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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: Thu, 27 Nov 2008 15:00:46 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84.htm/, Retrieved Thu, 27 Nov 2008 22:01:20 +0000

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/2008/Nov/27/t1227823271pgj3uy5coraws84.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

123,9 1 124,9 1 112,7 1 121,9 1 100,6 1 104,3 1 120,4 0 107,5 0 102,9 0 125,6 0 107,5 0 108,8 0 128,4 1 121,1 1 119,5 1 128,7 1 108,7 1 105,5 1 119,8 0 111,3 0 110,6 0 120,1 0 97,5 0 107,7 0 127,3 1 117,2 1 119,8 1 116,2 1 111 1 112,4 1 130,6 0 109,1 0 118,8 0 123,9 0 101,6 0 112,8 0 128 1 129,6 1 125,8 1 119,5 1 115,7 1 113,6 1 129,7 0 112 0 116,8 0 127 0 112,1 0 114,2 0 121,1 1 131,6 1 125 1 120,4 1 117,7 1 117,5 1 120,6 0 127,5 0 112,3 0 124,5 0 115,2 0 105,4 0

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Consumptieindex[t] = + 115.126666666667 + 3.85999999999999Dummy[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)	115.126666666667	1.517169	75.8826	0	0
Dummy	3.85999999999999	2.145601	1.799	0.077217	0.038609

Multiple Linear Regression - Regression Statistics
Multiple R	0.229896959660599
R-squared	0.0528526120611873
Adjusted R-squared	0.0365224846829320
F-TEST (value)	3.2365094795013
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0.0772171342105187
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.30987502845294
Sum Squared Residuals	4005.13333333333

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	123.9	118.986666666667	4.91333333333309
2	124.9	118.986666666667	5.91333333333335
3	112.7	118.986666666667	-6.28666666666665
4	121.9	118.986666666667	2.91333333333335
5	100.6	118.986666666667	-18.3866666666667
6	104.3	118.986666666667	-14.6866666666667
7	120.4	115.126666666667	5.27333333333334
8	107.5	115.126666666667	-7.62666666666666
9	102.9	115.126666666667	-12.2266666666667
10	125.6	115.126666666667	10.4733333333333
11	107.5	115.126666666667	-7.62666666666666
12	108.8	115.126666666667	-6.32666666666667
13	128.4	118.986666666667	9.41333333333335
14	121.1	118.986666666667	2.11333333333334
15	119.5	118.986666666667	0.513333333333343
16	128.7	118.986666666667	9.71333333333333
17	108.7	118.986666666667	-10.2866666666667
18	105.5	118.986666666667	-13.4866666666667
19	119.8	115.126666666667	4.67333333333333
20	111.3	115.126666666667	-3.82666666666667
21	110.6	115.126666666667	-4.52666666666667
22	120.1	115.126666666667	4.97333333333333
23	97.5	115.126666666667	-17.6266666666667
24	107.7	115.126666666667	-7.42666666666666
25	127.3	118.986666666667	8.31333333333334
26	117.2	118.986666666667	-1.78666666666665
27	119.8	118.986666666667	0.81333333333334
28	116.2	118.986666666667	-2.78666666666665
29	111	118.986666666667	-7.98666666666666
30	112.4	118.986666666667	-6.58666666666665
31	130.6	115.126666666667	15.4733333333333
32	109.1	115.126666666667	-6.02666666666667
33	118.8	115.126666666667	3.67333333333333
34	123.9	115.126666666667	8.77333333333334
35	101.6	115.126666666667	-13.5266666666667
36	112.8	115.126666666667	-2.32666666666667
37	128	118.986666666667	9.01333333333334
38	129.6	118.986666666667	10.6133333333333
39	125.8	118.986666666667	6.81333333333334
40	119.5	118.986666666667	0.513333333333343
41	115.7	118.986666666667	-3.28666666666665
42	113.6	118.986666666667	-5.38666666666666
43	129.7	115.126666666667	14.5733333333333
44	112	115.126666666667	-3.12666666666666
45	116.8	115.126666666667	1.67333333333333
46	127	115.126666666667	11.8733333333333
47	112.1	115.126666666667	-3.02666666666667
48	114.2	115.126666666667	-0.926666666666661
49	121.1	118.986666666667	2.11333333333334
50	131.6	118.986666666667	12.6133333333333
51	125	118.986666666667	6.01333333333334
52	120.4	118.986666666667	1.41333333333335
53	117.7	118.986666666667	-1.28666666666665
54	117.5	118.986666666667	-1.48666666666666
55	120.6	115.126666666667	5.47333333333333
56	127.5	115.126666666667	12.3733333333333
57	112.3	115.126666666667	-2.82666666666667
58	124.5	115.126666666667	9.37333333333334
59	115.2	115.126666666667	0.0733333333333387
60	105.4	115.126666666667	-9.72666666666666

