R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(97.57,0,97.74,0,97.92,0,98.19,0,98.23,0,98.41,0,98.59,0,98.71,0,99.14,0,99.62,0,100.18,1,100.66,1,101.19,1,101.75,1,102.2,1,102.87,1,98.81,0,97.6,0,96.68,0,95.96,0,98.89,0,99.05,0,99.2,0,99.11,0,99.19,0,99.77,0,100.6956867,0,100.7751938,0,100.5267342,0,101.013715,0,100.9242695,0,101.1031604,0,103.1107136,0,102.991453,0,102.3057046,0,102.6137945,0,103.6772014,0,104.7207315,0,107.6624925,0,108.8749752,0,108.1196581,0,107.6128006,0,106.4201948,0,105.6052475,0,105.7145697,0,105.4859869,0,105.5654939,0,105.177897,0,106.0922282,0,106.3406877,0,108.4675015,1,116.8654343,1,121.0793083,1,123.2657523,1,124.1800835,1,125.6012721,1,126.5652952,1,127.1814749,1,128.0361757,1,128.5529716,1,129.6660704,1),dim=c(2,61),dimnames=list(c('elektrictietsindex','dumivariable'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('elektrictietsindex','dumivariable'),1:61))
> 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
elektrictietsindex dumivariable M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 97.5700 0 1 0 0 0 0 0 0 0 0 0 0 1
2 97.7400 0 0 1 0 0 0 0 0 0 0 0 0 2
3 97.9200 0 0 0 1 0 0 0 0 0 0 0 0 3
4 98.1900 0 0 0 0 1 0 0 0 0 0 0 0 4
5 98.2300 0 0 0 0 0 1 0 0 0 0 0 0 5
6 98.4100 0 0 0 0 0 0 1 0 0 0 0 0 6
7 98.5900 0 0 0 0 0 0 0 1 0 0 0 0 7
8 98.7100 0 0 0 0 0 0 0 0 1 0 0 0 8
9 99.1400 0 0 0 0 0 0 0 0 0 1 0 0 9
10 99.6200 0 0 0 0 0 0 0 0 0 0 1 0 10
11 100.1800 1 0 0 0 0 0 0 0 0 0 0 1 11
12 100.6600 1 0 0 0 0 0 0 0 0 0 0 0 12
13 101.1900 1 1 0 0 0 0 0 0 0 0 0 0 13
14 101.7500 1 0 1 0 0 0 0 0 0 0 0 0 14
15 102.2000 1 0 0 1 0 0 0 0 0 0 0 0 15
16 102.8700 1 0 0 0 1 0 0 0 0 0 0 0 16
17 98.8100 0 0 0 0 0 1 0 0 0 0 0 0 17
18 97.6000 0 0 0 0 0 0 1 0 0 0 0 0 18
19 96.6800 0 0 0 0 0 0 0 1 0 0 0 0 19
20 95.9600 0 0 0 0 0 0 0 0 1 0 0 0 20
21 98.8900 0 0 0 0 0 0 0 0 0 1 0 0 21
22 99.0500 0 0 0 0 0 0 0 0 0 0 1 0 22
23 99.2000 0 0 0 0 0 0 0 0 0 0 0 1 23
24 99.1100 0 0 0 0 0 0 0 0 0 0 0 0 24
25 99.1900 0 1 0 0 0 0 0 0 0 0 0 0 25
26 99.7700 0 0 1 0 0 0 0 0 0 0 0 0 26
27 100.6957 0 0 0 1 0 0 0 0 0 0 0 0 27
28 100.7752 0 0 0 0 1 0 0 0 0 0 0 0 28
29 100.5267 0 0 0 0 0 1 0 0 0 0 0 0 29
30 101.0137 0 0 0 0 0 0 1 0 0 0 0 0 30
31 100.9243 0 0 0 0 0 0 0 1 0 0 0 0 31
32 101.1032 0 0 0 0 0 0 0 0 1 0 0 0 32
33 103.1107 0 0 0 0 0 0 0 0 0 1 0 0 33
34 102.9915 0 0 0 0 0 0 0 0 0 0 1 0 34
35 102.3057 0 0 0 0 0 0 0 0 0 0 0 1 35
36 102.6138 0 0 0 0 0 0 0 0 0 0 0 0 36
37 103.6772 0 1 0 0 0 0 0 0 0 0 0 0 37
38 104.7207 0 0 1 0 0 0 0 0 0 0 0 0 38
39 107.6625 0 0 0 1 0 0 0 0 0 0 0 0 39
40 108.8750 0 0 0 0 1 0 0 0 0 0 0 0 40
41 108.1197 0 0 0 0 0 1 0 0 0 0 0 0 41
42 107.6128 0 0 0 0 0 0 1 0 0 0 0 0 42
43 106.4202 0 0 0 0 0 0 0 1 0 0 0 0 43
