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.
R is a collaborative project with many contributors.
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(8.6
+ ,0
+ ,8.5
+ ,8.3
+ ,8.7
+ ,8.5
+ ,0
+ ,8.6
+ ,8.5
+ ,8.2
+ ,8.2
+ ,0
+ ,8.5
+ ,8.6
+ ,8.3
+ ,8.1
+ ,0
+ ,8.2
+ ,8.5
+ ,8.5
+ ,7.9
+ ,0
+ ,8.1
+ ,8.2
+ ,8.6
+ ,8.6
+ ,0
+ ,7.9
+ ,8.1
+ ,8.5
+ ,8.7
+ ,0
+ ,8.6
+ ,7.9
+ ,8.2
+ ,8.7
+ ,0
+ ,8.7
+ ,8.6
+ ,8.1
+ ,8.5
+ ,0
+ ,8.7
+ ,8.7
+ ,7.9
+ ,8.4
+ ,0
+ ,8.5
+ ,8.7
+ ,8.6
+ ,8.5
+ ,0
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,0
+ ,8.5
+ ,8.4
+ ,8.7
+ ,8.7
+ ,0
+ ,8.7
+ ,8.5
+ ,8.5
+ ,8.6
+ ,0
+ ,8.7
+ ,8.7
+ ,8.4
+ ,8.5
+ ,0
+ ,8.6
+ ,8.7
+ ,8.5
+ ,8.3
+ ,0
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8
+ ,0
+ ,8.3
+ ,8.5
+ ,8.7
+ ,8.2
+ ,0
+ ,8
+ ,8.3
+ ,8.6
+ ,8.1
+ ,0
+ ,8.2
+ ,8
+ ,8.5
+ ,8.1
+ ,0
+ ,8.1
+ ,8.2
+ ,8.3
+ ,8
+ ,0
+ ,8.1
+ ,8.1
+ ,8
+ ,7.9
+ ,0
+ ,8
+ ,8.1
+ ,8.2
+ ,7.9
+ ,0
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,0
+ ,7.9
+ ,7.9
+ ,8.1
+ ,8
+ ,0
+ ,8
+ ,7.9
+ ,8
+ ,7.9
+ ,0
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,0
+ ,7.9
+ ,8
+ ,7.9
+ ,7.7
+ ,0
+ ,8
+ ,7.9
+ ,8
+ ,7.2
+ ,0
+ ,7.7
+ ,8
+ ,8
+ ,7.5
+ ,0
+ ,7.2
+ ,7.7
+ ,7.9
+ ,7.3
+ ,0
+ ,7.5
+ ,7.2
+ ,8
+ ,7
+ ,0
+ ,7.3
+ ,7.5
+ ,7.7
+ ,7
+ ,0
+ ,7
+ ,7.3
+ ,7.2
+ ,7
+ ,0
+ ,7
+ ,7
+ ,7.5
+ ,7.2
+ ,0
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,0
+ ,7.2
+ ,7
+ ,7
+ ,7.1
+ ,0
+ ,7.3
+ ,7.2
+ ,7
+ ,6.8
+ ,0
+ ,7.1
+ ,7.3
+ ,7
+ ,6.4
+ ,0
+ ,6.8
+ ,7.1
+ ,7.2
+ ,6.1
+ ,0
+ ,6.4
+ ,6.8
+ ,7.3
+ ,6.5
+ ,0
+ ,6.1
+ ,6.4
+ ,7.1
+ ,7.7
+ ,0
+ ,6.5
+ ,6.1
+ ,6.8
+ ,7.9
+ ,0
+ ,7.7
+ ,6.5
+ ,6.4
+ ,7.5
+ ,1
+ ,7.9
+ ,7.7
+ ,6.1
+ ,6.9
+ ,1
+ ,7.5
+ ,7.9
+ ,6.5
+ ,6.6
+ ,1
+ ,6.9
+ ,7.5
+ ,7.7
+ ,6.9
+ ,1
+ ,6.6
+ ,6.9
+ ,7.9
+ ,7.7
+ ,1
+ ,6.9
+ ,6.6
+ ,7.5
+ ,8
+ ,1
+ ,7.7
+ ,6.9
+ ,6.9
+ ,8
+ ,1
+ ,8
+ ,7.7
+ ,6.6
+ ,7.7
+ ,1
+ ,8
+ ,8
+ ,6.9
+ ,7.3
+ ,1
+ ,7.7
+ ,8
+ ,7.7
+ ,7.4
+ ,1
+ ,7.3
+ ,7.7
+ ,8
+ ,8.1
+ ,1
+ ,7.4
+ ,7.3
+ ,8
+ ,8.3
+ ,1
+ ,8.1
+ ,7.4
+ ,7.7
+ ,8.2
+ ,1
+ ,8.3
+ ,8.1
+ ,7.3)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('Y','X','Y1','Y2','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 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.6 0 8.5 8.3 8.7 1 0 0 0 0 0 0 0 0 0 0 1
2 8.5 0 8.6 8.5 8.2 0 1 0 0 0 0 0 0 0 0 0 2
3 8.2 0 8.5 8.6 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.1 0 8.2 8.5 8.5 0 0 0 1 0 0 0 0 0 0 0 4
5 7.9 0 8.1 8.2 8.6 0 0 0 0 1 0 0 0 0 0 0 5
6 8.6 0 7.9 8.1 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 8.7 0 8.6 7.9 8.2 0 0 0 0 0 0 1 0 0 0 0 7
8 8.7 0 8.7 8.6 8.1 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 0 8.7 8.7 7.9 0 0 0 0 0 0 0 0 1 0 0 9
10 8.4 0 8.5 8.7 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 0 8.4 8.5 8.7 0 0 0 0 0 0 0 0 0 0 1 11
12 8.7 0 8.5 8.4 8.7 0 0 0 0 0 0 0 0 0 0 0 12
