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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 09:02:51 -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/18/t1258560854divybkmxhfdc9km.htm/, Retrieved Wed, 18 Nov 2009 17:14:26 +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/t1258560854divybkmxhfdc9km.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 «

17823,2 0 17872 0 17420,4 0 16704,4 0 15991,2 0 15583,6 0 19123,5 0 17838,7 0 17209,4 0 18586,5 0 16258,1 0 15141,6 0 19202,1 0 17746,5 0 19090,1 1 18040,3 1 17515,5 1 17751,8 1 21072,4 1 17170 1 19439,5 1 19795,4 1 17574,9 1 16165,4 1 19464,6 1 19932,1 1 19961,2 1 17343,4 1 18924,2 1 18574,1 1 21350,6 1 18594,6 1 19832,1 1 20844,4 1 19640,2 1 17735,4 1 19813,6 1 22160 1 20664,3 1 17877,4 1 20906,5 1 21164,1 1 21374,4 1 22952,3 1 21343,5 1 23899,3 1 22392,9 1 18274,1 1 22786,7 1 22321,5 1 17842,2 1 16373,5 1 15933,8 0 16446,1 0 17729 0 16643 0 16196,7 0 18252,1 0 17570,4 0 15836,8 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

Y[t] = + 17141.3227272727 + 2541.79569377990X[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)	17141.3227272727	364.026667	47.0881	0	0
X	2541.79569377990	457.421976	5.5568	1e-06	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.589422639982481
R-squared	0.347419048523917
Adjusted R-squared	0.336167652808813
F-TEST (value)	30.8778623844428
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	7.23816425240997e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1707.43641480912
Sum Squared Residuals	169089668.415742

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	17823.2	17141.3227272727	681.877272727303
2	17872	17141.3227272727	730.677272727266
3	17420.4	17141.3227272727	279.077272727273
4	16704.4	17141.3227272727	-436.922727272727
5	15991.2	17141.3227272727	-1150.12272727273
6	15583.6	17141.3227272727	-1557.72272727273
7	19123.5	17141.3227272727	1982.17727272727
8	17838.7	17141.3227272727	697.377272727272
9	17209.4	17141.3227272727	68.0772727272728
10	18586.5	17141.3227272727	1445.17727272727
11	16258.1	17141.3227272727	-883.222727272728
12	15141.6	17141.3227272727	-1999.72272727273
13	19202.1	17141.3227272727	2060.77727272727
14	17746.5	17141.3227272727	605.177272727271
15	19090.1	19683.1184210526	-593.018421052633
16	18040.3	19683.1184210526	-1642.81842105263
17	17515.5	19683.1184210526	-2167.61842105263
18	17751.8	19683.1184210526	-1931.31842105263
19	21072.4	19683.1184210526	1389.28157894737
20	17170	19683.1184210526	-2513.11842105263
21	19439.5	19683.1184210526	-243.618421052632
22	19795.4	19683.1184210526	112.281578947370
23	17574.9	19683.1184210526	-2108.21842105263
24	16165.4	19683.1184210526	-3517.71842105263
25	19464.6	19683.1184210526	-218.518421052633
26	19932.1	19683.1184210526	248.981578947367
27	19961.2	19683.1184210526	278.081578947369
28	17343.4	19683.1184210526	-2339.71842105263
29	18924.2	19683.1184210526	-758.918421052631
30	18574.1	19683.1184210526	-1109.01842105263
31	21350.6	19683.1184210526	1667.48157894737
32	18594.6	19683.1184210526	-1088.51842105263
33	19832.1	19683.1184210526	148.981578947367
34	20844.4	19683.1184210526	1161.28157894737
35	19640.2	19683.1184210526	-42.918421052631
36	17735.4	19683.1184210526	-1947.71842105263
37	19813.6	19683.1184210526	130.481578947367
38	22160	19683.1184210526	2476.88157894737
39	20664.3	19683.1184210526	981.181578947368
40	17877.4	19683.1184210526	-1805.71842105263
41	20906.5	19683.1184210526	1223.38157894737
42	21164.1	19683.1184210526	1480.98157894737
43	21374.4	19683.1184210526	1691.28157894737
44	22952.3	19683.1184210526	3269.18157894737
45	21343.5	19683.1184210526	1660.38157894737
46	23899.3	19683.1184210526	4216.18157894737
47	22392.9	19683.1184210526	2709.78157894737
48	18274.1	19683.1184210526	-1409.01842105263
49	22786.7	19683.1184210526	3103.58157894737
50	22321.5	19683.1184210526	2638.38157894737
51	17842.2	19683.1184210526	-1840.91842105263
52	16373.5	19683.1184210526	-3309.61842105263
53	15933.8	17141.3227272727	-1207.52272727273
54	16446.1	17141.3227272727	-695.22272727273
55	17729	17141.3227272727	587.677272727271
56	16643	17141.3227272727	-498.322727272729
57	16196.7	17141.3227272727	-944.622727272728
58	18252.1	17141.3227272727	1110.77727272727
59	17570.4	17141.3227272727	429.077272727273
60	15836.8	17141.3227272727	-1304.52272727273

