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(7.50
+ ,103.90
+ ,7.70
+ ,8.10
+ ,8.00
+ ,7.60
+ ,101.60
+ ,7.50
+ ,7.70
+ ,8.10
+ ,7.80
+ ,94.60
+ ,7.60
+ ,7.50
+ ,7.70
+ ,7.80
+ ,95.90
+ ,7.80
+ ,7.60
+ ,7.50
+ ,7.80
+ ,104.70
+ ,7.80
+ ,7.80
+ ,7.60
+ ,7.50
+ ,102.80
+ ,7.80
+ ,7.80
+ ,7.80
+ ,7.50
+ ,98.10
+ ,7.50
+ ,7.80
+ ,7.80
+ ,7.10
+ ,113.90
+ ,7.50
+ ,7.50
+ ,7.80
+ ,7.50
+ ,80.90
+ ,7.10
+ ,7.50
+ ,7.50
+ ,7.50
+ ,95.70
+ ,7.50
+ ,7.10
+ ,7.50
+ ,7.60
+ ,113.20
+ ,7.50
+ ,7.50
+ ,7.10
+ ,7.70
+ ,105.90
+ ,7.60
+ ,7.50
+ ,7.50
+ ,7.70
+ ,108.80
+ ,7.70
+ ,7.60
+ ,7.50
+ ,7.90
+ ,102.30
+ ,7.70
+ ,7.70
+ ,7.60
+ ,8.10
+ ,99.00
+ ,7.90
+ ,7.70
+ ,7.70
+ ,8.20
+ ,100.70
+ ,8.10
+ ,7.90
+ ,7.70
+ ,8.20
+ ,115.50
+ ,8.20
+ ,8.10
+ ,7.90
+ ,8.20
+ ,100.70
+ ,8.20
+ ,8.20
+ ,8.10
+ ,7.90
+ ,109.90
+ ,8.20
+ ,8.20
+ ,8.20
+ ,7.30
+ ,114.60
+ ,7.90
+ ,8.20
+ ,8.20
+ ,6.90
+ ,85.40
+ ,7.30
+ ,7.90
+ ,8.20
+ ,6.60
+ ,100.50
+ ,6.90
+ ,7.30
+ ,7.90
+ ,6.70
+ ,114.80
+ ,6.60
+ ,6.90
+ ,7.30
+ ,6.90
+ ,116.50
+ ,6.70
+ ,6.60
+ ,6.90
+ ,7.00
+ ,112.90
+ ,6.90
+ ,6.70
+ ,6.60
+ ,7.10
+ ,102.00
+ ,7.00
+ ,6.90
+ ,6.70
+ ,7.20
+ ,106.00
+ ,7.10
+ ,7.00
+ ,6.90
+ ,7.10
+ ,105.30
+ ,7.20
+ ,7.10
+ ,7.00
+ ,6.90
+ ,118.80
+ ,7.10
+ ,7.20
+ ,7.10
+ ,7.00
+ ,106.10
+ ,6.90
+ ,7.10
+ ,7.20
+ ,6.80
+ ,109.30
+ ,7.00
+ ,6.90
+ ,7.10
+ ,6.40
+ ,117.20
+ ,6.80
+ ,7.00
+ ,6.90
+ ,6.70
+ ,92.50
+ ,6.40
+ ,6.80
+ ,7.00
+ ,6.60
+ ,104.20
+ ,6.70
+ ,6.40
+ ,6.80
+ ,6.40
+ ,112.50
+ ,6.60
+ ,6.70
+ ,6.40
+ ,6.30
+ ,122.40
+ ,6.40
+ ,6.60
+ ,6.70
+ ,6.20
+ ,113.30
+ ,6.30
+ ,6.40
+ ,6.60
+ ,6.50
+ ,100.00
+ ,6.20
+ ,6.30
+ ,6.40
+ ,6.80
+ ,110.70
+ ,6.50
+ ,6.20
+ ,6.30
+ ,6.80
+ ,112.80
+ ,6.80
+ ,6.50
+ ,6.20
+ ,6.40
+ ,109.80
+ ,6.80
+ ,6.80
+ ,6.50
+ ,6.10
+ ,117.30
+ ,6.40
+ ,6.80
+ ,6.80
+ ,5.80
+ ,109.10
+ ,6.10
+ ,6.40
+ ,6.80
+ ,6.10
+ ,115.90
+ ,5.80
+ ,6.10
+ ,6.40
+ ,7.20
+ ,96.00
+ ,6.10
+ ,5.80
+ ,6.10
+ ,7.30
+ ,99.80
+ ,7.20
+ ,6.10
+ ,5.80
+ ,6.90
+ ,116.80
+ ,7.30
+ ,7.20
+ ,6.10
+ ,6.10
+ ,115.70
+ ,6.90
+ ,7.30
+ ,7.20
+ ,5.80
+ ,99.40
+ ,6.10
+ ,6.90
+ ,7.30
+ ,6.20
+ ,94.30
+ ,5.80
+ ,6.10
+ ,6.90
+ ,7.10
+ ,91.00
+ ,6.20
+ ,5.80
+ ,6.10
+ ,7.70
+ ,93.20
+ ,7.10
+ ,6.20
+ ,5.80
+ ,7.90
+ ,103.10
+ ,7.70
+ ,7.10
+ ,6.20
+ ,7.70
+ ,94.10
+ ,7.90
+ ,7.70
+ ,7.10
+ ,7.40
+ ,91.80
+ ,7.70
+ ,7.90
+ ,7.70
+ ,7.50
+ ,102.70
+ ,7.40
+ ,7.70
+ ,7.90
+ ,8.00
+ ,82.60
+ ,7.50
+ ,7.40
+ ,7.70)
+ ,dim=c(5
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3')
+ ,1:57))
> y <- array(NA,dim=c(5,57),dimnames=list(c('Y','X','Y1','Y2','Y3'),1:57))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.5 103.9 7.7 8.1 8.0 1 0 0 0 0 0 0 0 0 0 0 1
2 7.6 101.6 7.5 7.7 8.1 0 1 0 0 0 0 0 0 0 0 0 2
3 7.8 94.6 7.6 7.5 7.7 0 0 1 0 0 0 0 0 0 0 0 3
4 7.8 95.9 7.8 7.6 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 7.8 104.7 7.8 7.8 7.6 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 102.8 7.8 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 98.1 7.5 7.8 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.1 113.9 7.5 7.5 7.8 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 80.9 7.1 7.5 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 95.7 7.5 7.1 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.6 113.2 7.5 7.5 7.1 0 0 0 0 0 0 0 0 0 0 1 11
