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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: Thu, 09 Dec 2010 19:40:40 +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/09/t129192357972bo4y9langxqrx.htm/, Retrieved Thu, 09 Dec 2010 20:39:49 +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/09/t129192357972bo4y9langxqrx.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 «

8587 0 9743 9731 0 8587 9563 0 9731 9998 0 9563 9437 0 9998 10038 0 9437 9918 0 10038 9252 0 9918 9737 0 9252 9035 0 9737 9133 0 9035 9487 0 9133 8700 0 9487 9627 0 8700 8947 0 9627 9283 0 8947 8829 0 9283 9947 0 8829 9628 0 9947 9318 0 9628 9605 0 9318 8640 0 9605 9214 0 8640 9567 0 9214 8547 0 9567 9185 0 8547 9470 0 9185 9123 0 9470 9278 0 9123 10170 0 9278 9434 0 10170 9655 0 9434 9429 0 9655 8739 0 9429 9552 0 8739 9687 1 9552 9019 1 9687 9672 1 9019 9206 1 9672 9069 1 9206 9788 1 9069 10312 1 9788 10105 1 10312 9863 1 10105 9656 1 9863 9295 1 9656 9946 1 9295 9701 1 9946 9049 1 9701 10190 1 9049 9706 1 10190 9765 1 9706 9893 1 9765 9994 1 9893 10433 1 9994 10073 1 10433 10112 1 10073 9266 1 10112 9820 1 9266 10097 1 9820 9115 1 10097 10411 1 9115 9678 1 10411 10408 1 9678 10153 1 10408 10368 1 10153 10581 1 10368 10597 1 10581 10680 1 10597 9738 1 10680 9556 1 9738

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	10 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

births[t] = + 6947.20633706813 + 173.236282358347difference[t] + 0.258154051655289Y1[t] -873.773242778912M1[t] + 313.821678685072M2[t] -315.452804436626M3[t] -44.9190632971903M4[t] -141.316615424233M5[t] + 439.945673352842M6[t] + 164.362142445581M7[t] -33.1643734665533M8[t] + 95.9311315482093M9[t] -680.3389636175M10[t] -73.9005296879053M11[t] + 5.43525886352773t + 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)	6947.20633706813	1183.325377	5.8709	0	0
difference	173.236282358347	141.073038	1.228	0.224587	0.112294
Y1	0.258154051655289	0.128651	2.0066	0.049627	0.024813
M1	-873.773242778912	171.461961	-5.096	4e-06	2e-06
M2	313.821678685072	187.869952	1.6704	0.100417	0.050209
M3	-315.452804436626	173.39924	-1.8192	0.074224	0.037112
M4	-44.9190632971903	168.882586	-0.266	0.791233	0.395616
M5	-141.316615424233	169.356938	-0.8344	0.407584	0.203792
M6	439.945673352842	169.159421	2.6008	0.011875	0.005937
M7	164.362142445581	187.271435	0.8777	0.383874	0.191937
M8	-33.1643734665533	181.133234	-0.1831	0.855386	0.427693
M9	95.9311315482093	173.380351	0.5533	0.582261	0.291131
M10	-680.3389636175	175.816228	-3.8696	0.000287	0.000143
M11	-73.9005296879053	179.593853	-0.4115	0.682286	0.341143
t	5.43525886352773	3.508735	1.5491	0.127	0.0635

Multiple Linear Regression - Regression Statistics
Multiple R	0.872427654180786
R-squared	0.76113001177939
Adjusted R-squared	0.701412514724237
F-TEST (value)	12.7455109358726
F-TEST (DF numerator)	14
F-TEST (DF denominator)	56
p-value	1.08324460512677e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	278.193377872033
Sum Squared Residuals	4333927.10754370

