R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(112.3
+ ,0
+ ,117.2
+ ,96.8
+ ,80
+ ,126.1
+ ,117.3
+ ,0
+ ,112.3
+ ,117.2
+ ,96.8
+ ,80
+ ,111.1
+ ,1
+ ,117.3
+ ,112.3
+ ,117.2
+ ,96.8
+ ,102.2
+ ,1
+ ,111.1
+ ,117.3
+ ,112.3
+ ,117.2
+ ,104.3
+ ,1
+ ,102.2
+ ,111.1
+ ,117.3
+ ,112.3
+ ,122.9
+ ,1
+ ,104.3
+ ,102.2
+ ,111.1
+ ,117.3
+ ,107.6
+ ,1
+ ,122.9
+ ,104.3
+ ,102.2
+ ,111.1
+ ,121.3
+ ,1
+ ,107.6
+ ,122.9
+ ,104.3
+ ,102.2
+ ,131.5
+ ,1
+ ,121.3
+ ,107.6
+ ,122.9
+ ,104.3
+ ,89
+ ,1
+ ,131.5
+ ,121.3
+ ,107.6
+ ,122.9
+ ,104.4
+ ,1
+ ,89
+ ,131.5
+ ,121.3
+ ,107.6
+ ,128.9
+ ,1
+ ,104.4
+ ,89
+ ,131.5
+ ,121.3
+ ,135.9
+ ,1
+ ,128.9
+ ,104.4
+ ,89
+ ,131.5
+ ,133.3
+ ,1
+ ,135.9
+ ,128.9
+ ,104.4
+ ,89
+ ,121.3
+ ,1
+ ,133.3
+ ,135.9
+ ,128.9
+ ,104.4
+ ,120.5
+ ,0
+ ,121.3
+ ,133.3
+ ,135.9
+ ,128.9
+ ,120.4
+ ,0
+ ,120.5
+ ,121.3
+ ,133.3
+ ,135.9
+ ,137.9
+ ,0
+ ,120.4
+ ,120.5
+ ,121.3
+ ,133.3
+ ,126.1
+ ,0
+ ,137.9
+ ,120.4
+ ,120.5
+ ,121.3
+ ,133.2
+ ,0
+ ,126.1
+ ,137.9
+ ,120.4
+ ,120.5
+ ,151.1
+ ,0
+ ,133.2
+ ,126.1
+ ,137.9
+ ,120.4
+ ,105
+ ,0
+ ,151.1
+ ,133.2
+ ,126.1
+ ,137.9
+ ,119
+ ,0
+ ,105
+ ,151.1
+ ,133.2
+ ,126.1
+ ,140.4
+ ,0
+ ,119
+ ,105
+ ,151.1
+ ,133.2
+ ,156.6
+ ,0
+ ,140.4
+ ,119
+ ,105
+ ,151.1
+ ,137.1
+ ,0
+ ,156.6
+ ,140.4
+ ,119
+ ,105
+ ,122.7
+ ,0
+ ,137.1
+ ,156.6
+ ,140.4
+ ,119
+ ,125.8
+ ,0
+ ,122.7
+ ,137.1
+ ,156.6
+ ,140.4
+ ,139.3
+ ,0
+ ,125.8
+ ,122.7
+ ,137.1
+ ,156.6
+ ,134.9
+ ,0
+ ,139.3
+ ,125.8
+ ,122.7
+ ,137.1
+ ,149.2
+ ,0
+ ,134.9
+ ,139.3
+ ,125.8
+ ,122.7
+ ,132.3
+ ,0
+ ,149.2
+ ,134.9
+ ,139.3
+ ,125.8
+ ,149
+ ,0
+ ,132.3
+ ,149.2
+ ,134.9
+ ,139.3
+ ,117.2
+ ,0
+ ,149
+ ,132.3
+ ,149.2
+ ,134.9
+ ,119.6
+ ,0
+ ,117.2
+ ,149
+ ,132.3
+ ,149.2
+ ,152
+ ,0
+ ,119.6
+ ,117.2
+ ,149
+ ,132.3
+ ,149.4
+ ,0
+ ,152
+ ,119.6
+ ,117.2
+ ,149
+ ,127.3
+ ,0
+ ,149.4
+ ,152
+ ,119.6
+ ,117.2
+ ,114.1
+ ,0
+ ,127.3
+ ,149.4
+ ,152
+ ,119.6
+ ,102.1
