R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(94.6,116.1,95.9,107.5,104.7,116.7,102.8,112.5,98.1,113,113.9,126.4,80.9,114.1,95.7,112.5,113.2,112.4,105.9,113.1,108.8,116.3,102.3,111.7,99,118.8,100.7,116.5,115.5,125.1,100.7,113.1,109.9,119.6,114.6,114.4,85.4,114,100.5,117.8,114.8,117,116.5,120.9,112.9,115,102,117.3,106,119.4,105.3,114.9,118.8,125.8,106.1,117.6,109.3,117.6,117.2,114.9,92.5,121.9,104.2,117,112.5,106.4,122.4,110.5,113.3,113.6,100,114.2,110.7,125.4,112.8,124.6,109.8,120.2,117.3,120.8,109.1,111.4,115.9,124.1,96,120.2,99.8,125.5,116.8,116,115.7,117,99.4,105.7,94.3,102,91,106.4,93.2,96.9,103.1,107.6,94.1,98.8,91.8,101.1,102.7,105.7,82.6,104.6,89.1,103.2,104.5,101.6,105.1,106.7,95.1,99.5,88.7,101),dim=c(2,60),dimnames=list(c('T.I.P.','I.P.C.N.'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('T.I.P.','I.P.C.N.'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
I.P.C.N. T.I.P. M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 116.1 94.6 1 0 0 0 0 0 0 0 0 0 0
2 107.5 95.9 0 1 0 0 0 0 0 0 0 0 0
3 116.7 104.7 0 0 1 0 0 0 0 0 0 0 0
4 112.5 102.8 0 0 0 1 0 0 0 0 0 0 0
5 113.0 98.1 0 0 0 0 1 0 0 0 0 0 0
6 126.4 113.9 0 0 0 0 0 1 0 0 0 0 0
7 114.1 80.9 0 0 0 0 0 0 1 0 0 0 0
8 112.5 95.7 0 0 0 0 0 0 0 1 0 0 0
9 112.4 113.2 0 0 0 0 0 0 0 0 1 0 0
10 113.1 105.9 0 0 0 0 0 0 0 0 0 1 0
11 116.3 108.8 0 0 0 0 0 0 0 0 0 0 1
12 111.7 102.3 0 0 0 0 0 0 0 0 0 0 0
13 118.8 99.0 1 0 0 0 0 0 0 0 0 0 0
14 116.5 100.7 0 1 0 0 0 0 0 0 0 0 0
15 125.1 115.5 0 0 1 0 0 0 0 0 0 0 0
16 113.1 100.7 0 0 0 1 0 0 0 0 0 0 0
17 119.6 109.9 0 0 0 0 1 0 0 0 0 0 0
18 114.4 114.6 0 0 0 0 0 1 0 0 0 0 0
19 114.0 85.4 0 0 0 0 0 0 1 0 0 0 0
20 117.8 100.5 0 0 0 0 0 0 0 1 0 0 0
21 117.0 114.8 0 0 0 0 0 0 0 0 1 0 0
22 120.9 116.5 0 0 0 0 0 0 0 0 0 1 0
23 115.0 112.9 0 0 0 0 0 0 0 0 0 0 1
24 117.3 102.0 0 0 0 0 0 0 0 0 0 0 0
25 119.4 106.0 1 0 0 0 0 0 0 0 0 0 0
26 114.9 105.3 0 1 0 0 0 0 0 0 0 0 0
27 125.8 118.8 0 0 1 0 0 0 0 0 0 0 0
28 117.6 106.1 0 0 0 1 0 0 0 0 0 0 0
29 117.6 109.3 0 0 0 0 1 0 0 0 0 0 0
30 114.9 117.2 0 0 0 0 0 1 0 0 0 0 0
31 121.9 92.5 0 0 0 0 0 0 1 0 0 0 0
32 117.0 104.2 0 0 0 0 0 0 0 1 0 0 0
33 106.4 112.5 0 0 0 0 0 0 0 0 1 0 0
34 110.5 122.4 0 0 0 0 0 0 0 0 0 1 0
35 113.6 113.3 0 0 0 0 0 0 0 0 0 0 1
36 114.2 100.0 0 0 0 0 0 0 0 0 0 0 0
37 125.4 110.7 1 0 0 0 0 0 0 0 0 0 0
38 124.6 112.8 0 1 0 0 0 0 0 0 0 0 0
39 120.2 109.8 0 0 1 0 0 0 0 0 0 0 0
40 120.8 117.3 0 0 0 1 0 0 0 0 0 0 0
41 111.4 109.1 0 0 0 0 1 0 0 0 0 0 0
42 124.1 115.9 0 0 0 0 0 1 0 0 0 0 0
43 120.2 96.0 0 0 0 0 0 0 1 0 0 0 0
44 125.5 99.8 0 0 0 0 0 0 0 1 0 0 0
45 116.0 116.8 0 0 0 0 0 0 0 0 1 0 0
46 117.0 115.7 0 0 0 0 0 0 0 0 0 1 0
47 105.7 99.4 0 0 0 0 0 0 0 0 0 0 1
48 102.0 94.3 0 0 0 0 0 0 0 0 0 0 0
49 106.4 91.0 1 0 0 0 0 0 0 0 0 0 0
50 96.9 93.2 0 1 0 0 0 0 0 0 0 0 0
51 107.6 103.1 0 0 1 0 0 0 0 0 0 0 0
52 98.8 94.1 0 0 0 1 0 0 0 0 0 0 0
53 101.1 91.8 0 0 0 0 1 0 0 0 0 0 0
54 105.7 102.7 0 0 0 0 0 1 0 0 0 0 0
55 104.6 82.6 0 0 0 0 0 0 1 0 0 0 0
56 103.2 89.1 0 0 0 0 0 0 0 1 0 0 0
57 101.6 104.5 0 0 0 0 0 0 0 0 1 0 0
58 106.7 105.1 0 0 0 0 0 0 0 0 0 1 0
59 99.5 95.1 0 0 0 0 0 0 0 0 0 0 1
60 101.0 88.7 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T.I.P. M1 M2 M3 M4
23.6112 0.8786 5.5199 -0.7799 -1.5116 -2.6018
M5 M6 M7 M8 M9 M10
-2.1298 -5.6705 14.4885 5.6086 -11.6512 -9.3589
