R version 2.13.0 (2011-04-13)
Copyright (C) 2011 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.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(33907,71433,152,74272,99,765,35981,53655,99,78867,128,1371,36588,70556,92,80176,57,1880,16967,74702,138,36541,95,232,25333,61201,106,55107,205,230,21027,686,95,45527,51,828,21114,87586,145,46001,59,1833,28777,6615,181,62854,194,906,35612,89725,190,78112,27,1781,24183,40420,150,52653,9,1264,22262,49569,186,48467,24,1123,20637,13963,174,44873,189,1461,29948,62508,151,65605,37,820,22093,90901,112,48016,81,107,36997,89418,143,81110,72,1349,31089,83237,120,68019,81,870,19477,22183,169,42198,90,1471,31301,24346,135,68531,216,731,18497,74341,161,40071,216,1945,30142,24188,98,65849,13,521,21326,11781,142,46362,153,1920,16779,23072,190,36313,185,1924,38068,49119,169,83521,131,100,29707,67776,130,64932,136,34,35016,86910,160,76730,182,325,26131,69358,176,56982,139,1677,29251,16144,111,63793,42,1779,22855,77863,165,49740,213,477,31806,89070,117,69447,184,1007,34124,34790,122,74708,44,1527),dim=c(6,30),dimnames=list(c('Y','X1','X2','X3','X4','X5
'),1:30))
> y <- array(NA,dim=c(6,30),dimnames=list(c('Y','X1','X2','X3','X4','X5
'),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
Y X1 X2 X3 X4 X5\r
1 33907 71433 152 74272 99 765
2 35981 53655 99 78867 128 1371
3 36588 70556 92 80176 57 1880
4 16967 74702 138 36541 95 232
5 25333 61201 106 55107 205 230
6 21027 686 95 45527 51 828
7 21114 87586 145 46001 59 1833
8 28777 6615 181 62854 194 906
9 35612 89725 190 78112 27 1781
10 24183 40420 150 52653 9 1264
11 22262 49569 186 48467 24 1123
12 20637 13963 174 44873 189 1461
13 29948 62508 151 65605 37 820
14 22093 90901 112 48016 81 107
15 36997 89418 143 81110 72 1349
16 31089 83237 120 68019 81 870
17 19477 22183 169 42198 90 1471
18 31301 24346 135 68531 216 731
19 18497 74341 161 40071 216 1945
20 30142 24188 98 65849 13 521
21 21326 11781 142 46362 153 1920
22 16779 23072 190 36313 185 1924
23 38068 49119 169 83521 131 100
24 29707 67776 130 64932 136 34
25 35016 86910 160 76730 182 325
26 26131 69358 176 56982 139 1677
27 29251 16144 111 63793 42 1779
28 22855 77863 165 49740 213 477
29 31806 89070 117 69447 184 1007
30 34124 34790 122 74708 44 1527
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1 X2 X3 X4 `X5\r`
572.834888 -0.000146 -0.630378 0.450256 0.036592 -0.008100
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-54.287 -16.848 0.434 17.358 52.410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.728e+02 3.960e+01 14.467 2.38e-13 ***
X1 -1.460e-04 1.905e-04 -0.767 0.45081
X2 -6.304e-01 1.889e-01 -3.337 0.00275 **
X3 4.503e-01 3.891e-04 1157.105 < 2e-16 ***
X4 3.659e-02 8.158e-02 0.449 0.65778
`X5\r` -8.100e-03 8.937e-03 -0.906 0.37377
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 28.45 on 24 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.13e+05 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.5922723 0.8154554 0.4077277
[2,] 0.4224598 0.8449197 0.5775402
[3,] 0.3322118 0.6644236 0.6677882
[4,] 0.2085228 0.4170456 0.7914772
[5,] 0.8397423 0.3205153 0.1602577
[6,] 0.7866258 0.4267484 0.2133742
[7,] 0.7511215 0.4977569 0.2488785
[8,] 0.8699425 0.2601150 0.1300575
[9,] 0.8911525 0.2176951 0.1088475
[10,] 0.8807091 0.2385818 0.1192909
[11,] 0.8567663 0.2864674 0.1432337
[12,] 0.7359350 0.5281300 0.2640650
[13,] 0.6982142 0.6035715 0.3017858
> postscript(file="/var/wessaorg/rcomp/tmp/1o3jd1321627372.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/25ln11321627372.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3w8k11321627372.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4h0gf1321627372.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5u9iv1321627372.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.5620705 -25.5233705 -3.1327599 37.6576665 18.0174527 20.1803318
7 8 9 10 11 12
-54.1865008 19.0694616 15.0676899 13.1957599 -0.6935562 -23.5363198
13 14 15 16 17 18
-54.2872490 -17.5563584 15.3814188 -17.9257619 22.6508008 -41.6656591
19 20 21 22 23 24
2.1467416 -10.6984426 -20.4232674 -12.0311509 -0.9558884 -14.7227235
25 26 27 28 29 30
4.5341928 31.2401471 40.1769645 -2.1248395 52.4096537 6.1734960
