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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: Wed, 18 Nov 2009 14:53:28 -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/18/t1258581276rivurwtvlxkc6ae.htm/, Retrieved Wed, 18 Nov 2009 22:54:48 +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/18/t1258581276rivurwtvlxkc6ae.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 «

7,3 20,9 7,4 8,1 8,3 8,2 7,7 20,9 7,3 7,4 8,1 8,3 8 22,3 7,7 7,3 7,4 8,1 8 22,3 8 7,7 7,3 7,4 7,7 22,3 8 8 7,7 7,3 6,9 19,9 7,7 8 8 7,7 6,6 19,9 6,9 7,7 8 8 6,9 19,9 6,6 6,9 7,7 8 7,5 24,1 6,9 6,6 6,9 7,7 7,9 24,1 7,5 6,9 6,6 6,9 7,7 24,1 7,9 7,5 6,9 6,6 6,5 13,8 7,7 7,9 7,5 6,9 6,1 13,8 6,5 7,7 7,9 7,5 6,4 13,8 6,1 6,5 7,7 7,9 6,8 16,2 6,4 6,1 6,5 7,7 7,1 16,2 6,8 6,4 6,1 6,5 7,3 16,2 7,1 6,8 6,4 6,1 7,2 18,6 7,3 7,1 6,8 6,4 7 18,6 7,2 7,3 7,1 6,8 7 18,6 7 7,2 7,3 7,1 7 22,4 7 7 7,2 7,3 7,3 22,4 7 7 7 7,2 7,5 22,4 7,3 7 7 7 7,2 22,6 7,5 7,3 7 7 7,7 22,6 7,2 7,5 7,3 7 8 22,6 7,7 7,2 7,5 7,3 7,9 20 8 7,7 7,2 7,5 8 20 7,9 8 7,7 7,2 8 20 8 7,9 8 7,7 7,9 21,8 8 8 7,9 8 7,9 21,8 7,9 8 8 7,9 8 21,8 7,9 7,9 8 8 8,1 28,7 8 7,9 7,9 8 8,1 28,7 8,1 8 7,9 7,9 8,2 28,7 8,1 8,1 8 7,9 8 19,5 8,2 8,1 8,1 8 8,3 19,5 8 8,2 8,1 8,1 8,5 19,5 8,3 8 8,2 8,1 8,6 19,4 8,5 8,3 8 8,2 8,7 19,4 8,6 8,5 8,3 8 8,7 19,4 8,7 8,6 8,5 8,3 8,5 21,7 8,7 8,7 8,6 8,5 8,4 21,7 8,5 8,7 8,7 8,6 8,5 21,7 8,4 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

Y[t] = + 0.550379527945818 + 0.03191199081617X[t] + 1.29906637784746Y1[t] -0.702880030402592Y2[t] -0.105447463900118Y3[t] + 0.276468222179575Y4[t] + 0.85590625548584M1[t] + 0.494902925375232M2[t] + 0.345194798134066M3[t] + 0.563227115239073M4[t] + 0.500826415002446M5[t] + 0.255445622080153M6[t] + 0.401570798521914M7[t] + 0.542381338318757M8[t] + 0.274628508917084M9[t] + 0.305463665399061M10[t] + 0.284067963059267M11[t] + 0.00685825937152281t + 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)	0.550379527945818	0.39863	1.3807	0.175445	0.087723
X	0.03191199081617	0.011769	2.7115	0.010001	0.005001
Y1	1.29906637784746	0.154282	8.4201	0	0
Y2	-0.702880030402592	0.267159	-2.6309	0.012232	0.006116
Y3	-0.105447463900118	0.26533	-0.3974	0.693281	0.346641
Y4	0.276468222179575	0.138013	2.0032	0.052326	0.026163
M1	0.85590625548584	0.128063	6.6835	0	0
M2	0.494902925375232	0.150247	3.2939	0.002144	0.001072
M3	0.345194798134066	0.137688	2.5071	0.016568	0.008284
M4	0.563227115239073	0.103924	5.4196	4e-06	2e-06
M5	0.500826415002446	0.102889	4.8676	2e-05	1e-05
M6	0.255445622080153	0.10505	2.4316	0.019854	0.009927
M7	0.401570798521914	0.11359	3.5353	0.00109	0.000545
M8	0.542381338318757	0.117994	4.5967	4.6e-05	2.3e-05
M9	0.274628508917084	0.142074	1.933	0.060713	0.030357
M10	0.305463665399061	0.134773	2.2665	0.029197	0.014598
M11	0.284067963059267	0.130173	2.1822	0.035343	0.017672
t	0.00685825937152281	0.002036	3.3693	0.001739	0.00087

