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*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: Sat, 28 Nov 2009 01:23:38 -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/28/t1259396690nty8d6xsrioti6w.htm/, Retrieved Sat, 28 Nov 2009 09:25:02 +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/28/t1259396690nty8d6xsrioti6w.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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140.4 139.5 138.1 136.7 130 0 144.6 140.4 139.5 138.1 136.7 0 151.4 144.6 140.4 139.5 138.1 0 147.9 151.4 144.6 140.4 139.5 0 141.5 147.9 151.4 144.6 140.4 0 143.8 141.5 147.9 151.4 144.6 0 143.6 143.8 141.5 147.9 151.4 0 150.5 143.6 143.8 141.5 147.9 0 150.1 150.5 143.6 143.8 141.5 0 154.9 150.1 150.5 143.6 143.8 0 162.1 154.9 150.1 150.5 143.6 0 176.7 162.1 154.9 150.1 150.5 0 186.6 176.7 162.1 154.9 150.1 0 194.8 186.6 176.7 162.1 154.9 0 196.3 194.8 186.6 176.7 162.1 0 228.8 196.3 194.8 186.6 176.7 0 267.2 228.8 196.3 194.8 186.6 0 237.2 267.2 228.8 196.3 194.8 0 254.7 237.2 267.2 228.8 196.3 0 258.2 254.7 237.2 267.2 228.8 0 257.9 258.2 254.7 237.2 267.2 0 269.6 257.9 258.2 254.7 237.2 0 266.9 269.6 257.9 258.2 254.7 0 269.6 266.9 269.6 257.9 258.2 0 253.9 269.6 266.9 269.6 257.9 0 258.6 253.9 269.6 266.9 269.6 0 274.2 258.6 253.9 269.6 266.9 0 301.5 274.2 258.6 253.9 269.6 0 304.5 301.5 274.2 258.6 253.9 0 285.1 304.5 301.5 274.2 258.6 0 287.7 285.1 304.5 301.5 274.2 0 265 etc...

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 12 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y(t)[t] = + 7.29385563015886 + 1.16542650415886`Y(t-1)`[t] -0.129112947395121`Y(t-2)`[t] -0.0368990439218969`Y(t-3)`[t] -0.070065492642836`Y(t-4)`[t] -9.6442854673581`X(t)`[t] + 8.60005373467613M1[t] + 13.2524044882671M2[t] + 13.3517695343097M3[t] + 15.9060791381124M4[t] + 7.05102777228139M5[t] -5.17540387752344M6[t] + 11.8092378520582M7[t] + 3.27991340720070M8[t] + 0.412761016381833M9[t] -0.38427936608232M10[t] -3.10603649820027M11[t] + 0.217517579164888t + 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) 7.29385563015886 12.287407 0.5936 0.556204 0.278102 `Y(t-1)` 1.16542650415886 0.161998 7.1941 0 0 `Y(t-2)` -0.129112947395121 0.256751 -0.5029 0.617883 0.308941 `Y(t-3)` -0.0368990439218969 0.265336 -0.1391 0.890114 0.445057 `Y(t-4)` -0.070065492642836 0.170284 -0.4115 0.682986 0.341493 `X(t)` -9.6442854673581 9.799208 -0.9842 0.331089 0.165544 M1 8.60005373467613 11.518415 0.7466 0.459761 0.229881 M2 13.2524044882671 11.630962 1.1394 0.261483 0.130742 M3 13.3517695343097 11.835117 1.1281 0.26615 0.133075 M4 15.9060791381124 11.999806 1.3255 0.192709 0.096354 M5 7.05102777228139 12.243533 0.5759 0.567993 0.283996 M6 -5.17540387752344 12.130701 -0.4266 0.67199 0.335995 M7 11.8092378520582 11.826893 0.9985 0.324189 0.162094 M8 3.27991340720070 11.712746 0.28 0.780935 0.390467 M9 0.412761016381833 11.575729 0.0357 0.971737 0.485869 M10 -0.38427936608232 12.000398 -0.032 0.974618 0.487309 M11 -3.10603649820027 11.849186 -0.2621 0.7946 0.3973 t 0.217517579164888 0.29144 0.7464 0.459929 0.229964 Multiple Linear Regression - Regression Statistics Multiple R 0.972300937502626 R-squared 0.945369113068485 Adjusted R-squared 0.921555649534235 F-TEST (value) 39.6989338282856 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.6752372297033 Sum Squared Residuals 10844.4779300084 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 140.4 146.705312891202 -6.30531289120248 2 144.6 151.922209489150 -7.32220948915017 3 151.4 156.867931427979 -5.46793142797879 4 147.9 166.891083630937 -18.9910836309374 5 141.5 153.078554109578 -11.5785541095779 6 143.8 133.517617160435 10.2823828395646 7 143.6 153.879281595831 -10.2792815958315 8 150.5 145.518812755648 4.98118724435165 9 150.1 151.299994764063 -1.19999476406328 10 154.9 149.20965119778 5.69034880221999 11 162.1 152.110513739215 9.989486260785 12 176.7 162.736704217361 13.9632957826394 13 186.6 187.490800056908 -0.890800056908018 14 194.8 201.411354267945 -6.61135426794451 15 196.3 208.963318459755 -12.6633184597549 16 228.8 211.036302502909 17.7636974970915 17 267.2 239.085240142989 