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 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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(101.7
+ ,86.2
+ ,102.8
+ ,105.6
+ ,96.9
+ ,97.6
+ ,104.2
+ ,88.8
+ ,101.7
+ ,102.8
+ ,105.6
+ ,96.9
+ ,92.7
+ ,89.6
+ ,104.2
+ ,101.7
+ ,102.8
+ ,105.6
+ ,91.9
+ ,87.8
+ ,92.7
+ ,104.2
+ ,101.7
+ ,102.8
+ ,106.5
+ ,88.3
+ ,91.9
+ ,92.7
+ ,104.2
+ ,101.7
+ ,112.3
+ ,88.6
+ ,106.5
+ ,91.9
+ ,92.7
+ ,104.2
+ ,102.8
+ ,91
+ ,112.3
+ ,106.5
+ ,91.9
+ ,92.7
+ ,96.5
+ ,91.5
+ ,102.8
+ ,112.3
+ ,106.5
+ ,91.9
+ ,101
+ ,95.4
+ ,96.5
+ ,102.8
+ ,112.3
+ ,106.5
+ ,98.9
+ ,98.7
+ ,101
+ ,96.5
+ ,102.8
+ ,112.3
+ ,105.1
+ ,99.9
+ ,98.9
+ ,101
+ ,96.5
+ ,102.8
+ ,103
+ ,98.6
+ ,105.1
+ ,98.9
+ ,101
+ ,96.5
+ ,99
+ ,100.3
+ ,103
+ ,105.1
+ ,98.9
+ ,101
+ ,104.3
+ ,100.2
+ ,99
+ ,103
+ ,105.1
+ ,98.9
+ ,94.6
+ ,100.4
+ ,104.3
+ ,99
+ ,103
+ ,105.1
+ ,90.4
+ ,101.4
+ ,94.6
+ ,104.3
+ ,99
+ ,103
+ ,108.9
+ ,103
+ ,90.4
+ ,94.6
+ ,104.3
+ ,99
+ ,111.4
+ ,109.1
+ ,108.9
+ ,90.4
+ ,94.6
+ ,104.3
+ ,100.8
+ ,111.4
+ ,111.4
+ ,108.9
+ ,90.4
+ ,94.6
+ ,102.5
+ ,114.1
+ ,100.8
+ ,111.4
+ ,108.9
+ ,90.4
+ ,98.2
+ ,121.8
+ ,102.5
+ ,100.8
+ ,111.4
+ ,108.9
+ ,98.7
+ ,127.6
+ ,98.2
+ ,102.5
+ ,100.8
+ ,111.4
+ ,113.3
+ ,129.9
+ ,98.7
+ ,98.2
+ ,102.5
+ ,100.8
+ ,104.6
+ ,128
+ ,113.3
+ ,98.7
+ ,98.2
+ ,102.5
+ ,99.3
+ ,123.5
+ ,104.6
+ ,113.3
+ ,98.7
+ ,98.2
+ ,111.8
+ ,124
+ ,99.3
+ ,104.6
+ ,113.3
+ ,98.7
+ ,97.3
+ ,127.4
+ ,111.8
+ ,99.3
+ ,104.6
+ ,113.3
+ ,97.7
+ ,127.6
+ ,97.3
+ ,111.8
+ ,99.3
+ ,104.6
+ ,115.6
+ ,128.4
+ ,97.7
+ ,97.3
+ ,111.8
+ ,99.3
+ ,111.9
+ ,131.4
+ ,115.6
+ ,97.7
+ ,97.3
+ ,111.8
+ ,107
+ ,135.1
+ ,111.9
+ ,115.6
+ ,97.7
+ ,97.3
+ ,107.1
+ ,134
+ ,107
+ ,111.9
+ ,115.6
+ ,97.7
+ ,100.6
+ ,144.5
+ ,107.1
+ ,107
+ ,111.9
+ ,115.6
+ ,99.2
+ ,147.3
+ ,100.6
+ ,107.1
+ ,107
+ ,111.9
+ ,108.4
+ ,150.9
+ ,99.2
+ ,100.6
+ ,107.1
+ ,107
+ ,103
+ ,148.7
+ ,108.4
+ ,99.2
+ ,100.6
+ ,107.1
+ ,99.8
+ ,141.4
+ ,103
+ ,108.4
+ ,99.2
+ ,100.6
+ ,115
+ ,138.9
+ ,99.8
+ ,103
+ ,108.4
+ ,99.2
+ ,90.8
+ ,139.8
+ ,115
+ ,99.8
+ ,103
+ ,108.4
+ ,95.9
+ ,145.6
+ ,90.8
+ ,115
+ ,99.8
+ ,103
+ ,114.4
+ ,147.9
+ ,95.9
+ ,90.8
+ ,115
+ ,99.8
+ ,108.2
+ ,148.5
+ ,114.4
+ ,95.9
+ ,90.8
+ ,115
+ ,112.6
+ ,151.1
+ ,108.2
+ ,114.4
+ ,95.9
+ ,90.8
+ ,109.1
+ ,157.5
+ ,112.6
+ ,108.2
+ ,114.4
+ ,95.9
+ ,105
+ ,167.5
+ ,109.1
+ ,112.6
+ ,108.2
+ ,114.4
+ ,105
+ ,172.3
+ ,105
+ ,109.1
+ ,112.6
+ ,108.2
+ ,118.5
+ ,173.5
+ ,105
+ ,105
