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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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(29
+ ,27
+ ,24
+ ,25
+ ,22
+ ,24
+ ,26
+ ,28
+ ,29
+ ,24
+ ,25
+ ,22
+ ,26
+ ,25
+ ,26
+ ,29
+ ,24
+ ,25
+ ,21
+ ,19
+ ,26
+ ,26
+ ,29
+ ,24
+ ,23
+ ,19
+ ,21
+ ,26
+ ,26
+ ,29
+ ,22
+ ,19
+ ,23
+ ,21
+ ,26
+ ,26
+ ,21
+ ,20
+ ,22
+ ,23
+ ,21
+ ,26
+ ,16
+ ,16
+ ,21
+ ,22
+ ,23
+ ,21
+ ,19
+ ,22
+ ,16
+ ,21
+ ,22
+ ,23
+ ,16
+ ,21
+ ,19
+ ,16
+ ,21
+ ,22
+ ,25
+ ,25
+ ,16
+ ,19
+ ,16
+ ,21
+ ,27
+ ,29
+ ,25
+ ,16
+ ,19
+ ,16
+ ,23
+ ,28
+ ,27
+ ,25
+ ,16
+ ,19
+ ,22
+ ,25
+ ,23
+ ,27
+ ,25
+ ,16
+ ,23
+ ,26
+ ,22
+ ,23
+ ,27
+ ,25
+ ,20
+ ,24
+ ,23
+ ,22
+ ,23
+ ,27
+ ,24
+ ,28
+ ,20
+ ,23
+ ,22
+ ,23
+ ,23
+ ,28
+ ,24
+ ,20
+ ,23
+ ,22
+ ,20
+ ,28
+ ,23
+ ,24
+ ,20
+ ,23
+ ,21
+ ,28
+ ,20
+ ,23
+ ,24
+ ,20
+ ,22
+ ,32
+ ,21
+ ,20
+ ,23
+ ,24
+ ,17
+ ,31
+ ,22
+ ,21
+ ,20
+ ,23
+ ,21
+ ,22
+ ,17
+ ,22
+ ,21
+ ,20
+ ,19
+ ,29
+ ,21
+ ,17
+ ,22
+ ,21
+ ,23
+ ,31
+ ,19
+ ,21
+ ,17
+ ,22
+ ,22
+ ,29
+ ,23
+ ,19
+ ,21
+ ,17
+ ,15
+ ,32
+ ,22
+ ,23
+ ,19
+ ,21
+ ,23
+ ,32
+ ,15
+ ,22
+ ,23
+ ,19
+ ,21
+ ,31
+ ,23
+ ,15
+ ,22
+ ,23
+ ,18
+ ,29
+ ,21
+ ,23
+ ,15
+ ,22
+ ,18
+ ,28
+ ,18
+ ,21
+ ,23
+ ,15
+ ,18
+ ,28
+ ,18
+ ,18
+ ,21
+ ,23
+ ,18
+ ,29
+ ,18
+ ,18
+ ,18
+ ,21
+ ,10
+ ,22
+ ,18
+ ,18
+ ,18
+ ,18
+ ,13
+ ,26
+ ,10
+ ,18
+ ,18
+ ,18
+ ,10
+ ,24
+ ,13
+ ,10
+ ,18
+ ,18
+ ,9
+ ,27
+ ,10
+ ,13
+ ,10
+ ,18
+ ,9
+ ,27
+ ,9
+ ,10
+ ,13
+ ,10
+ ,6
+ ,23
+ ,9
+ ,9
+ ,10
+ ,13
+ ,11
+ ,21
+ ,6
+ ,9
+ ,9
+ ,10
+ ,9
+ ,19
+ ,11
+ ,6
+ ,9
+ ,9
+ ,10
+ ,17
+ ,9
+ ,11
+ ,6
+ ,9
+ ,9
+ ,19
+ ,10
+ ,9
+ ,11
+ ,6
+ ,16
+ ,21
+ ,9
+ ,10
+ ,9
+ ,11
+ ,10
+ ,13
+ ,16
+ ,9
+ ,10
+ ,9
+ ,7
+ ,8
+ ,10
+ ,16
+ ,9
+ ,10
+ ,7
+ ,5
+ ,7
+ ,10
+ ,16
+ ,9
+ ,14
+ ,10
+ ,7
+ ,7
+ ,10
+ ,16
+ ,11
+ ,6
+ ,14
+ ,7
+ ,7
+ ,10
