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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 09:18:28 -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/t1258561186ggtpnqtwuz2s1qh.htm/, Retrieved Wed, 18 Nov 2009 17:19:59 +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/t1258561186ggtpnqtwuz2s1qh.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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17823.2 0 17872 0 17420.4 0 16704.4 0 15991.2 0 15583.6 0 19123.5 0 17838.7 0 17209.4 0 18586.5 0 16258.1 0 15141.6 0 19202.1 0 17746.5 0 19090.1 1 18040.3 1 17515.5 1 17751.8 1 21072.4 1 17170 1 19439.5 1 19795.4 1 17574.9 1 16165.4 1 19464.6 1 19932.1 1 19961.2 1 17343.4 1 18924.2 1 18574.1 1 21350.6 1 18594.6 1 19832.1 1 20844.4 1 19640.2 1 17735.4 1 19813.6 1 22160 1 20664.3 1 17877.4 1 20906.5 1 21164.1 1 21374.4 1 22952.3 1 21343.5 1 23899.3 1 22392.9 1 18274.1 1 22786.7 1 22321.5 1 17842.2 1 16373.5 1 15933.8 0 16446.1 0 17729 0 16643 0 16196.7 0 18252.1 0 17570.4 0 15836.8 0

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 15013.5497058824 + 2695.18382352941X[t] + 3187.38000000000M1[t] + 3375.76M2[t] + 1825.94323529412M3[t] + 98.103235294117M4[t] + 1223.58000000000M5[t] + 1273.28M6[t] + 3499.32M7[t] + 2009.06M8[t] + 2173.58M9[t] + 3644.88M10[t] + 2056.64M11[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) 15013.5497058824 636.709675 23.5799 0 0 X 2695.18382352941 362.799008 7.4289 0 0 M1 3187.38000000000 846.185426 3.7668 0.00046 0.00023 M2 3375.76 846.185426 3.9894 0.00023 0.000115 M3 1825.94323529412 849.290704 2.15 0.036735 0.018368 M4 98.103235294117 849.290704 0.1155 0.908531 0.454266 M5 1223.58000000000 846.185426 1.446 0.154814 0.077407 M6 1273.28 846.185426 1.5047 0.139085 0.069542 M7 3499.32 846.185426 4.1354 0.000145 7.3e-05 M8 2009.06 846.185426 2.3743 0.02172 0.01086 M9 2173.58 846.185426 2.5687 0.013445 0.006722 M10 3644.88 846.185426 4.3074 8.4e-05 4.2e-05 M11 2056.64 846.185426 2.4305 0.01895 0.009475 Multiple Linear Regression - Regression Statistics Multiple R 0.821764579421597 R-squared 0.675297023991954 Adjusted R-squared 0.592394136500538 F-TEST (value) 8.14563936704709 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 5.5601213855283e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1337.93663438386 Sum Squared Residuals 84133498.5684411 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 17823.2 18200.9297058823 -377.729705882324 2 17872 18389.3097058824 -517.309705882358 3 17420.4 16839.4929411765 580.907058823533 4 16704.4 15111.6529411765 1592.74705882353 5 15991.2 16237.1297058824 -245.929705882350 6 15583.6 16286.8297058824 -703.229705882352 7 19123.5 18512.8697058824 610.630294117639 8 17838.7 17022.6097058824 816.090294117646 9 17209.4 17187.1297058824 22.2702941176465 10 18586.5 18658.4297058824 -71.9297058823531 11 16258.1 17070.1897058824 -812.089705882354 12 15141.6 15013.5497058824 128.050294117644 13 19202.1 18200.9297058824 1001.17029411764 14 17746.5 18389.3097058824 -642.809705882354 15 19090.1 19534.6767647059 -444.576764705885 16 18040.3 17806.8367647059 233.463235294118 17 17515.5 18932.3135294118 -1416.81352941177 18 17751.8 18982.0135294118 -1230.21352941176 19 21072.4 21208.0535294118 -135.653529411761 20 17170 19717.7935294118 -2547.79352941176 21 19439.5 19882.3135294118 -442.813529411764 22 19795.4 21353.6135294118 -1558.21352941176 23 17574.9 19765.3735294118 -2190.47352941176 24 16165.4 17708.7335294118 -1543.33352941176 25 19464.6 20896.1135294118 -1431.51352941177 26 19932.1 21084.4935294118 -1152.39352941177 27 19961.2 19534.6767647059 426.523235294117 28 17343.4 17806.8367647059 -463.43676470588 29 18924.2 18932.3135294118 -8.11352941176437 30 18574.1 18982.0135294118 -407.913529411765 31 21350.6 21208.0535294118 142.546470588236 32 18594.6 19717.7935294118 -1123.19352941177 33 19832.1 19882.3135294118 -50.213529411765 34 20844.4 21353.6135294118 -509.213529411763 35 19640.2 19765.3735294118 -125.173529411765 36 17735.4 17708.7335294118 26.6664705882384 37 19813.6 20896.1135294118 -1082.51352941177 38 22160 21084.4935294118 1075.50647058824 39 20664.3 19534.6767647059 1129.62323529412 40 17877.4 17806.8367647059 70.56323529412 41 20906.5 18932.3135294118 1974.18647058823 