+ ,109.1
+ ,112.6
+ ,103.7
+ ,187.5
+ ,118.5
+ ,105
+ ,105
+ ,109.1
+ ,112.5
+ ,205.5
+ ,103.7
+ ,118.5
+ ,105
+ ,105
+ ,116.6
+ ,195.1
+ ,112.5
+ ,103.7
+ ,118.5
+ ,105
+ ,96.6
+ ,204.5
+ ,116.6
+ ,112.5
+ ,103.7
+ ,118.5
+ ,101.9
+ ,204.5
+ ,96.6
+ ,116.6
+ ,112.5
+ ,103.7
+ ,116.5
+ ,201.7
+ ,101.9
+ ,96.6
+ ,116.6
+ ,112.5
+ ,119.3
+ ,207
+ ,116.5
+ ,101.9
+ ,96.6
+ ,116.6
+ ,115.4
+ ,206.6
+ ,119.3
+ ,116.5
+ ,101.9
+ ,96.6
+ ,108.5
+ ,210.6
+ ,115.4
+ ,119.3
+ ,116.5
+ ,101.9
+ ,111.5
+ ,211.1
+ ,108.5
+ ,115.4
+ ,119.3
+ ,116.5
+ ,108.8
+ ,215
+ ,111.5
+ ,108.5
+ ,115.4
+ ,119.3
+ ,121.8
+ ,223.9
+ ,108.8
+ ,111.5
+ ,108.5
+ ,115.4
+ ,109.6
+ ,238.2
+ ,121.8
+ ,108.8
+ ,111.5
+ ,108.5
+ ,112.2
+ ,238.9
+ ,109.6
+ ,121.8
+ ,108.8
+ ,111.5
+ ,119.6
+ ,229.6
+ ,112.2
+ ,109.6
+ ,121.8
+ ,108.8
+ ,104.1
+ ,232.2
+ ,119.6
+ ,112.2
+ ,109.6
+ ,121.8
+ ,105.3
+ ,222.1
+ ,104.1
+ ,119.6
+ ,112.2
+ ,109.6
+ ,115
+ ,221.6
+ ,105.3
+ ,104.1
+ ,119.6
+ ,112.2
+ ,124.1
+ ,227.3
+ ,115
+ ,105.3
+ ,104.1
+ ,119.6
+ ,116.8
+ ,221
+ ,124.1
+ ,115
+ ,105.3
+ ,104.1
+ ,107.5
+ ,213.6
+ ,116.8
+ ,124.1
+ ,115
+ ,105.3
+ ,115.6
+ ,243.4
+ ,107.5
+ ,116.8
+ ,124.1
+ ,115)
+ ,dim=c(6
+ ,69)
+ ,dimnames=list(c('tot_indus'
+ ,'prijsindex'
+ ,'y(t-1)'
+ ,'y(t-2)'
+ ,'y(t-3)'
+ ,'y(t-4)')
+ ,1:69))
> y <- array(NA,dim=c(6,69),dimnames=list(c('tot_indus','prijsindex','y(t-1)','y(t-2)','y(t-3)','y(t-4)'),1:69))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
tot_indus prijsindex y(t-1) y(t-2) y(t-3) y(t-4)
1 101.7 86.2 102.8 105.6 96.9 97.6
2 104.2 88.8 101.7 102.8 105.6 96.9
3 92.7 89.6 104.2 101.7 102.8 105.6
4 91.9 87.8 92.7 104.2 101.7 102.8
5 106.5 88.3 91.9 92.7 104.2 101.7
6 112.3 88.6 106.5 91.9 92.7 104.2
7 102.8 91.0 112.3 106.5 91.9 92.7
8 96.5 91.5 102.8 112.3 106.5 91.9
9 101.0 95.4 96.5 102.8 112.3 106.5
10 98.9 98.7 101.0 96.5 102.8 112.3
11 105.1 99.9 98.9 101.0 96.5 102.8
12 103.0 98.6 105.1 98.9 101.0 96.5
13 99.0 100.3 103.0 105.1 98.9 101.0
14 104.3 100.2 99.0 103.0 105.1 98.9
15 94.6 100.4 104.3 99.0 103.0 105.1
16 90.4 101.4 94.6 104.3 99.0 103.0
17 108.9 103.0 90.4 94.6 104.3 99.0
18 111.4 109.1 108.9 90.4 94.6 104.3
19 100.8 111.4 111.4 108.9 90.4 94.6
20 102.5 114.1 100.8 111.4 108.9 90.4
21 98.2 121.8 102.5 100.8 111.4 108.9
22 98.7 127.6 98.2 102.5 100.8 111.4
23 113.3 129.9 98.7 98.2 102.5 100.8
24 104.6 128.0 113.3 98.7 98.2 102.5
25 99.3 123.5 104.6 113.3 98.7 98.2
26 111.8 124.0 99.3 104.6 113.3 98.7
27 97.3 127.4 111.8 99.3 104.6 113.3
