R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9.9,8.2,9.8,8,9.3,7.5,8.3,6.8,8,6.5,8.5,6.6,10.4,7.6,11.1,8,10.9,8.1,10,7.7,9.2,7.5,9.2,7.6,9.5,7.8,9.6,7.8,9.5,7.8,9.1,7.5,8.9,7.5,9,7.1,10.1,7.5,10.3,7.5,10.2,7.6,9.6,7.7,9.2,7.7,9.3,7.9,9.4,8.1,9.4,8.2,9.2,8.2,9,8.2,9,7.9,9,7.3,9.8,6.9,10,6.6,9.8,6.7,9.3,6.9,9,7,9,7.1,9.1,7.2,9.1,7.1,9.1,6.9,9.2,7,8.8,6.8,8.3,6.4,8.4,6.7,8.1,6.6,7.7,6.4,7.9,6.3,7.9,6.2,8,6.5,7.9,6.8,7.6,6.8,7.1,6.4,6.8,6.1,6.5,5.8,6.9,6.1,8.2,7.2,8.7,7.3,8.3,6.9,7.9,6.1,7.5,5.8,7.8,6.2),dim=c(2,60),dimnames=list(c('WLVrouw','WLMan'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WLVrouw','WLMan'),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
WLMan WLVrouw M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.2 9.9 1 0 0 0 0 0 0 0 0 0 0
2 8.0 9.8 0 1 0 0 0 0 0 0 0 0 0
3 7.5 9.3 0 0 1 0 0 0 0 0 0 0 0
4 6.8 8.3 0 0 0 1 0 0 0 0 0 0 0
5 6.5 8.0 0 0 0 0 1 0 0 0 0 0 0
6 6.6 8.5 0 0 0 0 0 1 0 0 0 0 0
7 7.6 10.4 0 0 0 0 0 0 1 0 0 0 0
8 8.0 11.1 0 0 0 0 0 0 0 1 0 0 0
9 8.1 10.9 0 0 0 0 0 0 0 0 1 0 0
10 7.7 10.0 0 0 0 0 0 0 0 0 0 1 0
11 7.5 9.2 0 0 0 0 0 0 0 0 0 0 1
12 7.6 9.2 0 0 0 0 0 0 0 0 0 0 0
13 7.8 9.5 1 0 0 0 0 0 0 0 0 0 0
14 7.8 9.6 0 1 0 0 0 0 0 0 0 0 0
15 7.8 9.5 0 0 1 0 0 0 0 0 0 0 0
16 7.5 9.1 0 0 0 1 0 0 0 0 0 0 0
17 7.5 8.9 0 0 0 0 1 0 0 0 0 0 0
18 7.1 9.0 0 0 0 0 0 1 0 0 0 0 0
19 7.5 10.1 0 0 0 0 0 0 1 0 0 0 0
20 7.5 10.3 0 0 0 0 0 0 0 1 0 0 0
21 7.6 10.2 0 0 0 0 0 0 0 0 1 0 0
22 7.7 9.6 0 0 0 0 0 0 0 0 0 1 0
23 7.7 9.2 0 0 0 0 0 0 0 0 0 0 1
24 7.9 9.3 0 0 0 0 0 0 0 0 0 0 0
25 8.1 9.4 1 0 0 0 0 0 0 0 0 0 0
26 8.2 9.4 0 1 0 0 0 0 0 0 0 0 0
27 8.2 9.2 0 0 1 0 0 0 0 0 0 0 0
28 8.2 9.0 0 0 0 1 0 0 0 0 0 0 0
29 7.9 9.0 0 0 0 0 1 0 0 0 0 0 0
30 7.3 9.0 0 0 0 0 0 1 0 0 0 0 0
31 6.9 9.8 0 0 0 0 0 0 1 0 0 0 0
32 6.6 10.0 0 0 0 0 0 0 0 1 0 0 0
33 6.7 9.8 0 0 0 0 0 0 0 0 1 0 0
34 6.9 9.3 0 0 0 0 0 0 0 0 0 1 0
35 7.0 9.0 0 0 0 0 0 0 0 0 0 0 1
36 7.1 9.0 0 0 0 0 0 0 0 0 0 0 0
37 7.2 9.1 1 0 0 0 0 0 0 0 0 0 0
38 7.1 9.1 0 1 0 0 0 0 0 0 0 0 0
39 6.9 9.1 0 0 1 0 0 0 0 0 0 0 0
40 7.0 9.2 0 0 0 1 0 0 0 0 0 0 0
41 6.8 8.8 0 0 0 0 1 0 0 0 0 0 0
42 6.4 8.3 0 0 0 0 0 1 0 0 0 0 0
43 6.7 8.4 0 0 0 0 0 0 1 0 0 0 0
44 6.6 8.1 0 0 0 0 0 0 0 1 0 0 0
45 6.4 7.7 0 0 0 0 0 0 0 0 1 0 0
46 6.3 7.9 0 0 0 0 0 0 0 0 0 1 0
47 6.2 7.9 0 0 0 0 0 0 0 0 0 0 1
48 6.5 8.0 0 0 0 0 0 0 0 0 0 0 0
49 6.8 7.9 1 0 0 0 0 0 0 0 0 0 0
50 6.8 7.6 0 1 0 0 0 0 0 0 0 0 0
51 6.4 7.1 0 0 1 0 0 0 0 0 0 0 0
52 6.1 6.8 0 0 0 1 0 0 0 0 0 0 0
53 5.8 6.5 0 0 0 0 1 0 0 0 0 0 0
54 6.1 6.9 0 0 0 0 0 1 0 0 0 0 0
55 7.2 8.2 0 0 0 0 0 0 1 0 0 0 0
56 7.3 8.7 0 0 0 0 0 0 0 1 0 0 0
57 6.9 8.3 0 0 0 0 0 0 0 0 1 0 0
58 6.1 7.9 0 0 0 0 0 0 0 0 0 1 0
59 5.8 7.5 0 0 0 0 0 0 0 0 0 0 1
60 6.2 7.8 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) WLVrouw M1 M2 M3 M4
2.15404 0.56651 0.27675 0.27074 0.19803 0.16197
M5 M6 M7 M8 M9 M10
0.07793 -0.17872 -0.28789 -0.41518 -0.32789 -0.27862
M11
-0.16335
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.80394 -0.22451 -0.01986 0.22610 0.78542
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.15404 0.53099 4.057 0.000186 ***
WLVrouw 0.56651 0.05785 9.793 6.27e-13 ***
M1 0.27675 0.25058 1.104 0.275024
M2 0.27074 0.25020 1.082 0.284735
M3 0.19803 0.24912 0.795 0.430657
M4 0.16197 0.24912 0.650 0.518742
