R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
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> x <- array(list(11993
+ ,11992
+ ,11771
+ ,10900
+ ,10057
+ ,14504
+ ,11993
+ ,11992
+ ,11771
+ ,10900
+ ,11727
+ ,14504
+ ,11993
+ ,11992
+ ,11771
+ ,11477
+ ,11727
+ ,14504
+ ,11993
+ ,11992
+ ,13578
+ ,11477
+ ,11727
+ ,14504
+ ,11993
+ ,11555
+ ,13578
+ ,11477
+ ,11727
+ ,14504
+ ,11846
+ ,11555
+ ,13578
+ ,11477
+ ,11727
+ ,11397
+ ,11846
+ ,11555
+ ,13578
+ ,11477
+ ,10066
+ ,11397
+ ,11846
+ ,11555
+ ,13578
+ ,10269
+ ,10066
+ ,11397
+ ,11846
+ ,11555
+ ,14279
+ ,10269
+ ,10066
+ ,11397
+ ,11846
+ ,13870
+ ,14279
+ ,10269
+ ,10066
+ ,11397
+ ,13695
+ ,13870
+ ,14279
+ ,10269
+ ,10066
+ ,14420
+ ,13695
+ ,13870
+ ,14279
+ ,10269
+ ,11424
+ ,14420
+ ,13695
+ ,13870
+ ,14279
+ ,9704
+ ,11424
+ ,14420
+ ,13695
+ ,13870
+ ,12464
+ ,9704
+ ,11424
+ ,14420
+ ,13695
+ ,14301
+ ,12464
+ ,9704
+ ,11424
+ ,14420
+ ,13464
+ ,14301
+ ,12464
+ ,9704
+ ,11424
+ ,9893
+ ,13464
+ ,14301
+ ,12464
+ ,9704
+ ,11572
+ ,9893
+ ,13464
+ ,14301
+ ,12464
+ ,12380
+ ,11572
+ ,9893
+ ,13464
+ ,14301
+ ,16692
+ ,12380
+ ,11572
+ ,9893
+ ,13464
+ ,16052
+ ,16692
+ ,12380
+ ,11572
+ ,9893
+ ,16459
+ ,16052
+ ,16692
+ ,12380
+ ,11572
+ ,14761
+ ,16459
+ ,16052
+ ,16692
+ ,12380
+ ,13654
+ ,14761
+ ,16459
+ ,16052
+ ,16692
+ ,13480
+ ,13654
+ ,14761
+ ,16459
+ ,16052
+ ,18068
+ ,13480
+ ,13654
+ ,14761
+ ,16459
+ ,16560
+ ,18068
+ ,13480
+ ,13654
+ ,14761
+ ,14530
+ ,16560
+ ,18068
+ ,13480
+ ,13654
+ ,10650
+ ,14530
+ ,16560
+ ,18068
+ ,13480
+ ,11651
+ ,10650
+ ,14530
+ ,16560
+ ,18068
+ ,13735
+ ,11651
+ ,10650
+ ,14530
+ ,16560
+ ,13360
+ ,13735
+ ,11651
+ ,10650
+ ,14530
+ ,17818
+ ,13360
+ ,13735
+ ,11651
+ ,10650
+ ,20613
+ ,17818
+ ,13360
+ ,13735
+ ,11651
+ ,16231
+ ,20613
+ ,17818
+ ,13360
+ ,13735
+ ,13862
+ ,16231
+ ,20613
+ ,17818
+ ,13360
+ ,12004
+ ,13862
+ ,16231
+ ,20613
+ ,17818
+ ,17734
+ ,12004
+ ,13862
+ ,16231
+ ,20613
+ ,15034
+ ,17734
+ ,12004
+ ,13862
+ ,16231
+ ,12609
+ ,15034
+ ,17734
+ ,12004
+ ,13862
+ ,12320
+ ,12609
+ ,15034
+ ,17734
+ ,12004
+ ,10833
+ ,12320
+ ,12609
+ ,15034
+ ,17734
+ ,11350
+ ,10833
+ ,12320
+ ,12609
+ ,15034
+ ,13648
+ ,11350
+ ,10833
+ ,12320
+ ,12609
+ ,14890
+ ,13648
+ ,11350
+ ,10833
+ ,12320
+ ,16325
+ ,14890
+ ,13648
+ ,11350
+ ,10833
