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(8,0,8.1,0,7.7,0,7.5,0,7.6,0,7.8,0,7.8,0,7.8,0,7.5,0,7.5,0,7.1,0,7.5,0,7.5,0,7.6,0,7.7,0,7.7,0,7.9,0,8.1,0,8.2,0,8.2,0,8.2,0,7.9,0,7.3,0,6.9,0,6.6,0,6.7,0,6.9,0,7,0,7.1,0,7.2,0,7.1,0,6.9,0,7,0,6.8,0,6.4,0,6.7,0,6.6,0,6.4,0,6.3,0,6.2,0,6.5,0,6.8,1,6.8,1,6.4,1,6.1,1,5.8,1,6.1,1,7.2,1,7.3,1,6.9,1,6.1,1,5.8,1,6.2,1,7.1,1,7.7,1,7.9,1,7.7,1,7.4,1,7.5,1,8,1,8.1,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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 8.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 8.1 0 0 1 0 0 0 0 0 0 0 0 0 2
3 7.7 0 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 0 0 0 0 1 0 0 0 0 0 0 0 4
5 7.6 0 0 0 0 0 1 0 0 0 0 0 0 5
6 7.8 0 0 0 0 0 0 1 0 0 0 0 0 6
7 7.8 0 0 0 0 0 0 0 1 0 0 0 0 7
8 7.8 0 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 0 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 0 0 0 0 0 0 0 0 0 0 1 0 10
11 7.1 0 0 0 0 0 0 0 0 0 0 0 1 11
12 7.5 0 0 0 0 0 0 0 0 0 0 0 0 12
13 7.5 0 1 0 0 0 0 0 0 0 0 0 0 13
14 7.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 7.7 0 0 0 1 0 0 0 0 0 0 0 0 15
16 7.7 0 0 0 0 1 0 0 0 0 0 0 0 16
17 7.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 8.1 0 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 0 0 0 0 0 0 0 0 1 0 0 0 20
21 8.2 0 0 0 0 0 0 0 0 0 1 0 0 21
22 7.9 0 0 0 0 0 0 0 0 0 0 1 0 22
23 7.3 0 0 0 0 0 0 0 0 0 0 0 1 23
24 6.9 0 0 0 0 0 0 0 0 0 0 0 0 24
25 6.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 6.7 0 0 1 0 0 0 0 0 0 0 0 0 26
27 6.9 0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.0 0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.1 0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.2 0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 6.9 0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 6.8 0 0 0 0 0 0 0 0 0 0 1 0 34
35 6.4 0 0 0 0 0 0 0 0 0 0 0 1 35
36 6.7 0 0 0 0 0 0 0 0 0 0 0 0 36
37 6.6 0 1 0 0 0 0 0 0 0 0 0 0 37
38 6.4 0 0 1 0 0 0 0 0 0 0 0 0 38
39 6.3 0 0 0 1 0 0 0 0 0 0 0 0 39
40 6.2 0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.5 0 0 0 0 0 1 0 0 0 0 0 0 41
42 6.8 1 0 0 0 0 0 1 0 0 0 0 0 42
43 6.8 1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.4 1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.1 1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.8 1 0 0 0 0 0 0 0 0 0 1 0 46
47 6.1 1 0 0 0 0 0 0 0 0 0 0 1 47
48 7.2 1 0 0 0 0 0 0 0 0 0 0 0 48
49 7.3 1 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 6.1 1 0 0 1 0 0 0 0 0 0 0 0 51
52 5.8 1 0 0 0 1 0 0 0 0 0 0 0 52
53 6.2 1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 7.9 1 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 1 0 0 0 0 0 0 0 0 1 0 0 57
58 7.4 1 0 0 0 0 0 0 0 0 0 1 0 58
59 7.5 1 0 0 0 0 0 0 0 0 0 0 1 59
60 8.0 1 0 0 0 0 0 0 0 0 0 0 0 60
61 8.1 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
7.989714 0.291429 -0.008111 -0.296794 -0.473286 -0.549778
M5 M6 M7 M8 M9 M10
-0.306270 -0.001048 0.142460 0.085968 -0.030524 -0.227016
M11 t
-0.403508 -0.023508
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.17276 -0.38171 -0.04762 0.45410 1.26095
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.989714 0.337456 23.676 <2e-16 ***
