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(1.3,2,1.2,2.1,1.1,2.1,1.4,2.5,1.2,2.2,1.5,2.3,1.1,2.3,1.3,2.2,1.5,2.2,1.1,1.6,1.4,1.8,1.3,1.7,1.5,1.9,1.6,1.8,1.7,1.9,1.1,1.5,1.6,1,1.3,0.8,1.7,1.1,1.6,1.5,1.7,1.7,1.9,2.3,1.8,2.4,1.9,3,1.6,3,1.5,3.2,1.6,3.2,1.6,3.2,1.7,3.5,2,4,2,4.3,1.9,4.1,1.7,4,1.8,4.1,1.9,4.2,1.7,4.5,2,5.6,2.1,6.5,2.4,7.6,2.5,8.5,2.5,8.7,2.6,8.3,2.2,8.3,2.5,8.5,2.8,8.7,2.8,8.7,2.9,8.5,3,7.9,3.1,7,2.9,5.8,2.7,4.5,2.2,3.7,2.5,3.1,2.3,2.7,2.6,2.3,2.3,1.8,2.2,1.5,1.8,1.2,1.8,1),dim=c(2,59),dimnames=list(c('inflatie','inflatie_levensmiddelen'),1:59))
> y <- array(NA,dim=c(2,59),dimnames=list(c('inflatie','inflatie_levensmiddelen'),1:59))
> 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
inflatie inflatie_levensmiddelen M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.3 2.0 1 0 0 0 0 0 0 0 0 0 0
2 1.2 2.1 0 1 0 0 0 0 0 0 0 0 0
3 1.1 2.1 0 0 1 0 0 0 0 0 0 0 0
4 1.4 2.5 0 0 0 1 0 0 0 0 0 0 0
5 1.2 2.2 0 0 0 0 1 0 0 0 0 0 0
6 1.5 2.3 0 0 0 0 0 1 0 0 0 0 0
7 1.1 2.3 0 0 0 0 0 0 1 0 0 0 0
8 1.3 2.2 0 0 0 0 0 0 0 1 0 0 0
9 1.5 2.2 0 0 0 0 0 0 0 0 1 0 0
10 1.1 1.6 0 0 0 0 0 0 0 0 0 1 0
11 1.4 1.8 0 0 0 0 0 0 0 0 0 0 1
12 1.3 1.7 0 0 0 0 0 0 0 0 0 0 0
13 1.5 1.9 1 0 0 0 0 0 0 0 0 0 0
14 1.6 1.8 0 1 0 0 0 0 0 0 0 0 0
15 1.7 1.9 0 0 1 0 0 0 0 0 0 0 0
16 1.1 1.5 0 0 0 1 0 0 0 0 0 0 0
17 1.6 1.0 0 0 0 0 1 0 0 0 0 0 0
18 1.3 0.8 0 0 0 0 0 1 0 0 0 0 0
19 1.7 1.1 0 0 0 0 0 0 1 0 0 0 0
20 1.6 1.5 0 0 0 0 0 0 0 1 0 0 0
21 1.7 1.7 0 0 0 0 0 0 0 0 1 0 0
22 1.9 2.3 0 0 0 0 0 0 0 0 0 1 0
23 1.8 2.4 0 0 0 0 0 0 0 0 0 0 1
24 1.9 3.0 0 0 0 0 0 0 0 0 0 0 0
25 1.6 3.0 1 0 0 0 0 0 0 0 0 0 0
26 1.5 3.2 0 1 0 0 0 0 0 0 0 0 0
27 1.6 3.2 0 0 1 0 0 0 0 0 0 0 0
28 1.6 3.2 0 0 0 1 0 0 0 0 0 0 0
29 1.7 3.5 0 0 0 0 1 0 0 0 0 0 0
30 2.0 4.0 0 0 0 0 0 1 0 0 0 0 0
31 2.0 4.3 0 0 0 0 0 0 1 0 0 0 0
32 1.9 4.1 0 0 0 0 0 0 0 1 0 0 0
33 1.7 4.0 0 0 0 0 0 0 0 0 1 0 0
34 1.8 4.1 0 0 0 0 0 0 0 0 0 1 0
35 1.9 4.2 0 0 0 0 0 0 0 0 0 0 1
36 1.7 4.5 0 0 0 0 0 0 0 0 0 0 0
37 2.0 5.6 1 0 0 0 0 0 0 0 0 0 0
38 2.1 6.5 0 1 0 0 0 0 0 0 0 0 0
39 2.4 7.6 0 0 1 0 0 0 0 0 0 0 0
40 2.5 8.5 0 0 0 1 0 0 0 0 0 0 0
41 2.5 8.7 0 0 0 0 1 0 0 0 0 0 0
42 2.6 8.3 0 0 0 0 0 1 0 0 0 0 0
43 2.2 8.3 0 0 0 0 0 0 1 0 0 0 0
44 2.5 8.5 0 0 0 0 0 0 0 1 0 0 0
45 2.8 8.7 0 0 0 0 0 0 0 0 1 0 0
46 2.8 8.7 0 0 0 0 0 0 0 0 0 1 0
47 2.9 8.5 0 0 0 0 0 0 0 0 0 0 1
48 3.0 7.9 0 0 0 0 0 0 0 0 0 0 0
49 3.1 7.0 1 0 0 0 0 0 0 0 0 0 0
50 2.9 5.8 0 1 0 0 0 0 0 0 0 0 0
51 2.7 4.5 0 0 1 0 0 0 0 0 0 0 0
52 2.2 3.7 0 0 0 1 0 0 0 0 0 0 0
53 2.5 3.1 0 0 0 0 1 0 0 0 0 0 0
54 2.3 2.7 0 0 0 0 0 1 0 0 0 0 0
55 2.6 2.3 0 0 0 0 0 0 1 0 0 0 0
56 2.3 1.8 0 0 0 0 0 0 0 1 0 0 0
57 2.2 1.5 0 0 0 0 0 0 0 0 1 0 0
58 1.8 1.2 0 0 0 0 0 0 0 0 0 1 0
59 1.8 1.0 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) inflatie_levensmiddelen M1
1.265802 0.165894 -0.012790
M2 M3 M4
-0.049472 -0.006154 -0.149472
M5 M6 M7
0.020389 0.073661 0.047025
M8 M9 M10
0.053661 0.113661 0.020297
M11
0.100297
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.59438 -0.23700 -0.09963 0.14221 0.90562
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.265802 0.217350 5.824 5.32e-07 ***
inflatie_levensmiddelen 0.165894 0.021002 7.899 4.17e-10 ***
M1 -0.012790 0.265680 -0.048 0.962
