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(8.4,99,8.4,98.6,8.4,98.6,8.6,98.5,8.9,98.9,8.8,99.4,8.3,99.8,7.5,99.9,7.2,100,7.4,100.1,8.8,100.1,9.3,100.2,9.3,100.3,8.7,100,8.2,99.9,8.3,99.4,8.5,99.8,8.6,99.6,8.5,100,8.2,99.9,8.1,100.3,7.9,100.6,8.6,100.7,8.7,100.8,8.7,100.8,8.5,100.6,8.4,101.1,8.5,101.1,8.7,100.9,8.7,101.1,8.6,101.2,8.5,101.4,8.3,101.9,8,102.1,8.2,102.1,8.1,103,8.1,103.4,8,103.2,7.9,103.1,7.9,103,8,103.7,8,103.4,7.9,103.5,8,103.8,7.7,104,7.2,104.2,7.5,104.4,7.3,104.4,7,104.9,7,105.3,7,105.2,7.2,105.4,7.3,105.4,7.1,105.5,6.8,105.7,6.4,105.6,6.1,105.8,6.5,105.4,7.7,105.5,7.9,105.8,7.5,106.1,6.9,106,6.6,105.5,6.9,105.4,7.7,106,8,106.1,8,106.4,7.7,106,7.3,106,7.4,106,8.1,106,8.3,106.1,8.2,106.1),dim=c(2,73),dimnames=list(c('werkl','afzetp'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('werkl','afzetp'),1:73))
> 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
werkl afzetp M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.4 99.0 1 0 0 0 0 0 0 0 0 0 0 1
2 8.4 98.6 0 1 0 0 0 0 0 0 0 0 0 2
3 8.4 98.6 0 0 1 0 0 0 0 0 0 0 0 3
4 8.6 98.5 0 0 0 1 0 0 0 0 0 0 0 4
5 8.9 98.9 0 0 0 0 1 0 0 0 0 0 0 5
6 8.8 99.4 0 0 0 0 0 1 0 0 0 0 0 6
7 8.3 99.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 99.9 0 0 0 0 0 0 0 1 0 0 0 8
9 7.2 100.0 0 0 0 0 0 0 0 0 1 0 0 9
10 7.4 100.1 0 0 0 0 0 0 0 0 0 1 0 10
11 8.8 100.1 0 0 0 0 0 0 0 0 0 0 1 11
12 9.3 100.2 0 0 0 0 0 0 0 0 0 0 0 12
13 9.3 100.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 100.0 0 1 0 0 0 0 0 0 0 0 0 14
15 8.2 99.9 0 0 1 0 0 0 0 0 0 0 0 15
16 8.3 99.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 99.8 0 0 0 0 1 0 0 0 0 0 0 17
18 8.6 99.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 100.0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 99.9 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 100.3 0 0 0 0 0 0 0 0 1 0 0 21
22 7.9 100.6 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 100.7 0 0 0 0 0 0 0 0 0 0 1 23
24 8.7 100.8 0 0 0 0 0 0 0 0 0 0 0 24
25 8.7 100.8 1 0 0 0 0 0 0 0 0 0 0 25
26 8.5 100.6 0 1 0 0 0 0 0 0 0 0 0 26
27 8.4 101.1 0 0 1 0 0 0 0 0 0 0 0 27
28 8.5 101.1 0 0 0 1 0 0 0 0 0 0 0 28
29 8.7 100.9 0 0 0 0 1 0 0 0 0 0 0 29
30 8.7 101.1 0 0 0 0 0 1 0 0 0 0 0 30
31 8.6 101.2 0 0 0 0 0 0 1 0 0 0 0 31
32 8.5 101.4 0 0 0 0 0 0 0 1 0 0 0 32
33 8.3 101.9 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 102.1 0 0 0 0 0 0 0 0 0 1 0 34
