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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> x <- array(list(102.38
+ ,0
+ ,102.37
+ ,101.76
+ ,102.86
+ ,0
+ ,102.38
+ ,102.37
+ ,102.87
+ ,0
+ ,102.86
+ ,102.38
+ ,102.92
+ ,0
+ ,102.87
+ ,102.86
+ ,102.95
+ ,0
+ ,102.92
+ ,102.87
+ ,103.02
+ ,0
+ ,102.95
+ ,102.92
+ ,104.08
+ ,0
+ ,103.02
+ ,102.95
+ ,104.16
+ ,0
+ ,104.08
+ ,103.02
+ ,104.24
+ ,0
+ ,104.16
+ ,104.08
+ ,104.33
+ ,0
+ ,104.24
+ ,104.16
+ ,104.73
+ ,0
+ ,104.33
+ ,104.24
+ ,104.86
+ ,0
+ ,104.73
+ ,104.33
+ ,105.03
+ ,0
+ ,104.86
+ ,104.73
+ ,105.62
+ ,0
+ ,105.03
+ ,104.86
+ ,105.63
+ ,0
+ ,105.62
+ ,105.03
+ ,105.63
+ ,0
+ ,105.63
+ ,105.62
+ ,105.94
+ ,0
+ ,105.63
+ ,105.63
+ ,106.61
+ ,0
+ ,105.94
+ ,105.63
+ ,107.69
+ ,0
+ ,106.61
+ ,105.94
+ ,107.78
+ ,0
+ ,107.69
+ ,106.61
+ ,107.93
+ ,0
+ ,107.78
+ ,107.69
+ ,108.48
+ ,0
+ ,107.93
+ ,107.78
+ ,108.14
+ ,0
+ ,108.48
+ ,107.93
+ ,108.48
+ ,0
+ ,108.14
+ ,108.48
+ ,108.48
+ ,0
+ ,108.48
+ ,108.14
+ ,108.89
+ ,0
+ ,108.48
+ ,108.48
+ ,108.93
+ ,0
+ ,108.89
+ ,108.48
+ ,109.21
+ ,0
+ ,108.93
+ ,108.89
+ ,109.47
+ ,0
+ ,109.21
+ ,108.93
+ ,109.8
+ ,0
+ ,109.47
+ ,109.21
+ ,111.73
+ ,0
+ ,109.8
+ ,109.47
+ ,111.85
+ ,0
+ ,111.73
+ ,109.8
+ ,112.12
+ ,0
+ ,111.85
+ ,111.73
+ ,112.15
+ ,0
+ ,112.12
+ ,111.85
+ ,112.17
+ ,0
+ ,112.15
+ ,112.12
+ ,112.67
+ ,1
+ ,112.17
+ ,112.15
+ ,112.8
+ ,1
+ ,112.67
+ ,112.17
+ ,113.44
+ ,1
+ ,112.8
+ ,112.67
+ ,113.53
+ ,1
+ ,113.44
+ ,112.8
+ ,114.53
+ ,1
+ ,113.53
+ ,113.44
+ ,114.51
+ ,1
+ ,114.53
+ ,113.53
+ ,115.05
+ ,1
+ ,114.51
+ ,114.53
+ ,116.67
+ ,1
+ ,115.05
+ ,114.51
+ ,117.07
+ ,1
+ ,116.67
+ ,115.05
+ ,116.92
+ ,1
+ ,117.07
+ ,116.67
+ ,117
+ ,1
+ ,116.92
+ ,117.07
+ ,117.02
+ ,1
+ ,117
+ ,116.92
+ ,117.35
+ ,1
+ ,117.02
+ ,117
+ ,117.36
+ ,1
+ ,117.35
+ ,117.02
+ ,117.82
+ ,1
+ ,117.36
+ ,117.35
+ ,117.88
+ ,1
+ ,117.82
+ ,117.36
+ ,118.24
+ ,1
+ ,117.88
+ ,117.82
+ ,118.5
+ ,1
+ ,118.24
+ ,117.88
+ ,118.8
+ ,1
+ ,118.5
+ ,118.24
+ ,119.76
+ ,1
+ ,118.8
+ ,118.5
+ ,120.09
+ ,1
+ ,119.76
+ ,118.8)
+ ,dim=c(4
+ ,56)
+ ,dimnames=list(c('Vrijetijdsbesteding'
+ ,'x'
+ ,'y-1'
+ ,'y-2')
+ ,1:56))
> y <- array(NA,dim=c(4,56),dimnames=list(c('Vrijetijdsbesteding','x','y-1','y-2'),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
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
Vrijetijdsbesteding x y-1 y-2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 102.38 0 102.37 101.76 1 0 0 0 0 0 0 0 0 0 0 1
2 102.86 0 102.38 102.37 0 1 0 0 0 0 0 0 0 0 0 2
3 102.87 0 102.86 102.38 0 0 1 0 0 0 0 0 0 0 0 3
4 102.92 0 102.87 102.86 0 0 0 1 0 0 0 0 0 0 0 4
5 102.95 0 102.92 102.87 0 0 0 0 1 0 0 0 0 0 0 5
6 103.02 0 102.95 102.92 0 0 0 0 0 1 0 0 0 0 0 6
7 104.08 0 103.02 102.95 0 0 0 0 0 0 1 0 0 0 0 7
8 104.16 0 104.08 103.02 0 0 0 0 0 0 0 1 0 0 0 8
9 104.24 0 104.16 104.08 0 0 0 0 0 0 0 0 1 0 0 9
10 104.33 0 104.24 104.16 0 0 0 0 0 0 0 0 0 1 0 10
11 104.73 0 104.33 104.24 0 0 0 0 0 0 0 0 0 0 1 11
12 104.86 0 104.73 104.33 0 0 0 0 0 0 0 0 0 0 0 12