44 105.6052 0 0 0 0 0 0 0 0 1 0 0 0 44
45 105.7146 0 0 0 0 0 0 0 0 0 1 0 0 45
46 105.4860 0 0 0 0 0 0 0 0 0 0 1 0 46
47 105.5655 0 0 0 0 0 0 0 0 0 0 0 1 47
48 105.1779 0 0 0 0 0 0 0 0 0 0 0 0 48
49 106.0922 0 1 0 0 0 0 0 0 0 0 0 0 49
50 106.3407 0 0 1 0 0 0 0 0 0 0 0 0 50
51 108.4675 1 0 0 1 0 0 0 0 0 0 0 0 51
52 116.8654 1 0 0 0 1 0 0 0 0 0 0 0 52
53 121.0793 1 0 0 0 0 1 0 0 0 0 0 0 53
54 123.2658 1 0 0 0 0 0 1 0 0 0 0 0 54
55 124.1801 1 0 0 0 0 0 0 1 0 0 0 0 55
56 125.6013 1 0 0 0 0 0 0 0 1 0 0 0 56
57 126.5653 1 0 0 0 0 0 0 0 0 1 0 0 57
58 127.1815 1 0 0 0 0 0 0 0 0 0 1 0 58
59 128.0362 1 0 0 0 0 0 0 0 0 0 0 1 59
60 128.5530 1 0 0 0 0 0 0 0 0 0 0 0 60
61 129.6661 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dumivariable M1 M2 M3
90.8628 9.7125 1.3881 0.2492 -0.7150
M4 M5 M6 M7 M8
1.0644 2.4984 2.3792 1.8111 1.5016
M9 M10 M11 t
2.4433 2.2784 0.1811 0.3465
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.066 -2.492 -0.855 3.086 7.186
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 90.86278 2.05608 44.192 < 2e-16 ***
dumivariable 9.71252 1.23673 7.853 4.22e-10 ***
M1 1.38814 2.39585 0.579 0.565
M2 0.24917 2.51812 0.099 0.922
M3 -0.71501 2.51340 -0.284 0.777
M4 1.06444 2.51009 0.424 0.673
M5 2.49843 2.51155 0.995 0.325
M6 2.37922 2.51014 0.948 0.348
M7 1.81114 2.50911 0.722 0.474
M8 1.50163 2.50847 0.599 0.552
M9 2.44328 2.50822 0.974 0.335
M10 2.27842 2.50837 0.908 0.368
M11 0.18107 2.49780 0.072 0.943
t 0.34653 0.03126 11.084 1.04e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.949 on 47 degrees of freedom
Multiple R-squared: 0.8618, Adjusted R-squared: 0.8236
F-statistic: 22.54 on 13 and 47 DF, p-value: 7.083e-16
> 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,] 1.405851e-03 2.811701e-03 0.9985941
[2,] 7.493240e-04 1.498648e-03 0.9992507
[3,] 7.118813e-04 1.423763e-03 0.9992881
[4,] 6.703703e-04 1.340741e-03 0.9993296
[5,] 1.874484e-04 3.748968e-04 0.9998126
[6,] 3.909221e-05 7.818443e-05 0.9999609
[7,] 4.786382e-04 9.572764e-04 0.9995214
[8,] 2.345075e-04 4.690149e-04 0.9997655
[9,] 8.208918e-05 1.641784e-04 0.9999179
[10,] 2.734890e-05 5.469779e-05 0.9999727
[11,] 1.794195e-05 3.588389e-05 0.9999821
[12,] 5.550887e-06 1.110177e-05 0.9999944
[13,] 1.839277e-06 3.678554e-06 0.9999982
[14,] 1.175641e-06 2.351283e-06 0.9999988
[15,] 7.679087e-07 1.535817e-06 0.9999992
[16,] 6.218043e-07 1.243609e-06 0.9999994
[17,] 5.080263e-07 1.016053e-06 0.9999995
[18,] 2.337743e-07 4.675487e-07 0.9999998
[19,] 1.260826e-07 2.521651e-07 0.9999999
[20,] 6.201482e-08 1.240296e-07 0.9999999
[21,] 4.284753e-08 8.569505e-08 1.0000000
[22,] 3.065243e-08 6.130486e-08 1.0000000
[23,] 1.721082e-04 3.442164e-04 0.9998279
