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(4
+ ,7.2
+ ,102.9
+ ,271244
+ ,4.1
+ ,7.4
+ ,97.4
+ ,269907
+ ,4
+ ,8.8
+ ,111.4
+ ,271296
+ ,3.8
+ ,9.3
+ ,87.4
+ ,270157
+ ,4.7
+ ,9.3
+ ,96.8
+ ,271322
+ ,4.3
+ ,8.7
+ ,114.1
+ ,267179
+ ,3.9
+ ,8.2
+ ,110.3
+ ,264101
+ ,4
+ ,8.3
+ ,103.9
+ ,265518
+ ,4.3
+ ,8.5
+ ,101.6
+ ,269419
+ ,4.8
+ ,8.6
+ ,94.6
+ ,268714
+ ,4.4
+ ,8.5
+ ,95.9
+ ,272482
+ ,4.3
+ ,8.2
+ ,104.7
+ ,268351
+ ,4.7
+ ,8.1
+ ,102.8
+ ,268175
+ ,4.7
+ ,7.9
+ ,98.1
+ ,270674
+ ,4.9
+ ,8.6
+ ,113.9
+ ,272764
+ ,5
+ ,8.7
+ ,80.9
+ ,272599
+ ,4.2
+ ,8.7
+ ,95.7
+ ,270333
+ ,4.3
+ ,8.5
+ ,113.2
+ ,270846
+ ,4.8
+ ,8.4
+ ,105.9
+ ,270491
+ ,4.8
+ ,8.5
+ ,108.8
+ ,269160
+ ,4.8
+ ,8.7
+ ,102.3
+ ,274027
+ ,4.2
+ ,8.7
+ ,99
+ ,273784
+ ,4.6
+ ,8.6
+ ,100.7
+ ,276663
+ ,4.8
+ ,8.5
+ ,115.5
+ ,274525
+ ,4.5
+ ,8.3
+ ,100.7
+ ,271344
+ ,4.4
+ ,8
+ ,109.9
+ ,271115
+ ,4.3
+ ,8.2
+ ,114.6
+ ,270798
+ ,3.9
+ ,8.1
+ ,85.4
+ ,273911
+ ,3.7
+ ,8.1
+ ,100.5
+ ,273985
+ ,4
+ ,8
+ ,114.8
+ ,271917
+ ,4.1
+ ,7.9
+ ,116.5
+ ,273338
+ ,3.7
+ ,7.9
+ ,112.9
+ ,270601
+ ,3.8
+ ,8
+ ,102
+ ,273547
+ ,3.8
+ ,8
+ ,106
+ ,275363
+ ,3.8
+ ,7.9
+ ,105.3
+ ,281229
+ ,3.3
+ ,8
+ ,118.8
+ ,277793
+ ,3.3
+ ,7.7
+ ,106.1
+ ,279913
+ ,3.3
+ ,7.2
+ ,109.3
+ ,282500
+ ,3.2
+ ,7.5
+ ,117.2
+ ,280041
+ ,3.4
+ ,7.3
+ ,92.5
+ ,282166
+ ,4.2
+ ,7
+ ,104.2
+ ,290304
+ ,4.9
+ ,7
+ ,112.5
+ ,283519
+ ,5.1
+ ,7
+ ,122.4
+ ,287816
+ ,5.5
+ ,7.2
+ ,113.3
+ ,285226
+ ,5.6
+ ,7.3
+ ,100
+ ,287595
+ ,6.4
+ ,7.1
+ ,110.7
+ ,289741
+ ,6.1
+ ,6.8
+ ,112.8
+ ,289148
+ ,7.1
+ ,6.4
+ ,109.8
+ ,288301
+ ,7.8
+ ,6.1
+ ,117.3
+ ,290155
+ ,7.9
+ ,6.5
+ ,109.1
+ ,289648
+ ,7.4
+ ,7.7
+ ,115.9
+ ,288225
+ ,7.5
+ ,7.9
+ ,96
+ ,289351
+ ,6.8
+ ,7.5
+ ,99.8
+ ,294735
+ ,5.2
+ ,6.9
+ ,116.8
+ ,305333
+ ,4.7
+ ,6.6
+ ,115.7
+ ,309030
+ ,4.1
+ ,6.9
+ ,99.4
+ ,310215
+ ,3.9
+ ,7.7
+ ,94.3
+ ,321935
+ ,2.6
+ ,8
+ ,91
+ ,325734
+ ,2.7
+ ,8
+ ,93.2
+ ,320846
+ ,1.8
+ ,7.7
+ ,103.1
+ ,323023
+ ,1
+ ,7.3
+ ,94.1
+ ,319753
+ ,0.3
+ ,7.4
+ ,91.8
+ ,321753
+ ,1.3
+ ,8.1
+ ,102.7
+ ,320757
+ ,1
+ ,8.3
+ ,82.6
+ ,324479
+ ,1.1
+ ,8.2
+ ,89.1
+ ,324641)
+ ,dim=c(4
+ ,65)
+ ,dimnames=list(c('Cons.index'
+ ,'Werkl.graad'
+ ,'Industr.prod.'
