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.
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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.
Type 'q()' to quit R.
> x <- array(list(101.9
+ ,93.7
+ ,95.7
+ ,107.2
+ ,98.6
+ ,99.9
+ ,106.2
+ ,106.7
+ ,93.7
+ ,95.7
+ ,107.2
+ ,98.6
+ ,81
+ ,86.7
+ ,106.7
+ ,93.7
+ ,95.7
+ ,107.2
+ ,94.7
+ ,95.3
+ ,86.7
+ ,106.7
+ ,93.7
+ ,95.7
+ ,101
+ ,99.3
+ ,95.3
+ ,86.7
+ ,106.7
+ ,93.7
+ ,109.4
+ ,101.8
+ ,99.3
+ ,95.3
+ ,86.7
+ ,106.7
+ ,102.3
+ ,96
+ ,101.8
+ ,99.3
+ ,95.3
+ ,86.7
+ ,90.7
+ ,91.7
+ ,96
+ ,101.8
+ ,99.3
+ ,95.3
+ ,96.2
+ ,95.3
+ ,91.7
+ ,96
+ ,101.8
+ ,99.3
+ ,96.1
+ ,96.6
+ ,95.3
+ ,91.7
+ ,96
+ ,101.8
+ ,106
+ ,107.2
+ ,96.6
+ ,95.3
+ ,91.7
+ ,96
+ ,103.1
+ ,108
+ ,107.2
+ ,96.6
+ ,95.3
+ ,91.7
+ ,102
+ ,98.4
+ ,108
+ ,107.2
+ ,96.6
+ ,95.3
+ ,104.7
+ ,103.1
+ ,98.4
+ ,108
+ ,107.2
+ ,96.6
+ ,86
+ ,81.1
+ ,103.1
+ ,98.4
+ ,108
+ ,107.2
+ ,92.1
+ ,96.6
+ ,81.1
+ ,103.1
+ ,98.4
+ ,108
+ ,106.9
+ ,103.7
+ ,96.6
+ ,81.1
+ ,103.1
+ ,98.4
+ ,112.6
+ ,106.6
+ ,103.7
+ ,96.6
+ ,81.1
+ ,103.1
+ ,101.7
+ ,97.6
+ ,106.6
+ ,103.7
+ ,96.6
+ ,81.1
+ ,92
+ ,87.6
+ ,97.6
+ ,106.6
+ ,103.7
+ ,96.6
+ ,97.4
+ ,99.4
+ ,87.6
+ ,97.6
+ ,106.6
+ ,103.7
+ ,97
+ ,98.5
+ ,99.4
+ ,87.6
+ ,97.6
+ ,106.6
+ ,105.4
+ ,105.2
+ ,98.5
+ ,99.4
+ ,87.6
+ ,97.6
+ ,102.7
+ ,104.6
+ ,105.2
+ ,98.5
+ ,99.4
+ ,87.6
+ ,98.1
+ ,97.5
+ ,104.6
+ ,105.2
+ ,98.5
+ ,99.4
+ ,104.5
+ ,108.9
+ ,97.5
+ ,104.6
+ ,105.2
+ ,98.5
+ ,87.4
+ ,86.8
+ ,108.9
+ ,97.5
+ ,104.6
+ ,105.2
+ ,89.9
+ ,88.9
+ ,86.8
+ ,108.9
+ ,97.5
+ ,104.6
+ ,109.8
+ ,110.3
+ ,88.9
+ ,86.8
+ ,108.9
+ ,97.5
+ ,111.7
+ ,114.8
+ ,110.3
+ ,88.9
+ ,86.8
+ ,108.9
+ ,98.6
+ ,94.6
+ ,114.8
+ ,110.3
+ ,88.9
+ ,86.8
+ ,96.9
+ ,92
+ ,94.6
+ ,114.8
+ ,110.3
+ ,88.9
+ ,95.1
+ ,93.8
+ ,92
+ ,94.6
+ ,114.8
+ ,110.3
+ ,97
+ ,93.8
+ ,93.8
+ ,92
+ ,94.6
+ ,114.8
+ ,112.7
+ ,107.6
+ ,93.8
+ ,93.8
+ ,92
+ ,94.6
+ ,102.9
+ ,101
+ ,107.6
+ ,93.8
+ ,93.8
+ ,92
+ ,97.4
+ ,95.4
+ ,101
+ ,107.6
+ ,93.8
+ ,93.8
+ ,111.4
+ ,96.5
+ ,95.4
+ ,101
+ ,107.6
+ ,93.8
+ ,87.4
+ ,89.2
+ ,96.5
+ ,95.4
+ ,101
+ ,107.6
+ ,96.8
+ ,87.1
+ ,89.2
+ ,96.5
+ ,95.4
+ ,101
+ ,114.1
+ ,110.5
+ ,87.1
+ ,89.2
+ ,96.5
+ ,95.4
+ ,110.3
+ ,110.8
+ ,110.5
+ ,87.1
+ ,89.2
+ ,96.5
+ ,103.9
+ ,104.2
+ ,110.8
+ ,110.5
+ ,87.1
+ ,89.2
+ ,101.6
+ ,88.9
+ ,104.2
+ ,110.8
+ ,110.5
+ ,87.1
+ ,94.6
+ ,89.8
+ ,88.9
+ ,104.2
+ ,110.8
+ ,110.5
+ ,95.9
+ ,90
+ ,89.8
+ ,88.9
+ ,104.2
+ ,110.8
+ ,104.7
+ ,93.9
+ ,90
+ ,89.8
+ ,88.9
+ ,104.2
+ ,102.8
+ ,91.3
+ ,93.9
+ ,90
+ ,89.8
+ ,88.9
+ ,98.1
+ ,87.8
+ ,91.3
+ ,93.9
+ ,90
+ ,89.8
+ ,113.9
+ ,99.7
+ ,87.8