13 8.7 0 8.7 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.6 0 8.7 8.7 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 0 8.6 8.7 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.3 0 8.5 8.6 8.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.0 0 8.3 8.5 8.7 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 0 8.0 8.3 8.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.1 0 8.2 8.0 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.1 0 8.1 8.2 8.3 0 0 0 0 0 0 0 1 0 0 0 20
21 8.0 0 8.1 8.1 8.0 0 0 0 0 0 0 0 0 1 0 0 21
22 7.9 0 8.0 8.1 8.2 0 0 0 0 0 0 0 0 0 1 0 22
23 7.9 0 7.9 8.0 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 8.0 0 7.9 7.9 8.1 0 0 0 0 0 0 0 0 0 0 0 24
25 8.0 0 8.0 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 25
26 7.9 0 8.0 8.0 7.9 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 0 7.9 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 27
28 7.7 0 8.0 7.9 8.0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.2 0 7.7 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.5 0 7.2 7.7 7.9 0 0 0 0 0 1 0 0 0 0 0 30
31 7.3 0 7.5 7.2 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.0 0 7.3 7.5 7.7 0 0 0 0 0 0 0 1 0 0 0 32
33 7.0 0 7.0 7.3 7.2 0 0 0 0 0 0 0 0 1 0 0 33
34 7.0 0 7.0 7.0 7.5 0 0 0 0 0 0 0 0 0 1 0 34
35 7.2 0 7.0 7.0 7.3 0 0 0 0 0 0 0 0 0 0 1 35
36 7.3 0 7.2 7.0 7.0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.1 0 7.3 7.2 7.0 1 0 0 0 0 0 0 0 0 0 0 37
38 6.8 0 7.1 7.3 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 6.4 0 6.8 7.1 7.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.1 0 6.4 6.8 7.3 0 0 0 1 0 0 0 0 0 0 0 40
41 6.5 0 6.1 6.4 7.1 0 0 0 0 1 0 0 0 0 0 0 41
42 7.7 0 6.5 6.1 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 7.9 0 7.7 6.5 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 7.5 1 7.9 7.7 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.9 1 7.5 7.9 6.5 0 0 0 0 0 0 0 0 1 0 0 45
46 6.6 1 6.9 7.5 7.7 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 1 6.6 6.9 7.9 0 0 0 0 0 0 0 0 0 0 1 47
48 7.7 1 6.9 6.6 7.5 0 0 0 0 0 0 0 0 0 0 0 48
49 8.0 1 7.7 6.9 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 8.0 1 8.0 7.7 6.6 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 1 8.0 8.0 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 7.3 1 7.7 8.0 7.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.4 1 7.3 7.7 8.0 0 0 0 0 1 0 0 0 0 0 0 53
54 8.1 1 7.4 7.3 8.0 0 0 0 0 0 1 0 0 0 0 0 54
55 8.3 1 8.1 7.4 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.2 1 8.3 8.1 7.3 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 Y4 M1
0.933206 0.203417 1.558311 -0.965690 0.310028 -0.229526
M2 M3 M4 M5 M6 M7
-0.074505 -0.087331 -0.233405 -0.128830 0.438079 -0.517683
M8 M9 M10 M11 t
-0.096409 -0.012774 -0.137213 -0.004751 -0.004954
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.249508 -0.085866 -0.005577 0.090256 0.372254
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.933206 0.640580 1.457 0.153172
X 0.203417 0.093576 2.174 0.035852 *
Y1 1.558311 0.109835 14.188 < 2e-16 ***
Y2 -0.965690 0.130832 -7.381 6.46e-09 ***
Y4 0.310028 0.071426 4.341 9.76e-05 ***
M1 -0.229526 0.107774 -2.130 0.039564 *