+ ,'BrutoSchuld')
+ ,1:65))
> y <- array(NA,dim=c(4,65),dimnames=list(c('Cons.index','Werkl.graad','Industr.prod.','BrutoSchuld'),1:65))
> 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
Cons.index Werkl.graad Industr.prod. BrutoSchuld M1 M2 M3 M4 M5 M6 M7 M8 M9
1 4.0 7.2 102.9 271244 1 0 0 0 0 0 0 0 0
2 4.1 7.4 97.4 269907 0 1 0 0 0 0 0 0 0
3 4.0 8.8 111.4 271296 0 0 1 0 0 0 0 0 0
4 3.8 9.3 87.4 270157 0 0 0 1 0 0 0 0 0
5 4.7 9.3 96.8 271322 0 0 0 0 1 0 0 0 0
6 4.3 8.7 114.1 267179 0 0 0 0 0 1 0 0 0
7 3.9 8.2 110.3 264101 0 0 0 0 0 0 1 0 0
8 4.0 8.3 103.9 265518 0 0 0 0 0 0 0 1 0
9 4.3 8.5 101.6 269419 0 0 0 0 0 0 0 0 1
10 4.8 8.6 94.6 268714 0 0 0 0 0 0 0 0 0
11 4.4 8.5 95.9 272482 0 0 0 0 0 0 0 0 0
12 4.3 8.2 104.7 268351 0 0 0 0 0 0 0 0 0
13 4.7 8.1 102.8 268175 1 0 0 0 0 0 0 0 0
14 4.7 7.9 98.1 270674 0 1 0 0 0 0 0 0 0
15 4.9 8.6 113.9 272764 0 0 1 0 0 0 0 0 0
16 5.0 8.7 80.9 272599 0 0 0 1 0 0 0 0 0
17 4.2 8.7 95.7 270333 0 0 0 0 1 0 0 0 0
18 4.3 8.5 113.2 270846 0 0 0 0 0 1 0 0 0
19 4.8 8.4 105.9 270491 0 0 0 0 0 0 1 0 0
20 4.8 8.5 108.8 269160 0 0 0 0 0 0 0 1 0
21 4.8 8.7 102.3 274027 0 0 0 0 0 0 0 0 1
22 4.2 8.7 99.0 273784 0 0 0 0 0 0 0 0 0
23 4.6 8.6 100.7 276663 0 0 0 0 0 0 0 0 0
24 4.8 8.5 115.5 274525 0 0 0 0 0 0 0 0 0
25 4.5 8.3 100.7 271344 1 0 0 0 0 0 0 0 0
26 4.4 8.0 109.9 271115 0 1 0 0 0 0 0 0 0
27 4.3 8.2 114.6 270798 0 0 1 0 0 0 0 0 0
28 3.9 8.1 85.4 273911 0 0 0 1 0 0 0 0 0
29 3.7 8.1 100.5 273985 0 0 0 0 1 0 0 0 0
30 4.0 8.0 114.8 271917 0 0 0 0 0 1 0 0 0
31 4.1 7.9 116.5 273338 0 0 0 0 0 0 1 0 0
32 3.7 7.9 112.9 270601 0 0 0 0 0 0 0 1 0
33 3.8 8.0 102.0 273547 0 0 0 0 0 0 0 0 1
34 3.8 8.0 106.0 275363 0 0 0 0 0 0 0 0 0
35 3.8 7.9 105.3 281229 0 0 0 0 0 0 0 0 0
36 3.3 8.0 118.8 277793 0 0 0 0 0 0 0 0 0
37 3.3 7.7 106.1 279913 1 0 0 0 0 0 0 0 0
38 3.3 7.2 109.3 282500 0 1 0 0 0 0 0 0 0
39 3.2 7.5 117.2 280041 0 0 1 0 0 0 0 0 0
40 3.4 7.3 92.5 282166 0 0 0 1 0 0 0 0 0
41 4.2 7.0 104.2 290304 0 0 0 0 1 0 0 0 0
42 4.9 7.0 112.5 283519 0 0 0 0 0 1 0 0 0
43 5.1 7.0 122.4 287816 0 0 0 0 0 0 1 0 0
44 5.5 7.2 113.3 285226 0 0 0 0 0 0 0 1 0
45 5.6 7.3 100.0 287595 0 0 0 0 0 0 0 0 1
46 6.4 7.1 110.7 289741 0 0 0 0 0 0 0 0 0
47 6.1 6.8 112.8 289148 0 0 0 0 0 0 0 0 0
48 7.1 6.4 109.8 288301 0 0 0 0 0 0 0 0 0
49 7.8 6.1 117.3 290155 1 0 0 0 0 0 0 0 0
50 7.9 6.5 109.1 289648 0 1 0 0 0 0 0 0 0
51 7.4 7.7 115.9 288225 0 0 1 0 0 0 0 0 0
52 7.5 7.9 96.0 289351 0 0 0 1 0 0 0 0 0
53 6.8 7.5 99.8 294735 0 0 0 0 1 0 0 0 0
54 5.2 6.9 116.8 305333 0 0 0 0 0 1 0 0 0
55 4.7 6.6 115.7 309030 0 0 0 0 0 0 1 0 0
56 4.1 6.9 99.4 310215 0 0 0 0 0 0 0 1 0
57 3.9 7.7 94.3 321935 0 0 0 0 0 0 0 0 1
58 2.6 8.0 91.0 325734 0 0 0 0 0 0 0 0 0
59 2.7 8.0 93.2 320846 0 0 0 0 0 0 0 0 0
60 1.8 7.7 103.1 323023 0 0 0 0 0 0 0 0 0
61 1.0 7.3 94.1 319753 1 0 0 0 0 0 0 0 0
62 0.3 7.4 91.8 321753 0 1 0 0 0 0 0 0 0