M5 0.07793 0.25008 0.312 0.756700
M6 -0.17872 0.24959 -0.716 0.477503
M7 -0.28789 0.25236 -1.141 0.259746
M8 -0.41518 0.25528 -1.626 0.110553
M9 -0.32789 0.25236 -1.299 0.200188
M10 -0.27862 0.24943 -1.117 0.269651
M11 -0.16335 0.24897 -0.656 0.514955
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3935 on 47 degrees of freedom
Multiple R-squared: 0.7249, Adjusted R-squared: 0.6546
F-statistic: 10.32 on 12 and 47 DF, p-value: 1.578e-09
> 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,] 1.875860e-03 3.751720e-03 0.998124140
[2,] 5.632605e-04 1.126521e-03 0.999436739
[3,] 6.017371e-05 1.203474e-04 0.999939826
[4,] 7.325432e-05 1.465086e-04 0.999926746
[5,] 8.934906e-05 1.786981e-04 0.999910651
[6,] 2.268110e-05 4.536221e-05 0.999977319
[7,] 5.483919e-05 1.096784e-04 0.999945161
[8,] 3.328550e-05 6.657100e-05 0.999966714
[9,] 2.374112e-05 4.748223e-05 0.999976259
[10,] 5.813393e-05 1.162679e-04 0.999941866
[11,] 5.373727e-04 1.074745e-03 0.999462627
[12,] 8.588939e-03 1.717788e-02 0.991411061
[13,] 1.159421e-01 2.318842e-01 0.884057911
[14,] 3.009734e-01 6.019468e-01 0.699026593
[15,] 3.343363e-01 6.686725e-01 0.665663738
[16,] 3.394989e-01 6.789978e-01 0.660501102
[17,] 6.492365e-01 7.015270e-01 0.350763505
[18,] 8.115057e-01 3.769887e-01 0.188494342
[19,] 7.576905e-01 4.846190e-01 0.242309524
[20,] 8.391429e-01 3.217141e-01 0.160857063
[21,] 8.589598e-01 2.820804e-01 0.141040197
[22,] 7.994110e-01 4.011781e-01 0.200589042
[23,] 7.632591e-01 4.734818e-01 0.236740898
[24,] 7.850895e-01 4.298209e-01 0.214910464
[25,] 8.111921e-01 3.776157e-01 0.188807870
[26,] 7.552433e-01 4.895134e-01 0.244756684
[27,] 8.009837e-01 3.980326e-01 0.199016296
[28,] 9.925468e-01 1.490645e-02 0.007453227
[29,] 9.804277e-01 3.914469e-02 0.019572347
> postscript(file="/var/www/html/rcomp/tmp/1xwc81258731747.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2eppz1258731747.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qiy01258731747.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gy0m1258731747.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tfaw1258731747.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
0.160783924 0.023444252 -0.120593777 -0.218028522 -0.264038029 -0.190641314
7 8 9 10 11 12
-0.157838375 -0.027101988 0.098907519 0.159501296 0.297434745 0.234085566
13 14 15 16 17 18
-0.012612792 -0.063254105 0.066104581 0.028764909 0.226104581 0.026104581
19 20 21 22 23 24
-0.087885912 -0.073895419 -0.004536733 0.386104581 0.497434745 0.477434745
25 26 27 28 29 30
0.344038029 0.450047537 0.636057044 0.785415730 0.569453760 0.226104581
31 32 33 34 35 36
-0.517933449 -0.803942956 -0.677933449 -0.243942956 -0.089263613 -0.152612792
37 38 39 40 41 42
-0.386009507 -0.480000000 -0.607292135 -0.527885912 -0.417244598 -0.277339672
43 44 45 46 47 48
0.075178047 0.272422645 0.211733794 -0.050831461 -0.266104581 -0.186104581
49 50 51 52 53 54
-0.106199654 0.069762316 0.025724287 -0.068266206 -0.114275713 0.215771824
55 56 57 58 59 60
0.688479689 0.632517718 0.371828868 -0.250831461 -0.439501296 -0.372802939
> postscript(file="/var/www/html/rcomp/tmp/69jhm1258731747.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 0.160783924 NA
1 0.023444252 0.160783924
2 -0.120593777 0.023444252
3 -0.218028522 -0.120593777
4 -0.264038029 -0.218028522
