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(7.70
+ ,110.30
+ ,8.10
+ ,8.00
+ ,7.50
+ ,103.90
+ ,7.70
+ ,8.10
+ ,7.60
+ ,101.60
+ ,7.50
+ ,7.70
+ ,7.80
+ ,94.60
+ ,7.60
+ ,7.50
+ ,7.80
+ ,95.90
+ ,7.80
+ ,7.60
+ ,7.80
+ ,104.70
+ ,7.80
+ ,7.80
+ ,7.50
+ ,102.80
+ ,7.80
+ ,7.80
+ ,7.50
+ ,98.10
+ ,7.50
+ ,7.80
+ ,7.10
+ ,113.90
+ ,7.50
+ ,7.50
+ ,7.50
+ ,80.90
+ ,7.10
+ ,7.50
+ ,7.50
+ ,95.70
+ ,7.50
+ ,7.10
+ ,7.60
+ ,113.20
+ ,7.50
+ ,7.50
+ ,7.70
+ ,105.90
+ ,7.60
+ ,7.50
+ ,7.70
+ ,108.80
+ ,7.70
+ ,7.60
+ ,7.90
+ ,102.30
+ ,7.70
+ ,7.70
+ ,8.10
+ ,99.00
+ ,7.90
+ ,7.70
+ ,8.20
+ ,100.70
+ ,8.10
+ ,7.90
+ ,8.20
+ ,115.50
+ ,8.20
+ ,8.10
+ ,8.20
+ ,100.70
+ ,8.20
+ ,8.20
+ ,7.90
+ ,109.90
+ ,8.20
+ ,8.20
+ ,7.30
+ ,114.60
+ ,7.90
+ ,8.20
+ ,6.90
+ ,85.40
+ ,7.30
+ ,7.90
+ ,6.60
+ ,100.50
+ ,6.90
+ ,7.30
+ ,6.70
+ ,114.80
+ ,6.60
+ ,6.90
+ ,6.90
+ ,116.50
+ ,6.70
+ ,6.60
+ ,7.00
+ ,112.90
+ ,6.90
+ ,6.70
+ ,7.10
+ ,102.00
+ ,7.00
+ ,6.90
+ ,7.20
+ ,106.00
+ ,7.10
+ ,7.00
+ ,7.10
+ ,105.30
+ ,7.20
+ ,7.10
+ ,6.90
+ ,118.80
+ ,7.10
+ ,7.20
+ ,7.00
+ ,106.10
+ ,6.90
+ ,7.10
+ ,6.80
+ ,109.30
+ ,7.00
+ ,6.90
+ ,6.40
+ ,117.20
+ ,6.80
+ ,7.00
+ ,6.70
+ ,92.50
+ ,6.40
+ ,6.80
+ ,6.60
+ ,104.20
+ ,6.70
+ ,6.40
+ ,6.40
+ ,112.50
+ ,6.60
+ ,6.70
+ ,6.30
+ ,122.40
+ ,6.40
+ ,6.60
+ ,6.20
+ ,113.30
+ ,6.30
+ ,6.40
+ ,6.50
+ ,100.00
+ ,6.20
+ ,6.30
+ ,6.80
+ ,110.70
+ ,6.50
+ ,6.20
+ ,6.80
+ ,112.80
+ ,6.80
+ ,6.50
+ ,6.40
+ ,109.80
+ ,6.80
+ ,6.80
+ ,6.10
+ ,117.30
+ ,6.40
+ ,6.80
+ ,5.80
+ ,109.10
+ ,6.10
+ ,6.40
+ ,6.10
+ ,115.90
+ ,5.80
+ ,6.10
+ ,7.20
+ ,96.00
+ ,6.10
+ ,5.80
+ ,7.30
+ ,99.80
+ ,7.20
+ ,6.10
+ ,6.90
+ ,116.80
+ ,7.30
+ ,7.20
+ ,6.10
+ ,115.70
+ ,6.90
+ ,7.30
+ ,5.80
+ ,99.40
+ ,6.10
+ ,6.90
+ ,6.20
+ ,94.30
+ ,5.80
+ ,6.10
+ ,7.10
+ ,91.00
+ ,6.20
+ ,5.80
+ ,7.70
+ ,93.20
+ ,7.10
+ ,6.20
+ ,7.90
+ ,103.10
+ ,7.70
+ ,7.10
+ ,7.70
+ ,94.10
+ ,7.90
+ ,7.70
+ ,7.40
+ ,91.80
+ ,7.70
+ ,7.90
+ ,7.50
+ ,102.70
+ ,7.40
+ ,7.70
+ ,8.00
+ ,82.60
+ ,7.50
+ ,7.40)
+ ,dim=c(4
+ ,58)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.7 110.3 8.1 8.0 1 0 0 0 0 0 0 0 0 0 0 1
2 7.5 103.9 7.7 8.1 0 1 0 0 0 0 0 0 0 0 0 2
3 7.6 101.6 7.5 7.7 0 0 1 0 0 0 0 0 0 0 0 3
4 7.8 94.6 7.6 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 7.8 95.9 7.8 7.6 0 0 0 0 1 0 0 0 0 0 0 5
6 7.8 104.7 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 102.8 7.8 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 98.1 7.5 7.8 0 0 0 0 0 0 0 1 0 0 0 8
9 7.1 113.9 7.5 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 80.9 7.1 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.5 95.7 7.5 7.1 0 0 0 0 0 0 0 0 0 0 1 11
12 7.6 113.2 7.5 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 7.7 105.9 7.6 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.7 108.8 7.7 7.6 0 1 0 0 0 0 0 0 0 0 0 14
