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(9,9,8.9,8.9,9,9,9,8.9,9.1,9,9,8.9,9,9,9,8.9,9.1,9,9,9,9.1,9,9,9,9,9,9,9,9,9.1,9,9,9,9,9.1,9,9,9.1,9,9,8.9,9.1,9.1,9,8.9,9,9.1,9.1,8.9,9,9,9,8.9,9,9,9.1,8.8,9,9,9.1,8.8,8.9,9,9,8.7,8.9,8.9,9,8.7,8.9,8.9,9,8.5,8.9,8.9,9,8.5,8.8,8.9,8.9,8.4,8.8,8.8,8.9,8.2,8.7,8.8,8.9,8.2,8.7,8.7,8.9,8.1,8.5,8.7,8.8,8.1,8.5,8.5,8.8,8,8.4,8.5,8.7,7.9,8.2,8.4,8.7,7.8,8.2,8.2,8.5,7.7,8.1,8.2,8.5,7.6,8.1,8.1,8.4,7.5,8,8.1,8.2,7.5,7.9,8,8.2,7.5,7.8,7.9,8.1,7.5,7.7,7.8,8.1,7.5,7.6,7.7,8,7.4,7.5,7.6,7.9,7.4,7.5,7.5,7.8,7.3,7.5,7.5,7.7,7.3,7.5,7.5,7.6,7.3,7.5,7.5,7.5,7.2,7.4,7.5,7.5,7.2,7.4,7.4,7.5,7.3,7.3,7.4,7.5,7.4,7.3,7.3,7.5,7.4,7.3,7.3,7.4,7.5,7.2,7.3,7.4,7.6,7.2,7.2,7.3,7.7,7.3,7.2,7.3,7.9,7.4,7.3,7.3,8,7.4,7.4,7.2,8.2,7.5,7.4,7.2),dim=c(4,51),dimnames=list(c('Y-1','Y-7','Y-8','Y-11'),1:51))
> y <- array(NA,dim=c(4,51),dimnames=list(c('Y-1','Y-7','Y-8','Y-11'),1:51))
> 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-1 Y-7 Y-8 Y-11 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.0 9.0 8.9 8.9 1 0 0 0 0 0 0 0 0 0 0 1
2 9.0 9.0 9.0 8.9 0 1 0 0 0 0 0 0 0 0 0 2
3 9.1 9.0 9.0 8.9 0 0 1 0 0 0 0 0 0 0 0 3
4 9.0 9.0 9.0 8.9 0 0 0 1 0 0 0 0 0 0 0 4
5 9.1 9.0 9.0 9.0 0 0 0 0 1 0 0 0 0 0 0 5
6 9.1 9.0 9.0 9.0 0 0 0 0 0 1 0 0 0 0 0 6
7 9.0 9.0 9.0 9.0 0 0 0 0 0 0 1 0 0 0 0 7
8 9.0 9.1 9.0 9.0 0 0 0 0 0 0 0 1 0 0 0 8
9 9.0 9.0 9.1 9.0 0 0 0 0 0 0 0 0 1 0 0 9
10 9.0 9.1 9.0 9.0 0 0 0 0 0 0 0 0 0 1 0 10
11 8.9 9.1 9.1 9.0 0 0 0 0 0 0 0 0 0 0 1 11
12 8.9 9.0 9.1 9.1 0 0 0 0 0 0 0 0 0 0 0 12
13 8.9 9.0 9.0 9.0 1 0 0 0 0 0 0 0 0 0 0 13
14 8.9 9.0 9.0 9.1 0 1 0 0 0 0 0 0 0 0 0 14
15 8.8 9.0 9.0 9.1 0 0 1 0 0 0 0 0 0 0 0 15
16 8.8 8.9 9.0 9.0 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 8.9 8.9 9.0 0 0 0 0 1 0 0 0 0 0 0 17
18 8.7 8.9 8.9 9.0 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 8.9 8.9 9.0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 8.8 8.9 8.9 0 0 0 0 0 0 0 1 0 0 0 20
21 8.4 8.8 8.8 8.9 0 0 0 0 0 0 0 0 1 0 0 21
22 8.2 8.7 8.8 8.9 0 0 0 0 0 0 0 0 0 1 0 22
23 8.2 8.7 8.7 8.9 0 0 0 0 0 0 0 0 0 0 1 23
24 8.1 8.5 8.7 8.8 0 0 0 0 0 0 0 0 0 0 0 24
25 8.1 8.5 8.5 8.8 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 8.4 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 8.2 8.4 8.7 0 0 1 0 0 0 0 0 0 0 0 27
28 7.8 8.2 8.2 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 7.7 8.1 8.2 8.5 0 0 0 0 1 0 0 0 0 0 0 29
30 7.6 8.1 8.1 8.4 0 0 0 0 0 1 0 0 0 0 0 30
31 7.5 8.0 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 31
32 7.5 7.9 8.0 8.2 0 0 0 0 0 0 0 1 0 0 0 32
33 7.5 7.8 7.9 8.1 0 0 0 0 0 0 0 0 1 0 0 33
34 7.5 7.7 7.8 8.1 0 0 0 0 0 0 0 0 0 1 0 34
35 7.5 7.6 7.7 8.0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.4 7.5 7.6 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 7.4 7.5 7.5 7.8 1 0 0 0 0 0 0 0 0 0 0 37
38 7.3 7.5 7.5 7.7 0 1 0 0 0 0 0 0 0 0 0 38
39 7.3 7.5 7.5 7.6 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 7.5 7.5 7.5 0 0 0 1 0 0 0 0 0 0 0 40
41 7.2 7.4 7.5 7.5 0 0 0 0 1 0 0 0 0 0 0 41
