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 '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(97.4
+ ,114
+ ,102.9
+ ,112.7
+ ,97
+ ,95.1
+ ,111.4
+ ,116
+ ,97.4
+ ,102.9
+ ,112.7
+ ,97
+ ,87.4
+ ,153
+ ,111.4
+ ,97.4
+ ,102.9
+ ,112.7
+ ,96.8
+ ,162
+ ,87.4
+ ,111.4
+ ,97.4
+ ,102.9
+ ,114.1
+ ,161
+ ,96.8
+ ,87.4
+ ,111.4
+ ,97.4
+ ,110.3
+ ,149
+ ,114.1
+ ,96.8
+ ,87.4
+ ,111.4
+ ,103.9
+ ,139
+ ,110.3
+ ,114.1
+ ,96.8
+ ,87.4
+ ,101.6
+ ,135
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,94.6
+ ,130
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,95.9
+ ,127
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,104.7
+ ,122
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,102.8
+ ,117
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,98.1
+ ,112
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,113.9
+ ,113
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,80.9
+ ,149
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.7
+ ,157
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,113.2
+ ,157
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,105.9
+ ,147
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,108.8
+ ,137
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,102.3
+ ,132
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,99
+ ,125
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,100.7
+ ,123
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,115.5
+ ,117
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,100.7
+ ,114
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,109.9
+ ,111
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,114.6
+ ,112
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,85.4
+ ,144
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.5
+ ,150
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,114.8
+ ,149
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,116.5
+ ,134
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,112.9
+ ,123
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,102
+ ,116
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,106
+ ,117
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,105.3
+ ,111
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,118.8
+ ,105
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,106.1
+ ,102
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,109.3
+ ,95
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,117.2
+ ,93
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,92.5
+ ,124
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,104.2
+ ,130
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,112.5
+ ,124
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,122.4
+ ,115
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,113.3
+ ,106
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,100
+ ,105
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,110.7
+ ,105
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,112.8
+ ,101
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,109.8
+ ,95
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,117.3
+ ,93
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,109.1
+ ,84
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,115.9
+ ,87
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,96
+ ,116
