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 '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(9.5
+ ,101.6
+ ,9.2
+ ,9.2
+ ,10
+ ,10.9
+ ,9.6
+ ,94.6
+ ,9.5
+ ,9.2
+ ,9.2
+ ,10
+ ,9.5
+ ,95.9
+ ,9.6
+ ,9.5
+ ,9.2
+ ,9.2
+ ,9.1
+ ,104.7
+ ,9.5
+ ,9.6
+ ,9.5
+ ,9.2
+ ,8.9
+ ,102.8
+ ,9.1
+ ,9.5
+ ,9.6
+ ,9.5
+ ,9
+ ,98.1
+ ,8.9
+ ,9.1
+ ,9.5
+ ,9.6
+ ,10.1
+ ,113.9
+ ,9
+ ,8.9
+ ,9.1
+ ,9.5
+ ,10.3
+ ,80.9
+ ,10.1
+ ,9
+ ,8.9
+ ,9.1
+ ,10.2
+ ,95.7
+ ,10.3
+ ,10.1
+ ,9
+ ,8.9
+ ,9.6
+ ,113.2
+ ,10.2
+ ,10.3
+ ,10.1
+ ,9
+ ,9.2
+ ,105.9
+ ,9.6
+ ,10.2
+ ,10.3
+ ,10.1
+ ,9.3
+ ,108.8
+ ,9.2
+ ,9.6
+ ,10.2
+ ,10.3
+ ,9.4
+ ,102.3
+ ,9.3
+ ,9.2
+ ,9.6
+ ,10.2
+ ,9.4
+ ,99
+ ,9.4
+ ,9.3
+ ,9.2
+ ,9.6
+ ,9.2
+ ,100.7
+ ,9.4
+ ,9.4
+ ,9.3
+ ,9.2
+ ,9
+ ,115.5
+ ,9.2
+ ,9.4
+ ,9.4
+ ,9.3
+ ,9
+ ,100.7
+ ,9
+ ,9.2
+ ,9.4
+ ,9.4
+ ,9
+ ,109.9
+ ,9
+ ,9
+ ,9.2
+ ,9.4
+ ,9.8
+ ,114.6
+ ,9
+ ,9
+ ,9
+ ,9.2
+ ,10
+ ,85.4
+ ,9.8
+ ,9
+ ,9
+ ,9
+ ,9.8
+ ,100.5
+ ,10
+ ,9.8
+ ,9
+ ,9
+ ,9.3
+ ,114.8
+ ,9.8
+ ,10
+ ,9.8
+ ,9
+ ,9
+ ,116.5
+ ,9.3
+ ,9.8
+ ,10
+ ,9.8
+ ,9
+ ,112.9
+ ,9
+ ,9.3
+ ,9.8
+ ,10
+ ,9.1
+ ,102
+ ,9
+ ,9
+ ,9.3
+ ,9.8
+ ,9.1
+ ,106
+ ,9.1
+ ,9
+ ,9
+ ,9.3
+ ,9.1
+ ,105.3
+ ,9.1
+ ,9.1
+ ,9
+ ,9
+ ,9.2
+ ,118.8
+ ,9.1
+ ,9.1
+ ,9.1
+ ,9
+ ,8.8
+ ,106.1
+ ,9.2
+ ,9.1
+ ,9.1
+ ,9.1
+ ,8.3
+ ,109.3
+ ,8.8
+ ,9.2
+ ,9.1
+ ,9.1
+ ,8.4
+ ,117.2
+ ,8.3
+ ,8.8
+ ,9.2
+ ,9.1
+ ,8.1
+ ,92.5
+ ,8.4
+ ,8.3
+ ,8.8
+ ,9.2
+ ,7.7
+ ,104.2
+ ,8.1
+ ,8.4
+ ,8.3
+ ,8.8
+ ,7.9
+ ,112.5
+ ,7.7
+ ,8.1
+ ,8.4
+ ,8.3
+ ,7.9
+ ,122.4
+ ,7.9
+ ,7.7
+ ,8.1
+ ,8.4
+ ,8
+ ,113.3
+ ,7.9
+ ,7.9
+ ,7.7
+ ,8.1
+ ,7.9
+ ,100
+ ,8
+ ,7.9
+ ,7.9
+ ,7.7
+ ,7.6
+ ,110.7
+ ,7.9
+ ,8
+ ,7.9
+ ,7.9
+ ,7.1
+ ,112.8
+ ,7.6
+ ,7.9
+ ,8
+ ,7.9
+ ,6.8
+ ,109.8
+ ,7.1
+ ,7.6
+ ,7.9
+ ,8
+ ,6.5
+ ,117.3
+ ,6.8
+ ,7.1
+ ,7.6
+ ,7.9
+ ,6.9
+ ,109.1
+ ,6.5
+ ,6.8
+ ,7.1
+ ,7.6
+ ,8.2
+ ,115.9
+ ,6.9
+ ,6.5
+ ,6.8
+ ,7.1
+ ,8.7
+ ,96
+ ,8.2
+ ,6.9
+ ,6.5
+ ,6.8
+ ,8.3
+ ,99.8
+ ,8.7
+ ,8.2
+ ,6.9
+ ,6.5
+ ,7.9
+ ,116.8
+ ,8.3
+ ,8.7
+ ,8.2
+ ,6.9
+ ,7.5
+ ,115.7
+ ,7.9
+ ,8.3
+ ,8.7
+ ,8.2
+ ,7.8
+ ,99.4
+ ,7.5
+ ,7.9
