R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(22,78.1,21.8,74.5,21.5,74.6,21.3,75.5,21.1,76.9,21.2,76.3,21,73.8,20.8,73.4,20.5,75.8,20.4,76.9,20.1,73.2,19.9,72.1,19.6,74.3,19.4,73.1,19.2,72.2,19.1,69.4,19.1,70.8,18.9,71.1,18.7,71.2,18.7,70.6,18.7,71.1,18.4,70.3,18.4,68.3,18.3,68.9,18.4,71.9,18.3,73.3,18.3,70.9,18,70,17.7,65.5,17.7,70.1,17.9,66.6,17.6,67.4,17.7,67.8,17.4,69.4,17.1,69.4,16.8,66.7,16.5,65,16.2,63.1,15.8,65,15.5,63.9,15.2,63,14.9,62.2,14.6,61.4,14.4,61,14.5,58.8,14.2,61),dim=c(2,46),dimnames=list(c('Mortality','Marriages'),1:46))
> y <- array(NA,dim=c(2,46),dimnames=list(c('Mortality','Marriages'),1:46))
> 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
> 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
Mortality Marriages
1 22.0 78.1
2 21.8 74.5
3 21.5 74.6
4 21.3 75.5
5 21.1 76.9
6 21.2 76.3
7 21.0 73.8
8 20.8 73.4
9 20.5 75.8
10 20.4 76.9
11 20.1 73.2
12 19.9 72.1
13 19.6 74.3
14 19.4 73.1
15 19.2 72.2
16 19.1 69.4
17 19.1 70.8
18 18.9 71.1
19 18.7 71.2
20 18.7 70.6
21 18.7 71.1
22 18.4 70.3
23 18.4 68.3
24 18.3 68.9
25 18.4 71.9
26 18.3 73.3
27 18.3 70.9
28 18.0 70.0
29 17.7 65.5
30 17.7 70.1
31 17.9 66.6
32 17.6 67.4
33 17.7 67.8
34 17.4 69.4
35 17.1 69.4
36 16.8 66.7
37 16.5 65.0
38 16.2 63.1
39 15.8 65.0
40 15.5 63.9
41 15.2 63.0
42 14.9 62.2
43 14.6 61.4
44 14.4 61.0
45 14.5 58.8
46 14.2 61.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Marriages
-10.8466 0.4185
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.5321 -0.3930 -0.1741 0.4889 1.4656
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -10.84661 1.42447 -7.614 1.45e-09 ***
Marriages 0.41854 0.02039 20.525 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6642 on 44 degrees of freedom
Multiple R-squared: 0.9054, Adjusted R-squared: 0.9033
F-statistic: 421.3 on 1 and 44 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,] 0.2807812 0.56156241 0.71921879
[2,] 0.1905014 0.38100288 0.80949856
[3,] 0.1564571 0.31291424 0.84354288
[4,] 0.1443083 0.28861667 0.85569166
[5,] 0.2873781 0.57475610 0.71262195
[6,] 0.4442230 0.88844602 0.55577699
[7,] 0.5269104 0.94617922 0.47308961
[8,] 0.5602164 0.87956726 0.43978363
[9,] 0.7153238 0.56935232 0.28467616
[10,] 0.7567235 0.48655304 0.24327652
[11,] 0.7477541 0.50449189 0.25224594
[12,] 0.7635865 0.47282704 0.23641352
[13,] 0.7367962 0.52640762 0.26320381
[14,] 0.7078697 0.58426051 0.29213026
[15,] 0.6863257 0.62734859 0.31367430
[16,] 0.6396862 0.72062761 0.36031380
[17,] 0.5945335 0.81093308 0.40546654
[18,] 0.5403197 0.91936060 0.45968030
[19,] 0.5786234 0.84275329 0.42137665
[20,] 0.5630647 0.87387062 0.43693531
[21,] 0.5965053 0.80698947 0.40349474
[22,] 0.7993796 0.40124089 0.20062045
[23,] 0.7565597 0.48688060 0.24344030
[24,] 0.6997274 0.60054516 0.30027258
[25,] 0.8618312 0.27633756 0.13816878
[26,] 0.8516362 0.29672767 0.14836383
[27,] 0.9405778 0.11884436 0.05942218
[28,] 0.9454980 0.10900405 0.05450202
[29,] 0.9621013 0.07579743 0.03789872
[30,] 0.9447926 0.11041488 0.05520744
[31,] 0.9432380 0.11352402 0.05676201
[32,] 0.9035827 0.19283466 0.09641733
[33,] 0.8909325 0.21813494 0.10906747
[34,] 0.9839093 0.03218136 0.01609068
[35,] 0.9668720 0.06625601 0.03312801
[36,] 0.9443569 0.11128628 0.05564314
[37,] 0.9238013 0.15239735 0.07619868