5 -0.190641314 -0.264038029
6 -0.157838375 -0.190641314
7 -0.027101988 -0.157838375
8 0.098907519 -0.027101988
9 0.159501296 0.098907519
10 0.297434745 0.159501296
11 0.234085566 0.297434745
12 -0.012612792 0.234085566
13 -0.063254105 -0.012612792
14 0.066104581 -0.063254105
15 0.028764909 0.066104581
16 0.226104581 0.028764909
17 0.026104581 0.226104581
18 -0.087885912 0.026104581
19 -0.073895419 -0.087885912
20 -0.004536733 -0.073895419
21 0.386104581 -0.004536733
22 0.497434745 0.386104581
23 0.477434745 0.497434745
24 0.344038029 0.477434745
25 0.450047537 0.344038029
26 0.636057044 0.450047537
27 0.785415730 0.636057044
28 0.569453760 0.785415730
29 0.226104581 0.569453760
30 -0.517933449 0.226104581
31 -0.803942956 -0.517933449
32 -0.677933449 -0.803942956
33 -0.243942956 -0.677933449
34 -0.089263613 -0.243942956
35 -0.152612792 -0.089263613
36 -0.386009507 -0.152612792
37 -0.480000000 -0.386009507
38 -0.607292135 -0.480000000
39 -0.527885912 -0.607292135
40 -0.417244598 -0.527885912
41 -0.277339672 -0.417244598
42 0.075178047 -0.277339672
43 0.272422645 0.075178047
44 0.211733794 0.272422645
45 -0.050831461 0.211733794
46 -0.266104581 -0.050831461
47 -0.186104581 -0.266104581
48 -0.106199654 -0.186104581
49 0.069762316 -0.106199654
50 0.025724287 0.069762316
51 -0.068266206 0.025724287
52 -0.114275713 -0.068266206
53 0.215771824 -0.114275713
54 0.688479689 0.215771824
55 0.632517718 0.688479689
56 0.371828868 0.632517718
57 -0.250831461 0.371828868
58 -0.439501296 -0.250831461
59 -0.372802939 -0.439501296
60 NA -0.372802939
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.023444252 0.160783924
[2,] -0.120593777 0.023444252
[3,] -0.218028522 -0.120593777
[4,] -0.264038029 -0.218028522
[5,] -0.190641314 -0.264038029
[6,] -0.157838375 -0.190641314
[7,] -0.027101988 -0.157838375
[8,] 0.098907519 -0.027101988
[9,] 0.159501296 0.098907519
[10,] 0.297434745 0.159501296
[11,] 0.234085566 0.297434745
[12,] -0.012612792 0.234085566
[13,] -0.063254105 -0.012612792
[14,] 0.066104581 -0.063254105
[15,] 0.028764909 0.066104581
[16,] 0.226104581 0.028764909
[17,] 0.026104581 0.226104581
[18,] -0.087885912 0.026104581
[19,] -0.073895419 -0.087885912
[20,] -0.004536733 -0.073895419
[21,] 0.386104581 -0.004536733
[22,] 0.497434745 0.386104581
[23,] 0.477434745 0.497434745
[24,] 0.344038029 0.477434745
[25,] 0.450047537 0.344038029
[26,] 0.636057044 0.450047537
[27,] 0.785415730 0.636057044
[28,] 0.569453760 0.785415730
[29,] 0.226104581 0.569453760
[30,] -0.517933449 0.226104581
[31,] -0.803942956 -0.517933449
[32,] -0.677933449 -0.803942956
[33,] -0.243942956 -0.677933449
[34,] -0.089263613 -0.243942956
[35,] -0.152612792 -0.089263613
[36,] -0.386009507 -0.152612792
[37,] -0.480000000 -0.386009507
[38,] -0.607292135 -0.480000000
[39,] -0.527885912 -0.607292135
[40,] -0.417244598 -0.527885912
[41,] -0.277339672 -0.417244598
[42,] 0.075178047 -0.277339672
[43,] 0.272422645 0.075178047
[44,] 0.211733794 0.272422645
[45,] -0.050831461 0.211733794
[46,] -0.266104581 -0.050831461
[47,] -0.186104581 -0.266104581
[48,] -0.106199654 -0.186104581
[49,] 0.069762316 -0.106199654
[50,] 0.025724287 0.069762316
[51,] -0.068266206 0.025724287
[52,] -0.114275713 -0.068266206
[53,] 0.215771824 -0.114275713
[54,] 0.688479689 0.215771824
