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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 04:37:28 -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/t12277860535udi02qgsyruafk.htm/, Retrieved Thu, 27 Nov 2008 11:41:06 +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/t12277860535udi02qgsyruafk.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 «

7,1 0 6,8 0 6,5 0 6,3 0 6,1 0 6,1 0 6,3 0 6,3 0 6,0 0 6,2 0 6,4 0 6,8 0 7,5 0 7,5 0 7,6 0 7,6 0 7,4 0 7,3 0 7,1 0 6,9 0 6,8 0 7,5 0 7,6 0 7,8 0 8,0 0 8,1 0 8,2 0 8,3 0 8,2 0 8,0 0 7,9 0 7,6 0 7,6 0 8,2 0 8,3 0 8,4 0 8,4 0 8,4 0 8,6 0 8,9 0 8,8 0 8,3 0 7,5 0 7,2 0 7,5 0 8,8 0 9,3 0 9,3 0 8,7 1 8,2 1 8,3 1 8,5 1 8,6 1 8,6 1 8,2 1 8,1 1 8,0 1 8,6 1 8,7 1 8,8 1 8,5 1 8,4 1 8,5 1 8,7 1 8,7 1 8,6 1 8,5 1 8,3 1 8,1 1 8,2 1 8,1 1 8,1 1 7,9 1 7,9 1 7,9 1 8,0 1 8,0 1 7,9 1 8,0 1 7,7 1 7,2 1 7,5 1 7,3 1 7,0 1 7,0 1 7,0 1 7,2 1 7,3 1 7,1 1 6,8 1 6,6 1 6,2 1 6,2 1 6,8 1 6,9 1 6,8 1

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

w[t] = + 7.59583333333333 + 0.0370833333333313d[t] + 0.0655902777777697M1[t] -0.0392361111111113M2[t] + 0.0184375000000004M3[t] + 0.113611111111111M4[t] + 0.0212847222222220M5[t] -0.146041666666667M6[t] -0.338368055555555M7[t] -0.568194444444444M8[t] -0.685520833333334M9[t] -0.140347222222223M10[t] -0.0451736111111113M11[t] + 0.00482638888888895t + 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)	7.59583333333333	0.355588	21.3613	0	0
d	0.0370833333333313	0.343531	0.1079	0.914301	0.45715
M1	0.0655902777777697	0.416309	0.1576	0.875197	0.437599
M2	-0.0392361111111113	0.415323	-0.0945	0.924965	0.462483
M3	0.0184375000000004	0.41443	0.0445	0.964623	0.482311
M4	0.113611111111111	0.413628	0.2747	0.784261	0.39213
M5	0.0212847222222220	0.41292	0.0515	0.959015	0.479508
M6	-0.146041666666667	0.412305	-0.3542	0.724093	0.362046
M7	-0.338368055555555	0.411784	-0.8217	0.413624	0.206812
M8	-0.568194444444444	0.411358	-1.3813	0.17095	0.085475
M9	-0.685520833333334	0.411026	-1.6678	0.099164	0.049582
M10	-0.140347222222223	0.410788	-0.3417	0.733486	0.366743
M11	-0.0451736111111113	0.410646	-0.11	0.912673	0.456337
t	0.00482638888888895	0.006247	0.7726	0.441988	0.220994

Multiple Linear Regression - Regression Statistics
Multiple R	0.347431893044433
R-squared	0.120708920304438
Adjusted R-squared	-0.0186908850131511
F-TEST (value)	0.865918858562475
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0.590867401040405
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.82119626948016
Sum Squared Residuals	55.2977916666667

