R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(95.1
+ ,8.9
+ ,96.9
+ ,98.6
+ ,111.7
+ ,109.8
+ ,97
+ ,8.8
+ ,95.1
+ ,96.9
+ ,98.6
+ ,111.7
+ ,112.7
+ ,8.3
+ ,97
+ ,95.1
+ ,96.9
+ ,98.6
+ ,102.9
+ ,7.5
+ ,112.7
+ ,97
+ ,95.1
+ ,96.9
+ ,97.4
+ ,7.2
+ ,102.9
+ ,112.7
+ ,97
+ ,95.1
+ ,111.4
+ ,7.4
+ ,97.4
+ ,102.9
+ ,112.7
+ ,97
+ ,87.4
+ ,8.8
+ ,111.4
+ ,97.4
+ ,102.9
+ ,112.7
+ ,96.8
+ ,9.3
+ ,87.4
+ ,111.4
+ ,97.4
+ ,102.9
+ ,114.1
+ ,9.3
+ ,96.8
+ ,87.4
+ ,111.4
+ ,97.4
+ ,110.3
+ ,8.7
+ ,114.1
+ ,96.8
+ ,87.4
+ ,111.4
+ ,103.9
+ ,8.2
+ ,110.3
+ ,114.1
+ ,96.8
+ ,87.4
+ ,101.6
+ ,8.3
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,94.6
+ ,8.5
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,95.9
+ ,8.6
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,104.7
+ ,8.5
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,102.8
+ ,8.2
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,98.1
+ ,8.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,113.9
+ ,7.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,80.9
+ ,8.6
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.7
+ ,8.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,113.2
+ ,8.7
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,105.9
+ ,8.5
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,108.8
+ ,8.4
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,102.3
+ ,8.5
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,99
+ ,8.7
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,100.7
+ ,8.7
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,115.5
+ ,8.6
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,100.7
+ ,8.5
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,109.9
+ ,8.3
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,114.6
+ ,8
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,85.4
+ ,8.2
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.5
+ ,8.1
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,114.8
+ ,8.1
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,116.5
+ ,8
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,112.9
+ ,7.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,102
+ ,7.9
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,106
+ ,8
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,105.3
+ ,8
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,118.8
+ ,7.9
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,106.1
+ ,8
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,109.3
+ ,7.7
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,117.2
+ ,7.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,92.5
+ ,7.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,104.2
+ ,7.3
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,112.5
+ ,7
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,122.4
+ ,7
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,113.3
+ ,7
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,100
+ ,7.2
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,110.7
+ ,7.3
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,112.8
+ ,7.1
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,109.8
+ ,6.8
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,117.3
+ ,6.4
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,109.1
+ ,6.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,115.9
+ ,6.5
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,96
+ ,7.7
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,99.8
+ ,7.9
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,116.8
+ ,7.5
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,115.7
+ ,6.9
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9)
+ ,dim=c(6
+ ,58)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:58))
> y <- array(NA,dim=c(6,58),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:58))
> 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
X Y Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.9 95.1 96.9 98.6 111.7 109.8 1 0 0 0 0 0 0 0 0 0 0 1
2 8.8 97.0 95.1 96.9 98.6 111.7 0 1 0 0 0 0 0 0 0 0 0 2
3 8.3 112.7 97.0 95.1 96.9 98.6 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 102.9 112.7 97.0 95.1 96.9 0 0 0 1 0 0 0 0 0 0 0 4
5 7.2 97.4 102.9 112.7 97.0 95.1 0 0 0 0 1 0 0 0 0 0 0 5
6 7.4 111.4 97.4 102.9 112.7 97.0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.8 87.4 111.4 97.4 102.9 112.7 0 0 0 0 0 0 1 0 0 0 0 7
