*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: Wed, 22 Dec 2010 17:52:52 +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/Dec/22/t12930403991dk3ognc8ih880q.htm/, Retrieved Wed, 22 Dec 2010 18:53:30 +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/Dec/22/t12930403991dk3ognc8ih880q.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:

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User-defined keywords:

Dataseries X:

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2981.85 0 3080.58 0 3106.22 0 3119.31 0 3061.26 0 3097.31 0 3161.69 0 3257.16 0 3277.01 0 3295.32 0 3363.99 0 3494.17 0 3667.03 0 3813.06 0 3917.96 0 3895.51 0 3801.06 0 3570.12 0 3701.61 0 3862.27 0 3970.10 0 4138.52 0 4199.75 0 4290.89 0 4443.91 0 4502.64 0 4356.98 0 4591.27 0 4696.96 0 4621.40 0 4562.84 0 4202.52 0 4296.49 0 4435.23 0 4105.18 0 4116.68 0 3844.49 0 3720.98 0 3674.40 0 3857.62 0 3801.06 0 3504.37 0 3032.60 0 3047.03 0 2962.34 0 2197.82 0 2014.45 1 1862.83 1 1905.41 1 1810.99 1 1670.07 1 1864.44 1 2052.02 1 2029.60 1 2070.83 1 2293.41 1 2443.27 1 2513.17 1 2466.92 1 2502.66 1 2539.91 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Bel20[t] = + 3903.77204878049 -1625.81512195122Dummy[t] -131.400341463416M1[t] -192.959024390245M2[t] -233.483024390245M3[t] -112.979024390245M4[t] -96.1370243902443M5[t] -214.049024390245M6[t] -272.695024390244M7[t] -246.131024390244M8[t] -188.767024390244M9[t] -262.597024390244M10[t] -23.3880000000004M11[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) 3903.77204878049 262.047574 14.8972 0 0 Dummy -1625.81512195122 171.062353 -9.5042 0 0 M1 -131.400341463416 342.69444 -0.3834 0.703092 0.351546 M2 -192.959024390245 359.36668 -0.5369 0.593788 0.296894 M3 -233.483024390245 359.36668 -0.6497 0.518979 0.259489 M4 -112.979024390245 359.36668 -0.3144 0.754592 0.377296 M5 -96.1370243902443 359.36668 -0.2675 0.790217 0.395108 M6 -214.049024390245 359.36668 -0.5956 0.554222 0.277111 M7 -272.695024390244 359.36668 -0.7588 0.45167 0.225835 M8 -246.131024390244 359.36668 -0.6849 0.4967 0.24835 M9 -188.767024390244 359.36668 -0.5253 0.601809 0.300904 M10 -262.597024390244 359.36668 -0.7307 0.468501 0.234251 M11 -23.3880000000004 357.734423 -0.0654 0.948144 0.474072 Multiple Linear Regression - Regression Statistics Multiple R 0.809937779239358 R-squared 0.655999206239183 Adjusted R-squared 0.569999007798978 F-TEST (value) 7.62788014605916 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 1.21177609946344e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 565.6277869838 Sum Squared Residuals 15356870.0835932 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2981.85 3772.37170731708 -790.521707317076 2 3080.58 3710.81302439024 -630.233024390244 3 3106.22 3670.28902439024 -564.069024390244 4 3119.31 3790.79302439024 -671.483024390243 5 3061.26 3807.63502439024 -746.375024390243 6 3097.31 3689.72302439024 -592.413024390243 7 3161.69 3631.07702439024 -469.387024390244 8 3257.16 3657.64102439024 -400.481024390244 9 3277.01 3715.00502439024 -437.995024390244 10 3295.32 3641.17502439024 -345.855024390244 11 3363.99 3880.38404878049 -516.394048780488 12 3494.17 3903.77204878049 -409.602048780488 13 3667.03 3772.37170731707 -105.341707317072 14 3813.06 3710.81302439024 102.246975609756 15 3917.96 3670.28902439024 247.670975609756 16 3895.51 3790.79302439024 104.716975609756 17 3801.06 3807.63502439024 -6.57502439024415 18 3570.12 3689.72302439024 -119.603024390244 19 3701.61 3631.07702439024 70.5329756097562 20 3862.27 3657.64102439024 204.628975609756 21 3970.1 3715.00502439024 255.094975609756 22 4138.52 3641.17502439024 497.344975609756 23 4199.75 3880.38404878049 319.365951219512 24 4290.89 3903.77204878049 387.117951219512 25 4443.91 3772.37170731707 671.538292682927 26 4502.64 3710.81302439024 791.826975609757 27 4356.98 3670.28902439024 686.690975609756 28 4591.27 3790.79302439024 800.476975609756 29 4696.96 3807.63502439024 889.324975609756 30 4621.4 3689.72302439024 931.676975609756 31 4562.84 3631.07702439024 931.762975609756 32 4202.52 3657.64102439024 544.878975609756 33 4296.49 3715.00502439024 581.484975609756 34 4435.23 3641.17502439024 794.054975609755 35 4105.18 3880.38404878049 224.795951219512 36 4116.68 3903.77204878049 212.907951219512 37 3844.49 