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(0.6348,1.5291,0.634,1.5358,0.62915,1.5355,0.62168,1.5287,0.61328,1.5334,0.6089,1.5225,0.60857,1.5135,0.62672,1.5144,0.62291,1.4913,0.62393,1.4793,0.61838,1.4663,0.62012,1.4749,0.61659,1.4745,0.6116,1.4775,0.61573,1.4678,0.61407,1.4658,0.62823,1.4572,0.64405,1.4721,0.6387,1.4624,0.63633,1.4636,0.63059,1.4649,0.62994,1.465,0.63709,1.4673,0.64217,1.4679,0.65711,1.4621,0.66977,1.4674,0.68255,1.4695,0.68902,1.4964,0.71322,1.5155,0.70224,1.5411,0.70045,1.5476,0.69919,1.54,0.69693,1.5474,0.69763,1.5485,0.69278,1.559,0.70196,1.5544,0.69215,1.5657,0.6769,1.5734,0.67124,1.567,0.66532,1.5547,0.67157,1.54,0.66428,1.5192,0.66576,1.527,0.66942,1.5387,0.6813,1.5431,0.69144,1.5426,0.69862,1.5216,0.695,1.5364,0.69867,1.5469,0.68968,1.5501,0.69233,1.5494,0.68293,1.5475,0.68399,1.5448,0.66895,1.5391,0.68756,1.5578,0.68527,1.5528,0.6776,1.5496,0.68137,1.549,0.67933,1.5449,0.67922,1.5479),dim=c(2,60),dimnames=list(c('Britse_pond','Zwitserse_frank'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Britse_pond','Zwitserse_frank'),1:60))
> 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
Britse_pond Zwitserse_frank M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 0.63480 1.5291 1 0 0 0 0 0 0 0 0 0 0
2 0.63400 1.5358 0 1 0 0 0 0 0 0 0 0 0
3 0.62915 1.5355 0 0 1 0 0 0 0 0 0 0 0
4 0.62168 1.5287 0 0 0 1 0 0 0 0 0 0 0
5 0.61328 1.5334 0 0 0 0 1 0 0 0 0 0 0
6 0.60890 1.5225 0 0 0 0 0 1 0 0 0 0 0
7 0.60857 1.5135 0 0 0 0 0 0 1 0 0 0 0
8 0.62672 1.5144 0 0 0 0 0 0 0 1 0 0 0
9 0.62291 1.4913 0 0 0 0 0 0 0 0 1 0 0
10 0.62393 1.4793 0 0 0 0 0 0 0 0 0 1 0
11 0.61838 1.4663 0 0 0 0 0 0 0 0 0 0 1
12 0.62012 1.4749 0 0 0 0 0 0 0 0 0 0 0
13 0.61659 1.4745 1 0 0 0 0 0 0 0 0 0 0
14 0.61160 1.4775 0 1 0 0 0 0 0 0 0 0 0
15 0.61573 1.4678 0 0 1 0 0 0 0 0 0 0 0
16 0.61407 1.4658 0 0 0 1 0 0 0 0 0 0 0
17 0.62823 1.4572 0 0 0 0 1 0 0 0 0 0 0
18 0.64405 1.4721 0 0 0 0 0 1 0 0 0 0 0
19 0.63870 1.4624 0 0 0 0 0 0 1 0 0 0 0
20 0.63633 1.4636 0 0 0 0 0 0 0 1 0 0 0
21 0.63059 1.4649 0 0 0 0 0 0 0 0 1 0 0
22 0.62994 1.4650 0 0 0 0 0 0 0 0 0 1 0
23 0.63709 1.4673 0 0 0 0 0 0 0 0 0 0 1
24 0.64217 1.4679 0 0 0 0 0 0 0 0 0 0 0
25 0.65711 1.4621 1 0 0 0 0 0 0 0 0 0 0
26 0.66977 1.4674 0 1 0 0 0 0 0 0 0 0 0
27 0.68255 1.4695 0 0 1 0 0 0 0 0 0 0 0
28 0.68902 1.4964 0 0 0 1 0 0 0 0 0 0 0
29 0.71322 1.5155 0 0 0 0 1 0 0 0 0 0 0
30 0.70224 1.5411 0 0 0 0 0 1 0 0 0 0 0
31 0.70045 1.5476 0 0 0 0 0 0 1 0 0 0 0
32 0.69919 1.5400 0 0 0 0 0 0 0 1 0 0 0
33 0.69693 1.5474 0 0 0 0 0 0 0 0 1 0 0
34 0.69763 1.5485 0 0 0 0 0 0 0 0 0 1 0
35 0.69278 1.5590 0 0 0 0 0 0 0 0 0 0 1
36 0.70196 1.5544 0 0 0 0 0 0 0 0 0 0 0
37 0.69215 1.5657 1 0 0 0 0 0 0 0 0 0 0
38 0.67690 1.5734 0 1 0 0 0 0 0 0 0 0 0
39 0.67124 1.5670 0 0 1 0 0 0 0 0 0 0 0
40 0.66532 1.5547 0 0 0 1 0 0 0 0 0 0 0
41 0.67157 1.5400 0 0 0 0 1 0 0 0 0 0 0
42 0.66428 1.5192 0 0 0 0 0 1 0 0 0 0 0
