R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(96.9
+ ,8.6
+ ,8.4
+ ,8.4
+ ,95.1
+ ,8.9
+ ,8.6
+ ,8.4
+ ,97
+ ,8.8
+ ,8.9
+ ,8.6
+ ,112.7
+ ,8.3
+ ,8.8
+ ,8.9
+ ,102.9
+ ,7.5
+ ,8.3
+ ,8.8
+ ,97.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,111.4
+ ,7.4
+ ,7.2
+ ,7.5
+ ,87.4
+ ,8.8
+ ,7.4
+ ,7.2
+ ,96.8
+ ,9.3
+ ,8.8
+ ,7.4
+ ,114.1
+ ,9.3
+ ,9.3
+ ,8.8
+ ,110.3
+ ,8.7
+ ,9.3
+ ,9.3
+ ,103.9
+ ,8.2
+ ,8.7
+ ,9.3
+ ,101.6
+ ,8.3
+ ,8.2
+ ,8.7
+ ,94.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,95.9
+ ,8.6
+ ,8.5
+ ,8.3
+ ,104.7
+ ,8.5
+ ,8.6
+ ,8.5
+ ,102.8
+ ,8.2
+ ,8.5
+ ,8.6
+ ,98.1
+ ,8.1
+ ,8.2
+ ,8.5
+ ,113.9
+ ,7.9
+ ,8.1
+ ,8.2
+ ,80.9
+ ,8.6
+ ,7.9
+ ,8.1
+ ,95.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,113.2
+ ,8.7
+ ,8.7
+ ,8.6
+ ,105.9
+ ,8.5
+ ,8.7
+ ,8.7
+ ,108.8
+ ,8.4
+ ,8.5
+ ,8.7
+ ,102.3
+ ,8.5
+ ,8.4
+ ,8.5
+ ,99
+ ,8.7
+ ,8.5
+ ,8.4
+ ,100.7
+ ,8.7
+ ,8.7
+ ,8.5
+ ,115.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,100.7
+ ,8.5
+ ,8.6
+ ,8.7
+ ,109.9
+ ,8.3
+ ,8.5
+ ,8.6
+ ,114.6
+ ,8
+ ,8.3
+ ,8.5
+ ,85.4
+ ,8.2
+ ,8
+ ,8.3
+ ,100.5
+ ,8.1
+ ,8.2
+ ,8
+ ,114.8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,116.5
+ ,8
+ ,8.1
+ ,8.1
+ ,112.9
+ ,7.9
+ ,8
+ ,8.1
+ ,102
+ ,7.9
+ ,7.9
+ ,8
+ ,106
+ ,8
+ ,7.9
+ ,7.9
+ ,105.3
+ ,8
+ ,8
+ ,7.9
+ ,118.8
+ ,7.9
+ ,8
+ ,8
+ ,106.1
+ ,8
+ ,7.9
+ ,8
+ ,109.3
+ ,7.7
+ ,8
+ ,7.9
+ ,117.2
+ ,7.2
+ ,7.7
+ ,8
+ ,92.5
+ ,7.5
+ ,7.2
+ ,7.7
+ ,104.2
+ ,7.3
+ ,7.5
+ ,7.2
+ ,112.5
+ ,7
+ ,7.3
+ ,7.5
+ ,122.4
+ ,7
+ ,7
+ ,7.3
+ ,113.3
+ ,7
+ ,7
+ ,7
+ ,100
+ ,7.2
+ ,7
+ ,7
+ ,110.7
+ ,7.3
+ ,7.2
+ ,7
+ ,112.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,109.8
+ ,6.8
+ ,7.1
+ ,7.3
+ ,117.3
+ ,6.4
+ ,6.8
+ ,7.1
+ ,109.1
+ ,6.1
+ ,6.4
+ ,6.8
+ ,115.9
+ ,6.5
+ ,6.1
+ ,6.4
+ ,96
+ ,7.7
+ ,6.5
+ ,6.1
+ ,99.8
+ ,7.9
+ ,7.7
+ ,6.5
+ ,116.8
+ ,7.5
+ ,7.9
+ ,7.7
+ ,115.7
+ ,6.9
+ ,7.5
+ ,7.9)
+ ,dim=c(4
+ ,59)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y2'
+ ,'Y3')
+ ,1:59))
> y <- array(NA,dim=c(4,59),dimnames=list(c('Y','X','Y2','Y3'),1:59))
> 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 = '2'
> #'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
X Y Y2 Y3 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.6 96.9 8.4 8.4 1 0 0 0 0 0 0 0 0 0 0 1
2 8.9 95.1 8.6 8.4 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 97.0 8.9 8.6 0 0 1 0 0 0 0 0 0 0 0 3
4 8.3 112.7 8.8 8.9 0 0 0 1 0 0 0 0 0 0 0 4
5 7.5 102.9 8.3 8.8 0 0 0 0 1 0 0 0 0 0 0 5
6 7.2 97.4 7.5 8.3 0 0 0 0 0 1 0 0 0 0 0 6
7 7.4 111.4 7.2 7.5 0 0 0 0 0 0 1 0 0 0 0 7
8 8.8 87.4 7.4 7.2 0 0 0 0 0 0 0 1 0 0 0 8
9 9.3 96.8 8.8 7.4 0 0 0 0 0 0 0 0 1 0 0 9
10 9.3 114.1 9.3 8.8 0 0 0 0 0 0 0 0 0 1 0 10
11 8.7 110.3 9.3 9.3 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 103.9 8.7 9.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.3 101.6 8.2 8.7 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 94.6 8.3 8.2 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 95.9 8.5 8.3 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 104.7 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 102.8 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 17
18 8.1 98.1 8.2 8.5 0 0 0 0 0 1 0 0 0 0 0 18
19 7.9 113.9 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 8.6 80.9 7.9 8.1 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 95.7 8.6 7.9 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 113.2 8.7 8.6 0 0 0 0 0 0 0 0 0 1 0 22
23 8.5 105.9 8.7 8.7 0 0 0 0 0 0 0 0 0 0 1 23
24 8.4 108.8 8.5 8.7 0 0 0 0 0 0 0 0 0 0 0 24
25 8.5 102.3 8.4 8.5 1 0 0 0 0 0 0 0 0 0 0 25
26 8.7 99.0 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 26
