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(10.9,0,10,0,9.2,0,9.2,0,9.5,0,9.6,0,9.5,0,9.1,0,8.9,0,9,0,10.1,0,10.3,0,10.2,0,9.6,0,9.2,0,9.3,0,9.4,0,9.4,0,9.2,0,9,0,9,0,9,0,9.8,0,10,0,9.8,0,9.3,0,9,0,9,0,9.1,0,9.1,0,9.1,0,9.2,0,8.8,0,8.3,0,8.4,0,8.1,0,7.7,1,7.9,1,7.9,1,8,1,7.9,1,7.6,1,7.1,1,6.8,1,6.5,1,6.9,1,8.2,1,8.7,1,8.3,1,7.9,1,7.5,1,7.8,1,8.3,1,8.4,1,8.2,1,7.7,1,7.2,1,7.3,1,8.1,1,8.5,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 10.9 0 1 0 0 0 0 0 0 0 0 0 0 1
2 10.0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 9.2 0 0 0 1 0 0 0 0 0 0 0 0 3
4 9.2 0 0 0 0 1 0 0 0 0 0 0 0 4
5 9.5 0 0 0 0 0 1 0 0 0 0 0 0 5
6 9.6 0 0 0 0 0 0 1 0 0 0 0 0 6
7 9.5 0 0 0 0 0 0 0 1 0 0 0 0 7
8 9.1 0 0 0 0 0 0 0 0 1 0 0 0 8
9 8.9 0 0 0 0 0 0 0 0 0 1 0 0 9
10 9.0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 10.1 0 0 0 0 0 0 0 0 0 0 0 1 11
12 10.3 0 0 0 0 0 0 0 0 0 0 0 0 12
13 10.2 0 1 0 0 0 0 0 0 0 0 0 0 13
14 9.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 9.2 0 0 0 1 0 0 0 0 0 0 0 0 15
16 9.3 0 0 0 0 1 0 0 0 0 0 0 0 16
17 9.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 9.4 0 0 0 0 0 0 1 0 0 0 0 0 18
19 9.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 9.0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 9.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 9.0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 9.8 0 0 0 0 0 0 0 0 0 0 0 1 23
24 10.0 0 0 0 0 0 0 0 0 0 0 0 0 24
25 9.8 0 1 0 0 0 0 0 0 0 0 0 0 25
26 9.3 0 0 1 0 0 0 0 0 0 0 0 0 26
27 9.0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 9.0 0 0 0 0 1 0 0 0 0 0 0 0 28
29 9.1 0 0 0 0 0 1 0 0 0 0 0 0 29
30 9.1 0 0 0 0 0 0 1 0 0 0 0 0 30
31 9.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 9.2 0 0 0 0 0 0 0 0 1 0 0 0 32
33 8.8 0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.3 0 0 0 0 0 0 0 0 0 0 1 0 34
35 8.4 0 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 1 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 1 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 1 0 0 0 1 0 0 0 0 0 0 0 40
41 7.9 1 0 0 0 0 1 0 0 0 0 0 0 41
42 7.6 1 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.8 1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.5 1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 1 0 0 0 0 0 0 0 0 0 1 0 46
47 8.2 1 0 0 0 0 0 0 0 0 0 0 1 47
48 8.7 1 0 0 0 0 0 0 0 0 0 0 0 48
49 8.3 1 1 0 0 0 0 0 0 0 0 0 0 49
50 7.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.5 1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.8 1 0 0 0 1 0 0 0 0 0 0 0 52
53 8.3 1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.4 1 0 0 0 0 0 1 0 0 0 0 0 54
55 8.2 1 0 0 0 0 0 0 1 0 0 0 0 55
56 7.7 1 0 0 0 0 0 0 0 1 0 0 0 56
57 7.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 7.3 1 0 0 0 0 0 0 0 0 0 1 0 58
59 8.1 1 0 0 0 0 0 0 0 0 0 0 1 59
60 8.5 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
10.14889 -1.04722 0.07361 -0.34944 -0.71250 -0.59556
M5 M6 M7 M8 M9 M10
-0.39861 -0.40167 -0.58472 -0.82778 -1.09083 -1.05389
M11 t
-0.21694 -0.01694
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.43889 -0.11597 0.01778 0.24069 0.69444
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.148889 0.259067 39.175 < 2e-16 ***
X -1.047222 0.232649 -4.501 4.58e-05 ***
M1 0.073611 0.288788 0.255 0.799939
