Multivariate regressie calculator

*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: Thu, 19 Nov 2009 02:34:31 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36.htm/, Retrieved Thu, 19 Nov 2009 10:37:59 +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/2009/Nov/19/t12586234673sdw9pj0iw6za36.htm/}, year = {2009}, } @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 = {2009}, 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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121.6 0 97.2 111.5 114.0 118.8 118.8 0 102.5 97.2 111.5 114.0 114.0 1 113.4 102.5 97.2 111.5 111.5 1 109.8 113.4 102.5 97.2 97.2 1 104.9 109.8 113.4 102.5 102.5 1 126.1 104.9 109.8 113.4 113.4 1 80.0 126.1 104.9 109.8 109.8 1 96.8 80.0 126.1 104.9 104.9 1 117.2 96.8 80.0 126.1 126.1 1 112.3 117.2 96.8 80.0 80.0 1 117.3 112.3 117.2 96.8 96.8 1 111.1 117.3 112.3 117.2 117.2 1 102.2 111.1 117.3 112.3 112.3 1 104.3 102.2 111.1 117.3 117.3 1 122.9 104.3 102.2 111.1 111.1 0 107.6 122.9 104.3 102.2 102.2 0 121.3 107.6 122.9 104.3 104.3 0 131.5 121.3 107.6 122.9 122.9 0 89.0 131.5 121.3 107.6 107.6 0 104.4 89.0 131.5 121.3 121.3 0 128.9 104.4 89.0 131.5 131.5 0 135.9 128.9 104.4 89.0 89.0 0 133.3 135.9 128.9 104.4 104.4 0 121.3 133.3 135.9 128.9 128.9 0 120.5 121.3 133.3 135.9 135.9 0 120.4 120.5 121.3 133.3 133.3 0 137.9 120.4 120.5 121.3 121.3 0 126.1 137.9 120.4 120.5 120.5 0 133.2 126.1 137.9 120.4 120.4 0 151.1 133.2 126.1 137.9 137.9 0 105.0 151.1 133.2 126.1 126.1 0 119.0 105.0 1 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation X[t] = -14.5866928548540 + 3.81450091388516Y[t] -0.0614895038985489`y(t)`[t] + 0.298353875860903`y(t-1)`[t] + 0.443207090249879`y(t-2)`[t] + 0.182742933153958`y(t-3)`[t] + 32.2967210061925M1[t] + 42.8733727282386M2[t] + 36.7193713043985M3[t] + 23.9288546766903M4[t] + 13.7481929951856M5[t] + 20.4676144284120M6[t] + 22.442884965267M7[t] + 22.5990064410627M8[t] + 38.328605594057M9[t] + 48.7954535622456M10[t] -7.94034379372219M11[t] + 0.180699665271588t + 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) -14.5866928548540 14.154942 -1.0305 0.309289 0.154645 Y 3.81450091388516 2.597743 1.4684 0.150228 0.075114 `y(t)` -0.0614895038985489 0.141793 -0.4337 0.66699 0.333495 `y(t-1)` 0.298353875860903 0.136986 2.178 0.035682 0.017841 `y(t-2)` 0.443207090249879 0.142821 3.1032 0.003604 0.001802 `y(t-3)` 0.182742933153958 0.150411 1.215 0.231876 0.115938 M1 32.2967210061925 4.906805 6.582 0 0 M2 42.8733727282386 5.300258 8.0889 0 0 M3 36.7193713043985 6.063467 6.0558 0 0 M4 23.9288546766903 5.853191 4.0882 0.000217 0.000109 M5 13.7481929951856 5.137656 2.676 0.010934 0.005467 M6 20.4676144284120 6.186255 3.3086 0.002059 0.001029 M7 22.442884965267 5.628103 3.9876 0.000293 0.000146 M8 22.5990064410627 5.641269 4.006 0.000277 0.000139 M9 38.328605594057 8.767507 4.3717 9.2e-05 4.6e-05 M10 48.7954535622456 9.177321 5.317 5e-06 2e-06 M11 -7.94034379372219 6.759368 -1.1747 0.247417 0.123708 t 0.180699665271588 0.065854 2.7439 0.009215 0.004607 Multiple Linear Regression - Regression Statistics Multiple R 0.945747076096644 R-squared 0.894437531945352 Adjusted R-squared 0.847212217289326 F-TEST (value) 18.9397897813945 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 1.54765089632747e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.55580806681484 Sum Squared Residuals 1633.18753753875 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 121.6 117.415873943338 4.18412605666175 2 118.8 121.595686730419 -2.79568673041905 3 114 113.553207111846 0.446792888153958 4 111.5 104.152583244551 7.34741675544911 5 97.2 99.179340673761 -1.97934067376104 6 102.5 103.710302744370 -1.21030274436988 7 113.4 112.196451942892 1.20354805710789 8 109.8 106.246685682119 3.55331431788076 9 104.9 109.357247057662 -4.45724705766225 10 126.1 125.413942225588 0.686057774411688 11 80 79.200968941765 0.799031058234997 12 96.8 90.7512577983506 6.04874220164938 13 117.2 123.246736102969 -6.04673610296916 14 112.3 129.385440743158 -17.0854407431585 15 117.3 117.817428062606 -0.517428062606313 16 111.1 107.187604471400 3.9123955286003 