lineairiteit

*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: Thu, 19 Nov 2009 09:09:57 -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/19/t1258647101ch8no0tmm2q1a6h.htm/, Retrieved Thu, 19 Nov 2009 17:11:53 +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/19/t1258647101ch8no0tmm2q1a6h.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:

sdws7

Dataseries X:

» Textbox « » Textfile « » CSV «

593530.00 0 610943.00 0 612613.00 0 611324.00 0 594167.00 0 595454.00 0 590865.00 0 589379.00 0 584428.00 0 573100.00 0 567456.00 0 569028.00 0 620735.00 0 628884.00 0 628232.00 0 612117.00 0 595404.00 0 597141.00 0 593408.00 0 590072.00 0 579799.00 0 574205.00 0 572775.00 0 572942.00 0 619567.00 0 625809.00 0 619916.00 0 587625.00 0 565742.00 0 557274.00 0 560576.00 0 548854.00 0 531673.00 0 525919.00 0 511038.00 0 498662.00 0 555362.00 0 564591.00 0 541657.00 0 527070.00 0 509846.00 0 514258.00 0 516922.00 0 507561.00 0 492622.00 0 490243.00 0 469357.00 0 477580.00 0 528379.00 1 533590.00 1 517945.00 1 506174.00 1 501866.00 1 516141.00 1 528222.00 1 532638.00 1 536322.00 1 536535.00 1 523597.00 1 536214.00 1 586570.00 1 596594.00 1 580523.00 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 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 werklozen[t] = + 598258.288649425 + 30272.6688218391`crisis `[t] + 38903.9965996168M1[t] + 50321.6527777778M2[t] + 42440.4756226054M3[t] + 21659.5505747127M4[t] + 8242.20675287357M5[t] + 12930.4629310345M6[t] + 16915.1191091954M7[t] + 14656.9752873563M8[t] + 7964.63146551724M9[t] + 5035.88764367817M10[t] -4080.25617816091M11[t] -2039.65617816092t + 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) 598258.288649425 14112.302435 42.3927 0 0 `crisis ` 30272.6688218391 11633.384758 2.6022 0.012217 0.006109 M1 38903.9965996168 15804.680029 2.4615 0.017401 0.008701 M2 50321.6527777778 15763.564159 3.1923 0.002466 0.001233 M3 42440.4756226054 15727.038308 2.6986 0.009527 0.004763 M4 21659.5505747127 16405.200968 1.3203 0.192874 0.096437 M5 8242.20675287357 16371.396022 0.5035 0.616903 0.308451 M6 12930.4629310345 16342.041838 0.7912 0.432618 0.216309 M7 16915.1191091954 16317.162436 1.0366 0.30499 0.152495 M8 14656.9752873563 16296.778311 0.8994 0.37285 0.186425 M9 7964.63146551724 16280.906346 0.4892 0.626882 0.313441 M10 5035.88764367817 16269.559749 0.3095 0.758232 0.379116 M11 -4080.25617816091 16262.747991 -0.2509 0.802944 0.401472 t -2039.65617816092 271.785589 -7.5047 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.840177539990154 R-squared 0.705898298703907 Adjusted R-squared 0.627871316727393 F-TEST (value) 9.04684867750464 F-TEST (DF numerator) 13 F-TEST (DF denominator) 49 p-value 4.58399296299206e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 25710.0712175971 Sum Squared Residuals 32389380338.6819 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 593530 635122.629070882 -41592.6290708817 2 610943 644500.629070881 -33557.6290708812 3 612613 634579.795737548 -21966.7957375479 4 611324 611759.214511494 -435.214511494171 5 594167 596302.214511494 -2135.21451149429 6 595454 598950.814511494 -3496.81451149423 7 590865 600895.814511494 -10030.8145114942 8 589379 596598.014511494 -7219.01451149419 9 584428 587866.014511494 -3438.01451149423 10 573100 582897.614511494 -9797.61451149422 11 567456 571741.814511494 -4285.81451149422 12 569028 573782.414511494 -4754.41451149422 13 620735 610646.75493295 10088.2450670499 14 628884 620024.75493295 8859.24506704982 15 628232 610103.921599617 18128.0784003832 16 612117 587283.340373563 24833.6596264368 17 595404 571826.340373563 23577.6596264368 18 597141 574474.940373563 22666.0596264368 19 593408 576419.940373563 16988.0596264368 20 590072 572122.140373563 17949.8596264368 21 579799 563390.140373563 16408.8596264368 22 574205 558421.740373563 15783.2596264368 23 572775 547265.940373563 25509.0596264368 24 572942 549306.540373563 23635.4596264368 25 619567 586170.880795019 33396.1192049810 26 625809 595548.880795019 30260.1192049808 27 619916 585628.047461686 34287.9525383142 28 587625 562807.466235632 24817.5337643678 29 565742 547350.466235632 18391.5337643678 30 557274 549999.066235632 7274.93376436781 31 560576 551944.066235632 8631.93376436781 32 548854 547646.266235632 1207.73376436780 33 531673 538914.266235632 -7241.26623563219 34 525919 533945.866235632 -8026.86623563218 35 511038 522790.066235632 -11752.0662356322 36 498662 524830.666235632 -26168.6662356322 37 555362 561695.006657088 -6333.00665708804 38 564591 571073.006657088 -6482.00665708814 39 541657 561152.173323755 -19495.1733237548 40 527070 538331.592097701 -11261.5920977012 41 509846 522874.592097701 -13028.5920977012 42 514258 525523.192097701 -11265.1920977012 43 516922 527468.192097701 -10546.1920977012 44 507561 523170.392097701 -15609.3920977012 45 492622 514438.392097701 -21816.3920977012 46 490243 509469.992097701 -19226.9920977012 47 469357 498314.192097701 -28957.1920977012 48 477580 500354.792097701 -22774.7920977012 49 528379 567491.801340996 -39112.8013409961 50 533590 576869.801340996 -43279.8013409962 51 517945 566948.968007663 -49003.9680076628 52 506174 544128.386781609 -37954.3867816092 53 501866 528671.386781609 -26805.3867816092 54 516141 531319.986781609 -15178.9867816092 55 528222 533264.986781609 -5042.98678160921 56 532638 528967.186781609 3670.81321839078 57 536322 520235.186781609 16086.8132183908 58 536535 515266.786781609 21268.2132183908 59 523597 504110.986781609 19486.0132183908 60 536214 506151.586781609 30062.4132183908 61 586570 543015.927203065 43554.0727969349 62 596594 552393.927203065 44200.0727969349 63 580523 542473.093869732 38049.9061302682 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0507963836723774 0.101592767344755 0.949203616327623 18 0.0190547850049731 0.0381095700099462 0.980945214995027 19 0.00616048110580751 0.012320962211615 0.993839518894192 20 0.00202004007741811 0.00404008015483623 0.997979959922582 21 0.000891940292714654 0.00178388058542931 0.999108059707285 22 0.000246333993625878 0.000492667987251756 0.999753666006374 23 6.01413529265318e-05 0.000120282705853064 0.999939858647073 24 1.43100509070488e-05 2.86201018140975e-05 0.999985689949093 25 3.757602724711e-06 7.515205449422e-06 0.999996242397275 26 1.04691275238879e-06 2.09382550477758e-06 0.999998953087248 27 7.487340439449e-07 1.4974680878898e-06 0.999999251265956 28 4.34382450855794e-05 8.68764901711589e-05 0.999956561754914 29 0.000494796282451648 0.000989592564903295 0.999505203717548 30 0.00374114367050629 0.00748228734101257 0.996258856329494 31 0.00755413335107595 0.0151082667021519 0.992445866648924 32 0.0187625262546131 0.0375250525092262 0.981237473745387 33 0.0459269741645197 0.0918539483290393 0.95407302583548 34 0.0757942339813164 0.151588467962633 0.924205766018684 35 0.185001154602748 0.370002309205497 0.814998845397252 36 0.336041509750715 0.672083019501429 0.663958490249285 37 0.370898731594228 0.741797463188457 0.629101268405772 38 0.468319065031701 0.936638130063401 0.531680934968299 39 0.66896947306812 0.662061053863759 0.331030526931880 40 0.837016529177981 0.325966941644038 0.162983470822019 41 0.92269245275774 0.154615094484520 0.0773075472422602 42 0.968335376971356 0.0633292460572885 0.0316646230286442 43 0.992919535065931 0.0141609298681374 0.00708046493406868 44 0.99933442908733 0.00133114182533970 0.000665570912669852 45 0.998725583586919 0.00254883282616261 0.00127441641308131 46 0.99885953878881 0.00228092242237711 0.00114046121118855 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.466666666666667 NOK 5% type I error level 19 0.633333333333333 NOK 10% type I error level 21 0.7 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/10gh801258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/10gh801258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/1eoa21258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/1eoa21258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/2yamw1258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/2yamw1258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/3ukdt1258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/3ukdt1258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/414jw1258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/414jw1258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/5ay9h1258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/5ay9h1258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/6qtdb1258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/6qtdb1258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/77qdn1258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/77qdn1258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/8nczj1258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/8nczj1258646990.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/90n701258646990.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258647101ch8no0tmm2q1a6h/90n701258646990.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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