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(3.6,4.5,3.9,3.3,4.6,3.6,3.2,4.9,3.3,3.4,4.9,3.2,3.4,4.5,3.4,3.5,4.6,3.4,3.2,4.7,3.5,3.3,4.7,3.2,3.3,4.3,3.3,3.4,4.2,3.3,3.7,4.4,3.4,3.9,4,3.7,4,3.8,3.9,3.7,3.6,4,3.9,3.6,3.7,4.2,3.3,3.9,4.4,3.4,4.2,4.3,3.4,4.4,4.2,3.3,4.3,4.3,3.3,4.2,4.3,3.2,4.3,4.3,3.1,4.3,4.5,3.1,4.3,5,2.4,4.5,5.2,2.4,5,5.2,2.4,5.2,5.4,2.1,5.2,5.5,2,5.4,5.4,2,5.5,5.5,2.1,5.4,5.4,2.1,5.5,5.7,2,5.4,5.7,2,5.7,6.1,2,5.7,6.5,1.7,6.1,6.9,1.3,6.5,6.8,1.2,6.9,6.7,1.1,6.8,6.6,1.4,6.7,6.5,1.5,6.6,6.4,1.4,6.5,6.1,1.1,6.4,6.2,1.1,6.1,6.3,1,6.2,6.4,1.4,6.3,6.5,1.3,6.4,6.7,1.2,6.5,7,1.5,6.7,7,1.6,7,6.8,1.8,7,6.7,1.5,6.8,6.7,1.3,6.7,6.5,1.6,6.7,6.4,1.6,6.5,6.1,1.8,6.4,6.2,1.8,6.1,6,1.6,6.2,6.1,1.8,6,6.1,2,6.1),dim=c(3,59),dimnames=list(c('Werkl','Infl','M1(t)'),1:59))
> y <- array(NA,dim=c(3,59),dimnames=list(c('Werkl','Infl','M1(t)'),1:59))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
Werkl Infl M1(t) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 3.6 4.5 3.9 1 0 0 0 0 0 0 0 0 0 0 1
2 3.3 4.6 3.6 0 1 0 0 0 0 0 0 0 0 0 2
3 3.2 4.9 3.3 0 0 1 0 0 0 0 0 0 0 0 3
4 3.4 4.9 3.2 0 0 0 1 0 0 0 0 0 0 0 4
5 3.4 4.5 3.4 0 0 0 0 1 0 0 0 0 0 0 5
6 3.5 4.6 3.4 0 0 0 0 0 1 0 0 0 0 0 6
7 3.2 4.7 3.5 0 0 0 0 0 0 1 0 0 0 0 7
8 3.3 4.7 3.2 0 0 0 0 0 0 0 1 0 0 0 8
9 3.3 4.3 3.3 0 0 0 0 0 0 0 0 1 0 0 9
10 3.4 4.2 3.3 0 0 0 0 0 0 0 0 0 1 0 10
11 3.7 4.4 3.4 0 0 0 0 0 0 0 0 0 0 1 11
12 3.9 4.0 3.7 0 0 0 0 0 0 0 0 0 0 0 12
13 4.0 3.8 3.9 1 0 0 0 0 0 0 0 0 0 0 13
14 3.7 3.6 4.0 0 1 0 0 0 0 0 0 0 0 0 14
15 3.9 3.6 3.7 0 0 1 0 0 0 0 0 0 0 0 15
16 4.2 3.3 3.9 0 0 0 1 0 0 0 0 0 0 0 16
17 4.4 3.4 4.2 0 0 0 0 1 0 0 0 0 0 0 17
18 4.3 3.4 4.4 0 0 0 0 0 1 0 0 0 0 0 18
19 4.2 3.3 4.3 0 0 0 0 0 0 1 0 0 0 0 19
20 4.3 3.3 4.2 0 0 0 0 0 0 0 1 0 0 0 20
21 4.3 3.2 4.3 0 0 0 0 0 0 0 0 1 0 0 21
22 4.3 3.1 4.3 0 0 0 0 0 0 0 0 0 1 0 22
23 4.5 3.1 4.3 0 0 0 0 0 0 0 0 0 0 1 23
24 5.0 2.4 4.5 0 0 0 0 0 0 0 0 0 0 0 24
25 5.2 2.4 5.0 1 0 0 0 0 0 0 0 0 0 0 25
26 5.2 2.4 5.2 0 1 0 0 0 0 0 0 0 0 0 26
27 5.4 2.1 5.2 0 0 1 0 0 0 0 0 0 0 0 27
28 5.5 2.0 5.4 0 0 0 1 0 0 0 0 0 0 0 28
29 5.4 2.0 5.5 0 0 0 0 1 0 0 0 0 0 0 29
30 5.5 2.1 5.4 0 0 0 0 0 1 0 0 0 0 0 30
31 5.4 2.1 5.5 0 0 0 0 0 0 1 0 0 0 0 31
32 5.7 2.0 5.4 0 0 0 0 0 0 0 1 0 0 0 32
33 5.7 2.0 5.7 0 0 0 0 0 0 0 0 1 0 0 33
34 6.1 2.0 5.7 0 0 0 0 0 0 0 0 0 1 0 34
35 6.5 1.7 6.1 0 0 0 0 0 0 0 0 0 0 1 35
36 6.9 1.3 6.5 0 0 0 0 0 0 0 0 0 0 0 36
37 6.8 1.2 6.9 1 0 0 0 0 0 0 0 0 0 0 37
38 6.7 1.1 6.8 0 1 0 0 0 0 0 0 0 0 0 38
39 6.6 1.4 6.7 0 0 1 0 0 0 0 0 0 0 0 39
40 6.5 1.5 6.6 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 1.4 6.5 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 1.1 6.4 0 0 0 0 0 1 0 0 0 0 0 42
43 6.2 1.1 6.1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.3 1.0 6.2 0 0 0 0 0 0 0 1 0 0 0 44
