*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, 20 Nov 2009 10:44:44 -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/20/t1258740254r5fn9au22f1abrp.htm/, Retrieved Fri, 20 Nov 2009 19:04:26 +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/20/t1258740254r5fn9au22f1abrp.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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3,2 1 1,9 1 0 1 0,6 1 0,2 1 0,9 1 2,4 1 4,7 1 9,4 1 12,5 1 15,8 1 18,2 1 16,8 0 17,3 0 19,3 0 17,9 0 20,2 0 18,7 0 20,1 0 18,2 0 18,4 0 18,2 0 18,9 0 19,9 0 21,3 0 20 0 19,5 0 19,6 0 20,9 0 21 0 19,9 0 19,6 0 20,9 0 21,7 0 22,9 0 21,5 0 21,3 0 23,5 0 21,6 0 24,5 0 22,2 0 23,5 0 20,9 0 20,7 0 18,1 0 17,1 0 14,8 0 13,8 0 15,2 0 16 0 17,6 0 15 0 15 0 16,3 0 19,4 0 21,3 0 20,5 0 21,1 0 21,6 0 22,6 0

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 21.9379166666667 -13.6895833333334X[t] -3.64M1[t] -3.46000000000001M2[t] -3.59999999999999M3[t] -3.67999999999999M4[t] -3.50000000000000M5[t] -3.12M6[t] -2.66M7[t] -2.30000000000000M8[t] -1.73999999999999M9[t] -1.07999999999999M10[t] -0.399999999999995M11[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) 21.9379166666667 1.693442 12.9546 0 0 X -13.6895833333334 1.209602 -11.3174 0 0 M1 -3.64 2.370325 -1.5357 0.131328 0.065664 M2 -3.46000000000001 2.370325 -1.4597 0.15102 0.07551 M3 -3.59999999999999 2.370325 -1.5188 0.135517 0.067758 M4 -3.67999999999999 2.370325 -1.5525 0.127244 0.063622 M5 -3.50000000000000 2.370325 -1.4766 0.146455 0.073227 M6 -3.12 2.370325 -1.3163 0.194465 0.097233 M7 -2.66 2.370325 -1.1222 0.267474 0.133737 M8 -2.30000000000000 2.370325 -0.9703 0.336849 0.168424 M9 -1.73999999999999 2.370325 -0.7341 0.466549 0.233274 M10 -1.07999999999999 2.370325 -0.4556 0.650752 0.325376 M11 -0.399999999999995 2.370325 -0.1688 0.866715 0.433357 Multiple Linear Regression - Regression Statistics Multiple R 0.86126669839 R-squared 0.741780325755611 Adjusted R-squared 0.675851898288959 F-TEST (value) 11.2512971150543 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 3.96747745767811e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.74781342871466 Sum Squared Residuals 660.166958333334 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.2 4.60833333333334 -1.40833333333334 2 1.9 4.78833333333339 -2.88833333333339 3 0 4.64833333333333 -4.64833333333333 4 0.6 4.56833333333332 -3.96833333333332 5 0.2 4.74833333333334 -4.54833333333334 6 0.9 5.12833333333333 -4.22833333333333 7 2.4 5.58833333333333 -3.18833333333333 8 4.7 5.94833333333333 -1.24833333333333 9 9.4 6.50833333333332 2.89166666666668 10 12.5 7.16833333333332 5.33166666666668 11 15.8 7.84833333333333 7.95166666666667 12 18.2 8.24833333333333 9.95166666666667 13 16.8 18.2979166666667 -1.49791666666667 14 17.3 18.4779166666667 -1.17791666666665 15 19.3 18.3379166666667 0.962083333333332 16 17.9 18.2579166666667 -0.357916666666669 17 20.2 18.4379166666667 1.76208333333333 18 18.7 18.8179166666667 -0.117916666666668 19 20.1 19.2779166666667 0.822083333333333 20 18.2 19.6379166666667 -1.43791666666667 21 18.4 20.1979166666667 -1.79791666666667 22 18.2 20.8579166666667 -2.65791666666667 23 18.9 21.5379166666667 -2.63791666666667 24 19.9 21.9379166666667 -2.03791666666666 25 21.3 18.2979166666667 3.00208333333334 26 20 18.4779166666667 1.52208333333335 27 19.5 18.3379166666667 1.16208333333333 28 19.6 18.2579166666667 1.34208333333333 29 20.9 18.4379166666667 2.46208333333333 30 21 18.8179166666667 2.18208333333333 31 19.9 19.2779166666667 0.62208333333333 32 19.6 19.6379166666667 -0.037916666666668 33 20.9 20.1979166666667 0.702083333333327 34 21.7 20.8579166666667 0.842083333333333 35 22.9 21.5379166666667 1.36208333333333 36 21.5 21.9379166666667 -0.437916666666662 37 21.3 18.2979166666667 3.00208333333334 38 23.5 18.4779166666667 5.02208333333335 39 21.6 18.3379166666667 3.26208333333333 40 24.5 18.2579166666667 6.24208333333333 41 22.2 18.4379166666667 3.76208333333333 42 23.5 