28 97.7 127.6 97.3 111.8 99.3 104.6
29 115.6 128.4 97.7 97.3 111.8 99.3
30 111.9 131.4 115.6 97.7 97.3 111.8
31 107.0 135.1 111.9 115.6 97.7 97.3
32 107.1 134.0 107.0 111.9 115.6 97.7
33 100.6 144.5 107.1 107.0 111.9 115.6
34 99.2 147.3 100.6 107.1 107.0 111.9
35 108.4 150.9 99.2 100.6 107.1 107.0
36 103.0 148.7 108.4 99.2 100.6 107.1
37 99.8 141.4 103.0 108.4 99.2 100.6
38 115.0 138.9 99.8 103.0 108.4 99.2
39 90.8 139.8 115.0 99.8 103.0 108.4
40 95.9 145.6 90.8 115.0 99.8 103.0
41 114.4 147.9 95.9 90.8 115.0 99.8
42 108.2 148.5 114.4 95.9 90.8 115.0
43 112.6 151.1 108.2 114.4 95.9 90.8
44 109.1 157.5 112.6 108.2 114.4 95.9
45 105.0 167.5 109.1 112.6 108.2 114.4
46 105.0 172.3 105.0 109.1 112.6 108.2
47 118.5 173.5 105.0 105.0 109.1 112.6
48 103.7 187.5 118.5 105.0 105.0 109.1
49 112.5 205.5 103.7 118.5 105.0 105.0
50 116.6 195.1 112.5 103.7 118.5 105.0
51 96.6 204.5 116.6 112.5 103.7 118.5
52 101.9 204.5 96.6 116.6 112.5 103.7
53 116.5 201.7 101.9 96.6 116.6 112.5
54 119.3 207.0 116.5 101.9 96.6 116.6
55 115.4 206.6 119.3 116.5 101.9 96.6
56 108.5 210.6 115.4 119.3 116.5 101.9
57 111.5 211.1 108.5 115.4 119.3 116.5
58 108.8 215.0 111.5 108.5 115.4 119.3
59 121.8 223.9 108.8 111.5 108.5 115.4
60 109.6 238.2 121.8 108.8 111.5 108.5
61 112.2 238.9 109.6 121.8 108.8 111.5
62 119.6 229.6 112.2 109.6 121.8 108.8
63 104.1 232.2 119.6 112.2 109.6 121.8
64 105.3 222.1 104.1 119.6 112.2 109.6
65 115.0 221.6 105.3 104.1 119.6 112.2
66 124.1 227.3 115.0 105.3 104.1 119.6
67 116.8 221.0 124.1 115.0 105.3 104.1
68 107.5 213.6 116.8 124.1 115.0 105.3
69 115.6 243.4 107.5 116.8 124.1 115.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) prijsindex `y(t-1)` `y(t-2)` `y(t-3)` `y(t-4)`
209.29898 0.20066 -0.04310 -0.65254 -0.03028 -0.53534
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.321 -3.248 0.351 3.627 11.012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 209.29898 26.09173 8.022 3.27e-11 ***
prijsindex 0.20066 0.02794 7.181 9.68e-10 ***
`y(t-1)` -0.04310 0.11002 -0.392 0.697
`y(t-2)` -0.65254 0.11217 -5.817 2.17e-07 ***
`y(t-3)` -0.03028 0.11095 -0.273 0.786
`y(t-4)` -0.53534 0.11706 -4.573 2.30e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.358 on 63 degrees of freedom
Multiple R-squared: 0.5756, Adjusted R-squared: 0.5419
F-statistic: 17.09 on 5 and 63 DF, p-value: 1.203e-10
> 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,] 0.50742623 0.98514755 0.4925738
[2,] 0.35746383 0.71492767 0.6425362
[3,] 0.24170810 0.48341621 0.7582919
