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(151.7,105.2,121.3,105.2,133.0,105.6,119.6,105.6,122.2,106.2,117.4,106.3,106.7,106.4,87.5,106.9,81.0,107.2,110.3,107.3,87.0,107.3,55.7,107.4,146.0,107.55,137.5,107.87,138.5,108.37,135.6,108.38,107.3,107.92,99.0,108.03,91.4,108.14,68.4,108.3,82.6,108.64,98.4,108.66,71.3,109.04,47.6,109.03,130.8,109.03,113.6,109.54,125.7,109.75,113.6,109.83,97.1,109.65,104.4,109.82,91.8,109.95,75.1,110.12,89.2,110.15,110.2,110.2,78.4,109.99,68.4,110.14,122.8,110.14,129.7,110.81,159.1,110.97,139.0,110.99,102.2,109.73,113.6,109.81,81.5,110.02,77.4,110.18,87.6,110.21,101.2,110.25,87.2,110.36,64.9,110.51,133.1,110.64,118.0,110.95,135.9,111.18,125.7,111.19,108.0,111.69,128.3,111.7,84.7,111.83,86.4,111.77,92.2,111.73,95.8,112.01,92.3,111.86,54.3,112.04),dim=c(2,60),dimnames=list(c('Yt','Xt'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Yt','Xt'),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 = '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
Yt Xt M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 151.7 105.20 1 0 0 0 0 0 0 0 0 0 0 1
2 121.3 105.20 0 1 0 0 0 0 0 0 0 0 0 2
3 133.0 105.60 0 0 1 0 0 0 0 0 0 0 0 3
4 119.6 105.60 0 0 0 1 0 0 0 0 0 0 0 4
5 122.2 106.20 0 0 0 0 1 0 0 0 0 0 0 5
6 117.4 106.30 0 0 0 0 0 1 0 0 0 0 0 6
7 106.7 106.40 0 0 0 0 0 0 1 0 0 0 0 7
8 87.5 106.90 0 0 0 0 0 0 0 1 0 0 0 8
9 81.0 107.20 0 0 0 0 0 0 0 0 1 0 0 9
10 110.3 107.30 0 0 0 0 0 0 0 0 0 1 0 10
11 87.0 107.30 0 0 0 0 0 0 0 0 0 0 1 11
12 55.7 107.40 0 0 0 0 0 0 0 0 0 0 0 12
13 146.0 107.55 1 0 0 0 0 0 0 0 0 0 0 13
14 137.5 107.87 0 1 0 0 0 0 0 0 0 0 0 14
15 138.5 108.37 0 0 1 0 0 0 0 0 0 0 0 15
16 135.6 108.38 0 0 0 1 0 0 0 0 0 0 0 16
17 107.3 107.92 0 0 0 0 1 0 0 0 0 0 0 17
18 99.0 108.03 0 0 0 0 0 1 0 0 0 0 0 18
19 91.4 108.14 0 0 0 0 0 0 1 0 0 0 0 19
20 68.4 108.30 0 0 0 0 0 0 0 1 0 0 0 20
21 82.6 108.64 0 0 0 0 0 0 0 0 1 0 0 21
22 98.4 108.66 0 0 0 0 0 0 0 0 0 1 0 22
23 71.3 109.04 0 0 0 0 0 0 0 0 0 0 1 23
24 47.6 109.03 0 0 0 0 0 0 0 0 0 0 0 24
25 130.8 109.03 1 0 0 0 0 0 0 0 0 0 0 25
26 113.6 109.54 0 1 0 0 0 0 0 0 0 0 0 26
27 125.7 109.75 0 0 1 0 0 0 0 0 0 0 0 27
28 113.6 109.83 0 0 0 1 0 0 0 0 0 0 0 28
29 97.1 109.65 0 0 0 0 1 0 0 0 0 0 0 29
30 104.4 109.82 0 0 0 0 0 1 0 0 0 0 0 30
31 91.8 109.95 0 0 0 0 0 0 1 0 0 0 0 31
32 75.1 110.12 0 0 0 0 0 0 0 1 0 0 0 32
33 89.2 110.15 0 0 0 0 0 0 0 0 1 0 0 33
34 110.2 110.20 0 0 0 0 0 0 0 0 0 1 0 34
35 78.4 109.99 0 0 0 0 0 0 0 0 0 0 1 35
36 68.4 110.14 0 0 0 0 0 0 0 0 0 0 0 36
37 122.8 110.14 1 0 0 0 0 0 0 0 0 0 0 37
38 129.7 110.81 0 1 0 0 0 0 0 0 0 0 0 38
39 159.1 110.97 0 0 1 0 0 0 0 0 0 0 0 39
40 139.0 110.99 0 0 0 1 0 0 0 0 0 0 0 40
41 102.2 109.73 0 0 0 0 1 0 0 0 0 0 0 41
42 113.6 109.81 0 0 0 0 0 1 0 0 0 0 0 42
43 81.5 110.02 0 0 0 0 0 0 1 0 0 0 0 43
44 77.4 110.18 0 0 0 0 0 0 0 1 0 0 0 44
45 87.6 110.21 0 0 0 0 0 0 0 0 1 0 0 45
46 101.2 110.25 0 0 0 0 0 0 0 0 0 1 0 46
47 87.2 110.36 0 0 0 0 0 0 0 0 0 0 1 47
48 64.9 110.51 0 0 0 0 0 0 0 0 0 0 0 48
49 133.1 110.64 1 0 0 0 0 0 0 0 0 0 0 49
50 118.0 110.95 0 1 0 0 0 0 0 0 0 0 0 50
51 135.9 111.18 0 0 1 0 0 0 0 0 0 0 0 51
52 125.7 111.19 0 0 0 1 0 0 0 0 0 0 0 52
53 108.0 111.69 0 0 0 0 1 0 0 0 0 0 0 53
54 128.3 111.70 0 0 0 0 0 1 0 0 0 0 0 54
55 84.7 111.83 0 0 0 0 0 0 1 0 0 0 0 55
56 86.4 111.77 0 0 0 0 0 0 0 1 0 0 0 56
57 92.2 111.73 0 0 0 0 0 0 0 0 1 0 0 57