+ ,0
+ ,114.1
+ ,127.3
+ ,149.4
+ ,152
+ ,107.7
+ ,0
+ ,102.1
+ ,114.1
+ ,127.3
+ ,149.4
+ ,104.4
+ ,0
+ ,107.7
+ ,102.1
+ ,114.1
+ ,127.3
+ ,102.1
+ ,0
+ ,104.4
+ ,107.7
+ ,102.1
+ ,114.1
+ ,96
+ ,1
+ ,102.1
+ ,104.4
+ ,107.7
+ ,102.1
+ ,109.3
+ ,0
+ ,96
+ ,102.1
+ ,104.4
+ ,107.7
+ ,90
+ ,1
+ ,109.3
+ ,96
+ ,102.1
+ ,104.4
+ ,83.9
+ ,1
+ ,90
+ ,109.3
+ ,96
+ ,102.1
+ ,112
+ ,1
+ ,83.9
+ ,90
+ ,109.3
+ ,96
+ ,114.3
+ ,1
+ ,112
+ ,83.9
+ ,90
+ ,109.3
+ ,103.6
+ ,1
+ ,114.3
+ ,112
+ ,83.9
+ ,90
+ ,91.7
+ ,1
+ ,103.6
+ ,114.3
+ ,112
+ ,83.9
+ ,80.8
+ ,1
+ ,91.7
+ ,103.6
+ ,114.3
+ ,112
+ ,87.2
+ ,1
+ ,80.8
+ ,91.7
+ ,103.6
+ ,114.3
+ ,109.2
+ ,1
+ ,87.2
+ ,80.8
+ ,91.7
+ ,103.6
+ ,102.7
+ ,1
+ ,109.2
+ ,87.2
+ ,80.8
+ ,91.7
+ ,95.1
+ ,1
+ ,102.7
+ ,109.2
+ ,87.2
+ ,80.8
+ ,117.5
+ ,1
+ ,95.1
+ ,102.7
+ ,109.2
+ ,87.2
+ ,85.1
+ ,1
+ ,117.5
+ ,95.1
+ ,102.7
+ ,109.2
+ ,92.1
+ ,1
+ ,85.1
+ ,117.5
+ ,95.1
+ ,102.7
+ ,113.5
+ ,1
+ ,92.1
+ ,85.1
+ ,117.5
+ ,95.1)
+ ,dim=c(6
+ ,60)
+ ,dimnames=list(c('X'
+ ,'Y'
+ ,'y(t)'
+ ,'y(t-1)'
+ ,'y(t-2)'
+ ,'y(t-3)')
+ ,1:60))
> y <- array(NA,dim=c(6,60),dimnames=list(c('X','Y','y(t)','y(t-1)','y(t-2)','y(t-3)'),1:60))
> 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'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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 y(t) y(t-1) y(t-2) y(t-3) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 112.3 0 117.2 96.8 80.0 126.1 1 0 0 0 0 0 0 0 0 0 0 1
2 117.3 0 112.3 117.2 96.8 80.0 0 1 0 0 0 0 0 0 0 0 0 2
3 111.1 1 117.3 112.3 117.2 96.8 0 0 1 0 0 0 0 0 0 0 0 3
4 102.2 1 111.1 117.3 112.3 117.2 0 0 0 1 0 0 0 0 0 0 0 4
5 104.3 1 102.2 111.1 117.3 112.3 0 0 0 0 1 0 0 0 0 0 0 5
6 122.9 1 104.3 102.2 111.1 117.3 0 0 0 0 0 1 0 0 0 0 0 6
7 107.6 1 122.9 104.3 102.2 111.1 0 0 0 0 0 0 1 0 0 0 0 7
8 121.3 1 107.6 122.9 104.3 102.2 0 0 0 0 0 0 0 1 0 0 0 8
9 131.5 1 121.3 107.6 122.9 104.3 0 0 0 0 0 0 0 0 1 0 0 9
