*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 07:23:39 -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/t1258554282ef1vz70cn3luty7.htm/, Retrieved Wed, 18 Nov 2009 15:24:54 +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/t1258554282ef1vz70cn3luty7.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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785.8 35 819.3 31.3 849.4 30 880.4 31.3 900.1 33 937.2 31.3 948.9 29 952.6 28.7 947.3 28 974.2 29.7 1000.8 30.7 1032.8 24 1050.7 29 1057.3 33 1075.4 28 1118.4 28.7 1179.8 31.7 1227 34 1257.8 35.3 1251.5 27 1236.3 31.3 1170.6 38.7 1213.1 37.3 1265.5 37.3 1300.8 37.7 1348.4 34.7 1371.9 34.7 1403.3 33.7 1451.8 38.3 1474.2 38 1438.2 38.3 1513.6 42.7 1562.2 41.7 1546.2 39.7 1527.5 39.3 1418.7 39.3 1448.5 37.7 1492.1 38.3 1395.4 37.7 1403.7 37 1316.6 34.3 1274.5 29.7 1264.4 34.7 1323.9 32 1332.1 30.3 1250.2 28.3 1096.7 31.3 1080.8 17.7 1039.2 15.7 792 14.3 746.6 13.3 688.8 11 715.8 2.7 672.9 3.3 629.5 3.7 681.2 1.4 755.4 7.1 760.6 8.1 765.9 12.4 836.8 12.4 904.9 9.2

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 Herdiv[t] = + 608.354354748396 + 19.8380124426781handact[t] -63.268595470396M1[t] -108.022892010394M2[t] -90.7588323509631M3[t] -71.643627373892M4[t] -50.9987031433814M5[t] -31.9785739357996M6[t] -60.026305631917M7[t] + 13.2756372626106M8[t] + 9.18946083827558M9[t] -41.3129143417917M10[t] -86.6623305172731M11[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) 608.354354748396 91.47 6.6509 0 0 handact 19.8380124426781 1.960901 10.1168 0 0 M1 -63.268595470396 102.606768 -0.6166 0.540404 0.270202 M2 -108.022892010394 107.452094 -1.0053 0.319788 0.159894 M3 -90.7588323509631 107.260253 -0.8462 0.401665 0.200832 M4 -71.643627373892 107.225833 -0.6682 0.507234 0.253617 M5 -50.9987031433814 107.20108 -0.4757 0.636425 0.318213 M6 -31.9785739357996 107.161524 -0.2984 0.766675 0.383337 M7 -60.026305631917 107.215139 -0.5599 0.578173 0.289087 M8 13.2756372626106 107.139885 0.1239 0.901904 0.450952 M9 9.18946083827558 107.181566 0.0857 0.932032 0.466016 M10 -41.3129143417917 107.275624 -0.3851 0.701857 0.350929 M11 -86.6623305172731 107.434401 -0.8067 0.423845 0.211923 Multiple Linear Regression - Regression Statistics Multiple R 0.826492018036717 R-squared 0.683089055878405 Adjusted R-squared 0.603861319848006 F-TEST (value) 8.62184242670158 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 2.04565041572735e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 169.401659358048 Sum Squared Residuals 1377452.26527649 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 785.8 1239.41619477173 -453.616194771729 2 819.3 1121.26125219382 -301.961252193824 3 849.4 1112.73589567777 -263.335895677774 4 880.4 1157.64051683033 -277.240516830327 5 900.1 1212.01006221339 -311.910062213390 6 937.2 1197.30557026842 -260.105570268419 7 948.9 1123.63040995414 -174.730409954143 8 952.6 1190.98094911587 -238.380949115867 9 947.3 1173.00816398166 -225.708163981657 10 974.2 1156.23040995414 -182.030409954142 11 1000.8 1130.71900622134 -129.919006221339 12 1032.8 1084.46665337267 -51.6666533726691 13 1050.7 1120.38812011566 -69.6881201156632 14 1057.3 1154.98587334638 -97.6858733463773 15 1075.4 1073.05987079242 2.34012920758184 16 1118.4 1106.06168447936 12.338315520636 17 1179.8 1186.22064603791 -6.42064603790887 18 1227 1250.86820386365 -23.8682038636502 19 1257.8 1248.60988834301 9.19011165698564 20 1251.5 1157.25632796331 94.243672036686 21 1236.3 1238.47360504249 -2.17360504249465 22 1170.6 1334.77252193825 -164.172521938245 23 1213.1 1261.64988834301 -48.5498883430143 24 1265.5 1348.31221886029 -82.8122188602874 25 1300.8 1292.97882836696 7.8211716330373 26 1348.4 1188.71049449893 159.68950550107 27 1371.9 1205.97455415836 165.925445841639 28 1403.3 1205.25174669275 198.048253307246 29 1451.8 1317.15152815958 134.648471840416 30 1474.2 1330.22025363436 143.979746365637 31 1438.2 1308.12392567105 130.076074328952 32 1513.6 1468.71312331336 44.8868766866402 33 1562.2 1444.78893444635 117.411065553654 34 1546.2 1354.61053438092 191.589465619077 35 1527.5 1301.32591322837 226.174086771630 36 1418.7 1387.98824374564 30.7117562543565 37 1448.5 1292.97882836696 155.521171633037 38 1492.1 1260.12733929257 231.972660707429 39 1395.4 1265.48859148640 129.911408513604 40 1403.7 1270.71718775359 