M11
-6.6354
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.2935 -2.7289 0.1934 2.6974 8.5955
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.61120 9.05787 2.607 0.012210 *
T.I.P. 0.87860 0.09067 9.690 8.76e-13 ***
M1 5.51991 2.82341 1.955 0.056539 .
M2 -0.77985 2.83668 -0.275 0.784585
M3 -1.51157 3.04624 -0.496 0.622060
M4 -2.60180 2.87762 -0.904 0.370527
M5 -2.12978 2.86726 -0.743 0.461305
M6 -5.67051 3.13959 -1.806 0.077303 .
M7 14.48847 2.95399 4.905 1.16e-05 ***
M8 5.60856 2.81221 1.994 0.051931 .
M9 -11.65121 3.11969 -3.735 0.000508 ***
M10 -9.35895 3.15014 -2.971 0.004666 **
M11 -6.63542 2.91425 -2.277 0.027388 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.446 on 47 degrees of freedom
Multiple R-squared: 0.7191, Adjusted R-squared: 0.6474
F-statistic: 10.03 on 12 and 47 DF, p-value: 2.472e-09
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.16074884 0.32149768 0.8392512
[2,] 0.09773836 0.19547673 0.9022616
[3,] 0.56841318 0.86317364 0.4315868
[4,] 0.46678731 0.93357462 0.5332127
[5,] 0.35078941 0.70157882 0.6492106
[6,] 0.29334112 0.58668223 0.7066589
[7,] 0.23828729 0.47657458 0.7617127
[8,] 0.19121159 0.38242319 0.8087884
[9,] 0.18957168 0.37914336 0.8104283
[10,] 0.15285559 0.30571117 0.8471444
[11,] 0.10176949 0.20353897 0.8982305
[12,] 0.06237260 0.12474519 0.9376274
[13,] 0.05471231 0.10942462 0.9452877
[14,] 0.03538157 0.07076314 0.9646184
[15,] 0.06786519 0.13573037 0.9321348
[16,] 0.06053698 0.12107396 0.9394630
[17,] 0.05467455 0.10934911 0.9453254
[18,] 0.07724949 0.15449898 0.9227505
[19,] 0.50944299 0.98111402 0.4905570
[20,] 0.54505753 0.90988495 0.4549425
[21,] 0.47257686 0.94515372 0.5274231
[22,] 0.40170227 0.80340453 0.5982977
[23,] 0.43663812 0.87327623 0.5633619
[24,] 0.40587557 0.81175115 0.5941244
[25,] 0.31626585 0.63253171 0.6837341
[26,] 0.61128714 0.77742573 0.3887129
[27,] 0.50938131 0.98123738 0.4906187
[28,] 0.37066561 0.74133121 0.6293344
[29,] 0.82206811 0.35586377 0.1779319
> postscript(file="/var/www/rcomp/tmp/1g2l61292668600.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2g2l61292668600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/39u2r1292668600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/49u2r1292668600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/59u2r1292668600.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
3.85290207 0.41047416 2.61047416 1.17004645 5.32746951 8.38625121
7 8 9 10 11 12
4.92121830 -0.80221405 0.98197213 5.80352525 3.73204664 -1.79244629
13 14 15 16 17 18
2.68704180 5.19317205 1.52154442 3.61511612 1.55993517 -4.22877201
19 20 21 22 23 24
0.86749758 0.28048384 4.17620476 4.29031643 -1.17023224 4.07113509
25 26 27 28 29 30
-2.86319044 -0.44840913 -0.67785078 3.37065125 0.08709793 -6.01314399
31 32 33 34 35 36
2.52940488 -3.77035320 -4.40300464 -11.29345074 -2.92167408 2.72834430
37 38 39 40 41 42
-0.99263208 2.66205633 1.62959067 -3.26972033 -5.93718115 4.32904200
43 44 45 46 47 48
-2.24571124 8.59550707 1.41899555 1.09320012 1.39092994 -4.46360945
49 50 51 52 53 54
-2.68412135 -7.81729341 -5.08375847 -4.88609348 -1.03732147 -2.47337721
55 56 57 58 59 60
-6.07240953 -4.30342366 -2.17416780 0.10640894 -1.03107026 -0.54342366
> postscript(file="/var/www/rcomp/tmp/61l1u1292668600.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 3.85290207 NA