+ ,8.3
+ ,8.7
+ ,8.3
+ ,94.3
+ ,7.8
+ ,7.5
+ ,7.9
+ ,8.3
+ ,8.4
+ ,91
+ ,8.3
+ ,7.8
+ ,7.5
+ ,7.9
+ ,8.2
+ ,93.2
+ ,8.4
+ ,8.3
+ ,7.8
+ ,7.5
+ ,7.7
+ ,103.1
+ ,8.2
+ ,8.4
+ ,8.3
+ ,7.8
+ ,7.2
+ ,94.1
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.3
+ ,7.3
+ ,91.8
+ ,7.2
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.1
+ ,102.7
+ ,7.3
+ ,7.2
+ ,7.7
+ ,8.2
+ ,8.5
+ ,82.6
+ ,8.1
+ ,7.3
+ ,7.2
+ ,7.7)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),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
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.5 101.6 9.2 9.2 10.0 10.9 1 0 0 0 0 0 0 0 0 0 0 1
2 9.6 94.6 9.5 9.2 9.2 10.0 0 1 0 0 0 0 0 0 0 0 0 2
3 9.5 95.9 9.6 9.5 9.2 9.2 0 0 1 0 0 0 0 0 0 0 0 3
4 9.1 104.7 9.5 9.6 9.5 9.2 0 0 0 1 0 0 0 0 0 0 0 4
5 8.9 102.8 9.1 9.5 9.6 9.5 0 0 0 0 1 0 0 0 0 0 0 5
6 9.0 98.1 8.9 9.1 9.5 9.6 0 0 0 0 0 1 0 0 0 0 0 6
7 10.1 113.9 9.0 8.9 9.1 9.5 0 0 0 0 0 0 1 0 0 0 0 7
8 10.3 80.9 10.1 9.0 8.9 9.1 0 0 0 0 0 0 0 1 0 0 0 8
9 10.2 95.7 10.3 10.1 9.0 8.9 0 0 0 0 0 0 0 0 1 0 0 9
10 9.6 113.2 10.2 10.3 10.1 9.0 0 0 0 0 0 0 0 0 0 1 0 10
11 9.2 105.9 9.6 10.2 10.3 10.1 0 0 0 0 0 0 0 0 0 0 1 11
12 9.3 108.8 9.2 9.6 10.2 10.3 0 0 0 0 0 0 0 0 0 0 0 12
13 9.4 102.3 9.3 9.2 9.6 10.2 1 0 0 0 0 0 0 0 0 0 0 13
14 9.4 99.0 9.4 9.3 9.2 9.6 0 1 0 0 0 0 0 0 0 0 0 14
15 9.2 100.7 9.4 9.4 9.3 9.2 0 0 1 0 0 0 0 0 0 0 0 15
16 9.0 115.5 9.2 9.4 9.4 9.3 0 0 0 1 0 0 0 0 0 0 0 16
17 9.0 100.7 9.0 9.2 9.4 9.4 0 0 0 0 1 0 0 0 0 0 0 17
18 9.0 109.9 9.0 9.0 9.2 9.4 0 0 0 0 0 1 0 0 0 0 0 18
19 9.8 114.6 9.0 9.0 9.0 9.2 0 0 0 0 0 0 1 0 0 0 0 19
20 10.0 85.4 9.8 9.0 9.0 9.0 0 0 0 0 0 0 0 1 0 0 0 20
21 9.8 100.5 10.0 9.8 9.0 9.0 0 0 0 0 0 0 0 0 1 0 0 21
22 9.3 114.8 9.8 10.0 9.8 9.0 0 0 0 0 0 0 0 0 0 1 0 22
23 9.0 116.5 9.3 9.8 10.0 9.8 0 0 0 0 0 0 0 0 0 0 1 23
24 9.0 112.9 9.0 9.3 9.8 10.0 0 0 0 0 0 0 0 0 0 0 0 24
25 9.1 102.0 9.0 9.0 9.3 9.8 1 0 0 0 0 0 0 0 0 0 0 25
26 9.1 106.0 9.1 9.0 9.0 9.3 0 1 0 0 0 0 0 0 0 0 0 26
27 9.1 105.3 9.1 9.1 9.0 9.0 0 0 1 0 0 0 0 0 0 0 0 27
28 9.2 118.8 9.1 9.1 9.1 9.0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.8 106.1 9.2 9.1 9.1 9.1 0 0 0 0 1 0 0 0 0 0 0 29