15 7.9 102.3 7.7 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 8.1 99.0 7.9 7.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 100.7 8.1 7.9 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 115.5 8.2 8.1 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 100.7 8.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 7.9 109.9 8.2 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 7.3 114.6 7.9 8.2 0 0 0 0 0 0 0 0 1 0 0 21
22 6.9 85.4 7.3 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 6.6 100.5 6.9 7.3 0 0 0 0 0 0 0 0 0 0 1 23
24 6.7 114.8 6.6 6.9 0 0 0 0 0 0 0 0 0 0 0 24
25 6.9 116.5 6.7 6.6 1 0 0 0 0 0 0 0 0 0 0 25
26 7.0 112.9 6.9 6.7 0 1 0 0 0 0 0 0 0 0 0 26
27 7.1 102.0 7.0 6.9 0 0 1 0 0 0 0 0 0 0 0 27
28 7.2 106.0 7.1 7.0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.1 105.3 7.2 7.1 0 0 0 0 1 0 0 0 0 0 0 29
30 6.9 118.8 7.1 7.2 0 0 0 0 0 1 0 0 0 0 0 30
31 7.0 106.1 6.9 7.1 0 0 0 0 0 0 1 0 0 0 0 31
32 6.8 109.3 7.0 6.9 0 0 0 0 0 0 0 1 0 0 0 32
33 6.4 117.2 6.8 7.0 0 0 0 0 0 0 0 0 1 0 0 33
34 6.7 92.5 6.4 6.8 0 0 0 0 0 0 0 0 0 1 0 34
35 6.6 104.2 6.7 6.4 0 0 0 0 0 0 0 0 0 0 1 35
36 6.4 112.5 6.6 6.7 0 0 0 0 0 0 0 0 0 0 0 36
37 6.3 122.4 6.4 6.6 1 0 0 0 0 0 0 0 0 0 0 37
38 6.2 113.3 6.3 6.4 0 1 0 0 0 0 0 0 0 0 0 38
39 6.5 100.0 6.2 6.3 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 110.7 6.5 6.2 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 112.8 6.8 6.5 0 0 0 0 1 0 0 0 0 0 0 41
42 6.4 109.8 6.8 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.1 117.3 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 43
44 5.8 109.1 6.1 6.4 0 0 0 0 0 0 0 1 0 0 0 44
45 6.1 115.9 5.8 6.1 0 0 0 0 0 0 0 0 1 0 0 45
46 7.2 96.0 6.1 5.8 0 0 0 0 0 0 0 0 0 1 0 46
47 7.3 99.8 7.2 6.1 0 0 0 0 0 0 0 0 0 0 1 47
48 6.9 116.8 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 115.7 6.9 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 5.8 99.4 6.1 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 6.2 94.3 5.8 6.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.1 91.0 6.2 5.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 93.2 7.1 6.2 0 0 0 0 1 0 0 0 0 0 0 53
54 7.9 103.1 7.7 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 94.1 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 7.4 91.8 7.7 7.9 0 0 0 0 0 0 0 1 0 0 0 56
57 7.5 102.7 7.4 7.7 0 0 0 0 0 0 0 0 1 0 0 57
58 8.0 82.6 7.5 7.4 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
4.07581 -0.01658 1.37884 -0.67583 -0.09634 -0.06391
M3 M4 M5 M6 M7 M8
0.03745 0.01518 -0.15804 -0.02278 -0.06885 -0.15415
M9 M10 M11 t
0.01249 0.10369 -0.48169 -0.00501
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.479544 -0.130501 0.003955 0.144064 0.400840
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.075808 1.004399 4.058 0.000211 ***
X -0.016584 0.005499 -3.016 0.004338 **
Y1 1.378839 0.112875 12.216 2.06e-15 ***
Y2 -0.675825 0.120155 -5.625 1.37e-06 ***
M1 -0.096343 0.145639 -0.662 0.511891
M2 -0.063909 0.151328 -0.422 0.674943
M3 0.037451 0.169160 0.221 0.825857
M4 0.015182 0.170499 0.089 0.929469
M5 -0.158041 0.165870 -0.953 0.346141
M6 -0.022776 0.149099 -0.153 0.879323
M7 -0.068847 0.153843 -0.448 0.656804
M8 -0.154153 0.154904 -0.995 0.325361
