*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: Tue, 21 Dec 2010 12:39:17 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23.htm/, Retrieved Tue, 21 Dec 2010 13:38:52 +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/2010/Dec/21/t129293513284rr05ui143wd23.htm/}, year = {2010}, } @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 = {2010}, 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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-999,000 645,000 3,000 2,000 42,000 3,000 -999,000 60,000 1,000 -999,000 25,000 3,000 1,800 624,000 4,000 0,700 180,000 4,000 3,900 35,000 1,000 1,000 392,000 4,000 3,600 63,000 1,000 1,400 230,000 1,000 1,500 112,000 4,000 0,700 281,000 5,000 2,700 -999,000 2,000 -999,000 365,000 5,000 2,100 42,000 1,000 0,000 28,000 2,000 4,100 42,000 2,000 1,200 120,000 2,000 1,300 -999,000 1,000 6,100 -999,000 1,000 0,300 400,000 5,000 0,500 148,000 5,000 3,400 16,000 2,000 -999,000 252,000 1,000 1,500 310,000 1,000 -999,000 63,000 1,000 3,400 28,000 3,000 0,800 68,000 4,000 0,800 336,000 5,000 -999,000 100,000 1,000 -999,000 33,000 4,000 1,400 21,500 4,000 2,000 50,000 1,000 1,900 267,000 1,000 2,400 30,000 1,000 2,800 45,000 3,000 1,300 19,000 3,000 2,000 30,000 3,000 5,600 12,000 1,000 3,100 120,000 1,000 1,000 440,000 5,000 1,800 140,000 2,000 0,900 170,000 4,000 1,800 17,000 2,000 1,900 115,000 4,000 0,900 31,000 5,000 -999,000 63,000 2,000 2,600 21,000 3,000 2,400 52,0 etc...

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 24 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation PS[t] = -269.584992675584 -0.232155250771579tg[t] + 35.8886851345589`D `[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) -269.584992675584 107.802798 -2.5007 0.015191 0.007596 tg -0.232155250771579 0.172049 -1.3494 0.182379 0.091189 `D ` 35.8886851345589 37.758977 0.9505 0.345752 0.172876 Multiple Linear Regression - Regression Statistics Multiple R 0.184686213917441 R-squared 0.0341089976111589 Adjusted R-squared 0.00136692973357111 F-TEST (value) 1.04174842403606 F-TEST (DF numerator) 2 F-TEST (DF denominator) 59 p-value 0.359235753691446 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 398.420841056119 Sum Squared Residuals 9365610.82868407 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -999 -311.659074019576 -687.340925980424 2 2 -171.669457804314 173.669457804314 3 -999 -247.62562258732 -751.37437741268 4 -999 -167.722818541197 -831.277181458803 5 1.8 -270.895128618814 272.695128618814 6 0.7 -167.818197276233 168.518197276233 7 3.9 -241.821741318031 245.721741318031 8 1 -217.035110439808 218.035110439808 9 3.6 -248.322088339635 251.922088339635 10 1.4 -287.092015218489 288.492015218489 11 1.5 -152.031640223766 153.531640223766 12 0.7 -155.377192469604 156.077192469604 13 2.7 34.1154731143405 -31.4154731143405 14 -999 -174.878233534416 -824.121766465584 15 2.1 -243.446828073432 245.546828073432 16 0 -204.307969428071 204.307969428071 17 4.1 -207.558142938873 211.658142938873 18 1.2 -225.666252499056 226.866252499056 19 1.3 -1.77321202021838 3.07321202021838 20 6.1 -1.77321202021838 7.87321202021838 21 0.3 -183.003667311421 183.303667311421 22 0.5 -124.500544116984 125.000544116984 23 3.4 -201.522106418812 204.922106418812 24 -999 -292.199430735463 -706.800569264537 25 1.5 -305.664435280215 307.164435280215 26 -999 -248.322088339635 -750.677911660365 27 3.4 -168.419284293512 171.819284293512 28 0.8 -141.816809189816 142.616809189816 29 0.8 -168.145731262040 168.945731262040 30 -999 -256.911832618183 -742.088167381817 31 -999 -133.691375412811 -865.308624587189 32 1.4 -131.021590028938 132.421590028938 33 2 -245.304070079604 247.304070079604 34 1.9 -295.681759497037 297.581759497037 35 2.4 -240.660965064173 243.060965064173 36 2.8 -172.365923556629 175.165923556629 37 1.3 -166.329887036568 167.629887036568 38 2 -168.883594795055 170.883594795055 39 5.6 -236.482170550284 242.082170550284 40 3.1 -261.554937633615 264.654937633615 41 1 -192.289877342285 193.289877342285 42 1.8 -230.309357514488 232.109357514488 43 0.9 -165.496644768517 166.396644768517 44 1.8 -201.754261669583 203.554261669583 45 1.9 -152.728105976080 154.628105976080 46 0.9 -97.3383797767089 98.2383797767089 47 -999 -212.433403205076 -786.566596794924 48 2.6 -166.794197538111 169.394197538111 49 2.4 -245.768380581148 248.168380581148 50 1.2 -235.881083533005 237.081083533005 51 0.9 -250.042553830072 250.942553830072 52 0.5 -214.153868695513 214.653868695513 53 -999 -124.964854618527 -874.035145381473 54 0.6 -125.197009869298 125.797009869298 55 -999 -218.701594975909 -780.298405024091 56 2.2 34.1154731143405 -31.9154731143405 57 2.3 -211.736937452761 