45 6.4 1.4 6.3 0 0 0 0 0 0 0 0 1 0 0 45
46 6.5 1.3 6.4 0 0 0 0 0 0 0 0 0 1 0 46
47 6.7 1.2 6.5 0 0 0 0 0 0 0 0 0 0 1 47
48 7.0 1.5 6.7 0 0 0 0 0 0 0 0 0 0 0 48
49 7.0 1.6 7.0 1 0 0 0 0 0 0 0 0 0 0 49
50 6.8 1.8 7.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.7 1.5 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 6.7 1.3 6.7 0 0 0 1 0 0 0 0 0 0 0 52
53 6.5 1.6 6.7 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 1.6 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.1 1.8 6.4 0 0 0 0 0 0 1 0 0 0 0 55
56 6.2 1.8 6.1 0 0 0 0 0 0 0 1 0 0 0 56
57 6.0 1.6 6.2 0 0 0 0 0 0 0 0 1 0 0 57
58 6.1 1.8 6.0 0 0 0 0 0 0 0 0 0 1 0 58
59 6.1 2.0 6.1 0 0 0 0 0 0 0 0 0 0 1 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl `M1(t)` M1 M2 M3
1.815902 -0.200998 0.824986 -0.302564 -0.463822 -0.293082
M4 M5 M6 M7 M8 M9
-0.227439 -0.351715 -0.380492 -0.460711 -0.211010 -0.356325
M10 M11 t
-0.201603 -0.094858 -0.002243
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.300028 -0.079979 0.002446 0.082515 0.261529
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.815902 0.416745 4.357 7.78e-05 ***
Infl -0.200998 0.052076 -3.860 0.000368 ***
`M1(t)` 0.824986 0.066791 12.352 6.74e-16 ***
M1 -0.302564 0.091674 -3.300 0.001920 **
M2 -0.463822 0.090772 -5.110 6.71e-06 ***
M3 -0.293082 0.088408 -3.315 0.001841 **
M4 -0.227439 0.087970 -2.585 0.013117 *
M5 -0.351715 0.088296 -3.983 0.000252 ***
M6 -0.380492 0.088137 -4.317 8.84e-05 ***
M7 -0.460711 0.088588 -5.201 4.96e-06 ***
M8 -0.211010 0.090105 -2.342 0.023781 *
M9 -0.356325 0.089442 -3.984 0.000251 ***
M10 -0.201603 0.090357 -2.231 0.030813 *
M11 -0.094858 0.089853 -1.056 0.296865
t -0.002243 0.003246 -0.691 0.493251
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1307 on 44 degrees of freedom
Multiple R-squared: 0.9921, Adjusted R-squared: 0.9895
F-statistic: 393.2 on 14 and 44 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,] 0.6291857 0.7416286 0.3708143
[2,] 0.5515172 0.8969657 0.4484828
[3,] 0.4067373 0.8134747 0.5932627
[4,] 0.2876663 0.5753327 0.7123337
[5,] 0.3007713 0.6015427 0.6992287
[6,] 0.3006061 0.6012122 0.6993939
[7,] 0.3088354 0.6176708 0.6911646
[8,] 0.3303544 0.6607087 0.6696456
[9,] 0.4784510 0.9569019 0.5215490
[10,] 0.4226388 0.8452776 0.5773612
[11,] 0.3696265 0.7392530 0.6303735
[12,] 0.3878880 0.7757759 0.6121120
[13,] 0.3253104 0.6506208 0.6746896
[14,] 0.3029502 0.6059004 0.6970498
[15,] 0.2918184 0.5836369 0.7081816
[16,] 0.2592961 0.5185923 0.7407039
[17,] 0.4003671 0.8007343 0.5996329
[18,] 0.4789092 0.9578185 0.5210908
[19,] 0.3758214 0.7516429 0.6241786
[20,] 0.3672466 0.7344933 0.6327534
[21,] 0.2615337 0.5230674 0.7384663
[22,] 0.2084990 0.4169980 0.7915010
[23,] 0.1876072 0.3752145 0.8123928
[24,] 0.1062310 0.2124620 0.8937690
