BBWS7-1

*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: Mon, 23 Nov 2009 12:46:23 -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/23/t1259005645c7krkumvpev6wpr.htm/, Retrieved Mon, 23 Nov 2009 20:47:37 +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/23/t1259005645c7krkumvpev6wpr.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:

Multiple lineair regression software

Dataseries X:

» Textbox « » Textfile « » CSV «

3922 8,1 3759 7,7 4138 7,5 4634 7,6 3996 7,8 4308 7,8 4143 7,8 4429 7,5 5219 7,5 4929 7,1 5755 7,5 5592 7,5 4163 7,6 4962 7,7 5208 7,7 4755 7,9 4491 8,1 5732 8,2 5731 8,2 5040 8,2 6102 7,9 4904 7,3 5369 6,9 5578 6,6 4619 6,7 4731 6,9 5011 7 5299 7,1 4146 7,2 4625 7,1 4736 6,9 4219 7 5116 6,8 4205 6,4 4121 6,7 5103 6,6 4300 6,4 4578 6,3 3809 6,2 5526 6,5 4247 6,8 3830 6,8 4394 6,4 4826 6,1 4409 5,8 4569 6,1 4106 7,2 4794 7,3 3914 6,9 3793 6,1 4405 5,8 4022 6,2 4100 7,1 4788 7,7 3163 7,9 3585 7,7 3903 7,4 4178 7,5 3863 8 4187 8,1

