R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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(99.9
+ ,11554.5
+ ,98.6
+ ,13182.1
+ ,107.2
+ ,14800.1
+ ,95.7
+ ,12150.7
+ ,93.7
+ ,14478.2
+ ,106.7
+ ,13253.9
+ ,86.7
+ ,12036.8
+ ,95.3
+ ,12653.2
+ ,99.3
+ ,14035.4
+ ,101.8
+ ,14571.4
+ ,96
+ ,15400.9
+ ,91.7
+ ,14283.2
+ ,95.3
+ ,14485.3
+ ,96.6
+ ,14196.3
+ ,107.2
+ ,15559.1
+ ,108
+ ,13767.4
+ ,98.4
+ ,14634
+ ,103.1
+ ,14381.1
+ ,81.1
+ ,12509.9
+ ,96.6
+ ,12122.3
+ ,103.7
+ ,13122.3
+ ,106.6
+ ,13908.7
+ ,97.6
+ ,13456.5
+ ,87.6
+ ,12441.6
+ ,99.4
+ ,12953
+ ,98.5
+ ,13057.2
+ ,105.2
+ ,14350.1
+ ,104.6
+ ,13830.2
+ ,97.5
+ ,13755.5
+ ,108.9
+ ,13574.4
+ ,86.8
+ ,12802.6
+ ,88.9
+ ,11737.3
+ ,110.3
+ ,13850.2
+ ,114.8
+ ,15081.8
+ ,94.6
+ ,13653.3
+ ,92
+ ,14019.1
+ ,93.8
+ ,13962
+ ,93.8
+ ,13768.7
+ ,107.6
+ ,14747.1
+ ,101
+ ,13858.1
+ ,95.4
+ ,13188
+ ,96.5
+ ,13693.1
+ ,89.2
+ ,12970
+ ,87.1
+ ,11392.8
+ ,110.5
+ ,13985.2
+ ,110.8
+ ,14994.7
+ ,104.2
+ ,13584.7
+ ,88.9
+ ,14257.8
+ ,89.8
+ ,13553.4
+ ,90
+ ,14007.3
+ ,93.9
+ ,16535.8
+ ,91.3
+ ,14721.4
+ ,87.8
+ ,13664.6
+ ,99.7
+ ,16405.9
+ ,73.5
+ ,13829.4
+ ,79.2
+ ,13735.6
+ ,96.9
+ ,15870.5
+ ,95.2
+ ,15962.4
+ ,95.6
+ ,15744.1
+ ,89.7
+ ,16083.7
+ ,92.8
+ ,14863.9
+ ,88
+ ,15533.1
+ ,101.1
+ ,17473.1
+ ,92.7
+ ,15925.5
+ ,95.8
+ ,15573.7
+ ,103.8
+ ,17495
+ ,81.8
+ ,14155.8
+ ,87.1
+ ,14913.9
+ ,105.9
+ ,17250.4
+ ,108.1
+ ,15879.8
+ ,102.6
+ ,17647.8
+ ,93.7
+ ,17749.9)
+ ,dim=c(2
+ ,72)
+ ,dimnames=list(c('metallurgie'
+ ,'Invoer')
+ ,1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('metallurgie','Invoer'),1:72))
> 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
metallurgie Invoer M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 99.9 11554.5 1 0 0 0 0 0 0 0 0 0 0
2 98.6 13182.1 0 1 0 0 0 0 0 0 0 0 0
3 107.2 14800.1 0 0 1 0 0 0 0 0 0 0 0
4 95.7 12150.7 0 0 0 1 0 0 0 0 0 0 0
5 93.7 14478.2 0 0 0 0 1 0 0 0 0 0 0
6 106.7 13253.9 0 0 0 0 0 1 0 0 0 0 0
7 86.7 12036.8 0 0 0 0 0 0 1 0 0 0 0
8 95.3 12653.2 0 0 0 0 0 0 0 1 0 0 0
9 99.3 14035.4 0 0 0 0 0 0 0 0 1 0 0
10 101.8 14571.4 0 0 0 0 0 0 0 0 0 1 0
11 96.0 15400.9 0 0 0 0 0 0 0 0 0 0 1
12 91.7 14283.2 0 0 0 0 0 0 0 0 0 0 0
13 95.3 14485.3 1 0 0 0 0 0 0 0 0 0 0
14 96.6 14196.3 0 1 0 0 0 0 0 0 0 0 0
15 107.2 15559.1 0 0 1 0 0 0 0 0 0 0 0
16 108.0 13767.4 0 0 0 1 0 0 0 0 0 0 0
17 98.4 14634.0 0 0 0 0 1 0 0 0 0 0 0
18 103.1 14381.1 0 0 0 0 0 1 0 0 0 0 0
19 81.1 12509.9 0 0 0 0 0 0 1 0 0 0 0
20 96.6 12122.3 0 0 0 0 0 0 0 1 0 0 0
21 103.7 13122.3 0 0 0 0 0 0 0 0 1 0 0
22 106.6 13908.7 0 0 0 0 0 0 0 0 0 1 0
