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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> x <- array(list(9.3,98.3,9.3,112.3,8.7,113.9,8.2,106.2,8.3,98.6,8.5,96.5,8.6,95.9,8.5,103.7,8.2,103.1,8.1,103.7,7.9,112.1,8.6,86.9,8.7,95,8.7,111.8,8.5,108.8,8.4,109.3,8.5,101.4,8.7,100.5,8.7,100.7,8.6,113.5,8.5,106.1,8.3,111.6,8,114.9,8.2,88.6,8.1,99.5,8.1,115.1,8,118,7.9,111.4,7.9,107.3,8,105.3,8,105.3,7.9,117.9,8,110.2,7.7,112.4,7.2,117.5,7.5,93,7.3,103.5,7,116.3,7,120,7,114.3,7.2,104.7,7.3,109.8,7.1,112.6,6.8,114.4,6.4,115.7,6.1,114.7,6.5,118.4,7.7,94.9,7.9,103.8,7.5,115.1,6.9,113.7,6.6,104,6.9,94.3,7.7,92.5,8,93.2,8,104.7,7.7,94,7.3,98.1,7.4,102.7,8.1,82.4),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = 'No 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 9.3 98.3 1 0 0 0 0 0 0 0 0 0 0
2 9.3 112.3 0 1 0 0 0 0 0 0 0 0 0
3 8.7 113.9 0 0 1 0 0 0 0 0 0 0 0
4 8.2 106.2 0 0 0 1 0 0 0 0 0 0 0
5 8.3 98.6 0 0 0 0 1 0 0 0 0 0 0
6 8.5 96.5 0 0 0 0 0 1 0 0 0 0 0
7 8.6 95.9 0 0 0 0 0 0 1 0 0 0 0
8 8.5 103.7 0 0 0 0 0 0 0 1 0 0 0
9 8.2 103.1 0 0 0 0 0 0 0 0 1 0 0
10 8.1 103.7 0 0 0 0 0 0 0 0 0 1 0
11 7.9 112.1 0 0 0 0 0 0 0 0 0 0 1
12 8.6 86.9 0 0 0 0 0 0 0 0 0 0 0
13 8.7 95.0 1 0 0 0 0 0 0 0 0 0 0
14 8.7 111.8 0 1 0 0 0 0 0 0 0 0 0
15 8.5 108.8 0 0 1 0 0 0 0 0 0 0 0
16 8.4 109.3 0 0 0 1 0 0 0 0 0 0 0
17 8.5 101.4 0 0 0 0 1 0 0 0 0 0 0
18 8.7 100.5 0 0 0 0 0 1 0 0 0 0 0
19 8.7 100.7 0 0 0 0 0 0 1 0 0 0 0
20 8.6 113.5 0 0 0 0 0 0 0 1 0 0 0
21 8.5 106.1 0 0 0 0 0 0 0 0 1 0 0
22 8.3 111.6 0 0 0 0 0 0 0 0 0 1 0
23 8.0 114.9 0 0 0 0 0 0 0 0 0 0 1
24 8.2 88.6 0 0 0 0 0 0 0 0 0 0 0
25 8.1 99.5 1 0 0 0 0 0 0 0 0 0 0
26 8.1 115.1 0 1 0 0 0 0 0 0 0 0 0
27 8.0 118.0 0 0 1 0 0 0 0 0 0 0 0
28 7.9 111.4 0 0 0 1 0 0 0 0 0 0 0
29 7.9 107.3 0 0 0 0 1 0 0 0 0 0 0
30 8.0 105.3 0 0 0 0 0 1 0 0 0 0 0
31 8.0 105.3 0 0 0 0 0 0 1 0 0 0 0
32 7.9 117.9 0 0 0 0 0 0 0 1 0 0 0
33 8.0 110.2 0 0 0 0 0 0 0 0 1 0 0
34 7.7 112.4 0 0 0 0 0 0 0 0 0 1 0
35 7.2 117.5 0 0 0 0 0 0 0 0 0 0 1
36 7.5 93.0 0 0 0 0 0 0 0 0 0 0 0
37 7.3 103.5 1 0 0 0 0 0 0 0 0 0 0
38 7.0 116.3 0 1 0 0 0 0 0 0 0 0 0
39 7.0 120.0 0 0 1 0 0 0 0 0 0 0 0
40 7.0 114.3 0 0 0 1 0 0 0 0 0 0 0
41 7.2 104.7 0 0 0 0 1 0 0 0 0 0 0
42 7.3 109.8 0 0 0 0 0 1 0 0 0 0 0
43 7.1 112.6 0 0 0 0 0 0 1 0 0 0 0
44 6.8 114.4 0 0 0 0 0 0 0 1 0 0 0
45 6.4 115.7 0 0 0 0 0 0 0 0 1 0 0
46 6.1 114.7 0 0 0 0 0 0 0 0 0 1 0
47 6.5 118.4 0 0 0 0 0 0 0 0 0 0 1
48 7.7 94.9 0 0 0 0 0 0 0 0 0 0 0
49 7.9 103.8 1 0 0 0 0 0 0 0 0 0 0
50 7.5 115.1 0 1 0 0 0 0 0 0 0 0 0
51 6.9 113.7 0 0 1 0 0 0 0 0 0 0 0
52 6.6 104.0 0 0 0 1 0 0 0 0 0 0 0
53 6.9 94.3 0 0 0 0 1 0 0 0 0 0 0
54 7.7 92.5 0 0 0 0 0 1 0 0 0 0 0
55 8.0 93.2 0 0 0 0 0 0 1 0 0 0 0
56 8.0 104.7 0 0 0 0 0 0 0 1 0 0 0
57 7.7 94.0 0 0 0 0 0 0 0 0 1 0 0
58 7.3 98.1 0 0 0 0 0 0 0 0 0 1 0
59 7.4 102.7 0 0 0 0 0 0 0 0 0 0 1
60 8.1 82.4 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
12.14621 -0.04628 0.74259 1.25512 0.99029 0.52002
M5 M6 M7 M8 M9 M10
0.29997 0.56424 0.63293 0.94332 0.51100 0.35652
M11
0.48884
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.2532 -0.4717 0.1584 0.4442 1.0958
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.14621 1.55747 7.799 5.1e-10 ***
X -0.04628 0.01712 -2.703 0.00954 **
M1 0.74259 0.47363 1.568 0.12362
