
51 -0.43578366 -0.13578366
52 -0.06899558 -0.43578366
53 -0.74899558 -0.06899558
54 -0.88899558 -0.74899558
55 -0.96899558 -0.88899558
56 0.35100442 -0.96899558
57 -0.16899558 0.35100442
58 -1.02899558 -0.16899558
59 -0.96899558 -1.02899558
60 -0.17240618 -0.96899558
61 NA -0.17240618
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.11742826 -4.58294702
[2,] -1.71742826 -3.11742826
[3,] -1.91742826 -1.71742826
[4,] -0.05064018 -1.91742826
[5,] 1.06935982 -0.05064018
[6,] 1.32935982 1.06935982
[7,] 0.74935982 1.32935982
[8,] -0.93064018 0.74935982
[9,] -1.25064018 -0.93064018
[10,] 0.28935982 -1.25064018
[11,] 0.84935982 0.28935982
[12,] 2.44594923 0.84935982
[13,] 1.91146799 2.44594923
[14,] 2.31146799 1.91146799
[15,] 2.61146799 2.31146799
[16,] 0.54431567 2.61146799
[17,] 0.36431567 0.54431567
[18,] -0.07568433 0.36431567
[19,] -0.45568433 -0.07568433
[20,] -0.33568433 -0.45568433
[21,] 0.64431567 -0.33568433
[22,] 0.68431567 0.64431567
[23,] 0.24431567 0.68431567
[24,] 1.04090508 0.24431567
[25,] 0.60642384 1.04090508
[26,] 0.10642384 0.60642384
[27,] 0.10642384 0.10642384
[28,] -0.02678808 0.10642384
[29,] 0.39321192 -0.02678808
[30,] 0.95321192 0.39321192
[31,] 1.57321192 0.95321192
[32,] 0.19321192 1.57321192
[33,] -0.32678808 0.19321192
[34,] -0.88678808 -0.32678808
[35,] -1.02678808 -0.88678808
[36,] -0.13019868 -1.02678808
[37,] -0.06467991 -0.13019868
[38,] -0.56467991 -0.06467991
[39,] -0.36467991 -0.56467991
[40,] -0.39789183 -0.36467991
[41,] -1.07789183 -0.39789183
[42,] -1.31789183 -1.07789183
[43,] -0.89789183 -1.31789183
[44,] 0.72210817 -0.89789183
[45,] 1.10210817 0.72210817
[46,] 0.94210817 1.10210817
[47,] 0.90210817 0.94210817
[48,] 1.39869757 0.90210817
[49,] 0.66421634 1.39869757
[50,] -0.13578366 0.66421634
[51,] -0.43578366 -0.13578366
[52,] -0.06899558 -0.43578366
[53,] -0.74899558 -0.06899558
[54,] -0.88899558 -0.74899558
[55,] -0.96899558 -0.88899558
[56,] 0.35100442 -0.96899558
[57,] -0.16899558 0.35100442
[58,] -1.02899558 -0.16899558
[59,] -0.96899558 -1.02899558
[60,] -0.17240618 -0.96899558
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.11742826 -4.58294702
2 -1.71742826 -3.11742826
3 -1.91742826 -1.71742826
4 -0.05064018 -1.91742826
5 1.06935982 -0.05064018
6 1.32935982 1.06935982
7 0.74935982 1.32935982
8 -0.93064018 0.74935982
9 -1.25064018 -0.93064018
10 0.28935982 -1.25064018
11 0.84935982 0.28935982
12 2.44594923 0.84935982
13 1.91146799 2.44594923
14 2.31146799 1.91146799
15 2.61146799 2.31146799
16 0.54431567 2.61146799
17 0.36431567 0.54431567
18 -0.07568433 0.36431567
19 -0.45568433 -0.07568433
20 -0.33568433 -0.45568433
21 0.64431567 -0.33568433
22 0.68431567 0.64431567
23 0.24431567 0.68431567
24 1.04090508 0.24431567
25 0.60642384 1.04090508
26 0.10642384 0.60642384
27 0.10642384 0.10642384
28 -0.02678808 0.10642384
29 0.39321192 -0.02678808
30 0.95321192 0.39321192
31 1.57321192 0.95321192
32 0.19321192 1.57321192
33 -0.32678808 0.19321192
34 -0.88678808 -0.32678808
35 -1.02678808 -0.88678808
36 -0.13019868 -1.02678808
37 -0.06467991 -0.13019868
38 -0.56467991 -0.06467991
39 -0.36467991 -0.56467991
40 -0.39789183 -0.36467991
41 -1.07789183 -0.39789183
42 -1.31789183 -1.07789183
43 -0.89789183 -1.31789183
44 0.72210817 -0.89789183
45 1.10210817 0.72210817
46 0.94210817 1.10210817
47 0.90210817 0.94210817
48 1.39869757 0.90210817
49 0.66421634 1.39869757
50 -0.13578366 0.66421634
51 -0.43578366 -0.13578366
52 -0.06899558 -0.43578366
53 -0.74899558 -0.06899558
54 -0.88899558 -0.74899558
55 -0.96899558 -0.88899558
56 0.35100442 -0.96899558
57 -0.16899558 0.35100442
58 -1.02899558 -0.16899558
59 -0.96899558 -1.02899558
60 -0.17240618 -0.96899558
> 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/7gasq1227804168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nrua1227804168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rwwi1227804168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10l97a1227804169.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116j351227804169.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/1242pa1227804169.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/13pmpc1227804169.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/14r5lu1227804169.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/152zmo1227804169.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/16bq301227804169.tab")
+ }
>
> system("convert tmp/1dwnw1227804168.ps tmp/1dwnw1227804168.png")
> system("convert tmp/233lu1227804168.ps tmp/233lu1227804168.png")
> system("convert tmp/3o3a71227804168.ps tmp/3o3a71227804168.png")
> system("convert tmp/4dhse1227804168.ps tmp/4dhse1227804168.png")
> system("convert tmp/53pk21227804168.ps tmp/53pk21227804168.png")
> system("convert tmp/6v53c1227804168.ps tmp/6v53c1227804168.png")
> system("convert tmp/7gasq1227804168.ps tmp/7gasq1227804168.png")
> system("convert tmp/8nrua1227804168.ps tmp/8nrua1227804168.png")
> system("convert tmp/9rwwi1227804168.ps tmp/9rwwi1227804168.png")
> system("convert tmp/10l97a1227804169.ps tmp/10l97a1227804169.png")
>
>
> proc.time()
user system elapsed
2.390 1.567 3.826