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,99.8
+ ,120
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,116.8
+ ,117
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,115.7
+ ,109
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,99.4
+ ,105
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,94.3
+ ,107
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,91
+ ,109
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),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
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 97.4 114 102.9 112.7 97.0 95.1 1 0 0 0 0 0 0 0 0 0 0 1
2 111.4 116 97.4 102.9 112.7 97.0 0 1 0 0 0 0 0 0 0 0 0 2
3 87.4 153 111.4 97.4 102.9 112.7 0 0 1 0 0 0 0 0 0 0 0 3
4 96.8 162 87.4 111.4 97.4 102.9 0 0 0 1 0 0 0 0 0 0 0 4
5 114.1 161 96.8 87.4 111.4 97.4 0 0 0 0 1 0 0 0 0 0 0 5
6 110.3 149 114.1 96.8 87.4 111.4 0 0 0 0 0 1 0 0 0 0 0 6
7 103.9 139 110.3 114.1 96.8 87.4 0 0 0 0 0 0 1 0 0 0 0 7
8 101.6 135 103.9 110.3 114.1 96.8 0 0 0 0 0 0 0 1 0 0 0 8
9 94.6 130 101.6 103.9 110.3 114.1 0 0 0 0 0 0 0 0 1 0 0 9
10 95.9 127 94.6 101.6 103.9 110.3 0 0 0 0 0 0 0 0 0 1 0 10
11 104.7 122 95.9 94.6 101.6 103.9 0 0 0 0 0 0 0 0 0 0 1 11
12 102.8 117 104.7 95.9 94.6 101.6 0 0 0 0 0 0 0 0 0 0 0 12
13 98.1 112 102.8 104.7 95.9 94.6 1 0 0 0 0 0 0 0 0 0 0 13
14 113.9 113 98.1 102.8 104.7 95.9 0 1 0 0 0 0 0 0 0 0 0 14
15 80.9 149 113.9 98.1 102.8 104.7 0 0 1 0 0 0 0 0 0 0 0 15
16 95.7 157 80.9 113.9 98.1 102.8 0 0 0 1 0 0 0 0 0 0 0 16
17 113.2 157 95.7 80.9 113.9 98.1 0 0 0 0 1 0 0 0 0 0 0 17
18 105.9 147 113.2 95.7 80.9 113.9 0 0 0 0 0 1 0 0 0 0 0 18
19 108.8 137 105.9 113.2 95.7 80.9 0 0 0 0 0 0 1 0 0 0 0 19
20 102.3 132 108.8 105.9 113.2 95.7 0 0 0 0 0 0 0 1 0 0 0 20
21 99.0 125 102.3 108.8 105.9 113.2 0 0 0 0 0 0 0 0 1 0 0 21
22 100.7 123 99.0 102.3 108.8 105.9 0 0 0 0 0 0 0 0 0 1 0 22
23 115.5 117 100.7 99.0 102.3 108.8 0 0 0 0 0 0 0 0 0 0 1 23
24 100.7 114 115.5 100.7 99.0 102.3 0 0 0 0 0 0 0 0 0 0 0 24
25 109.9 111 100.7 115.5 100.7 99.0 1 0 0 0 0 0 0 0 0 0 0 25
26 114.6 112 109.9 100.7 115.5 100.7 0 1 0 0 0 0 0 0 0 0 0 26
27 85.4 144 114.6 109.9 100.7 115.5 0 0 1 0 0 0 0 0 0 0 0 27
28 100.5 150 85.4 114.6 109.9 100.7 0 0 0 1 0 0 0 0 0 0 0 28
29 114.8 149 100.5 85.4 114.6 109.9 0 0 0 0 1 0 0 0 0 0 0 29
30 116.5 134 114.8 100.5 85.4 114.6 0 0 0 0 0 1 0 0 0 0 0 30
31 112.9 123 116.5 114.8 100.5 85.4 0 0 0 0 0 0 1 0 0 0 0 31
32 102.0 116 112.9 116.5 114.8 100.5 0 0 0 0 0 0 0 1 0 0 0 32
33 106.0 117 102.0 112.9 116.5 114.8 0 0 0 0 0 0 0 0 1 0 0 33
34 105.3 111 106.0 102.0 112.9 116.5 0 0 0 0 0 0 0 0 0 1 0 34
35 118.8 105 105.3 106.0 102.0 112.9 0 0 0 0 0 0 0 0 0 0 1 35
36 106.1 102 118.8 105.3 106.0 102.0 0 0 0 0 0 0 0 0 0 0 0 36
37 109.3 95 106.1 118.8 105.3 106.0 1 0 0 0 0 0 0 0 0 0 0 37
38 117.2 93 109.3 106.1 118.8 105.3 0 1 0 0 0 0 0 0 0 0 0 38
39 92.5 124 117.2 109.3 106.1 118.8 0 0 1 0 0 0 0 0 0 0 0 39
40 104.2 130 92.5 117.2 109.3 106.1 0 0 0 1 0 0 0 0 0 0 0 40
41 112.5 124 104.2 92.5 117.2 109.3 0 0 0 0 1 0 0 0 0 0 0 41
42 122.4 115 112.5 104.2 92.5 117.2 0 0 0 0 0 1 0 0 0 0 0 42
43 113.3 106 122.4 112.5 104.2 92.5 0 0 0 0 0 0 1 0 0 0 0 43
44 100.0 105 113.3 122.4 112.5 104.2 0 0 0 0 0 0 0 1 0 0 0 44
45 110.7 105 100.0 113.3 122.4 112.5 0 0 0 0 0 0 0 0 1 0 0 45
46 112.8 101 110.7 100.0 113.3 122.4 0 0 0 0 0 0 0 0 0 1 0 46
47 109.8 95 112.8 110.7 100.0 113.3 0 0 0 0 0 0 0 0 0 0 1 47