63 1.3 8.1 102.7 320757 0 0 1 0 0 0 0 0 0
64 1.0 8.3 82.6 324479 0 0 0 1 0 0 0 0 0
65 1.1 8.2 89.1 324641 0 0 0 0 1 0 0 0 0
M10 M11
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 1 0
11 0 1
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 0 0
18 0 0
19 0 0
20 0 0
21 0 0
22 1 0
23 0 1
24 0 0
25 0 0
26 0 0
27 0 0
28 0 0
29 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 1 0
35 0 1
36 0 0
37 0 0
38 0 0
39 0 0
40 0 0
41 0 0
42 0 0
43 0 0
44 0 0
45 0 0
46 1 0
47 0 1
48 0 0
49 0 0
50 0 0
51 0 0
52 0 0
53 0 0
54 0 0
55 0 0
56 0 0
57 0 0
58 1 0
59 0 1
60 0 0
61 0 0
62 0 0
63 0 0
64 0 0
65 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Werkl.graad Industr.prod. BrutoSchuld M1
1.918e+01 -1.128e+00 6.117e-02 -4.511e-05 -1.355e-01
M2 M3 M4 M5 M6
-1.696e-01 1.172e-01 1.770e+00 1.106e+00 -1.904e-01
M7 M8 M9 M10 M11
-3.747e-01 4.422e-02 1.119e+00 1.092e+00 8.994e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.24589 -0.74334 0.06685 0.57200 2.69985
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.918e+01 9.161e+00 2.094 0.04140 *
Werkl.graad -1.128e+00 3.547e-01 -3.179 0.00254 **
Industr.prod. 6.117e-02 3.746e-02 1.633 0.10872
BrutoSchuld -4.511e-05 1.243e-05 -3.628 0.00067 ***
M1 -1.355e-01 8.273e-01 -0.164 0.87053
M2 -1.696e-01 8.527e-01 -0.199 0.84314
M3 1.172e-01 7.723e-01 0.152 0.87997
M4 1.770e+00 1.071e+00 1.653 0.10456
M5 1.106e+00 8.493e-01 1.303 0.19864
M6 -1.904e-01 7.936e-01 -0.240 0.81134
M7 -3.747e-01 7.909e-01 -0.474 0.63774
M8 4.422e-02 8.007e-01 0.055 0.95617
M9 1.119e+00 8.548e-01 1.309 0.19651
M10 1.092e+00 8.459e-01 1.291 0.20265
M11 8.994e-01 8.322e-01 1.081 0.28498
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.24 on 50 degrees of freedom
Multiple R-squared: 0.4914, Adjusted R-squared: 0.349
F-statistic: 3.45 on 14 and 50 DF, p-value: 0.0006122
> 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,] 7.920899e-02 1.584180e-01 0.9207910
[2,] 3.201988e-02 6.403976e-02 0.9679801
[3,] 1.391945e-02 2.783889e-02 0.9860806
[4,] 4.406578e-03 8.813156e-03 0.9955934
[5,] 2.924122e-03 5.848243e-03 0.9970759
[6,] 1.015662e-03 2.031324e-03 0.9989843
[7,] 4.047880e-04 8.095759e-04 0.9995952
[8,] 2.404460e-04 4.808920e-04 0.9997596
[9,] 8.055967e-05 1.611193e-04 0.9999194
[10,] 2.338909e-05 4.677818e-05 0.9999766
[11,] 7.443027e-06 1.488605e-05 0.9999926
[12,] 2.421876e-06 4.843753e-06 0.9999976
[13,] 6.185938e-07 1.237188e-06 0.9999994
[14,] 1.931701e-07 3.863401e-07 0.9999998
[15,] 5.633791e-08 1.126758e-07 0.9999999
[16,] 2.127252e-08 4.254504e-08 1.0000000
[17,] 4.799145e-09 9.598290e-09 1.0000000
[18,] 1.252619e-09 2.505238e-09 1.0000000
[19,] 2.424763e-09 4.849525e-09 1.0000000
[20,] 4.560922e-09 9.121843e-09 1.0000000
[21,] 3.512763e-09 7.025526e-09 1.0000000
[22,] 1.074816e-08 2.149632e-08 1.0000000
[23,] 4.785357e-08 9.570713e-08 1.0000000
