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
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(103.52,0,103.5,0,103.52,0,103.53,0,103.53,0,103.53,0,103.52,0,103.54,0,103.59,0,103.59,0,103.59,0,103.59,0,103.63,0,103.74,0,103.7,0,103.72,0,103.81,0,103.8,0,104.22,0,106.91,1,107.06,1,107.17,1,107.25,1,107.28,1,107.24,1,107.23,1,107.34,1,107.34,1,107.3,1,107.24,1,107.3,1,107.32,1,107.28,1,107.33,1,107.33,1,107.33,1,107.28,1,107.28,1,107.29,1,107.29,1,107.23,1,107.24,1,107.24,1,107.2,1,107.23,1,107.2,1,107.21,1,107.24,1,107.21,1,113.89,1,114.05,1,114.05,1,114.05,1,114.05,1,115.12,1,115.68,1,116.05,1,116.18,1,116.35,1,116.44,1,117,1,117.61,1,118.17,1,118.33,1,118.33,1,118.42,1,118.5,1,118.67,1,119.09,1,119.14,1,119.23,1,119.33,1),dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 103.52 0 1 0 0 0 0 0 0 0 0 0 0 1
2 103.50 0 0 1 0 0 0 0 0 0 0 0 0 2
3 103.52 0 0 0 1 0 0 0 0 0 0 0 0 3
4 103.53 0 0 0 0 1 0 0 0 0 0 0 0 4
5 103.53 0 0 0 0 0 1 0 0 0 0 0 0 5
6 103.53 0 0 0 0 0 0 1 0 0 0 0 0 6
7 103.52 0 0 0 0 0 0 0 1 0 0 0 0 7
8 103.54 0 0 0 0 0 0 0 0 1 0 0 0 8
9 103.59 0 0 0 0 0 0 0 0 0 1 0 0 9
10 103.59 0 0 0 0 0 0 0 0 0 0 1 0 10
11 103.59 0 0 0 0 0 0 0 0 0 0 0 1 11
12 103.59 0 0 0 0 0 0 0 0 0 0 0 0 12
13 103.63 0 1 0 0 0 0 0 0 0 0 0 0 13
14 103.74 0 0 1 0 0 0 0 0 0 0 0 0 14
15 103.70 0 0 0 1 0 0 0 0 0 0 0 0 15
16 103.72 0 0 0 0 1 0 0 0 0 0 0 0 16
17 103.81 0 0 0 0 0 1 0 0 0 0 0 0 17
18 103.80 0 0 0 0 0 0 1 0 0 0 0 0 18
19 104.22 0 0 0 0 0 0 0 1 0 0 0 0 19
20 106.91 1 0 0 0 0 0 0 0 1 0 0 0 20
21 107.06 1 0 0 0 0 0 0 0 0 1 0 0 21
22 107.17 1 0 0 0 0 0 0 0 0 0 1 0 22
23 107.25 1 0 0 0 0 0 0 0 0 0 0 1 23
24 107.28 1 0 0 0 0 0 0 0 0 0 0 0 24
25 107.24 1 1 0 0 0 0 0 0 0 0 0 0 25
26 107.23 1 0 1 0 0 0 0 0 0 0 0 0 26
27 107.34 1 0 0 1 0 0 0 0 0 0 0 0 27
28 107.34 1 0 0 0 1 0 0 0 0 0 0 0 28
29 107.30 1 0 0 0 0 1 0 0 0 0 0 0 29
30 107.24 1 0 0 0 0 0 1 0 0 0 0 0 30
31 107.30 1 0 0 0 0 0 0 1 0 0 0 0 31
32 107.32 1 0 0 0 0 0 0 0 1 0 0 0 32
33 107.28 1 0 0 0 0 0 0 0 0 1 0 0 33
34 107.33 1 0 0 0 0 0 0 0 0 0 1 0 34
35 107.33 1 0 0 0 0 0 0 0 0 0 0 1 35
36 107.33 1 0 0 0 0 0 0 0 0 0 0 0 36
37 107.28 1 1 0 0 0 0 0 0 0 0 0 0 37
38 107.28 1 0 1 0 0 0 0 0 0 0 0 0 38
39 107.29 1 0 0 1 0 0 0 0 0 0 0 0 39
40 107.29 1 0 0 0 1 0 0 0 0 0 0 0 40
41 107.23 1 0 0 0 0 1 0 0 0 0 0 0 41
42 107.24 1 0 0 0 0 0 1 0 0 0 0 0 42
43 107.24 1 0 0 0 0 0 0 1 0 0 0 0 43
44 107.20 1 0 0 0 0 0 0 0 1 0 0 0 44
45 107.23 1 0 0 0 0 0 0 0 0 1 0 0 45
46 107.20 1 0 0 0 0 0 0 0 0 0 1 0 46
47 107.21 1 0 0 0 0 0 0 0 0 0 0 1 47
48 107.24 1 0 0 0 0 0 0 0 0 0 0 0 48
49 107.21 1 1 0 0 0 0 0 0 0 0 0 0 49
50 113.89 1 0 1 0 0 0 0 0 0 0 0 0 50
51 114.05 1 0 0 1 0 0 0 0 0 0 0 0 51
52 114.05 1 0 0 0 1 0 0 0 0 0 0 0 52
53 114.05 1 0 0 0 0 1 0 0 0 0 0 0 53
54 114.05 1 0 0 0 0 0 1 0 0 0 0 0 54
55 115.12 1 0 0 0 0 0 0 1 0 0 0 0 55
56 115.68 1 0 0 0 0 0 0 0 1 0 0 0 56
57 116.05 1 0 0 0 0 0 0 0 0 1 0 0 57
58 116.18 1 0 0 0 0 0 0 0 0 0 1 0 58
59 116.35 1 0 0 0 0 0 0 0 0 0 0 1 59
60 116.44 1 0 0 0 0 0 0 0 0 0 0 0 60
