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(192.37,47.91,3720,0,601.73,192.65,51.56,3683,0,564.01,193.77,56.06,3635,0,513.92,194.54,60.36,3589,0,492.44,198.63,64.19,3590,0,540.36,202.3,67.31,3609,0,520.92,206.05,68.18,3632,0,451.40,210.94,69.24,365,0,397.62,220.57,70.05,3716,0,408.69,228.55,72.22,3760,0,390.15,235.61,74.72,3794,0,361.02,239.86,77.08,3798,0,304.83,243.05,78.81,3779,0,307.09,241.37,80.78,3872,0,270.57,249.31,82.71,3857,0,316.00,259.98,83.76,3914,0,308.64,262.85,85.26,3939,0,282.78,273.13,86.53,3966,0,297.18,278.37,87.32,4035,0,287.67,288.19,88.31,4090,0,259.49,299.13,90.67,4173,0,268.33,301.26,92.88,4231,0,301.05,305.36,94.33,4226,0,310.44,307.75,95.75,4230,0,329.26,317.2,97.53,4270,0,319.59,323.6,100,4331,0,329.16,332.31,102.33,4384,0,381.06,341.59,104.19,4455,0,487.13,344.3,108.87,4532,1,527.37,335.17,108.86,4515,1,606.35),dim=c(5,30),dimnames=list(c('BBP','inflatie','werkeloosheid','crisis','goudprijzen'),1:30))
> y <- array(NA,dim=c(5,30),dimnames=list(c('BBP','inflatie','werkeloosheid','crisis','goudprijzen'),1:30))
> 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'
> 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
BBP inflatie werkeloosheid crisis goudprijzen
1 192.37 47.91 3720 0 601.73
2 192.65 51.56 3683 0 564.01
3 193.77 56.06 3635 0 513.92
4 194.54 60.36 3589 0 492.44
5 198.63 64.19 3590 0 540.36
6 202.30 67.31 3609 0 520.92
7 206.05 68.18 3632 0 451.40
8 210.94 69.24 365 0 397.62
9 220.57 70.05 3716 0 408.69
10 228.55 72.22 3760 0 390.15
11 235.61 74.72 3794 0 361.02
12 239.86 77.08 3798 0 304.83
13 243.05 78.81 3779 0 307.09
14 241.37 80.78 3872 0 270.57
15 249.31 82.71 3857 0 316.00
16 259.98 83.76 3914 0 308.64
17 262.85 85.26 3939 0 282.78
18 273.13 86.53 3966 0 297.18
19 278.37 87.32 4035 0 287.67
20 288.19 88.31 4090 0 259.49
21 299.13 90.67 4173 0 268.33
22 301.26 92.88 4231 0 301.05
23 305.36 94.33 4226 0 310.44
24 307.75 95.75 4230 0 329.26
25 317.20 97.53 4270 0 319.59
26 323.60 100.00 4331 0 329.16
27 332.31 102.33 4384 0 381.06
28 341.59 104.19 4455 0 487.13
29 344.30 108.87 4532 1 527.37
30 335.17 108.86 4515 1 606.35
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) inflatie werkeloosheid crisis goudprijzen
-41.681149 3.168896 0.005673 -23.209733 0.059945
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-21.0190 -4.9444 -0.5404 6.4199 25.0537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.681149 24.117987 -1.728 0.0963 .
inflatie 3.168896 0.209296 15.141 4.25e-14 ***
werkeloosheid 0.005673 0.003256 1.743 0.0937 .
crisis -23.209733 12.786228 -1.815 0.0815 .
goudprijzen 0.059945 0.028657 2.092 0.0468 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.79 on 25 degrees of freedom
Multiple R-squared: 0.9614, Adjusted R-squared: 0.9552
F-statistic: 155.6 on 4 and 25 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.5229587 0.954082665 0.477041332
[2,] 0.9446718 0.110656330 0.055328165
[3,] 0.9718978 0.056204379 0.028102190
[4,] 0.9713830 0.057233952 0.028616976
[5,] 0.9490981 0.101803883 0.050901941
[6,] 0.9559239 0.088152241 0.044076121
[7,] 0.9935907 0.012818668 0.006409334
[8,] 0.9949628 0.010074497 0.005037249
[9,] 0.9969381 0.006123762 0.003061881
[10,] 0.9976731 0.004653727 0.002326864
[11,] 0.9978725 0.004254985 0.002127492
[12,] 0.9961545 0.007690964 0.003845482
[13,] 0.9958481 0.008303877 0.004151939
[14,] 0.9961515 0.007696912 0.003848456
[15,] 0.9903564 0.019287177 0.009643588
> postscript(file="/var/www/rcomp/tmp/14g2a1293562560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2xp1e1293562560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3xp1e1293562560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4xp1e1293562560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/58g0y1293562560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 30
Frequency = 1
1 2 3 4 5 6
25.0536648 16.2382231 6.3731428 -4.9345215 -15.8596143 -21.0190415
7 8 9 10 11 12
-15.9891186 7.3007901 -5.3112872 -3.3460492 -2.6549989 -2.5379979
13 14 15 16 17 18
-4.8578678 -11.1190443 -11.9331973 -4.4727315 -4.9477431 0.2913732
19 20 21 22 23 24
3.2065518 11.2665457 13.7271450 6.5634366 5.5340243 2.2733399
25 26 27 28 29 30