+ ,10
+ ,6
+ ,11
+ ,14
+ ,7
+ ,7
+ ,6
+ ,8
+ ,10
+ ,11
+ ,14
+ ,7
+ ,8
+ ,11
+ ,6
+ ,10
+ ,11
+ ,14
+ ,13
+ ,12
+ ,8
+ ,6
+ ,10
+ ,11
+ ,12
+ ,13
+ ,13
+ ,8
+ ,6
+ ,10
+ ,15
+ ,19
+ ,12
+ ,13
+ ,8
+ ,6
+ ,16
+ ,19
+ ,15
+ ,12
+ ,13
+ ,8
+ ,16
+ ,18
+ ,16
+ ,15
+ ,12
+ ,13)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('s'
+ ,'consv'
+ ,'y(t-1)'
+ ,'y(t-2)'
+ ,'y(t-3)'
+ ,'y(t-4)')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('s','consv','y(t-1)','y(t-2)','y(t-3)','y(t-4)'),1:57))
> 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
s consv y(t-1) y(t-2) y(t-3) y(t-4) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 29 27 24 25 22 24 1 0 0 0 0 0 0 0 0 0 0 1
2 26 28 29 24 25 22 0 1 0 0 0 0 0 0 0 0 0 2
3 26 25 26 29 24 25 0 0 1 0 0 0 0 0 0 0 0 3
4 21 19 26 26 29 24 0 0 0 1 0 0 0 0 0 0 0 4
5 23 19 21 26 26 29 0 0 0 0 1 0 0 0 0 0 0 5
6 22 19 23 21 26 26 0 0 0 0 0 1 0 0 0 0 0 6
7 21 20 22 23 21 26 0 0 0 0 0 0 1 0 0 0 0 7
8 16 16 21 22 23 21 0 0 0 0 0 0 0 1 0 0 0 8
9 19 22 16 21 22 23 0 0 0 0 0 0 0 0 1 0 0 9
10 16 21 19 16 21 22 0 0 0 0 0 0 0 0 0 1 0 10
11 25 25 16 19 16 21 0 0 0 0 0 0 0 0 0 0 1 11
12 27 29 25 16 19 16 0 0 0 0 0 0 0 0 0 0 0 12
13 23 28 27 25 16 19 1 0 0 0 0 0 0 0 0 0 0 13
14 22 25 23 27 25 16 0 1 0 0 0 0 0 0 0 0 0 14
15 23 26 22 23 27 25 0 0 1 0 0 0 0 0 0 0 0 15
16 20 24 23 22 23 27 0 0 0 1 0 0 0 0 0 0 0 16
17 24 28 20 23 22 23 0 0 0 0 1 0 0 0 0 0 0 17
18 23 28 24 20 23 22 0 0 0 0 0 1 0 0 0 0 0 18
19 20 28 23 24 20 23 0 0 0 0 0 0 1 0 0 0 0 19
20 21 28 20 23 24 20 0 0 0 0 0 0 0 1 0 0 0 20
21 22 32 21 20 23 24 0 0 0 0 0 0 0 0 1 0 0 21
22 17 31 22 21 20 23 0 0 0 0 0 0 0 0 0 1 0 22
23 21 22 17 22 21 20 0 0 0 0 0 0 0 0 0 0 1 23
24 19 29 21 17 22 21 0 0 0 0 0 0 0 0 0 0 0 24
25 23 31 19 21 17 22 1 0 0 0 0 0 0 0 0 0 0 25
26 22 29 23 19 21 17 0 1 0 0 0 0 0 0 0 0 0 26
27 15 32 22 23 19 21 0 0 1 0 0 0 0 0 0 0 0 27
