R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(7.3
+ ,20.9
+ ,7.4
+ ,8.1
+ ,8.2
+ ,7.7
+ ,20.9
+ ,7.3
+ ,7.4
+ ,8.3
+ ,8
+ ,22.3
+ ,7.7
+ ,7.3
+ ,8.1
+ ,8
+ ,22.3
+ ,8
+ ,7.7
+ ,7.4
+ ,7.7
+ ,22.3
+ ,8
+ ,8
+ ,7.3
+ ,6.9
+ ,19.9
+ ,7.7
+ ,8
+ ,7.7
+ ,6.6
+ ,19.9
+ ,6.9
+ ,7.7
+ ,8
+ ,6.9
+ ,19.9
+ ,6.6
+ ,6.9
+ ,8
+ ,7.5
+ ,24.1
+ ,6.9
+ ,6.6
+ ,7.7
+ ,7.9
+ ,24.1
+ ,7.5
+ ,6.9
+ ,6.9
+ ,7.7
+ ,24.1
+ ,7.9
+ ,7.5
+ ,6.6
+ ,6.5
+ ,13.8
+ ,7.7
+ ,7.9
+ ,6.9
+ ,6.1
+ ,13.8
+ ,6.5
+ ,7.7
+ ,7.5
+ ,6.4
+ ,13.8
+ ,6.1
+ ,6.5
+ ,7.9
+ ,6.8
+ ,16.2
+ ,6.4
+ ,6.1
+ ,7.7
+ ,7.1
+ ,16.2
+ ,6.8
+ ,6.4
+ ,6.5
+ ,7.3
+ ,16.2
+ ,7.1
+ ,6.8
+ ,6.1
+ ,7.2
+ ,18.6
+ ,7.3
+ ,7.1
+ ,6.4
+ ,7
+ ,18.6
+ ,7.2
+ ,7.3
+ ,6.8
+ ,7
+ ,18.6
+ ,7
+ ,7.2
+ ,7.1
+ ,7
+ ,22.4
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,22.4
+ ,7
+ ,7
+ ,7.2
+ ,7.5
+ ,22.4
+ ,7.3
+ ,7
+ ,7
+ ,7.2
+ ,22.6
+ ,7.5
+ ,7.3
+ ,7
+ ,7.7
+ ,22.6
+ ,7.2
+ ,7.5
+ ,7
+ ,8
+ ,22.6
+ ,7.7
+ ,7.2
+ ,7.3
+ ,7.9
+ ,20
+ ,8
+ ,7.7
+ ,7.5
+ ,8
+ ,20
+ ,7.9
+ ,8
+ ,7.2
+ ,8
+ ,20
+ ,8
+ ,7.9
+ ,7.7
+ ,7.9
+ ,21.8
+ ,8
+ ,8
+ ,8
+ ,7.9
+ ,21.8
+ ,7.9
+ ,8
+ ,7.9
+ ,8
+ ,21.8
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,28.7
+ ,8
+ ,7.9
+ ,8
+ ,8.1
+ ,28.7
+ ,8.1
+ ,8
+ ,7.9
+ ,8.2
+ ,28.7
+ ,8.1
+ ,8.1
+ ,7.9
+ ,8
+ ,19.5
+ ,8.2
+ ,8.1
+ ,8
+ ,8.3
+ ,19.5
+ ,8
+ ,8.2
+ ,8.1
+ ,8.5
+ ,19.5
+ ,8.3
+ ,8
+ ,8.1
+ ,8.6
+ ,19.4
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,19.4
+ ,8.6
+ ,8.5
+ ,8
+ ,8.7
+ ,19.4
+ ,8.7
+ ,8.6
+ ,8.3
+ ,8.5
+ ,21.7
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,21.7
+ ,8.5
+ ,8.7
+ ,8.6
+ ,8.5
+ ,21.7
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,26.2
+ ,8.5
+ ,8.4
+ ,8.7
+ ,8.7
+ ,26.2
+ ,8.7
+ ,8.5
+ ,8.5
+ ,8.6
+ ,26.2
+ ,8.7
+ ,8.7
+ ,8.4
+ ,7.9
+ ,19.1
+ ,8.6
+ ,8.7
+ ,8.5
+ ,8.1
+ ,19.1
+ ,7.9
+ ,8.6
+ ,8.7
+ ,8.2
+ ,19.1
+ ,8.1
+ ,7.9
+ ,8.7
+ ,8.5
+ ,21.3
+ ,8.2
+ ,8.1
+ ,8.6
+ ,8.6
+ ,21.3
+ ,8.5
+ ,8.2
+ ,7.9
+ ,8.5
+ ,21.3
+ ,8.6
+ ,8.5
+ ,8.1
+ ,8.3
+ ,24.1
+ ,8.5
+ ,8.6
+ ,8.2
+ ,8.2
+ ,24.1
+ ,8.3
+ ,8.5
+ ,8.5
+ ,8.7
+ ,24.1
+ ,8.2
+ ,8.3
+ ,8.6)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,''
+ ,''
+ ,'')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('Y','X','','',''),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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.3 20.9 7.4 8.1 8.2 1 0 0 0 0 0 0 0 0 0 0 1
2 7.7 20.9 7.3 7.4 8.3 0 1 0 0 0 0 0 0 0 0 0 2
3 8.0 22.3 7.7 7.3 8.1 0 0 1 0 0 0 0 0 0 0 0 3
4 8.0 22.3 8.0 7.7 7.4 0 0 0 1 0 0 0 0 0 0 0 4
5 7.7 22.3 8.0 8.0 7.3 0 0 0 0 1 0 0 0 0 0 0 5
6 6.9 19.9 7.7 8.0 7.7 0 0 0 0 0 1 0 0 0 0 0 6
7 6.6 19.9 6.9 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 7
8 6.9 19.9 6.6 6.9 8.0 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 24.1 6.9 6.6 7.7 0 0 0 0 0 0 0 0 1 0 0 9
10 7.9 24.1 7.5 6.9 6.9 0 0 0 0 0 0 0 0 0 1 0 10
11 7.7 24.1 7.9 7.5 6.6 0 0 0 0 0 0 0 0 0 0 1 11
12 6.5 13.8 7.7 7.9 6.9 0 0 0 0 0 0 0 0 0 0 0 12
