R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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(6.3,0,3,2.1,3.406028945,4,9.1,1.02325246,4,15.8,-1.638272164,1,5.2,2.204119983,4,10.9,0.51851394,1,8.3,1.717337583,1,11,-0.37161107,4,3.2,2.667452953,5,6.3,-1.124938737,1,6.6,-0.105130343,2,9.5,-0.698970004,2,3.3,1.441852176,5,11,-0.920818754,2,4.7,1.929418926,1,10.4,-0.995678626,3,7.4,0.017033339,4,2.1,2.716837723,5,17.9,-2,1,6.1,1.792391689,1,11.9,-1.638272164,3,13.8,0.230448921,1,14.3,0.544068044,1,15.2,-0.318758763,2,10,1,4,11.9,0.209515015,2,6.5,2.283301229,4,7.5,0.397940009,5,10.6,-0.552841969,3,7.4,0.626853415,1,8.4,0.832508913,2,5.7,-0.124938737,2,4.9,0.556302501,3,3.2,1.744292983,5,11,-0.045757491,2,4.9,0.301029996,3,13.2,-0.982966661,2,9.7,0.622214023,4,12.8,0.544068044,1),dim=c(3,39),dimnames=list(c('SWS','log_Wb','D'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('SWS','log_Wb','D'),1:39))
> 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
SWS log_Wb D
1 6.3 0.00000000 3
2 2.1 3.40602895 4
3 9.1 1.02325246 4
4 15.8 -1.63827216 1
5 5.2 2.20411998 4
6 10.9 0.51851394 1
7 8.3 1.71733758 1
8 11.0 -0.37161107 4
9 3.2 2.66745295 5
10 6.3 -1.12493874 1
11 6.6 -0.10513034 2
12 9.5 -0.69897000 2
13 3.3 1.44185218 5
14 11.0 -0.92081875 2
15 4.7 1.92941893 1
16 10.4 -0.99567863 3
17 7.4 0.01703334 4
18 2.1 2.71683772 5
19 17.9 -2.00000000 1
20 6.1 1.79239169 1
21 11.9 -1.63827216 3
22 13.8 0.23044892 1
23 14.3 0.54406804 1
24 15.2 -0.31875876 2
25 10.0 1.00000000 4
26 11.9 0.20951501 2
27 6.5 2.28330123 4
28 7.5 0.39794001 5
29 10.6 -0.55284197 3
30 7.4 0.62685342 1
31 8.4 0.83250891 2
32 5.7 -0.12493874 2
33 4.9 0.55630250 3
34 3.2 1.74429298 5
35 11.0 -0.04575749 2
36 4.9 0.30103000 3
37 13.2 -0.98296666 2
38 9.7 0.62221402 4
39 12.8 0.54406804 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) log_Wb D
11.6991 -1.8149 -0.8062
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.6345 -1.6456 0.3162 2.0518 4.5348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.6991 0.9411 12.431 1.37e-14 ***
log_Wb -1.8149 0.3729 -4.866 2.26e-05 ***
D -0.8062 0.3370 -2.393 0.0221 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.661 on 36 degrees of freedom
Multiple R-squared: 0.5741, Adjusted R-squared: 0.5505
F-statistic: 24.27 on 2 and 36 DF, p-value: 2.124e-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,] 0.4874176 0.9748352 0.5125824
[2,] 0.3145223 0.6290446 0.6854777
[3,] 0.2118516 0.4237032 0.7881484
[4,] 0.1186435 0.2372870 0.8813565
[5,] 0.6866983 0.6266033 0.3133017
[6,] 0.7152216 0.5695569 0.2847784
[7,] 0.6410260 0.7179479 0.3589740
[8,] 0.5852073 0.8295854 0.4147927
[9,] 0.4931101 0.9862202 0.5068899
[10,] 0.4659547 0.9319093 0.5340453
[11,] 0.3727594 0.7455188 0.6272406
[12,] 0.2914924 0.5829848 0.7085076
[13,] 0.2167447 0.4334894 0.7832553
[14,] 0.3077384 0.6154768 0.6922616
[15,] 0.2636949 0.5273898 0.7363051
[16,] 0.1882603 0.3765205 0.8117397
[17,] 0.2275901 0.4551802 0.7724099
[18,] 0.3396932 0.6793864 0.6603068
