R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> 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 = '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 t
1 8.1 92.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.7 107.7 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 103.5 0 0 1 0 0 0 0 0 0 0 0 3
4 7.6 91.1 0 0 0 1 0 0 0 0 0 0 0 4
5 7.8 79.8 0 0 0 0 1 0 0 0 0 0 0 5
6 7.8 71.9 0 0 0 0 0 1 0 0 0 0 0 6
7 7.8 82.9 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 90.1 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 100.7 0 0 0 0 0 0 0 0 1 0 0 9
10 7.1 90.7 0 0 0 0 0 0 0 0 0 1 0 10
11 7.5 108.8 0 0 0 0 0 0 0 0 0 0 1 11
12 7.5 44.1 0 0 0 0 0 0 0 0 0 0 0 12
13 7.6 93.6 1 0 0 0 0 0 0 0 0 0 0 13
14 7.7 107.4 0 1 0 0 0 0 0 0 0 0 0 14
15 7.7 96.5 0 0 1 0 0 0 0 0 0 0 0 15
16 7.9 93.6 0 0 0 1 0 0 0 0 0 0 0 16
17 8.1 76.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 76.7 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 84.0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 103.3 0 0 0 0 0 0 0 1 0 0 0 20
21 7.9 88.5 0 0 0 0 0 0 0 0 1 0 0 21
22 7.3 99.0 0 0 0 0 0 0 0 0 0 1 0 22
23 6.9 105.9 0 0 0 0 0 0 0 0 0 0 1 23
24 6.6 44.7 0 0 0 0 0 0 0 0 0 0 0 24
25 6.7 94.0 1 0 0 0 0 0 0 0 0 0 0 25
26 6.9 107.1 0 1 0 0 0 0 0 0 0 0 0 26
27 7.0 104.8 0 0 1 0 0 0 0 0 0 0 0 27
28 7.1 102.5 0 0 0 1 0 0 0 0 0 0 0 28
29 7.2 77.7 0 0 0 0 1 0 0 0 0 0 0 29
30 7.1 85.2 0 0 0 0 0 1 0 0 0 0 0 30
31 6.9 91.3 0 0 0 0 0 0 1 0 0 0 0 31
32 7.0 106.5 0 0 0 0 0 0 0 1 0 0 0 32
33 6.8 92.4 0 0 0 0 0 0 0 0 1 0 0 33
34 6.4 97.5 0 0 0 0 0 0 0 0 0 1 0 34
35 6.7 107.0 0 0 0 0 0 0 0 0 0 0 1 35
36 6.6 51.1 0 0 0 0 0 0 0 0 0 0 0 36
37 6.4 98.6 1 0 0 0 0 0 0 0 0 0 0 37
38 6.3 102.2 0 1 0 0 0 0 0 0 0 0 0 38
39 6.2 114.3 0 0 1 0 0 0 0 0 0 0 0 39
40 6.5 99.4 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 72.5 0 0 0 0 1 0 0 0 0 0 0 41
42 6.8 92.3 0 0 0 0 0 1 0 0 0 0 0 42
43 6.4 99.4 0 0 0 0 0 0 1 0 0 0 0 43
44 6.1 85.9 0 0 0 0 0 0 0 1 0 0 0 44
45 5.8 109.4 0 0 0 0 0 0 0 0 1 0 0 45
46 6.1 97.6 0 0 0 0 0 0 0 0 0 1 0 46
47 7.2 104.7 0 0 0 0 0 0 0 0 0 0 1 47
48 7.3 56.9 0 0 0 0 0 0 0 0 0 0 0 48
49 6.9 86.7 1 0 0 0 0 0 0 0 0 0 0 49
50 6.1 108.5 0 1 0 0 0 0 0 0 0 0 0 50
51 5.8 103.4 0 0 1 0 0 0 0 0 0 0 0 51
52 6.2 86.2 0 0 0 1 0 0 0 0 0 0 0 52
53 7.1 71.0 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 75.9 0 0 0 0 0 1 0 0 0 0 0 54
55 7.9 87.1 0 0 0 0 0 0 1 0 0 0 0 55
56 7.7 102.0 0 0 0 0 0 0 0 1 0 0 0 56
57 7.4 88.5 0 0 0 0 0 0 0 0 1 0 0 57
58 7.5 87.8 0 0 0 0 0 0 0 0 0 1 0 58
59 8.0 100.8 0 0 0 0 0 0 0 0 0 0 1 59
60 8.1 50.6 0 0 0 0 0 0 0 0 0 0 0 60
61 8.0 85.9 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) Bruto_index M1 M2 M3 M4
9.26995 -0.03106 1.31095 1.35066 1.20033 1.12590
M5 M6 M7 M8 M9 M10
0.88824 1.17467 1.37414 1.51610 1.25881 1.03021
M11 t
1.76359 -0.01426
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.39099 -0.43815 -0.02385 0.41908 1.25701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.269949 0.771489 12.016 6.19e-16 ***
Bruto_index -0.031055 0.014272 -2.176 0.03461 *
M1 1.310953 0.713043 1.839 0.07231 .
