BDM 3

*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 10:57:26 -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/t12585670779gi09bra9hc0ocb.htm/, Retrieved Wed, 18 Nov 2009 18:58:09 +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/t12585670779gi09bra9hc0ocb.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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101.3 0 106.3 0 94 0 102.8 0 102 0 105.1 1 92.4 0 81.4 0 105.8 0 120.3 1 100.7 0 88.8 0 94.3 0 99.9 0 103.4 0 103.3 0 98.8 0 104.2 0 91.2 0 74.7 0 108.5 0 114.5 0 96.9 0 89.6 0 97.1 0 100.3 0 122.6 0 115.4 1 109 0 129.1 1 102.8 1 96.2 0 127.7 1 128.9 1 126.5 1 119.8 1 113.2 1 114.1 1 134.1 1 130 1 121.8 1 132.1 1 105.3 1 103 1 117.1 1 126.3 1 138.1 1 119.5 1 138 1 135.5 1 178.6 1 162.2 1 176.9 1 204.9 1 132.2 1 142.5 1 164.3 1 174.9 1 175.4 1 143 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Omzet[t] = + 91.1761764705882 + 34.9397058823529Uitvoer[t] + 3.62794117647051M1[t] + 6.06794117647057M2[t] + 21.3879411764706M3[t] + 10.6M4[t] + 16.5479411764706M5[t] + 15.9520588235294M6[t] -7.36000000000002M7[t] -5.59205882352942M8[t] + 12.5400000000000M9[t] + 13.8520588235294M10[t] + 15.38M11[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) 91.1761764705882 9.291201 9.8132 0 0 Uitvoer 34.9397058823529 5.294153 6.5997 0 0 M1 3.62794117647051 12.393295 0.2927 0.771014 0.385507 M2 6.06794117647057 12.393295 0.4896 0.626684 0.313342 M3 21.3879411764706 12.393295 1.7258 0.090959 0.04548 M4 10.6 12.347981 0.8584 0.395006 0.197503 M5 16.5479411764706 12.393295 1.3352 0.188231 0.094116 M6 15.9520588235294 12.393295 1.2872 0.204344 0.102172 M7 -7.36000000000002 12.347981 -0.596 0.554002 0.277001 M8 -5.59205882352942 12.393295 -0.4512 0.653909 0.326954 M9 12.5400000000000 12.347981 1.0156 0.315043 0.157521 M10 13.8520588235294 12.393295 1.1177 0.269373 0.134687 M11 15.38 12.347981 1.2455 0.219104 0.109552 Multiple Linear Regression - Regression Statistics Multiple R 0.754982442025881 R-squared 0.569998487767363 Adjusted R-squared 0.46021086762286 F-TEST (value) 5.19182843217777 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.85978556016542e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19.5238722173125 Sum Squared Residuals 17915.5345588235 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.3 94.804117647059 6.495882352941 2 106.3 97.2441176470588 9.05588235294119 3 94 112.564117647059 -18.5641176470588 4 102.8 101.776176470588 1.02382352941178 5 102 107.724117647059 -5.72411764705881 6 105.1 142.067941176471 -36.9679411764706 7 92.4 83.8161764705882 8.58382352941176 8 81.4 85.5841176470588 -4.18411764705878 9 105.8 103.716176470588 2.08382352941176 10 120.3 139.967941176471 -19.6679411764706 11 100.7 106.556176470588 -5.85617647058823 12 88.8 91.1761764705882 -2.37617647058824 13 94.3 94.8041176470588 -0.504117647058771 14 99.9 97.2441176470588 2.65588235294118 15 103.4 112.564117647059 -9.16411764705882 16 103.3 101.776176470588 1.52382352941178 17 98.8 107.724117647059 -8.92411764705883 18 104.2 107.128235294118 -2.92823529411761 19 91.2 83.8161764705882 7.38382352941178 20 74.7 85.5841176470588 -10.8841176470588 21 108.5 103.716176470588 4.78382352941176 22 114.5 105.028235294118 9.47176470588236 23 96.9 106.556176470588 -9.65617647058823 24 89.6 91.1761764705882 -1.57617647058825 25 97.1 94.8041176470588 2.29588235294123 26 100.3 97.2441176470588 3.05588235294118 27 122.6 112.564117647059 10.0358823529412 28 115.4 136.715882352941 -21.3158823529412 29 109 107.724117647059 1.27588235294117 30 129.1 142.067941176471 -12.9679411764706 31 102.8 118.755882352941 -15.9558823529412 32 96.2 85.5841176470588 10.6158823529412 33 127.7 138.655882352941 -10.9558823529412 34 128.9 139.967941176471 -11.0679411764706 35 126.5 141.495882352941 -14.9958823529412 36 119.8 126.115882352941 -6.3158823529412 37 113.2 129.743823529412 -16.5438235294117 38 114.1 132.183823529412 -18.0838235294118 39 134.1 147.503823529412 -13.4038235294118 40 130 136.715882352941 -6.71588235294116 41 121.8 142.663823529412 -20.8638235294118 42 132.1 142.067941176471 -9.96794117647057 43 105.3 118.755882352941 -13.4558823529412 44 103 120.523823529412 -17.5238235294118 45 117.1 138.655882352941 -21.5558823529412 46 126.3 139.967941176471 -13.6679411764706 47 138.1 141.495882352941 -3.39588235294119 48 119.5 126.115882352941 -6.61588235294119 49 138 129.743823529412 8.25617647058828 50 135.5 132.183823529412 3.31617647058823 51 178.6 147.503823529412 31.0961764705882 52 162.2 136.715882352941 25.4841176470588 53 176.9 142.663823529412 34.2361764705882 54 204.9 142.067941176471 62.8320588235294 55 132.2 118.755882352941 13.4441176470588 56 142.5 120.523823529412 21.9761764705882 57 164.3 138.655882352941 25.6441176470588 58 174.9 139.967941176471 34.9320588235294 59 175.4 141.495882352941 33.9041176470588 60 143 126.115882352941 16.8841176470588 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0210722142046296 0.0421444284092592 0.97892778579537 17 0.00473309488321537 0.00946618976643074 0.995266905116785 18 0.000895536764595618 0.00179107352919124 0.999104463235404 19 0.000155556846135896 0.000311113692271792 0.999844443153864 20 5.1549681727388e-05 0.000103099363454776 0.999948450318273 21 9.25139126149646e-06 1.85027825229929e-05 0.999990748608738 22 1.71843965460931e-06 3.43687930921863e-06 0.999998281560345 23 3.43965027587830e-07 6.87930055175661e-07 0.999999656034972 24 4.87044627428815e-08 9.7408925485763e-08 0.999999951295537 25 6.42635628234036e-09 1.28527125646807e-08 0.999999993573644 26 1.01887020912044e-09 2.03774041824088e-09 0.99999999898113 27 7.78484229568258e-07 1.55696845913652e-06 0.99999922151577 28 3.47997786054136e-07 6.95995572108271e-07 0.999999652002214 29 1.42870188891634e-07 2.85740377783267e-07 0.99999985712981 30 9.32392528906108e-07 1.86478505781222e-06 0.99999906760747 31 2.56749932360320e-07 5.13499864720639e-07 0.999999743250068 32 3.9051627942342e-07 7.8103255884684e-07 0.99999960948372 33 1.53411455687691e-07 3.06822911375382e-07 0.999999846588544 34 5.30425715474767e-08 1.06085143094953e-07 0.999999946957428 35 4.95144624957403e-08 9.90289249914807e-08 0.999999950485537 36 3.08675315219607e-08 6.17350630439215e-08 0.999999969132468 37 9.5521799715624e-09 1.91043599431248e-08 0.99999999044782 38 2.99762243246193e-09 5.99524486492386e-09 0.999999997002378 39 3.21234140771963e-09 6.42468281543925e-09 0.999999996787659 40 2.22593196810489e-09 4.45186393620979e-09 0.999999997774068 41 3.79043964896869e-09 7.58087929793738e-09 0.99999999620956 42 2.69120282944244e-07 5.38240565888488e-07 0.999999730879717 43 1.42454228209095e-07 2.84908456418189e-07 0.999999857545772 44 2.40560867962235e-07 4.8112173592447e-07 0.999999759439132 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 28 0.96551724137931 NOK 5% type I error level 29 1 NOK 10% type I error level 29 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/1067k01258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/1067k01258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/1jsp91258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/1jsp91258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/2hind1258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/2hind1258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/3brmf1258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/3brmf1258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/4oxt11258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/4oxt11258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/50xkw1258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/50xkw1258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/6m6ay1258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/6m6ay1258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/7djtt1258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/7djtt1258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/85lmu1258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/85lmu1258567042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/923dl1258567042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585670779gi09bra9hc0ocb/923dl1258567042.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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