[19,] 0.5035276 0.9929449 0.4964724
[20,] 0.5394326 0.9211349 0.4605674
[21,] 0.5129440 0.9741121 0.4870560
[22,] 0.4907645 0.9815291 0.5092355
[23,] 0.3908121 0.7816243 0.6091879
[24,] 0.2888068 0.5776137 0.7111932
[25,] 0.2474804 0.4949607 0.7525196
[26,] 0.1555120 0.3110241 0.8444880
[27,] 0.2939875 0.5879749 0.7060125
[28,] 0.3338171 0.6676341 0.6661829
> postscript(file="/var/www/rcomp/tmp/1y9ql1291922228.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ri8o1291922228.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3ri8o1291922228.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ri8o1291922228.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5297r1291922228.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 = 39
Frequency = 1
1 2 3 4 5 6 7
-2.9804580 -0.1927817 2.4828170 1.9338766 0.7259241 0.9481374 0.5238323
8 9 10 11 12 13 14
1.8513376 0.3730246 -6.6344960 -3.6774715 -1.8552063 -1.7512670 -0.7578303
15 16 17 18 19 20 21
-2.6912701 -0.6874734 -1.0433279 -0.6373490 3.3773919 -1.5399551 -0.3536895
22 23 24 25 26 27 28
3.3253403 4.3945145 4.5348232 3.3406171 2.1935651 2.1696268 0.5541805
29 30 31 32 33 34 35
0.3162123 -2.3552418 -0.1757893 -4.6134210 -3.3708478 -1.3023798 0.8302818
36 37 38 39
-3.8341312 1.3293801 2.3549891 2.8945145
> postscript(file="/var/www/rcomp/tmp/6297r1291922228.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 = 39
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.9804580 NA
1 -0.1927817 -2.9804580
2 2.4828170 -0.1927817
3 1.9338766 2.4828170
4 0.7259241 1.9338766
5 0.9481374 0.7259241
6 0.5238323 0.9481374
7 1.8513376 0.5238323
8 0.3730246 1.8513376
9 -6.6344960 0.3730246
10 -3.6774715 -6.6344960
11 -1.8552063 -3.6774715
12 -1.7512670 -1.8552063
13 -0.7578303 -1.7512670
14 -2.6912701 -0.7578303
15 -0.6874734 -2.6912701
16 -1.0433279 -0.6874734
17 -0.6373490 -1.0433279
18 3.3773919 -0.6373490
19 -1.5399551 3.3773919
20 -0.3536895 -1.5399551
21 3.3253403 -0.3536895
22 4.3945145 3.3253403
23 4.5348232 4.3945145
24 3.3406171 4.5348232
25 2.1935651 3.3406171
26 2.1696268 2.1935651
27 0.5541805 2.1696268
28 0.3162123 0.5541805
29 -2.3552418 0.3162123
30 -0.1757893 -2.3552418
31 -4.6134210 -0.1757893
32 -3.3708478 -4.6134210
33 -1.3023798 -3.3708478
34 0.8302818 -1.3023798
35 -3.8341312 0.8302818
36 1.3293801 -3.8341312
37 2.3549891 1.3293801
38 2.8945145 2.3549891
39 NA 2.8945145
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1927817 -2.9804580
[2,] 2.4828170 -0.1927817
[3,] 1.9338766 2.4828170
[4,] 0.7259241 1.9338766
[5,] 0.9481374 0.7259241
[6,] 0.5238323 0.9481374
[7,] 1.8513376 0.5238323
[8,] 0.3730246 1.8513376
[9,] -6.6344960 0.3730246
[10,] -3.6774715 -6.6344960
[11,] -1.8552063 -3.6774715
[12,] -1.7512670 -1.8552063
[13,] -0.7578303 -1.7512670
[14,] -2.6912701 -0.7578303
[15,] -0.6874734 -2.6912701
[16,] -1.0433279 -0.6874734
[17,] -0.6373490 -1.0433279
[18,] 3.3773919 -0.6373490
[19,] -1.5399551 3.3773919
[20,] -0.3536895 -1.5399551
[21,] 3.3253403 -0.3536895
[22,] 4.3945145 3.3253403
[23,] 4.5348232 4.3945145
