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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: Fri, 20 Nov 2009 06:10:25 -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/t1258723036q9eqtvv62p8ffu6.htm/, Retrieved Fri, 20 Nov 2009 14:17:28 +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/t1258723036q9eqtvv62p8ffu6.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 «

10144 112 10751 304 11752 794 13808 901 16203 1232 17432 1240 18014 1032 16956 1145 17982 1588 19435 2264 19990 2209 20154 2917 10327 243 9807 558 10862 1238 13743 1502 16458 2000 18466 2146 18810 2066 17361 2046 17411 1952 18517 2771 18525 3278 17859 4000 9499 410 9490 1107 9255 1622 10758 1986 12375 2036 14617 2400 15427 2736 14136 2901 14308 2883 15293 3747 15679 4075 16319 4996 11196 575 11169 999 12158 1411 14251 1493 16237 1846 19706 2899 18960 2372 18537 2856 19103 3468 19691 4193 19464 4440 17264 4186 8957 655 9703 1453 9166 1989 9519 2209 10535 2667 11526 3005 9630 2195 7061 2236 6021 2489 4728 2651 2657 2636 1264 2819

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	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 7378.28355988144 + 3.95381014494266X[t] + 6461.74593137428M1[t] + 4918.47723861162M2[t] + 3506.72080584833M3[t] + 4679.62057135073M4[t] + 5504.75273192363M5[t] + 6198.70800814803M6[t] + 7252.52025307776M7[t] + 5491.07357394325M8[t] + 4915.84217683649M9[t] + 3132.54862030323M10[t] + 2278.21743653034M11[t] -215.719989563514t + 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)	7378.28355988144	2945.752256	2.5047	0.015861	0.00793
X	3.95381014494266	0.812262	4.8677	1.4e-05	7e-06
M1	6461.74593137428	3219.306232	2.0072	0.050627	0.025313
M2	4918.47723861162	2925.344267	1.6813	0.099477	0.049738
M3	3506.72080584833	2629.965705	1.3334	0.188976	0.094488
M4	4679.62057135073	2527.999725	1.8511	0.070579	0.03529
M5	5504.75273192363	2368.659055	2.324	0.024597	0.012299
M6	6198.70800814803	2210.59078	2.8041	0.007366	0.003683
M7	7252.52025307776	2327.574203	3.1159	0.003156	0.001578
M8	5491.07357394325	2266.580054	2.4226	0.019405	0.009702
M9	4915.84217683649	2177.697695	2.2574	0.028777	0.014389
M10	3132.54862030323	1991.630336	1.5729	0.122606	0.061303
M11	2278.21743653034	1959.230055	1.1628	0.250903	0.125451
t	-215.719989563514	28.521856	-7.5633	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.813060614890317
R-squared	0.661067563485821
Adjusted R-squared	0.565282309688335
F-TEST (value)	6.90155882327656
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	3.96688436321568e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3046.67568215229
Sum Squared Residuals	426982704.762034