48 117.3 93 109.8 112.8 110.7 100.0 0 0 0 0 0 0 0 0 0 0 0 48
49 109.1 84 117.3 109.8 112.8 110.7 1 0 0 0 0 0 0 0 0 0 0 49
50 115.9 87 109.1 117.3 109.8 112.8 0 1 0 0 0 0 0 0 0 0 0 50
51 96.0 116 115.9 109.1 117.3 109.8 0 0 1 0 0 0 0 0 0 0 0 51
52 99.8 120 96.0 115.9 109.1 117.3 0 0 0 1 0 0 0 0 0 0 0 52
53 116.8 117 99.8 96.0 115.9 109.1 0 0 0 0 1 0 0 0 0 0 0 53
54 115.7 109 116.8 99.8 96.0 115.9 0 0 0 0 0 1 0 0 0 0 0 54
55 99.4 105 115.7 116.8 99.8 96.0 0 0 0 0 0 0 1 0 0 0 0 55
56 94.3 107 99.4 115.7 116.8 99.8 0 0 0 0 0 0 0 1 0 0 0 56
57 91.0 109 94.3 99.4 115.7 116.8 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) X Y1 Y2 Y3 Y4
-38.00093 0.07024 0.12112 0.45274 0.73382 0.03109
M1 M2 M3 M4 M5 M6
-4.98250 0.51193 -24.21475 -13.87003 4.55422 17.37624
M7 M8 M9 M10 M11 t
-2.40027 -20.17099 -16.27639 -7.26354 7.27102 -0.05698
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.55436 -3.24706 0.06082 2.81342 7.96125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -38.00093 47.00674 -0.808 0.423756
X 0.07024 0.14712 0.477 0.635739
Y1 0.12112 0.16445 0.737 0.465815
Y2 0.45274 0.15444 2.932 0.005616 **
Y3 0.73382 0.18531 3.960 0.000309 ***
Y4 0.03109 0.20566 0.151 0.880611
M1 -4.98250 3.39870 -1.466 0.150664
M2 0.51193 3.91657 0.131 0.896677
M3 -24.21475 6.27432 -3.859 0.000416 ***
M4 -13.87003 8.07399 -1.718 0.093752 .
M5 4.55422 6.98010 0.652 0.517934
M6 17.37624 5.25860 3.304 0.002048 **
M7 -2.40027 4.93354 -0.487 0.629321
M8 -20.17099 4.98350 -4.048 0.000237 ***
M9 -16.27639 6.51345 -2.499 0.016779 *
M10 -7.26354 5.49370 -1.322 0.193818
M11 7.27102 4.00651 1.815 0.077251 .
t -0.05698 0.10722 -0.531 0.598123
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.222 on 39 degrees of freedom
Multiple R-squared: 0.8536, Adjusted R-squared: 0.7898
F-statistic: 13.38 on 17 and 39 DF, p-value: 2.493e-11
> 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,] 0.6470655 0.7058690 0.3529345
[2,] 0.6280109 0.7439783 0.3719891
[3,] 0.5317177 0.9365645 0.4682823
[4,] 0.6257765 0.7484471 0.3742235
[5,] 0.5594257 0.8811486 0.4405743
[6,] 0.4398450 0.8796900 0.5601550
[7,] 0.4326641 0.8653282 0.5673359
[8,] 0.3402671 0.6805343 0.6597329
[9,] 0.2452355 0.4904711 0.7547645
[10,] 0.2921871 0.5843741 0.7078129
[11,] 0.4147778 0.8295556 0.5852222
[12,] 0.3939430 0.7878861 0.6060570
[13,] 0.4197152 0.8394303 0.5802848
[14,] 0.4965843 0.9931686 0.5034157
[15,] 0.6973382 0.6053237 0.3026618
[16,] 0.5248331 0.9503338 0.4751669
> postscript(file="/var/www/html/rcomp/tmp/1obcy1260975859.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21g4k1260975859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lpau1260975859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xeeq1260975859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5txpr1260975859.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 6
-5.19210406 -3.24706273 2.43546099 1.82487016 0.45260857 -4.44434646
7 8 9 10 11 12
-3.83236226 1.48451138 -3.57515320 -4.31652509 -4.74438726 4.58863312
13 14 15 16 17 18
0.78898707 6.01273006 -3.39738255 0.90474625 1.73711285 -2.72091902
19 20 21 22 23 24
3.84163956 5.17224184 2.81342221 -2.86054823 3.85112316 -3.34872286
25 26 27 28 29 30
5.04861428 -1.08624307 -2.08430789 -2.57556272 1.08352455 3.78505703
31 32 33 34 35 36
3.93822423 0.06082150 1.41096102 -0.78417432 5.04409042 -4.03180928
