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 '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(7.2
+ ,97.78
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,97.69
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,96.67
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,98.29
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,98.2
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,98.71
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,98.54
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,98.2
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,96.92
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,99.06
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,99.65
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,99.82
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,99.99
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,100.33
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,99.31
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,101.1
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,101.1
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,100.93
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,100.85
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,100.93
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,99.6
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,101.88
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,101.81
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,102.38
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,102.74
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,102.82
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,101.72
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,103.47
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,102.98
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,102.68
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,102.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,103.03
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,101.29
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,103.69
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,103.68
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,104.2
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,104.08
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,104.16
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,103.05
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,104.66
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,104.46
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,104.95
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,105.85
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,106.23
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,104.86
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,107.44
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,108.23
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,108.45
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,109.39
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,110.15
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,109.13
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,110.28
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,110.17
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,109.99
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,109.26
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,109.11
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9)
+ ,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 7.2 97.78 7.5 8.3 8.8 8.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.4 97.69 7.2 7.5 8.3 8.8 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 96.67 7.4 7.2 7.5 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 98.29 8.8 7.4 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 9.3 98.20 9.3 8.8 7.4 7.2 0 0 0 0 1 0 0 0 0 0 0 5
6 8.7 98.71 9.3 9.3 8.8 7.4 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 98.54 8.7 9.3 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 98.20 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 96.92 8.3 8.2 8.7 9.3 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 99.06 8.5 8.3 8.2 8.7 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 99.65 8.6 8.5 8.3 8.2 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 99.82 8.5 8.6 8.5 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 99.99 8.2 8.5 8.6 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 100.33 8.1 8.2 8.5 8.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 99.31 7.9 8.1 8.2 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 101.10 8.6 7.9 8.1 8.2 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 101.10 8.7 8.6 7.9 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 100.93 8.7 8.7 8.6 7.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 100.85 8.5 8.7 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 100.93 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 99.60 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 101.88 8.7 8.5 8.4 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 101.81 8.7 8.7 8.5 8.4 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 102.38 8.6 8.7 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 102.74 8.5 8.6 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 102.82 8.3 8.5 8.6 8.7 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 101.72 8.0 8.3 8.5 8.6 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 103.47 8.2 8.0 8.3 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 102.98 8.1 8.2 8.0 8.3 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 102.68 8.1 8.1 8.2 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 102.90 8.0 8.1 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 103.03 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 101.29 7.9 7.9 8.0 8.1 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 103.69 8.0 7.9 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 103.68 8.0 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 104.20 7.9 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 104.08 8.0 7.9 8.0 8.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 104.16 7.7 8.0 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.5 103.05 7.2 7.7 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 104.66 7.5 7.2 7.7 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 104.46 7.3 7.5 7.2 7.7 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 104.95 7.0 7.3 7.5 7.2 0 0 0 0 0 1 0 0 0 0 0 42
43 7.0 105.85 7.0 7.0 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 106.23 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 104.86 7.2 7.0 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.1 107.44 7.3 7.2 7.0 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 108.23 7.1 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 108.45 6.8 7.1 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 109.39 6.4 6.8 7.1 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 110.15 6.1 6.4 6.8 7.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 109.13 6.5 6.1 6.4 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 110.28 7.7 6.5 6.1 6.4 0 0 0 1 0 0 0 0 0 0 0 52
53 7.5 110.17 7.9 7.7 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.9 109.99 7.5 7.9 7.7 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 109.26 6.9 7.5 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 109.11 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) X Y1 Y2 Y3 Y4
-4.262427 0.054420 1.465330 -0.781224 -0.145033 0.348491
M1 M2 M3 M4 M5 M6
-0.148355 -0.124116 0.673763 -0.401218 0.005331 0.140525
M7 M8 M9 M10 M11 t
0.036508 0.196595 0.134987 -0.088918 -0.009669 -0.017630
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.273661 -0.076955 -0.001517 0.068942 0.346918
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.262427 3.264481 -1.306 0.19951
X 0.054420 0.030272 1.798 0.08017 .
Y1 1.465330 0.136492 10.736 4.58e-13 ***
Y2 -0.781224 0.261591 -2.986 0.00492 **
Y3 -0.145033 0.262515 -0.552 0.58386
Y4 0.348491 0.143398 2.430 0.01992 *
M1 -0.148355 0.102582 -1.446 0.15632
M2 -0.124116 0.105851 -1.173 0.24828
M3 0.673763 0.111311 6.053 4.82e-07 ***
M4 -0.401218 0.139823 -2.869 0.00668 **
M5 0.005331 0.153972 0.035 0.97256
M6 0.140525 0.125257 1.122 0.26895
M7 0.036508 0.100300 0.364 0.71788
M8 0.196595 0.103155 1.906 0.06426 .