13 105.03 0 104.86 104.73 1 0 0 0 0 0 0 0 0 0 0 13
14 105.62 0 105.03 104.86 0 1 0 0 0 0 0 0 0 0 0 14
15 105.63 0 105.62 105.03 0 0 1 0 0 0 0 0 0 0 0 15
16 105.63 0 105.63 105.62 0 0 0 1 0 0 0 0 0 0 0 16
17 105.94 0 105.63 105.63 0 0 0 0 1 0 0 0 0 0 0 17
18 106.61 0 105.94 105.63 0 0 0 0 0 1 0 0 0 0 0 18
19 107.69 0 106.61 105.94 0 0 0 0 0 0 1 0 0 0 0 19
20 107.78 0 107.69 106.61 0 0 0 0 0 0 0 1 0 0 0 20
21 107.93 0 107.78 107.69 0 0 0 0 0 0 0 0 1 0 0 21
22 108.48 0 107.93 107.78 0 0 0 0 0 0 0 0 0 1 0 22
23 108.14 0 108.48 107.93 0 0 0 0 0 0 0 0 0 0 1 23
24 108.48 0 108.14 108.48 0 0 0 0 0 0 0 0 0 0 0 24
25 108.48 0 108.48 108.14 1 0 0 0 0 0 0 0 0 0 0 25
26 108.89 0 108.48 108.48 0 1 0 0 0 0 0 0 0 0 0 26
27 108.93 0 108.89 108.48 0 0 1 0 0 0 0 0 0 0 0 27
28 109.21 0 108.93 108.89 0 0 0 1 0 0 0 0 0 0 0 28
29 109.47 0 109.21 108.93 0 0 0 0 1 0 0 0 0 0 0 29
30 109.80 0 109.47 109.21 0 0 0 0 0 1 0 0 0 0 0 30
31 111.73 0 109.80 109.47 0 0 0 0 0 0 1 0 0 0 0 31
32 111.85 0 111.73 109.80 0 0 0 0 0 0 0 1 0 0 0 32
33 112.12 0 111.85 111.73 0 0 0 0 0 0 0 0 1 0 0 33
34 112.15 0 112.12 111.85 0 0 0 0 0 0 0 0 0 1 0 34
35 112.17 0 112.15 112.12 0 0 0 0 0 0 0 0 0 0 1 35
36 112.67 1 112.17 112.15 0 0 0 0 0 0 0 0 0 0 0 36
37 112.80 1 112.67 112.17 1 0 0 0 0 0 0 0 0 0 0 37
38 113.44 1 112.80 112.67 0 1 0 0 0 0 0 0 0 0 0 38
39 113.53 1 113.44 112.80 0 0 1 0 0 0 0 0 0 0 0 39
40 114.53 1 113.53 113.44 0 0 0 1 0 0 0 0 0 0 0 40
41 114.51 1 114.53 113.53 0 0 0 0 1 0 0 0 0 0 0 41
42 115.05 1 114.51 114.53 0 0 0 0 0 1 0 0 0 0 0 42
43 116.67 1 115.05 114.51 0 0 0 0 0 0 1 0 0 0 0 43
44 117.07 1 116.67 115.05 0 0 0 0 0 0 0 1 0 0 0 44
45 116.92 1 117.07 116.67 0 0 0 0 0 0 0 0 1 0 0 45
46 117.00 1 116.92 117.07 0 0 0 0 0 0 0 0 0 1 0 46
47 117.02 1 117.00 116.92 0 0 0 0 0 0 0 0 0 0 1 47
48 117.35 1 117.02 117.00 0 0 0 0 0 0 0 0 0 0 0 48
49 117.36 1 117.35 117.02 1 0 0 0 0 0 0 0 0 0 0 49
50 117.82 1 117.36 117.35 0 1 0 0 0 0 0 0 0 0 0 50
51 117.88 1 117.82 117.36 0 0 1 0 0 0 0 0 0 0 0 51
52 118.24 1 117.88 117.82 0 0 0 1 0 0 0 0 0 0 0 52
53 118.50 1 118.24 117.88 0 0 0 0 1 0 0 0 0 0 0 53
54 118.80 1 118.50 118.24 0 0 0 0 0 1 0 0 0 0 0 54
55 119.76 1 118.80 118.50 0 0 0 0 0 0 1 0 0 0 0 55
56 120.09 1 119.76 118.80 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) x `y-1` `y-2` M1 M2
23.238669 0.328013 0.706707 0.065783 -0.171641 0.205094
M3 M4 M5 M6 M7 M8
-0.190684 0.014784 -0.127753 0.044378 1.024458 0.194502
M9 M10 M11 t
-0.007326 0.038083 -0.144088 0.068907
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.37313 -0.11883 -0.02178 0.08164 0.53304
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.238669 7.472222 3.110 0.00344 **
x 0.328013 0.154567 2.122 0.04007 *
`y-1` 0.706707 0.148857 4.748 2.64e-05 ***
`y-2` 0.065783 0.139888 0.470 0.64073
M1 -0.171641 0.154281 -1.113 0.27256
M2 0.205094 0.145419 1.410 0.16616
M3 -0.190684 0.160855 -1.185 0.24284
M4 0.014784 0.145301 0.102 0.91947
M5 -0.127753 0.151890 -0.841 0.40530
M6 0.044378 0.147301 0.301 0.76476