35 8.2 102.1 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 103.0 0 0 0 0 0 0 0 0 0 0 0 36
37 8.1 103.4 1 0 0 0 0 0 0 0 0 0 0 37
38 8.0 103.2 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 103.1 0 0 1 0 0 0 0 0 0 0 0 39
40 7.9 103.0 0 0 0 1 0 0 0 0 0 0 0 40
41 8.0 103.7 0 0 0 0 1 0 0 0 0 0 0 41
42 8.0 103.4 0 0 0 0 0 1 0 0 0 0 0 42
43 7.9 103.5 0 0 0 0 0 0 1 0 0 0 0 43
44 8.0 103.8 0 0 0 0 0 0 0 1 0 0 0 44
45 7.7 104.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.2 104.2 0 0 0 0 0 0 0 0 0 1 0 46
47 7.5 104.4 0 0 0 0 0 0 0 0 0 0 1 47
48 7.3 104.4 0 0 0 0 0 0 0 0 0 0 0 48
49 7.0 104.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.0 105.3 0 1 0 0 0 0 0 0 0 0 0 50
51 7.0 105.2 0 0 1 0 0 0 0 0 0 0 0 51
52 7.2 105.4 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 105.4 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 105.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.8 105.7 0 0 0 0 0 0 1 0 0 0 0 55
56 6.4 105.6 0 0 0 0 0 0 0 1 0 0 0 56
57 6.1 105.8 0 0 0 0 0 0 0 0 1 0 0 57
58 6.5 105.4 0 0 0 0 0 0 0 0 0 1 0 58
59 7.7 105.5 0 0 0 0 0 0 0 0 0 0 1 59
60 7.9 105.8 0 0 0 0 0 0 0 0 0 0 0 60
61 7.5 106.1 1 0 0 0 0 0 0 0 0 0 0 61
62 6.9 106.0 0 1 0 0 0 0 0 0 0 0 0 62
63 6.6 105.5 0 0 1 0 0 0 0 0 0 0 0 63
64 6.9 105.4 0 0 0 1 0 0 0 0 0 0 0 64
65 7.7 106.0 0 0 0 0 1 0 0 0 0 0 0 65
66 8.0 106.1 0 0 0 0 0 1 0 0 0 0 0 66
67 8.0 106.4 0 0 0 0 0 0 1 0 0 0 0 67
68 7.7 106.0 0 0 0 0 0 0 0 1 0 0 0 68
69 7.3 106.0 0 0 0 0 0 0 0 0 1 0 0 69
70 7.4 106.0 0 0 0 0 0 0 0 0 0 1 0 70
71 8.1 106.0 0 0 0 0 0 0 0 0 0 0 1 71
72 8.3 106.1 0 0 0 0 0 0 0 0 0 0 0 72
73 8.2 106.1 1 0 0 0 0 0 0 0 0 0 0 73
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) afzetp M1 M2 M3 M4
68.14802 -0.60011 -0.10242 -0.49582 -0.74392 -0.70536
M5 M6 M7 M8 M9 M10
-0.28342 -0.27818 -0.36292 -0.71435 -0.89242 -0.95384
M11 t
-0.21526 0.05143
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.73255 -0.23741 0.01251 0.25822 0.77690
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.14802 8.04570 8.470 8.81e-12 ***
afzetp -0.60011 0.08179 -7.338 7.25e-10 ***
M1 -0.10242 0.20811 -0.492 0.624445
M2 -0.49582 0.21644 -2.291 0.025563 *
M3 -0.74392 0.21618 -3.441 0.001070 **
M4 -0.70536 0.21724 -3.247 0.001925 **
M5 -0.28342 0.21591 -1.313 0.194370
M6 -0.27818 0.21598 -1.288 0.202774
M7 -0.36292 0.21553 -1.684 0.097502 .
