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(8.2
+ ,103.9
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.3
+ ,101.6
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.5
+ ,94.6
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.6
+ ,95.9
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.5
+ ,104.7
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,102.8
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.1
+ ,98.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,7.9
+ ,113.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,80.9
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,95.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.7
+ ,113.2
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.5
+ ,105.9
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.4
+ ,108.8
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,102.3
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,99
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,100.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.6
+ ,115.5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.5
+ ,100.7
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.3
+ ,109.9
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8
+ ,114.6
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.2
+ ,85.4
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,100.5
+ ,8.2
+ ,8
+ ,8.3
+ ,8.1
+ ,114.8
+ ,8.1
+ ,8.2
+ ,8
+ ,8
+ ,116.5
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,112.9
+ ,8
+ ,8.1
+ ,8.1
+ ,7.9
+ ,102
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,106
+ ,7.9
+ ,7.9
+ ,8
+ ,8
+ ,105.3
+ ,8
+ ,7.9
+ ,7.9
+ ,7.9
+ ,118.8
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,106.1
+ ,7.9
+ ,8
+ ,8
+ ,7.7
+ ,109.3
+ ,8
+ ,7.9
+ ,8
+ ,7.2
+ ,117.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.5
+ ,92.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7.3
+ ,104.2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,112.5
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,122.4
+ ,7
+ ,7.3
+ ,7.5
+ ,7
+ ,113.3
+ ,7
+ ,7
+ ,7.3
+ ,7.2
+ ,100
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,110.7
+ ,7.2
+ ,7
+ ,7
+ ,7.1
+ ,112.8
+ ,7.3
+ ,7.2
+ ,7
+ ,6.8
+ ,109.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.4
+ ,117.3
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.1
+ ,109.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,6.5
+ ,115.9
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.7
+ ,96
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.9
+ ,99.8
+ ,7.7
+ ,6.5
+ ,6.1
+ ,7.5
+ ,116.8
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.9
+ ,115.7
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.6
+ ,99.4
+ ,6.9
+ ,7.5
+ ,7.9
+ ,6.9
+ ,94.3
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.7
+ ,91
+ ,6.9
+ ,6.6
+ ,6.9
+ ,8
+ ,93.2
+ ,7.7
+ ,6.9
+ ,6.6
+ ,8
+ ,103.1
+ ,8
+ ,7.7
+ ,6.9
+ ,7.7
+ ,94.1
+ ,8
+ ,8
+ ,7.7
+ ,7.3
+ ,91.8
+ ,7.7
+ ,8
+ ,8
+ ,7.4
+ ,102.7
+ ,7.3
+ ,7.7
+ ,8
+ ,8.1
+ ,82.6
+ ,7.4
+ ,7.3
+ ,7.7)
+ ,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 8.2 103.9 8.7 9.3 9.3 1 0 0 0 0 0 0 0 0 0 0 1
2 8.3 101.6 8.2 8.7 9.3 0 1 0 0 0 0 0 0 0 0 0 2
3 8.5 94.6 8.3 8.2 8.7 0 0 1 0 0 0 0 0 0 0 0 3
4 8.6 95.9 8.5 8.3 8.2 0 0 0 1 0 0 0 0 0 0 0 4
5 8.5 104.7 8.6 8.5 8.3 0 0 0 0 1 0 0 0 0 0 0 5
6 8.2 102.8 8.5 8.6 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 8.1 98.1 8.2 8.5 8.6 0 0 0 0 0 0 1 0 0 0 0 7
8 7.9 113.9 8.1 8.2 8.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.6 80.9 7.9 8.1 8.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 95.7 8.6 7.9 8.1 0 0 0 0 0 0 0 0 0 1 0 10
11 8.7 113.2 8.7 8.6 7.9 0 0 0 0 0 0 0 0 0 0 1 11
12 8.5 105.9 8.7 8.7 8.6 0 0 0 0 0 0 0 0 0 0 0 12
13 8.4 108.8 8.5 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 102.3 8.4 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.7 99.0 8.5 8.4 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 100.7 8.7 8.5 8.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.6 115.5 8.7 8.7 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 100.7 8.6 8.7 8.7 0 0 0 0 0 1 0 0 0 0 0 18