+ ,18045
+ ,16325
+ ,14890
+ ,13648
+ ,11350
+ ,15616
+ ,18045
+ ,16325
+ ,14890
+ ,13648
+ ,11926
+ ,15616
+ ,18045
+ ,16325
+ ,14890
+ ,16855
+ ,11926
+ ,15616
+ ,18045
+ ,16325
+ ,15083
+ ,16855
+ ,11926
+ ,15616
+ ,18045
+ ,12520
+ ,15083
+ ,16855
+ ,11926
+ ,15616
+ ,12355
+ ,12520
+ ,15083
+ ,16855
+ ,11926)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('Y','Y1','Y2','Y3','Y4'),1:56))
> 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
> 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
Y Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 11993 11992 11771 10900 10057 1 0 0 0 0 0 0 0 0 0 0 1
2 14504 11993 11992 11771 10900 0 1 0 0 0 0 0 0 0 0 0 2
3 11727 14504 11993 11992 11771 0 0 1 0 0 0 0 0 0 0 0 3
4 11477 11727 14504 11993 11992 0 0 0 1 0 0 0 0 0 0 0 4
5 13578 11477 11727 14504 11993 0 0 0 0 1 0 0 0 0 0 0 5
6 11555 13578 11477 11727 14504 0 0 0 0 0 1 0 0 0 0 0 6
7 11846 11555 13578 11477 11727 0 0 0 0 0 0 1 0 0 0 0 7
8 11397 11846 11555 13578 11477 0 0 0 0 0 0 0 1 0 0 0 8
9 10066 11397 11846 11555 13578 0 0 0 0 0 0 0 0 1 0 0 9
10 10269 10066 11397 11846 11555 0 0 0 0 0 0 0 0 0 1 0 10
11 14279 10269 10066 11397 11846 0 0 0 0 0 0 0 0 0 0 1 11
12 13870 14279 10269 10066 11397 0 0 0 0 0 0 0 0 0 0 0 12
13 13695 13870 14279 10269 10066 1 0 0 0 0 0 0 0 0 0 0 13
14 14420 13695 13870 14279 10269 0 1 0 0 0 0 0 0 0 0 0 14
15 11424 14420 13695 13870 14279 0 0 1 0 0 0 0 0 0 0 0 15
16 9704 11424 14420 13695 13870 0 0 0 1 0 0 0 0 0 0 0 16
17 12464 9704 11424 14420 13695 0 0 0 0 1 0 0 0 0 0 0 17
18 14301 12464 9704 11424 14420 0 0 0 0 0 1 0 0 0 0 0 18
19 13464 14301 12464 9704 11424 0 0 0 0 0 0 1 0 0 0 0 19
20 9893 13464 14301 12464 9704 0 0 0 0 0 0 0 1 0 0 0 20
21 11572 9893 13464 14301 12464 0 0 0 0 0 0 0 0 1 0 0 21
22 12380 11572 9893 13464 14301 0 0 0 0 0 0 0 0 0 1 0 22
23 16692 12380 11572 9893 13464 0 0 0 0 0 0 0 0 0 0 1 23
24 16052 16692 12380 11572 9893 0 0 0 0 0 0 0 0 0 0 0 24
25 16459 16052 16692 12380 11572 1 0 0 0 0 0 0 0 0 0 0 25
26 14761 16459 16052 16692 12380 0 1 0 0 0 0 0 0 0 0 0 26
27 13654 14761 16459 16052 16692 0 0 1 0 0 0 0 0 0 0 0 27
28 13480 13654 14761 16459 16052 0 0 0 1 0 0 0 0 0 0 0 28
29 18068 13480 13654 14761 16459 0 0 0 0 1 0 0 0 0 0 0 29
30 16560 18068 13480 13654 14761 0 0 0 0 0 1 0 0 0 0 0 30
31 14530 16560 18068 13480 13654 0 0 0 0 0 0 1 0 0 0 0 31