10 89.0 1 131.5 121.3 107.6 122.9 0 0 0 0 0 0 0 0 0 1 0 10
11 104.4 1 89.0 131.5 121.3 107.6 0 0 0 0 0 0 0 0 0 0 1 11
12 128.9 1 104.4 89.0 131.5 121.3 0 0 0 0 0 0 0 0 0 0 0 12
13 135.9 1 128.9 104.4 89.0 131.5 1 0 0 0 0 0 0 0 0 0 0 13
14 133.3 1 135.9 128.9 104.4 89.0 0 1 0 0 0 0 0 0 0 0 0 14
15 121.3 1 133.3 135.9 128.9 104.4 0 0 1 0 0 0 0 0 0 0 0 15
16 120.5 0 121.3 133.3 135.9 128.9 0 0 0 1 0 0 0 0 0 0 0 16
17 120.4 0 120.5 121.3 133.3 135.9 0 0 0 0 1 0 0 0 0 0 0 17
18 137.9 0 120.4 120.5 121.3 133.3 0 0 0 0 0 1 0 0 0 0 0 18
19 126.1 0 137.9 120.4 120.5 121.3 0 0 0 0 0 0 1 0 0 0 0 19
20 133.2 0 126.1 137.9 120.4 120.5 0 0 0 0 0 0 0 1 0 0 0 20
21 151.1 0 133.2 126.1 137.9 120.4 0 0 0 0 0 0 0 0 1 0 0 21
22 105.0 0 151.1 133.2 126.1 137.9 0 0 0 0 0 0 0 0 0 1 0 22
23 119.0 0 105.0 151.1 133.2 126.1 0 0 0 0 0 0 0 0 0 0 1 23
24 140.4 0 119.0 105.0 151.1 133.2 0 0 0 0 0 0 0 0 0 0 0 24
25 156.6 0 140.4 119.0 105.0 151.1 1 0 0 0 0 0 0 0 0 0 0 25
26 137.1 0 156.6 140.4 119.0 105.0 0 1 0 0 0 0 0 0 0 0 0 26
27 122.7 0 137.1 156.6 140.4 119.0 0 0 1 0 0 0 0 0 0 0 0 27
28 125.8 0 122.7 137.1 156.6 140.4 0 0 0 1 0 0 0 0 0 0 0 28
29 139.3 0 125.8 122.7 137.1 156.6 0 0 0 0 1 0 0 0 0 0 0 29
30 134.9 0 139.3 125.8 122.7 137.1 0 0 0 0 0 1 0 0 0 0 0 30
31 149.2 0 134.9 139.3 125.8 122.7 0 0 0 0 0 0 1 0 0 0 0 31
32 132.3 0 149.2 134.9 139.3 125.8 0 0 0 0 0 0 0 1 0 0 0 32
33 149.0 0 132.3 149.2 134.9 139.3 0 0 0 0 0 0 0 0 1 0 0 33
34 117.2 0 149.0 132.3 149.2 134.9 0 0 0 0 0 0 0 0 0 1 0 34
35 119.6 0 117.2 149.0 132.3 149.2 0 0 0 0 0 0 0 0 0 0 1 35
36 152.0 0 119.6 117.2 149.0 132.3 0 0 0 0 0 0 0 0 0 0 0 36
37 149.4 0 152.0 119.6 117.2 149.0 1 0 0 0 0 0 0 0 0 0 0 37
38 127.3 0 149.4 152.0 119.6 117.2 0 1 0 0 0 0 0 0 0 0 0 38
39 114.1 0 127.3 149.4 152.0 119.6 0 0 1 0 0 0 0 0 0 0 0 39
40 102.1 0 114.1 127.3 149.4 152.0 0 0 0 1 0 0 0 0 0 0 0 40
41 107.7 0 102.1 114.1 127.3 149.4 0 0 0 0 1 0 0 0 0 0 0 41
42 104.4 0 107.7 102.1 114.1 127.3 0 0 0 0 0 1 0 0 0 0 0 42