43 0.66576 1.5270 0 0 0 0 0 0 1 0 0 0 0
44 0.66942 1.5387 0 0 0 0 0 0 0 1 0 0 0
45 0.68130 1.5431 0 0 0 0 0 0 0 0 1 0 0
46 0.69144 1.5426 0 0 0 0 0 0 0 0 0 1 0
47 0.69862 1.5216 0 0 0 0 0 0 0 0 0 0 1
48 0.69500 1.5364 0 0 0 0 0 0 0 0 0 0 0
49 0.69867 1.5469 1 0 0 0 0 0 0 0 0 0 0
50 0.68968 1.5501 0 1 0 0 0 0 0 0 0 0 0
51 0.69233 1.5494 0 0 1 0 0 0 0 0 0 0 0
52 0.68293 1.5475 0 0 0 1 0 0 0 0 0 0 0
53 0.68399 1.5448 0 0 0 0 1 0 0 0 0 0 0
54 0.66895 1.5391 0 0 0 0 0 1 0 0 0 0 0
55 0.68756 1.5578 0 0 0 0 0 0 1 0 0 0 0
56 0.68527 1.5528 0 0 0 0 0 0 0 1 0 0 0
57 0.67760 1.5496 0 0 0 0 0 0 0 0 1 0 0
58 0.68137 1.5490 0 0 0 0 0 0 0 0 0 1 0
59 0.67933 1.5449 0 0 0 0 0 0 0 0 0 0 1
60 0.67922 1.5479 0 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) Zwitserse_frank M1 M2
-1.931e-01 5.677e-01 -7.467e-03 -1.388e-02
M3 M4 M5 M6
-1.037e-02 -1.441e-02 -6.703e-03 -1.143e-02
M7 M8 M9 M10
-1.053e-02 -7.487e-03 -7.508e-03 -3.161e-03
M11
8.932e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0574185 -0.0142636 0.0007732 0.0133627 0.0526835
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.931e-01 1.476e-01 -1.308 0.197
Zwitserse_frank 5.677e-01 9.703e-02 5.851 4.52e-07 ***
M1 -7.467e-03 1.702e-02 -0.439 0.663
M2 -1.388e-02 1.703e-02 -0.815 0.419
M3 -1.037e-02 1.702e-02 -0.609 0.545
M4 -1.441e-02 1.702e-02 -0.846 0.402
M5 -6.703e-03 1.702e-02 -0.394 0.696
M6 -1.143e-02 1.702e-02 -0.671 0.505
M7 -1.053e-02 1.703e-02 -0.618 0.539
M8 -7.487e-03 1.703e-02 -0.440 0.662
M9 -7.508e-03 1.702e-02 -0.441 0.661
M10 -3.161e-03 1.702e-02 -0.186 0.853
M11 8.932e-05 1.703e-02 0.005 0.996
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02691 on 47 degrees of freedom
Multiple R-squared: 0.4296, Adjusted R-squared: 0.2839
F-statistic: 2.95 on 12 and 47 DF, p-value: 0.003944
> 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,] 0.03249482 6.498964e-02 9.675052e-01
[2,] 0.19657592 3.931518e-01 8.034241e-01
[3,] 0.44440975 8.888195e-01 5.555902e-01
[4,] 0.48815956 9.763191e-01 5.118404e-01
[5,] 0.38498680 7.699736e-01 6.150132e-01
[6,] 0.31114542 6.222908e-01 6.888546e-01
[7,] 0.28313073 5.662615e-01 7.168693e-01
[8,] 0.32733494 6.546699e-01 6.726651e-01
[9,] 0.43503044 8.700609e-01 5.649696e-01
[10,] 0.61507719 7.698456e-01 3.849228e-01
[11,] 0.80046104 3.990779e-01 1.995390e-01
[12,] 0.92625787 1.474843e-01 7.374213e-02
[13,] 0.98319540 3.360920e-02 1.680460e-02
[14,] 0.99945036 1.099271e-03 5.496353e-04
[15,] 0.99995787 8.426270e-05 4.213135e-05
[16,] 0.99998717 2.565235e-05 1.282618e-05
[17,] 0.99999208 1.583993e-05 7.919967e-06
[18,] 0.99999151 1.697855e-05 8.489275e-06
[19,] 0.99998289 3.421084e-05 1.710542e-05
[20,] 0.99994720 1.056009e-04 5.280047e-05
[21,] 0.99991505 1.698945e-04 8.494727e-05
[22,] 0.99971612 5.677539e-04 2.838769e-04
[23,] 0.99920216 1.595685e-03 7.978424e-04
[24,] 0.99879872 2.402561e-03 1.201280e-03
[25,] 0.99784851 4.302971e-03 2.151486e-03