58 95.8 112.01 0 0 0 0 0 0 0 0 0 1 0 58
59 92.3 111.86 0 0 0 0 0 0 0 0 0 0 1 59
60 54.3 112.04 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Xt M1 M2 M3 M4
247.3808 -1.7746 78.1126 65.7367 80.5309 68.6752
M5 M6 M7 M8 M9 M10
48.8930 54.0815 32.8446 20.7564 28.3924 45.0681
M11 t
25.0160 0.1583
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.596 -6.001 -1.717 6.820 21.948
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 247.3808 262.8640 0.941 0.351571
Xt -1.7746 2.4765 -0.717 0.477250
M1 78.1126 6.1680 12.664 < 2e-16 ***
M2 65.7367 6.1455 10.697 4.58e-14 ***
M3 80.5309 6.1719 13.048 < 2e-16 ***
M4 68.6752 6.1459 11.174 1.06e-14 ***
M5 48.8930 6.1223 7.986 3.10e-10 ***
M6 54.0815 6.1172 8.841 1.76e-11 ***
M7 32.8446 6.1104 5.375 2.47e-06 ***
M8 20.7564 6.1064 3.399 0.001406 **
M9 28.3924 6.1049 4.651 2.81e-05 ***
M10 45.0681 6.1024 7.385 2.42e-09 ***
M11 25.0160 6.0990 4.102 0.000166 ***
t 0.1583 0.2649 0.597 0.553196
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.643 on 46 degrees of freedom
Multiple R-squared: 0.8898, Adjusted R-squared: 0.8586
F-statistic: 28.57 on 13 and 46 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,] 0.21642997 0.43285994 0.78357003
[2,] 0.10456451 0.20912901 0.89543549
[3,] 0.05411409 0.10822819 0.94588591
[4,] 0.03342474 0.06684948 0.96657526
[5,] 0.25903801 0.51807602 0.74096199
[6,] 0.18631911 0.37263822 0.81368089
[7,] 0.14323787 0.28647573 0.85676213
[8,] 0.10082755 0.20165510 0.89917245
[9,] 0.06893010 0.13786020 0.93106990
[10,] 0.05660508 0.11321015 0.94339492
[11,] 0.06078056 0.12156113 0.93921944
[12,] 0.06511679 0.13023357 0.93488321
[13,] 0.04556461 0.09112922 0.95443539
[14,] 0.14104800 0.28209600 0.85895200
[15,] 0.11701304 0.23402608 0.88298696
[16,] 0.20107313 0.40214626 0.79892687
[17,] 0.50219923 0.99560154 0.49780077
[18,] 0.58469225 0.83061550 0.41530775
[19,] 0.78796271 0.42407459 0.21203729
[20,] 0.84023492 0.31953016 0.15976508
[21,] 0.95778616 0.08442768 0.04221384
[22,] 0.94795149 0.10409703 0.05204851
[23,] 0.96878762 0.06242476 0.03121238
[24,] 0.94622457 0.10755085 0.05377543
[25,] 0.88969562 0.22060875 0.11030438
[26,] 0.87892897 0.24214205 0.12107103
[27,] 0.78752680 0.42494640 0.21247320
> postscript(file="/var/www/html/rcomp/tmp/1fpik1258738442.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kuf21258738442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qshk1258738442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fsnj1258738442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vdg11258738442.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 6
12.74072276 -5.44169786 -7.98423360 -9.68682502 13.60190336 3.63255122
7 8 9 10 11 12
14.18866409 7.80590187 -5.95595817 6.68759111 3.28145040 -2.98339459
13 14 15 16 17 18
9.31196336 13.59742837 0.53235689 9.34751189 -0.14488089 -13.59648661
19 20 21 22 23 24
0.07737268 -10.70876803 -3.69964236 -4.69806449 -11.22984100 -10.08989668
25 26 27 28 29 30
-5.16073513 -9.23808802 -11.71780585 -11.97842587 -9.17391872 -6.91904588
31 32 33 34 35 36
1.79030626 -2.67808802 3.68089843 7.93571559 -4.34310007 10.78078707
37 38 39 40 41 42
-13.09005138 7.21653855 21.94808858 13.58099001 -5.83111686 0.36403814
43 44 45 46 47 48
-10.28463831 -2.17077901 0.28820744 -2.87472183 3.21434815 6.03823528
49 50 51 52 53 54