61 117.00 1 1 0 0 0 0 0 0 0 0 0 0 61
62 117.61 1 0 1 0 0 0 0 0 0 0 0 0 62
63 118.17 1 0 0 1 0 0 0 0 0 0 0 0 63
64 118.33 1 0 0 0 1 0 0 0 0 0 0 0 64
65 118.33 1 0 0 0 0 1 0 0 0 0 0 0 65
66 118.42 1 0 0 0 0 0 1 0 0 0 0 0 66
67 118.50 1 0 0 0 0 0 0 1 0 0 0 0 67
68 118.67 1 0 0 0 0 0 0 0 1 0 0 0 68
69 119.09 1 0 0 0 0 0 0 0 0 1 0 0 69
70 119.14 1 0 0 0 0 0 0 0 0 0 1 0 70
71 119.23 1 0 0 0 0 0 0 0 0 0 0 1 71
72 119.33 1 0 0 0 0 0 0 0 0 0 0 0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
100.3352 -2.3271 0.1491 1.0964 0.9519 0.7025
M5 M6 M7 M8 M9 M10
0.4198 0.1437 0.1326 0.8094 0.6916 0.4622
M11 t
0.2394 0.2811
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.7206 -1.3552 0.7278 1.3938 2.7546
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.33520 1.03062 97.355 <2e-16 ***
X -2.32705 0.90212 -2.580 0.0124 *
M1 0.14912 1.24979 0.119 0.9054
M2 1.09637 1.24848 0.878 0.3835
M3 0.95195 1.24746 0.763 0.4485
M4 0.70252 1.24673 0.563 0.5753
M5 0.41977 1.24629 0.337 0.7375
M6 0.14368 1.24615 0.115 0.9086
M7 0.13260 1.24629 0.106 0.9156
M8 0.80935 1.24466 0.650 0.5181
M9 0.69160 1.24363 0.556 0.5803
M10 0.46218 1.24290 0.372 0.7114
M11 0.23942 1.24247 0.193 0.8479
t 0.28109 0.01906 14.747 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.152 on 58 degrees of freedom
Multiple R-squared: 0.8741, Adjusted R-squared: 0.8459
F-statistic: 30.97 on 13 and 58 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,] 1.549551e-05 3.099102e-05 9.999845e-01
[2,] 4.276739e-07 8.553477e-07 9.999996e-01
[3,] 2.943408e-06 5.886817e-06 9.999971e-01
[4,] 1.789544e-07 3.579087e-07 9.999998e-01
[5,] 1.157896e-08 2.315791e-08 1.000000e+00
[6,] 9.598692e-10 1.919738e-09 1.000000e+00
[7,] 1.042059e-10 2.084119e-10 1.000000e+00
[8,] 1.286381e-11 2.572761e-11 1.000000e+00
[9,] 1.312427e-12 2.624854e-12 1.000000e+00
[10,] 9.787482e-14 1.957496e-13 1.000000e+00
[11,] 7.033015e-15 1.406603e-14 1.000000e+00
[12,] 5.002421e-16 1.000484e-15 1.000000e+00
[13,] 4.053958e-17 8.107916e-17 1.000000e+00
[14,] 4.525018e-18 9.050036e-18 1.000000e+00
[15,] 1.909040e-18 3.818081e-18 1.000000e+00
[16,] 2.199609e-19 4.399218e-19 1.000000e+00
[17,] 2.765678e-20 5.531357e-20 1.000000e+00
[18,] 4.575964e-21 9.151929e-21 1.000000e+00
[19,] 1.338917e-21 2.677835e-21 1.000000e+00
[20,] 8.239918e-22 1.647984e-21 1.000000e+00
[21,] 9.596463e-22 1.919293e-21 1.000000e+00
[22,] 1.658645e-22 3.317290e-22 1.000000e+00
[23,] 2.703040e-23 5.406079e-23 1.000000e+00
[24,] 3.907073e-24 7.814146e-24 1.000000e+00
[25,] 1.065470e-24 2.130940e-24 1.000000e+00
[26,] 1.539726e-25 3.079453e-25 1.000000e+00
[27,] 1.433117e-25 2.866233e-25 1.000000e+00
[28,] 2.061712e-26 4.123424e-26 1.000000e+00
[29,] 5.379407e-27 1.075881e-26 1.000000e+00
[30,] 4.792894e-27 9.585787e-27 1.000000e+00
[31,] 1.934967e-26 3.869935e-26 1.000000e+00
[32,] 3.331309e-24 6.662619e-24 1.000000e+00
[33,] 1.993617e-09 3.987234e-09 1.000000e+00
[34,] 9.686142e-01 6.277170e-02 3.138585e-02
[35,] 9.915089e-01 1.698216e-02 8.491079e-03
