ws7777

*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: Fri, 20 Nov 2009 05:13:23 -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/20/t1258719317t9ow1wpyne8frx0.htm/, Retrieved Fri, 20 Nov 2009 13:15:30 +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/20/t1258719317t9ow1wpyne8frx0.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:

» Textbox « » Textfile « » CSV «

100 0 95.84395716 0 105.5073942 1 118.1540031 1 101.8612953 1 109.8419174 1 105.6348802 1 112.927078 1 133.0698623 1 125.6756757 1 146.736359 1 142.5803162 1 106.1448241 1 126.5170831 1 132.7893932 1 121.2391637 1 114.5079041 1 146.1499235 1 146.1244263 1 128.5058644 1 155.5838858 1 125.0382458 1 136.8944416 1 142.2233554 1 117.7715451 1 120.627231 1 127.7664457 1 135.1096379 1 105.7113717 1 117.9245283 1 120.754717 1 107.572667 1 130.4436512 1 107.2157063 1 105.0739419 1 130.1121877 1 109.6379398 1 116.7261601 1 97.11881693 0 140.8975013 1 108.2865885 1 97.65425803 0 112.0346762 1 123.0494646 1 112.4171341 1 116.4966854 1 104.6914839 1 122.2335543 1 99.79602244 0 96.71086181 0 112.3151453 1 102.5497195 1 104.5385008 1 122.0805711 1 80.64762876 0 91.40744518 0 99.51555329 0 106.527282 1 98.49566548 0 106.7567568 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 Y[t] = + 116.597165706393 + 21.2164030996859X[t] -16.3844866073163M1[t] -11.5185960744808M2[t] -11.6965980755825M3[t] -7.19841447468412M4[t] -23.5563893078486M5[t] -7.31310291507592M6[t] -12.7531787022404M7[t] -12.8490423714049M8[t] + 0.915369317430634M9[t] -13.0923114136710M10[t] -6.41047327089834M11[t] -0.250898186835515t + 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) 116.597165706393 8.692869 13.413 0 0 X 21.2164030996859 4.775629 4.4426 5.5e-05 2.8e-05 M1 -16.3844866073163 8.017077 -2.0437 0.046734 0.023367 M2 -11.5185960744808 7.996703 -1.4404 0.156521 0.078261 M3 -11.6965980755825 7.767435 -1.5059 0.138942 0.069471 M4 -7.19841447468412 7.660879 -0.9396 0.352314 0.176157 M5 -23.5563893078486 7.65157 -3.0786 0.003501 0.00175 M6 -7.31310291507592 7.727571 -0.9464 0.348907 0.174454 M7 -12.7531787022404 7.716701 -1.6527 0.105208 0.052604 M8 -12.8490423714049 7.707049 -1.6672 0.102274 0.051137 M9 0.915369317430634 7.698619 0.1189 0.905872 0.452936 M10 -13.0923114136710 7.623575 -1.7173 0.092643 0.046321 M11 -6.41047327089834 7.685441 -0.8341 0.408532 0.204266 t -0.250898186835515 0.097482 -2.5738 0.013346 0.006673 Multiple Linear Regression - Regression Statistics Multiple R 0.745176801073555 R-squared 0.555288464858216 Adjusted R-squared 0.429609117970321 F-TEST (value) 4.4182952776921 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 8.43903345039143e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.0499880817301 Sum Squared Residuals 6679.3017874125 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 99.961780912241 0.038219087758995 2 95.84395716 104.576773258241 -8.7328160982408 3 105.5073942 125.364276169990 -19.8568819699895 4 118.1540031 129.611561584052 -11.4575584840524 5 101.8612953 113.002688564052 -11.1413932640523 6 109.8419174 128.995076769990 -19.1531593699895 7 105.6348802 123.304102795990 -17.6692225959895 8 112.927078 122.957340939990 -10.0302629399895 9 133.0698623 136.470854441990 -3.40099214198950 10 125.6756757 122.212275524052 3.46340017594767 11 146.736359 128.643215479990 18.0931435200105 12 142.5803162 134.802790564052 7.77752563594764 13 106.1448241 118.167405769900 -12.0225816699005 14 126.5170831 122.782398115901 3.73468498409947 15 132.7893932 122.353497927963 10.4358952720367 16 121.2391637 126.600783342026 -5.36161964202617 17 114.5079041 109.991910322026 4.51599377797383 18 146.1499235 125.984298527963 20.1656249720367 19 146.1244263 120.293324553963 25.8311017460367 20 128.5058644 119.946562697963 8.55930170203665 21 155.5838858 133.460076199963 22.1238096000366 22 125.0382458 119.201497282026 5.83674851797382 23 136.8944416 125.632437237963 11.2620043620366 24 142.2233554 131.792012322026 10.4313430779738 25 117.7715451 115.156627527874 2.61491757212569 26 120.627231 119.771619873874 0.855611126125644 27 127.7664457 119.342719685937 8.42372601406283 28 135.1096379 123.5900051 11.5196328 29 105.7113717 106.98113208 -1.26976038000000 30 117.9245283 122.973520285937 -5.04899198593717 31 120.754717 117.282546311937 3.47217068806282 32 107.572667 116.935784455937 -9.36311745593718 33 130.4436512 