R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1.6,0,1.4,1.7,0,1.6,2,0,1.7,2,0,2,2.1,0,2,2.5,0,2.1,2.5,0,2.5,2.6,0,2.5,2.7,0,2.6,3.7,0,2.7,4,0,3.7,5,0,4,5.1,0,5,5.1,0,5.1,5,0,5.1,5.1,0,5,4.7,0,5.1,4.5,0,4.7,4.5,0,4.5,4.6,0,4.5,4.6,0,4.6,4.6,0,4.6,4.6,0,4.6,5.3,0,4.6,5.4,0,5.3,5.3,0,5.4,5.2,0,5.3,5,0,5.2,4.2,0,5,4.3,0,4.2,4.3,0,4.3,4.3,0,4.3,4,0,4.3,4,0,4,4.1,0,4,4.4,0,4.1,3.6,0,4.4,3.7,0,3.6,3.8,0,3.7,3.3,0,3.8,3.3,0,3.3,3.3,0,3.3,3.5,0,3.3,3.3,0,3.5,3.3,0,3.3,3.4,0,3.3,3.4,0,3.4,5.2,0,3.4,5.3,0,5.2,4.8,1,5.3,5,1,4.8,4.6,1,5,4.6,1,4.6,3.5,1,4.6,3.5,1,3.5),dim=c(3,55),dimnames=list(c('IndGez','InvlMex','IndGez-1'),1:55))
> y <- array(NA,dim=c(3,55),dimnames=list(c('IndGez','InvlMex','IndGez-1'),1:55))
> 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
IndGez InvlMex IndGez-1 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.6 0 1.4 1 0 0 0 0 0 0 0 0 0 0
2 1.7 0 1.6 0 1 0 0 0 0 0 0 0 0 0
3 2.0 0 1.7 0 0 1 0 0 0 0 0 0 0 0
4 2.0 0 2.0 0 0 0 1 0 0 0 0 0 0 0
5 2.1 0 2.0 0 0 0 0 1 0 0 0 0 0 0
6 2.5 0 2.1 0 0 0 0 0 1 0 0 0 0 0
7 2.5 0 2.5 0 0 0 0 0 0 1 0 0 0 0
8 2.6 0 2.5 0 0 0 0 0 0 0 1 0 0 0
9 2.7 0 2.6 0 0 0 0 0 0 0 0 1 0 0
10 3.7 0 2.7 0 0 0 0 0 0 0 0 0 1 0
11 4.0 0 3.7 0 0 0 0 0 0 0 0 0 0 1
12 5.0 0 4.0 0 0 0 0 0 0 0 0 0 0 0
13 5.1 0 5.0 1 0 0 0 0 0 0 0 0 0 0
14 5.1 0 5.1 0 1 0 0 0 0 0 0 0 0 0
15 5.0 0 5.1 0 0 1 0 0 0 0 0 0 0 0
16 5.1 0 5.0 0 0 0 1 0 0 0 0 0 0 0
17 4.7 0 5.1 0 0 0 0 1 0 0 0 0 0 0
18 4.5 0 4.7 0 0 0 0 0 1 0 0 0 0 0
19 4.5 0 4.5 0 0 0 0 0 0 1 0 0 0 0
20 4.6 0 4.5 0 0 0 0 0 0 0 1 0 0 0
21 4.6 0 4.6 0 0 0 0 0 0 0 0 1 0 0
22 4.6 0 4.6 0 0 0 0 0 0 0 0 0 1 0
23 4.6 0 4.6 0 0 0 0 0 0 0 0 0 0 1
24 5.3 0 4.6 0 0 0 0 0 0 0 0 0 0 0
25 5.4 0 5.3 1 0 0 0 0 0 0 0 0 0 0
26 5.3 0 5.4 0 1 0 0 0 0 0 0 0 0 0
27 5.2 0 5.3 0 0 1 0 0 0 0 0 0 0 0
28 5.0 0 5.2 0 0 0 1 0 0 0 0 0 0 0
29 4.2 0 5.0 0 0 0 0 1 0 0 0 0 0 0
30 4.3 0 4.2 0 0 0 0 0 1 0 0 0 0 0
31 4.3 0 4.3 0 0 0 0 0 0 1 0 0 0 0
32 4.3 0 4.3 0 0 0 0 0 0 0 1 0 0 0
33 4.0 0 4.3 0 0 0 0 0 0 0 0 1 0 0
34 4.0 0 4.0 0 0 0 0 0 0 0 0 0 1 0
35 4.1 0 4.0 0 0 0 0 0 0 0 0 0 0 1
36 4.4 0 4.1 0 0 0 0 0 0 0 0 0 0 0
37 3.6 0 4.4 1 0 0 0 0 0 0 0 0 0 0
38 3.7 0 3.6 0 1 0 0 0 0 0 0 0 0 0
39 3.8 0 3.7 0 0 1 0 0 0 0 0 0 0 0
40 3.3 0 3.8 0 0 0 1 0 0 0 0 0 0 0
41 3.3 0 3.3 0 0 0 0 1 0 0 0 0 0 0
42 3.3 0 3.3 0 0 0 0 0 1 0 0 0 0 0
43 3.5 0 3.3 0 0 0 0 0 0 1 0 0 0 0
44 3.3 0 3.5 0 0 0 0 0 0 0 1 0 0 0
45 3.3 0 3.3 0 0 0 0 0 0 0 0 1 0 0
46 3.4 0 3.3 0 0 0 0 0 0 0 0 0 1 0
47 3.4 0 3.4 0 0 0 0 0 0 0 0 0 0 1
48 5.2 0 3.4 0 0 0 0 0 0 0 0 0 0 0
49 5.3 0 5.2 1 0 0 0 0 0 0 0 0 0 0
50 4.8 1 5.3 0 1 0 0 0 0 0 0 0 0 0
51 5.0 1 4.8 0 0 1 0 0 0 0 0 0 0 0
52 4.6 1 5.0 0 0 0 1 0 0 0 0 0 0 0
53 4.6 1 4.6 0 0 0 0 1 0 0 0 0 0 0
54 3.5 1 4.6 0 0 0 0 0 1 0 0 0 0 0
55 3.5 1 3.5 0 0 0 0 0 0 1 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) InvlMex `IndGez-1` M1 M2 M3
1.4244 -0.1672 0.8821 -0.9823 -0.9759 -0.8254
M4 M5 M6 M7 M8 M9
-1.0959 -1.1395 -1.1054 -0.9243 -0.9883 -1.0383
M10 M11
-0.7192 -0.8618
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.72350 -0.15822 0.02036 0.12834 0.77634
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.42438 0.23421 6.082 3.31e-07 ***