32 10650 14530 16560 18068 13480 0 0 0 0 0 0 0 1 0 0 0 32
33 11651 10650 14530 16560 18068 0 0 0 0 0 0 0 0 1 0 0 33
34 13735 11651 10650 14530 16560 0 0 0 0 0 0 0 0 0 1 0 34
35 13360 13735 11651 10650 14530 0 0 0 0 0 0 0 0 0 0 1 35
36 17818 13360 13735 11651 10650 0 0 0 0 0 0 0 0 0 0 0 36
37 20613 17818 13360 13735 11651 1 0 0 0 0 0 0 0 0 0 0 37
38 16231 20613 17818 13360 13735 0 1 0 0 0 0 0 0 0 0 0 38
39 13862 16231 20613 17818 13360 0 0 1 0 0 0 0 0 0 0 0 39
40 12004 13862 16231 20613 17818 0 0 0 1 0 0 0 0 0 0 0 40
41 17734 12004 13862 16231 20613 0 0 0 0 1 0 0 0 0 0 0 41
42 15034 17734 12004 13862 16231 0 0 0 0 0 1 0 0 0 0 0 42
43 12609 15034 17734 12004 13862 0 0 0 0 0 0 1 0 0 0 0 43
44 12320 12609 15034 17734 12004 0 0 0 0 0 0 0 1 0 0 0 44
45 10833 12320 12609 15034 17734 0 0 0 0 0 0 0 0 1 0 0 45
46 11350 10833 12320 12609 15034 0 0 0 0 0 0 0 0 0 1 0 46
47 13648 11350 10833 12320 12609 0 0 0 0 0 0 0 0 0 0 1 47
48 14890 13648 11350 10833 12320 0 0 0 0 0 0 0 0 0 0 0 48
49 16325 14890 13648 11350 10833 1 0 0 0 0 0 0 0 0 0 0 49
50 18045 16325 14890 13648 11350 0 1 0 0 0 0 0 0 0 0 0 50
51 15616 18045 16325 14890 13648 0 0 1 0 0 0 0 0 0 0 0 51
52 11926 15616 18045 16325 14890 0 0 0 1 0 0 0 0 0 0 0 52
53 16855 11926 15616 18045 16325 0 0 0 0 1 0 0 0 0 0 0 53
54 15083 16855 11926 15616 18045 0 0 0 0 0 1 0 0 0 0 0 54
55 12520 15083 16855 11926 15616 0 0 0 0 0 0 1 0 0 0 0 55
56 12355 12520 15083 16855 11926 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y1 Y2 Y3 Y4 M1
8.912e+03 3.975e-01 -1.303e-01 1.562e-01 -7.951e-03 2.916e+02
M2 M3 M4 M5 M6 M7
-5.312e+02 -2.826e+03 -3.627e+03 7.140e+02 -1.986e+03 -2.293e+03
M8 M9 M10 M11 t
-4.193e+03 -3.505e+03 -2.699e+03 -2.213e+02 3.014e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2186.0 -1086.1 -174.8 877.4 2900.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.912e+03 2.561e+03 3.480 0.00125 **
Y1 3.975e-01 1.584e-01 2.510 0.01632 *
Y2 -1.303e-01 1.722e-01 -0.757 0.45367
Y3 1.562e-01 1.712e-01 0.912 0.36739
Y4 -7.951e-03 1.604e-01 -0.050 0.96073
M1 2.916e+02 1.051e+03 0.277 0.78295
M2 -5.312e+02 1.151e+03 -0.461 0.64702
M3 -2.826e+03 1.271e+03 -2.223 0.03212 *
M4 -3.627e+03 1.393e+03 -2.603 0.01299 *
M5 7.140e+02 1.409e+03 0.507 0.61530
M6 -1.986e+03 1.214e+03 -1.635 0.11006
M7 -2.293e+03 1.229e+03 -1.865 0.06977 .
