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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> x <- array(list(82.7,0,88.9,0,105.9,0,100.8,0,94,0,105,0,58.5,0,87.6,0,113.1,0,112.5,0,89.6,0,74.5,0,82.7,0,90.1,0,109.4,0,96,0,89.2,0,109.1,0,49.1,0,92.9,0,107.7,0,103.5,0,91.1,0,79.8,0,71.9,0,82.9,0,90.1,0,100.7,0,90.7,0,108.8,0,44.1,0,93.6,0,107.4,0,96.5,0,93.6,0,76.5,0,76.7,1,84,1,103.3,1,88.5,1,99,1,105.9,1,44.7,1,94,1,107.1,1,104.8,1,102.5,1,77.7,1,85.2,1,91.3,1,106.5,1,92.4,1,97.5,1,107,1,51.1,1,98.6,1,102.2,1,114.3,1,99.4,1,72.5,1,92.3,1,99.4,1,85.9,1,109.4,1,97.6,1),dim=c(2,65),dimnames=list(c('Bouwproductie','d'),1:65))
> y <- array(NA,dim=c(2,65),dimnames=list(c('Bouwproductie','d'),1:65))
> 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
Bouwproductie d M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 82.7 0 1 0 0 0 0 0 0 0 0 0 0 1
2 88.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 105.9 0 0 0 1 0 0 0 0 0 0 0 0 3
4 100.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 94.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 105.0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 58.5 0 0 0 0 0 0 0 1 0 0 0 0 7
8 87.6 0 0 0 0 0 0 0 0 1 0 0 0 8
9 113.1 0 0 0 0 0 0 0 0 0 1 0 0 9
10 112.5 0 0 0 0 0 0 0 0 0 0 1 0 10
11 89.6 0 0 0 0 0 0 0 0 0 0 0 1 11
12 74.5 0 0 0 0 0 0 0 0 0 0 0 0 12
13 82.7 0 1 0 0 0 0 0 0 0 0 0 0 13
14 90.1 0 0 1 0 0 0 0 0 0 0 0 0 14
15 109.4 0 0 0 1 0 0 0 0 0 0 0 0 15
16 96.0 0 0 0 0 1 0 0 0 0 0 0 0 16
17 89.2 0 0 0 0 0 1 0 0 0 0 0 0 17
18 109.1 0 0 0 0 0 0 1 0 0 0 0 0 18
19 49.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 92.9 0 0 0 0 0 0 0 0 1 0 0 0 20
21 107.7 0 0 0 0 0 0 0 0 0 1 0 0 21
22 103.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 91.1 0 0 0 0 0 0 0 0 0 0 0 1 23
24 79.8 0 0 0 0 0 0 0 0 0 0 0 0 24
25 71.9 0 1 0 0 0 0 0 0 0 0 0 0 25
26 82.9 0 0 1 0 0 0 0 0 0 0 0 0 26
27 90.1 0 0 0 1 0 0 0 0 0 0 0 0 27
28 100.7 0 0 0 0 1 0 0 0 0 0 0 0 28
29 90.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 108.8 0 0 0 0 0 0 1 0 0 0 0 0 30
31 44.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 93.6 0 0 0 0 0 0 0 0 1 0 0 0 32
33 107.4 0 0 0 0 0 0 0 0 0 1 0 0 33
34 96.5 0 0 0 0 0 0 0 0 0 0 1 0 34
35 93.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 76.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 76.7 1 1 0 0 0 0 0 0 0 0 0 0 37
38 84.0 1 0 1 0 0 0 0 0 0 0 0 0 38
39 103.3 1 0 0 1 0 0 0 0 0 0 0 0 39
40 88.5 1 0 0 0 1 0 0 0 0 0 0 0 40
41 99.0 1 0 0 0 0 1 0 0 0 0 0 0 41
42 105.9 1 0 0 0 0 0 1 0 0 0 0 0 42
43 44.7 1 0 0 0 0 0 0 1 0 0 0 0 43
44 94.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 107.1 1 0 0 0 0 0 0 0 0 1 0 0 45
46 104.8 1 0 0 0 0 0 0 0 0 0 1 0 46