42 7.2 7.4 7.4 7.5 0 0 0 0 0 1 0 0 0 0 0 42
43 7.3 7.3 7.4 7.5 0 0 0 0 0 0 1 0 0 0 0 43
44 7.4 7.3 7.3 7.5 0 0 0 0 0 0 0 1 0 0 0 44
45 7.4 7.3 7.3 7.4 0 0 0 0 0 0 0 0 1 0 0 45
46 7.5 7.2 7.3 7.4 0 0 0 0 0 0 0 0 0 1 0 46
47 7.6 7.2 7.2 7.3 0 0 0 0 0 0 0 0 0 0 1 47
48 7.7 7.3 7.2 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.9 7.4 7.3 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 8.0 7.4 7.4 7.2 0 1 0 0 0 0 0 0 0 0 0 50
51 8.2 7.5 7.4 7.2 0 0 1 0 0 0 0 0 0 0 0 51
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Y-7` `Y-8` `Y-11` M1 M2
3.068266 1.725961 0.265700 -1.324952 -0.060428 -0.103472
M3 M4 M5 M6 M7 M8
-0.064075 -0.217517 -0.135390 -0.174166 -0.223052 -0.168679
M9 M10 M11 t
-0.160924 -0.080278 -0.084029 -0.006063
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.33557 -0.10488 0.01365 0.12702 0.24189
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.068266 1.229860 2.495 0.017471 *
`Y-7` 1.725961 0.449823 3.837 0.000499 ***
`Y-8` 0.265700 0.570509 0.466 0.644298
`Y-11` -1.324952 0.230478 -5.749 1.66e-06 ***
M1 -0.060428 0.135006 -0.448 0.657203
M2 -0.103472 0.127033 -0.815 0.420849
M3 -0.064075 0.126967 -0.505 0.616960
M4 -0.217517 0.133917 -1.624 0.113291
M5 -0.135390 0.132595 -1.021 0.314224
M6 -0.174166 0.136201 -1.279 0.209404
M7 -0.223052 0.132209 -1.687 0.100474
M8 -0.168679 0.133242 -1.266 0.213887
M9 -0.160924 0.132056 -1.219 0.231144
M10 -0.080278 0.132017 -0.608 0.547056
M11 -0.084029 0.133353 -0.630 0.532706
t -0.006063 0.006136 -0.988 0.329914
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1859 on 35 degrees of freedom
Multiple R-squared: 0.9464, Adjusted R-squared: 0.9234
F-statistic: 41.18 on 15 and 35 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,] 0.2192500 0.43850002 0.78074999
[2,] 0.3020820 0.60416406 0.69791797
[3,] 0.7474850 0.50502997 0.25251498
[4,] 0.8595956 0.28080883 0.14040442
[5,] 0.9091513 0.18169738 0.09084869
[6,] 0.8903220 0.21935591 0.10967796
[7,] 0.8897617 0.22047662 0.11023831
[8,] 0.8896655 0.22066905 0.11033453
[9,] 0.9579360 0.08412794 0.04206397
[10,] 0.9374424 0.12511511 0.06255755
[11,] 0.8825629 0.23487420 0.11743710
[12,] 0.8012559 0.39748818 0.19874409
[13,] 0.7208133 0.55837350 0.27918675
[14,] 0.6420291 0.71594190 0.35797095
> postscript(file="/var/www/html/rcomp/tmp/1kp8c1258711168.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qp791258711168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qwet1258711168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zjwo1258711168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lpha1258711168.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 = 51
Frequency = 1
1 2 3 4 5 6
-0.10808689 -0.08555005 -0.01888424 0.04062019 0.19705100 0.24188979
7 8 9 10 11 12
0.19683931 -0.02406704 0.12026608 -0.10034308 -0.21709954 0.01002593
13 14 15 16 17 18
-0.02940840 0.15219361 0.01885941 0.21846480 0.06897046 0.11380925
19 20 21 22 23 24
-0.03124123 -0.03945052 -0.11457349 -0.21656043 -0.18017688 -0.14544561
25 26 27 28 29 30
