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(8.1,92.9,7.7,107.7,7.5,103.5,7.6,91.1,7.8,79.8,7.8,71.9,7.8,82.9,7.5,90.1,7.5,100.7,7.1,90.7,7.5,108.8,7.5,44.1,7.6,93.6,7.7,107.4,7.7,96.5,7.9,93.6,8.1,76.5,8.2,76.7,8.2,84,8.2,103.3,7.9,88.5,7.3,99,6.9,105.9,6.6,44.7,6.7,94,6.9,107.1,7,104.8,7.1,102.5,7.2,77.7,7.1,85.2,6.9,91.3,7,106.5,6.8,92.4,6.4,97.5,6.7,107,6.6,51.1,6.4,98.6,6.3,102.2,6.2,114.3,6.5,99.4,6.8,72.5,6.8,92.3,6.4,99.4,6.1,85.9,5.8,109.4,6.1,97.6,7.2,104.7,7.3,56.9,6.9,86.7,6.1,108.5,5.8,103.4,6.2,86.2,7.1,71,7.7,75.9,7.9,87.1,7.7,102,7.4,88.5,7.5,87.8,8,100.8,8.1,50.6,8,85.9),dim=c(2,61),dimnames=list(c('Werkloosheidsgraad','Bruto_index'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Werkloosheidsgraad','Bruto_index'),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 = 'No 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
Werkloosheidsgraad Bruto_index M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.1 92.9 1 0 0 0 0 0 0 0 0 0 0
2 7.7 107.7 0 1 0 0 0 0 0 0 0 0 0
3 7.5 103.5 0 0 1 0 0 0 0 0 0 0 0
4 7.6 91.1 0 0 0 1 0 0 0 0 0 0 0
5 7.8 79.8 0 0 0 0 1 0 0 0 0 0 0
6 7.8 71.9 0 0 0 0 0 1 0 0 0 0 0
7 7.8 82.9 0 0 0 0 0 0 1 0 0 0 0
8 7.5 90.1 0 0 0 0 0 0 0 1 0 0 0
9 7.5 100.7 0 0 0 0 0 0 0 0 1 0 0
10 7.1 90.7 0 0 0 0 0 0 0 0 0 1 0
11 7.5 108.8 0 0 0 0 0 0 0 0 0 0 1
12 7.5 44.1 0 0 0 0 0 0 0 0 0 0 0
13 7.6 93.6 1 0 0 0 0 0 0 0 0 0 0
14 7.7 107.4 0 1 0 0 0 0 0 0 0 0 0
15 7.7 96.5 0 0 1 0 0 0 0 0 0 0 0
16 7.9 93.6 0 0 0 1 0 0 0 0 0 0 0
17 8.1 76.5 0 0 0 0 1 0 0 0 0 0 0
18 8.2 76.7 0 0 0 0 0 1 0 0 0 0 0
19 8.2 84.0 0 0 0 0 0 0 1 0 0 0 0
20 8.2 103.3 0 0 0 0 0 0 0 1 0 0 0
21 7.9 88.5 0 0 0 0 0 0 0 0 1 0 0
22 7.3 99.0 0 0 0 0 0 0 0 0 0 1 0
23 6.9 105.9 0 0 0 0 0 0 0 0 0 0 1
24 6.6 44.7 0 0 0 0 0 0 0 0 0 0 0
25 6.7 94.0 1 0 0 0 0 0 0 0 0 0 0
26 6.9 107.1 0 1 0 0 0 0 0 0 0 0 0
27 7.0 104.8 0 0 1 0 0 0 0 0 0 0 0
28 7.1 102.5 0 0 0 1 0 0 0 0 0 0 0
29 7.2 77.7 0 0 0 0 1 0 0 0 0 0 0
30 7.1 85.2 0 0 0 0 0 1 0 0 0 0 0
31 6.9 91.3 0 0 0 0 0 0 1 0 0 0 0
32 7.0 106.5 0 0 0 0 0 0 0 1 0 0 0
33 6.8 92.4 0 0 0 0 0 0 0 0 1 0 0
34 6.4 97.5 0 0 0 0 0 0 0 0 0 1 0
35 6.7 107.0 0 0 0 0 0 0 0 0 0 0 1
36 6.6 51.1 0 0 0 0 0 0 0 0 0 0 0
37 6.4 98.6 1 0 0 0 0 0 0 0 0 0 0
38 6.3 102.2 0 1 0 0 0 0 0 0 0 0 0
39 6.2 114.3 0 0 1 0 0 0 0 0 0 0 0
40 6.5 99.4 0 0 0 1 0 0 0 0 0 0 0
41 6.8 72.5 0 0 0 0 1 0 0 0 0 0 0
42 6.8 92.3 0 0 0 0 0 1 0 0 0 0 0
43 6.4 99.4 0 0 0 0 0 0 1 0 0 0 0
44 6.1 85.9 0 0 0 0 0 0 0 1 0 0 0
45 5.8 109.4 0 0 0 0 0 0 0 0 1 0 0
46 6.1 97.6 0 0 0 0 0 0 0 0 0 1 0
47 7.2 104.7 0 0 0 0 0 0 0 0 0 0 1
48 7.3 56.9 0 0 0 0 0 0 0 0 0 0 0
49 6.9 86.7 1 0 0 0 0 0 0 0 0 0 0
50 6.1 108.5 0 1 0 0 0 0 0 0 0 0 0
51 5.8 103.4 0 0 1 0 0 0 0 0 0 0 0
52 6.2 86.2 0 0 0 1 0 0 0 0 0 0 0
53 7.1 71.0 0 0 0 0 1 0 0 0 0 0 0
54 7.7 75.9 0 0 0 0 0 1 0 0 0 0 0
55 7.9 87.1 0 0 0 0 0 0 1 0 0 0 0
56 7.7 102.0 0 0 0 0 0 0 0 1 0 0 0
57 7.4 88.5 0 0 0 0 0 0 0 0 1 0 0
58 7.5 87.8 0 0 0 0 0 0 0 0 0 1 0
59 8.0 100.8 0 0 0 0 0 0 0 0 0 0 1
60 8.1 50.6 0 0 0 0 0 0 0 0 0 0 0
61 8.0 85.9 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Bruto_index M1 M2 M3 M4
8.81968 -0.03233 1.43638 1.56603 1.39879 1.29743
M5 M6 M7 M8 M9 M10
1.02122 1.29964 1.49574 1.63442 1.36075 1.11614
M11
1.84918
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.57697 -0.46370 0.03452 0.54354 1.08557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.81968 0.82441 10.698 2.65e-14 ***
