R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(24,24,22,23,25,24,24,24,29,27,26,28,26,25,21,19,23,19,22,19,21,20,16,16,19,22,16,21,25,25,27,29,23,28,22,25,23,26,20,24,24,28,23,28,20,28,21,28,22,32,17,31,21,22,19,29,23,31,22,29,15,32,23,32,21,31,18,29,18,28,18,28,18,29,10,22,13,26,10,24,9,27,9,27,6,23,11,21,9,19,10,17,9,19,16,21,10,13,7,8,7,5,14,10,11,6,10,6,6,8,8,11,13,12,12,13,15,19,16,19,16,18),dim=c(2,61),dimnames=list(c('s','consv'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('s','consv'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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
s consv M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 24 24 1 0 0 0 0 0 0 0 0 0 0
2 22 23 0 1 0 0 0 0 0 0 0 0 0
3 25 24 0 0 1 0 0 0 0 0 0 0 0
4 24 24 0 0 0 1 0 0 0 0 0 0 0
5 29 27 0 0 0 0 1 0 0 0 0 0 0
6 26 28 0 0 0 0 0 1 0 0 0 0 0
7 26 25 0 0 0 0 0 0 1 0 0 0 0
8 21 19 0 0 0 0 0 0 0 1 0 0 0
9 23 19 0 0 0 0 0 0 0 0 1 0 0
10 22 19 0 0 0 0 0 0 0 0 0 1 0
11 21 20 0 0 0 0 0 0 0 0 0 0 1
12 16 16 0 0 0 0 0 0 0 0 0 0 0
13 19 22 1 0 0 0 0 0 0 0 0 0 0
14 16 21 0 1 0 0 0 0 0 0 0 0 0
15 25 25 0 0 1 0 0 0 0 0 0 0 0
16 27 29 0 0 0 1 0 0 0 0 0 0 0
17 23 28 0 0 0 0 1 0 0 0 0 0 0
18 22 25 0 0 0 0 0 1 0 0 0 0 0
19 23 26 0 0 0 0 0 0 1 0 0 0 0
20 20 24 0 0 0 0 0 0 0 1 0 0 0
21 24 28 0 0 0 0 0 0 0 0 1 0 0
22 23 28 0 0 0 0 0 0 0 0 0 1 0
23 20 28 0 0 0 0 0 0 0 0 0 0 1
24 21 28 0 0 0 0 0 0 0 0 0 0 0
25 22 32 1 0 0 0 0 0 0 0 0 0 0
26 17 31 0 1 0 0 0 0 0 0 0 0 0
27 21 22 0 0 1 0 0 0 0 0 0 0 0
28 19 29 0 0 0 1 0 0 0 0 0 0 0
29 23 31 0 0 0 0 1 0 0 0 0 0 0
30 22 29 0 0 0 0 0 1 0 0 0 0 0
31 15 32 0 0 0 0 0 0 1 0 0 0 0
32 23 32 0 0 0 0 0 0 0 1 0 0 0
33 21 31 0 0 0 0 0 0 0 0 1 0 0
34 18 29 0 0 0 0 0 0 0 0 0 1 0
35 18 28 0 0 0 0 0 0 0 0 0 0 1
36 18 28 0 0 0 0 0 0 0 0 0 0 0
37 18 29 1 0 0 0 0 0 0 0 0 0 0
38 10 22 0 1 0 0 0 0 0 0 0 0 0
39 13 26 0 0 1 0 0 0 0 0 0 0 0
40 10 24 0 0 0 1 0 0 0 0 0 0 0
41 9 27 0 0 0 0 1 0 0 0 0 0 0
42 9 27 0 0 0 0 0 1 0 0 0 0 0
43 6 23 0 0 0 0 0 0 1 0 0 0 0
44 11 21 0 0 0 0 0 0 0 1 0 0 0
45 9 19 0 0 0 0 0 0 0 0 1 0 0
46 10 17 0 0 0 0 0 0 0 0 0 1 0
47 9 19 0 0 0 0 0 0 0 0 0 0 1
48 16 21 0 0 0 0 0 0 0 0 0 0 0
49 10 13 1 0 0 0 0 0 0 0 0 0 0
50 7 8 0 1 0 0 0 0 0 0 0 0 0
51 7 5 0 0 1 0 0 0 0 0 0 0 0
52 14 10 0 0 0 1 0 0 0 0 0 0 0
53 11 6 0 0 0 0 1 0 0 0 0 0 0
54 10 6 0 0 0 0 0 1 0 0 0 0 0
55 6 8 0 0 0 0 0 0 1 0 0 0 0
56 8 11 0 0 0 0 0 0 0 1 0 0 0
57 13 12 0 0 0 0 0 0 0 0 1 0 0
58 12 13 0 0 0 0 0 0 0 0 0 1 0
59 15 19 0 0 0 0 0 0 0 0 0 0 1
60 16 19 0 0 0 0 0 0 0 0 0 0 0
61 16 18 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) consv M1 M2 M3 M4
5.6212 0.5258 0.4512 -2.2638 1.8517 0.9793
M5 M6 M7 M8 M9 M10
0.8638 0.0845 -2.4103 -0.2742 0.9155 0.2310
M11
-1.0103
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.6827 -2.9083 0.4625 2.7398 9.6432
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.62125 3.21246 1.750 0.0865 .