M2 1.25512 0.61026 2.057 0.04529 *
M3 0.99029 0.61944 1.599 0.11659
M4 0.52002 0.55283 0.941 0.35169
M5 0.29997 0.48236 0.622 0.53702
M6 0.56424 0.47989 1.176 0.24561
M7 0.63293 0.48444 1.307 0.19773
M8 0.94332 0.57233 1.648 0.10598
M9 0.51100 0.52070 0.981 0.33143
M10 0.35652 0.54307 0.656 0.51471
M11 0.48884 0.59839 0.817 0.41809
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6887 on 47 degrees of freedom
Multiple R-squared: 0.2463, Adjusted R-squared: 0.0539
F-statistic: 1.28 on 12 and 47 DF, p-value: 0.2617
> 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,] 0.0874404191 0.174880838 0.91255958
[2,] 0.0326868477 0.065373695 0.96731315
[3,] 0.0119940871 0.023988174 0.98800591
[4,] 0.0052398776 0.010479755 0.99476012
[5,] 0.0036453471 0.007290694 0.99635465
[6,] 0.0018380014 0.003676003 0.99816200
[7,] 0.0010394489 0.002078898 0.99896055
[8,] 0.0004803610 0.000960722 0.99951964
[9,] 0.0005339893 0.001067979 0.99946601
[10,] 0.0111578031 0.022315606 0.98884220
[11,] 0.0417669273 0.083533855 0.95823307
[12,] 0.0598655982 0.119731196 0.94013440
[13,] 0.0742342116 0.148468423 0.92576579
[14,] 0.0924425217 0.184885043 0.90755748
[15,] 0.0872233292 0.174446658 0.91277667
[16,] 0.0770876445 0.154175289 0.92291236
[17,] 0.0764994099 0.152998820 0.92350059
[18,] 0.1290950300 0.258190060 0.87090497
[19,] 0.3332933600 0.666586720 0.66670664
[20,] 0.3859723519 0.771944704 0.61402765
[21,] 0.3685619447 0.737123889 0.63143806
[22,] 0.5386920663 0.922615867 0.46130793
[23,] 0.7205500361 0.558899928 0.27944996
[24,] 0.7089784119 0.582043176 0.29102159
[25,] 0.8181588166 0.363682367 0.18184118
[26,] 0.9158554566 0.168289087 0.08414454
[27,] 0.9292040743 0.141591851 0.07079593
[28,] 0.8797713144 0.240457371 0.12022869
[29,] 0.9562144881 0.087571024 0.04378551
> postscript(file="/var/www/html/rcomp/tmp/14o9l1258750484.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2aw7f1258750484.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32n8m1258750484.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43uyi1258750484.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5g07n1258750484.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 = 60
Frequency = 1
1 2 3 4 5 6
0.960400532 1.095772656 0.834646815 0.448568320 0.416898497 0.255447879
7 8 9 10 11 12
0.258987791 0.209569651 0.314121772 0.396373454 0.452795664 0.475410001
13 14 15 16 17 18
0.207680623 0.472633276 0.398625137 0.792032478 0.746479026 0.640562921
19 20 21 22 23 24
0.581125841 0.763101503 0.752958053 0.961975661 0.682376194 0.154083894
25 26 27 28 29 30
-0.184064955 0.025353185 0.324389733 0.389217875 0.419523713 0.162700971
31 32 33 34 35 36
0.094008139 0.266728049 0.442700971 0.398998670 0.002700971 -0.342289560
37 38 39 40 41 42
-0.798949914 -1.019112302 -0.583052747 -0.376573720 -0.400801064 -0.329044607
43 44 45 46 47 48
-0.468156910 -0.995247613 -0.902765847 -1.094560181 -0.655648145 -0.054359915
49 50 51 52 53 54
-0.185066286 -0.574646815 -0.974608937 -1.253244953 -1.182100173 -0.729667163
55 56 57 58 59 60
-0.465964862 -0.244151589 -0.607014948 -0.662787604 -0.482224684 -0.232844420
> postscript(file="/var/www/html/rcomp/tmp/66ktn1258750484.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.960400532 NA
1 1.095772656 0.960400532
2 0.834646815 1.095772656
3 0.448568320 0.834646815