+ ,91.3
+ ,93.9
+ ,90
+ ,80.9
+ ,73.5
+ ,99.7
+ ,87.8
+ ,91.3
+ ,93.9
+ ,95.7
+ ,79.2
+ ,73.5
+ ,99.7
+ ,87.8
+ ,91.3
+ ,113.2
+ ,96.9
+ ,79.2
+ ,73.5
+ ,99.7
+ ,87.8
+ ,105.9
+ ,95.2
+ ,96.9
+ ,79.2
+ ,73.5
+ ,99.7
+ ,108.8
+ ,95.6
+ ,95.2
+ ,96.9
+ ,79.2
+ ,73.5
+ ,102.3
+ ,89.7
+ ,95.6
+ ,95.2
+ ,96.9
+ ,79.2)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('ProdMetal'
+ ,'ProdInd'
+ ,'(t-1)'
+ ,'(t-2)'
+ ,'(t-3)'
+ ,'(t-4)')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('ProdMetal','ProdInd','(t-1)','(t-2)','(t-3)','(t-4)'),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 = '2'
> #'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
ProdInd ProdMetal (t-1) (t-2) (t-3) (t-4) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 93.7 101.9 95.7 107.2 98.6 99.9 1 0 0 0 0 0 0 0 0 0 0
2 106.7 106.2 93.7 95.7 107.2 98.6 0 1 0 0 0 0 0 0 0 0 0
3 86.7 81.0 106.7 93.7 95.7 107.2 0 0 1 0 0 0 0 0 0 0 0
4 95.3 94.7 86.7 106.7 93.7 95.7 0 0 0 1 0 0 0 0 0 0 0
5 99.3 101.0 95.3 86.7 106.7 93.7 0 0 0 0 1 0 0 0 0 0 0
6 101.8 109.4 99.3 95.3 86.7 106.7 0 0 0 0 0 1 0 0 0 0 0
7 96.0 102.3 101.8 99.3 95.3 86.7 0 0 0 0 0 0 1 0 0 0 0
8 91.7 90.7 96.0 101.8 99.3 95.3 0 0 0 0 0 0 0 1 0 0 0
9 95.3 96.2 91.7 96.0 101.8 99.3 0 0 0 0 0 0 0 0 1 0 0
10 96.6 96.1 95.3 91.7 96.0 101.8 0 0 0 0 0 0 0 0 0 1 0
11 107.2 106.0 96.6 95.3 91.7 96.0 0 0 0 0 0 0 0 0 0 0 1
12 108.0 103.1 107.2 96.6 95.3 91.7 0 0 0 0 0 0 0 0 0 0 0
13 98.4 102.0 108.0 107.2 96.6 95.3 1 0 0 0 0 0 0 0 0 0 0
14 103.1 104.7 98.4 108.0 107.2 96.6 0 1 0 0 0 0 0 0 0 0 0
15 81.1 86.0 103.1 98.4 108.0 107.2 0 0 1 0 0 0 0 0 0 0 0
16 96.6 92.1 81.1 103.1 98.4 108.0 0 0 0 1 0 0 0 0 0 0 0
17 103.7 106.9 96.6 81.1 103.1 98.4 0 0 0 0 1 0 0 0 0 0 0
18 106.6 112.6 103.7 96.6 81.1 103.1 0 0 0 0 0 1 0 0 0 0 0
19 97.6 101.7 106.6 103.7 96.6 81.1 0 0 0 0 0 0 1 0 0 0 0
20 87.6 92.0 97.6 106.6 103.7 96.6 0 0 0 0 0 0 0 1 0 0 0
21 99.4 97.4 87.6 97.6 106.6 103.7 0 0 0 0 0 0 0 0 1 0 0
22 98.5 97.0 99.4 87.6 97.6 106.6 0 0 0 0 0 0 0 0 0 1 0
23 105.2 105.4 98.5 99.4 87.6 97.6 0 0 0 0 0 0 0 0 0 0 1
24 104.6 102.7 105.2 98.5 99.4 87.6 0 0 0 0 0 0 0 0 0 0 0
25 97.5 98.1 104.6 105.2 98.5 99.4 1 0 0 0 0 0 0 0 0 0 0
26 108.9 104.5 97.5 104.6 105.2 98.5 0 1 0 0 0 0 0 0 0 0 0
27 86.8 87.4 108.9 97.5 104.6 105.2 0 0 1 0 0 0 0 0 0 0 0
28 88.9 89.9 86.8 108.9 97.5 104.6 0 0 0 1 0 0 0 0 0 0 0
29 110.3 109.8 88.9 86.8 108.9 97.5 0 0 0 0 1 0 0 0 0 0 0
30 114.8 111.7 110.3 88.9 86.8 108.9 0 0 0 0 0 1 0 0 0 0 0
31 94.6 98.6 114.8 110.3 88.9 86.8 0 0 0 0 0 0 1 0 0 0 0
32 92.0 96.9 94.6 114.8 110.3 88.9 0 0 0 0 0 0 0 1 0 0 0
33 93.8 95.1 92.0 94.6 114.8 110.3 0 0 0 0 0 0 0 0 1 0 0
