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
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Type 'q()' to quit R.
> x <- array(list(7.2
+ ,1.9
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,1.6
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,1.7
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,1.6
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,1.4
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,2.1
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,1.9
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,1.7
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,1.8
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,2
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,2.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,2.1
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,2.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,2.3
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,2.4
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,2.4
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,2.3
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,1.7
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,2
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,2.3
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,2
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,2
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,1.3
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,1.7
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,1.9
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,1.7
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,1.6
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,1.7
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,1.8
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,1.9
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,1.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,1.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,2
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,2.1
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,1.9
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,1.9
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,1.3
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,1.3
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,1.4
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,1.2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,1.3
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,1.8
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,2.2
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,2.6
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,2.8
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,3.1
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,3.9
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,3.7
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,4.6
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,5.1
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,5.2
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,4.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,5.1
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,4.8
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,3.9
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,3.5
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('TWIB'
+ ,'GI'
+ ,'TWIB1'
+ ,'TWIB2'
+ ,'TWIB3'
+ ,'TWIB4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('TWIB','GI','TWIB1','TWIB2','TWIB3','TWIB4'),1:56))
> 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
TWIB GI TWIB1 TWIB2 TWIB3 TWIB4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.2 1.9 7.5 8.3 8.8 8.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.4 1.6 7.2 7.5 8.3 8.8 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 1.7 7.4 7.2 7.5 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 1.6 8.8 7.4 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 9.3 1.4 9.3 8.8 7.4 7.2 0 0 0 0 1 0 0 0 0 0 0 5
6 8.7 2.1 9.3 9.3 8.8 7.4 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 1.9 8.7 9.3 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 1.7 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 1.8 8.3 8.2 8.7 9.3 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 2.0 8.5 8.3 8.2 8.7 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 2.5 8.6 8.5 8.3 8.2 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 2.1 8.5 8.6 8.5 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 2.1 8.2 8.5 8.6 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 2.3 8.1 8.2 8.5 8.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 2.4 7.9 8.1 8.2 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 2.4 8.6 7.9 8.1 8.2 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 2.3 8.7 8.6 7.9 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 1.7 8.7 8.7 8.6 7.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 2.0 8.5 8.7 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 2.3 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 2.0 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 2.0 8.7 8.5 8.4 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 1.3 8.7 8.7 8.5 8.4 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 1.7 8.6 8.7 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 1.9 8.5 8.6 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 1.7 8.3 8.5 8.6 8.7 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 1.6 8.0 8.3 8.5 8.6 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 1.7 8.2 8.0 8.3 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 1.8 8.1 8.2 8.0 8.3 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 1.9 8.1 8.1 8.2 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 1.9 8.0 8.1 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 1.9 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 2.0 7.9 7.9 8.0 8.1 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 2.1 8.0 7.9 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1.9 8.0 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 1.9 7.9 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 1.3 8.0 7.9 8.0 8.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 1.3 7.7 8.0 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.5 1.4 7.2 7.7 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 1.2 7.5 7.2 7.7 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 1.3 7.3 7.5 7.2 7.7 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 1.8 7.0 7.3 7.5 7.2 0 0 0 0 0 1 0 0 0 0 0 42
43 7.0 2.2 7.0 7.0 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 2.6 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 2.8 7.2 7.0 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.1 3.1 7.3 7.2 7.0 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 3.9 7.1 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 3.7 6.8 7.1 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 4.6 6.4 6.8 7.1 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 5.1 6.1 6.4 6.8 7.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 5.2 6.5 6.1 6.4 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 4.9 7.7 6.5 6.1 6.4 0 0 0 1 0 0 0 0 0 0 0 52
53 7.5 5.1 7.9 7.7 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.9 4.8 7.5 7.9 7.7 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 3.9 6.9 7.5 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 3.5 6.6 6.9 7.5 7.9 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) GI TWIB1 TWIB2 TWIB3 TWIB4
0.867934 0.050913 1.475638 -0.787677 -0.140644 0.349849
M1 M2 M3 M4 M5 M6
-0.144828 -0.118928 0.608834 -0.392339 -0.002540 0.120655
M7 M8 M9 M10 M11 t
0.012358 0.162524 0.009459 -0.104619 -0.022345 -0.007034
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.252708 -0.083455 0.005908 0.072701 0.355246
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.867934 0.677724 1.281 0.20807
GI 0.050913 0.028725 1.772 0.08434 .