M2 -0.074505 0.117601 -0.634 0.530078
M3 -0.087331 0.119010 -0.734 0.467454
M4 -0.233405 0.114254 -2.043 0.047865 *
M5 -0.128830 0.113909 -1.131 0.264969
M6 0.438079 0.108506 4.037 0.000245 ***
M7 -0.517683 0.117601 -4.402 8.09e-05 ***
M8 -0.096409 0.119378 -0.808 0.424219
M9 -0.012774 0.133967 -0.095 0.924521
M10 -0.137213 0.118853 -1.154 0.255331
M11 -0.004751 0.114667 -0.041 0.967162
t -0.004954 0.003622 -1.368 0.179211
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1572 on 39 degrees of freedom
Multiple R-squared: 0.9598, Adjusted R-squared: 0.9434
F-statistic: 58.25 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,] 0.7118373 0.5763254 0.2881627
[2,] 0.5558234 0.8883533 0.4441766
[3,] 0.4070123 0.8140247 0.5929877
[4,] 0.2709461 0.5418922 0.7290539
[5,] 0.1679486 0.3358971 0.8320514
[6,] 0.1345163 0.2690326 0.8654837
[7,] 0.0768095 0.1536190 0.9231905
[8,] 0.3396726 0.6793452 0.6603274
[9,] 0.3580377 0.7160753 0.6419623
[10,] 0.4252217 0.8504434 0.5747783
[11,] 0.4079449 0.8158899 0.5920551
[12,] 0.3664911 0.7329821 0.6335089
[13,] 0.3305169 0.6610338 0.6694831
[14,] 0.5135339 0.9729322 0.4864661
[15,] 0.4533859 0.9067717 0.5466141
[16,] 0.7931665 0.4136670 0.2068335
[17,] 0.6431598 0.7136805 0.3568402
> postscript(file="/var/www/html/rcomp/tmp/1936w1261671312.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cp401261671312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a1hx1261671312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wuq11261671312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dj441261671312.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
-0.026385373 -0.084130727 -0.144954398 0.214992735 -0.249507682 0.134633240
7 8 9 10 11 12
0.004402205 0.139237558 0.019131109 0.143166287 0.047347942 -0.004849228
13 14 15 16 17 18
0.076542964 0.050617346 0.093224660 0.041509664 -0.143018827 -0.199615855
19 20 21 22 23 24
0.090733754 0.085388994 -0.096852653 0.026365622 -0.010878044 -0.007244150
25 26 27 28 29 30
0.102407260 -0.020087373 0.253522773 -0.178851519 -0.214410916 0.044085170
31 32 33 34 35 36
-0.176539980 -0.198481828 0.152206368 -0.101116299 0.033380818 -0.085069905
37 38 39 40 41 42
-0.013283298 -0.055118634 -0.224990053 -0.071347053 0.372254141 0.190276310
43 44 45 46 47 48
-0.008693126 -0.088254656 -0.074484825 -0.068415610 -0.069850717 0.097163283
49 50 51 52 53 54
-0.139281554 0.108719388 0.023197018 -0.006303827 0.234683285 -0.169378864
55 56
0.090097146 0.062109932
> postscript(file="/var/www/html/rcomp/tmp/6zukt1261671312.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 -0.026385373 NA
1 -0.084130727 -0.026385373
2 -0.144954398 -0.084130727
3 0.214992735 -0.144954398
4 -0.249507682 0.214992735
5 0.134633240 -0.249507682
6 0.004402205 0.134633240
7 0.139237558 0.004402205
8 0.019131109 0.139237558
9 0.143166287 0.019131109
10 0.047347942 0.143166287
11 -0.004849228 0.047347942
12 0.076542964 -0.004849228
13 0.050617346 0.076542964
14 0.093224660 0.050617346
15 0.041509664 0.093224660
16 -0.143018827 0.041509664
