R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9.3,4,9.3,3.8,8.7,4.7,8.2,4.3,8.3,3.9,8.5,4,8.6,4.3,8.5,4.8,8.2,4.4,8.1,4.3,7.9,4.7,8.6,4.7,8.7,4.9,8.7,5,8.5,4.2,8.4,4.3,8.5,4.8,8.7,4.8,8.7,4.8,8.6,4.2,8.5,4.6,8.3,4.8,8,4.5,8.2,4.4,8.1,4.3,8.1,3.9,8,3.7,7.9,4,7.9,4.1,8,3.7,8,3.8,7.9,3.8,8,3.8,7.7,3.3,7.2,3.3,7.5,3.3,7.3,3.2,7,3.4,7,4.2,7,4.9,7.2,5.1,7.3,5.5,7.1,5.6,6.8,6.4,6.4,6.1,6.1,7.1,6.5,7.8,7.7,7.9,7.9,7.4,7.5,7.5,6.9,6.8,6.6,5.2,6.9,4.7,7.7,4.1,8,3.9,8,2.6,7.7,2.7,7.3,1.8,7.4,1,8.1,0.3),dim=c(2,60),dimnames=list(c('werklh','inflatie'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('werklh','inflatie'),1:60))
> 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
werklh inflatie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.3 4.0 1 0 0 0 0 0 0 0 0 0 0 1
2 9.3 3.8 0 1 0 0 0 0 0 0 0 0 0 2
3 8.7 4.7 0 0 1 0 0 0 0 0 0 0 0 3
4 8.2 4.3 0 0 0 1 0 0 0 0 0 0 0 4
5 8.3 3.9 0 0 0 0 1 0 0 0 0 0 0 5
6 8.5 4.0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.6 4.3 0 0 0 0 0 0 1 0 0 0 0 7
8 8.5 4.8 0 0 0 0 0 0 0 1 0 0 0 8
9 8.2 4.4 0 0 0 0 0 0 0 0 1 0 0 9
10 8.1 4.3 0 0 0 0 0 0 0 0 0 1 0 10
11 7.9 4.7 0 0 0 0 0 0 0 0 0 0 1 11
12 8.6 4.7 0 0 0 0 0 0 0 0 0 0 0 12
13 8.7 4.9 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 5.0 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 4.2 0 0 1 0 0 0 0 0 0 0 0 15
16 8.4 4.3 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 4.8 0 0 0 0 1 0 0 0 0 0 0 17
18 8.7 4.8 0 0 0 0 0 1 0 0 0 0 0 18
19 8.7 4.8 0 0 0 0 0 0 1 0 0 0 0 19
20 8.6 4.2 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 4.6 0 0 0 0 0 0 0 0 1 0 0 21
22 8.3 4.8 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 4.5 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 4.4 0 0 0 0 0 0 0 0 0 0 0 24
25 8.1 4.3 1 0 0 0 0 0 0 0 0 0 0 25
26 8.1 3.9 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 3.7 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 4.0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.9 4.1 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 3.7 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 3.8 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 3.8 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 3.8 0 0 0 0 0 0 0 0 1 0 0 33
34 7.7 3.3 0 0 0 0 0 0 0 0 0 1 0 34
35 7.2 3.3 0 0 0 0 0 0 0 0 0 0 1 35
36 7.5 3.3 0 0 0 0 0 0 0 0 0 0 0 36
37 7.3 3.2 1 0 0 0 0 0 0 0 0 0 0 37
38 7.0 3.4 0 1 0 0 0 0 0 0 0 0 0 38
39 7.0 4.2 0 0 1 0 0 0 0 0 0 0 0 39
40 7.0 4.9 0 0 0 1 0 0 0 0 0 0 0 40
41 7.2 5.1 0 0 0 0 1 0 0 0 0 0 0 41
42 7.3 5.5 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 5.6 0 0 0 0 0 0 1 0 0 0 0 43
44 6.8 6.4 0 0 0 0 0 0 0 1 0 0 0 44
45 6.4 6.1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.1 7.1 0 0 0 0 0 0 0 0 0 1 0 46
47 6.5 7.8 0 0 0 0 0 0 0 0 0 0 1 47
48 7.7 7.9 0 0 0 0 0 0 0 0 0 0 0 48
49 7.9 7.4 1 0 0 0 0 0 0 0 0 0 0 49
50 7.5 7.5 0 1 0 0 0 0 0 0 0 0 0 50
51 6.9 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 6.6 5.2 0 0 0 1 0 0 0 0 0 0 0 52
53 6.9 4.7 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 4.1 0 0 0 0 0 1 0 0 0 0 0 54
55 8.0 3.9 0 0 0 0 0 0 1 0 0 0 0 55
56 8.0 2.6 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 2.7 0 0 0 0 0 0 0 0 1 0 0 57
58 7.3 1.8 0 0 0 0 0 0 0 0 0 1 0 58
59 7.4 1.0 0 0 0 0 0 0 0 0 0 0 1 59
60 8.1 0.3 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) inflatie M1 M2 M3 M4
