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 = 'No 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
1 10.9 8.1 1 0 0 0 0 0 0 0 0 0 0
2 10.0 7.7 0 1 0 0 0 0 0 0 0 0 0
3 9.2 7.5 0 0 1 0 0 0 0 0 0 0 0
4 9.2 7.6 0 0 0 1 0 0 0 0 0 0 0
5 9.5 7.8 0 0 0 0 1 0 0 0 0 0 0
6 9.6 7.8 0 0 0 0 0 1 0 0 0 0 0
7 9.5 7.8 0 0 0 0 0 0 1 0 0 0 0
8 9.1 7.5 0 0 0 0 0 0 0 1 0 0 0
9 8.9 7.5 0 0 0 0 0 0 0 0 1 0 0
10 9.0 7.1 0 0 0 0 0 0 0 0 0 1 0
11 10.1 7.5 0 0 0 0 0 0 0 0 0 0 1
12 10.3 7.5 0 0 0 0 0 0 0 0 0 0 0
13 10.2 7.6 1 0 0 0 0 0 0 0 0 0 0
14 9.6 7.7 0 1 0 0 0 0 0 0 0 0 0
15 9.2 7.7 0 0 1 0 0 0 0 0 0 0 0
16 9.3 7.9 0 0 0 1 0 0 0 0 0 0 0
17 9.4 8.1 0 0 0 0 1 0 0 0 0 0 0
18 9.4 8.2 0 0 0 0 0 1 0 0 0 0 0
19 9.2 8.2 0 0 0 0 0 0 1 0 0 0 0
20 9.0 8.2 0 0 0 0 0 0 0 1 0 0 0
21 9.0 7.9 0 0 0 0 0 0 0 0 1 0 0
22 9.0 7.3 0 0 0 0 0 0 0 0 0 1 0
23 9.8 6.9 0 0 0 0 0 0 0 0 0 0 1
24 10.0 6.6 0 0 0 0 0 0 0 0 0 0 0
25 9.8 6.7 1 0 0 0 0 0 0 0 0 0 0
26 9.3 6.9 0 1 0 0 0 0 0 0 0 0 0
27 9.0 7.0 0 0 1 0 0 0 0 0 0 0 0
28 9.0 7.1 0 0 0 1 0 0 0 0 0 0 0
29 9.1 7.2 0 0 0 0 1 0 0 0 0 0 0
30 9.1 7.1 0 0 0 0 0 1 0 0 0 0 0
31 9.1 6.9 0 0 0 0 0 0 1 0 0 0 0
32 9.2 7.0 0 0 0 0 0 0 0 1 0 0 0
33 8.8 6.8 0 0 0 0 0 0 0 0 1 0 0
34 8.3 6.4 0 0 0 0 0 0 0 0 0 1 0
35 8.4 6.7 0 0 0 0 0 0 0 0 0 0 1
36 8.1 6.6 0 0 0 0 0 0 0 0 0 0 0
37 7.7 6.4 1 0 0 0 0 0 0 0 0 0 0
38 7.9 6.3 0 1 0 0 0 0 0 0 0 0 0
39 7.9 6.2 0 0 1 0 0 0 0 0 0 0 0
40 8.0 6.5 0 0 0 1 0 0 0 0 0 0 0
41 7.9 6.8 0 0 0 0 1 0 0 0 0 0 0
42 7.6 6.8 0 0 0 0 0 1 0 0 0 0 0
43 7.1 6.4 0 0 0 0 0 0 1 0 0 0 0
44 6.8 6.1 0 0 0 0 0 0 0 1 0 0 0
45 6.5 5.8 0 0 0 0 0 0 0 0 1 0 0
46 6.9 6.1 0 0 0 0 0 0 0 0 0 1 0
47 8.2 7.2 0 0 0 0 0 0 0 0 0 0 1
48 8.7 7.3 0 0 0 0 0 0 0 0 0 0 0
49 8.3 6.9 1 0 0 0 0 0 0 0 0 0 0
50 7.9 6.1 0 1 0 0 0 0 0 0 0 0 0
51 7.5 5.8 0 0 1 0 0 0 0 0 0 0 0
52 7.8 6.2 0 0 0 1 0 0 0 0 0 0 0
53 8.3 7.1 0 0 0 0 1 0 0 0 0 0 0
54 8.4 7.7 0 0 0 0 0 1 0 0 0 0 0
55 8.2 7.9 0 0 0 0 0 0 1 0 0 0 0
56 7.7 7.7 0 0 0 0 0 0 0 1 0 0 0
57 7.2 7.4 0 0 0 0 0 0 0 0 1 0 0
58 7.3 7.5 0 0 0 0 0 0 0 0 0 1 0
59 8.1 8.0 0 0 0 0 0 0 0 0 0 0 1
60 8.5 8.1 0 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) Werkl_Mannen M1 M2 M3
2.77768 0.87844 0.33027 0.06596 -0.22619
M4 M5 M6 M7 M8
-0.31945 -0.43812 -0.56353 -0.69326 -0.83027
M9 M10 M11
-0.91702 -0.72133 -0.23514
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.47004 -0.48092 -0.04149 0.53180 1.42463
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.77768 1.19758 2.319 0.0248 *
Werkl_Mannen 0.87844 0.15901 5.524 1.4e-06 ***
M1 0.33027 0.48229 0.685 0.4968
M2 0.06596 0.48418 0.136 0.8922
M3 -0.22619 0.48590 -0.466 0.6437
M4 -0.31945 0.48280 -0.662 0.5114
M5 -0.43812 0.48297 -0.907 0.3690
M6 -0.56353 0.48448 -1.163 0.2506
M7 -0.69326 0.48339 -1.434 0.1582
M8 -0.83027 0.48229 -1.722 0.0917 .
