W7: Linear Trend

*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 07:22:35 -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/t12588134670coiglzelq0oaut.htm/, Retrieved Sat, 21 Nov 2009 15:24:39 +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/t12588134670coiglzelq0oaut.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:

cvm

Dataseries X:

» Textbox « » Textfile « » CSV «

6,3 2 6,2 1,8 6,1 2,7 6,3 2,3 6,5 1,9 6,6 2 6,5 2,3 6,2 2,8 6,2 2,4 5,9 2,3 6,1 2,7 6,1 2,7 6,1 2,9 6,1 3 6,1 2,2 6,4 2,3 6,7 2,8 6,9 2,8 7 2,8 7 2,2 6,8 2,6 6,4 2,8 5,9 2,5 5,5 2,4 5,5 2,3 5,6 1,9 5,8 1,7 5,9 2 6,1 2,1 6,1 1,7 6 1,8 6 1,8 5,9 1,8 5,5 1,3 5,6 1,3 5,4 1,3 5,2 1,2 5,2 1,4 5,2 2,2 5,5 2,9 5,8 3,1 5,8 3,5 5,5 3,6 5,3 4,4 5,1 4,1 5,2 5,1 5,8 5,8 5,8 5,9 5,5 5,4 5 5,5 4,9 4,8 5,3 3,2 6,1 2,7 6,5 2,1 6,8 1,9 6,6 0,6 6,4 0,7 6,4 -0,2 6,6 -1 6,7 -1,7