9.653695 -0.140310 0.007248 -0.109041 -0.379719 -0.575652
M5 M6 M7 M8 M9 M10
-0.409135 -0.113843 -0.036102 -0.143617 -0.319906 -0.559002
M11 t
-0.629679 -0.029323
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.953336 -0.219124 -0.002741 0.271011 0.714164
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.653695 0.271248 35.590 < 2e-16 ***
inflatie -0.140310 0.039035 -3.594 0.000789 ***
M1 0.007248 0.270519 0.027 0.978740
M2 -0.109041 0.269961 -0.404 0.688147
M3 -0.379719 0.269588 -1.409 0.165703
M4 -0.575652 0.268710 -2.142 0.037497 *
M5 -0.409135 0.268366 -1.525 0.134220
M6 -0.113843 0.267901 -0.425 0.672857
M7 -0.036102 0.267801 -0.135 0.893351
M8 -0.143617 0.267409 -0.537 0.593810
M9 -0.319906 0.267218 -1.197 0.237373
M10 -0.559002 0.267058 -2.093 0.041875 *
M11 -0.629679 0.266998 -2.358 0.022661 *
t -0.029323 0.003212 -9.128 6.83e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.422 on 46 degrees of freedom
Multiple R-squared: 0.723, Adjusted R-squared: 0.6448
F-statistic: 9.237 on 13 and 46 DF, p-value: 6.057e-09
> 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,] 0.227403661 0.454807322 0.77259634
[2,] 0.171675838 0.343351676 0.82832416
[3,] 0.096881614 0.193763228 0.90311839
[4,] 0.046803457 0.093606915 0.95319654
[5,] 0.032677489 0.065354977 0.96732251
[6,] 0.024227379 0.048454757 0.97577262
[7,] 0.012978443 0.025956886 0.98702156
[8,] 0.012837667 0.025675335 0.98716233
[9,] 0.044806529 0.089613059 0.95519347
[10,] 0.052604547 0.105209095 0.94739545
[11,] 0.036475032 0.072950064 0.96352497
[12,] 0.029854814 0.059709629 0.97014519
[13,] 0.022387584 0.044775167 0.97761242
[14,] 0.013066551 0.026133102 0.98693345
[15,] 0.007511426 0.015022851 0.99248857
[16,] 0.004654682 0.009309364 0.99534532
[17,] 0.008504061 0.017008122 0.99149594
[18,] 0.038963590 0.077927181 0.96103641
[19,] 0.052473289 0.104946579 0.94752671
[20,] 0.036591970 0.073183941 0.96340803
[21,] 0.060155657 0.120311313 0.93984434
[22,] 0.170210909 0.340421819 0.82978909
[23,] 0.166654107 0.333308213 0.83334589
[24,] 0.222426431 0.444852862 0.77757357
[25,] 0.538895349 0.922209302 0.46110465
[26,] 0.761235259 0.477529482 0.23876474
[27,] 0.910014558 0.179970885 0.08998544
> postscript(file="/var/www/html/rcomp/tmp/1rqze1261058825.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rmfa1261058825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jonj1261058825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41zrp1261058825.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rsie1261058825.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 = 60
Frequency = 1
1 2 3 4 5 6
0.22961891 0.34716931 0.17344832 -0.15741988 -0.25073769 -0.30267569
7 8 9 10 11 12
-0.20900128 -0.10200908 -0.25252068 -0.09813308 -0.14200908 -0.04236568
13 14 15 16 17 18
0.10777066 0.26741406 0.25516606 0.39445286 0.42741406 0.36144506
19 20 21 22 23 24
0.31302646 0.26567766 0.42741406 0.52389466 0.28180166 -0.13258594
25 26 27 28 29 30
-0.22454260 -0.13505420 0.03688379 0.20423260 0.08106980 -0.14102320
31 32 33 34 35 36
-0.17541080 -0.13857360 0.16703880 0.06530240 -0.33469760 -0.63505420
37 38 39 40 41 42
-0.82701087 -0.95333647 -0.54108846 -0.21761566 -0.12674746 -0.23659245
43 44 45 46 47 48
-0.47098005 -0.52189485 -0.75837545 -0.64964684 -0.05142984 0.56224456
49 50 51 52 53 54
0.71416390 0.47380730 0.07559029 -0.22364991 -0.13099872 0.31884628
55 56 57 58 59 60