47 102.5 1 0 0 0 0 0 0 0 0 0 0 1 47
48 77.7 1 0 0 0 0 0 0 0 0 0 0 0 48
49 85.2 1 1 0 0 0 0 0 0 0 0 0 0 49
50 91.3 1 0 1 0 0 0 0 0 0 0 0 0 50
51 106.5 1 0 0 1 0 0 0 0 0 0 0 0 51
52 92.4 1 0 0 0 1 0 0 0 0 0 0 0 52
53 97.5 1 0 0 0 0 1 0 0 0 0 0 0 53
54 107.0 1 0 0 0 0 0 1 0 0 0 0 0 54
55 51.1 1 0 0 0 0 0 0 1 0 0 0 0 55
56 98.6 1 0 0 0 0 0 0 0 1 0 0 0 56
57 102.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 114.3 1 0 0 0 0 0 0 0 0 0 1 0 58
59 99.4 1 0 0 0 0 0 0 0 0 0 0 1 59
60 72.5 1 0 0 0 0 0 0 0 0 0 0 0 60
61 92.3 1 1 0 0 0 0 0 0 0 0 0 0 61
62 99.4 1 0 1 0 0 0 0 0 0 0 0 0 62
63 85.9 1 0 0 1 0 0 0 0 0 0 0 0 63
64 109.4 1 0 0 0 1 0 0 0 0 0 0 0 64
65 97.6 1 0 0 0 0 1 0 0 0 0 0 0 65
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d M1 M2 M3 M4
76.35775 2.97696 5.23167 12.78580 23.57326 21.39405
M5 M6 M7 M8 M9 M10
18.13151 30.73525 -26.88730 16.99016 31.18762 30.04508
M11 t
19.00254 -0.03746
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.6480 -3.3987 0.3413 3.0696 11.0686
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.35775 3.29924 23.144 < 2e-16 ***
d 2.97696 3.03352 0.981 0.33105
M1 5.23167 3.68458 1.420 0.16173
M2 12.78580 3.67093 3.483 0.00103 **
M3 23.57326 3.65899 6.443 4.13e-08 ***
M4 21.39405 3.64878 5.863 3.36e-07 ***
M5 18.13151 3.64031 4.981 7.65e-06 ***
M6 30.73525 3.81251 8.062 1.16e-10 ***
M7 -26.88730 3.80319 -7.070 4.22e-09 ***
M8 16.99016 3.79555 4.476 4.29e-05 ***
M9 31.18762 3.78959 8.230 6.36e-11 ***
M10 30.04508 3.78534 7.937 1.82e-10 ***
M11 19.00254 3.78278 5.023 6.59e-06 ***
t -0.03746 0.08032 -0.466 0.64293
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.98 on 51 degrees of freedom
Multiple R-squared: 0.8921, Adjusted R-squared: 0.8646
F-statistic: 32.43 on 13 and 51 DF, p-value: < 2.2e-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,] 0.149906700 0.299813399 0.8500933
[2,] 0.107365625 0.214731249 0.8926344
[3,] 0.175185576 0.350371152 0.8248144
[4,] 0.153515106 0.307030211 0.8464849
[5,] 0.115706816 0.231413632 0.8842932
[6,] 0.118609310 0.237218620 0.8813907
[7,] 0.076702381 0.153404761 0.9232976
[8,] 0.081855190 0.163710380 0.9181448
[9,] 0.119463753 0.238927507 0.8805362
[10,] 0.084210502 0.168421004 0.9157895
[11,] 0.194828948 0.389657897 0.8051711
[12,] 0.217174449 0.434348899 0.7828256
[13,] 0.166985225 0.333970450 0.8330148
[14,] 0.149217469 0.298434937 0.8507825
[15,] 0.119899712 0.239799424 0.8801003
[16,] 0.107420547 0.214841094 0.8925795
[17,] 0.084468120 0.168936240 0.9155319
[18,] 0.092945963 0.185891927 0.9070540
[19,] 0.089397063 0.178794125 0.9106029
[20,] 0.059403312 0.118806623 0.9405967
[21,] 0.048311585 0.096623169 0.9516884
[22,] 0.040941177 0.081882354 0.9590588
[23,] 0.042328096 0.084656192 0.9576719