[4,] 0.34704305 0.69408610 0.6529569
[5,] 0.23673373 0.47346745 0.7632663
[6,] 0.16736261 0.33472522 0.8326374
[7,] 0.24478756 0.48957513 0.7552124
[8,] 0.29885097 0.59770194 0.7011490
[9,] 0.21868218 0.43736437 0.7813178
[10,] 0.15682178 0.31364356 0.8431782
[11,] 0.10792430 0.21584860 0.8920757
[12,] 0.07504169 0.15008338 0.9249583
[13,] 0.04901281 0.09802563 0.9509872
[14,] 0.03942729 0.07885458 0.9605727
[15,] 0.03378434 0.06756868 0.9662157
[16,] 0.02612607 0.05225215 0.9738739
[17,] 0.01873906 0.03747812 0.9812609
[18,] 0.02871204 0.05742407 0.9712880
[19,] 0.01990067 0.03980134 0.9800993
[20,] 0.01420872 0.02841744 0.9857913
[21,] 0.01073537 0.02147075 0.9892646
[22,] 0.02659286 0.05318572 0.9734071
[23,] 0.03478948 0.06957896 0.9652105
[24,] 0.02995070 0.05990141 0.9700493
[25,] 0.02208426 0.04416853 0.9779157
[26,] 0.01428773 0.02857546 0.9857123
[27,] 0.00920689 0.01841378 0.9907931
[28,] 0.01362311 0.02724621 0.9863769
[29,] 0.01264365 0.02528731 0.9873563
[30,] 0.01502332 0.03004665 0.9849767
[31,] 0.17822489 0.35644978 0.8217751
[32,] 0.17695490 0.35390980 0.8230451
[33,] 0.16515126 0.33030252 0.8348487
[34,] 0.13656959 0.27313918 0.8634304
[35,] 0.11388035 0.22776070 0.8861197
[36,] 0.08352894 0.16705787 0.9164711
[37,] 0.07990674 0.15981348 0.9200933
[38,] 0.05626079 0.11252158 0.9437392
[39,] 0.16777511 0.33555021 0.8322249
[40,] 0.18749060 0.37498120 0.8125094
[41,] 0.15745194 0.31490388 0.8425481
[42,] 0.11835597 0.23671194 0.8816440
[43,] 0.24951267 0.49902534 0.7504873
[44,] 0.38490676 0.76981352 0.6150932
[45,] 0.33501592 0.67003183 0.6649841
[46,] 0.28013054 0.56026108 0.7198695
[47,] 0.19943745 0.39887490 0.8005626
[48,] 0.13505604 0.27011208 0.8649440
[49,] 0.10693810 0.21387619 0.8930619
[50,] 0.07055409 0.14110817 0.9294459
[51,] 0.08364003 0.16728007 0.9163600
[52,] 0.24690238 0.49380475 0.7530976
> postscript(file="/var/www/html/rcomp/tmp/1iz7a1258645213.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/257ku1258645214.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qvz41258645214.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pn7j1258645214.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5anv51258645214.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 = 69
Frequency = 1
1 2 3 4 5 6
3.6267934 3.6192457 -4.0786533 -4.9140608 1.5337298 8.3709466
7 8 9 10 11 12
1.9858530 -1.0254163 4.2128879 0.3509868 3.8796421 -2.2989754
13 14 15 16 17 18
-0.3394179 2.5014170 -6.3649182 -8.9705377 0.7168519 2.5931688
19 20 21 22 23 24
