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(12.6,18,15.7,16,13.2,19,20.3,18,12.8,23,8,20,0.9,20,3.6,15,14.1,17,21.7,16,24.5,15,18.9,10,13.9,13,11,10,5.8,19,15.5,21,22.4,17,31.7,16,30.3,17,31.4,14,20.2,18,19.7,17,10.8,14,13.2,15,15.1,16,15.6,11,15.5,15,12.7,13,10.9,17,10,16,9.1,9,10.3,17,16.9,15,22,12,27.6,12,28.9,12,31,12,32.9,4,38.1,7,28.8,4,29,3,21.8,3,28.8,0,25.6,5,28.2,3,20.2,4,17.9,3,16.3,10,13.2,4,8.1,1,4.5,1,-0.1,8,0,5,2.3,4,2.8,0,2.9,2,0.1,7,3.5,6,8.6,9,13.8,10),dim=c(2,60),dimnames=list(c('Rvnp','Svdg'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Rvnp','Svdg'),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 = '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
Rvnp Svdg M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 12.6 18 1 0 0 0 0 0 0 0 0 0 0
2 15.7 16 0 1 0 0 0 0 0 0 0 0 0
3 13.2 19 0 0 1 0 0 0 0 0 0 0 0
4 20.3 18 0 0 0 1 0 0 0 0 0 0 0
5 12.8 23 0 0 0 0 1 0 0 0 0 0 0
6 8.0 20 0 0 0 0 0 1 0 0 0 0 0
7 0.9 20 0 0 0 0 0 0 1 0 0 0 0
8 3.6 15 0 0 0 0 0 0 0 1 0 0 0
9 14.1 17 0 0 0 0 0 0 0 0 1 0 0
10 21.7 16 0 0 0 0 0 0 0 0 0 1 0
11 24.5 15 0 0 0 0 0 0 0 0 0 0 1
12 18.9 10 0 0 0 0 0 0 0 0 0 0 0
13 13.9 13 1 0 0 0 0 0 0 0 0 0 0
14 11.0 10 0 1 0 0 0 0 0 0 0 0 0
15 5.8 19 0 0 1 0 0 0 0 0 0 0 0
16 15.5 21 0 0 0 1 0 0 0 0 0 0 0
17 22.4 17 0 0 0 0 1 0 0 0 0 0 0
18 31.7 16 0 0 0 0 0 1 0 0 0 0 0
19 30.3 17 0 0 0 0 0 0 1 0 0 0 0
20 31.4 14 0 0 0 0 0 0 0 1 0 0 0
21 20.2 18 0 0 0 0 0 0 0 0 1 0 0
22 19.7 17 0 0 0 0 0 0 0 0 0 1 0
23 10.8 14 0 0 0 0 0 0 0 0 0 0 1
24 13.2 15 0 0 0 0 0 0 0 0 0 0 0
25 15.1 16 1 0 0 0 0 0 0 0 0 0 0
26 15.6 11 0 1 0 0 0 0 0 0 0 0 0
27 15.5 15 0 0 1 0 0 0 0 0 0 0 0
28 12.7 13 0 0 0 1 0 0 0 0 0 0 0
29 10.9 17 0 0 0 0 1 0 0 0 0 0 0
30 10.0 16 0 0 0 0 0 1 0 0 0 0 0
31 9.1 9 0 0 0 0 0 0 1 0 0 0 0
32 10.3 17 0 0 0 0 0 0 0 1 0 0 0
33 16.9 15 0 0 0 0 0 0 0 0 1 0 0
34 22.0 12 0 0 0 0 0 0 0 0 0 1 0
35 27.6 12 0 0 0 0 0 0 0 0 0 0 1
36 28.9 12 0 0 0 0 0 0 0 0 0 0 0
37 31.0 12 1 0 0 0 0 0 0 0 0 0 0
38 32.9 4 0 1 0 0 0 0 0 0 0 0 0
39 38.1 7 0 0 1 0 0 0 0 0 0 0 0
40 28.8 4 0 0 0 1 0 0 0 0 0 0 0
41 29.0 3 0 0 0 0 1 0 0 0 0 0 0
42 21.8 3 0 0 0 0 0 1 0 0 0 0 0
43 28.8 0 0 0 0 0 0 0 1 0 0 0 0
44 25.6 5 0 0 0 0 0 0 0 1 0 0 0
45 28.2 3 0 0 0 0 0 0 0 0 1 0 0
46 20.2 4 0 0 0 0 0 0 0 0 0 1 0
47 17.9 3 0 0 0 0 0 0 0 0 0 0 1
48 16.3 10 0 0 0 0 0 0 0 0 0 0 0
49 13.2 4 1 0 0 0 0 0 0 0 0 0 0
50 8.1 1 0 1 0 0 0 0 0 0 0 0 0
51 4.5 1 0 0 1 0 0 0 0 0 0 0 0
52 -0.1 8 0 0 0 1 0 0 0 0 0 0 0
53 0.0 5 0 0 0 0 1 0 0 0 0 0 0
54 2.3 4 0 0 0 0 0 1 0 0 0 0 0
55 2.8 0 0 0 0 0 0 0 1 0 0 0 0
56 2.9 2 0 0 0 0 0 0 0 1 0 0 0
57 0.1 7 0 0 0 0 0 0 0 0 1 0 0
58 3.5 6 0 0 0 0 0 0 0 0 0 1 0
59 8.6 9 0 0 0 0 0 0 0 0 0 0 1
60 13.8 10 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) Svdg M1 M2 M3 M4
18.4526 -0.0204 -1.0355 -1.6212 -2.7837 -2.7514
M5 M6 M7 M8 M9 M10
-3.1674 -3.4518 -3.8849 -3.4763 -2.3078 -0.8082
M11
-0.3563
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.902 -5.869 -1.823 6.747 22.574
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.4526 5.4813 3.366 0.00153 **
Svdg -0.0204 0.2300 -0.089 0.92971
