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(107.1,32.5,115.6,100.6,33.5,111.9,99.2,31.5,107,108.4,31.2,107.1,103,27,100.6,99.8,26.7,99.2,115,26.5,108.4,90.8,26,103,95.9,27.2,99.8,114.4,30.5,115,108.2,33.7,90.8,112.6,34.2,95.9,109.1,36.7,114.4,105,36.2,108.2,105,38.5,112.6,118.5,40,109.1,103.7,42.5,105,112.5,43.5,105,116.6,43.3,118.5,96.6,45.5,103.7,101.9,44.3,112.5,116.5,43,116.6,119.3,43.5,96.6,115.4,41.5,101.9,108.5,42.5,116.5,111.5,41.3,119.3,108.8,39.5,115.4,121.8,38.5,108.5,109.6,41,111.5,112.2,44.5,108.8,119.6,46,121.8,104.1,44,109.6,105.3,41.5,112.2,115,41.3,119.6,124.1,38,104.1,116.8,38,105.3,107.5,36.2,115,115.6,38.7,124.1,116.2,38.7,116.8,116.3,39.2,107.5,119,35.7,115.6,111.9,36.5,116.2,118.6,36.7,116.3,106.9,34.7,119,103.2,35,111.9,118.6,28.2,118.6,118.7,23.7,106.9,102.8,15,103.2,100.6,8.7,118.6,94.9,11,118.7,94.5,7.5,102.8,102.9,5.7,100.6,95.3,9.3,94.9,92.5,10.2,94.5,102.7,15.7,102.9,91.5,18.1,95.3,89.5,20.8,92.5),dim=c(3,57),dimnames=list(c('Ipzb','Cvn','Y3'),1:57))
> y <- array(NA,dim=c(3,57),dimnames=list(c('Ipzb','Cvn','Y3'),1:57))
> 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
Ipzb Cvn Y3 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 107.1 32.5 115.6 1 0 0 0 0 0 0 0 0 0 0 1
2 100.6 33.5 111.9 0 1 0 0 0 0 0 0 0 0 0 2
3 99.2 31.5 107.0 0 0 1 0 0 0 0 0 0 0 0 3
4 108.4 31.2 107.1 0 0 0 1 0 0 0 0 0 0 0 4
5 103.0 27.0 100.6 0 0 0 0 1 0 0 0 0 0 0 5
6 99.8 26.7 99.2 0 0 0 0 0 1 0 0 0 0 0 6
7 115.0 26.5 108.4 0 0 0 0 0 0 1 0 0 0 0 7
8 90.8 26.0 103.0 0 0 0 0 0 0 0 1 0 0 0 8
9 95.9 27.2 99.8 0 0 0 0 0 0 0 0 1 0 0 9
10 114.4 30.5 115.0 0 0 0 0 0 0 0 0 0 1 0 10
11 108.2 33.7 90.8 0 0 0 0 0 0 0 0 0 0 1 11
12 112.6 34.2 95.9 0 0 0 0 0 0 0 0 0 0 0 12
13 109.1 36.7 114.4 1 0 0 0 0 0 0 0 0 0 0 13
14 105.0 36.2 108.2 0 1 0 0 0 0 0 0 0 0 0 14
15 105.0 38.5 112.6 0 0 1 0 0 0 0 0 0 0 0 15
16 118.5 40.0 109.1 0 0 0 1 0 0 0 0 0 0 0 16
17 103.7 42.5 105.0 0 0 0 0 1 0 0 0 0 0 0 17
18 112.5 43.5 105.0 0 0 0 0 0 1 0 0 0 0 0 18
19 116.6 43.3 118.5 0 0 0 0 0 0 1 0 0 0 0 19
20 96.6 45.5 103.7 0 0 0 0 0 0 0 1 0 0 0 20
21 101.9 44.3 112.5 0 0 0 0 0 0 0 0 1 0 0 21
22 116.5 43.0 116.6 0 0 0 0 0 0 0 0 0 1 0 22
23 119.3 43.5 96.6 0 0 0 0 0 0 0 0 0 0 1 23
24 115.4 41.5 101.9 0 0 0 0 0 0 0 0 0 0 0 24
25 108.5 42.5 116.5 1 0 0 0 0 0 0 0 0 0 0 25
26 111.5 41.3 119.3 0 1 0 0 0 0 0 0 0 0 0 26
27 108.8 39.5 115.4 0 0 1 0 0 0 0 0 0 0 0 27
28 121.8 38.5 108.5 0 0 0 1 0 0 0 0 0 0 0 28
29 109.6 41.0 111.5 0 0 0 0 1 0 0 0 0 0 0 29
30 112.2 44.5 108.8 0 0 0 0 0 1 0 0 0 0 0 30
31 119.6 46.0 121.8 0 0 0 0 0 0 1 0 0 0 0 31
32 104.1 44.0 109.6 0 0 0 0 0 0 0 1 0 0 0 32