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8587	8594.0632784302	-7.06327843019715
2	9731	9488.66737504422	242.332624955782
3	9563	9160.1563858797	402.843614120302
4	9998	9392.75550520457	605.244494795426
5	9437	9414.0902244111	22.909775588891
6	10038	9855.9633490731	182.036650926906
7	9918	9740.96566207419	177.03433792581
8	9252	9517.89591882695	-265.895918826949
9	9737	9480.49608430282	256.503915697183
10	9035	8834.86596305345	200.134036946549
11	9133	9265.51551158456	-132.515511584560
12	9487	9370.15039719821	116.849602801789
13	8700	8593.1989475688	106.801052431202
14	9627	9583.0618892436	43.9381107564011
15	8947	9198.53147086988	-251.531470869882
16	9283	9298.95571574725	-15.955715747248
17	8829	9294.73318383991	-465.73318383991
18	9947	9764.2287920290	182.771207970988
19	9628	9782.6967497359	-154.696749735891
20	9318	9508.25435020925	-190.254350209248
21	9605	9562.7573580744	42.2426419256015
22	8640	8866.01273459729	-226.012734597285
23	9214	9228.76776754305	-14.7677675430531
24	9567	9456.28398174462	110.716018255378
25	8547	8679.07437806355	-132.074378063555
26	9185	9608.78742570267	-423.787425702672
27	9470	9149.65048640058	320.349513599423
28	9123	9499.1933911253	-376.193391125297
29	9278	9318.6516419374	-40.6516419373967
30	10170	9945.36306758457	224.636932415431
31	9434	9905.48820961735	-471.488209617353
32	9655	9523.39557055045	131.604429449546
33	9429	9714.97837984456	-285.978379844564
34	8739	8885.80072786829	-146.800727868287
35	9552	9319.54812501926	232.451874980740
36	9687	9781.99943992479	-94.9994399247896
37	9019	8948.51225298287	70.4877470171306
38	9672	9969.09552680465	-297.095526804649
39	9206	9513.83089827738	-307.830898277383
40	9069	9669.50011020898	-600.50011020898
41	9788	9543.17071186869	244.829288131309
42	10312	10315.4810226494	-3.48102264944673
43	10105	10180.6054736731	-75.6054736730843
44	9863	9935.07632793183	-72.076327931833
45	9656	10007.1338113095	-351.133811309543
46	9295	9182.86108631472	112.138913685282
47	9946	9701.54116646028	244.45883353972
48	9701	9948.9352426393	-247.935242639306
49	9049	9017.34951606838	31.6504839316238
50	10190	10042.0632547166	147.93674528336
51	9706	9712.77780339716	-6.77780339715467
52	9765	9863.80024239896	-98.8002423989576
53	9893	9788.0690381831	104.930961816895
54	9994	10407.8103044356	-413.810304435585
55	10433	10163.7355916090	269.264408390965
56	10073	10084.9739632371	-11.9739632371005
57	10112	10126.5692685195	-14.5692685194869
58	9266	9365.80244023186	-99.8024402318617
59	9820	9759.2778053246	60.7221946753907
60	10097	9981.63093849307	115.369061506928
61	9115	9184.8016268862	-69.8016268862032
62	10411	10124.3245284882	286.675471511778
63	9678	9835.0529551753	-157.052955175306
64	10408	9921.79503531494	486.204964685058
65	10153	10019.2851997598	133.714800240212
66	10368	10540.1534642283	-172.153464228293
67	10581	10325.5083132904	255.491686709554
68	10597	10188.4038692444	408.596130755584
69	10680	10327.0650979492	352.934902050809
70	9738	9577.6570479344	160.342952065601
71	9556	9946.34962406824	-390.349624068238

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.783911325223041	0.432177349553918	0.216088674776959
19	0.653444255151926	0.693111489696149	0.346555744848074
20	0.632834847657737	0.734330304684525	0.367165152342263
21	0.51151932448441	0.97696135103118	0.48848067551559
22	0.413539200541369	0.827078401082737	0.586460799458631
23	0.423534582835073	0.847069165670146	0.576465417164927
24	0.359766653577378	0.719533307154756	0.640233346422622
25	0.271347952874445	0.54269590574889	0.728652047125555
26	0.276462359933734	0.552924719867469	0.723537640066266
27	0.479308208006073	0.958616416012145	0.520691791993927
28	0.456396370538607	0.912792741077213	0.543603629461393
29	0.473160779647047	0.946321559294093	0.526839220352953
30	0.567161367952205	0.86567726409559	0.432838632047795
31	0.532126891319965	0.93574621736007	0.467873108680035
32	0.602857996101842	0.794284007796317	0.397142003898158
33	0.535790519287707	0.928418961424585	0.464209480712292
34	0.499436145789224	0.998872291578449	0.500563854210776
35	0.535696875140347	0.928606249719307	0.464303124859653
36	0.456095691191572	0.912191382383145	0.543904308808428
37	0.430719431351817	0.861438862703634	0.569280568648183
38	0.372658347878394	0.745316695756788	0.627341652121606
39	0.335703744977743	0.671407489955485	0.664296255022258
40	0.58865916020399	0.822681679592021	0.411340839796010
41	0.594987551261185	0.810024897477631	0.405012448738815
42	0.650687232836624	0.698625534326752	0.349312767163376
43	0.583050764522214	0.833898470955571	0.416949235477786
44	0.516438623917899	0.967122752164203	0.483561376082101
45	0.56059966125744	0.87880067748512	0.43940033874256
46	0.480248767911185	0.96049753582237	0.519751232088815
47	0.779995353867149	0.440009292265703	0.220004646132851
48	0.683603864608243	0.632792270783514	0.316396135391757
49	0.611655056699373	0.776689886601254	0.388344943300627
50	0.555335907067507	0.889328185864987	0.444664092932493
51	0.584492940876188	0.831014118247623	0.415507059123812
52	0.460344974872109	0.920689949744218	0.539655025127891
53	0.31531753218212	0.63063506436424	0.68468246781788

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/2010/Dec/09/t129192357972bo4y9langxqrx/10kz1v1291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/10kz1v1291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/1vym11291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/1vym11291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/2vym11291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/2vym11291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/36q441291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/36q441291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/46q441291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/46q441291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/56q441291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/56q441291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/6yz371291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/6yz371291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/7r82a1291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/7r82a1291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/8r82a1291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/8r82a1291923627.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/9r82a1291923627.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t129192357972bo4y9langxqrx/9r82a1291923627.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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