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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: Wed, 29 Dec 2010 18:38:10 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7.htm/, Retrieved Wed, 29 Dec 2010 19:35:56 +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/2010/Dec/29/t1293647748pkdivys4rx6zhc7.htm/},
    year = {2010},
}
@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 = {2010},
    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 «

597141 25 593408 24 590072 21 579799 22 574205 20 572775 24 572942 24 619567 24 625809 24 619916 28 587625 27 565742 18 557274 25 560576 27 548854 25 531673 28 525919 28 511038 27 498662 25 555362 24 564591 24 541657 25 527070 18 509846 22 514258 20 516922 23 507561 23 492622 19 490243 17 469357 15 477580 13 528379 15 533590 17 517945 9 506174 4 501866 1 516141 6 528222 2 532638 2 536322 4 536535 7 523597 8 536214 9 586570 15 596594 15 580523 14 564478 16 557560 11 575093 11 580112 11 574761 13 563250 18 551531 13 537034 17 544686 19 600991 22 604378 22 586111 24 563668 26 548604 24

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' @ www.wessa.org

Multiple Linear Regression - Estimated Regression Equation

werkloos[t] = + 513222.488502579 + 1430.61638726532cv[t] + 12646.9207958182M1[t] + 16464.7501732907M2[t] + 12203.5493831224M3[t] + 107.915818423545M4[t] -3270.71513938551M5[t] -17962.6254266313M6[t] -14468.6727717057M7[t] + 34778.3238312362M8[t] + 40975.9066538026M9[t] + 25737.3825861813M10[t] + 8836.32146073147M11[t] + 48.7706225274404t + 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)	513222.488502579	23744.845748	21.6141	0	0
cv	1430.61638726532	690.584512	2.0716	0.043937	0.021969
M1	12646.9207958182	21491.830879	0.5885	0.559107	0.279554
M2	16464.7501732907	21459.396456	0.7673	0.446852	0.223426
M3	12203.5493831224	21431.599871	0.5694	0.571842	0.285921
M4	107.915818423545	21422.244562	0.005	0.996002	0.498001
M5	-3270.71513938551	21382.122604	-0.153	0.879095	0.439547
M6	-17962.6254266313	21393.616404	-0.8396	0.405461	0.202731
M7	-14468.6727717057	21377.336001	-0.6768	0.501908	0.250954
M8	34778.3238312362	21502.953895	1.6174	0.112635	0.056318
M9	40975.9066538026	21550.192892	1.9014	0.063522	0.031761
M10	25737.3825861813	21524.394911	1.1957	0.237929	0.118965
M11	8836.32146073147	21393.915693	0.413	0.681504	0.340752
t	48.7706225274404	296.510848	0.1645	0.870073	0.435036

Multiple Linear Regression - Regression Statistics
Multiple R	0.584519492891654
R-squared	0.341663037570316
Adjusted R-squared	0.155611287318449
F-TEST (value)	1.83638711867956
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	0.0655093381447696
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	33687.4434771532
Sum Squared Residuals	52202817009.214