[36,] 9.949997e-01 1.000062e-02 5.000308e-03
[37,] 9.965051e-01 6.989834e-03 3.494917e-03
[38,] 9.998838e-01 2.323197e-04 1.161599e-04
[39,] 9.999604e-01 7.912178e-05 3.956089e-05
> postscript(file="/var/www/html/rcomp/tmp/1h0kf1259058875.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hkoe1259058875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39i1v1259058875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4y3kc1259058875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ce0m1259058875.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 = 72
Frequency = 1
1 2 3 4 5 6
2.75459127 1.50625793 1.38959127 1.36792460 1.36959127 1.36459127
7 8 9 10 11 12
1.08459127 0.14674897 0.03341564 -0.01825103 -0.07658436 -0.11825103
13 14 15 16 17 18
-0.50845958 -1.62679291 -1.80345958 -1.81512625 -1.72345958 -1.73845958
19 20 21 22 23 24
-1.58845958 2.47075190 2.45741857 2.51575190 2.53741857 2.52575190
25 26 27 28 29 30
2.05554335 0.81721002 0.79054335 0.75887668 0.72054335 0.65554335
31 32 33 34 35 36
0.44554335 -0.49229895 -0.69563228 -0.69729895 -0.75563228 -0.79729895
37 38 39 40 41 42
-1.27750750 -2.50584083 -2.63250750 -2.66417416 -2.72250750 -2.71750750
43 44 45 46 47 48
-2.98750750 -3.98534979 -4.11868313 -4.20034979 -4.24868313 -4.26034979
49 50 51 52 53 54
-4.72055835 0.73110832 0.75444165 0.72277499 0.72444165 0.71944165
55 56 57 58 59 60
1.51944165 1.12159936 1.32826602 1.40659936 1.51826602 1.56659936
61 62 63 64 65 66
1.69639081 1.07805747 1.50139081 1.62972414 1.63139081 1.71639081
67 68 69 70 71 72
1.52639081 0.73854851 0.99521518 0.99354851 1.02521518 1.08354851
> postscript(file="/var/www/html/rcomp/tmp/6e3ke1259058875.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 2.75459127 NA
1 1.50625793 2.75459127
2 1.38959127 1.50625793
3 1.36792460 1.38959127
4 1.36959127 1.36792460
5 1.36459127 1.36959127
6 1.08459127 1.36459127
7 0.14674897 1.08459127
8 0.03341564 0.14674897
9 -0.01825103 0.03341564
10 -0.07658436 -0.01825103
11 -0.11825103 -0.07658436
12 -0.50845958 -0.11825103
13 -1.62679291 -0.50845958
14 -1.80345958 -1.62679291
15 -1.81512625 -1.80345958
16 -1.72345958 -1.81512625
17 -1.73845958 -1.72345958
18 -1.58845958 -1.73845958
19 2.47075190 -1.58845958
20 2.45741857 2.47075190
21 2.51575190 2.45741857
22 2.53741857 2.51575190
23 2.52575190 2.53741857
24 2.05554335 2.52575190
25 0.81721002 2.05554335
26 0.79054335 0.81721002
27 0.75887668 0.79054335
28 0.72054335 0.75887668
29 0.65554335 0.72054335
30 0.44554335 0.65554335
31 -0.49229895 0.44554335
32 -0.69563228 -0.49229895
33 -0.69729895 -0.69563228
34 -0.75563228 -0.69729895
35 -0.79729895 -0.75563228
36 -1.27750750 -0.79729895
37 -2.50584083 -1.27750750
38 -2.63250750 -2.50584083
39 -2.66417416 -2.63250750
40 -2.72250750 -2.66417416
41 -2.71750750 -2.72250750
42 -2.98750750 -2.71750750
43 -3.98534979 -2.98750750
44 -4.11868313 -3.98534979
45 -4.20034979 -4.11868313
46 -4.24868313 -4.20034979
47 -4.26034979 -4.24868313
48 -4.72055835 -4.26034979