8 9.3 96.8 87.4 111.4 97.4 102.9 0 0 0 0 0 0 0 1 0 0 0 8
9 9.3 114.1 96.8 87.4 111.4 97.4 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 110.3 114.1 96.8 87.4 111.4 0 0 0 0 0 0 0 0 0 1 0 10
11 8.2 103.9 110.3 114.1 96.8 87.4 0 0 0 0 0 0 0 0 0 0 1 11
12 8.3 101.6 103.9 110.3 114.1 96.8 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 94.6 101.6 103.9 110.3 114.1 1 0 0 0 0 0 0 0 0 0 0 13
14 8.6 95.9 94.6 101.6 103.9 110.3 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 104.7 95.9 94.6 101.6 103.9 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 102.8 104.7 95.9 94.6 101.6 0 0 0 1 0 0 0 0 0 0 0 16
17 8.1 98.1 102.8 104.7 95.9 94.6 0 0 0 0 1 0 0 0 0 0 0 17
18 7.9 113.9 98.1 102.8 104.7 95.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.6 80.9 113.9 98.1 102.8 104.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.7 95.7 80.9 113.9 98.1 102.8 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 113.2 95.7 80.9 113.9 98.1 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 105.9 113.2 95.7 80.9 113.9 0 0 0 0 0 0 0 0 0 1 0 22
23 8.4 108.8 105.9 113.2 95.7 80.9 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 102.3 108.8 105.9 113.2 95.7 0 0 0 0 0 0 0 0 0 0 0 24
25 8.7 99.0 102.3 108.8 105.9 113.2 1 0 0 0 0 0 0 0 0 0 0 25
26 8.7 100.7 99.0 102.3 108.8 105.9 0 1 0 0 0 0 0 0 0 0 0 26
27 8.6 115.5 100.7 99.0 102.3 108.8 0 0 1 0 0 0 0 0 0 0 0 27
28 8.5 100.7 115.5 100.7 99.0 102.3 0 0 0 1 0 0 0 0 0 0 0 28
29 8.3 109.9 100.7 115.5 100.7 99.0 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 114.6 109.9 100.7 115.5 100.7 0 0 0 0 0 1 0 0 0 0 0 30
31 8.2 85.4 114.6 109.9 100.7 115.5 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 100.5 85.4 114.6 109.9 100.7 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 114.8 100.5 85.4 114.6 109.9 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 116.5 114.8 100.5 85.4 114.6 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 112.9 116.5 114.8 100.5 85.4 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 102.0 112.9 116.5 114.8 100.5 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 106.0 102.0 112.9 116.5 114.8 1 0 0 0 0 0 0 0 0 0 0 37
38 8.0 105.3 106.0 102.0 112.9 116.5 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 118.8 105.3 106.0 102.0 112.9 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 106.1 118.8 105.3 106.0 102.0 0 0 0 1 0 0 0 0 0 0 0 40
41 7.7 109.3 106.1 118.8 105.3 106.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.2 117.2 109.3 106.1 118.8 105.3 0 0 0 0 0 1 0 0 0 0 0 42
43 7.5 92.5 117.2 109.3 106.1 118.8 0 0 0 0 0 0 1 0 0 0 0 43
44 7.3 104.2 92.5 117.2 109.3 106.1 0 0 0 0 0 0 0 1 0 0 0 44
45 7.0 112.5 104.2 92.5 117.2 109.3 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 122.4 112.5 104.2 92.5 117.2 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 113.3 122.4 112.5 104.2 92.5 0 0 0 0 0 0 0 0 0 0 1 47
48 7.2 100.0 113.3 122.4 112.5 104.2 0 0 0 0 0 0 0 0 0 0 0 48
49 7.3 110.7 100.0 113.3 122.4 112.5 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 112.8 110.7 100.0 113.3 122.4 0 1 0 0 0 0 0 0 0 0 0 50
51 6.8 109.8 112.8 110.7 100.0 113.3 0 0 1 0 0 0 0 0 0 0 0 51
52 6.4 117.3 109.8 112.8 110.7 100.0 0 0 0 1 0 0 0 0 0 0 0 52
53 6.1 109.1 117.3 109.8 112.8 110.7 0 0 0 0 1 0 0 0 0 0 0 53
54 6.5 115.9 109.1 117.3 109.8 112.8 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 96.0 115.9 109.1 117.3 109.8 0 0 0 0 0 0 1 0 0 0 0 55
56 7.9 99.8 96.0 115.9 109.1 117.3 0 0 0 0 0 0 0 1 0 0 0 56
57 7.5 116.8 99.8 96.0 115.9 109.1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.9 115.7 116.8 99.8 96.0 115.9 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y Y1 Y2 Y3 Y4
18.330793 0.003739 -0.009942 -0.028609 -0.035192 -0.020545
M1 M2 M3 M4 M5 M6
0.246350 -0.176443 -0.759247 -1.013094 -0.960018 -0.882069
M7 M8 M9 M10 M11 t
0.032410 -0.047981 -0.549765 -1.094622 -0.872759 -0.011655
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.92272 -0.23514 0.01380 0.23167 0.77138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.330793 4.676352 3.920 0.000338 ***
Y 0.003739 0.021192 0.176 0.860844
Y1 -0.009942 0.022279 -0.446 0.657810
Y2 -0.028609 0.020082 -1.425 0.162020
Y3 -0.035192 0.022778 -1.545 0.130231
Y4 -0.020545 0.021909 -0.938 0.354017
M1 0.246350 0.561956 0.438 0.663469
M2 -0.176443 0.621947 -0.284 0.778107
M3 -0.759247 0.677966 -1.120 0.269440
M4 -1.013094 0.553894 -1.829 0.074856 .