X 0.291429 0.292590 0.996 0.324
M1 -0.008111 0.372191 -0.022 0.983
M2 -0.296794 0.390476 -0.760 0.451
M3 -0.473286 0.389954 -1.214 0.231
M4 -0.549778 0.389587 -1.411 0.165
M5 -0.306270 0.389374 -0.787 0.435
M6 -0.001048 0.390607 -0.003 0.998
M7 0.142460 0.389757 0.366 0.716
M8 0.085968 0.389060 0.221 0.826
M9 -0.030524 0.388517 -0.079 0.938
M10 -0.227016 0.388129 -0.585 0.561
M11 -0.403508 0.387895 -1.040 0.304
t -0.023508 0.007766 -3.027 0.004 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6132 on 47 degrees of freedom
Multiple R-squared: 0.3298, Adjusted R-squared: 0.1445
F-statistic: 1.779 on 13 and 47 DF, p-value: 0.07523
> 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,] 0.10651459 0.21302919 0.8934854
[2,] 0.05868524 0.11737048 0.9413148
[3,] 0.03647219 0.07294437 0.9635278
[4,] 0.02335178 0.04670356 0.9766482
[5,] 0.03148275 0.06296549 0.9685173
[6,] 0.02939892 0.05879785 0.9706011
[7,] 0.02043550 0.04087101 0.9795645
[8,] 0.03041882 0.06083763 0.9695812
[9,] 0.13824062 0.27648123 0.8617594
[10,] 0.20588783 0.41177566 0.7941122
[11,] 0.24971306 0.49942611 0.7502869
[12,] 0.43710441 0.87420881 0.5628956
[13,] 0.77858125 0.44283750 0.2214188
[14,] 0.78559707 0.42880585 0.2144029
[15,] 0.74707275 0.50585449 0.2529272
[16,] 0.71883923 0.56232153 0.2811608
[17,] 0.72221681 0.55556639 0.2777832
[18,] 0.80657145 0.38685710 0.1934285
[19,] 0.75169978 0.49660043 0.2483002
[20,] 0.67955337 0.64089326 0.3204466
[21,] 0.69161884 0.61676231 0.3083812
[22,] 0.75097183 0.49805634 0.2490282
[23,] 0.66415689 0.67168622 0.3358431
[24,] 0.56174842 0.87650316 0.4382516
[25,] 0.43504617 0.87009234 0.5649538
[26,] 0.64772882 0.70454236 0.3522712
[27,] 0.57108179 0.85783642 0.4289182
[28,] 0.43833454 0.87666909 0.5616655
> postscript(file="/var/www/html/rcomp/tmp/1l0e21258896748.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ehz61258896748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3za601258896748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4m7w51258896748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5r2yv1258896748.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 = 61
Frequency = 1
1 2 3 4 5 6
0.04190476 0.45409524 0.25409524 0.15409524 0.03409524 -0.04761905
7 8 9 10 11 12
-0.16761905 -0.08761905 -0.24761905 -0.02761905 -0.22761905 -0.20761905
13 14 15 16 17 18
-0.17600000 0.23619048 0.53619048 0.63619048 0.61619048 0.53447619
19 20 21 22 23 24
0.51447619 0.59447619 0.73447619 0.65447619 0.25447619 -0.52552381
25 26 27 28 29 30
-0.79390476 -0.38171429 0.01828571 0.21828571 0.09828571 -0.08342857
31 32 33 34 35 36
-0.30342857 -0.42342857 -0.18342857 -0.16342857 -0.36342857 -0.44342857
37 38 39 40 41 42
-0.51180952 -0.39961905 -0.29961905 -0.29961905 -0.21961905 -0.49276190
43 44 45 46 47 48
-0.61276190 -0.93276190 -1.09276190 -1.17276190 -0.67276190 0.04723810
49 50 51 52 53 54
0.17885714 0.09104762 -0.50895238 -0.70895238 -0.52895238 0.08933333
55 56 57 58 59 60
0.56933333 0.84933333 0.78933333 0.70933333 1.00933333 1.12933333
61
1.26095238
> postscript(file="/var/www/html/rcomp/tmp/68z5g1258896748.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.04190476 NA
1 0.45409524 0.04190476