M2 1.350661 0.905939 1.491 0.14267
M3 1.200325 0.878948 1.366 0.17855
M4 1.125896 0.754345 1.493 0.14224
M5 0.888242 0.540410 1.644 0.10692
M6 1.174672 0.590435 1.990 0.05248 .
M7 1.374143 0.686184 2.003 0.05101 .
M8 1.516099 0.790151 1.919 0.06110 .
M9 1.258806 0.769445 1.636 0.10852
M10 1.030209 0.752364 1.369 0.17741
M11 1.763592 0.889103 1.984 0.05316 .
t -0.014259 0.004558 -3.128 0.00302 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6174 on 47 degrees of freedom
Multiple R-squared: 0.3207, Adjusted R-squared: 0.1328
F-statistic: 1.707 on 13 and 47 DF, p-value: 0.09076
> 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,] 0.070928890 0.141857780 0.9290711
[2,] 0.038229484 0.076458967 0.9617705
[3,] 0.017360761 0.034721522 0.9826392
[4,] 0.010374821 0.020749642 0.9896252
[5,] 0.009492928 0.018985857 0.9905071
[6,] 0.004827688 0.009655377 0.9951723
[7,] 0.010995539 0.021991078 0.9890045
[8,] 0.036288369 0.072576738 0.9637116
[9,] 0.106858898 0.213717795 0.8931411
[10,] 0.115936919 0.231873838 0.8840631
[11,] 0.136395972 0.272791945 0.8636040
[12,] 0.182011577 0.364023154 0.8179884
[13,] 0.184128256 0.368256512 0.8158717
[14,] 0.166284327 0.332568655 0.8337157
[15,] 0.156458220 0.312916441 0.8435418
[16,] 0.177120869 0.354241737 0.8228791
[17,] 0.230626402 0.461252804 0.7693736
[18,] 0.211609078 0.423218157 0.7883909
[19,] 0.149096161 0.298192322 0.8509038
[20,] 0.098132587 0.196265174 0.9018674
[21,] 0.065061252 0.130122505 0.9349387
[22,] 0.092943626 0.185887253 0.9070564
[23,] 0.205841100 0.411682200 0.7941589
[24,] 0.557627918 0.884744164 0.4423721
[25,] 0.763046216 0.473907567 0.2369538
[26,] 0.698575404 0.602849192 0.3014246
[27,] 0.600515540 0.798968920 0.3994845
[28,] 0.852855832 0.294288335 0.1471442
> postscript(file="/var/www/html/rcomp/tmp/1xa4z1261134643.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29z1u1261134644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qdeq1261134644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4b8hd1261134644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ihnp1261134644.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
0.418389157 0.452557879 0.286720769 0.090324877 0.191313544 -0.326193536
7 8 9 10 11 12
-0.169797644 -0.373896086 0.226841165 -0.240855010 0.002121618 -0.229301185
13 14 15 16 17 18
0.111239821 0.614353302 0.440446080 0.639074974 0.559943245 0.393983614
19 20 21 22 23 24
0.435475116 0.907145086 0.419079224 0.388015483 -0.516826585 -0.939556046
25 26 27 28 29 30
-0.605226088 -0.023851275 0.169316572 0.286578611 -0.131678471 -0.270934845
31 32 33 34 35 36
-0.466709632 -0.022366149 -0.388693342 -0.387455383 -0.511553825 -0.569690510
37 38 39 40 41 42
-0.591259985 -0.604909959 -0.164546646 -0.238580640 -0.522053727 -0.179330641
43 44 45 46 47 48
-0.544050188 -1.390992111 -0.689642256 -0.513237864 0.088131117 0.481541881
49 50 51 52 53 54
-0.289705354 -0.438149947 -0.731936774 -0.777397821 -0.097524592 0.382475408
55 56 57 58 59 60
0.745082348 0.880109259 0.432415210 0.753532773 0.938127674 1.257005860
61
0.956562449
> postscript(file="/var/www/html/rcomp/tmp/6dg351261134644.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 0.418389157 NA
1 0.452557879 0.418389157
2 0.286720769 0.452557879
3 0.090324877 0.286720769
4 0.191313544 0.090324877
5 -0.326193536 0.191313544
6 -0.169797644 -0.326193536
7 -0.373896086 -0.169797644
