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*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 03:06:32 -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/t1258626341kdjdcuz539hl65l.htm/, Retrieved Thu, 19 Nov 2009 11:25:53 +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/t1258626341kdjdcuz539hl65l.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 «

96.8 610763 114.1 612613 110.3 611324 103.9 594167 101.6 595454 94.6 590865 95.9 589379 104.7 584428 102.8 573100 98.1 567456 113.9 569028 80.9 620735 95.7 628884 113.2 628232 105.9 612117 108.8 595404 102.3 597141 99 593408 100.7 590072 115.5 579799 100.7 574205 109.9 572775 114.6 572942 85.4 619567 100.5 625809 114.8 619916 116.5 587625 112.9 565742 102 557274 106 560576 105.3 548854 118.8 531673 106.1 525919 109.3 511038 117.2 498662 92.5 555362 104.2 564591 112.5 541657 122.4 527070 113.3 509846 100 514258 110.7 516922 112.8 507561 109.8 492622 117.3 490243 109.1 469357 115.9 477580 96 528379 99.8 533590 116.8 517945 115.7 506174 99.4 501866 94.3 516141 91 528222 93.2 532638 103.1 536322 94.1 536535 91.8 523597 102.7 536214 82.6 586570 89.1 596594

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

Tot_nietwerkende_werkzoekenden[t] = + 838838.18799994 -2239.06649810638Tot_ind_productie[t] + 25644.8903432349M1[t] + 45056.4124976968M2[t] + 31267.1814830794M3[t] + 2946.30621054342M4[t] -9776.7235398718M5[t] -5649.07194513298M6[t] -5301.24720247713M7[t] + 7360.59494601442M8[t] -9755.17904712758M9[t] -20474.7993209117M10[t] + 3900.0507567845M11[t] -1690.05696515542t + 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)	838838.18799994	44098.410987	19.022	0	0
Tot_ind_productie	-2239.06649810638	484.43716	-4.622	3e-05	1.5e-05
M1	25644.8903432349	14160.566997	1.811	0.076534	0.038267
M2	45056.4124976968	19007.24327	2.3705	0.021918	0.010959
M3	31267.1814830794	18958.917067	1.6492	0.105774	0.052887
M4	2946.30621054342	16970.249867	0.1736	0.862913	0.431456
M5	-9776.7235398718	15149.022085	-0.6454	0.521826	0.260913
M6	-5649.07194513298	15183.182751	-0.3721	0.71152	0.35576
M7	-5301.24720247713	15446.860603	-0.3432	0.732984	0.366492
M8	7360.59494601442	17738.506462	0.415	0.680067	0.340033
M9	-9755.17904712758	16039.319974	-0.6082	0.54598	0.27299
M10	-20474.7993209117	15903.31724	-1.2875	0.20424	0.10212
M11	3900.0507567845	18513.800654	0.2107	0.834066	0.417033
t	-1690.05696515542	161.692331	-10.4523	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.890095379048494
R-squared	0.792269783803483
Adjusted R-squared	0.734812489961893
F-TEST (value)	13.7888461295754
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	6.57685017557696e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	21892.9363583838
Sum Squared Residuals	22527131132.4354