Bruto_index -0.03233 0.01552 -2.084 0.0425 *
M1 1.43638 0.77434 1.855 0.0697 .
M2 1.56603 0.98253 1.594 0.1175
M3 1.39879 0.95353 1.467 0.1489
M4 1.29743 0.81832 1.585 0.1194
M5 1.02122 0.58598 1.743 0.0878 .
M6 1.29964 0.64074 2.028 0.0481 *
M7 1.49574 0.74516 2.007 0.0504 .
M8 1.63442 0.85845 1.904 0.0629 .
M9 1.36075 0.83616 1.627 0.1102
M10 1.11614 0.81779 1.365 0.1787
M11 1.84918 0.96661 1.913 0.0617 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6715 on 48 degrees of freedom
Multiple R-squared: 0.1792, Adjusted R-squared: -0.02594
F-statistic: 0.8736 on 12 and 48 DF, p-value: 0.5782
> 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,] 0.059139441 0.11827888 0.9408606
[2,] 0.024087566 0.04817513 0.9759124
[3,] 0.019119356 0.03823871 0.9808806
[4,] 0.012477326 0.02495465 0.9875227
[5,] 0.010021013 0.02004203 0.9899790
[6,] 0.016735884 0.03347177 0.9832641
[7,] 0.008215713 0.01643143 0.9917843
[8,] 0.007484595 0.01496919 0.9925154
[9,] 0.020721780 0.04144356 0.9792782
[10,] 0.077973143 0.15594629 0.9220269
[11,] 0.097999893 0.19599979 0.9020001
[12,] 0.105767417 0.21153483 0.8942326
[13,] 0.124432410 0.24886482 0.8755676
[14,] 0.126675692 0.25335138 0.8733243
[15,] 0.115908474 0.23181695 0.8840915
[16,] 0.121137074 0.24227415 0.8788629
[17,] 0.101009505 0.20201901 0.8989905
[18,] 0.106029065 0.21205813 0.8939709
[19,] 0.086713060 0.17342612 0.9132869
[20,] 0.075224755 0.15044951 0.9247752
[21,] 0.087079568 0.17415914 0.9129204
[22,] 0.088462108 0.17692422 0.9115379
[23,] 0.112724352 0.22544870 0.8872756
[24,] 0.101906720 0.20381344 0.8980933
[25,] 0.104381931 0.20876386 0.8956181
[26,] 0.092993990 0.18598798 0.9070060
[27,] 0.053674197 0.10734839 0.9463258
[28,] 0.045575829 0.09115166 0.9544242
[29,] 0.743157148 0.51368570 0.2568429
[30,] 0.753700562 0.49259888 0.2462994
> postscript(file="/var/www/html/rcomp/tmp/1ozf11261135058.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dfsx1261135058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3boa31261135058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45myn1261135058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56dhq1261135058.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
0.847380033 0.796209442 0.627670141 0.428138688 0.539018394 0.005196198
7 8 9 10 11 12
0.164727651 -0.041180749 0.575183324 0.096499938 0.348628326 0.106065358
13 14 15 16 17 18
0.370010934 0.786510484 0.601361127 0.808963335 0.732329859 0.560379521
19 20 21 22 23 24
0.600290496 1.085573391 0.580759043 0.564837769 -0.345128265 -0.774536726
25 26 27 28 29 30
-0.517057122 -0.023188473 0.169698958 0.296699081 -0.128874310 -0.264816676
31 32 33 34 35 36
-0.463701533 -0.010971060 -0.393154507 -0.383657020 -0.509565420 -0.567625628
37 38 39 40 41 42
-0.668339770 -0.781604783 -0.323167381 -0.403523482 -0.696989577 -0.335274677
43 44 45 46 47 48
-0.701829674 -1.576966157 -0.843546902 -0.680424034 -0.083924096 0.319887554
49 50 51 52 53 54
-0.553065093 -0.777926671 -1.075562845 -1.130277622 -0.445484366 0.034515634
55 56 57 58 59 60
0.400513059 0.543544574 0.080759043 0.402743347 0.589989454 0.916209442
61
0.521071019
> postscript(file="/var/www/html/rcomp/tmp/683451261135058.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 0.847380033 NA
1 0.796209442 0.847380033
2 0.627670141 0.796209442
3 0.428138688 0.627670141
4 0.539018394 0.428138688
5 0.005196198 0.539018394