30 8.3 109.3 8.8 9.2 9.1 9.1 0 0 0 0 0 1 0 0 0 0 0 30
31 8.4 117.2 8.3 8.8 9.2 9.1 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 92.5 8.4 8.3 8.8 9.2 0 0 0 0 0 0 0 1 0 0 0 32
33 7.7 104.2 8.1 8.4 8.3 8.8 0 0 0 0 0 0 0 0 1 0 0 33
34 7.9 112.5 7.7 8.1 8.4 8.3 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 122.4 7.9 7.7 8.1 8.4 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 113.3 7.9 7.9 7.7 8.1 0 0 0 0 0 0 0 0 0 0 0 36
37 7.9 100.0 8.0 7.9 7.9 7.7 1 0 0 0 0 0 0 0 0 0 0 37
38 7.6 110.7 7.9 8.0 7.9 7.9 0 1 0 0 0 0 0 0 0 0 0 38
39 7.1 112.8 7.6 7.9 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 109.8 7.1 7.6 7.9 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.5 117.3 6.8 7.1 7.6 7.9 0 0 0 0 1 0 0 0 0 0 0 41
42 6.9 109.1 6.5 6.8 7.1 7.6 0 0 0 0 0 1 0 0 0 0 0 42
43 8.2 115.9 6.9 6.5 6.8 7.1 0 0 0 0 0 0 1 0 0 0 0 43
44 8.7 96.0 8.2 6.9 6.5 6.8 0 0 0 0 0 0 0 1 0 0 0 44
45 8.3 99.8 8.7 8.2 6.9 6.5 0 0 0 0 0 0 0 0 1 0 0 45
46 7.9 116.8 8.3 8.7 8.2 6.9 0 0 0 0 0 0 0 0 0 1 0 46
47 7.5 115.7 7.9 8.3 8.7 8.2 0 0 0 0 0 0 0 0 0 0 1 47
48 7.8 99.4 7.5 7.9 8.3 8.7 0 0 0 0 0 0 0 0 0 0 0 48
49 8.3 94.3 7.8 7.5 7.9 8.3 1 0 0 0 0 0 0 0 0 0 0 49
50 8.4 91.0 8.3 7.8 7.5 7.9 0 1 0 0 0 0 0 0 0 0 0 50
51 8.2 93.2 8.4 8.3 7.8 7.5 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 103.1 8.2 8.4 8.3 7.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.2 94.1 7.7 8.2 8.4 8.3 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 91.8 7.2 7.7 8.2 8.4 0 0 0 0 0 1 0 0 0 0 0 54
55 8.1 102.7 7.3 7.2 7.7 8.2 0 0 0 0 0 0 1 0 0 0 0 55
56 8.5 82.6 8.1 7.3 7.2 7.7 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) X Y1 Y2 Y3 Y4
2.223767 -0.007949 1.437184 -0.559931 -0.368555 0.368299
M1 M2 M3 M4 M5 M6
-0.276787 -0.470758 -0.348946 -0.228263 -0.364396 -0.153384
M7 M8 M9 M10 M11 t
0.537200 -0.634309 -0.512369 0.064362 -0.134759 -0.004979
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.245653 -0.139958 0.002908 0.126897 0.344470
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.223767 0.985077 2.257 0.029808 *
X -0.007949 0.004484 -1.773 0.084248 .