28 23 32 15 22 23 19 0 0 0 1 0 0 0 0 0 0 0 28
29 21 31 23 15 22 23 0 0 0 0 1 0 0 0 0 0 0 29
30 18 29 21 23 15 22 0 0 0 0 0 1 0 0 0 0 0 30
31 18 28 18 21 23 15 0 0 0 0 0 0 1 0 0 0 0 31
32 18 28 18 18 21 23 0 0 0 0 0 0 0 1 0 0 0 32
33 18 29 18 18 18 21 0 0 0 0 0 0 0 0 1 0 0 33
34 10 22 18 18 18 18 0 0 0 0 0 0 0 0 0 1 0 34
35 13 26 10 18 18 18 0 0 0 0 0 0 0 0 0 0 1 35
36 10 24 13 10 18 18 0 0 0 0 0 0 0 0 0 0 0 36
37 9 27 10 13 10 18 1 0 0 0 0 0 0 0 0 0 0 37
38 9 27 9 10 13 10 0 1 0 0 0 0 0 0 0 0 0 38
39 6 23 9 9 10 13 0 0 1 0 0 0 0 0 0 0 0 39
40 11 21 6 9 9 10 0 0 0 1 0 0 0 0 0 0 0 40
41 9 19 11 6 9 9 0 0 0 0 1 0 0 0 0 0 0 41
42 10 17 9 11 6 9 0 0 0 0 0 1 0 0 0 0 0 42
43 9 19 10 9 11 6 0 0 0 0 0 0 1 0 0 0 0 43
44 16 21 9 10 9 11 0 0 0 0 0 0 0 1 0 0 0 44
45 10 13 16 9 10 9 0 0 0 0 0 0 0 0 1 0 0 45
46 7 8 10 16 9 10 0 0 0 0 0 0 0 0 0 1 0 46
47 7 5 7 10 16 9 0 0 0 0 0 0 0 0 0 0 1 47
48 14 10 7 7 10 16 0 0 0 0 0 0 0 0 0 0 0 48
49 11 6 14 7 7 10 1 0 0 0 0 0 0 0 0 0 0 49
50 10 6 11 14 7 7 0 1 0 0 0 0 0 0 0 0 0 50
51 6 8 10 11 14 7 0 0 1 0 0 0 0 0 0 0 0 51
52 8 11 6 10 11 14 0 0 0 1 0 0 0 0 0 0 0 52
53 13 12 8 6 10 11 0 0 0 0 1 0 0 0 0 0 0 53
54 12 13 13 8 6 10 0 0 0 0 0 1 0 0 0 0 0 54
55 15 19 12 13 8 6 0 0 0 0 0 0 1 0 0 0 0 55
56 16 19 15 12 13 8 0 0 0 0 0 0 0 1 0 0 0 56
57 16 18 16 15 12 13 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) consv `y(t-1)` `y(t-2)` `y(t-3)` `y(t-4)`
8.53758 0.11619 0.43184 0.31462 -0.10429 -0.03034
M1 M2 M3 M4 M5 M6
-2.05606 -3.08836 -4.95093 -1.76784 -0.23895 -2.44085
M7 M8 M9 M10 M11 t
-2.88724 -1.28390 -1.93080 -6.85809 -0.41202 -0.08102
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.4190063 -2.0639078 -0.0001928 1.9369133 4.4925032
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.53758 4.84348 1.763 0.08579 .
consv 0.11619 0.07401 1.570 0.12453
`y(t-1)` 0.43184 0.15628 2.763 0.00869 **
`y(t-2)` 0.31462 0.17132 1.836 0.07392 .