17 -0.199615855 -0.143018827
18 0.090733754 -0.199615855
19 0.085388994 0.090733754
20 -0.096852653 0.085388994
21 0.026365622 -0.096852653
22 -0.010878044 0.026365622
23 -0.007244150 -0.010878044
24 0.102407260 -0.007244150
25 -0.020087373 0.102407260
26 0.253522773 -0.020087373
27 -0.178851519 0.253522773
28 -0.214410916 -0.178851519
29 0.044085170 -0.214410916
30 -0.176539980 0.044085170
31 -0.198481828 -0.176539980
32 0.152206368 -0.198481828
33 -0.101116299 0.152206368
34 0.033380818 -0.101116299
35 -0.085069905 0.033380818
36 -0.013283298 -0.085069905
37 -0.055118634 -0.013283298
38 -0.224990053 -0.055118634
39 -0.071347053 -0.224990053
40 0.372254141 -0.071347053
41 0.190276310 0.372254141
42 -0.008693126 0.190276310
43 -0.088254656 -0.008693126
44 -0.074484825 -0.088254656
45 -0.068415610 -0.074484825
46 -0.069850717 -0.068415610
47 0.097163283 -0.069850717
48 -0.139281554 0.097163283
49 0.108719388 -0.139281554
50 0.023197018 0.108719388
51 -0.006303827 0.023197018
52 0.234683285 -0.006303827
53 -0.169378864 0.234683285
54 0.090097146 -0.169378864
55 0.062109932 0.090097146
56 NA 0.062109932
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.084130727 -0.026385373
[2,] -0.144954398 -0.084130727
[3,] 0.214992735 -0.144954398
[4,] -0.249507682 0.214992735
[5,] 0.134633240 -0.249507682
[6,] 0.004402205 0.134633240
[7,] 0.139237558 0.004402205
[8,] 0.019131109 0.139237558
[9,] 0.143166287 0.019131109
[10,] 0.047347942 0.143166287
[11,] -0.004849228 0.047347942
[12,] 0.076542964 -0.004849228
[13,] 0.050617346 0.076542964
[14,] 0.093224660 0.050617346
[15,] 0.041509664 0.093224660
[16,] -0.143018827 0.041509664
[17,] -0.199615855 -0.143018827
[18,] 0.090733754 -0.199615855
[19,] 0.085388994 0.090733754
[20,] -0.096852653 0.085388994
[21,] 0.026365622 -0.096852653
[22,] -0.010878044 0.026365622
[23,] -0.007244150 -0.010878044
[24,] 0.102407260 -0.007244150
[25,] -0.020087373 0.102407260
[26,] 0.253522773 -0.020087373
[27,] -0.178851519 0.253522773
[28,] -0.214410916 -0.178851519
[29,] 0.044085170 -0.214410916
[30,] -0.176539980 0.044085170
[31,] -0.198481828 -0.176539980
[32,] 0.152206368 -0.198481828
[33,] -0.101116299 0.152206368
[34,] 0.033380818 -0.101116299
[35,] -0.085069905 0.033380818
[36,] -0.013283298 -0.085069905
[37,] -0.055118634 -0.013283298
[38,] -0.224990053 -0.055118634
[39,] -0.071347053 -0.224990053
[40,] 0.372254141 -0.071347053
[41,] 0.190276310 0.372254141
[42,] -0.008693126 0.190276310
[43,] -0.088254656 -0.008693126
[44,] -0.074484825 -0.088254656
[45,] -0.068415610 -0.074484825
[46,] -0.069850717 -0.068415610
[47,] 0.097163283 -0.069850717
[48,] -0.139281554 0.097163283
[49,] 0.108719388 -0.139281554
[50,] 0.023197018 0.108719388
[51,] -0.006303827 0.023197018
[52,] 0.234683285 -0.006303827
[53,] -0.169378864 0.234683285
[54,] 0.090097146 -0.169378864
[55,] 0.062109932 0.090097146
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.084130727 -0.026385373
2 -0.144954398 -0.084130727
3 0.214992735 -0.144954398
4 -0.249507682 0.214992735
