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 '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(33907,0,152,74272,99,765,35981,71433,99,78867,128,1371,36588,53655,92,80176,57,1880,16967,70556,138,36541,95,232,25333,74702,106,55107,205,230,21027,61201,95,45527,51,828,21114,686,145,46001,59,1833,28777,87586,181,62854,194,906,35612,6615,190,78112,27,1781,24183,89725,150,52653,9,1264,22262,40420,186,48467,24,1123,20637,49569,174,44873,189,1461,29948,13963,151,65605,37,820,22093,62508,112,48016,81,107,36997,90901,143,81110,72,1349,31089,89418,120,68019,81,870,19477,83237,169,42198,90,1471,31301,22183,135,68531,216,731,18497,24346,161,40071,216,1945,30142,74341,98,65849,13,521,21326,24188,142,46362,153,1920,16779,11781,190,36313,185,1924,38068,23072,169,83521,131,100,29707,49119,130,64932,136,34,35016,67776,160,76730,182,325,26131,86910,176,56982,139,1677,29251,69358,111,63793,42,1779,22855,16144,165,49740,213,477,31806,77863,117,69447,184,1007,34124,89070,122,74708,44,1527),dim=c(6,30),dimnames=list(c('Y_t','X_1t','X_2t','X_3t','X_4t','X_5t'),1:30))
> y <- array(NA,dim=c(6,30),dimnames=list(c('Y_t','X_1t','X_2t','X_3t','X_4t','X_5t'),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_t X_1t X_2t X_3t X_4t X_5t
1 33907 0 152 74272 99 765
2 35981 71433 99 78867 128 1371
3 36588 53655 92 80176 57 1880
4 16967 70556 138 36541 95 232
5 25333 74702 106 55107 205 230
6 21027 61201 95 45527 51 828
7 21114 686 145 46001 59 1833
8 28777 87586 181 62854 194 906
9 35612 6615 190 78112 27 1781
10 24183 89725 150 52653 9 1264
11 22262 40420 186 48467 24 1123
12 20637 49569 174 44873 189 1461
13 29948 13963 151 65605 37 820
14 22093 62508 112 48016 81 107
15 36997 90901 143 81110 72 1349
16 31089 89418 120 68019 81 870
17 19477 83237 169 42198 90 1471
18 31301 22183 135 68531 216 731
19 18497 24346 161 40071 216 1945
20 30142 74341 98 65849 13 521
21 21326 24188 142 46362 153 1920
22 16779 11781 190 36313 185 1924
23 38068 23072 169 83521 131 100
24 29707 49119 130 64932 136 34
25 35016 67776 160 76730 182 325
26 26131 86910 176 56982 139 1677
27 29251 69358 111 63793 42 1779
28 22855 16144 165 49740 213 477
29 31806 77863 117 69447 184 1007
30 34124 89070 122 74708 44 1527
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t X_2t X_3t X_4t X_5t
5.141e+02 5.346e-04 -4.629e-01 4.502e-01 5.408e-02 -5.441e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-36.153 -9.604 -0.154 10.835 41.818
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.141e+02 3.617e+01 14.215 3.48e-13 ***
X_1t 5.346e-04 1.523e-04 3.510 0.0018 **
X_2t -4.629e-01 1.631e-01 -2.837 0.0091 **
X_3t 4.502e-01 3.093e-04 1455.407 < 2e-16 ***
X_4t 5.408e-02 6.726e-02 0.804 0.4293
X_5t -5.441e-03 7.247e-03 -0.751 0.4601
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 23.41 on 24 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 4.623e+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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.4150983 0.8301966 0.5849017
[2,] 0.4877173 0.9754347 0.5122827
[3,] 0.4123873 0.8247746 0.5876127
[4,] 0.3544688 0.7089375 0.6455312
[5,] 0.7684143 0.4631715 0.2315857
[6,] 0.7602925 0.4794150 0.2397075
[7,] 0.6700922 0.6598157 0.3299078
[8,] 0.8011006 0.3977989 0.1988994
[9,] 0.6998395 0.6003210 0.3001605
[10,] 0.7696372 0.4607256 0.2303628
[11,] 0.6662698 0.6674605 0.3337302
[12,] 0.5056123 0.9887754 0.4943877
[13,] 0.5651496 0.8697008 0.4348504
> postscript(file="/var/wessaorg/rcomp/tmp/1uugs1321625531.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2higs1321625531.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3yqvp1321625531.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4vjyy1321625531.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/51qq71321625531.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
28.0971881 -27.3619952 3.2581383 26.0335102 11.4564626 31.6559969
7 8 9 10 11 12
-34.1857517 0.1988531 27.9724661 -5.3032065 -0.5064622 -25.1768641
13 14 15 16 17 18
-33.6799317 -21.1567173 -8.1954743 -36.1534297 4.0828313 -19.8000528
19 20 21 22 23 24
5.1120273 -6.6589351 -3.2558743 0.4996684 15.7922196 -9.8712688
25 26 27 28 29 30