[24,] 3.548703e-02 7.097405e-02 0.9645130
[25,] 2.767298e-01 5.534596e-01 0.7232702
[26,] 6.312533e-01 7.374934e-01 0.3687467
[27,] 8.498990e-01 3.002019e-01 0.1501010
[28,] 8.689929e-01 2.620142e-01 0.1310071
> postscript(file="/var/www/html/rcomp/tmp/1tk081229947591.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ll9u1229947591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34gjx1229947591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4selz1229947591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ye1s1229947591.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 = 61
Frequency = 1
1 2 3 4 5 6
4.97254667 5.93498574 6.73263671 4.87665219 3.13612946 3.08881600
7 8 9 10 11 12
3.49036002 3.57333358 2.71515388 3.01348662 -4.38821836 -4.07367614
13 14 15 16 17 18
-5.27835285 -3.92591379 -2.85826281 -4.31424733 -0.44225370 -1.87956716
19 20 21 22 23 24
-2.57802314 -3.33504958 -1.69322928 -1.71489654 0.18591486 -0.06954292
25 26 27 28 29 30
-1.72421963 -0.35178057 1.19155711 -0.85492031 -2.88390265 -2.62423531
31 32 33 34 35 36
-2.49213679 -2.35027233 -1.63089883 -1.93182669 -0.86676369 -0.72413157
37 38 39 40 41 42
-1.39540139 0.44056778 3.99997976 3.08647794 0.55063810 -0.18353286
43 44 45 46 47 48
-1.15459464 -2.00656838 -3.18542588 -3.59567594 -1.76535754 -2.31841222
49 50 51 52 53 54
-3.13875774 -2.09785917 -9.06591076 -2.79396248 -0.36061122 1.59851932
55 56 57 58 59 60
2.73439454 4.11855670 3.79440010 4.22891254 6.83442474 7.18576286
61
6.56418494
> postscript(file="/var/www/html/rcomp/tmp/662hz1229947591.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 4.97254667 NA
1 5.93498574 4.97254667
2 6.73263671 5.93498574
3 4.87665219 6.73263671
4 3.13612946 4.87665219
5 3.08881600 3.13612946
6 3.49036002 3.08881600
7 3.57333358 3.49036002
8 2.71515388 3.57333358
9 3.01348662 2.71515388
10 -4.38821836 3.01348662
11 -4.07367614 -4.38821836
12 -5.27835285 -4.07367614
13 -3.92591379 -5.27835285
14 -2.85826281 -3.92591379
15 -4.31424733 -2.85826281
16 -0.44225370 -4.31424733
17 -1.87956716 -0.44225370
18 -2.57802314 -1.87956716
19 -3.33504958 -2.57802314
20 -1.69322928 -3.33504958
21 -1.71489654 -1.69322928
22 0.18591486 -1.71489654
23 -0.06954292 0.18591486
24 -1.72421963 -0.06954292
25 -0.35178057 -1.72421963
26 1.19155711 -0.35178057
27 -0.85492031 1.19155711
28 -2.88390265 -0.85492031
29 -2.62423531 -2.88390265
30 -2.49213679 -2.62423531
31 -2.35027233 -2.49213679
32 -1.63089883 -2.35027233
33 -1.93182669 -1.63089883
34 -0.86676369 -1.93182669
35 -0.72413157 -0.86676369
36 -1.39540139 -0.72413157
37 0.44056778 -1.39540139
38 3.99997976 0.44056778
39 3.08647794 3.99997976
40 0.55063810 3.08647794
41 -0.18353286 0.55063810