49 0.73110832 -4.72055835
50 0.75444165 0.73110832
51 0.72277499 0.75444165
52 0.72444165 0.72277499
53 0.71944165 0.72444165
54 1.51944165 0.71944165
55 1.12159936 1.51944165
56 1.32826602 1.12159936
57 1.40659936 1.32826602
58 1.51826602 1.40659936
59 1.56659936 1.51826602
60 1.69639081 1.56659936
61 1.07805747 1.69639081
62 1.50139081 1.07805747
63 1.62972414 1.50139081
64 1.63139081 1.62972414
65 1.71639081 1.63139081
66 1.52639081 1.71639081
67 0.73854851 1.52639081
68 0.99521518 0.73854851
69 0.99354851 0.99521518
70 1.02521518 0.99354851
71 1.08354851 1.02521518
72 NA 1.08354851
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.50625793 2.75459127
[2,] 1.38959127 1.50625793
[3,] 1.36792460 1.38959127
[4,] 1.36959127 1.36792460
[5,] 1.36459127 1.36959127
[6,] 1.08459127 1.36459127
[7,] 0.14674897 1.08459127
[8,] 0.03341564 0.14674897
[9,] -0.01825103 0.03341564
[10,] -0.07658436 -0.01825103
[11,] -0.11825103 -0.07658436
[12,] -0.50845958 -0.11825103
[13,] -1.62679291 -0.50845958
[14,] -1.80345958 -1.62679291
[15,] -1.81512625 -1.80345958
[16,] -1.72345958 -1.81512625
[17,] -1.73845958 -1.72345958
[18,] -1.58845958 -1.73845958
[19,] 2.47075190 -1.58845958
[20,] 2.45741857 2.47075190
[21,] 2.51575190 2.45741857
[22,] 2.53741857 2.51575190
[23,] 2.52575190 2.53741857
[24,] 2.05554335 2.52575190
[25,] 0.81721002 2.05554335
[26,] 0.79054335 0.81721002
[27,] 0.75887668 0.79054335
[28,] 0.72054335 0.75887668
[29,] 0.65554335 0.72054335
[30,] 0.44554335 0.65554335
[31,] -0.49229895 0.44554335
[32,] -0.69563228 -0.49229895
[33,] -0.69729895 -0.69563228
[34,] -0.75563228 -0.69729895
[35,] -0.79729895 -0.75563228
[36,] -1.27750750 -0.79729895
[37,] -2.50584083 -1.27750750
[38,] -2.63250750 -2.50584083
[39,] -2.66417416 -2.63250750
[40,] -2.72250750 -2.66417416
[41,] -2.71750750 -2.72250750
[42,] -2.98750750 -2.71750750
[43,] -3.98534979 -2.98750750
[44,] -4.11868313 -3.98534979
[45,] -4.20034979 -4.11868313
[46,] -4.24868313 -4.20034979
[47,] -4.26034979 -4.24868313
[48,] -4.72055835 -4.26034979
[49,] 0.73110832 -4.72055835
[50,] 0.75444165 0.73110832
[51,] 0.72277499 0.75444165
[52,] 0.72444165 0.72277499
[53,] 0.71944165 0.72444165
[54,] 1.51944165 0.71944165
[55,] 1.12159936 1.51944165
[56,] 1.32826602 1.12159936
[57,] 1.40659936 1.32826602
[58,] 1.51826602 1.40659936
[59,] 1.56659936 1.51826602
[60,] 1.69639081 1.56659936
[61,] 1.07805747 1.69639081
[62,] 1.50139081 1.07805747
[63,] 1.62972414 1.50139081
[64,] 1.63139081 1.62972414
[65,] 1.71639081 1.63139081
[66,] 1.52639081 1.71639081
[67,] 0.73854851 1.52639081
[68,] 0.99521518 0.73854851
[69,] 0.99354851 0.99521518
[70,] 1.02521518 0.99354851
[71,] 1.08354851 1.02521518
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.50625793 2.75459127
2 1.38959127 1.50625793
3 1.36792460 1.38959127
4 1.36959127 1.36792460
5 1.36459127 1.36959127
6 1.08459127 1.36459127
7 0.14674897 1.08459127
8 0.03341564 0.14674897
9 -0.01825103 0.03341564
10 -0.07658436 -0.01825103
11 -0.11825103 -0.07658436
