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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: Fri, 20 Nov 2009 09:22:51 -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/20/t1258734408z37n75d0q118kzx.htm/, Retrieved Fri, 20 Nov 2009 17:27:00 +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/20/t1258734408z37n75d0q118kzx.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 «

100 100 97.82226485 99.87129987 94.04971502 99.54459954 91.12460521 99.81189981 93.13202153 100.4851005 93.88342812 101.1385011 92.55349954 101.3662014 94.43494835 101.5147015 96.25017563 101.8216018 100.4355715 102.4354024 101.5036685 102.5344025 99.39789728 102.6532027 99.68990733 102.4651025 101.6895041 102.4354024 103.6652759 102.4156024 103.0532766 102.4453024 100.9500712 102.8908029 102.345366 102.8512029 101.6472299 103.3561034 99.56809393 103.7422037 95.67727392 103.7224037 96.58494865 104.0788041 96.32604937 104.2075042 95.37109101 103.9105039 96.00056203 103.7026037 96.88367859 103.960004 94.85280372 104.0986041 92.46943974 104.1481041 93.99180173 104.7124047 93.45262168 104.7223047 92.26698759 105.1975052 90.39653498 105.0688051 90.43001228 105.0589051 91.04995327 105.5044055 89.07845784 105.3757054 89.69314509 105.4747055 87.92459054 106.029106 85.8789319 107.019107 83.20612366 107.3161073 83.85722053 107.7517078 83.01393462 108.5239085 82.84508195 109.3159 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

wisselkoers[t] = + 229.996777988985 -1.30967774785332consumptieprijzen[t] + 0.785022580191187M1[t] + 1.96611624075313M2[t] + 1.47713232739671M3[t] -0.366566192051892M4[t] + 0.779934307996218M5[t] + 1.92797452086387M6[t] + 0.506668150275282M7[t] + 0.0127663425833398M8[t] -0.647583681956498M9[t] + 0.537257220167209M10[t] -0.0653346407386438M11[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)	229.996777988985	20.929821	10.989	0	0
consumptieprijzen	-1.30967774785332	0.194962	-6.7176	0	0
M1	0.785022580191187	3.43633	0.2284	0.820289	0.410145
M2	1.96611624075313	3.434496	0.5725	0.569738	0.284869
M3	1.47713232739671	3.435127	0.43	0.669155	0.334578
M4	-0.366566192051892	3.431486	-0.1068	0.915383	0.457692
M5	0.779934307996218	3.422234	0.2279	0.820711	0.410355
M6	1.92797452086387	3.420301	0.5637	0.575648	0.287824
M7	0.506668150275282	3.417063	0.1483	0.882759	0.44138
M8	0.0127663425833398	3.415417	0.0037	0.997033	0.498517
M9	-0.647583681956498	3.414858	-0.1896	0.85041	0.425205
M10	0.537257220167209	3.414403	0.1574	0.875643	0.437821
M11	-0.0653346407386438	3.414368	-0.0191	0.984814	0.492407

Multiple Linear Regression - Regression Statistics
Multiple R	0.719881802483796
R-squared	0.518229809547319
Adjusted R-squared	0.395224654538124
F-TEST (value)	4.21307391148427
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.000173715412039699
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.39857808069091
Sum Squared Residuals	1369.79832878587