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.887918492625185	0.224163014749629	0.112081507374815
6	0.913708143752524	0.172583712494952	0.0862918562474762
7	0.851035544249124	0.297928911501752	0.148964455750876
8	0.843477037340876	0.313045925318249	0.156522962659124
9	0.850997553074287	0.298004893851426	0.149002446925713
10	0.900106603094732	0.199786793810536	0.0998933969052682
11	0.875414822413656	0.249170355172687	0.124585177586344
12	0.834433980634046	0.331132038731907	0.165566019365954
13	0.873209003907274	0.253581992185452	0.126790996092726
14	0.828525476310801	0.342949047378398	0.171474523689199
15	0.768010267252192	0.463979465495616	0.231989732747808
16	0.79161483270352	0.416770334592961	0.208385167296481
17	0.80785082218358	0.384298355632841	0.192149177816420
18	0.867457171280109	0.265085657439782	0.132542828719891
19	0.84394201765677	0.31211596468646	0.15605798234323
20	0.798002220183143	0.403995559633714	0.201997779816857
21	0.749743642281484	0.500512715437033	0.250256357718516
22	0.716761880286514	0.566476239426973	0.283238119713486
23	0.879424910751629	0.241150178496743	0.120575089248371
24	0.869998109712115	0.26000378057577	0.130001890287885
25	0.874561729885046	0.250876540229907	0.125438270114954
26	0.833641178334519	0.332717643330963	0.166358821665481
27	0.783304294132465	0.433391411735071	0.216695705867535
28	0.732716145863105	0.53456770827379	0.267283854136895
29	0.737733401296806	0.524533197406388	0.262266598703194
30	0.730342500168402	0.539314999663197	0.269657499831598
31	0.873421770540369	0.253156458919262	0.126578229459631
32	0.860079532898173	0.279840934203655	0.139920467101827
33	0.823359205396571	0.353281589206858	0.176640794603429
34	0.827274183864978	0.345451632270045	0.172725816135022
35	0.919942291366896	0.160115417266208	0.080057708633104
36	0.897328171461464	0.205343657077073	0.102671828538536
37	0.89636327965346	0.207273440693080	0.103636720346540
38	0.911801229345267	0.176397541309467	0.0881987706547333
39	0.896483836833697	0.207032326332606	0.103516163166303
40	0.853822310639728	0.292355378720543	0.146177689360272
41	0.818457532464739	0.363084935070521	0.181542467535261
42	0.811360522832484	0.377278954335031	0.188639477167516
43	0.900291490063256	0.199417019873487	0.0997085099367436
44	0.874219606706134	0.251560786587733	0.125780393293866
45	0.820374002306946	0.359251995386108	0.179625997693054
46	0.866884861161642	0.266230277676717	0.133115138838358
47	0.827181595860226	0.345636808279548	0.172818404139774
48	0.763683702283429	0.472632595433143	0.236316297716571
49	0.676345595673511	0.647308808652977	0.323654404326489
50	0.751186051289099	0.497627897421802	0.248813948710901
51	0.70191831602756	0.596163367944881	0.298081683972440
52	0.59189694055955	0.8162061188809	0.40810305944045
53	0.45689206710923	0.91378413421846	0.54310793289077
54	0.317577547455715	0.635155094911429	0.682422452544285
55	0.205983842465045	0.41196768493009	0.794016157534955

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://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/10qx6f1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/10qx6f1227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/15ev61227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/15ev61227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/2dkpx1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/2dkpx1227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/326un1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/326un1227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/4wijb1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/4wijb1227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/5blfp1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/5blfp1227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/65h1o1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/65h1o1227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/7dxk01227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/7dxk01227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/8zsuf1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/8zsuf1227823241.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/9ej4k1227823241.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227823271pgj3uy5coraws84/9ej4k1227823241.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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