R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(19,80.2,18,74.8,19,77.8,19,73,22,72,23,75.8,20,72.6,14,71.9,14,74.8,14,72.9,15,72.9,11,79.9,17,74,16,76,20,69.6,24,77.3,23,75.2,20,75.8,21,77.6,19,76.7,23,77,23,77.9,23,76.7,23,71.9,27,73.4,26,72.5,17,73.7,24,69.5,26,74.7,24,72.5,27,72.1,27,70.7,26,71.4,24,69.5,23,73.5,23,72.4,24,74.5,17,72.2,21,73,19,73.3,22,71.3,22,73.6,18,71.3,16,71.2,14,81.4,12,76.1,14,71.1,16,75.7,8,70,3,68.5,0,56.7,5,57.9,1,58.8,1,59.3,3,61.3,6,62.9,7,61.4,8,64.5,14,63.8,14,61.6,13,64.7),dim=c(2,61),dimnames=list(c('indcvtr','dzcg
'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('indcvtr','dzcg
'),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 = '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
dzcg\r indcvtr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 80.2 19 1 0 0 0 0 0 0 0 0 0 0 1
2 74.8 18 0 1 0 0 0 0 0 0 0 0 0 2
3 77.8 19 0 0 1 0 0 0 0 0 0 0 0 3
4 73.0 19 0 0 0 1 0 0 0 0 0 0 0 4
5 72.0 22 0 0 0 0 1 0 0 0 0 0 0 5
6 75.8 23 0 0 0 0 0 1 0 0 0 0 0 6
7 72.6 20 0 0 0 0 0 0 1 0 0 0 0 7
8 71.9 14 0 0 0 0 0 0 0 1 0 0 0 8
9 74.8 14 0 0 0 0 0 0 0 0 1 0 0 9
10 72.9 14 0 0 0 0 0 0 0 0 0 1 0 10
11 72.9 15 0 0 0 0 0 0 0 0 0 0 1 11
12 79.9 11 0 0 0 0 0 0 0 0 0 0 0 12
13 74.0 17 1 0 0 0 0 0 0 0 0 0 0 13
14 76.0 16 0 1 0 0 0 0 0 0 0 0 0 14
15 69.6 20 0 0 1 0 0 0 0 0 0 0 0 15
16 77.3 24 0 0 0 1 0 0 0 0 0 0 0 16
17 75.2 23 0 0 0 0 1 0 0 0 0 0 0 17
18 75.8 20 0 0 0 0 0 1 0 0 0 0 0 18
19 77.6 21 0 0 0 0 0 0 1 0 0 0 0 19
20 76.7 19 0 0 0 0 0 0 0 1 0 0 0 20
21 77.0 23 0 0 0 0 0 0 0 0 1 0 0 21
22 77.9 23 0 0 0 0 0 0 0 0 0 1 0 22
23 76.7 23 0 0 0 0 0 0 0 0 0 0 1 23
24 71.9 23 0 0 0 0 0 0 0 0 0 0 0 24
25 73.4 27 1 0 0 0 0 0 0 0 0 0 0 25
26 72.5 26 0 1 0 0 0 0 0 0 0 0 0 26
27 73.7 17 0 0 1 0 0 0 0 0 0 0 0 27
28 69.5 24 0 0 0 1 0 0 0 0 0 0 0 28
29 74.7 26 0 0 0 0 1 0 0 0 0 0 0 29
30 72.5 24 0 0 0 0 0 1 0 0 0 0 0 30
31 72.1 27 0 0 0 0 0 0 1 0 0 0 0 31
32 70.7 27 0 0 0 0 0 0 0 1 0 0 0 32
33 71.4 26 0 0 0 0 0 0 0 0 1 0 0 33
34 69.5 24 0 0 0 0 0 0 0 0 0 1 0 34
35 73.5 23 0 0 0 0 0 0 0 0 0 0 1 35
36 72.4 23 0 0 0 0 0 0 0 0 0 0 0 36
37 74.5 24 1 0 0 0 0 0 0 0 0 0 0 37
38 72.2 17 0 1 0 0 0 0 0 0 0 0 0 38
39 73.0 21 0 0 1 0 0 0 0 0 0 0 0 39
40 73.3 19 0 0 0 1 0 0 0 0 0 0 0 40
41 71.3 22 0 0 0 0 1 0 0 0 0 0 0 41
42 73.6 22 0 0 0 0 0 1 0 0 0 0 0 42
43 71.3 18 0 0 0 0 0 0 1 0 0 0 0 43
44 71.2 16 0 0 0 0 0 0 0 1 0 0 0 44
45 81.4 14 0 0 0 0 0 0 0 0 1 0 0 45
46 76.1 12 0 0 0 0 0 0 0 0 0 1 0 46
47 71.1 14 0 0 0 0 0 0 0 0 0 0 1 47
48 75.7 16 0 0 0 0 0 0 0 0 0 0 0 48
49 70.0 8 1 0 0 0 0 0 0 0 0 0 0 49
50 68.5 3 0 1 0 0 0 0 0 0 0 0 0 50
51 56.7 0 0 0 1 0 0 0 0 0 0 0 0 51
52 57.9 5 0 0 0 1 0 0 0 0 0 0 0 52
53 58.8 1 0 0 0 0 1 0 0 0 0 0 0 53
54 59.3 1 0 0 0 0 0 1 0 0 0 0 0 54
55 61.3 3 0 0 0 0 0 0 1 0 0 0 0 55
56 62.9 6 0 0 0 0 0 0 0 1 0 0 0 56
57 61.4 7 0 0 0 0 0 0 0 0 1 0 0 57
58 64.5 8 0 0 0 0 0 0 0 0 0 1 0 58
59 63.8 14 0 0 0 0 0 0 0 0 0 0 1 59
60 61.6 14 0 0 0 0 0 0 0 0 0 0 0 60
61 64.7 13 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) indcvtr M1 M2 M3 M4
72.49080 0.31943 -0.49011 -0.64971 -2.93836 -3.63307
M5 M6 M7 M8 M9 M10
-3.46504 -2.04980 -2.24623 -1.93933 0.61258 -0.05607
M11 t
-0.98746 -0.15969
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.7814 -2.5527 -0.8166 2.5641 11.0107