-8.8013262 2.7289464 41.8182567 8.9692062 37.9888989 -5.5573801
> postscript(file="/var/wessaorg/rcomp/tmp/6v4nf1321625531.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 28.0971881 NA
1 -27.3619952 28.0971881
2 3.2581383 -27.3619952
3 26.0335102 3.2581383
4 11.4564626 26.0335102
5 31.6559969 11.4564626
6 -34.1857517 31.6559969
7 0.1988531 -34.1857517
8 27.9724661 0.1988531
9 -5.3032065 27.9724661
10 -0.5064622 -5.3032065
11 -25.1768641 -0.5064622
12 -33.6799317 -25.1768641
13 -21.1567173 -33.6799317
14 -8.1954743 -21.1567173
15 -36.1534297 -8.1954743
16 4.0828313 -36.1534297
17 -19.8000528 4.0828313
18 5.1120273 -19.8000528
19 -6.6589351 5.1120273
20 -3.2558743 -6.6589351
21 0.4996684 -3.2558743
22 15.7922196 0.4996684
23 -9.8712688 15.7922196
24 -8.8013262 -9.8712688
25 2.7289464 -8.8013262
26 41.8182567 2.7289464
27 8.9692062 41.8182567
28 37.9888989 8.9692062
29 -5.5573801 37.9888989
30 NA -5.5573801
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -27.3619952 28.0971881
[2,] 3.2581383 -27.3619952
[3,] 26.0335102 3.2581383
[4,] 11.4564626 26.0335102
[5,] 31.6559969 11.4564626
[6,] -34.1857517 31.6559969
[7,] 0.1988531 -34.1857517
[8,] 27.9724661 0.1988531
[9,] -5.3032065 27.9724661
[10,] -0.5064622 -5.3032065
[11,] -25.1768641 -0.5064622
[12,] -33.6799317 -25.1768641
[13,] -21.1567173 -33.6799317
[14,] -8.1954743 -21.1567173
[15,] -36.1534297 -8.1954743
[16,] 4.0828313 -36.1534297
[17,] -19.8000528 4.0828313
[18,] 5.1120273 -19.8000528
[19,] -6.6589351 5.1120273
[20,] -3.2558743 -6.6589351
[21,] 0.4996684 -3.2558743
[22,] 15.7922196 0.4996684
[23,] -9.8712688 15.7922196
[24,] -8.8013262 -9.8712688
[25,] 2.7289464 -8.8013262
[26,] 41.8182567 2.7289464
[27,] 8.9692062 41.8182567
[28,] 37.9888989 8.9692062
[29,] -5.5573801 37.9888989
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -27.3619952 28.0971881
2 3.2581383 -27.3619952
3 26.0335102 3.2581383
4 11.4564626 26.0335102
5 31.6559969 11.4564626
6 -34.1857517 31.6559969
7 0.1988531 -34.1857517
8 27.9724661 0.1988531
9 -5.3032065 27.9724661
10 -0.5064622 -5.3032065
11 -25.1768641 -0.5064622
12 -33.6799317 -25.1768641
13 -21.1567173 -33.6799317
14 -8.1954743 -21.1567173
15 -36.1534297 -8.1954743
16 4.0828313 -36.1534297
17 -19.8000528 4.0828313
18 5.1120273 -19.8000528
19 -6.6589351 5.1120273
20 -3.2558743 -6.6589351
21 0.4996684 -3.2558743
22 15.7922196 0.4996684
23 -9.8712688 15.7922196
24 -8.8013262 -9.8712688
25 2.7289464 -8.8013262
26 41.8182567 2.7289464
27 8.9692062 41.8182567
28 37.9888989 8.9692062
29 -5.5573801 37.9888989
> 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/7y7a61321625531.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/8mgbg1321625531.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/9ar9s1321625531.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/109qk81321625531.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11hgxc1321625531.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/120wa11321625531.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13n08i1321625531.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/145wx41321625531.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/1538bq1321625531.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/16om7z1321625531.tab")
+ }
>
> try(system("convert tmp/1uugs1321625531.ps tmp/1uugs1321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/2higs1321625531.ps tmp/2higs1321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yqvp1321625531.ps tmp/3yqvp1321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vjyy1321625531.ps tmp/4vjyy1321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/51qq71321625531.ps tmp/51qq71321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v4nf1321625531.ps tmp/6v4nf1321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/7y7a61321625531.ps tmp/7y7a61321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mgbg1321625531.ps tmp/8mgbg1321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ar9s1321625531.ps tmp/9ar9s1321625531.png",intern=TRUE))
character(0)
> try(system("convert tmp/109qk81321625531.ps tmp/109qk81321625531.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.870 0.467 3.365