42 -1.15459464 -0.18353286
43 -2.00656838 -1.15459464
44 -3.18542588 -2.00656838
45 -3.59567594 -3.18542588
46 -1.76535754 -3.59567594
47 -2.31841222 -1.76535754
48 -3.13875774 -2.31841222
49 -2.09785917 -3.13875774
50 -9.06591076 -2.09785917
51 -2.79396248 -9.06591076
52 -0.36061122 -2.79396248
53 1.59851932 -0.36061122
54 2.73439454 1.59851932
55 4.11855670 2.73439454
56 3.79440010 4.11855670
57 4.22891254 3.79440010
58 6.83442474 4.22891254
59 7.18576286 6.83442474
60 6.56418494 7.18576286
61 NA 6.56418494
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.93498574 4.97254667
[2,] 6.73263671 5.93498574
[3,] 4.87665219 6.73263671
[4,] 3.13612946 4.87665219
[5,] 3.08881600 3.13612946
[6,] 3.49036002 3.08881600
[7,] 3.57333358 3.49036002
[8,] 2.71515388 3.57333358
[9,] 3.01348662 2.71515388
[10,] -4.38821836 3.01348662
[11,] -4.07367614 -4.38821836
[12,] -5.27835285 -4.07367614
[13,] -3.92591379 -5.27835285
[14,] -2.85826281 -3.92591379
[15,] -4.31424733 -2.85826281
[16,] -0.44225370 -4.31424733
[17,] -1.87956716 -0.44225370
[18,] -2.57802314 -1.87956716
[19,] -3.33504958 -2.57802314
[20,] -1.69322928 -3.33504958
[21,] -1.71489654 -1.69322928
[22,] 0.18591486 -1.71489654
[23,] -0.06954292 0.18591486
[24,] -1.72421963 -0.06954292
[25,] -0.35178057 -1.72421963
[26,] 1.19155711 -0.35178057
[27,] -0.85492031 1.19155711
[28,] -2.88390265 -0.85492031
[29,] -2.62423531 -2.88390265
[30,] -2.49213679 -2.62423531
[31,] -2.35027233 -2.49213679
[32,] -1.63089883 -2.35027233
[33,] -1.93182669 -1.63089883
[34,] -0.86676369 -1.93182669
[35,] -0.72413157 -0.86676369
[36,] -1.39540139 -0.72413157
[37,] 0.44056778 -1.39540139
[38,] 3.99997976 0.44056778
[39,] 3.08647794 3.99997976
[40,] 0.55063810 3.08647794
[41,] -0.18353286 0.55063810
[42,] -1.15459464 -0.18353286
[43,] -2.00656838 -1.15459464
[44,] -3.18542588 -2.00656838
[45,] -3.59567594 -3.18542588
[46,] -1.76535754 -3.59567594
[47,] -2.31841222 -1.76535754
[48,] -3.13875774 -2.31841222
[49,] -2.09785917 -3.13875774
[50,] -9.06591076 -2.09785917
[51,] -2.79396248 -9.06591076
[52,] -0.36061122 -2.79396248
[53,] 1.59851932 -0.36061122
[54,] 2.73439454 1.59851932
[55,] 4.11855670 2.73439454
[56,] 3.79440010 4.11855670
[57,] 4.22891254 3.79440010
[58,] 6.83442474 4.22891254
[59,] 7.18576286 6.83442474
[60,] 6.56418494 7.18576286
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.93498574 4.97254667
2 6.73263671 5.93498574
3 4.87665219 6.73263671
4 3.13612946 4.87665219
5 3.08881600 3.13612946
6 3.49036002 3.08881600
7 3.57333358 3.49036002
8 2.71515388 3.57333358
9 3.01348662 2.71515388
10 -4.38821836 3.01348662
11 -4.07367614 -4.38821836
12 -5.27835285 -4.07367614
13 -3.92591379 -5.27835285