-0.02581477 -0.03660697 0.20182107 0.04947520 0.04600697 -0.11507940
31 32 33 34 35 36
-0.25252408 -0.10166820 -0.03669022 0.08789284 0.16437735 0.05308252
37 38 39 40 41 42
0.01364819 -0.16974012 -0.33556948 -0.30856020 -0.31202843 -0.24061964
43 44 45 46 47 48
0.08692600 0.16518576 0.03099762 0.22901068 0.23289907 0.08233716
49 50 51
0.14966186 0.13970354 0.13377324
> postscript(file="/var/www/html/rcomp/tmp/6nzzs1258711168.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 = 51
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.10808689 NA
1 -0.08555005 -0.10808689
2 -0.01888424 -0.08555005
3 0.04062019 -0.01888424
4 0.19705100 0.04062019
5 0.24188979 0.19705100
6 0.19683931 0.24188979
7 -0.02406704 0.19683931
8 0.12026608 -0.02406704
9 -0.10034308 0.12026608
10 -0.21709954 -0.10034308
11 0.01002593 -0.21709954
12 -0.02940840 0.01002593
13 0.15219361 -0.02940840
14 0.01885941 0.15219361
15 0.21846480 0.01885941
16 0.06897046 0.21846480
17 0.11380925 0.06897046
18 -0.03124123 0.11380925
19 -0.03945052 -0.03124123
20 -0.11457349 -0.03945052
21 -0.21656043 -0.11457349
22 -0.18017688 -0.21656043
23 -0.14544561 -0.18017688
24 -0.02581477 -0.14544561
25 -0.03660697 -0.02581477
26 0.20182107 -0.03660697
27 0.04947520 0.20182107
28 0.04600697 0.04947520
29 -0.11507940 0.04600697
30 -0.25252408 -0.11507940
31 -0.10166820 -0.25252408
32 -0.03669022 -0.10166820
33 0.08789284 -0.03669022
34 0.16437735 0.08789284
35 0.05308252 0.16437735
36 0.01364819 0.05308252
37 -0.16974012 0.01364819
38 -0.33556948 -0.16974012
39 -0.30856020 -0.33556948
40 -0.31202843 -0.30856020
41 -0.24061964 -0.31202843
42 0.08692600 -0.24061964
43 0.16518576 0.08692600
44 0.03099762 0.16518576
45 0.22901068 0.03099762
46 0.23289907 0.22901068
47 0.08233716 0.23289907
48 0.14966186 0.08233716
49 0.13970354 0.14966186
50 0.13377324 0.13970354
51 NA 0.13377324
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.08555005 -0.10808689
[2,] -0.01888424 -0.08555005
[3,] 0.04062019 -0.01888424
[4,] 0.19705100 0.04062019
[5,] 0.24188979 0.19705100
[6,] 0.19683931 0.24188979
[7,] -0.02406704 0.19683931
[8,] 0.12026608 -0.02406704
[9,] -0.10034308 0.12026608
[10,] -0.21709954 -0.10034308
[11,] 0.01002593 -0.21709954
[12,] -0.02940840 0.01002593
[13,] 0.15219361 -0.02940840
[14,] 0.01885941 0.15219361
[15,] 0.21846480 0.01885941
[16,] 0.06897046 0.21846480
[17,] 0.11380925 0.06897046
[18,] -0.03124123 0.11380925
[19,] -0.03945052 -0.03124123
[20,] -0.11457349 -0.03945052
[21,] -0.21656043 -0.11457349
[22,] -0.18017688 -0.21656043
[23,] -0.14544561 -0.18017688
[24,] -0.02581477 -0.14544561
[25,] -0.03660697 -0.02581477
[26,] 0.20182107 -0.03660697
[27,] 0.04947520 0.20182107
[28,] 0.04600697 0.04947520
[29,] -0.11507940 0.04600697
[30,] -0.25252408 -0.11507940
[31,] -0.10166820 -0.25252408
[32,] -0.03669022 -0.10166820
[33,] 0.08789284 -0.03669022
[34,] 0.16437735 0.08789284
[35,] 0.05308252 0.16437735
[36,] 0.01364819 0.05308252
[37,] -0.16974012 0.01364819
[38,] -0.33556948 -0.16974012
[39,] -0.30856020 -0.33556948
[40,] -0.31202843 -0.30856020
[41,] -0.24061964 -0.31202843