43 102.1 0 104.4 107.7 102.1 114.1 0 0 0 0 0 0 1 0 0 0 0 43
44 96.0 1 102.1 104.4 107.7 102.1 0 0 0 0 0 0 0 1 0 0 0 44
45 109.3 0 96.0 102.1 104.4 107.7 0 0 0 0 0 0 0 0 1 0 0 45
46 90.0 1 109.3 96.0 102.1 104.4 0 0 0 0 0 0 0 0 0 1 0 46
47 83.9 1 90.0 109.3 96.0 102.1 0 0 0 0 0 0 0 0 0 0 1 47
48 112.0 1 83.9 90.0 109.3 96.0 0 0 0 0 0 0 0 0 0 0 0 48
49 114.3 1 112.0 83.9 90.0 109.3 1 0 0 0 0 0 0 0 0 0 0 49
50 103.6 1 114.3 112.0 83.9 90.0 0 1 0 0 0 0 0 0 0 0 0 50
51 91.7 1 103.6 114.3 112.0 83.9 0 0 1 0 0 0 0 0 0 0 0 51
52 80.8 1 91.7 103.6 114.3 112.0 0 0 0 1 0 0 0 0 0 0 0 52
53 87.2 1 80.8 91.7 103.6 114.3 0 0 0 0 1 0 0 0 0 0 0 53
54 109.2 1 87.2 80.8 91.7 103.6 0 0 0 0 0 1 0 0 0 0 0 54
55 102.7 1 109.2 87.2 80.8 91.7 0 0 0 0 0 0 1 0 0 0 0 55
56 95.1 1 102.7 109.2 87.2 80.8 0 0 0 0 0 0 0 1 0 0 0 56
57 117.5 1 95.1 102.7 109.2 87.2 0 0 0 0 0 0 0 0 1 0 0 57
58 85.1 1 117.5 95.1 102.7 109.2 0 0 0 0 0 0 0 0 0 1 0 58
59 92.1 1 85.1 117.5 95.1 102.7 0 0 0 0 0 0 0 0 0 0 1 59
60 113.5 1 92.1 85.1 117.5 95.1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y `y(t)` `y(t-1)` `y(t-2)` `y(t-3)`
24.16984 3.12409 0.35411 0.38669 0.31630 -0.07924
M1 M2 M3 M4 M5 M6
4.29883 -22.29685 -39.56917 -35.92971 -20.33786 -6.96633
M7 M8 M9 M10 M11 t
-15.93235 -22.99397 -6.55872 -44.28328 -31.40455 -0.09838
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.2924 -3.8712 -0.7825 5.4700 15.3720
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.16984 18.63898 1.297 0.20180
Y 3.12409 3.77308 0.828 0.41235
`y(t)` 0.35411 0.15350 2.307 0.02606 *
`y(t-1)` 0.38669 0.16145 2.395 0.02116 *
`y(t-2)` 0.31630 0.15627 2.024 0.04936 *
`y(t-3)` -0.07924 0.15790 -0.502 0.61842
M1 4.29883 9.79949 0.439 0.66314
M2 -22.29685 10.74913 -2.074 0.04422 *
M3 -39.56917 7.99930 -4.947 1.27e-05 ***
M4 -35.92971 5.97498 -6.013 3.80e-07 ***
M5 -20.33786 6.11528 -3.326 0.00184 **
M6 -6.96633 6.52299 -1.068 0.29164
M7 -15.93235 7.90012 -2.017 0.05015 .
