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(12.1,0,0,12,0,0,11.8,0,0,12.7,0,0,12.3,0,0,11.9,0,0,12,0,0,12.3,0,0,12.8,0,0,12.4,0,0,12.3,0,0,12.7,0,0,12.7,0,0,12.9,0,0,13,0,0,12.2,0,0,12.3,0,0,12.8,0,0,12.8,0,0,12.8,0,0,12.2,0,0,12.6,0,0,12.8,0,0,12.5,0,0,12.4,0,0,12.3,1,0,11.9,1,0,11.7,1,0,12,1,0,12.1,1,0,11.7,1,0,11.8,1,0,11.8,1,0,11.8,1,0,11.3,1,0,11.3,1,0,11.3,1,0,11.2,0,1,11.4,0,1,12.2,0,1,12.9,0,1,13.1,0,1,13.5,0,1,13.6,0,1,14.4,0,1,14.1,0,1,15.1,0,1,15.8,0,1,15.9,0,1,15.4,0,1,15.5,0,1,14.8,0,1,13.2,0,1,12.7,0,1,12.1,0,1,11.9,0,1,10.6,0,1,10.7,0,1,9.8,0,1,9,0,1,8.3,0,1,9.3,0,1,9,0,1,9.1,0,1,10,0,1),dim=c(3,65),dimnames=list(c('Gzhdsidx','Vr_crisis','NA_crisis'),1:65))
> y <- array(NA,dim=c(3,65),dimnames=list(c('Gzhdsidx','Vr_crisis','NA_crisis'),1:65))
> 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
Gzhdsidx Vr_crisis NA_crisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 12.1 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 12.0 0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 11.8 0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 12.7 0 0 0 0 0 1 0 0 0 0 0 0 0 4
5 12.3 0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 11.9 0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 12.0 0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 12.3 0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 12.8 0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 12.4 0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 12.3 0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 12.7 0 0 0 0 0 0 0 0 0 0 0 0 0 12
13 12.7 0 0 1 0 0 0 0 0 0 0 0 0 0 13
14 12.9 0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 13.0 0 0 0 0 1 0 0 0 0 0 0 0 0 15
16 12.2 0 0 0 0 0 1 0 0 0 0 0 0 0 16
17 12.3 0 0 0 0 0 0 1 0 0 0 0 0 0 17
18 12.8 0 0 0 0 0 0 0 1 0 0 0 0 0 18
19 12.8 0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 12.8 0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 12.2 0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 12.6 0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 12.8 0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 12.5 0 0 0 0 0 0 0 0 0 0 0 0 0 24
25 12.4 0 0 1 0 0 0 0 0 0 0 0 0 0 25
26 12.3 1 0 0 1 0 0 0 0 0 0 0 0 0 26
27 11.9 1 0 0 0 1 0 0 0 0 0 0 0 0 27
28 11.7 1 0 0 0 0 1 0 0 0 0 0 0 0 28
29 12.0 1 0 0 0 0 0 1 0 0 0 0 0 0 29
30 12.1 1 0 0 0 0 0 0 1 0 0 0 0 0 30
31 11.7 1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 11.8 1 0 0 0 0 0 0 0 0 1 0 0 0 32
33 11.8 1 0 0 0 0 0 0 0 0 0 1 0 0 33
34 11.8 1 0 0 0 0 0 0 0 0 0 0 1 0 34
35 11.3 1 0 0 0 0 0 0 0 0 0 0 0 1 35
36 11.3 1 0 0 0 0 0 0 0 0 0 0 0 0 36
37 11.3 1 0 1 0 0 0 0 0 0 0 0 0 0 37
38 11.2 0 1 0 1 0 0 0 0 0 0 0 0 0 38
39 11.4 0 1 0 0 1 0 0 0 0 0 0 0 0 39
40 12.2 0 1 0 0 0 1 0 0 0 0 0 0 0 40
41 12.9 0 1 0 0 0 0 1 0 0 0 0 0 0 41
42 13.1 0 1 0 0 0 0 0 1 0 0 0 0 0 42
43 13.5 0 1 0 0 0 0 0 0 1 0 0 0 0 43
