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(95.1,117.1,97,118.7,112.7,126.5,102.9,127.5,97.4,134.6,111.4,131.8,87.4,135.9,96.8,142.7,114.1,141.7,110.3,153.4,103.9,145,101.6,137.7,94.6,148.3,95.9,152.2,104.7,169.4,102.8,168.6,98.1,161.1,113.9,174.1,80.9,179,95.7,190.6,113.2,190,105.9,181.6,108.8,174.8,102.3,180.5,99,196.8,100.7,193.8,115.5,197,100.7,216.3,109.9,221.4,114.6,217.9,85.4,229.7,100.5,227.4,114.8,204.2,116.5,196.6,112.9,198.8,102,207.5,106,190.7,105.3,201.6,118.8,210.5,106.1,223.5,109.3,223.8,117.2,231.2,92.5,244,104.2,234.7,112.5,250.2,122.4,265.7,113.3,287.6,100,283.3,110.7,295.4,112.8,312.3,109.8,333.8,117.3,347.7,109.1,383.2,115.9,407.1,96,413.6,99.8,362.7,116.8,321.9,115.7,239.4,99.4,191,94.3,159.7,91,163.4),dim=c(2,61),dimnames=list(c('tot.ind.prod.index','prijsindex.grondst.incl.energie'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('tot.ind.prod.index','prijsindex.grondst.incl.energie'),1:61))
> 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
tot.ind.prod.index prijsindex.grondst.incl.energie M1 M2 M3 M4 M5 M6 M7 M8
1 95.1 117.1 1 0 0 0 0 0 0 0
2 97.0 118.7 0 1 0 0 0 0 0 0
3 112.7 126.5 0 0 1 0 0 0 0 0
4 102.9 127.5 0 0 0 1 0 0 0 0
5 97.4 134.6 0 0 0 0 1 0 0 0
6 111.4 131.8 0 0 0 0 0 1 0 0
7 87.4 135.9 0 0 0 0 0 0 1 0
8 96.8 142.7 0 0 0 0 0 0 0 1
9 114.1 141.7 0 0 0 0 0 0 0 0
10 110.3 153.4 0 0 0 0 0 0 0 0
11 103.9 145.0 0 0 0 0 0 0 0 0
12 101.6 137.7 0 0 0 0 0 0 0 0
13 94.6 148.3 1 0 0 0 0 0 0 0
14 95.9 152.2 0 1 0 0 0 0 0 0
15 104.7 169.4 0 0 1 0 0 0 0 0
16 102.8 168.6 0 0 0 1 0 0 0 0
17 98.1 161.1 0 0 0 0 1 0 0 0
18 113.9 174.1 0 0 0 0 0 1 0 0
19 80.9 179.0 0 0 0 0 0 0 1 0
20 95.7 190.6 0 0 0 0 0 0 0 1
21 113.2 190.0 0 0 0 0 0 0 0 0
22 105.9 181.6 0 0 0 0 0 0 0 0
23 108.8 174.8 0 0 0 0 0 0 0 0
24 102.3 180.5 0 0 0 0 0 0 0 0
25 99.0 196.8 1 0 0 0 0 0 0 0
26 100.7 193.8 0 1 0 0 0 0 0 0
27 115.5 197.0 0 0 1 0 0 0 0 0
28 100.7 216.3 0 0 0 1 0 0 0 0
29 109.9 221.4 0 0 0 0 1 0 0 0
30 114.6 217.9 0 0 0 0 0 1 0 0
31 85.4 229.7 0 0 0 0 0 0 1 0
32 100.5 227.4 0 0 0 0 0 0 0 1
33 114.8 204.2 0 0 0 0 0 0 0 0
34 116.5 196.6 0 0 0 0 0 0 0 0
35 112.9 198.8 0 0 0 0 0 0 0 0
36 102.0 207.5 0 0 0 0 0 0 0 0
37 106.0 190.7 1 0 0 0 0 0 0 0
38 105.3 201.6 0 1 0 0 0 0 0 0
39 118.8 210.5 0 0 1 0 0 0 0 0
40 106.1 223.5 0 0 0 1 0 0 0 0
41 109.3 223.8 0 0 0 0 1 0 0 0
42 117.2 231.2 0 0 0 0 0 1 0 0
43 92.5 244.0 0 0 0 0 0 0 1 0
44 104.2 234.7 0 0 0 0 0 0 0 1
45 112.5 250.2 0 0 0 0 0 0 0 0
46 122.4 265.7 0 0 0 0 0 0 0 0
47 113.3 287.6 0 0 0 0 0 0 0 0
48 100.0 283.3 0 0 0 0 0 0 0 0
49 110.7 295.4 1 0 0 0 0 0 0 0
50 112.8 312.3 0 1 0 0 0 0 0 0
51 109.8 333.8 0 0 1 0 0 0 0 0
52 117.3 347.7 0 0 0 1 0 0 0 0
53 109.1 383.2 0 0 0 0 1 0 0 0
54 115.9 407.1 0 0 0 0 0 1 0 0