0.54236568 0.49679987 0.41644327 0.15858287 0.24633487 0.24776126
> postscript(file="/var/www/html/rcomp/tmp/60l4l1261058826.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.22961891 NA
1 0.34716931 0.22961891
2 0.17344832 0.34716931
3 -0.15741988 0.17344832
4 -0.25073769 -0.15741988
5 -0.30267569 -0.25073769
6 -0.20900128 -0.30267569
7 -0.10200908 -0.20900128
8 -0.25252068 -0.10200908
9 -0.09813308 -0.25252068
10 -0.14200908 -0.09813308
11 -0.04236568 -0.14200908
12 0.10777066 -0.04236568
13 0.26741406 0.10777066
14 0.25516606 0.26741406
15 0.39445286 0.25516606
16 0.42741406 0.39445286
17 0.36144506 0.42741406
18 0.31302646 0.36144506
19 0.26567766 0.31302646
20 0.42741406 0.26567766
21 0.52389466 0.42741406
22 0.28180166 0.52389466
23 -0.13258594 0.28180166
24 -0.22454260 -0.13258594
25 -0.13505420 -0.22454260
26 0.03688379 -0.13505420
27 0.20423260 0.03688379
28 0.08106980 0.20423260
29 -0.14102320 0.08106980
30 -0.17541080 -0.14102320
31 -0.13857360 -0.17541080
32 0.16703880 -0.13857360
33 0.06530240 0.16703880
34 -0.33469760 0.06530240
35 -0.63505420 -0.33469760
36 -0.82701087 -0.63505420
37 -0.95333647 -0.82701087
38 -0.54108846 -0.95333647
39 -0.21761566 -0.54108846
40 -0.12674746 -0.21761566
41 -0.23659245 -0.12674746
42 -0.47098005 -0.23659245
43 -0.52189485 -0.47098005
44 -0.75837545 -0.52189485
45 -0.64964684 -0.75837545
46 -0.05142984 -0.64964684
47 0.56224456 -0.05142984
48 0.71416390 0.56224456
49 0.47380730 0.71416390
50 0.07559029 0.47380730
51 -0.22364991 0.07559029
52 -0.13099872 -0.22364991
53 0.31884628 -0.13099872
54 0.54236568 0.31884628
55 0.49679987 0.54236568
56 0.41644327 0.49679987
57 0.15858287 0.41644327
58 0.24633487 0.15858287
59 0.24776126 0.24633487
60 NA 0.24776126
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.34716931 0.22961891
[2,] 0.17344832 0.34716931
[3,] -0.15741988 0.17344832
[4,] -0.25073769 -0.15741988
[5,] -0.30267569 -0.25073769
[6,] -0.20900128 -0.30267569
[7,] -0.10200908 -0.20900128
[8,] -0.25252068 -0.10200908
[9,] -0.09813308 -0.25252068
[10,] -0.14200908 -0.09813308
[11,] -0.04236568 -0.14200908
[12,] 0.10777066 -0.04236568
[13,] 0.26741406 0.10777066
[14,] 0.25516606 0.26741406
[15,] 0.39445286 0.25516606
[16,] 0.42741406 0.39445286
[17,] 0.36144506 0.42741406
[18,] 0.31302646 0.36144506
[19,] 0.26567766 0.31302646
[20,] 0.42741406 0.26567766
[21,] 0.52389466 0.42741406
[22,] 0.28180166 0.52389466
[23,] -0.13258594 0.28180166
[24,] -0.22454260 -0.13258594
[25,] -0.13505420 -0.22454260
[26,] 0.03688379 -0.13505420
[27,] 0.20423260 0.03688379
[28,] 0.08106980 0.20423260
[29,] -0.14102320 0.08106980
[30,] -0.17541080 -0.14102320
[31,] -0.13857360 -0.17541080
[32,] 0.16703880 -0.13857360
[33,] 0.06530240 0.16703880
[34,] -0.33469760 0.06530240
[35,] -0.63505420 -0.33469760
[36,] -0.82701087 -0.63505420
[37,] -0.95333647 -0.82701087
[38,] -0.54108846 -0.95333647
[39,] -0.21761566 -0.54108846
[40,] -0.12674746 -0.21761566
[41,] -0.23659245 -0.12674746
[42,] -0.47098005 -0.23659245
[43,] -0.52189485 -0.47098005
[44,] -0.75837545 -0.52189485
[45,] -0.64964684 -0.75837545
[46,] -0.05142984 -0.64964684
[47,] 0.56224456 -0.05142984
[48,] 0.71416390 0.56224456
[49,] 0.47380730 0.71416390
[50,] 0.07559029 0.47380730
[51,] -0.22364991 0.07559029
[52,] -0.13099872 -0.22364991
[53,] 0.31884628 -0.13099872
[54,] 0.54236568 0.31884628
[55,] 0.49679987 0.54236568
