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(96.8,92.9,114.1,107.7,110.3,103.5,103.9,91.1,101.6,79.8,94.6,71.9,95.9,82.9,104.7,90.1,102.8,100.7,98.1,90.7,113.9,108.8,80.9,44.1,95.7,93.6,113.2,107.4,105.9,96.5,108.8,93.6,102.3,76.5,99,76.7,100.7,84,115.5,103.3,100.7,88.5,109.9,99,114.6,105.9,85.4,44.7,100.5,94,114.8,107.1,116.5,104.8,112.9,102.5,102,77.7,106,85.2,105.3,91.3,118.8,106.5,106.1,92.4,109.3,97.5,117.2,107,92.5,51.1,104.2,98.6,112.5,102.2,122.4,114.3,113.3,99.4,100,72.5,110.7,92.3,112.8,99.4,109.8,85.9,117.3,109.4,109.1,97.6,115.9,104.7,96,56.9,99.8,86.7,116.8,108.5,115.7,103.4,99.4,86.2,94.3,71,91,75.9,93.2,87.1,103.1,102,94.1,88.5,91.8,87.8,102.7,100.8,82.6,50.6),dim=c(2,60),dimnames=list(c('Totind','Bouw'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Totind','Bouw'),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
Totind Bouw M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 96.8 92.9 1 0 0 0 0 0 0 0 0 0 0 1
2 114.1 107.7 0 1 0 0 0 0 0 0 0 0 0 2
3 110.3 103.5 0 0 1 0 0 0 0 0 0 0 0 3
4 103.9 91.1 0 0 0 1 0 0 0 0 0 0 0 4
5 101.6 79.8 0 0 0 0 1 0 0 0 0 0 0 5
6 94.6 71.9 0 0 0 0 0 1 0 0 0 0 0 6
7 95.9 82.9 0 0 0 0 0 0 1 0 0 0 0 7
8 104.7 90.1 0 0 0 0 0 0 0 1 0 0 0 8
9 102.8 100.7 0 0 0 0 0 0 0 0 1 0 0 9
10 98.1 90.7 0 0 0 0 0 0 0 0 0 1 0 10
11 113.9 108.8 0 0 0 0 0 0 0 0 0 0 1 11
12 80.9 44.1 0 0 0 0 0 0 0 0 0 0 0 12
13 95.7 93.6 1 0 0 0 0 0 0 0 0 0 0 13
14 113.2 107.4 0 1 0 0 0 0 0 0 0 0 0 14
15 105.9 96.5 0 0 1 0 0 0 0 0 0 0 0 15
16 108.8 93.6 0 0 0 1 0 0 0 0 0 0 0 16
17 102.3 76.5 0 0 0 0 1 0 0 0 0 0 0 17
18 99.0 76.7 0 0 0 0 0 1 0 0 0 0 0 18
19 100.7 84.0 0 0 0 0 0 0 1 0 0 0 0 19
20 115.5 103.3 0 0 0 0 0 0 0 1 0 0 0 20
21 100.7 88.5 0 0 0 0 0 0 0 0 1 0 0 21
22 109.9 99.0 0 0 0 0 0 0 0 0 0 1 0 22
23 114.6 105.9 0 0 0 0 0 0 0 0 0 0 1 23
24 85.4 44.7 0 0 0 0 0 0 0 0 0 0 0 24
25 100.5 94.0 1 0 0 0 0 0 0 0 0 0 0 25
26 114.8 107.1 0 1 0 0 0 0 0 0 0 0 0 26
27 116.5 104.8 0 0 1 0 0 0 0 0 0 0 0 27
28 112.9 102.5 0 0 0 1 0 0 0 0 0 0 0 28
29 102.0 77.7 0 0 0 0 1 0 0 0 0 0 0 29
30 106.0 85.2 0 0 0 0 0 1 0 0 0 0 0 30
31 105.3 91.3 0 0 0 0 0 0 1 0 0 0 0 31
32 118.8 106.5 0 0 0 0 0 0 0 1 0 0 0 32
33 106.1 92.4 0 0 0 0 0 0 0 0 1 0 0 33
34 109.3 97.5 0 0 0 0 0 0 0 0 0 1 0 34
35 117.2 107.0 0 0 0 0 0 0 0 0 0 0 1 35
36 92.5 51.1 0 0 0 0 0 0 0 0 0 0 0 36
37 104.2 98.6 1 0 0 0 0 0 0 0 0 0 0 37
38 112.5 102.2 0 1 0 0 0 0 0 0 0 0 0 38
39 122.4 114.3 0 0 1 0 0 0 0 0 0 0 0 39
40 113.3 99.4 0 0 0 1 0 0 0 0 0 0 0 40
41 100.0 72.5 0 0 0 0 1 0 0 0 0 0 0 41
42 110.7 92.3 0 0 0 0 0 1 0 0 0 0 0 42
43 112.8 99.4 0 0 0 0 0 0 1 0 0 0 0 43
44 109.8 85.9 0 0 0 0 0 0 0 1 0 0 0 44