2 0.25409524 0.45409524
3 0.15409524 0.25409524
4 0.03409524 0.15409524
5 -0.04761905 0.03409524
6 -0.16761905 -0.04761905
7 -0.08761905 -0.16761905
8 -0.24761905 -0.08761905
9 -0.02761905 -0.24761905
10 -0.22761905 -0.02761905
11 -0.20761905 -0.22761905
12 -0.17600000 -0.20761905
13 0.23619048 -0.17600000
14 0.53619048 0.23619048
15 0.63619048 0.53619048
16 0.61619048 0.63619048
17 0.53447619 0.61619048
18 0.51447619 0.53447619
19 0.59447619 0.51447619
20 0.73447619 0.59447619
21 0.65447619 0.73447619
22 0.25447619 0.65447619
23 -0.52552381 0.25447619
24 -0.79390476 -0.52552381
25 -0.38171429 -0.79390476
26 0.01828571 -0.38171429
27 0.21828571 0.01828571
28 0.09828571 0.21828571
29 -0.08342857 0.09828571
30 -0.30342857 -0.08342857
31 -0.42342857 -0.30342857
32 -0.18342857 -0.42342857
33 -0.16342857 -0.18342857
34 -0.36342857 -0.16342857
35 -0.44342857 -0.36342857
36 -0.51180952 -0.44342857
37 -0.39961905 -0.51180952
38 -0.29961905 -0.39961905
39 -0.29961905 -0.29961905
40 -0.21961905 -0.29961905
41 -0.49276190 -0.21961905
42 -0.61276190 -0.49276190
43 -0.93276190 -0.61276190
44 -1.09276190 -0.93276190
45 -1.17276190 -1.09276190
46 -0.67276190 -1.17276190
47 0.04723810 -0.67276190
48 0.17885714 0.04723810
49 0.09104762 0.17885714
50 -0.50895238 0.09104762
51 -0.70895238 -0.50895238
52 -0.52895238 -0.70895238
53 0.08933333 -0.52895238
54 0.56933333 0.08933333
55 0.84933333 0.56933333
56 0.78933333 0.84933333
57 0.70933333 0.78933333
58 1.00933333 0.70933333
59 1.12933333 1.00933333
60 1.26095238 1.12933333
61 NA 1.26095238
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.45409524 0.04190476
[2,] 0.25409524 0.45409524
[3,] 0.15409524 0.25409524
[4,] 0.03409524 0.15409524
[5,] -0.04761905 0.03409524
[6,] -0.16761905 -0.04761905
[7,] -0.08761905 -0.16761905
[8,] -0.24761905 -0.08761905
[9,] -0.02761905 -0.24761905
[10,] -0.22761905 -0.02761905
[11,] -0.20761905 -0.22761905
[12,] -0.17600000 -0.20761905
[13,] 0.23619048 -0.17600000
[14,] 0.53619048 0.23619048
[15,] 0.63619048 0.53619048
[16,] 0.61619048 0.63619048
[17,] 0.53447619 0.61619048
[18,] 0.51447619 0.53447619
[19,] 0.59447619 0.51447619
[20,] 0.73447619 0.59447619
[21,] 0.65447619 0.73447619
[22,] 0.25447619 0.65447619
[23,] -0.52552381 0.25447619
[24,] -0.79390476 -0.52552381
[25,] -0.38171429 -0.79390476
[26,] 0.01828571 -0.38171429
[27,] 0.21828571 0.01828571
[28,] 0.09828571 0.21828571
[29,] -0.08342857 0.09828571
[30,] -0.30342857 -0.08342857
[31,] -0.42342857 -0.30342857
[32,] -0.18342857 -0.42342857
[33,] -0.16342857 -0.18342857
[34,] -0.36342857 -0.16342857
[35,] -0.44342857 -0.36342857
[36,] -0.51180952 -0.44342857
[37,] -0.39961905 -0.51180952
[38,] -0.29961905 -0.39961905
[39,] -0.29961905 -0.29961905
[40,] -0.21961905 -0.29961905
[41,] -0.49276190 -0.21961905
[42,] -0.61276190 -0.49276190
[43,] -0.93276190 -0.61276190
[44,] -1.09276190 -0.93276190
[45,] -1.17276190 -1.09276190
[46,] -0.67276190 -1.17276190
[47,] 0.04723810 -0.67276190
[48,] 0.17885714 0.04723810
[49,] 0.09104762 0.17885714
[50,] -0.50895238 0.09104762
[51,] -0.70895238 -0.50895238
[52,] -0.52895238 -0.70895238
[53,] 0.08933333 -0.52895238
[54,] 0.56933333 0.08933333
[55,] 0.84933333 0.56933333