TWIB1 1.475638 0.136411 10.818 3.68e-13 ***
TWIB2 -0.787677 0.261456 -3.013 0.00459 **
TWIB3 -0.140644 0.262464 -0.536 0.59518
TWIB4 0.349849 0.144111 2.428 0.02004 *
M1 -0.144828 0.102505 -1.413 0.16583
M2 -0.118928 0.105678 -1.125 0.26749
M3 0.608834 0.107385 5.670 1.62e-06 ***
M4 -0.392339 0.140127 -2.800 0.00799 **
M5 -0.002540 0.154355 -0.016 0.98695
M6 0.120655 0.122740 0.983 0.33182
M7 0.012358 0.100250 0.123 0.90254
M8 0.162524 0.103143 1.576 0.12338
M9 0.009459 0.111740 0.085 0.93299
M10 -0.104619 0.113050 -0.925 0.36059
M11 -0.022345 0.106781 -0.209 0.83536
t -0.007034 0.002396 -2.935 0.00562 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1478 on 38 degrees of freedom
Multiple R-squared: 0.9727, Adjusted R-squared: 0.9605
F-statistic: 79.75 on 17 and 38 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,] 0.01390351 0.02780702 0.9860965
[2,] 0.14897265 0.29794530 0.8510274
[3,] 0.07959532 0.15919064 0.9204047
[4,] 0.05084526 0.10169052 0.9491547
[5,] 0.03493665 0.06987330 0.9650634
[6,] 0.01862543 0.03725087 0.9813746
[7,] 0.33331858 0.66663716 0.6666814
[8,] 0.23814387 0.47628773 0.7618561
[9,] 0.16067283 0.32134565 0.8393272
[10,] 0.20368618 0.40737236 0.7963138
[11,] 0.15462302 0.30924603 0.8453770
[12,] 0.13821576 0.27643153 0.8617842
[13,] 0.10277954 0.20555907 0.8972205
[14,] 0.09724512 0.19449025 0.9027549
[15,] 0.07799577 0.15599154 0.9220042
> postscript(file="/var/www/html/rcomp/tmp/1nvci1258756994.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26woa1258756994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3guen1258756994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lp1f1258756994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mzki1258756994.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 = 56
Frequency = 1
1 2 3 4 5 6
-0.018357509 -0.044736816 0.160423168 0.003050388 0.128480576 -0.102549238
7 8 9 10 11 12
-0.011119319 0.046220303 -0.224560429 -0.090403934 -0.092138951 -0.167609608
13 14 15 16 17 18
0.192270464 -0.174566382 0.008766531 0.017382476 0.050375240 0.011949761
19 20 21 22 23 24
0.076304901 -0.027056536 0.093856160 0.054513251 0.121497571 0.126529141
25 26 27 28 29 30
0.067034749 -0.039353021 -0.248912692 0.129628954 0.074648770 -0.092288062
31 32 33 34 35 36
-0.013427277 -0.052777465 0.109398365 0.098775384 0.047471305 0.293788398
37 38 39 40 41 42
-0.085118554 -0.096589700 0.028156770 -0.067161135 -0.188953789 0.171702136
43 44 45 46 47 48
-0.102718252 -0.038437895 0.021305904 -0.062884701 -0.076829925 -0.252707932
49 50 51 52 53 54
-0.155829150 0.355245920 0.051566222 -0.082900682 -0.064550796 0.011185404
55 56
0.050959947 0.072051592
> postscript(file="/var/www/html/rcomp/tmp/67jfd1258756994.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.018357509 NA
1 -0.044736816 -0.018357509
2 0.160423168 -0.044736816
3 0.003050388 0.160423168
4 0.128480576 0.003050388
5 -0.102549238 0.128480576
6 -0.011119319 -0.102549238
7 0.046220303 -0.011119319
8 -0.224560429 0.046220303
9 -0.090403934 -0.224560429
