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Multiple Regression - werkloosheid

*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, 21 Nov 2008 08:51:01 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/21/t1227282725t1wqu9lxm2mxwvo.htm/, Retrieved Fri, 21 Nov 2008 15:52:05 +0000

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/2008/Nov/21/t1227282725t1wqu9lxm2mxwvo.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

multiple regression - werkloosheid

Dataseries X:

» Textbox « » Textfile « » CSV «

8,4 7,6 9,5 25 6,6 8,4 7,9 9,1 23,6 6,7 8,4 7,9 9 22,3 6,8 8,6 8,1 9,3 21,8 7,2 8,9 8,2 9,9 20,8 7,6 8,8 8 9,8 19,7 7,6 8,3 7,5 9,4 18,3 7,3 7,5 6,8 8,3 17,4 6,4 7,2 6,5 8 17 6,1 7,5 6,6 8,5 18,1 6,3 8,8 7,6 10,4 23,9 7,1 9,3 8 11,1 25,6 7,5 9,3 8 10,9 25,3 7,4 8,7 7,7 9,9 23,6 7,1 8,2 7,5 9,2 21,9 6,8 8,3 7,6 9,2 21,4 6,9 8,5 7,7 9,5 20,6 7,2 8,6 7,9 9,6 20,5 7,4 8,6 7,8 9,5 20,2 7,3 8,2 7,5 9,1 20,6 6,9 8,1 7,5 8,9 19,7 6,9 8 7,1 9 19,3 6,8 8,6 7,5 10,1 22,8 7,1 8,7 7,5 10,3 23,5 7,2 8,8 7,6 10,2 23,8 7,1 8,5 7,7 9,6 22,6 7 8,4 7,7 9,2 22 6,9 8,5 7,9 9,3 21,7 7 8,7 8,1 9,4 20,7 7,4 8,7 8,2 9,4 20,2 7,5 8,6 8,2 9,2 19,1 7,5 8,5 8,1 9 19,5 7,4 8,3 7,9 9 18,7 7,3 8,1 7,3 9 18,6 7 8,2 6,9 9,8 22,2 6,7 8,1 6,6 10 23,2 6,5 8,1 6,7 9,9 23,5 6,5 7,9 6,9 9,3 21,3 6,5 7,9 7 9 20 6,6 7,9 7,1 9 18,7 6,8 8 7,2 9,1 18,9 6,9 8 7,1 9,1 18,3 6,9 7,9 6,9 9,1 18,4 6,8 8 7 9,2 19,9 6,8 7,7 6,8 8,8 19,2 6,5 7,2 6,4 8,3 18,5 6,1 7,5 6,7 8,4 20,9 6 7,3 6,7 8,1 20,5 5,9 7 6,4 7,8 19,4 5,8 7 6,3 7,9 18,1 5,9 7 6,2 7,9 17 5,9 7,2 6,5 8 17 6,2 7,3 6,8 7,9 17,3 6,3 7,1 6,8 7,5 16,7 6,2 6,8 6,5 7,2 15,5 6 6,6 6,3 6,9 15,3 5,8 6,2 5,9 6,6 13,7 5,5 6,2 5,9 6,7 14,1 5,5 6,8 6,4 7,3 17,3 5,7 6,9 6,4 7,5 18,1 5,8 6,8 6,5 7,2 18,1 5,7

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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Totaal[t] = + 0.0915442743664928 + 0.465788320786374Mannen[t] + 0.392832052720299Vrouwen[t] + 0.0140978689669122`<25j`[t] + 0.106007892077027`>25j`[t] + 0.0222051691603468M1[t] + 0.00784697233190004M2[t] + 0.0458619353068849M3[t] + 0.0271220437832206M4[t] + 0.0333285255821577M5[t] + 0.0290675940015739M6[t] + 0.0363209336876756M7[t] + 0.0483014035067723M8[t] + 0.0190382857229988M9[t] + 0.0366810978139367M10[t] + 0.0245809750151976M11[t] -0.000376055884064269t + 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)	0.0915442743664928	0.111586	0.8204	0.416414	0.208207
Mannen	0.465788320786374	0.081462	5.7179	1e-06	0
Vrouwen	0.392832052720299	0.06567	5.9819	0	0
`<25j`	0.0140978689669122	0.016358	0.8618	0.393464	0.196732
`>25j`	0.106007892077027	0.133213	0.7958	0.430435	0.215217
M1	0.0222051691603468	0.022903	0.9695	0.337577	0.168788
M2	0.00784697233190004	0.024543	0.3197	0.750691	0.375346
M3	0.0458619353068849	0.026433	1.735	0.089741	0.04487
M4	0.0271220437832206	0.029273	0.9265	0.359223	0.179612
M5	0.0333285255821577	0.033336	0.9998	0.322882	0.161441
M6	0.0290675940015739	0.036031	0.8067	0.424157	0.212078
M7	0.0363209336876756	0.03731	0.9735	0.335633	0.167817
M8	0.0483014035067723	0.033236	1.4533	0.153235	0.076618
M9	0.0190382857229988	0.033865	0.5622	0.576845	0.288423
M10	0.0366810978139367	0.032535	1.1274	0.265669	0.132834
M11	0.0245809750151976	0.024235	1.0143	0.316002	0.158001
t	-0.000376055884064269	0.000519	-0.725	0.472284	0.236142

