R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(100
+ ,100
+ ,96.21064363
+ ,97.82226485
+ ,96.31280765
+ ,94.04971502
+ ,107.1793443
+ ,91.12460521
+ ,114.9066592
+ ,93.13202153
+ ,92.56060184
+ ,93.88342812
+ ,114.9995356
+ ,92.55349954
+ ,107.1236185
+ ,94.43494835
+ ,117.7765394
+ ,96.25017563
+ ,107.3650971
+ ,100.4355715
+ ,106.2970187
+ ,101.5036685
+ ,114.5072908
+ ,99.39789728
+ ,98.0031578
+ ,99.68990733
+ ,103.0649206
+ ,101.6895041
+ ,100.2879168
+ ,103.6652759
+ ,104.6066685
+ ,103.0532766
+ ,111.1544534
+ ,100.9500712
+ ,104.9874617
+ ,102.345366
+ ,109.9284852
+ ,101.6472299
+ ,111.5352466
+ ,99.56809393
+ ,132.4974459
+ ,95.67727392
+ ,100.3436426
+ ,96.58494865
+ ,123.0983561
+ ,96.32604937
+ ,114.2379493
+ ,95.37109101
+ ,104.569518
+ ,96.00056203
+ ,109.0833101
+ ,96.88367859
+ ,106.9843039
+ ,94.85280372
+ ,133.6769759
+ ,92.46943974
+ ,124.8537197
+ ,93.99180173
+ ,122.5132349
+ ,93.45262168
+ ,116.8013374
+ ,92.26698759
+ ,116.0118882
+ ,90.39653498
+ ,129.7575926
+ ,90.43001228
+ ,125.1973623
+ ,91.04995327
+ ,143.7912139
+ ,89.07845784
+ ,127.9465032
+ ,89.69314509
+ ,130.2962757
+ ,87.92459054
+ ,108.4424631
+ ,85.8789319
+ ,129.3675118
+ ,83.20612366
+ ,143.6797622
+ ,83.85722053
+ ,131.8844618
+ ,83.01393462
+ ,117.6186496
+ ,82.84508195
+ ,118.9560695
+ ,78.68864276
+ ,104.8202842
+ ,77.56959675
+ ,134.624315
+ ,78.53689529
+ ,140.401226
+ ,78.55717715
+ ,143.8005015
+ ,77.4761291
+ ,153.4317823
+ ,81.58931659
+ ,153.2924677
+ ,85.02428326
+ ,127.3149438
+ ,91.71290159
+ ,153.5525216
+ ,95.96293061
+ ,136.9276493
+ ,90.84689022
+ ,131.7730101
+ ,92.28788036
+ ,144.3391845
+ ,95.56511274
+ ,107.4208229
+ ,93.62452884
+ ,113.6249652
+ ,92.63071726
+ ,124.2221603
+ ,89.50914211
+ ,102.0618557
+ ,87.17171779
+ ,96.36853348
+ ,86.72624975
+ ,111.6838488
+ ,85.63212844)
+ ,dim=c(2
+ ,60)
+ ,dimnames=list(c('Import'
+ ,'Wisselkoers')
+ ,1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Import','Wisselkoers'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Import Wisselkoers M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 100.00000 100.00000 1 0 0 0 0 0 0 0 0 0 0
2 96.21064 97.82226 0 1 0 0 0 0 0 0 0 0 0
3 96.31281 94.04972 0 0 1 0 0 0 0 0 0 0 0
4 107.17934 91.12461 0 0 0 1 0 0 0 0 0 0 0
5 114.90666 93.13202 0 0 0 0 1 0 0 0 0 0 0
6 92.56060 93.88343 0 0 0 0 0 1 0 0 0 0 0
7 114.99954 92.55350 0 0 0 0 0 0 1 0 0 0 0
8 107.12362 94.43495 0 0 0 0 0 0 0 1 0 0 0
9 117.77654 96.25018 0 0 0 0 0 0 0 0 1 0 0
10 107.36510 100.43557 0 0 0 0 0 0 0 0 0 1 0
11 106.29702 101.50367 0 0 0 0 0 0 0 0 0 0 1
12 114.50729 99.39790 0 0 0 0 0 0 0 0 0 0 0
13 98.00316 99.68991 1 0 0 0 0 0 0 0 0 0 0
14 103.06492 101.68950 0 1 0 0 0 0 0 0 0 0 0
15 100.28792 103.66528 0 0 1 0 0 0 0 0 0 0 0
16 104.60667 103.05328 0 0 0 1 0 0 0 0 0 0 0
17 111.15445 100.95007 0 0 0 0 1 0 0 0 0 0 0
18 104.98746 102.34537 0 0 0 0 0 1 0 0 0 0 0
19 109.92849 101.64723 0 0 0 0 0 0 1 0 0 0 0
