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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: Sat, 19 Dec 2009 15:14:52 -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/19/t1261260945ndwnw8sd61c59tf.htm/, Retrieved Sat, 19 Dec 2009 23:15:58 +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/19/t1261260945ndwnw8sd61c59tf.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 «

19915 23322 20858 19761 19843 22558 21968 20858 19761 19185 23061 21968 20858 17869 22661 23061 21968 21515 22269 22661 23061 17686 21857 22269 22661 18044 21568 21857 22269 20398 21274 21568 21857 22894 20987 21274 21568 22016 19683 20987 21274 25325 19381 19683 20987 27683 19071 19381 19683 17333 20772 19071 19381 20190 22485 20772 19071 22589 24181 22485 20772 14588 23479 24181 22485 14296 22782 23479 24181 12237 22067 22782 23479 7607 21489 22067 22782 9303 20903 21489 22067 9226 20330 20903 21489 9351 19736 20330 20903 21266 19483 19736 20330 21377 19242 19483 19736 22034 20334 19242 19483 22483 21423 20334 19242 15122 22523 21423 20334 18982 21986 22523 21423 19653 21462 21986 22523 16653 20908 21462 21986 23528 20575 20908 21462 24612 20237 20575 20908 24733 19904 20237 20575 21839 19610 19904 20237 22421 19251 19610 19904 26543 18941 19251 19610 27067 20450 18941 19251 31403 21946 20450 18941 25762 23409 21946 20450 29359 22741 23409 21946 34174 22069 22741 234 etc...

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' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

X[t] = + 9900.68216907901 -0.00471812005872899Y[t] -0.56526794567411X1[t] + 1.11839925045814X2[t] + 117.241170415241M1[t] -790.09409085441M2[t] -1676.42406361342M3[t] -2187.26848383598M4[t] -600.875914435188M5[t] + 1023.58767688915M6[t] + 785.650338535605M7[t] + 457.358804732008M8[t] + 193.922136287785M9[t] -216.611067566657M10[t] + 212.757644255395M11[t] -6.13384599065014t + 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)	9900.68216907901	2342.98532	4.2257	0.00013	6.5e-05
Y	-0.00471812005872899	0.007216	-0.6538	0.516885	0.258442
X1	-0.56526794567411	0.216706	-2.6085	0.012633	0.006317
X2	1.11839925045814	0.211778	5.281	5e-06	2e-06
M1	117.241170415241	393.990629	0.2976	0.767531	0.383766
M2	-790.09409085441	447.69196	-1.7648	0.085043	0.042522
M3	-1676.42406361342	563.017217	-2.9776	0.00486	0.00243
M4	-2187.26848383598	484.977155	-4.51	5.3e-05	2.7e-05
M5	-600.875914435188	426.212172	-1.4098	0.166138	0.083069
M6	1023.58767688915	386.506818	2.6483	0.011432	0.005716
M7	785.650338535605	342.966385	2.2908	0.027191	0.013596
M8	457.358804732008	307.147616	1.4891	0.144126	0.072063
M9	193.922136287785	284.791364	0.6809	0.499746	0.249873
M10	-216.611067566657	278.867676	-0.7768	0.441763	0.220882
M11	212.757644255395	232.946172	0.9133	0.366407	0.183203
t	-6.13384599065014	3.388652	-1.8101	0.077612	0.038806

Multiple Linear Regression - Regression Statistics
Multiple R	0.974951444117692
R-squared	0.950530318387173
Adjusted R-squared	0.93243165438248
F-TEST (value)	52.5193637574966
F-TEST (DF numerator)	15
F-TEST (DF denominator)	41
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	326.7935313191
Sum Squared Residuals	4378554.49659231