4 0.416898497 0.448568320
5 0.255447879 0.416898497
6 0.258987791 0.255447879
7 0.209569651 0.258987791
8 0.314121772 0.209569651
9 0.396373454 0.314121772
10 0.452795664 0.396373454
11 0.475410001 0.452795664
12 0.207680623 0.475410001
13 0.472633276 0.207680623
14 0.398625137 0.472633276
15 0.792032478 0.398625137
16 0.746479026 0.792032478
17 0.640562921 0.746479026
18 0.581125841 0.640562921
19 0.763101503 0.581125841
20 0.752958053 0.763101503
21 0.961975661 0.752958053
22 0.682376194 0.961975661
23 0.154083894 0.682376194
24 -0.184064955 0.154083894
25 0.025353185 -0.184064955
26 0.324389733 0.025353185
27 0.389217875 0.324389733
28 0.419523713 0.389217875
29 0.162700971 0.419523713
30 0.094008139 0.162700971
31 0.266728049 0.094008139
32 0.442700971 0.266728049
33 0.398998670 0.442700971
34 0.002700971 0.398998670
35 -0.342289560 0.002700971
36 -0.798949914 -0.342289560
37 -1.019112302 -0.798949914
38 -0.583052747 -1.019112302
39 -0.376573720 -0.583052747
40 -0.400801064 -0.376573720
41 -0.329044607 -0.400801064
42 -0.468156910 -0.329044607
43 -0.995247613 -0.468156910
44 -0.902765847 -0.995247613
45 -1.094560181 -0.902765847
46 -0.655648145 -1.094560181
47 -0.054359915 -0.655648145
48 -0.185066286 -0.054359915
49 -0.574646815 -0.185066286
50 -0.974608937 -0.574646815
51 -1.253244953 -0.974608937
52 -1.182100173 -1.253244953
53 -0.729667163 -1.182100173
54 -0.465964862 -0.729667163
55 -0.244151589 -0.465964862
56 -0.607014948 -0.244151589
57 -0.662787604 -0.607014948
58 -0.482224684 -0.662787604
59 -0.232844420 -0.482224684
60 NA -0.232844420
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.095772656 0.960400532
[2,] 0.834646815 1.095772656
[3,] 0.448568320 0.834646815
[4,] 0.416898497 0.448568320
[5,] 0.255447879 0.416898497
[6,] 0.258987791 0.255447879
[7,] 0.209569651 0.258987791
[8,] 0.314121772 0.209569651
[9,] 0.396373454 0.314121772
[10,] 0.452795664 0.396373454
[11,] 0.475410001 0.452795664
[12,] 0.207680623 0.475410001
[13,] 0.472633276 0.207680623
[14,] 0.398625137 0.472633276
[15,] 0.792032478 0.398625137
[16,] 0.746479026 0.792032478
[17,] 0.640562921 0.746479026
[18,] 0.581125841 0.640562921
[19,] 0.763101503 0.581125841
[20,] 0.752958053 0.763101503
[21,] 0.961975661 0.752958053
[22,] 0.682376194 0.961975661
[23,] 0.154083894 0.682376194
[24,] -0.184064955 0.154083894
[25,] 0.025353185 -0.184064955
[26,] 0.324389733 0.025353185
[27,] 0.389217875 0.324389733
[28,] 0.419523713 0.389217875
[29,] 0.162700971 0.419523713
[30,] 0.094008139 0.162700971
[31,] 0.266728049 0.094008139
[32,] 0.442700971 0.266728049
[33,] 0.398998670 0.442700971
[34,] 0.002700971 0.398998670
[35,] -0.342289560 0.002700971
[36,] -0.798949914 -0.342289560
[37,] -1.019112302 -0.798949914
[38,] -0.583052747 -1.019112302
[39,] -0.376573720 -0.583052747
[40,] -0.400801064 -0.376573720
[41,] -0.329044607 -0.400801064
[42,] -0.468156910 -0.329044607
[43,] -0.995247613 -0.468156910
[44,] -0.902765847 -0.995247613
[45,] -1.094560181 -0.902765847
[46,] -0.655648145 -1.094560181
[47,] -0.054359915 -0.655648145
[48,] -0.185066286 -0.054359915
[49,] -0.574646815 -0.185066286
[50,] -0.974608937 -0.574646815
[51,] -1.253244953 -0.974608937
[52,] -1.182100173 -1.253244953
[53,] -0.729667163 -1.182100173
[54,] -0.465964862 -0.729667163