consv 0.52584 0.09648 5.450 1.71e-06 ***
M1 0.45116 3.21873 0.140 0.8891
M2 -2.26383 3.36402 -0.673 0.5042
M3 1.85167 3.36684 0.550 0.5849
M4 0.97933 3.36219 0.291 0.7721
M5 0.86383 3.36402 0.257 0.7984
M6 0.08450 3.36181 0.025 0.9801
M7 -2.41033 3.36153 -0.717 0.4768
M8 -0.27416 3.36269 -0.082 0.9354
M9 0.91550 3.36181 0.272 0.7865
M10 0.23100 3.36330 0.069 0.9455
M11 -1.01033 3.36153 -0.301 0.7650
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.315 on 48 degrees of freedom
Multiple R-squared: 0.4111, Adjusted R-squared: 0.2639
F-statistic: 2.792 on 12 and 48 DF, p-value: 0.005744
> 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
+ }
[,1] [,2] [,3]
[1,] 0.13338062 0.26676125 0.86661938
[2,] 0.25028360 0.50056721 0.74971640
[3,] 0.15360220 0.30720440 0.84639780
[4,] 0.16931735 0.33863469 0.83068265
[5,] 0.15569129 0.31138257 0.84430871
[6,] 0.12052065 0.24104129 0.87947935
[7,] 0.08622627 0.17245255 0.91377373
[8,] 0.06042144 0.12084289 0.93957856
[9,] 0.04049027 0.08098054 0.95950973
[10,] 0.02428526 0.04857052 0.97571474
[11,] 0.02045494 0.04090989 0.97954506
[12,] 0.02935003 0.05870007 0.97064997
[13,] 0.06038738 0.12077477 0.93961262
[14,] 0.07767054 0.15534108 0.92232946
[15,] 0.10568503 0.21137006 0.89431497
[16,] 0.28382347 0.56764694 0.71617653
[17,] 0.46320903 0.92641805 0.53679097
[18,] 0.58228056 0.83543887 0.41771944
[19,] 0.65163599 0.69672802 0.34836401
[20,] 0.69386420 0.61227159 0.30613580
[21,] 0.60839856 0.78320287 0.39160144
[22,] 0.63744387 0.72511225 0.36255613
[23,] 0.70850275 0.58299451 0.29149725
[24,] 0.88550330 0.22899341 0.11449670
[25,] 0.93468945 0.13062111 0.06531055
[26,] 0.94973188 0.10053625 0.05026812
[27,] 0.93906759 0.12186482 0.06093241
[28,] 0.91434326 0.17131349 0.08565674
[29,] 0.85712625 0.28574750 0.14287375
[30,] 0.83109709 0.33780582 0.16890291
> postscript(file="/var/www/html/rcomp/tmp/199w21258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2r4bg1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3n9m91258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u6ba1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nh5l1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
5.3074963 6.5483259 4.9069866 4.7793303 8.3173214 5.5708147
7 8 9 10 11 12
9.6431584 5.6620090 6.4723438 6.1568416 5.8723438 1.9653572
13 14 15 16 17 18
1.3591704 1.6000000 4.3811495 5.1501450 1.7914843 3.1483259
19 20 21 22 23 24
6.1173214 2.0328236 2.7398102 2.4243079 0.6656472 0.6553124
25 26 27 28 29 30
-0.8992003 -2.6583707 1.9586607 -2.8498550 0.2139731 1.0449776
31 32 33 34 35 36
-5.0377010 0.8261271 -1.8377010 -3.1015291 -1.3343528 -2.3446876
37 38 39 40 41 42
-3.3216891 -4.9258371 -8.1446876 -9.2206697 -11.6826786 -10.9033483
43 44 45 46 47 48
-9.3051674 -5.3896652 -7.5276562 -4.7914843 -5.6018191 -0.6638281
49 50 51 52 53 54
-2.9082960 -0.5641181 -3.1021091 2.1410493 1.3598998 1.1392302
55 56 57 58 59 60
-1.4176114 -3.1312945 0.1532033 -0.6881360 0.3981809 0.3878460
61
0.4625187
> postscript(file="/var/www/html/rcomp/tmp/6pi4h1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 5.3074963 NA
1 6.5483259 5.3074963
2 4.9069866 6.5483259
3 4.7793303 4.9069866