12 -0.50845958 -0.11825103
13 -1.62679291 -0.50845958
14 -1.80345958 -1.62679291
15 -1.81512625 -1.80345958
16 -1.72345958 -1.81512625
17 -1.73845958 -1.72345958
18 -1.58845958 -1.73845958
19 2.47075190 -1.58845958
20 2.45741857 2.47075190
21 2.51575190 2.45741857
22 2.53741857 2.51575190
23 2.52575190 2.53741857
24 2.05554335 2.52575190
25 0.81721002 2.05554335
26 0.79054335 0.81721002
27 0.75887668 0.79054335
28 0.72054335 0.75887668
29 0.65554335 0.72054335
30 0.44554335 0.65554335
31 -0.49229895 0.44554335
32 -0.69563228 -0.49229895
33 -0.69729895 -0.69563228
34 -0.75563228 -0.69729895
35 -0.79729895 -0.75563228
36 -1.27750750 -0.79729895
37 -2.50584083 -1.27750750
38 -2.63250750 -2.50584083
39 -2.66417416 -2.63250750
40 -2.72250750 -2.66417416
41 -2.71750750 -2.72250750
42 -2.98750750 -2.71750750
43 -3.98534979 -2.98750750
44 -4.11868313 -3.98534979
45 -4.20034979 -4.11868313
46 -4.24868313 -4.20034979
47 -4.26034979 -4.24868313
48 -4.72055835 -4.26034979
49 0.73110832 -4.72055835
50 0.75444165 0.73110832
51 0.72277499 0.75444165
52 0.72444165 0.72277499
53 0.71944165 0.72444165
54 1.51944165 0.71944165
55 1.12159936 1.51944165
56 1.32826602 1.12159936
57 1.40659936 1.32826602
58 1.51826602 1.40659936
59 1.56659936 1.51826602
60 1.69639081 1.56659936
61 1.07805747 1.69639081
62 1.50139081 1.07805747
63 1.62972414 1.50139081
64 1.63139081 1.62972414
65 1.71639081 1.63139081
66 1.52639081 1.71639081
67 0.73854851 1.52639081
68 0.99521518 0.73854851
69 0.99354851 0.99521518
70 1.02521518 0.99354851
71 1.08354851 1.02521518
> 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/7hx9c1259058875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jsoy1259058875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ldbc1259058875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108gyf1259058875.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11anqr1259058875.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/12oull1259058875.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/13bsqm1259058876.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/14n8b61259058876.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/15ghvf1259058876.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/16ib3h1259058876.tab")
+ }
>
> system("convert tmp/1h0kf1259058875.ps tmp/1h0kf1259058875.png")
> system("convert tmp/2hkoe1259058875.ps tmp/2hkoe1259058875.png")
> system("convert tmp/39i1v1259058875.ps tmp/39i1v1259058875.png")
> system("convert tmp/4y3kc1259058875.ps tmp/4y3kc1259058875.png")
> system("convert tmp/5ce0m1259058875.ps tmp/5ce0m1259058875.png")
> system("convert tmp/6e3ke1259058875.ps tmp/6e3ke1259058875.png")
> system("convert tmp/7hx9c1259058875.ps tmp/7hx9c1259058875.png")
> system("convert tmp/8jsoy1259058875.ps tmp/8jsoy1259058875.png")
> system("convert tmp/9ldbc1259058875.ps tmp/9ldbc1259058875.png")
> system("convert tmp/108gyf1259058875.ps tmp/108gyf1259058875.png")
>
>
> proc.time()
user system elapsed
2.488 1.572 3.625