13 6.1 13.8 6.5 7.7 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 6.4 13.8 6.1 6.5 7.9 0 1 0 0 0 0 0 0 0 0 0 14
15 6.8 16.2 6.4 6.1 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 7.1 16.2 6.8 6.4 6.5 0 0 0 1 0 0 0 0 0 0 0 16
17 7.3 16.2 7.1 6.8 6.1 0 0 0 0 1 0 0 0 0 0 0 17
18 7.2 18.6 7.3 7.1 6.4 0 0 0 0 0 1 0 0 0 0 0 18
19 7.0 18.6 7.2 7.3 6.8 0 0 0 0 0 0 1 0 0 0 0 19
20 7.0 18.6 7.0 7.2 7.1 0 0 0 0 0 0 0 1 0 0 0 20
21 7.0 22.4 7.0 7.0 7.3 0 0 0 0 0 0 0 0 1 0 0 21
22 7.3 22.4 7.0 7.0 7.2 0 0 0 0 0 0 0 0 0 1 0 22
23 7.5 22.4 7.3 7.0 7.0 0 0 0 0 0 0 0 0 0 0 1 23
24 7.2 22.6 7.5 7.3 7.0 0 0 0 0 0 0 0 0 0 0 0 24
25 7.7 22.6 7.2 7.5 7.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 22.6 7.7 7.2 7.3 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 20.0 8.0 7.7 7.5 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 20.0 7.9 8.0 7.2 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 20.0 8.0 7.9 7.7 0 0 0 0 1 0 0 0 0 0 0 29
30 7.9 21.8 8.0 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 21.8 7.9 8.0 7.9 0 0 0 0 0 0 1 0 0 0 0 31
32 8.0 21.8 7.9 7.9 8.0 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 28.7 8.0 7.9 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.1 28.7 8.1 8.0 7.9 0 0 0 0 0 0 0 0 0 1 0 34
35 8.2 28.7 8.1 8.1 7.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 19.5 8.2 8.1 8.0 0 0 0 0 0 0 0 0 0 0 0 36
37 8.3 19.5 8.0 8.2 8.1 1 0 0 0 0 0 0 0 0 0 0 37
38 8.5 19.5 8.3 8.0 8.1 0 1 0 0 0 0 0 0 0 0 0 38
39 8.6 19.4 8.5 8.3 8.2 0 0 1 0 0 0 0 0 0 0 0 39
40 8.7 19.4 8.6 8.5 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 8.7 19.4 8.7 8.6 8.3 0 0 0 0 1 0 0 0 0 0 0 41
42 8.5 21.7 8.7 8.7 8.5 0 0 0 0 0 1 0 0 0 0 0 42
43 8.4 21.7 8.5 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 43
44 8.5 21.7 8.4 8.5 8.7 0 0 0 0 0 0 0 1 0 0 0 44
45 8.7 26.2 8.5 8.4 8.7 0 0 0 0 0 0 0 0 1 0 0 45
46 8.7 26.2 8.7 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 46
47 8.6 26.2 8.7 8.7 8.4 0 0 0 0 0 0 0 0 0 0 1 47
48 7.9 19.1 8.6 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 48
49 8.1 19.1 7.9 8.6 8.7 1 0 0 0 0 0 0 0 0 0 0 49
50 8.2 19.1 8.1 7.9 8.7 0 1 0 0 0 0 0 0 0 0 0 50
51 8.5 21.3 8.2 8.1 8.6 0 0 1 0 0 0 0 0 0 0 0 51
52 8.6 21.3 8.5 8.2 7.9 0 0 0 1 0 0 0 0 0 0 0 52
53 8.5 21.3 8.6 8.5 8.1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.3 24.1 8.5 8.6 8.2 0 0 0 0 0 1 0 0 0 0 0 54
55 8.2 24.1 8.3 8.5 8.5 0 0 0 0 0 0 1 0 0 0 0 55
56 8.7 24.1 8.2 8.3 8.6 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 V3 V4 V5 M1
0.547157 0.032167 1.334882 -0.795642 0.227810 0.868739
M2 M3 M4 M5 M6 M7
0.458689 0.372251 0.566569 0.495916 0.254353 0.403918
M8 M9 M10 M11 t
0.533870 0.275340 0.310975 0.283748 0.006814
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.26988 -0.09786 -0.00492 0.08671 0.26052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.547157 0.394221 1.388 0.173033
X 0.032167 0.011624 2.767 0.008598 **
V3 1.334882 0.123865 10.777 2.95e-13 ***
V4 -0.795642 0.128564 -6.189 2.83e-07 ***
V5 0.227810 0.063004 3.616 0.000847 ***