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	100	99.814025783844	0.185974216155999
2	97.82226485	101.163675140813	-3.34141029081277
3	94.04971502	101.102563379874	-7.0528483598737
4	91.12460521	98.908787644811	-7.7841824348109
5	93.13202153	99.1736121813265	-6.04159065132651
6	93.88342812	99.4659081679402	-5.58248004794015
7	92.55349954	97.746387781262	-5.19288824126204
8	94.43494835	97.057998697046	-2.62305034704610
9	96.25017563	95.9957081787868	0.254467451213242
10	100.4355715	96.3766680934715	4.05890340652855
11	101.5036685	95.6444180045604	5.85925049543965
12	99.39789728	95.5541626669185	3.84373461308152
13	99.68990733	96.5855358934164	3.10437143658359
14	101.6895041	97.8055271140574	3.88397698594263
15	103.6652759	97.3424748201084	6.32280107989155
16	103.0532766	95.4598788715486	7.59339772845141
17	100.9500712	96.0229172800892	4.92715391991081
18	102.345366	97.2228207317718	5.12254526822818
19	101.6472299	95.1402574114532	6.50697248854677
20	99.56809393	94.1406886324118	5.42740529758822
21	95.67727392	93.5062702272794	2.17100369272056
22	96.58494865	94.2243414561971	2.36060719380287
23	96.32604937	93.4531939381748	2.87285543182523
24	95.37109101	93.9075032629292	1.46358774707081
25	96.00056203	94.9648081088346	1.03575392116537
26	96.88367859	95.8087903241958	1.0748882658042
27	94.85280372	95.138284944019	-0.285481224019131
28	92.46943974	93.2297573760518	-0.760317636051798
29	93.99180173	93.6372059371796	0.354595792820371
30	93.45262168	94.7722803403435	-1.31965866034354
31	92.26698759	92.7286144491362	-0.461626859136179
32	90.39653498	92.4032682985607	-2.00673331856072
33	90.43001228	91.7558840837246	-1.32587180372463
34	91.04995327	92.3572630253086	-1.30730975530858
35	89.07845784	91.9232268215192	-2.84476898151924
36	89.69314509	91.8589032342526	-2.16575814425263
37	87.92459054	91.917839816195	-3.99324927619506
38	85.8789319	91.8023511967045	-5.92341929670446
39	83.20612366	90.9243925993323	-7.71826893933228
40	83.85722053	88.5101977980799	-4.65297726807989
41	83.01393462	88.6453642244612	-5.63142960446125
42	82.84508195	88.7561386132869	-5.91105666328688
43	78.68864276	87.0106866072013	-8.32204384720128
44	77.56959675	85.2331684601283	-7.66357171012832
45	78.53689529	83.7689373171817	-5.23204202718169
46	78.55717715	84.1887947919454	-5.63161764194539
47	77.4761291	84.4808445864048	-7.0047154864048
48	81.58931659	84.3127945215082	-2.72347793150821
49	85.02428326	85.3571335577099	-0.332850297709895
50	91.71290159	87.4069372542296	4.30596433577041
51	95.96293061	87.2291331666664	8.73379744333356
52	90.84689022	85.2428106095088	5.60407961049117
53	92.28788036	85.8966098169434	6.39127054305657
54	95.56511274	87.8744626366576	7.6906501033424
55	93.62452884	86.1549423809473	7.46958645905273
56	92.63071726	85.764767181853	6.86595007814692
57	89.50914211	85.3766994230275	4.13244268697251
58	87.17171779	86.6523009930774	0.519416796922556
59	86.72624975	85.6088712093408	1.11737854065915
60	85.63212844	86.0502147243915	-0.418086284391485

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.299256083453493	0.598512166906986	0.700743916546507
17	0.166934019272065	0.33386803854413	0.833065980727935
18	0.112109854681927	0.224219709363855	0.887890145318073
19	0.0742482319072327	0.148496463814465	0.925751768092767
20	0.0400677453772614	0.0801354907545229	0.959932254622739
21	0.0403275150353397	0.0806550300706794	0.95967248496466
22	0.0634423480827344	0.126884696165469	0.936557651917266
23	0.103628122100241	0.207256244200481	0.89637187789976
24	0.099832382228866	0.199664764457732	0.900167617771134
25	0.132370096520959	0.264740193041918	0.867629903479041
26	0.130370674531247	0.260741349062495	0.869629325468753
27	0.126383944245729	0.252767888491458	0.87361605575427
28	0.113645420936221	0.227290841872443	0.886354579063779
29	0.0848832715958072	0.169766543191614	0.915116728404193
30	0.0656749047976685	0.131349809595337	0.934325095202332
31	0.0497478589669461	0.0994957179338922	0.950252141033054
32	0.0408747370125671	0.0817494740251341	0.959125262987433
33	0.0297305842214212	0.0594611684428425	0.970269415778579
34	0.027343330219844	0.054686660439688	0.972656669780156
35	0.0315918547454339	0.0631837094908678	0.968408145254566
36	0.0310441827933885	0.0620883655867771	0.968955817206612
37	0.0330794754274407	0.0661589508548814	0.966920524572559
38	0.0260495946022288	0.0520991892044575	0.973950405397771
39	0.0286397736625877	0.0572795473251754	0.971360226337412
40	0.0204013637657919	0.0408027275315838	0.979598636234208
41	0.0281504827951351	0.0563009655902702	0.971849517204865
42	0.059763857447541	0.119527714895082	0.94023614255246
43	0.35666480841494	0.71332961682988	0.64333519158506
44	0.770069239353579	0.459861521292842	0.229930760646421

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	1	0.0344827586206897	OK
10% type I error level	13	0.448275862068966	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/108vdv1258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/108vdv1258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/1fq9o1258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/1fq9o1258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/2eug21258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/2eug21258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/323te1258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/323te1258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/4otp91258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/4otp91258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/52v2m1258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/52v2m1258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/69y2w1258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/69y2w1258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/7ecye1258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/7ecye1258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/854if1258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/854if1258734167.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/9kvh61258734167.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258734408z37n75d0q118kzx/9kvh61258734167.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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