M9 0.134987 0.128021 1.054 0.29835
M10 -0.088918 0.112482 -0.791 0.43414
M11 -0.009669 0.106662 -0.091 0.92825
t -0.017630 0.006378 -2.764 0.00876 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1476 on 38 degrees of freedom
Multiple R-squared: 0.9728, Adjusted R-squared: 0.9606
F-statistic: 79.93 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,] 0.10843081 0.21686163 0.8915692
[2,] 0.06562303 0.13124606 0.9343770
[3,] 0.02434723 0.04869447 0.9756528
[4,] 0.03730260 0.07460520 0.9626974
[5,] 0.01630485 0.03260969 0.9836952
[6,] 0.01464049 0.02928097 0.9853595
[7,] 0.30518847 0.61037693 0.6948115
[8,] 0.20622948 0.41245895 0.7937705
[9,] 0.13505980 0.27011961 0.8649402
[10,] 0.15996979 0.31993958 0.8400302
[11,] 0.11758208 0.23516416 0.8824179
[12,] 0.10039468 0.20078937 0.8996053
[13,] 0.07482237 0.14964473 0.9251776
[14,] 0.04820045 0.09640089 0.9517996
[15,] 0.02168771 0.04337542 0.9783123
> postscript(file="/var/www/html/rcomp/tmp/1mfqd1258556542.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/203y81258556542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33rq21258556542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lvz01258556542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ft8a1258556542.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
-0.023869900 -0.048630005 0.157414654 0.001930631 0.112512026 -0.108845360
7 8 9 10 11 12
-0.014120615 0.051610647 -0.223659494 -0.076949570 -0.072216398 -0.154694180
13 14 15 16 17 18
0.208320723 -0.153978681 0.027563105 0.030830126 0.048077187 -0.010891275
19 20 21 22 23 24
0.078733949 -0.012637143 0.085316746 0.043024895 0.090811980 0.108443750
25 26 27 28 29 30
0.053549544 -0.056973744 -0.273661119 0.102123686 0.068836183 -0.076970116
31 32 33 34 35 36
-0.004965175 -0.051236694 0.130065741 0.114804962 0.066701086 0.307399600
37 38 39 40 41 42
-0.079589850 -0.087335320 0.040471718 -0.063103979 -0.181675500 0.175204356
43 44 45 46 47 48
-0.120049006 -0.056997035 0.008277007 -0.080880287 -0.085296668 -0.261149170
49 50 51 52 53 54
-0.158410516 0.346917750 0.048211642 -0.071780463 -0.047749895 0.021502394
55 56
0.060400847 0.069260224
> postscript(file="/var/www/html/rcomp/tmp/64pws1258556542.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 -0.023869900 NA
1 -0.048630005 -0.023869900
2 0.157414654 -0.048630005
3 0.001930631 0.157414654
4 0.112512026 0.001930631
5 -0.108845360 0.112512026
6 -0.014120615 -0.108845360
7 0.051610647 -0.014120615
8 -0.223659494 0.051610647
9 -0.076949570 -0.223659494
10 -0.072216398 -0.076949570
11 -0.154694180 -0.072216398
12 0.208320723 -0.154694180
13 -0.153978681 0.208320723
14 0.027563105 -0.153978681
15 0.030830126 0.027563105
16 0.048077187 0.030830126
17 -0.010891275 0.048077187
18 0.078733949 -0.010891275
19 -0.012637143 0.078733949
20 0.085316746 -0.012637143
21 0.043024895 0.085316746
22 0.090811980 0.043024895
23 0.108443750 0.090811980
24 0.053549544 0.108443750
25 -0.056973744 0.053549544
26 -0.273661119 -0.056973744
27 0.102123686 -0.273661119
28 0.068836183 0.102123686
29 -0.076970116 0.068836183
30 -0.004965175 -0.076970116
31 -0.051236694 -0.004965175
32 0.130065741 -0.051236694
33 0.114804962 0.130065741
34 0.066701086 0.114804962
35 0.307399600 0.066701086
36 -0.079589850 0.307399600
37 -0.087335320 -0.079589850
38 0.040471718 -0.087335320
39 -0.063103979 0.040471718
40 -0.181675500 -0.063103979
41 0.175204356 -0.181675500
42 -0.120049006 0.175204356
43 -0.056997035 -0.120049006
44 0.008277007 -0.056997035
45 -0.080880287 0.008277007
46 -0.085296668 -0.080880287
47 -0.261149170 -0.085296668
48 -0.158410516 -0.261149170
49 0.346917750 -0.158410516
50 0.048211642 0.346917750
51 -0.071780463 0.048211642
52 -0.047749895 -0.071780463
53 0.021502394 -0.047749895
54 0.060400847 0.021502394
55 0.069260224 0.060400847
56 NA 0.069260224
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.048630005 -0.023869900
[2,] 0.157414654 -0.048630005
[3,] 0.001930631 0.157414654
[4,] 0.112512026 0.001930631
[5,] -0.108845360 0.112512026
[6,] -0.014120615 -0.108845360
[7,] 0.051610647 -0.014120615
[8,] -0.223659494 0.051610647
[9,] -0.076949570 -0.223659494
[10,] -0.072216398 -0.076949570
[11,] -0.154694180 -0.072216398
[12,] 0.208320723 -0.154694180
[13,] -0.153978681 0.208320723
[14,] 0.027563105 -0.153978681