M1 0.868739 0.122580 7.087 1.63e-08 ***
M2 0.458689 0.118164 3.882 0.000389 ***
M3 0.372251 0.118381 3.145 0.003177 **
M4 0.566569 0.102459 5.530 2.33e-06 ***
M5 0.495916 0.101036 4.908 1.67e-05 ***
M6 0.254353 0.103875 2.449 0.018938 *
M7 0.403918 0.112205 3.600 0.000887 ***
M8 0.533870 0.114775 4.651 3.74e-05 ***
M9 0.275340 0.140520 1.959 0.057238 .
M10 0.310975 0.132602 2.345 0.024200 *
M11 0.283748 0.128757 2.204 0.033514 *
t 0.006814 0.002010 3.389 0.001614 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1491 on 39 degrees of freedom
Multiple R-squared: 0.9654, Adjusted R-squared: 0.9512
F-statistic: 67.97 on 16 and 39 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,] 0.10565899 0.21131797 0.8943410
[2,] 0.04457909 0.08915819 0.9554209
[3,] 0.33580763 0.67161526 0.6641924
[4,] 0.43354671 0.86709341 0.5664533
[5,] 0.51305491 0.97389018 0.4869451
[6,] 0.43900755 0.87801509 0.5609925
[7,] 0.48956934 0.97913868 0.5104307
[8,] 0.58239991 0.83520018 0.4176001
[9,] 0.75245181 0.49509638 0.2475482
[10,] 0.64562546 0.70874908 0.3543745
[11,] 0.54906745 0.90186510 0.4509325
[12,] 0.46244399 0.92488799 0.5375560
[13,] 0.42638689 0.85277377 0.5736131
[14,] 0.41132426 0.82264852 0.5886757
[15,] 0.47601984 0.95203968 0.5239802
[16,] 0.68157296 0.63685408 0.3184270
[17,] 0.58283016 0.83433969 0.4171698
> postscript(file="/var/www/html/rcomp/tmp/18hy01258583556.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mj391258583556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3we2z1258583556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4waz51258583556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rsw11258583556.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
-0.096477280 0.260516393 0.027150026 -0.096723553 -0.071411558 -0.250119398
7 8 9 10 11 12
0.054371523 -0.018444044 0.127354057 0.104915310 -0.062896004 -0.137746998
13 14 15 16 17 18
-0.107255266 0.084039206 -0.186699049 -0.109720306 0.163034056 0.123954643
19 20 21 22 23 24
-0.030931984 -0.048629184 -0.123839939 0.156491420 0.022002135 -0.035781857
25 26 27 28 29 30
0.148258323 -0.122982479 -0.107929187 0.231462148 -0.031656644 0.056412491
31 32 33 34 35 36
0.056302213 -0.082809548 -0.086537453 -0.160130119 0.039847600 0.256452729
37 38 39 40 41 42
0.004659513 0.048302247 0.180077567 0.150147273 0.091718805 0.086485145
43 44 45 46 47 48
0.074301194 -0.010886542 0.083023336 -0.101276611 0.001046270 -0.082923875
49 50 51 52 53 54
0.050814709 -0.269875367 0.087400643 -0.175165562 -0.151684660 -0.016732881
55 56
-0.154042947 0.160769317
> postscript(file="/var/www/html/rcomp/tmp/6x9fa1258583556.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 -0.096477280 NA
1 0.260516393 -0.096477280
2 0.027150026 0.260516393
3 -0.096723553 0.027150026
4 -0.071411558 -0.096723553
5 -0.250119398 -0.071411558
6 0.054371523 -0.250119398
7 -0.018444044 0.054371523
8 0.127354057 -0.018444044
9 0.104915310 0.127354057
10 -0.062896004 0.104915310
11 -0.137746998 -0.062896004