23 97.6 13456.5 0 0 0 0 0 0 0 0 0 0 1
24 87.6 12441.6 0 0 0 0 0 0 0 0 0 0 0
25 99.4 12953.0 1 0 0 0 0 0 0 0 0 0 0
26 98.5 13057.2 0 1 0 0 0 0 0 0 0 0 0
27 105.2 14350.1 0 0 1 0 0 0 0 0 0 0 0
28 104.6 13830.2 0 0 0 1 0 0 0 0 0 0 0
29 97.5 13755.5 0 0 0 0 1 0 0 0 0 0 0
30 108.9 13574.4 0 0 0 0 0 1 0 0 0 0 0
31 86.8 12802.6 0 0 0 0 0 0 1 0 0 0 0
32 88.9 11737.3 0 0 0 0 0 0 0 1 0 0 0
33 110.3 13850.2 0 0 0 0 0 0 0 0 1 0 0
34 114.8 15081.8 0 0 0 0 0 0 0 0 0 1 0
35 94.6 13653.3 0 0 0 0 0 0 0 0 0 0 1
36 92.0 14019.1 0 0 0 0 0 0 0 0 0 0 0
37 93.8 13962.0 1 0 0 0 0 0 0 0 0 0 0
38 93.8 13768.7 0 1 0 0 0 0 0 0 0 0 0
39 107.6 14747.1 0 0 1 0 0 0 0 0 0 0 0
40 101.0 13858.1 0 0 0 1 0 0 0 0 0 0 0
41 95.4 13188.0 0 0 0 0 1 0 0 0 0 0 0
42 96.5 13693.1 0 0 0 0 0 1 0 0 0 0 0
43 89.2 12970.0 0 0 0 0 0 0 1 0 0 0 0
44 87.1 11392.8 0 0 0 0 0 0 0 1 0 0 0
45 110.5 13985.2 0 0 0 0 0 0 0 0 1 0 0
46 110.8 14994.7 0 0 0 0 0 0 0 0 0 1 0
47 104.2 13584.7 0 0 0 0 0 0 0 0 0 0 1
48 88.9 14257.8 0 0 0 0 0 0 0 0 0 0 0
49 89.8 13553.4 1 0 0 0 0 0 0 0 0 0 0
50 90.0 14007.3 0 1 0 0 0 0 0 0 0 0 0
51 93.9 16535.8 0 0 1 0 0 0 0 0 0 0 0
52 91.3 14721.4 0 0 0 1 0 0 0 0 0 0 0
53 87.8 13664.6 0 0 0 0 1 0 0 0 0 0 0
54 99.7 16405.9 0 0 0 0 0 1 0 0 0 0 0
55 73.5 13829.4 0 0 0 0 0 0 1 0 0 0 0
56 79.2 13735.6 0 0 0 0 0 0 0 1 0 0 0
57 96.9 15870.5 0 0 0 0 0 0 0 0 1 0 0
58 95.2 15962.4 0 0 0 0 0 0 0 0 0 1 0
59 95.6 15744.1 0 0 0 0 0 0 0 0 0 0 1
60 89.7 16083.7 0 0 0 0 0 0 0 0 0 0 0
61 92.8 14863.9 1 0 0 0 0 0 0 0 0 0 0
62 88.0 15533.1 0 1 0 0 0 0 0 0 0 0 0
63 101.1 17473.1 0 0 1 0 0 0 0 0 0 0 0
64 92.7 15925.5 0 0 0 1 0 0 0 0 0 0 0
65 95.8 15573.7 0 0 0 0 1 0 0 0 0 0 0
66 103.8 17495.0 0 0 0 0 0 1 0 0 0 0 0
67 81.8 14155.8 0 0 0 0 0 0 1 0 0 0 0
68 87.1 14913.9 0 0 0 0 0 0 0 1 0 0 0
69 105.9 17250.4 0 0 0 0 0 0 0 0 1 0 0
70 108.1 15879.8 0 0 0 0 0 0 0 0 0 1 0
71 102.6 17647.8 0 0 0 0 0 0 0 0 0 0 1
72 93.7 17749.9 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) Invoer M1 M2 M3 M4
104.81323 -0.00096 3.37259 2.83553 13.84078 7.55023
M5 M6 M7 M8 M9 M10
3.60008 12.51156 -9.10155 -3.53144 13.71793 15.86682
M11
7.93765
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.1566 -2.7632 0.2284 3.4099 8.8529
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.048e+02 7.609e+00 13.775 < 2e-16 ***
Invoer -9.600e-04 4.949e-04 -1.940 0.05719 .
M1 3.373e+00 2.966e+00 1.137 0.26016
M2 2.836e+00 2.932e+00 0.967 0.33744
M3 1.384e+01 2.927e+00 4.729 1.45e-05 ***
M4 7.550e+00 2.926e+00 2.580 0.01239 *
M5 3.600e+00 2.916e+00 1.234 0.22195
M6 1.251e+01 2.902e+00 4.312 6.24e-05 ***