M8 -22.99397 7.83731 -2.934 0.00540 **
M9 -6.55872 6.46951 -1.014 0.31649
M10 -44.28328 7.36444 -6.013 3.80e-07 ***
M11 -31.40455 8.39478 -3.741 0.00055 ***
t -0.09838 0.06592 -1.492 0.14306
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.735 on 42 degrees of freedom
Multiple R-squared: 0.8816, Adjusted R-squared: 0.8337
F-statistic: 18.4 on 17 and 42 DF, p-value: 3.198e-14
> 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.025997349 0.051994698 0.9740027
[2,] 0.008086278 0.016172556 0.9919137
[3,] 0.001803604 0.003607208 0.9981964
[4,] 0.002275122 0.004550244 0.9977249
[5,] 0.000865485 0.001730970 0.9991345
[6,] 0.004872591 0.009745182 0.9951274
[7,] 0.114008742 0.228017484 0.8859913
[8,] 0.307805443 0.615610887 0.6921946
[9,] 0.332383313 0.664766626 0.6676167
[10,] 0.291140181 0.582280362 0.7088598
[11,] 0.593927548 0.812144904 0.4060725
[12,] 0.517518877 0.964962246 0.4824811
[13,] 0.704306281 0.591387438 0.2956937
[14,] 0.599772058 0.800455883 0.4002279
[15,] 0.512029590 0.975940819 0.4879704
[16,] 0.744961239 0.510077523 0.2550388
[17,] 0.638956287 0.722087427 0.3610437
[18,] 0.634458321 0.731083359 0.3655417
[19,] 0.527649136 0.944701729 0.4723509
> postscript(file="/var/wessaorg/rcomp/tmp/1z4lo1322332188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2txq21322332188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3253w1322332188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4a2in1322332188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rjkr1322332188.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 60
Frequency = 1
1 2 3 4 5 6
-10.3148771 6.2593538 9.3088810 0.2961017 -9.5181154 0.8638286
7 8 9 10 11 12
-10.4464181 7.2696335 -3.5189414 -10.7923562 -2.6129541 -0.5789336
13 14 15 16 17 18
1.8408807 5.7438979 2.7994220 6.5642930 -2.7286468 5.4325110
19 20 21 22 23 24
-4.1590853 7.4806111 5.5493486 -6.6927624 0.7488321 -1.3877752
25 26 27 28 29 30
13.6199147 -1.2785563 -3.3265464 5.4435071 15.3719888 -5.2707478
31 32 33 34 35 36
12.3099424 -4.8167533 -1.5374332 0.2352140 1.1362943 7.0556184
37 38 39 40 41 42
-0.7644455 -11.0571336 -8.1131642 -7.0446752 -0.8004671 -12.2923809
43 44 45 46 47 48
-3.7752220 -6.4709078 -1.8467570 11.6672498 -3.7745955 -2.0477768
49 50 51 52 53 54
-4.3814727 0.3324382 -0.6685924 -5.2592266 -2.3247595 11.2667891
55 56 57 58 59 60
6.0707830 -3.4625834 1.3537830 5.5826548 4.5024232 -3.0411327
> postscript(file="/var/wessaorg/rcomp/tmp/6cj7x1322332188.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -10.3148771 NA
1 6.2593538 -10.3148771
2 9.3088810 6.2593538
3 0.2961017 9.3088810
4 -9.5181154 0.2961017
5 0.8638286 -9.5181154
6 -10.4464181 0.8638286
7 7.2696335 -10.4464181
8 -3.5189414 7.2696335
9 -10.7923562 -3.5189414
10 -2.6129541 -10.7923562
11 -0.5789336 -2.6129541
12 1.8408807 -0.5789336
13 5.7438979 1.8408807
14 2.7994220 5.7438979
15 6.5642930 2.7994220
16 -2.7286468 6.5642930
17 5.4325110 -2.7286468
18 -4.1590853 5.4325110