20 111.53525 99.56809 0 0 0 0 0 0 0 1 0 0 0
21 132.49745 95.67727 0 0 0 0 0 0 0 0 1 0 0
22 100.34364 96.58495 0 0 0 0 0 0 0 0 0 1 0
23 123.09836 96.32605 0 0 0 0 0 0 0 0 0 0 1
24 114.23795 95.37109 0 0 0 0 0 0 0 0 0 0 0
25 104.56952 96.00056 1 0 0 0 0 0 0 0 0 0 0
26 109.08331 96.88368 0 1 0 0 0 0 0 0 0 0 0
27 106.98430 94.85280 0 0 1 0 0 0 0 0 0 0 0
28 133.67698 92.46944 0 0 0 1 0 0 0 0 0 0 0
29 124.85372 93.99180 0 0 0 0 1 0 0 0 0 0 0
30 122.51323 93.45262 0 0 0 0 0 1 0 0 0 0 0
31 116.80134 92.26699 0 0 0 0 0 0 1 0 0 0 0
32 116.01189 90.39653 0 0 0 0 0 0 0 1 0 0 0
33 129.75759 90.43001 0 0 0 0 0 0 0 0 1 0 0
34 125.19736 91.04995 0 0 0 0 0 0 0 0 0 1 0
35 143.79121 89.07846 0 0 0 0 0 0 0 0 0 0 1
36 127.94650 89.69315 0 0 0 0 0 0 0 0 0 0 0
37 130.29628 87.92459 1 0 0 0 0 0 0 0 0 0 0
38 108.44246 85.87893 0 1 0 0 0 0 0 0 0 0 0
39 129.36751 83.20612 0 0 1 0 0 0 0 0 0 0 0
40 143.67976 83.85722 0 0 0 1 0 0 0 0 0 0 0
41 131.88446 83.01393 0 0 0 0 1 0 0 0 0 0 0
42 117.61865 82.84508 0 0 0 0 0 1 0 0 0 0 0
43 118.95607 78.68864 0 0 0 0 0 0 1 0 0 0 0
44 104.82028 77.56960 0 0 0 0 0 0 0 1 0 0 0
45 134.62431 78.53690 0 0 0 0 0 0 0 0 1 0 0
46 140.40123 78.55718 0 0 0 0 0 0 0 0 0 1 0
47 143.80050 77.47613 0 0 0 0 0 0 0 0 0 0 1
48 153.43178 81.58932 0 0 0 0 0 0 0 0 0 0 0
49 153.29247 85.02428 1 0 0 0 0 0 0 0 0 0 0
50 127.31494 91.71290 0 1 0 0 0 0 0 0 0 0 0
51 153.55252 95.96293 0 0 1 0 0 0 0 0 0 0 0
52 136.92765 90.84689 0 0 0 1 0 0 0 0 0 0 0
53 131.77301 92.28788 0 0 0 0 1 0 0 0 0 0 0
54 144.33918 95.56511 0 0 0 0 0 1 0 0 0 0 0
55 107.42082 93.62453 0 0 0 0 0 0 1 0 0 0 0
56 113.62497 92.63072 0 0 0 0 0 0 0 1 0 0 0
57 124.22216 89.50914 0 0 0 0 0 0 0 0 1 0 0
58 102.06186 87.17172 0 0 0 0 0 0 0 0 0 1 0
59 96.36853 86.72625 0 0 0 0 0 0 0 0 0 0 1
60 111.68385 85.63213 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Wisselkoers M1 M2 M3 M4
222.724 -1.089 -3.437 -10.681 -2.693 2.958
M5 M6 M7 M8 M9 M10
1.099 -4.384 -9.195 -13.103 3.135 -8.827
M11
-1.815
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-30.1090 -8.8732 0.1699 7.1205 38.0106
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 222.724 25.925 8.591 3.39e-11 ***
Wisselkoers -1.089 0.278 -3.917 0.000289 ***
M1 -3.437 9.159 -0.375 0.709169
M2 -10.681 9.194 -1.162 0.251208
M3 -2.693 9.178 -0.293 0.770456
M4 2.958 9.126 0.324 0.747281
M5 1.099 9.133 0.120 0.904721
M6 -4.385 9.156 -0.479 0.634243
M7 -9.195 9.119 -1.008 0.318458
M8 -13.103 9.112 -1.438 0.157036
M9 3.135 9.110 0.344 0.732266
M10 -8.827 9.111 -0.969 0.337590
M11 -1.815 9.110 -0.199 0.842932
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.4 on 47 degrees of freedom
Multiple R-squared: 0.3473, Adjusted R-squared: 0.1806
F-statistic: 2.084 on 12 and 47 DF, p-value: 0.03686
> 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,] 0.0164602011 0.0329204022 0.9835398
[2,] 0.0049042353 0.0098084707 0.9950958
[3,] 0.0077307882 0.0154615763 0.9922692
[4,] 0.0036076785 0.0072153570 0.9963923