214.036937452761 58 0.5 -208.349987426223 208.849987426223 59 2.6 -208.486763941959 211.086763941959 60 0.6 -174.782854799380 175.382854799380 61 6.6 -236.946481051828 243.546481051828 62 -999 -242.518207070346 -756.481792929654 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.839852142036227 0.320295715927545 0.160147857963773 7 0.955859542005133 0.0882809159897346 0.0441404579948673 8 0.9272706051341 0.145458789731801 0.0727293948659004 9 0.934116030925708 0.131767938148584 0.0658839690742918 10 0.926086629168276 0.147826741663448 0.0739133708317241 11 0.8873035366247 0.225392926750598 0.112696463375299 12 0.834499199641234 0.331001600717531 0.165500800358766 13 0.765866231977426 0.468267536045149 0.234133768022574 14 0.900889648035132 0.198220703929736 0.0991103519648679 15 0.872430386932688 0.255139226134625 0.127569613067312 16 0.834078240131054 0.331843519737892 0.165921759868946 17 0.78970661559277 0.420586768814459 0.210293384407229 18 0.741531422965644 0.516937154068712 0.258468577034356 19 0.671302290093741 0.657395419812517 0.328697709906259 20 0.595659331221237 0.808681337557525 0.404340668778763 21 0.542951693457384 0.914096613085232 0.457048306542616 22 0.474002852448795 0.94800570489759 0.525997147551205 23 0.412655343557942 0.825310687115884 0.587344656442058 24 0.569669700214338 0.860660599571324 0.430330299785662 25 0.536505956763078 0.926988086473844 0.463494043236922 26 0.700552931206974 0.598894137586052 0.299447068793026 27 0.64381008613799 0.71237982772402 0.35618991386201 28 0.578761676656134 0.842476646687732 0.421238323343866 29 0.513118922517321 0.973762154965357 0.486881077482679 30 0.685809206429484 0.628381587141032 0.314190793570516 31 0.880262117620172 0.239475764759656 0.119737882379828 32 0.842524766222403 0.314950467555194 0.157475233777597 33 0.811532547052057 0.376934905895886 0.188467452947943 34 0.782010301976394 0.435979396047212 0.217989698023606 35 0.740310101461788 0.519379797076424 0.259689898538212 36 0.684917105152127 0.630165789695747 0.315082894847873 37 0.623813654224541 0.752372691550917 0.376186345775458 38 0.559506826822895 0.88098634635421 0.440493173177105 39 0.504590138092142 0.990819723815715 0.495409861907858 40 0.454222859636834 0.908445719273668 0.545777140363166 41 0.388134946409086 0.776269892818171 0.611865053590914 42 0.334538606159839 0.669077212319678 0.665461393840161 43 0.273960178799941 0.547920357599883 0.726039821200059 44 0.225065497206511 0.450130994413021 0.77493450279349 45 0.175785133446941 0.351570266893881 0.82421486655306 46 0.131938902010782 0.263877804021563 0.868061097989218 47 0.272755139008582 0.545510278017163 0.727244860991418 48 0.215431329373552 0.430862658747105 0.784568670626448 49 0.167296812389055 0.33459362477811 0.832703187610945 50 0.128956583776401 0.257913167552802 0.8710434162236 51 0.100816807644902 0.201633615289805 0.899183192355098 52 0.0773529823455096 0.154705964691019 0.92264701765449 53 0.284307287328595 0.56861457465719 0.715692712671405 54 0.202054379161704 0.404108758323408 0.797945620838296 55 0.438771501545622 0.877543003091244 0.561228498454378 56 0.368966470119056 0.737932940238111 0.631033529880944 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 1 0.0196078431372549 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/10bs7z1292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/10bs7z1292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/159an1292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/159an1292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/259an1292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/259an1292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/3xir81292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/3xir81292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/4xir81292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/4xir81292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/5xir81292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/5xir81292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/6q9qb1292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/6q9qb1292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/7j0qw1292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/7j0qw1292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/8j0qw1292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/8j0qw1292935130.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/9j0qw1292935130.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293513284rr05ui143wd23/9j0qw1292935130.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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