M5 -0.960018 0.433549 -2.214 0.032568 *
M6 -0.882069 0.488682 -1.805 0.078607 .
M7 0.032410 0.487602 0.066 0.947336
M8 -0.047981 0.641393 -0.075 0.940741
M9 -0.549765 0.771893 -0.712 0.480456
M10 -1.094622 0.933406 -1.173 0.247845
M11 -0.872759 0.693778 -1.258 0.215694
t -0.011655 0.010160 -1.147 0.258110
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4711 on 40 degrees of freedom
Multiple R-squared: 0.7019, Adjusted R-squared: 0.5753
F-statistic: 5.541 on 17 and 40 DF, p-value: 4.244e-06
> 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,] 0.7927112 0.4145776 0.2072888
[2,] 0.7432180 0.5135641 0.2567820
[3,] 0.6383026 0.7233948 0.3616974
[4,] 0.5200613 0.9598775 0.4799387
[5,] 0.3928042 0.7856084 0.6071958
[6,] 0.2838800 0.5677600 0.7161200
[7,] 0.1862920 0.3725839 0.8137080
[8,] 0.2331328 0.4662655 0.7668672
[9,] 0.1682801 0.3365602 0.8317199
[10,] 0.1029827 0.2059654 0.8970173
[11,] 0.1005585 0.2011171 0.8994415
[12,] 0.2745400 0.5490800 0.7254600
[13,] 0.4382129 0.8764259 0.5617871
[14,] 0.3718213 0.7436425 0.6281787
[15,] 0.2683523 0.5367046 0.7316477
[16,] 0.1665771 0.3331543 0.8334229
[17,] 0.0967359 0.1934718 0.9032641
> postscript(file="/var/www/html/rcomp/tmp/13y421258744965.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2v7o01258744965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3032d1258744965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mis01258744965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58wa01258744965.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 = 58
Frequency = 1
1 2 3 4 5 6
-0.050070732 -0.211234832 -0.537044728 -0.922719207 -0.861968935 -0.524112434
7 8 9 10 11 12
0.022317261 0.346228864 0.581520935 0.436191602 0.044792849 -0.078117399
13 14 15 16 17 18
-0.070908339 0.019984885 0.081777053 -0.114527349 -0.103571784 -0.193631848
19 20 21 22 23 24
-0.136511860 -0.080319540 0.030215381 0.174702964 0.124588195 0.127695244
25 26 27 28 29 30
0.226311858 0.387716438 0.580165316 0.747113070 0.739585283 0.579541969
31 32 33 34 35 36
0.079050187 -0.121517424 0.007621603 0.100907608 0.161657195 0.167618990
37 38 39 40 41 42
0.160220107 0.233455163 0.327362535 0.771375313 0.735485205 0.268844486
43 44 45 46 47 48
-0.241115184 -0.560687940 -0.624834557 -0.395026235 -0.331038239 -0.217196836
49 50 51 52 53 54
-0.265552895 -0.429921654 -0.452260176 -0.481241828 -0.509529769 -0.130642173
55 56 57 58
0.276259596 0.416296040 0.005476638 -0.316775939
> postscript(file="/var/www/html/rcomp/tmp/60ms01258744965.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.050070732 NA
1 -0.211234832 -0.050070732
2 -0.537044728 -0.211234832
3 -0.922719207 -0.537044728
4 -0.861968935 -0.922719207
5 -0.524112434 -0.861968935
6 0.022317261 -0.524112434
7 0.346228864 0.022317261
8 0.581520935 0.346228864
9 0.436191602 0.581520935
10 0.044792849 0.436191602
11 -0.078117399 0.044792849
12 -0.070908339 -0.078117399
13 0.019984885 -0.070908339
14 0.081777053 0.019984885
15 -0.114527349 0.081777053
16 -0.103571784 -0.114527349
