WS7

*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: Mon, 23 Nov 2009 01:26:14 -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/23/t1258964911zsgvmz69mvzsi3k.htm/, Retrieved Mon, 23 Nov 2009 09:28:43 +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/23/t1258964911zsgvmz69mvzsi3k.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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286602 326011 283042 328282 276687 317480 277915 317539 277128 313737 277103 312276 275037 309391 270150 302950 267140 300316 264993 304035 287259 333476 291186 337698 292300 335932 288186 323931 281477 313927 282656 314485 280190 313218 280408 309664 276836 302963 275216 298989 274352 298423 271311 301631 289802 329765 290726 335083 292300 327616 278506 309119 269826 295916 265861 291413 269034 291542 264176 284678 255198 276475 253353 272566 246057 264981 235372 263290 258556 296806 260993 303598 254663 286994 250643 276427 243422 266424 247105 267153 248541 268381 245039 262522 237080 255542 237085 253158 225554 243803 226839 250741 247934 280445 248333 285257 246969 270976 245098 261076 246263 255603 255765 260376 264319 263903 268347 264291 273046 263276 273963 262572 267430 256167 271993 264221 292710 293860 295881 300713 293299 287224

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 15 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 99313.0632111415 + 0.570009443436961X[t] + 4071.3473931428M1[t] -1088.08404190897M2[t] -1006.70058021337M3[t] + 1134.47236766780M4[t] + 3137.56271707497M5[t] + 4287.69548580122M6[t] + 3651.92018371694M7[t] + 4150.92106954182M8[t] + 1330.30120474864M9[t] -2980.72899961993M10[t] + 1020.11087758092M11[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) 99313.0632111415 21099.663435 4.7069 2.2e-05 1.1e-05 X 0.570009443436961 0.065472 8.7062 0 0 M1 4071.3473931428 7006.292031 0.5811 0.563893 0.281946 M2 -1088.08404190897 7350.771891 -0.148 0.882945 0.441472 M3 -1006.70058021337 7451.947022 -0.1351 0.893104 0.446552 M4 1134.47236766780 7447.774331 0.1523 0.87957 0.439785 M5 3137.56271707497 7448.249093 0.4212 0.675454 0.337727 M6 4287.69548580122 7496.120553 0.572 0.569999 0.284999 M7 3651.92018371694 7579.293654 0.4818 0.632117 0.316059 M8 4150.92106954182 7643.388894 0.5431 0.589592 0.294796 M9 1330.30120474864 7752.995588 0.1716 0.864485 0.432242 M10 -2980.72899961993 7668.185931 -0.3887 0.699207 0.349603 M11 1020.11087758092 7312.767325 0.1395 0.889641 0.44482 Multiple Linear Regression - Regression Statistics Multiple R 0.822464504467495 R-squared 0.676447861108961 Adjusted R-squared 0.595559826386202 F-TEST (value) 8.36276791032763 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 3.21437509942513e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11547.9618811135 Sum Squared Residuals 6401060333.16722 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 289213.759268611 -2611.75926861116 2 283042 285348.819279605 -2306.81927960492 3 276687 279272.960733294 -2585.96073329448 4 277915 281447.764238338 -3532.76423833845 5 277128 281283.678683798 -4155.67868379829 6 277103 281601.027655663 -4498.02765566314 7 275037 279320.775109263 -4283.77510926323 8 270150 276148.345169911 -5998.34516991063 9 267140 271826.320431105 -4686.3204311045 10 264993 269635.155346878 -4642.155346878 11 287259 290417.643248306 -3158.64324830641 12 291186 291804.112240916 -618.112240916341 13 292300 294868.822956949 -2568.82295694946 14 288186 282868.708191211 5317.29180878927 15 281477 277247.717180763 4229.28281923703 16 282656 279706.955398082 2949.04460191804 17 280190 280987.843782654 -797.843782654503 18 280408 280112.162989406 295.837010594203 19 276836 275656.754406850 1179.24559314956 20 275216 273890.537764457 1325.46223554316 21 274352 270747.292554678 3604.70744532166 22 271311 268264.852644856 3046.14735514446 23 289802 288302.338203712 1499.66179628816 24 290726 290313.537546329 412.462453671317 25 292300 290128.624425328 2171.37557467230 26 278506 274425.728315022 4080.27168497754 27 269826 266981.27709502 2844.72290498013 28 265861 266555.697519104 -694.697519104405 29 269034 268632.319086715 401.68091328506 30 264176 265869.90703569 -1693.90703568989 31 255198 260558.344269092 -5360.34426909223 32 253353 258829.178240522 -5476.17824052202 33 246057 251685.036747259 -5628.0367472595 34 235372 246410.120574039 -11038.1205740390 35 258556 269515.396957473 -10959.3969574731 36 260993 272366.790219716 -11373.7902197160 37 254663 266973.700814031 -12310.7008140315 38 250643 255790.979590181 -5147.97959018134 39 243422 250170.558589177 -6748.55858917701 40 247105 252727.268421324 -5622.26842132374 41 248541 255430.330367271 -6889.33036727149 42 245039 253240.777806901 -8201.77780690059 43 237080 248626.336589626 -11546.3365896263 44 237085 247766.434962297 -10681.4349622975 45 225554 239613.376754152 -14059.3767541515 46 226839 239257.072068349 -12418.0720683486 47 247934 260189.472453401 -12255.4724534009 48 248333 261912.247017639 -13579.2470176387 49 246969 257843.289549058 -10874.2895490582 50 245098 247040.764623981 -1942.76462398055 51 246263 244002.486401746 2260.51359825434 52 255765 248864.314423151 6900.68557684855 53 264319 252877.828079561 11441.1719204392 54 268347 254249.124512341 14097.8754876594 55 273046 253034.789625168 20011.2103748322 56 273963 253132.503862813 20830.4961371870 57 267430 246660.973512806 20769.0264871939 58 271993 246940.799365879 25052.2006341212 59 292710 267836.149137108 24873.8508628922 60 295881 270722.312975400 25158.6870245997 61 293299 267104.802986022 26194.1970139780 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0361574124876587 0.0723148249753174 0.963842587512341 17 0.0102431201087589 0.0204862402175179 0.98975687989124 18 0.00283627414949324 0.00567254829898647 0.997163725850507 19 0.000625656413114197 0.00125131282622839 0.999374343586886 20 0.000201536363387954 0.000403072726775909 0.999798463636612 21 0.000111206643688309 0.000222413287376619 0.999888793356312 22 4.40888230042918e-05 8.81776460085836e-05 0.999955911176996 23 9.59997479415066e-06 1.91999495883013e-05 0.999990400025206 24 1.99393507935029e-06 3.98787015870057e-06 0.99999800606492 25 4.20626438169753e-07 8.41252876339505e-07 0.999999579373562 26 5.72864020105818e-07 1.14572804021164e-06 0.99999942713598 27 2.25370969516494e-07 4.50741939032988e-07 0.99999977462903 28 1.09300029242478e-07 2.18600058484956e-07 0.99999989069997 29 2.37697567555960e-08 4.75395135111919e-08 0.999999976230243 30 6.95674108363666e-09 1.39134821672733e-08 0.999999993043259 31 5.19946381891729e-09 1.03989276378346e-08 0.999999994800536 32 2.55950641597323e-09 5.11901283194646e-09 0.999999997440494 33 1.81636801990149e-09 3.63273603980298e-09 0.999999998183632 34 3.85047558976354e-09 7.70095117952708e-09 0.999999996149524 35 8.48396973784984e-09 1.69679394756997e-08 0.99999999151603 36 4.01153849425996e-08 8.02307698851991e-08 0.999999959884615 37 2.50488613453317e-07 5.00977226906634e-07 0.999999749511387 38 1.03071818179347e-06 2.06143636358695e-06 0.999998969281818 39 1.30070342602233e-05 2.60140685204466e-05 0.99998699296574 40 0.0002354227028672 0.0004708454057344 0.999764577297133 41 0.0418446970518164 0.0836893941036328 0.958155302948184 42 0.326957920252609 0.653915840505218 0.673042079747391 43 0.650982881818908 0.698034236362184 0.349017118181092 44 0.785359222727716 0.429281554544568 0.214640777272284 45 0.712848695877078 0.574302608245844 0.287151304122922 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.766666666666667 NOK 5% type I error level 24 0.8 NOK 10% type I error level 26 0.866666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/106pe21258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/106pe21258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/1gaxg1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/1gaxg1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/216ia1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/216ia1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/3h60l1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/3h60l1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/40x4o1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/40x4o1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/5v9at1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/5v9at1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/6tzeh1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/6tzeh1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/7p4zb1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/7p4zb1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/8iuif1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/8iuif1258964758.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/9bloo1258964758.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258964911zsgvmz69mvzsi3k/9bloo1258964758.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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