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	610763	646051.384361324	-35288.3843613239
2	612613	625036.999133389	-12423.9991333891
3	611324	618066.163846421	-6742.16384642067
4	594167	602385.25719661	-8218.25719661001
5	595454	593122.023426684	2331.97657331597
6	590865	611233.083543012	-20368.0835430122
7	589379	606980.064872974	-17601.0648729742
8	584428	598248.064872974	-13820.0648729742
9	573100	583696.460261079	-10596.4602610789
10	567456	581810.39556324	-14354.3955632393
11	569028	569117.938005699	-89.9380056993582
12	620735	637417.02472127	-16682.02472127
13	628884	628233.673927375	650.326072625002
14	628232	606771.47539982	21460.5246001800
15	612117	607637.372856224	4479.6271437763
16	595404	571133.147774024	24270.8522259763
17	597141	571273.993296145	25867.0067038554
18	593408	581100.507369479	12307.4926305210
19	590072	575951.862100199	14120.1378998014
20	579799	553785.46311156	26013.5368884397
21	574205	568117.816325237	6087.18367476267
22	572775	535108.727303719	37666.2726962809
23	572942	547269.90787516	25672.0921248401
24	619567	607060.541897926	12506.4581020737
25	625809	597205.4711546	28603.5288454006
26	619916	582908.285420985	37007.7145790153
27	587625	563622.584394431	24002.4156055689
28	565742	541672.291549923	24069.7084500774
29	557274	551665.029663712	5608.9703362885
30	560576	545146.358300869	15429.6416991306
31	548854	545371.472627044	3482.52737295573
32	531673	526115.860085944	5557.13991405572
33	525919	535746.173653598	-9827.17365359789
34	511038	516171.483620718	-5133.48362071788
35	498662	521167.651398218	-22505.6513982183
36	555362	570882.486179506	-15520.4861795060
37	564591	568640.241529741	-4049.24152974076
38	541657	567777.454784764	-26120.4547847644
39	527070	530131.408473738	-3061.40847373838
40	509846	520495.981368815	-10649.981368815
41	514258	535862.479078059	-21604.4790780593
42	516922	514342.062177904	2579.93782209561
43	507561	508297.790309381	-736.790309381435
44	492622	525986.774987037	-33364.7749870367
45	490243	490387.945292941	-144.945292941415
46	469357	496338.613338474	-26981.6133384741
47	477580	503797.754263892	-26217.7542638916
48	528379	542765.069854269	-14386.0698542686
49	533590	558211.450539544	-24621.4505395439
50	517945	537868.785261042	-19923.7852610419
51	506174	524852.470429186	-18678.4704291862
52	501866	531338.322110629	-29472.3221106287
53	516141	528344.4745354	-12203.4745354006
54	528222	538170.988608735	-9948.98860873506
55	532638	531902.810090401	735.189909598565
56	536322	520707.836942484	15614.1630575156
57	536535	522053.604467144	14481.3955328556
58	523597	514793.78017385	8803.21982615045
59	536214	513072.748457031	23141.2515429692
60	586570	552487.877347029	34082.1226529709
61	596594	561888.778487417	34705.2215125829

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0449456463730226	0.0898912927460451	0.955054353626977
18	0.0120948457859071	0.0241896915718143	0.987905154214093
19	0.00301650233514609	0.00603300467029218	0.996983497664854
20	0.000691896626749456	0.00138379325349891	0.99930810337325
21	0.000251291950908134	0.000502583901816267	0.999748708049092
22	0.000108709446470507	0.000217418892941014	0.99989129055353
23	2.53150873273935e-05	5.0630174654787e-05	0.999974684912673
24	6.54273682993918e-06	1.30854736598784e-05	0.99999345726317
25	1.28483206443595e-06	2.5696641288719e-06	0.999998715167935
26	1.31865181637720e-06	2.63730363275439e-06	0.999998681348184
27	9.5789886066480e-05	0.000191579772132960	0.999904210113933
28	0.00188486755170638	0.00376973510341276	0.998115132448294
29	0.0263494601183569	0.0526989202367138	0.973650539881643
30	0.0377421796525934	0.0754843593051868	0.962257820347407
31	0.0572657790469781	0.114531558093956	0.942734220953022
32	0.128419100403106	0.256838200806212	0.871580899596894
33	0.152219558788507	0.304439117577014	0.847780441211493
34	0.255141372062021	0.510282744124042	0.744858627937979
35	0.373069123518523	0.746138247037046	0.626930876481477
36	0.350353785133524	0.700707570267048	0.649646214866476
37	0.311405186014815	0.622810372029629	0.688594813985185
38	0.361560810857307	0.723121621714614	0.638439189142693
39	0.516256142066779	0.967487715866442	0.483743857933221
40	0.692893595895738	0.614212808208525	0.307106404104262
41	0.901398501987874	0.197202996024252	0.098601498012126
42	0.956080718695533	0.0878385626089333	0.0439192813044667
43	0.985721508536231	0.0285569829275371	0.0142784914637686
44	0.99998238717709	3.52256458211766e-05	1.76128229105883e-05

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	11	0.392857142857143	NOK
5% type I error level	13	0.464285714285714	NOK
10% type I error level	17	0.607142857142857	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/10encb1258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/10encb1258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/143e41258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/143e41258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/23jiv1258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/23jiv1258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/35q241258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/35q241258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/4jmr21258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/4jmr21258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/5rlvz1258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/5rlvz1258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/6t5w71258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/6t5w71258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/7a0tq1258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/7a0tq1258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/88ez21258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/88ez21258625187.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/97psm1258625187.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626341kdjdcuz539hl65l/97psm1258625187.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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