Home » date » 2008 » Nov » 26 »

Gilliam Schoorel

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 26 Nov 2008 03:32:39 -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/26/t1227695605z7pft920lqmyi43.htm/, Retrieved Wed, 26 Nov 2008 10:33:25 +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/26/t1227695605z7pft920lqmyi43.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)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

-3.3 0 -3.5 0 -3.5 0 -8.4 0 -15.7 0 -18.7 0 -22.8 0 -20.7 0 -14 0 -6.3 0 0.7 1 0.2 1 0.8 1 1.2 1 4.5 1 0.4 1 5.9 1 6.5 1 12.8 1 4.2 1 -3.3 0 -12.5 0 -16.3 0 -10.5 0 -11.8 0 -11.4 0 -17.7 0 -17.3 0 -18.6 0 -17.9 0 -21.4 0 -19.4 0 -15.5 0 -7.7 0 -0.7 0 -1.6 0 1.4 1 0.7 1 9.5 1 1.4 1 4.1 1 6.6 1 18.4 1 16.9 1 9.2 1 -4.3 0 -5.9 0 -7.7 0 -5.4 0 -2.3 0 -4.8 0 2.3 0 -5.2 0 -10 0 -17.1 0 -14.4 0 -3.9 0 3.7 1 6.5 1 0.9 1 -4.1 0 -7 0 -12.2 0 -2.5 0 4.4 1 13.7 1 12.3 1 13.4 1 2.2 1 1.7 1 -7.2 0 -4.8 0 -2.9 0 -2.4 0 -2.5 0 -5.3 0 -7.1 0 -8 0 -8.9 0 -7.7 0 -1.1 0 4 1 9.6 1 10.9 1 13 1 14.9 1 20.1 1 10.8 1 11 1 3.8 1 10.8 1 7.6 1 10.2 1 2.2 1 -0.1 0 -1.7 0 -4.8 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	8 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Multiple Linear Regression - Estimated Regression Equation

Registraties[t] = -12.8175140054531 + 15.2694308354279D[t] + 1.01483530723242M1[t] + 1.54471804484586M2[t] + 1.84649624036127M3[t] + 0.248274435876682M4[t] -2.08362622303640M5[t] -2.53184802752098M6[t] -1.61756983200557M7[t] -2.24079163649015M8[t] + 0.0571654134537557M9[t] -2.32473524545932M10[t] + 0.210721804484585M11[t] + 0.0982218044845852t + 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)	-12.8175140054531	2.26228	-5.6657	0	0
D	15.2694308354279	1.172352	13.0246	0	0
M1	1.01483530723242	2.703229	0.3754	0.708309	0.354154
M2	1.54471804484586	2.787107	0.5542	0.580907	0.290453
M3	1.84649624036127	2.785658	0.6629	0.509258	0.254629
M4	0.248274435876682	2.78436	0.0892	0.929164	0.464582
M5	-2.08362622303640	2.788447	-0.7472	0.457033	0.228517
M6	-2.53184802752098	2.78726	-0.9084	0.366316	0.183158
M7	-1.61756983200557	2.786224	-0.5806	0.56311	0.281555
M8	-2.24079163649015	2.78534	-0.8045	0.42341	0.211705
M9	0.0571654134537557	2.780158	0.0206	0.983644	0.491822
M10	-2.32473524545932	2.78403	-0.835	0.406101	0.20305
M11	0.210721804484585	2.779547	0.0758	0.939751	0.469876
t	0.0982218044845852	0.020618	4.7638	8e-06	4e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.853799987790563
R-squared	0.728974419151166
Adjusted R-squared	0.686524629379662
F-TEST (value)	17.172627310407
F-TEST (DF numerator)	13
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.55894021277874
Sum Squared Residuals	2564.85075200763

