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(120.9,611,0,0,119.6,594,0,0,125.9,595,0,0,116.1,591,0,0,107.5,589,0,0,116.7,584,0,0,112.5,573,0,0,113,567,0,0,126.4,569,0,0,114.1,621,0,0,112.5,629,0,0,112.4,628,0,0,113.1,612,0,0,116.3,595,0,0,111.7,597,0,0,118.8,593,0,0,116.5,590,0,0,125.1,580,0,0,113.1,574,0,0,119.6,573,0,0,114.4,573,0,0,114,620,0,0,117.8,626,0,0,117,620,0,0,120.9,588,0,0,115,566,0,0,117.3,557,0,0,119.4,561,0,0,114.9,549,0,0,125.8,532,0,0,117.6,526,0,0,117.6,511,0,0,114.9,499,0,0,121.9,555,0,0,117,565,0,1,106.4,542,0,1,110.5,527,0,1,113.6,510,0,1,114.2,514,0,1,125.4,517,0,1,124.6,508,0,1,120.2,493,0,1,120.8,490,0,1,111.4,469,0,1,124.1,478,0,1,120.2,528,0,1,125.5,534,0,1,116,518,1,0,117,506,1,0,105.7,502,1,0,102,516,1,0,106.4,528,1,0,96.9,533,1,0,107.6,536,1,0,98.8,537,1,0,101.1,524,1,0,105.7,536,1,0,104.6,587,1,0,103.2,597,1,0,101.6,581,1,0),dim=c(4,60),dimnames=list(c('ChemischeIndustrie','Werkloosheid','Dummy','Dummy2'),1:60))
> y <- array(NA,dim=c(4,60),dimnames=list(c('ChemischeIndustrie','Werkloosheid','Dummy','Dummy2'),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
ChemischeIndustrie Werkloosheid Dummy Dummy2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 120.9 611 0 0 1 0 0 0 0 0 0 0 0 0
2 119.6 594 0 0 0 1 0 0 0 0 0 0 0 0
3 125.9 595 0 0 0 0 1 0 0 0 0 0 0 0
4 116.1 591 0 0 0 0 0 1 0 0 0 0 0 0
5 107.5 589 0 0 0 0 0 0 1 0 0 0 0 0
6 116.7 584 0 0 0 0 0 0 0 1 0 0 0 0
7 112.5 573 0 0 0 0 0 0 0 0 1 0 0 0
8 113.0 567 0 0 0 0 0 0 0 0 0 1 0 0
9 126.4 569 0 0 0 0 0 0 0 0 0 0 1 0
10 114.1 621 0 0 0 0 0 0 0 0 0 0 0 1
11 112.5 629 0 0 0 0 0 0 0 0 0 0 0 0
12 112.4 628 0 0 0 0 0 0 0 0 0 0 0 0
13 113.1 612 0 0 1 0 0 0 0 0 0 0 0 0
14 116.3 595 0 0 0 1 0 0 0 0 0 0 0 0
15 111.7 597 0 0 0 0 1 0 0 0 0 0 0 0
16 118.8 593 0 0 0 0 0 1 0 0 0 0 0 0
17 116.5 590 0 0 0 0 0 0 1 0 0 0 0 0
18 125.1 580 0 0 0 0 0 0 0 1 0 0 0 0
19 113.1 574 0 0 0 0 0 0 0 0 1 0 0 0
20 119.6 573 0 0 0 0 0 0 0 0 0 1 0 0
21 114.4 573 0 0 0 0 0 0 0 0 0 0 1 0
22 114.0 620 0 0 0 0 0 0 0 0 0 0 0 1
23 117.8 626 0 0 0 0 0 0 0 0 0 0 0 0
24 117.0 620 0 0 0 0 0 0 0 0 0 0 0 0
25 120.9 588 0 0 1 0 0 0 0 0 0 0 0 0
26 115.0 566 0 0 0 1 0 0 0 0 0 0 0 0
27 117.3 557 0 0 0 0 1 0 0 0 0 0 0 0
28 119.4 561 0 0 0 0 0 1 0 0 0 0 0 0
29 114.9 549 0 0 0 0 0 0 1 0 0 0 0 0
30 125.8 532 0 0 0 0 0 0 0 1 0 0 0 0
31 117.6 526 0 0 0 0 0 0 0 0 1 0 0 0
32 117.6 511 0 0 0 0 0 0 0 0 0 1 0 0
33 114.9 499 0 0 0 0 0 0 0 0 0 0 1 0
34 121.9 555 0 0 0 0 0 0 0 0 0 0 0 1
35 117.0 565 0 1 0 0 0 0 0 0 0 0 0 0
36 106.4 542 0 1 0 0 0 0 0 0 0 0 0 0
37 110.5 527 0 1 1 0 0 0 0 0 0 0 0 0
38 113.6 510 0 1 0 1 0 0 0 0 0 0 0 0
39 114.2 514 0 1 0 0 1 0 0 0 0 0 0 0
40 125.4 517 0 1 0 0 0 1 0 0 0 0 0 0
41 124.6 508 0 1 0 0 0 0 1 0 0 0 0 0
42 120.2 493 0 1 0 0 0 0 0 1 0 0 0 0
43 120.8 490 0 1 0 0 0 0 0 0 1 0 0 0
44 111.4 469 0 1 0 0 0 0 0 0 0 1 0 0
45 124.1 478 0 1 0 0 0 0 0 0 0 0 1 0
46 120.2 528 0 1 0 0 0 0 0 0 0 0 0 1
47 125.5 534 0 1 0 0 0 0 0 0 0 0 0 0
48 116.0 518 1 0 0 0 0 0 0 0 0 0 0 0
49 117.0 506 1 0 1 0 0 0 0 0 0 0 0 0
50 105.7 502 1 0 0 1 0 0 0 0 0 0 0 0
51 102.0 516 1 0 0 0 1 0 0 0 0 0 0 0
52 106.4 528 1 0 0 0 0 1 0 0 0 0 0 0
53 96.9 533 1 0 0 0 0 0 1 0 0 0 0 0
54 107.6 536 1 0 0 0 0 0 0 1 0 0 0 0
55 98.8 537 1 0 0 0 0 0 0 0 1 0 0 0
56 101.1 524 1 0 0 0 0 0 0 0 0 1 0 0
57 105.7 536 1 0 0 0 0 0 0 0 0 0 1 0
58 104.6 587 1 0 0 0 0 0 0 0 0 0 0 1