10 -0.092138951 -0.090403934
11 -0.167609608 -0.092138951
12 0.192270464 -0.167609608
13 -0.174566382 0.192270464
14 0.008766531 -0.174566382
15 0.017382476 0.008766531
16 0.050375240 0.017382476
17 0.011949761 0.050375240
18 0.076304901 0.011949761
19 -0.027056536 0.076304901
20 0.093856160 -0.027056536
21 0.054513251 0.093856160
22 0.121497571 0.054513251
23 0.126529141 0.121497571
24 0.067034749 0.126529141
25 -0.039353021 0.067034749
26 -0.248912692 -0.039353021
27 0.129628954 -0.248912692
28 0.074648770 0.129628954
29 -0.092288062 0.074648770
30 -0.013427277 -0.092288062
31 -0.052777465 -0.013427277
32 0.109398365 -0.052777465
33 0.098775384 0.109398365
34 0.047471305 0.098775384
35 0.293788398 0.047471305
36 -0.085118554 0.293788398
37 -0.096589700 -0.085118554
38 0.028156770 -0.096589700
39 -0.067161135 0.028156770
40 -0.188953789 -0.067161135
41 0.171702136 -0.188953789
42 -0.102718252 0.171702136
43 -0.038437895 -0.102718252
44 0.021305904 -0.038437895
45 -0.062884701 0.021305904
46 -0.076829925 -0.062884701
47 -0.252707932 -0.076829925
48 -0.155829150 -0.252707932
49 0.355245920 -0.155829150
50 0.051566222 0.355245920
51 -0.082900682 0.051566222
52 -0.064550796 -0.082900682
53 0.011185404 -0.064550796
54 0.050959947 0.011185404
55 0.072051592 0.050959947
56 NA 0.072051592
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.044736816 -0.018357509
[2,] 0.160423168 -0.044736816
[3,] 0.003050388 0.160423168
[4,] 0.128480576 0.003050388
[5,] -0.102549238 0.128480576
[6,] -0.011119319 -0.102549238
[7,] 0.046220303 -0.011119319
[8,] -0.224560429 0.046220303
[9,] -0.090403934 -0.224560429
[10,] -0.092138951 -0.090403934
[11,] -0.167609608 -0.092138951
[12,] 0.192270464 -0.167609608
[13,] -0.174566382 0.192270464
[14,] 0.008766531 -0.174566382
[15,] 0.017382476 0.008766531
[16,] 0.050375240 0.017382476
[17,] 0.011949761 0.050375240
[18,] 0.076304901 0.011949761
[19,] -0.027056536 0.076304901
[20,] 0.093856160 -0.027056536
[21,] 0.054513251 0.093856160
[22,] 0.121497571 0.054513251
[23,] 0.126529141 0.121497571
[24,] 0.067034749 0.126529141
[25,] -0.039353021 0.067034749
[26,] -0.248912692 -0.039353021
[27,] 0.129628954 -0.248912692
[28,] 0.074648770 0.129628954
[29,] -0.092288062 0.074648770
[30,] -0.013427277 -0.092288062
[31,] -0.052777465 -0.013427277
[32,] 0.109398365 -0.052777465
[33,] 0.098775384 0.109398365
[34,] 0.047471305 0.098775384
[35,] 0.293788398 0.047471305
[36,] -0.085118554 0.293788398
[37,] -0.096589700 -0.085118554
[38,] 0.028156770 -0.096589700
[39,] -0.067161135 0.028156770
[40,] -0.188953789 -0.067161135
[41,] 0.171702136 -0.188953789
[42,] -0.102718252 0.171702136
[43,] -0.038437895 -0.102718252
[44,] 0.021305904 -0.038437895
[45,] -0.062884701 0.021305904
[46,] -0.076829925 -0.062884701
[47,] -0.252707932 -0.076829925
[48,] -0.155829150 -0.252707932
[49,] 0.355245920 -0.155829150
[50,] 0.051566222 0.355245920
[51,] -0.082900682 0.051566222
[52,] -0.064550796 -0.082900682
[53,] 0.011185404 -0.064550796
[54,] 0.050959947 0.011185404
[55,] 0.072051592 0.050959947