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	7.1	7.66625000000005	-0.566250000000055
2	6.8	7.56625	-0.766249999999999
3	6.5	7.62875	-1.12875000000000
4	6.3	7.72875	-1.42875000000000
5	6.1	7.64125	-1.54125
6	6.1	7.47875	-1.37875000000000
7	6.3	7.29125	-0.99125
8	6.3	7.06625	-0.766249999999998
9	6	6.95375	-0.953749999999998
10	6.2	7.50375	-1.30375000000000
11	6.4	7.60375	-1.20375000000000
12	6.8	7.65375	-0.853749999999999
13	7.5	7.72416666666666	-0.224166666666658
14	7.5	7.62416666666667	-0.124166666666667
15	7.6	7.68666666666666	-0.0866666666666655
16	7.6	7.78666666666667	-0.186666666666667
17	7.4	7.69916666666666	-0.299166666666665
18	7.3	7.53666666666667	-0.236666666666666
19	7.1	7.34916666666667	-0.249166666666666
20	6.9	7.12416666666667	-0.224166666666666
21	6.8	7.01166666666667	-0.211666666666666
22	7.5	7.56166666666667	-0.0616666666666656
23	7.6	7.66166666666667	-0.0616666666666661
24	7.8	7.71166666666667	0.088333333333334
25	8	7.78208333333333	0.217916666666675
26	8.1	7.68208333333333	0.417916666666665
27	8.2	7.74458333333333	0.455416666666667
28	8.3	7.84458333333333	0.455416666666666
29	8.2	7.75708333333333	0.442916666666667
30	8	7.59458333333333	0.405416666666667
31	7.9	7.40708333333333	0.492916666666667
32	7.6	7.18208333333333	0.417916666666666
33	7.6	7.06958333333333	0.530416666666667
34	8.2	7.61958333333333	0.580416666666666
35	8.3	7.71958333333333	0.580416666666667
36	8.4	7.76958333333333	0.630416666666667
37	8.4	7.83999999999999	0.560000000000008
38	8.4	7.74	0.659999999999999
39	8.6	7.8025	0.7975
40	8.9	7.9025	0.9975
41	8.8	7.815	0.985000000000001
42	8.3	7.6525	0.6475
43	7.5	7.465	0.0349999999999994
44	7.2	7.24	-0.0400000000000007
45	7.5	7.1275	0.372499999999999
46	8.8	7.6775	1.1225
47	9.3	7.7775	1.5225
48	9.3	7.8275	1.4725
49	8.7	7.93499999999999	0.765000000000008
50	8.2	7.835	0.364999999999999
51	8.3	7.8975	0.402500000000002
52	8.5	7.9975	0.502499999999999
53	8.6	7.91	0.690000000000001
54	8.6	7.7475	0.8525
55	8.2	7.56	0.64
56	8.1	7.335	0.765
57	8	7.2225	0.777500000000001
58	8.6	7.7725	0.8275
59	8.7	7.8725	0.8275
60	8.8	7.9225	0.877500000000002
61	8.5	7.99291666666666	0.507083333333341
62	8.4	7.89291666666667	0.507083333333333
63	8.5	7.95541666666667	0.544583333333334
64	8.7	8.05541666666667	0.644583333333331
65	8.7	7.96791666666667	0.732083333333333
66	8.6	7.80541666666667	0.794583333333333
67	8.5	7.61791666666667	0.882083333333333
68	8.3	7.39291666666667	0.907083333333334
69	8.1	7.28041666666667	0.819583333333333
70	8.2	7.83041666666667	0.369583333333332
71	8.1	7.93041666666667	0.169583333333333
72	8.1	7.98041666666667	0.119583333333333
73	7.9	8.05083333333333	-0.150833333333326
74	7.9	7.95083333333334	-0.0508333333333349
75	7.9	8.01333333333333	-0.113333333333333
76	8	8.11333333333334	-0.113333333333335
77	8	8.02583333333333	-0.0258333333333336
78	7.9	7.86333333333333	0.0366666666666662
79	8	7.67583333333333	0.324166666666666
80	7.7	7.45083333333333	0.249166666666666
81	7.2	7.33833333333333	-0.138333333333334
82	7.5	7.88833333333333	-0.388333333333334
83	7.3	7.98833333333333	-0.688333333333335
84	7	8.03833333333333	-1.03833333333333
85	7	8.10875	-1.10874999999999
86	7	8.00875	-1.00875000000000
87	7.2	8.07125	-0.871250000000001
88	7.3	8.17125	-0.871250000000003
89	7.1	8.08375	-0.98375
90	6.8	7.92125	-1.12125000000000
91	6.6	7.73375	-1.13375000000000
92	6.2	7.50875	-1.30875
93	6.2	7.39625	-1.19625
94	6.8	7.94625	-1.14625000000000
95	6.9	8.04625	-1.14625000000000
96	6.8	8.09625	-1.29625000000000