M8 -0.71435 0.21573 -3.311 0.001588 **
M9 -0.89242 0.21537 -4.144 0.000111 ***
M10 -0.95384 0.21541 -4.428 4.18e-05 ***
M11 -0.21526 0.21555 -0.999 0.322024
t 0.05143 0.01002 5.133 3.35e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3729 on 59 degrees of freedom
Multiple R-squared: 0.7641, Adjusted R-squared: 0.7121
F-statistic: 14.7 on 13 and 59 DF, p-value: 5.603e-14
> 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,] 0.49281596 0.98563191 0.5071840
[2,] 0.50074289 0.99851422 0.4992571
[3,] 0.47116374 0.94232748 0.5288363
[4,] 0.57802101 0.84395799 0.4219790
[5,] 0.62728135 0.74543729 0.3727186
[6,] 0.53550460 0.92899080 0.4644954
[7,] 0.48579460 0.97158920 0.5142054
[8,] 0.55698290 0.88603420 0.4430171
[9,] 0.49006846 0.98013692 0.5099315
[10,] 0.40853393 0.81706786 0.5914661
[11,] 0.32605621 0.65211242 0.6739438
[12,] 0.24875890 0.49751781 0.7512411
[13,] 0.19733128 0.39466257 0.8026687
[14,] 0.14985150 0.29970301 0.8501485
[15,] 0.12100694 0.24201388 0.8789931
[16,] 0.13473957 0.26947915 0.8652604
[17,] 0.13572379 0.27144757 0.8642762
[18,] 0.09849723 0.19699446 0.9015028
[19,] 0.13826797 0.27653594 0.8617320
[20,] 0.18757130 0.37514260 0.8124287
[21,] 0.15983229 0.31966457 0.8401677
[22,] 0.11840457 0.23680915 0.8815954
[23,] 0.09949427 0.19898855 0.9005057
[24,] 0.07054897 0.14109793 0.9294510
[25,] 0.04775434 0.09550867 0.9522457
[26,] 0.03209625 0.06419249 0.9679038
[27,] 0.02149785 0.04299570 0.9785021
[28,] 0.02688974 0.05377949 0.9731103
[29,] 0.07698538 0.15397077 0.9230146
[30,] 0.13587571 0.27175142 0.8641243
[31,] 0.14979138 0.29958275 0.8502086
[32,] 0.21112164 0.42224328 0.7888784
[33,] 0.24865040 0.49730079 0.7513496
[34,] 0.34468193 0.68936387 0.6553181
[35,] 0.54441515 0.91116970 0.4555848
[36,] 0.75036390 0.49927220 0.2496361
[37,] 0.75673021 0.48653957 0.2432698
[38,] 0.64044075 0.71911851 0.3595593
[39,] 0.56095735 0.87808530 0.4390426
[40,] 0.62349744 0.75300513 0.3765026
> postscript(file="/var/www/html/rcomp/tmp/16m9v1258203522.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2u8mj1258203522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3csb51258203522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ldzm1258203522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5uqpr1258203522.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 = 73
Frequency = 1
1 2 3 4 5 6
-0.28608151 -0.18415953 0.01251267 0.06251267 0.12918855 0.27256982
7 8 9 10 11 12
0.04591975 -0.39406919 -0.50741727 -0.23741358 0.37257904 0.66589578
13 14 15 16 17 18
0.77689646 0.33882950 -0.02450936 -0.31455361 -0.34787773 -0.42457389
19 20 21 22 23 24
-0.25122397 -0.31123503 -0.04454993 -0.05452411 -0.08452042 -0.19120369
25 26 27 28 29 30
-0.14021407 -0.11826996 0.27845755 0.28846861 -0.10492188 -0.04157380
31 32 33 34 35 36
-0.04825706 0.27176507 0.49846123 0.32847598 -0.26153139 -0.08812615
37 38 39 40 41 42
0.20290771 0.32485182 0.36151295 0.21151295 0.25822203 0.02151480
43 44 45 46 47 48
0.01483154 0.59486473 0.54152770 0.17154245 -0.19844280 -0.66513712
49 50 51 52 53 54
-0.61409219 -0.03208171 0.10457943 0.33461261 -0.03875575 -0.23541873
55 56 57 58 59 60
-0.38209093 -0.54210199 -0.59543901 -0.42549064 0.04451305 0.15785191
61 62 63 64 65 66
-0.01112528 -0.32917011 -0.73255323 -0.58255323 0.10414478 0.40748180
67 68 69 70 71 72
0.62082067 0.38077642 0.10741727 0.21740990 0.12740252 0.12071926
73
0.07170888
> postscript(file="/var/www/html/rcomp/tmp/6cmds1258203522.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.28608151 NA
1 -0.18415953 -0.28608151
2 0.01251267 -0.18415953
3 0.06251267 0.01251267
4 0.12918855 0.06251267