[42,] 0.08692600 -0.24061964
[43,] 0.16518576 0.08692600
[44,] 0.03099762 0.16518576
[45,] 0.22901068 0.03099762
[46,] 0.23289907 0.22901068
[47,] 0.08233716 0.23289907
[48,] 0.14966186 0.08233716
[49,] 0.13970354 0.14966186
[50,] 0.13377324 0.13970354
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.08555005 -0.10808689
2 -0.01888424 -0.08555005
3 0.04062019 -0.01888424
4 0.19705100 0.04062019
5 0.24188979 0.19705100
6 0.19683931 0.24188979
7 -0.02406704 0.19683931
8 0.12026608 -0.02406704
9 -0.10034308 0.12026608
10 -0.21709954 -0.10034308
11 0.01002593 -0.21709954
12 -0.02940840 0.01002593
13 0.15219361 -0.02940840
14 0.01885941 0.15219361
15 0.21846480 0.01885941
16 0.06897046 0.21846480
17 0.11380925 0.06897046
18 -0.03124123 0.11380925
19 -0.03945052 -0.03124123
20 -0.11457349 -0.03945052
21 -0.21656043 -0.11457349
22 -0.18017688 -0.21656043
23 -0.14544561 -0.18017688
24 -0.02581477 -0.14544561
25 -0.03660697 -0.02581477
26 0.20182107 -0.03660697
27 0.04947520 0.20182107
28 0.04600697 0.04947520
29 -0.11507940 0.04600697
30 -0.25252408 -0.11507940
31 -0.10166820 -0.25252408
32 -0.03669022 -0.10166820
33 0.08789284 -0.03669022
34 0.16437735 0.08789284
35 0.05308252 0.16437735
36 0.01364819 0.05308252
37 -0.16974012 0.01364819
38 -0.33556948 -0.16974012
39 -0.30856020 -0.33556948
40 -0.31202843 -0.30856020
41 -0.24061964 -0.31202843
42 0.08692600 -0.24061964
43 0.16518576 0.08692600
44 0.03099762 0.16518576
45 0.22901068 0.03099762
46 0.23289907 0.22901068
47 0.08233716 0.23289907
48 0.14966186 0.08233716
49 0.13970354 0.14966186
50 0.13377324 0.13970354
> 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/7vfmw1258711168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sqwm1258711168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9i2x41258711168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10f0a41258711168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h5n01258711168.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/12wk831258711168.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/1357c11258711168.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/14ciir1258711168.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/15nf8k1258711168.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/16wjs71258711168.tab")
+ }
>
> system("convert tmp/1kp8c1258711168.ps tmp/1kp8c1258711168.png")
> system("convert tmp/2qp791258711168.ps tmp/2qp791258711168.png")
> system("convert tmp/3qwet1258711168.ps tmp/3qwet1258711168.png")
> system("convert tmp/4zjwo1258711168.ps tmp/4zjwo1258711168.png")
> system("convert tmp/5lpha1258711168.ps tmp/5lpha1258711168.png")
> system("convert tmp/6nzzs1258711168.ps tmp/6nzzs1258711168.png")
> system("convert tmp/7vfmw1258711168.ps tmp/7vfmw1258711168.png")
> system("convert tmp/8sqwm1258711168.ps tmp/8sqwm1258711168.png")
> system("convert tmp/9i2x41258711168.ps tmp/9i2x41258711168.png")
> system("convert tmp/10f0a41258711168.ps tmp/10f0a41258711168.png")
>
>
> proc.time()
user system elapsed
2.246 1.546 2.761