M2 -0.349444 0.287144 -1.217 0.229824
M3 -0.712500 0.285648 -2.494 0.016274 *
M4 -0.595556 0.284302 -2.095 0.041725 *
M5 -0.398611 0.283110 -1.408 0.165862
M6 -0.401667 0.282072 -1.424 0.161199
M7 -0.584722 0.281192 -2.079 0.043178 *
M8 -0.827778 0.280469 -2.951 0.004964 **
M9 -1.090833 0.279905 -3.897 0.000314 ***
M10 -1.053889 0.279502 -3.771 0.000463 ***
M11 -0.216944 0.279260 -0.777 0.441221
t -0.016944 0.006716 -2.523 0.015157 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4414 on 46 degrees of freedom
Multiple R-squared: 0.8321, Adjusted R-squared: 0.7846
F-statistic: 17.53 on 13 and 46 DF, p-value: 1.259e-13
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.659194e-01 3.318388e-01 0.8340806
[2,] 6.850618e-02 1.370124e-01 0.9314938
[3,] 2.622905e-02 5.245811e-02 0.9737709
[4,] 9.791987e-03 1.958397e-02 0.9902080
[5,] 6.142451e-03 1.228490e-02 0.9938575
[6,] 2.952837e-03 5.905674e-03 0.9970472
[7,] 1.567804e-03 3.135608e-03 0.9984322
[8,] 1.056479e-03 2.112959e-03 0.9989435
[9,] 2.122839e-03 4.245679e-03 0.9978772
[10,] 9.243864e-04 1.848773e-03 0.9990756
[11,] 4.349446e-04 8.698891e-04 0.9995651
[12,] 1.623814e-04 3.247628e-04 0.9998376
[13,] 5.223805e-05 1.044761e-04 0.9999478
[14,] 1.615782e-05 3.231564e-05 0.9999838
[15,] 6.245532e-06 1.249106e-05 0.9999938
[16,] 5.597755e-05 1.119551e-04 0.9999440
[17,] 4.963099e-04 9.926198e-04 0.9995037
[18,] 6.055141e-03 1.211028e-02 0.9939449
[19,] 1.805530e-01 3.611060e-01 0.8194470
[20,] 5.508083e-01 8.983834e-01 0.4491917
[21,] 4.581946e-01 9.163891e-01 0.5418054
[22,] 4.256132e-01 8.512265e-01 0.5743868
[23,] 5.539418e-01 8.921164e-01 0.4460582
[24,] 6.098378e-01 7.803245e-01 0.3901622
[25,] 4.781660e-01 9.563320e-01 0.5218340
[26,] 3.649511e-01 7.299022e-01 0.6350489
[27,] 4.128865e-01 8.257731e-01 0.5871135
> postscript(file="/var/www/html/rcomp/tmp/1v9181258798301.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2n3xb1258798301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33l2o1258798301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lj5m1258798301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/568bl1258798301.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
0.694444444 0.234444444 -0.185555556 -0.285555556 -0.165555556 -0.045555556
7 8 9 10 11 12
0.054444444 -0.085555556 -0.005555556 0.074444444 0.354444444 0.354444444
13 14 15 16 17 18
0.197777778 0.037777778 0.017777778 0.017777778 -0.062222222 -0.042222222
19 20 21 22 23 24
-0.042222222 0.017777778 0.297777778 0.277777778 0.257777778 0.257777778
25 26 27 28 29 30
0.001111111 -0.058888889 0.021111111 -0.078888889 -0.158888889 -0.138888889
31 32 33 34 35 36
0.061111111 0.421111111 0.301111111 -0.218888889 -0.938888889 -1.438888889
37 38 39 40 41 42
-0.848333333 -0.208333333 0.171666667 0.171666667 -0.108333333 -0.388333333
43 44 45 46 47 48
-0.688333333 -0.728333333 -0.748333333 -0.368333333 0.111666667 0.411666667
49 50 51 52 53 54
-0.045000000 -0.005000000 -0.025000000 0.175000000 0.495000000 0.615000000
55 56 57 58 59 60
0.615000000 0.375000000 0.155000000 0.235000000 0.215000000 0.415000000
> postscript(file="/var/www/html/rcomp/tmp/6wsxq1258798301.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 0.694444444 NA
1 0.234444444 0.694444444
2 -0.185555556 0.234444444
3 -0.285555556 -0.185555556
