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(112.3,0,117.3,0,111.1,1,102.2,1,104.3,1,122.9,1,107.6,1,121.3,1,131.5,1,89,1,104.4,1,128.9,1,135.9,1,133.3,1,121.3,1,120.5,0,120.4,0,137.9,0,126.1,0,133.2,0,151.1,0,105,0,119,0,140.4,0,156.6,0,137.1,0,122.7,0,125.8,0,139.3,0,134.9,0,149.2,0,132.3,0,149,0,117.2,0,119.6,0,152,0,149.4,0,127.3,0,114.1,0,102.1,0,107.7,0,104.4,0,102.1,0,96,1,109.3,0,90,1,83.9,1,112,1,114.3,1,103.6,1,91.7,1,80.8,1,87.2,1,109.2,1,102.7,1,95.1,1,117.5,1,85.1,1,92.1,1,113.5,1),dim=c(2,60),dimnames=list(c('Promet','Dummy'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Promet','Dummy'),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
Promet Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 112.3 0 1 0 0 0 0 0 0 0 0 0 0 1
2 117.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 111.1 1 0 0 1 0 0 0 0 0 0 0 0 3
4 102.2 1 0 0 0 1 0 0 0 0 0 0 0 4
5 104.3 1 0 0 0 0 1 0 0 0 0 0 0 5
6 122.9 1 0 0 0 0 0 1 0 0 0 0 0 6
7 107.6 1 0 0 0 0 0 0 1 0 0 0 0 7
8 121.3 1 0 0 0 0 0 0 0 1 0 0 0 8
9 131.5 1 0 0 0 0 0 0 0 0 1 0 0 9
10 89.0 1 0 0 0 0 0 0 0 0 0 1 0 10
11 104.4 1 0 0 0 0 0 0 0 0 0 0 1 11
12 128.9 1 0 0 0 0 0 0 0 0 0 0 0 12
13 135.9 1 1 0 0 0 0 0 0 0 0 0 0 13
14 133.3 1 0 1 0 0 0 0 0 0 0 0 0 14
15 121.3 1 0 0 1 0 0 0 0 0 0 0 0 15
16 120.5 0 0 0 0 1 0 0 0 0 0 0 0 16
17 120.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 137.9 0 0 0 0 0 0 1 0 0 0 0 0 18
19 126.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 133.2 0 0 0 0 0 0 0 0 1 0 0 0 20
21 151.1 0 0 0 0 0 0 0 0 0 1 0 0 21
22 105.0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 119.0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 140.4 0 0 0 0 0 0 0 0 0 0 0 0 24
25 156.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 137.1 0 0 1 0 0 0 0 0 0 0 0 0 26
27 122.7 0 0 0 1 0 0 0 0 0 0 0 0 27
28 125.8 0 0 0 0 1 0 0 0 0 0 0 0 28
29 139.3 0 0 0 0 0 1 0 0 0 0 0 0 29
30 134.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 149.2 0 0 0 0 0 0 0 1 0 0 0 0 31
32 132.3 0 0 0 0 0 0 0 0 1 0 0 0 32
33 149.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 117.2 0 0 0 0 0 0 0 0 0 0 1 0 34
35 119.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 152.0 0 0 0 0 0 0 0 0 0 0 0 0 36
37 149.4 0 1 0 0 0 0 0 0 0 0 0 0 37
38 127.3 0 0 1 0 0 0 0 0 0 0 0 0 38
39 114.1 0 0 0 1 0 0 0 0 0 0 0 0 39
40 102.1 0 0 0 0 1 0 0 0 0 0 0 0 40
41 107.7 0 0 0 0 0 1 0 0 0 0 0 0 41
42 104.4 0 0 0 0 0 0 1 0 0 0 0 0 42
43 102.1 0 0 0 0 0 0 0 1 0 0 0 0 43
44 96.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 109.3 0 0 0 0 0 0 0 0 0 1 0 0 45
46 90.0 1 0 0 0 0 0 0 0 0 0 1 0 46
47 83.9 1 0 0 0 0 0 0 0 0 0 0 1 47
48 112.0 1 0 0 0 0 0 0 0 0 0 0 0 48
49 114.3 1 1 0 0 0 0 0 0 0 0 0 0 49
50 103.6 1 0 1 0 0 0 0 0 0 0 0 0 50
51 91.7 1 0 0 1 0 0 0 0 0 0 0 0 51
52 80.8 1 0 0 0 1 0 0 0 0 0 0 0 52
53 87.2 1 0 0 0 0 1 0 0 0 0 0 0 53
54 109.2 1 0 0 0 0 0 1 0 0 0 0 0 54
55 102.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 95.1 1 0 0 0 0 0 0 0 1 0 0 0 56
57 117.5 1 0 0 0 0 0 0 0 0 1 0 0 57
58 85.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 92.1 1 0 0 0 0 0 0 0 0 0 0 1 59
60 113.5 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) Dummy M1 M2 M3 M4
152.0629 -18.0166 -2.8973 -12.5469 -20.1532 -29.3262
