R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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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
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> x <- array(list(100.35,102.1,100.35,102.86,100.36,102.99,100.39,103.73,100.34,105.02,100.34,104.43,100.35,104.63,100.43,104.93,100.47,105.87,100.67,105.66,100.75,106.76,100.78,106,100.79,107.22,100.67,107.33,100.64,107.11,100.64,108.86,100.76,107.72,100.79,107.88,100.79,108.38,100.9,107.72,100.98,108.41,101.11,109.9,101.18,111.45,101.22,112.18,101.23,113.34,101.09,113.46,101.26,114.06,101.28,115.54,101.43,116.39,101.53,115.94,101.54,116.97,101.54,115.94,101.79,115.91,102.18,116.43,102.37,116.26,102.46,116.35,102.46,117.9,102.03,117.7,102.26,117.53,102.33,117.86,102.44,117.65,102.5,116.51,102.52,115.93,102.66,115.31,102.72,115),dim=c(2,45),dimnames=list(c('ktot','vmtot'),1:45))
> y <- array(NA,dim=c(2,45),dimnames=list(c('ktot','vmtot'),1:45))
> 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 = 'Do not include Seasonal 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
ktot vmtot
1 100.35 102.10
2 100.35 102.86
3 100.36 102.99
4 100.39 103.73
5 100.34 105.02
6 100.34 104.43
7 100.35 104.63
8 100.43 104.93
9 100.47 105.87
10 100.67 105.66
11 100.75 106.76
12 100.78 106.00
13 100.79 107.22
14 100.67 107.33
15 100.64 107.11
16 100.64 108.86
17 100.76 107.72
18 100.79 107.88
19 100.79 108.38
20 100.90 107.72
21 100.98 108.41
22 101.11 109.90
23 101.18 111.45
24 101.22 112.18
25 101.23 113.34
26 101.09 113.46
27 101.26 114.06
28 101.28 115.54
29 101.43 116.39
30 101.53 115.94
31 101.54 116.97
32 101.54 115.94
33 101.79 115.91
34 102.18 116.43
35 102.37 116.26
36 102.46 116.35
37 102.46 117.90
38 102.03 117.70
39 102.26 117.53
40 102.33 117.86
41 102.44 117.65
42 102.50 116.51
43 102.52 115.93
44 102.66 115.31
45 102.72 115.00
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) vmtot
86.1933 0.1359
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.61202 -0.15550 -0.02041 0.17318 0.90136
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 86.193313 1.098820 78.44 <2e-16 ***
vmtot 0.135872 0.009884 13.75 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3413 on 43 degrees of freedom
Multiple R-squared: 0.8146, Adjusted R-squared: 0.8103
F-statistic: 189 on 1 and 43 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 3.423691e-04 6.847382e-04 0.999657631
[2,] 2.196312e-05 4.392623e-05 0.999978037
[3,] 1.042392e-06 2.084783e-06 0.999998958
[4,] 2.461882e-06 4.923763e-06 0.999997538
[5,] 1.516319e-06 3.032639e-06 0.999998484
[6,] 1.095558e-04 2.191116e-04 0.999890444
[7,] 1.357679e-04 2.715359e-04 0.999864232
[8,] 2.024202e-04 4.048404e-04 0.999797580
[9,] 7.134259e-05 1.426852e-04 0.999928657
[10,] 2.135392e-05 4.270784e-05 0.999978646
[11,] 6.106725e-06 1.221345e-05 0.999993893
[12,] 4.913254e-06 9.826508e-06 0.999995087
[13,] 1.418439e-06 2.836878e-06 0.999998582
[14,] 4.124115e-07 8.248230e-07 0.999999588
[15,] 9.850925e-08 1.970185e-07 0.999999901
[16,] 8.037745e-08 1.607549e-07 0.999999920
[17,] 6.635282e-08 1.327056e-07 0.999999934
[18,] 3.705265e-08 7.410530e-08 0.999999963
[19,] 9.654749e-09 1.930950e-08 0.999999990
[20,] 2.204219e-09 4.408437e-09 0.999999998
[21,] 6.971634e-10 1.394327e-09 0.999999999
[22,] 1.134407e-09 2.268815e-09 0.999999999
[23,] 7.558586e-10 1.511717e-09 0.999999999
[24,] 3.492080e-09 6.984160e-09 0.999999997
