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(105.29,0,101.23,0,102.33,0,100.26,0,104.13,0,103.54,0,100.02,0,98.66,0,108.64,0,105.67,0,102.66,0,100.3,0,95.13,0,93.2,0,102.84,0,101.36,0,102.55,0,103.12,0,96.3,0,99.13,0,102.23,0,104.3,0,99.58,0,98.45,0,96.23,0,97.62,0,102.32,0,105.23,0,100.05,0,102.66,0,100.98,0,99.2,0,98.36,0,102.56,0,97.33,0,96.22,0,99.22,0,102.32,0,104.22,0,100.06,0,107.23,0,99.62,0,98.32,1,101.23,1,102.33,1,100.6,1,95.63,1,94.63,1,95.66,1,100.78,1,90.36,1,95.45,1,103.65,1,99.89,1,97.68,1,99.62,1,98.33,1,96.23,1,102.65,1,99.35,1,92.65,1,100.6,1,97.67,1),dim=c(2,63),dimnames=list(c('Y','X'),1:63))
> y <- array(NA,dim=c(2,63),dimnames=list(c('Y','X'),1:63))
> 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
1 105.29 0 1 0 0 0 0 0 0 0 0 0 0 1
2 101.23 0 0 1 0 0 0 0 0 0 0 0 0 2
3 102.33 0 0 0 1 0 0 0 0 0 0 0 0 3
4 100.26 0 0 0 0 1 0 0 0 0 0 0 0 4
5 104.13 0 0 0 0 0 1 0 0 0 0 0 0 5
6 103.54 0 0 0 0 0 0 1 0 0 0 0 0 6
7 100.02 0 0 0 0 0 0 0 1 0 0 0 0 7
8 98.66 0 0 0 0 0 0 0 0 1 0 0 0 8
9 108.64 0 0 0 0 0 0 0 0 0 1 0 0 9
10 105.67 0 0 0 0 0 0 0 0 0 0 1 0 10
11 102.66 0 0 0 0 0 0 0 0 0 0 0 1 11
12 100.30 0 0 0 0 0 0 0 0 0 0 0 0 12
13 95.13 0 1 0 0 0 0 0 0 0 0 0 0 13
14 93.20 0 0 1 0 0 0 0 0 0 0 0 0 14
15 102.84 0 0 0 1 0 0 0 0 0 0 0 0 15
16 101.36 0 0 0 0 1 0 0 0 0 0 0 0 16
17 102.55 0 0 0 0 0 1 0 0 0 0 0 0 17
18 103.12 0 0 0 0 0 0 1 0 0 0 0 0 18
19 96.30 0 0 0 0 0 0 0 1 0 0 0 0 19
20 99.13 0 0 0 0 0 0 0 0 1 0 0 0 20
21 102.23 0 0 0 0 0 0 0 0 0 1 0 0 21
22 104.30 0 0 0 0 0 0 0 0 0 0 1 0 22
23 99.58 0 0 0 0 0 0 0 0 0 0 0 1 23
24 98.45 0 0 0 0 0 0 0 0 0 0 0 0 24
25 96.23 0 1 0 0 0 0 0 0 0 0 0 0 25
26 97.62 0 0 1 0 0 0 0 0 0 0 0 0 26
27 102.32 0 0 0 1 0 0 0 0 0 0 0 0 27
28 105.23 0 0 0 0 1 0 0 0 0 0 0 0 28
29 100.05 0 0 0 0 0 1 0 0 0 0 0 0 29
30 102.66 0 0 0 0 0 0 1 0 0 0 0 0 30