[5,] 0.0011472030 0.0022944060 0.9988528
[6,] 0.0025546376 0.0051092752 0.9974454
[7,] 0.0012118698 0.0024237397 0.9987881
[8,] 0.0025889710 0.0051779420 0.9974110
[9,] 0.0009971958 0.0019943916 0.9990028
[10,] 0.0008201781 0.0016403562 0.9991798
[11,] 0.0005685728 0.0011371455 0.9994314
[12,] 0.0007524917 0.0015049834 0.9992475
[13,] 0.0083232682 0.0166465364 0.9916767
[14,] 0.0058385260 0.0116770521 0.9941615
[15,] 0.0102390448 0.0204780897 0.9897610
[16,] 0.0051434543 0.0102869086 0.9948565
[17,] 0.0025568856 0.0051137713 0.9974431
[18,] 0.0011369326 0.0022738651 0.9988631
[19,] 0.0009980551 0.0019961102 0.9990019
[20,] 0.0021604753 0.0043209506 0.9978395
[21,] 0.0009972154 0.0019944307 0.9990028
[22,] 0.0012174885 0.0024349770 0.9987825
[23,] 0.0010771370 0.0021542739 0.9989229
[24,] 0.0015324660 0.0030649320 0.9984675
[25,] 0.0008504710 0.0017009420 0.9991495
[26,] 0.0003502077 0.0007004154 0.9996498
[27,] 0.0014380514 0.0028761027 0.9985619
[28,] 0.0011013167 0.0022026334 0.9988987
[29,] 0.0386942440 0.0773884880 0.9613058
> postscript(file="/var/www/html/rcomp/tmp/1bcq01258731778.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2iine1258731778.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qojl1258731778.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lqux1258731778.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bc8b1258731778.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
-10.40290298 -9.31906389 -21.31230469 -19.28220540 -7.51033038 -23.55456522
7 8 9 10 11 12
2.24644560 0.32767668 -3.28146643 2.82659862 -4.09023490 0.01204106
13 14 15 16 17 18
-12.73738813 1.74603842 -6.86733712 -8.86640323 -2.74988942 -1.91396393
19 20 21 22 23 24
7.07706091 10.32850631 10.81563873 -8.38758854 7.07347616 -4.64186959
25 26 27 28 29 30
-10.18815428 2.53162732 -9.76636907 8.67974298 3.37289776 5.92898627
31 32 33 34 35 36
3.73628021 4.81873890 2.36232917 10.43937737 19.87482787 2.88427950
37 38 39 40 41 42
6.74511969 -10.09168656 -0.06459420 9.30515473 -1.54955932 -10.51556898
43 44 45 46 47 48
-8.89370486 -20.33941671 -5.72071316 12.04054031 7.25097417 19.54574289
49 50 51 52 53 54
26.58332570 15.13308471 38.01060509 10.16371093 8.43688136 30.05511186
55 56 57 58 59 60
-4.16608186 4.86449481 -4.17578830 -16.91892776 -30.10904331 -17.80019386
> postscript(file="/var/www/html/rcomp/tmp/664o61258731778.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 -10.40290298 NA
1 -9.31906389 -10.40290298
2 -21.31230469 -9.31906389
3 -19.28220540 -21.31230469
4 -7.51033038 -19.28220540
5 -23.55456522 -7.51033038
6 2.24644560 -23.55456522
7 0.32767668 2.24644560
8 -3.28146643 0.32767668
9 2.82659862 -3.28146643
10 -4.09023490 2.82659862
11 0.01204106 -4.09023490
12 -12.73738813 0.01204106
13 1.74603842 -12.73738813
14 -6.86733712 1.74603842
15 -8.86640323 -6.86733712
16 -2.74988942 -8.86640323
17 -1.91396393 -2.74988942
18 7.07706091 -1.91396393
19 10.32850631 7.07706091
20 10.81563873 10.32850631