InvlMex -0.16723 0.15041 -1.112 0.272681
`IndGez-1` 0.88214 0.04239 20.808 < 2e-16 ***
M1 -0.98230 0.21547 -4.559 4.58e-05 ***
M2 -0.97593 0.21722 -4.493 5.64e-05 ***
M3 -0.82536 0.21724 -3.799 0.000473 ***
M4 -1.09593 0.21722 -5.045 9.70e-06 ***
M5 -1.13950 0.21737 -5.242 5.13e-06 ***
M6 -1.10543 0.21791 -5.073 8.87e-06 ***
M7 -0.92429 0.21855 -4.229 0.000128 ***
M8 -0.98830 0.22730 -4.348 8.86e-05 ***
M9 -1.03830 0.22730 -4.568 4.45e-05 ***
M10 -0.71920 0.22744 -3.162 0.002945 **
M11 -0.86179 0.22692 -3.798 0.000475 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3209 on 41 degrees of freedom
Multiple R-squared: 0.9258, Adjusted R-squared: 0.9023
F-statistic: 39.36 on 13 and 41 DF, p-value: < 2.2e-16
> 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,] 0.1788803412 0.3577606823 0.8211197
[2,] 0.1811033077 0.3622066155 0.8188967
[3,] 0.0977050454 0.1954100908 0.9022950
[4,] 0.0513025329 0.1026050659 0.9486975
[5,] 0.0231229580 0.0462459161 0.9768770
[6,] 0.1341288884 0.2682577767 0.8658711
[7,] 0.0854344386 0.1708688772 0.9145656
[8,] 0.0551698136 0.1103396272 0.9448302
[9,] 0.0384806594 0.0769613187 0.9615193
[10,] 0.0203123530 0.0406247060 0.9796876
[11,] 0.0097297036 0.0194594072 0.9902703
[12,] 0.0052893671 0.0105787342 0.9947106
[13,] 0.0123146804 0.0246293609 0.9876853
[14,] 0.0097327933 0.0194655867 0.9902672
[15,] 0.0044976245 0.0089952490 0.9955024
[16,] 0.0022275564 0.0044551127 0.9977724
[17,] 0.0012394802 0.0024789603 0.9987605
[18,] 0.0011138437 0.0022276873 0.9988862
[19,] 0.0004397941 0.0008795882 0.9995602
[20,] 0.0233682984 0.0467365967 0.9766317
[21,] 0.1957035673 0.3914071347 0.8042964
[22,] 0.1152942106 0.2305884212 0.8847058
> postscript(file="/var/www/html/rcomp/tmp/1rnjk1259095591.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26f3v1259095591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lsyv1259095591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nhcb1259095591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vemt1259095591.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 = 55
Frequency = 1
1 2 3 4 5 6
-0.077076065 -0.159878952 -0.098664375 -0.092735447 0.050836306 0.328551110
7 8 9 10 11 12
-0.205447982 -0.041430517 0.020355360 0.613034174 0.173481778 0.047053531
13 14 15 16 17 18
0.247215485 0.152626721 -0.097944578 0.360840845 -0.083801526 0.034983896
19 20 21 22 23 24
0.030269546 0.194287011 0.156072887 -0.163034174 -0.020445334 -0.182231211
25 26 27 28 29 30
0.282573114 0.087984350 -0.074372825 0.084412597 -0.495587403 0.276054514
31 32 33 34 35 36
0.006697793 0.070715258 -0.179284742 -0.233749433 0.008839407 -0.641160593
37 38 39 40 41 42
-0.723499773 0.075838575 -0.062946847 -0.380589672 0.104052699 0.069981627
43 44 45 46 47 48
0.088839029 -0.223571753 0.002856494 -0.216250567 -0.161875851 0.776338273
49 50 51 52 53 54
0.270787238 -0.156570694 0.333928625 0.028071677 0.424499924 -0.709571148
55
0.079641614
> postscript(file="/var/www/html/rcomp/tmp/6gdkz1259095591.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 = 55