Y1 1.437184 0.147512 9.743 7.01e-12 ***
Y2 -0.559931 0.270110 -2.073 0.045006 *
Y3 -0.368555 0.267209 -1.379 0.175874
Y4 0.368299 0.141530 2.602 0.013131 *
M1 -0.276787 0.139330 -1.987 0.054217 .
M2 -0.470758 0.142090 -3.313 0.002033 **
M3 -0.348946 0.139890 -2.494 0.017081 *
M4 -0.228263 0.134440 -1.698 0.097707 .
M5 -0.364396 0.130901 -2.784 0.008327 **
M6 -0.153384 0.130524 -1.175 0.247249
M7 0.537200 0.131918 4.072 0.000228 ***
M8 -0.634309 0.195992 -3.236 0.002511 **
M9 -0.512369 0.188841 -2.713 0.009958 **
M10 0.064362 0.175997 0.366 0.716617
M11 -0.134759 0.142327 -0.947 0.349710
t -0.004979 0.003513 -1.417 0.164548
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1845 on 38 degrees of freedom
Multiple R-squared: 0.9719, Adjusted R-squared: 0.9593
F-statistic: 77.2 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,] 0.24188239 0.48376478 0.7581176
[2,] 0.17554594 0.35109188 0.8244541
[3,] 0.12739644 0.25479287 0.8726036
[4,] 0.09621731 0.19243461 0.9037827
[5,] 0.04961261 0.09922522 0.9503874
[6,] 0.02461390 0.04922779 0.9753861
[7,] 0.02228569 0.04457137 0.9777143
[8,] 0.36794293 0.73588587 0.6320571
[9,] 0.41973447 0.83946895 0.5802655
[10,] 0.39448225 0.78896450 0.6055178
[11,] 0.81959346 0.36081307 0.1804065
[12,] 0.76695423 0.46609153 0.2330458
[13,] 0.74424653 0.51150694 0.2557535
[14,] 0.74686118 0.50627763 0.2531388
[15,] 0.75890799 0.48218402 0.2410920
> postscript(file="/var/www/html/rcomp/tmp/1ejvc1258714811.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vf671258714811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tccj1258714811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4edae1258714811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5l6yb1258714811.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
-0.033961716 -0.185187980 -0.072787281 -0.208257424 0.162996083 0.009379675
7 8 9 10 11 12
0.183079599 -0.154066350 0.185628112 -0.222722635 -0.001754379 0.119918667
13 14 15 16 17 18
-0.101981378 0.056567812 -0.006583603 0.082827620 0.244906905 -0.073687594
19 20 21 22 23 24
0.078018470 0.146293906 0.109878514 -0.153928848 0.149362981 -0.005216463
25 26 27 28 29 30
0.011302625 0.171915625 0.216000037 0.344469991 -0.195925677 -0.245653123
31 32 33 34 35 36
-0.236982523 -0.164782926 -0.138544644 0.183582953 -0.192422772 -0.219489217
37 38 39 40 41 42
-0.066139629 0.043922310 -0.144199918 -0.106825018 -0.128639705 0.189529709
43 44 45 46 47 48
0.188710809 0.062560564 -0.156961983 0.193068530 0.044814171 0.104787012
49 50 51 52 53 54
0.190780099 -0.087217767 0.007570764 -0.112215168 -0.083337606 0.120431332
55 56
-0.212826356 0.109994806
> postscript(file="/var/www/html/rcomp/tmp/687bk1258714811.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 -0.033961716 NA
1 -0.185187980 -0.033961716
2 -0.072787281 -0.185187980
3 -0.208257424 -0.072787281
4 0.162996083 -0.208257424
5 0.009379675 0.162996083
6 0.183079599 0.009379675
7 -0.154066350 0.183079599
8 0.185628112 -0.154066350
9 -0.222722635 0.185628112
10 -0.001754379 -0.222722635