-1.6085293 -0.9641072 -3.6733701 -2.3958197 3.3351791 -3.2481159
25 26 27 28 29 30
-0.7798722 6.4239863 -4.1254101 -1.0517006 4.7843622 7.7676423
31 32 33 34 35 36
5.8959168 4.3471613 2.0176434 -2.2882353 -0.7325952 -6.3514349
37 38 39 40 41 42
-5.8381062 5.7309777 -15.3209665 -5.4969777 -4.2830000 0.9263708
43 44 45 46 47 48
3.8086626 -1.5412568 4.7885199 -1.8211249 11.0121803 -8.0129574
49 50 51 52 53 54
3.1516952 0.4690011 -8.7191532 -9.2623569 -2.0877678 5.3258324
55 56 57 58 59 60
0.6075943 -2.1566557 5.8014152 -0.6735131 10.0851341 -9.7887833
61 62 63 64 65 66
2.1521919 2.5175869 -4.8985541 -3.9636161 -2.6100059 10.0395786
67 68 69
2.4641987 1.2086465 3.6329645
> postscript(file="/var/www/html/rcomp/tmp/6x2hs1258645214.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 = 69
Frequency = 1
lag(myerror, k = 1) myerror
0 3.6267934 NA
1 3.6192457 3.6267934
2 -4.0786533 3.6192457
3 -4.9140608 -4.0786533
4 1.5337298 -4.9140608
5 8.3709466 1.5337298
6 1.9858530 8.3709466
7 -1.0254163 1.9858530
8 4.2128879 -1.0254163
9 0.3509868 4.2128879
10 3.8796421 0.3509868
11 -2.2989754 3.8796421
12 -0.3394179 -2.2989754
13 2.5014170 -0.3394179
14 -6.3649182 2.5014170
15 -8.9705377 -6.3649182
16 0.7168519 -8.9705377
17 2.5931688 0.7168519
18 -1.6085293 2.5931688
19 -0.9641072 -1.6085293
20 -3.6733701 -0.9641072
21 -2.3958197 -3.6733701
22 3.3351791 -2.3958197
23 -3.2481159 3.3351791
24 -0.7798722 -3.2481159
25 6.4239863 -0.7798722
26 -4.1254101 6.4239863
27 -1.0517006 -4.1254101
28 4.7843622 -1.0517006
29 7.7676423 4.7843622
30 5.8959168 7.7676423
31 4.3471613 5.8959168
32 2.0176434 4.3471613
33 -2.2882353 2.0176434
34 -0.7325952 -2.2882353
35 -6.3514349 -0.7325952
36 -5.8381062 -6.3514349
37 5.7309777 -5.8381062
38 -15.3209665 5.7309777
39 -5.4969777 -15.3209665
40 -4.2830000 -5.4969777
41 0.9263708 -4.2830000
42 3.8086626 0.9263708
43 -1.5412568 3.8086626
44 4.7885199 -1.5412568
45 -1.8211249 4.7885199
46 11.0121803 -1.8211249
47 -8.0129574 11.0121803
48 3.1516952 -8.0129574
49 0.4690011 3.1516952
50 -8.7191532 0.4690011
51 -9.2623569 -8.7191532
52 -2.0877678 -9.2623569
53 5.3258324 -2.0877678
54 0.6075943 5.3258324
55 -2.1566557 0.6075943
56 5.8014152 -2.1566557
57 -0.6735131 5.8014152
58 10.0851341 -0.6735131
59 -9.7887833 10.0851341
60 2.1521919 -9.7887833
61 2.5175869 2.1521919
62 -4.8985541 2.5175869
63 -3.9636161 -4.8985541
64 -2.6100059 -3.9636161
65 10.0395786 -2.6100059
66 2.4641987 10.0395786
67 1.2086465 2.4641987
68 3.6329645 1.2086465
69 NA 3.6329645