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.077076065 NA
1 -0.159878952 -0.077076065
2 -0.098664375 -0.159878952
3 -0.092735447 -0.098664375
4 0.050836306 -0.092735447
5 0.328551110 0.050836306
6 -0.205447982 0.328551110
7 -0.041430517 -0.205447982
8 0.020355360 -0.041430517
9 0.613034174 0.020355360
10 0.173481778 0.613034174
11 0.047053531 0.173481778
12 0.247215485 0.047053531
13 0.152626721 0.247215485
14 -0.097944578 0.152626721
15 0.360840845 -0.097944578
16 -0.083801526 0.360840845
17 0.034983896 -0.083801526
18 0.030269546 0.034983896
19 0.194287011 0.030269546
20 0.156072887 0.194287011
21 -0.163034174 0.156072887
22 -0.020445334 -0.163034174
23 -0.182231211 -0.020445334
24 0.282573114 -0.182231211
25 0.087984350 0.282573114
26 -0.074372825 0.087984350
27 0.084412597 -0.074372825
28 -0.495587403 0.084412597
29 0.276054514 -0.495587403
30 0.006697793 0.276054514
31 0.070715258 0.006697793
32 -0.179284742 0.070715258
33 -0.233749433 -0.179284742
34 0.008839407 -0.233749433
35 -0.641160593 0.008839407
36 -0.723499773 -0.641160593
37 0.075838575 -0.723499773
38 -0.062946847 0.075838575
39 -0.380589672 -0.062946847
40 0.104052699 -0.380589672
41 0.069981627 0.104052699
42 0.088839029 0.069981627
43 -0.223571753 0.088839029
44 0.002856494 -0.223571753
45 -0.216250567 0.002856494
46 -0.161875851 -0.216250567
47 0.776338273 -0.161875851
48 0.270787238 0.776338273
49 -0.156570694 0.270787238
50 0.333928625 -0.156570694
51 0.028071677 0.333928625
52 0.424499924 0.028071677
53 -0.709571148 0.424499924
54 0.079641614 -0.709571148
55 NA 0.079641614
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.159878952 -0.077076065
[2,] -0.098664375 -0.159878952
[3,] -0.092735447 -0.098664375
[4,] 0.050836306 -0.092735447
[5,] 0.328551110 0.050836306
[6,] -0.205447982 0.328551110
[7,] -0.041430517 -0.205447982
[8,] 0.020355360 -0.041430517
[9,] 0.613034174 0.020355360
[10,] 0.173481778 0.613034174
[11,] 0.047053531 0.173481778
[12,] 0.247215485 0.047053531
[13,] 0.152626721 0.247215485
[14,] -0.097944578 0.152626721
[15,] 0.360840845 -0.097944578
[16,] -0.083801526 0.360840845
[17,] 0.034983896 -0.083801526
[18,] 0.030269546 0.034983896
[19,] 0.194287011 0.030269546
[20,] 0.156072887 0.194287011
[21,] -0.163034174 0.156072887
[22,] -0.020445334 -0.163034174
[23,] -0.182231211 -0.020445334
[24,] 0.282573114 -0.182231211
[25,] 0.087984350 0.282573114
[26,] -0.074372825 0.087984350
[27,] 0.084412597 -0.074372825
[28,] -0.495587403 0.084412597
[29,] 0.276054514 -0.495587403
[30,] 0.006697793 0.276054514
[31,] 0.070715258 0.006697793
[32,] -0.179284742 0.070715258
[33,] -0.233749433 -0.179284742
[34,] 0.008839407 -0.233749433
[35,] -0.641160593 0.008839407
[36,] -0.723499773 -0.641160593
[37,] 0.075838575 -0.723499773
[38,] -0.062946847 0.075838575
[39,] -0.380589672 -0.062946847
[40,] 0.104052699 -0.380589672
[41,] 0.069981627 0.104052699
[42,] 0.088839029 0.069981627
[43,] -0.223571753 0.088839029
[44,] 0.002856494 -0.223571753
[45,] -0.216250567 0.002856494
[46,] -0.161875851 -0.216250567
[47,] 0.776338273 -0.161875851
[48,] 0.270787238 0.776338273
[49,] -0.156570694 0.270787238
[50,] 0.333928625 -0.156570694