4 -0.165555556 -0.285555556
5 -0.045555556 -0.165555556
6 0.054444444 -0.045555556
7 -0.085555556 0.054444444
8 -0.005555556 -0.085555556
9 0.074444444 -0.005555556
10 0.354444444 0.074444444
11 0.354444444 0.354444444
12 0.197777778 0.354444444
13 0.037777778 0.197777778
14 0.017777778 0.037777778
15 0.017777778 0.017777778
16 -0.062222222 0.017777778
17 -0.042222222 -0.062222222
18 -0.042222222 -0.042222222
19 0.017777778 -0.042222222
20 0.297777778 0.017777778
21 0.277777778 0.297777778
22 0.257777778 0.277777778
23 0.257777778 0.257777778
24 0.001111111 0.257777778
25 -0.058888889 0.001111111
26 0.021111111 -0.058888889
27 -0.078888889 0.021111111
28 -0.158888889 -0.078888889
29 -0.138888889 -0.158888889
30 0.061111111 -0.138888889
31 0.421111111 0.061111111
32 0.301111111 0.421111111
33 -0.218888889 0.301111111
34 -0.938888889 -0.218888889
35 -1.438888889 -0.938888889
36 -0.848333333 -1.438888889
37 -0.208333333 -0.848333333
38 0.171666667 -0.208333333
39 0.171666667 0.171666667
40 -0.108333333 0.171666667
41 -0.388333333 -0.108333333
42 -0.688333333 -0.388333333
43 -0.728333333 -0.688333333
44 -0.748333333 -0.728333333
45 -0.368333333 -0.748333333
46 0.111666667 -0.368333333
47 0.411666667 0.111666667
48 -0.045000000 0.411666667
49 -0.005000000 -0.045000000
50 -0.025000000 -0.005000000
51 0.175000000 -0.025000000
52 0.495000000 0.175000000
53 0.615000000 0.495000000
54 0.615000000 0.615000000
55 0.375000000 0.615000000
56 0.155000000 0.375000000
57 0.235000000 0.155000000
58 0.215000000 0.235000000
59 0.415000000 0.215000000
60 NA 0.415000000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.234444444 0.694444444
[2,] -0.185555556 0.234444444
[3,] -0.285555556 -0.185555556
[4,] -0.165555556 -0.285555556
[5,] -0.045555556 -0.165555556
[6,] 0.054444444 -0.045555556
[7,] -0.085555556 0.054444444
[8,] -0.005555556 -0.085555556
[9,] 0.074444444 -0.005555556
[10,] 0.354444444 0.074444444
[11,] 0.354444444 0.354444444
[12,] 0.197777778 0.354444444
[13,] 0.037777778 0.197777778
[14,] 0.017777778 0.037777778
[15,] 0.017777778 0.017777778
[16,] -0.062222222 0.017777778
[17,] -0.042222222 -0.062222222
[18,] -0.042222222 -0.042222222
[19,] 0.017777778 -0.042222222
[20,] 0.297777778 0.017777778
[21,] 0.277777778 0.297777778
[22,] 0.257777778 0.277777778
[23,] 0.257777778 0.257777778
[24,] 0.001111111 0.257777778
[25,] -0.058888889 0.001111111
[26,] 0.021111111 -0.058888889
[27,] -0.078888889 0.021111111
[28,] -0.158888889 -0.078888889
[29,] -0.138888889 -0.158888889
[30,] 0.061111111 -0.138888889
[31,] 0.421111111 0.061111111
[32,] 0.301111111 0.421111111
[33,] -0.218888889 0.301111111
[34,] -0.938888889 -0.218888889
[35,] -1.438888889 -0.938888889
[36,] -0.848333333 -1.438888889
[37,] -0.208333333 -0.848333333
[38,] 0.171666667 -0.208333333
[39,] 0.171666667 0.171666667
[40,] -0.108333333 0.171666667
[41,] -0.388333333 -0.108333333
[42,] -0.688333333 -0.388333333
[43,] -0.728333333 -0.688333333
[44,] -0.748333333 -0.728333333
[45,] -0.368333333 -0.748333333
[46,] 0.111666667 -0.368333333
[47,] 0.411666667 0.111666667
[48,] -0.045000000 0.411666667
[49,] -0.005000000 -0.045000000
[50,] -0.025000000 -0.005000000
[51,] 0.175000000 -0.025000000
[52,] 0.495000000 0.175000000
[53,] 0.615000000 0.495000000
[54,] 0.615000000 0.615000000