12 7.7 105.9 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 7.7 108.8 7.7 7.6 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 102.3 7.7 7.7 7.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.1 99.0 7.9 7.7 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 100.7 8.1 7.9 7.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 115.5 8.2 8.1 7.9 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 100.7 8.2 8.2 8.1 0 0 0 0 0 1 0 0 0 0 0 18
19 7.9 109.9 8.2 8.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 7.3 114.6 7.9 8.2 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 6.9 85.4 7.3 7.9 8.2 0 0 0 0 0 0 0 0 1 0 0 21
22 6.6 100.5 6.9 7.3 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 6.7 114.8 6.6 6.9 7.3 0 0 0 0 0 0 0 0 0 0 1 23
24 6.9 116.5 6.7 6.6 6.9 0 0 0 0 0 0 0 0 0 0 0 24
25 7.0 112.9 6.9 6.7 6.6 1 0 0 0 0 0 0 0 0 0 0 25
26 7.1 102.0 7.0 6.9 6.7 0 1 0 0 0 0 0 0 0 0 0 26
27 7.2 106.0 7.1 7.0 6.9 0 0 1 0 0 0 0 0 0 0 0 27
28 7.1 105.3 7.2 7.1 7.0 0 0 0 1 0 0 0 0 0 0 0 28
29 6.9 118.8 7.1 7.2 7.1 0 0 0 0 1 0 0 0 0 0 0 29
30 7.0 106.1 6.9 7.1 7.2 0 0 0 0 0 1 0 0 0 0 0 30
31 6.8 109.3 7.0 6.9 7.1 0 0 0 0 0 0 1 0 0 0 0 31
32 6.4 117.2 6.8 7.0 6.9 0 0 0 0 0 0 0 1 0 0 0 32
33 6.7 92.5 6.4 6.8 7.0 0 0 0 0 0 0 0 0 1 0 0 33
34 6.6 104.2 6.7 6.4 6.8 0 0 0 0 0 0 0 0 0 1 0 34
35 6.4 112.5 6.6 6.7 6.4 0 0 0 0 0 0 0 0 0 0 1 35
36 6.3 122.4 6.4 6.6 6.7 0 0 0 0 0 0 0 0 0 0 0 36
37 6.2 113.3 6.3 6.4 6.6 1 0 0 0 0 0 0 0 0 0 0 37
38 6.5 100.0 6.2 6.3 6.4 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 110.7 6.5 6.2 6.3 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 112.8 6.8 6.5 6.2 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 109.8 6.8 6.8 6.5 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 117.3 6.4 6.8 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 5.8 109.1 6.1 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 43
44 6.1 115.9 5.8 6.1 6.4 0 0 0 0 0 0 0 1 0 0 0 44
45 7.2 96.0 6.1 5.8 6.1 0 0 0 0 0 0 0 0 1 0 0 45
46 7.3 99.8 7.2 6.1 5.8 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 116.8 7.3 7.2 6.1 0 0 0 0 0 0 0 0 0 0 1 47
48 6.1 115.7 6.9 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 5.8 99.4 6.1 6.9 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.2 94.3 5.8 6.1 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 7.1 91.0 6.2 5.8 6.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 93.2 7.1 6.2 5.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.9 103.1 7.7 7.1 6.2 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 94.1 7.9 7.7 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.4 91.8 7.7 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 7.5 102.7 7.4 7.7 7.9 0 0 0 0 0 0 0 1 0 0 0 56
57 8.0 82.6 7.5 7.4 7.7 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 M1
2.985184 -0.014594 1.602145 -1.147679 0.354459 0.019360
M2 M3 M4 M5 M6 M7
0.082891 0.032549 -0.082186 0.089013 0.006990 -0.128437
M8 M9 M10 M11 t