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.156567405799712	0.313134811599423	0.843432594200288
6	0.171012419352994	0.342024838705987	0.828987580647006
7	0.275425461108639	0.550850922217278	0.724574538891361
8	0.182027865024098	0.364055730048197	0.817972134975902
9	0.105936150213182	0.211872300426363	0.894063849786818
10	0.0918592845167842	0.183718569033568	0.908140715483216
11	0.0713183092387693	0.142636618477539	0.92868169076123
12	0.112716238866065	0.225432477732129	0.887283761133935
13	0.149476663480803	0.298953326961605	0.850523336519197
14	0.102480784433466	0.204961568866933	0.897519215566534
15	0.0652054695565306	0.130410939113061	0.93479453044347
16	0.0476752896832778	0.0953505793665556	0.952324710316722
17	0.0386279610573766	0.0772559221147532	0.961372038942623
18	0.0274238941986022	0.0548477883972043	0.972576105801398
19	0.0621984751138182	0.124396950227636	0.937801524886182
20	0.0682132154672921	0.136426430934584	0.931786784532708
21	0.0507137187772953	0.101427437554591	0.949286281222705
22	0.0393061092295214	0.0786122184590428	0.960693890770479
23	0.0377831170614508	0.0755662341229015	0.96221688293855
24	0.0951901351105647	0.190380270221129	0.904809864889435
25	0.0761740001031471	0.152348000206294	0.923825999896853
26	0.0652991592507382	0.130598318501476	0.934700840749262
27	0.0539004698588536	0.107800939717707	0.946099530141146
28	0.0672623660293146	0.134524732058629	0.932737633970685
29	0.0514398852931384	0.102879770586277	0.948560114706862
30	0.0417960544136825	0.083592108827365	0.958203945586317
31	0.0624150351248792	0.124830070249758	0.93758496487512
32	0.0523642398341106	0.104728479668221	0.94763576016589
33	0.0402306685051936	0.0804613370103873	0.959769331494806
34	0.0393894193981887	0.0787788387963775	0.960610580601811
35	0.0285271709517792	0.0570543419035584	0.97147282904822
36	0.0398629700495858	0.0797259400991717	0.960137029950414
37	0.0301381061949388	0.0602762123898776	0.969861893805061
38	0.0534393891368671	0.106878778273734	0.946560610863133
39	0.042575527077787	0.085151054155574	0.957424472922213
40	0.0626981987321992	0.125396397464398	0.9373018012678
41	0.0519037308868316	0.103807461773663	0.948096269113168
42	0.0441366852980736	0.0882733705961473	0.955863314701926
43	0.0384253886592674	0.0768507773185348	0.961574611340733
44	0.0745342180733787	0.149068436146757	0.925465781926621
45	0.0606471876917601	0.121294375383520	0.93935281230824
46	0.217279741100127	0.434559482200255	0.782720258899873
47	0.301555475475394	0.603110950950788	0.698444524524606
48	0.267556359743309	0.535112719486617	0.732443640256691
49	0.510995577724501	0.978008844550998	0.489004422275499
50	0.969865879273942	0.0602682414521157	0.0301341207260578
51	0.963843414489693	0.072313171020615	0.0361565855103075
52	0.93614051680442	0.127718966391159	0.0638594831955796
53	0.909811681176304	0.180376637647392	0.0901883188236962
54	0.836606741744857	0.326786516510286	0.163393258255143
55	0.735453305952004	0.529093388095992	0.264546694047996

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	16	0.313725490196078	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/10pzf1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/10pzf1258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/10q14e1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/10q14e1258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/2c0k31258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/2c0k31258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/3l2at1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/3l2at1258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/4aepv1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/4aepv1258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/5e3u31258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/5e3u31258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/6mhuf1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/6mhuf1258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/7pfrf1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/7pfrf1258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/8saxc1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/8saxc1258560166.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/9cm0y1258560166.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560854divybkmxhfdc9km/9cm0y1258560166.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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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