> postscript(file="/var/wessaorg/rcomp/tmp/6oshn1321627372.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 1.5620705 NA
1 -25.5233705 1.5620705
2 -3.1327599 -25.5233705
3 37.6576665 -3.1327599
4 18.0174527 37.6576665
5 20.1803318 18.0174527
6 -54.1865008 20.1803318
7 19.0694616 -54.1865008
8 15.0676899 19.0694616
9 13.1957599 15.0676899
10 -0.6935562 13.1957599
11 -23.5363198 -0.6935562
12 -54.2872490 -23.5363198
13 -17.5563584 -54.2872490
14 15.3814188 -17.5563584
15 -17.9257619 15.3814188
16 22.6508008 -17.9257619
17 -41.6656591 22.6508008
18 2.1467416 -41.6656591
19 -10.6984426 2.1467416
20 -20.4232674 -10.6984426
21 -12.0311509 -20.4232674
22 -0.9558884 -12.0311509
23 -14.7227235 -0.9558884
24 4.5341928 -14.7227235
25 31.2401471 4.5341928
26 40.1769645 31.2401471
27 -2.1248395 40.1769645
28 52.4096537 -2.1248395
29 6.1734960 52.4096537
30 NA 6.1734960
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -25.5233705 1.5620705
[2,] -3.1327599 -25.5233705
[3,] 37.6576665 -3.1327599
[4,] 18.0174527 37.6576665
[5,] 20.1803318 18.0174527
[6,] -54.1865008 20.1803318
[7,] 19.0694616 -54.1865008
[8,] 15.0676899 19.0694616
[9,] 13.1957599 15.0676899
[10,] -0.6935562 13.1957599
[11,] -23.5363198 -0.6935562
[12,] -54.2872490 -23.5363198
[13,] -17.5563584 -54.2872490
[14,] 15.3814188 -17.5563584
[15,] -17.9257619 15.3814188
[16,] 22.6508008 -17.9257619
[17,] -41.6656591 22.6508008
[18,] 2.1467416 -41.6656591
[19,] -10.6984426 2.1467416
[20,] -20.4232674 -10.6984426
[21,] -12.0311509 -20.4232674
[22,] -0.9558884 -12.0311509
[23,] -14.7227235 -0.9558884
[24,] 4.5341928 -14.7227235
[25,] 31.2401471 4.5341928
[26,] 40.1769645 31.2401471
[27,] -2.1248395 40.1769645
[28,] 52.4096537 -2.1248395
[29,] 6.1734960 52.4096537
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -25.5233705 1.5620705
2 -3.1327599 -25.5233705
3 37.6576665 -3.1327599
4 18.0174527 37.6576665
5 20.1803318 18.0174527
6 -54.1865008 20.1803318
7 19.0694616 -54.1865008
8 15.0676899 19.0694616
9 13.1957599 15.0676899
10 -0.6935562 13.1957599
11 -23.5363198 -0.6935562
12 -54.2872490 -23.5363198
13 -17.5563584 -54.2872490
14 15.3814188 -17.5563584
15 -17.9257619 15.3814188
16 22.6508008 -17.9257619
17 -41.6656591 22.6508008
18 2.1467416 -41.6656591
19 -10.6984426 2.1467416
20 -20.4232674 -10.6984426
21 -12.0311509 -20.4232674
22 -0.9558884 -12.0311509
23 -14.7227235 -0.9558884
24 4.5341928 -14.7227235
25 31.2401471 4.5341928
26 40.1769645 31.2401471
27 -2.1248395 40.1769645
28 52.4096537 -2.1248395
29 6.1734960 52.4096537
> 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/wessaorg/rcomp/tmp/703wp1321627372.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/wessaorg/rcomp/tmp/8gsy41321627372.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/wessaorg/rcomp/tmp/9whi11321627372.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/wessaorg/rcomp/tmp/10353z1321627372.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11eo1v1321627372.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12ohbj1321627372.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13quef1321627372.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14mv3n1321627372.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/wessaorg/rcomp/tmp/15yzcf1321627372.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/wessaorg/rcomp/tmp/16rbjn1321627372.tab")
+ }
>
> try(system("convert tmp/1o3jd1321627372.ps tmp/1o3jd1321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/25ln11321627372.ps tmp/25ln11321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w8k11321627372.ps tmp/3w8k11321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h0gf1321627372.ps tmp/4h0gf1321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/5u9iv1321627372.ps tmp/5u9iv1321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oshn1321627372.ps tmp/6oshn1321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/703wp1321627372.ps tmp/703wp1321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gsy41321627372.ps tmp/8gsy41321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/9whi11321627372.ps tmp/9whi11321627372.png",intern=TRUE))
character(0)
> try(system("convert tmp/10353z1321627372.ps tmp/10353z1321627372.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.807 0.456 3.374