M9 0.012487 0.147277 0.085 0.932834
M10 0.103689 0.207027 0.501 0.619094
M11 -0.481693 0.181600 -2.652 0.011227 *
t -0.005010 0.002393 -2.093 0.042392 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2167 on 42 degrees of freedom
Multiple R-squared: 0.9176, Adjusted R-squared: 0.8881
F-statistic: 31.17 on 15 and 42 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,] 0.04247555 0.08495109 0.9575245
[2,] 0.02193403 0.04386805 0.9780660
[3,] 0.04556730 0.09113460 0.9544327
[4,] 0.57724484 0.84551032 0.4227552
[5,] 0.53368381 0.93263237 0.4663162
[6,] 0.49550011 0.99100022 0.5044999
[7,] 0.43050938 0.86101876 0.5694906
[8,] 0.39887206 0.79774411 0.6011279
[9,] 0.48375595 0.96751189 0.5162441
[10,] 0.37611026 0.75222052 0.6238897
[11,] 0.29315326 0.58630652 0.7068467
[12,] 0.29621067 0.59242133 0.7037893
[13,] 0.35504363 0.71008725 0.6449564
[14,] 0.32117892 0.64235784 0.6788211
[15,] 0.40542015 0.81084029 0.5945799
[16,] 0.39486931 0.78973862 0.6051307
[17,] 0.38474608 0.76949215 0.6152539
[18,] 0.37546809 0.75093618 0.6245319
[19,] 0.73789900 0.52420200 0.2621010
[20,] 0.61562251 0.76875498 0.3843775
[21,] 0.54678216 0.90643568 0.4532178
> postscript(file="/var/www/html/rcomp/tmp/12d3n1258724109.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2efc01258724109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wmed1258724109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44q2x1258724109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xkxj1258724109.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 = 58
Frequency = 1
1 2 3 4 5 6
-0.207266054 0.078292157 0.049236151 -0.112619928 -0.121013216 0.029831885
7 8 9 10 11 12
-0.250596111 0.175427691 -0.326928290 -0.008846590 0.005117936 0.188978675
13 14 15 16 17 18
0.131386487 0.081753529 0.145191004 0.041975759 0.207798490 0.320261807
19 20 21 22 23 24
0.193486835 0.136372185 -0.133663395 -0.479543560 0.207302068 0.211086316
25 26 27 28 29 30
0.199999648 0.004688860 -0.175143215 -0.051831137 -0.055507907 0.043581848
31 32 33 34 35 36
0.192235294 -0.137430435 -0.224699567 -0.004138382 -0.003699389 -0.202107201
37 38 39 40 41 42
0.171609085 -0.104007941 -0.050620721 -0.027130823 -0.024975959 -0.402235783
43 44 45 46 47 48
0.024758392 -0.177590879 0.284451643 0.351844765 -0.208720615 -0.197957790
49 50 51 52 53 54
-0.295729165 -0.060726605 0.031336780 0.149606129 -0.006301408 0.008560244
55 56 57 58
-0.159884409 0.003221439 0.400839609 0.140683766
> postscript(file="/var/www/html/rcomp/tmp/69keq1258724109.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.207266054 NA
1 0.078292157 -0.207266054
2 0.049236151 0.078292157
3 -0.112619928 0.049236151
4 -0.121013216 -0.112619928
5 0.029831885 -0.121013216
6 -0.250596111 0.029831885
7 0.175427691 -0.250596111
8 -0.326928290 0.175427691
9 -0.008846590 -0.326928290
10 0.005117936 -0.008846590
11 0.188978675 0.005117936
12 0.131386487 0.188978675
13 0.081753529 0.131386487
14 0.145191004 0.081753529
15 0.041975759 0.145191004
16 0.207798490 0.041975759