[25,] 9.738666e-09 1.947733e-08 0.999999990
[26,] 4.974822e-08 9.949643e-08 0.999999950
[27,] 4.578617e-07 9.157233e-07 0.999999542
[28,] 1.562760e-04 3.125520e-04 0.999843724
[29,] 1.663009e-01 3.326019e-01 0.833699073
[30,] 7.375158e-01 5.249685e-01 0.262484239
[31,] 9.083390e-01 1.833220e-01 0.091661007
[32,] 9.322526e-01 1.354948e-01 0.067747396
[33,] 9.568453e-01 8.630941e-02 0.043154703
[34,] 9.959516e-01 8.096705e-03 0.004048352
[35,] 9.974589e-01 5.082186e-03 0.002541093
[36,] 9.896048e-01 2.079042e-02 0.010395212
> postscript(file="/var/www/html/rcomp/tmp/1pyza1258750353.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fbvj1258750353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mvyc1258750353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/427wj1258750353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/571ey1258750353.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 = 45
Frequency = 1
1 2 3 4 5 6
0.28411027 0.18084722 0.17318380 0.10263819 -0.12263726 -0.04247251
7 8 9 10 11 12
-0.05964700 -0.02040874 -0.10812883 0.12040438 0.05094470 0.18420775
13 14 15 16 17 18
0.02844337 -0.10650259 -0.10661066 -0.34438743 -0.06949285 -0.06123244
19 20 21 22 23 24
-0.12916866 0.07050715 0.05675517 -0.01569477 -0.15629705 -0.21548393
25 26 27 28 29 30
-0.36309597 -0.51940066 -0.43092412 -0.61201534 -0.57750691 -0.41636431
31 32 33 34 35 36
-0.54631293 -0.40636431 -0.15228814 0.16705819 0.38015651 0.45792799
37 38 39 40 41 42
0.24732570 -0.15549981 0.09759850 0.12276060 0.26129381 0.47618839
43 44 45
0.57499441 0.79923532 0.90135578
> postscript(file="/var/www/html/rcomp/tmp/696cw1258750353.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 = 45
Frequency = 1
lag(myerror, k = 1) myerror
0 0.28411027 NA
1 0.18084722 0.28411027
2 0.17318380 0.18084722
3 0.10263819 0.17318380
4 -0.12263726 0.10263819
5 -0.04247251 -0.12263726
6 -0.05964700 -0.04247251
7 -0.02040874 -0.05964700
8 -0.10812883 -0.02040874
9 0.12040438 -0.10812883
10 0.05094470 0.12040438
11 0.18420775 0.05094470
12 0.02844337 0.18420775
13 -0.10650259 0.02844337
14 -0.10661066 -0.10650259
15 -0.34438743 -0.10661066
16 -0.06949285 -0.34438743
17 -0.06123244 -0.06949285
18 -0.12916866 -0.06123244
19 0.07050715 -0.12916866
20 0.05675517 0.07050715
21 -0.01569477 0.05675517
22 -0.15629705 -0.01569477
23 -0.21548393 -0.15629705
24 -0.36309597 -0.21548393
25 -0.51940066 -0.36309597
26 -0.43092412 -0.51940066
27 -0.61201534 -0.43092412
28 -0.57750691 -0.61201534
29 -0.41636431 -0.57750691
30 -0.54631293 -0.41636431
31 -0.40636431 -0.54631293
32 -0.15228814 -0.40636431
33 0.16705819 -0.15228814
34 0.38015651 0.16705819
35 0.45792799 0.38015651
36 0.24732570 0.45792799
37 -0.15549981 0.24732570
38 0.09759850 -0.15549981
39 0.12276060 0.09759850
40 0.26129381 0.12276060
41 0.47618839 0.26129381
42 0.57499441 0.47618839
43 0.79923532 0.57499441
44 0.90135578 0.79923532
45 NA 0.90135578
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.18084722 0.28411027
[2,] 0.17318380 0.18084722
[3,] 0.10263819 0.17318380
[4,] -0.12263726 0.10263819
[5,] -0.04247251 -0.12263726
[6,] -0.05964700 -0.04247251
[7,] -0.02040874 -0.05964700
[8,] -0.10812883 -0.02040874
[9,] 0.12040438 -0.10812883
[10,] 0.05094470 0.12040438
[11,] 0.18420775 0.05094470
[12,] 0.02844337 0.18420775
[13,] -0.10650259 0.02844337
[14,] -0.10661066 -0.10650259
[15,] -0.34438743 -0.10661066
[16,] -0.06949285 -0.34438743
[17,] -0.06123244 -0.06949285