M1 -1.0355 6.8127 -0.152 0.87984
M2 -1.6212 6.8420 -0.237 0.81373
M3 -2.7837 6.8096 -0.409 0.68455
M4 -2.7514 6.8147 -0.404 0.68823
M5 -3.1674 6.8170 -0.465 0.64435
M6 -3.4518 6.8077 -0.507 0.61449
M7 -3.8849 6.8259 -0.569 0.57197
M8 -3.4763 6.8096 -0.511 0.61209
M9 -2.3078 6.8085 -0.339 0.73615
M10 -0.8082 6.8077 -0.119 0.90601
M11 -0.3563 6.8096 -0.052 0.95849
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.76 on 47 degrees of freedom
Multiple R-squared: 0.01775, Adjusted R-squared: -0.233
F-statistic: 0.07077 on 12 and 47 DF, p-value: 1
> 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,] 0.049623355 0.099246710 0.9503766
[2,] 0.034947407 0.069894815 0.9650526
[3,] 0.155143790 0.310287580 0.8448562
[4,] 0.342525595 0.685051189 0.6574744
[5,] 0.515116605 0.969766790 0.4848834
[6,] 0.419313392 0.838626784 0.5806866
[7,] 0.311839550 0.623679100 0.6881605
[8,] 0.287761689 0.575523378 0.7122383
[9,] 0.206790358 0.413580715 0.7932096
[10,] 0.142739350 0.285478700 0.8572606
[11,] 0.092278597 0.184557194 0.9077214
[12,] 0.057409140 0.114818279 0.9425909
[13,] 0.051748971 0.103497943 0.9482510
[14,] 0.037524381 0.075048762 0.9624756
[15,] 0.028871691 0.057743382 0.9711283
[16,] 0.023306966 0.046613932 0.9766930
[17,] 0.015806818 0.031613635 0.9841932
[18,] 0.008624663 0.017249327 0.9913753
[19,] 0.004279806 0.008559613 0.9957202
[20,] 0.002738177 0.005476355 0.9972618
[21,] 0.002297875 0.004595750 0.9977021
[22,] 0.002542412 0.005084823 0.9974576
[23,] 0.003521854 0.007043707 0.9964781
[24,] 0.039802499 0.079604997 0.9601975
[25,] 0.030227320 0.060454640 0.9697727
[26,] 0.039128699 0.078257398 0.9608713
[27,] 0.038083501 0.076167002 0.9619165
[28,] 0.072929673 0.145859345 0.9270703
[29,] 0.396013244 0.792026488 0.6039868
> postscript(file="/var/www/html/rcomp/tmp/1b17v1258740233.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gvde1258740233.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/303vd1258740233.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41w8m1258740233.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52cvl1258740233.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
-4.4498392 -0.8049589 -2.0812790 4.9660808 -2.0159985 -6.5927188
7 8 9 10 11 12
-13.2596784 -11.0702394 -1.6979993 4.3820007 6.7097606 0.6514398
13 14 15 16 17 18
-3.2518399 -5.6273598 -9.4812790 0.2272812 7.4616006 17.0256806
19 20 21 22 23 24
16.0791211 16.7093605 4.4224009 2.4024009 -7.0106395 -4.9465595
25 26 27 28 29 30
-1.9906395 -1.0069596 0.1371204 -2.7359200 -4.0383994 -4.6743194
31 32 33 34 35 36
-5.2840800 -4.3294391 1.0612004 4.6004001 9.7485602 10.6922401
37 38 39 40 41 42
13.8277599 16.1502394 22.5739192 13.1804787 13.7759985 6.8604787
43 44 45 46 47 48
14.2323187 10.7257592 12.1163987 2.6371990 -0.1350411 -1.9485602
49 50 51 52 53 54
-4.1354413 -8.7109611 -11.1484816 -15.6379207 -15.1832012 -12.6191211
55 56 57 58 59 60
-11.7676813 -12.0354413 -15.9020007 -14.0220007 -9.3126402 -4.4485602
> postscript(file="/var/www/html/rcomp/tmp/6aqy71258740233.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 -4.4498392 NA