33 105.3 41.5 112.2 0 0 0 0 0 0 0 0 1 0 0 33
34 115.0 41.3 119.6 0 0 0 0 0 0 0 0 0 1 0 34
35 124.1 38.0 104.1 0 0 0 0 0 0 0 0 0 0 1 35
36 116.8 38.0 105.3 0 0 0 0 0 0 0 0 0 0 0 36
37 107.5 36.2 115.0 1 0 0 0 0 0 0 0 0 0 0 37
38 115.6 38.7 124.1 0 1 0 0 0 0 0 0 0 0 0 38
39 116.2 38.7 116.8 0 0 1 0 0 0 0 0 0 0 0 39
40 116.3 39.2 107.5 0 0 0 1 0 0 0 0 0 0 0 40
41 119.0 35.7 115.6 0 0 0 0 1 0 0 0 0 0 0 41
42 111.9 36.5 116.2 0 0 0 0 0 1 0 0 0 0 0 42
43 118.6 36.7 116.3 0 0 0 0 0 0 1 0 0 0 0 43
44 106.9 34.7 119.0 0 0 0 0 0 0 0 1 0 0 0 44
45 103.2 35.0 111.9 0 0 0 0 0 0 0 0 1 0 0 45
46 118.6 28.2 118.6 0 0 0 0 0 0 0 0 0 1 0 46
47 118.7 23.7 106.9 0 0 0 0 0 0 0 0 0 0 1 47
48 102.8 15.0 103.2 0 0 0 0 0 0 0 0 0 0 0 48
49 100.6 8.7 118.6 1 0 0 0 0 0 0 0 0 0 0 49
50 94.9 11.0 118.7 0 1 0 0 0 0 0 0 0 0 0 50
51 94.5 7.5 102.8 0 0 1 0 0 0 0 0 0 0 0 51
52 102.9 5.7 100.6 0 0 0 1 0 0 0 0 0 0 0 52
53 95.3 9.3 94.9 0 0 0 0 1 0 0 0 0 0 0 53
54 92.5 10.2 94.5 0 0 0 0 0 1 0 0 0 0 0 54
55 102.7 15.7 102.9 0 0 0 0 0 0 1 0 0 0 0 55
56 91.5 18.1 95.3 0 0 0 0 0 0 0 1 0 0 0 56
57 89.5 20.8 92.5 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Cvn Y3 M1 M2 M3
51.00218 0.35852 0.46914 -11.52516 -13.11318 -11.00202
M4 M5 M6 M7 M8 M9
-0.09471 -7.18834 -7.64246 -3.61421 -16.69862 -15.45196
M10 M11 t
-4.39026 5.74431 0.05700
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.5792 -1.9278 0.1342 2.0260 6.8579
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.00218 8.63695 5.905 5.44e-07 ***
Cvn 0.35852 0.06060 5.917 5.24e-07 ***
Y3 0.46914 0.09786 4.794 2.07e-05 ***
M1 -11.52516 2.71509 -4.245 0.000118 ***
M2 -13.11318 2.70851 -4.841 1.78e-05 ***
M3 -11.00202 2.45663 -4.478 5.67e-05 ***
M4 -0.09471 2.31307 -0.041 0.967533
M5 -7.18834 2.28449 -3.147 0.003034 **
M6 -7.64246 2.26065 -3.381 0.001573 **
M7 -3.61421 2.49769 -1.447 0.155314
M8 -16.69862 2.27069 -7.354 4.55e-09 ***
M9 -15.45196 2.26478 -6.823 2.61e-08 ***
M10 -4.39026 2.77136 -1.584 0.120660
M11 5.74431 2.37975 2.414 0.020224 *
t 0.05700 0.03452 1.651 0.106214
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.339 on 42 degrees of freedom
Multiple R-squared: 0.8938, Adjusted R-squared: 0.8584
F-statistic: 25.24 on 14 and 42 DF, p-value: 5.53e-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,] 0.8738480 0.2523041 0.1261520
[2,] 0.8744281 0.2511438 0.1255719
[3,] 0.8040843 0.3918314 0.1959157
[4,] 0.6986596 0.6026808 0.3013404
[5,] 0.6278730 0.7442540 0.3721270
[6,] 0.6396550 0.7206900 0.3603450
[7,] 0.5857424 0.8285152 0.4142576
[8,] 0.6243791 0.7512419 0.3756209
[9,] 0.5720575 0.8558849 0.4279425
[10,] 0.4885695 0.9771391 0.5114305
