w7

*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: Sun, 22 Nov 2009 06:21:07 -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/22/t1258896131smrtlilx4q3qngx.htm/, Retrieved Sun, 22 Nov 2009 14:22:23 +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/22/t1258896131smrtlilx4q3qngx.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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8 0 8,1 0 7,7 0 7,5 0 7,6 0 7,8 0 7,8 0 7,8 0 7,5 0 7,5 0 7,1 0 7,5 0 7,5 0 7,6 0 7,7 0 7,7 0 7,9 0 8,1 0 8,2 0 8,2 0 8,2 0 7,9 0 7,3 0 6,9 0 6,6 0 6,7 0 6,9 0 7 0 7,1 0 7,2 0 7,1 0 6,9 0 7 0 6,8 0 6,4 0 6,7 0 6,6 0 6,4 0 6,3 0 6,2 0 6,5 0 6,8 1 6,8 1 6,4 1 6,1 1 5,8 1 6,1 1 7,2 1 7,3 1 6,9 1 6,1 1 5,8 1 6,2 1 7,1 1 7,7 1 7,9 1 7,7 1 7,4 1 7,5 1 8 1 8,1 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] = + 7.43134020618557 -0.428350515463918X[t] + 0.0614432989690721M1[t] -0.205670103092784M2[t] -0.405670103092784M3[t] -0.505670103092785M4[t] -0.285670103092784M5[t] + 0.139999999999999M6[t] + 0.259999999999999M7[t] + 0.179999999999999M8[t] + 0.0399999999999992M9[t] -0.180000000000001M10[t] -0.380000000000001M11[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) 7.43134020618557 0.30567 24.3116 0 0 X -0.428350515463918 0.184438 -2.3225 0.024495 0.012247 M1 0.0614432989690721 0.401832 0.1529 0.879112 0.439556 M2 -0.205670103092784 0.421122 -0.4884 0.6275 0.31375 M3 -0.405670103092784 0.421122 -0.9633 0.340221 0.17011 M4 -0.505670103092785 0.421122 -1.2008 0.235733 0.117866 M5 -0.285670103092784 0.421122 -0.6784 0.500804 0.250402 M6 0.139999999999999 0.419503 0.3337 0.740038 0.370019 M7 0.259999999999999 0.419503 0.6198 0.538333 0.269167 M8 0.179999999999999 0.419503 0.4291 0.669785 0.334893 M9 0.0399999999999992 0.419503 0.0954 0.924433 0.462217 M10 -0.180000000000001 0.419503 -0.4291 0.669785 0.334893 M11 -0.380000000000001 0.419503 -0.9058 0.369549 0.184774 Multiple Linear Regression - Regression Statistics Multiple R 0.446265759861158 R-squared 0.199153128424457 Adjusted R-squared -0.00105858946942883 F-TEST (value) 0.994712649786114 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.467970416533147 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.663292578485196 Sum Squared Residuals 21.1179381443299 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.49278350515463 0.507216494845367 2 8.1 7.22567010309278 0.874329896907217 3 7.7 7.02567010309278 0.674329896907217 4 7.5 6.92567010309278 0.574329896907215 5 7.6 7.14567010309278 0.454329896907216 6 7.8 7.57134020618557 0.228659793814433 7 7.8 7.69134020618557 0.108659793814433 8 7.8 7.61134020618557 0.188659793814433 9 7.5 7.47134020618557 0.0286597938144331 10 7.5 7.25134020618557 0.248659793814433 11 7.1 7.05134020618557 0.0486597938144325 12 7.5 7.43134020618557 0.0686597938144325 13 7.5 7.49278350515464 0.00721649484536001 14 7.6 7.22567010309278 0.374329896907217 15 7.7 7.02567010309278 0.674329896907217 16 7.7 6.92567010309278 0.774329896907217 17 7.9 7.14567010309278 0.754329896907217 18 8.1 7.57134020618557 0.528659793814433 19 8.2 7.69134020618557 0.508659793814433 20 8.2 7.61134020618557 0.588659793814432 21 8.2 7.47134020618557 0.728659793814432 22 7.9 7.25134020618557 0.648659793814433 23 7.3 7.05134020618557 0.248659793814433 24 6.9 7.43134020618557 -0.531340206185567 25 6.6 7.49278350515464 -0.89278350515464 26 6.7 7.22567010309278 -0.525670103092783 27 6.9 7.02567010309278 -0.125670103092783 28 7 6.92567010309278 0.0743298969072169 29 7.1 7.14567010309278 -0.0456701030927837 30 7.2 7.57134020618557 -0.371340206185567 31 7.1 7.69134020618557 -0.591340206185567 32 6.9 7.61134020618557 -0.711340206185567 33 7 7.47134020618557 -0.471340206185567 34 6.8 7.25134020618557 -0.451340206185567 35 6.4 7.05134020618557 -0.651340206185567 36 6.7 7.43134020618557 -0.731340206185568 37 6.6 7.49278350515464 -0.89278350515464 38 6.4 7.22567010309278 -0.825670103092783 39 6.3 7.02567010309278 -0.725670103092784 40 6.2 6.92567010309278 -0.725670103092783 41 6.5 7.14567010309278 -0.645670103092783 42 6.8 7.14298969072165 -0.342989690721649 43 6.8 