[24,] 0.060748648 0.121497297 0.9392514
[25,] 0.065654632 0.131309265 0.9343454
[26,] 0.038531350 0.077062701 0.9614686
[27,] 0.025689902 0.051379804 0.9743101
[28,] 0.015606674 0.031213348 0.9843933
[29,] 0.009128600 0.018257201 0.9908714
[30,] 0.006253975 0.012507949 0.9937460
[31,] 0.005874367 0.011748734 0.9941256
[32,] 0.002859137 0.005718273 0.9971409
> postscript(file="/var/www/html/rcomp/tmp/16s0a1229035030.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mq5e1229035030.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3e8xl1229035030.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qc1d1229035030.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n90j1229035030.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 = 65
Frequency = 1
1 2 3 4 5 6
1.1480392 -0.1686275 6.0813725 3.1980392 -0.3019608 -1.8682353
7 8 9 10 11 12
9.2917647 -5.4482353 5.8917647 6.4717647 -5.3482353 -1.4082353
13 14 15 16 17 18
1.5975490 1.4808824 10.0308824 -1.1524510 -4.6524510 2.6812745
19 20 21 22 23 24
0.3412745 0.3012745 0.9412745 -2.0787255 -3.3987255 4.3412745
25 26 27 28 29 30
-8.7529412 -5.2696078 -8.8196078 3.9970588 -2.7029412 2.8307843
31 32 33 34 35 36
-4.2092157 1.4507843 1.0907843 -8.6292157 -0.4492157 1.4907843
37 38 39 40 41 42
-6.4803922 -6.6970588 1.8529412 -10.7303922 3.0696078 -2.5966667
43 44 45 46 47 48
-6.1366667 -0.6766667 -1.7366667 -2.8566667 5.9233333 0.1633333
49 50 51 52 53 54
2.4691176 1.0524510 5.5024510 -6.3808824 2.0191176 -1.0471569
55 56 57 58 59 60
0.7128431 4.3728431 -6.1871569 7.0928431 3.2728431 -4.5871569
61 62 63 64 65
10.0186275 9.6019608 -14.6480392 11.0686275 2.5686275
> postscript(file="/var/www/html/rcomp/tmp/6byp61229035030.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 = 65
Frequency = 1
lag(myerror, k = 1) myerror
0 1.1480392 NA
1 -0.1686275 1.1480392
2 6.0813725 -0.1686275
3 3.1980392 6.0813725
4 -0.3019608 3.1980392
5 -1.8682353 -0.3019608
6 9.2917647 -1.8682353
7 -5.4482353 9.2917647
8 5.8917647 -5.4482353
9 6.4717647 5.8917647
10 -5.3482353 6.4717647
11 -1.4082353 -5.3482353
12 1.5975490 -1.4082353
13 1.4808824 1.5975490
14 10.0308824 1.4808824
15 -1.1524510 10.0308824
16 -4.6524510 -1.1524510
17 2.6812745 -4.6524510
18 0.3412745 2.6812745
19 0.3012745 0.3412745
20 0.9412745 0.3012745
21 -2.0787255 0.9412745
22 -3.3987255 -2.0787255
23 4.3412745 -3.3987255
24 -8.7529412 4.3412745
25 -5.2696078 -8.7529412
26 -8.8196078 -5.2696078
27 3.9970588 -8.8196078
28 -2.7029412 3.9970588
29 2.8307843 -2.7029412
30 -4.2092157 2.8307843
31 1.4507843 -4.2092157
32 1.0907843 1.4507843
33 -8.6292157 1.0907843
34 -0.4492157 -8.6292157
35 1.4907843 -0.4492157
36 -6.4803922 1.4907843
37 -6.6970588 -6.4803922
38 1.8529412 -6.6970588