8 0.226841165 -0.373896086
9 -0.240855010 0.226841165
10 0.002121618 -0.240855010
11 -0.229301185 0.002121618
12 0.111239821 -0.229301185
13 0.614353302 0.111239821
14 0.440446080 0.614353302
15 0.639074974 0.440446080
16 0.559943245 0.639074974
17 0.393983614 0.559943245
18 0.435475116 0.393983614
19 0.907145086 0.435475116
20 0.419079224 0.907145086
21 0.388015483 0.419079224
22 -0.516826585 0.388015483
23 -0.939556046 -0.516826585
24 -0.605226088 -0.939556046
25 -0.023851275 -0.605226088
26 0.169316572 -0.023851275
27 0.286578611 0.169316572
28 -0.131678471 0.286578611
29 -0.270934845 -0.131678471
30 -0.466709632 -0.270934845
31 -0.022366149 -0.466709632
32 -0.388693342 -0.022366149
33 -0.387455383 -0.388693342
34 -0.511553825 -0.387455383
35 -0.569690510 -0.511553825
36 -0.591259985 -0.569690510
37 -0.604909959 -0.591259985
38 -0.164546646 -0.604909959
39 -0.238580640 -0.164546646
40 -0.522053727 -0.238580640
41 -0.179330641 -0.522053727
42 -0.544050188 -0.179330641
43 -1.390992111 -0.544050188
44 -0.689642256 -1.390992111
45 -0.513237864 -0.689642256
46 0.088131117 -0.513237864
47 0.481541881 0.088131117
48 -0.289705354 0.481541881
49 -0.438149947 -0.289705354
50 -0.731936774 -0.438149947
51 -0.777397821 -0.731936774
52 -0.097524592 -0.777397821
53 0.382475408 -0.097524592
54 0.745082348 0.382475408
55 0.880109259 0.745082348
56 0.432415210 0.880109259
57 0.753532773 0.432415210
58 0.938127674 0.753532773
59 1.257005860 0.938127674
60 0.956562449 1.257005860
61 NA 0.956562449
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.452557879 0.418389157
[2,] 0.286720769 0.452557879
[3,] 0.090324877 0.286720769
[4,] 0.191313544 0.090324877
[5,] -0.326193536 0.191313544
[6,] -0.169797644 -0.326193536
[7,] -0.373896086 -0.169797644
[8,] 0.226841165 -0.373896086
[9,] -0.240855010 0.226841165
[10,] 0.002121618 -0.240855010
[11,] -0.229301185 0.002121618
[12,] 0.111239821 -0.229301185
[13,] 0.614353302 0.111239821
[14,] 0.440446080 0.614353302
[15,] 0.639074974 0.440446080
[16,] 0.559943245 0.639074974
[17,] 0.393983614 0.559943245
[18,] 0.435475116 0.393983614
[19,] 0.907145086 0.435475116
[20,] 0.419079224 0.907145086
[21,] 0.388015483 0.419079224
[22,] -0.516826585 0.388015483
[23,] -0.939556046 -0.516826585
[24,] -0.605226088 -0.939556046
[25,] -0.023851275 -0.605226088
[26,] 0.169316572 -0.023851275
[27,] 0.286578611 0.169316572
[28,] -0.131678471 0.286578611
[29,] -0.270934845 -0.131678471
[30,] -0.466709632 -0.270934845
[31,] -0.022366149 -0.466709632
[32,] -0.388693342 -0.022366149
[33,] -0.387455383 -0.388693342
[34,] -0.511553825 -0.387455383
[35,] -0.569690510 -0.511553825
[36,] -0.591259985 -0.569690510
[37,] -0.604909959 -0.591259985
[38,] -0.164546646 -0.604909959
[39,] -0.238580640 -0.164546646
[40,] -0.522053727 -0.238580640
[41,] -0.179330641 -0.522053727
[42,] -0.544050188 -0.179330641
[43,] -1.390992111 -0.544050188
[44,] -0.689642256 -1.390992111
[45,] -0.513237864 -0.689642256
[46,] 0.088131117 -0.513237864
[47,] 0.481541881 0.088131117
[48,] -0.289705354 0.481541881
[49,] -0.438149947 -0.289705354
[50,] -0.731936774 -0.438149947
[51,] -0.777397821 -0.731936774
[52,] -0.097524592 -0.777397821
[53,] 0.382475408 -0.097524592
[54,] 0.745082348 0.382475408
[55,] 0.880109259 0.745082348
[56,] 0.432415210 0.880109259
[57,] 0.753532773 0.432415210
[58,] 0.938127674 0.753532773
[59,] 1.257005860 0.938127674