55 96.0 413.6 0 0 0 0 0 0 1 0
56 99.8 362.7 0 0 0 0 0 0 0 1
57 116.8 321.9 0 0 0 0 0 0 0 0
58 115.7 239.4 0 0 0 0 0 0 0 0
59 99.4 191.0 0 0 0 0 0 0 0 0
60 94.3 159.7 0 0 0 0 0 0 0 0
61 91.0 163.4 1 0 0 0 0 0 0 0
M9 M10 M11 t
1 0 0 0 1
2 0 0 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 0 5
6 0 0 0 6
7 0 0 0 7
8 0 0 0 8
9 1 0 0 9
10 0 1 0 10
11 0 0 1 11
12 0 0 0 12
13 0 0 0 13
14 0 0 0 14
15 0 0 0 15
16 0 0 0 16
17 0 0 0 17
18 0 0 0 18
19 0 0 0 19
20 0 0 0 20
21 1 0 0 21
22 0 1 0 22
23 0 0 1 23
24 0 0 0 24
25 0 0 0 25
26 0 0 0 26
27 0 0 0 27
28 0 0 0 28
29 0 0 0 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 1 0 0 33
34 0 1 0 34
35 0 0 1 35
36 0 0 0 36
37 0 0 0 37
38 0 0 0 38
39 0 0 0 39
40 0 0 0 40
41 0 0 0 41
42 0 0 0 42
43 0 0 0 43
44 0 0 0 44
45 1 0 0 45
46 0 1 0 46
47 0 0 1 47
48 0 0 0 48
49 0 0 0 49
50 0 0 0 50
51 0 0 0 51
52 0 0 0 52
53 0 0 0 53
54 0 0 0 54
55 0 0 0 55
56 0 0 0 56
57 1 0 0 57
58 0 1 0 58
59 0 0 1 59
60 0 0 0 60
61 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) prijsindex.grondst.incl.energie
91.82251 0.04658
M1 M2
-0.35816 1.98362
M3 M4
11.42012 4.67027
M5 M6
3.11538 12.62379
M7 M8
-13.88737 -2.49412
M9 M10
12.87503 13.44168
M11 t
7.33208 -0.02242
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.8479 -2.8432 0.5579 3.0038 6.6264
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.82251 2.60259 35.281 < 2e-16 ***
prijsindex.grondst.incl.energie 0.04658 0.01450 3.213 0.002374 **
M1 -0.35816 2.75546 -0.130 0.897135
M2 1.98362 2.93332 0.676 0.502205
M3 11.42012 2.95321 3.867 0.000338 ***
M4 4.67027 2.96968 1.573 0.122508
M5 3.11538 2.98442 1.044 0.301879
M6 12.62379 2.99903 4.209 0.000115 ***
M7 -13.88737 3.01709 -4.603 3.18e-05 ***
M8 -2.49412 2.96690 -0.841 0.404802
M9 12.87503 2.92346 4.404 6.10e-05 ***
M10 13.44168 2.88577 4.658 2.65e-05 ***
M11 7.33208 2.87329 2.552 0.014028 *
t -0.02242 0.05818 -0.385 0.701795
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.538 on 47 degrees of freedom
Multiple R-squared: 0.8058, Adjusted R-squared: 0.752
F-statistic: 15 on 13 and 47 DF, p-value: 1.482e-12
> 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,] 0.09377934 0.18755869 0.9062207
[2,] 0.11919618 0.23839236 0.8808038
[3,] 0.08596242 0.17192484 0.9140376
[4,] 0.05178696 0.10357391 0.9482130
[5,] 0.02649644 0.05299288 0.9735036
[6,] 0.03241225 0.06482451 0.9675877
[7,] 0.03913291 0.07826581 0.9608671
[8,] 0.02284128 0.04568255 0.9771587
[9,] 0.03967846 0.07935693 0.9603215
[10,] 0.05217972 0.10435943 0.9478203
[11,] 0.05424689 0.10849378 0.9457531
[12,] 0.08151674 0.16303348 0.9184833
[13,] 0.26117765 0.52235530 0.7388223
[14,] 0.18955342 0.37910685 0.8104466
[15,] 0.22860572 0.45721145 0.7713943
[16,] 0.19071888 0.38143777 0.8092811