12 -0.107255266 -0.137746998
13 0.084039206 -0.107255266
14 -0.186699049 0.084039206
15 -0.109720306 -0.186699049
16 0.163034056 -0.109720306
17 0.123954643 0.163034056
18 -0.030931984 0.123954643
19 -0.048629184 -0.030931984
20 -0.123839939 -0.048629184
21 0.156491420 -0.123839939
22 0.022002135 0.156491420
23 -0.035781857 0.022002135
24 0.148258323 -0.035781857
25 -0.122982479 0.148258323
26 -0.107929187 -0.122982479
27 0.231462148 -0.107929187
28 -0.031656644 0.231462148
29 0.056412491 -0.031656644
30 0.056302213 0.056412491
31 -0.082809548 0.056302213
32 -0.086537453 -0.082809548
33 -0.160130119 -0.086537453
34 0.039847600 -0.160130119
35 0.256452729 0.039847600
36 0.004659513 0.256452729
37 0.048302247 0.004659513
38 0.180077567 0.048302247
39 0.150147273 0.180077567
40 0.091718805 0.150147273
41 0.086485145 0.091718805
42 0.074301194 0.086485145
43 -0.010886542 0.074301194
44 0.083023336 -0.010886542
45 -0.101276611 0.083023336
46 0.001046270 -0.101276611
47 -0.082923875 0.001046270
48 0.050814709 -0.082923875
49 -0.269875367 0.050814709
50 0.087400643 -0.269875367
51 -0.175165562 0.087400643
52 -0.151684660 -0.175165562
53 -0.016732881 -0.151684660
54 -0.154042947 -0.016732881
55 0.160769317 -0.154042947
56 NA 0.160769317
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.260516393 -0.096477280
[2,] 0.027150026 0.260516393
[3,] -0.096723553 0.027150026
[4,] -0.071411558 -0.096723553
[5,] -0.250119398 -0.071411558
[6,] 0.054371523 -0.250119398
[7,] -0.018444044 0.054371523
[8,] 0.127354057 -0.018444044
[9,] 0.104915310 0.127354057
[10,] -0.062896004 0.104915310
[11,] -0.137746998 -0.062896004
[12,] -0.107255266 -0.137746998
[13,] 0.084039206 -0.107255266
[14,] -0.186699049 0.084039206
[15,] -0.109720306 -0.186699049
[16,] 0.163034056 -0.109720306
[17,] 0.123954643 0.163034056
[18,] -0.030931984 0.123954643
[19,] -0.048629184 -0.030931984
[20,] -0.123839939 -0.048629184
[21,] 0.156491420 -0.123839939
[22,] 0.022002135 0.156491420
[23,] -0.035781857 0.022002135
[24,] 0.148258323 -0.035781857
[25,] -0.122982479 0.148258323
[26,] -0.107929187 -0.122982479
[27,] 0.231462148 -0.107929187
[28,] -0.031656644 0.231462148
[29,] 0.056412491 -0.031656644
[30,] 0.056302213 0.056412491
[31,] -0.082809548 0.056302213
[32,] -0.086537453 -0.082809548
[33,] -0.160130119 -0.086537453
[34,] 0.039847600 -0.160130119
[35,] 0.256452729 0.039847600
[36,] 0.004659513 0.256452729
[37,] 0.048302247 0.004659513
[38,] 0.180077567 0.048302247
[39,] 0.150147273 0.180077567
[40,] 0.091718805 0.150147273
[41,] 0.086485145 0.091718805
[42,] 0.074301194 0.086485145
[43,] -0.010886542 0.074301194
[44,] 0.083023336 -0.010886542
[45,] -0.101276611 0.083023336
[46,] 0.001046270 -0.101276611
[47,] -0.082923875 0.001046270
[48,] 0.050814709 -0.082923875
[49,] -0.269875367 0.050814709
[50,] 0.087400643 -0.269875367
[51,] -0.175165562 0.087400643
[52,] -0.151684660 -0.175165562
[53,] -0.016732881 -0.151684660
[54,] -0.154042947 -0.016732881
[55,] 0.160769317 -0.154042947