17 -0.193631848 -0.103571784
18 -0.136511860 -0.193631848
19 -0.080319540 -0.136511860
20 0.030215381 -0.080319540
21 0.174702964 0.030215381
22 0.124588195 0.174702964
23 0.127695244 0.124588195
24 0.226311858 0.127695244
25 0.387716438 0.226311858
26 0.580165316 0.387716438
27 0.747113070 0.580165316
28 0.739585283 0.747113070
29 0.579541969 0.739585283
30 0.079050187 0.579541969
31 -0.121517424 0.079050187
32 0.007621603 -0.121517424
33 0.100907608 0.007621603
34 0.161657195 0.100907608
35 0.167618990 0.161657195
36 0.160220107 0.167618990
37 0.233455163 0.160220107
38 0.327362535 0.233455163
39 0.771375313 0.327362535
40 0.735485205 0.771375313
41 0.268844486 0.735485205
42 -0.241115184 0.268844486
43 -0.560687940 -0.241115184
44 -0.624834557 -0.560687940
45 -0.395026235 -0.624834557
46 -0.331038239 -0.395026235
47 -0.217196836 -0.331038239
48 -0.265552895 -0.217196836
49 -0.429921654 -0.265552895
50 -0.452260176 -0.429921654
51 -0.481241828 -0.452260176
52 -0.509529769 -0.481241828
53 -0.130642173 -0.509529769
54 0.276259596 -0.130642173
55 0.416296040 0.276259596
56 0.005476638 0.416296040
57 -0.316775939 0.005476638
58 NA -0.316775939
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.211234832 -0.050070732
[2,] -0.537044728 -0.211234832
[3,] -0.922719207 -0.537044728
[4,] -0.861968935 -0.922719207
[5,] -0.524112434 -0.861968935
[6,] 0.022317261 -0.524112434
[7,] 0.346228864 0.022317261
[8,] 0.581520935 0.346228864
[9,] 0.436191602 0.581520935
[10,] 0.044792849 0.436191602
[11,] -0.078117399 0.044792849
[12,] -0.070908339 -0.078117399
[13,] 0.019984885 -0.070908339
[14,] 0.081777053 0.019984885
[15,] -0.114527349 0.081777053
[16,] -0.103571784 -0.114527349
[17,] -0.193631848 -0.103571784
[18,] -0.136511860 -0.193631848
[19,] -0.080319540 -0.136511860
[20,] 0.030215381 -0.080319540
[21,] 0.174702964 0.030215381
[22,] 0.124588195 0.174702964
[23,] 0.127695244 0.124588195
[24,] 0.226311858 0.127695244
[25,] 0.387716438 0.226311858
[26,] 0.580165316 0.387716438
[27,] 0.747113070 0.580165316
[28,] 0.739585283 0.747113070
[29,] 0.579541969 0.739585283
[30,] 0.079050187 0.579541969
[31,] -0.121517424 0.079050187
[32,] 0.007621603 -0.121517424
[33,] 0.100907608 0.007621603
[34,] 0.161657195 0.100907608
[35,] 0.167618990 0.161657195
[36,] 0.160220107 0.167618990
[37,] 0.233455163 0.160220107
[38,] 0.327362535 0.233455163
[39,] 0.771375313 0.327362535
[40,] 0.735485205 0.771375313
[41,] 0.268844486 0.735485205
[42,] -0.241115184 0.268844486
[43,] -0.560687940 -0.241115184
[44,] -0.624834557 -0.560687940
[45,] -0.395026235 -0.624834557
[46,] -0.331038239 -0.395026235
[47,] -0.217196836 -0.331038239
[48,] -0.265552895 -0.217196836
[49,] -0.429921654 -0.265552895
[50,] -0.452260176 -0.429921654
[51,] -0.481241828 -0.452260176
[52,] -0.509529769 -0.481241828
[53,] -0.130642173 -0.509529769
[54,] 0.276259596 -0.130642173
[55,] 0.416296040 0.276259596
[56,] 0.005476638 0.416296040
[57,] -0.316775939 0.005476638
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.211234832 -0.050070732