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.49080 2.87474 25.216 < 2e-16 ***
indcvtr 0.31943 0.08218 3.887 0.000317 ***
M1 -0.49011 2.33374 -0.210 0.834567
M2 -0.64971 2.46642 -0.263 0.793377
M3 -2.93836 2.46727 -1.191 0.239658
M4 -3.63307 2.44440 -1.486 0.143882
M5 -3.46504 2.44078 -1.420 0.162311
M6 -2.04980 2.43927 -0.840 0.404976
M7 -2.24623 2.43743 -0.922 0.361466
M8 -1.93933 2.44009 -0.795 0.430737
M9 0.61258 2.43630 0.251 0.802569
M10 -0.05607 2.43701 -0.023 0.981743
M11 -0.98746 2.43288 -0.406 0.686671
t -0.15969 0.03341 -4.780 1.76e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.846 on 47 degrees of freedom
Multiple R-squared: 0.6418, Adjusted R-squared: 0.5427
F-statistic: 6.478 on 13 and 47 DF, p-value: 8.058e-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,] 0.56517246 0.86965508 0.4348275
[2,] 0.44061280 0.88122561 0.5593872
[3,] 0.38590334 0.77180669 0.6140967
[4,] 0.26619370 0.53238739 0.7338063
[5,] 0.19495272 0.38990544 0.8050473
[6,] 0.11830823 0.23661647 0.8816918
[7,] 0.06753506 0.13507012 0.9324649
[8,] 0.23019399 0.46038799 0.7698060
[9,] 0.20943069 0.41886138 0.7905693
[10,] 0.17790052 0.35580104 0.8220995
[11,] 0.12012022 0.24024045 0.8798798
[12,] 0.11035882 0.22071764 0.8896412
[13,] 0.07340890 0.14681780 0.9265911
[14,] 0.04832262 0.09664525 0.9516774
[15,] 0.03178659 0.06357317 0.9682134
[16,] 0.02506871 0.05013743 0.9749313
[17,] 0.03923253 0.07846506 0.9607675
[18,] 0.12003664 0.24007328 0.8799634
[19,] 0.10375242 0.20750483 0.8962476
[20,] 0.16974139 0.33948277 0.8302586
[21,] 0.31123240 0.62246480 0.6887676
[22,] 0.51683085 0.96633831 0.4831692
[23,] 0.42179340 0.84358680 0.5782066
[24,] 0.34699941 0.69399881 0.6530006
[25,] 0.25063035 0.50126070 0.7493697
[26,] 0.15849120 0.31698240 0.8415088
[27,] 0.14884733 0.29769465 0.8511527
[28,] 0.64024339 0.71951323 0.3597566
> postscript(file="/var/www/html/rcomp/tmp/163ql1260642475.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xx5a1260642475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3djow1260642475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4c3wm1260642475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t6ex1260642475.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
2.28984164 -2.47144188 2.65747120 -1.28812669 -3.25475707 -1.02972986
7 8 9 10 11 12
-2.91532775 -1.84595284 -1.33818111 -2.40983871 -1.63818111 5.81176447
13 14 15 16 17 18
-1.35500809 1.28370839 -3.94566652 3.33101829 1.54210521 1.84484973
19 20 21 22 23 24
3.68153454 3.27319214 -0.09675344 1.63158896 1.52267588 -4.10509585
25 26 27 28 29 30
-3.23300975 -3.49429327 3.02891308 -2.55269010 2.00010884 -0.81657596
31 32 33 34 35 36
-1.81874981 -3.36595087 -4.73874981 -5.17154875 0.23896750 -1.68880423
37 38 39 40 41 42
0.74156984 0.99686229 2.96748738 4.76074815 1.79411776 2.83857430
43 44 45 46 47 48
2.17240575 2.56406334 11.01069373 7.17789479 2.63012306 5.76349267
49 50 51 52 53 54
3.26873069 3.68516448 -4.70820513 -4.25094965 -2.08157475 -2.83711821
55 56 57 58 59 60
-1.11986273 -0.62535177 -4.83700937 -1.22809629 -2.75358533 -5.78135706
61
-1.71212433
> postscript(file="/var/www/html/rcomp/tmp/6vn571260642475.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 2.28984164 NA
1 -2.47144188 2.28984164