> postscript(file="/var/www/html/rcomp/tmp/1jt0x1260104796.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ewz41260104796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hnph1260104796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4w2wj1260104796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rteb1260104796.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 = 59
Frequency = 1
1 2 3 4 5
-0.2240484785 -0.0929526421 -0.0536549192 0.1654431869 0.0465653336
6 7 8 9 10
0.1976856711 -0.0822521741 0.0177854795 0.0024455636 -0.0701339994
11 12 13 14 15
0.0830650499 -0.1374451153 0.0621651638 -0.1970327506 0.0819655471
16 17 18 19 20
0.0932684837 0.1923910909 -0.0415855428 0.0032741580 -0.0616853512
21 22 23 24 25
-0.0167258420 -0.1893054050 -0.0938073910 0.0078816000 0.1001957517
26 27 28 29 30
0.0986988726 0.0699020008 -0.0785954458 -0.1345754841 0.0990434348
31 32 33 34 35
-0.0009942188 0.1139464636 0.0140086183 0.2615288637 0.1667331269
36 37 38 39 40
0.0637243802 -0.0815626951 0.0443363617 -0.0813630783 -0.1421651638
41 42 43 44 45
-0.0532478477 -0.3000281623 0.1299285098 -0.1201279707 0.1253305804
46 47 48 49 50
-0.0297475640 -0.0368479398 0.0658391351 0.1432502580 0.1469501585
51 52 53 54 55
-0.0168495504 -0.0379510610 -0.0511330928 0.0448845992 -0.0499562748
56 57 58 59
0.0500813788 -0.1250589204 0.0276581047 -0.1191428460
> postscript(file="/var/www/html/rcomp/tmp/6jlng1260104796.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.2240484785 NA
1 -0.0929526421 -0.2240484785
2 -0.0536549192 -0.0929526421
3 0.1654431869 -0.0536549192
4 0.0465653336 0.1654431869
5 0.1976856711 0.0465653336
6 -0.0822521741 0.1976856711
7 0.0177854795 -0.0822521741
8 0.0024455636 0.0177854795
9 -0.0701339994 0.0024455636
10 0.0830650499 -0.0701339994
11 -0.1374451153 0.0830650499
12 0.0621651638 -0.1374451153
13 -0.1970327506 0.0621651638
14 0.0819655471 -0.1970327506
15 0.0932684837 0.0819655471
16 0.1923910909 0.0932684837
17 -0.0415855428 0.1923910909
18 0.0032741580 -0.0415855428
19 -0.0616853512 0.0032741580
20 -0.0167258420 -0.0616853512
21 -0.1893054050 -0.0167258420
22 -0.0938073910 -0.1893054050
23 0.0078816000 -0.0938073910
24 0.1001957517 0.0078816000
25 0.0986988726 0.1001957517
26 0.0699020008 0.0986988726
27 -0.0785954458 0.0699020008
28 -0.1345754841 -0.0785954458
29 0.0990434348 -0.1345754841
30 -0.0009942188 0.0990434348
31 0.1139464636 -0.0009942188
32 0.0140086183 0.1139464636
33 0.2615288637 0.0140086183
34 0.1667331269 0.2615288637
35 0.0637243802 0.1667331269
36 -0.0815626951 0.0637243802
37 0.0443363617 -0.0815626951
38 -0.0813630783 0.0443363617
39 -0.1421651638 -0.0813630783
40 -0.0532478477 -0.1421651638
41 -0.3000281623 -0.0532478477
42 0.1299285098 -0.3000281623
43 -0.1201279707 0.1299285098
44 0.1253305804 -0.1201279707
45 -0.0297475640 0.1253305804