6.4354321 4.0885089 2.0031619 -1.3721271 6.8681453 -6.8681453
> postscript(file="/var/www/rcomp/tmp/68g0y1293562560.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 = 30
Frequency = 1
lag(myerror, k = 1) myerror
0 25.0536648 NA
1 16.2382231 25.0536648
2 6.3731428 16.2382231
3 -4.9345215 6.3731428
4 -15.8596143 -4.9345215
5 -21.0190415 -15.8596143
6 -15.9891186 -21.0190415
7 7.3007901 -15.9891186
8 -5.3112872 7.3007901
9 -3.3460492 -5.3112872
10 -2.6549989 -3.3460492
11 -2.5379979 -2.6549989
12 -4.8578678 -2.5379979
13 -11.1190443 -4.8578678
14 -11.9331973 -11.1190443
15 -4.4727315 -11.9331973
16 -4.9477431 -4.4727315
17 0.2913732 -4.9477431
18 3.2065518 0.2913732
19 11.2665457 3.2065518
20 13.7271450 11.2665457
21 6.5634366 13.7271450
22 5.5340243 6.5634366
23 2.2733399 5.5340243
24 6.4354321 2.2733399
25 4.0885089 6.4354321
26 2.0031619 4.0885089
27 -1.3721271 2.0031619
28 6.8681453 -1.3721271
29 -6.8681453 6.8681453
30 NA -6.8681453
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 16.2382231 25.0536648
[2,] 6.3731428 16.2382231
[3,] -4.9345215 6.3731428
[4,] -15.8596143 -4.9345215
[5,] -21.0190415 -15.8596143
[6,] -15.9891186 -21.0190415
[7,] 7.3007901 -15.9891186
[8,] -5.3112872 7.3007901
[9,] -3.3460492 -5.3112872
[10,] -2.6549989 -3.3460492
[11,] -2.5379979 -2.6549989
[12,] -4.8578678 -2.5379979
[13,] -11.1190443 -4.8578678
[14,] -11.9331973 -11.1190443
[15,] -4.4727315 -11.9331973
[16,] -4.9477431 -4.4727315
[17,] 0.2913732 -4.9477431
[18,] 3.2065518 0.2913732
[19,] 11.2665457 3.2065518
[20,] 13.7271450 11.2665457
[21,] 6.5634366 13.7271450
[22,] 5.5340243 6.5634366
[23,] 2.2733399 5.5340243
[24,] 6.4354321 2.2733399
[25,] 4.0885089 6.4354321
[26,] 2.0031619 4.0885089
[27,] -1.3721271 2.0031619
[28,] 6.8681453 -1.3721271
[29,] -6.8681453 6.8681453
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 16.2382231 25.0536648
2 6.3731428 16.2382231
3 -4.9345215 6.3731428
4 -15.8596143 -4.9345215
5 -21.0190415 -15.8596143
6 -15.9891186 -21.0190415
7 7.3007901 -15.9891186
8 -5.3112872 7.3007901
9 -3.3460492 -5.3112872
10 -2.6549989 -3.3460492
11 -2.5379979 -2.6549989
12 -4.8578678 -2.5379979
13 -11.1190443 -4.8578678
14 -11.9331973 -11.1190443
15 -4.4727315 -11.9331973
16 -4.9477431 -4.4727315
17 0.2913732 -4.9477431
18 3.2065518 0.2913732
19 11.2665457 3.2065518
20 13.7271450 11.2665457
21 6.5634366 13.7271450
22 5.5340243 6.5634366
23 2.2733399 5.5340243
24 6.4354321 2.2733399
25 4.0885089 6.4354321
26 2.0031619 4.0885089
27 -1.3721271 2.0031619
28 6.8681453 -1.3721271
29 -6.8681453 6.8681453
> 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/71q0k1293562560.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/81q0k1293562560.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/9thzm1293562560.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')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10thzm1293562560.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11fixs1293562560.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12i0wg1293562560.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/137jta1293562560.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14zasv1293562560.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/153brj1293562560.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/16zl6r1293562560.tab")
+ }
>
> try(system("convert tmp/14g2a1293562560.ps tmp/14g2a1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xp1e1293562560.ps tmp/2xp1e1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xp1e1293562560.ps tmp/3xp1e1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xp1e1293562560.ps tmp/4xp1e1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/58g0y1293562560.ps tmp/58g0y1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/68g0y1293562560.ps tmp/68g0y1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/71q0k1293562560.ps tmp/71q0k1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/81q0k1293562560.ps tmp/81q0k1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/9thzm1293562560.ps tmp/9thzm1293562560.png",intern=TRUE))
character(0)
> try(system("convert tmp/10thzm1293562560.ps tmp/10thzm1293562560.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.750 1.680 4.488