19 8.3 109.9 8.5 8.6 8.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.0 114.6 8.3 8.5 8.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.2 85.4 8.0 8.3 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.1 100.5 8.2 8.0 8.3 0 0 0 0 0 0 0 0 0 1 0 22
23 8.1 114.8 8.1 8.2 8.0 0 0 0 0 0 0 0 0 0 0 1 23
24 8.0 116.5 8.1 8.1 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 112.9 8.0 8.1 8.1 1 0 0 0 0 0 0 0 0 0 0 25
26 7.9 102.0 7.9 8.0 8.1 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 106.0 7.9 7.9 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 105.3 8.0 7.9 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 7.9 118.8 8.0 8.0 7.9 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 106.1 7.9 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.7 109.3 8.0 7.9 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.2 117.2 7.7 8.0 7.9 0 0 0 0 0 0 0 1 0 0 0 32
33 7.5 92.5 7.2 7.7 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.3 104.2 7.5 7.2 7.7 0 0 0 0 0 0 0 0 0 1 0 34
35 7.0 112.5 7.3 7.5 7.2 0 0 0 0 0 0 0 0 0 0 1 35
36 7.0 122.4 7.0 7.3 7.5 0 0 0 0 0 0 0 0 0 0 0 36
37 7.0 113.3 7.0 7.0 7.3 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 100.0 7.0 7.0 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.3 110.7 7.2 7.0 7.0 0 0 1 0 0 0 0 0 0 0 0 39
40 7.1 112.8 7.3 7.2 7.0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 109.8 7.1 7.3 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 6.4 117.3 6.8 7.1 7.3 0 0 0 0 0 1 0 0 0 0 0 42
43 6.1 109.1 6.4 6.8 7.1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.5 115.9 6.1 6.4 6.8 0 0 0 0 0 0 0 1 0 0 0 44
45 7.7 96.0 6.5 6.1 6.4 0 0 0 0 0 0 0 0 1 0 0 45
46 7.9 99.8 7.7 6.5 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.5 116.8 7.9 7.7 6.5 0 0 0 0 0 0 0 0 0 0 1 47
48 6.9 115.7 7.5 7.9 7.7 0 0 0 0 0 0 0 0 0 0 0 48
49 6.6 99.4 6.9 7.5 7.9 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 94.3 6.6 6.9 7.5 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 91.0 6.9 6.6 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 8.0 93.2 7.7 6.9 6.6 0 0 0 1 0 0 0 0 0 0 0 52
53 8.0 103.1 8.0 7.7 6.9 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 94.1 8.0 8.0 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 7.3 91.8 7.7 8.0 8.0 0 0 0 0 0 0 1 0 0 0 0 55
56 7.4 102.7 7.3 7.7 8.0 0 0 0 0 0 0 0 1 0 0 0 56
57 8.1 82.6 7.4 7.3 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
2.476062 -0.010130 1.608282 -1.174993 0.403743 -0.032332
M2 M3 M4 M5 M6 M7
0.060319 0.007668 -0.139876 0.041565 -0.089935 -0.186944
M8 M9 M10 M11 t
0.042212 0.345355 -0.565778 0.173536 -0.004156
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.25521 -0.10745 -0.01654 0.08740 0.30261
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.476062 0.926371 2.673 0.01083 *
X -0.010130 0.004085 -2.480 0.01746 *
Y1 1.608282 0.130647 12.310 3.49e-15 ***
Y2 -1.174993 0.205054 -5.730 1.13e-06 ***
Y3 0.403743 0.131816 3.063 0.00391 **
M1 -0.032332 0.114051 -0.283 0.77827
M2 0.060319 0.130854 0.461 0.64732
M3 0.007668 0.135075 0.057 0.95501
M4 -0.139876 0.130083 -1.075 0.28869
M5 0.041565 0.113909 0.365 0.71711
M6 -0.089935 0.116120 -0.774 0.44319
M7 -0.186944 0.118169 -1.582 0.12153
M8 0.042212 0.110943 0.380 0.70560
M9 0.345355 0.163829 2.108 0.04134 *
M10 -0.565778 0.161505 -3.503 0.00115 **
M11 0.173536 0.133558 1.299 0.20127
t -0.004156 0.002591 -1.604 0.11659
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1612 on 40 degrees of freedom
Multiple R-squared: 0.9576, Adjusted R-squared: 0.9406
F-statistic: 56.4 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,] 0.05796713 0.1159343 0.9420329
[2,] 0.65655095 0.6868981 0.3434491
[3,] 0.57407364 0.8518527 0.4259264
[4,] 0.46973957 0.9394791 0.5302604
[5,] 0.36521435 0.7304287 0.6347857
[6,] 0.30524362 0.6104872 0.6947564
[7,] 0.29540232 0.5908046 0.7045977
[8,] 0.19818384 0.3963677 0.8018162
[9,] 0.14433207 0.2886641 0.8556679
[10,] 0.10377243 0.2075449 0.8962276
[11,] 0.34982048 0.6996410 0.6501795
[12,] 0.40777091 0.8155418 0.5922291