31 100.98 0 0 0 0 0 0 0 1 0 0 0 0 31
32 99.20 0 0 0 0 0 0 0 0 1 0 0 0 32
33 98.36 0 0 0 0 0 0 0 0 0 1 0 0 33
34 102.56 0 0 0 0 0 0 0 0 0 0 1 0 34
35 97.33 0 0 0 0 0 0 0 0 0 0 0 1 35
36 96.22 0 0 0 0 0 0 0 0 0 0 0 0 36
37 99.22 0 1 0 0 0 0 0 0 0 0 0 0 37
38 102.32 0 0 1 0 0 0 0 0 0 0 0 0 38
39 104.22 0 0 0 1 0 0 0 0 0 0 0 0 39
40 100.06 0 0 0 0 1 0 0 0 0 0 0 0 40
41 107.23 0 0 0 0 0 1 0 0 0 0 0 0 41
42 99.62 0 0 0 0 0 0 1 0 0 0 0 0 42
43 98.32 1 0 0 0 0 0 0 1 0 0 0 0 43
44 101.23 1 0 0 0 0 0 0 0 1 0 0 0 44
45 102.33 1 0 0 0 0 0 0 0 0 1 0 0 45
46 100.60 1 0 0 0 0 0 0 0 0 0 1 0 46
47 95.63 1 0 0 0 0 0 0 0 0 0 0 1 47
48 94.63 1 0 0 0 0 0 0 0 0 0 0 0 48
49 95.66 1 1 0 0 0 0 0 0 0 0 0 0 49
50 100.78 1 0 1 0 0 0 0 0 0 0 0 0 50
51 90.36 1 0 0 1 0 0 0 0 0 0 0 0 51
52 95.45 1 0 0 0 1 0 0 0 0 0 0 0 52
53 103.65 1 0 0 0 0 1 0 0 0 0 0 0 53
54 99.89 1 0 0 0 0 0 1 0 0 0 0 0 54
55 97.68 1 0 0 0 0 0 0 1 0 0 0 0 55
56 99.62 1 0 0 0 0 0 0 0 1 0 0 0 56
57 98.33 1 0 0 0 0 0 0 0 0 1 0 0 57
58 96.23 1 0 0 0 0 0 0 0 0 0 1 0 58
59 102.65 1 0 0 0 0 0 0 0 0 0 0 1 59
60 99.35 1 0 0 0 0 0 0 0 0 0 0 0 60
61 92.65 1 1 0 0 0 0 0 0 0 0 0 0 61
62 100.60 1 0 1 0 0 0 0 0 0 0 0 0 62
63 97.67 1 0 0 1 0 0 0 0 0 0 0 0 63
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
99.81614 -1.16315 -0.72100 1.25069 1.95905 2.10251
M5 M6 M7 M8 M9 M10
5.19587 3.48322 0.65321 1.60457 4.05793 3.99528
M11 t
1.73664 -0.04336
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.0408 -1.8839 0.0951 1.7223 6.2382
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.81614 1.69538 58.875 <2e-16 ***
X -1.16315 1.47918 -0.786 0.4355
M1 -0.72100 1.90516 -0.378 0.7067
M2 1.25069 1.90335 0.657 0.5142
M3 1.95905 1.90230 1.030 0.3081
M4 2.10251 1.99396 1.054 0.2969
M5 5.19587 1.99313 2.607 0.0121 *
M6 3.48322 1.99302 1.748 0.0868 .
