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R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 21 Dec 2009 06:27:31 -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/Dec/21/t1261402114zs0mxcjac5fegtu.htm/, Retrieved Mon, 21 Dec 2009 14:28:46 +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/Dec/21/t1261402114zs0mxcjac5fegtu.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:

» Textbox « » Textfile « » CSV «

2350.44 0 2440.25 0 2408.64 0 2472.81 0 2407.6 0 2454.62 0 2448.05 0 2497.84 0 2645.64 0 2756.76 0 2849.27 0 2921.44 0 2981.85 0 3080.58 0 3106.22 0 3119.31 0 3061.26 0 3097.31 0 3161.69 0 3257.16 0 3277.01 0 3295.32 0 3363.99 0 3494.17 0 3667.03 0 3813.06 0 3917.96 0 3895.51 0 3801.06 0 3570.12 0 3701.61 0 3862.27 0 3970.1 0 4138.52 0 4199.75 0 4290.89 0 4443.91 0 4502.64 1 4356.98 1 4591.27 1 4696.96 1 4621.4 1 4562.84 1 4202.52 1 4296.49 1 4435.23 1 4105.18 1 4116.68 1 3844.49 1 3720.98 1 3674.4 1 3857.62 1 3801.06 1 3504.37 1 3032.6 1 3047.03 1 2962.34 1 2197.82 1 2014.45 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Bel20[t] = + 2850.23778669725 -331.827809633028Dummy[t] -108.42336286315M1[t] -19.3836064602451M2[t] -69.329411983946M3[t] -6.14921750764652M4[t] -71.1490230313466M5[t] -206.456828555047M6[t] -305.946634078747M7[t] -345.224439602447M8[t] -319.556245126148M9[t] -416.426050649848M10[t] -505.911856173549M11[t] + 31.2838055237003t + 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) 2850.23778669725 406.106862 7.0184 0 0 Dummy -331.827809633028 345.374831 -0.9608 0.341799 0.1709 M1 -108.42336286315 452.547159 -0.2396 0.81174 0.40587 M2 -19.3836064602451 459.596347 -0.0422 0.966546 0.483273 M3 -69.329411983946 457.90489 -0.1514 0.880332 0.440166 M4 -6.14921750764652 456.421055 -0.0135 0.98931 0.494655 M5 -71.1490230313466 455.146873 -0.1563 0.876479 0.43824 M6 -206.456828555047 454.084107 -0.4547 0.651535 0.325768 M7 -305.946634078747 453.234246 -0.675 0.503111 0.251555 M8 -345.224439602447 452.598488 -0.7628 0.449586 0.224793 M9 -319.556245126148 452.177738 -0.7067 0.483393 0.241697 M10 -416.426050649848 451.972594 -0.9214 0.36178 0.18089 M11 -505.911856173549 451.983352 -1.1193 0.268945 0.134472 t 31.2838055237003 9.879391 3.1666 0.002769 0.001384 Multiple Linear Regression - Regression Statistics Multiple R 0.573493498817874 R-squared 0.328894793186366 Adjusted R-squared 0.135019955662428 F-TEST (value) 1.69642846584336 F-TEST (DF numerator) 13 F-TEST (DF denominator) 45 p-value 0.0948706913653125 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 672.488729898232 Sum Squared Residuals 20350849.1328062 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2350.44 2773.09822935779 -422.658229357794 2 2440.25 2893.42179128440 -453.171791284402 3 2408.64 2874.75979128440 -466.119791284403 4 2472.81 2969.22379128440 -496.413791284405 5 2407.6 2935.50779128440 -527.907791284404 6 2454.62 2831.48379128440 -376.863791284404 7 2448.05 2763.27779128440 -315.227791284403 8 2497.84 2755.28379128440 -257.443791284404 9 2645.64 2812.23579128440 -166.595791284404 10 2756.76 2746.64979128440 10.1102087155960 11 2849.27 2688.44779128440 160.822208715595 12 2921.44 3225.64345298165 -304.203452981652 13 2981.85 3148.5038956422 -166.653895642203 14 3080.58 3268.82745756881 -188.247457568809 15 3106.22 3250.16545756881 -143.945457568807 16 3119.31 3344.62945756881 -225.319457568807 17 3061.26 3310.91345756881 -249.653457568807 18 3097.31 3206.88945756881 -109.579457568807 19 3161.69 3138.68345756881 23.0065424311928 20 3257.16 3130.68945756881 126.470542431193 21 3277.01 3187.64145756881 89.368542431193 22 3295.32 3122.05545756881 173.264542431193 23 3363.99 3063.85345756881 300.136542431193 24 3494.17 3601.04911926606 -106.879119266056 25 3667.03 3523.90956192661 143.120438073394 26 3813.06 3644.23312385321 168.826876146789 27 3917.96 3625.57112385321 292.388876146789 28 3895.51 3720.03512385321 175.474876146790 29 3801.06 3686.31912385321 114.740876146789 30 3570.12 3582.29512385321 -12.1751238532106 31 3701.61 3514.08912385321 187.520876146789 32 3862.27 3506.09512385321 356.174876146789 33 3970.1 3563.04712385321 407.052876146789 34 4138.52 3497.46112385321 641.05887614679 35 4199.75 3439.25912385321 760.49087614679 36 4290.89 3976.45478555046 