M2 -0.049472 0.265693 -0.186 0.853
M3 -0.006154 0.265706 -0.023 0.982
M4 -0.149472 0.265693 -0.563 0.576
M5 0.020389 0.265838 0.077 0.939
M6 0.073661 0.265919 0.277 0.783
M7 0.047025 0.265877 0.177 0.860
M8 0.053661 0.265919 0.202 0.841
M9 0.113661 0.265919 0.427 0.671
M10 0.020297 0.265964 0.076 0.940
M11 0.100297 0.265964 0.377 0.708
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3959 on 46 degrees of freedom
Multiple R-squared: 0.5803, Adjusted R-squared: 0.4708
F-statistic: 5.3 on 12 and 46 DF, p-value: 1.607e-05
> 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,] 0.44092877 0.88185754 0.5590712
[2,] 0.31821988 0.63643976 0.6817801
[3,] 0.25012387 0.50024775 0.7498761
[4,] 0.23681392 0.47362785 0.7631861
[5,] 0.15794032 0.31588065 0.8420597
[6,] 0.09530230 0.19060460 0.9046977
[7,] 0.20648667 0.41297334 0.7935133
[8,] 0.17132301 0.34264603 0.8286770
[9,] 0.16792451 0.33584901 0.8320755
[10,] 0.13044554 0.26089109 0.8695545
[11,] 0.10577797 0.21155593 0.8942220
[12,] 0.08831318 0.17662636 0.9116868
[13,] 0.07059013 0.14118026 0.9294099
[14,] 0.05648286 0.11296571 0.9435171
[15,] 0.04215700 0.08431399 0.9578430
[16,] 0.02833299 0.05666598 0.9716670
[17,] 0.01876532 0.03753064 0.9812347
[18,] 0.02101496 0.04202992 0.9789850
[19,] 0.01440548 0.02881095 0.9855945
[20,] 0.00941825 0.01883650 0.9905818
[21,] 0.02259493 0.04518985 0.9774051
[22,] 0.05737890 0.11475781 0.9426211
[23,] 0.11633684 0.23267367 0.8836632
[24,] 0.11876603 0.23753206 0.8812340
[25,] 0.07129383 0.14258767 0.9287062
[26,] 0.05676078 0.11352156 0.9432392
[27,] 0.02867008 0.05734016 0.9713299
[28,] 0.28061259 0.56122517 0.7193874
> postscript(file="/var/www/html/rcomp/tmp/15eff1258719324.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2pm5j1258719324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3h1ve1258719324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4azn21258719325.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ou651258719325.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 = 59
Frequency = 1
1 2 3 4 5 6
-0.284800771 -0.364708091 -0.508025977 -0.131065823 -0.451158504 -0.221019483
7 8 9 10 11 12
-0.594383710 -0.384430050 -0.244430050 -0.451529225 -0.264708091 -0.247822098
13 14 15 16 17 18
-0.068211338 0.085060208 0.125152889 -0.265171492 0.147914694 -0.172177987
19 20 21 22 23 24
0.204689487 0.031695982 0.038517115 0.232344744 0.035755311 0.136515272
25 26 27 28 29 30
-0.150695102 -0.247191855 -0.190509742 -0.047191855 -0.166821134 -0.003039846
31 32 33 34 35 36
-0.026172372 -0.099629279 -0.343039846 -0.166265052 -0.162854485 -0.312326224
37 38 39 40 41 42
-0.182020363 -0.194643147 -0.120444798 -0.026431809 -0.229471655 -0.116385469
43 44 45 46 47 48
-0.489749696 -0.229564335 -0.022743201 0.070621025 0.123799892 0.423633050
49 50 51 52 53 54
0.685727574 0.721482885 0.693827628 0.469860980 0.699536599 0.512622784
55 56 57 58 59
0.905616290 0.681927682 0.571695982 0.314828508 0.268007374
> postscript(file="/var/www/html/rcomp/tmp/6925d1258719325.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.284800771 NA
1 -0.364708091 -0.284800771
2 -0.508025977 -0.364708091
3 -0.131065823 -0.508025977
4 -0.451158504 -0.131065823
5 -0.221019483 -0.451158504