59 103.2 597 1 0 0 0 0 0 0 0 0 0 0 0
60 101.6 581 1 0 0 0 0 0 0 0 0 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Werkloosheid Dummy Dummy2 M1
176.02589 -0.09714 -13.03790 -4.05345 1.34207
M2 M3 M4 M5 M6
-2.50510 -2.00323 1.29921 -4.16003 2.07391
M7 M8 M9 M10 M11
-4.84303 -5.86222 -0.99978 1.92234 3.83886
t
-0.08874
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.7299 -2.4616 -0.5028 2.8929 9.9398
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 176.02589 24.39807 7.215 5.54e-09 ***
Werkloosheid -0.09714 0.03798 -2.557 0.014073 *
Dummy -13.03790 3.65423 -3.568 0.000883 ***
Dummy2 -4.05345 2.96192 -1.369 0.178096
M1 1.34207 3.32948 0.403 0.688837
M2 -2.50510 3.50370 -0.715 0.478394
M3 -2.00323 3.43854 -0.583 0.563149
M4 1.29921 3.38391 0.384 0.702874
M5 -4.16003 3.41800 -1.217 0.230057
M6 2.07391 3.52843 0.588 0.559691
M7 -4.84303 3.58954 -1.349 0.184173
M8 -5.86222 3.77629 -1.552 0.127737
M9 -0.99978 3.70907 -0.270 0.788766
M10 1.92234 3.21984 0.597 0.553547
M11 3.83886 3.28514 1.169 0.248876
t -0.08874 0.10755 -0.825 0.413760
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.005 on 44 degrees of freedom
Multiple R-squared: 0.6415, Adjusted R-squared: 0.5192
F-statistic: 5.248 on 15 and 44 DF, p-value: 8.112e-06
> 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,] 0.8449090 0.3101819 0.15509097
[2,] 0.9141746 0.1716508 0.08582542
[3,] 0.9050579 0.1898843 0.09494213
[4,] 0.8369126 0.3261748 0.16308739
[5,] 0.7517715 0.4964570 0.24822848
[6,] 0.6860072 0.6279855 0.31399276
[7,] 0.6670465 0.6659070 0.33295350
[8,] 0.6508751 0.6982499 0.34912494
[9,] 0.6004530 0.7990940 0.39954698
[10,] 0.4948082 0.9896165 0.50519176
[11,] 0.3887593 0.7775186 0.61124071
[12,] 0.3359078 0.6718157 0.66409217
[13,] 0.2519588 0.5039176 0.74804120
[14,] 0.2451467 0.4902934 0.75485332
[15,] 0.2842549 0.5685098 0.71574511
[16,] 0.2273468 0.4546937 0.77265316
[17,] 0.1606629 0.3213258 0.83933712
[18,] 0.2384708 0.4769416 0.76152922
[19,] 0.4713150 0.9426300 0.52868498
[20,] 0.4636137 0.9272274 0.53638630
[21,] 0.4716155 0.9432310 0.52838450
[22,] 0.4655175 0.9310350 0.53448249
[23,] 0.5776250 0.8447499 0.42237497
> postscript(file="/var/www/html/rcomp/tmp/1qmgl1261318125.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ffqc1261318125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31oah1261318125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4tsu41261318125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5aua31261318125.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 = 60
Frequency = 1
1 2 3 4 5 6
2.97116524 3.95574688 9.93975582 -3.46249034 -6.70879009 -4.13967148
7 8 9 10 11 12
-2.40249034 -1.37738066 7.44319204 -2.63909877 -5.28978973 -1.55932393
13 14 15 16 17 18
-3.66683723 1.81774441 -3.00111018 0.49664367 3.45320744 4.93664367
19 20 21 22 23 24
-0.64049281 6.87029925 -3.10340100 -1.77137420 0.78366189 3.32844531
25 26 27 28 29 30
2.86674840 -1.23435234 -0.22170817 -0.94686251 -1.06452703 2.03895387
31 32 33 34 35 36
0.26181739 -0.08730121 -8.72663917 0.87961591 -0.82334731 -9.72988398
37 38 39 40 41 42
-8.34026080 -2.95567917 -2.38026080 5.89744838 9.77119329 -2.23105286
43 44 45 46 47 48
5.08322009 -5.24871737 3.55181066 1.67524689 5.73028298 7.58815178
49 50 51 52 53 54
6.16918439 -1.58345978 -4.33667666 -1.98473920 -5.45108362 -0.60487320
55 56 57 58 59 60
