*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 09:28:03 -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/t1258648357tgsim9okgry4bck.htm/, Retrieved Thu, 19 Nov 2009 17:32:49 +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/t1258648357tgsim9okgry4bck.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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56.6 0 56 0 54.8 0 52.7 0 50.9 0 50.6 0 52.1 0 53.3 0 53.9 0 54.3 0 54.2 0 54.2 0 53.5 0 51.4 0 50.5 0 50.3 0 49.8 0 50.7 0 52.8 0 55.3 0 57.3 0 57.5 0 56.8 0 56.4 0 56.3 0 56.4 0 57 0 57.9 0 58.9 0 58.8 0 56.5 1 51.9 1 47.4 1 44.9 1 43.9 1 43.4 1 42.9 1 42.6 1 42.2 1 41.2 1 40.2 1 39.3 1 38.5 1 38.3 1 37.9 1 37.6 1 37.3 1 36 1 34.5 1 33.5 1 32.9 1 32.9 1 32.8 1 31.9 1 30.5 1 29.2 1 28.7 1 28.4 1 28 1 27.4 1 26.9 1

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 Y[t] = + 53.7889795918367 -17.1816326530612X[t] -0.0814965986394639M1[t] + 1.06367346938775M2[t] + 0.563673469387756M3[t] + 0.0836734693877571M4[t] -0.396326530612249M5[t] -0.656326530612247M6[t] + 2.60000000000000M7[t] + 2.12M8[t] + 1.56000000000000M9[t] + 1.06000000000000M10[t] + 0.559999999999999M11[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) 53.7889795918367 2.875464 18.7062 0 0 X -17.1816326530612 1.582891 -10.8546 0 0 M1 -0.0814965986394639 3.678306 -0.0222 0.982415 0.491208 M2 1.06367346938775 3.85134 0.2762 0.783593 0.391797 M3 0.563673469387756 3.85134 0.1464 0.884252 0.442126 M4 0.0836734693877571 3.85134 0.0217 0.982757 0.491378 M5 -0.396326530612249 3.85134 -0.1029 0.918466 0.459233 M6 -0.656326530612247 3.85134 -0.1704 0.8654 0.4327 M7 2.60000000000000 3.838306 0.6774 0.501416 0.250708 M8 2.12 3.838306 0.5523 0.583287 0.291644 M9 1.56000000000000 3.838306 0.4064 0.686234 0.343117 M10 1.06000000000000 3.838306 0.2762 0.783608 0.391804 M11 0.559999999999999 3.838306 0.1459 0.884613 0.442307 Multiple Linear Regression - Regression Statistics Multiple R 0.845833695305805 R-squared 0.715434640114673 Adjusted R-squared 0.644293300143341 F-TEST (value) 10.0565246648851 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 1.91935778381946e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.06889503628554 Sum Squared Residuals 1767.91137414966 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 56.6 53.7074829931973 2.89251700680269 2 56 54.8526530612245 1.14734693877548 3 54.8 54.3526530612245 0.44734693877551 4 52.7 53.8726530612245 -1.17265306122449 5 50.9 53.3926530612245 -2.49265306122449 6 50.6 53.1326530612245 -2.53265306122449 7 52.1 56.3889795918367 -4.28897959183673 8 53.3 55.9089795918367 -2.60897959183673 9 53.9 55.3489795918367 -1.44897959183673 10 54.3 54.8489795918367 -0.548979591836734 11 54.2 54.3489795918367 -0.148979591836729 12 54.2 53.7889795918367 0.41102040816327 13 53.5 53.7074829931973 -0.207482993197271 14 51.4 54.8526530612245 -3.45265306122448 15 50.5 54.3526530612245 -3.85265306122449 16 50.3 53.8726530612245 -3.57265306122449 17 49.8 53.3926530612245 -3.59265306122449 18 50.7 53.1326530612245 -2.43265306122449 19 52.8 56.3889795918367 -3.58897959183674 20 55.3 55.9089795918367 -0.608979591836735 21 57.3 55.3489795918367 1.95102040816327 22 57.5 54.8489795918367 2.65102040816327 23 56.8 54.3489795918367 2.45102040816326 24 56.4 53.7889795918367 2.61102040816326 25 56.3 53.7074829931973 2.59251700680273 26 56.4 54.8526530612245 1.54734693877552 27 57 54.3526530612245 2.64734693877551 28 57.9 53.8726530612245 4.02734693877551 29 58.9 53.3926530612245 5.50734693877552 30 58.8 53.1326530612245 5.66734693877551 31 56.5 39.2073469387755 17.2926530612245 32 51.9 38.7273469387755 13.1726530612245 33 47.4 38.1673469387755 9.23265306122449 34 44.9 37.6673469387755 7.23265306122449 35 43.9 37.1673469387755 6.73265306122449 36 43.4 36.6073469387755 6.79265306122449 37 42.9 36.5258503401360 6.37414965986395 38 42.6 37.6710204081633 4.92897959183674 39 42.2 37.1710204081633 5.02897959183673 40 41.2 36.6910204081633 4.50897959183674 41 40.2 36.2110204081633 3.98897959183674 42 39.3 35.9510204081633 3.34897959183673 43 38.5 39.2073469387755 -0.707346938775512 44 38.3 38.7273469387755 -0.427346938775513 45 37.9 