45 117.3 109.4 0 0 0 0 0 0 0 0 1 0 0 45
46 109.1 97.6 0 0 0 0 0 0 0 0 0 1 0 46
47 115.9 104.7 0 0 0 0 0 0 0 0 0 0 1 47
48 96.0 56.9 0 0 0 0 0 0 0 0 0 0 0 48
49 99.8 86.7 1 0 0 0 0 0 0 0 0 0 0 49
50 116.8 108.5 0 1 0 0 0 0 0 0 0 0 0 50
51 115.7 103.4 0 0 1 0 0 0 0 0 0 0 0 51
52 99.4 86.2 0 0 0 1 0 0 0 0 0 0 0 52
53 94.3 71.0 0 0 0 0 1 0 0 0 0 0 0 53
54 91.0 75.9 0 0 0 0 0 1 0 0 0 0 0 54
55 93.2 87.1 0 0 0 0 0 0 1 0 0 0 0 55
56 103.1 102.0 0 0 0 0 0 0 0 1 0 0 0 56
57 94.1 88.5 0 0 0 0 0 0 0 0 1 0 0 57
58 91.8 87.8 0 0 0 0 0 0 0 0 0 1 0 58
59 102.7 100.8 0 0 0 0 0 0 0 0 0 0 1 59
60 82.6 50.6 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) Bouw M1 M2 M3 M4
46.83452 0.83186 -24.57313 -20.84242 -19.21784 -17.43482
M5 M6 M7 M8 M9 M10
-9.18521 -13.02703 -18.79683 -17.15317 -21.93797 -21.33570
M11 t
-21.18533 -0.01431
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.6301 -1.9750 0.5794 2.1582 9.2912
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.83452 4.88821 9.581 1.56e-12 ***
Bouw 0.83186 0.09124 9.117 7.08e-12 ***
M1 -24.57313 4.71499 -5.212 4.31e-06 ***
M2 -20.84242 5.78846 -3.601 0.000775 ***
M3 -19.21784 5.61425 -3.423 0.001311 **
M4 -17.43482 4.81363 -3.622 0.000727 ***
M5 -9.18521 3.43644 -2.673 0.010371 *
M6 -13.02703 3.75790 -3.467 0.001154 **
M7 -18.79683 4.37270 -4.299 8.83e-05 ***
M8 -17.15317 5.03915 -3.404 0.001386 **
M9 -21.93797 4.90522 -4.472 5.04e-05 ***
M10 -21.33570 4.79455 -4.450 5.42e-05 ***
M11 -21.18533 5.67052 -3.736 0.000515 ***
t -0.01431 0.02968 -0.482 0.632092
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.893 on 46 degrees of freedom
Multiple R-squared: 0.8682, Adjusted R-squared: 0.8309
F-statistic: 23.3 on 13 and 46 DF, p-value: 6.04e-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,] 1.138742e-01 0.2277484266 0.8861258
[2,] 4.100113e-02 0.0820022623 0.9589989
[3,] 2.945426e-02 0.0589085220 0.9705457
[4,] 1.079587e-02 0.0215917432 0.9892041
[5,] 1.500316e-02 0.0300063132 0.9849968
[6,] 2.056919e-02 0.0411383863 0.9794308
[7,] 9.429960e-03 0.0188599195 0.9905700
[8,] 5.117958e-03 0.0102359154 0.9948820
[9,] 2.948148e-03 0.0058962966 0.9970519
[10,] 2.434397e-03 0.0048687934 0.9975656
[11,] 1.662364e-03 0.0033247284 0.9983376
[12,] 2.549718e-03 0.0050994351 0.9974503
[13,] 2.858026e-03 0.0057160513 0.9971420
[14,] 1.334982e-03 0.0026699635 0.9986650
[15,] 6.878304e-04 0.0013756608 0.9993122
[16,] 3.294018e-04 0.0006588035 0.9996706
[17,] 2.742971e-04 0.0005485942 0.9997257
[18,] 1.217506e-04 0.0002435012 0.9998782
[19,] 6.143269e-05 0.0001228654 0.9999386
[20,] 7.805448e-05 0.0001561090 0.9999219
[21,] 3.467020e-04 0.0006934040 0.9996533
