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: Fri, 20 Nov 2009 11:34:54 -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/20/t1258742159j11bsaf9vbc5hk4.htm/, Retrieved Fri, 20 Nov 2009 19:36:11 +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/20/t1258742159j11bsaf9vbc5hk4.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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3016.70 2756.76 3052.40 2849.27 3099.60 2921.44 3103.30 2981.85 3119.80 3080.58 3093.70 3106.22 3164.90 3119.31 3311.50 3061.26 3410.60 3097.31 3392.60 3161.69 3338.20 3257.16 3285.10 3277.01 3294.80 3295.32 3611.20 3363.99 3611.30 3494.17 3521.00 3667.03 3519.30 3813.06 3438.30 3917.96 3534.90 3895.51 3705.80 3801.06 3807.60 3570.12 3663.00 3701.61 3604.50 3862.27 3563.80 3970.10 3511.40 4138.52 3546.50 4199.75 3525.40 4290.89 3529.90 4443.91 3591.60 4502.64 3668.30 4356.98 3728.80 4591.27 3853.60 4696.96 3897.70 4621.40 3640.70 4562.84 3495.50 4202.52 3495.10 4296.49 3268.00 4435.23 3479.10 4105.18 3417.80 4116.68 3521.30 3844.49 3487.10 3720.98 3529.90 3674.40 3544.30 3857.62 3710.80 3801.06 3641.90 3504.37 3447.10 3032.60 3386.80 3047.03 3438.50 2962.34 3364.30 2197.82 3462.70 2014.45 3291.90 1862.83 3550.00 1905.41 3611.00 1810.99 3708.60 1670.07 3771.10 1864.44 4042.70 2052.02 3988.40 2029.60 3851.20 2070.83 3876.70 2293.41

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 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 Zichtrekeningen[t] = + 2943.37797804525 + 0.0683218595762599`Bel20 `[t] -94.2882744750586M1[t] + 40.5455669616858M2[t] -11.2127651932675M3[t] + 34.0636735807399M4[t] + 45.0719224853597M5[t] + 61.357970088517M6[t] + 105.682285287828M7[t] + 272.128981094533M8[t] + 296.062320766578M9[t] + 141.266497108976M10[t] + 72.3889677968816M11[t] + 8.48262743431114t + 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) 2943.37797804525 133.930697 21.9769 0 0 `Bel20 ` 0.0683218595762599 0.025232 2.7078 0.009542 0.004771 M1 -94.2882744750586 103.144258 -0.9141 0.365516 0.182758 M2 40.5455669616858 103.176576 0.393 0.696195 0.348098 M3 -11.2127651932675 103.023002 -0.1088 0.913815 0.456908 M4 34.0636735807399 102.895519 0.3311 0.742143 0.371071 M5 45.0719224853597 102.817848 0.4384 0.663218 0.331609 M6 61.357970088517 102.851013 0.5966 0.553783 0.276892 M7 105.682285287828 102.677205 1.0293 0.308855 0.154427 M8 272.128981094533 102.671439 2.6505 0.011054 0.005527 M9 296.062320766578 102.807401 2.8798 0.006073 0.003036 M10 141.266497108976 102.916341 1.3726 0.176669 0.088334 M11 72.3889677968816 102.916029 0.7034 0.485443 0.242722 t 8.48262743431114 1.259542 6.7347 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.81148330821063 R-squared 0.658505159504467 Adjusted R-squared 0.559851094472425 F-TEST (value) 6.67489129100342 F-TEST (DF numerator) 13 F-TEST (DF denominator) 45 p-value 7.13518564787741e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 152.955565226744 Sum Squared Residuals 1052793.22202247 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3016.7 3045.91930060995 -29.2193006099471 2 3052.4 3195.55622471041 -143.156224710411 3 3099.6 3157.21130859539 -57.6113085953879 4 3103.3 3215.09769834071 -111.797698340707 5 3119.8 3241.3339918756 -121.533991875603 6 3093.7 3267.85443939261 -174.154439392607 7 3164.9 3321.55571516808 -156.655715168082 8 3311.5 3492.51895446070 -181.018954460697 9 3410.6 3527.39792460478 -116.797924604777 10 3392.6 3385.48328970101 7.11671029899395 11 3338.2 3331.61107575697 6.58892424303202 12 3285.1 3269.06092430699 16.0390756930139 13 3294.8 3184.50625051508 110.293749484920 14 3611.2 3332.51438148324 278.685618516763 15 3611.3 3298.13281644223 313.167183557767 16 3521 3363.70199929690 157.298000703097 17 3519.3 3393.16991678976 126.130083210245 18 3438.3 3425.10555489677 13.1944451032266 19 3534.9 3476.37867178291 58.5213282170912 20 3705.8 3644.85499538695 60.9450046130524 21 3807.6 3661.49271224276 146.107287757238 22 3663 3524.16315733515 138.836842664846 23 3604.5 3474.74484541689 129.755154583108 24 3563.8 3418.20565117243 145.594348827570 25 3511.4 3343.90677172152 167.493228278484 26 3546.5 3491.40658805443 55.0934119455739 27 3525.4 3454.35773761556 71.0422623844358 28 3529.9 3518.57141477624 11.3285852237581 29 3591.6 3542.07483392809 49.5251660719132 30 3668.3 3556.89174689968 111.408253100323 31 3728.8 