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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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(103.9
+ ,91.1
+ ,110.3
+ ,114.1
+ ,96.8
+ ,101.6
+ ,79.8
+ ,103.9
+ ,110.3
+ ,114.1
+ ,94.6
+ ,71.9
+ ,101.6
+ ,103.9
+ ,110.3
+ ,95.9
+ ,82.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,104.7
+ ,90.1
+ ,95.9
+ ,94.6
+ ,101.6
+ ,102.8
+ ,100.7
+ ,104.7
+ ,95.9
+ ,94.6
+ ,98.1
+ ,90.7
+ ,102.8
+ ,104.7
+ ,95.9
+ ,113.9
+ ,108.8
+ ,98.1
+ ,102.8
+ ,104.7
+ ,80.9
+ ,44.1
+ ,113.9
+ ,98.1
+ ,102.8
+ ,95.7
+ ,93.6
+ ,80.9
+ ,113.9
+ ,98.1
+ ,113.2
+ ,107.4
+ ,95.7
+ ,80.9
+ ,113.9
+ ,105.9
+ ,96.5
+ ,113.2
+ ,95.7
+ ,80.9
+ ,108.8
+ ,93.6
+ ,105.9
+ ,113.2
+ ,95.7
+ ,102.3
+ ,76.5
+ ,108.8
+ ,105.9
+ ,113.2
+ ,99
+ ,76.7
+ ,102.3
+ ,108.8
+ ,105.9
+ ,100.7
+ ,84
+ ,99
+ ,102.3
+ ,108.8
+ ,115.5
+ ,103.3
+ ,100.7
+ ,99
+ ,102.3
+ ,100.7
+ ,88.5
+ ,115.5
+ ,100.7
+ ,99
+ ,109.9
+ ,99
+ ,100.7
+ ,115.5
+ ,100.7
+ ,114.6
+ ,105.9
+ ,109.9
+ ,100.7
+ ,115.5
+ ,85.4
+ ,44.7
+ ,114.6
+ ,109.9
+ ,100.7
+ ,100.5
+ ,94
+ ,85.4
+ ,114.6
+ ,109.9
+ ,114.8
+ ,107.1
+ ,100.5
+ ,85.4
+ ,114.6
+ ,116.5
+ ,104.8
+ ,114.8
+ ,100.5
+ ,85.4
+ ,112.9
+ ,102.5
+ ,116.5
+ ,114.8
+ ,100.5
+ ,102
+ ,77.7
+ ,112.9
+ ,116.5
+ ,114.8
+ ,106
+ ,85.2
+ ,102
+ ,112.9
+ ,116.5
+ ,105.3
+ ,91.3
+ ,106
+ ,102
+ ,112.9
+ ,118.8
+ ,106.5
+ ,105.3
+ ,106
+ ,102
+ ,106.1
+ ,92.4
+ ,118.8
+ ,105.3
+ ,106
+ ,109.3
+ ,97.5
+ ,106.1
+ ,118.8
+ ,105.3
+ ,117.2
+ ,107
+ ,109.3
+ ,106.1
+ ,118.8
+ ,92.5
+ ,51.1
+ ,117.2
+ ,109.3
+ ,106.1
+ ,104.2
+ ,98.6
+ ,92.5
+ ,117.2
+ ,109.3
+ ,112.5
+ ,102.2
+ ,104.2
+ ,92.5
+ ,117.2
+ ,122.4
+ ,114.3
+ ,112.5
+ ,104.2
+ ,92.5
+ ,113.3
+ ,99.4
+ ,122.4
+ ,112.5
+ ,104.2
+ ,100
+ ,72.5
+ ,113.3
+ ,122.4
+ ,112.5
+ ,110.7
+ ,92.3
+ ,100
+ ,113.3
+ ,122.4
+ ,112.8
+ ,99.4
+ ,110.7
+ ,100
+ ,113.3
+ ,109.8
+ ,85.9
+ ,112.8
+ ,110.7
+ ,100
+ ,117.3
+ ,109.4
+ ,109.8
+ ,112.8
+ ,110.7
+ ,109.1
+ ,97.6
+ ,117.3
+ ,109.8
+ ,112.8
+ ,115.9
+ ,104.7
+ ,109.1
+ ,117.3
+ ,109.8
+ ,96
+ ,56.9
+ ,115.9
+ ,109.1
+ ,117.3
+ ,99.8
+ ,86.7
+ ,96
+ ,115.9
+ ,109.1
+ ,116.8
+ ,108.5
+ ,99.8
+ ,96
+ ,115.9
+ ,115.7
+ ,103.4
+ ,116.8
+ ,99.8
+ ,96
+ ,99.4
+ ,86.2
+ ,115.7
+ ,116.8
+ ,99.8
+ ,94.3
+ ,71
+ ,99.4
+ ,115.7
+ ,116.8
+ ,91
+ ,75.9
+ ,94.3
+ ,99.4
+ ,115.7
+ ,93.2
+ ,87.1
+ ,91
+ ,94.3
+ ,99.4
+ ,103.1
+ ,102
+ ,93.2
+ ,91
+ ,94.3
+ ,94.1
+ ,88.5
+ ,103.1
+ ,93.2
+ ,91
+ ,91.8
+ ,87.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,102.7
+ ,100.8
+ ,91.8
+ ,94.1
+ ,103.1
+ ,82.6
+ ,50.6
+ ,102.7
+ ,91.8
+ ,94.1)
+ ,dim=c(5
+ ,57)