M7 -9.102e+00 3.029e+00 -3.005 0.00390 **
M8 -3.531e+00 3.073e+00 -1.149 0.25519
M9 1.372e+01 2.902e+00 4.726 1.46e-05 ***
M10 1.587e+01 2.905e+00 5.463 9.88e-07 ***
M11 7.938e+00 2.902e+00 2.735 0.00823 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.026 on 59 degrees of freedom
Multiple R-squared: 0.6907, Adjusted R-squared: 0.6278
F-statistic: 10.98 on 12 and 59 DF, p-value: 4.702e-11
> 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,] 0.62580624 0.74838752 0.3741938
[2,] 0.52140751 0.95718497 0.4785925
[3,] 0.40732078 0.81464156 0.5926792
[4,] 0.34933962 0.69867925 0.6506604
[5,] 0.27680777 0.55361553 0.7231922
[6,] 0.21821253 0.43642506 0.7817875
[7,] 0.17117961 0.34235921 0.8288204
[8,] 0.11137635 0.22275270 0.8886237
[9,] 0.09845833 0.19691667 0.9015417
[10,] 0.06880160 0.13760319 0.9311984
[11,] 0.04635816 0.09271632 0.9536418
[12,] 0.02949326 0.05898652 0.9705067
[13,] 0.02517961 0.05035922 0.9748204
[14,] 0.01560401 0.03120802 0.9843960
[15,] 0.01455409 0.02910818 0.9854459
[16,] 0.01049662 0.02099324 0.9895034
[17,] 0.01500072 0.03000144 0.9849993
[18,] 0.02958886 0.05917772 0.9704111
[19,] 0.08510679 0.17021357 0.9148932
[20,] 0.07325583 0.14651166 0.9267442
[21,] 0.04932870 0.09865741 0.9506713
[22,] 0.04227427 0.08454854 0.9577257
[23,] 0.03520865 0.07041731 0.9647913
[24,] 0.03579976 0.07159952 0.9642002
[25,] 0.03744628 0.07489256 0.9625537
[26,] 0.02537136 0.05074272 0.9746286
[27,] 0.04671914 0.09343828 0.9532809
[28,] 0.08062350 0.16124701 0.9193765
[29,] 0.07862190 0.15724380 0.9213781
[30,] 0.14919284 0.29838569 0.8508072
[31,] 0.23331102 0.46662205 0.7666890
[32,] 0.52057487 0.95885026 0.4794251
[33,] 0.50954538 0.98090924 0.4904546
[34,] 0.47233431 0.94466863 0.5276657
[35,] 0.56572218 0.86855563 0.4342778
[36,] 0.60851910 0.78296180 0.3914809
[37,] 0.58733564 0.82532872 0.4126644
[38,] 0.50383776 0.99232447 0.4961622
[39,] 0.37333628 0.74667256 0.6266637
[40,] 0.39828230 0.79656460 0.6017177
[41,] 0.30286075 0.60572151 0.6971392
> postscript(file="/var/www/html/rcomp/tmp/1p0bk1229764819.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/27epp1229764819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3uefn1229764819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zq8z1229764819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59z341229764819.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 = 72
Frequency = 1
1 2 3 4 5 6
2.8061738 3.6056858 2.7536699 -4.9991362 -0.8146420 2.0985768
7 8 9 10 11 12
2.5433032 6.1649256 -5.7575711 -4.8919167 -1.9664510 0.5982387
13 14 15 16 17 18