44 13.6 0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 14.4 0 1 0 0 0 0 0 0 0 0 1 0 0 45
46 14.1 0 1 0 0 0 0 0 0 0 0 0 1 0 46
47 15.1 0 1 0 0 0 0 0 0 0 0 0 0 1 47
48 15.8 0 1 0 0 0 0 0 0 0 0 0 0 0 48
49 15.9 0 1 1 0 0 0 0 0 0 0 0 0 0 49
50 15.4 0 1 0 1 0 0 0 0 0 0 0 0 0 50
51 15.5 0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 14.8 0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 13.2 0 1 0 0 0 0 1 0 0 0 0 0 0 53
54 12.7 0 1 0 0 0 0 0 1 0 0 0 0 0 54
55 12.1 0 1 0 0 0 0 0 0 1 0 0 0 0 55
56 11.9 0 1 0 0 0 0 0 0 0 1 0 0 0 56
57 10.6 0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 10.7 0 1 0 0 0 0 0 0 0 0 0 1 0 58
59 9.8 0 1 0 0 0 0 0 0 0 0 0 0 1 59
60 9.0 0 1 0 0 0 0 0 0 0 0 0 0 0 60
61 8.3 0 1 1 0 0 0 0 0 0 0 0 0 0 61
62 9.3 0 1 0 1 0 0 0 0 0 0 0 0 0 62
63 9.0 0 1 0 0 1 0 0 0 0 0 0 0 0 63
64 9.1 0 1 0 0 0 1 0 0 0 0 0 0 0 64
65 10.0 0 1 0 0 0 0 1 0 0 0 0 0 0 65
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Vr_crisis NA_crisis M1 M2 M3
14.12357 1.14362 3.74369 -0.35421 -0.81178 -0.79540
M4 M5 M6 M7 M8 M9
-0.67901 -0.57930 -0.33830 -0.33858 -0.17886 -0.19915
M10 M11 t
-0.13943 -0.09972 -0.09972
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.13038 -0.84698 0.02258 0.80961 3.51365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.12357 0.87812 16.084 < 2e-16 ***
Vr_crisis 1.14362 0.76273 1.499 0.14006
NA_crisis 3.74369 1.19191 3.141 0.00283 **
M1 -0.35421 0.94480 -0.375 0.70931
M2 -0.81178 0.96600 -0.840 0.40471
M3 -0.79540 0.96025 -0.828 0.41143
M4 -0.67901 0.95533 -0.711 0.48053
M5 -0.57930 0.95124 -0.609 0.54529
M6 -0.33830 0.99932 -0.339 0.73638
M7 -0.33858 0.99481 -0.340 0.73502
M8 -0.17886 0.99110 -0.180 0.85751
M9 -0.19915 0.98821 -0.202 0.84111
M10 -0.13943 0.98614 -0.141 0.88813
M11 -0.09972 0.98489 -0.101 0.91976
t -0.09972 0.02860 -3.487 0.00103 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.557 on 50 degrees of freedom
Multiple R-squared: 0.2243, Adjusted R-squared: 0.007071
F-statistic: 1.033 on 14 and 50 DF, p-value: 0.4384
> 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,] 5.251141e-02 1.050228e-01 0.9474886
[2,] 1.518618e-02 3.037235e-02 0.9848138
[3,] 3.764222e-03 7.528444e-03 0.9962358
[4,] 2.911138e-03 5.822276e-03 0.9970889
[5,] 7.764767e-04 1.552953e-03 0.9992235
[6,] 1.863908e-04 3.727816e-04 0.9998136
[7,] 6.186287e-05 1.237257e-04 0.9999381
[8,] 1.946937e-05 3.893874e-05 0.9999805
[9,] 4.114680e-06 8.229361e-06 0.9999959
[10,] 9.141671e-07 1.828334e-06 0.9999991
[11,] 2.202297e-07 4.404594e-07 0.9999998
[12,] 4.170679e-08 8.341357e-08 1.0000000
[13,] 7.552834e-09 1.510567e-08 1.0000000
[14,] 1.482430e-09 2.964859e-09 1.0000000
[15,] 2.806906e-10 5.613811e-10 1.0000000
[16,] 4.626434e-11 9.252867e-11 1.0000000
[17,] 7.154412e-12 1.430882e-11 1.0000000
[18,] 3.177196e-12 6.354392e-12 1.0000000
[19,] 1.236740e-12 2.473481e-12 1.0000000
[20,] 3.532467e-13 7.064935e-13 1.0000000
[21,] 2.976329e-13 5.952657e-13 1.0000000