14 -2.85826281 -3.92591379
15 -4.31424733 -2.85826281
16 -0.44225370 -4.31424733
17 -1.87956716 -0.44225370
18 -2.57802314 -1.87956716
19 -3.33504958 -2.57802314
20 -1.69322928 -3.33504958
21 -1.71489654 -1.69322928
22 0.18591486 -1.71489654
23 -0.06954292 0.18591486
24 -1.72421963 -0.06954292
25 -0.35178057 -1.72421963
26 1.19155711 -0.35178057
27 -0.85492031 1.19155711
28 -2.88390265 -0.85492031
29 -2.62423531 -2.88390265
30 -2.49213679 -2.62423531
31 -2.35027233 -2.49213679
32 -1.63089883 -2.35027233
33 -1.93182669 -1.63089883
34 -0.86676369 -1.93182669
35 -0.72413157 -0.86676369
36 -1.39540139 -0.72413157
37 0.44056778 -1.39540139
38 3.99997976 0.44056778
39 3.08647794 3.99997976
40 0.55063810 3.08647794
41 -0.18353286 0.55063810
42 -1.15459464 -0.18353286
43 -2.00656838 -1.15459464
44 -3.18542588 -2.00656838
45 -3.59567594 -3.18542588
46 -1.76535754 -3.59567594
47 -2.31841222 -1.76535754
48 -3.13875774 -2.31841222
49 -2.09785917 -3.13875774
50 -9.06591076 -2.09785917
51 -2.79396248 -9.06591076
52 -0.36061122 -2.79396248
53 1.59851932 -0.36061122
54 2.73439454 1.59851932
55 4.11855670 2.73439454
56 3.79440010 4.11855670
57 4.22891254 3.79440010
58 6.83442474 4.22891254
59 7.18576286 6.83442474
60 6.56418494 7.18576286
> 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/7n8091229947591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8blo11229947591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tzfq1229947591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10sum11229947591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112we91229947591.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/12fari1229947591.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/13s4sx1229947591.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/14ivka1229947591.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/15xkkr1229947591.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/167yvk1229947591.tab")
+ }
>
> system("convert tmp/1tk081229947591.ps tmp/1tk081229947591.png")
> system("convert tmp/2ll9u1229947591.ps tmp/2ll9u1229947591.png")
> system("convert tmp/34gjx1229947591.ps tmp/34gjx1229947591.png")
> system("convert tmp/4selz1229947591.ps tmp/4selz1229947591.png")
> system("convert tmp/5ye1s1229947591.ps tmp/5ye1s1229947591.png")
> system("convert tmp/662hz1229947591.ps tmp/662hz1229947591.png")
> system("convert tmp/7n8091229947591.ps tmp/7n8091229947591.png")
> system("convert tmp/8blo11229947591.ps tmp/8blo11229947591.png")
> system("convert tmp/9tzfq1229947591.ps tmp/9tzfq1229947591.png")
> system("convert tmp/10sum11229947591.ps tmp/10sum11229947591.png")
>
>
> proc.time()
user system elapsed
2.442 1.579 2.993