[11,] 0.5901489 0.8197023 0.4098511
[12,] 0.5941704 0.8116593 0.4058296
[13,] 0.5128899 0.9742202 0.4871101
[14,] 0.4705284 0.9410568 0.5294716
[15,] 0.4088327 0.8176653 0.5911673
[16,] 0.3304283 0.6608566 0.6695717
[17,] 0.6603174 0.6793651 0.3396826
[18,] 0.5679478 0.8641043 0.4320522
[19,] 0.5253237 0.9493526 0.4746763
[20,] 0.5698879 0.8602241 0.4301121
[21,] 0.7321494 0.5357012 0.2678506
[22,] 0.6834626 0.6330748 0.3165374
> postscript(file="/var/www/html/rcomp/tmp/1d6ct1258729285.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mlgh1258729285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ez2b1258729285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4iway1258729285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59a3e1258729285.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 = 57
Frequency = 1
1 2 3 4 5 6
1.68187474 -1.90981693 -2.46216028 -4.16582713 2.02598092 -0.01254304
7 8 9 10 11 12
6.85786099 -1.60212904 3.26522782 2.33254021 -3.85319562 3.66226318
13 14 15 16 17 18
2.05510299 2.57403430 -2.48291935 1.15696676 -5.57924500 3.25936239
19 20 21 22 23 24
-2.98752030 -3.80563511 -3.50746460 -1.48353993 0.32835853 0.34629316
25 26 27 28 29 30
-2.29345303 1.35421515 -1.03896890 4.59228425 -2.87479628 0.13417640
31 32 33 34 35 36
-3.18762419 0.78029548 0.35318957 -4.46540940 2.89775579 0.72210736
37 38 39 40 41 42
-1.01501076 3.45056920 5.30711083 -1.37349043 5.81796064 -1.45320958
43 44 45 46 47 48
1.04292760 1.82071423 0.04037236 3.61640912 0.62708131 -4.73066370
49 50 51 52 53 54
-0.42851394 -5.46900172 0.67693770 -0.20993345 0.61009972 -1.92778618
55 56 57
-1.72564410 2.80675444 -0.15132515
> postscript(file="/var/www/html/rcomp/tmp/6badh1258729285.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 1.68187474 NA
1 -1.90981693 1.68187474
2 -2.46216028 -1.90981693
3 -4.16582713 -2.46216028
4 2.02598092 -4.16582713
5 -0.01254304 2.02598092
6 6.85786099 -0.01254304
7 -1.60212904 6.85786099
8 3.26522782 -1.60212904
9 2.33254021 3.26522782
10 -3.85319562 2.33254021
11 3.66226318 -3.85319562
12 2.05510299 3.66226318
13 2.57403430 2.05510299
14 -2.48291935 2.57403430
15 1.15696676 -2.48291935
16 -5.57924500 1.15696676
17 3.25936239 -5.57924500
18 -2.98752030 3.25936239
19 -3.80563511 -2.98752030
20 -3.50746460 -3.80563511
21 -1.48353993 -3.50746460
22 0.32835853 -1.48353993
23 0.34629316 0.32835853
24 -2.29345303 0.34629316
25 1.35421515 -2.29345303
26 -1.03896890 1.35421515
27 4.59228425 -1.03896890
28 -2.87479628 4.59228425
29 0.13417640 -2.87479628
30 -3.18762419 0.13417640
31 0.78029548 -3.18762419
32 0.35318957 0.78029548