3772.37170731707 72.118292682927 38 3720.98 3710.81302439024 10.1669756097563 39 3674.4 3670.28902439024 4.1109756097562 40 3857.62 3790.79302439024 66.826975609756 41 3801.06 3807.63502439024 -6.57502439024415 42 3504.37 3689.72302439024 -185.353024390244 43 3032.6 3631.07702439024 -598.477024390244 44 3047.03 3657.64102439024 -610.611024390244 45 2962.34 3715.00502439024 -752.665024390244 46 2197.82 3641.17502439024 -1443.35502439024 47 2014.45 2254.56892682927 -240.118926829268 48 1862.83 2277.95692682927 -415.126926829269 49 1905.41 2146.55658536585 -241.146585365853 50 1810.99 2084.99790243902 -274.007902439024 51 1670.07 2044.47390243902 -374.403902439025 52 1864.44 2164.97790243902 -300.537902439024 53 2052.02 2181.81990243902 -129.799902439025 54 2029.6 2063.90790243902 -34.3079024390245 55 2070.83 2005.26190243902 65.5680975609754 56 2293.41 2031.82590243902 261.584097560975 57 2443.27 2089.18990243902 354.080097560976 58 2513.17 2015.35990243902 497.810097560976 59 2466.92 2254.56892682927 212.351073170732 60 2502.66 2277.95692682927 224.703073170731 61 2539.91 2146.55658536585 393.353414634146 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.677075679342848 0.645848641314304 0.322924320657152 17 0.62503788147409 0.74992423705182 0.37496211852591 18 0.525039209519413 0.949921580961175 0.474960790480587 19 0.438718751683493 0.877437503366986 0.561281248316507 20 0.372398189428657 0.744796378857313 0.627601810571343 21 0.329154225707488 0.658308451414975 0.670845774292512 22 0.326601485201433 0.653202970402867 0.673398514798567 23 0.308399876372569 0.616799752745138 0.691600123627431 24 0.282837896009789 0.565675792019578 0.717162103990211 25 0.35772777193853 0.71545554387706 0.64227222806147 26 0.425395801320619 0.850791602641238 0.574604198679381 27 0.440541160001622 0.881082320003243 0.559458839998378 28 0.504540799871396 0.99091840025721 0.495459200128604 29 0.601787427417201 0.796425145165599 0.398212572582799 30 0.69999406816709 0.600011863665821 0.300005931832910 31 0.791783346687518 0.416433306624964 0.208216653312482 32 0.779511818452699 0.440976363094603 0.220488181547301 33 0.780583555589304 0.438832888821391 0.219416444410696 34 0.87762481029379 0.244750379412419 0.122375189706210 35 0.841676742854649 0.316646514290703 0.158323257145351 36 0.812205739818195 0.37558852036361 0.187794260181805 37 0.749207200536376 0.501585598927248 0.250792799463624 38 0.710428223893135 0.57914355221373 0.289571776106865 39 0.709155177679012 0.581689644641976 0.290844822320988 40 0.750917164637866 0.498165670724268 0.249082835362134 41 0.790565531338551 0.418868937322898 0.209434468661449 42 0.825353161940836 0.349293676118329 0.174646838059164 43 0.79600688210739 0.40798623578522 0.20399311789261 44 0.747414977660894 0.505170044678212 0.252585022339106 45 0.704792479975285 0.59041504004943 0.295207520024715 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 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/1055wu1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/1055wu1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/19dy31293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/19dy31293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/2k4go1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/2k4go1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/3k4go1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/3k4go1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/4k4go1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/4k4go1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/5k4go1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/5k4go1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/6cwfr1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/6cwfr1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/755wu1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/755wu1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/855wu1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/855wu1293040365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/955wu1293040365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t12930403991dk3ognc8ih880q/955wu1293040365.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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