7.26298969072165 -0.462989690721649 44 6.4 7.18298969072165 -0.78298969072165 45 6.1 7.04298969072165 -0.94298969072165 46 5.8 6.82298969072165 -1.02298969072165 47 6.1 6.62298969072165 -0.52298969072165 48 7.2 7.00298969072165 0.19701030927835 49 7.3 7.06443298969072 0.235567010309277 50 6.9 6.79731958762887 0.102680412371135 51 6.1 6.59731958762887 -0.497319587628867 52 5.8 6.49731958762887 -0.697319587628865 53 6.2 6.71731958762887 -0.517319587628865 54 7.1 7.14298969072165 -0.0429896907216496 55 7.7 7.26298969072165 0.437010309278351 56 7.9 7.18298969072165 0.717010309278351 57 7.7 7.04298969072165 0.657010309278351 58 7.4 6.82298969072165 0.577010309278351 59 7.5 6.62298969072165 0.87701030927835 60 8 7.00298969072165 0.99701030927835 61 8.1 7.06443298969072 1.03556701030928 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0953793667316105 0.190758733463221 0.90462063326839 17 0.0483540671206405 0.096708134241281 0.95164593287936 18 0.0243475170311011 0.0486950340622023 0.975652482968899 19 0.0155892021620359 0.0311784043240718 0.984410797837964 20 0.0109927949238662 0.0219855898477323 0.989007205076134 21 0.0196141655782057 0.0392283311564113 0.980385834421794 22 0.0175842421751386 0.0351684843502772 0.982415757824861 23 0.00976926058884005 0.0195385211776801 0.99023073941116 24 0.0094380431986718 0.0188760863973436 0.990561956801328 25 0.0567596484800693 0.113519296960139 0.943240351519931 26 0.119787631532279 0.239575263064557 0.880212368467721 27 0.137556716923897 0.275113433847793 0.862443283076103 28 0.151320016471579 0.302640032943158 0.84867998352842 29 0.156793986789835 0.313587973579669 0.843206013210165 30 0.152067505644162 0.304135011288323 0.847932494355838 31 0.153091945772326 0.306183891544653 0.846908054227673 32 0.173047313956359 0.346094627912718 0.826952686043641 33 0.160767358160553 0.321534716321107 0.839232641839447 34 0.156476175523885 0.31295235104777 0.843523824476115 35 0.130028691014554 0.260057382029108 0.869971308985446 36 0.105551645866392 0.211103291732784 0.894448354133608 37 0.124369743336329 0.248739486672658 0.875630256663671 38 0.131775862374726 0.263551724749451 0.868224137625274 39 0.121113333600433 0.242226667200867 0.878886666399567 40 0.108580411073130 0.217160822146261 0.89141958892687 41 0.083678964882828 0.167357929765656 0.916321035117172 42 0.0481182690631435 0.096236538126287 0.951881730936857 43 0.0327098088619943 0.0654196177239886 0.967290191138006 44 0.0392898552201762 0.0785797104403524 0.960710144779824 45 0.0612634952180893 0.122526990436179 0.93873650478191 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 7 0.233333333333333 NOK 10% type I error level 11 0.366666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/10ynkc1258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/10ynkc1258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/1mop11258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/1mop11258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/2o8rz1258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/2o8rz1258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/3paic1258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/3paic1258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/4t0e71258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/4t0e71258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/535fm1258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/535fm1258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/6oez41258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/6oez41258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/74udq1258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/74udq1258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/8ums71258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/8ums71258896063.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/9jymh1258896063.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258896131smrtlilx4q3qngx/9jymh1258896063.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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