39 -10.7303922 1.8529412
40 3.0696078 -10.7303922
41 -2.5966667 3.0696078
42 -6.1366667 -2.5966667
43 -0.6766667 -6.1366667
44 -1.7366667 -0.6766667
45 -2.8566667 -1.7366667
46 5.9233333 -2.8566667
47 0.1633333 5.9233333
48 2.4691176 0.1633333
49 1.0524510 2.4691176
50 5.5024510 1.0524510
51 -6.3808824 5.5024510
52 2.0191176 -6.3808824
53 -1.0471569 2.0191176
54 0.7128431 -1.0471569
55 4.3728431 0.7128431
56 -6.1871569 4.3728431
57 7.0928431 -6.1871569
58 3.2728431 7.0928431
59 -4.5871569 3.2728431
60 10.0186275 -4.5871569
61 9.6019608 10.0186275
62 -14.6480392 9.6019608
63 11.0686275 -14.6480392
64 2.5686275 11.0686275
65 NA 2.5686275
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1686275 1.1480392
[2,] 6.0813725 -0.1686275
[3,] 3.1980392 6.0813725
[4,] -0.3019608 3.1980392
[5,] -1.8682353 -0.3019608
[6,] 9.2917647 -1.8682353
[7,] -5.4482353 9.2917647
[8,] 5.8917647 -5.4482353
[9,] 6.4717647 5.8917647
[10,] -5.3482353 6.4717647
[11,] -1.4082353 -5.3482353
[12,] 1.5975490 -1.4082353
[13,] 1.4808824 1.5975490
[14,] 10.0308824 1.4808824
[15,] -1.1524510 10.0308824
[16,] -4.6524510 -1.1524510
[17,] 2.6812745 -4.6524510
[18,] 0.3412745 2.6812745
[19,] 0.3012745 0.3412745
[20,] 0.9412745 0.3012745
[21,] -2.0787255 0.9412745
[22,] -3.3987255 -2.0787255
[23,] 4.3412745 -3.3987255
[24,] -8.7529412 4.3412745
[25,] -5.2696078 -8.7529412
[26,] -8.8196078 -5.2696078
[27,] 3.9970588 -8.8196078
[28,] -2.7029412 3.9970588
[29,] 2.8307843 -2.7029412
[30,] -4.2092157 2.8307843
[31,] 1.4507843 -4.2092157
[32,] 1.0907843 1.4507843
[33,] -8.6292157 1.0907843
[34,] -0.4492157 -8.6292157
[35,] 1.4907843 -0.4492157
[36,] -6.4803922 1.4907843
[37,] -6.6970588 -6.4803922
[38,] 1.8529412 -6.6970588
[39,] -10.7303922 1.8529412
[40,] 3.0696078 -10.7303922
[41,] -2.5966667 3.0696078
[42,] -6.1366667 -2.5966667
[43,] -0.6766667 -6.1366667
[44,] -1.7366667 -0.6766667
[45,] -2.8566667 -1.7366667
[46,] 5.9233333 -2.8566667
[47,] 0.1633333 5.9233333
[48,] 2.4691176 0.1633333
[49,] 1.0524510 2.4691176
[50,] 5.5024510 1.0524510
[51,] -6.3808824 5.5024510
[52,] 2.0191176 -6.3808824
[53,] -1.0471569 2.0191176
[54,] 0.7128431 -1.0471569
[55,] 4.3728431 0.7128431
[56,] -6.1871569 4.3728431
[57,] 7.0928431 -6.1871569
[58,] 3.2728431 7.0928431
[59,] -4.5871569 3.2728431
[60,] 10.0186275 -4.5871569
[61,] 9.6019608 10.0186275
[62,] -14.6480392 9.6019608
[63,] 11.0686275 -14.6480392
[64,] 2.5686275 11.0686275
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1686275 1.1480392
2 6.0813725 -0.1686275
3 3.1980392 6.0813725
4 -0.3019608 3.1980392
5 -1.8682353 -0.3019608
6 9.2917647 -1.8682353
7 -5.4482353 9.2917647
8 5.8917647 -5.4482353
9 6.4717647 5.8917647
10 -5.3482353 6.4717647
11 -1.4082353 -5.3482353