[24,] 3.162332e-06 6.324665e-06 0.9999968
[25,] 3.763685e-06 7.527370e-06 0.9999962
[26,] 9.971958e-06 1.994392e-05 0.9999900
[27,] 2.747464e-05 5.494928e-05 0.9999725
[28,] 3.888239e-04 7.776478e-04 0.9996112
[29,] 2.788240e-02 5.576480e-02 0.9721176
[30,] 7.852885e-01 4.294230e-01 0.2147115
> postscript(file="/var/www/html/rcomp/tmp/1kzc01258649899.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28gd91258649899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3sdp11258649899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a1p41258649899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5y9bq1258649899.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 = 65
Frequency = 1
1 2 3 4 5 6
-0.98209585 -0.34635576 0.05189601 0.17976343 1.22080622 0.19589959
7 8 9 10 11 12
-0.49013729 -0.24084399 -0.47330973 0.56272754 0.33304228 0.06947494
13 14 15 16 17 18
0.60051764 0.80928502 0.63965193 1.21090260 0.06684682 0.19083562
19 20 21 22 23 24
1.19282171 0.64926463 0.41729176 0.03506969 0.54081163 0.52567577
25 26 27 28 29 30
0.89747897 -0.07985605 -0.54294478 -0.78180624 -1.23865050 -0.72258018
31 32 33 34 35 36
-0.59100582 -1.31315669 -1.37541338 -1.51129124 -1.12399087 -1.59261771
37 38 39 40 41 42
-0.92290370 -1.53172560 -2.17441727 -2.24588686 -1.46928319 -0.28621472
43 44 45 46 47 48
-0.31372081 0.33273895 0.39126078 0.43488669 -0.16601251 1.42760420
49 50 51 52 53 54
1.54968210 2.61356814 2.69984568 2.64077950 2.16361627 0.62205969
55 56 57 58 59 60
0.20204222 0.57199711 1.04017057 0.47860732 0.41614947 -0.43013720
61 62 63 64 65
-1.14267915 -1.46491575 -0.67403156 -1.00375243 -0.74333561
> postscript(file="/var/www/html/rcomp/tmp/6kfrk1258649899.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 = 65
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.98209585 NA
1 -0.34635576 -0.98209585
2 0.05189601 -0.34635576
3 0.17976343 0.05189601
4 1.22080622 0.17976343
5 0.19589959 1.22080622
6 -0.49013729 0.19589959
7 -0.24084399 -0.49013729
8 -0.47330973 -0.24084399
9 0.56272754 -0.47330973
10 0.33304228 0.56272754
11 0.06947494 0.33304228
12 0.60051764 0.06947494
13 0.80928502 0.60051764
14 0.63965193 0.80928502
15 1.21090260 0.63965193
16 0.06684682 1.21090260
17 0.19083562 0.06684682
18 1.19282171 0.19083562
19 0.64926463 1.19282171
20 0.41729176 0.64926463
21 0.03506969 0.41729176
22 0.54081163 0.03506969
23 0.52567577 0.54081163
24 0.89747897 0.52567577
25 -0.07985605 0.89747897
26 -0.54294478 -0.07985605
27 -0.78180624 -0.54294478
28 -1.23865050 -0.78180624
29 -0.72258018 -1.23865050
30 -0.59100582 -0.72258018
31 -1.31315669 -0.59100582
32 -1.37541338 -1.31315669
33 -1.51129124 -1.37541338
34 -1.12399087 -1.51129124
35 -1.59261771 -1.12399087
36 -0.92290370 -1.59261771
37 -1.53172560 -0.92290370
38 -2.17441727 -1.53172560
39 -2.24588686 -2.17441727