1 0.41047416 3.85290207
2 2.61047416 0.41047416
3 1.17004645 2.61047416
4 5.32746951 1.17004645
5 8.38625121 5.32746951
6 4.92121830 8.38625121
7 -0.80221405 4.92121830
8 0.98197213 -0.80221405
9 5.80352525 0.98197213
10 3.73204664 5.80352525
11 -1.79244629 3.73204664
12 2.68704180 -1.79244629
13 5.19317205 2.68704180
14 1.52154442 5.19317205
15 3.61511612 1.52154442
16 1.55993517 3.61511612
17 -4.22877201 1.55993517
18 0.86749758 -4.22877201
19 0.28048384 0.86749758
20 4.17620476 0.28048384
21 4.29031643 4.17620476
22 -1.17023224 4.29031643
23 4.07113509 -1.17023224
24 -2.86319044 4.07113509
25 -0.44840913 -2.86319044
26 -0.67785078 -0.44840913
27 3.37065125 -0.67785078
28 0.08709793 3.37065125
29 -6.01314399 0.08709793
30 2.52940488 -6.01314399
31 -3.77035320 2.52940488
32 -4.40300464 -3.77035320
33 -11.29345074 -4.40300464
34 -2.92167408 -11.29345074
35 2.72834430 -2.92167408
36 -0.99263208 2.72834430
37 2.66205633 -0.99263208
38 1.62959067 2.66205633
39 -3.26972033 1.62959067
40 -5.93718115 -3.26972033
41 4.32904200 -5.93718115
42 -2.24571124 4.32904200
43 8.59550707 -2.24571124
44 1.41899555 8.59550707
45 1.09320012 1.41899555
46 1.39092994 1.09320012
47 -4.46360945 1.39092994
48 -2.68412135 -4.46360945
49 -7.81729341 -2.68412135
50 -5.08375847 -7.81729341
51 -4.88609348 -5.08375847
52 -1.03732147 -4.88609348
53 -2.47337721 -1.03732147
54 -6.07240953 -2.47337721
55 -4.30342366 -6.07240953
56 -2.17416780 -4.30342366
57 0.10640894 -2.17416780
58 -1.03107026 0.10640894
59 -0.54342366 -1.03107026
60 NA -0.54342366
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.41047416 3.85290207
[2,] 2.61047416 0.41047416
[3,] 1.17004645 2.61047416
[4,] 5.32746951 1.17004645
[5,] 8.38625121 5.32746951
[6,] 4.92121830 8.38625121
[7,] -0.80221405 4.92121830
[8,] 0.98197213 -0.80221405
[9,] 5.80352525 0.98197213
[10,] 3.73204664 5.80352525
[11,] -1.79244629 3.73204664
[12,] 2.68704180 -1.79244629
[13,] 5.19317205 2.68704180
[14,] 1.52154442 5.19317205
[15,] 3.61511612 1.52154442
[16,] 1.55993517 3.61511612
[17,] -4.22877201 1.55993517
[18,] 0.86749758 -4.22877201
[19,] 0.28048384 0.86749758
[20,] 4.17620476 0.28048384
[21,] 4.29031643 4.17620476
[22,] -1.17023224 4.29031643
[23,] 4.07113509 -1.17023224
[24,] -2.86319044 4.07113509
[25,] -0.44840913 -2.86319044
[26,] -0.67785078 -0.44840913
[27,] 3.37065125 -0.67785078
[28,] 0.08709793 3.37065125
[29,] -6.01314399 0.08709793
[30,] 2.52940488 -6.01314399
[31,] -3.77035320 2.52940488
[32,] -4.40300464 -3.77035320
[33,] -11.29345074 -4.40300464
[34,] -2.92167408 -11.29345074
[35,] 2.72834430 -2.92167408
[36,] -0.99263208 2.72834430
[37,] 2.66205633 -0.99263208
[38,] 1.62959067 2.66205633
[39,] -3.26972033 1.62959067
[40,] -5.93718115 -3.26972033
[41,] 4.32904200 -5.93718115
[42,] -2.24571124 4.32904200
[43,] 8.59550707 -2.24571124
[44,] 1.41899555 8.59550707
[45,] 1.09320012 1.41899555
[46,] 1.39092994 1.09320012
[47,] -4.46360945 1.39092994
[48,] -2.68412135 -4.46360945
[49,] -7.81729341 -2.68412135
[50,] -5.08375847 -7.81729341
[51,] -4.88609348 -5.08375847
[52,] -1.03732147 -4.88609348
[53,] -2.47337721 -1.03732147
[54,] -6.07240953 -2.47337721
[55,] -4.30342366 -6.07240953
[56,] -2.17416780 -4.30342366
[57,] 0.10640894 -2.17416780
[58,] -1.03107026 0.10640894