46 -0.0368479398 -0.0297475640
47 0.0658391351 -0.0368479398
48 0.1432502580 0.0658391351
49 0.1469501585 0.1432502580
50 -0.0168495504 0.1469501585
51 -0.0379510610 -0.0168495504
52 -0.0511330928 -0.0379510610
53 0.0448845992 -0.0511330928
54 -0.0499562748 0.0448845992
55 0.0500813788 -0.0499562748
56 -0.1250589204 0.0500813788
57 0.0276581047 -0.1250589204
58 -0.1191428460 0.0276581047
59 NA -0.1191428460
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0929526421 -0.2240484785
[2,] -0.0536549192 -0.0929526421
[3,] 0.1654431869 -0.0536549192
[4,] 0.0465653336 0.1654431869
[5,] 0.1976856711 0.0465653336
[6,] -0.0822521741 0.1976856711
[7,] 0.0177854795 -0.0822521741
[8,] 0.0024455636 0.0177854795
[9,] -0.0701339994 0.0024455636
[10,] 0.0830650499 -0.0701339994
[11,] -0.1374451153 0.0830650499
[12,] 0.0621651638 -0.1374451153
[13,] -0.1970327506 0.0621651638
[14,] 0.0819655471 -0.1970327506
[15,] 0.0932684837 0.0819655471
[16,] 0.1923910909 0.0932684837
[17,] -0.0415855428 0.1923910909
[18,] 0.0032741580 -0.0415855428
[19,] -0.0616853512 0.0032741580
[20,] -0.0167258420 -0.0616853512
[21,] -0.1893054050 -0.0167258420
[22,] -0.0938073910 -0.1893054050
[23,] 0.0078816000 -0.0938073910
[24,] 0.1001957517 0.0078816000
[25,] 0.0986988726 0.1001957517
[26,] 0.0699020008 0.0986988726
[27,] -0.0785954458 0.0699020008
[28,] -0.1345754841 -0.0785954458
[29,] 0.0990434348 -0.1345754841
[30,] -0.0009942188 0.0990434348
[31,] 0.1139464636 -0.0009942188
[32,] 0.0140086183 0.1139464636
[33,] 0.2615288637 0.0140086183
[34,] 0.1667331269 0.2615288637
[35,] 0.0637243802 0.1667331269
[36,] -0.0815626951 0.0637243802
[37,] 0.0443363617 -0.0815626951
[38,] -0.0813630783 0.0443363617
[39,] -0.1421651638 -0.0813630783
[40,] -0.0532478477 -0.1421651638
[41,] -0.3000281623 -0.0532478477
[42,] 0.1299285098 -0.3000281623
[43,] -0.1201279707 0.1299285098
[44,] 0.1253305804 -0.1201279707
[45,] -0.0297475640 0.1253305804
[46,] -0.0368479398 -0.0297475640
[47,] 0.0658391351 -0.0368479398
[48,] 0.1432502580 0.0658391351
[49,] 0.1469501585 0.1432502580
[50,] -0.0168495504 0.1469501585
[51,] -0.0379510610 -0.0168495504
[52,] -0.0511330928 -0.0379510610
[53,] 0.0448845992 -0.0511330928
[54,] -0.0499562748 0.0448845992
[55,] 0.0500813788 -0.0499562748
[56,] -0.1250589204 0.0500813788
[57,] 0.0276581047 -0.1250589204
[58,] -0.1191428460 0.0276581047
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0929526421 -0.2240484785
2 -0.0536549192 -0.0929526421
3 0.1654431869 -0.0536549192
4 0.0465653336 0.1654431869
5 0.1976856711 0.0465653336
6 -0.0822521741 0.1976856711
7 0.0177854795 -0.0822521741
8 0.0024455636 0.0177854795
9 -0.0701339994 0.0024455636
10 0.0830650499 -0.0701339994
11 -0.1374451153 0.0830650499
12 0.0621651638 -0.1374451153
13 -0.1970327506 0.0621651638