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	-3.3	-11.7044568937360	8.40445689373604
2	-3.5	-11.0763523516381	7.57635235163808
3	-3.5	-10.6763523516381	7.17635235163805
4	-8.4	-12.1763523516381	3.77635235163805
5	-15.7	-14.4100312060665	-1.28996879393346
6	-18.7	-14.7600312060665	-3.93996879393346
7	-22.8	-13.7475312060665	-9.05246879393346
8	-20.7	-14.2725312060665	-6.42746879393345
9	-14	-11.8763523516381	-2.12364764836195
10	-6.3	-14.1600312060665	7.86003120606655
11	0.7	3.74307848378988	-3.04307848378988
12	0.2	3.63057848378988	-3.43057848378988
13	0.8	4.74363559550689	-3.94363559550689
14	1.2	5.37174013760491	-4.17174013760491
15	4.5	5.77174013760491	-1.27174013760491
16	0.4	4.27174013760490	-3.87174013760490
17	5.9	2.03806128317642	3.86193871682359
18	6.5	1.68806128317641	4.81193871682359
19	12.8	2.70056128317642	10.0994387168236
20	4.2	2.17556128317642	2.02443871682358
21	-3.3	-10.6976906978230	7.39769069782303
22	-12.5	-12.9813695522515	0.481369552251523
23	-16.3	-10.3476906978230	-5.95230930217697
24	-10.5	-10.4601906978230	-0.0398093021769683
25	-11.8	-9.34713358610603	-2.45286641389397
26	-11.4	-8.719029044008	-2.68097095599199
27	-17.7	-8.319029044008	-9.380970955992
28	-17.3	-9.819029044008	-7.480970955992
29	-18.6	-12.0527078984365	-6.5472921015635
30	-17.9	-12.4027078984365	-5.4972921015635
31	-21.4	-11.3902078984365	-10.0097921015635
32	-19.4	-11.9152078984365	-7.4847921015635
33	-15.5	-9.519029044008	-5.98097095599199
34	-7.7	-11.8027078984365	4.1027078984365
35	-0.7	-9.169029044008	8.469029044008
36	-1.6	-9.281529044008	7.68152904400801
37	1.4	7.10095890313694	-5.70095890313694
38	0.7	7.72906344523496	-7.02906344523496
39	9.5	8.12906344523495	1.37093655476505
40	1.4	6.62906344523495	-5.22906344523495
41	4.1	4.39538459080646	-0.29538459080646
42	6.6	4.04538459080646	2.55461540919354
43	18.4	5.05788459080646	13.3421154091935
44	16.9	4.53288459080646	12.3671154091935
45	9.2	6.92906344523495	2.27093655476505
46	-4.3	-10.6240462446215	6.32404624462148
47	-5.9	-7.99036739019299	2.09036739019299
48	-7.7	-8.10286739019299	0.402867390192989
49	-5.4	-6.98981027847598	1.58981027847598
50	-2.3	-6.36170573637796	4.06170573637796
51	-4.8	-5.96170573637797	1.16170573637797
52	2.3	-7.46170573637796	9.76170573637796
53	-5.2	-9.69538459080646	4.49538459080646
54	-10	-10.0453845908065	0.0453845908064588
55	-17.1	-9.03288459080646	-8.06711540919355
56	-14.4	-9.55788459080646	-4.84211540919354
57	-3.9	-7.16170573637797	3.26170573637797
58	3.7	5.82404624462148	-2.12404624462148
59	6.5	8.45772509904997	-1.95772509904997
60	0.9	8.34522509904997	-7.44522509904997
61	-4.1	-5.81114862466096	1.71114862466096
62	-7	-5.18304408256294	-1.81695591743706
63	-12.2	-4.78304408256294	-7.41695591743705
64	-2.5	-6.28304408256294	3.78304408256294
65	4.4	6.7527078984365	-2.35270789843650
66	13.7	6.4027078984365	7.2972921015635
67	12.3	7.4152078984365	4.8847921015635
68	13.4	6.8902078984365	6.5097921015635
69	2.2	9.286386752865	-7.08638675286499
70	1.7	7.0027078984365	-5.3027078984365
71	-7.2	-5.63304408256294	-1.56695591743705
72	-4.8	-5.74554408256294	0.945544082562945
73	-2.9	-4.63248697084594	1.73248697084594
74	-2.4	-4.00438242874792	1.60438242874792
75	-2.5	-3.60438242874792	1.10438242874792
76	-5.3	-5.10438242874792	-0.195617571252077
77	-7.1	-7.33806128317642	0.238061283176417
78	-8	-7.68806128317642	-0.311938716823583
79	-8.9	-6.67556128317642	-2.22443871682359
80	-7.7	-7.20056128317642	-0.499438716823585
81	-1.1	-4.80438242874792	3.70438242874792
82	4	8.18136955225152	-4.18136955225153
83	9.6	10.8150484066800	-1.21504840668002
84	10.9	10.7025484066800	0.197451593319985
85	13	11.8156055183970	1.18439448160298
86	14.9	12.4437100604950	2.45628993950496
87	20.1	12.8437100604950	7.25628993950496
88	10.8	11.3437100604950	-0.543710060495038
89	11	9.11003120606654	1.88996879393345
90	3.8	8.76003120606654	-4.96003120606654
91	10.8	9.77253120606654	1.02746879393346
92	7.6	9.24753120606655	-1.64753120606655
93	10.2	11.6437100604950	-1.44371006049504
94	2.2	9.36003120606654	-7.16003120606655
95	-0.1	-3.2757207749329	3.1757207749329
96	-1.7	-3.3882207749329	1.6882207749329
97	-4.8	-2.27516366321590	-2.5248363367841