1 -0.8049589 -4.4498392
2 -2.0812790 -0.8049589
3 4.9660808 -2.0812790
4 -2.0159985 4.9660808
5 -6.5927188 -2.0159985
6 -13.2596784 -6.5927188
7 -11.0702394 -13.2596784
8 -1.6979993 -11.0702394
9 4.3820007 -1.6979993
10 6.7097606 4.3820007
11 0.6514398 6.7097606
12 -3.2518399 0.6514398
13 -5.6273598 -3.2518399
14 -9.4812790 -5.6273598
15 0.2272812 -9.4812790
16 7.4616006 0.2272812
17 17.0256806 7.4616006
18 16.0791211 17.0256806
19 16.7093605 16.0791211
20 4.4224009 16.7093605
21 2.4024009 4.4224009
22 -7.0106395 2.4024009
23 -4.9465595 -7.0106395
24 -1.9906395 -4.9465595
25 -1.0069596 -1.9906395
26 0.1371204 -1.0069596
27 -2.7359200 0.1371204
28 -4.0383994 -2.7359200
29 -4.6743194 -4.0383994
30 -5.2840800 -4.6743194
31 -4.3294391 -5.2840800
32 1.0612004 -4.3294391
33 4.6004001 1.0612004
34 9.7485602 4.6004001
35 10.6922401 9.7485602
36 13.8277599 10.6922401
37 16.1502394 13.8277599
38 22.5739192 16.1502394
39 13.1804787 22.5739192
40 13.7759985 13.1804787
41 6.8604787 13.7759985
42 14.2323187 6.8604787
43 10.7257592 14.2323187
44 12.1163987 10.7257592
45 2.6371990 12.1163987
46 -0.1350411 2.6371990
47 -1.9485602 -0.1350411
48 -4.1354413 -1.9485602
49 -8.7109611 -4.1354413
50 -11.1484816 -8.7109611
51 -15.6379207 -11.1484816
52 -15.1832012 -15.6379207
53 -12.6191211 -15.1832012
54 -11.7676813 -12.6191211
55 -12.0354413 -11.7676813
56 -15.9020007 -12.0354413
57 -14.0220007 -15.9020007
58 -9.3126402 -14.0220007
59 -4.4485602 -9.3126402
60 NA -4.4485602
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.8049589 -4.4498392
[2,] -2.0812790 -0.8049589
[3,] 4.9660808 -2.0812790
[4,] -2.0159985 4.9660808
[5,] -6.5927188 -2.0159985
[6,] -13.2596784 -6.5927188
[7,] -11.0702394 -13.2596784
[8,] -1.6979993 -11.0702394
[9,] 4.3820007 -1.6979993
[10,] 6.7097606 4.3820007
[11,] 0.6514398 6.7097606
[12,] -3.2518399 0.6514398
[13,] -5.6273598 -3.2518399
[14,] -9.4812790 -5.6273598
[15,] 0.2272812 -9.4812790
[16,] 7.4616006 0.2272812
[17,] 17.0256806 7.4616006
[18,] 16.0791211 17.0256806
[19,] 16.7093605 16.0791211
[20,] 4.4224009 16.7093605
[21,] 2.4024009 4.4224009
[22,] -7.0106395 2.4024009
[23,] -4.9465595 -7.0106395
[24,] -1.9906395 -4.9465595
[25,] -1.0069596 -1.9906395
[26,] 0.1371204 -1.0069596
[27,] -2.7359200 0.1371204
[28,] -4.0383994 -2.7359200
[29,] -4.6743194 -4.0383994
[30,] -5.2840800 -4.6743194
[31,] -4.3294391 -5.2840800
[32,] 1.0612004 -4.3294391
[33,] 4.6004001 1.0612004
[34,] 9.7485602 4.6004001
[35,] 10.6922401 9.7485602
[36,] 13.8277599 10.6922401
[37,] 16.1502394 13.8277599
[38,] 22.5739192 16.1502394
[39,] 13.1804787 22.5739192
[40,] 13.7759985 13.1804787
[41,] 6.8604787 13.7759985
[42,] 14.2323187 6.8604787
[43,] 10.7257592 14.2323187
[44,] 12.1163987 10.7257592
[45,] 2.6371990 12.1163987
[46,] -0.1350411 2.6371990
[47,] -1.9485602 -0.1350411
[48,] -4.1354413 -1.9485602
[49,] -8.7109611 -4.1354413
[50,] -11.1484816 -8.7109611
[51,] -15.6379207 -11.1484816
[52,] -15.1832012 -15.6379207
[53,] -12.6191211 -15.1832012
[54,] -11.7676813 -12.6191211
[55,] -12.0354413 -11.7676813