1.0196594 2.5792893 3.4822885 8.8528504 4.0349216 -0.4193429
19 20 21 22 23 24
-2.6025341 6.9552766 -2.2341214 -0.7280901 -2.2330203 -5.2696455
25 26 27 28 29 30
3.6486945 3.3857853 0.3216825 5.5131366 2.2915863 4.6062478
31 32 33 34 35 36
3.3784496 -1.1143126 5.0646422 8.5980529 -5.0440979 0.6447101
37 38 39 40 41 42
-0.9826939 -0.6311947 3.1027913 1.9399198 -0.3531978 -7.6798035
43 44 45 46 47 48
5.9391489 -3.2450229 5.3942384 4.5144394 4.4900481 -2.2261446
49 50 51 52 53 54
-5.3749384 -4.2021454 -8.8801069 -6.9313364 -7.4956752 -1.8755918
55 56 57 58 59 60
-8.9358512 -8.8960008 -6.3959266 -10.1565959 -2.0369887 0.3266681
61 62 63 64 65 66
-1.1168953 -4.7374203 -0.7803253 -4.3754343 2.3370072 3.2699136
67 68 69 70 71 72
-0.3225164 0.1351341 3.9287386 2.6641105 6.7905098 5.9261732
> postscript(file="/var/www/html/rcomp/tmp/64j7d1229764819.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 2.8061738 NA
1 3.6056858 2.8061738
2 2.7536699 3.6056858
3 -4.9991362 2.7536699
4 -0.8146420 -4.9991362
5 2.0985768 -0.8146420
6 2.5433032 2.0985768
7 6.1649256 2.5433032
8 -5.7575711 6.1649256
9 -4.8919167 -5.7575711
10 -1.9664510 -4.8919167
11 0.5982387 -1.9664510
12 1.0196594 0.5982387
13 2.5792893 1.0196594
14 3.4822885 2.5792893
15 8.8528504 3.4822885
16 4.0349216 8.8528504
17 -0.4193429 4.0349216
18 -2.6025341 -0.4193429
19 6.9552766 -2.6025341
20 -2.2341214 6.9552766
21 -0.7280901 -2.2341214
22 -2.2330203 -0.7280901
23 -5.2696455 -2.2330203
24 3.6486945 -5.2696455
25 3.3857853 3.6486945
26 0.3216825 3.3857853
27 5.5131366 0.3216825
28 2.2915863 5.5131366
29 4.6062478 2.2915863
30 3.3784496 4.6062478
31 -1.1143126 3.3784496
32 5.0646422 -1.1143126
33 8.5980529 5.0646422
34 -5.0440979 8.5980529
35 0.6447101 -5.0440979
36 -0.9826939 0.6447101
37 -0.6311947 -0.9826939
38 3.1027913 -0.6311947
39 1.9399198 3.1027913
40 -0.3531978 1.9399198
41 -7.6798035 -0.3531978
42 5.9391489 -7.6798035
43 -3.2450229 5.9391489
44 5.3942384 -3.2450229
45 4.5144394 5.3942384
46 4.4900481 4.5144394
47 -2.2261446 4.4900481
48 -5.3749384 -2.2261446
49 -4.2021454 -5.3749384
50 -8.8801069 -4.2021454
51 -6.9313364 -8.8801069
52 -7.4956752 -6.9313364
53 -1.8755918 -7.4956752
54 -8.9358512 -1.8755918
55 -8.8960008 -8.9358512
56 -6.3959266 -8.8960008
57 -10.1565959 -6.3959266
58 -2.0369887 -10.1565959
59 0.3266681 -2.0369887
60 -1.1168953 0.3266681
61 -4.7374203 -1.1168953
62 -0.7803253 -4.7374203
63 -4.3754343 -0.7803253
64 2.3370072 -4.3754343
65 3.2699136 2.3370072
66 -0.3225164 3.2699136
67 0.1351341 -0.3225164
68 3.9287386 0.1351341
69 2.6641105 3.9287386
70 6.7905098 2.6641105
71 5.9261732 6.7905098