[56,] 0.41644327 0.49679987
[57,] 0.15858287 0.41644327
[58,] 0.24633487 0.15858287
[59,] 0.24776126 0.24633487
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.34716931 0.22961891
2 0.17344832 0.34716931
3 -0.15741988 0.17344832
4 -0.25073769 -0.15741988
5 -0.30267569 -0.25073769
6 -0.20900128 -0.30267569
7 -0.10200908 -0.20900128
8 -0.25252068 -0.10200908
9 -0.09813308 -0.25252068
10 -0.14200908 -0.09813308
11 -0.04236568 -0.14200908
12 0.10777066 -0.04236568
13 0.26741406 0.10777066
14 0.25516606 0.26741406
15 0.39445286 0.25516606
16 0.42741406 0.39445286
17 0.36144506 0.42741406
18 0.31302646 0.36144506
19 0.26567766 0.31302646
20 0.42741406 0.26567766
21 0.52389466 0.42741406
22 0.28180166 0.52389466
23 -0.13258594 0.28180166
24 -0.22454260 -0.13258594
25 -0.13505420 -0.22454260
26 0.03688379 -0.13505420
27 0.20423260 0.03688379
28 0.08106980 0.20423260
29 -0.14102320 0.08106980
30 -0.17541080 -0.14102320
31 -0.13857360 -0.17541080
32 0.16703880 -0.13857360
33 0.06530240 0.16703880
34 -0.33469760 0.06530240
35 -0.63505420 -0.33469760
36 -0.82701087 -0.63505420
37 -0.95333647 -0.82701087
38 -0.54108846 -0.95333647
39 -0.21761566 -0.54108846
40 -0.12674746 -0.21761566
41 -0.23659245 -0.12674746
42 -0.47098005 -0.23659245
43 -0.52189485 -0.47098005
44 -0.75837545 -0.52189485
45 -0.64964684 -0.75837545
46 -0.05142984 -0.64964684
47 0.56224456 -0.05142984
48 0.71416390 0.56224456
49 0.47380730 0.71416390
50 0.07559029 0.47380730
51 -0.22364991 0.07559029
52 -0.13099872 -0.22364991
53 0.31884628 -0.13099872
54 0.54236568 0.31884628
55 0.49679987 0.54236568
56 0.41644327 0.49679987
57 0.15858287 0.41644327
58 0.24633487 0.15858287
59 0.24776126 0.24633487
> 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/77fo31261058826.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v7mo1261058826.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9pqlo1261058826.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10i8v41261058826.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xqvx1261058826.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/125m2m1261058826.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/13eojd1261058826.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/14jtwu1261058826.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/15cyp01261058826.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/16ty3p1261058826.tab")
+ }
>
> try(system("convert tmp/1rqze1261058825.ps tmp/1rqze1261058825.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rmfa1261058825.ps tmp/2rmfa1261058825.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jonj1261058825.ps tmp/3jonj1261058825.png",intern=TRUE))
character(0)
> try(system("convert tmp/41zrp1261058825.ps tmp/41zrp1261058825.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rsie1261058825.ps tmp/5rsie1261058825.png",intern=TRUE))
character(0)
> try(system("convert tmp/60l4l1261058826.ps tmp/60l4l1261058826.png",intern=TRUE))
character(0)
> try(system("convert tmp/77fo31261058826.ps tmp/77fo31261058826.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v7mo1261058826.ps tmp/8v7mo1261058826.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pqlo1261058826.ps tmp/9pqlo1261058826.png",intern=TRUE))
character(0)
> try(system("convert tmp/10i8v41261058826.ps tmp/10i8v41261058826.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.379 1.558 3.451