[60,] 0.956562449 1.257005860
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.452557879 0.418389157
2 0.286720769 0.452557879
3 0.090324877 0.286720769
4 0.191313544 0.090324877
5 -0.326193536 0.191313544
6 -0.169797644 -0.326193536
7 -0.373896086 -0.169797644
8 0.226841165 -0.373896086
9 -0.240855010 0.226841165
10 0.002121618 -0.240855010
11 -0.229301185 0.002121618
12 0.111239821 -0.229301185
13 0.614353302 0.111239821
14 0.440446080 0.614353302
15 0.639074974 0.440446080
16 0.559943245 0.639074974
17 0.393983614 0.559943245
18 0.435475116 0.393983614
19 0.907145086 0.435475116
20 0.419079224 0.907145086
21 0.388015483 0.419079224
22 -0.516826585 0.388015483
23 -0.939556046 -0.516826585
24 -0.605226088 -0.939556046
25 -0.023851275 -0.605226088
26 0.169316572 -0.023851275
27 0.286578611 0.169316572
28 -0.131678471 0.286578611
29 -0.270934845 -0.131678471
30 -0.466709632 -0.270934845
31 -0.022366149 -0.466709632
32 -0.388693342 -0.022366149
33 -0.387455383 -0.388693342
34 -0.511553825 -0.387455383
35 -0.569690510 -0.511553825
36 -0.591259985 -0.569690510
37 -0.604909959 -0.591259985
38 -0.164546646 -0.604909959
39 -0.238580640 -0.164546646
40 -0.522053727 -0.238580640
41 -0.179330641 -0.522053727
42 -0.544050188 -0.179330641
43 -1.390992111 -0.544050188
44 -0.689642256 -1.390992111
45 -0.513237864 -0.689642256
46 0.088131117 -0.513237864
47 0.481541881 0.088131117
48 -0.289705354 0.481541881
49 -0.438149947 -0.289705354
50 -0.731936774 -0.438149947
51 -0.777397821 -0.731936774
52 -0.097524592 -0.777397821
53 0.382475408 -0.097524592
54 0.745082348 0.382475408
55 0.880109259 0.745082348
56 0.432415210 0.880109259
57 0.753532773 0.432415210
58 0.938127674 0.753532773
59 1.257005860 0.938127674
60 0.956562449 1.257005860
> 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/7sptr1261134644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xwpl1261134644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xpq01261134644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k7o11261134644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116tan1261134644.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/12nwpy1261134644.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/13ztgo1261134644.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/14q62n1261134644.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/15rpim1261134644.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/16ms4c1261134644.tab")
+ }
>
> try(system("convert tmp/1xa4z1261134643.ps tmp/1xa4z1261134643.png",intern=TRUE))
character(0)
> try(system("convert tmp/29z1u1261134644.ps tmp/29z1u1261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qdeq1261134644.ps tmp/3qdeq1261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b8hd1261134644.ps tmp/4b8hd1261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ihnp1261134644.ps tmp/5ihnp1261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dg351261134644.ps tmp/6dg351261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sptr1261134644.ps tmp/7sptr1261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xwpl1261134644.ps tmp/8xwpl1261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xpq01261134644.ps tmp/9xpq01261134644.png",intern=TRUE))
character(0)
> try(system("convert tmp/10k7o11261134644.ps tmp/10k7o11261134644.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.380 1.521 3.192