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 WMan>25[t] = + 6.59619621729017 -0.1480526479753Infl[t] -0.202067711946028M1[t] -0.297369689987745M2[t] -0.286749562110452M3[t] -0.042778910868714M4[t] + 0.324880164049072M5[t] + 0.460695027128835M6[t] + 0.460198313884645M7[t] + 0.313052124004902M8[t] + 0.177750145963182M9[t] -0.0205128850380429M10[t] + 0.110107242839249M11[t] -0.0106201278772925t + 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) 6.59619621729017 0.236136 27.9338 0 0 Infl -0.1480526479753 0.039238 -3.7732 0.00046 0.00023 M1 -0.202067711946028 0.271928 -0.7431 0.461204 0.230602 M2 -0.297369689987745 0.271368 -1.0958 0.278862 0.139431 M3 -0.286749562110452 0.270993 -1.0581 0.295515 0.147757 M4 -0.042778910868714 0.27011 -0.1584 0.874854 0.437427 M5 0.324880164049072 0.269764 1.2043 0.234629 0.117315 M6 0.460695027128835 0.269296 1.7107 0.093867 0.046933 M7 0.460198313884645 0.269197 1.7095 0.094093 0.047046 M8 0.313052124004902 0.268802 1.1646 0.250178 0.125089 M9 0.177750145963182 0.26861 0.6617 0.511439 0.25572 M10 -0.0205128850380429 0.268449 -0.0764 0.939422 0.469711 M11 0.110107242839249 0.268389 0.4103 0.683525 0.341763 t -0.0106201278772925 0.003229 -3.2888 0.001934 0.000967 Multiple Linear Regression - Regression Statistics Multiple R 0.714346509058098 R-squared 0.510290935003491 Adjusted R-squared 0.371894894895783 F-TEST (value) 3.68717872712506 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.000506791225271641 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.424236906606732 Sum Squared Residuals 8.27893983465347 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.3 6.08740308151626 0.212596918483744 2 6.2 6.0110915051923 0.188908494807704 3 6.1 5.87784412201453 0.222155877985473 4 6.3 6.17041570456909 0.129584295430907 5 6.5 6.58667571079971 -0.086675710799707 6 6.6 6.69706518120465 -0.0970651812046475 7 6.5 6.64153254569058 -0.141532545690575 8 6.2 6.40973990394589 -0.209739903945889 9 6.2 6.323038857217 -0.123038857216996 10 5.9 6.12896096313601 -0.228960963136009 11 6.1 6.18973990394589 -0.0897399039458891 12 6.1 6.06901253322935 0.0309874667706529 13 6.1 5.82671416381097 0.273285836189033 14 6.1 5.70598679309443 0.394013206905573 15 6.1 5.82442891147467 0.275571088525333 16 6.4 6.04297417004158 0.357025829958418 17 6.7 6.32598679309443 0.374013206905574 18 6.9 6.4511815282969 0.448818471703104 19 7 6.44006468717541 0.559935312824586 20 7 6.37112995820356 0.628870041796442 21 6.8 6.16598679309443 0.634013206905573 22 6.4 5.92749310462085 0.472506895379152 23 5.9 6.09190889901344 -0.191908899013438 24 5.5 5.98598679309443 -0.485986793094426 25 5.5 5.78810421806864 -0.288104218068636 26 5.6 5.74140317133975 -0.141403171339746 27 5.8 5.7710137009348 0.0289862990651938 28 5.9 5.95994842990666 -0.0599484299066617 29 6.1 6.30218211214963 -0.202182112149626 30 6.1 6.48659790654222 -0.386597906542216 31 6 6.4606758006232 -0.460675800623204 32 6 6.30290948286617 -0.302909482866168 33 5.9 6.15698737694716 -0.256987376947156 34 5.5 6.02213054205629 -0.522130542056288 35 5.6 6.14213054205629 -0.542130542056288 36 5.4 6.02140317133975 -0.621403171339746 37 5.2 5.82352059631396 -0.623520596313956 38 5.2 5.68798796079989 -0.487987960799885 39 5.2 5.56954584241965 -0.369545842419645 40 5.5 5.69925951220138 -0.199259512201382 41 5.8 6.02668792964682 -0.226687929646815 42 5.8 6.09266160565917 -0.292661605659166 43 5.5 6.06673949974015 -0.566739499740154 44 5.3 5.79053106360288 -0.490531063602878 45 5.1 5.68902475207646 -0.589024752076456 46 5.2 5.33208894522264 -0.132088945222638 47 5.8 5.34845209163993 0.451547908360072 48 5.8 5.21291945612586 0.587080543874144 49 5.5 5.07425794029019 0.425742059709814 50 5 4.95353056957364 0.0464694304263549 51 4.9 5.05716742315636 -0.157167423156355 52 5.3 5.52740218328128 -0.227402183281281 53 6.1 5.95846745430942 0.141532545690575 54 6.5 6.17249377829708 0.327506221702925 55 6.8 6.19098746677065 0.609012533229347 56 6.6 6.2256895913815 0.374310408618493 57 6.4 6.06496222066496 0.335037779335035 58 6.4 5.98932644496422 0.410673555035783 59 6.6 6.22776856334446 0.372231436655543 60 6.7 6.21067804621063 0.489321953789375 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0228237385891184 0.0456474771782368 0.977176261410882 18 0.0170870224254627 0.0341740448509254 0.982912977574537 19 0.0263743178267607 0.0527486356535213 0.97362568217324 20 0.050910110264536 0.101820220529072 0.949089889735464 21 0.0680921184520517 0.136184236904103 0.931907881547948 22 0.0880984262143142 0.176196852428628 0.911901573785686 23 0.128496283092188 0.256992566184375 0.871503716907812 24 0.274534680392768 0.549069360785535 0.725465319607232 25 0.445323216724325 0.89064643344865 0.554676783275675 26 0.45659819856532 0.91319639713064 0.54340180143468 27 0.49387995294783 0.98775990589566 0.50612004705217 28 0.558483191378076 0.883033617243849 0.441516808621924 29 0.554332291922516 0.891335416154967 0.445667708077484 30 0.503571043374252 0.992857913251496 0.496428956625748 31 0.449963312095454 0.899926624190908 0.550036687904546 32 0.443758464526993 0.887516929053986 0.556241535473007 33 0.55145353521146 0.89709292957708 0.44854646478854 34 0.481007851065149 0.962015702130297 0.518992148934851 35 0.394998133622609 0.789996267245218 0.605001866377391 36 0.333688748381936 0.667377496763871 0.666311251618064 37 0.296074091495032 0.592148182990063 0.703925908504968 38 0.214623283452552 0.429246566905105 0.785376716547448 39 0.22898359763709 0.45796719527418 0.77101640236291 40 0.508449766939761 0.983100466120478 0.491550233060239 41 0.787227727542107 0.425544544915787 0.212772272457893 42 0.98609118224482 0.0278176355103586 0.0139088177551793 43 0.980742358854364 0.0385152822912712 0.0192576411456356 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 4 0.148148148148148 NOK 10% type I error level 5 0.185185185185185 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/10k45v1258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/10k45v1258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/1qjp81258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/1qjp81258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/2zzf21258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/2zzf21258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/3afe01258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/3afe01258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/4w70h1258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/4w70h1258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/55n4u1258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/55n4u1258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/60mtu1258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/60mtu1258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/7g8h11258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/7g8h11258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/89o0q1258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/89o0q1258813351.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/9u84d1258813351.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588134670coiglzelq0oaut/9u84d1258813351.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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