[22,] 7.769447e-13 1.553889e-12 1.0000000
[23,] 1.704438e-11 3.408875e-11 1.0000000
[24,] 3.876118e-09 7.752236e-09 1.0000000
[25,] 1.431171e-07 2.862342e-07 0.9999999
[26,] 6.052895e-06 1.210579e-05 0.9999939
[27,] 2.686345e-04 5.372690e-04 0.9997314
[28,] 1.500380e-03 3.000761e-03 0.9984996
[29,] 8.505902e-03 1.701180e-02 0.9914941
[30,] 1.347298e-02 2.694596e-02 0.9865270
> postscript(file="/var/www/html/rcomp/tmp/1kf771261149177.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2pglf1261149177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tg4z1261149177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nz4h1261149177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pbj81261149177.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 = 65
Frequency = 1
1 2 3 4 5 6
-1.56964577 -1.11236428 -1.22903094 -0.34569761 -0.74569761 -1.28698324
7 8 9 10 11 12
-1.08698324 -0.84698324 -0.22698324 -0.58698324 -0.62698324 -0.22698324
13 14 15 16 17 18
0.22694591 0.98422740 1.16756074 0.35089407 0.45089407 0.80960844
19 20 21 22 23 24
0.90960844 0.84960844 0.36960844 0.80960844 1.06960844 0.76960844
25 26 27 28 29 30
1.12353760 0.43719632 0.12052966 -0.09613701 0.20386299 0.16257736
31 32 33 34 35 36
-0.13742264 -0.09742264 0.02257736 0.06257736 -0.37742264 -0.37742264
37 38 39 40 41 42
0.07650651 -2.06627816 -1.78294483 -0.99961150 -0.29961150 -0.24089712
43 44 45 46 47 48
0.25910288 0.29910288 1.21910288 0.95910288 2.01910288 2.71910288
49 50 51 52 53 54
3.27303203 3.33031352 3.51364685 2.79698018 1.19698018 0.55569456
55 56 57 58 59 60
0.05569456 -0.20430544 -1.38430544 -1.24430544 -2.08430544 -2.88430544
61 62 63 64 65
-3.13037629 -1.57309480 -1.78976147 -1.70642814 -0.80642814
> postscript(file="/var/www/html/rcomp/tmp/6lty51261149177.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 = 65
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.56964577 NA
1 -1.11236428 -1.56964577
2 -1.22903094 -1.11236428
3 -0.34569761 -1.22903094
4 -0.74569761 -0.34569761
5 -1.28698324 -0.74569761
6 -1.08698324 -1.28698324
7 -0.84698324 -1.08698324
8 -0.22698324 -0.84698324
9 -0.58698324 -0.22698324
10 -0.62698324 -0.58698324
11 -0.22698324 -0.62698324
12 0.22694591 -0.22698324
13 0.98422740 0.22694591
14 1.16756074 0.98422740
15 0.35089407 1.16756074
16 0.45089407 0.35089407
17 0.80960844 0.45089407
18 0.90960844 0.80960844
19 0.84960844 0.90960844
20 0.36960844 0.84960844
21 0.80960844 0.36960844
22 1.06960844 0.80960844
23 0.76960844 1.06960844
24 1.12353760 0.76960844
25 0.43719632 1.12353760
26 0.12052966 0.43719632
27 -0.09613701 0.12052966
28 0.20386299 -0.09613701
29 0.16257736 0.20386299
30 -0.13742264 0.16257736
31 -0.09742264 -0.13742264
32 0.02257736 -0.09742264
33 0.06257736 0.02257736
34 -0.37742264 0.06257736
35 -0.37742264 -0.37742264
36 0.07650651 -0.37742264
37 -2.06627816 0.07650651
38 -1.78294483 -2.06627816
39 -0.99961150 -1.78294483
40 -0.29961150 -0.99961150