M5 M6 M7 M8 M9 M10
-23.4958 -13.0855 -17.0751 -15.1014 -2.2744 -32.7607
M11 t
-25.8904 -0.3304
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-36.5352 -5.4689 0.1078 5.8525 24.4534
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 152.0629 6.5178 23.331 < 2e-16 ***
Dummy -18.0166 3.2078 -5.616 1.08e-06 ***
M1 -2.8973 7.7270 -0.375 0.709417
M2 -12.5469 7.7162 -1.626 0.110773
M3 -20.1532 7.6882 -2.621 0.011833 *
M4 -29.3262 7.6980 -3.810 0.000411 ***
M5 -23.4958 7.6906 -3.055 0.003736 **
M6 -13.0855 7.6843 -1.703 0.095338 .
M7 -17.0751 7.6791 -2.224 0.031128 *
M8 -15.1014 7.6519 -1.974 0.054456 .
M9 -2.2744 7.6720 -0.296 0.768219
M10 -32.7607 7.6451 -4.285 9.23e-05 ***
M11 -25.8904 7.6434 -3.387 0.001455 **
t -0.3304 0.0926 -3.568 0.000855 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.08 on 46 degrees of freedom
Multiple R-squared: 0.6836, Adjusted R-squared: 0.5941
F-statistic: 7.644 on 13 and 46 DF, p-value: 9.702e-08
> 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,] 5.555878e-02 1.111176e-01 0.94444122
[2,] 1.543921e-02 3.087842e-02 0.98456079
[3,] 5.283868e-03 1.056774e-02 0.99471613
[4,] 1.897092e-03 3.794185e-03 0.99810291
[5,] 6.909051e-04 1.381810e-03 0.99930909
[6,] 2.532100e-04 5.064200e-04 0.99974679
[7,] 7.066492e-05 1.413298e-04 0.99992934
[8,] 4.577111e-05 9.154222e-05 0.99995423
[9,] 7.456359e-05 1.491272e-04 0.99992544
[10,] 8.899209e-04 1.779842e-03 0.99911008
[11,] 4.967729e-03 9.935459e-03 0.99503227
[12,] 3.274111e-03 6.548222e-03 0.99672589
[13,] 2.776067e-03 5.552134e-03 0.99722393
[14,] 7.016934e-03 1.403387e-02 0.99298307
[15,] 1.957598e-02 3.915196e-02 0.98042402
[16,] 3.234037e-02 6.468074e-02 0.96765963
[17,] 5.141607e-02 1.028321e-01 0.94858393
[18,] 3.429051e-02 6.858102e-02 0.96570949
[19,] 3.607855e-02 7.215709e-02 0.96392145
[20,] 7.779659e-02 1.555932e-01 0.92220341
[21,] 1.702635e-01 3.405270e-01 0.82973648
[22,] 3.540613e-01 7.081225e-01 0.64593874
[23,] 5.448586e-01 9.102829e-01 0.45514143
[24,] 7.932891e-01 4.134218e-01 0.20671092
[25,] 9.896964e-01 2.060722e-02 0.01030361
[26,] 9.801715e-01 3.965697e-02 0.01982848
[27,] 9.641246e-01 7.175081e-02 0.03587541
> postscript(file="/var/www/html/rcomp/tmp/12hpj1291725674.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22hpj1291725674.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32hpj1291725674.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4dqom1291725674.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dqom1291725674.ps",horizontal=F,onefile=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
-36.5352437 -21.5552437 -1.8019965 -1.1986847 -4.5986847 3.9213153
7 8 9 10 11 12
-7.0586847 4.9980035 2.7013153 -8.9819965 -0.1219965 -1.1819965
13 14 15 16 17 18
9.0456254 16.4256254 12.3623136 3.0490664 -2.5509336 4.8690664
19 20 21 22 23 24
-2.6109336 2.8457546 8.2490664 -7.0342454 0.4257546 -3.7342454
25 26 27 28 29 30
15.6933764 6.1733764 -0.2899354 12.3133764 20.3133764 5.8333764
31 32 33 34 35 36
24.4533764 5.9100646 10.1133764 9.1300646 4.9900646 11.8300646
37 38 39 40 41 42
12.4576864 0.3376864 -4.9256254 -7.4223136 -7.3223136 -20.7023136
43 44 45 46 47 48
-18.6823136 -8.4090664 -25.6223136 3.9109336 -8.7290664 -6.1890664
49 50 51 52 53 54