5 0.134633240 -0.249507682
6 0.004402205 0.134633240
7 0.139237558 0.004402205
8 0.019131109 0.139237558
9 0.143166287 0.019131109
10 0.047347942 0.143166287
11 -0.004849228 0.047347942
12 0.076542964 -0.004849228
13 0.050617346 0.076542964
14 0.093224660 0.050617346
15 0.041509664 0.093224660
16 -0.143018827 0.041509664
17 -0.199615855 -0.143018827
18 0.090733754 -0.199615855
19 0.085388994 0.090733754
20 -0.096852653 0.085388994
21 0.026365622 -0.096852653
22 -0.010878044 0.026365622
23 -0.007244150 -0.010878044
24 0.102407260 -0.007244150
25 -0.020087373 0.102407260
26 0.253522773 -0.020087373
27 -0.178851519 0.253522773
28 -0.214410916 -0.178851519
29 0.044085170 -0.214410916
30 -0.176539980 0.044085170
31 -0.198481828 -0.176539980
32 0.152206368 -0.198481828
33 -0.101116299 0.152206368
34 0.033380818 -0.101116299
35 -0.085069905 0.033380818
36 -0.013283298 -0.085069905
37 -0.055118634 -0.013283298
38 -0.224990053 -0.055118634
39 -0.071347053 -0.224990053
40 0.372254141 -0.071347053
41 0.190276310 0.372254141
42 -0.008693126 0.190276310
43 -0.088254656 -0.008693126
44 -0.074484825 -0.088254656
45 -0.068415610 -0.074484825
46 -0.069850717 -0.068415610
47 0.097163283 -0.069850717
48 -0.139281554 0.097163283
49 0.108719388 -0.139281554
50 0.023197018 0.108719388
51 -0.006303827 0.023197018
52 0.234683285 -0.006303827
53 -0.169378864 0.234683285
54 0.090097146 -0.169378864
55 0.062109932 0.090097146
> 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/7xkzr1261671312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ack41261671312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9y6b41261671312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106eii1261671312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1129zk1261671312.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/12l58b1261671312.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/13014x1261671312.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/14p6d71261671312.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/15czzq1261671312.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/16035h1261671312.tab")
+ }
>
> try(system("convert tmp/1936w1261671312.ps tmp/1936w1261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cp401261671312.ps tmp/2cp401261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a1hx1261671312.ps tmp/3a1hx1261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wuq11261671312.ps tmp/4wuq11261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dj441261671312.ps tmp/5dj441261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zukt1261671312.ps tmp/6zukt1261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xkzr1261671312.ps tmp/7xkzr1261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ack41261671312.ps tmp/8ack41261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/9y6b41261671312.ps tmp/9y6b41261671312.png",intern=TRUE))
character(0)
> try(system("convert tmp/106eii1261671312.ps tmp/106eii1261671312.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.342 1.567 2.997