21 -8.38758854 10.81563873
22 7.07347616 -8.38758854
23 -4.64186959 7.07347616
24 -10.18815428 -4.64186959
25 2.53162732 -10.18815428
26 -9.76636907 2.53162732
27 8.67974298 -9.76636907
28 3.37289776 8.67974298
29 5.92898627 3.37289776
30 3.73628021 5.92898627
31 4.81873890 3.73628021
32 2.36232917 4.81873890
33 10.43937737 2.36232917
34 19.87482787 10.43937737
35 2.88427950 19.87482787
36 6.74511969 2.88427950
37 -10.09168656 6.74511969
38 -0.06459420 -10.09168656
39 9.30515473 -0.06459420
40 -1.54955932 9.30515473
41 -10.51556898 -1.54955932
42 -8.89370486 -10.51556898
43 -20.33941671 -8.89370486
44 -5.72071316 -20.33941671
45 12.04054031 -5.72071316
46 7.25097417 12.04054031
47 19.54574289 7.25097417
48 26.58332570 19.54574289
49 15.13308471 26.58332570
50 38.01060509 15.13308471
51 10.16371093 38.01060509
52 8.43688136 10.16371093
53 30.05511186 8.43688136
54 -4.16608186 30.05511186
55 4.86449481 -4.16608186
56 -4.17578830 4.86449481
57 -16.91892776 -4.17578830
58 -30.10904331 -16.91892776
59 -17.80019386 -30.10904331
60 NA -17.80019386
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -9.31906389 -10.40290298
[2,] -21.31230469 -9.31906389
[3,] -19.28220540 -21.31230469
[4,] -7.51033038 -19.28220540
[5,] -23.55456522 -7.51033038
[6,] 2.24644560 -23.55456522
[7,] 0.32767668 2.24644560
[8,] -3.28146643 0.32767668
[9,] 2.82659862 -3.28146643
[10,] -4.09023490 2.82659862
[11,] 0.01204106 -4.09023490
[12,] -12.73738813 0.01204106
[13,] 1.74603842 -12.73738813
[14,] -6.86733712 1.74603842
[15,] -8.86640323 -6.86733712
[16,] -2.74988942 -8.86640323
[17,] -1.91396393 -2.74988942
[18,] 7.07706091 -1.91396393
[19,] 10.32850631 7.07706091
[20,] 10.81563873 10.32850631
[21,] -8.38758854 10.81563873
[22,] 7.07347616 -8.38758854
[23,] -4.64186959 7.07347616
[24,] -10.18815428 -4.64186959
[25,] 2.53162732 -10.18815428
[26,] -9.76636907 2.53162732
[27,] 8.67974298 -9.76636907
[28,] 3.37289776 8.67974298
[29,] 5.92898627 3.37289776
[30,] 3.73628021 5.92898627
[31,] 4.81873890 3.73628021
[32,] 2.36232917 4.81873890
[33,] 10.43937737 2.36232917
[34,] 19.87482787 10.43937737
[35,] 2.88427950 19.87482787
[36,] 6.74511969 2.88427950
[37,] -10.09168656 6.74511969
[38,] -0.06459420 -10.09168656
[39,] 9.30515473 -0.06459420
[40,] -1.54955932 9.30515473
[41,] -10.51556898 -1.54955932
[42,] -8.89370486 -10.51556898
[43,] -20.33941671 -8.89370486
[44,] -5.72071316 -20.33941671
[45,] 12.04054031 -5.72071316
[46,] 7.25097417 12.04054031
[47,] 19.54574289 7.25097417
[48,] 26.58332570 19.54574289
[49,] 15.13308471 26.58332570
[50,] 38.01060509 15.13308471
[51,] 10.16371093 38.01060509
[52,] 8.43688136 10.16371093
[53,] 30.05511186 8.43688136
[54,] -4.16608186 30.05511186
[55,] 4.86449481 -4.16608186
[56,] -4.17578830 4.86449481
[57,] -16.91892776 -4.17578830
[58,] -30.10904331 -16.91892776
[59,] -17.80019386 -30.10904331
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -9.31906389 -10.40290298
2 -21.31230469 -9.31906389