38.1673469387755 -0.26734693877551 46 37.6 37.6673469387755 -0.0673469387755091 47 37.3 37.1673469387755 0.132653061224487 48 36 36.6073469387755 -0.607346938775511 49 34.5 36.5258503401360 -2.02585034013605 50 33.5 37.6710204081633 -4.17102040816326 51 32.9 37.1710204081633 -4.27102040816327 52 32.9 36.6910204081633 -3.79102040816327 53 32.8 36.2110204081633 -3.41102040816327 54 31.9 35.9510204081633 -4.05102040816327 55 30.5 39.2073469387755 -8.70734693877551 56 29.2 38.7273469387755 -9.5273469387755 57 28.7 38.1673469387755 -9.46734693877551 58 28.4 37.6673469387755 -9.26734693877551 59 28 37.1673469387755 -9.16734693877551 60 27.4 36.6073469387755 -9.20734693877552 61 26.9 36.5258503401360 -9.62585034013605 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0907914733058338 0.181582946611668 0.909208526694166 17 0.0324229654828009 0.0648459309656018 0.96757703451720 18 0.0101204999314691 0.0202409998629381 0.98987950006853 19 0.00317813335186961 0.00635626670373922 0.99682186664813 20 0.00106843026887692 0.00213686053775383 0.998931569731123 21 0.000509209005742083 0.00101841801148417 0.999490790994258 22 0.000222614175948935 0.000445228351897871 0.99977738582405 23 8.04947863379647e-05 0.000160989572675929 0.999919505213662 24 2.57810506126255e-05 5.1562101225251e-05 0.999974218949387 25 6.88833087188077e-06 1.37766617437615e-05 0.999993111669128 26 2.82165500876413e-06 5.64331001752827e-06 0.999997178344991 27 2.51778939777423e-06 5.03557879554846e-06 0.999997482210602 28 6.18612248012076e-06 1.23722449602415e-05 0.99999381387752 29 2.97176721365546e-05 5.94353442731092e-05 0.999970282327863 30 6.04808323064724e-05 0.000120961664612945 0.999939519167694 31 0.000112671216721647 0.000225342433443294 0.999887328783278 32 0.000288804216643076 0.000577608433286153 0.999711195783357 33 0.000976820486026288 0.00195364097205258 0.999023179513974 34 0.00249830561678552 0.00499661123357103 0.997501694383214 35 0.00449709043797511 0.00899418087595022 0.995502909562025 36 0.0079416963394494 0.0158833926788988 0.99205830366055 37 0.0169449647852534 0.0338899295705067 0.983055035214747 38 0.0175388076101513 0.0350776152203026 0.982461192389849 39 0.018346960758706 0.036693921517412 0.981653039241294 40 0.0176554930598935 0.0353109861197869 0.982344506940107 41 0.0155925813454596 0.0311851626909191 0.98440741865454 42 0.0144066725763584 0.0288133451527168 0.985593327423642 43 0.0218292575560557 0.0436585151121113 0.978170742443944 44 0.0334849099877462 0.0669698199754923 0.966515090012254 45 0.0464960165265584 0.0929920330531168 0.953503983473442 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.566666666666667 NOK 5% type I error level 26 0.866666666666667 NOK 10% type I error level 29 0.966666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/104woa1258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/104woa1258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/1kvt51258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/1kvt51258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/2wzae1258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/2wzae1258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/3mpqx1258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/3mpqx1258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/41rdv1258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/41rdv1258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/5z2kx1258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/5z2kx1258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/63hzn1258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/63hzn1258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/7b8cc1258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/7b8cc1258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/8uvg41258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/8uvg41258648079.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/9lqu21258648079.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648357tgsim9okgry4bck/9lqu21258648079.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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