72 NA 5.9261732
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.6056858 2.8061738
[2,] 2.7536699 3.6056858
[3,] -4.9991362 2.7536699
[4,] -0.8146420 -4.9991362
[5,] 2.0985768 -0.8146420
[6,] 2.5433032 2.0985768
[7,] 6.1649256 2.5433032
[8,] -5.7575711 6.1649256
[9,] -4.8919167 -5.7575711
[10,] -1.9664510 -4.8919167
[11,] 0.5982387 -1.9664510
[12,] 1.0196594 0.5982387
[13,] 2.5792893 1.0196594
[14,] 3.4822885 2.5792893
[15,] 8.8528504 3.4822885
[16,] 4.0349216 8.8528504
[17,] -0.4193429 4.0349216
[18,] -2.6025341 -0.4193429
[19,] 6.9552766 -2.6025341
[20,] -2.2341214 6.9552766
[21,] -0.7280901 -2.2341214
[22,] -2.2330203 -0.7280901
[23,] -5.2696455 -2.2330203
[24,] 3.6486945 -5.2696455
[25,] 3.3857853 3.6486945
[26,] 0.3216825 3.3857853
[27,] 5.5131366 0.3216825
[28,] 2.2915863 5.5131366
[29,] 4.6062478 2.2915863
[30,] 3.3784496 4.6062478
[31,] -1.1143126 3.3784496
[32,] 5.0646422 -1.1143126
[33,] 8.5980529 5.0646422
[34,] -5.0440979 8.5980529
[35,] 0.6447101 -5.0440979
[36,] -0.9826939 0.6447101
[37,] -0.6311947 -0.9826939
[38,] 3.1027913 -0.6311947
[39,] 1.9399198 3.1027913
[40,] -0.3531978 1.9399198
[41,] -7.6798035 -0.3531978
[42,] 5.9391489 -7.6798035
[43,] -3.2450229 5.9391489
[44,] 5.3942384 -3.2450229
[45,] 4.5144394 5.3942384
[46,] 4.4900481 4.5144394
[47,] -2.2261446 4.4900481
[48,] -5.3749384 -2.2261446
[49,] -4.2021454 -5.3749384
[50,] -8.8801069 -4.2021454
[51,] -6.9313364 -8.8801069
[52,] -7.4956752 -6.9313364
[53,] -1.8755918 -7.4956752
[54,] -8.9358512 -1.8755918
[55,] -8.8960008 -8.9358512
[56,] -6.3959266 -8.8960008
[57,] -10.1565959 -6.3959266
[58,] -2.0369887 -10.1565959
[59,] 0.3266681 -2.0369887
[60,] -1.1168953 0.3266681
[61,] -4.7374203 -1.1168953
[62,] -0.7803253 -4.7374203
[63,] -4.3754343 -0.7803253
[64,] 2.3370072 -4.3754343
[65,] 3.2699136 2.3370072
[66,] -0.3225164 3.2699136
[67,] 0.1351341 -0.3225164
[68,] 3.9287386 0.1351341
[69,] 2.6641105 3.9287386
[70,] 6.7905098 2.6641105
[71,] 5.9261732 6.7905098
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.6056858 2.8061738
2 2.7536699 3.6056858
3 -4.9991362 2.7536699
4 -0.8146420 -4.9991362
5 2.0985768 -0.8146420
6 2.5433032 2.0985768
7 6.1649256 2.5433032
8 -5.7575711 6.1649256
9 -4.8919167 -5.7575711
10 -1.9664510 -4.8919167
11 0.5982387 -1.9664510
12 1.0196594 0.5982387
13 2.5792893 1.0196594
14 3.4822885 2.5792893
15 8.8528504 3.4822885
16 4.0349216 8.8528504
17 -0.4193429 4.0349216
18 -2.6025341 -0.4193429
19 6.9552766 -2.6025341
20 -2.2341214 6.9552766
21 -0.7280901 -2.2341214
22 -2.2330203 -0.7280901
23 -5.2696455 -2.2330203