19 7.4806111 -4.1590853
20 5.5493486 7.4806111
21 -6.6927624 5.5493486
22 0.7488321 -6.6927624
23 -1.3877752 0.7488321
24 13.6199147 -1.3877752
25 -1.2785563 13.6199147
26 -3.3265464 -1.2785563
27 5.4435071 -3.3265464
28 15.3719888 5.4435071
29 -5.2707478 15.3719888
30 12.3099424 -5.2707478
31 -4.8167533 12.3099424
32 -1.5374332 -4.8167533
33 0.2352140 -1.5374332
34 1.1362943 0.2352140
35 7.0556184 1.1362943
36 -0.7644455 7.0556184
37 -11.0571336 -0.7644455
38 -8.1131642 -11.0571336
39 -7.0446752 -8.1131642
40 -0.8004671 -7.0446752
41 -12.2923809 -0.8004671
42 -3.7752220 -12.2923809
43 -6.4709078 -3.7752220
44 -1.8467570 -6.4709078
45 11.6672498 -1.8467570
46 -3.7745955 11.6672498
47 -2.0477768 -3.7745955
48 -4.3814727 -2.0477768
49 0.3324382 -4.3814727
50 -0.6685924 0.3324382
51 -5.2592266 -0.6685924
52 -2.3247595 -5.2592266
53 11.2667891 -2.3247595
54 6.0707830 11.2667891
55 -3.4625834 6.0707830
56 1.3537830 -3.4625834
57 5.5826548 1.3537830
58 4.5024232 5.5826548
59 -3.0411327 4.5024232
60 NA -3.0411327
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.2593538 -10.3148771
[2,] 9.3088810 6.2593538
[3,] 0.2961017 9.3088810
[4,] -9.5181154 0.2961017
[5,] 0.8638286 -9.5181154
[6,] -10.4464181 0.8638286
[7,] 7.2696335 -10.4464181
[8,] -3.5189414 7.2696335
[9,] -10.7923562 -3.5189414
[10,] -2.6129541 -10.7923562
[11,] -0.5789336 -2.6129541
[12,] 1.8408807 -0.5789336
[13,] 5.7438979 1.8408807
[14,] 2.7994220 5.7438979
[15,] 6.5642930 2.7994220
[16,] -2.7286468 6.5642930
[17,] 5.4325110 -2.7286468
[18,] -4.1590853 5.4325110
[19,] 7.4806111 -4.1590853
[20,] 5.5493486 7.4806111
[21,] -6.6927624 5.5493486
[22,] 0.7488321 -6.6927624
[23,] -1.3877752 0.7488321
[24,] 13.6199147 -1.3877752
[25,] -1.2785563 13.6199147
[26,] -3.3265464 -1.2785563
[27,] 5.4435071 -3.3265464
[28,] 15.3719888 5.4435071
[29,] -5.2707478 15.3719888
[30,] 12.3099424 -5.2707478
[31,] -4.8167533 12.3099424
[32,] -1.5374332 -4.8167533
[33,] 0.2352140 -1.5374332
[34,] 1.1362943 0.2352140
[35,] 7.0556184 1.1362943
[36,] -0.7644455 7.0556184
[37,] -11.0571336 -0.7644455
[38,] -8.1131642 -11.0571336
[39,] -7.0446752 -8.1131642
[40,] -0.8004671 -7.0446752
[41,] -12.2923809 -0.8004671
[42,] -3.7752220 -12.2923809
[43,] -6.4709078 -3.7752220
[44,] -1.8467570 -6.4709078
[45,] 11.6672498 -1.8467570
[46,] -3.7745955 11.6672498
[47,] -2.0477768 -3.7745955
[48,] -4.3814727 -2.0477768
[49,] 0.3324382 -4.3814727
[50,] -0.6685924 0.3324382
[51,] -5.2592266 -0.6685924
[52,] -2.3247595 -5.2592266
[53,] 11.2667891 -2.3247595
[54,] 6.0707830 11.2667891
[55,] -3.4625834 6.0707830
[56,] 1.3537830 -3.4625834
[57,] 5.5826548 1.3537830
[58,] 4.5024232 5.5826548
[59,] -3.0411327 4.5024232
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.2593538 -10.3148771
2 9.3088810 6.2593538
3 0.2961017 9.3088810
4 -9.5181154 0.2961017
5 0.8638286 -9.5181154
6 -10.4464181 0.8638286
7 7.2696335 -10.4464181
8 -3.5189414 7.2696335
9 -10.7923562 -3.5189414