6 0.164727651 0.005196198
7 -0.041180749 0.164727651
8 0.575183324 -0.041180749
9 0.096499938 0.575183324
10 0.348628326 0.096499938
11 0.106065358 0.348628326
12 0.370010934 0.106065358
13 0.786510484 0.370010934
14 0.601361127 0.786510484
15 0.808963335 0.601361127
16 0.732329859 0.808963335
17 0.560379521 0.732329859
18 0.600290496 0.560379521
19 1.085573391 0.600290496
20 0.580759043 1.085573391
21 0.564837769 0.580759043
22 -0.345128265 0.564837769
23 -0.774536726 -0.345128265
24 -0.517057122 -0.774536726
25 -0.023188473 -0.517057122
26 0.169698958 -0.023188473
27 0.296699081 0.169698958
28 -0.128874310 0.296699081
29 -0.264816676 -0.128874310
30 -0.463701533 -0.264816676
31 -0.010971060 -0.463701533
32 -0.393154507 -0.010971060
33 -0.383657020 -0.393154507
34 -0.509565420 -0.383657020
35 -0.567625628 -0.509565420
36 -0.668339770 -0.567625628
37 -0.781604783 -0.668339770
38 -0.323167381 -0.781604783
39 -0.403523482 -0.323167381
40 -0.696989577 -0.403523482
41 -0.335274677 -0.696989577
42 -0.701829674 -0.335274677
43 -1.576966157 -0.701829674
44 -0.843546902 -1.576966157
45 -0.680424034 -0.843546902
46 -0.083924096 -0.680424034
47 0.319887554 -0.083924096
48 -0.553065093 0.319887554
49 -0.777926671 -0.553065093
50 -1.075562845 -0.777926671
51 -1.130277622 -1.075562845
52 -0.445484366 -1.130277622
53 0.034515634 -0.445484366
54 0.400513059 0.034515634
55 0.543544574 0.400513059
56 0.080759043 0.543544574
57 0.402743347 0.080759043
58 0.589989454 0.402743347
59 0.916209442 0.589989454
60 0.521071019 0.916209442
61 NA 0.521071019
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.796209442 0.847380033
[2,] 0.627670141 0.796209442
[3,] 0.428138688 0.627670141
[4,] 0.539018394 0.428138688
[5,] 0.005196198 0.539018394
[6,] 0.164727651 0.005196198
[7,] -0.041180749 0.164727651
[8,] 0.575183324 -0.041180749
[9,] 0.096499938 0.575183324
[10,] 0.348628326 0.096499938
[11,] 0.106065358 0.348628326
[12,] 0.370010934 0.106065358
[13,] 0.786510484 0.370010934
[14,] 0.601361127 0.786510484
[15,] 0.808963335 0.601361127
[16,] 0.732329859 0.808963335
[17,] 0.560379521 0.732329859
[18,] 0.600290496 0.560379521
[19,] 1.085573391 0.600290496
[20,] 0.580759043 1.085573391
[21,] 0.564837769 0.580759043
[22,] -0.345128265 0.564837769
[23,] -0.774536726 -0.345128265
[24,] -0.517057122 -0.774536726
[25,] -0.023188473 -0.517057122
[26,] 0.169698958 -0.023188473
[27,] 0.296699081 0.169698958
[28,] -0.128874310 0.296699081
[29,] -0.264816676 -0.128874310
[30,] -0.463701533 -0.264816676
[31,] -0.010971060 -0.463701533
[32,] -0.393154507 -0.010971060
[33,] -0.383657020 -0.393154507
[34,] -0.509565420 -0.383657020
[35,] -0.567625628 -0.509565420
[36,] -0.668339770 -0.567625628
[37,] -0.781604783 -0.668339770
[38,] -0.323167381 -0.781604783
[39,] -0.403523482 -0.323167381
[40,] -0.696989577 -0.403523482
[41,] -0.335274677 -0.696989577
[42,] -0.701829674 -0.335274677
[43,] -1.576966157 -0.701829674
[44,] -0.843546902 -1.576966157
[45,] -0.680424034 -0.843546902
[46,] -0.083924096 -0.680424034
[47,] 0.319887554 -0.083924096
[48,] -0.553065093 0.319887554
[49,] -0.777926671 -0.553065093
[50,] -1.075562845 -0.777926671
[51,] -1.130277622 -1.075562845
[52,] -0.445484366 -1.130277622
[53,] 0.034515634 -0.445484366
[54,] 0.400513059 0.034515634
[55,] 0.543544574 0.400513059
[56,] 0.080759043 0.543544574
[57,] 0.402743347 0.080759043
[58,] 0.589989454 0.402743347