[55,] 0.632517718 0.688479689
[56,] 0.371828868 0.632517718
[57,] -0.250831461 0.371828868
[58,] -0.439501296 -0.250831461
[59,] -0.372802939 -0.439501296
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.023444252 0.160783924
2 -0.120593777 0.023444252
3 -0.218028522 -0.120593777
4 -0.264038029 -0.218028522
5 -0.190641314 -0.264038029
6 -0.157838375 -0.190641314
7 -0.027101988 -0.157838375
8 0.098907519 -0.027101988
9 0.159501296 0.098907519
10 0.297434745 0.159501296
11 0.234085566 0.297434745
12 -0.012612792 0.234085566
13 -0.063254105 -0.012612792
14 0.066104581 -0.063254105
15 0.028764909 0.066104581
16 0.226104581 0.028764909
17 0.026104581 0.226104581
18 -0.087885912 0.026104581
19 -0.073895419 -0.087885912
20 -0.004536733 -0.073895419
21 0.386104581 -0.004536733
22 0.497434745 0.386104581
23 0.477434745 0.497434745
24 0.344038029 0.477434745
25 0.450047537 0.344038029
26 0.636057044 0.450047537
27 0.785415730 0.636057044
28 0.569453760 0.785415730
29 0.226104581 0.569453760
30 -0.517933449 0.226104581
31 -0.803942956 -0.517933449
32 -0.677933449 -0.803942956
33 -0.243942956 -0.677933449
34 -0.089263613 -0.243942956
35 -0.152612792 -0.089263613
36 -0.386009507 -0.152612792
37 -0.480000000 -0.386009507
38 -0.607292135 -0.480000000
39 -0.527885912 -0.607292135
40 -0.417244598 -0.527885912
41 -0.277339672 -0.417244598
42 0.075178047 -0.277339672
43 0.272422645 0.075178047
44 0.211733794 0.272422645
45 -0.050831461 0.211733794
46 -0.266104581 -0.050831461
47 -0.186104581 -0.266104581
48 -0.106199654 -0.186104581
49 0.069762316 -0.106199654
50 0.025724287 0.069762316
51 -0.068266206 0.025724287
52 -0.114275713 -0.068266206
53 0.215771824 -0.114275713
54 0.688479689 0.215771824
55 0.632517718 0.688479689
56 0.371828868 0.632517718
57 -0.250831461 0.371828868
58 -0.439501296 -0.250831461
59 -0.372802939 -0.439501296
> 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/7vawe1258731747.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84y221258731747.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nn7p1258731747.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107eag1258731747.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u3yn1258731747.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/123b6v1258731747.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/13iyha1258731747.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/14b50q1258731747.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/15ilk51258731747.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/16houc1258731747.tab")
+ }
>
> system("convert tmp/1xwc81258731747.ps tmp/1xwc81258731747.png")
> system("convert tmp/2eppz1258731747.ps tmp/2eppz1258731747.png")
> system("convert tmp/3qiy01258731747.ps tmp/3qiy01258731747.png")
> system("convert tmp/4gy0m1258731747.ps tmp/4gy0m1258731747.png")
> system("convert tmp/5tfaw1258731747.ps tmp/5tfaw1258731747.png")
> system("convert tmp/69jhm1258731747.ps tmp/69jhm1258731747.png")
> system("convert tmp/7vawe1258731747.ps tmp/7vawe1258731747.png")
> system("convert tmp/84y221258731747.ps tmp/84y221258731747.png")
> system("convert tmp/9nn7p1258731747.ps tmp/9nn7p1258731747.png")
> system("convert tmp/107eag1258731747.ps tmp/107eag1258731747.png")
>
>
> proc.time()
user system elapsed
2.383 1.531 2.880