14 0.0819655471 -0.1970327506
15 0.0932684837 0.0819655471
16 0.1923910909 0.0932684837
17 -0.0415855428 0.1923910909
18 0.0032741580 -0.0415855428
19 -0.0616853512 0.0032741580
20 -0.0167258420 -0.0616853512
21 -0.1893054050 -0.0167258420
22 -0.0938073910 -0.1893054050
23 0.0078816000 -0.0938073910
24 0.1001957517 0.0078816000
25 0.0986988726 0.1001957517
26 0.0699020008 0.0986988726
27 -0.0785954458 0.0699020008
28 -0.1345754841 -0.0785954458
29 0.0990434348 -0.1345754841
30 -0.0009942188 0.0990434348
31 0.1139464636 -0.0009942188
32 0.0140086183 0.1139464636
33 0.2615288637 0.0140086183
34 0.1667331269 0.2615288637
35 0.0637243802 0.1667331269
36 -0.0815626951 0.0637243802
37 0.0443363617 -0.0815626951
38 -0.0813630783 0.0443363617
39 -0.1421651638 -0.0813630783
40 -0.0532478477 -0.1421651638
41 -0.3000281623 -0.0532478477
42 0.1299285098 -0.3000281623
43 -0.1201279707 0.1299285098
44 0.1253305804 -0.1201279707
45 -0.0297475640 0.1253305804
46 -0.0368479398 -0.0297475640
47 0.0658391351 -0.0368479398
48 0.1432502580 0.0658391351
49 0.1469501585 0.1432502580
50 -0.0168495504 0.1469501585
51 -0.0379510610 -0.0168495504
52 -0.0511330928 -0.0379510610
53 0.0448845992 -0.0511330928
54 -0.0499562748 0.0448845992
55 0.0500813788 -0.0499562748
56 -0.1250589204 0.0500813788
57 0.0276581047 -0.1250589204
58 -0.1191428460 0.0276581047
> 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/72gw01260104796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86djv1260104796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/963tp1260104796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104y0t1260104796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11cw6p1260104796.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/122r3e1260104796.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/13jian1260104796.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/14fchr1260104796.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/15u5gs1260104796.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/16hy9d1260104796.tab")
+ }
>
> system("convert tmp/1jt0x1260104796.ps tmp/1jt0x1260104796.png")
> system("convert tmp/2ewz41260104796.ps tmp/2ewz41260104796.png")
> system("convert tmp/3hnph1260104796.ps tmp/3hnph1260104796.png")
> system("convert tmp/4w2wj1260104796.ps tmp/4w2wj1260104796.png")
> system("convert tmp/5rteb1260104796.ps tmp/5rteb1260104796.png")
> system("convert tmp/6jlng1260104796.ps tmp/6jlng1260104796.png")
> system("convert tmp/72gw01260104796.ps tmp/72gw01260104796.png")
> system("convert tmp/86djv1260104796.ps tmp/86djv1260104796.png")
> system("convert tmp/963tp1260104796.ps tmp/963tp1260104796.png")
> system("convert tmp/104y0t1260104796.ps tmp/104y0t1260104796.png")
>
>
> proc.time()
user system elapsed
2.367 1.536 3.497