0.029073 0.159023 -0.545004 0.185452 -0.002795
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.47471 -0.10440 0.01749 0.11394 0.34007
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.985184 1.032594 2.891 0.00618 **
X -0.014594 0.005253 -2.778 0.00828 **
Y1 1.602145 0.134769 11.888 1.05e-14 ***
Y2 -1.147679 0.215066 -5.336 4.03e-06 ***
Y3 0.354459 0.141146 2.511 0.01617 *
M1 0.019360 0.143425 0.135 0.89330
M2 0.082891 0.159809 0.519 0.60684
M3 0.032549 0.161876 0.201 0.84166
M4 -0.082186 0.158924 -0.517 0.60791
M5 0.089013 0.144348 0.617 0.54096
M6 0.006990 0.146545 0.048 0.96219
M7 -0.128437 0.146643 -0.876 0.38634
M8 0.029073 0.138753 0.210 0.83510
M9 0.159023 0.195947 0.812 0.42185
M10 -0.545004 0.176414 -3.089 0.00364 **
M11 0.185452 0.154617 1.199 0.23742
t -0.002795 0.002487 -1.124 0.26785
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2033 on 40 degrees of freedom
Multiple R-squared: 0.93, Adjusted R-squared: 0.9019
F-statistic: 33.19 on 16 and 40 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,] 0.1790663 0.3581325 0.8209337
[2,] 0.7513638 0.4972724 0.2486362
[3,] 0.7606384 0.4787231 0.2393616
[4,] 0.6801899 0.6396203 0.3198101
[5,] 0.5599710 0.8800580 0.4400290
[6,] 0.5059866 0.9880269 0.4940134
[7,] 0.5437068 0.9125864 0.4562932
[8,] 0.4293768 0.8587535 0.5706232
[9,] 0.3223720 0.6447440 0.6776280
[10,] 0.2635261 0.5270521 0.7364739
[11,] 0.3228934 0.6457869 0.6771066
[12,] 0.2476486 0.4952973 0.7523514
[13,] 0.3060281 0.6120563 0.6939719
[14,] 0.2769135 0.5538270 0.7230865
[15,] 0.2532225 0.5064450 0.7467775
[16,] 0.2221043 0.4442086 0.7778957
[17,] 0.7394224 0.5211553 0.2605776
[18,] 0.6626839 0.6746321 0.3373161
> postscript(file="/var/www/html/rcomp/tmp/1kpd41258723835.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2z57w1258723835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zyni1258723835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kdq31258723835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5a6ys1258723835.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 = 57
Frequency = 1
1 2 3 4 5 6
0.138623071 -0.029768896 -0.126759719 -0.125026785 0.029090751 -0.284713557
7 8 9 10 11 12
0.265557858 -0.402867615 0.135554135 -0.041554389 0.187043299 0.066751800
13 14 15 16 17 18
0.047064103 0.170785496 0.019885858 0.171332590 0.217356644 0.130051226
19 20 21 22 23 24
0.067096626 -0.138380619 -0.474712100 0.211075918 0.026363805 0.076686595
25 26 27 28 29 30
0.008257955 -0.077683084 0.017493519 -0.056085796 0.012072521 0.181754790
31 32 33 34 35 36
-0.187625138 -0.120954814 0.067281819 -0.023963266 -0.184188253 0.147867150
37 38 39 40 41 42
-0.135383067 0.026112361 -0.024554889 0.022728904 -0.351492579 0.077303484
43 44 45 46 47 48
-0.182577237 0.340074101 0.303878547 -0.145558262 -0.029218850 -0.291305545
49 50 51 52 53 54
-0.058562063 -0.089445877 0.113935231 -0.012948913 0.092972664 -0.104395943
55 56 57
0.037547891 0.322128947 -0.032002401
> postscript(file="/var/www/html/rcomp/tmp/64mfw1258723835.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 0.138623071 NA
1 -0.029768896 0.138623071
2 -0.126759719 -0.029768896
3 -0.125026785 -0.126759719
