*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, 18 Nov 2009 09:38:41 -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/18/t1258562408six2zg3mbchp0he.htm/, Retrieved Wed, 18 Nov 2009 17:40:21 +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/18/t1258562408six2zg3mbchp0he.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:

ws7multipleregressionlineairtrendwmanecogr

Dataseries X:

» Textbox « » Textfile « » CSV «

8,00 96,80 8,10 114,10 7,70 110,30 7,50 103,90 7,60 101,60 7,80 94,60 7,80 95,90 7,80 104,70 7,50 102,80 7,50 98,10 7,10 113,90 7,50 80,90 7,50 95,70 7,60 113,20 7,70 105,90 7,70 108,80 7,90 102,30 8,10 99,00 8,20 100,70 8,20 115,50 8,20 100,70 7,90 109,90 7,30 114,60 6,90 85,40 6,60 100,50 6,70 114,80 6,90 116,50 7,00 112,90 7,10 102,00 7,20 106,00 7,10 105,30 6,90 118,80 7,00 106,10 6,80 109,30 6,40 117,20 6,70 92,50 6,60 104,20 6,40 112,50 6,30 122,40 6,20 113,30 6,50 100,00 6,80 110,70 6,80 112,80 6,40 109,80 6,10 117,30 5,80 109,10 6,10 115,90 7,20 96,00 7,30 99,80 6,90 116,80 6,10 115,70 5,80 99,40 6,20 94,30 7,10 91,00 7,70 93,20 7,90 103,10 7,70 94,10 7,40 91,80 7,50 102,70 8,00 82,60