M8 -4.193e+03 1.262e+03 -3.322 0.00195 **
M9 -3.505e+03 1.432e+03 -2.448 0.01898 *
M10 -2.699e+03 1.293e+03 -2.087 0.04349 *
M11 -2.213e+02 1.166e+03 -0.190 0.85041
t 3.014e+01 1.904e+01 1.583 0.12151
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1476 on 39 degrees of freedom
Multiple R-squared: 0.7331, Adjusted R-squared: 0.6236
F-statistic: 6.694 on 16 and 39 DF, p-value: 6.465e-07
> 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,] 0.6515083 0.6969834 0.3484917
[2,] 0.5539678 0.8920644 0.4460322
[3,] 0.4651645 0.9303291 0.5348355
[4,] 0.5885339 0.8229322 0.4114661
[5,] 0.5661289 0.8677423 0.4338711
[6,] 0.5795667 0.8408666 0.4204333
[7,] 0.7622744 0.4754512 0.2377256
[8,] 0.7184685 0.5630630 0.2815315
[9,] 0.6247125 0.7505749 0.3752875
[10,] 0.6527417 0.6945166 0.3472583
[11,] 0.5455906 0.9088189 0.4544094
[12,] 0.4226881 0.8453762 0.5773119
[13,] 0.4685272 0.9370544 0.5314728
[14,] 0.3891532 0.7783064 0.6108468
[15,] 0.2630888 0.5261776 0.7369112
[16,] 0.3496346 0.6992692 0.6503654
[17,] 0.5545166 0.8909668 0.4454834
> postscript(file="/var/www/rcomp/tmp/1b4x11290777540.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2b4x11290777540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3mde41290777540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4mde41290777540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5mde41290777540.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 = 56
Frequency = 1
1 2 3 4 5 6 7
-2095.9052 1106.9145 -431.2568 1522.3365 -1402.2369 -1170.0237 492.7512
8 9 10 11 12 13 14
1204.7837 -295.4187 -519.8256 800.9051 -1222.7734 -1076.7352 -167.3241
15 16 17 18 19 20 21
-1114.1495 -753.7552 -2186.0325 1472.6743 786.7723 -786.4252 1219.8529
22 23 24 25 26 27 28
203.9822 2457.0104 -333.7438 454.9250 -1362.4620 657.1763 1404.2316
29 30 31 32 33 34 35
1814.5671 1288.8396 751.3823 -1365.6384 466.9032 1115.9792 -1874.7866
36 37 38 39 40 41 42
2565.2493 2900.0470 -1144.2064 158.1323 -959.2839 1536.1216 -679.2565
43 44 45 46 47 48 49
-736.1393 547.8042 -1391.3374 -800.1358 -1383.1289 -1008.7320 -182.3316
50 51 52 53 54 55 56
1567.0781 730.0977 -1213.5290 237.5806 -912.2336 -1294.7664 399.4758
> postscript(file="/var/www/rcomp/tmp/6x5d71290777540.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -2095.9052 NA
1 1106.9145 -2095.9052
2 -431.2568 1106.9145
3 1522.3365 -431.2568
4 -1402.2369 1522.3365
5 -1170.0237 -1402.2369
6 492.7512 -1170.0237
7 1204.7837 492.7512
8 -295.4187 1204.7837
9 -519.8256 -295.4187
10 800.9051 -519.8256
11 -1222.7734 800.9051
12 -1076.7352 -1222.7734
13 -167.3241 -1076.7352
14 -1114.1495 -167.3241
15 -753.7552 -1114.1495
16 -2186.0325 -753.7552
17 1472.6743 -2186.0325
18 786.7723 1472.6743
19 -786.4252 786.7723
20 1219.8529 -786.4252
21 203.9822 1219.8529
22 2457.0104 203.9822
23 -333.7438 2457.0104
24 454.9250 -333.7438
25 -1362.4620 454.9250
26 657.1763 -1362.4620
27 1404.2316 657.1763
28 1814.5671 1404.2316
29 1288.8396 1814.5671
30 751.3823 1288.8396
31 -1365.6384 751.3823
32 466.9032 -1365.6384
33 1115.9792 466.9032
34 -1874.7866 1115.9792
35 2565.2493 -1874.7866
36 2900.0470 2565.2493
37 -1144.2064 2900.0470
38 158.1323 -1144.2064
39 -959.2839 158.1323
40 1536.1216 -959.2839
41 -679.2565 1536.1216
42 -736.1393 -679.2565
43 547.8042 -736.1393
44 -1391.3374 547.8042
45 -800.1358 -1391.3374
46 -1383.1289 -800.1358
47 -1008.7320 -1383.1289
48 -182.3316 -1008.7320
49 1567.0781 -182.3316
50 730.0977 1567.0781
51 -1213.5290 730.0977
52 237.5806 -1213.5290
53 -912.2336 237.5806
54 -1294.7664 -912.2336
55 399.4758 -1294.7664
56 NA 399.4758
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1106.9145 -2095.9052
[2,] -431.2568 1106.9145
[3,] 1522.3365 -431.2568
[4,] -1402.2369 1522.3365
[5,] -1170.0237 -1402.2369
[6,] 492.7512 -1170.0237
[7,] 1204.7837 492.7512
[8,] -295.4187 1204.7837
[9,] -519.8256 -295.4187
[10,] 800.9051 -519.8256
[11,] -1222.7734 800.9051
[12,] -1076.7352 -1222.7734
[13,] -167.3241 -1076.7352
[14,] -1114.1495 -167.3241
[15,] -753.7552 -1114.1495
[16,] -2186.0325 -753.7552