4 0.029090751 -0.125026785
5 -0.284713557 0.029090751
6 0.265557858 -0.284713557
7 -0.402867615 0.265557858
8 0.135554135 -0.402867615
9 -0.041554389 0.135554135
10 0.187043299 -0.041554389
11 0.066751800 0.187043299
12 0.047064103 0.066751800
13 0.170785496 0.047064103
14 0.019885858 0.170785496
15 0.171332590 0.019885858
16 0.217356644 0.171332590
17 0.130051226 0.217356644
18 0.067096626 0.130051226
19 -0.138380619 0.067096626
20 -0.474712100 -0.138380619
21 0.211075918 -0.474712100
22 0.026363805 0.211075918
23 0.076686595 0.026363805
24 0.008257955 0.076686595
25 -0.077683084 0.008257955
26 0.017493519 -0.077683084
27 -0.056085796 0.017493519
28 0.012072521 -0.056085796
29 0.181754790 0.012072521
30 -0.187625138 0.181754790
31 -0.120954814 -0.187625138
32 0.067281819 -0.120954814
33 -0.023963266 0.067281819
34 -0.184188253 -0.023963266
35 0.147867150 -0.184188253
36 -0.135383067 0.147867150
37 0.026112361 -0.135383067
38 -0.024554889 0.026112361
39 0.022728904 -0.024554889
40 -0.351492579 0.022728904
41 0.077303484 -0.351492579
42 -0.182577237 0.077303484
43 0.340074101 -0.182577237
44 0.303878547 0.340074101
45 -0.145558262 0.303878547
46 -0.029218850 -0.145558262
47 -0.291305545 -0.029218850
48 -0.058562063 -0.291305545
49 -0.089445877 -0.058562063
50 0.113935231 -0.089445877
51 -0.012948913 0.113935231
52 0.092972664 -0.012948913
53 -0.104395943 0.092972664
54 0.037547891 -0.104395943
55 0.322128947 0.037547891
56 -0.032002401 0.322128947
57 NA -0.032002401
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.029768896 0.138623071
[2,] -0.126759719 -0.029768896
[3,] -0.125026785 -0.126759719
[4,] 0.029090751 -0.125026785
[5,] -0.284713557 0.029090751
[6,] 0.265557858 -0.284713557
[7,] -0.402867615 0.265557858
[8,] 0.135554135 -0.402867615
[9,] -0.041554389 0.135554135
[10,] 0.187043299 -0.041554389
[11,] 0.066751800 0.187043299
[12,] 0.047064103 0.066751800
[13,] 0.170785496 0.047064103
[14,] 0.019885858 0.170785496
[15,] 0.171332590 0.019885858
[16,] 0.217356644 0.171332590
[17,] 0.130051226 0.217356644
[18,] 0.067096626 0.130051226
[19,] -0.138380619 0.067096626
[20,] -0.474712100 -0.138380619
[21,] 0.211075918 -0.474712100
[22,] 0.026363805 0.211075918
[23,] 0.076686595 0.026363805
[24,] 0.008257955 0.076686595
[25,] -0.077683084 0.008257955
[26,] 0.017493519 -0.077683084
[27,] -0.056085796 0.017493519
[28,] 0.012072521 -0.056085796
[29,] 0.181754790 0.012072521
[30,] -0.187625138 0.181754790
[31,] -0.120954814 -0.187625138
[32,] 0.067281819 -0.120954814
[33,] -0.023963266 0.067281819
[34,] -0.184188253 -0.023963266
[35,] 0.147867150 -0.184188253
[36,] -0.135383067 0.147867150
[37,] 0.026112361 -0.135383067
[38,] -0.024554889 0.026112361
[39,] 0.022728904 -0.024554889
[40,] -0.351492579 0.022728904
[41,] 0.077303484 -0.351492579
[42,] -0.182577237 0.077303484
[43,] 0.340074101 -0.182577237
[44,] 0.303878547 0.340074101
[45,] -0.145558262 0.303878547
[46,] -0.029218850 -0.145558262
[47,] -0.291305545 -0.029218850
[48,] -0.058562063 -0.291305545
[49,] -0.089445877 -0.058562063
[50,] 0.113935231 -0.089445877
[51,] -0.012948913 0.113935231
[52,] 0.092972664 -0.012948913