34 93.8 97.0 93.8 92.0 94.6 114.8 0 0 0 0 0 0 0 0 0 1 0
35 107.6 112.7 93.8 93.8 92.0 94.6 0 0 0 0 0 0 0 0 0 0 1
36 101.0 102.9 107.6 93.8 93.8 92.0 0 0 0 0 0 0 0 0 0 0 0
37 95.4 97.4 101.0 107.6 93.8 93.8 1 0 0 0 0 0 0 0 0 0 0
38 96.5 111.4 95.4 101.0 107.6 93.8 0 1 0 0 0 0 0 0 0 0 0
39 89.2 87.4 96.5 95.4 101.0 107.6 0 0 1 0 0 0 0 0 0 0 0
40 87.1 96.8 89.2 96.5 95.4 101.0 0 0 0 1 0 0 0 0 0 0 0
41 110.5 114.1 87.1 89.2 96.5 95.4 0 0 0 0 1 0 0 0 0 0 0
42 110.8 110.3 110.5 87.1 89.2 96.5 0 0 0 0 0 1 0 0 0 0 0
43 104.2 103.9 110.8 110.5 87.1 89.2 0 0 0 0 0 0 1 0 0 0 0
44 88.9 101.6 104.2 110.8 110.5 87.1 0 0 0 0 0 0 0 1 0 0 0
45 89.8 94.6 88.9 104.2 110.8 110.5 0 0 0 0 0 0 0 0 1 0 0
46 90.0 95.9 89.8 88.9 104.2 110.8 0 0 0 0 0 0 0 0 0 1 0
47 93.9 104.7 90.0 89.8 88.9 104.2 0 0 0 0 0 0 0 0 0 0 1
48 91.3 102.8 93.9 90.0 89.8 88.9 0 0 0 0 0 0 0 0 0 0 0
49 87.8 98.1 91.3 93.9 90.0 89.8 1 0 0 0 0 0 0 0 0 0 0
50 99.7 113.9 87.8 91.3 93.9 90.0 0 1 0 0 0 0 0 0 0 0 0
51 73.5 80.9 99.7 87.8 91.3 93.9 0 0 1 0 0 0 0 0 0 0 0
52 79.2 95.7 73.5 99.7 87.8 91.3 0 0 0 1 0 0 0 0 0 0 0
53 96.9 113.2 79.2 73.5 99.7 87.8 0 0 0 0 1 0 0 0 0 0 0
54 95.2 105.9 96.9 79.2 73.5 99.7 0 0 0 0 0 1 0 0 0 0 0
55 95.6 108.8 95.2 96.9 79.2 73.5 0 0 0 0 0 0 1 0 0 0 0
56 89.7 102.3 95.6 95.2 96.9 79.2 0 0 0 0 0 0 0 1 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ProdMetal `(t-1)` `(t-2)` `(t-3)` `(t-4)`
-28.76360 0.73853 0.37677 0.10826 -0.09453 0.19197
M1 M2 M3 M4 M5 M6
-5.51809 0.03005 -6.57335 -0.65805 4.71602 -3.84132
M7 M8 M9 M10 M11 t
-4.77496 -4.41089 1.15568 -1.37957 0.27650 -0.11921
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.22299 -2.57129 -0.08453 2.62046 6.84316
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -28.76360 29.72241 -0.968 0.33929
ProdMetal 0.73853 0.20924 3.530 0.00111 **
`(t-1)` 0.37677 0.13769 2.736 0.00940 **
`(t-2)` 0.10826 0.14537 0.745 0.46101
`(t-3)` -0.09453 0.15078 -0.627 0.53448
`(t-4)` 0.19197 0.15111 1.270 0.21167
M1 -5.51809 3.27276 -1.686 0.09998 .
M2 0.03005 3.65169 0.008 0.99348
M3 -6.57335 4.72343 -1.392 0.17212
M4 -0.65805 4.83473 -0.136 0.89245
M5 4.71602 4.23495 1.114 0.27245
M6 -3.84132 4.74035 -0.810 0.42279
M7 -4.77496 3.44569 -1.386 0.17389
M8 -4.41089 4.09229 -1.078 0.28789
M9 1.15568 4.65935 0.248 0.80544
M10 -1.37957 4.25807 -0.324 0.74772
M11 0.27650 3.70153 0.075 0.94085
t -0.11921 0.04309 -2.766 0.00871 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.188 on 38 degrees of freedom
Multiple R-squared: 0.831, Adjusted R-squared: 0.7555
F-statistic: 11 on 17 and 38 DF, p-value: 7.009e-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,] 0.13997747 0.27995494 0.8600225