132.982812246408 41 1316.6 1237.79947838887 78.8005216111281 42 1274.5 1165.56475036013 108.935249639866 43 1264.4 1236.70708087741 27.6929191225925 44 1323.9 1256.44639017670 67.4536098232958 45 1332.1 1218.63559259982 113.464407400183 46 1250.2 1128.45719253439 121.742807465607 47 1096.7 1142.62181368695 -45.9218136869458 48 1080.8 959.487174983797 121.312825016203 49 1039.2 856.542554628045 182.657445371955 50 792 784.015040668297 7.98495933170262 51 746.6 781.44108788505 -34.8410878850506 52 688.8 754.928864243962 -66.1288642439623 53 715.8 610.918285200245 104.881714799755 54 672.9 641.841221873433 31.0587781265665 55 629.5 621.728695154387 7.77130484561275 56 681.2 649.403209430755 31.7967905692447 57 755.4 758.393703929685 -2.99370392968532 58 760.6 727.729341192296 32.8706588077039 59 765.9 767.68337852033 -1.78337852033029 60 836.8 854.345709037603 -17.5457090376035 61 904.9 727.595473750638 177.304526249362 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.917820964638697 0.164358070722607 0.0821790353613034 17 0.93778557942504 0.124428841149921 0.0622144205749604 18 0.992030107045266 0.0159397859094675 0.00796989295473376 19 0.997789404945303 0.00442119010939391 0.00221059505469696 20 0.998139417714793 0.00372116457041489 0.00186058228520745 21 0.998369374098517 0.00326125180296655 0.00163062590148327 22 0.999651442141735 0.000697115716530403 0.000348557858265202 23 0.999560829614097 0.000878340771806141 0.000439170385903070 24 0.999573119081085 0.00085376183783031 0.000426880918915155 25 0.999946082676146 0.000107834647707617 5.39173238538085e-05 26 0.999966471564354 6.70568712917934e-05 3.35284356458967e-05 27 0.99996760987774 6.4780244519632e-05 3.2390122259816e-05 28 0.999983269316873 3.34613662538918e-05 1.67306831269459e-05 29 0.999966905244717 6.61895105662292e-05 3.30947552831146e-05 30 0.999930214490675 0.000139571018650796 6.97855093253981e-05 31 0.999845285081858 0.000309429836283371 0.000154714918141686 32 0.99972055471703 0.000558890565938418 0.000279445282969209 33 0.999282152715018 0.00143569456996397 0.000717847284981985 34 0.998902314872837 0.00219537025432549 0.00109768512716274 35 0.999692338038207 0.00061532392358583 0.000307661961792915 36 0.999426453193996 0.00114709361200748 0.000573546806003739 37 0.999337731982765 0.00132453603446956 0.000662268017234779 38 0.9995089350419 0.000982129916198306 0.000491064958099153 39 0.999187053417664 0.00162589316467268 0.000812946582336342 40 0.9994845528612 0.00103089427760125 0.000515447138800624 41 0.999000537611206 0.00199892477758775 0.000999462388793877 42 0.996948860650978 0.0061022786980446 0.0030511393490223 43 0.990647694779517 0.0187046104409652 0.00935230522048259 44 0.973516384476293 0.0529672310474141 0.0264836155237070 45 0.931737054264197 0.136525891471607 0.0682629457358033 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.8 NOK 5% type I error level 26 0.866666666666667 NOK 10% type I error level 27 0.9 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/10380a1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/10380a1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/1j2ye1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/1j2ye1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/2b7sc1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/2b7sc1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/3yxcz1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/3yxcz1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/4w4oi1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/4w4oi1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/58w6v1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/58w6v1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/6zd8j1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/6zd8j1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/7fv0z1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/7fv0z1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/8wsuq1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/8wsuq1258554214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/94gbc1258554214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258554282ef1vz70cn3luty7/94gbc1258554214.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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