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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 14:23:28 +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/t1293632469qhy3nb7ga9okadr.htm/, Retrieved Wed, 29 Dec 2010 15:21:09 +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/t1293632469qhy3nb7ga9okadr.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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Werkloos[t] = + 515846.69583124 + 1373.48053741838cv[t] + 12236.1428176794M1[t] + 16102.7428176796M2[t] + 11856.0311401306M3[t] -110.841612255150M4[t] -3509.26496735309M5[t] -18083.8416122552M6[t] -14552.5455047715M7[t] + 34857.4934203918M8[t] + 41126.7012054244M9[t] + 25914.0934203918M10[t] + 8958.95838774484M11[t] + 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)	515846.69583124	17403.234813	29.6409	0	0
cv	1373.48053741838	590.649844	2.3254	0.024419	0.01221
M1	12236.1428176794	21124.135667	0.5792	0.565187	0.282594
M2	16102.7428176796	21124.135667	0.7623	0.449695	0.224848
M3	11856.0311401306	21105.300054	0.5618	0.576951	0.288476
M4	-110.841612255150	21158.459192	-0.0052	0.995842	0.497921
M5	-3509.26496735309	21110.919452	-0.1662	0.868689	0.434345
M6	-18083.8416122552	21158.459192	-0.8547	0.397061	0.19853
M7	-14552.5455047715	21148.89381	-0.6881	0.494772	0.247386
M8	34857.4934203918	21273.892249	1.6385	0.107993	0.053997
M9	41126.7012054244	21306.664694	1.9302	0.059625	0.029813
M10	25914.0934203918	21273.892249	1.2181	0.229259	0.11463
M11	8958.95838774484	21158.459192	0.4234	0.673919	0.336959

Multiple Linear Regression - Regression Statistics
Multiple R	0.584188194038514
R-squared	0.34127584605398
Adjusted R-squared	0.173090955684783
F-TEST (value)	2.02917066631144
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.0424051193047795
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	33336.9386056947
Sum Squared Residuals	52233519353.1933

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	597141	562419.85208438	34721.1479156195
2	593408	564912.971546961	28495.0284530387
3	590072	556545.818257157	33526.1817428428
4	579799	545952.42604219	33846.5739578102
5	574205	539807.041612255	34397.9583877449
6	572775	530726.387117027	42048.6128829734
7	572942	534257.68322451	38684.3167754897
8	619567	583667.722149674	35899.2778503265
9	625809	589936.929934706	35872.0700652938
10	619916	580218.244299347	39697.755700653
11	587625	561889.628729282	25735.3712707182
12	565742	540569.345504771	25172.6544952285
13	557274	562419.85208438	-5145.85208437949
14	560576	569033.413159216	-8457.41315921646
15	548854	562039.740406831	-13185.7404068307
16	531673	554193.3092667	-22520.3092667001
17	525919	550794.885911602	-24875.8859116022
18	511038	534846.828729282	-23808.8287292817
19	498662	535631.163761929	-36969.1637619287
20	555362	583667.722149674	-28305.7221496735
21	564591	589936.929934706	-25345.9299347062
22	541657	576097.802687092	-34440.8026870919
23	527070	549528.303892516	-22458.3038925163
24	509846	546063.267654445	-36217.267654445
25	514258	555552.449397288	-41294.4493972876
26	516922	563539.491009543	-46617.4910095429
27	507561	559292.779331994	-51731.7793319939
28	492622	541831.984429935	-49209.9844299347
29	490243	535686.6	-45443.6
30	469357	518365.062280261	-49008.0622802612
31	477580	519149.397312908	-41569.3973129081
32	528379	571306.397312908	-42927.3973129081
33	533590	580322.566172778	-46732.5661727775
34	517945	554122.114088398	-36177.1140883978
35	506174	530299.576368659	-24125.576368659
36	501866	517220.176368659	-15354.176368659
37	516141	536323.72187343	-20182.7218734303
38	528222	534696.399723757	-6474.39972375694
39	532638	530449.688046208	2188.31195379204
40	536322	521229.776368659	15092.223631341
41	536535	521951.794625816	14583.2053741838
42	523597	508750.698518333	14846.3014816675
43	536214	513655.475163235	22558.5248367654
44	586570	571306.397312908	15263.6026870919
45	596594	577575.605097941	19018.3949020592
46	580523	560989.51677549	19533.4832245103
47	564478	546781.34281768	17696.6571823204
48	557560	530954.981742843	26605.0182571572
49	575093	543191.124560522	31901.8754394778
50	580112	547057.724560522	33054.2754394776
51	574761	545557.97395781	29203.0260421898
52	563250	540458.503892516	22791.4961074837
53	551531	530192.677850327	21338.3221496735
54	537034	521112.023355098	15921.9766449021
55	544686	527390.280537418	17295.7194625816
56	600991	580920.761074837	20070.2389251633
57	604378	587189.968859869	17188.0311401306
58	586111	574724.322149673	11386.6778503265
59	563668	560516.148191863	3151.85180813665
60	548604	548810.228729282	-206.228729281760

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.159426709308393	0.318853418616787	0.840573290691607
17	0.0842719082923595	0.168543816584719	0.91572809170764
18	0.100542537367769	0.201085074735539	0.89945746263223
19	0.259916908035517	0.519833816071034	0.740083091964483
20	0.365154033622105	0.730308067244211	0.634845966377894
21	0.418968258055817	0.837936516111633	0.581031741944183
22	0.663607605244657	0.672784789510687	0.336392394755343
23	0.772203924548201	0.455592150903598	0.227796075451799
24	0.742724155287996	0.514551689424008	0.257275844712004
25	0.788811332926463	0.422377334147074	0.211188667073537
26	0.822482535020077	0.355034929959846	0.177517464979923
27	0.880572118845538	0.238855762308924	0.119427881154462
28	0.929720131210504	0.140559737578992	0.0702798687894958
29	0.958684070774412	0.0826318584511767	0.0413159292255884
30	0.977382909224663	0.0452341815506742	0.0226170907753371
31	0.985676528908621	0.0286469421827571	0.0143234710913786
32	0.99183050892368	0.0163389821526393	0.00816949107631967
33	0.99834160719497	0.00331678561006075	0.00165839280503038
34	0.99898998446782	0.00202003106436012	0.00101001553218006
35	0.998306309128852	0.00338738174229574	0.00169369087114787
36	0.997241249693596	0.00551750061280846	0.00275875030640423
37	0.999346311493762	0.00130737701247645	0.000653688506238226
38	0.99984770211053	0.000304595778941214	0.000152297889470607
39	0.999961918573835	7.61628523294494e-05	3.80814261647247e-05
40	0.999950006463415	9.99870731706652e-05	4.99935365853326e-05
41	0.999869785590087	0.000260428819825984	0.000130214409912992
42	0.999558631235605	0.000882737528789861	0.000441368764394930
43	0.997827016889745	0.00434596622050918	0.00217298311025459
44	0.9957915645158	0.00841687096839874	0.00420843548419937

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	12	0.413793103448276	NOK
5% type I error level	15	0.517241379310345	NOK
10% type I error level	16	0.551724137931034	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/10z26y1293632600.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/10z26y1293632600.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/1i9p91293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/1i9p91293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/2i9p91293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/2i9p91293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/3b0ou1293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/3b0ou1293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/4b0ou1293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/4b0ou1293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/5b0ou1293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/5b0ou1293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/63r5e1293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/63r5e1293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/7w1501293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/7w1501293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/8w1501293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/8w1501293632599.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/9w1501293632599.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293632469qhy3nb7ga9okadr/9w1501293632599.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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