2 -0.537044728 -0.211234832
3 -0.922719207 -0.537044728
4 -0.861968935 -0.922719207
5 -0.524112434 -0.861968935
6 0.022317261 -0.524112434
7 0.346228864 0.022317261
8 0.581520935 0.346228864
9 0.436191602 0.581520935
10 0.044792849 0.436191602
11 -0.078117399 0.044792849
12 -0.070908339 -0.078117399
13 0.019984885 -0.070908339
14 0.081777053 0.019984885
15 -0.114527349 0.081777053
16 -0.103571784 -0.114527349
17 -0.193631848 -0.103571784
18 -0.136511860 -0.193631848
19 -0.080319540 -0.136511860
20 0.030215381 -0.080319540
21 0.174702964 0.030215381
22 0.124588195 0.174702964
23 0.127695244 0.124588195
24 0.226311858 0.127695244
25 0.387716438 0.226311858
26 0.580165316 0.387716438
27 0.747113070 0.580165316
28 0.739585283 0.747113070
29 0.579541969 0.739585283
30 0.079050187 0.579541969
31 -0.121517424 0.079050187
32 0.007621603 -0.121517424
33 0.100907608 0.007621603
34 0.161657195 0.100907608
35 0.167618990 0.161657195
36 0.160220107 0.167618990
37 0.233455163 0.160220107
38 0.327362535 0.233455163
39 0.771375313 0.327362535
40 0.735485205 0.771375313
41 0.268844486 0.735485205
42 -0.241115184 0.268844486
43 -0.560687940 -0.241115184
44 -0.624834557 -0.560687940
45 -0.395026235 -0.624834557
46 -0.331038239 -0.395026235
47 -0.217196836 -0.331038239
48 -0.265552895 -0.217196836
49 -0.429921654 -0.265552895
50 -0.452260176 -0.429921654
51 -0.481241828 -0.452260176
52 -0.509529769 -0.481241828
53 -0.130642173 -0.509529769
54 0.276259596 -0.130642173
55 0.416296040 0.276259596
56 0.005476638 0.416296040
57 -0.316775939 0.005476638
> 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/73x7j1258744965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8d8vu1258744965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gy891258744965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10i2e51258744965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1151k41258744965.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/12z6df1258744965.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/13fqa81258744965.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/14n5jv1258744965.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/15tl0z1258744965.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/162jtl1258744965.tab")
+ }
> system("convert tmp/13y421258744965.ps tmp/13y421258744965.png")
> system("convert tmp/2v7o01258744965.ps tmp/2v7o01258744965.png")
> system("convert tmp/3032d1258744965.ps tmp/3032d1258744965.png")
> system("convert tmp/4mis01258744965.ps tmp/4mis01258744965.png")
> system("convert tmp/58wa01258744965.ps tmp/58wa01258744965.png")
> system("convert tmp/60ms01258744965.ps tmp/60ms01258744965.png")
> system("convert tmp/73x7j1258744965.ps tmp/73x7j1258744965.png")
> system("convert tmp/8d8vu1258744965.ps tmp/8d8vu1258744965.png")
> system("convert tmp/9gy891258744965.ps tmp/9gy891258744965.png")
> system("convert tmp/10i2e51258744965.ps tmp/10i2e51258744965.png")
>
>
> proc.time()
user system elapsed
2.433 1.586 7.170