R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(0,98.1,0,101.1,0,111.1,0,93.3,0,100,0,108,0,70.4,0,75.4,1,105.5,1,112.3,1,102.5,1,93.5,1,86.7,1,95.2,1,103.8,1,97,1,95.5,1,101,1,67.5,1,64,1,106.7,1,100.6,1,101.2,1,93.1,1,84.2,1,85.8,1,91.8,1,92.4,1,80.3,1,79.7,1,62.5,1,57.1,1,100.8,1,100.7,1,86.2,1,83.2),dim=c(2,36),dimnames=list(c('Dummy','Cons'),1:36))
> y <- array(NA,dim=c(2,36),dimnames=list(c('Dummy','Cons'),1:36))
> 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 = '2'
> #'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
Cons Dummy
1 98.1 0
2 101.1 0
3 111.1 0
4 93.3 0
5 100.0 0
6 108.0 0
7 70.4 0
8 75.4 0
9 105.5 1
10 112.3 1
11 102.5 1
12 93.5 1
13 86.7 1
14 95.2 1
15 103.8 1
16 97.0 1
17 95.5 1
18 101.0 1
19 67.5 1
20 64.0 1
21 106.7 1
22 100.6 1
23 101.2 1
24 93.1 1
25 84.2 1
26 85.8 1
27 91.8 1
28 92.4 1
29 80.3 1
30 79.7 1
31 62.5 1
32 57.1 1
33 100.8 1
34 100.7 1
35 86.2 1
36 83.2 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy
94.675 -4.289
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-33.286 -6.436 3.270 10.464 21.914
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 94.675 5.044 18.77 <2e-16 ***
Dummy -4.289 5.720 -0.75 0.458
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.27 on 34 degrees of freedom
Multiple R-squared: 0.01627, Adjusted R-squared: -0.01266
F-statistic: 0.5623 on 1 and 34 DF, p-value: 0.4585
> 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,] 0.14843956 0.2968791 0.8515604
[2,] 0.10894277 0.2178855 0.8910572
[3,] 0.59650402 0.8069920 0.4034960
[4,] 0.65771085 0.6845783 0.3422892
[5,] 0.56191705 0.8761659 0.4380830
[6,] 0.52038181 0.9592364 0.4796182
[7,] 0.44348103 0.8869621 0.5565190
[8,] 0.39227955 0.7845591 0.6077204
[9,] 0.37312393 0.7462479 0.6268761
[10,] 0.28928608 0.5785722 0.7107139
[11,] 0.24463312 0.4892662 0.7553669
[12,] 0.18359781 0.3671956 0.8164022
[13,] 0.13342778 0.2668556 0.8665722
[14,] 0.10522923 0.2104585 0.8947708
[15,] 0.28313351 0.5662670 0.7168665
[16,] 0.52967520 0.9406496 0.4703248
[17,] 0.56589825 0.8682035 0.4341018
[18,] 0.53404725 0.9319055 0.4659527
[19,] 0.52476240 0.9504752 0.4752376
[20,] 0.44676359 0.8935272 0.5532364
[21,] 0.35836069 0.7167214 0.6416393
[22,] 0.26933986 0.5386797 0.7306601
[23,] 0.20104641 0.4020928 0.7989536
[24,] 0.14884791 0.2976958 0.8511521
[25,] 0.09719103 0.1943821 0.9028090
[26,] 0.05728823 0.1145765 0.9427118
[27,] 0.10439199 0.2087840 0.8956080
> postscript(file="/var/www/html/rcomp/tmp/1js8t1230060973.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cne61230060973.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mxw31230060974.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4genb1230060974.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5of8v1230060974.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 = 36
Frequency = 1
1 2 3 4 5 6 7
3.425000 6.425000 16.425000 -1.375000 5.325000 13.325000 -24.275000
8 9 10 11 12 13 14
-19.275000 15.114286 21.914286 12.114286 3.114286 -3.685714 4.814286
15 16 17 18 19 20 21
13.414286 6.614286 5.114286 10.614286 -22.885714 -26.385714 16.314286
22 23 24 25 26 27 28
10.214286 10.814286 2.714286 -6.185714 -4.585714 1.414286 2.014286
29 30 31 32 33 34 35
-10.085714 -10.685714 -27.885714 -33.285714 10.414286 10.314286 -4.185714
36
-7.185714
> postscript(file="/var/www/html/rcomp/tmp/6emde1230060974.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 = 36
Frequency = 1
lag(myerror, k = 1) myerror
0 3.425000 NA
1 6.425000 3.425000
2 16.425000 6.425000
3 -1.375000 16.425000
4 5.325000 -1.375000
5 13.325000 5.325000
6 -24.275000 13.325000
7 -19.275000 -24.275000
8 15.114286 -19.275000
9 21.914286 15.114286
10 12.114286 21.914286
11 3.114286 12.114286
12 -3.685714 3.114286
13 4.814286 -3.685714
