R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(3295.32,0,3363.99,0,3494.17,0,3667.03,0,3813.06,0,3917.96,0,3895.51,0,3801.06,0,3570.12,0,3701.61,0,3862.27,0,3970.1,0,4138.52,0,4199.75,0,4290.89,0,4443.91,0,4502.64,0,4356.98,0,4591.27,0,4696.96,0,4621.4,0,4562.84,0,4202.52,0,4296.49,0,4435.23,0,4105.18,0,4116.68,1,3844.49,1,3720.98,1,3674.4,1,3857.62,1,3801.06,1,3504.37,1,3032.6,1,3047.03,1,2962.34,1,2197.82,1),dim=c(2,37),dimnames=list(c('bel20','dummy'),1:37))
> y <- array(NA,dim=c(2,37),dimnames=list(c('bel20','dummy'),1:37))
> 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
bel20 dummy
1 3295.32 0
2 3363.99 0
3 3494.17 0
4 3667.03 0
5 3813.06 0
6 3917.96 0
7 3895.51 0
8 3801.06 0
9 3570.12 0
10 3701.61 0
11 3862.27 0
12 3970.10 0
13 4138.52 0
14 4199.75 0
15 4290.89 0
16 4443.91 0
17 4502.64 0
18 4356.98 0
19 4591.27 0
20 4696.96 0
21 4621.40 0
22 4562.84 0
23 4202.52 0
24 4296.49 0
25 4435.23 0
26 4105.18 0
27 4116.68 1
28 3844.49 1
29 3720.98 1
30 3674.40 1
31 3857.62 1
32 3801.06 1
33 3504.37 1
34 3032.60 1
35 3047.03 1
36 2962.34 1
37 2197.82 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy
4069.1 -636.4
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1234.9 -367.5 71.7 368.4 684.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4069.1 89.4 45.515 < 2e-16 ***
dummy -636.4 164.0 -3.882 0.000439 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 455.9 on 35 degrees of freedom
Multiple R-squared: 0.3009, Adjusted R-squared: 0.281
F-statistic: 15.07 on 1 and 35 DF, p-value: 0.0004392
> 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,] 0.20834078 0.41668157 0.7916592
[2,] 0.21691846 0.43383692 0.7830815
[3,] 0.17555903 0.35111805 0.8244410
[4,] 0.11507454 0.23014907 0.8849255
[5,] 0.08007675 0.16015350 0.9199233
[6,] 0.05291579 0.10583159 0.9470842
[7,] 0.04200447 0.08400894 0.9579955
[8,] 0.04214472 0.08428943 0.9578553
[9,] 0.06267994 0.12535988 0.9373201
[10,] 0.08565233 0.17130466 0.9143477
[11,] 0.11721040 0.23442079 0.8827896
[12,] 0.17808070 0.35616140 0.8219193
[13,] 0.23318883 0.46637766 0.7668112
[14,] 0.21846380 0.43692761 0.7815362
[15,] 0.25770455 0.51540910 0.7422955
[16,] 0.31624700 0.63249400 0.6837530
[17,] 0.32454112 0.64908223 0.6754589
[18,] 0.30494183 0.60988366 0.6950582
[19,] 0.22603697 0.45207394 0.7739630
[20,] 0.16310385 0.32620770 0.8368962
[21,] 0.12598041 0.25196083 0.8740196
[22,] 0.07851009 0.15702017 0.9214899
[23,] 0.08804280 0.17608561 0.9119572
[24,] 0.07577532 0.15155064 0.9242247
[25,] 0.05881548 0.11763096 0.9411845
[26,] 0.04449594 0.08899187 0.9555041
[27,] 0.05978399 0.11956798 0.9402160
[28,] 0.12132162 0.24264323 0.8786784
> postscript(file="/var/www/html/rcomp/tmp/1mxlq1229173596.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2f8vf1229173596.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30v741229173596.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4teue1229173596.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t45j1229173596.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 = 37
Frequency = 1
1 2 3 4 5 6
-773.78692 -705.11692 -574.93692 -402.07692 -256.04692 -151.14692
7 8 9 10 11 12
-173.59692 -268.04692 -498.98692 -367.49692 -206.83692 -99.00692
13 14 15 16 17 18
69.41308 130.64308 221.78308 374.80308 433.53308 287.87308
19 20 21 22 23 24
522.16308 627.85308 552.29308 493.73308 133.41308 227.38308
25 26 27 28 29 30
366.12308 36.07308 684.00818 411.81818 288.30818 241.72818
31 32 33 34 35 36
424.94818 368.38818 71.69818 -400.07182 -385.64182 -470.33182
37
-1234.85182
> postscript(file="/var/www/html/rcomp/tmp/6mnib1229173596.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 = 37
Frequency = 1
lag(myerror, k = 1) myerror
0 -773.78692 NA
1 -705.11692 -773.78692
2 -574.93692 -705.11692
3 -402.07692 -574.93692
4 -256.04692 -402.07692
5 -151.14692 -256.04692
6 -173.59692 -151.14692
7 -268.04692 -173.59692
8 -498.98692 -268.04692
9 -367.49692 -498.98692
10 -206.83692 -367.49692