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	597141	561683.589602558	35457.4103974424
2	593408	564119.573215292	29288.4267847075
3	590072	555615.293885856	34456.7061141442
4	579799	544999.04733095	34799.9526690503
5	574205	538807.954221137	35397.0457788626
6	572775	529887.28010548	42887.7198945197
7	572942	533430.003382933	39511.9966170666
8	619567	582725.770608403	36841.2293915973
9	625809	588972.124053497	36836.8759465034
10	619916	579504.836157464	40411.163842536
11	587625	561221.929267276	26403.0707327237
12	565742	539558.830943684	26183.1690563157
13	557274	562268.837072887	-4994.83707288724
14	560576	568996.669847418	-8420.66984741784
15	548854	561923.006905246	-13069.0069052464
16	531673	554167.993124871	-22494.9931248709
17	525919	550838.132789589	-24919.1327895893
18	511038	534764.376737606	-23726.3767376055
19	498662	535445.867240528	-36783.867240528
20	555362	583311.018078732	-27949.018078732
21	564591	589557.371523826	-24966.3715238259
22	541657	575798.234465997	-34141.2344659974
23	527070	548931.629252218	-21861.6292522177
24	509846	545866.543963075	-36020.5439630749
25	514258	555701.00260689	-41443.0026068899
26	516922	563859.451768686	-46937.4517686858
27	507561	559647.021601045	-52086.021601045
28	492622	541877.693109812	-49255.6931098123
29	490243	535686.6	-45443.6
30	469357	518182.227560751	-48825.227560751
31	477580	518863.718063673	-41283.7180636734
32	528379	571020.718063673	-42641.7180636734
33	533590	580128.304283298	-46538.3042832979
34	517945	553493.619740081	-35548.6197400815
35	506174	529488.247300832	-23314.2473008324
36	501866	516408.847300832	-14542.8473008324
37	516141	536257.620655505	-20116.6206555047
38	528222	534401.755106443	-6179.75510644334
39	532638	530189.324938803	2448.67506119748
40	536322	521003.694771162	15318.3052288383
41	536535	521965.683597676	14569.3164023239
42	523597	508753.160320223	14843.839679777
43	536214	513726.499984941	22487.5000150586
44	586570	571605.965534003	14964.0344659973
45	596594	577852.318979096	18741.6810209035
46	580523	561231.949146737	19291.0508532626
47	564478	547240.891418346	17237.1085816544
48	557560	531300.258643815	26259.7413561851
49	575093	543995.950062161	31097.0499378394
50	580112	547862.55006216	32249.4499378395
51	574761	546511.35266905	28249.6473309497
52	563250	541617.571663206	21632.4283367945
53	551531	531134.629391597	20396.3706084027
54	537034	522213.95527594	14820.0447240598
55	544686	528617.911327924	16068.0886720761
56	600991	582205.527715189	18785.4722848108
57	604378	588451.881160283	15926.1188397169
58	586111	576123.36048972	9987.63951028013
59	563668	562132.302761328	1535.69723867192
60	548604	550483.519148593	-1879.51914859341

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0103250392942059	0.0206500785884118	0.989674960705794
18	0.0469742386537515	0.093948477307503	0.953025761346249
19	0.118155152838888	0.236310305677777	0.881844847161112
20	0.0793058183196281	0.158611636639256	0.920694181680372
21	0.0530395909051934	0.106079181810387	0.946960409094807
22	0.0475310814427198	0.0950621628854397	0.95246891855728
23	0.0652226547267292	0.130445309453458	0.934777345273271
24	0.067779904557981	0.135559809115962	0.932220095442019
25	0.149772874426467	0.299545748852933	0.850227125573534
26	0.195285346120626	0.390570692241251	0.804714653879374
27	0.201354132987171	0.402708265974342	0.798645867012829
28	0.164976074454834	0.329952148909668	0.835023925545166
29	0.152149216003679	0.304298432007358	0.847850783996321
30	0.106740865194655	0.213481730389311	0.893259134805345
31	0.0771633884802887	0.154326776960577	0.922836611519711
32	0.0511763246097407	0.102352649219481	0.94882367539026
33	0.0308698139328526	0.0617396278657052	0.969130186067147
34	0.0251234805286414	0.0502469610572828	0.974876519471359
35	0.0292794128568357	0.0585588257136715	0.970720587143164
36	0.0544704359977565	0.108940871995513	0.945529564002244
37	0.278643481658899	0.557286963317797	0.721356518341101
38	0.691281311487255	0.61743737702549	0.308718688512745
39	0.951698557884602	0.096602884230796	0.048301442115398
40	0.999703989109236	0.00059202178152823	0.000296010890764115
41	0.999338209798336	0.0013235804033282	0.000661790201664098
42	0.998980196657014	0.00203960668597168	0.00101980334298584
43	0.998053921811745	0.00389215637651067	0.00194607818825533

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.148148148148148	NOK
5% type I error level	5	0.185185185185185	NOK
10% type I error level	11	0.407407407407407	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/108wom1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/108wom1293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/11drs1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/11drs1293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/2cmqd1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/2cmqd1293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/3cmqd1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/3cmqd1293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/4cmqd1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/4cmqd1293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/55vqg1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/55vqg1293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/65vqg1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/65vqg1293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/7xn711293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/7xn711293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/8xn711293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/8xn711293647886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/98wom1293647886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293647748pkdivys4rx6zhc7/98wom1293647886.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')

}

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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