WS 7: 1ste link

*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: Sat, 21 Nov 2009 06:39:42 -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/21/t1258811083dr1eqh87svojjlw.htm/, Retrieved Sat, 21 Nov 2009 14:44:55 +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/21/t1258811083dr1eqh87svojjlw.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:

Multivariate variabele

Dataseries X:

» Textbox « » Textfile « » CSV «

8.1 10.9 7.7 10.0 7.5 9.2 7.6 9.2 7.8 9.5 7.8 9.6 7.8 9.5 7.5 9.1 7.5 8.9 7.1 9.0 7.5 10.1 7.5 10.3 7.6 10.2 7.7 9.6 7.7 9.2 7.9 9.3 8.1 9.4 8.2 9.4 8.2 9.2 8.2 9.0 7.9 9.0 7.3 9.0 6.9 9.8 6.6 10.0 6.7 9.8 6.9 9.3 7.0 9.0 7.1 9.0 7.2 9.1 7.1 9.1 6.9 9.1 7.0 9.2 6.8 8.8 6.4 8.3 6.7 8.4 6.6 8.1 6.4 7.7 6.3 7.9 6.2 7.9 6.5 8.0 6.8 7.9 6.8 7.6 6.4 7.1 6.1 6.8 5.8 6.5 6.1 6.9 7.2 8.2 7.3 8.7 6.9 8.3 6.1 7.9 5.8 7.5 6.2 7.8 7.1 8.3 7.7 8.4 7.9 8.2 7.7 7.7 7.4 7.2 7.5 7.3 8.0 8.1 8.1 8.5