37 38 39 40 41 42
0.51488563 -1.40468709 2.99574261 1.44829694 -4.32862557 5.01584499
43 44 45 46 47 48
3.60686315 -1.62963318 3.44073705 7.96124764 -4.15082632 2.79189901
49 50 51 52 53 54
-1.16038293 -0.27473717 0.05048685 -1.60235063 1.05537961 -1.63563654
55 56 57
-7.55436468 -5.08794154 -4.08996708
> postscript(file="/var/www/html/rcomp/tmp/6gmgw1260975859.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 -5.19210406 NA
1 -3.24706273 -5.19210406
2 2.43546099 -3.24706273
3 1.82487016 2.43546099
4 0.45260857 1.82487016
5 -4.44434646 0.45260857
6 -3.83236226 -4.44434646
7 1.48451138 -3.83236226
8 -3.57515320 1.48451138
9 -4.31652509 -3.57515320
10 -4.74438726 -4.31652509
11 4.58863312 -4.74438726
12 0.78898707 4.58863312
13 6.01273006 0.78898707
14 -3.39738255 6.01273006
15 0.90474625 -3.39738255
16 1.73711285 0.90474625
17 -2.72091902 1.73711285
18 3.84163956 -2.72091902
19 5.17224184 3.84163956
20 2.81342221 5.17224184
21 -2.86054823 2.81342221
22 3.85112316 -2.86054823
23 -3.34872286 3.85112316
24 5.04861428 -3.34872286
25 -1.08624307 5.04861428
26 -2.08430789 -1.08624307
27 -2.57556272 -2.08430789
28 1.08352455 -2.57556272
29 3.78505703 1.08352455
30 3.93822423 3.78505703
31 0.06082150 3.93822423
32 1.41096102 0.06082150
33 -0.78417432 1.41096102
34 5.04409042 -0.78417432
35 -4.03180928 5.04409042
36 0.51488563 -4.03180928
37 -1.40468709 0.51488563
38 2.99574261 -1.40468709
39 1.44829694 2.99574261
40 -4.32862557 1.44829694
41 5.01584499 -4.32862557
42 3.60686315 5.01584499
43 -1.62963318 3.60686315
44 3.44073705 -1.62963318
45 7.96124764 3.44073705
46 -4.15082632 7.96124764
47 2.79189901 -4.15082632
48 -1.16038293 2.79189901
49 -0.27473717 -1.16038293
50 0.05048685 -0.27473717
51 -1.60235063 0.05048685
52 1.05537961 -1.60235063
53 -1.63563654 1.05537961
54 -7.55436468 -1.63563654
55 -5.08794154 -7.55436468
56 -4.08996708 -5.08794154
57 NA -4.08996708
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.24706273 -5.19210406
[2,] 2.43546099 -3.24706273
[3,] 1.82487016 2.43546099
[4,] 0.45260857 1.82487016
[5,] -4.44434646 0.45260857
[6,] -3.83236226 -4.44434646
[7,] 1.48451138 -3.83236226
[8,] -3.57515320 1.48451138
[9,] -4.31652509 -3.57515320
[10,] -4.74438726 -4.31652509
[11,] 4.58863312 -4.74438726
[12,] 0.78898707 4.58863312
[13,] 6.01273006 0.78898707
[14,] -3.39738255 6.01273006
[15,] 0.90474625 -3.39738255
[16,] 1.73711285 0.90474625
[17,] -2.72091902 1.73711285
[18,] 3.84163956 -2.72091902
[19,] 5.17224184 3.84163956
[20,] 2.81342221 5.17224184
[21,] -2.86054823 2.81342221
[22,] 3.85112316 -2.86054823
[23,] -3.34872286 3.85112316
[24,] 5.04861428 -3.34872286
[25,] -1.08624307 5.04861428
[26,] -2.08430789 -1.08624307
[27,] -2.57556272 -2.08430789
[28,] 1.08352455 -2.57556272
[29,] 3.78505703 1.08352455
[30,] 3.93822423 3.78505703
[31,] 0.06082150 3.93822423
[32,] 1.41096102 0.06082150
[33,] -0.78417432 1.41096102
[34,] 5.04409042 -0.78417432
[35,] -4.03180928 5.04409042
[36,] 0.51488563 -4.03180928
[37,] -1.40468709 0.51488563
[38,] 2.99574261 -1.40468709
[39,] 1.44829694 2.99574261
[40,] -4.32862557 1.44829694
[41,] 5.01584499 -4.32862557
[42,] 3.60686315 5.01584499
[43,] -1.62963318 3.60686315
[44,] 3.44073705 -1.62963318
[45,] 7.96124764 3.44073705
[46,] -4.15082632 7.96124764
[47,] 2.79189901 -4.15082632
[48,] -1.16038293 2.79189901