[24,] 3.3406171 4.5348232
[25,] 2.1935651 3.3406171
[26,] 2.1696268 2.1935651
[27,] 0.5541805 2.1696268
[28,] 0.3162123 0.5541805
[29,] -2.3552418 0.3162123
[30,] -0.1757893 -2.3552418
[31,] -4.6134210 -0.1757893
[32,] -3.3708478 -4.6134210
[33,] -1.3023798 -3.3708478
[34,] 0.8302818 -1.3023798
[35,] -3.8341312 0.8302818
[36,] 1.3293801 -3.8341312
[37,] 2.3549891 1.3293801
[38,] 2.8945145 2.3549891
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1927817 -2.9804580
2 2.4828170 -0.1927817
3 1.9338766 2.4828170
4 0.7259241 1.9338766
5 0.9481374 0.7259241
6 0.5238323 0.9481374
7 1.8513376 0.5238323
8 0.3730246 1.8513376
9 -6.6344960 0.3730246
10 -3.6774715 -6.6344960
11 -1.8552063 -3.6774715
12 -1.7512670 -1.8552063
13 -0.7578303 -1.7512670
14 -2.6912701 -0.7578303
15 -0.6874734 -2.6912701
16 -1.0433279 -0.6874734
17 -0.6373490 -1.0433279
18 3.3773919 -0.6373490
19 -1.5399551 3.3773919
20 -0.3536895 -1.5399551
21 3.3253403 -0.3536895
22 4.3945145 3.3253403
23 4.5348232 4.3945145
24 3.3406171 4.5348232
25 2.1935651 3.3406171
26 2.1696268 2.1935651
27 0.5541805 2.1696268
28 0.3162123 0.5541805
29 -2.3552418 0.3162123
30 -0.1757893 -2.3552418
31 -4.6134210 -0.1757893
32 -3.3708478 -4.6134210
33 -1.3023798 -3.3708478
34 0.8302818 -1.3023798
35 -3.8341312 0.8302818
36 1.3293801 -3.8341312
37 2.3549891 1.3293801
38 2.8945145 2.3549891
> 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/rcomp/tmp/7c06u1291922228.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8c06u1291922228.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9na5f1291922228.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10na5f1291922228.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/111jl61291922228.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/rcomp/tmp/12ct2r1291922228.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/rcomp/tmp/13jcz21291922228.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/rcomp/tmp/144uy81291922228.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/rcomp/tmp/15pdxw1291922228.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/rcomp/tmp/16tdd21291922228.tab")
+ }
>
> try(system("convert tmp/1y9ql1291922228.ps tmp/1y9ql1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ri8o1291922228.ps tmp/2ri8o1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ri8o1291922228.ps tmp/3ri8o1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ri8o1291922228.ps tmp/4ri8o1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/5297r1291922228.ps tmp/5297r1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/6297r1291922228.ps tmp/6297r1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/7c06u1291922228.ps tmp/7c06u1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/8c06u1291922228.ps tmp/8c06u1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/9na5f1291922228.ps tmp/9na5f1291922228.png",intern=TRUE))
character(0)
> try(system("convert tmp/10na5f1291922228.ps tmp/10na5f1291922228.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.760 1.810 4.545