M9 -0.91702 0.48264 -1.900 0.0636 .
M10 -0.72133 0.48515 -1.487 0.1437
M11 -0.23514 0.48217 -0.488 0.6281
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7623 on 47 degrees of freedom
Multiple R-squared: 0.4883, Adjusted R-squared: 0.3577
F-statistic: 3.738 on 12 and 47 DF, p-value: 0.0005454
> 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,] 2.423216e-02 4.846431e-02 0.975767843
[2,] 1.045662e-02 2.091325e-02 0.989543376
[3,] 5.721768e-03 1.144354e-02 0.994278232
[4,] 2.948110e-03 5.896220e-03 0.997051890
[5,] 8.800513e-04 1.760103e-03 0.999119949
[6,] 2.419958e-04 4.839916e-04 0.999758004
[7,] 7.070548e-05 1.414110e-04 0.999929295
[8,] 3.767481e-05 7.534962e-05 0.999962325
[9,] 2.458468e-05 4.916936e-05 0.999975415
[10,] 4.450393e-05 8.900786e-05 0.999955496
[11,] 2.088996e-05 4.177993e-05 0.999979110
[12,] 7.281579e-06 1.456316e-05 0.999992718
[13,] 2.418984e-06 4.837968e-06 0.999997581
[14,] 8.903275e-07 1.780655e-06 0.999999110
[15,] 4.515735e-07 9.031469e-07 0.999999548
[16,] 8.029950e-07 1.605990e-06 0.999999197
[17,] 3.333531e-05 6.667061e-05 0.999966665
[18,] 2.237867e-03 4.475735e-03 0.997762133
[19,] 1.447430e-01 2.894860e-01 0.855257023
[20,] 8.727822e-01 2.544356e-01 0.127217795
[21,] 9.814817e-01 3.703657e-02 0.018518286
[22,] 9.961879e-01 7.624167e-03 0.003812083
[23,] 9.929087e-01 1.418255e-02 0.007091273
[24,] 9.852518e-01 2.949630e-02 0.014748151
[25,] 9.669643e-01 6.607143e-02 0.033035713
[26,] 9.443644e-01 1.112712e-01 0.055635579
[27,] 9.300008e-01 1.399984e-01 0.069999222
[28,] 9.320094e-01 1.359812e-01 0.067990610
[29,] 9.080666e-01 1.838668e-01 0.091933396
> postscript(file="/var/www/html/rcomp/tmp/15vdw1258743702.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23d6m1258743702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b75g1258743702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45c171258743702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nbcy1258743702.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
0.676700313 0.392387748 0.060231465 0.065643926 0.308625131 0.534037591
7 8 9 10 11 12
0.563762617 0.564312565 0.451056387 0.706743822 0.969175078 0.934037591
13 14 15 16 17 18
0.415918900 -0.007612252 -0.115455969 -0.097887226 -0.054906022 -0.017337278
19 20 21 22 23 24
-0.087612252 -0.150593456 0.199681518 0.531056387 1.196237383 1.424631048
25 26 27 28 29 30
0.806512356 0.395137487 0.299450052 0.304862513 0.435687435 0.648943613
31 32 33 34 35 36
0.954356074 1.103531152 0.965962409 0.621649843 -0.028075183 -0.475368952
37 38 39 40 41 42
-1.029956491 -0.477800209 -0.097800209 -0.168075183 -0.412937696 -0.587525235
43 44 45 46 47 48
-0.606425339 -0.505875392 -0.455600418 -0.514819005 -0.667293770 -0.490274974
49 50 51 52 53 54
-0.869175078 -0.302112774 -0.146425339 -0.104544031 -0.276468848 -0.578118691
55 56 57 58 59 60
-0.824081100 -1.011374869 -1.161099896 -1.344631048 -1.470043509 -1.393024713
> postscript(file="/var/www/html/rcomp/tmp/6aw151258743702.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 0.676700313 NA
1 0.392387748 0.676700313
2 0.060231465 0.392387748
3 0.065643926 0.060231465
4 0.308625131 0.065643926
5 0.534037591 0.308625131
6 0.563762617 0.534037591
7 0.564312565 0.563762617
8 0.451056387 0.564312565