> postscript(file="/var/wessaorg/rcomp/tmp/1xpyd1321877125.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2d8da1321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/30e3x1321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4vbmb1321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ijml1321877125.ps",horizontal=F,onefile=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 = 46
Frequency = 1
1 2 3 4 5 6
0.158915688 1.465646717 1.123793077 0.547110320 -0.238840636 0.112281202
7 8 9 10 11 12
0.958622195 0.926036754 -0.378450599 -0.938840636 0.309744033 0.570134070
13 14 15 16 17 18
-0.650646004 -0.348402327 -0.171719570 0.900182342 0.314231386 -0.011329533
19 20 21 22 23 24
-0.253183173 -0.002061335 -0.211329533 -0.176500416 0.660572378 0.309450540
25 26 27 28 29 30
-0.846158651 -1.532109607 -0.527622254 -0.450939496 1.132474290 -0.792793136
31 32 33 34 35 36
0.872084253 0.237255136 0.169840577 -0.799817658 -1.099817658 -0.269769386
37 38 39 40 41 42
0.141742489 0.636961643 -0.558257511 -0.397867475 -0.321184717 -0.286355600
43 44 45 46
-0.251526482 -0.284111923 0.736668150 -0.484111923
> postscript(file="/var/wessaorg/rcomp/tmp/6bsm61321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 46
Frequency = 1
lag(myerror, k = 1) myerror
0 0.158915688 NA
1 1.465646717 0.158915688
2 1.123793077 1.465646717
3 0.547110320 1.123793077
4 -0.238840636 0.547110320
5 0.112281202 -0.238840636
6 0.958622195 0.112281202
7 0.926036754 0.958622195
8 -0.378450599 0.926036754
9 -0.938840636 -0.378450599
10 0.309744033 -0.938840636
11 0.570134070 0.309744033
12 -0.650646004 0.570134070
13 -0.348402327 -0.650646004
14 -0.171719570 -0.348402327
15 0.900182342 -0.171719570
16 0.314231386 0.900182342
17 -0.011329533 0.314231386
18 -0.253183173 -0.011329533
19 -0.002061335 -0.253183173
20 -0.211329533 -0.002061335
21 -0.176500416 -0.211329533
22 0.660572378 -0.176500416
23 0.309450540 0.660572378
24 -0.846158651 0.309450540
25 -1.532109607 -0.846158651
26 -0.527622254 -1.532109607
27 -0.450939496 -0.527622254
28 1.132474290 -0.450939496
29 -0.792793136 1.132474290
30 0.872084253 -0.792793136
31 0.237255136 0.872084253
32 0.169840577 0.237255136
33 -0.799817658 0.169840577
34 -1.099817658 -0.799817658
35 -0.269769386 -1.099817658
36 0.141742489 -0.269769386
37 0.636961643 0.141742489
38 -0.558257511 0.636961643
39 -0.397867475 -0.558257511
40 -0.321184717 -0.397867475
41 -0.286355600 -0.321184717
42 -0.251526482 -0.286355600
43 -0.284111923 -0.251526482
44 0.736668150 -0.284111923
45 -0.484111923 0.736668150
46 NA -0.484111923
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.465646717 0.158915688
[2,] 1.123793077 1.465646717
[3,] 0.547110320 1.123793077
[4,] -0.238840636 0.547110320
[5,] 0.112281202 -0.238840636
[6,] 0.958622195 0.112281202
[7,] 0.926036754 0.958622195
[8,] -0.378450599 0.926036754
[9,] -0.938840636 -0.378450599
[10,] 0.309744033 -0.938840636
[11,] 0.570134070 0.309744033
[12,] -0.650646004 0.570134070
[13,] -0.348402327 -0.650646004
[14,] -0.171719570 -0.348402327
[15,] 0.900182342 -0.171719570
[16,] 0.314231386 0.900182342
[17,] -0.011329533 0.314231386
[18,] -0.253183173 -0.011329533
[19,] -0.002061335 -0.253183173
[20,] -0.211329533 -0.002061335
[21,] -0.176500416 -0.211329533
[22,] 0.660572378 -0.176500416
[23,] 0.309450540 0.660572378
[24,] -0.846158651 0.309450540
[25,] -1.532109607 -0.846158651
[26,] -0.527622254 -1.532109607
[27,] -0.450939496 -0.527622254
[28,] 1.132474290 -0.450939496
[29,] -0.792793136 1.132474290
[30,] 0.872084253 -0.792793136
[31,] 0.237255136 0.872084253
[32,] 0.169840577 0.237255136