18.8179166666667 4.68208333333333 43 20.9 19.2779166666667 1.62208333333333 44 20.7 19.6379166666667 1.06208333333333 45 18.1 20.1979166666667 -2.09791666666667 46 17.1 20.8579166666667 -3.75791666666667 47 14.8 21.5379166666667 -6.73791666666667 48 13.8 21.9379166666667 -8.13791666666666 49 15.2 18.2979166666667 -3.09791666666666 50 16 18.4779166666667 -2.47791666666665 51 17.6 18.3379166666667 -0.737916666666667 52 15 18.2579166666667 -3.25791666666667 53 15 18.4379166666667 -3.43791666666667 54 16.3 18.8179166666667 -2.51791666666667 55 19.4 19.2779166666667 0.12208333333333 56 21.3 19.6379166666667 1.66208333333333 57 20.5 20.1979166666667 0.302083333333329 58 21.1 20.8579166666667 0.242083333333335 59 21.6 21.5379166666667 0.0620833333333334 60 22.6 21.9379166666667 0.66208333333334 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.201201831085324 0.402403662170647 0.798798168914676 17 0.182777349027733 0.365554698055467 0.817222650972267 18 0.093651966815556 0.187303933631112 0.906348033184444 19 0.0432497359799072 0.0864994719598143 0.956750264020093 20 0.0508328812616245 0.101665762523249 0.949167118738375 21 0.215315722536364 0.430631445072728 0.784684277463636 22 0.529838618523108 0.940322762953784 0.470161381476892 23 0.774829944832408 0.450340110335184 0.225170055167592 24 0.883076123983437 0.233847752033126 0.116923876016563 25 0.867031269771325 0.265937460457350 0.132968730228675 26 0.825676152428543 0.348647695142914 0.174323847571457 27 0.769798977147356 0.460402045705288 0.230201022852644 28 0.71013143744968 0.579737125100639 0.289868562550320 29 0.65741006916749 0.685179861665021 0.342589930832511 30 0.598241420643587 0.803517158712826 0.401758579356413 31 0.504943418103237 0.990113163793526 0.495056581896763 32 0.412425193941908 0.824850387883815 0.587574806058092 33 0.323361440443741 0.646722880887483 0.676638559556259 34 0.25079101166211 0.50158202332422 0.74920898833789 35 0.213624063104908 0.427248126209816 0.786375936895092 36 0.184386766364251 0.368773532728503 0.815613233635749 37 0.170050290433641 0.340100580867281 0.82994970956636 38 0.212129937490241 0.424259874980481 0.78787006250976 39 0.175837011859600 0.351674023719200 0.8241629881404 40 0.307793853483 0.615587706966 0.692206146517 41 0.345896382041282 0.691792764082564 0.654103617958718 42 0.416289534437712 0.832579068875425 0.583710465562288 43 0.289120304056000 0.578240608111999 0.710879695944 44 0.169432061365860 0.338864122731719 0.83056793863414 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 0.0344827586206897 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/109z811258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/109z811258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/1lrec1258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/1lrec1258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/2mcck1258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/2mcck1258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/3pzqg1258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/3pzqg1258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/46yeq1258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/46yeq1258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/5lez51258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/5lez51258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/6ucrn1258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/6ucrn1258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/7oix61258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/7oix61258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/8tpxz1258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/8tpxz1258739076.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/9um9q1258739076.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740254r5fn9au22f1abrp/9um9q1258739076.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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