[59,] 0.916209442 0.589989454
[60,] 0.521071019 0.916209442
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.796209442 0.847380033
2 0.627670141 0.796209442
3 0.428138688 0.627670141
4 0.539018394 0.428138688
5 0.005196198 0.539018394
6 0.164727651 0.005196198
7 -0.041180749 0.164727651
8 0.575183324 -0.041180749
9 0.096499938 0.575183324
10 0.348628326 0.096499938
11 0.106065358 0.348628326
12 0.370010934 0.106065358
13 0.786510484 0.370010934
14 0.601361127 0.786510484
15 0.808963335 0.601361127
16 0.732329859 0.808963335
17 0.560379521 0.732329859
18 0.600290496 0.560379521
19 1.085573391 0.600290496
20 0.580759043 1.085573391
21 0.564837769 0.580759043
22 -0.345128265 0.564837769
23 -0.774536726 -0.345128265
24 -0.517057122 -0.774536726
25 -0.023188473 -0.517057122
26 0.169698958 -0.023188473
27 0.296699081 0.169698958
28 -0.128874310 0.296699081
29 -0.264816676 -0.128874310
30 -0.463701533 -0.264816676
31 -0.010971060 -0.463701533
32 -0.393154507 -0.010971060
33 -0.383657020 -0.393154507
34 -0.509565420 -0.383657020
35 -0.567625628 -0.509565420
36 -0.668339770 -0.567625628
37 -0.781604783 -0.668339770
38 -0.323167381 -0.781604783
39 -0.403523482 -0.323167381
40 -0.696989577 -0.403523482
41 -0.335274677 -0.696989577
42 -0.701829674 -0.335274677
43 -1.576966157 -0.701829674
44 -0.843546902 -1.576966157
45 -0.680424034 -0.843546902
46 -0.083924096 -0.680424034
47 0.319887554 -0.083924096
48 -0.553065093 0.319887554
49 -0.777926671 -0.553065093
50 -1.075562845 -0.777926671
51 -1.130277622 -1.075562845
52 -0.445484366 -1.130277622
53 0.034515634 -0.445484366
54 0.400513059 0.034515634
55 0.543544574 0.400513059
56 0.080759043 0.543544574
57 0.402743347 0.080759043
58 0.589989454 0.402743347
59 0.916209442 0.589989454
60 0.521071019 0.916209442
> 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/7sq6g1261135058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89agg1261135058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9txfh1261135058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cjji1261135058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vq5v1261135058.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/12ib3h1261135058.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/13xa4c1261135058.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/14eycy1261135058.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/155qnf1261135058.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/16t1bh1261135058.tab")
+ }
>
> try(system("convert tmp/1ozf11261135058.ps tmp/1ozf11261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dfsx1261135058.ps tmp/2dfsx1261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/3boa31261135058.ps tmp/3boa31261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/45myn1261135058.ps tmp/45myn1261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/56dhq1261135058.ps tmp/56dhq1261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/683451261135058.ps tmp/683451261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sq6g1261135058.ps tmp/7sq6g1261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/89agg1261135058.ps tmp/89agg1261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/9txfh1261135058.ps tmp/9txfh1261135058.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cjji1261135058.ps tmp/10cjji1261135058.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.405 1.551 3.327