3 -19.28220540 -21.31230469
4 -7.51033038 -19.28220540
5 -23.55456522 -7.51033038
6 2.24644560 -23.55456522
7 0.32767668 2.24644560
8 -3.28146643 0.32767668
9 2.82659862 -3.28146643
10 -4.09023490 2.82659862
11 0.01204106 -4.09023490
12 -12.73738813 0.01204106
13 1.74603842 -12.73738813
14 -6.86733712 1.74603842
15 -8.86640323 -6.86733712
16 -2.74988942 -8.86640323
17 -1.91396393 -2.74988942
18 7.07706091 -1.91396393
19 10.32850631 7.07706091
20 10.81563873 10.32850631
21 -8.38758854 10.81563873
22 7.07347616 -8.38758854
23 -4.64186959 7.07347616
24 -10.18815428 -4.64186959
25 2.53162732 -10.18815428
26 -9.76636907 2.53162732
27 8.67974298 -9.76636907
28 3.37289776 8.67974298
29 5.92898627 3.37289776
30 3.73628021 5.92898627
31 4.81873890 3.73628021
32 2.36232917 4.81873890
33 10.43937737 2.36232917
34 19.87482787 10.43937737
35 2.88427950 19.87482787
36 6.74511969 2.88427950
37 -10.09168656 6.74511969
38 -0.06459420 -10.09168656
39 9.30515473 -0.06459420
40 -1.54955932 9.30515473
41 -10.51556898 -1.54955932
42 -8.89370486 -10.51556898
43 -20.33941671 -8.89370486
44 -5.72071316 -20.33941671
45 12.04054031 -5.72071316
46 7.25097417 12.04054031
47 19.54574289 7.25097417
48 26.58332570 19.54574289
49 15.13308471 26.58332570
50 38.01060509 15.13308471
51 10.16371093 38.01060509
52 8.43688136 10.16371093
53 30.05511186 8.43688136
54 -4.16608186 30.05511186
55 4.86449481 -4.16608186
56 -4.17578830 4.86449481
57 -16.91892776 -4.17578830
58 -30.10904331 -16.91892776
59 -17.80019386 -30.10904331
> 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/7nd821258731778.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ssr61258731778.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90oex1258731778.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jp1r1258731778.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11q7jl1258731778.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/12v4uw1258731778.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/13tqk81258731778.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/144llt1258731778.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/15vzjv1258731778.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/16abbt1258731778.tab")
+ }
>
> system("convert tmp/1bcq01258731778.ps tmp/1bcq01258731778.png")
> system("convert tmp/2iine1258731778.ps tmp/2iine1258731778.png")
> system("convert tmp/3qojl1258731778.ps tmp/3qojl1258731778.png")
> system("convert tmp/4lqux1258731778.ps tmp/4lqux1258731778.png")
> system("convert tmp/5bc8b1258731778.ps tmp/5bc8b1258731778.png")
> system("convert tmp/664o61258731778.ps tmp/664o61258731778.png")
> system("convert tmp/7nd821258731778.ps tmp/7nd821258731778.png")
> system("convert tmp/8ssr61258731778.ps tmp/8ssr61258731778.png")
> system("convert tmp/90oex1258731778.ps tmp/90oex1258731778.png")
> system("convert tmp/10jp1r1258731778.ps tmp/10jp1r1258731778.png")
>
>
> proc.time()
user system elapsed
2.473 1.640 4.249