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 Wman[t] = + 11.8416227316364 -0.0442772635382484Ecogr[t] + 0.251375956642180M1[t] + 0.869895185794386M2[t] + 0.684255461872863M3[t] + 0.316126796577316M4[t] + 0.218407596118930M5[t] + 0.587822141800411M6[t] + 0.785941677373967M7[t] + 1.11525514421362M8[t] + 0.721295203250312M9[t] + 0.49617348337196M10[t] + 0.724083400897677M11[t] -0.0196735477030671t + 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) 11.8416227316364 1.050389 11.2736 0 0 Ecogr -0.0442772635382484 0.011625 -3.8088 0.000412 0.000206 M1 0.251375956642180 0.356335 0.7054 0.484088 0.242044 M2 0.869895185794386 0.452407 1.9228 0.060707 0.030354 M3 0.684255461872863 0.45109 1.5169 0.136136 0.068068 M4 0.316126796577316 0.402454 0.7855 0.436189 0.218094 M5 0.218407596118930 0.357688 0.6106 0.544463 0.272232 M6 0.587822141800411 0.358444 1.6399 0.107842 0.053921 M7 0.785941677373967 0.364831 2.1543 0.036492 0.018246 M8 1.11525514421362 0.420702 2.6509 0.010972 0.005486 M9 0.721295203250312 0.379138 1.9025 0.063382 0.031691 M10 0.49617348337196 0.375714 1.3206 0.193162 0.096581 M11 0.724083400897677 0.439165 1.6488 0.106008 0.053004 t -0.0196735477030671 0.003912 -5.0288 8e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.723465284054046 R-squared 0.523402017231401 Adjusted R-squared 0.38871128297071 F-TEST (value) 3.88595414602439 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.000308225772254089 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.514224918714163 Sum Squared Residuals 12.1636542832230 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.78728603007306 0.212713969926935 2 8.1 7.6201350523105 0.479864947689505 3 7.7 7.58307538213125 0.116924617868750 4 7.5 7.47864765577743 0.0213523442225741 5 7.6 7.46309261375394 0.136907386246056 6 7.8 8.1227744565001 -0.322774456500098 7 7.8 8.24366000177086 -0.443660001770861 8 7.8 8.16366000177086 -0.363660001770861 9 7.5 7.83415331382716 -0.334153313827159 10 7.5 7.7974611848755 -0.297461184875506 11 7.1 7.30611679079383 -0.206116790793833 12 7.5 8.02350953895529 -0.523509538955286 13 7.5 7.59990844752832 -0.0999084475283223 14 7.6 7.42390201705811 0.176097982941885 15 7.7 7.54181276926274 0.158187230737263 16 7.7 7.0256064920032 0.674393507996798 17 7.9 7.19601595684036 0.703984043159636 18 8.1 7.691871924495 0.408128075505002 19 8.2 7.79504656435046 0.404953435649535 20 8.2 7.44938298312097 0.750617016879025 21 8.2 7.69105299482067 0.508947005179325 22 7.9 7.03890690268737 0.86109309731263 23 7.3 7.03904013388025 0.260959866119747 24 6.9 7.58817928059636 -0.688179280596362 25 6.6 7.15129501010792 -0.551295010107925 26 6.7 7.11697582296011 -0.416975822960111 27 6.9 6.8363912033205 0.0636087966795017 28 7 6.60798713905958 0.392012860940422 29 7.1 6.97321656346503 0.126783436534967 30 7.2 7.14584850729045 0.0541514927095463 31 7.1 7.35528857963772 -0.255288579637716 32 6.9 7.06718544100795 -0.167185441007948 33 7 7.21587319927733 -0.215873199277328 34 6.8 6.82939068837351 -0.0293906883735145 35 6.4 6.687836676244 -0.287836676244001 36 6.7 7.037728137038 -0.337728137037993 37 6.6 6.7513865625796 -0.1513865625796 38 6.4 6.98273095666128 -0.582730956661276 39 6.3 6.33907277600803 -0.0390727760080276 40 6.2 6.35419366120747 -0.154193661207474 41 6.5 6.82568851810472 -0.325688518104725 42 6.8 6.70166279622388 0.0983372037761192 43 6.8 6.78712653066405 0.0128734693359522 44 6.4 7.22959824041538 -0.829598240415378 45 6.1 6.48388527521214 -0.383885275212141 46 5.8 6.60216356864436 -0.802163568644359 47 6.1 6.50931454640692 -0.409314546406919 48 7.2 6.64667514221732 0.553324857782682 49 7.3 6.71012394971109 0.589876050288912 50 6.9 6.55625615101 0.343743848989997 51 6.1 6.39964786927749 -0.299647869277487 52 5.8 6.73356505195232 -0.93356505195232 53 6.2 6.84198634783593 -0.641986347835935 54 7.1 7.33784231549057 -0.237842315490569 55 7.7 7.41887832357691 0.281121676423090 56 7.9 7.29017333368484 0.609826666315162 57 7.7 7.2750352168627 0.424964783137302 58 7.4 7.13207765541925 0.26792234458075 59 7.5 6.85769185267499 0.642308147325008 60 8 7.00390790119304 0.996092098806958 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.147429620956153 0.294859241912306 0.852570379043847 18 0.063752527024913 0.127505054049826 0.936247472975087 19 0.0263198878313664 0.0526397756627327 0.973680112168634 20 0.0128537885881923 0.0257075771763845 0.987146211411808 21 0.0393057533149338 0.0786115066298677 0.960694246685066 22 0.0307417208146687 0.0614834416293375 0.969258279185331 23 0.0168362559604542 0.0336725119209084 0.983163744039546 24 0.0434780519826284 0.0869561039652568 0.956521948017372 25 0.226705208065335 0.453410416130671 0.773294791934665 26 0.284454858784502 0.568909717569005 0.715545141215498 27 0.268249175293195 0.536498350586389 0.731750824706805 28 0.318559901551083 0.637119803102166 0.681440098448917 29 0.334600665179904 0.669201330359808 0.665399334820096 30 0.313028563259182 0.626057126518364 0.686971436740818 31 0.261039508401398 0.522079016802795 0.738960491598602 32 0.253604823462481 0.507209646924962 0.746395176537519 33 0.201604151094104 0.403208302188209 0.798395848905896 34 0.237948450182779 0.475896900365558 0.762051549817221 35 0.186630321527715 0.373260643055430 0.813369678472285 36 0.129385508582333 0.258771017164667 0.870614491417667 37 0.084021429982498 0.168042859964996 0.915978570017502 38 0.062670383875596 0.125340767751192 0.937329616124404 39 0.0518940226178594 0.103788045235719 0.94810597738214 40 0.129616229573661 0.259232459147322 0.870383770426339 41 0.381592153885610 0.763184307771219 0.61840784611439 42 0.641306396953161 0.717387206093679 0.358693603046839 43 0.639963795239619 0.720072409520763 0.360036204760381 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 2 0.0740740740740741 NOK 10% type I error level 6 0.222222222222222 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/1054l71258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/1054l71258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/1exho1258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/1exho1258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/2hgi21258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/2hgi21258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/31krw1258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/31krw1258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/47bz31258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/47bz31258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/5xejj1258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/5xejj1258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/6wz5q1258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/6wz5q1258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/7wnjk1258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/7wnjk1258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/8m7sf1258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/8m7sf1258562316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/9rhm81258562316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562408six2zg3mbchp0he/9rhm81258562316.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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