M7 1.024458 0.154241 6.642 5.93e-08 ***
M8 0.194502 0.237914 0.818 0.41847
M9 -0.007326 0.169158 -0.043 0.96567
M10 0.038083 0.163564 0.233 0.81708
M11 -0.144088 0.162537 -0.886 0.38065
t 0.068907 0.021924 3.143 0.00315 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2163 on 40 degrees of freedom
Multiple R-squared: 0.9989, Adjusted R-squared: 0.9984
F-statistic: 2325 on 15 and 40 DF, p-value: < 2.2e-16
> 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,] 0.5932760 0.8134481 0.4067240
[2,] 0.4611370 0.9222741 0.5388630
[3,] 0.3167763 0.6335527 0.6832237
[4,] 0.3865210 0.7730420 0.6134790
[5,] 0.7585423 0.4829154 0.2414577
[6,] 0.6603431 0.6793138 0.3396569
[7,] 0.5830975 0.8338050 0.4169025
[8,] 0.5284906 0.9430187 0.4715094
[9,] 0.4315488 0.8630976 0.5684512
[10,] 0.5876486 0.8247028 0.4123514
[11,] 0.6049150 0.7901701 0.3950850
[12,] 0.5939426 0.8121149 0.4060574
[13,] 0.8488146 0.3023709 0.1511854
[14,] 0.7942695 0.4114610 0.2057305
[15,] 0.7212525 0.5574951 0.2787475
[16,] 0.6458795 0.7082411 0.3541205
[17,] 0.5018400 0.9963201 0.4981600
[18,] 0.3514179 0.7028357 0.6485821
[19,] 0.2156837 0.4313674 0.7843163
> postscript(file="/var/www/html/rcomp/tmp/16rk71291226997.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hijr1291226997.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hijr1291226997.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hijr1291226997.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hijr1291226997.ps",horizontal=F,onefile=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
0.204362660 0.191526209 0.188519961 -0.074497217 -0.006860574 -0.202389402
7 8 9 10 11 12
-0.242818753 -0.155483474 -0.068829544 -0.154944276 0.289453018 -0.082144943
13 14 15 16 17 18
0.072403443 0.088069941 -0.003199409 -0.323452764 0.059519221 0.269401646
19 20 21 22 23 24
-0.193471173 -0.149740087 -0.001468894 0.322289061 -0.303003637 0.028101096
25 26 27 28 29 30
-0.087079221 -0.145087079 -0.067966014 -0.117579557 0.015541007 -0.097660584
31 32 33 34 35 36
0.533036097 0.028432725 0.219586801 -0.063433569 0.030867284 -0.026247965
37 38 39 40 41 42
-0.148183412 -0.078588508 -0.122561864 0.497359063 -0.161638476 0.085673783
43 44 45 46 47 48
0.276381385 0.257042618 -0.149288363 -0.103911215 -0.017316665 0.080291812
49 50 51 52 53 54
-0.041503470 -0.055920562 0.005207326 0.018170475 0.093438822 -0.055025443
55 56
-0.373127557 0.019748219
> postscript(file="/var/www/html/rcomp/tmp/6r9ic1291226997.ps",horizontal=F,onefile=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 0.204362660 NA
1 0.191526209 0.204362660
2 0.188519961 0.191526209
3 -0.074497217 0.188519961
4 -0.006860574 -0.074497217
5 -0.202389402 -0.006860574
6 -0.242818753 -0.202389402
7 -0.155483474 -0.242818753
8 -0.068829544 -0.155483474
9 -0.154944276 -0.068829544
10 0.289453018 -0.154944276
11 -0.082144943 0.289453018
12 0.072403443 -0.082144943
13 0.088069941 0.072403443
14 -0.003199409 0.088069941
15 -0.323452764 -0.003199409
16 0.059519221 -0.323452764
17 0.269401646 0.059519221