28.1147598570110 18 237.2 267.002647436154 -29.8026474361536 19 254.7 242.979757273736 11.7202427262642 20 258.2 255.242250855184 2.95774914481612 21 257.9 252.828588628843 5.07141137115671 22 269.6 252.903774069065 16.6962259309347 23 266.9 262.718465724013 4.18153427598678 24 269.6 261.150587244553 8.44941275544693 25 253.9 273.052715911497 -19.1527159114966 26 258.6 258.556644325659 0.0433556743405659 27 274.2 266.467654206064 7.73234579393643 28 301.5 287.203442160590 14.2965578394097 29 304.5 309.294492686157 -4.79449268615674 30 285.1 296.352141763504 -11.2521417635036 31 287.7 288.457322465087 -0.75732246508697 32 265.5 283.656930608758 -18.1569306087576 33 264.1 255.304778715706 8.79522128429407 34 276.1 257.22329928183 18.8767007181699 35 258.9 269.521924399331 -10.6219243993313 36 239.1 252.857899834584 -13.7578998345841 37 250.1 230.831786756555 19.268213243445 38 276.8 250.871660637224 25.9283393627757 39 297.6 282.820916045237 14.7790839547627 40 295.4 307.367706090447 -11.9677060904467 41 283 291.724759797027 -8.72475979702665 42 275.8 262.910356791947 12.8896432080530 43 279.7 271.946261468106 7.75373853189355 44 254.6 269.720923418324 -15.120923418324 45 234.6 238.450028082451 -3.85002808245054 46 176.9 218.163275451325 -41.2632754513247 47 148.1 151.649096137441 -3.54909613744054 48 122.7 131.354808703502 -8.6548087035022 49 124.9 117.819384383838 7.08061561616208 50 121.6 133.638131280022 -12.0381312800216 51 128.4 132.780179860965 -4.38017986096548 52 144.5 145.601465615117 -1.10146561511707 53 151.8 154.816953264250 -3.01695326424972 54 167.1 149.217236847960 17.8827631520395 55 173.8 182.237377197239 -8.43737719723928 56 203.7 178.361082362086 25.3389176379138 57 199.8 208.616609808937 -8.81660980893696 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.760953456202154 0.478093087595692 0.239046543797846 22 0.609242443893144 0.781515112213713 0.390757556106856 23 0.472938190792624 0.945876381585248 0.527061809207376 24 0.362236591442021 0.724473182884042 0.63776340855798 25 0.494201511185571 0.988403022371142 0.505798488814429 26 0.375585132848542 0.751170265697085 0.624414867151458 27 0.264417637507138 0.528835275014276 0.735582362492862 28 0.200155774572858 0.400311549145716 0.799844225427142 29 0.144290296861843 0.288580593723685 0.855709703138157 30 0.155294579496135 0.31058915899227 0.844705420503865 31 0.102572131677218 0.205144263354436 0.897427868322782 32 0.180493307820132 0.360986615640264 0.819506692179868 33 0.124134932023621 0.248269864047243 0.875865067976379 34 0.201101003337762 0.402202006675523 0.798898996662238 35 0.128699382509755 0.257398765019511 0.871300617490245 36 0.0894927212522188 0.178985442504438 0.910507278747781 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/2009/Nov/28/t1259396690nty8d6xsrioti6w/1045pb1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/1045pb1259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/11dj41259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/11dj41259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/2022o1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/2022o1259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/3qvzn1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/3qvzn1259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/4qq6w1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/4qq6w1259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/56q9f1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/56q9f1259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/6y0n01259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/6y0n01259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/718je1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/718je1259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/8yepd1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/8yepd1259396605.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/9ednu1259396605.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259396690nty8d6xsrioti6w/9ednu1259396605.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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