6 -0.594383710 -0.221019483
7 -0.384430050 -0.594383710
8 -0.244430050 -0.384430050
9 -0.451529225 -0.244430050
10 -0.264708091 -0.451529225
11 -0.247822098 -0.264708091
12 -0.068211338 -0.247822098
13 0.085060208 -0.068211338
14 0.125152889 0.085060208
15 -0.265171492 0.125152889
16 0.147914694 -0.265171492
17 -0.172177987 0.147914694
18 0.204689487 -0.172177987
19 0.031695982 0.204689487
20 0.038517115 0.031695982
21 0.232344744 0.038517115
22 0.035755311 0.232344744
23 0.136515272 0.035755311
24 -0.150695102 0.136515272
25 -0.247191855 -0.150695102
26 -0.190509742 -0.247191855
27 -0.047191855 -0.190509742
28 -0.166821134 -0.047191855
29 -0.003039846 -0.166821134
30 -0.026172372 -0.003039846
31 -0.099629279 -0.026172372
32 -0.343039846 -0.099629279
33 -0.166265052 -0.343039846
34 -0.162854485 -0.166265052
35 -0.312326224 -0.162854485
36 -0.182020363 -0.312326224
37 -0.194643147 -0.182020363
38 -0.120444798 -0.194643147
39 -0.026431809 -0.120444798
40 -0.229471655 -0.026431809
41 -0.116385469 -0.229471655
42 -0.489749696 -0.116385469
43 -0.229564335 -0.489749696
44 -0.022743201 -0.229564335
45 0.070621025 -0.022743201
46 0.123799892 0.070621025
47 0.423633050 0.123799892
48 0.685727574 0.423633050
49 0.721482885 0.685727574
50 0.693827628 0.721482885
51 0.469860980 0.693827628
52 0.699536599 0.469860980
53 0.512622784 0.699536599
54 0.905616290 0.512622784
55 0.681927682 0.905616290
56 0.571695982 0.681927682
57 0.314828508 0.571695982
58 0.268007374 0.314828508
59 NA 0.268007374
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.364708091 -0.284800771
[2,] -0.508025977 -0.364708091
[3,] -0.131065823 -0.508025977
[4,] -0.451158504 -0.131065823
[5,] -0.221019483 -0.451158504
[6,] -0.594383710 -0.221019483
[7,] -0.384430050 -0.594383710
[8,] -0.244430050 -0.384430050
[9,] -0.451529225 -0.244430050
[10,] -0.264708091 -0.451529225
[11,] -0.247822098 -0.264708091
[12,] -0.068211338 -0.247822098
[13,] 0.085060208 -0.068211338
[14,] 0.125152889 0.085060208
[15,] -0.265171492 0.125152889
[16,] 0.147914694 -0.265171492
[17,] -0.172177987 0.147914694
[18,] 0.204689487 -0.172177987
[19,] 0.031695982 0.204689487
[20,] 0.038517115 0.031695982
[21,] 0.232344744 0.038517115
[22,] 0.035755311 0.232344744
[23,] 0.136515272 0.035755311
[24,] -0.150695102 0.136515272
[25,] -0.247191855 -0.150695102
[26,] -0.190509742 -0.247191855
[27,] -0.047191855 -0.190509742
[28,] -0.166821134 -0.047191855
[29,] -0.003039846 -0.166821134
[30,] -0.026172372 -0.003039846
[31,] -0.099629279 -0.026172372
[32,] -0.343039846 -0.099629279
[33,] -0.166265052 -0.343039846
[34,] -0.162854485 -0.166265052
[35,] -0.312326224 -0.162854485
[36,] -0.182020363 -0.312326224
[37,] -0.194643147 -0.182020363
[38,] -0.120444798 -0.194643147
[39,] -0.026431809 -0.120444798
[40,] -0.229471655 -0.026431809
[41,] -0.116385469 -0.229471655
[42,] -0.489749696 -0.116385469
[43,] -0.229564335 -0.489749696
[44,] -0.022743201 -0.229564335
[45,] 0.070621025 -0.022743201
[46,] 0.123799892 0.070621025
[47,] 0.423633050 0.123799892
[48,] 0.685727574 0.423633050
[49,] 0.721482885 0.685727574
[50,] 0.693827628 0.721482885
[51,] 0.469860980 0.693827628
[52,] 0.699536599 0.469860980
[53,] 0.512622784 0.699536599
[54,] 0.905616290 0.512622784