[2,] 0.05363118 0.10726237 0.9463688
[3,] 0.05602409 0.11204819 0.9439759
[4,] 0.07085304 0.14170608 0.9291470
[5,] 0.03661950 0.07323899 0.9633805
[6,] 0.05958453 0.11916907 0.9404155
[7,] 0.05829040 0.11658080 0.9417096
[8,] 0.16067527 0.32135054 0.8393247
[9,] 0.24548872 0.49097745 0.7545113
[10,] 0.29287557 0.58575115 0.7071244
[11,] 0.22475187 0.44950374 0.7752481
[12,] 0.31000446 0.62000891 0.6899955
[13,] 0.44580941 0.89161882 0.5541906
[14,] 0.36851848 0.73703695 0.6314815
[15,] 0.32059161 0.64118322 0.6794084
> postscript(file="/var/www/html/rcomp/tmp/1bzbn1259092686.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cfs91259092686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30pac1259092686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4j1s71259092686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5l2wn1259092686.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
-4.67561750 2.78080901 0.69499890 1.52755096 -3.84223829 -5.69361280
7 8 9 10 11 12
-1.91982066 2.74405306 -1.44877970 0.66052280 2.23961638 2.60836086
13 14 15 16 17 18
-2.55916606 -0.99935939 -5.15694183 6.76094619 -3.49512636 -3.46315290
19 20 21 22 23 24
0.46690723 -1.84164641 4.17609182 1.45528961 0.25882535 2.65677367
25 26 27 28 29 30
1.74169782 6.53226884 -1.08561610 -0.09162304 5.39869305 4.60438396
31 32 33 34 35 36
-4.43921406 2.71466214 -0.11963527 -2.03836988 2.06697099 -1.42981929
37 38 39 40 41 42
3.31649865 -7.22299484 6.84316380 -4.62615916 3.50290793 5.79567356
43 44 45 46 47 48
3.53163798 -5.24537370 -2.60767684 -0.07744253 -4.56541272 -3.83531523
49 50 51 52 53 54
2.17658709 -1.09072363 -1.29560476 -3.57071495 -1.56423633 -1.24329182
55 56
2.36048952 1.62830491
> postscript(file="/var/www/html/rcomp/tmp/60e8b1259092686.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 -4.67561750 NA
1 2.78080901 -4.67561750
2 0.69499890 2.78080901
3 1.52755096 0.69499890
4 -3.84223829 1.52755096
5 -5.69361280 -3.84223829
6 -1.91982066 -5.69361280
7 2.74405306 -1.91982066
8 -1.44877970 2.74405306
9 0.66052280 -1.44877970
10 2.23961638 0.66052280
11 2.60836086 2.23961638
12 -2.55916606 2.60836086
13 -0.99935939 -2.55916606
14 -5.15694183 -0.99935939
15 6.76094619 -5.15694183
16 -3.49512636 6.76094619
17 -3.46315290 -3.49512636
18 0.46690723 -3.46315290
19 -1.84164641 0.46690723
20 4.17609182 -1.84164641
21 1.45528961 4.17609182
22 0.25882535 1.45528961
23 2.65677367 0.25882535
24 1.74169782 2.65677367
25 6.53226884 1.74169782
26 -1.08561610 6.53226884
27 -0.09162304 -1.08561610
28 5.39869305 -0.09162304
29 4.60438396 5.39869305