+ ,dimnames=list(c('Totind'
+ ,'Bouw'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3')
+ ,1:57))
> y <- array(NA,dim=c(5,57),dimnames=list(c('Totind','Bouw','Yt-1','Yt-2','Yt-3'),1:57))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Totind Bouw Yt-1 Yt-2 Yt-3 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 103.9 91.1 110.3 114.1 96.8 1 0 0 0 0 0 0 0 0 0 0 1
2 101.6 79.8 103.9 110.3 114.1 0 1 0 0 0 0 0 0 0 0 0 2
3 94.6 71.9 101.6 103.9 110.3 0 0 1 0 0 0 0 0 0 0 0 3
4 95.9 82.9 94.6 101.6 103.9 0 0 0 1 0 0 0 0 0 0 0 4
5 104.7 90.1 95.9 94.6 101.6 0 0 0 0 1 0 0 0 0 0 0 5
6 102.8 100.7 104.7 95.9 94.6 0 0 0 0 0 1 0 0 0 0 0 6
7 98.1 90.7 102.8 104.7 95.9 0 0 0 0 0 0 1 0 0 0 0 7
8 113.9 108.8 98.1 102.8 104.7 0 0 0 0 0 0 0 1 0 0 0 8
9 80.9 44.1 113.9 98.1 102.8 0 0 0 0 0 0 0 0 1 0 0 9
10 95.7 93.6 80.9 113.9 98.1 0 0 0 0 0 0 0 0 0 1 0 10
11 113.2 107.4 95.7 80.9 113.9 0 0 0 0 0 0 0 0 0 0 1 11
12 105.9 96.5 113.2 95.7 80.9 0 0 0 0 0 0 0 0 0 0 0 12
13 108.8 93.6 105.9 113.2 95.7 1 0 0 0 0 0 0 0 0 0 0 13
14 102.3 76.5 108.8 105.9 113.2 0 1 0 0 0 0 0 0 0 0 0 14
15 99.0 76.7 102.3 108.8 105.9 0 0 1 0 0 0 0 0 0 0 0 15
16 100.7 84.0 99.0 102.3 108.8 0 0 0 1 0 0 0 0 0 0 0 16
17 115.5 103.3 100.7 99.0 102.3 0 0 0 0 1 0 0 0 0 0 0 17
18 100.7 88.5 115.5 100.7 99.0 0 0 0 0 0 1 0 0 0 0 0 18
19 109.9 99.0 100.7 115.5 100.7 0 0 0 0 0 0 1 0 0 0 0 19
20 114.6 105.9 109.9 100.7 115.5 0 0 0 0 0 0 0 1 0 0 0 20
21 85.4 44.7 114.6 109.9 100.7 0 0 0 0 0 0 0 0 1 0 0 21
22 100.5 94.0 85.4 114.6 109.9 0 0 0 0 0 0 0 0 0 1 0 22
23 114.8 107.1 100.5 85.4 114.6 0 0 0 0 0 0 0 0 0 0 1 23
24 116.5 104.8 114.8 100.5 85.4 0 0 0 0 0 0 0 0 0 0 0 24
25 112.9 102.5 116.5 114.8 100.5 1 0 0 0 0 0 0 0 0 0 0 25
26 102.0 77.7 112.9 116.5 114.8 0 1 0 0 0 0 0 0 0 0 0 26
27 106.0 85.2 102.0 112.9 116.5 0 0 1 0 0 0 0 0 0 0 0 27
28 105.3 91.3 106.0 102.0 112.9 0 0 0 1 0 0 0 0 0 0 0 28
29 118.8 106.5 105.3 106.0 102.0 0 0 0 0 1 0 0 0 0 0 0 29
30 106.1 92.4 118.8 105.3 106.0 0 0 0 0 0 1 0 0 0 0 0 30
31 109.3 97.5 106.1 118.8 105.3 0 0 0 0 0 0 1 0 0 0 0 31
32 117.2 107.0 109.3 106.1 118.8 0 0 0 0 0 0 0 1 0 0 0 32
33 92.5 51.1 117.2 109.3 106.1 0 0 0 0 0 0 0 0 1 0 0 33
34 104.2 98.6 92.5 117.2 109.3 0 0 0 0 0 0 0 0 0 1 0 34
35 112.5 102.2 104.2 92.5 117.2 0 0 0 0 0 0 0 0 0 0 1 35
36 122.4 114.3 112.5 104.2 92.5 0 0 0 0 0 0 0 0 0 0 0 36
37 113.3 99.4 122.4 112.5 104.2 1 0 0 0 0 0 0 0 0 0 0 37
38 100.0 72.5 113.3 122.4 112.5 0 1 0 0 0 0 0 0 0 0 0 38
39 110.7 92.3 100.0 113.3 122.4 0 0 1 0 0 0 0 0 0 0 0 39
40 112.8 99.4 110.7 100.0 113.3 0 0 0 1 0 0 0 0 0 0 0 40
41 109.8 85.9 112.8 110.7 100.0 0 0 0 0 1 0 0 0 0 0 0 41
