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.
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Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> 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 = '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 t
1 192.37 47.91 3720 0 601.73 1
2 192.65 51.56 3683 0 564.01 2
3 193.77 56.06 3635 0 513.92 3
4 194.54 60.36 3589 0 492.44 4
5 198.63 64.19 3590 0 540.36 5
6 202.30 67.31 3609 0 520.92 6
7 206.05 68.18 3632 0 451.40 7
8 210.94 69.24 365 0 397.62 8
9 220.57 70.05 3716 0 408.69 9
10 228.55 72.22 3760 0 390.15 10
11 235.61 74.72 3794 0 361.02 11
12 239.86 77.08 3798 0 304.83 12
13 243.05 78.81 3779 0 307.09 13
14 241.37 80.78 3872 0 270.57 14
15 249.31 82.71 3857 0 316.00 15
16 259.98 83.76 3914 0 308.64 16
17 262.85 85.26 3939 0 282.78 17
18 273.13 86.53 3966 0 297.18 18
19 278.37 87.32 4035 0 287.67 19
20 288.19 88.31 4090 0 259.49 20
21 299.13 90.67 4173 0 268.33 21
22 301.26 92.88 4231 0 301.05 22
23 305.36 94.33 4226 0 310.44 23
24 307.75 95.75 4230 0 329.26 24
25 317.20 97.53 4270 0 319.59 25
26 323.60 100.00 4331 0 329.16 26
27 332.31 102.33 4384 0 381.06 27
28 341.59 104.19 4455 0 487.13 28
29 344.30 108.87 4532 1 527.37 29
30 335.17 108.86 4515 1 606.35 30
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) inflatie werkeloosheid crisis goudprijzen
239.816249 -1.576122 0.001228 -6.421018 0.021689
t
8.816099
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.827 -1.577 -0.252 1.607 9.827
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 239.816249 24.379757 9.837 6.77e-10 ***
inflatie -1.576122 0.389705 -4.044 0.000471 ***
werkeloosheid 0.001228 0.001270 0.967 0.343174
crisis -6.421018 4.972173 -1.291 0.208868
goudprijzen 0.021689 0.011158 1.944 0.063726 .
t 8.816099 0.709283 12.430 6.01e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.037 on 24 degrees of freedom
Multiple R-squared: 0.9948, Adjusted R-squared: 0.9937
F-statistic: 919.6 on 5 and 24 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.01856895 0.03713791 0.9814310
[2,] 0.03474870 0.06949740 0.9652513
[3,] 0.07502552 0.15005104 0.9249745
[4,] 0.06532213 0.13064426 0.9346779
[5,] 0.06298376 0.12596753 0.9370162
[6,] 0.60133547 0.79732906 0.3986645
[7,] 0.61122475 0.77755050 0.3887752
[8,] 0.48490037 0.96980074 0.5150996
[9,] 0.57545613 0.84908774 0.4245439
[10,] 0.45178022 0.90356044 0.5482198
[11,] 0.40461453 0.80922906 0.5953855
[12,] 0.31909304 0.63818608 0.6809070
[13,] 0.59655214 0.80689573 0.4034479
> postscript(file="/var/www/rcomp/tmp/18yay1293562694.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/28yay1293562694.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3179j1293562694.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4179j1293562694.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5179j1293562694.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.63069469 -0.28902237 0.25276234 -0.49364860 -0.22375280 -0.05405349
7 8 9 10 11 12
-2.26936822 0.65354947 -1.61104372 1.32111949 4.09536697 4.46269528
13 14 15 16 17 18
1.53760328 -5.17566662 -3.97674969 -0.37828762 -3.43003238 -0.30993044
19 20 21 22 23 24
-2.51936541 0.58854518 6.13844118 2.15469168 -0.47354745 -5.07464726
25 26 27 28 29 30
-1.47463888 -0.28018603 2.09534889 3.10312253 9.82698080 -9.82698080
> postscript(file="/var/www/rcomp/tmp/6tyq41293562694.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 1.63069469 NA
1 -0.28902237 1.63069469
2 0.25276234 -0.28902237
3 -0.49364860 0.25276234
4 -0.22375280 -0.49364860
5 -0.05405349 -0.22375280
6 -2.26936822 -0.05405349
7 0.65354947 -2.26936822
8 -1.61104372 0.65354947
9 1.32111949 -1.61104372
10 4.09536697 1.32111949
11 4.46269528 4.09536697
12 1.53760328 4.46269528
13 -5.17566662 1.53760328