24 3.6486945 -5.2696455
25 3.3857853 3.6486945
26 0.3216825 3.3857853
27 5.5131366 0.3216825
28 2.2915863 5.5131366
29 4.6062478 2.2915863
30 3.3784496 4.6062478
31 -1.1143126 3.3784496
32 5.0646422 -1.1143126
33 8.5980529 5.0646422
34 -5.0440979 8.5980529
35 0.6447101 -5.0440979
36 -0.9826939 0.6447101
37 -0.6311947 -0.9826939
38 3.1027913 -0.6311947
39 1.9399198 3.1027913
40 -0.3531978 1.9399198
41 -7.6798035 -0.3531978
42 5.9391489 -7.6798035
43 -3.2450229 5.9391489
44 5.3942384 -3.2450229
45 4.5144394 5.3942384
46 4.4900481 4.5144394
47 -2.2261446 4.4900481
48 -5.3749384 -2.2261446
49 -4.2021454 -5.3749384
50 -8.8801069 -4.2021454
51 -6.9313364 -8.8801069
52 -7.4956752 -6.9313364
53 -1.8755918 -7.4956752
54 -8.9358512 -1.8755918
55 -8.8960008 -8.9358512
56 -6.3959266 -8.8960008
57 -10.1565959 -6.3959266
58 -2.0369887 -10.1565959
59 0.3266681 -2.0369887
60 -1.1168953 0.3266681
61 -4.7374203 -1.1168953
62 -0.7803253 -4.7374203
63 -4.3754343 -0.7803253
64 2.3370072 -4.3754343
65 3.2699136 2.3370072
66 -0.3225164 3.2699136
67 0.1351341 -0.3225164
68 3.9287386 0.1351341
69 2.6641105 3.9287386
70 6.7905098 2.6641105
71 5.9261732 6.7905098
> 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/7we211229764819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8em191229764819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c79u1229764819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wq4h1229764819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11uxpe1229764819.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/12gk881229764819.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/13q8n41229764819.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/14kcx81229764819.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/15k2zx1229764819.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/16ua3i1229764819.tab")
+ }
>
> system("convert tmp/1p0bk1229764819.ps tmp/1p0bk1229764819.png")
> system("convert tmp/27epp1229764819.ps tmp/27epp1229764819.png")
> system("convert tmp/3uefn1229764819.ps tmp/3uefn1229764819.png")
> system("convert tmp/4zq8z1229764819.ps tmp/4zq8z1229764819.png")
> system("convert tmp/59z341229764819.ps tmp/59z341229764819.png")
> system("convert tmp/64j7d1229764819.ps tmp/64j7d1229764819.png")
> system("convert tmp/7we211229764819.ps tmp/7we211229764819.png")
> system("convert tmp/8em191229764819.ps tmp/8em191229764819.png")
> system("convert tmp/9c79u1229764819.ps tmp/9c79u1229764819.png")
> system("convert tmp/10wq4h1229764819.ps tmp/10wq4h1229764819.png")
>
>
> proc.time()
user system elapsed
2.621 1.623 8.917