-3.80189962 -6.13418104 -2.77840602 -1.26325102 1.54801311 16.51894313
55 56 57 58 59 60
-5.77170473 7.75173319 5.68649466 -7.05052038 9.07714252 -3.74573107
> postscript(file="/var/www/html/rcomp/tmp/6pens1258738442.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 12.74072276 NA
1 -5.44169786 12.74072276
2 -7.98423360 -5.44169786
3 -9.68682502 -7.98423360
4 13.60190336 -9.68682502
5 3.63255122 13.60190336
6 14.18866409 3.63255122
7 7.80590187 14.18866409
8 -5.95595817 7.80590187
9 6.68759111 -5.95595817
10 3.28145040 6.68759111
11 -2.98339459 3.28145040
12 9.31196336 -2.98339459
13 13.59742837 9.31196336
14 0.53235689 13.59742837
15 9.34751189 0.53235689
16 -0.14488089 9.34751189
17 -13.59648661 -0.14488089
18 0.07737268 -13.59648661
19 -10.70876803 0.07737268
20 -3.69964236 -10.70876803
21 -4.69806449 -3.69964236
22 -11.22984100 -4.69806449
23 -10.08989668 -11.22984100
24 -5.16073513 -10.08989668
25 -9.23808802 -5.16073513
26 -11.71780585 -9.23808802
27 -11.97842587 -11.71780585
28 -9.17391872 -11.97842587
29 -6.91904588 -9.17391872
30 1.79030626 -6.91904588
31 -2.67808802 1.79030626
32 3.68089843 -2.67808802
33 7.93571559 3.68089843
34 -4.34310007 7.93571559
35 10.78078707 -4.34310007
36 -13.09005138 10.78078707
37 7.21653855 -13.09005138
38 21.94808858 7.21653855
39 13.58099001 21.94808858
40 -5.83111686 13.58099001
41 0.36403814 -5.83111686
42 -10.28463831 0.36403814
43 -2.17077901 -10.28463831
44 0.28820744 -2.17077901
45 -2.87472183 0.28820744
46 3.21434815 -2.87472183
47 6.03823528 3.21434815
48 -3.80189962 6.03823528
49 -6.13418104 -3.80189962
50 -2.77840602 -6.13418104
51 -1.26325102 -2.77840602
52 1.54801311 -1.26325102
53 16.51894313 1.54801311
54 -5.77170473 16.51894313
55 7.75173319 -5.77170473
56 5.68649466 7.75173319
57 -7.05052038 5.68649466
58 9.07714252 -7.05052038
59 -3.74573107 9.07714252
60 NA -3.74573107
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.44169786 12.74072276
[2,] -7.98423360 -5.44169786
[3,] -9.68682502 -7.98423360
[4,] 13.60190336 -9.68682502
[5,] 3.63255122 13.60190336
[6,] 14.18866409 3.63255122
[7,] 7.80590187 14.18866409
[8,] -5.95595817 7.80590187
[9,] 6.68759111 -5.95595817
[10,] 3.28145040 6.68759111
[11,] -2.98339459 3.28145040
[12,] 9.31196336 -2.98339459
[13,] 13.59742837 9.31196336
[14,] 0.53235689 13.59742837
[15,] 9.34751189 0.53235689
[16,] -0.14488089 9.34751189
[17,] -13.59648661 -0.14488089
[18,] 0.07737268 -13.59648661
[19,] -10.70876803 0.07737268
[20,] -3.69964236 -10.70876803
[21,] -4.69806449 -3.69964236
[22,] -11.22984100 -4.69806449
[23,] -10.08989668 -11.22984100
[24,] -5.16073513 -10.08989668
[25,] -9.23808802 -5.16073513
[26,] -11.71780585 -9.23808802
[27,] -11.97842587 -11.71780585
[28,] -9.17391872 -11.97842587
[29,] -6.91904588 -9.17391872
[30,] 1.79030626 -6.91904588
[31,] -2.67808802 1.79030626
[32,] 3.68089843 -2.67808802
[33,] 7.93571559 3.68089843
[34,] -4.34310007 7.93571559
[35,] 10.78078707 -4.34310007
[36,] -13.09005138 10.78078707
[37,] 7.21653855 -13.09005138
[38,] 21.94808858 7.21653855
[39,] 13.58099001 21.94808858
[40,] -5.83111686 13.58099001
[41,] 0.36403814 -5.83111686
[42,] -10.28463831 0.36403814
[43,] -2.17077901 -10.28463831
[44,] 0.28820744 -2.17077901
[45,] -2.87472183 0.28820744
[46,] 3.21434815 -2.87472183
[47,] 6.03823528 3.21434815
[48,] -3.80189962 6.03823528
[49,] -6.13418104 -3.80189962