M7 0.65321 1.99480 0.327 0.7447
M8 1.60457 1.99152 0.806 0.4243
M9 4.05793 1.98897 2.040 0.0467 *
M10 3.99528 1.98715 2.011 0.0499 *
M11 1.73664 1.98606 0.874 0.3862
t -0.04336 0.03808 -1.139 0.2604
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.14 on 49 degrees of freedom
Multiple R-squared: 0.3798, Adjusted R-squared: 0.2153
F-statistic: 2.309 on 13 and 49 DF, p-value: 0.01767
> 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,] 0.8548108 0.2903785 0.1451892
[2,] 0.7829411 0.4341178 0.2170589
[3,] 0.6832005 0.6335990 0.3167995
[4,] 0.6205788 0.7588425 0.3794212
[5,] 0.5559732 0.8880536 0.4440268
[6,] 0.4687468 0.9374937 0.5312532
[7,] 0.3549768 0.7099536 0.6450232
[8,] 0.2609714 0.5219429 0.7390286
[9,] 0.1800207 0.3600414 0.8199793
[10,] 0.2199544 0.4399088 0.7800456
[11,] 0.2016052 0.4032104 0.7983948
[12,] 0.4186514 0.8373027 0.5813486
[13,] 0.4216736 0.8433473 0.5783264
[14,] 0.3503122 0.7006245 0.6496878
[15,] 0.3454867 0.6909733 0.6545133
[16,] 0.3021261 0.6042522 0.6978739
[17,] 0.3745993 0.7491986 0.6254007
[18,] 0.2920514 0.5841029 0.7079486
[19,] 0.2927360 0.5854720 0.7072640
[20,] 0.2859160 0.5718319 0.7140840
[21,] 0.2330204 0.4660407 0.7669796
[22,] 0.3078558 0.6157115 0.6921442
[23,] 0.4271413 0.8542827 0.5728587
[24,] 0.3464899 0.6929798 0.6535101
[25,] 0.3543298 0.7086596 0.6456702
[26,] 0.2612805 0.5225611 0.7387195
[27,] 0.1759100 0.3518200 0.8240900
[28,] 0.1212082 0.2424164 0.8787918
[29,] 0.1182676 0.2365352 0.8817324
[30,] 0.1851582 0.3703164 0.8148418
> postscript(file="/var/www/html/rcomp/tmp/183rg1258990276.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xmjj1258990276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nu5l1258990276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lp2d1258990276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5f5ht1258990276.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 = 63
Frequency = 1
1 2 3 4 5 6
6.23821911 0.24988577 0.68488577 -1.48521495 -0.66521495 0.50078505
7 8 9 10 11 12
-0.14584424 -2.41384424 5.15615576 2.29215576 1.58415576 1.00415576
13 14 15 16 17 18
-3.40148806 -7.25982139 1.71517861 0.13507788 -1.72492212 0.60107788
19 20 21 22 23 24
-3.34555140 -1.42355140 -0.73355140 1.44244860 -0.97555140 -0.32555140
25 26 27 28 29 30
-1.78119522 -2.31952856 1.71547144 4.52537072 -3.70462928 0.66137072
31 32 33 34 35 36
1.85474143 -0.83325857 -4.08325857 0.22274143 -2.70525857 -2.03525857
37 38 39 40 41 42
1.72909761 2.90076428 4.13576428 -0.12433645 3.99566355 -1.85833645
43 44 45 46 47 48
0.87818069 2.88018069 1.57018069 -0.05381931 -2.72181931 -1.94181931
49 50 51 52 53 54
-0.14746314 3.04420353 -8.04079647 -3.05089720 2.09910280 0.09510280
55 56 57 58 59 60
0.75847352 1.79047352 -1.90952648 -3.90352648 4.81847352 3.29847352
61 62 63
-2.63717030 3.38449637 -0.21050363
> postscript(file="/var/www/html/rcomp/tmp/6v8m01258990276.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 = 63
Frequency = 1
lag(myerror, k = 1) myerror
0 6.23821911 NA
1 0.24988577 6.23821911
2 0.68488577 0.24988577
3 -1.48521495 0.68488577
4 -0.66521495 -1.48521495
5 0.50078505 -0.66521495
6 -0.14584424 0.50078505
7 -2.41384424 -0.14584424