[26,] 0.99438740 1.122519e-02 5.612597e-03
[27,] 0.98228739 3.542522e-02 1.771261e-02
[28,] 0.98505562 2.988877e-02 1.494438e-02
[29,] 0.99878973 2.420533e-03 1.210267e-03
> postscript(file="/var/www/html/rcomp/tmp/18qt11258651479.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fzda1258651479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33zth1258651479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4sp771258651479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5eurl1258651479.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 = 60
Frequency = 1
1 2 3 4 5
-0.0326939655 -0.0308828782 -0.0390756838 -0.0386464741 -0.0574184817
6 7 8 9 10
-0.0508845113 -0.0470055210 -0.0324082068 -0.0230829468 -0.0195976174
11 12 13 14 15
-0.0210180336 -0.0240709813 -0.0199072307 -0.0201856322 -0.0140620035
16 17 18 19 20
-0.0105477815 0.0007906975 0.0128778592 0.0121342436 0.0060412461
21 22 23 24 25
-0.0004155146 -0.0054694249 -0.0028757394 0.0019529590 0.0276523208
26 27 28 29 30
0.0437181961 0.0517928967 0.0470304221 0.0526834514 0.0318961614
31 32 33 34 35
0.0255157125 0.0255285257 0.0190887598 0.0148171437 0.0007556420
36 37 38 39 40
0.0126364104 0.0038780035 -0.0093286150 -0.0148684154 -0.0097668240
41 42 43 44 45
-0.0028753398 0.0063689177 0.0025204512 -0.0035034568 0.0058998946
46 47 48 49 50
0.0119766077 0.0278278376 0.0158951141 0.0210708719 0.0166789293
51 52 53 54 55
0.0162132061 0.0119306575 0.0068196725 -0.0002584270 0.0068351137
56 57 58 59 60
0.0043418919 -0.0014901929 -0.0017267092 -0.0046897067 -0.0064135022
> postscript(file="/var/www/html/rcomp/tmp/6k0cg1258651479.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0326939655 NA
1 -0.0308828782 -0.0326939655
2 -0.0390756838 -0.0308828782
3 -0.0386464741 -0.0390756838
4 -0.0574184817 -0.0386464741
5 -0.0508845113 -0.0574184817
6 -0.0470055210 -0.0508845113
7 -0.0324082068 -0.0470055210
8 -0.0230829468 -0.0324082068
9 -0.0195976174 -0.0230829468
10 -0.0210180336 -0.0195976174
11 -0.0240709813 -0.0210180336
12 -0.0199072307 -0.0240709813
13 -0.0201856322 -0.0199072307
14 -0.0140620035 -0.0201856322
15 -0.0105477815 -0.0140620035
16 0.0007906975 -0.0105477815
17 0.0128778592 0.0007906975
18 0.0121342436 0.0128778592
19 0.0060412461 0.0121342436
20 -0.0004155146 0.0060412461
21 -0.0054694249 -0.0004155146
22 -0.0028757394 -0.0054694249
23 0.0019529590 -0.0028757394
24 0.0276523208 0.0019529590
25 0.0437181961 0.0276523208
26 0.0517928967 0.0437181961
27 0.0470304221 0.0517928967
28 0.0526834514 0.0470304221
29 0.0318961614 0.0526834514
30 0.0255157125 0.0318961614
31 0.0255285257 0.0255157125
32 0.0190887598 0.0255285257
33 0.0148171437 0.0190887598
34 0.0007556420 0.0148171437
35 0.0126364104 0.0007556420
36 0.0038780035 0.0126364104
37 -0.0093286150 0.0038780035
38 -0.0148684154 -0.0093286150
39 -0.0097668240 -0.0148684154
40 -0.0028753398 -0.0097668240
41 0.0063689177 -0.0028753398