42 117.3 109.4 109.8 112.8 110.7 0 0 0 0 0 1 0 0 0 0 0 42
43 109.1 97.6 117.3 109.8 112.8 0 0 0 0 0 0 1 0 0 0 0 43
44 115.9 104.7 109.1 117.3 109.8 0 0 0 0 0 0 0 1 0 0 0 44
45 96.0 56.9 115.9 109.1 117.3 0 0 0 0 0 0 0 0 1 0 0 45
46 99.8 86.7 96.0 115.9 109.1 0 0 0 0 0 0 0 0 0 1 0 46
47 116.8 108.5 99.8 96.0 115.9 0 0 0 0 0 0 0 0 0 0 1 47
48 115.7 103.4 116.8 99.8 96.0 0 0 0 0 0 0 0 0 0 0 0 48
49 99.4 86.2 115.7 116.8 99.8 1 0 0 0 0 0 0 0 0 0 0 49
50 94.3 71.0 99.4 115.7 116.8 0 1 0 0 0 0 0 0 0 0 0 50
51 91.0 75.9 94.3 99.4 115.7 0 0 1 0 0 0 0 0 0 0 0 51
52 93.2 87.1 91.0 94.3 99.4 0 0 0 1 0 0 0 0 0 0 0 52
53 103.1 102.0 93.2 91.0 94.3 0 0 0 0 1 0 0 0 0 0 0 53
54 94.1 88.5 103.1 93.2 91.0 0 0 0 0 0 1 0 0 0 0 0 54
55 91.8 87.8 94.1 103.1 93.2 0 0 0 0 0 0 1 0 0 0 0 55
56 102.7 100.8 91.8 94.1 103.1 0 0 0 0 0 0 0 1 0 0 0 56
57 82.6 50.6 102.7 91.8 94.1 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Bouw `Yt-1` `Yt-2` `Yt-3` M1
-24.05167 0.67039 0.25400 0.26998 0.16064 -6.37661
M2 M3 M4 M5 M6 M7
-1.88111 -1.19157 -2.50634 1.38914 -6.26009 -6.86827
M8 M9 M10 M11 t
-4.52699 6.45482 -7.94932 0.51710 -0.04483
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.630027 -0.994329 -0.008976 0.930098 4.229584
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.05167 6.17579 -3.895 0.000365 ***
Bouw 0.67039 0.04824 13.897 < 2e-16 ***
`Yt-1` 0.25400 0.05667 4.482 6.06e-05 ***
`Yt-2` 0.26998 0.05135 5.258 5.19e-06 ***
`Yt-3` 0.16064 0.06655 2.414 0.020462 *
M1 -6.37661 1.71596 -3.716 0.000619 ***
M2 -1.88111 3.21020 -0.586 0.561183
M3 -1.19157 3.24308 -0.367 0.715244
M4 -2.50634 2.56130 -0.979 0.333686
M5 1.38914 1.89124 0.735 0.466922
M6 -6.26009 1.66124 -3.768 0.000531 ***
M7 -6.86827 2.04321 -3.362 0.001716 **
M8 -4.52699 2.17372 -2.083 0.043727 *
M9 6.45482 3.53398 1.827 0.075245 .
M10 -7.94932 3.09398 -2.569 0.014025 *
M11 0.51710 2.66372 0.194 0.847058
t -0.04483 0.01455 -3.082 0.003713 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.747 on 40 degrees of freedom
Multiple R-squared: 0.9761, Adjusted R-squared: 0.9665
F-statistic: 102.1 on 16 and 40 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,] 0.22573200 0.4514640 0.7742680
[2,] 0.16156478 0.3231296 0.8384352
[3,] 0.09003874 0.1800775 0.9099613
[4,] 0.09443937 0.1888787 0.9055606
[5,] 0.07536843 0.1507369 0.9246316
[6,] 0.15565609 0.3113122 0.8443439
[7,] 0.44615367 0.8923073 0.5538463
[8,] 0.51810158 0.9637968 0.4818984
[9,] 0.39944718 0.7988944 0.6005528
[10,] 0.30341439 0.6068288 0.6965856
[11,] 0.22597470 0.4519494 0.7740253
[12,] 0.19723774 0.3944755 0.8027623
[13,] 0.14659187 0.2931837 0.8534081
[14,] 0.10050674 0.2010135 0.8994933
