*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 19 Nov 2010 14:51:10 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u.htm/, Retrieved Fri, 19 Nov 2010 15:49:29 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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8 78 284 9.100000381 109 9.300000191 68 433 8.699999809 144 7.5 70 739 7.199999809 113 8.899999619 96 1792 8.899999619 97 10.19999981 74 477 8.300000191 206 8.300000191 111 362 10.89999962 124 8.800000191 77 671 10 152 8.800000191 168 636 9.100000381 162 10.69999981 82 329 8.699999809 150 11.69999981 89 634 7.599999905 134 8.5 149 631 10.80000019 292 8.300000191 60 257 9.5 108 8.199999809 96 284 8.800000191 111 7.900000095 83 603 9.5 182 10.30000019 130 686 8.699999809 129 7.400000095 145 345 11.19999981 158 9.600000381 112 1357 9.699999809 186 9.300000191 131 544 9.600000381 177 10.60000038 80 205 9.100000381 127 9.699999809 130 1264 9.199999809 179 11.60000038 140 688 8.300000191 80 8.100000381 154 354 8.399999619 103 9.800000191 118 1632 9.399999619 101 7.400000095 94 348 9.800000191 117 9.399999619 119 370 10.39999962 88 11.19999981 153 648 9.899999619 78 9.100000381 116 366 9.199999809 102 10.5 97 540 10.30000019 95 11.89999962 176 680 8.899999619 80 8.399999619 75 345 9.6000 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation X1[t] = + 12.2662551669423 + 0.00739161503025132X2[t] + 0.0005837156555437X3[t] -0.330230236582663X4[t] -0.00946288487507264X5[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 12.2662551669423 2.020147 6.072 0 0 X2 0.00739161503025132 0.006934 1.0661 0.291734 0.145867 X3 0.0005837156555437 0.000722 0.8086 0.422753 0.211377 X4 -0.330230236582663 0.234552 -1.4079 0.165598 0.082799 X5 -0.00946288487507264 0.004887 -1.9364 0.058717 0.029358 Multiple Linear Regression - Regression Statistics Multiple R 0.379083047311434 R-squared 0.143703956758923 Adjusted R-squared 0.0723459531555002 F-TEST (value) 2.01384497186283 F-TEST (DF numerator) 4 F-TEST (DF denominator) 48 p-value 0.107455004361819 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.60126238508799 Sum Squared Residuals 123.073978843088 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 8.97202665537339 -0.97202665537339 2 9.300000191 8.78597545064412 0.51402474035588 3 7.5 9.76807045730224 -2.26807045730224 4 8.899999619 10.1649198519307 -1.26492023293067 5 10.19999981 8.40140173590016 1.79859807409984 6 8.300000191 8.52512232483443 -0.225122133834433 7 8.800000191 8.48642186230376 0.313578328696236 8 8.800000191 9.34120702046856 -0.541206829468555 9 10.69999981 8.77197432364066 1.92802548635934 10 11.69999981 9.51640829033323 2.18359151966677 11 8.5 7.40628138374007 1.09371861625993 12 8.300000191 8.70058817818893 -0.400587987188927 13 8.199999809 9.18521908988633 -0.985219280886327 14 7.900000095 8.37230745994745 -0.472307364947453 15 10.30000019 9.53387891649835 0.766121273501653 16 7.400000095 8.34570685024772 -0.945706755247721 17 9.600000381 8.92288837636184 0.677112004638156 18 9.300000191 8.70695703262182 0.593043158378185 19 10.60000038 8.77036442089465 1.82963595910535 20 9.699999809 9.23300720335764 0.466992605642356 21 11.60000038 10.2047358254756 1.39526455452438 22 8.100000381 9.8625882200543 -1.7625878390543 23 9.800000191 10.0311742199176 -0.231174028917586 24 7.400000095 8.82078611594752 -1.42078602094752 25 9.399999619 9.09470394411474 0.305295674885261 26 11.19999981 9.76803577475672 1.43196403524328 27 9.100000381 9.33399006963647 -0.233989688636476 28 10.5 8.99810271619316 1.50189728380684 29 11.89999962 10.2680262882624 1.63197333173758 30 8.399999619 8.98121238985572 -0.581212770855718 31 5 8.96948730635205 -3.96948730635205 32 9.800000191 9.5077519056557 0.292248285344308 33 9.800000191 9.54535484170044 0.254645349299557 34 10.80000019 9.63475526718102 1.16524492281898 35 10.10000038 9.36153393830083 0.738466441699168 36 10.89999962 10.2900942141976 0.609905405802424 37 9.199999809 8.54019535178266 0.659804457217337 38 8.300000191 10.0478169924589 -1.74781680145892 39 7.300000191 8.7554226820658 -1.45542249106579 40 9.399999619 9.72189488979475 -0.321895270794744 41 9.399999619 9.50067315307057 -0.10067353407057 42 9.800000191 9.66447590239719 0.135524288602814 43 3.599999905 9.24040974054532 -5.64040983554532 44 8.399999619 9.95069516680641 -1.55069554780641 45 10.80000019 9.8835612581701 0.9164389318299 46 10.10000038 8.69060905795837 1.40939132204163 