17 0.320261807 0.207798490
18 0.193486835 0.320261807
19 0.136372185 0.193486835
20 -0.133663395 0.136372185
21 -0.479543560 -0.133663395
22 0.207302068 -0.479543560
23 0.211086316 0.207302068
24 0.199999648 0.211086316
25 0.004688860 0.199999648
26 -0.175143215 0.004688860
27 -0.051831137 -0.175143215
28 -0.055507907 -0.051831137
29 0.043581848 -0.055507907
30 0.192235294 0.043581848
31 -0.137430435 0.192235294
32 -0.224699567 -0.137430435
33 -0.004138382 -0.224699567
34 -0.003699389 -0.004138382
35 -0.202107201 -0.003699389
36 0.171609085 -0.202107201
37 -0.104007941 0.171609085
38 -0.050620721 -0.104007941
39 -0.027130823 -0.050620721
40 -0.024975959 -0.027130823
41 -0.402235783 -0.024975959
42 0.024758392 -0.402235783
43 -0.177590879 0.024758392
44 0.284451643 -0.177590879
45 0.351844765 0.284451643
46 -0.208720615 0.351844765
47 -0.197957790 -0.208720615
48 -0.295729165 -0.197957790
49 -0.060726605 -0.295729165
50 0.031336780 -0.060726605
51 0.149606129 0.031336780
52 -0.006301408 0.149606129
53 0.008560244 -0.006301408
54 -0.159884409 0.008560244
55 0.003221439 -0.159884409
56 0.400839609 0.003221439
57 0.140683766 0.400839609
58 NA 0.140683766
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.078292157 -0.207266054
[2,] 0.049236151 0.078292157
[3,] -0.112619928 0.049236151
[4,] -0.121013216 -0.112619928
[5,] 0.029831885 -0.121013216
[6,] -0.250596111 0.029831885
[7,] 0.175427691 -0.250596111
[8,] -0.326928290 0.175427691
[9,] -0.008846590 -0.326928290
[10,] 0.005117936 -0.008846590
[11,] 0.188978675 0.005117936
[12,] 0.131386487 0.188978675
[13,] 0.081753529 0.131386487
[14,] 0.145191004 0.081753529
[15,] 0.041975759 0.145191004
[16,] 0.207798490 0.041975759
[17,] 0.320261807 0.207798490
[18,] 0.193486835 0.320261807
[19,] 0.136372185 0.193486835
[20,] -0.133663395 0.136372185
[21,] -0.479543560 -0.133663395
[22,] 0.207302068 -0.479543560
[23,] 0.211086316 0.207302068
[24,] 0.199999648 0.211086316
[25,] 0.004688860 0.199999648
[26,] -0.175143215 0.004688860
[27,] -0.051831137 -0.175143215
[28,] -0.055507907 -0.051831137
[29,] 0.043581848 -0.055507907
[30,] 0.192235294 0.043581848
[31,] -0.137430435 0.192235294
[32,] -0.224699567 -0.137430435
[33,] -0.004138382 -0.224699567
[34,] -0.003699389 -0.004138382
[35,] -0.202107201 -0.003699389
[36,] 0.171609085 -0.202107201
[37,] -0.104007941 0.171609085
[38,] -0.050620721 -0.104007941
[39,] -0.027130823 -0.050620721
[40,] -0.024975959 -0.027130823
[41,] -0.402235783 -0.024975959
[42,] 0.024758392 -0.402235783
[43,] -0.177590879 0.024758392
[44,] 0.284451643 -0.177590879
[45,] 0.351844765 0.284451643
[46,] -0.208720615 0.351844765
[47,] -0.197957790 -0.208720615
[48,] -0.295729165 -0.197957790
[49,] -0.060726605 -0.295729165
[50,] 0.031336780 -0.060726605
[51,] 0.149606129 0.031336780
[52,] -0.006301408 0.149606129
[53,] 0.008560244 -0.006301408
[54,] -0.159884409 0.008560244
[55,] 0.003221439 -0.159884409
[56,] 0.400839609 0.003221439
[57,] 0.140683766 0.400839609
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.078292157 -0.207266054