[13,] 0.47997368 0.9599474 0.5200263
[14,] 0.36841661 0.7368332 0.6315834
[15,] 0.33353457 0.6670691 0.6664654
[16,] 0.25938823 0.5187765 0.7406118
[17,] 0.56218687 0.8756263 0.4378131
[18,] 0.70234479 0.5953104 0.2976552
> postscript(file="/var/www/html/rcomp/tmp/1sb5c1261060525.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2514b1261060525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nxne1261060525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4e3641261060525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53wwq1261060525.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
-0.006525982 0.080826528 -0.239353483 0.023229594 -0.131117040 -0.117128674
7 8 9 10 11 12
0.161037953 -0.255208045 0.136805190 -0.018407426 0.166119026 -0.095255619
13 14 15 16 17 18
0.151890482 0.023382839 0.049182680 0.054320499 0.121580079 0.087396374
19 20 21 22 23 24
0.125083368 -0.107775217 -0.214688908 0.160153263 0.086798916 -0.016535865
25 26 27 28 29 30
0.084687750 -0.070891154 0.049309194 0.073464681 0.050430671 0.277893560
31 32 33 34 35 36
-0.166853841 -0.171470699 -0.009392365 -0.124443574 -0.199499913 0.204839580
37 38 39 40 41 42
-0.122601795 -0.024697950 -0.081160142 -0.034017258 -0.183283064 -0.164543030
43 44 45 46 47 48
-0.074877826 0.302614751 0.167733298 -0.017302263 -0.053418029 -0.093048096
49 50 51 52 53 54
-0.107450455 -0.008620262 0.222021751 -0.116997517 0.142389352 -0.083618230
55 56 57
-0.044389655 0.231839211 -0.080457215
> postscript(file="/var/www/html/rcomp/tmp/6sguc1261060525.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 -0.006525982 NA
1 0.080826528 -0.006525982
2 -0.239353483 0.080826528
3 0.023229594 -0.239353483
4 -0.131117040 0.023229594
5 -0.117128674 -0.131117040
6 0.161037953 -0.117128674
7 -0.255208045 0.161037953
8 0.136805190 -0.255208045
9 -0.018407426 0.136805190
10 0.166119026 -0.018407426
11 -0.095255619 0.166119026
12 0.151890482 -0.095255619
13 0.023382839 0.151890482
14 0.049182680 0.023382839
15 0.054320499 0.049182680
16 0.121580079 0.054320499
17 0.087396374 0.121580079
18 0.125083368 0.087396374
19 -0.107775217 0.125083368
20 -0.214688908 -0.107775217
21 0.160153263 -0.214688908
22 0.086798916 0.160153263
23 -0.016535865 0.086798916
24 0.084687750 -0.016535865
25 -0.070891154 0.084687750
26 0.049309194 -0.070891154
27 0.073464681 0.049309194
28 0.050430671 0.073464681
29 0.277893560 0.050430671
30 -0.166853841 0.277893560
31 -0.171470699 -0.166853841
32 -0.009392365 -0.171470699
33 -0.124443574 -0.009392365
34 -0.199499913 -0.124443574
35 0.204839580 -0.199499913
36 -0.122601795 0.204839580
37 -0.024697950 -0.122601795
38 -0.081160142 -0.024697950
39 -0.034017258 -0.081160142
40 -0.183283064 -0.034017258
41 -0.164543030 -0.183283064
42 -0.074877826 -0.164543030
43 0.302614751 -0.074877826
44 0.167733298 0.302614751
45 -0.017302263 0.167733298
46 -0.053418029 -0.017302263
47 -0.093048096 -0.053418029
48 -0.107450455 -0.093048096
49 -0.008620262 -0.107450455
50 0.222021751 -0.008620262
51 -0.116997517 0.222021751
52 0.142389352 -0.116997517
53 -0.083618230 0.142389352
54 -0.044389655 -0.083618230
55 0.231839211 -0.044389655
56 -0.080457215 0.231839211
57 NA -0.080457215
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.080826528 -0.006525982
[2,] -0.239353483 0.080826528
[3,] 0.023229594 -0.239353483
[4,] -0.131117040 0.023229594
[5,] -0.117128674 -0.131117040
[6,] 0.161037953 -0.117128674
[7,] -0.255208045 0.161037953
[8,] 0.136805190 -0.255208045
[9,] -0.018407426 0.136805190
[10,] 0.166119026 -0.018407426
[11,] -0.095255619 0.166119026
[12,] 0.151890482 -0.095255619
[13,] 0.023382839 0.151890482
[14,] 0.049182680 0.023382839
[15,] 0.054320499 0.049182680
[16,] 0.121580079 0.054320499
[17,] 0.087396374 0.121580079
[18,] 0.125083368 0.087396374
[19,] -0.107775217 0.125083368
[20,] -0.214688908 -0.107775217
[21,] 0.160153263 -0.214688908
[22,] 0.086798916 0.160153263
[23,] -0.016535865 0.086798916
[24,] 0.084687750 -0.016535865
[25,] -0.070891154 0.084687750
[26,] 0.049309194 -0.070891154
[27,] 0.073464681 0.049309194
[28,] 0.050430671 0.073464681