47 9 9.79037539189222 -0.790375391892218 48 10 9.48117937801253 0.518820621987475 49 11.30000019 10.5017292099824 0.79827098001764 50 11.30000019 10.0638896066784 1.23611058332162 51 12.80000019 10.0093598651099 2.79064032489013 52 10 9.30479142463087 0.695208575369128 53 6.699999809 9.4698627215658 -2.7698629125658 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0473553825929688 0.0947107651859376 0.952644617407031 9 0.184318860787197 0.368637721574395 0.815681139212803 10 0.456542321059515 0.91308464211903 0.543457678940485 11 0.446800652785577 0.893601305571153 0.553199347214423 12 0.332082883733871 0.664165767467743 0.667917116266129 13 0.248000681360965 0.49600136272193 0.751999318639035 14 0.206725426181044 0.413450852362088 0.793274573818956 15 0.178799229751228 0.357598459502457 0.821200770248772 16 0.123153959520348 0.246307919040696 0.876846040479652 17 0.0884439860502594 0.176887972100519 0.91155601394974 18 0.0707876071194103 0.141575214238821 0.92921239288059 19 0.13920768978113 0.278415379562261 0.86079231021887 20 0.137916709962615 0.27583341992523 0.862083290037385 21 0.181657720468494 0.363315440936988 0.818342279531506 22 0.1959261371128 0.391852274225599 0.8040738628872 23 0.182345043219856 0.364690086439711 0.817654956780144 24 0.153209605806006 0.306419211612013 0.846790394193994 25 0.134699957584158 0.269399915168316 0.865300042415842 26 0.165692584534905 0.33138516906981 0.834307415465095 27 0.143930953388227 0.287861906776453 0.856069046611773 28 0.168671795072084 0.337343590144168 0.831328204927916 29 0.220469570733862 0.440939141467724 0.779530429266138 30 0.16535668634319 0.330713372686381 0.83464331365681 31 0.391314853624548 0.782629707249097 0.608685146375452 32 0.317061574752159 0.634123149504317 0.682938425247841 33 0.250879786147044 0.501759572294087 0.749120213852956 34 0.211400369687696 0.422800739375392 0.788599630312304 35 0.165184659874056 0.330369319748112 0.834815340125944 36 0.159096700854752 0.318193401709504 0.840903299145248 37 0.158864397184553 0.317728794369107 0.841135602815447 38 0.131296178937304 0.262592357874608 0.868703821062696 39 0.0954163490551875 0.190832698110375 0.904583650944813 40 0.0749176467845392 0.149835293569078 0.92508235321546 41 0.124932119998985 0.249864239997971 0.875067880001015 42 0.095770376374879 0.191540752749758 0.904229623625121 43 0.312682773275941 0.625365546551883 0.687317226724059 44 0.333287418281879 0.666574836563758 0.666712581718121 45 0.211761949553625 0.423523899107251 0.788238050446375 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 1 0.0263157894736842 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/10k5vg1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/10k5vg1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/1v4ym1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/1v4ym1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/2v4ym1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/2v4ym1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/36vfp1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/36vfp1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/46vfp1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/46vfp1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/56vfp1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/56vfp1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/6g4fa1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/6g4fa1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/7rwwv1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/7rwwv1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/8rwwv1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/8rwwv1290178260.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/9rwwv1290178260.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290178166rq4ufcpe0ecnc2u/9rwwv1290178260.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) 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 k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z 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() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$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$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.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='mytable5.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='mytable6.tab') }

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