[22,] 6.298484e-03 0.0125969671 0.9937015
[23,] 8.298075e-02 0.1659615083 0.9170192
[24,] 9.981260e-02 0.1996252095 0.9001874
[25,] 8.261016e-01 0.3477967031 0.1738984
[26,] 7.177429e-01 0.5645141940 0.2822571
[27,] 6.303185e-01 0.7393629747 0.3696815
> postscript(file="/var/www/html/rcomp/tmp/1fpc61258730153.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2g9sb1258730153.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33yo61258730153.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ix761258730154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rtil1258730154.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
-2.72709549 -1.45506557 -3.37151733 -1.22513588 -2.36038793 1.06745051
7 8 9 10 11 12
-0.99893094 0.18231363 -5.73631912 -2.70566542 -2.09843730 -2.44796011
13 14 15 16 17 18
-4.23770930 -1.93381700 -1.77679083 1.76689803 1.25644777 1.64620095
19 20 21 22 23 24
3.05771029 0.17342010 2.48409174 2.36156669 1.18565345 1.72461231
25 26 27 28 29 30
0.40123560 0.08743156 2.09044129 -1.36498728 0.12990277 1.74706059
31 32 33 34 35 36
1.75680479 0.98315034 4.81151832 3.18105011 3.04229469 3.67238295
37 38 39 40 41 42
0.44635852 2.03524707 0.25943855 1.78547594 2.62727699 0.71252755
43 44 45 46 47 48
2.69040938 9.29120518 2.04154775 3.06955373 3.82726801 2.51927101
49 50 51 52 53 54
6.11721067 1.26620394 2.79842832 -0.96225081 -1.65323959 -5.17323959
55 56 57 58 59 60
-6.50599352 -10.63008925 -3.60083870 -5.90650511 -5.95677886 -5.46830616
> postscript(file="/var/www/html/rcomp/tmp/6mxpz1258730154.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 -2.72709549 NA
1 -1.45506557 -2.72709549
2 -3.37151733 -1.45506557
3 -1.22513588 -3.37151733
4 -2.36038793 -1.22513588
5 1.06745051 -2.36038793
6 -0.99893094 1.06745051
7 0.18231363 -0.99893094
8 -5.73631912 0.18231363
9 -2.70566542 -5.73631912
10 -2.09843730 -2.70566542
11 -2.44796011 -2.09843730
12 -4.23770930 -2.44796011
13 -1.93381700 -4.23770930
14 -1.77679083 -1.93381700
15 1.76689803 -1.77679083
16 1.25644777 1.76689803
17 1.64620095 1.25644777
18 3.05771029 1.64620095
19 0.17342010 3.05771029
20 2.48409174 0.17342010
21 2.36156669 2.48409174
22 1.18565345 2.36156669
23 1.72461231 1.18565345
24 0.40123560 1.72461231
25 0.08743156 0.40123560
26 2.09044129 0.08743156
27 -1.36498728 2.09044129
28 0.12990277 -1.36498728
29 1.74706059 0.12990277
30 1.75680479 1.74706059
31 0.98315034 1.75680479
32 4.81151832 0.98315034
33 3.18105011 4.81151832
34 3.04229469 3.18105011
35 3.67238295 3.04229469
36 0.44635852 3.67238295
37 2.03524707 0.44635852
38 0.25943855 2.03524707
39 1.78547594 0.25943855
40 2.62727699 1.78547594