17 102.2 100.407833989356 1.79216601064425 18 104.3 107.386160323223 -3.0861603232233 19 122.9 118.474614233987 4.42538576601275 20 107.6 112.208747795687 -4.60874779568654 21 121.3 114.234880039246 7.06511996075367 22 131.5 130.820485634814 0.679514365186276 23 89 90.1865526669731 -1.18655266697309 24 104.4 105.849401589532 -1.44940158953221 25 128.9 134.922629451212 -6.02262945121231 26 135.9 139.653829979032 -3.75382997903233 27 133.3 130.027145644606 3.27285435539431 28 121.3 123.173582600190 -1.87358260018965 29 120.5 116.954319157176 3.54568084282432 30 120.4 122.840248319748 -2.44024831974766 31 137.9 134.161822759065 3.73817724093501 32 126.1 129.114558909231 -3.01455890923095 33 133.2 130.725188249057 2.47481175094310 34 151.1 144.541828907885 6.55817109211522 35 105 106.062142127660 -1.06214212765973 36 119 120.341387495048 -1.34138749504774 37 140.4 142.649792149251 -2.24979214925078 38 156.6 143.556262953006 13.0437370469935 39 137.1 140.623736078071 -3.52373607807136 40 122.7 132.371660967248 -9.67166096724821 41 125.8 128.194250392190 -2.39425039219045 42 139.3 134.559108758229 4.74089124177082 43 134.9 138.775979030085 -3.87597903008539 44 149.2 137.605275860176 11.5947241398243 45 132.3 137.382684654035 -5.08268465403452 46 149 156.923743231713 -7.92374323171319 47 117.2 115.750336263602 1.44966373639782 48 119.6 122.857953117069 -3.25795311706941 49 152 141.864968353230 10.1350316467705 50 149.4 138.808779594384 10.5912204056163 51 127.3 126.978483102871 0.321516897129407 52 114.1 113.814568716612 0.285431283388453 53 102.1 103.064255787517 -0.964255787517077 54 107.7 105.70417985443 1.99582014557002 55 104.4 109.891132033970 -5.49113203397025 56 102.1 109.624731752788 -7.52473175278754 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.202620487538644 0.405240975077288 0.797379512461356 22 0.15980078098405 0.3196015619681 0.84019921901595 23 0.0783236120741228 0.156647224148246 0.921676387925877 24 0.0345918655051737 0.0691837310103475 0.965408134494826 25 0.0364899466248148 0.0729798932496296 0.963510053375185 26 0.366320329033942 0.732640658067885 0.633679670966058 27 0.269772251075367 0.539544502150734 0.730227748924633 28 0.176329536206567 0.352659072413134 0.823670463793433 29 0.152075696227899 0.304151392455798 0.8479243037721 30 0.121445856615829 0.242891713231658 0.87855414338417 31 0.0821513498158866 0.164302699631773 0.917848650184113 32 0.122347327374468 0.244694654748936 0.877652672625532 33 0.156405537238638 0.312811074477277 0.843594462761362 34 0.41917317944019 0.83834635888038 0.58082682055981 35 0.270029898777281 0.540059797554561 0.729970101222719 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 2 0.133333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/101izy1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/101izy1258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/1dxph1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/1dxph1258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/222ei1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/222ei1258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/3ylix1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/3ylix1258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/4z2jh1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/4z2jh1258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/52i941258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/52i941258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/6bpkp1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/6bpkp1258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/7h6lz1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/7h6lz1258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/8x0w41258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/8x0w41258623266.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/9lyid1258623266.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586234673sdw9pj0iw6za36/9lyid1258623266.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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