[50,] -2.77840602 -6.13418104
[51,] -1.26325102 -2.77840602
[52,] 1.54801311 -1.26325102
[53,] 16.51894313 1.54801311
[54,] -5.77170473 16.51894313
[55,] 7.75173319 -5.77170473
[56,] 5.68649466 7.75173319
[57,] -7.05052038 5.68649466
[58,] 9.07714252 -7.05052038
[59,] -3.74573107 9.07714252
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.44169786 12.74072276
2 -7.98423360 -5.44169786
3 -9.68682502 -7.98423360
4 13.60190336 -9.68682502
5 3.63255122 13.60190336
6 14.18866409 3.63255122
7 7.80590187 14.18866409
8 -5.95595817 7.80590187
9 6.68759111 -5.95595817
10 3.28145040 6.68759111
11 -2.98339459 3.28145040
12 9.31196336 -2.98339459
13 13.59742837 9.31196336
14 0.53235689 13.59742837
15 9.34751189 0.53235689
16 -0.14488089 9.34751189
17 -13.59648661 -0.14488089
18 0.07737268 -13.59648661
19 -10.70876803 0.07737268
20 -3.69964236 -10.70876803
21 -4.69806449 -3.69964236
22 -11.22984100 -4.69806449
23 -10.08989668 -11.22984100
24 -5.16073513 -10.08989668
25 -9.23808802 -5.16073513
26 -11.71780585 -9.23808802
27 -11.97842587 -11.71780585
28 -9.17391872 -11.97842587
29 -6.91904588 -9.17391872
30 1.79030626 -6.91904588
31 -2.67808802 1.79030626
32 3.68089843 -2.67808802
33 7.93571559 3.68089843
34 -4.34310007 7.93571559
35 10.78078707 -4.34310007
36 -13.09005138 10.78078707
37 7.21653855 -13.09005138
38 21.94808858 7.21653855
39 13.58099001 21.94808858
40 -5.83111686 13.58099001
41 0.36403814 -5.83111686
42 -10.28463831 0.36403814
43 -2.17077901 -10.28463831
44 0.28820744 -2.17077901
45 -2.87472183 0.28820744
46 3.21434815 -2.87472183
47 6.03823528 3.21434815
48 -3.80189962 6.03823528
49 -6.13418104 -3.80189962
50 -2.77840602 -6.13418104
51 -1.26325102 -2.77840602
52 1.54801311 -1.26325102
53 16.51894313 1.54801311
54 -5.77170473 16.51894313
55 7.75173319 -5.77170473
56 5.68649466 7.75173319
57 -7.05052038 5.68649466
58 9.07714252 -7.05052038
59 -3.74573107 9.07714252
> 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/7xihf1258738442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rmuf1258738442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9me3b1258738442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cjha1258738442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11j7ue1258738442.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/12ab231258738442.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/13wxbr1258738442.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/143ec11258738443.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/15gwbh1258738443.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/1683ab1258738443.tab")
+ }
>
> system("convert tmp/1fpik1258738442.ps tmp/1fpik1258738442.png")
> system("convert tmp/2kuf21258738442.ps tmp/2kuf21258738442.png")
> system("convert tmp/3qshk1258738442.ps tmp/3qshk1258738442.png")
> system("convert tmp/4fsnj1258738442.ps tmp/4fsnj1258738442.png")
> system("convert tmp/5vdg11258738442.ps tmp/5vdg11258738442.png")
> system("convert tmp/6pens1258738442.ps tmp/6pens1258738442.png")
> system("convert tmp/7xihf1258738442.ps tmp/7xihf1258738442.png")
> system("convert tmp/8rmuf1258738442.ps tmp/8rmuf1258738442.png")
> system("convert tmp/9me3b1258738442.ps tmp/9me3b1258738442.png")
> system("convert tmp/10cjha1258738442.ps tmp/10cjha1258738442.png")
>
>
> proc.time()
user system elapsed
2.493 1.623 2.908