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3.67573123516923 + 0.402023229674035X[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) 3.67573123516923 0.649784 5.6568 0 0 X 0.402023229674035 0.074253 5.4143 1e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.579423898585698 R-squared 0.335732054252249 Adjusted R-squared 0.324279158635909 F-TEST (value) 29.3141634656340 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.2302241262363e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.542502420533747 Sum Squared Residuals 17.0699148245285 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.1 8.05778443861623 0.0422155613837746 2 7.7 7.69596353190958 0.00403646809042381 3 7.5 7.37434494817035 0.12565505182965 4 7.6 7.37434494817035 0.225655051829650 5 7.8 7.49495191707256 0.305048082927439 6 7.8 7.53515424003996 0.264845759960036 7 7.8 7.49495191707256 0.305048082927439 8 7.5 7.33414262520295 0.165857374797053 9 7.5 7.25373797926814 0.24626202073186 10 7.1 7.29394030223554 -0.193940302235544 11 7.5 7.73616585487698 -0.236165854876981 12 7.5 7.81657050081179 -0.316570500811789 13 7.6 7.77636817784438 -0.176368177844385 14 7.7 7.53515424003996 0.164845759960036 15 7.7 7.37434494817035 0.32565505182965 16 7.9 7.41454727113775 0.485452728862246 17 8.1 7.45474959410516 0.645250405894842 18 8.2 7.45474959410516 0.745250405894842 19 8.2 7.37434494817035 0.82565505182965 20 8.2 7.29394030223554 0.906059697764456 21 7.9 7.29394030223554 0.606059697764457 22 7.3 7.29394030223554 0.00605969776445643 23 6.9 7.61555888597477 -0.715558885974771 24 6.6 7.69596353190958 -1.09596353190958 25 6.7 7.61555888597477 -0.915558885974771 26 6.9 7.41454727113775 -0.514547271137754 27 7 7.29394030223554 -0.293940302235543 28 7.1 7.29394030223554 -0.193940302235544 29 7.2 7.33414262520295 -0.134142625202947 30 7.1 7.33414262520295 -0.234142625202947 31 6.9 7.33414262520295 -0.434142625202946 32 7 7.37434494817035 -0.37434494817035 33 6.8 7.21353565630074 -0.413535656300737 34 6.4 7.01252404146372 -0.612524041463719 35 6.7 7.05272636443112 -0.352726364431123 36 6.6 6.93211939552891 -0.332119395528913 37 6.4 6.7713101036593 -0.371310103659298 38 6.3 6.8517147495941 -0.551714749594106 39 6.2 6.8517147495941 -0.651714749594105 40 6.5 6.89191707256151 -0.391917072561509 41 6.8 6.8517147495941 -0.0517147495941058 42 6.8 6.7311077806919 0.0688922193081048 43 6.4 6.53009616585488 -0.130096165854877 44 6.1 6.40948919695267 -0.309489196952668 45 5.8 6.28888222805046 -0.488882228050457 46 6.1 6.44969151992007 -0.349691519920072 47 7.2 6.97232171849632 0.227678281503685 48 7.3 7.17333333333333 0.126666666666667 49 6.9 7.01252404146372 -0.112524041463719 50 6.1 6.8517147495941 -0.751714749594106 51 5.8 6.69090545772449 -0.890905457724492 52 6.2 6.8115124266267 -0.611512426626702 53 7.1 7.01252404146372 0.0874759585362801 54 7.7 7.05272636443112 0.647273635568877 55 7.9 6.97232171849632 0.927678281503685 56 7.7 6.7713101036593 0.928689896340702 57 7.4 6.57029848882228 0.829701511177719 58 7.5 6.61050081178968 0.889499188210315 59 8 6.93211939552891 1.06788060447109 60 8.1 7.09292868739853 1.00707131260147 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0105822228305548 0.0211644456611096 0.989417777169445 6 0.00261828000294297 0.00523656000588594 0.997381719997057 7 0.000719650760157085 0.00143930152031417 0.999280349239843 8 0.000161031092502012 0.000322062185004024 0.999838968907498 9 2.60833088699367e-05 5.21666177398734e-05 0.99997391669113 10 0.000436205111084818 0.000872410222169637 0.999563794888915 11 0.000568543469934409 0.00113708693986882 0.999431456530066 12 0.0005673223903331 0.0011346447806662 0.999432677609667 13 0.000230805306514122 0.000461610613028245 0.999769194693486 14 7.9631048681344e-05 0.000159262097362688 0.999920368951319 15 3.50720239782191e-05 7.01440479564381e-05 0.999964927976022 16 3.84245944272907e-05 7.68491888545814e-05 0.999961575405573 17 0.000118759520151867 0.000237519040303735 0.999881240479848 18 0.000433305220527797 0.000866610441055594 0.999566694779472 19 0.00120583432926947 0.00241166865853894 0.99879416567073 20 0.00288250177360682 0.00576500354721365 0.997117498226393 21 0.0023419042188278 0.0046838084376556 0.997658095781172 22 0.00235399044557404 0.00470798089114808 0.997646009554426 23 0.00970699266533434 0.0194139853306687 0.990293007334666 24 0.0570089631166454 0.114017926233291 0.942991036883355 25 0.119812343004072 0.239624686008145 0.880187656995928 26 0.144435915875177 0.288871831750354 0.855564084124823 27 0.143101492697724 0.286202985395447 0.856898507302276 28 0.123740190872588 0.247480381745176 0.876259809127412 29 0.0974023760364813 0.194804752072963 0.902597623963519 30 0.080547860209265 0.16109572041853 0.919452139790735 31 0.0821027181097286 0.164205436219457 0.917897281890271 32 0.0768016100018586 0.153603220003717 0.923198389998141 33 0.0825417737943412 0.165083547588682 0.917458226205659 34 0.119524504654914 0.239049009309827 0.880475495345086 35 0.112992758302853 0.225985516605706 0.887007241697147 36 0.0997738475409641 0.199547695081928 0.900226152459036 37 0.0839493818229047 0.167898763645809 0.916050618177095 38 0.0880224310367037 0.176044862073407 0.911977568963296 39 0.107329784627382 0.214659569254765 0.892670215372618 40 0.0999621557543874 0.199924311508775 0.900037844245613 41 0.0736487294504937 0.147297458900987 0.926351270549506 42 0.051381245249576 0.102762490499152 0.948618754750424 43 0.0333019366956406 0.0666038733912812 0.96669806330436 44 0.0211217025272002 0.0422434050544004 0.9788782974728 45 0.0143217855820677 0.0286435711641354 0.985678214417932 46 0.0101977770995209 0.0203955541990418 0.98980222290048 47 0.00631289475979272 0.0126257895195854 0.993687105240207 48 0.00380806791567726 0.00761613583135452 0.996191932084323 49 0.00278007998291759 0.00556015996583518 0.997219920017082 50 0.0111947421952532 0.0223894843905064 0.988805257804747 51 0.113687444093367 0.227374888186734 0.886312555906633 52 0.763740294023648 0.472519411952704 0.236259705976352 53 0.988947078319696 0.0221058433606083 0.0110529216803042 54 0.999410312828243 0.00117937434351467 0.000589687171757336 55 0.99789148724512 0.00421702550976095 0.00210851275488048 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.411764705882353 NOK 5% type I error level 29 0.568627450980392 NOK 10% type I error level 30 0.588235294117647 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/10j65s1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/10j65s1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/14q1h1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/14q1h1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/2atpd1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/2atpd1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/3rjas1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/3rjas1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/4puph1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/4puph1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/5gueo1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/5gueo1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/69rdw1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/69rdw1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/7u7e91258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/7u7e91258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/8js6u1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/8js6u1258810777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/9qsal1258810777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258811083dr1eqh87svojjlw/9qsal1258810777.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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