314.435214449541 37 4443.91 3899.31522821101 544.59477178899 38 4502.64 3687.81098050459 814.829019495413 39 4356.98 3669.14898050459 687.831019495412 40 4591.27 3763.61298050459 827.657019495413 41 4696.96 3729.89698050459 967.063019495413 42 4621.4 3625.87298050459 995.527019495412 43 4562.84 3557.66698050459 1005.17301949541 44 4202.52 3549.67298050459 652.847019495413 45 4296.49 3606.62498050459 689.865019495413 46 4435.23 3541.03898050459 894.191019495412 47 4105.18 3482.83698050459 622.343019495413 48 4116.68 4020.03264220184 96.6473577981641 49 3844.49 3942.89308486239 -98.4030848623867 50 3720.98 4063.21664678899 -342.236646788991 51 3674.4 4044.55464678899 -370.154646788991 52 3857.62 4139.01864678899 -281.398646788990 53 3801.06 4105.30264678899 -304.242646788991 54 3504.37 4001.27864678899 -496.908646788991 55 3032.6 3933.07264678899 -900.47264678899 56 3047.03 3925.07864678899 -878.04864678899 57 2962.34 3982.03064678899 -1019.69064678899 58 2197.82 3916.44464678899 -1718.62464678899 59 2014.45 3858.24264678899 -1843.79264678899 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 5.05423349397846e-05 0.000101084669879569 0.99994945766506 18 1.68118041740724e-06 3.36236083481449e-06 0.999998318819583 19 3.48832462337070e-07 6.97664924674141e-07 0.999999651167538 20 2.04597604852596e-07 4.09195209705192e-07 0.999999795402395 21 2.06172266214545e-08 4.12344532429091e-08 0.999999979382773 22 2.23306650158922e-08 4.46613300317844e-08 0.999999977669335 23 1.81170428996373e-08 3.62340857992747e-08 0.999999981882957 24 8.03107091827873e-09 1.60621418365575e-08 0.99999999196893 25 4.26246511919446e-09 8.52493023838892e-09 0.999999995737535 26 1.32928481633966e-09 2.65856963267932e-09 0.999999998670715 27 2.9726943100405e-09 5.945388620081e-09 0.999999997027306 28 1.21236711793792e-09 2.42473423587583e-09 0.999999998787633 29 5.60296188791632e-10 1.12059237758326e-09 0.999999999439704 30 1.53746469842774e-08 3.07492939685547e-08 0.999999984625353 31 2.06665590903830e-08 4.13331181807660e-08 0.99999997933344 32 9.91111256768274e-09 1.98222251353655e-08 0.999999990088887 33 6.38877443871536e-09 1.27775488774307e-08 0.999999993611226 34 3.20069511183936e-09 6.40139022367873e-09 0.999999996799305 35 9.695730814927e-10 1.9391461629854e-09 0.999999999030427 36 3.18975701035518e-10 6.37951402071037e-10 0.999999999681024 37 8.97846236880828e-11 1.79569247376166e-10 0.999999999910215 38 3.86016201120075e-11 7.7203240224015e-11 0.999999999961398 39 1.3876706466326e-10 2.7753412932652e-10 0.999999999861233 40 8.21200488997979e-10 1.64240097799596e-09 0.9999999991788 41 4.14451483928315e-08 8.2890296785663e-08 0.999999958554852 42 2.18616900010062e-07 4.37233800020124e-07 0.9999997813831 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 26 1 NOK 5% type I error level 26 1 NOK 10% type I error level 26 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/10m6ss1261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/10m6ss1261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/1nxzp1261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/1nxzp1261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/2jywk1261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/2jywk1261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/3jon71261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/3jon71261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/4iuki1261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/4iuki1261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/5fmj61261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/5fmj61261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/6xj3n1261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/6xj3n1261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/7z0ll1261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/7z0ll1261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/80r241261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/80r241261402042.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/9bwab1261402042.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261402114zs0mxcjac5fegtu/9bwab1261402042.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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