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(8.1,10.9,7.7,10,7.5,9.2,7.6,9.2,7.8,9.5,7.8,9.6,7.8,9.5,7.5,9.1,7.5,8.9,7.1,9,7.5,10.1,7.5,10.3,7.6,10.2,7.7,9.6,7.7,9.2,7.9,9.3,8.1,9.4,8.2,9.4,8.2,9.2,8.2,9,7.9,9,7.3,9,6.9,9.8,6.6,10,6.7,9.8,6.9,9.3,7,9,7.1,9,7.2,9.1,7.1,9.1,6.9,9.1,7,9.2,6.8,8.8,6.4,8.3,6.7,8.4,6.6,8.1,6.4,7.7,6.3,7.9,6.2,7.9,6.5,8,6.8,7.9,6.8,7.6,6.4,7.1,6.1,6.8,5.8,6.5,6.1,6.9,7.2,8.2,7.3,8.7,6.9,8.3,6.1,7.9,5.8,7.5,6.2,7.8,7.1,8.3,7.7,8.4,7.9,8.2,7.7,7.7,7.4,7.2,7.5,7.3,8,8.1,8.1,8.5),dim=c(2,60),dimnames=list(c('Werkl_Mannen','Werkl_Vrouwen'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Werkl_Mannen','Werkl_Vrouwen'),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 = '2'
> #'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
Werkl_Vrouwen Werkl_Mannen M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 10.9 8.1 1 0 0 0 0 0 0 0 0 0 0 1
2 10.0 7.7 0 1 0 0 0 0 0 0 0 0 0 2
3 9.2 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 9.2 7.6 0 0 0 1 0 0 0 0 0 0 0 4
5 9.5 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 9.6 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 9.5 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 9.1 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.9 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 9.0 7.1 0 0 0 0 0 0 0 0 0 1 0 10
11 10.1 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 10.3 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 10.2 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 9.6 7.7 0 1 0 0 0 0 0 0 0 0 0 14
15 9.2 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 9.3 7.9 0 0 0 1 0 0 0 0 0 0 0 16
17 9.4 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 9.4 8.2 0 0 0 0 0 1 0 0 0 0 0 18
19 9.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 9.0 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 9.0 7.9 0 0 0 0 0 0 0 0 1 0 0 21
22 9.0 7.3 0 0 0 0 0 0 0 0 0 1 0 22
23 9.8 6.9 0 0 0 0 0 0 0 0 0 0 1 23
24 10.0 6.6 0 0 0 0 0 0 0 0 0 0 0 24
25 9.8 6.7 1 0 0 0 0 0 0 0 0 0 0 25
26 9.3 6.9 0 1 0 0 0 0 0 0 0 0 0 26
27 9.0 7.0 0 0 1 0 0 0 0 0 0 0 0 27
28 9.0 7.1 0 0 0 1 0 0 0 0 0 0 0 28
29 9.1 7.2 0 0 0 0 1 0 0 0 0 0 0 29
30 9.1 7.1 0 0 0 0 0 1 0 0 0 0 0 30
31 9.1 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 9.2 7.0 0 0 0 0 0 0 0 1 0 0 0 32
33 8.8 6.8 0 0 0 0 0 0 0 0 1 0 0 33
34 8.3 6.4 0 0 0 0 0 0 0 0 0 1 0 34
35 8.4 6.7 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 6.6 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 6.4 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 6.3 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 6.2 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 6.5 0 0 0 1 0 0 0 0 0 0 0 40
41 7.9 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 7.6 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 6.8 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.5 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 8.2 7.2 0 0 0 0 0 0 0 0 0 0 1 47
48 8.7 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 8.3 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.9 6.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.5 5.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.8 6.2 0 0 0 1 0 0 0 0 0 0 0 52
53 8.3 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.4 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 8.2 7.9 0 0 0 0 0 0 1 0 0 0 0 55