41 0.71252755 2.62727699
42 2.69040938 0.71252755
43 9.29120518 2.69040938
44 2.04154775 9.29120518
45 3.06955373 2.04154775
46 3.82726801 3.06955373
47 2.51927101 3.82726801
48 6.11721067 2.51927101
49 1.26620394 6.11721067
50 2.79842832 1.26620394
51 -0.96225081 2.79842832
52 -1.65323959 -0.96225081
53 -5.17323959 -1.65323959
54 -6.50599352 -5.17323959
55 -10.63008925 -6.50599352
56 -3.60083870 -10.63008925
57 -5.90650511 -3.60083870
58 -5.95677886 -5.90650511
59 -5.46830616 -5.95677886
60 NA -5.46830616
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.45506557 -2.72709549
[2,] -3.37151733 -1.45506557
[3,] -1.22513588 -3.37151733
[4,] -2.36038793 -1.22513588
[5,] 1.06745051 -2.36038793
[6,] -0.99893094 1.06745051
[7,] 0.18231363 -0.99893094
[8,] -5.73631912 0.18231363
[9,] -2.70566542 -5.73631912
[10,] -2.09843730 -2.70566542
[11,] -2.44796011 -2.09843730
[12,] -4.23770930 -2.44796011
[13,] -1.93381700 -4.23770930
[14,] -1.77679083 -1.93381700
[15,] 1.76689803 -1.77679083
[16,] 1.25644777 1.76689803
[17,] 1.64620095 1.25644777
[18,] 3.05771029 1.64620095
[19,] 0.17342010 3.05771029
[20,] 2.48409174 0.17342010
[21,] 2.36156669 2.48409174
[22,] 1.18565345 2.36156669
[23,] 1.72461231 1.18565345
[24,] 0.40123560 1.72461231
[25,] 0.08743156 0.40123560
[26,] 2.09044129 0.08743156
[27,] -1.36498728 2.09044129
[28,] 0.12990277 -1.36498728
[29,] 1.74706059 0.12990277
[30,] 1.75680479 1.74706059
[31,] 0.98315034 1.75680479
[32,] 4.81151832 0.98315034
[33,] 3.18105011 4.81151832
[34,] 3.04229469 3.18105011
[35,] 3.67238295 3.04229469
[36,] 0.44635852 3.67238295
[37,] 2.03524707 0.44635852
[38,] 0.25943855 2.03524707
[39,] 1.78547594 0.25943855
[40,] 2.62727699 1.78547594
[41,] 0.71252755 2.62727699
[42,] 2.69040938 0.71252755
[43,] 9.29120518 2.69040938
[44,] 2.04154775 9.29120518
[45,] 3.06955373 2.04154775
[46,] 3.82726801 3.06955373
[47,] 2.51927101 3.82726801
[48,] 6.11721067 2.51927101
[49,] 1.26620394 6.11721067
[50,] 2.79842832 1.26620394
[51,] -0.96225081 2.79842832
[52,] -1.65323959 -0.96225081
[53,] -5.17323959 -1.65323959
[54,] -6.50599352 -5.17323959
[55,] -10.63008925 -6.50599352
[56,] -3.60083870 -10.63008925
[57,] -5.90650511 -3.60083870
[58,] -5.95677886 -5.90650511
[59,] -5.46830616 -5.95677886
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.45506557 -2.72709549
2 -3.37151733 -1.45506557
3 -1.22513588 -3.37151733
4 -2.36038793 -1.22513588
5 1.06745051 -2.36038793
6 -0.99893094 1.06745051
7 0.18231363 -0.99893094
8 -5.73631912 0.18231363
9 -2.70566542 -5.73631912
10 -2.09843730 -2.70566542
11 -2.44796011 -2.09843730
12 -4.23770930 -2.44796011