[18,] -0.12916866 -0.06123244
[19,] 0.07050715 -0.12916866
[20,] 0.05675517 0.07050715
[21,] -0.01569477 0.05675517
[22,] -0.15629705 -0.01569477
[23,] -0.21548393 -0.15629705
[24,] -0.36309597 -0.21548393
[25,] -0.51940066 -0.36309597
[26,] -0.43092412 -0.51940066
[27,] -0.61201534 -0.43092412
[28,] -0.57750691 -0.61201534
[29,] -0.41636431 -0.57750691
[30,] -0.54631293 -0.41636431
[31,] -0.40636431 -0.54631293
[32,] -0.15228814 -0.40636431
[33,] 0.16705819 -0.15228814
[34,] 0.38015651 0.16705819
[35,] 0.45792799 0.38015651
[36,] 0.24732570 0.45792799
[37,] -0.15549981 0.24732570
[38,] 0.09759850 -0.15549981
[39,] 0.12276060 0.09759850
[40,] 0.26129381 0.12276060
[41,] 0.47618839 0.26129381
[42,] 0.57499441 0.47618839
[43,] 0.79923532 0.57499441
[44,] 0.90135578 0.79923532
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.18084722 0.28411027
2 0.17318380 0.18084722
3 0.10263819 0.17318380
4 -0.12263726 0.10263819
5 -0.04247251 -0.12263726
6 -0.05964700 -0.04247251
7 -0.02040874 -0.05964700
8 -0.10812883 -0.02040874
9 0.12040438 -0.10812883
10 0.05094470 0.12040438
11 0.18420775 0.05094470
12 0.02844337 0.18420775
13 -0.10650259 0.02844337
14 -0.10661066 -0.10650259
15 -0.34438743 -0.10661066
16 -0.06949285 -0.34438743
17 -0.06123244 -0.06949285
18 -0.12916866 -0.06123244
19 0.07050715 -0.12916866
20 0.05675517 0.07050715
21 -0.01569477 0.05675517
22 -0.15629705 -0.01569477
23 -0.21548393 -0.15629705
24 -0.36309597 -0.21548393
25 -0.51940066 -0.36309597
26 -0.43092412 -0.51940066
27 -0.61201534 -0.43092412
28 -0.57750691 -0.61201534
29 -0.41636431 -0.57750691
30 -0.54631293 -0.41636431
31 -0.40636431 -0.54631293
32 -0.15228814 -0.40636431
33 0.16705819 -0.15228814
34 0.38015651 0.16705819
35 0.45792799 0.38015651
36 0.24732570 0.45792799
37 -0.15549981 0.24732570
38 0.09759850 -0.15549981
39 0.12276060 0.09759850
40 0.26129381 0.12276060
41 0.47618839 0.26129381
42 0.57499441 0.47618839
43 0.79923532 0.57499441
44 0.90135578 0.79923532
> 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/7085p1258750353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/847lh1258750353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9y8i71258750353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10t9uu1258750353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113lww1258750353.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/1227ty1258750353.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/13klim1258750353.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/14ey8e1258750353.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/15wplp1258750353.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/162t0h1258750353.tab")
+ }
>
> system("convert tmp/1pyza1258750353.ps tmp/1pyza1258750353.png")
> system("convert tmp/2fbvj1258750353.ps tmp/2fbvj1258750353.png")
> system("convert tmp/3mvyc1258750353.ps tmp/3mvyc1258750353.png")
> system("convert tmp/427wj1258750353.ps tmp/427wj1258750353.png")
> system("convert tmp/571ey1258750353.ps tmp/571ey1258750353.png")
> system("convert tmp/696cw1258750353.ps tmp/696cw1258750353.png")
> system("convert tmp/7085p1258750353.ps tmp/7085p1258750353.png")
> system("convert tmp/847lh1258750353.ps tmp/847lh1258750353.png")
> system("convert tmp/9y8i71258750353.ps tmp/9y8i71258750353.png")
> system("convert tmp/10t9uu1258750353.ps tmp/10t9uu1258750353.png")
>
>
> proc.time()
user system elapsed
2.309 1.524 2.763