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.6192457 3.6267934
[2,] -4.0786533 3.6192457
[3,] -4.9140608 -4.0786533
[4,] 1.5337298 -4.9140608
[5,] 8.3709466 1.5337298
[6,] 1.9858530 8.3709466
[7,] -1.0254163 1.9858530
[8,] 4.2128879 -1.0254163
[9,] 0.3509868 4.2128879
[10,] 3.8796421 0.3509868
[11,] -2.2989754 3.8796421
[12,] -0.3394179 -2.2989754
[13,] 2.5014170 -0.3394179
[14,] -6.3649182 2.5014170
[15,] -8.9705377 -6.3649182
[16,] 0.7168519 -8.9705377
[17,] 2.5931688 0.7168519
[18,] -1.6085293 2.5931688
[19,] -0.9641072 -1.6085293
[20,] -3.6733701 -0.9641072
[21,] -2.3958197 -3.6733701
[22,] 3.3351791 -2.3958197
[23,] -3.2481159 3.3351791
[24,] -0.7798722 -3.2481159
[25,] 6.4239863 -0.7798722
[26,] -4.1254101 6.4239863
[27,] -1.0517006 -4.1254101
[28,] 4.7843622 -1.0517006
[29,] 7.7676423 4.7843622
[30,] 5.8959168 7.7676423
[31,] 4.3471613 5.8959168
[32,] 2.0176434 4.3471613
[33,] -2.2882353 2.0176434
[34,] -0.7325952 -2.2882353
[35,] -6.3514349 -0.7325952
[36,] -5.8381062 -6.3514349
[37,] 5.7309777 -5.8381062
[38,] -15.3209665 5.7309777
[39,] -5.4969777 -15.3209665
[40,] -4.2830000 -5.4969777
[41,] 0.9263708 -4.2830000
[42,] 3.8086626 0.9263708
[43,] -1.5412568 3.8086626
[44,] 4.7885199 -1.5412568
[45,] -1.8211249 4.7885199
[46,] 11.0121803 -1.8211249
[47,] -8.0129574 11.0121803
[48,] 3.1516952 -8.0129574
[49,] 0.4690011 3.1516952
[50,] -8.7191532 0.4690011
[51,] -9.2623569 -8.7191532
[52,] -2.0877678 -9.2623569
[53,] 5.3258324 -2.0877678
[54,] 0.6075943 5.3258324
[55,] -2.1566557 0.6075943
[56,] 5.8014152 -2.1566557
[57,] -0.6735131 5.8014152
[58,] 10.0851341 -0.6735131
[59,] -9.7887833 10.0851341
[60,] 2.1521919 -9.7887833
[61,] 2.5175869 2.1521919
[62,] -4.8985541 2.5175869
[63,] -3.9636161 -4.8985541
[64,] -2.6100059 -3.9636161
[65,] 10.0395786 -2.6100059
[66,] 2.4641987 10.0395786
[67,] 1.2086465 2.4641987
[68,] 3.6329645 1.2086465
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.6192457 3.6267934
2 -4.0786533 3.6192457
3 -4.9140608 -4.0786533
4 1.5337298 -4.9140608
5 8.3709466 1.5337298
6 1.9858530 8.3709466
7 -1.0254163 1.9858530
8 4.2128879 -1.0254163
9 0.3509868 4.2128879
10 3.8796421 0.3509868
11 -2.2989754 3.8796421
12 -0.3394179 -2.2989754
13 2.5014170 -0.3394179
14 -6.3649182 2.5014170
15 -8.9705377 -6.3649182
16 0.7168519 -8.9705377
17 2.5931688 0.7168519
18 -1.6085293 2.5931688
19 -0.9641072 -1.6085293
20 -3.6733701 -0.9641072
21 -2.3958197 -3.6733701
22 3.3351791 -2.3958197
23 -3.2481159 3.3351791
24 -0.7798722 -3.2481159