30 -4.43921406 4.60438396
31 2.71466214 -4.43921406
32 -0.11963527 2.71466214
33 -2.03836988 -0.11963527
34 2.06697099 -2.03836988
35 -1.42981929 2.06697099
36 3.31649865 -1.42981929
37 -7.22299484 3.31649865
38 6.84316380 -7.22299484
39 -4.62615916 6.84316380
40 3.50290793 -4.62615916
41 5.79567356 3.50290793
42 3.53163798 5.79567356
43 -5.24537370 3.53163798
44 -2.60767684 -5.24537370
45 -0.07744253 -2.60767684
46 -4.56541272 -0.07744253
47 -3.83531523 -4.56541272
48 2.17658709 -3.83531523
49 -1.09072363 2.17658709
50 -1.29560476 -1.09072363
51 -3.57071495 -1.29560476
52 -1.56423633 -3.57071495
53 -1.24329182 -1.56423633
54 2.36048952 -1.24329182
55 1.62830491 2.36048952
56 NA 1.62830491
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.78080901 -4.67561750
[2,] 0.69499890 2.78080901
[3,] 1.52755096 0.69499890
[4,] -3.84223829 1.52755096
[5,] -5.69361280 -3.84223829
[6,] -1.91982066 -5.69361280
[7,] 2.74405306 -1.91982066
[8,] -1.44877970 2.74405306
[9,] 0.66052280 -1.44877970
[10,] 2.23961638 0.66052280
[11,] 2.60836086 2.23961638
[12,] -2.55916606 2.60836086
[13,] -0.99935939 -2.55916606
[14,] -5.15694183 -0.99935939
[15,] 6.76094619 -5.15694183
[16,] -3.49512636 6.76094619
[17,] -3.46315290 -3.49512636
[18,] 0.46690723 -3.46315290
[19,] -1.84164641 0.46690723
[20,] 4.17609182 -1.84164641
[21,] 1.45528961 4.17609182
[22,] 0.25882535 1.45528961
[23,] 2.65677367 0.25882535
[24,] 1.74169782 2.65677367
[25,] 6.53226884 1.74169782
[26,] -1.08561610 6.53226884
[27,] -0.09162304 -1.08561610
[28,] 5.39869305 -0.09162304
[29,] 4.60438396 5.39869305
[30,] -4.43921406 4.60438396
[31,] 2.71466214 -4.43921406
[32,] -0.11963527 2.71466214
[33,] -2.03836988 -0.11963527
[34,] 2.06697099 -2.03836988
[35,] -1.42981929 2.06697099
[36,] 3.31649865 -1.42981929
[37,] -7.22299484 3.31649865
[38,] 6.84316380 -7.22299484
[39,] -4.62615916 6.84316380
[40,] 3.50290793 -4.62615916
[41,] 5.79567356 3.50290793
[42,] 3.53163798 5.79567356
[43,] -5.24537370 3.53163798
[44,] -2.60767684 -5.24537370
[45,] -0.07744253 -2.60767684
[46,] -4.56541272 -0.07744253
[47,] -3.83531523 -4.56541272
[48,] 2.17658709 -3.83531523
[49,] -1.09072363 2.17658709
[50,] -1.29560476 -1.09072363
[51,] -3.57071495 -1.29560476
[52,] -1.56423633 -3.57071495
[53,] -1.24329182 -1.56423633
[54,] 2.36048952 -1.24329182
[55,] 1.62830491 2.36048952
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.78080901 -4.67561750
2 0.69499890 2.78080901
3 1.52755096 0.69499890
4 -3.84223829 1.52755096
5 -5.69361280 -3.84223829
6 -1.91982066 -5.69361280
7 2.74405306 -1.91982066
8 -1.44877970 2.74405306
9 0.66052280 -1.44877970
10 2.23961638 0.66052280