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.044736816 -0.018357509
2 0.160423168 -0.044736816
3 0.003050388 0.160423168
4 0.128480576 0.003050388
5 -0.102549238 0.128480576
6 -0.011119319 -0.102549238
7 0.046220303 -0.011119319
8 -0.224560429 0.046220303
9 -0.090403934 -0.224560429
10 -0.092138951 -0.090403934
11 -0.167609608 -0.092138951
12 0.192270464 -0.167609608
13 -0.174566382 0.192270464
14 0.008766531 -0.174566382
15 0.017382476 0.008766531
16 0.050375240 0.017382476
17 0.011949761 0.050375240
18 0.076304901 0.011949761
19 -0.027056536 0.076304901
20 0.093856160 -0.027056536
21 0.054513251 0.093856160
22 0.121497571 0.054513251
23 0.126529141 0.121497571
24 0.067034749 0.126529141
25 -0.039353021 0.067034749
26 -0.248912692 -0.039353021
27 0.129628954 -0.248912692
28 0.074648770 0.129628954
29 -0.092288062 0.074648770
30 -0.013427277 -0.092288062
31 -0.052777465 -0.013427277
32 0.109398365 -0.052777465
33 0.098775384 0.109398365
34 0.047471305 0.098775384
35 0.293788398 0.047471305
36 -0.085118554 0.293788398
37 -0.096589700 -0.085118554
38 0.028156770 -0.096589700
39 -0.067161135 0.028156770
40 -0.188953789 -0.067161135
41 0.171702136 -0.188953789
42 -0.102718252 0.171702136
43 -0.038437895 -0.102718252
44 0.021305904 -0.038437895
45 -0.062884701 0.021305904
46 -0.076829925 -0.062884701
47 -0.252707932 -0.076829925
48 -0.155829150 -0.252707932
49 0.355245920 -0.155829150
50 0.051566222 0.355245920
51 -0.082900682 0.051566222
52 -0.064550796 -0.082900682
53 0.011185404 -0.064550796
54 0.050959947 0.011185404
55 0.072051592 0.050959947
> 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/7x57b1258756994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/811d41258756994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vux11258756994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109b7c1258756994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113fad1258756994.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/123ovu1258756994.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/13haj61258756995.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/14p40t1258756995.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/15i58r1258756995.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/16uyee1258756995.tab")
+ }
>
> system("convert tmp/1nvci1258756994.ps tmp/1nvci1258756994.png")
> system("convert tmp/26woa1258756994.ps tmp/26woa1258756994.png")
> system("convert tmp/3guen1258756994.ps tmp/3guen1258756994.png")
> system("convert tmp/4lp1f1258756994.ps tmp/4lp1f1258756994.png")
> system("convert tmp/5mzki1258756994.ps tmp/5mzki1258756994.png")
> system("convert tmp/67jfd1258756994.ps tmp/67jfd1258756994.png")
> system("convert tmp/7x57b1258756994.ps tmp/7x57b1258756994.png")
> system("convert tmp/811d41258756994.ps tmp/811d41258756994.png")
> system("convert tmp/9vux11258756994.ps tmp/9vux11258756994.png")
> system("convert tmp/109b7c1258756994.ps tmp/109b7c1258756994.png")
>
>
> proc.time()
user system elapsed
2.299 1.634 2.788