9 0.706743822 0.451056387
10 0.969175078 0.706743822
11 0.934037591 0.969175078
12 0.415918900 0.934037591
13 -0.007612252 0.415918900
14 -0.115455969 -0.007612252
15 -0.097887226 -0.115455969
16 -0.054906022 -0.097887226
17 -0.017337278 -0.054906022
18 -0.087612252 -0.017337278
19 -0.150593456 -0.087612252
20 0.199681518 -0.150593456
21 0.531056387 0.199681518
22 1.196237383 0.531056387
23 1.424631048 1.196237383
24 0.806512356 1.424631048
25 0.395137487 0.806512356
26 0.299450052 0.395137487
27 0.304862513 0.299450052
28 0.435687435 0.304862513
29 0.648943613 0.435687435
30 0.954356074 0.648943613
31 1.103531152 0.954356074
32 0.965962409 1.103531152
33 0.621649843 0.965962409
34 -0.028075183 0.621649843
35 -0.475368952 -0.028075183
36 -1.029956491 -0.475368952
37 -0.477800209 -1.029956491
38 -0.097800209 -0.477800209
39 -0.168075183 -0.097800209
40 -0.412937696 -0.168075183
41 -0.587525235 -0.412937696
42 -0.606425339 -0.587525235
43 -0.505875392 -0.606425339
44 -0.455600418 -0.505875392
45 -0.514819005 -0.455600418
46 -0.667293770 -0.514819005
47 -0.490274974 -0.667293770
48 -0.869175078 -0.490274974
49 -0.302112774 -0.869175078
50 -0.146425339 -0.302112774
51 -0.104544031 -0.146425339
52 -0.276468848 -0.104544031
53 -0.578118691 -0.276468848
54 -0.824081100 -0.578118691
55 -1.011374869 -0.824081100
56 -1.161099896 -1.011374869
57 -1.344631048 -1.161099896
58 -1.470043509 -1.344631048
59 -1.393024713 -1.470043509
60 NA -1.393024713
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.392387748 0.676700313
[2,] 0.060231465 0.392387748
[3,] 0.065643926 0.060231465
[4,] 0.308625131 0.065643926
[5,] 0.534037591 0.308625131
[6,] 0.563762617 0.534037591
[7,] 0.564312565 0.563762617
[8,] 0.451056387 0.564312565
[9,] 0.706743822 0.451056387
[10,] 0.969175078 0.706743822
[11,] 0.934037591 0.969175078
[12,] 0.415918900 0.934037591
[13,] -0.007612252 0.415918900
[14,] -0.115455969 -0.007612252
[15,] -0.097887226 -0.115455969
[16,] -0.054906022 -0.097887226
[17,] -0.017337278 -0.054906022
[18,] -0.087612252 -0.017337278
[19,] -0.150593456 -0.087612252
[20,] 0.199681518 -0.150593456
[21,] 0.531056387 0.199681518
[22,] 1.196237383 0.531056387
[23,] 1.424631048 1.196237383
[24,] 0.806512356 1.424631048
[25,] 0.395137487 0.806512356
[26,] 0.299450052 0.395137487
[27,] 0.304862513 0.299450052
[28,] 0.435687435 0.304862513
[29,] 0.648943613 0.435687435
[30,] 0.954356074 0.648943613
[31,] 1.103531152 0.954356074
[32,] 0.965962409 1.103531152
[33,] 0.621649843 0.965962409
[34,] -0.028075183 0.621649843
[35,] -0.475368952 -0.028075183
[36,] -1.029956491 -0.475368952
[37,] -0.477800209 -1.029956491
[38,] -0.097800209 -0.477800209
[39,] -0.168075183 -0.097800209
[40,] -0.412937696 -0.168075183
[41,] -0.587525235 -0.412937696
[42,] -0.606425339 -0.587525235
[43,] -0.505875392 -0.606425339
[44,] -0.455600418 -0.505875392
[45,] -0.514819005 -0.455600418
[46,] -0.667293770 -0.514819005
[47,] -0.490274974 -0.667293770
[48,] -0.869175078 -0.490274974
[49,] -0.302112774 -0.869175078
[50,] -0.146425339 -0.302112774
[51,] -0.104544031 -0.146425339
[52,] -0.276468848 -0.104544031
[53,] -0.578118691 -0.276468848
[54,] -0.824081100 -0.578118691
[55,] -1.011374869 -0.824081100
[56,] -1.161099896 -1.011374869