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.176077488307277	0.352154976614554	0.823922511692723
18	0.0955045510875216	0.191009102175043	0.904495448912478
19	0.0512832829510283	0.102566565902057	0.948716717048972
20	0.0372577399209485	0.074515479841897	0.962742260079052
21	0.0215885877862048	0.0431771755724096	0.978411412213795
22	0.0198773350872958	0.0397546701745917	0.980122664912704
23	0.0160788775237890	0.0321577550475781	0.983921122476211
24	0.0107128167208118	0.0214256334416236	0.989287183279188
25	0.0366887609703367	0.0733775219406735	0.963311239029663
26	0.0302898227871098	0.0605796455742195	0.96971017721289
27	0.0188908611774333	0.0377817223548666	0.981109138822567
28	0.0123402809228972	0.0246805618457945	0.987659719077103
29	0.00932705192729616	0.0186541038545923	0.990672948072704
30	0.00652006540312166	0.0130401308062433	0.993479934596878
31	0.00426955008751054	0.00853910017502108	0.99573044991249
32	0.00386963413722260	0.00773926827444519	0.996130365862777
33	0.00263586359079807	0.00527172718159615	0.997364136409202
34	0.00193444453276651	0.00386888906553302	0.998065555467234
35	0.00143724932265811	0.00287449864531621	0.998562750677342
36	0.00107054724838659	0.00214109449677318	0.998929452751613
37	0.00674971112881029	0.0134994222576206	0.99325028887119
38	0.0125228553689138	0.0250457107378275	0.987477144631086
39	0.0101753120778445	0.0203506241556889	0.989824687922155
40	0.00622096007286147	0.0124419201457229	0.993779039927138
41	0.00373748235419338	0.00747496470838676	0.996262517645807
42	0.00302609568295094	0.00605219136590188	0.99697390431705
43	0.0465873343011137	0.0931746686022275	0.953412665698886
44	0.346178955025729	0.692357910051459	0.653821044974271
45	0.596171705760659	0.807656588478681	0.403828294239341
46	0.584361132421027	0.831277735157945	0.415638867578973
47	0.583143124456752	0.833713751086496	0.416856875543248
48	0.533323303890997	0.933353392218007	0.466676696109003
49	0.47413918223817	0.94827836447634	0.52586081776183
50	0.540719422300476	0.918561155399048	0.459280577699524
51	0.614293511239043	0.771412977521913	0.385706488760957
52	0.688476897004626	0.623046205990748	0.311523102995374
53	0.737590534414305	0.52481893117139	0.262409465585695
54	0.765878122494182	0.468243755011635	0.234121877505818
55	0.897913482991564	0.204173034016871	0.102086517008436
56	0.957739182888873	0.0845216342222538	0.0422608171111269
57	0.983550924595783	0.0328981508084345	0.0164490754042172
58	0.987108367909101	0.0257832641817975	0.0128916320908988
59	0.985366714964055	0.0292665700718909	0.0146332850359455
60	0.97734335697961	0.0453132860407803	0.0226566430203902
61	0.981376202720322	0.0372475945593554	0.0186237972796777
62	0.984277472115996	0.0314450557680076	0.0157225278840038
63	0.985112005319038	0.0297759893619237	0.0148879946809619
64	0.981004207103506	0.0379915857929881	0.0189957928964941
65	0.971499257834321	0.0570014843313579	0.0285007421656790
66	0.955614865802465	0.0887702683950701	0.0443851341975351
67	0.930951303493795	0.138097393012411	0.0690486965062053
68	0.899312770192574	0.201374459614852	0.100687229807426
69	0.867401237169132	0.265197525661735	0.132598762830868
70	0.861680506093888	0.276638987812224	0.138319493906112
71	0.88351527447604	0.232969451047921	0.116484725523960
72	0.88401407175216	0.231971856495679	0.115985928247839
73	0.899014937899393	0.201970124201213	0.100985062100607
74	0.883724088369335	0.232551823261329	0.116275911630665
75	0.859900535876363	0.280198928247274	0.140099464123637
76	0.818574367847053	0.362851264305894	0.181425632152947
77	0.738680270952049	0.522639458095903	0.261319729047951
78	0.637522397782562	0.724955204434875	0.362477602217438
79	0.593633559367885	0.812732881264229	0.406366440632115

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.126984126984127	NOK
5% type I error level	28	0.444444444444444	NOK
10% type I error level	35	0.555555555555556	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/10dhce1227785843.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/10dhce1227785843.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/1mk6o1227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/1mk6o1227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/23vlm1227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/23vlm1227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/33umy1227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/33umy1227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/44dq91227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/44dq91227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/5l3gm1227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/5l3gm1227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/6gaec1227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/6gaec1227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/7hx2j1227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/7hx2j1227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/8jy8y1227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/8jy8y1227785842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/9yx871227785842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277860535udi02qgsyruafk/9yx871227785842.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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