41 -0.24089712 -0.29961150
42 0.25910288 -0.24089712
43 0.29910288 0.25910288
44 1.21910288 0.29910288
45 0.95910288 1.21910288
46 2.01910288 0.95910288
47 2.71910288 2.01910288
48 3.27303203 2.71910288
49 3.33031352 3.27303203
50 3.51364685 3.33031352
51 2.79698018 3.51364685
52 1.19698018 2.79698018
53 0.55569456 1.19698018
54 0.05569456 0.55569456
55 -0.20430544 0.05569456
56 -1.38430544 -0.20430544
57 -1.24430544 -1.38430544
58 -2.08430544 -1.24430544
59 -2.88430544 -2.08430544
60 -3.13037629 -2.88430544
61 -1.57309480 -3.13037629
62 -1.78976147 -1.57309480
63 -1.70642814 -1.78976147
64 -0.80642814 -1.70642814
65 NA -0.80642814
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.11236428 -1.56964577
[2,] -1.22903094 -1.11236428
[3,] -0.34569761 -1.22903094
[4,] -0.74569761 -0.34569761
[5,] -1.28698324 -0.74569761
[6,] -1.08698324 -1.28698324
[7,] -0.84698324 -1.08698324
[8,] -0.22698324 -0.84698324
[9,] -0.58698324 -0.22698324
[10,] -0.62698324 -0.58698324
[11,] -0.22698324 -0.62698324
[12,] 0.22694591 -0.22698324
[13,] 0.98422740 0.22694591
[14,] 1.16756074 0.98422740
[15,] 0.35089407 1.16756074
[16,] 0.45089407 0.35089407
[17,] 0.80960844 0.45089407
[18,] 0.90960844 0.80960844
[19,] 0.84960844 0.90960844
[20,] 0.36960844 0.84960844
[21,] 0.80960844 0.36960844
[22,] 1.06960844 0.80960844
[23,] 0.76960844 1.06960844
[24,] 1.12353760 0.76960844
[25,] 0.43719632 1.12353760
[26,] 0.12052966 0.43719632
[27,] -0.09613701 0.12052966
[28,] 0.20386299 -0.09613701
[29,] 0.16257736 0.20386299
[30,] -0.13742264 0.16257736
[31,] -0.09742264 -0.13742264
[32,] 0.02257736 -0.09742264
[33,] 0.06257736 0.02257736
[34,] -0.37742264 0.06257736
[35,] -0.37742264 -0.37742264
[36,] 0.07650651 -0.37742264
[37,] -2.06627816 0.07650651
[38,] -1.78294483 -2.06627816
[39,] -0.99961150 -1.78294483
[40,] -0.29961150 -0.99961150
[41,] -0.24089712 -0.29961150
[42,] 0.25910288 -0.24089712
[43,] 0.29910288 0.25910288
[44,] 1.21910288 0.29910288
[45,] 0.95910288 1.21910288
[46,] 2.01910288 0.95910288
[47,] 2.71910288 2.01910288
[48,] 3.27303203 2.71910288
[49,] 3.33031352 3.27303203
[50,] 3.51364685 3.33031352
[51,] 2.79698018 3.51364685
[52,] 1.19698018 2.79698018
[53,] 0.55569456 1.19698018
[54,] 0.05569456 0.55569456
[55,] -0.20430544 0.05569456
[56,] -1.38430544 -0.20430544
[57,] -1.24430544 -1.38430544
[58,] -2.08430544 -1.24430544
[59,] -2.88430544 -2.08430544
[60,] -3.13037629 -2.88430544
[61,] -1.57309480 -3.13037629
[62,] -1.78976147 -1.57309480
[63,] -1.70642814 -1.78976147
[64,] -0.80642814 -1.70642814
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.11236428 -1.56964577
2 -1.22903094 -1.11236428
3 -0.34569761 -1.22903094
4 -0.74569761 -0.34569761
5 -1.28698324 -0.74569761
6 -1.08698324 -1.28698324
7 -0.84698324 -1.08698324
8 -0.22698324 -0.84698324
9 -0.58698324 -0.22698324
10 -0.62698324 -0.58698324
11 -0.22698324 -0.62698324
12 0.22694591 -0.22698324
13 0.98422740 0.22694591
14 1.16756074 0.98422740