2 0.049236151 0.078292157
3 -0.112619928 0.049236151
4 -0.121013216 -0.112619928
5 0.029831885 -0.121013216
6 -0.250596111 0.029831885
7 0.175427691 -0.250596111
8 -0.326928290 0.175427691
9 -0.008846590 -0.326928290
10 0.005117936 -0.008846590
11 0.188978675 0.005117936
12 0.131386487 0.188978675
13 0.081753529 0.131386487
14 0.145191004 0.081753529
15 0.041975759 0.145191004
16 0.207798490 0.041975759
17 0.320261807 0.207798490
18 0.193486835 0.320261807
19 0.136372185 0.193486835
20 -0.133663395 0.136372185
21 -0.479543560 -0.133663395
22 0.207302068 -0.479543560
23 0.211086316 0.207302068
24 0.199999648 0.211086316
25 0.004688860 0.199999648
26 -0.175143215 0.004688860
27 -0.051831137 -0.175143215
28 -0.055507907 -0.051831137
29 0.043581848 -0.055507907
30 0.192235294 0.043581848
31 -0.137430435 0.192235294
32 -0.224699567 -0.137430435
33 -0.004138382 -0.224699567
34 -0.003699389 -0.004138382
35 -0.202107201 -0.003699389
36 0.171609085 -0.202107201
37 -0.104007941 0.171609085
38 -0.050620721 -0.104007941
39 -0.027130823 -0.050620721
40 -0.024975959 -0.027130823
41 -0.402235783 -0.024975959
42 0.024758392 -0.402235783
43 -0.177590879 0.024758392
44 0.284451643 -0.177590879
45 0.351844765 0.284451643
46 -0.208720615 0.351844765
47 -0.197957790 -0.208720615
48 -0.295729165 -0.197957790
49 -0.060726605 -0.295729165
50 0.031336780 -0.060726605
51 0.149606129 0.031336780
52 -0.006301408 0.149606129
53 0.008560244 -0.006301408
54 -0.159884409 0.008560244
55 0.003221439 -0.159884409
56 0.400839609 0.003221439
57 0.140683766 0.400839609
> 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/7c8se1258724109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fmch1258724109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yzoa1258724109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qexg1258724109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t7sn1258724109.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/12s17j1258724109.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/13kdvx1258724109.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/14714e1258724109.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/15vjeg1258724109.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/16wudz1258724109.tab")
+ }
>
> system("convert tmp/12d3n1258724109.ps tmp/12d3n1258724109.png")
> system("convert tmp/2efc01258724109.ps tmp/2efc01258724109.png")
> system("convert tmp/3wmed1258724109.ps tmp/3wmed1258724109.png")
> system("convert tmp/44q2x1258724109.ps tmp/44q2x1258724109.png")
> system("convert tmp/5xkxj1258724109.ps tmp/5xkxj1258724109.png")
> system("convert tmp/69keq1258724109.ps tmp/69keq1258724109.png")
> system("convert tmp/7c8se1258724109.ps tmp/7c8se1258724109.png")
> system("convert tmp/8fmch1258724109.ps tmp/8fmch1258724109.png")
> system("convert tmp/9yzoa1258724109.ps tmp/9yzoa1258724109.png")
> system("convert tmp/10qexg1258724109.ps tmp/10qexg1258724109.png")
>
>
> proc.time()
user system elapsed
2.361 1.593 2.808