8 5.15615576 -2.41384424
9 2.29215576 5.15615576
10 1.58415576 2.29215576
11 1.00415576 1.58415576
12 -3.40148806 1.00415576
13 -7.25982139 -3.40148806
14 1.71517861 -7.25982139
15 0.13507788 1.71517861
16 -1.72492212 0.13507788
17 0.60107788 -1.72492212
18 -3.34555140 0.60107788
19 -1.42355140 -3.34555140
20 -0.73355140 -1.42355140
21 1.44244860 -0.73355140
22 -0.97555140 1.44244860
23 -0.32555140 -0.97555140
24 -1.78119522 -0.32555140
25 -2.31952856 -1.78119522
26 1.71547144 -2.31952856
27 4.52537072 1.71547144
28 -3.70462928 4.52537072
29 0.66137072 -3.70462928
30 1.85474143 0.66137072
31 -0.83325857 1.85474143
32 -4.08325857 -0.83325857
33 0.22274143 -4.08325857
34 -2.70525857 0.22274143
35 -2.03525857 -2.70525857
36 1.72909761 -2.03525857
37 2.90076428 1.72909761
38 4.13576428 2.90076428
39 -0.12433645 4.13576428
40 3.99566355 -0.12433645
41 -1.85833645 3.99566355
42 0.87818069 -1.85833645
43 2.88018069 0.87818069
44 1.57018069 2.88018069
45 -0.05381931 1.57018069
46 -2.72181931 -0.05381931
47 -1.94181931 -2.72181931
48 -0.14746314 -1.94181931
49 3.04420353 -0.14746314
50 -8.04079647 3.04420353
51 -3.05089720 -8.04079647
52 2.09910280 -3.05089720
53 0.09510280 2.09910280
54 0.75847352 0.09510280
55 1.79047352 0.75847352
56 -1.90952648 1.79047352
57 -3.90352648 -1.90952648
58 4.81847352 -3.90352648
59 3.29847352 4.81847352
60 -2.63717030 3.29847352
61 3.38449637 -2.63717030
62 -0.21050363 3.38449637
63 NA -0.21050363
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.24988577 6.23821911
[2,] 0.68488577 0.24988577
[3,] -1.48521495 0.68488577
[4,] -0.66521495 -1.48521495
[5,] 0.50078505 -0.66521495
[6,] -0.14584424 0.50078505
[7,] -2.41384424 -0.14584424
[8,] 5.15615576 -2.41384424
[9,] 2.29215576 5.15615576
[10,] 1.58415576 2.29215576
[11,] 1.00415576 1.58415576
[12,] -3.40148806 1.00415576
[13,] -7.25982139 -3.40148806
[14,] 1.71517861 -7.25982139
[15,] 0.13507788 1.71517861
[16,] -1.72492212 0.13507788
[17,] 0.60107788 -1.72492212
[18,] -3.34555140 0.60107788
[19,] -1.42355140 -3.34555140
[20,] -0.73355140 -1.42355140
[21,] 1.44244860 -0.73355140
[22,] -0.97555140 1.44244860
[23,] -0.32555140 -0.97555140
[24,] -1.78119522 -0.32555140
[25,] -2.31952856 -1.78119522
[26,] 1.71547144 -2.31952856
[27,] 4.52537072 1.71547144
[28,] -3.70462928 4.52537072
[29,] 0.66137072 -3.70462928
[30,] 1.85474143 0.66137072
[31,] -0.83325857 1.85474143
[32,] -4.08325857 -0.83325857
[33,] 0.22274143 -4.08325857
[34,] -2.70525857 0.22274143
[35,] -2.03525857 -2.70525857
[36,] 1.72909761 -2.03525857
[37,] 2.90076428 1.72909761
[38,] 4.13576428 2.90076428
[39,] -0.12433645 4.13576428
[40,] 3.99566355 -0.12433645
[41,] -1.85833645 3.99566355
[42,] 0.87818069 -1.85833645
[43,] 2.88018069 0.87818069
[44,] 1.57018069 2.88018069
[45,] -0.05381931 1.57018069
[46,] -2.72181931 -0.05381931
[47,] -1.94181931 -2.72181931
[48,] -0.14746314 -1.94181931
[49,] 3.04420353 -0.14746314
[50,] -8.04079647 3.04420353
[51,] -3.05089720 -8.04079647
[52,] 2.09910280 -3.05089720
[53,] 0.09510280 2.09910280
[54,] 0.75847352 0.09510280
[55,] 1.79047352 0.75847352
[56,] -1.90952648 1.79047352
[57,] -3.90352648 -1.90952648
[58,] 4.81847352 -3.90352648
[59,] 3.29847352 4.81847352