15 0.35089407 1.16756074
16 0.45089407 0.35089407
17 0.80960844 0.45089407
18 0.90960844 0.80960844
19 0.84960844 0.90960844
20 0.36960844 0.84960844
21 0.80960844 0.36960844
22 1.06960844 0.80960844
23 0.76960844 1.06960844
24 1.12353760 0.76960844
25 0.43719632 1.12353760
26 0.12052966 0.43719632
27 -0.09613701 0.12052966
28 0.20386299 -0.09613701
29 0.16257736 0.20386299
30 -0.13742264 0.16257736
31 -0.09742264 -0.13742264
32 0.02257736 -0.09742264
33 0.06257736 0.02257736
34 -0.37742264 0.06257736
35 -0.37742264 -0.37742264
36 0.07650651 -0.37742264
37 -2.06627816 0.07650651
38 -1.78294483 -2.06627816
39 -0.99961150 -1.78294483
40 -0.29961150 -0.99961150
41 -0.24089712 -0.29961150
42 0.25910288 -0.24089712
43 0.29910288 0.25910288
44 1.21910288 0.29910288
45 0.95910288 1.21910288
46 2.01910288 0.95910288
47 2.71910288 2.01910288
48 3.27303203 2.71910288
49 3.33031352 3.27303203
50 3.51364685 3.33031352
51 2.79698018 3.51364685
52 1.19698018 2.79698018
53 0.55569456 1.19698018
54 0.05569456 0.55569456
55 -0.20430544 0.05569456
56 -1.38430544 -0.20430544
57 -1.24430544 -1.38430544
58 -2.08430544 -1.24430544
59 -2.88430544 -2.08430544
60 -3.13037629 -2.88430544
61 -1.57309480 -3.13037629
62 -1.78976147 -1.57309480
63 -1.70642814 -1.78976147
64 -0.80642814 -1.70642814
> 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/79cmj1261149177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88cco1261149177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98jhs1261149177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10n4aw1261149177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nmvx1261149177.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/125rqp1261149177.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/138dmd1261149177.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/14fdy41261149177.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/15gts01261149177.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/16oz8a1261149177.tab")
+ }
>
> try(system("convert tmp/1kf771261149177.ps tmp/1kf771261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/2pglf1261149177.ps tmp/2pglf1261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tg4z1261149177.ps tmp/3tg4z1261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nz4h1261149177.ps tmp/4nz4h1261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pbj81261149177.ps tmp/5pbj81261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lty51261149177.ps tmp/6lty51261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/79cmj1261149177.ps tmp/79cmj1261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/88cco1261149177.ps tmp/88cco1261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/98jhs1261149177.ps tmp/98jhs1261149177.png",intern=TRUE))
character(0)
> try(system("convert tmp/10n4aw1261149177.ps tmp/10n4aw1261149177.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.454 1.582 3.064