14 13.414286 4.814286
15 6.614286 13.414286
16 5.114286 6.614286
17 10.614286 5.114286
18 -22.885714 10.614286
19 -26.385714 -22.885714
20 16.314286 -26.385714
21 10.214286 16.314286
22 10.814286 10.214286
23 2.714286 10.814286
24 -6.185714 2.714286
25 -4.585714 -6.185714
26 1.414286 -4.585714
27 2.014286 1.414286
28 -10.085714 2.014286
29 -10.685714 -10.085714
30 -27.885714 -10.685714
31 -33.285714 -27.885714
32 10.414286 -33.285714
33 10.314286 10.414286
34 -4.185714 10.314286
35 -7.185714 -4.185714
36 NA -7.185714
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.425000 3.425000
[2,] 16.425000 6.425000
[3,] -1.375000 16.425000
[4,] 5.325000 -1.375000
[5,] 13.325000 5.325000
[6,] -24.275000 13.325000
[7,] -19.275000 -24.275000
[8,] 15.114286 -19.275000
[9,] 21.914286 15.114286
[10,] 12.114286 21.914286
[11,] 3.114286 12.114286
[12,] -3.685714 3.114286
[13,] 4.814286 -3.685714
[14,] 13.414286 4.814286
[15,] 6.614286 13.414286
[16,] 5.114286 6.614286
[17,] 10.614286 5.114286
[18,] -22.885714 10.614286
[19,] -26.385714 -22.885714
[20,] 16.314286 -26.385714
[21,] 10.214286 16.314286
[22,] 10.814286 10.214286
[23,] 2.714286 10.814286
[24,] -6.185714 2.714286
[25,] -4.585714 -6.185714
[26,] 1.414286 -4.585714
[27,] 2.014286 1.414286
[28,] -10.085714 2.014286
[29,] -10.685714 -10.085714
[30,] -27.885714 -10.685714
[31,] -33.285714 -27.885714
[32,] 10.414286 -33.285714
[33,] 10.314286 10.414286
[34,] -4.185714 10.314286
[35,] -7.185714 -4.185714
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.425000 3.425000
2 16.425000 6.425000
3 -1.375000 16.425000
4 5.325000 -1.375000
5 13.325000 5.325000
6 -24.275000 13.325000
7 -19.275000 -24.275000
8 15.114286 -19.275000
9 21.914286 15.114286
10 12.114286 21.914286
11 3.114286 12.114286
12 -3.685714 3.114286
13 4.814286 -3.685714
14 13.414286 4.814286
15 6.614286 13.414286
16 5.114286 6.614286
17 10.614286 5.114286
18 -22.885714 10.614286
19 -26.385714 -22.885714
20 16.314286 -26.385714
21 10.214286 16.314286
22 10.814286 10.214286
23 2.714286 10.814286
24 -6.185714 2.714286
25 -4.585714 -6.185714
26 1.414286 -4.585714
27 2.014286 1.414286
28 -10.085714 2.014286
29 -10.685714 -10.085714
30 -27.885714 -10.685714
31 -33.285714 -27.885714
32 10.414286 -33.285714
33 10.314286 10.414286
34 -4.185714 10.314286
35 -7.185714 -4.185714
> 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/76zo51230060974.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x5yv1230060974.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96ro91230060974.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105s5j1230060974.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11y9p61230060974.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/12gz5z1230060974.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/13is231230060974.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/14zycn1230060974.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/150xtm1230060974.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/16b0151230060974.tab")
+ }
>
> system("convert tmp/1js8t1230060973.ps tmp/1js8t1230060973.png")
> system("convert tmp/2cne61230060973.ps tmp/2cne61230060973.png")
> system("convert tmp/3mxw31230060974.ps tmp/3mxw31230060974.png")
> system("convert tmp/4genb1230060974.ps tmp/4genb1230060974.png")
> system("convert tmp/5of8v1230060974.ps tmp/5of8v1230060974.png")
> system("convert tmp/6emde1230060974.ps tmp/6emde1230060974.png")
> system("convert tmp/76zo51230060974.ps tmp/76zo51230060974.png")
> system("convert tmp/8x5yv1230060974.ps tmp/8x5yv1230060974.png")
> system("convert tmp/96ro91230060974.ps tmp/96ro91230060974.png")
> system("convert tmp/105s5j1230060974.ps tmp/105s5j1230060974.png")
>
>
> proc.time()
user system elapsed
2.316 1.586 3.020