[49,] -0.27473717 -1.16038293
[50,] 0.05048685 -0.27473717
[51,] -1.60235063 0.05048685
[52,] 1.05537961 -1.60235063
[53,] -1.63563654 1.05537961
[54,] -7.55436468 -1.63563654
[55,] -5.08794154 -7.55436468
[56,] -4.08996708 -5.08794154
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.24706273 -5.19210406
2 2.43546099 -3.24706273
3 1.82487016 2.43546099
4 0.45260857 1.82487016
5 -4.44434646 0.45260857
6 -3.83236226 -4.44434646
7 1.48451138 -3.83236226
8 -3.57515320 1.48451138
9 -4.31652509 -3.57515320
10 -4.74438726 -4.31652509
11 4.58863312 -4.74438726
12 0.78898707 4.58863312
13 6.01273006 0.78898707
14 -3.39738255 6.01273006
15 0.90474625 -3.39738255
16 1.73711285 0.90474625
17 -2.72091902 1.73711285
18 3.84163956 -2.72091902
19 5.17224184 3.84163956
20 2.81342221 5.17224184
21 -2.86054823 2.81342221
22 3.85112316 -2.86054823
23 -3.34872286 3.85112316
24 5.04861428 -3.34872286
25 -1.08624307 5.04861428
26 -2.08430789 -1.08624307
27 -2.57556272 -2.08430789
28 1.08352455 -2.57556272
29 3.78505703 1.08352455
30 3.93822423 3.78505703
31 0.06082150 3.93822423
32 1.41096102 0.06082150
33 -0.78417432 1.41096102
34 5.04409042 -0.78417432
35 -4.03180928 5.04409042
36 0.51488563 -4.03180928
37 -1.40468709 0.51488563
38 2.99574261 -1.40468709
39 1.44829694 2.99574261
40 -4.32862557 1.44829694
41 5.01584499 -4.32862557
42 3.60686315 5.01584499
43 -1.62963318 3.60686315
44 3.44073705 -1.62963318
45 7.96124764 3.44073705
46 -4.15082632 7.96124764
47 2.79189901 -4.15082632
48 -1.16038293 2.79189901
49 -0.27473717 -1.16038293
50 0.05048685 -0.27473717
51 -1.60235063 0.05048685
52 1.05537961 -1.60235063
53 -1.63563654 1.05537961
54 -7.55436468 -1.63563654
55 -5.08794154 -7.55436468
56 -4.08996708 -5.08794154
> 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/707j91260975859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80vmm1260975859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zl9u1260975859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102ng41260975859.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jvy81260975860.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/122sxm1260975860.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/136m401260975860.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/14sovs1260975860.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/15jax01260975860.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/16xvqk1260975860.tab")
+ }
> try(system("convert tmp/1obcy1260975859.ps tmp/1obcy1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/21g4k1260975859.ps tmp/21g4k1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lpau1260975859.ps tmp/3lpau1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xeeq1260975859.ps tmp/4xeeq1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/5txpr1260975859.ps tmp/5txpr1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gmgw1260975859.ps tmp/6gmgw1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/707j91260975859.ps tmp/707j91260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/80vmm1260975859.ps tmp/80vmm1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zl9u1260975859.ps tmp/9zl9u1260975859.png",intern=TRUE))
character(0)
> try(system("convert tmp/102ng41260975859.ps tmp/102ng41260975859.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.322 1.517 4.054