40 -1.46928319 -2.24588686
41 -0.28621472 -1.46928319
42 -0.31372081 -0.28621472
43 0.33273895 -0.31372081
44 0.39126078 0.33273895
45 0.43488669 0.39126078
46 -0.16601251 0.43488669
47 1.42760420 -0.16601251
48 1.54968210 1.42760420
49 2.61356814 1.54968210
50 2.69984568 2.61356814
51 2.64077950 2.69984568
52 2.16361627 2.64077950
53 0.62205969 2.16361627
54 0.20204222 0.62205969
55 0.57199711 0.20204222
56 1.04017057 0.57199711
57 0.47860732 1.04017057
58 0.41614947 0.47860732
59 -0.43013720 0.41614947
60 -1.14267915 -0.43013720
61 -1.46491575 -1.14267915
62 -0.67403156 -1.46491575
63 -1.00375243 -0.67403156
64 -0.74333561 -1.00375243
65 NA -0.74333561
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.34635576 -0.98209585
[2,] 0.05189601 -0.34635576
[3,] 0.17976343 0.05189601
[4,] 1.22080622 0.17976343
[5,] 0.19589959 1.22080622
[6,] -0.49013729 0.19589959
[7,] -0.24084399 -0.49013729
[8,] -0.47330973 -0.24084399
[9,] 0.56272754 -0.47330973
[10,] 0.33304228 0.56272754
[11,] 0.06947494 0.33304228
[12,] 0.60051764 0.06947494
[13,] 0.80928502 0.60051764
[14,] 0.63965193 0.80928502
[15,] 1.21090260 0.63965193
[16,] 0.06684682 1.21090260
[17,] 0.19083562 0.06684682
[18,] 1.19282171 0.19083562
[19,] 0.64926463 1.19282171
[20,] 0.41729176 0.64926463
[21,] 0.03506969 0.41729176
[22,] 0.54081163 0.03506969
[23,] 0.52567577 0.54081163
[24,] 0.89747897 0.52567577
[25,] -0.07985605 0.89747897
[26,] -0.54294478 -0.07985605
[27,] -0.78180624 -0.54294478
[28,] -1.23865050 -0.78180624
[29,] -0.72258018 -1.23865050
[30,] -0.59100582 -0.72258018
[31,] -1.31315669 -0.59100582
[32,] -1.37541338 -1.31315669
[33,] -1.51129124 -1.37541338
[34,] -1.12399087 -1.51129124
[35,] -1.59261771 -1.12399087
[36,] -0.92290370 -1.59261771
[37,] -1.53172560 -0.92290370
[38,] -2.17441727 -1.53172560
[39,] -2.24588686 -2.17441727
[40,] -1.46928319 -2.24588686
[41,] -0.28621472 -1.46928319
[42,] -0.31372081 -0.28621472
[43,] 0.33273895 -0.31372081
[44,] 0.39126078 0.33273895
[45,] 0.43488669 0.39126078
[46,] -0.16601251 0.43488669
[47,] 1.42760420 -0.16601251
[48,] 1.54968210 1.42760420
[49,] 2.61356814 1.54968210
[50,] 2.69984568 2.61356814
[51,] 2.64077950 2.69984568
[52,] 2.16361627 2.64077950
[53,] 0.62205969 2.16361627
[54,] 0.20204222 0.62205969
[55,] 0.57199711 0.20204222
[56,] 1.04017057 0.57199711
[57,] 0.47860732 1.04017057
[58,] 0.41614947 0.47860732
[59,] -0.43013720 0.41614947
[60,] -1.14267915 -0.43013720
[61,] -1.46491575 -1.14267915
[62,] -0.67403156 -1.46491575
[63,] -1.00375243 -0.67403156
[64,] -0.74333561 -1.00375243
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.34635576 -0.98209585
2 0.05189601 -0.34635576
3 0.17976343 0.05189601
4 1.22080622 0.17976343
5 0.19589959 1.22080622
6 -0.49013729 0.19589959
7 -0.24084399 -0.49013729
8 -0.47330973 -0.24084399
9 0.56272754 -0.47330973
10 0.33304228 0.56272754