[56,] 0.78933333 0.84933333
[57,] 0.70933333 0.78933333
[58,] 1.00933333 0.70933333
[59,] 1.12933333 1.00933333
[60,] 1.26095238 1.12933333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.45409524 0.04190476
2 0.25409524 0.45409524
3 0.15409524 0.25409524
4 0.03409524 0.15409524
5 -0.04761905 0.03409524
6 -0.16761905 -0.04761905
7 -0.08761905 -0.16761905
8 -0.24761905 -0.08761905
9 -0.02761905 -0.24761905
10 -0.22761905 -0.02761905
11 -0.20761905 -0.22761905
12 -0.17600000 -0.20761905
13 0.23619048 -0.17600000
14 0.53619048 0.23619048
15 0.63619048 0.53619048
16 0.61619048 0.63619048
17 0.53447619 0.61619048
18 0.51447619 0.53447619
19 0.59447619 0.51447619
20 0.73447619 0.59447619
21 0.65447619 0.73447619
22 0.25447619 0.65447619
23 -0.52552381 0.25447619
24 -0.79390476 -0.52552381
25 -0.38171429 -0.79390476
26 0.01828571 -0.38171429
27 0.21828571 0.01828571
28 0.09828571 0.21828571
29 -0.08342857 0.09828571
30 -0.30342857 -0.08342857
31 -0.42342857 -0.30342857
32 -0.18342857 -0.42342857
33 -0.16342857 -0.18342857
34 -0.36342857 -0.16342857
35 -0.44342857 -0.36342857
36 -0.51180952 -0.44342857
37 -0.39961905 -0.51180952
38 -0.29961905 -0.39961905
39 -0.29961905 -0.29961905
40 -0.21961905 -0.29961905
41 -0.49276190 -0.21961905
42 -0.61276190 -0.49276190
43 -0.93276190 -0.61276190
44 -1.09276190 -0.93276190
45 -1.17276190 -1.09276190
46 -0.67276190 -1.17276190
47 0.04723810 -0.67276190
48 0.17885714 0.04723810
49 0.09104762 0.17885714
50 -0.50895238 0.09104762
51 -0.70895238 -0.50895238
52 -0.52895238 -0.70895238
53 0.08933333 -0.52895238
54 0.56933333 0.08933333
55 0.84933333 0.56933333
56 0.78933333 0.84933333
57 0.70933333 0.78933333
58 1.00933333 0.70933333
59 1.12933333 1.00933333
60 1.26095238 1.12933333
> 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/71ku81258896748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8iqiz1258896748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o3w31258896748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fm2u1258896748.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118mcg1258896748.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/127ydd1258896749.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/13rcbp1258896749.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/14gjk01258896749.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/15sxcg1258896749.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/16xrzc1258896749.tab")
+ }
>
> system("convert tmp/1l0e21258896748.ps tmp/1l0e21258896748.png")
> system("convert tmp/2ehz61258896748.ps tmp/2ehz61258896748.png")
> system("convert tmp/3za601258896748.ps tmp/3za601258896748.png")
> system("convert tmp/4m7w51258896748.ps tmp/4m7w51258896748.png")
> system("convert tmp/5r2yv1258896748.ps tmp/5r2yv1258896748.png")
> system("convert tmp/68z5g1258896748.ps tmp/68z5g1258896748.png")
> system("convert tmp/71ku81258896748.ps tmp/71ku81258896748.png")
> system("convert tmp/8iqiz1258896748.ps tmp/8iqiz1258896748.png")
> system("convert tmp/9o3w31258896748.ps tmp/9o3w31258896748.png")
> system("convert tmp/10fm2u1258896748.ps tmp/10fm2u1258896748.png")
>
>
> proc.time()
user system elapsed
2.363 1.569 3.572