[17,] 0.13654755 0.27309509 0.8634525
[18,] 0.18756663 0.37513326 0.8124334
[19,] 0.13595884 0.27191768 0.8640412
[20,] 0.10251857 0.20503714 0.8974814
[21,] 0.07120307 0.14240615 0.9287969
[22,] 0.07489532 0.14979065 0.9251047
[23,] 0.10658346 0.21316692 0.8934165
[24,] 0.27071365 0.54142729 0.7292864
[25,] 0.18423311 0.36846622 0.8157669
[26,] 0.14459023 0.28918046 0.8554098
[27,] 0.08470029 0.16940057 0.9152997
[28,] 0.15331835 0.30663670 0.8466816
> postscript(file="/var/www/html/rcomp/tmp/1p85f1258644475.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dl5b1258644475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3n0yx1258644475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gm3a1258644475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hysq1258644475.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 = 61
Frequency = 1
1 2 3 4 5 6
-1.79646511 -2.29036130 3.63223281 0.55791628 -3.69550359 0.94892977
7 8 9 10 11 12
3.29152388 1.00394225 3.00378947 -1.88543070 -1.76214064 3.63238757
13 14 15 16 17 18
-3.48078371 -4.68181420 -6.09707331 -1.18754560 -3.96089557 1.74757173
19 20 21 22 23 24
-4.94709827 -2.05826452 0.12295065 -7.33000891 2.01875295 2.60773946
25 26 27 28 29 30
-1.07093856 -1.55056615 3.68629656 -5.24043635 5.29930404 0.67634349
31 32 33 34 35 36
-2.53972940 1.29656815 1.33049426 2.84027060 5.26981128 1.31905741
37 38 39 40 41 42
6.48218169 2.95509029 6.62644626 0.09316818 4.85649319 2.92580922
43 44 45 46 47 48
4.16315620 4.92551466 -2.84321026 5.79056507 1.80247719 -3.94273499
49 50 51 52 53 54
6.57422354 5.56765136 -7.84790231 5.77689749 -2.49939808 -6.29865420
55 56 57 58 59 60
0.03214760 -5.16776053 -1.61402413 0.58460395 -7.32890079 -3.61644946
61
-6.70821784
> postscript(file="/var/www/html/rcomp/tmp/68jp21258644475.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.79646511 NA
1 -2.29036130 -1.79646511
2 3.63223281 -2.29036130
3 0.55791628 3.63223281
4 -3.69550359 0.55791628
5 0.94892977 -3.69550359
6 3.29152388 0.94892977
7 1.00394225 3.29152388
8 3.00378947 1.00394225
9 -1.88543070 3.00378947
10 -1.76214064 -1.88543070
11 3.63238757 -1.76214064
12 -3.48078371 3.63238757
13 -4.68181420 -3.48078371
14 -6.09707331 -4.68181420
15 -1.18754560 -6.09707331
16 -3.96089557 -1.18754560
17 1.74757173 -3.96089557
18 -4.94709827 1.74757173
19 -2.05826452 -4.94709827
20 0.12295065 -2.05826452
21 -7.33000891 0.12295065
22 2.01875295 -7.33000891
23 2.60773946 2.01875295
24 -1.07093856 2.60773946
25 -1.55056615 -1.07093856
26 3.68629656 -1.55056615
27 -5.24043635 3.68629656
28 5.29930404 -5.24043635
29 0.67634349 5.29930404
30 -2.53972940 0.67634349
31 1.29656815 -2.53972940
32 1.33049426 1.29656815
33 2.84027060 1.33049426
34 5.26981128 2.84027060
35 1.31905741 5.26981128