11 0.119918667 -0.001754379
12 -0.101981378 0.119918667
13 0.056567812 -0.101981378
14 -0.006583603 0.056567812
15 0.082827620 -0.006583603
16 0.244906905 0.082827620
17 -0.073687594 0.244906905
18 0.078018470 -0.073687594
19 0.146293906 0.078018470
20 0.109878514 0.146293906
21 -0.153928848 0.109878514
22 0.149362981 -0.153928848
23 -0.005216463 0.149362981
24 0.011302625 -0.005216463
25 0.171915625 0.011302625
26 0.216000037 0.171915625
27 0.344469991 0.216000037
28 -0.195925677 0.344469991
29 -0.245653123 -0.195925677
30 -0.236982523 -0.245653123
31 -0.164782926 -0.236982523
32 -0.138544644 -0.164782926
33 0.183582953 -0.138544644
34 -0.192422772 0.183582953
35 -0.219489217 -0.192422772
36 -0.066139629 -0.219489217
37 0.043922310 -0.066139629
38 -0.144199918 0.043922310
39 -0.106825018 -0.144199918
40 -0.128639705 -0.106825018
41 0.189529709 -0.128639705
42 0.188710809 0.189529709
43 0.062560564 0.188710809
44 -0.156961983 0.062560564
45 0.193068530 -0.156961983
46 0.044814171 0.193068530
47 0.104787012 0.044814171
48 0.190780099 0.104787012
49 -0.087217767 0.190780099
50 0.007570764 -0.087217767
51 -0.112215168 0.007570764
52 -0.083337606 -0.112215168
53 0.120431332 -0.083337606
54 -0.212826356 0.120431332
55 0.109994806 -0.212826356
56 NA 0.109994806
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.185187980 -0.033961716
[2,] -0.072787281 -0.185187980
[3,] -0.208257424 -0.072787281
[4,] 0.162996083 -0.208257424
[5,] 0.009379675 0.162996083
[6,] 0.183079599 0.009379675
[7,] -0.154066350 0.183079599
[8,] 0.185628112 -0.154066350
[9,] -0.222722635 0.185628112
[10,] -0.001754379 -0.222722635
[11,] 0.119918667 -0.001754379
[12,] -0.101981378 0.119918667
[13,] 0.056567812 -0.101981378
[14,] -0.006583603 0.056567812
[15,] 0.082827620 -0.006583603
[16,] 0.244906905 0.082827620
[17,] -0.073687594 0.244906905
[18,] 0.078018470 -0.073687594
[19,] 0.146293906 0.078018470
[20,] 0.109878514 0.146293906
[21,] -0.153928848 0.109878514
[22,] 0.149362981 -0.153928848
[23,] -0.005216463 0.149362981
[24,] 0.011302625 -0.005216463
[25,] 0.171915625 0.011302625
[26,] 0.216000037 0.171915625
[27,] 0.344469991 0.216000037
[28,] -0.195925677 0.344469991
[29,] -0.245653123 -0.195925677
[30,] -0.236982523 -0.245653123
[31,] -0.164782926 -0.236982523
[32,] -0.138544644 -0.164782926
[33,] 0.183582953 -0.138544644
[34,] -0.192422772 0.183582953
[35,] -0.219489217 -0.192422772
[36,] -0.066139629 -0.219489217
[37,] 0.043922310 -0.066139629
[38,] -0.144199918 0.043922310
[39,] -0.106825018 -0.144199918
[40,] -0.128639705 -0.106825018
[41,] 0.189529709 -0.128639705
[42,] 0.188710809 0.189529709
[43,] 0.062560564 0.188710809
[44,] -0.156961983 0.062560564
[45,] 0.193068530 -0.156961983
[46,] 0.044814171 0.193068530
[47,] 0.104787012 0.044814171
[48,] 0.190780099 0.104787012
[49,] -0.087217767 0.190780099
[50,] 0.007570764 -0.087217767
[51,] -0.112215168 0.007570764
[52,] -0.083337606 -0.112215168
[53,] 0.120431332 -0.083337606
[54,] -0.212826356 0.120431332
[55,] 0.109994806 -0.212826356