`y(t-3)` -0.10429 0.17345 -0.601 0.55112
`y(t-4)` -0.03034 0.16730 -0.181 0.85702
M1 -2.05606 2.37974 -0.864 0.39288
M2 -3.08836 2.42868 -1.272 0.21104
M3 -4.95093 2.28532 -2.166 0.03645 *
M4 -1.76784 2.28886 -0.772 0.44455
M5 -0.23895 2.12187 -0.113 0.91091
M6 -2.44085 2.24626 -1.087 0.28387
M7 -2.88724 2.35987 -1.223 0.22849
M8 -1.28390 2.24543 -0.572 0.57075
M9 -1.93080 2.20079 -0.877 0.38569
M10 -6.85809 2.35885 -2.907 0.00598 **
M11 -0.41202 2.52801 -0.163 0.87137
t -0.08102 0.06030 -1.344 0.18686
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.098 on 39 degrees of freedom
Multiple R-squared: 0.8233, Adjusted R-squared: 0.7463
F-statistic: 10.69 on 17 and 39 DF, p-value: 7.566e-10
> 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,] 0.052871594 0.10574319 0.94712841
[2,] 0.024239121 0.04847824 0.97576088
[3,] 0.009892404 0.01978481 0.99010760
[4,] 0.051864726 0.10372945 0.94813527
[5,] 0.041105299 0.08221060 0.95889470
[6,] 0.053907846 0.10781569 0.94609215
[7,] 0.251690295 0.50338059 0.74830971
[8,] 0.346103570 0.69220714 0.65389643
[9,] 0.312378970 0.62475794 0.68762103
[10,] 0.230791753 0.46158351 0.76920825
[11,] 0.211404932 0.42280986 0.78859507
[12,] 0.161491625 0.32298325 0.83850837
[13,] 0.247843083 0.49568617 0.75215692
[14,] 0.331374982 0.66274996 0.66862502
[15,] 0.761414159 0.47717168 0.23858584
[16,] 0.946465190 0.10706962 0.05353481
> postscript(file="/var/www/html/rcomp/tmp/11u271258650466.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29ouu1258650466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33shp1258650466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4aiiz1258650466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ilee1258650466.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 = 57
Frequency = 1
1 2 3 4 5
4.2552649436 0.6599761871 2.6613150389 -3.3086183367 -0.7584258269
6 7 8 9 10
1.1428641944 -0.1647820793 -5.4190062500 0.0419941411 2.3094215761
11 12 13 14 15
4.2794562398 2.7021054015 -2.9617432677 -0.5540966470 4.4453057813
16 17 18 19 20
-1.8980987251 0.9445099544 1.5178530195 -2.0639077965 -0.6499357182
21 22 23 24 25
1.1423077492 0.1771180756 0.7156606275 -2.4483302799 2.5704450811
26 27 28 29 30
2.0834608325 -4.2353748597 4.3565734458 -0.2104409160 -3.1088155000
31 32 33 34 35
0.0815109462 -0.4627964296 -0.2246413656 -2.4940170401 -2.8690842635
36 37 38 39 40
-4.7462783176 -4.4404576507 -1.8813088389 -2.3801825722 0.8503364021
41 42 43 44 45
-3.6108544977 -1.1178490357 -1.1949865688 4.1106561541 -2.8965738374
46 47 48 49 50
0.0074773884 -2.1260326038 4.4925031959 0.5764908938 -0.3080315336
51 52 53 54 55
-0.4910633883 -0.0001927861 3.6352112862 1.5659473219 3.3421654983
56 57
2.4210822437 1.9369133128
> postscript(file="/var/www/html/rcomp/tmp/60yv01258650466.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 4.2552649436 NA
1 0.6599761871 4.2552649436
2 2.6613150389 0.6599761871
3 -3.3086183367 2.6613150389
4 -0.7584258269 -3.3086183367
5 1.1428641944 -0.7584258269
6 -0.1647820793 1.1428641944
7 -5.4190062500 -0.1647820793
8 0.0419941411 -5.4190062500
9 2.3094215761 0.0419941411
10 4.2794562398 2.3094215761
11 2.7021054015 4.2794562398
12 -2.9617432677 2.7021054015