[15,] 0.08281197 0.1656239 0.9171880
[16,] 0.11478466 0.2295693 0.8852153
[17,] 0.11139114 0.2227823 0.8886089
[18,] 0.05580666 0.1116133 0.9441933
> postscript(file="/var/www/html/rcomp/tmp/1kgq51258732249.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2eol61258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wkl41258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xc7a1258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tfl51258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 57
Frequency = 1
1 2 3 4 5 6
-1.069683629 -0.372519572 0.201325947 -1.086306183 0.965370801 -1.808374380
7 8 9 10 11 12
-1.253532074 0.409129481 -2.592815013 -1.656746035 0.782319345 -1.788100344
13 14 15 16 17 18
4.229583878 3.165605966 1.127539954 1.420516396 0.934661703 0.062423755
19 20 21 22 23 24
2.366770819 -0.573916597 -0.983292001 0.185540537 0.574850412 1.360469227
25 26 27 28 29 30
-0.994329491 -1.561089292 0.233717958 -0.690974117 -0.382672809 0.181301190
31 32 33 34 35 36
0.308851010 -0.008976487 0.998324681 -1.069237422 -1.176592704 -0.125515905
37 38 39 40 41 42
0.549826364 -0.862115564 0.164159847 1.198749242 2.112666874 0.028813265
43 44 45 46 47 48
-1.040000366 -0.756334519 -0.266926785 2.540442920 -0.180577053 0.553147022
49 50 51 52 53 54
-2.715397122 -0.369881538 -1.726743706 -0.841985340 -3.630026570 1.535836169
55 56 57
-0.382089389 0.930098121 2.844709119
> postscript(file="/var/www/html/rcomp/tmp/6uchk1258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.069683629 NA
1 -0.372519572 -1.069683629
2 0.201325947 -0.372519572
3 -1.086306183 0.201325947
4 0.965370801 -1.086306183
5 -1.808374380 0.965370801
6 -1.253532074 -1.808374380
7 0.409129481 -1.253532074
8 -2.592815013 0.409129481
9 -1.656746035 -2.592815013
10 0.782319345 -1.656746035
11 -1.788100344 0.782319345
12 4.229583878 -1.788100344
13 3.165605966 4.229583878
14 1.127539954 3.165605966
15 1.420516396 1.127539954
16 0.934661703 1.420516396
17 0.062423755 0.934661703
18 2.366770819 0.062423755
19 -0.573916597 2.366770819
20 -0.983292001 -0.573916597
21 0.185540537 -0.983292001
22 0.574850412 0.185540537
23 1.360469227 0.574850412
24 -0.994329491 1.360469227
25 -1.561089292 -0.994329491
26 0.233717958 -1.561089292
27 -0.690974117 0.233717958
28 -0.382672809 -0.690974117
29 0.181301190 -0.382672809
30 0.308851010 0.181301190
31 -0.008976487 0.308851010
32 0.998324681 -0.008976487
33 -1.069237422 0.998324681
34 -1.176592704 -1.069237422
35 -0.125515905 -1.176592704
36 0.549826364 -0.125515905
37 -0.862115564 0.549826364
38 0.164159847 -0.862115564
39 1.198749242 0.164159847
40 2.112666874 1.198749242