[55,] 0.681927682 0.905616290
[56,] 0.571695982 0.681927682
[57,] 0.314828508 0.571695982
[58,] 0.268007374 0.314828508
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.364708091 -0.284800771
2 -0.508025977 -0.364708091
3 -0.131065823 -0.508025977
4 -0.451158504 -0.131065823
5 -0.221019483 -0.451158504
6 -0.594383710 -0.221019483
7 -0.384430050 -0.594383710
8 -0.244430050 -0.384430050
9 -0.451529225 -0.244430050
10 -0.264708091 -0.451529225
11 -0.247822098 -0.264708091
12 -0.068211338 -0.247822098
13 0.085060208 -0.068211338
14 0.125152889 0.085060208
15 -0.265171492 0.125152889
16 0.147914694 -0.265171492
17 -0.172177987 0.147914694
18 0.204689487 -0.172177987
19 0.031695982 0.204689487
20 0.038517115 0.031695982
21 0.232344744 0.038517115
22 0.035755311 0.232344744
23 0.136515272 0.035755311
24 -0.150695102 0.136515272
25 -0.247191855 -0.150695102
26 -0.190509742 -0.247191855
27 -0.047191855 -0.190509742
28 -0.166821134 -0.047191855
29 -0.003039846 -0.166821134
30 -0.026172372 -0.003039846
31 -0.099629279 -0.026172372
32 -0.343039846 -0.099629279
33 -0.166265052 -0.343039846
34 -0.162854485 -0.166265052
35 -0.312326224 -0.162854485
36 -0.182020363 -0.312326224
37 -0.194643147 -0.182020363
38 -0.120444798 -0.194643147
39 -0.026431809 -0.120444798
40 -0.229471655 -0.026431809
41 -0.116385469 -0.229471655
42 -0.489749696 -0.116385469
43 -0.229564335 -0.489749696
44 -0.022743201 -0.229564335
45 0.070621025 -0.022743201
46 0.123799892 0.070621025
47 0.423633050 0.123799892
48 0.685727574 0.423633050
49 0.721482885 0.685727574
50 0.693827628 0.721482885
51 0.469860980 0.693827628
52 0.699536599 0.469860980
53 0.512622784 0.699536599
54 0.905616290 0.512622784
55 0.681927682 0.905616290
56 0.571695982 0.681927682
57 0.314828508 0.571695982
58 0.268007374 0.314828508
> 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/754d41258719325.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8agg31258719325.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9usio1258719325.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qz9b1258719325.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vs9o1258719325.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/12a1k21258719325.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/13d2s31258719325.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/14a8av1258719325.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/154gwv1258719325.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/16z5ux1258719325.tab")
+ }
>
> system("convert tmp/15eff1258719324.ps tmp/15eff1258719324.png")
> system("convert tmp/2pm5j1258719324.ps tmp/2pm5j1258719324.png")
> system("convert tmp/3h1ve1258719324.ps tmp/3h1ve1258719324.png")
> system("convert tmp/4azn21258719325.ps tmp/4azn21258719325.png")
> system("convert tmp/5ou651258719325.ps tmp/5ou651258719325.png")
> system("convert tmp/6925d1258719325.ps tmp/6925d1258719325.png")
> system("convert tmp/754d41258719325.ps tmp/754d41258719325.png")
> system("convert tmp/8agg31258719325.ps tmp/8agg31258719325.png")
> system("convert tmp/9usio1258719325.ps tmp/9usio1258719325.png")
> system("convert tmp/10qz9b1258719325.ps tmp/10qz9b1258719325.png")
>
>
> proc.time()
user system elapsed
2.375 1.540 5.294