42 21164.1 18982.0135294118 2182.08647058823 43 21374.4 21208.0535294118 166.346470588239 44 22952.3 19717.7935294118 3234.50647058824 45 21343.5 19882.3135294118 1461.18647058824 46 23899.3 21353.6135294118 2545.68647058823 47 22392.9 19765.3735294118 2627.52647058824 48 18274.1 17708.7335294118 565.366470588235 49 22786.7 20896.1135294118 1890.58647058823 50 22321.5 21084.4935294118 1237.00647058824 51 17842.2 19534.6767647059 -1692.47676470588 52 16373.5 17806.8367647059 -1433.33676470588 53 15933.8 16237.1297058824 -303.329705882356 54 16446.1 16286.8297058824 159.270294117645 55 17729 18512.8697058824 -783.869705882353 56 16643 17022.6097058824 -379.609705882354 57 16196.7 17187.1297058824 -990.429705882353 58 18252.1 18658.4297058824 -406.329705882356 59 17570.4 17070.1897058824 500.210294117645 60 15836.8 15013.5497058824 823.250294117646 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0629547564938385 0.125909512987677 0.937045243506161 17 0.0197926356949563 0.0395852713899127 0.980207364305044 18 0.00824088904861209 0.0164817780972242 0.991759110951388 19 0.00233962311051191 0.00467924622102383 0.997660376889488 20 0.0255894369059925 0.0511788738119851 0.974410563094007 21 0.0145780577229675 0.0291561154459350 0.985421942277033 22 0.00777082355430918 0.0155416471086184 0.99222917644569 23 0.00616560212276189 0.0123312042455238 0.993834397877238 24 0.00393346075266989 0.00786692150533978 0.99606653924733 25 0.00227184213865423 0.00454368427730845 0.997728157861346 26 0.00218137804012561 0.00436275608025121 0.997818621959874 27 0.00186415977967823 0.00372831955935645 0.998135840220322 28 0.00112019531279579 0.00224039062559158 0.998879804687204 29 0.00196600105239059 0.00393200210478118 0.99803399894761 30 0.00280903871179816 0.00561807742359632 0.997190961288202 31 0.00139001220655887 0.00278002441311774 0.99860998779344 32 0.00270519617319871 0.00541039234639741 0.997294803826801 33 0.00184792845452560 0.00369585690905119 0.998152071545474 34 0.00320993221842082 0.00641986443684164 0.99679006778158 35 0.0213834429854741 0.0427668859709481 0.978616557014526 36 0.0301503604795196 0.0603007209590392 0.96984963952048 37 0.0794650997367702 0.158930199473540 0.92053490026323 38 0.141234225166798 0.282468450333597 0.858765774833202 39 0.374427781554891 0.748855563109783 0.625572218445109 40 0.393062883039592 0.786125766079185 0.606937116960408 41 0.449408499792950 0.898816999585899 0.550591500207050 42 0.474792213048932 0.949584426097864 0.525207786951068 43 0.397070553985122 0.794141107970245 0.602929446014878 44 0.560929334580668 0.878141330838665 0.439070665419332 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.413793103448276 NOK 5% type I error level 18 0.620689655172414 NOK 10% type I error level 20 0.689655172413793 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/107lu71258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/107lu71258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/1bk111258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/1bk111258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/25rrf1258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/25rrf1258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/332lc1258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/332lc1258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/4yp551258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/4yp551258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/5ya881258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/5ya881258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/6agiz1258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/6agiz1258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/71zhg1258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/71zhg1258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/82fgu1258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/82fgu1258561101.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/9m6lg1258561101.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561186ggtpnqtwuz2s1qh/9m6lg1258561101.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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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