[55,] 0.375000000 0.615000000
[56,] 0.155000000 0.375000000
[57,] 0.235000000 0.155000000
[58,] 0.215000000 0.235000000
[59,] 0.415000000 0.215000000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.234444444 0.694444444
2 -0.185555556 0.234444444
3 -0.285555556 -0.185555556
4 -0.165555556 -0.285555556
5 -0.045555556 -0.165555556
6 0.054444444 -0.045555556
7 -0.085555556 0.054444444
8 -0.005555556 -0.085555556
9 0.074444444 -0.005555556
10 0.354444444 0.074444444
11 0.354444444 0.354444444
12 0.197777778 0.354444444
13 0.037777778 0.197777778
14 0.017777778 0.037777778
15 0.017777778 0.017777778
16 -0.062222222 0.017777778
17 -0.042222222 -0.062222222
18 -0.042222222 -0.042222222
19 0.017777778 -0.042222222
20 0.297777778 0.017777778
21 0.277777778 0.297777778
22 0.257777778 0.277777778
23 0.257777778 0.257777778
24 0.001111111 0.257777778
25 -0.058888889 0.001111111
26 0.021111111 -0.058888889
27 -0.078888889 0.021111111
28 -0.158888889 -0.078888889
29 -0.138888889 -0.158888889
30 0.061111111 -0.138888889
31 0.421111111 0.061111111
32 0.301111111 0.421111111
33 -0.218888889 0.301111111
34 -0.938888889 -0.218888889
35 -1.438888889 -0.938888889
36 -0.848333333 -1.438888889
37 -0.208333333 -0.848333333
38 0.171666667 -0.208333333
39 0.171666667 0.171666667
40 -0.108333333 0.171666667
41 -0.388333333 -0.108333333
42 -0.688333333 -0.388333333
43 -0.728333333 -0.688333333
44 -0.748333333 -0.728333333
45 -0.368333333 -0.748333333
46 0.111666667 -0.368333333
47 0.411666667 0.111666667
48 -0.045000000 0.411666667
49 -0.005000000 -0.045000000
50 -0.025000000 -0.005000000
51 0.175000000 -0.025000000
52 0.495000000 0.175000000
53 0.615000000 0.495000000
54 0.615000000 0.615000000
55 0.375000000 0.615000000
56 0.155000000 0.375000000
57 0.235000000 0.155000000
58 0.215000000 0.235000000
59 0.415000000 0.215000000
> 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/7i6tk1258798301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kv9f1258798301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9akct1258798301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109olp1258798301.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zlvw1258798301.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/122p8n1258798301.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/13p1ky1258798301.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/14b42f1258798301.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/15wam71258798301.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/16fk3y1258798301.tab")
+ }
>
> system("convert tmp/1v9181258798301.ps tmp/1v9181258798301.png")
> system("convert tmp/2n3xb1258798301.ps tmp/2n3xb1258798301.png")
> system("convert tmp/33l2o1258798301.ps tmp/33l2o1258798301.png")
> system("convert tmp/4lj5m1258798301.ps tmp/4lj5m1258798301.png")
> system("convert tmp/568bl1258798301.ps tmp/568bl1258798301.png")
> system("convert tmp/6wsxq1258798301.ps tmp/6wsxq1258798301.png")
> system("convert tmp/7i6tk1258798301.ps tmp/7i6tk1258798301.png")
> system("convert tmp/8kv9f1258798301.ps tmp/8kv9f1258798301.png")
> system("convert tmp/9akct1258798301.ps tmp/9akct1258798301.png")
> system("convert tmp/109olp1258798301.ps tmp/109olp1258798301.png")
>
>
> proc.time()
user system elapsed
2.356 1.548 2.962