Multiple Linear Regression - Regression Statistics
Multiple R	0.999091516927074
R-squared	0.998183859195642
Adjusted R-squared	0.997523444357693
F-TEST (value)	1511.44977647128
F-TEST (DF numerator)	16
F-TEST (DF denominator)	44
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0371328161706541
Sum Squared Residuals	0.0606692256175981

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.4	8.43736793834323	-0.0373679383432272
2	8.4	8.39610113343254	0.00389886656746406
3	8.4	8.38673039480214	0.0132696051978572
4	8.6	8.61397594971513	-0.0139759497151340
5	8.9	8.93038972720472	-0.0303897272047208
6	8.8	8.77780421444717	0.0221957855528353
7	8.3	8.34311513259111	-0.0431151325911107
8	7.5	7.48845727904381	0.0115427209561911
9	7.2	7.1637904781141	0.0362095218859039
10	7.5	7.46076132703876	0.0392386729612349
11	8.8	8.82702832298062	-0.0270283229806143
12	9.3	9.32973859137467	-0.0297385913746721
13	9.3	9.25817114420912	0.041828855790881
14	8.7	8.65509959767354	0.0449004023264599
15	8.2	8.26882965883612	-0.0688296588361176
16	8.3	8.29984439823127	0.000155601768728534
17	8.5	8.49062734449045	0.0093726555095492
18	8.6	8.63822301797382	-0.0382230179738217
19	8.6	8.54440811452742	0.0555918854725846
20	8.2	8.22237920189437	-0.0223792018943709
21	8.1	8.10148553561225	-0.00148553561225234
22	8	7.95548023198214	0.044519768017862
23	8.6	8.64257954861351	-0.0425795486135137
24	8.7	8.71665822574285	-0.0166582257428535
25	8.8	8.73941153730811	0.0605884626918876
26	8.5	8.50803865307406	-0.00803865307406344
27	8.4	8.36948522848902	0.0305147715109858
28	8.5	8.48918157902822	0.0108184209717799
29	8.7	8.6557581622363	0.0442418377637036
30	8.7	8.70125186157453	-0.00125186157453201
31	8.6	8.6140550789689	-0.0140550789689056
32	8.5	8.4955526086603	0.00444739133969665
33	8.3	8.35087668645396	-0.0508766864539579
34	8.1	8.0554582956692	0.0445417043307912
35	8.2	8.18988239150587	0.0101176084941286
36	8.1	8.09665156546626	0.00334843453373694
37	8.1	8.13000566623923	-0.0300056662392272
38	7.9	7.9417145343246	-0.0417145343246045
39	7.9	7.90035621722879	-0.000356217228789279
40	7.9	7.93069345065812	-0.0306934506581173
41	8	8.03580627692474	-0.0358062769247431
42	8	7.97613173600131	0.0238682639986899
43	7.9	7.88066035333506	0.0193396466649388
44	8	7.99927360807113	0.000726391928870802
45	7.7	7.67767307325795	0.0223269267420489
46	7.2	7.25993680968248	-0.0599368096824769
47	7.5	7.4497144288205	0.0502855711794979
48	7.3	7.29066784531068	0.00933215468931686
49	7	7.02880240146366	-0.0288024014636577
50	7	6.99904608149526	0.000953918504743992
51	7	6.97459850064394	0.025401499356064
52	7.2	7.16630462236726	0.033695377632743
53	7.3	7.28741848914379	0.0125815108562111
54	7.1	7.10658917000317	-0.00658917000317149
55	6.8	6.8177613205775	-0.0177613205775072
56	6.6	6.59433730233039	0.00566269766961238
57	6.2	6.20617422656174	-0.00617422656174262
58	6.2	6.26836333562741	-0.0683633356274111
59	6.8	6.7907953080795	0.00920469192050143
60	6.9	6.86628377210553	0.0337162278944717
61	6.8	6.80624131243666	-0.00624131243665649

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.924627508779029	0.150744982441942	0.075372491220971
21	0.850279362244945	0.29944127551011	0.149720637755055
22	0.831155588612794	0.337688822774412	0.168844411387206
23	0.781472489173354	0.437055021653291	0.218527510826646
24	0.75232762344398	0.495344753112041	0.247672376556021
25	0.806079920836184	0.387840158327632	0.193920079163816
26	0.867376525015772	0.265246949968455	0.132623474984228
27	0.824358873491935	0.351282253016131	0.175641126508065
28	0.781551328690184	0.436897342619631	0.218448671309816
29	0.803954080682777	0.392091838634446	0.196045919317223
30	0.724830097269571	0.550339805460857	0.275169902730429
31	0.663006971309987	0.673986057380027	0.336993028690013
32	0.566293282077333	0.867413435845334	0.433706717922667
33	0.74214926264716	0.51570147470568	0.25785073735284
34	0.96127775125451	0.0774444974909793	0.0387222487454896
35	0.928700064601785	0.142599870796429	0.0712999353982145
36	0.89968175218017	0.200636495639661	0.100318247819830
37	0.94863520674761	0.102729586504778	0.0513647932523892
38	0.943680521365056	0.112638957269888	0.0563194786349438
39	0.935746668017511	0.128506663964978	0.064253331982489
40	0.866599903703257	0.266800192593487	0.133400096296743
41	0.757589514748416	0.484820970503168	0.242410485251584

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	0	0	OK
10% type I error level	1	0.0454545454545455	OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227282725t1wqu9lxm2mxwvo/10t7nm1227282650.png (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227282725t1wqu9lxm2mxwvo/9g40n1227282650.png (open in new window)

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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')

}

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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