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3917.35496549577 + 90.7009806465004X[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) 3917.35496549577 892.667656 4.3884 4.9e-05 2.4e-05 X 90.7009806465004 123.927842 0.7319 0.467186 0.233593 Multiple Linear Regression - Regression Statistics Multiple R 0.0956605444102581 R-squared 0.00915093975686697 Adjusted R-squared -0.00793266473008347 F-TEST (value) 0.535656264101465 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.467185578170894 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 628.221676726639 Sum Squared Residuals 22890423.5563353 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3922 4652.03290873243 -730.032908732433 2 3759 4615.75251647382 -856.752516473823 3 4138 4597.61232034452 -459.612320344523 4 4634 4606.68241840917 27.3175815908267 5 3996 4624.82261453847 -628.822614538473 6 4308 4624.82261453847 -316.822614538473 7 4143 4624.82261453847 -481.822614538473 8 4429 4597.61232034452 -168.612320344523 9 5219 4597.61232034452 621.387679655477 10 4929 4561.33192808592 367.668071914077 11 5755 4597.61232034452 1157.38767965548 12 5592 4597.61232034452 994.387679655477 13 4163 4606.68241840917 -443.682418409173 14 4962 4615.75251647382 346.247483526177 15 5208 4615.75251647382 592.247483526177 16 4755 4633.89271260312 121.107287396876 17 4491 4652.03290873242 -161.032908732424 18 5732 4661.10300679707 1070.89699320293 19 5731 4661.10300679707 1069.89699320293 20 5040 4661.10300679707 378.896993202926 21 6102 4633.89271260312 1468.10728739688 22 4904 4579.47212421522 324.527875784777 23 5369 4543.19173195662 825.808268043377 24 5578 4515.98143776267 1062.01856223733 25 4619 4525.05153582732 93.948464172677 26 4731 4543.19173195662 187.808268043377 27 5011 4552.26183002127 458.738169978727 28 5299 4561.33192808592 737.668071914077 29 4146 4570.40202615057 -424.402026150573 30 4625 4561.33192808592 63.6680719140768 31 4736 4543.19173195662 192.808268043377 32 4219 4552.26183002127 -333.261830021273 33 5116 4534.12163389197 581.878366108027 34 4205 4497.84124163337 -292.841241633373 35 4121 4525.05153582732 -404.051535827323 36 5103 4515.98143776267 587.018562237327 37 4300 4497.84124163337 -197.841241633373 38 4578 4488.77114356872 89.2288564312771 39 3809 4479.70104550407 -670.701045504073 40 5526 4506.91133969802 1019.08866030198 41 4247 4534.12163389197 -287.121633891973 42 3830 4534.12163389197 -704.121633891973 43 4394 4497.84124163337 -103.841241633373 44 4826 4470.63094743942 355.369052560577 45 4409 4443.42065324547 -34.4206532454726 46 4569 4470.63094743942 98.3690525605772 47 4106 4570.40202615057 -464.402026150573 48 4794 4579.47212421522 214.527875784777 49 3914 4543.19173195662 -629.191731956623 50 3793 4470.63094743942 -677.630947439423 51 4405 4443.42065324547 -38.4206532454726 52 4022 4479.70104550407 -457.701045504073 53 4100 4561.33192808592 -461.331928085923 54 4788 4615.75251647382 172.247483526177 55 3163 4633.89271260312 -1470.89271260312 56 3585 4615.75251647382 -1030.75251647382 57 3903 4588.54222227987 -685.542222279873 58 4178 4597.61232034452 -419.612320344523 59 3863 4642.96281066777 -779.962810667774 60 4187 4652.03290873242 -465.032908732423 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.174312253152891 0.348624506305782 0.82568774684711 6 0.0957809864661272 0.191561972932254 0.904219013533873 7 0.0415653953846395 0.083130790769279 0.95843460461536 8 0.0176865389068755 0.035373077813751 0.982313461093125 9 0.080991982565971 0.161983965131942 0.919008017434029 10 0.0433942058009597 0.0867884116019194 0.95660579419904 11 0.272725379825278 0.545450759650557 0.727274620174722 12 0.396386558079887 0.792773116159775 0.603613441920113 13 0.349147672913405 0.69829534582681 0.650852327086595 14 0.322418136506957 0.644836273013914 0.677581863493043 15 0.352367929740242 0.704735859480484 0.647632070259758 16 0.312715831255611 0.625431662511222 0.68728416874439 17 0.261703211307275 0.523406422614549 0.738296788692725 18 0.562268287329653 0.875463425340694 0.437731712670347 19 0.718255293218167 0.563489413563665 0.281744706781833 20 0.676426706771686 0.647146586456627 0.323573293228314 21 0.918148128994509 0.163703742010983 0.0818518710054915 22 0.899988487301953 0.200023025396094 0.100011512698047 23 0.92221504647089 0.155569907058219 0.0777849535291097 24 0.953973424490071 0.0920531510198573 0.0460265755099286 25 0.939500997763482 0.120998004473036 0.0604990022365178 26 0.921577014589049 0.156845970821903 0.0784229854109514 27 0.917242788312519 0.165514423374962 0.0827572116874811 28 0.947456606774806 0.105086786450388 0.0525433932251939 29 0.941385157671058 0.117229684657884 0.0586148423289421 30 0.926335037603302 0.147329924793395 0.0736649623966977 31 0.911411463321285 0.177177073357430 0.0885885366787149 32 0.893276081767562 0.213447836464875 0.106723918232438 33 0.91510588647732 0.169788227045360 0.0848941135226798 34 0.89714401465184 0.20571197069632 0.10285598534816 35 0.876915754760519 0.246168490478962 0.123084245239481 36 0.902432309175104 0.195135381649792 0.097567690824896 37 0.869866667553853 0.260266664892294 0.130133332446147 38 0.829413469848836 0.341173060302329 0.170586530151164 39 0.846786496182612 0.306427007634776 0.153213503817388 40 0.970130305977693 0.0597393880446136 0.0298696940223068 41 0.955080432227258 0.0898391355454848 0.0449195677727424 42 0.95230311450269 0.09539377099462 0.04769688549731 43 0.92687572206463 0.146248555870739 0.0731242779353696 44 0.925268868364692 0.149462263270616 0.0747311316353079 45 0.88739898190869 0.225202036182620 0.112601018091310 46 0.859473383760799 0.281053232478402 0.140526616239201 47 0.808605531419117 0.382788937161766 0.191394468580883 48 0.873621080638376 0.252757838723248 0.126378919361624 49 0.825290428104898 0.349419143790205 0.174709571895102 50 0.796475601790174 0.407048796419652 0.203524398209826 51 0.709694409630477 0.580611180739045 0.290305590369523 52 0.608836218163751 0.782327563672498 0.391163781836249 53 0.484325120405626 0.968650240811251 0.515674879594374 54 0.69119268860264 0.617614622794721 0.308807311397360 55 0.88313433381305 0.233731332373899 0.116865666186949 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 1 0.0196078431372549 OK 10% type I error level 7 0.137254901960784 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/10qug81259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/10qug81259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/1zykh1259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/1zykh1259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/2mqf31259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/2mqf31259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/3jk781259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/3jk781259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/4amd71259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/4amd71259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/581yi1259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/581yi1259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/6olcb1259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/6olcb1259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/7yfw41259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/7yfw41259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/8ple81259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/8ple81259005578.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/9q57s1259005578.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005645c7krkumvpev6wpr/9q57s1259005578.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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