4 8.3173214 4.7793303
5 5.5708147 8.3173214
6 9.6431584 5.5708147
7 5.6620090 9.6431584
8 6.4723438 5.6620090
9 6.1568416 6.4723438
10 5.8723438 6.1568416
11 1.9653572 5.8723438
12 1.3591704 1.9653572
13 1.6000000 1.3591704
14 4.3811495 1.6000000
15 5.1501450 4.3811495
16 1.7914843 5.1501450
17 3.1483259 1.7914843
18 6.1173214 3.1483259
19 2.0328236 6.1173214
20 2.7398102 2.0328236
21 2.4243079 2.7398102
22 0.6656472 2.4243079
23 0.6553124 0.6656472
24 -0.8992003 0.6553124
25 -2.6583707 -0.8992003
26 1.9586607 -2.6583707
27 -2.8498550 1.9586607
28 0.2139731 -2.8498550
29 1.0449776 0.2139731
30 -5.0377010 1.0449776
31 0.8261271 -5.0377010
32 -1.8377010 0.8261271
33 -3.1015291 -1.8377010
34 -1.3343528 -3.1015291
35 -2.3446876 -1.3343528
36 -3.3216891 -2.3446876
37 -4.9258371 -3.3216891
38 -8.1446876 -4.9258371
39 -9.2206697 -8.1446876
40 -11.6826786 -9.2206697
41 -10.9033483 -11.6826786
42 -9.3051674 -10.9033483
43 -5.3896652 -9.3051674
44 -7.5276562 -5.3896652
45 -4.7914843 -7.5276562
46 -5.6018191 -4.7914843
47 -0.6638281 -5.6018191
48 -2.9082960 -0.6638281
49 -0.5641181 -2.9082960
50 -3.1021091 -0.5641181
51 2.1410493 -3.1021091
52 1.3598998 2.1410493
53 1.1392302 1.3598998
54 -1.4176114 1.1392302
55 -3.1312945 -1.4176114
56 0.1532033 -3.1312945
57 -0.6881360 0.1532033
58 0.3981809 -0.6881360
59 0.3878460 0.3981809
60 0.4625187 0.3878460
61 NA 0.4625187
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.5483259 5.3074963
[2,] 4.9069866 6.5483259
[3,] 4.7793303 4.9069866
[4,] 8.3173214 4.7793303
[5,] 5.5708147 8.3173214
[6,] 9.6431584 5.5708147
[7,] 5.6620090 9.6431584
[8,] 6.4723438 5.6620090
[9,] 6.1568416 6.4723438
[10,] 5.8723438 6.1568416
[11,] 1.9653572 5.8723438
[12,] 1.3591704 1.9653572
[13,] 1.6000000 1.3591704
[14,] 4.3811495 1.6000000
[15,] 5.1501450 4.3811495
[16,] 1.7914843 5.1501450
[17,] 3.1483259 1.7914843
[18,] 6.1173214 3.1483259
[19,] 2.0328236 6.1173214
[20,] 2.7398102 2.0328236
[21,] 2.4243079 2.7398102
[22,] 0.6656472 2.4243079
[23,] 0.6553124 0.6656472
[24,] -0.8992003 0.6553124
[25,] -2.6583707 -0.8992003
[26,] 1.9586607 -2.6583707
[27,] -2.8498550 1.9586607
[28,] 0.2139731 -2.8498550
[29,] 1.0449776 0.2139731
[30,] -5.0377010 1.0449776
[31,] 0.8261271 -5.0377010
[32,] -1.8377010 0.8261271
[33,] -3.1015291 -1.8377010
[34,] -1.3343528 -3.1015291
[35,] -2.3446876 -1.3343528
[36,] -3.3216891 -2.3446876
[37,] -4.9258371 -3.3216891
[38,] -8.1446876 -4.9258371
[39,] -9.2206697 -8.1446876
[40,] -11.6826786 -9.2206697
[41,] -10.9033483 -11.6826786
[42,] -9.3051674 -10.9033483
[43,] -5.3896652 -9.3051674
[44,] -7.5276562 -5.3896652
[45,] -4.7914843 -7.5276562
[46,] -5.6018191 -4.7914843
[47,] -0.6638281 -5.6018191
[48,] -2.9082960 -0.6638281
[49,] -0.5641181 -2.9082960
[50,] -3.1021091 -0.5641181
[51,] 2.1410493 -3.1021091
[52,] 1.3598998 2.1410493
[53,] 1.1392302 1.3598998
[54,] -1.4176114 1.1392302
[55,] -3.1312945 -1.4176114
[56,] 0.1532033 -3.1312945
[57,] -0.6881360 0.1532033
[58,] 0.3981809 -0.6881360
[59,] 0.3878460 0.3981809
[60,] 0.4625187 0.3878460
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.5483259 5.3074963
2 4.9069866 6.5483259
3 4.7793303 4.9069866
4 8.3173214 4.7793303