13 -1.93381700 -4.23770930
14 -1.77679083 -1.93381700
15 1.76689803 -1.77679083
16 1.25644777 1.76689803
17 1.64620095 1.25644777
18 3.05771029 1.64620095
19 0.17342010 3.05771029
20 2.48409174 0.17342010
21 2.36156669 2.48409174
22 1.18565345 2.36156669
23 1.72461231 1.18565345
24 0.40123560 1.72461231
25 0.08743156 0.40123560
26 2.09044129 0.08743156
27 -1.36498728 2.09044129
28 0.12990277 -1.36498728
29 1.74706059 0.12990277
30 1.75680479 1.74706059
31 0.98315034 1.75680479
32 4.81151832 0.98315034
33 3.18105011 4.81151832
34 3.04229469 3.18105011
35 3.67238295 3.04229469
36 0.44635852 3.67238295
37 2.03524707 0.44635852
38 0.25943855 2.03524707
39 1.78547594 0.25943855
40 2.62727699 1.78547594
41 0.71252755 2.62727699
42 2.69040938 0.71252755
43 9.29120518 2.69040938
44 2.04154775 9.29120518
45 3.06955373 2.04154775
46 3.82726801 3.06955373
47 2.51927101 3.82726801
48 6.11721067 2.51927101
49 1.26620394 6.11721067
50 2.79842832 1.26620394
51 -0.96225081 2.79842832
52 -1.65323959 -0.96225081
53 -5.17323959 -1.65323959
54 -6.50599352 -5.17323959
55 -10.63008925 -6.50599352
56 -3.60083870 -10.63008925
57 -5.90650511 -3.60083870
58 -5.95677886 -5.90650511
59 -5.46830616 -5.95677886
> 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/7t9cr1258730154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81hdj1258730154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99llc1258730154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gage1258730154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dyod1258730154.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/121gu61258730154.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/13bkfn1258730154.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/14rqzp1258730154.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/155xdg1258730154.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/163bjm1258730154.tab")
+ }
>
> system("convert tmp/1fpc61258730153.ps tmp/1fpc61258730153.png")
> system("convert tmp/2g9sb1258730153.ps tmp/2g9sb1258730153.png")
> system("convert tmp/33yo61258730153.ps tmp/33yo61258730153.png")
> system("convert tmp/4ix761258730154.ps tmp/4ix761258730154.png")
> system("convert tmp/5rtil1258730154.ps tmp/5rtil1258730154.png")
> system("convert tmp/6mxpz1258730154.ps tmp/6mxpz1258730154.png")
> system("convert tmp/7t9cr1258730154.ps tmp/7t9cr1258730154.png")
> system("convert tmp/81hdj1258730154.ps tmp/81hdj1258730154.png")
> system("convert tmp/99llc1258730154.ps tmp/99llc1258730154.png")
> system("convert tmp/10gage1258730154.ps tmp/10gage1258730154.png")
>
>
> proc.time()
user system elapsed
2.391 1.601 2.965