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10144	14067.1362379258	-3923.13623792581
2	10751	13067.2791034286	-2316.2791034286
3	11752	13377.1696521237	-1625.16965212370
4	13808	14757.4071135715	-949.407113571461
5	16203	16675.5304425569	-472.530442556864
6	17432	17185.3962103773	246.603789622711
7	18014	17201.0959555954	812.904044404558
8	16956	15670.7098332759	1285.29016672406
9	17982	16631.2963408153	1350.70365918475
10	19435	17305.0584526997	2129.94154730029
11	19990	16017.5477213915	3972.45227860853
12	20154	16322.9078779170	3831.09212208298
13	10327	11996.4454921511	-1669.44549215111
14	9807	11482.9070054819	-1675.90700548187
15	10862	12544.0214817161	-1682.02148171608
16	13743	14545.0071359198	-802.00713591983
17	16458	17123.4167591107	-665.416759110656
18	18466	18178.9083269332	287.091673066823
19	18810	18700.6957707040	109.304229296019
20	17361	16644.4528991071	716.547100892892
21	17411	15481.8433588122	1929.15664118778
22	18517	16721.0003214235	1795.99967857652
23	18525	17655.530891573	869.469108426989
24	17859	18016.2443901278	-157.244390127759
25	9499	10068.0919115944	-569.091911594363
26	9490	11064.9089002932	-1574.90890029323
27	9255	11473.6447026119	-2218.64470261189
28	10758	13870.0113713099	-3112.01137130991
29	12375	14677.1140495664	-2302.11404956642
30	14617	16594.5362289864	-1977.53622898645
31	15427	18761.1086930534	-3334.1086930534
32	14136	17436.3206982709	-3300.32069827092
33	14308	16574.2007289917	-2266.20072899166
34	15293	17991.2791481253	-2698.27914812535
35	15679	18218.0777023301	-2539.07770233015
36	16319	19365.5994197285	-3046.59941972848
37	11196	8131.83071074773	3064.16928925227
38	11169	8049.25752987725	3119.74247012275
39	12158	8050.75088726682	4107.24911273318
40	14251	9332.14309509101	4918.85690490899
41	16237	11337.2502472651	4899.74975273485
42	19706	15978.8476165507	3727.15238344933
43	18960	14733.2819255321	4226.7180744679
44	18537	14669.7593669863	3867.24063301367
45	19103	16298.5397890210	2804.46021097905
46	19691	17166.0385980076	2524.96140199239
47	19464	17072.5785304720	2391.42146952795
48	17264	13574.3733275628	3689.62667243724
49	8957	5859.49564758098	3097.50435241902
50	9703	7255.64746091905	2447.35253908095
51	9166	7747.41327628151	1418.58672371849
52	9519	9574.43128410779	-55.4312841077877
53	10535	11994.6885015009	-1459.68850150091
54	11526	13809.3116171524	-2283.31161715242
55	9630	11444.8176551151	-1814.81765511508
56	7061	9629.75720235971	-2568.75720235971
57	6021	9839.11978235992	-3818.11978235992
58	4728	8480.62347974385	-3752.62347974385
59	2657	7351.26515423332	-4694.26515423332
60	1264	5580.87498466397	-4316.87498466397

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.00356809153681226	0.00713618307362452	0.996431908463188
18	0.00075022431047229	0.00150044862094458	0.999249775689528
19	8.56748506512946e-05	0.000171349701302589	0.999914325149349
20	9.2675548917958e-06	1.85351097835916e-05	0.999990732445108
21	1.12815764249729e-06	2.25631528499459e-06	0.999998871842358
22	3.74303397903608e-07	7.48606795807215e-07	0.999999625696602
23	2.74098465785763e-06	5.48196931571526e-06	0.999997259015342
24	7.34818240664762e-06	1.46963648132952e-05	0.999992651817593
25	1.48352498926296e-06	2.96704997852593e-06	0.99999851647501
26	3.11337802941747e-07	6.22675605883494e-07	0.999999688662197
27	2.25773476370176e-07	4.51546952740352e-07	0.999999774226524
28	7.62023095888406e-07	1.52404619177681e-06	0.999999237976904
29	2.7765244417018e-06	5.5530488834036e-06	0.999997223475558
30	2.22377507220397e-06	4.44755014440795e-06	0.999997776224928
31	3.16853245085323e-06	6.33706490170646e-06	0.99999683146755
32	4.59469066445211e-06	9.18938132890421e-06	0.999995405309336
33	4.51110223721642e-06	9.02220447443284e-06	0.999995488897763
34	9.63390611245494e-06	1.92678122249099e-05	0.999990366093888
35	3.00676300578268e-05	6.01352601156536e-05	0.999969932369942
36	0.0383677813774291	0.0767355627548582	0.96163221862257
37	0.335151964786386	0.670303929572773	0.664848035213614
38	0.861772785746588	0.276454428506825	0.138227214253412
39	0.9929569169324	0.0140861661351998	0.00704308306759989
40	0.991517976782284	0.0169640464354322	0.00848202321771608
41	0.98256033462206	0.0348793307558788	0.0174396653779394
42	0.971726372760808	0.056547254478384	0.028273627239192
43	0.918865441294573	0.162269117410854	0.081134558705427

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	19	0.703703703703704	NOK
5% type I error level	22	0.814814814814815	NOK
10% type I error level	24	0.888888888888889	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/108tw81258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/108tw81258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/1kvuu1258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/1kvuu1258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/2gywb1258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/2gywb1258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/3clu81258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/3clu81258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/4y8te1258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/4y8te1258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/5y0bp1258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/5y0bp1258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/6pdu21258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/6pdu21258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/7napx1258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/7napx1258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/8k3ib1258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/8k3ib1258722619.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/9cu391258722619.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723036q9eqtvv62p8ffu6/9cu391258722619.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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