[59,] -0.54342366 -1.03107026
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.41047416 3.85290207
2 2.61047416 0.41047416
3 1.17004645 2.61047416
4 5.32746951 1.17004645
5 8.38625121 5.32746951
6 4.92121830 8.38625121
7 -0.80221405 4.92121830
8 0.98197213 -0.80221405
9 5.80352525 0.98197213
10 3.73204664 5.80352525
11 -1.79244629 3.73204664
12 2.68704180 -1.79244629
13 5.19317205 2.68704180
14 1.52154442 5.19317205
15 3.61511612 1.52154442
16 1.55993517 3.61511612
17 -4.22877201 1.55993517
18 0.86749758 -4.22877201
19 0.28048384 0.86749758
20 4.17620476 0.28048384
21 4.29031643 4.17620476
22 -1.17023224 4.29031643
23 4.07113509 -1.17023224
24 -2.86319044 4.07113509
25 -0.44840913 -2.86319044
26 -0.67785078 -0.44840913
27 3.37065125 -0.67785078
28 0.08709793 3.37065125
29 -6.01314399 0.08709793
30 2.52940488 -6.01314399
31 -3.77035320 2.52940488
32 -4.40300464 -3.77035320
33 -11.29345074 -4.40300464
34 -2.92167408 -11.29345074
35 2.72834430 -2.92167408
36 -0.99263208 2.72834430
37 2.66205633 -0.99263208
38 1.62959067 2.66205633
39 -3.26972033 1.62959067
40 -5.93718115 -3.26972033
41 4.32904200 -5.93718115
42 -2.24571124 4.32904200
43 8.59550707 -2.24571124
44 1.41899555 8.59550707
45 1.09320012 1.41899555
46 1.39092994 1.09320012
47 -4.46360945 1.39092994
48 -2.68412135 -4.46360945
49 -7.81729341 -2.68412135
50 -5.08375847 -7.81729341
51 -4.88609348 -5.08375847
52 -1.03732147 -4.88609348
53 -2.47337721 -1.03732147
54 -6.07240953 -2.47337721
55 -4.30342366 -6.07240953
56 -2.17416780 -4.30342366
57 0.10640894 -2.17416780
58 -1.03107026 0.10640894
59 -0.54342366 -1.03107026
> 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/rcomp/tmp/7uc1f1292668600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8uc1f1292668600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9uc1f1292668600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1053001292668600.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/118myo1292668600.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/rcomp/tmp/12u4xb1292668600.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/rcomp/tmp/138ed21292668600.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/rcomp/tmp/14bxbq1292668600.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/rcomp/tmp/15efse1292668600.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/rcomp/tmp/16iyqk1292668600.tab")
+ }
>
> try(system("convert tmp/1g2l61292668600.ps tmp/1g2l61292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g2l61292668600.ps tmp/2g2l61292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/39u2r1292668600.ps tmp/39u2r1292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/49u2r1292668600.ps tmp/49u2r1292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/59u2r1292668600.ps tmp/59u2r1292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/61l1u1292668600.ps tmp/61l1u1292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uc1f1292668600.ps tmp/7uc1f1292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uc1f1292668600.ps tmp/8uc1f1292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/9uc1f1292668600.ps tmp/9uc1f1292668600.png",intern=TRUE))
character(0)
> try(system("convert tmp/1053001292668600.ps tmp/1053001292668600.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.130 1.610 4.774