[29,] 0.277893560 0.050430671
[30,] -0.166853841 0.277893560
[31,] -0.171470699 -0.166853841
[32,] -0.009392365 -0.171470699
[33,] -0.124443574 -0.009392365
[34,] -0.199499913 -0.124443574
[35,] 0.204839580 -0.199499913
[36,] -0.122601795 0.204839580
[37,] -0.024697950 -0.122601795
[38,] -0.081160142 -0.024697950
[39,] -0.034017258 -0.081160142
[40,] -0.183283064 -0.034017258
[41,] -0.164543030 -0.183283064
[42,] -0.074877826 -0.164543030
[43,] 0.302614751 -0.074877826
[44,] 0.167733298 0.302614751
[45,] -0.017302263 0.167733298
[46,] -0.053418029 -0.017302263
[47,] -0.093048096 -0.053418029
[48,] -0.107450455 -0.093048096
[49,] -0.008620262 -0.107450455
[50,] 0.222021751 -0.008620262
[51,] -0.116997517 0.222021751
[52,] 0.142389352 -0.116997517
[53,] -0.083618230 0.142389352
[54,] -0.044389655 -0.083618230
[55,] 0.231839211 -0.044389655
[56,] -0.080457215 0.231839211
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.080826528 -0.006525982
2 -0.239353483 0.080826528
3 0.023229594 -0.239353483
4 -0.131117040 0.023229594
5 -0.117128674 -0.131117040
6 0.161037953 -0.117128674
7 -0.255208045 0.161037953
8 0.136805190 -0.255208045
9 -0.018407426 0.136805190
10 0.166119026 -0.018407426
11 -0.095255619 0.166119026
12 0.151890482 -0.095255619
13 0.023382839 0.151890482
14 0.049182680 0.023382839
15 0.054320499 0.049182680
16 0.121580079 0.054320499
17 0.087396374 0.121580079
18 0.125083368 0.087396374
19 -0.107775217 0.125083368
20 -0.214688908 -0.107775217
21 0.160153263 -0.214688908
22 0.086798916 0.160153263
23 -0.016535865 0.086798916
24 0.084687750 -0.016535865
25 -0.070891154 0.084687750
26 0.049309194 -0.070891154
27 0.073464681 0.049309194
28 0.050430671 0.073464681
29 0.277893560 0.050430671
30 -0.166853841 0.277893560
31 -0.171470699 -0.166853841
32 -0.009392365 -0.171470699
33 -0.124443574 -0.009392365
34 -0.199499913 -0.124443574
35 0.204839580 -0.199499913
36 -0.122601795 0.204839580
37 -0.024697950 -0.122601795
38 -0.081160142 -0.024697950
39 -0.034017258 -0.081160142
40 -0.183283064 -0.034017258
41 -0.164543030 -0.183283064
42 -0.074877826 -0.164543030
43 0.302614751 -0.074877826
44 0.167733298 0.302614751
45 -0.017302263 0.167733298
46 -0.053418029 -0.017302263
47 -0.093048096 -0.053418029
48 -0.107450455 -0.093048096
49 -0.008620262 -0.107450455
50 0.222021751 -0.008620262
51 -0.116997517 0.222021751
52 0.142389352 -0.116997517
53 -0.083618230 0.142389352
54 -0.044389655 -0.083618230
55 0.231839211 -0.044389655
56 -0.080457215 0.231839211
> 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/7s6sl1261060525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hgki1261060525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hk8o1261060525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10936y1261060525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sk9e1261060525.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/120bqz1261060525.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/13t2nq1261060525.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/14iol61261060525.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/15nznz1261060526.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/1670nq1261060526.tab")
+ }
>
> try(system("convert tmp/1sb5c1261060525.ps tmp/1sb5c1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/2514b1261060525.ps tmp/2514b1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nxne1261060525.ps tmp/3nxne1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/4e3641261060525.ps tmp/4e3641261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/53wwq1261060525.ps tmp/53wwq1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sguc1261060525.ps tmp/6sguc1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/7s6sl1261060525.ps tmp/7s6sl1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hgki1261060525.ps tmp/8hgki1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hk8o1261060525.ps tmp/9hk8o1261060525.png",intern=TRUE))
character(0)
> try(system("convert tmp/10936y1261060525.ps tmp/10936y1261060525.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.378 1.558 3.137