[56,] -15.9020007 -12.0354413
[57,] -14.0220007 -15.9020007
[58,] -9.3126402 -14.0220007
[59,] -4.4485602 -9.3126402
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.8049589 -4.4498392
2 -2.0812790 -0.8049589
3 4.9660808 -2.0812790
4 -2.0159985 4.9660808
5 -6.5927188 -2.0159985
6 -13.2596784 -6.5927188
7 -11.0702394 -13.2596784
8 -1.6979993 -11.0702394
9 4.3820007 -1.6979993
10 6.7097606 4.3820007
11 0.6514398 6.7097606
12 -3.2518399 0.6514398
13 -5.6273598 -3.2518399
14 -9.4812790 -5.6273598
15 0.2272812 -9.4812790
16 7.4616006 0.2272812
17 17.0256806 7.4616006
18 16.0791211 17.0256806
19 16.7093605 16.0791211
20 4.4224009 16.7093605
21 2.4024009 4.4224009
22 -7.0106395 2.4024009
23 -4.9465595 -7.0106395
24 -1.9906395 -4.9465595
25 -1.0069596 -1.9906395
26 0.1371204 -1.0069596
27 -2.7359200 0.1371204
28 -4.0383994 -2.7359200
29 -4.6743194 -4.0383994
30 -5.2840800 -4.6743194
31 -4.3294391 -5.2840800
32 1.0612004 -4.3294391
33 4.6004001 1.0612004
34 9.7485602 4.6004001
35 10.6922401 9.7485602
36 13.8277599 10.6922401
37 16.1502394 13.8277599
38 22.5739192 16.1502394
39 13.1804787 22.5739192
40 13.7759985 13.1804787
41 6.8604787 13.7759985
42 14.2323187 6.8604787
43 10.7257592 14.2323187
44 12.1163987 10.7257592
45 2.6371990 12.1163987
46 -0.1350411 2.6371990
47 -1.9485602 -0.1350411
48 -4.1354413 -1.9485602
49 -8.7109611 -4.1354413
50 -11.1484816 -8.7109611
51 -15.6379207 -11.1484816
52 -15.1832012 -15.6379207
53 -12.6191211 -15.1832012
54 -11.7676813 -12.6191211
55 -12.0354413 -11.7676813
56 -15.9020007 -12.0354413
57 -14.0220007 -15.9020007
58 -9.3126402 -14.0220007
59 -4.4485602 -9.3126402
> 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/7egxd1258740233.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ip451258740233.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99qwx1258740233.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bs371258740233.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11959f1258740233.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/12u70g1258740233.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/13vl371258740233.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/14xev61258740233.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/15euu71258740233.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/160ela1258740233.tab")
+ }
> system("convert tmp/1b17v1258740233.ps tmp/1b17v1258740233.png")
> system("convert tmp/2gvde1258740233.ps tmp/2gvde1258740233.png")
> system("convert tmp/303vd1258740233.ps tmp/303vd1258740233.png")
> system("convert tmp/41w8m1258740233.ps tmp/41w8m1258740233.png")
> system("convert tmp/52cvl1258740233.ps tmp/52cvl1258740233.png")
> system("convert tmp/6aqy71258740233.ps tmp/6aqy71258740233.png")
> system("convert tmp/7egxd1258740233.ps tmp/7egxd1258740233.png")
> system("convert tmp/8ip451258740233.ps tmp/8ip451258740233.png")
> system("convert tmp/99qwx1258740233.ps tmp/99qwx1258740233.png")
> system("convert tmp/10bs371258740233.ps tmp/10bs371258740233.png")
>
>
> proc.time()
user system elapsed
2.389 1.546 2.775