-2.30205434 -0.15690000 0.83503747 1.85561017 -0.40080783 0.37261082
> postscript(file="/var/www/html/rcomp/tmp/6vry01261318125.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 2.97116524 NA
1 3.95574688 2.97116524
2 9.93975582 3.95574688
3 -3.46249034 9.93975582
4 -6.70879009 -3.46249034
5 -4.13967148 -6.70879009
6 -2.40249034 -4.13967148
7 -1.37738066 -2.40249034
8 7.44319204 -1.37738066
9 -2.63909877 7.44319204
10 -5.28978973 -2.63909877
11 -1.55932393 -5.28978973
12 -3.66683723 -1.55932393
13 1.81774441 -3.66683723
14 -3.00111018 1.81774441
15 0.49664367 -3.00111018
16 3.45320744 0.49664367
17 4.93664367 3.45320744
18 -0.64049281 4.93664367
19 6.87029925 -0.64049281
20 -3.10340100 6.87029925
21 -1.77137420 -3.10340100
22 0.78366189 -1.77137420
23 3.32844531 0.78366189
24 2.86674840 3.32844531
25 -1.23435234 2.86674840
26 -0.22170817 -1.23435234
27 -0.94686251 -0.22170817
28 -1.06452703 -0.94686251
29 2.03895387 -1.06452703
30 0.26181739 2.03895387
31 -0.08730121 0.26181739
32 -8.72663917 -0.08730121
33 0.87961591 -8.72663917
34 -0.82334731 0.87961591
35 -9.72988398 -0.82334731
36 -8.34026080 -9.72988398
37 -2.95567917 -8.34026080
38 -2.38026080 -2.95567917
39 5.89744838 -2.38026080
40 9.77119329 5.89744838
41 -2.23105286 9.77119329
42 5.08322009 -2.23105286
43 -5.24871737 5.08322009
44 3.55181066 -5.24871737
45 1.67524689 3.55181066
46 5.73028298 1.67524689
47 7.58815178 5.73028298
48 6.16918439 7.58815178
49 -1.58345978 6.16918439
50 -4.33667666 -1.58345978
51 -1.98473920 -4.33667666
52 -5.45108362 -1.98473920
53 -0.60487320 -5.45108362
54 -2.30205434 -0.60487320
55 -0.15690000 -2.30205434
56 0.83503747 -0.15690000
57 1.85561017 0.83503747
58 -0.40080783 1.85561017
59 0.37261082 -0.40080783
60 NA 0.37261082
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.95574688 2.97116524
[2,] 9.93975582 3.95574688
[3,] -3.46249034 9.93975582
[4,] -6.70879009 -3.46249034
[5,] -4.13967148 -6.70879009
[6,] -2.40249034 -4.13967148
[7,] -1.37738066 -2.40249034
[8,] 7.44319204 -1.37738066
[9,] -2.63909877 7.44319204
[10,] -5.28978973 -2.63909877
[11,] -1.55932393 -5.28978973
[12,] -3.66683723 -1.55932393
[13,] 1.81774441 -3.66683723
[14,] -3.00111018 1.81774441
[15,] 0.49664367 -3.00111018
[16,] 3.45320744 0.49664367
[17,] 4.93664367 3.45320744
[18,] -0.64049281 4.93664367
[19,] 6.87029925 -0.64049281
[20,] -3.10340100 6.87029925
[21,] -1.77137420 -3.10340100
[22,] 0.78366189 -1.77137420
[23,] 3.32844531 0.78366189
[24,] 2.86674840 3.32844531
[25,] -1.23435234 2.86674840
[26,] -0.22170817 -1.23435234
[27,] -0.94686251 -0.22170817
[28,] -1.06452703 -0.94686251
[29,] 2.03895387 -1.06452703
[30,] 0.26181739 2.03895387
[31,] -0.08730121 0.26181739
[32,] -8.72663917 -0.08730121
[33,] 0.87961591 -8.72663917
[34,] -0.82334731 0.87961591
[35,] -9.72988398 -0.82334731
[36,] -8.34026080 -9.72988398
[37,] -2.95567917 -8.34026080
[38,] -2.38026080 -2.95567917
[39,] 5.89744838 -2.38026080
[40,] 9.77119329 5.89744838
[41,] -2.23105286 9.77119329
[42,] 5.08322009 -2.23105286
[43,] -5.24871737 5.08322009
[44,] 3.55181066 -5.24871737
[45,] 1.67524689 3.55181066
[46,] 5.73028298 1.67524689
[47,] 7.58815178 5.73028298
[48,] 6.16918439 7.58815178
[49,] -1.58345978 6.16918439
[50,] -4.33667666 -1.58345978
[51,] -1.98473920 -4.33667666
[52,] -5.45108362 -1.98473920
[53,] -0.60487320 -5.45108362
[54,] -2.30205434 -0.60487320