-0.6614445 -1.3814445 -5.3447563 -6.7414445 -5.8414445 6.0785555
55 56 57 58 59 60
3.8985555 -5.3447563 4.5585555 2.9752437 3.4352437 -0.7247563
> postscript(file="/var/www/html/rcomp/tmp/6dqom1291725674.ps",horizontal=F,onefile=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 -36.5352437 NA
1 -21.5552437 -36.5352437
2 -1.8019965 -21.5552437
3 -1.1986847 -1.8019965
4 -4.5986847 -1.1986847
5 3.9213153 -4.5986847
6 -7.0586847 3.9213153
7 4.9980035 -7.0586847
8 2.7013153 4.9980035
9 -8.9819965 2.7013153
10 -0.1219965 -8.9819965
11 -1.1819965 -0.1219965
12 9.0456254 -1.1819965
13 16.4256254 9.0456254
14 12.3623136 16.4256254
15 3.0490664 12.3623136
16 -2.5509336 3.0490664
17 4.8690664 -2.5509336
18 -2.6109336 4.8690664
19 2.8457546 -2.6109336
20 8.2490664 2.8457546
21 -7.0342454 8.2490664
22 0.4257546 -7.0342454
23 -3.7342454 0.4257546
24 15.6933764 -3.7342454
25 6.1733764 15.6933764
26 -0.2899354 6.1733764
27 12.3133764 -0.2899354
28 20.3133764 12.3133764
29 5.8333764 20.3133764
30 24.4533764 5.8333764
31 5.9100646 24.4533764
32 10.1133764 5.9100646
33 9.1300646 10.1133764
34 4.9900646 9.1300646
35 11.8300646 4.9900646
36 12.4576864 11.8300646
37 0.3376864 12.4576864
38 -4.9256254 0.3376864
39 -7.4223136 -4.9256254
40 -7.3223136 -7.4223136
41 -20.7023136 -7.3223136
42 -18.6823136 -20.7023136
43 -8.4090664 -18.6823136
44 -25.6223136 -8.4090664
45 3.9109336 -25.6223136
46 -8.7290664 3.9109336
47 -6.1890664 -8.7290664
48 -0.6614445 -6.1890664
49 -1.3814445 -0.6614445
50 -5.3447563 -1.3814445
51 -6.7414445 -5.3447563
52 -5.8414445 -6.7414445
53 6.0785555 -5.8414445
54 3.8985555 6.0785555
55 -5.3447563 3.8985555
56 4.5585555 -5.3447563
57 2.9752437 4.5585555
58 3.4352437 2.9752437
59 -0.7247563 3.4352437
60 NA -0.7247563
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -21.5552437 -36.5352437
[2,] -1.8019965 -21.5552437
[3,] -1.1986847 -1.8019965
[4,] -4.5986847 -1.1986847
[5,] 3.9213153 -4.5986847
[6,] -7.0586847 3.9213153
[7,] 4.9980035 -7.0586847
[8,] 2.7013153 4.9980035
[9,] -8.9819965 2.7013153
[10,] -0.1219965 -8.9819965
[11,] -1.1819965 -0.1219965
[12,] 9.0456254 -1.1819965
[13,] 16.4256254 9.0456254
[14,] 12.3623136 16.4256254
[15,] 3.0490664 12.3623136
[16,] -2.5509336 3.0490664
[17,] 4.8690664 -2.5509336
[18,] -2.6109336 4.8690664
[19,] 2.8457546 -2.6109336
[20,] 8.2490664 2.8457546
[21,] -7.0342454 8.2490664
[22,] 0.4257546 -7.0342454
[23,] -3.7342454 0.4257546
[24,] 15.6933764 -3.7342454
[25,] 6.1733764 15.6933764
[26,] -0.2899354 6.1733764
[27,] 12.3133764 -0.2899354
[28,] 20.3133764 12.3133764
[29,] 5.8333764 20.3133764
[30,] 24.4533764 5.8333764
[31,] 5.9100646 24.4533764
[32,] 10.1133764 5.9100646
[33,] 9.1300646 10.1133764
[34,] 4.9900646 9.1300646
[35,] 11.8300646 4.9900646
[36,] 12.4576864 11.8300646
[37,] 0.3376864 12.4576864
[38,] -4.9256254 0.3376864
[39,] -7.4223136 -4.9256254
[40,] -7.3223136 -7.4223136
[41,] -20.7023136 -7.3223136
[42,] -18.6823136 -20.7023136
[43,] -8.4090664 -18.6823136
[44,] -25.6223136 -8.4090664
[45,] 3.9109336 -25.6223136
[46,] -8.7290664 3.9109336
[47,] -6.1890664 -8.7290664
[48,] -0.6614445 -6.1890664
[49,] -1.3814445 -0.6614445
[50,] -5.3447563 -1.3814445
[51,] -6.7414445 -5.3447563
[52,] -5.8414445 -6.7414445
[53,] 6.0785555 -5.8414445