18 -0.193471173 0.269401646
19 -0.149740087 -0.193471173
20 -0.001468894 -0.149740087
21 0.322289061 -0.001468894
22 -0.303003637 0.322289061
23 0.028101096 -0.303003637
24 -0.087079221 0.028101096
25 -0.145087079 -0.087079221
26 -0.067966014 -0.145087079
27 -0.117579557 -0.067966014
28 0.015541007 -0.117579557
29 -0.097660584 0.015541007
30 0.533036097 -0.097660584
31 0.028432725 0.533036097
32 0.219586801 0.028432725
33 -0.063433569 0.219586801
34 0.030867284 -0.063433569
35 -0.026247965 0.030867284
36 -0.148183412 -0.026247965
37 -0.078588508 -0.148183412
38 -0.122561864 -0.078588508
39 0.497359063 -0.122561864
40 -0.161638476 0.497359063
41 0.085673783 -0.161638476
42 0.276381385 0.085673783
43 0.257042618 0.276381385
44 -0.149288363 0.257042618
45 -0.103911215 -0.149288363
46 -0.017316665 -0.103911215
47 0.080291812 -0.017316665
48 -0.041503470 0.080291812
49 -0.055920562 -0.041503470
50 0.005207326 -0.055920562
51 0.018170475 0.005207326
52 0.093438822 0.018170475
53 -0.055025443 0.093438822
54 -0.373127557 -0.055025443
55 0.019748219 -0.373127557
56 NA 0.019748219
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.191526209 0.204362660
[2,] 0.188519961 0.191526209
[3,] -0.074497217 0.188519961
[4,] -0.006860574 -0.074497217
[5,] -0.202389402 -0.006860574
[6,] -0.242818753 -0.202389402
[7,] -0.155483474 -0.242818753
[8,] -0.068829544 -0.155483474
[9,] -0.154944276 -0.068829544
[10,] 0.289453018 -0.154944276
[11,] -0.082144943 0.289453018
[12,] 0.072403443 -0.082144943
[13,] 0.088069941 0.072403443
[14,] -0.003199409 0.088069941
[15,] -0.323452764 -0.003199409
[16,] 0.059519221 -0.323452764
[17,] 0.269401646 0.059519221
[18,] -0.193471173 0.269401646
[19,] -0.149740087 -0.193471173
[20,] -0.001468894 -0.149740087
[21,] 0.322289061 -0.001468894
[22,] -0.303003637 0.322289061
[23,] 0.028101096 -0.303003637
[24,] -0.087079221 0.028101096
[25,] -0.145087079 -0.087079221
[26,] -0.067966014 -0.145087079
[27,] -0.117579557 -0.067966014
[28,] 0.015541007 -0.117579557
[29,] -0.097660584 0.015541007
[30,] 0.533036097 -0.097660584
[31,] 0.028432725 0.533036097
[32,] 0.219586801 0.028432725
[33,] -0.063433569 0.219586801
[34,] 0.030867284 -0.063433569
[35,] -0.026247965 0.030867284
[36,] -0.148183412 -0.026247965
[37,] -0.078588508 -0.148183412
[38,] -0.122561864 -0.078588508
[39,] 0.497359063 -0.122561864
[40,] -0.161638476 0.497359063
[41,] 0.085673783 -0.161638476
[42,] 0.276381385 0.085673783
[43,] 0.257042618 0.276381385
[44,] -0.149288363 0.257042618
[45,] -0.103911215 -0.149288363
[46,] -0.017316665 -0.103911215
[47,] 0.080291812 -0.017316665
[48,] -0.041503470 0.080291812
[49,] -0.055920562 -0.041503470
[50,] 0.005207326 -0.055920562
[51,] 0.018170475 0.005207326
[52,] 0.093438822 0.018170475
[53,] -0.055025443 0.093438822
[54,] -0.373127557 -0.055025443
[55,] 0.019748219 -0.373127557
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.191526209 0.204362660
2 0.188519961 0.191526209
3 -0.074497217 0.188519961
4 -0.006860574 -0.074497217