[17,] 1472.6743 -2186.0325
[18,] 786.7723 1472.6743
[19,] -786.4252 786.7723
[20,] 1219.8529 -786.4252
[21,] 203.9822 1219.8529
[22,] 2457.0104 203.9822
[23,] -333.7438 2457.0104
[24,] 454.9250 -333.7438
[25,] -1362.4620 454.9250
[26,] 657.1763 -1362.4620
[27,] 1404.2316 657.1763
[28,] 1814.5671 1404.2316
[29,] 1288.8396 1814.5671
[30,] 751.3823 1288.8396
[31,] -1365.6384 751.3823
[32,] 466.9032 -1365.6384
[33,] 1115.9792 466.9032
[34,] -1874.7866 1115.9792
[35,] 2565.2493 -1874.7866
[36,] 2900.0470 2565.2493
[37,] -1144.2064 2900.0470
[38,] 158.1323 -1144.2064
[39,] -959.2839 158.1323
[40,] 1536.1216 -959.2839
[41,] -679.2565 1536.1216
[42,] -736.1393 -679.2565
[43,] 547.8042 -736.1393
[44,] -1391.3374 547.8042
[45,] -800.1358 -1391.3374
[46,] -1383.1289 -800.1358
[47,] -1008.7320 -1383.1289
[48,] -182.3316 -1008.7320
[49,] 1567.0781 -182.3316
[50,] 730.0977 1567.0781
[51,] -1213.5290 730.0977
[52,] 237.5806 -1213.5290
[53,] -912.2336 237.5806
[54,] -1294.7664 -912.2336
[55,] 399.4758 -1294.7664
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1106.9145 -2095.9052
2 -431.2568 1106.9145
3 1522.3365 -431.2568
4 -1402.2369 1522.3365
5 -1170.0237 -1402.2369
6 492.7512 -1170.0237
7 1204.7837 492.7512
8 -295.4187 1204.7837
9 -519.8256 -295.4187
10 800.9051 -519.8256
11 -1222.7734 800.9051
12 -1076.7352 -1222.7734
13 -167.3241 -1076.7352
14 -1114.1495 -167.3241
15 -753.7552 -1114.1495
16 -2186.0325 -753.7552
17 1472.6743 -2186.0325
18 786.7723 1472.6743
19 -786.4252 786.7723
20 1219.8529 -786.4252
21 203.9822 1219.8529
22 2457.0104 203.9822
23 -333.7438 2457.0104
24 454.9250 -333.7438
25 -1362.4620 454.9250
26 657.1763 -1362.4620
27 1404.2316 657.1763
28 1814.5671 1404.2316
29 1288.8396 1814.5671
30 751.3823 1288.8396
31 -1365.6384 751.3823
32 466.9032 -1365.6384
33 1115.9792 466.9032
34 -1874.7866 1115.9792
35 2565.2493 -1874.7866
36 2900.0470 2565.2493
37 -1144.2064 2900.0470
38 158.1323 -1144.2064
39 -959.2839 158.1323
40 1536.1216 -959.2839
41 -679.2565 1536.1216
42 -736.1393 -679.2565
43 547.8042 -736.1393
44 -1391.3374 547.8042
45 -800.1358 -1391.3374
46 -1383.1289 -800.1358
47 -1008.7320 -1383.1289
48 -182.3316 -1008.7320
49 1567.0781 -182.3316
50 730.0977 1567.0781
51 -1213.5290 730.0977
52 237.5806 -1213.5290
53 -912.2336 237.5806
54 -1294.7664 -912.2336
55 399.4758 -1294.7664
> 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/rcomp/tmp/7peda1290777540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8peda1290777540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9peda1290777540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1005cu1290777540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11l6s01290777540.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/rcomp/tmp/12p6961290777540.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/rcomp/tmp/13w7oi1290777540.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/rcomp/tmp/146gnl1290777540.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/rcomp/tmp/15ahm91290777540.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/rcomp/tmp/16or1i1290777540.tab")
+ }
>
> try(system("convert tmp/1b4x11290777540.ps tmp/1b4x11290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b4x11290777540.ps tmp/2b4x11290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mde41290777540.ps tmp/3mde41290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mde41290777540.ps tmp/4mde41290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mde41290777540.ps tmp/5mde41290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/6x5d71290777540.ps tmp/6x5d71290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/7peda1290777540.ps tmp/7peda1290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/8peda1290777540.ps tmp/8peda1290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/9peda1290777540.ps tmp/9peda1290777540.png",intern=TRUE))
character(0)
> try(system("convert tmp/1005cu1290777540.ps tmp/1005cu1290777540.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.61 1.72 5.30