13 -0.5540966470 -2.9617432677
14 4.4453057813 -0.5540966470
15 -1.8980987251 4.4453057813
16 0.9445099544 -1.8980987251
17 1.5178530195 0.9445099544
18 -2.0639077965 1.5178530195
19 -0.6499357182 -2.0639077965
20 1.1423077492 -0.6499357182
21 0.1771180756 1.1423077492
22 0.7156606275 0.1771180756
23 -2.4483302799 0.7156606275
24 2.5704450811 -2.4483302799
25 2.0834608325 2.5704450811
26 -4.2353748597 2.0834608325
27 4.3565734458 -4.2353748597
28 -0.2104409160 4.3565734458
29 -3.1088155000 -0.2104409160
30 0.0815109462 -3.1088155000
31 -0.4627964296 0.0815109462
32 -0.2246413656 -0.4627964296
33 -2.4940170401 -0.2246413656
34 -2.8690842635 -2.4940170401
35 -4.7462783176 -2.8690842635
36 -4.4404576507 -4.7462783176
37 -1.8813088389 -4.4404576507
38 -2.3801825722 -1.8813088389
39 0.8503364021 -2.3801825722
40 -3.6108544977 0.8503364021
41 -1.1178490357 -3.6108544977
42 -1.1949865688 -1.1178490357
43 4.1106561541 -1.1949865688
44 -2.8965738374 4.1106561541
45 0.0074773884 -2.8965738374
46 -2.1260326038 0.0074773884
47 4.4925031959 -2.1260326038
48 0.5764908938 4.4925031959
49 -0.3080315336 0.5764908938
50 -0.4910633883 -0.3080315336
51 -0.0001927861 -0.4910633883
52 3.6352112862 -0.0001927861
53 1.5659473219 3.6352112862
54 3.3421654983 1.5659473219
55 2.4210822437 3.3421654983
56 1.9369133128 2.4210822437
57 NA 1.9369133128
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.6599761871 4.2552649436
[2,] 2.6613150389 0.6599761871
[3,] -3.3086183367 2.6613150389
[4,] -0.7584258269 -3.3086183367
[5,] 1.1428641944 -0.7584258269
[6,] -0.1647820793 1.1428641944
[7,] -5.4190062500 -0.1647820793
[8,] 0.0419941411 -5.4190062500
[9,] 2.3094215761 0.0419941411
[10,] 4.2794562398 2.3094215761
[11,] 2.7021054015 4.2794562398
[12,] -2.9617432677 2.7021054015
[13,] -0.5540966470 -2.9617432677
[14,] 4.4453057813 -0.5540966470
[15,] -1.8980987251 4.4453057813
[16,] 0.9445099544 -1.8980987251
[17,] 1.5178530195 0.9445099544
[18,] -2.0639077965 1.5178530195
[19,] -0.6499357182 -2.0639077965
[20,] 1.1423077492 -0.6499357182
[21,] 0.1771180756 1.1423077492
[22,] 0.7156606275 0.1771180756
[23,] -2.4483302799 0.7156606275
[24,] 2.5704450811 -2.4483302799
[25,] 2.0834608325 2.5704450811
[26,] -4.2353748597 2.0834608325
[27,] 4.3565734458 -4.2353748597
[28,] -0.2104409160 4.3565734458
[29,] -3.1088155000 -0.2104409160
[30,] 0.0815109462 -3.1088155000
[31,] -0.4627964296 0.0815109462
[32,] -0.2246413656 -0.4627964296
[33,] -2.4940170401 -0.2246413656
[34,] -2.8690842635 -2.4940170401
[35,] -4.7462783176 -2.8690842635
[36,] -4.4404576507 -4.7462783176
[37,] -1.8813088389 -4.4404576507
[38,] -2.3801825722 -1.8813088389
[39,] 0.8503364021 -2.3801825722
[40,] -3.6108544977 0.8503364021
[41,] -1.1178490357 -3.6108544977
[42,] -1.1949865688 -1.1178490357
[43,] 4.1106561541 -1.1949865688
[44,] -2.8965738374 4.1106561541
[45,] 0.0074773884 -2.8965738374
[46,] -2.1260326038 0.0074773884
[47,] 4.4925031959 -2.1260326038
[48,] 0.5764908938 4.4925031959
[49,] -0.3080315336 0.5764908938
[50,] -0.4910633883 -0.3080315336
[51,] -0.0001927861 -0.4910633883
[52,] 3.6352112862 -0.0001927861
[53,] 1.5659473219 3.6352112862
[54,] 3.3421654983 1.5659473219
[55,] 2.4210822437 3.3421654983