33 -4.46540940 0.35318957
34 2.89775579 -4.46540940
35 0.72210736 2.89775579
36 -1.01501076 0.72210736
37 3.45056920 -1.01501076
38 5.30711083 3.45056920
39 -1.37349043 5.30711083
40 5.81796064 -1.37349043
41 -1.45320958 5.81796064
42 1.04292760 -1.45320958
43 1.82071423 1.04292760
44 0.04037236 1.82071423
45 3.61640912 0.04037236
46 0.62708131 3.61640912
47 -4.73066370 0.62708131
48 -0.42851394 -4.73066370
49 -5.46900172 -0.42851394
50 0.67693770 -5.46900172
51 -0.20993345 0.67693770
52 0.61009972 -0.20993345
53 -1.92778618 0.61009972
54 -1.72564410 -1.92778618
55 2.80675444 -1.72564410
56 -0.15132515 2.80675444
57 NA -0.15132515
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.90981693 1.68187474
[2,] -2.46216028 -1.90981693
[3,] -4.16582713 -2.46216028
[4,] 2.02598092 -4.16582713
[5,] -0.01254304 2.02598092
[6,] 6.85786099 -0.01254304
[7,] -1.60212904 6.85786099
[8,] 3.26522782 -1.60212904
[9,] 2.33254021 3.26522782
[10,] -3.85319562 2.33254021
[11,] 3.66226318 -3.85319562
[12,] 2.05510299 3.66226318
[13,] 2.57403430 2.05510299
[14,] -2.48291935 2.57403430
[15,] 1.15696676 -2.48291935
[16,] -5.57924500 1.15696676
[17,] 3.25936239 -5.57924500
[18,] -2.98752030 3.25936239
[19,] -3.80563511 -2.98752030
[20,] -3.50746460 -3.80563511
[21,] -1.48353993 -3.50746460
[22,] 0.32835853 -1.48353993
[23,] 0.34629316 0.32835853
[24,] -2.29345303 0.34629316
[25,] 1.35421515 -2.29345303
[26,] -1.03896890 1.35421515
[27,] 4.59228425 -1.03896890
[28,] -2.87479628 4.59228425
[29,] 0.13417640 -2.87479628
[30,] -3.18762419 0.13417640
[31,] 0.78029548 -3.18762419
[32,] 0.35318957 0.78029548
[33,] -4.46540940 0.35318957
[34,] 2.89775579 -4.46540940
[35,] 0.72210736 2.89775579
[36,] -1.01501076 0.72210736
[37,] 3.45056920 -1.01501076
[38,] 5.30711083 3.45056920
[39,] -1.37349043 5.30711083
[40,] 5.81796064 -1.37349043
[41,] -1.45320958 5.81796064
[42,] 1.04292760 -1.45320958
[43,] 1.82071423 1.04292760
[44,] 0.04037236 1.82071423
[45,] 3.61640912 0.04037236
[46,] 0.62708131 3.61640912
[47,] -4.73066370 0.62708131
[48,] -0.42851394 -4.73066370
[49,] -5.46900172 -0.42851394
[50,] 0.67693770 -5.46900172
[51,] -0.20993345 0.67693770
[52,] 0.61009972 -0.20993345
[53,] -1.92778618 0.61009972
[54,] -1.72564410 -1.92778618
[55,] 2.80675444 -1.72564410
[56,] -0.15132515 2.80675444
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.90981693 1.68187474
2 -2.46216028 -1.90981693
3 -4.16582713 -2.46216028
4 2.02598092 -4.16582713
5 -0.01254304 2.02598092
6 6.85786099 -0.01254304
7 -1.60212904 6.85786099
8 3.26522782 -1.60212904
9 2.33254021 3.26522782
10 -3.85319562 2.33254021
11 3.66226318 -3.85319562