42 0.0025204512 0.0063689177
43 -0.0035034568 0.0025204512
44 0.0058998946 -0.0035034568
45 0.0119766077 0.0058998946
46 0.0278278376 0.0119766077
47 0.0158951141 0.0278278376
48 0.0210708719 0.0158951141
49 0.0166789293 0.0210708719
50 0.0162132061 0.0166789293
51 0.0119306575 0.0162132061
52 0.0068196725 0.0119306575
53 -0.0002584270 0.0068196725
54 0.0068351137 -0.0002584270
55 0.0043418919 0.0068351137
56 -0.0014901929 0.0043418919
57 -0.0017267092 -0.0014901929
58 -0.0046897067 -0.0017267092
59 -0.0064135022 -0.0046897067
60 NA -0.0064135022
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0308828782 -0.0326939655
[2,] -0.0390756838 -0.0308828782
[3,] -0.0386464741 -0.0390756838
[4,] -0.0574184817 -0.0386464741
[5,] -0.0508845113 -0.0574184817
[6,] -0.0470055210 -0.0508845113
[7,] -0.0324082068 -0.0470055210
[8,] -0.0230829468 -0.0324082068
[9,] -0.0195976174 -0.0230829468
[10,] -0.0210180336 -0.0195976174
[11,] -0.0240709813 -0.0210180336
[12,] -0.0199072307 -0.0240709813
[13,] -0.0201856322 -0.0199072307
[14,] -0.0140620035 -0.0201856322
[15,] -0.0105477815 -0.0140620035
[16,] 0.0007906975 -0.0105477815
[17,] 0.0128778592 0.0007906975
[18,] 0.0121342436 0.0128778592
[19,] 0.0060412461 0.0121342436
[20,] -0.0004155146 0.0060412461
[21,] -0.0054694249 -0.0004155146
[22,] -0.0028757394 -0.0054694249
[23,] 0.0019529590 -0.0028757394
[24,] 0.0276523208 0.0019529590
[25,] 0.0437181961 0.0276523208
[26,] 0.0517928967 0.0437181961
[27,] 0.0470304221 0.0517928967
[28,] 0.0526834514 0.0470304221
[29,] 0.0318961614 0.0526834514
[30,] 0.0255157125 0.0318961614
[31,] 0.0255285257 0.0255157125
[32,] 0.0190887598 0.0255285257
[33,] 0.0148171437 0.0190887598
[34,] 0.0007556420 0.0148171437
[35,] 0.0126364104 0.0007556420
[36,] 0.0038780035 0.0126364104
[37,] -0.0093286150 0.0038780035
[38,] -0.0148684154 -0.0093286150
[39,] -0.0097668240 -0.0148684154
[40,] -0.0028753398 -0.0097668240
[41,] 0.0063689177 -0.0028753398
[42,] 0.0025204512 0.0063689177
[43,] -0.0035034568 0.0025204512
[44,] 0.0058998946 -0.0035034568
[45,] 0.0119766077 0.0058998946
[46,] 0.0278278376 0.0119766077
[47,] 0.0158951141 0.0278278376
[48,] 0.0210708719 0.0158951141
[49,] 0.0166789293 0.0210708719
[50,] 0.0162132061 0.0166789293
[51,] 0.0119306575 0.0162132061
[52,] 0.0068196725 0.0119306575
[53,] -0.0002584270 0.0068196725
[54,] 0.0068351137 -0.0002584270
[55,] 0.0043418919 0.0068351137
[56,] -0.0014901929 0.0043418919
[57,] -0.0017267092 -0.0014901929
[58,] -0.0046897067 -0.0017267092
[59,] -0.0064135022 -0.0046897067
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0308828782 -0.0326939655
2 -0.0390756838 -0.0308828782
3 -0.0386464741 -0.0390756838
4 -0.0574184817 -0.0386464741
5 -0.0508845113 -0.0574184817
6 -0.0470055210 -0.0508845113
7 -0.0324082068 -0.0470055210
8 -0.0230829468 -0.0324082068
9 -0.0195976174 -0.0230829468
10 -0.0210180336 -0.0195976174