36 6.48218169 1.31905741
37 2.95509029 6.48218169
38 6.62644626 2.95509029
39 0.09316818 6.62644626
40 4.85649319 0.09316818
41 2.92580922 4.85649319
42 4.16315620 2.92580922
43 4.92551466 4.16315620
44 -2.84321026 4.92551466
45 5.79056507 -2.84321026
46 1.80247719 5.79056507
47 -3.94273499 1.80247719
48 6.57422354 -3.94273499
49 5.56765136 6.57422354
50 -7.84790231 5.56765136
51 5.77689749 -7.84790231
52 -2.49939808 5.77689749
53 -6.29865420 -2.49939808
54 0.03214760 -6.29865420
55 -5.16776053 0.03214760
56 -1.61402413 -5.16776053
57 0.58460395 -1.61402413
58 -7.32890079 0.58460395
59 -3.61644946 -7.32890079
60 -6.70821784 -3.61644946
61 NA -6.70821784
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.29036130 -1.79646511
[2,] 3.63223281 -2.29036130
[3,] 0.55791628 3.63223281
[4,] -3.69550359 0.55791628
[5,] 0.94892977 -3.69550359
[6,] 3.29152388 0.94892977
[7,] 1.00394225 3.29152388
[8,] 3.00378947 1.00394225
[9,] -1.88543070 3.00378947
[10,] -1.76214064 -1.88543070
[11,] 3.63238757 -1.76214064
[12,] -3.48078371 3.63238757
[13,] -4.68181420 -3.48078371
[14,] -6.09707331 -4.68181420
[15,] -1.18754560 -6.09707331
[16,] -3.96089557 -1.18754560
[17,] 1.74757173 -3.96089557
[18,] -4.94709827 1.74757173
[19,] -2.05826452 -4.94709827
[20,] 0.12295065 -2.05826452
[21,] -7.33000891 0.12295065
[22,] 2.01875295 -7.33000891
[23,] 2.60773946 2.01875295
[24,] -1.07093856 2.60773946
[25,] -1.55056615 -1.07093856
[26,] 3.68629656 -1.55056615
[27,] -5.24043635 3.68629656
[28,] 5.29930404 -5.24043635
[29,] 0.67634349 5.29930404
[30,] -2.53972940 0.67634349
[31,] 1.29656815 -2.53972940
[32,] 1.33049426 1.29656815
[33,] 2.84027060 1.33049426
[34,] 5.26981128 2.84027060
[35,] 1.31905741 5.26981128
[36,] 6.48218169 1.31905741
[37,] 2.95509029 6.48218169
[38,] 6.62644626 2.95509029
[39,] 0.09316818 6.62644626
[40,] 4.85649319 0.09316818
[41,] 2.92580922 4.85649319
[42,] 4.16315620 2.92580922
[43,] 4.92551466 4.16315620
[44,] -2.84321026 4.92551466
[45,] 5.79056507 -2.84321026
[46,] 1.80247719 5.79056507
[47,] -3.94273499 1.80247719
[48,] 6.57422354 -3.94273499
[49,] 5.56765136 6.57422354
[50,] -7.84790231 5.56765136
[51,] 5.77689749 -7.84790231
[52,] -2.49939808 5.77689749
[53,] -6.29865420 -2.49939808
[54,] 0.03214760 -6.29865420
[55,] -5.16776053 0.03214760
[56,] -1.61402413 -5.16776053
[57,] 0.58460395 -1.61402413
[58,] -7.32890079 0.58460395
[59,] -3.61644946 -7.32890079
[60,] -6.70821784 -3.61644946
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.29036130 -1.79646511
2 3.63223281 -2.29036130
3 0.55791628 3.63223281
4 -3.69550359 0.55791628
5 0.94892977 -3.69550359
6 3.29152388 0.94892977
7 1.00394225 3.29152388
8 3.00378947 1.00394225
9 -1.88543070 3.00378947
10 -1.76214064 -1.88543070