10 -2.6129541 -10.7923562
11 -0.5789336 -2.6129541
12 1.8408807 -0.5789336
13 5.7438979 1.8408807
14 2.7994220 5.7438979
15 6.5642930 2.7994220
16 -2.7286468 6.5642930
17 5.4325110 -2.7286468
18 -4.1590853 5.4325110
19 7.4806111 -4.1590853
20 5.5493486 7.4806111
21 -6.6927624 5.5493486
22 0.7488321 -6.6927624
23 -1.3877752 0.7488321
24 13.6199147 -1.3877752
25 -1.2785563 13.6199147
26 -3.3265464 -1.2785563
27 5.4435071 -3.3265464
28 15.3719888 5.4435071
29 -5.2707478 15.3719888
30 12.3099424 -5.2707478
31 -4.8167533 12.3099424
32 -1.5374332 -4.8167533
33 0.2352140 -1.5374332
34 1.1362943 0.2352140
35 7.0556184 1.1362943
36 -0.7644455 7.0556184
37 -11.0571336 -0.7644455
38 -8.1131642 -11.0571336
39 -7.0446752 -8.1131642
40 -0.8004671 -7.0446752
41 -12.2923809 -0.8004671
42 -3.7752220 -12.2923809
43 -6.4709078 -3.7752220
44 -1.8467570 -6.4709078
45 11.6672498 -1.8467570
46 -3.7745955 11.6672498
47 -2.0477768 -3.7745955
48 -4.3814727 -2.0477768
49 0.3324382 -4.3814727
50 -0.6685924 0.3324382
51 -5.2592266 -0.6685924
52 -2.3247595 -5.2592266
53 11.2667891 -2.3247595
54 6.0707830 11.2667891
55 -3.4625834 6.0707830
56 1.3537830 -3.4625834
57 5.5826548 1.3537830
58 4.5024232 5.5826548
59 -3.0411327 4.5024232
> 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/wessaorg/rcomp/tmp/7w59k1322332188.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/wessaorg/rcomp/tmp/846v41322332188.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/wessaorg/rcomp/tmp/9agt31322332188.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/wessaorg/rcomp/tmp/10ej6g1322332188.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11oacy1322332188.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12cum41322332188.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13d7wq1322332188.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14q3nm1322332188.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/wessaorg/rcomp/tmp/158x6d1322332188.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/wessaorg/rcomp/tmp/1632hd1322332188.tab")
+ }
>
> try(system("convert tmp/1z4lo1322332188.ps tmp/1z4lo1322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/2txq21322332188.ps tmp/2txq21322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/3253w1322332188.ps tmp/3253w1322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a2in1322332188.ps tmp/4a2in1322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rjkr1322332188.ps tmp/5rjkr1322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cj7x1322332188.ps tmp/6cj7x1322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w59k1322332188.ps tmp/7w59k1322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/846v41322332188.ps tmp/846v41322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/9agt31322332188.ps tmp/9agt31322332188.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ej6g1322332188.ps tmp/10ej6g1322332188.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.231 0.491 3.779