[54,] 3.8985555 6.0785555
[55,] -5.3447563 3.8985555
[56,] 4.5585555 -5.3447563
[57,] 2.9752437 4.5585555
[58,] 3.4352437 2.9752437
[59,] -0.7247563 3.4352437
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -21.5552437 -36.5352437
2 -1.8019965 -21.5552437
3 -1.1986847 -1.8019965
4 -4.5986847 -1.1986847
5 3.9213153 -4.5986847
6 -7.0586847 3.9213153
7 4.9980035 -7.0586847
8 2.7013153 4.9980035
9 -8.9819965 2.7013153
10 -0.1219965 -8.9819965
11 -1.1819965 -0.1219965
12 9.0456254 -1.1819965
13 16.4256254 9.0456254
14 12.3623136 16.4256254
15 3.0490664 12.3623136
16 -2.5509336 3.0490664
17 4.8690664 -2.5509336
18 -2.6109336 4.8690664
19 2.8457546 -2.6109336
20 8.2490664 2.8457546
21 -7.0342454 8.2490664
22 0.4257546 -7.0342454
23 -3.7342454 0.4257546
24 15.6933764 -3.7342454
25 6.1733764 15.6933764
26 -0.2899354 6.1733764
27 12.3133764 -0.2899354
28 20.3133764 12.3133764
29 5.8333764 20.3133764
30 24.4533764 5.8333764
31 5.9100646 24.4533764
32 10.1133764 5.9100646
33 9.1300646 10.1133764
34 4.9900646 9.1300646
35 11.8300646 4.9900646
36 12.4576864 11.8300646
37 0.3376864 12.4576864
38 -4.9256254 0.3376864
39 -7.4223136 -4.9256254
40 -7.3223136 -7.4223136
41 -20.7023136 -7.3223136
42 -18.6823136 -20.7023136
43 -8.4090664 -18.6823136
44 -25.6223136 -8.4090664
45 3.9109336 -25.6223136
46 -8.7290664 3.9109336
47 -6.1890664 -8.7290664
48 -0.6614445 -6.1890664
49 -1.3814445 -0.6614445
50 -5.3447563 -1.3814445
51 -6.7414445 -5.3447563
52 -5.8414445 -6.7414445
53 6.0785555 -5.8414445
54 3.8985555 6.0785555
55 -5.3447563 3.8985555
56 4.5585555 -5.3447563
57 2.9752437 4.5585555
58 3.4352437 2.9752437
59 -0.7247563 3.4352437
> 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/75z561291725674.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8g9nr1291725674.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9g9nr1291725674.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10g9nr1291725674.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117dbm1291725674.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/125s2l1291725674.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/13cthx1291725674.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/1442g01291725674.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/15q3fo1291725674.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/164cce1291725674.tab")
+ }
>
> try(system("convert tmp/12hpj1291725674.ps tmp/12hpj1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/22hpj1291725674.ps tmp/22hpj1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/32hpj1291725674.ps tmp/32hpj1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dqom1291725674.ps tmp/4dqom1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dqom1291725674.ps tmp/5dqom1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dqom1291725674.ps tmp/6dqom1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/75z561291725674.ps tmp/75z561291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/8g9nr1291725674.ps tmp/8g9nr1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/9g9nr1291725674.ps tmp/9g9nr1291725674.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g9nr1291725674.ps tmp/10g9nr1291725674.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.444 1.645 6.429