[55,] -0.244151589 -0.465964862
[56,] -0.607014948 -0.244151589
[57,] -0.662787604 -0.607014948
[58,] -0.482224684 -0.662787604
[59,] -0.232844420 -0.482224684
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.095772656 0.960400532
2 0.834646815 1.095772656
3 0.448568320 0.834646815
4 0.416898497 0.448568320
5 0.255447879 0.416898497
6 0.258987791 0.255447879
7 0.209569651 0.258987791
8 0.314121772 0.209569651
9 0.396373454 0.314121772
10 0.452795664 0.396373454
11 0.475410001 0.452795664
12 0.207680623 0.475410001
13 0.472633276 0.207680623
14 0.398625137 0.472633276
15 0.792032478 0.398625137
16 0.746479026 0.792032478
17 0.640562921 0.746479026
18 0.581125841 0.640562921
19 0.763101503 0.581125841
20 0.752958053 0.763101503
21 0.961975661 0.752958053
22 0.682376194 0.961975661
23 0.154083894 0.682376194
24 -0.184064955 0.154083894
25 0.025353185 -0.184064955
26 0.324389733 0.025353185
27 0.389217875 0.324389733
28 0.419523713 0.389217875
29 0.162700971 0.419523713
30 0.094008139 0.162700971
31 0.266728049 0.094008139
32 0.442700971 0.266728049
33 0.398998670 0.442700971
34 0.002700971 0.398998670
35 -0.342289560 0.002700971
36 -0.798949914 -0.342289560
37 -1.019112302 -0.798949914
38 -0.583052747 -1.019112302
39 -0.376573720 -0.583052747
40 -0.400801064 -0.376573720
41 -0.329044607 -0.400801064
42 -0.468156910 -0.329044607
43 -0.995247613 -0.468156910
44 -0.902765847 -0.995247613
45 -1.094560181 -0.902765847
46 -0.655648145 -1.094560181
47 -0.054359915 -0.655648145
48 -0.185066286 -0.054359915
49 -0.574646815 -0.185066286
50 -0.974608937 -0.574646815
51 -1.253244953 -0.974608937
52 -1.182100173 -1.253244953
53 -0.729667163 -1.182100173
54 -0.465964862 -0.729667163
55 -0.244151589 -0.465964862
56 -0.607014948 -0.244151589
57 -0.662787604 -0.607014948
58 -0.482224684 -0.662787604
59 -0.232844420 -0.482224684
> 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/7sz7v1258750484.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8o8ys1258750484.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c8t61258750484.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dx1x1258750485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zeh31258750485.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/12tnnn1258750485.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/137kpa1258750485.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/14c17p1258750485.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/157qvq1258750485.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/16rwbs1258750485.tab")
+ }
>
> system("convert tmp/14o9l1258750484.ps tmp/14o9l1258750484.png")
> system("convert tmp/2aw7f1258750484.ps tmp/2aw7f1258750484.png")
> system("convert tmp/32n8m1258750484.ps tmp/32n8m1258750484.png")
> system("convert tmp/43uyi1258750484.ps tmp/43uyi1258750484.png")
> system("convert tmp/5g07n1258750484.ps tmp/5g07n1258750484.png")
> system("convert tmp/66ktn1258750484.ps tmp/66ktn1258750484.png")
> system("convert tmp/7sz7v1258750484.ps tmp/7sz7v1258750484.png")
> system("convert tmp/8o8ys1258750484.ps tmp/8o8ys1258750484.png")
> system("convert tmp/9c8t61258750484.ps tmp/9c8t61258750484.png")
> system("convert tmp/10dx1x1258750485.ps tmp/10dx1x1258750485.png")
>
>
> proc.time()
user system elapsed
2.413 1.584 2.850