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.260516393 -0.096477280
2 0.027150026 0.260516393
3 -0.096723553 0.027150026
4 -0.071411558 -0.096723553
5 -0.250119398 -0.071411558
6 0.054371523 -0.250119398
7 -0.018444044 0.054371523
8 0.127354057 -0.018444044
9 0.104915310 0.127354057
10 -0.062896004 0.104915310
11 -0.137746998 -0.062896004
12 -0.107255266 -0.137746998
13 0.084039206 -0.107255266
14 -0.186699049 0.084039206
15 -0.109720306 -0.186699049
16 0.163034056 -0.109720306
17 0.123954643 0.163034056
18 -0.030931984 0.123954643
19 -0.048629184 -0.030931984
20 -0.123839939 -0.048629184
21 0.156491420 -0.123839939
22 0.022002135 0.156491420
23 -0.035781857 0.022002135
24 0.148258323 -0.035781857
25 -0.122982479 0.148258323
26 -0.107929187 -0.122982479
27 0.231462148 -0.107929187
28 -0.031656644 0.231462148
29 0.056412491 -0.031656644
30 0.056302213 0.056412491
31 -0.082809548 0.056302213
32 -0.086537453 -0.082809548
33 -0.160130119 -0.086537453
34 0.039847600 -0.160130119
35 0.256452729 0.039847600
36 0.004659513 0.256452729
37 0.048302247 0.004659513
38 0.180077567 0.048302247
39 0.150147273 0.180077567
40 0.091718805 0.150147273
41 0.086485145 0.091718805
42 0.074301194 0.086485145
43 -0.010886542 0.074301194
44 0.083023336 -0.010886542
45 -0.101276611 0.083023336
46 0.001046270 -0.101276611
47 -0.082923875 0.001046270
48 0.050814709 -0.082923875
49 -0.269875367 0.050814709
50 0.087400643 -0.269875367
51 -0.175165562 0.087400643
52 -0.151684660 -0.175165562
53 -0.016732881 -0.151684660
54 -0.154042947 -0.016732881
55 0.160769317 -0.154042947
> 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/7jqyn1258583556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8t2dl1258583556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ssfj1258583556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10e9he1258583556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110yz91258583556.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/128h0l1258583556.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/13hox91258583556.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/14mk4h1258583556.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/15pio31258583556.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/16rpt11258583556.tab")
+ }
>
> system("convert tmp/18hy01258583556.ps tmp/18hy01258583556.png")
> system("convert tmp/2mj391258583556.ps tmp/2mj391258583556.png")
> system("convert tmp/3we2z1258583556.ps tmp/3we2z1258583556.png")
> system("convert tmp/4waz51258583556.ps tmp/4waz51258583556.png")
> system("convert tmp/5rsw11258583556.ps tmp/5rsw11258583556.png")
> system("convert tmp/6x9fa1258583556.ps tmp/6x9fa1258583556.png")
> system("convert tmp/7jqyn1258583556.ps tmp/7jqyn1258583556.png")
> system("convert tmp/8t2dl1258583556.ps tmp/8t2dl1258583556.png")
> system("convert tmp/9ssfj1258583556.ps tmp/9ssfj1258583556.png")
> system("convert tmp/10e9he1258583556.ps tmp/10e9he1258583556.png")
>
>
> proc.time()
user system elapsed
2.342 1.543 2.785