41 0.028813265 2.112666874
42 -1.040000366 0.028813265
43 -0.756334519 -1.040000366
44 -0.266926785 -0.756334519
45 2.540442920 -0.266926785
46 -0.180577053 2.540442920
47 0.553147022 -0.180577053
48 -2.715397122 0.553147022
49 -0.369881538 -2.715397122
50 -1.726743706 -0.369881538
51 -0.841985340 -1.726743706
52 -3.630026570 -0.841985340
53 1.535836169 -3.630026570
54 -0.382089389 1.535836169
55 0.930098121 -0.382089389
56 2.844709119 0.930098121
57 NA 2.844709119
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.372519572 -1.069683629
[2,] 0.201325947 -0.372519572
[3,] -1.086306183 0.201325947
[4,] 0.965370801 -1.086306183
[5,] -1.808374380 0.965370801
[6,] -1.253532074 -1.808374380
[7,] 0.409129481 -1.253532074
[8,] -2.592815013 0.409129481
[9,] -1.656746035 -2.592815013
[10,] 0.782319345 -1.656746035
[11,] -1.788100344 0.782319345
[12,] 4.229583878 -1.788100344
[13,] 3.165605966 4.229583878
[14,] 1.127539954 3.165605966
[15,] 1.420516396 1.127539954
[16,] 0.934661703 1.420516396
[17,] 0.062423755 0.934661703
[18,] 2.366770819 0.062423755
[19,] -0.573916597 2.366770819
[20,] -0.983292001 -0.573916597
[21,] 0.185540537 -0.983292001
[22,] 0.574850412 0.185540537
[23,] 1.360469227 0.574850412
[24,] -0.994329491 1.360469227
[25,] -1.561089292 -0.994329491
[26,] 0.233717958 -1.561089292
[27,] -0.690974117 0.233717958
[28,] -0.382672809 -0.690974117
[29,] 0.181301190 -0.382672809
[30,] 0.308851010 0.181301190
[31,] -0.008976487 0.308851010
[32,] 0.998324681 -0.008976487
[33,] -1.069237422 0.998324681
[34,] -1.176592704 -1.069237422
[35,] -0.125515905 -1.176592704
[36,] 0.549826364 -0.125515905
[37,] -0.862115564 0.549826364
[38,] 0.164159847 -0.862115564
[39,] 1.198749242 0.164159847
[40,] 2.112666874 1.198749242
[41,] 0.028813265 2.112666874
[42,] -1.040000366 0.028813265
[43,] -0.756334519 -1.040000366
[44,] -0.266926785 -0.756334519
[45,] 2.540442920 -0.266926785
[46,] -0.180577053 2.540442920
[47,] 0.553147022 -0.180577053
[48,] -2.715397122 0.553147022
[49,] -0.369881538 -2.715397122
[50,] -1.726743706 -0.369881538
[51,] -0.841985340 -1.726743706
[52,] -3.630026570 -0.841985340
[53,] 1.535836169 -3.630026570
[54,] -0.382089389 1.535836169
[55,] 0.930098121 -0.382089389
[56,] 2.844709119 0.930098121
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.372519572 -1.069683629
2 0.201325947 -0.372519572
3 -1.086306183 0.201325947
4 0.965370801 -1.086306183
5 -1.808374380 0.965370801
6 -1.253532074 -1.808374380
7 0.409129481 -1.253532074
8 -2.592815013 0.409129481
9 -1.656746035 -2.592815013
10 0.782319345 -1.656746035
11 -1.788100344 0.782319345
12 4.229583878 -1.788100344
13 3.165605966 4.229583878