3625.70581801342 103.094181986579 32 3853.6 3807.85607859305 45.7439214069471 33 3897.7 3835.10964598983 62.5903540101733 34 3640.7 3684.79552166975 -44.0955216697502 35 3495.5 3599.78288734945 -104.282887349448 36 3495.1 3542.29675213126 -47.1967521312591 37 3268 3465.97007988812 -197.970079888122 38 3479.1 3586.73691900603 -107.636919006033 39 3417.8 3544.24691567052 -126.446915670517 40 3521.3 3579.40945492077 -58.1094549207738 41 3487.1 3590.46189838344 -103.361898383441 42 3529.9 3612.04814120185 -82.1481412018471 43 3544.3 3677.37301494703 -133.073014947032 44 3710.8 3848.43805381042 -137.638053810415 45 3641.9 3860.58360839909 -218.683608399091 46 3447.1 3682.03820848351 -234.938208483508 47 3386.8 3622.62919103941 -235.829191039409 48 3438.5 3552.93667238933 -114.436672389325 49 3364.3 3414.89759726534 -50.5975972653355 50 3462.7 3545.68588674589 -82.9858867458928 51 3291.9 3492.0512216763 -200.151221676298 52 3550 3548.71943266537 1.28056733462649 53 3611 3561.75935902311 49.240640976886 54 3708.6 3576.90011760910 131.699882390904 55 3771.1 3642.98678008856 128.113219911444 56 4042.7 3830.73191774889 211.968082251113 57 3988.4 3861.61610876354 126.783891236457 58 3851.2 3718.11982281058 133.080177189418 59 3876.7 3672.93200043728 203.767999562718 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.26656486185633 0.53312972371266 0.73343513814367 18 0.143584059405276 0.287168118810551 0.856415940594724 19 0.0698116322527439 0.139623264505488 0.930188367747256 20 0.0345302262187998 0.0690604524375996 0.9654697737812 21 0.01866116669697 0.03732233339394 0.98133883330303 22 0.0236714714494563 0.0473429428989125 0.976328528550544 23 0.0206699324676816 0.0413398649353632 0.979330067532318 24 0.0131216663814902 0.0262433327629803 0.98687833361851 25 0.0209264043802624 0.0418528087605247 0.979073595619738 26 0.0637220751920967 0.127444150384193 0.936277924807903 27 0.138518077589591 0.277036155179182 0.861481922410409 28 0.117665006867943 0.235330013735885 0.882334993132057 29 0.0971145027717917 0.194229005543583 0.902885497228208 30 0.0839443225765095 0.167888645153019 0.91605567742349 31 0.075473550188365 0.15094710037673 0.924526449811635 32 0.0624453554020166 0.124890710804033 0.937554644597983 33 0.0943920232853623 0.188784046570725 0.905607976714638 34 0.201855059328963 0.403710118657926 0.798144940671037 35 0.707644009845512 0.584711980308975 0.292355990154488 36 0.964720567444756 0.0705588651104884 0.0352794325552442 37 0.968315898551286 0.0633682028974274 0.0316841014487137 38 0.950833004342556 0.0983339913148873 0.0491669956574436 39 0.974155432234907 0.0516891355301868 0.0258445677650934 40 0.99227258050098 0.0154548389980412 0.00772741949902059 41 0.99598503365077 0.0080299326984592 0.0040149663492296 42 0.992824708361972 0.0143505832760563 0.00717529163802814 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0384615384615385 NOK 5% type I error level 8 0.307692307692308 NOK 10% type I error level 13 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/10dpym1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/10dpym1258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/19hva1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/19hva1258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/2ympw1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/2ympw1258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/3sfu31258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/3sfu31258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/4687m1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/4687m1258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/5emfa1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/5emfa1258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/6tpcu1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/6tpcu1258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/71kq51258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/71kq51258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/8ymst1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/8ymst1258742090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/94zie1258742090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742159j11bsaf9vbc5hk4/94zie1258742090.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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