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.185187980 -0.033961716
2 -0.072787281 -0.185187980
3 -0.208257424 -0.072787281
4 0.162996083 -0.208257424
5 0.009379675 0.162996083
6 0.183079599 0.009379675
7 -0.154066350 0.183079599
8 0.185628112 -0.154066350
9 -0.222722635 0.185628112
10 -0.001754379 -0.222722635
11 0.119918667 -0.001754379
12 -0.101981378 0.119918667
13 0.056567812 -0.101981378
14 -0.006583603 0.056567812
15 0.082827620 -0.006583603
16 0.244906905 0.082827620
17 -0.073687594 0.244906905
18 0.078018470 -0.073687594
19 0.146293906 0.078018470
20 0.109878514 0.146293906
21 -0.153928848 0.109878514
22 0.149362981 -0.153928848
23 -0.005216463 0.149362981
24 0.011302625 -0.005216463
25 0.171915625 0.011302625
26 0.216000037 0.171915625
27 0.344469991 0.216000037
28 -0.195925677 0.344469991
29 -0.245653123 -0.195925677
30 -0.236982523 -0.245653123
31 -0.164782926 -0.236982523
32 -0.138544644 -0.164782926
33 0.183582953 -0.138544644
34 -0.192422772 0.183582953
35 -0.219489217 -0.192422772
36 -0.066139629 -0.219489217
37 0.043922310 -0.066139629
38 -0.144199918 0.043922310
39 -0.106825018 -0.144199918
40 -0.128639705 -0.106825018
41 0.189529709 -0.128639705
42 0.188710809 0.189529709
43 0.062560564 0.188710809
44 -0.156961983 0.062560564
45 0.193068530 -0.156961983
46 0.044814171 0.193068530
47 0.104787012 0.044814171
48 0.190780099 0.104787012
49 -0.087217767 0.190780099
50 0.007570764 -0.087217767
51 -0.112215168 0.007570764
52 -0.083337606 -0.112215168
53 0.120431332 -0.083337606
54 -0.212826356 0.120431332
55 0.109994806 -0.212826356
> 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/7ay951258714811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89ykk1258714811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9aduq1258714811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108c1d1258714811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h9701258714811.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/12k4mq1258714812.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/136ouz1258714812.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/14d51l1258714812.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/15vmt01258714812.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/16tdd51258714812.tab")
+ }
>
> system("convert tmp/1ejvc1258714811.ps tmp/1ejvc1258714811.png")
> system("convert tmp/2vf671258714811.ps tmp/2vf671258714811.png")
> system("convert tmp/3tccj1258714811.ps tmp/3tccj1258714811.png")
> system("convert tmp/4edae1258714811.ps tmp/4edae1258714811.png")
> system("convert tmp/5l6yb1258714811.ps tmp/5l6yb1258714811.png")
> system("convert tmp/687bk1258714811.ps tmp/687bk1258714811.png")
> system("convert tmp/7ay951258714811.ps tmp/7ay951258714811.png")
> system("convert tmp/89ykk1258714811.ps tmp/89ykk1258714811.png")
> system("convert tmp/9aduq1258714811.ps tmp/9aduq1258714811.png")
> system("convert tmp/108c1d1258714811.ps tmp/108c1d1258714811.png")
>
>
> proc.time()
user system elapsed
2.306 1.525 2.808