12 2.05510299 3.66226318
13 2.57403430 2.05510299
14 -2.48291935 2.57403430
15 1.15696676 -2.48291935
16 -5.57924500 1.15696676
17 3.25936239 -5.57924500
18 -2.98752030 3.25936239
19 -3.80563511 -2.98752030
20 -3.50746460 -3.80563511
21 -1.48353993 -3.50746460
22 0.32835853 -1.48353993
23 0.34629316 0.32835853
24 -2.29345303 0.34629316
25 1.35421515 -2.29345303
26 -1.03896890 1.35421515
27 4.59228425 -1.03896890
28 -2.87479628 4.59228425
29 0.13417640 -2.87479628
30 -3.18762419 0.13417640
31 0.78029548 -3.18762419
32 0.35318957 0.78029548
33 -4.46540940 0.35318957
34 2.89775579 -4.46540940
35 0.72210736 2.89775579
36 -1.01501076 0.72210736
37 3.45056920 -1.01501076
38 5.30711083 3.45056920
39 -1.37349043 5.30711083
40 5.81796064 -1.37349043
41 -1.45320958 5.81796064
42 1.04292760 -1.45320958
43 1.82071423 1.04292760
44 0.04037236 1.82071423
45 3.61640912 0.04037236
46 0.62708131 3.61640912
47 -4.73066370 0.62708131
48 -0.42851394 -4.73066370
49 -5.46900172 -0.42851394
50 0.67693770 -5.46900172
51 -0.20993345 0.67693770
52 0.61009972 -0.20993345
53 -1.92778618 0.61009972
54 -1.72564410 -1.92778618
55 2.80675444 -1.72564410
56 -0.15132515 2.80675444
> 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/70oak1258729285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81cf71258729285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vzby1258729285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pqtb1258729285.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113f2j1258729285.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/12t6z91258729285.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/13vz4v1258729285.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/140rvi1258729285.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/15inon1258729285.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/166zz91258729285.tab")
+ }
>
> system("convert tmp/1d6ct1258729285.ps tmp/1d6ct1258729285.png")
> system("convert tmp/2mlgh1258729285.ps tmp/2mlgh1258729285.png")
> system("convert tmp/3ez2b1258729285.ps tmp/3ez2b1258729285.png")
> system("convert tmp/4iway1258729285.ps tmp/4iway1258729285.png")
> system("convert tmp/59a3e1258729285.ps tmp/59a3e1258729285.png")
> system("convert tmp/6badh1258729285.ps tmp/6badh1258729285.png")
> system("convert tmp/70oak1258729285.ps tmp/70oak1258729285.png")
> system("convert tmp/81cf71258729285.ps tmp/81cf71258729285.png")
> system("convert tmp/9vzby1258729285.ps tmp/9vzby1258729285.png")
> system("convert tmp/10pqtb1258729285.ps tmp/10pqtb1258729285.png")
>
>
> proc.time()
user system elapsed
2.337 1.557 2.748