11 -99.00692 -206.83692
12 69.41308 -99.00692
13 130.64308 69.41308
14 221.78308 130.64308
15 374.80308 221.78308
16 433.53308 374.80308
17 287.87308 433.53308
18 522.16308 287.87308
19 627.85308 522.16308
20 552.29308 627.85308
21 493.73308 552.29308
22 133.41308 493.73308
23 227.38308 133.41308
24 366.12308 227.38308
25 36.07308 366.12308
26 684.00818 36.07308
27 411.81818 684.00818
28 288.30818 411.81818
29 241.72818 288.30818
30 424.94818 241.72818
31 368.38818 424.94818
32 71.69818 368.38818
33 -400.07182 71.69818
34 -385.64182 -400.07182
35 -470.33182 -385.64182
36 -1234.85182 -470.33182
37 NA -1234.85182
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -705.11692 -773.78692
[2,] -574.93692 -705.11692
[3,] -402.07692 -574.93692
[4,] -256.04692 -402.07692
[5,] -151.14692 -256.04692
[6,] -173.59692 -151.14692
[7,] -268.04692 -173.59692
[8,] -498.98692 -268.04692
[9,] -367.49692 -498.98692
[10,] -206.83692 -367.49692
[11,] -99.00692 -206.83692
[12,] 69.41308 -99.00692
[13,] 130.64308 69.41308
[14,] 221.78308 130.64308
[15,] 374.80308 221.78308
[16,] 433.53308 374.80308
[17,] 287.87308 433.53308
[18,] 522.16308 287.87308
[19,] 627.85308 522.16308
[20,] 552.29308 627.85308
[21,] 493.73308 552.29308
[22,] 133.41308 493.73308
[23,] 227.38308 133.41308
[24,] 366.12308 227.38308
[25,] 36.07308 366.12308
[26,] 684.00818 36.07308
[27,] 411.81818 684.00818
[28,] 288.30818 411.81818
[29,] 241.72818 288.30818
[30,] 424.94818 241.72818
[31,] 368.38818 424.94818
[32,] 71.69818 368.38818
[33,] -400.07182 71.69818
[34,] -385.64182 -400.07182
[35,] -470.33182 -385.64182
[36,] -1234.85182 -470.33182
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -705.11692 -773.78692
2 -574.93692 -705.11692
3 -402.07692 -574.93692
4 -256.04692 -402.07692
5 -151.14692 -256.04692
6 -173.59692 -151.14692
7 -268.04692 -173.59692
8 -498.98692 -268.04692
9 -367.49692 -498.98692
10 -206.83692 -367.49692
11 -99.00692 -206.83692
12 69.41308 -99.00692
13 130.64308 69.41308
14 221.78308 130.64308
15 374.80308 221.78308
16 433.53308 374.80308
17 287.87308 433.53308
18 522.16308 287.87308
19 627.85308 522.16308
20 552.29308 627.85308
21 493.73308 552.29308
22 133.41308 493.73308
23 227.38308 133.41308
24 366.12308 227.38308
25 36.07308 366.12308
26 684.00818 36.07308
27 411.81818 684.00818
28 288.30818 411.81818
29 241.72818 288.30818
30 424.94818 241.72818
31 368.38818 424.94818
32 71.69818 368.38818
33 -400.07182 71.69818
34 -385.64182 -400.07182
35 -470.33182 -385.64182
36 -1234.85182 -470.33182
> 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/7ydwc1229173596.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8u3gi1229173596.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mkn01229173596.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10u0do1229173596.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117lf51229173596.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/12517c1229173597.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/13yzr31229173597.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/14mh2u1229173597.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/15s9mc1229173597.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/16laqn1229173597.tab")
+ }
>
> system("convert tmp/1mxlq1229173596.ps tmp/1mxlq1229173596.png")
> system("convert tmp/2f8vf1229173596.ps tmp/2f8vf1229173596.png")
> system("convert tmp/30v741229173596.ps tmp/30v741229173596.png")
> system("convert tmp/4teue1229173596.ps tmp/4teue1229173596.png")
> system("convert tmp/5t45j1229173596.ps tmp/5t45j1229173596.png")
> system("convert tmp/6mnib1229173596.ps tmp/6mnib1229173596.png")
> system("convert tmp/7ydwc1229173596.ps tmp/7ydwc1229173596.png")
> system("convert tmp/8u3gi1229173596.ps tmp/8u3gi1229173596.png")
> system("convert tmp/9mkn01229173596.ps tmp/9mkn01229173596.png")
> system("convert tmp/10u0do1229173596.ps tmp/10u0do1229173596.png")
>
>
> proc.time()
user system elapsed
2.293 1.599 5.233