5 -0.202389402 -0.006860574
6 -0.242818753 -0.202389402
7 -0.155483474 -0.242818753
8 -0.068829544 -0.155483474
9 -0.154944276 -0.068829544
10 0.289453018 -0.154944276
11 -0.082144943 0.289453018
12 0.072403443 -0.082144943
13 0.088069941 0.072403443
14 -0.003199409 0.088069941
15 -0.323452764 -0.003199409
16 0.059519221 -0.323452764
17 0.269401646 0.059519221
18 -0.193471173 0.269401646
19 -0.149740087 -0.193471173
20 -0.001468894 -0.149740087
21 0.322289061 -0.001468894
22 -0.303003637 0.322289061
23 0.028101096 -0.303003637
24 -0.087079221 0.028101096
25 -0.145087079 -0.087079221
26 -0.067966014 -0.145087079
27 -0.117579557 -0.067966014
28 0.015541007 -0.117579557
29 -0.097660584 0.015541007
30 0.533036097 -0.097660584
31 0.028432725 0.533036097
32 0.219586801 0.028432725
33 -0.063433569 0.219586801
34 0.030867284 -0.063433569
35 -0.026247965 0.030867284
36 -0.148183412 -0.026247965
37 -0.078588508 -0.148183412
38 -0.122561864 -0.078588508
39 0.497359063 -0.122561864
40 -0.161638476 0.497359063
41 0.085673783 -0.161638476
42 0.276381385 0.085673783
43 0.257042618 0.276381385
44 -0.149288363 0.257042618
45 -0.103911215 -0.149288363
46 -0.017316665 -0.103911215
47 0.080291812 -0.017316665
48 -0.041503470 0.080291812
49 -0.055920562 -0.041503470
50 0.005207326 -0.055920562
51 0.018170475 0.005207326
52 0.093438822 0.018170475
53 -0.055025443 0.093438822
54 -0.373127557 -0.055025443
55 0.019748219 -0.373127557
> 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/7kjhx1291226997.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kjhx1291226997.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kjhx1291226997.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vazi1291226997.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gbf61291226997.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/12kbwc1291226997.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/13y3bl1291226997.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/14j3a91291226997.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/1554qx1291226997.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/168mp21291226997.tab")
+ }
>
> try(system("convert tmp/16rk71291226997.ps tmp/16rk71291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hijr1291226997.ps tmp/2hijr1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hijr1291226997.ps tmp/3hijr1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hijr1291226997.ps tmp/4hijr1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hijr1291226997.ps tmp/5hijr1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/6r9ic1291226997.ps tmp/6r9ic1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kjhx1291226997.ps tmp/7kjhx1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kjhx1291226997.ps tmp/8kjhx1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kjhx1291226997.ps tmp/9kjhx1291226997.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vazi1291226997.ps tmp/10vazi1291226997.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.460 1.741 6.153