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.641574429676838	0.716851140646325	0.358425570323162
18	0.828100265507422	0.343799468985156	0.171899734492578
19	0.98154434758391	0.036911304832178	0.018455652416089
20	0.976158182653984	0.0476836346920329	0.0238418173460165
21	0.963562636198372	0.072874727603256	0.036437363801628
22	0.968259198505401	0.0634816029891975	0.0317408014945988
23	0.952750198014957	0.0944996039700868	0.0472498019850434
24	0.928511494427941	0.142977011144118	0.071488505572059
25	0.901428878185846	0.197142243628308	0.098571121814154
26	0.862445649113704	0.275108701772591	0.137554350886296
27	0.886270894540841	0.227458210918318	0.113729105459159
28	0.86796381628268	0.264072367434641	0.132036183717321
29	0.843081380257906	0.313837239484188	0.156918619742094
30	0.808726814196283	0.382546371607435	0.191273185803717
31	0.837650106915405	0.324699786169190	0.162349893084595
32	0.844752058198789	0.310495883602422	0.155247941801211
33	0.840166589737704	0.319666820524593	0.159833410262296
34	0.81318527349577	0.373629453008459	0.186814726504230
35	0.93841292991004	0.123174140179920	0.0615870700899602
36	0.963873429012082	0.0722531419758358	0.0361265709879179
37	0.960451718940691	0.079096562118617	0.0395482810593085
38	0.965820798264847	0.0683584034703052	0.0341792017351526
39	0.95715543616761	0.0856891276647812	0.0428445638323906
40	0.965599745496723	0.0688005090065538	0.0344002545032769
41	0.957276580407655	0.0854468391846892	0.0427234195923446
42	0.94827488671438	0.10345022657124	0.05172511328562
43	0.991013905749806	0.0179721885003889	0.00898609425019446
44	0.998348886814245	0.00330222637151072	0.00165111318575536
45	0.997248521411865	0.00550295717627026	0.00275147858813513
46	0.998467482521048	0.00306503495790449	0.00153251747895224
47	0.997628595830484	0.0047428083390313	0.00237140416951565
48	0.996050959874054	0.00789808025189243	0.00394904012594622
49	0.993965595311448	0.0120688093771033	0.00603440468855164
50	0.99256018512608	0.0148796297478386	0.00743981487391928
51	0.988382945559308	0.0232341088813841	0.0116170544406920
52	0.995348038438512	0.00930392312297501	0.00465196156148751
53	0.99480500597952	0.0103899880409614	0.00519499402048069
54	0.991429995912103	0.0171400081757937	0.00857000408789687
55	0.995135266797706	0.00972946640458887	0.00486473320229443
56	0.99505920569225	0.00988158861550024	0.00494079430775012
57	0.993954200273632	0.0120915994527366	0.00604579972636832
58	0.993353838179787	0.0132923236404266	0.0066461618202133
59	0.989484501941393	0.0210309961172139	0.0105154980586070
60	0.994519667026886	0.0109606659462284	0.00548033297311421
61	0.990932715761837	0.0181345684763255	0.00906728423816277
62	0.986916920440417	0.026166159119166	0.013083079559583
63	0.997257777912856	0.00548444417428843	0.00274222208714422
64	0.995946056005199	0.00810788798960282	0.00405394399480141
65	0.995057463004733	0.0098850739905346	0.0049425369952673
66	0.998013628949952	0.00397274210009619	0.00198637105004810
67	0.997913500530889	0.00417299893822226	0.00208649946911113
68	0.999465865030699	0.00106826993860246	0.000534134969301231
69	0.999864639854202	0.000270720291595181	0.000135360145797591
70	0.999689566567202	0.000620866865596186	0.000310433432798093
71	0.999400879670852	0.00119824065829646	0.000599120329148228
72	0.99844458583752	0.00311082832496194	0.00155541416248097
73	0.996726439136205	0.00654712172759	0.003273560863795
74	0.99229644105986	0.0154071178802809	0.00770355894014046
75	0.995314416488588	0.0093711670228234	0.0046855835114117
76	0.988003454907258	0.0239930901854837	0.0119965450927419
77	0.978668193467282	0.0426636130654368	0.0213318065327184
78	0.95748740222844	0.0850251955431205	0.0425125977715603
79	0.960287955695649	0.0794240886087024	0.0397120443043512
80	0.922039425321173	0.155921149357654	0.077960574678827

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.3125	NOK
5% type I error level	37	0.578125	NOK
10% type I error level	48	0.75	NOK

Charts produced by software:

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695605z7pft920lqmyi43/6xtt51227695545.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695605z7pft920lqmyi43/8o01j1227695546.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695605z7pft920lqmyi43/9gulp1227695546.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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