[51,] 0.028071677 0.333928625
[52,] 0.424499924 0.028071677
[53,] -0.709571148 0.424499924
[54,] 0.079641614 -0.709571148
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.159878952 -0.077076065
2 -0.098664375 -0.159878952
3 -0.092735447 -0.098664375
4 0.050836306 -0.092735447
5 0.328551110 0.050836306
6 -0.205447982 0.328551110
7 -0.041430517 -0.205447982
8 0.020355360 -0.041430517
9 0.613034174 0.020355360
10 0.173481778 0.613034174
11 0.047053531 0.173481778
12 0.247215485 0.047053531
13 0.152626721 0.247215485
14 -0.097944578 0.152626721
15 0.360840845 -0.097944578
16 -0.083801526 0.360840845
17 0.034983896 -0.083801526
18 0.030269546 0.034983896
19 0.194287011 0.030269546
20 0.156072887 0.194287011
21 -0.163034174 0.156072887
22 -0.020445334 -0.163034174
23 -0.182231211 -0.020445334
24 0.282573114 -0.182231211
25 0.087984350 0.282573114
26 -0.074372825 0.087984350
27 0.084412597 -0.074372825
28 -0.495587403 0.084412597
29 0.276054514 -0.495587403
30 0.006697793 0.276054514
31 0.070715258 0.006697793
32 -0.179284742 0.070715258
33 -0.233749433 -0.179284742
34 0.008839407 -0.233749433
35 -0.641160593 0.008839407
36 -0.723499773 -0.641160593
37 0.075838575 -0.723499773
38 -0.062946847 0.075838575
39 -0.380589672 -0.062946847
40 0.104052699 -0.380589672
41 0.069981627 0.104052699
42 0.088839029 0.069981627
43 -0.223571753 0.088839029
44 0.002856494 -0.223571753
45 -0.216250567 0.002856494
46 -0.161875851 -0.216250567
47 0.776338273 -0.161875851
48 0.270787238 0.776338273
49 -0.156570694 0.270787238
50 0.333928625 -0.156570694
51 0.028071677 0.333928625
52 0.424499924 0.028071677
53 -0.709571148 0.424499924
54 0.079641614 -0.709571148
> 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/7rwgu1259095591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vwjq1259095591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dieg1259095591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kj2u1259095591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h8gl1259095591.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/12yczl1259095591.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/13bymh1259095591.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/14mwxa1259095591.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/15jlco1259095591.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/160r7w1259095591.tab")
+ }
>
> system("convert tmp/1rnjk1259095591.ps tmp/1rnjk1259095591.png")
> system("convert tmp/26f3v1259095591.ps tmp/26f3v1259095591.png")
> system("convert tmp/3lsyv1259095591.ps tmp/3lsyv1259095591.png")
> system("convert tmp/4nhcb1259095591.ps tmp/4nhcb1259095591.png")
> system("convert tmp/5vemt1259095591.ps tmp/5vemt1259095591.png")
> system("convert tmp/6gdkz1259095591.ps tmp/6gdkz1259095591.png")
> system("convert tmp/7rwgu1259095591.ps tmp/7rwgu1259095591.png")
> system("convert tmp/8vwjq1259095591.ps tmp/8vwjq1259095591.png")
> system("convert tmp/9dieg1259095591.ps tmp/9dieg1259095591.png")
> system("convert tmp/10kj2u1259095591.ps tmp/10kj2u1259095591.png")
>
>
> proc.time()
user system elapsed
2.335 1.540 2.756