Multiple Linear Regression - Regression Statistics
Multiple R	0.982609821560171
R-squared	0.965522061426511
Adjusted R-squared	0.95009772048574
F-TEST (value)	62.5972976825443
F-TEST (DF numerator)	17
F-TEST (DF denominator)	38
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.150754515256455
Sum Squared Residuals	0.863623107067927

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	7.3	7.39169307217292	-0.0916930721729179
2	7.7	7.44839369992886	0.251606300071140
3	8	7.95865475367527	0.0413452463247315
4	8	8.10913022220931	-0.109130222209308
5	7.7	7.77289796444542	-0.0728979644454228
6	6.9	7.1470197892834	-0.247019789283400
7	6.6	6.55455459859336	0.0454454014066356
8	6.9	6.9064417478996	-0.00644174789959902
9	7.5	7.3815789662386	0.118421033761397
10	7.9	7.79830786110618	0.101692138893823
11	7.7	7.76709424521143	-0.0670942452114273
12	6.5	6.6398977367004	-0.139897736700404
13	6.1	6.20806055196903	-0.108060551969030
14	6.4	6.30942174822592	0.0905782517740782
15	6.8	6.98527589607459	-0.185275896074591
16	7.1	7.22934613351388	-0.129346133513884
17	7.3	7.14015006580012	0.159849934199884
18	7.2	7.0679270577507	0.132072942249306
19	7	7.02938089940051	-0.0293808994005084
20	7	7.04937539991349	-0.0493753999134895
21	7	7.11616079189123	-0.116160791891231
22	7.3	7.1472968783068	0.152703121693203
23	7.5	7.46718570425685	0.0328142957431514
24	7.2	7.24530766518105	-0.0453076651810543
25	7.7	7.54614202143363	0.153857978566375
26	8	8.1142451226129	-0.114245122612897
27	7.9	8.0136318603801	-0.113631860380104
28	8	7.76208759134718	0.237912408652821
29	8	8.01333966322683	-0.0133396632268325
30	7.9	7.8554559231488	0.0445440768512077
31	7.9	7.84034115256936	0.0596588474306385
32	8	8.08594477699594	-0.0859447769959444
33	8.1	8.18569432777213	-0.0856943277721255
34	8.1	8.25535955615215	-0.155359556152155
35	8.2	8.15998936375361	0.0400106362463874
36	8	7.7361980581698	0.263801941830205
37	8.3	8.29650811663537	0.00349188336463572
38	8.5	8.46211421894103	0.0378857810589745
39	8.6	8.41375873343646	0.186241266563541
40	8.7	8.54105205801127	0.158947941988733
41	8.7	8.6069792257645	0.0930207742355004
42	8.5	8.41631516609656	0.083684833903437
43	8.4	8.3265874021683	0.0734125978316991
44	8.5	8.5125723918504	-0.012572391850397
45	8.7	8.61656591409804	0.083434085901959
46	8.7	8.79903570443487	-0.0990357044348705
47	8.6	8.60573068677811	-0.00573068677811138
48	7.9	7.97859653994875	-0.0785965399487474
49	8.1	8.05759623778906	0.0424037622109373
50	8.2	8.4658252102913	-0.265825210291295
51	8.5	8.42867875643358	0.0713212435664224
52	8.6	8.75838399491836	-0.158383994918362
53	8.5	8.66663308076313	-0.166633080763129
54	8.3	8.31328206372055	-0.013282063720551
55	8.2	8.34913594726846	-0.149135947268465
56	8.7	8.54566568334057	0.15433431665943

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.103832179590666	0.207664359181331	0.896167820409334
22	0.431121748453515	0.86224349690703	0.568878251546485
23	0.512236535559941	0.975526928880118	0.487763464440059
24	0.571427368792363	0.857145262415274	0.428572631207637
25	0.489693765176208	0.979387530352417	0.510306234823792
26	0.520465041085379	0.959069917829241	0.479534958914621
27	0.603619840106981	0.792760319786039	0.396380159893019
28	0.74246014147664	0.515079717046719	0.257539858523360
29	0.633495722856039	0.733008554287923	0.366504277143961
30	0.546927597477049	0.906144805045901	0.453072402522951
31	0.444025733022278	0.888051466044557	0.555974266977722
32	0.463521709824191	0.927043419648382	0.536478290175809
33	0.449529389554259	0.899058779108517	0.550470610445741
34	0.485563329880863	0.971126659761727	0.514436670119137
35	0.549125012222208	0.901749975555585	0.450874987777792

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	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/10x3f71258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/10x3f71258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/1ic4b1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/1ic4b1258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/2hxpc1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/2hxpc1258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/3hn5i1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/3hn5i1258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/45kwj1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/45kwj1258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/5cg2n1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/5cg2n1258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/68ksz1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/68ksz1258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/7pon91258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/7pon91258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/8h5xg1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/8h5xg1258581204.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/9825t1258581204.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258581276rivurwtvlxkc6ae/9825t1258581204.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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