[56,] 1.9369133128 2.4210822437
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.6599761871 4.2552649436
2 2.6613150389 0.6599761871
3 -3.3086183367 2.6613150389
4 -0.7584258269 -3.3086183367
5 1.1428641944 -0.7584258269
6 -0.1647820793 1.1428641944
7 -5.4190062500 -0.1647820793
8 0.0419941411 -5.4190062500
9 2.3094215761 0.0419941411
10 4.2794562398 2.3094215761
11 2.7021054015 4.2794562398
12 -2.9617432677 2.7021054015
13 -0.5540966470 -2.9617432677
14 4.4453057813 -0.5540966470
15 -1.8980987251 4.4453057813
16 0.9445099544 -1.8980987251
17 1.5178530195 0.9445099544
18 -2.0639077965 1.5178530195
19 -0.6499357182 -2.0639077965
20 1.1423077492 -0.6499357182
21 0.1771180756 1.1423077492
22 0.7156606275 0.1771180756
23 -2.4483302799 0.7156606275
24 2.5704450811 -2.4483302799
25 2.0834608325 2.5704450811
26 -4.2353748597 2.0834608325
27 4.3565734458 -4.2353748597
28 -0.2104409160 4.3565734458
29 -3.1088155000 -0.2104409160
30 0.0815109462 -3.1088155000
31 -0.4627964296 0.0815109462
32 -0.2246413656 -0.4627964296
33 -2.4940170401 -0.2246413656
34 -2.8690842635 -2.4940170401
35 -4.7462783176 -2.8690842635
36 -4.4404576507 -4.7462783176
37 -1.8813088389 -4.4404576507
38 -2.3801825722 -1.8813088389
39 0.8503364021 -2.3801825722
40 -3.6108544977 0.8503364021
41 -1.1178490357 -3.6108544977
42 -1.1949865688 -1.1178490357
43 4.1106561541 -1.1949865688
44 -2.8965738374 4.1106561541
45 0.0074773884 -2.8965738374
46 -2.1260326038 0.0074773884
47 4.4925031959 -2.1260326038
48 0.5764908938 4.4925031959
49 -0.3080315336 0.5764908938
50 -0.4910633883 -0.3080315336
51 -0.0001927861 -0.4910633883
52 3.6352112862 -0.0001927861
53 1.5659473219 3.6352112862
54 3.3421654983 1.5659473219
55 2.4210822437 3.3421654983
56 1.9369133128 2.4210822437
> 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/7m7231258650466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8uhzk1258650466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nib01258650466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10i7r11258650466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jg211258650466.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/12kk301258650466.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/13mafi1258650466.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/143p691258650466.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/15jbny1258650466.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/16tnpi1258650466.tab")
+ }
>
> system("convert tmp/11u271258650466.ps tmp/11u271258650466.png")
> system("convert tmp/29ouu1258650466.ps tmp/29ouu1258650466.png")
> system("convert tmp/33shp1258650466.ps tmp/33shp1258650466.png")
> system("convert tmp/4aiiz1258650466.ps tmp/4aiiz1258650466.png")
> system("convert tmp/5ilee1258650466.ps tmp/5ilee1258650466.png")
> system("convert tmp/60yv01258650466.ps tmp/60yv01258650466.png")
> system("convert tmp/7m7231258650466.ps tmp/7m7231258650466.png")
> system("convert tmp/8uhzk1258650466.ps tmp/8uhzk1258650466.png")
> system("convert tmp/9nib01258650466.ps tmp/9nib01258650466.png")
> system("convert tmp/10i7r11258650466.ps tmp/10i7r11258650466.png")
>
>
> proc.time()
user system elapsed
2.353 1.571 2.811