2 2.65747120 -2.47144188
3 -1.28812669 2.65747120
4 -3.25475707 -1.28812669
5 -1.02972986 -3.25475707
6 -2.91532775 -1.02972986
7 -1.84595284 -2.91532775
8 -1.33818111 -1.84595284
9 -2.40983871 -1.33818111
10 -1.63818111 -2.40983871
11 5.81176447 -1.63818111
12 -1.35500809 5.81176447
13 1.28370839 -1.35500809
14 -3.94566652 1.28370839
15 3.33101829 -3.94566652
16 1.54210521 3.33101829
17 1.84484973 1.54210521
18 3.68153454 1.84484973
19 3.27319214 3.68153454
20 -0.09675344 3.27319214
21 1.63158896 -0.09675344
22 1.52267588 1.63158896
23 -4.10509585 1.52267588
24 -3.23300975 -4.10509585
25 -3.49429327 -3.23300975
26 3.02891308 -3.49429327
27 -2.55269010 3.02891308
28 2.00010884 -2.55269010
29 -0.81657596 2.00010884
30 -1.81874981 -0.81657596
31 -3.36595087 -1.81874981
32 -4.73874981 -3.36595087
33 -5.17154875 -4.73874981
34 0.23896750 -5.17154875
35 -1.68880423 0.23896750
36 0.74156984 -1.68880423
37 0.99686229 0.74156984
38 2.96748738 0.99686229
39 4.76074815 2.96748738
40 1.79411776 4.76074815
41 2.83857430 1.79411776
42 2.17240575 2.83857430
43 2.56406334 2.17240575
44 11.01069373 2.56406334
45 7.17789479 11.01069373
46 2.63012306 7.17789479
47 5.76349267 2.63012306
48 3.26873069 5.76349267
49 3.68516448 3.26873069
50 -4.70820513 3.68516448
51 -4.25094965 -4.70820513
52 -2.08157475 -4.25094965
53 -2.83711821 -2.08157475
54 -1.11986273 -2.83711821
55 -0.62535177 -1.11986273
56 -4.83700937 -0.62535177
57 -1.22809629 -4.83700937
58 -2.75358533 -1.22809629
59 -5.78135706 -2.75358533
60 -1.71212433 -5.78135706
61 NA -1.71212433
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.47144188 2.28984164
[2,] 2.65747120 -2.47144188
[3,] -1.28812669 2.65747120
[4,] -3.25475707 -1.28812669
[5,] -1.02972986 -3.25475707
[6,] -2.91532775 -1.02972986
[7,] -1.84595284 -2.91532775
[8,] -1.33818111 -1.84595284
[9,] -2.40983871 -1.33818111
[10,] -1.63818111 -2.40983871
[11,] 5.81176447 -1.63818111
[12,] -1.35500809 5.81176447
[13,] 1.28370839 -1.35500809
[14,] -3.94566652 1.28370839
[15,] 3.33101829 -3.94566652
[16,] 1.54210521 3.33101829
[17,] 1.84484973 1.54210521
[18,] 3.68153454 1.84484973
[19,] 3.27319214 3.68153454
[20,] -0.09675344 3.27319214
[21,] 1.63158896 -0.09675344
[22,] 1.52267588 1.63158896
[23,] -4.10509585 1.52267588
[24,] -3.23300975 -4.10509585
[25,] -3.49429327 -3.23300975
[26,] 3.02891308 -3.49429327
[27,] -2.55269010 3.02891308
[28,] 2.00010884 -2.55269010
[29,] -0.81657596 2.00010884
[30,] -1.81874981 -0.81657596
[31,] -3.36595087 -1.81874981
[32,] -4.73874981 -3.36595087
[33,] -5.17154875 -4.73874981
[34,] 0.23896750 -5.17154875
[35,] -1.68880423 0.23896750
[36,] 0.74156984 -1.68880423
[37,] 0.99686229 0.74156984
[38,] 2.96748738 0.99686229
[39,] 4.76074815 2.96748738
[40,] 1.79411776 4.76074815
[41,] 2.83857430 1.79411776
[42,] 2.17240575 2.83857430
[43,] 2.56406334 2.17240575
[44,] 11.01069373 2.56406334
[45,] 7.17789479 11.01069373
[46,] 2.63012306 7.17789479
[47,] 5.76349267 2.63012306
[48,] 3.26873069 5.76349267
[49,] 3.68516448 3.26873069
[50,] -4.70820513 3.68516448
[51,] -4.25094965 -4.70820513
[52,] -2.08157475 -4.25094965
[53,] -2.83711821 -2.08157475
[54,] -1.11986273 -2.83711821
[55,] -0.62535177 -1.11986273
[56,] -4.83700937 -0.62535177
[57,] -1.22809629 -4.83700937
[58,] -2.75358533 -1.22809629