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	19915	20212.0822749267	-297.082274926661
2	19843	19901.6543694456	-58.6543694455693
3	19761	19648.6900730407	112.309926959266
4	20858	20586.4384118452	271.561588154798
5	21968	21923.7202040422	44.2797959577834
6	23061	23354.5935185189	-293.593518518917
7	22661	22811.4151923048	-150.415192304763
8	22269	22308.8547505381	-39.8547505380529
9	21857	21860.9303292104	-3.93032921036739
10	21568	21864.5346050544	-296.534605054392
11	21274	20984.4755086076	289.524491392376
12	20987	20591.9351807837	395.06481921629
13	19683	19443.6505045825	239.349495417539
14	19381	19450.7948624039	-69.7948624039197
15	19071	19504.1357538049	-433.135753804873
16	20772	21318.5303928218	-546.53039282179
17	22485	22509.0422916023	-24.0422916023209
18	24181	23761.7289497246	419.271050275406
19	23479	23066.5720697744	412.427930225621
20	22782	22408.9570077607	373.042992239250
21	22067	21808.2663606732	258.733639326807
22	21489	21085.9359350387	403.064064961334
23	20903	20931.6380358537	-28.6380358537261
24	20330	20565.4973988227	-235.497398822712
25	19736	19786.6981023322	-50.6981023321769
26	19483	19476.8257478267	6.17425217330837
27	19242	19215.2340543367	26.7659456632720
28	20334	20213.8319070278	120.16809297222
29	21423	21486.5447779157	-63.5447779157272
30	22523	22846.146118089	-323.146118088991
31	21986	22138.2788994967	-152.278899496704
32	21462	21617.3726927941	-155.372692794082
33	20908	21157.4465650867	-249.446565086729
34	20575	20548.1955803172	26.8044196827740
35	20237	20842.8063131368	-605.80631313676
36	19904	20378.1944642531	-474.194464253134
37	19610	19287.1363961027	322.863603897305
38	19251	19195.2331424806	55.7668575193872
39	18941	19175.5225131464	-234.522513146397
40	20450	20655.3902602125	-205.390260212509
41	21946	21845.7005957268	100.299404273165
42	23409	23078.1636361028	330.83636389722
43	22741	22415.2967881444	325.703211855635
44	22069	21819.5810301379	249.418969862111
45	21539	21416.4918527254	122.508147274589
46	21189	21322.3338795897	-133.333879589717
47	20960	20615.0801424019	344.919857598110
48	20704	20389.3729561404	314.627043859556
49	19697	19911.432722056	-214.432722056006
50	19598	19531.4918778432	66.5081221567936
51	19456	18927.4176056713	528.582394328732
52	20316	19955.8090280927	360.190971907281
53	21083	21139.9921307129	-56.9921307129004
54	22158	22291.3677775647	-133.367777564716
55	21469	21904.4370502798	-435.43705027979
56	20892	21319.2345187692	-427.234518769225
57	20578	20705.8648923043	-127.864892304300

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.247291058659135	0.494582117318269	0.752708941340865
20	0.318993875242251	0.637987750484502	0.681006124757749
21	0.43258640201674	0.86517280403348	0.56741359798326
22	0.451685297221257	0.903370594442513	0.548314702778743
23	0.451791994197155	0.90358398839431	0.548208005802845
24	0.565575264537042	0.868849470925917	0.434424735462958
25	0.454304358226409	0.908608716452818	0.545695641773591
26	0.351940526486939	0.703881052973877	0.648059473513061
27	0.285549926093568	0.571099852187136	0.714450073906432
28	0.249354510431320	0.498709020862639	0.75064548956868
29	0.225820582474728	0.451641164949455	0.774179417525272
30	0.213175845109689	0.426351690219378	0.78682415489031
31	0.139090994688237	0.278181989376475	0.860909005311763
32	0.0869566479857104	0.173913295971421	0.91304335201429
33	0.0497850903508387	0.0995701807016774	0.950214909649161
34	0.0347706725071397	0.0695413450142794	0.96522932749286
35	0.0333847342056253	0.0667694684112505	0.966615265794375
36	0.143496051434300	0.286992102868600	0.8565039485657
37	0.176223132716885	0.352446265433769	0.823776867283115
38	0.12064462804803	0.24128925609606	0.87935537195197

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	3	0.15	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/101ubh1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/101ubh1261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/1a07s1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/1a07s1261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/23x911261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/23x911261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/3oype1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/3oype1261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/4zjog1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/4zjog1261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/555jx1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/555jx1261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/6xpsy1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/6xpsy1261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/7y2j11261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/7y2j11261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/8mbgm1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/8mbgm1261260888.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/98qor1261260888.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261260945ndwnw8sd61c59tf/98qor1261260888.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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