11 2.60836086 2.23961638
12 -2.55916606 2.60836086
13 -0.99935939 -2.55916606
14 -5.15694183 -0.99935939
15 6.76094619 -5.15694183
16 -3.49512636 6.76094619
17 -3.46315290 -3.49512636
18 0.46690723 -3.46315290
19 -1.84164641 0.46690723
20 4.17609182 -1.84164641
21 1.45528961 4.17609182
22 0.25882535 1.45528961
23 2.65677367 0.25882535
24 1.74169782 2.65677367
25 6.53226884 1.74169782
26 -1.08561610 6.53226884
27 -0.09162304 -1.08561610
28 5.39869305 -0.09162304
29 4.60438396 5.39869305
30 -4.43921406 4.60438396
31 2.71466214 -4.43921406
32 -0.11963527 2.71466214
33 -2.03836988 -0.11963527
34 2.06697099 -2.03836988
35 -1.42981929 2.06697099
36 3.31649865 -1.42981929
37 -7.22299484 3.31649865
38 6.84316380 -7.22299484
39 -4.62615916 6.84316380
40 3.50290793 -4.62615916
41 5.79567356 3.50290793
42 3.53163798 5.79567356
43 -5.24537370 3.53163798
44 -2.60767684 -5.24537370
45 -0.07744253 -2.60767684
46 -4.56541272 -0.07744253
47 -3.83531523 -4.56541272
48 2.17658709 -3.83531523
49 -1.09072363 2.17658709
50 -1.29560476 -1.09072363
51 -3.57071495 -1.29560476
52 -1.56423633 -3.57071495
53 -1.24329182 -1.56423633
54 2.36048952 -1.24329182
55 1.62830491 2.36048952
> 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/78owo1259092686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/860pt1259092686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90fa21259092686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107nf61259092686.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11szro1259092686.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/125m1v1259092686.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/130o4m1259092686.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/14zkaj1259092686.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/15t9up1259092686.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/163anh1259092686.tab")
+ }
>
> system("convert tmp/1bzbn1259092686.ps tmp/1bzbn1259092686.png")
> system("convert tmp/2cfs91259092686.ps tmp/2cfs91259092686.png")
> system("convert tmp/30pac1259092686.ps tmp/30pac1259092686.png")
> system("convert tmp/4j1s71259092686.ps tmp/4j1s71259092686.png")
> system("convert tmp/5l2wn1259092686.ps tmp/5l2wn1259092686.png")
> system("convert tmp/60e8b1259092686.ps tmp/60e8b1259092686.png")
> system("convert tmp/78owo1259092686.ps tmp/78owo1259092686.png")
> system("convert tmp/860pt1259092686.ps tmp/860pt1259092686.png")
> system("convert tmp/90fa21259092686.ps tmp/90fa21259092686.png")
> system("convert tmp/107nf61259092686.ps tmp/107nf61259092686.png")
>
>
> proc.time()
user system elapsed
2.384 1.573 2.970