14 1.127539954 3.165605966
15 1.420516396 1.127539954
16 0.934661703 1.420516396
17 0.062423755 0.934661703
18 2.366770819 0.062423755
19 -0.573916597 2.366770819
20 -0.983292001 -0.573916597
21 0.185540537 -0.983292001
22 0.574850412 0.185540537
23 1.360469227 0.574850412
24 -0.994329491 1.360469227
25 -1.561089292 -0.994329491
26 0.233717958 -1.561089292
27 -0.690974117 0.233717958
28 -0.382672809 -0.690974117
29 0.181301190 -0.382672809
30 0.308851010 0.181301190
31 -0.008976487 0.308851010
32 0.998324681 -0.008976487
33 -1.069237422 0.998324681
34 -1.176592704 -1.069237422
35 -0.125515905 -1.176592704
36 0.549826364 -0.125515905
37 -0.862115564 0.549826364
38 0.164159847 -0.862115564
39 1.198749242 0.164159847
40 2.112666874 1.198749242
41 0.028813265 2.112666874
42 -1.040000366 0.028813265
43 -0.756334519 -1.040000366
44 -0.266926785 -0.756334519
45 2.540442920 -0.266926785
46 -0.180577053 2.540442920
47 0.553147022 -0.180577053
48 -2.715397122 0.553147022
49 -0.369881538 -2.715397122
50 -1.726743706 -0.369881538
51 -0.841985340 -1.726743706
52 -3.630026570 -0.841985340
53 1.535836169 -3.630026570
54 -0.382089389 1.535836169
55 0.930098121 -0.382089389
56 2.844709119 0.930098121
> 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/7ydg81258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8yl6p1258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97x8t1258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10slk61258732249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hahl1258732249.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/12mb1j1258732249.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/13ri0z1258732249.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/146tlw1258732249.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/1573561258732249.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/168hct1258732249.tab")
+ }
>
> system("convert tmp/1kgq51258732249.ps tmp/1kgq51258732249.png")
> system("convert tmp/2eol61258732249.ps tmp/2eol61258732249.png")
> system("convert tmp/3wkl41258732249.ps tmp/3wkl41258732249.png")
> system("convert tmp/4xc7a1258732249.ps tmp/4xc7a1258732249.png")
> system("convert tmp/5tfl51258732249.ps tmp/5tfl51258732249.png")
> system("convert tmp/6uchk1258732249.ps tmp/6uchk1258732249.png")
> system("convert tmp/7ydg81258732249.ps tmp/7ydg81258732249.png")
> system("convert tmp/8yl6p1258732249.ps tmp/8yl6p1258732249.png")
> system("convert tmp/97x8t1258732249.ps tmp/97x8t1258732249.png")
> system("convert tmp/10slk61258732249.ps tmp/10slk61258732249.png")
>
>
> proc.time()
user system elapsed
2.319 1.544 2.758