[60,] -2.63717030 3.29847352
[61,] 3.38449637 -2.63717030
[62,] -0.21050363 3.38449637
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.24988577 6.23821911
2 0.68488577 0.24988577
3 -1.48521495 0.68488577
4 -0.66521495 -1.48521495
5 0.50078505 -0.66521495
6 -0.14584424 0.50078505
7 -2.41384424 -0.14584424
8 5.15615576 -2.41384424
9 2.29215576 5.15615576
10 1.58415576 2.29215576
11 1.00415576 1.58415576
12 -3.40148806 1.00415576
13 -7.25982139 -3.40148806
14 1.71517861 -7.25982139
15 0.13507788 1.71517861
16 -1.72492212 0.13507788
17 0.60107788 -1.72492212
18 -3.34555140 0.60107788
19 -1.42355140 -3.34555140
20 -0.73355140 -1.42355140
21 1.44244860 -0.73355140
22 -0.97555140 1.44244860
23 -0.32555140 -0.97555140
24 -1.78119522 -0.32555140
25 -2.31952856 -1.78119522
26 1.71547144 -2.31952856
27 4.52537072 1.71547144
28 -3.70462928 4.52537072
29 0.66137072 -3.70462928
30 1.85474143 0.66137072
31 -0.83325857 1.85474143
32 -4.08325857 -0.83325857
33 0.22274143 -4.08325857
34 -2.70525857 0.22274143
35 -2.03525857 -2.70525857
36 1.72909761 -2.03525857
37 2.90076428 1.72909761
38 4.13576428 2.90076428
39 -0.12433645 4.13576428
40 3.99566355 -0.12433645
41 -1.85833645 3.99566355
42 0.87818069 -1.85833645
43 2.88018069 0.87818069
44 1.57018069 2.88018069
45 -0.05381931 1.57018069
46 -2.72181931 -0.05381931
47 -1.94181931 -2.72181931
48 -0.14746314 -1.94181931
49 3.04420353 -0.14746314
50 -8.04079647 3.04420353
51 -3.05089720 -8.04079647
52 2.09910280 -3.05089720
53 0.09510280 2.09910280
54 0.75847352 0.09510280
55 1.79047352 0.75847352
56 -1.90952648 1.79047352
57 -3.90352648 -1.90952648
58 4.81847352 -3.90352648
59 3.29847352 4.81847352
60 -2.63717030 3.29847352
61 3.38449637 -2.63717030
62 -0.21050363 3.38449637
> 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/7sm4f1258990276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80qe01258990276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9l6g61258990276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10y2651258990276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o9wh1258990276.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/127wuj1258990276.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/13de8r1258990276.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/14ifha1258990276.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/15aerv1258990276.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/16joct1258990276.tab")
+ }
>
> system("convert tmp/183rg1258990276.ps tmp/183rg1258990276.png")
> system("convert tmp/2xmjj1258990276.ps tmp/2xmjj1258990276.png")
> system("convert tmp/3nu5l1258990276.ps tmp/3nu5l1258990276.png")
> system("convert tmp/4lp2d1258990276.ps tmp/4lp2d1258990276.png")
> system("convert tmp/5f5ht1258990276.ps tmp/5f5ht1258990276.png")
> system("convert tmp/6v8m01258990276.ps tmp/6v8m01258990276.png")
> system("convert tmp/7sm4f1258990276.ps tmp/7sm4f1258990276.png")
> system("convert tmp/80qe01258990276.ps tmp/80qe01258990276.png")
> system("convert tmp/9l6g61258990276.ps tmp/9l6g61258990276.png")
> system("convert tmp/10y2651258990276.ps tmp/10y2651258990276.png")
>
>
> proc.time()
user system elapsed
2.450 1.596 3.568