25 6.4239863 -0.7798722
26 -4.1254101 6.4239863
27 -1.0517006 -4.1254101
28 4.7843622 -1.0517006
29 7.7676423 4.7843622
30 5.8959168 7.7676423
31 4.3471613 5.8959168
32 2.0176434 4.3471613
33 -2.2882353 2.0176434
34 -0.7325952 -2.2882353
35 -6.3514349 -0.7325952
36 -5.8381062 -6.3514349
37 5.7309777 -5.8381062
38 -15.3209665 5.7309777
39 -5.4969777 -15.3209665
40 -4.2830000 -5.4969777
41 0.9263708 -4.2830000
42 3.8086626 0.9263708
43 -1.5412568 3.8086626
44 4.7885199 -1.5412568
45 -1.8211249 4.7885199
46 11.0121803 -1.8211249
47 -8.0129574 11.0121803
48 3.1516952 -8.0129574
49 0.4690011 3.1516952
50 -8.7191532 0.4690011
51 -9.2623569 -8.7191532
52 -2.0877678 -9.2623569
53 5.3258324 -2.0877678
54 0.6075943 5.3258324
55 -2.1566557 0.6075943
56 5.8014152 -2.1566557
57 -0.6735131 5.8014152
58 10.0851341 -0.6735131
59 -9.7887833 10.0851341
60 2.1521919 -9.7887833
61 2.5175869 2.1521919
62 -4.8985541 2.5175869
63 -3.9636161 -4.8985541
64 -2.6100059 -3.9636161
65 10.0395786 -2.6100059
66 2.4641987 10.0395786
67 1.2086465 2.4641987
68 3.6329645 1.2086465
> 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/7rez01258645214.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tz8g1258645214.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9x0951258645214.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qh421258645214.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jshm1258645214.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/12tl661258645214.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/13g2a71258645214.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/146ghp1258645214.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/15yne81258645214.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/160zoc1258645214.tab")
+ }
>
> system("convert tmp/1iz7a1258645213.ps tmp/1iz7a1258645213.png")
> system("convert tmp/257ku1258645214.ps tmp/257ku1258645214.png")
> system("convert tmp/3qvz41258645214.ps tmp/3qvz41258645214.png")
> system("convert tmp/4pn7j1258645214.ps tmp/4pn7j1258645214.png")
> system("convert tmp/5anv51258645214.ps tmp/5anv51258645214.png")
> system("convert tmp/6x2hs1258645214.ps tmp/6x2hs1258645214.png")
> system("convert tmp/7rez01258645214.ps tmp/7rez01258645214.png")
> system("convert tmp/8tz8g1258645214.ps tmp/8tz8g1258645214.png")
> system("convert tmp/9x0951258645214.ps tmp/9x0951258645214.png")
> system("convert tmp/10qh421258645214.ps tmp/10qh421258645214.png")
>
>
> proc.time()
user system elapsed
2.644 1.625 3.319