5 0.27256982 0.12918855
6 0.04591975 0.27256982
7 -0.39406919 0.04591975
8 -0.50741727 -0.39406919
9 -0.23741358 -0.50741727
10 0.37257904 -0.23741358
11 0.66589578 0.37257904
12 0.77689646 0.66589578
13 0.33882950 0.77689646
14 -0.02450936 0.33882950
15 -0.31455361 -0.02450936
16 -0.34787773 -0.31455361
17 -0.42457389 -0.34787773
18 -0.25122397 -0.42457389
19 -0.31123503 -0.25122397
20 -0.04454993 -0.31123503
21 -0.05452411 -0.04454993
22 -0.08452042 -0.05452411
23 -0.19120369 -0.08452042
24 -0.14021407 -0.19120369
25 -0.11826996 -0.14021407
26 0.27845755 -0.11826996
27 0.28846861 0.27845755
28 -0.10492188 0.28846861
29 -0.04157380 -0.10492188
30 -0.04825706 -0.04157380
31 0.27176507 -0.04825706
32 0.49846123 0.27176507
33 0.32847598 0.49846123
34 -0.26153139 0.32847598
35 -0.08812615 -0.26153139
36 0.20290771 -0.08812615
37 0.32485182 0.20290771
38 0.36151295 0.32485182
39 0.21151295 0.36151295
40 0.25822203 0.21151295
41 0.02151480 0.25822203
42 0.01483154 0.02151480
43 0.59486473 0.01483154
44 0.54152770 0.59486473
45 0.17154245 0.54152770
46 -0.19844280 0.17154245
47 -0.66513712 -0.19844280
48 -0.61409219 -0.66513712
49 -0.03208171 -0.61409219
50 0.10457943 -0.03208171
51 0.33461261 0.10457943
52 -0.03875575 0.33461261
53 -0.23541873 -0.03875575
54 -0.38209093 -0.23541873
55 -0.54210199 -0.38209093
56 -0.59543901 -0.54210199
57 -0.42549064 -0.59543901
58 0.04451305 -0.42549064
59 0.15785191 0.04451305
60 -0.01112528 0.15785191
61 -0.32917011 -0.01112528
62 -0.73255323 -0.32917011
63 -0.58255323 -0.73255323
64 0.10414478 -0.58255323
65 0.40748180 0.10414478
66 0.62082067 0.40748180
67 0.38077642 0.62082067
68 0.10741727 0.38077642
69 0.21740990 0.10741727
70 0.12740252 0.21740990
71 0.12071926 0.12740252
72 0.07170888 0.12071926
73 NA 0.07170888
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.18415953 -0.28608151
[2,] 0.01251267 -0.18415953
[3,] 0.06251267 0.01251267
[4,] 0.12918855 0.06251267
[5,] 0.27256982 0.12918855
[6,] 0.04591975 0.27256982
[7,] -0.39406919 0.04591975
[8,] -0.50741727 -0.39406919
[9,] -0.23741358 -0.50741727
[10,] 0.37257904 -0.23741358
[11,] 0.66589578 0.37257904
[12,] 0.77689646 0.66589578
[13,] 0.33882950 0.77689646
[14,] -0.02450936 0.33882950
[15,] -0.31455361 -0.02450936
[16,] -0.34787773 -0.31455361
[17,] -0.42457389 -0.34787773
[18,] -0.25122397 -0.42457389
[19,] -0.31123503 -0.25122397
[20,] -0.04454993 -0.31123503
[21,] -0.05452411 -0.04454993
[22,] -0.08452042 -0.05452411
[23,] -0.19120369 -0.08452042
[24,] -0.14021407 -0.19120369
[25,] -0.11826996 -0.14021407
[26,] 0.27845755 -0.11826996
[27,] 0.28846861 0.27845755
[28,] -0.10492188 0.28846861
[29,] -0.04157380 -0.10492188
[30,] -0.04825706 -0.04157380
[31,] 0.27176507 -0.04825706
[32,] 0.49846123 0.27176507
[33,] 0.32847598 0.49846123
[34,] -0.26153139 0.32847598
[35,] -0.08812615 -0.26153139
[36,] 0.20290771 -0.08812615
[37,] 0.32485182 0.20290771
[38,] 0.36151295 0.32485182
[39,] 0.21151295 0.36151295
[40,] 0.25822203 0.21151295
[41,] 0.02151480 0.25822203
[42,] 0.01483154 0.02151480
[43,] 0.59486473 0.01483154
[44,] 0.54152770 0.59486473
[45,] 0.17154245 0.54152770
[46,] -0.19844280 0.17154245
[47,] -0.66513712 -0.19844280
[48,] -0.61409219 -0.66513712
[49,] -0.03208171 -0.61409219
[50,] 0.10457943 -0.03208171
[51,] 0.33461261 0.10457943
[52,] -0.03875575 0.33461261
[53,] -0.23541873 -0.03875575
[54,] -0.38209093 -0.23541873
[55,] -0.54210199 -0.38209093
[56,] -0.59543901 -0.54210199
[57,] -0.42549064 -0.59543901
[58,] 0.04451305 -0.42549064
[59,] 0.15785191 0.04451305
[60,] -0.01112528 0.15785191
[61,] -0.32917011 -0.01112528
[62,] -0.73255323 -0.32917011