[59,] -5.78135706 -2.75358533
[60,] -1.71212433 -5.78135706
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.47144188 2.28984164
2 2.65747120 -2.47144188
3 -1.28812669 2.65747120
4 -3.25475707 -1.28812669
5 -1.02972986 -3.25475707
6 -2.91532775 -1.02972986
7 -1.84595284 -2.91532775
8 -1.33818111 -1.84595284
9 -2.40983871 -1.33818111
10 -1.63818111 -2.40983871
11 5.81176447 -1.63818111
12 -1.35500809 5.81176447
13 1.28370839 -1.35500809
14 -3.94566652 1.28370839
15 3.33101829 -3.94566652
16 1.54210521 3.33101829
17 1.84484973 1.54210521
18 3.68153454 1.84484973
19 3.27319214 3.68153454
20 -0.09675344 3.27319214
21 1.63158896 -0.09675344
22 1.52267588 1.63158896
23 -4.10509585 1.52267588
24 -3.23300975 -4.10509585
25 -3.49429327 -3.23300975
26 3.02891308 -3.49429327
27 -2.55269010 3.02891308
28 2.00010884 -2.55269010
29 -0.81657596 2.00010884
30 -1.81874981 -0.81657596
31 -3.36595087 -1.81874981
32 -4.73874981 -3.36595087
33 -5.17154875 -4.73874981
34 0.23896750 -5.17154875
35 -1.68880423 0.23896750
36 0.74156984 -1.68880423
37 0.99686229 0.74156984
38 2.96748738 0.99686229
39 4.76074815 2.96748738
40 1.79411776 4.76074815
41 2.83857430 1.79411776
42 2.17240575 2.83857430
43 2.56406334 2.17240575
44 11.01069373 2.56406334
45 7.17789479 11.01069373
46 2.63012306 7.17789479
47 5.76349267 2.63012306
48 3.26873069 5.76349267
49 3.68516448 3.26873069
50 -4.70820513 3.68516448
51 -4.25094965 -4.70820513
52 -2.08157475 -4.25094965
53 -2.83711821 -2.08157475
54 -1.11986273 -2.83711821
55 -0.62535177 -1.11986273
56 -4.83700937 -0.62535177
57 -1.22809629 -4.83700937
58 -2.75358533 -1.22809629
59 -5.78135706 -2.75358533
60 -1.71212433 -5.78135706
> 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/707vx1260642475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8y7dq1260642475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wtwu1260642475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10p3t11260642475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11l6te1260642476.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/12pie61260642476.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/13jgvd1260642476.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/14u5w81260642476.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/15uud31260642476.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/161ksf1260642476.tab")
+ }
>
> try(system("convert tmp/163ql1260642475.ps tmp/163ql1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xx5a1260642475.ps tmp/2xx5a1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/3djow1260642475.ps tmp/3djow1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c3wm1260642475.ps tmp/4c3wm1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t6ex1260642475.ps tmp/5t6ex1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vn571260642475.ps tmp/6vn571260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/707vx1260642475.ps tmp/707vx1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/8y7dq1260642475.ps tmp/8y7dq1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wtwu1260642475.ps tmp/9wtwu1260642475.png",intern=TRUE))
character(0)
> try(system("convert tmp/10p3t11260642475.ps tmp/10p3t11260642475.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.384 1.530 3.605