11 3.63238757 -1.76214064
12 -3.48078371 3.63238757
13 -4.68181420 -3.48078371
14 -6.09707331 -4.68181420
15 -1.18754560 -6.09707331
16 -3.96089557 -1.18754560
17 1.74757173 -3.96089557
18 -4.94709827 1.74757173
19 -2.05826452 -4.94709827
20 0.12295065 -2.05826452
21 -7.33000891 0.12295065
22 2.01875295 -7.33000891
23 2.60773946 2.01875295
24 -1.07093856 2.60773946
25 -1.55056615 -1.07093856
26 3.68629656 -1.55056615
27 -5.24043635 3.68629656
28 5.29930404 -5.24043635
29 0.67634349 5.29930404
30 -2.53972940 0.67634349
31 1.29656815 -2.53972940
32 1.33049426 1.29656815
33 2.84027060 1.33049426
34 5.26981128 2.84027060
35 1.31905741 5.26981128
36 6.48218169 1.31905741
37 2.95509029 6.48218169
38 6.62644626 2.95509029
39 0.09316818 6.62644626
40 4.85649319 0.09316818
41 2.92580922 4.85649319
42 4.16315620 2.92580922
43 4.92551466 4.16315620
44 -2.84321026 4.92551466
45 5.79056507 -2.84321026
46 1.80247719 5.79056507
47 -3.94273499 1.80247719
48 6.57422354 -3.94273499
49 5.56765136 6.57422354
50 -7.84790231 5.56765136
51 5.77689749 -7.84790231
52 -2.49939808 5.77689749
53 -6.29865420 -2.49939808
54 0.03214760 -6.29865420
55 -5.16776053 0.03214760
56 -1.61402413 -5.16776053
57 0.58460395 -1.61402413
58 -7.32890079 0.58460395
59 -3.61644946 -7.32890079
60 -6.70821784 -3.61644946
> 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/7n2xt1258644475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8r8o21258644475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97khd1258644475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10v3c11258644475.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vcnx1258644475.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/1246qw1258644475.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/13wart1258644475.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/14p4a71258644475.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/156oqh1258644475.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/16sqpj1258644475.tab")
+ }
>
> system("convert tmp/1p85f1258644475.ps tmp/1p85f1258644475.png")
> system("convert tmp/2dl5b1258644475.ps tmp/2dl5b1258644475.png")
> system("convert tmp/3n0yx1258644475.ps tmp/3n0yx1258644475.png")
> system("convert tmp/4gm3a1258644475.ps tmp/4gm3a1258644475.png")
> system("convert tmp/5hysq1258644475.ps tmp/5hysq1258644475.png")
> system("convert tmp/68jp21258644475.ps tmp/68jp21258644475.png")
> system("convert tmp/7n2xt1258644475.ps tmp/7n2xt1258644475.png")
> system("convert tmp/8r8o21258644475.ps tmp/8r8o21258644475.png")
> system("convert tmp/97khd1258644475.ps tmp/97khd1258644475.png")
> system("convert tmp/10v3c11258644475.ps tmp/10v3c11258644475.png")
>
>
> proc.time()
user system elapsed
2.352 1.535 2.850