5 5.5708147 8.3173214
6 9.6431584 5.5708147
7 5.6620090 9.6431584
8 6.4723438 5.6620090
9 6.1568416 6.4723438
10 5.8723438 6.1568416
11 1.9653572 5.8723438
12 1.3591704 1.9653572
13 1.6000000 1.3591704
14 4.3811495 1.6000000
15 5.1501450 4.3811495
16 1.7914843 5.1501450
17 3.1483259 1.7914843
18 6.1173214 3.1483259
19 2.0328236 6.1173214
20 2.7398102 2.0328236
21 2.4243079 2.7398102
22 0.6656472 2.4243079
23 0.6553124 0.6656472
24 -0.8992003 0.6553124
25 -2.6583707 -0.8992003
26 1.9586607 -2.6583707
27 -2.8498550 1.9586607
28 0.2139731 -2.8498550
29 1.0449776 0.2139731
30 -5.0377010 1.0449776
31 0.8261271 -5.0377010
32 -1.8377010 0.8261271
33 -3.1015291 -1.8377010
34 -1.3343528 -3.1015291
35 -2.3446876 -1.3343528
36 -3.3216891 -2.3446876
37 -4.9258371 -3.3216891
38 -8.1446876 -4.9258371
39 -9.2206697 -8.1446876
40 -11.6826786 -9.2206697
41 -10.9033483 -11.6826786
42 -9.3051674 -10.9033483
43 -5.3896652 -9.3051674
44 -7.5276562 -5.3896652
45 -4.7914843 -7.5276562
46 -5.6018191 -4.7914843
47 -0.6638281 -5.6018191
48 -2.9082960 -0.6638281
49 -0.5641181 -2.9082960
50 -3.1021091 -0.5641181
51 2.1410493 -3.1021091
52 1.3598998 2.1410493
53 1.1392302 1.3598998
54 -1.4176114 1.1392302
55 -3.1312945 -1.4176114
56 0.1532033 -3.1312945
57 -0.6881360 0.1532033
58 0.3981809 -0.6881360
59 0.3878460 0.3981809
60 0.4625187 0.3878460
> 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()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7q86m1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dyea1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ydvm1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10awzf1258565284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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="/var/www/html/rcomp/tmp/113ic81258565284.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
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="/var/www/html/rcomp/tmp/12b0iy1258565284.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="/var/www/html/rcomp/tmp/13y6041258565284.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
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
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="/var/www/html/rcomp/tmp/14ad351258565284.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="/var/www/html/rcomp/tmp/15pk6b1258565284.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="/var/www/html/rcomp/tmp/16478o1258565284.tab")
+ }
>
> system("convert tmp/199w21258565284.ps tmp/199w21258565284.png")
> system("convert tmp/2r4bg1258565284.ps tmp/2r4bg1258565284.png")
> system("convert tmp/3n9m91258565284.ps tmp/3n9m91258565284.png")
> system("convert tmp/4u6ba1258565284.ps tmp/4u6ba1258565284.png")
> system("convert tmp/5nh5l1258565284.ps tmp/5nh5l1258565284.png")
> system("convert tmp/6pi4h1258565284.ps tmp/6pi4h1258565284.png")
> system("convert tmp/7q86m1258565284.ps tmp/7q86m1258565284.png")
> system("convert tmp/8dyea1258565284.ps tmp/8dyea1258565284.png")
> system("convert tmp/9ydvm1258565284.ps tmp/9ydvm1258565284.png")
> system("convert tmp/10awzf1258565284.ps tmp/10awzf1258565284.png")
>
>
> proc.time()
user system elapsed
2.363 1.525 2.773