[63,] -0.58255323 -0.73255323
[64,] 0.10414478 -0.58255323
[65,] 0.40748180 0.10414478
[66,] 0.62082067 0.40748180
[67,] 0.38077642 0.62082067
[68,] 0.10741727 0.38077642
[69,] 0.21740990 0.10741727
[70,] 0.12740252 0.21740990
[71,] 0.12071926 0.12740252
[72,] 0.07170888 0.12071926
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.18415953 -0.28608151
2 0.01251267 -0.18415953
3 0.06251267 0.01251267
4 0.12918855 0.06251267
5 0.27256982 0.12918855
6 0.04591975 0.27256982
7 -0.39406919 0.04591975
8 -0.50741727 -0.39406919
9 -0.23741358 -0.50741727
10 0.37257904 -0.23741358
11 0.66589578 0.37257904
12 0.77689646 0.66589578
13 0.33882950 0.77689646
14 -0.02450936 0.33882950
15 -0.31455361 -0.02450936
16 -0.34787773 -0.31455361
17 -0.42457389 -0.34787773
18 -0.25122397 -0.42457389
19 -0.31123503 -0.25122397
20 -0.04454993 -0.31123503
21 -0.05452411 -0.04454993
22 -0.08452042 -0.05452411
23 -0.19120369 -0.08452042
24 -0.14021407 -0.19120369
25 -0.11826996 -0.14021407
26 0.27845755 -0.11826996
27 0.28846861 0.27845755
28 -0.10492188 0.28846861
29 -0.04157380 -0.10492188
30 -0.04825706 -0.04157380
31 0.27176507 -0.04825706
32 0.49846123 0.27176507
33 0.32847598 0.49846123
34 -0.26153139 0.32847598
35 -0.08812615 -0.26153139
36 0.20290771 -0.08812615
37 0.32485182 0.20290771
38 0.36151295 0.32485182
39 0.21151295 0.36151295
40 0.25822203 0.21151295
41 0.02151480 0.25822203
42 0.01483154 0.02151480
43 0.59486473 0.01483154
44 0.54152770 0.59486473
45 0.17154245 0.54152770
46 -0.19844280 0.17154245
47 -0.66513712 -0.19844280
48 -0.61409219 -0.66513712
49 -0.03208171 -0.61409219
50 0.10457943 -0.03208171
51 0.33461261 0.10457943
52 -0.03875575 0.33461261
53 -0.23541873 -0.03875575
54 -0.38209093 -0.23541873
55 -0.54210199 -0.38209093
56 -0.59543901 -0.54210199
57 -0.42549064 -0.59543901
58 0.04451305 -0.42549064
59 0.15785191 0.04451305
60 -0.01112528 0.15785191
61 -0.32917011 -0.01112528
62 -0.73255323 -0.32917011
63 -0.58255323 -0.73255323
64 0.10414478 -0.58255323
65 0.40748180 0.10414478
66 0.62082067 0.40748180
67 0.38077642 0.62082067
68 0.10741727 0.38077642
69 0.21740990 0.10741727
70 0.12740252 0.21740990
71 0.12071926 0.12740252
72 0.07170888 0.12071926
> 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/7io401258203522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kpet1258203522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hr1r1258203522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zkrq1258203522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113qkv1258203522.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/12n47k1258203522.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/13wi981258203522.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/14a6oq1258203522.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/15ckqq1258203522.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/16cuwz1258203522.tab")
+ }
>
> system("convert tmp/16m9v1258203522.ps tmp/16m9v1258203522.png")
> system("convert tmp/2u8mj1258203522.ps tmp/2u8mj1258203522.png")
> system("convert tmp/3csb51258203522.ps tmp/3csb51258203522.png")
> system("convert tmp/4ldzm1258203522.ps tmp/4ldzm1258203522.png")
> system("convert tmp/5uqpr1258203522.ps tmp/5uqpr1258203522.png")
> system("convert tmp/6cmds1258203522.ps tmp/6cmds1258203522.png")
> system("convert tmp/7io401258203522.ps tmp/7io401258203522.png")
> system("convert tmp/8kpet1258203522.ps tmp/8kpet1258203522.png")
> system("convert tmp/9hr1r1258203522.ps tmp/9hr1r1258203522.png")
> system("convert tmp/10zkrq1258203522.ps tmp/10zkrq1258203522.png")
>
>
> proc.time()
user system elapsed
2.597 1.606 3.545