56 7.7 7.7 0 0 0 0 0 0 0 1 0 0 0 56
57 7.2 7.4 0 0 0 0 0 0 0 0 1 0 0 57
58 7.3 7.5 0 0 0 0 0 0 0 0 0 1 0 58
59 8.1 8.0 0 0 0 0 0 0 0 0 0 0 1 59
60 8.5 8.1 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) Werkl_Mannen M1 M2 M3
7.38673 0.41964 -0.10259 -0.42265 -0.72467
M4 M5 M6 M7 M8
-0.68097 -0.60764 -0.64198 -0.77239 -0.93763
M9 M10 M11 t
-1.08929 -0.94935 -0.25280 -0.03601
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.9373 -0.2295 -0.1030 0.2995 0.9659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.386733 0.882223 8.373 8.4e-11 ***
Werkl_Mannen 0.419638 0.108377 3.872 0.000339 ***
M1 -0.102588 0.294941 -0.348 0.729557
M2 -0.422646 0.297106 -1.423 0.161615
M3 -0.724668 0.298324 -2.429 0.019100 *
M4 -0.680974 0.294082 -2.316 0.025093 *
M5 -0.607636 0.292094 -2.080 0.043098 *
M6 -0.641978 0.292536 -2.195 0.033285 *
M7 -0.772393 0.291882 -2.646 0.011104 *
M8 -0.937629 0.291328 -3.218 0.002364 **
M9 -1.089294 0.291911 -3.732 0.000522 ***
M10 -0.949352 0.293879 -3.230 0.002285 **
M11 -0.252800 0.291020 -0.869 0.389538
t -0.036014 0.003953 -9.112 7.2e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4601 on 46 degrees of freedom
Multiple R-squared: 0.8176, Adjusted R-squared: 0.766
F-statistic: 15.86 on 13 and 46 DF, p-value: 7.724e-13
> 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,] 7.302366e-03 1.460473e-02 0.992697634
[2,] 5.581966e-03 1.116393e-02 0.994418034
[3,] 4.210796e-03 8.421592e-03 0.995789204
[4,] 2.162261e-03 4.324523e-03 0.997837739
[5,] 1.235792e-03 2.471583e-03 0.998764208
[6,] 5.752571e-04 1.150514e-03 0.999424743
[7,] 3.631861e-04 7.263722e-04 0.999636814
[8,] 2.929052e-04 5.858103e-04 0.999707095
[9,] 1.546512e-04 3.093025e-04 0.999845349
[10,] 4.756973e-05 9.513945e-05 0.999952430
[11,] 5.933226e-05 1.186645e-04 0.999940668
[12,] 4.951759e-05 9.903519e-05 0.999950482
[13,] 1.997497e-05 3.994993e-05 0.999980025
[14,] 6.261529e-06 1.252306e-05 0.999993738
[15,] 7.787737e-06 1.557547e-05 0.999992212
[16,] 2.521292e-04 5.042584e-04 0.999747871
[17,] 1.858766e-03 3.717532e-03 0.998141234
[18,] 5.572313e-02 1.114463e-01 0.944276865
[19,] 7.094991e-01 5.810019e-01 0.290500945
[20,] 9.341437e-01 1.317125e-01 0.065856271
[21,] 9.850895e-01 2.982103e-02 0.014910513
[22,] 9.759834e-01 4.803320e-02 0.024016599
[23,] 9.494331e-01 1.011338e-01 0.050566924
[24,] 8.999781e-01 2.000439e-01 0.100021942
[25,] 9.028255e-01 1.943489e-01 0.097174470
[26,] 9.772953e-01 4.540936e-02 0.022704679
[27,] 9.926809e-01 1.463819e-02 0.007319097
> postscript(file="/var/www/html/rcomp/tmp/1d0ex1258798962.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kfrt1258798962.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lyx81258798962.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4472n1258798962.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ql2t1258798962.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
0.25280018 -0.12327218 -0.50130835 -0.55095176 -0.37220241 -0.20184582
7 8 9 10 11 12
-0.13541688 -0.20827477 -0.22059518 -0.05666753 0.21493971 0.19815418
13 14 15 16 17 18
0.19479285 -0.09109862 -0.15306244 -0.14466968 -0.16592032 -0.13752756
19 20 21 22 23 24
-0.17109862 -0.16984797 0.14372309 0.29157838 0.59889620 0.70800215
25 26 27 28 29 30
0.60464082 0.37678553 0.37285788 0.32321447 0.34392765 0.45624806
31 32 33 34 35 36
0.70660465 0.96589147 0.83749871 0.40142635 -0.28500259 -0.75982430