[53,] -0.104395943 0.092972664
[54,] 0.037547891 -0.104395943
[55,] 0.322128947 0.037547891
[56,] -0.032002401 0.322128947
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.029768896 0.138623071
2 -0.126759719 -0.029768896
3 -0.125026785 -0.126759719
4 0.029090751 -0.125026785
5 -0.284713557 0.029090751
6 0.265557858 -0.284713557
7 -0.402867615 0.265557858
8 0.135554135 -0.402867615
9 -0.041554389 0.135554135
10 0.187043299 -0.041554389
11 0.066751800 0.187043299
12 0.047064103 0.066751800
13 0.170785496 0.047064103
14 0.019885858 0.170785496
15 0.171332590 0.019885858
16 0.217356644 0.171332590
17 0.130051226 0.217356644
18 0.067096626 0.130051226
19 -0.138380619 0.067096626
20 -0.474712100 -0.138380619
21 0.211075918 -0.474712100
22 0.026363805 0.211075918
23 0.076686595 0.026363805
24 0.008257955 0.076686595
25 -0.077683084 0.008257955
26 0.017493519 -0.077683084
27 -0.056085796 0.017493519
28 0.012072521 -0.056085796
29 0.181754790 0.012072521
30 -0.187625138 0.181754790
31 -0.120954814 -0.187625138
32 0.067281819 -0.120954814
33 -0.023963266 0.067281819
34 -0.184188253 -0.023963266
35 0.147867150 -0.184188253
36 -0.135383067 0.147867150
37 0.026112361 -0.135383067
38 -0.024554889 0.026112361
39 0.022728904 -0.024554889
40 -0.351492579 0.022728904
41 0.077303484 -0.351492579
42 -0.182577237 0.077303484
43 0.340074101 -0.182577237
44 0.303878547 0.340074101
45 -0.145558262 0.303878547
46 -0.029218850 -0.145558262
47 -0.291305545 -0.029218850
48 -0.058562063 -0.291305545
49 -0.089445877 -0.058562063
50 0.113935231 -0.089445877
51 -0.012948913 0.113935231
52 0.092972664 -0.012948913
53 -0.104395943 0.092972664
54 0.037547891 -0.104395943
55 0.322128947 0.037547891
56 -0.032002401 0.322128947
> 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/7byhw1258723835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tt9f1258723835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z26i1258723835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lwcm1258723835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dtly1258723835.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/12x30k1258723835.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/13i3491258723835.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/14fxs11258723835.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/15skn61258723835.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/1636ss1258723835.tab")
+ }
>
> system("convert tmp/1kpd41258723835.ps tmp/1kpd41258723835.png")
> system("convert tmp/2z57w1258723835.ps tmp/2z57w1258723835.png")
> system("convert tmp/3zyni1258723835.ps tmp/3zyni1258723835.png")
> system("convert tmp/4kdq31258723835.ps tmp/4kdq31258723835.png")
> system("convert tmp/5a6ys1258723835.ps tmp/5a6ys1258723835.png")
> system("convert tmp/64mfw1258723835.ps tmp/64mfw1258723835.png")
> system("convert tmp/7byhw1258723835.ps tmp/7byhw1258723835.png")
> system("convert tmp/8tt9f1258723835.ps tmp/8tt9f1258723835.png")
> system("convert tmp/9z26i1258723835.ps tmp/9z26i1258723835.png")
> system("convert tmp/10lwcm1258723835.ps tmp/10lwcm1258723835.png")
>
>
> proc.time()
user system elapsed
2.413 1.588 5.568