12 1.5975490 -1.4082353
13 1.4808824 1.5975490
14 10.0308824 1.4808824
15 -1.1524510 10.0308824
16 -4.6524510 -1.1524510
17 2.6812745 -4.6524510
18 0.3412745 2.6812745
19 0.3012745 0.3412745
20 0.9412745 0.3012745
21 -2.0787255 0.9412745
22 -3.3987255 -2.0787255
23 4.3412745 -3.3987255
24 -8.7529412 4.3412745
25 -5.2696078 -8.7529412
26 -8.8196078 -5.2696078
27 3.9970588 -8.8196078
28 -2.7029412 3.9970588
29 2.8307843 -2.7029412
30 -4.2092157 2.8307843
31 1.4507843 -4.2092157
32 1.0907843 1.4507843
33 -8.6292157 1.0907843
34 -0.4492157 -8.6292157
35 1.4907843 -0.4492157
36 -6.4803922 1.4907843
37 -6.6970588 -6.4803922
38 1.8529412 -6.6970588
39 -10.7303922 1.8529412
40 3.0696078 -10.7303922
41 -2.5966667 3.0696078
42 -6.1366667 -2.5966667
43 -0.6766667 -6.1366667
44 -1.7366667 -0.6766667
45 -2.8566667 -1.7366667
46 5.9233333 -2.8566667
47 0.1633333 5.9233333
48 2.4691176 0.1633333
49 1.0524510 2.4691176
50 5.5024510 1.0524510
51 -6.3808824 5.5024510
52 2.0191176 -6.3808824
53 -1.0471569 2.0191176
54 0.7128431 -1.0471569
55 4.3728431 0.7128431
56 -6.1871569 4.3728431
57 7.0928431 -6.1871569
58 3.2728431 7.0928431
59 -4.5871569 3.2728431
60 10.0186275 -4.5871569
61 9.6019608 10.0186275
62 -14.6480392 9.6019608
63 11.0686275 -14.6480392
64 2.5686275 11.0686275
> 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/7tpi41229035030.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nlxk1229035030.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vsn91229035030.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10f5571229035030.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jcfm1229035030.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/12j7751229035030.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/13a4au1229035030.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/14xk7h1229035030.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/158zqd1229035031.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/16pt1w1229035031.tab")
+ }
>
> system("convert tmp/16s0a1229035030.ps tmp/16s0a1229035030.png")
> system("convert tmp/2mq5e1229035030.ps tmp/2mq5e1229035030.png")
> system("convert tmp/3e8xl1229035030.ps tmp/3e8xl1229035030.png")
> system("convert tmp/4qc1d1229035030.ps tmp/4qc1d1229035030.png")
> system("convert tmp/5n90j1229035030.ps tmp/5n90j1229035030.png")
> system("convert tmp/6byp61229035030.ps tmp/6byp61229035030.png")
> system("convert tmp/7tpi41229035030.ps tmp/7tpi41229035030.png")
> system("convert tmp/8nlxk1229035030.ps tmp/8nlxk1229035030.png")
> system("convert tmp/9vsn91229035030.ps tmp/9vsn91229035030.png")
> system("convert tmp/10f5571229035030.ps tmp/10f5571229035030.png")
>
>
> proc.time()
user system elapsed
2.525 1.635 3.795