37 38 39 40 41 42
-0.93729415 -0.33925797 0.04074203 0.00717097 -0.25604350 -0.48568691
43 44 45 46 47 48
-0.65140268 -0.62426056 -0.61068950 -0.44050862 -0.26264815 -0.02139750
49 50 51 52 53 54
-0.11493971 0.17684323 0.24077088 0.36523600 0.45023859 0.36881224
55 56 57 58 59 60
0.25131353 0.03649183 -0.14993712 -0.19582859 -0.26618517 -0.12493453
> postscript(file="/var/www/html/rcomp/tmp/62k6m1258798962.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 0.25280018 NA
1 -0.12327218 0.25280018
2 -0.50130835 -0.12327218
3 -0.55095176 -0.50130835
4 -0.37220241 -0.55095176
5 -0.20184582 -0.37220241
6 -0.13541688 -0.20184582
7 -0.20827477 -0.13541688
8 -0.22059518 -0.20827477
9 -0.05666753 -0.22059518
10 0.21493971 -0.05666753
11 0.19815418 0.21493971
12 0.19479285 0.19815418
13 -0.09109862 0.19479285
14 -0.15306244 -0.09109862
15 -0.14466968 -0.15306244
16 -0.16592032 -0.14466968
17 -0.13752756 -0.16592032
18 -0.17109862 -0.13752756
19 -0.16984797 -0.17109862
20 0.14372309 -0.16984797
21 0.29157838 0.14372309
22 0.59889620 0.29157838
23 0.70800215 0.59889620
24 0.60464082 0.70800215
25 0.37678553 0.60464082
26 0.37285788 0.37678553
27 0.32321447 0.37285788
28 0.34392765 0.32321447
29 0.45624806 0.34392765
30 0.70660465 0.45624806
31 0.96589147 0.70660465
32 0.83749871 0.96589147
33 0.40142635 0.83749871
34 -0.28500259 0.40142635
35 -0.75982430 -0.28500259
36 -0.93729415 -0.75982430
37 -0.33925797 -0.93729415
38 0.04074203 -0.33925797
39 0.00717097 0.04074203
40 -0.25604350 0.00717097
41 -0.48568691 -0.25604350
42 -0.65140268 -0.48568691
43 -0.62426056 -0.65140268
44 -0.61068950 -0.62426056
45 -0.44050862 -0.61068950
46 -0.26264815 -0.44050862
47 -0.02139750 -0.26264815
48 -0.11493971 -0.02139750
49 0.17684323 -0.11493971
50 0.24077088 0.17684323
51 0.36523600 0.24077088
52 0.45023859 0.36523600
53 0.36881224 0.45023859
54 0.25131353 0.36881224
55 0.03649183 0.25131353
56 -0.14993712 0.03649183
57 -0.19582859 -0.14993712
58 -0.26618517 -0.19582859
59 -0.12493453 -0.26618517
60 NA -0.12493453
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.12327218 0.25280018
[2,] -0.50130835 -0.12327218
[3,] -0.55095176 -0.50130835
[4,] -0.37220241 -0.55095176
[5,] -0.20184582 -0.37220241
[6,] -0.13541688 -0.20184582
[7,] -0.20827477 -0.13541688
[8,] -0.22059518 -0.20827477
[9,] -0.05666753 -0.22059518
[10,] 0.21493971 -0.05666753
[11,] 0.19815418 0.21493971
[12,] 0.19479285 0.19815418
[13,] -0.09109862 0.19479285
[14,] -0.15306244 -0.09109862
[15,] -0.14466968 -0.15306244
[16,] -0.16592032 -0.14466968
[17,] -0.13752756 -0.16592032
[18,] -0.17109862 -0.13752756
[19,] -0.16984797 -0.17109862
[20,] 0.14372309 -0.16984797
[21,] 0.29157838 0.14372309
[22,] 0.59889620 0.29157838
[23,] 0.70800215 0.59889620
[24,] 0.60464082 0.70800215
[25,] 0.37678553 0.60464082
[26,] 0.37285788 0.37678553
[27,] 0.32321447 0.37285788
[28,] 0.34392765 0.32321447
[29,] 0.45624806 0.34392765
[30,] 0.70660465 0.45624806
[31,] 0.96589147 0.70660465
[32,] 0.83749871 0.96589147
[33,] 0.40142635 0.83749871
[34,] -0.28500259 0.40142635
[35,] -0.75982430 -0.28500259
[36,] -0.93729415 -0.75982430
[37,] -0.33925797 -0.93729415
[38,] 0.04074203 -0.33925797
[39,] 0.00717097 0.04074203
[40,] -0.25604350 0.00717097
[41,] -0.48568691 -0.25604350
[42,] -0.65140268 -0.48568691
[43,] -0.62426056 -0.65140268
[44,] -0.61068950 -0.62426056
[45,] -0.44050862 -0.61068950
[46,] -0.26264815 -0.44050862