11 -0.0240709813 -0.0210180336
12 -0.0199072307 -0.0240709813
13 -0.0201856322 -0.0199072307
14 -0.0140620035 -0.0201856322
15 -0.0105477815 -0.0140620035
16 0.0007906975 -0.0105477815
17 0.0128778592 0.0007906975
18 0.0121342436 0.0128778592
19 0.0060412461 0.0121342436
20 -0.0004155146 0.0060412461
21 -0.0054694249 -0.0004155146
22 -0.0028757394 -0.0054694249
23 0.0019529590 -0.0028757394
24 0.0276523208 0.0019529590
25 0.0437181961 0.0276523208
26 0.0517928967 0.0437181961
27 0.0470304221 0.0517928967
28 0.0526834514 0.0470304221
29 0.0318961614 0.0526834514
30 0.0255157125 0.0318961614
31 0.0255285257 0.0255157125
32 0.0190887598 0.0255285257
33 0.0148171437 0.0190887598
34 0.0007556420 0.0148171437
35 0.0126364104 0.0007556420
36 0.0038780035 0.0126364104
37 -0.0093286150 0.0038780035
38 -0.0148684154 -0.0093286150
39 -0.0097668240 -0.0148684154
40 -0.0028753398 -0.0097668240
41 0.0063689177 -0.0028753398
42 0.0025204512 0.0063689177
43 -0.0035034568 0.0025204512
44 0.0058998946 -0.0035034568
45 0.0119766077 0.0058998946
46 0.0278278376 0.0119766077
47 0.0158951141 0.0278278376
48 0.0210708719 0.0158951141
49 0.0166789293 0.0210708719
50 0.0162132061 0.0166789293
51 0.0119306575 0.0162132061
52 0.0068196725 0.0119306575
53 -0.0002584270 0.0068196725
54 0.0068351137 -0.0002584270
55 0.0043418919 0.0068351137
56 -0.0014901929 0.0043418919
57 -0.0017267092 -0.0014901929
58 -0.0046897067 -0.0017267092
59 -0.0064135022 -0.0046897067
> 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/7au6y1258651479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lyv51258651479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96vx81258651479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10l1ni1258651479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11htzi1258651479.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/12yu7l1258651480.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/13cuo41258651480.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/144i871258651480.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/156u1l1258651480.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/16xazh1258651480.tab")
+ }
>
> system("convert tmp/18qt11258651479.ps tmp/18qt11258651479.png")
> system("convert tmp/2fzda1258651479.ps tmp/2fzda1258651479.png")
> system("convert tmp/33zth1258651479.ps tmp/33zth1258651479.png")
> system("convert tmp/4sp771258651479.ps tmp/4sp771258651479.png")
> system("convert tmp/5eurl1258651479.ps tmp/5eurl1258651479.png")
> system("convert tmp/6k0cg1258651479.ps tmp/6k0cg1258651479.png")
> system("convert tmp/7au6y1258651479.ps tmp/7au6y1258651479.png")
> system("convert tmp/8lyv51258651479.ps tmp/8lyv51258651479.png")
> system("convert tmp/96vx81258651479.ps tmp/96vx81258651479.png")
> system("convert tmp/10l1ni1258651479.ps tmp/10l1ni1258651479.png")
>
>
> proc.time()
user system elapsed
2.407 1.557 2.803