[33,] -0.799817658 0.169840577
[34,] -1.099817658 -0.799817658
[35,] -0.269769386 -1.099817658
[36,] 0.141742489 -0.269769386
[37,] 0.636961643 0.141742489
[38,] -0.558257511 0.636961643
[39,] -0.397867475 -0.558257511
[40,] -0.321184717 -0.397867475
[41,] -0.286355600 -0.321184717
[42,] -0.251526482 -0.286355600
[43,] -0.284111923 -0.251526482
[44,] 0.736668150 -0.284111923
[45,] -0.484111923 0.736668150
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.465646717 0.158915688
2 1.123793077 1.465646717
3 0.547110320 1.123793077
4 -0.238840636 0.547110320
5 0.112281202 -0.238840636
6 0.958622195 0.112281202
7 0.926036754 0.958622195
8 -0.378450599 0.926036754
9 -0.938840636 -0.378450599
10 0.309744033 -0.938840636
11 0.570134070 0.309744033
12 -0.650646004 0.570134070
13 -0.348402327 -0.650646004
14 -0.171719570 -0.348402327
15 0.900182342 -0.171719570
16 0.314231386 0.900182342
17 -0.011329533 0.314231386
18 -0.253183173 -0.011329533
19 -0.002061335 -0.253183173
20 -0.211329533 -0.002061335
21 -0.176500416 -0.211329533
22 0.660572378 -0.176500416
23 0.309450540 0.660572378
24 -0.846158651 0.309450540
25 -1.532109607 -0.846158651
26 -0.527622254 -1.532109607
27 -0.450939496 -0.527622254
28 1.132474290 -0.450939496
29 -0.792793136 1.132474290
30 0.872084253 -0.792793136
31 0.237255136 0.872084253
32 0.169840577 0.237255136
33 -0.799817658 0.169840577
34 -1.099817658 -0.799817658
35 -0.269769386 -1.099817658
36 0.141742489 -0.269769386
37 0.636961643 0.141742489
38 -0.558257511 0.636961643
39 -0.397867475 -0.558257511
40 -0.321184717 -0.397867475
41 -0.286355600 -0.321184717
42 -0.251526482 -0.286355600
43 -0.284111923 -0.251526482
44 0.736668150 -0.284111923
45 -0.484111923 0.736668150
> 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/wessaorg/rcomp/tmp/7bfzj1321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8yasv1321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9zr9d1321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10zmn01321877125.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/111qyx1321877125.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/wessaorg/rcomp/tmp/122k8k1321877125.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/wessaorg/rcomp/tmp/133u6f1321877126.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/wessaorg/rcomp/tmp/14ntmz1321877126.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/wessaorg/rcomp/tmp/158lj61321877126.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/wessaorg/rcomp/tmp/16vb1v1321877126.tab")
+ }
>
> try(system("convert tmp/1xpyd1321877125.ps tmp/1xpyd1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d8da1321877125.ps tmp/2d8da1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/30e3x1321877125.ps tmp/30e3x1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vbmb1321877125.ps tmp/4vbmb1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ijml1321877125.ps tmp/5ijml1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bsm61321877125.ps tmp/6bsm61321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bfzj1321877125.ps tmp/7bfzj1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yasv1321877125.ps tmp/8yasv1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zr9d1321877125.ps tmp/9zr9d1321877125.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zmn01321877125.ps tmp/10zmn01321877125.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.190 0.522 3.873