[47,] -0.02139750 -0.26264815
[48,] -0.11493971 -0.02139750
[49,] 0.17684323 -0.11493971
[50,] 0.24077088 0.17684323
[51,] 0.36523600 0.24077088
[52,] 0.45023859 0.36523600
[53,] 0.36881224 0.45023859
[54,] 0.25131353 0.36881224
[55,] 0.03649183 0.25131353
[56,] -0.14993712 0.03649183
[57,] -0.19582859 -0.14993712
[58,] -0.26618517 -0.19582859
[59,] -0.12493453 -0.26618517
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.12327218 0.25280018
2 -0.50130835 -0.12327218
3 -0.55095176 -0.50130835
4 -0.37220241 -0.55095176
5 -0.20184582 -0.37220241
6 -0.13541688 -0.20184582
7 -0.20827477 -0.13541688
8 -0.22059518 -0.20827477
9 -0.05666753 -0.22059518
10 0.21493971 -0.05666753
11 0.19815418 0.21493971
12 0.19479285 0.19815418
13 -0.09109862 0.19479285
14 -0.15306244 -0.09109862
15 -0.14466968 -0.15306244
16 -0.16592032 -0.14466968
17 -0.13752756 -0.16592032
18 -0.17109862 -0.13752756
19 -0.16984797 -0.17109862
20 0.14372309 -0.16984797
21 0.29157838 0.14372309
22 0.59889620 0.29157838
23 0.70800215 0.59889620
24 0.60464082 0.70800215
25 0.37678553 0.60464082
26 0.37285788 0.37678553
27 0.32321447 0.37285788
28 0.34392765 0.32321447
29 0.45624806 0.34392765
30 0.70660465 0.45624806
31 0.96589147 0.70660465
32 0.83749871 0.96589147
33 0.40142635 0.83749871
34 -0.28500259 0.40142635
35 -0.75982430 -0.28500259
36 -0.93729415 -0.75982430
37 -0.33925797 -0.93729415
38 0.04074203 -0.33925797
39 0.00717097 0.04074203
40 -0.25604350 0.00717097
41 -0.48568691 -0.25604350
42 -0.65140268 -0.48568691
43 -0.62426056 -0.65140268
44 -0.61068950 -0.62426056
45 -0.44050862 -0.61068950
46 -0.26264815 -0.44050862
47 -0.02139750 -0.26264815
48 -0.11493971 -0.02139750
49 0.17684323 -0.11493971
50 0.24077088 0.17684323
51 0.36523600 0.24077088
52 0.45023859 0.36523600
53 0.36881224 0.45023859
54 0.25131353 0.36881224
55 0.03649183 0.25131353
56 -0.14993712 0.03649183
57 -0.19582859 -0.14993712
58 -0.26618517 -0.19582859
59 -0.12493453 -0.26618517
> 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/7ao2o1258798962.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kekc1258798962.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ll0s1258798962.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104maj1258798962.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tktd1258798963.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/12ozn11258798963.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/13z63l1258798963.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/143uea1258798963.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/15v4oa1258798963.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/16e5vp1258798963.tab")
+ }
>
> system("convert tmp/1d0ex1258798962.ps tmp/1d0ex1258798962.png")
> system("convert tmp/2kfrt1258798962.ps tmp/2kfrt1258798962.png")
> system("convert tmp/3lyx81258798962.ps tmp/3lyx81258798962.png")
> system("convert tmp/4472n1258798962.ps tmp/4472n1258798962.png")
> system("convert tmp/5ql2t1258798962.ps tmp/5ql2t1258798962.png")
> system("convert tmp/62k6m1258798962.ps tmp/62k6m1258798962.png")
> system("convert tmp/7ao2o1258798962.ps tmp/7ao2o1258798962.png")
> system("convert tmp/8kekc1258798962.ps tmp/8kekc1258798962.png")
> system("convert tmp/9ll0s1258798962.ps tmp/9ll0s1258798962.png")
> system("convert tmp/104maj1258798962.ps tmp/104maj1258798962.png")
>
>
> proc.time()
user system elapsed
2.388 1.524 3.072