130.449297957937 -0.00564675793717828 34 107.2157063 116.19071904 -8.97501274000001 35 105.0739419 122.621658995937 -17.5477170959372 36 130.1121877 128.78123408 1.33095361999999 37 109.6379398 112.145849285848 -2.50790948584814 38 116.7261601 116.760841631848 -0.0346815318481742 39 97.11881693 95.1155383442251 2.00327858577489 40 140.8975013 120.579226857974 20.3182744420262 41 108.2865885 103.970353837974 4.31623466202616 42 97.65425803 98.7463389442251 -1.09208091422512 43 112.0346762 114.271768069911 -2.23709186991100 44 123.0494646 113.925006213911 9.12445838608899 45 112.4171341 127.438519715911 -15.021385615911 46 116.4966854 113.179940797974 3.31674460202618 47 104.6914839 119.610880753911 -14.919396853911 48 122.2335543 125.770455837974 -3.53690153797383 49 99.79602244 87.918667944136 11.8773544958639 50 96.71086181 92.5336602901361 4.17720151986388 51 112.3151453 113.321163201885 -1.00601790188483 52 102.5497195 117.568448615948 -15.0187291159477 53 104.5385008 100.959575595948 3.57892520405234 54 122.0805711 116.951963801885 5.12860729811517 55 80.64762876 90.044586728199 -9.39695796819894 56 91.40744518 89.697824872199 1.70962030780106 57 99.51555329 103.211338374199 -3.69578508419894 58 106.527282 110.169162555948 -3.64188055594766 59 98.49566548 95.383699412199 3.11196606780106 60 106.7567568 122.759677595948 -16.0029207959476 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.604552859462969 0.790894281074062 0.395447140537031 18 0.682095506616478 0.635808986767044 0.317904493383522 19 0.767262159361426 0.465475681277148 0.232737840638574 20 0.67762493180306 0.644750136393879 0.322375068196940 21 0.663723893525296 0.672552212949408 0.336276106474704 22 0.738617353631946 0.522765292736108 0.261382646368054 23 0.888823848279442 0.222352303441117 0.111176151720558 24 0.894857716236257 0.210284567527486 0.105142283763743 25 0.852270079434066 0.295459841131868 0.147729920565934 26 0.813244966565468 0.373510066869065 0.186755033434533 27 0.775606839528113 0.448786320943775 0.224393160471887 28 0.707073997843951 0.585852004312099 0.292926002156049 29 0.714600413342365 0.57079917331527 0.285399586657635 30 0.751815989439243 0.496368021121515 0.248184010560757 31 0.739462004762985 0.521075990474029 0.260537995237015 32 0.789918083266202 0.420163833467595 0.210081916733798 33 0.79060736499741 0.418785270005178 0.209392635002589 34 0.809586244861173 0.380827510277654 0.190413755138827 35 0.897084760127789 0.205830479744423 0.102915239872211 36 0.851878321099208 0.296243357801583 0.148121678900792 37 0.816065596072008 0.367868807855983 0.183934403927992 38 0.730846078721746 0.538307842556509 0.269153921278254 39 0.629217036628341 0.741565926743318 0.370782963371659 40 0.887788009738538 0.224423980522925 0.112211990261462 41 0.796686413522134 0.406627172955731 0.203313586477866 42 0.806702804769619 0.386594390460763 0.193297195230382 43 0.769224658496167 0.461550683007665 0.230775341503833 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/20/t1258719317t9ow1wpyne8frx0/10t3aa1258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/10t3aa1258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/12ao31258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/12ao31258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/2osvi1258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/2osvi1258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/3qrfo1258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/3qrfo1258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/4ztkm1258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/4ztkm1258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/5gei61258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/5gei61258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/68k0z1258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/68k0z1258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/7eum61258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/7eum61258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/8mshr1258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/8mshr1258719199.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/9pfyx1258719199.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719317t9ow1wpyne8frx0/9pfyx1258719199.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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