14 -3.97674969 -5.17566662
15 -0.37828762 -3.97674969
16 -3.43003238 -0.37828762
17 -0.30993044 -3.43003238
18 -2.51936541 -0.30993044
19 0.58854518 -2.51936541
20 6.13844118 0.58854518
21 2.15469168 6.13844118
22 -0.47354745 2.15469168
23 -5.07464726 -0.47354745
24 -1.47463888 -5.07464726
25 -0.28018603 -1.47463888
26 2.09534889 -0.28018603
27 3.10312253 2.09534889
28 9.82698080 3.10312253
29 -9.82698080 9.82698080
30 NA -9.82698080
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.28902237 1.63069469
[2,] 0.25276234 -0.28902237
[3,] -0.49364860 0.25276234
[4,] -0.22375280 -0.49364860
[5,] -0.05405349 -0.22375280
[6,] -2.26936822 -0.05405349
[7,] 0.65354947 -2.26936822
[8,] -1.61104372 0.65354947
[9,] 1.32111949 -1.61104372
[10,] 4.09536697 1.32111949
[11,] 4.46269528 4.09536697
[12,] 1.53760328 4.46269528
[13,] -5.17566662 1.53760328
[14,] -3.97674969 -5.17566662
[15,] -0.37828762 -3.97674969
[16,] -3.43003238 -0.37828762
[17,] -0.30993044 -3.43003238
[18,] -2.51936541 -0.30993044
[19,] 0.58854518 -2.51936541
[20,] 6.13844118 0.58854518
[21,] 2.15469168 6.13844118
[22,] -0.47354745 2.15469168
[23,] -5.07464726 -0.47354745
[24,] -1.47463888 -5.07464726
[25,] -0.28018603 -1.47463888
[26,] 2.09534889 -0.28018603
[27,] 3.10312253 2.09534889
[28,] 9.82698080 3.10312253
[29,] -9.82698080 9.82698080
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.28902237 1.63069469
2 0.25276234 -0.28902237
3 -0.49364860 0.25276234
4 -0.22375280 -0.49364860
5 -0.05405349 -0.22375280
6 -2.26936822 -0.05405349
7 0.65354947 -2.26936822
8 -1.61104372 0.65354947
9 1.32111949 -1.61104372
10 4.09536697 1.32111949
11 4.46269528 4.09536697
12 1.53760328 4.46269528
13 -5.17566662 1.53760328
14 -3.97674969 -5.17566662
15 -0.37828762 -3.97674969
16 -3.43003238 -0.37828762
17 -0.30993044 -3.43003238
18 -2.51936541 -0.30993044
19 0.58854518 -2.51936541
20 6.13844118 0.58854518
21 2.15469168 6.13844118
22 -0.47354745 2.15469168
23 -5.07464726 -0.47354745
24 -1.47463888 -5.07464726
25 -0.28018603 -1.47463888
26 2.09534889 -0.28018603
27 3.10312253 2.09534889
28 9.82698080 3.10312253
29 -9.82698080 9.82698080
> 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/74qq71293562694.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/84qq71293562694.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/94qq71293562694.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/10xzps1293562694.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11ihng1293562694.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/123iml1293562694.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/130s2u1293562694.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14lsi01293562694.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/156bh61293562694.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/16atxc1293562694.tab")
+ }
>
> try(system("convert tmp/18yay1293562694.ps tmp/18yay1293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/28yay1293562694.ps tmp/28yay1293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/3179j1293562694.ps tmp/3179j1293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/4179j1293562694.ps tmp/4179j1293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/5179j1293562694.ps tmp/5179j1293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tyq41293562694.ps tmp/6tyq41293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/74qq71293562694.ps tmp/74qq71293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/84qq71293562694.ps tmp/84qq71293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/94qq71293562694.ps tmp/94qq71293562694.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xzps1293562694.ps tmp/10xzps1293562694.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.880 1.550 4.502