[57,] -1.344631048 -1.161099896
[58,] -1.470043509 -1.344631048
[59,] -1.393024713 -1.470043509
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.392387748 0.676700313
2 0.060231465 0.392387748
3 0.065643926 0.060231465
4 0.308625131 0.065643926
5 0.534037591 0.308625131
6 0.563762617 0.534037591
7 0.564312565 0.563762617
8 0.451056387 0.564312565
9 0.706743822 0.451056387
10 0.969175078 0.706743822
11 0.934037591 0.969175078
12 0.415918900 0.934037591
13 -0.007612252 0.415918900
14 -0.115455969 -0.007612252
15 -0.097887226 -0.115455969
16 -0.054906022 -0.097887226
17 -0.017337278 -0.054906022
18 -0.087612252 -0.017337278
19 -0.150593456 -0.087612252
20 0.199681518 -0.150593456
21 0.531056387 0.199681518
22 1.196237383 0.531056387
23 1.424631048 1.196237383
24 0.806512356 1.424631048
25 0.395137487 0.806512356
26 0.299450052 0.395137487
27 0.304862513 0.299450052
28 0.435687435 0.304862513
29 0.648943613 0.435687435
30 0.954356074 0.648943613
31 1.103531152 0.954356074
32 0.965962409 1.103531152
33 0.621649843 0.965962409
34 -0.028075183 0.621649843
35 -0.475368952 -0.028075183
36 -1.029956491 -0.475368952
37 -0.477800209 -1.029956491
38 -0.097800209 -0.477800209
39 -0.168075183 -0.097800209
40 -0.412937696 -0.168075183
41 -0.587525235 -0.412937696
42 -0.606425339 -0.587525235
43 -0.505875392 -0.606425339
44 -0.455600418 -0.505875392
45 -0.514819005 -0.455600418
46 -0.667293770 -0.514819005
47 -0.490274974 -0.667293770
48 -0.869175078 -0.490274974
49 -0.302112774 -0.869175078
50 -0.146425339 -0.302112774
51 -0.104544031 -0.146425339
52 -0.276468848 -0.104544031
53 -0.578118691 -0.276468848
54 -0.824081100 -0.578118691
55 -1.011374869 -0.824081100
56 -1.161099896 -1.011374869
57 -1.344631048 -1.161099896
58 -1.470043509 -1.344631048
59 -1.393024713 -1.470043509
> 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/753jy1258743702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8og2b1258743702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fj8s1258743702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k13k1258743702.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t7971258743702.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/129b9q1258743702.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/13008k1258743702.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/14efkd1258743702.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/1511kn1258743702.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/167pf81258743702.tab")
+ }
>
> system("convert tmp/15vdw1258743702.ps tmp/15vdw1258743702.png")
> system("convert tmp/23d6m1258743702.ps tmp/23d6m1258743702.png")
> system("convert tmp/3b75g1258743702.ps tmp/3b75g1258743702.png")
> system("convert tmp/45c171258743702.ps tmp/45c171258743702.png")
> system("convert tmp/5nbcy1258743702.ps tmp/5nbcy1258743702.png")
> system("convert tmp/6aw151258743702.ps tmp/6aw151258743702.png")
> system("convert tmp/753jy1258743702.ps tmp/753jy1258743702.png")
> system("convert tmp/8og2b1258743702.ps tmp/8og2b1258743702.png")
> system("convert tmp/9fj8s1258743702.ps tmp/9fj8s1258743702.png")
> system("convert tmp/10k13k1258743702.ps tmp/10k13k1258743702.png")
>
>
> proc.time()
user system elapsed
2.488 1.598 2.864