[55,] -0.15690000 -2.30205434
[56,] 0.83503747 -0.15690000
[57,] 1.85561017 0.83503747
[58,] -0.40080783 1.85561017
[59,] 0.37261082 -0.40080783
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.95574688 2.97116524
2 9.93975582 3.95574688
3 -3.46249034 9.93975582
4 -6.70879009 -3.46249034
5 -4.13967148 -6.70879009
6 -2.40249034 -4.13967148
7 -1.37738066 -2.40249034
8 7.44319204 -1.37738066
9 -2.63909877 7.44319204
10 -5.28978973 -2.63909877
11 -1.55932393 -5.28978973
12 -3.66683723 -1.55932393
13 1.81774441 -3.66683723
14 -3.00111018 1.81774441
15 0.49664367 -3.00111018
16 3.45320744 0.49664367
17 4.93664367 3.45320744
18 -0.64049281 4.93664367
19 6.87029925 -0.64049281
20 -3.10340100 6.87029925
21 -1.77137420 -3.10340100
22 0.78366189 -1.77137420
23 3.32844531 0.78366189
24 2.86674840 3.32844531
25 -1.23435234 2.86674840
26 -0.22170817 -1.23435234
27 -0.94686251 -0.22170817
28 -1.06452703 -0.94686251
29 2.03895387 -1.06452703
30 0.26181739 2.03895387
31 -0.08730121 0.26181739
32 -8.72663917 -0.08730121
33 0.87961591 -8.72663917
34 -0.82334731 0.87961591
35 -9.72988398 -0.82334731
36 -8.34026080 -9.72988398
37 -2.95567917 -8.34026080
38 -2.38026080 -2.95567917
39 5.89744838 -2.38026080
40 9.77119329 5.89744838
41 -2.23105286 9.77119329
42 5.08322009 -2.23105286
43 -5.24871737 5.08322009
44 3.55181066 -5.24871737
45 1.67524689 3.55181066
46 5.73028298 1.67524689
47 7.58815178 5.73028298
48 6.16918439 7.58815178
49 -1.58345978 6.16918439
50 -4.33667666 -1.58345978
51 -1.98473920 -4.33667666
52 -5.45108362 -1.98473920
53 -0.60487320 -5.45108362
54 -2.30205434 -0.60487320
55 -0.15690000 -2.30205434
56 0.83503747 -0.15690000
57 1.85561017 0.83503747
58 -0.40080783 1.85561017
59 0.37261082 -0.40080783
> 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/769s91261318125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mhax1261318125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9haln1261318125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qb1j1261318125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11541p1261318125.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/12guf41261318125.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/13m98m1261318125.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/14szjc1261318125.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/157c7a1261318125.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/168n3j1261318125.tab")
+ }
>
> try(system("convert tmp/1qmgl1261318125.ps tmp/1qmgl1261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ffqc1261318125.ps tmp/2ffqc1261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/31oah1261318125.ps tmp/31oah1261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tsu41261318125.ps tmp/4tsu41261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/5aua31261318125.ps tmp/5aua31261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vry01261318125.ps tmp/6vry01261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/769s91261318125.ps tmp/769s91261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mhax1261318125.ps tmp/8mhax1261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/9haln1261318125.ps tmp/9haln1261318125.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qb1j1261318125.ps tmp/10qb1j1261318125.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.348 1.564 2.768