11 0.06947494 0.33304228
12 0.60051764 0.06947494
13 0.80928502 0.60051764
14 0.63965193 0.80928502
15 1.21090260 0.63965193
16 0.06684682 1.21090260
17 0.19083562 0.06684682
18 1.19282171 0.19083562
19 0.64926463 1.19282171
20 0.41729176 0.64926463
21 0.03506969 0.41729176
22 0.54081163 0.03506969
23 0.52567577 0.54081163
24 0.89747897 0.52567577
25 -0.07985605 0.89747897
26 -0.54294478 -0.07985605
27 -0.78180624 -0.54294478
28 -1.23865050 -0.78180624
29 -0.72258018 -1.23865050
30 -0.59100582 -0.72258018
31 -1.31315669 -0.59100582
32 -1.37541338 -1.31315669
33 -1.51129124 -1.37541338
34 -1.12399087 -1.51129124
35 -1.59261771 -1.12399087
36 -0.92290370 -1.59261771
37 -1.53172560 -0.92290370
38 -2.17441727 -1.53172560
39 -2.24588686 -2.17441727
40 -1.46928319 -2.24588686
41 -0.28621472 -1.46928319
42 -0.31372081 -0.28621472
43 0.33273895 -0.31372081
44 0.39126078 0.33273895
45 0.43488669 0.39126078
46 -0.16601251 0.43488669
47 1.42760420 -0.16601251
48 1.54968210 1.42760420
49 2.61356814 1.54968210
50 2.69984568 2.61356814
51 2.64077950 2.69984568
52 2.16361627 2.64077950
53 0.62205969 2.16361627
54 0.20204222 0.62205969
55 0.57199711 0.20204222
56 1.04017057 0.57199711
57 0.47860732 1.04017057
58 0.41614947 0.47860732
59 -0.43013720 0.41614947
60 -1.14267915 -0.43013720
61 -1.46491575 -1.14267915
62 -0.67403156 -1.46491575
63 -1.00375243 -0.67403156
64 -0.74333561 -1.00375243
> 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/7maq91258649899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lm741258649899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dmto1258649899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103hs21258649899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jyz71258649899.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/126j131258649900.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/137is11258649900.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/14d86a1258649900.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/15wilb1258649900.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/16qr9m1258649900.tab")
+ }
>
> system("convert tmp/1kzc01258649899.ps tmp/1kzc01258649899.png")
> system("convert tmp/28gd91258649899.ps tmp/28gd91258649899.png")
> system("convert tmp/3sdp11258649899.ps tmp/3sdp11258649899.png")
> system("convert tmp/4a1p41258649899.ps tmp/4a1p41258649899.png")
> system("convert tmp/5y9bq1258649899.ps tmp/5y9bq1258649899.png")
> system("convert tmp/6kfrk1258649899.ps tmp/6kfrk1258649899.png")
> system("convert tmp/7maq91258649899.ps tmp/7maq91258649899.png")
> system("convert tmp/8lm741258649899.ps tmp/8lm741258649899.png")
> system("convert tmp/9dmto1258649899.ps tmp/9dmto1258649899.png")
> system("convert tmp/103hs21258649899.ps tmp/103hs21258649899.png")
>
>
> proc.time()
user system elapsed
2.443 1.550 2.861