[15,] 0.030830126 0.027563105
[16,] 0.048077187 0.030830126
[17,] -0.010891275 0.048077187
[18,] 0.078733949 -0.010891275
[19,] -0.012637143 0.078733949
[20,] 0.085316746 -0.012637143
[21,] 0.043024895 0.085316746
[22,] 0.090811980 0.043024895
[23,] 0.108443750 0.090811980
[24,] 0.053549544 0.108443750
[25,] -0.056973744 0.053549544
[26,] -0.273661119 -0.056973744
[27,] 0.102123686 -0.273661119
[28,] 0.068836183 0.102123686
[29,] -0.076970116 0.068836183
[30,] -0.004965175 -0.076970116
[31,] -0.051236694 -0.004965175
[32,] 0.130065741 -0.051236694
[33,] 0.114804962 0.130065741
[34,] 0.066701086 0.114804962
[35,] 0.307399600 0.066701086
[36,] -0.079589850 0.307399600
[37,] -0.087335320 -0.079589850
[38,] 0.040471718 -0.087335320
[39,] -0.063103979 0.040471718
[40,] -0.181675500 -0.063103979
[41,] 0.175204356 -0.181675500
[42,] -0.120049006 0.175204356
[43,] -0.056997035 -0.120049006
[44,] 0.008277007 -0.056997035
[45,] -0.080880287 0.008277007
[46,] -0.085296668 -0.080880287
[47,] -0.261149170 -0.085296668
[48,] -0.158410516 -0.261149170
[49,] 0.346917750 -0.158410516
[50,] 0.048211642 0.346917750
[51,] -0.071780463 0.048211642
[52,] -0.047749895 -0.071780463
[53,] 0.021502394 -0.047749895
[54,] 0.060400847 0.021502394
[55,] 0.069260224 0.060400847
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.048630005 -0.023869900
2 0.157414654 -0.048630005
3 0.001930631 0.157414654
4 0.112512026 0.001930631
5 -0.108845360 0.112512026
6 -0.014120615 -0.108845360
7 0.051610647 -0.014120615
8 -0.223659494 0.051610647
9 -0.076949570 -0.223659494
10 -0.072216398 -0.076949570
11 -0.154694180 -0.072216398
12 0.208320723 -0.154694180
13 -0.153978681 0.208320723
14 0.027563105 -0.153978681
15 0.030830126 0.027563105
16 0.048077187 0.030830126
17 -0.010891275 0.048077187
18 0.078733949 -0.010891275
19 -0.012637143 0.078733949
20 0.085316746 -0.012637143
21 0.043024895 0.085316746
22 0.090811980 0.043024895
23 0.108443750 0.090811980
24 0.053549544 0.108443750
25 -0.056973744 0.053549544
26 -0.273661119 -0.056973744
27 0.102123686 -0.273661119
28 0.068836183 0.102123686
29 -0.076970116 0.068836183
30 -0.004965175 -0.076970116
31 -0.051236694 -0.004965175
32 0.130065741 -0.051236694
33 0.114804962 0.130065741
34 0.066701086 0.114804962
35 0.307399600 0.066701086
36 -0.079589850 0.307399600
37 -0.087335320 -0.079589850
38 0.040471718 -0.087335320
39 -0.063103979 0.040471718
40 -0.181675500 -0.063103979
41 0.175204356 -0.181675500
42 -0.120049006 0.175204356
43 -0.056997035 -0.120049006
44 0.008277007 -0.056997035
45 -0.080880287 0.008277007
46 -0.085296668 -0.080880287
47 -0.261149170 -0.085296668
48 -0.158410516 -0.261149170
49 0.346917750 -0.158410516
50 0.048211642 0.346917750
51 -0.071780463 0.048211642
52 -0.047749895 -0.071780463
53 0.021502394 -0.047749895
54 0.060400847 0.021502394
55 0.069260224 0.060400847
> 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/7kx1v1258556542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8h6hl1258556542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uerj1258556542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ys7g1258556542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11x4tz1258556542.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/12gh8u1258556542.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/13z2tk1258556542.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/14ltp81258556542.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/150et61258556542.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/16x0t41258556542.tab")
+ }
>
> system("convert tmp/1mfqd1258556542.ps tmp/1mfqd1258556542.png")
> system("convert tmp/203y81258556542.ps tmp/203y81258556542.png")
> system("convert tmp/33rq21258556542.ps tmp/33rq21258556542.png")
> system("convert tmp/4lvz01258556542.ps tmp/4lvz01258556542.png")
> system("convert tmp/5ft8a1258556542.ps tmp/5ft8a1258556542.png")
> system("convert tmp/64pws1258556542.ps tmp/64pws1258556542.png")
> system("convert tmp/7kx1v1258556542.ps tmp/7kx1v1258556542.png")
> system("convert tmp/8h6hl1258556542.ps tmp/8h6hl1258556542.png")
> system("convert tmp/9uerj1258556542.ps tmp/9uerj1258556542.png")
> system("convert tmp/10ys7g1258556542.ps tmp/10ys7g1258556542.png")
>
>
> proc.time()
user system elapsed
2.430 1.614 3.430