27 8.7 100.7 8.7 8.5 0 0 1 0 0 0 0 0 0 0 0 27
28 8.6 115.5 8.7 8.7 0 0 0 1 0 0 0 0 0 0 0 28
29 8.5 100.7 8.6 8.7 0 0 0 0 1 0 0 0 0 0 0 29
30 8.3 109.9 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 114.6 8.3 8.5 0 0 0 0 0 0 1 0 0 0 0 31
32 8.2 85.4 8.0 8.3 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 100.5 8.2 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.1 114.8 8.1 8.2 0 0 0 0 0 0 0 0 0 1 0 34
35 8.0 116.5 8.1 8.1 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 112.9 8.0 8.1 0 0 0 0 0 0 0 0 0 0 0 36
37 7.9 102.0 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 37
38 8.0 106.0 7.9 7.9 0 1 0 0 0 0 0 0 0 0 0 38
39 8.0 105.3 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.9 118.8 8.0 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 8.0 106.1 7.9 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.7 109.3 8.0 7.9 0 0 0 0 0 1 0 0 0 0 0 42
43 7.2 117.2 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 43
44 7.5 92.5 7.2 7.7 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 104.2 7.5 7.2 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 112.5 7.3 7.5 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 122.4 7.0 7.3 0 0 0 0 0 0 0 0 0 0 1 47
48 7.0 113.3 7.0 7.0 0 0 0 0 0 0 0 0 0 0 0 48
49 7.2 100.0 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 49
50 7.3 110.7 7.2 7.0 0 1 0 0 0 0 0 0 0 0 0 50
51 7.1 112.8 7.3 7.2 0 0 1 0 0 0 0 0 0 0 0 51
52 6.8 109.8 7.1 7.3 0 0 0 1 0 0 0 0 0 0 0 52
53 6.4 117.3 6.8 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.1 109.1 6.4 6.8 0 0 0 0 0 1 0 0 0 0 0 54
55 6.5 115.9 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 55
56 7.7 96.0 6.5 6.1 0 0 0 0 0 0 0 1 0 0 0 56
57 7.9 99.8 7.7 6.5 0 0 0 0 0 0 0 0 1 0 0 57
58 7.5 116.8 7.9 7.7 0 0 0 0 0 0 0 0 0 1 0 58
59 6.9 115.7 7.5 7.9 0 0 0 0 0 0 0 0 0 0 1 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y Y2 Y3 M1 M2
2.919052 -0.004532 1.415409 -0.690016 0.137105 0.060706
M3 M4 M5 M6 M7 M8
-0.137858 -0.124199 -0.161449 -0.126373 -0.021384 0.575373
M9 M10 M11 t
-0.398167 -0.080157 -0.105846 -0.007696
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.428488 -0.123343 -0.009715 0.132527 0.377236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.919052 1.021101 2.859 0.00653 **
Y -0.004532 0.007543 -0.601 0.55106
Y2 1.415409 0.113061 12.519 6.21e-16 ***
Y3 -0.690016 0.112329 -6.143 2.27e-07 ***
M1 0.137105 0.146224 0.938 0.35367
M2 0.060706 0.150161 0.404 0.68801
M3 -0.137858 0.147784 -0.933 0.35611
M4 -0.124199 0.133191 -0.932 0.35629
M5 -0.161449 0.132865 -1.215 0.23095
M6 -0.126373 0.137069 -0.922 0.36169
M7 -0.021384 0.138145 -0.155 0.87771
M8 0.575373 0.217551 2.645 0.01137 *
M9 -0.398167 0.192458 -2.069 0.04461 *
M10 -0.080157 0.137849 -0.581 0.56395
M11 -0.105846 0.133625 -0.792 0.43264
t -0.007696 0.002721 -2.828 0.00708 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1937 on 43 degrees of freedom
Multiple R-squared: 0.9466, Adjusted R-squared: 0.9279
F-statistic: 50.79 on 15 and 43 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,] 0.54952962 0.9009408 0.4504704
[2,] 0.38984939 0.7796988 0.6101506
[3,] 0.29683695 0.5936739 0.7031631
[4,] 0.19369896 0.3873979 0.8063010
[5,] 0.15542627 0.3108525 0.8445737
[6,] 0.14345636 0.2869127 0.8565436
[7,] 0.14108744 0.2821749 0.8589126
[8,] 0.08299020 0.1659804 0.9170098
[9,] 0.04643480 0.0928696 0.9535652
[10,] 0.03931170 0.0786234 0.9606883
[11,] 0.08023969 0.1604794 0.9197603
[12,] 0.06219451 0.1243890 0.9378055
[13,] 0.03961565 0.0792313 0.9603844
[14,] 0.22107922 0.4421584 0.7789208
[15,] 0.17748764 0.3549753 0.8225124
[16,] 0.18221891 0.3644378 0.8177811
[17,] 0.26541964 0.5308393 0.7345804
[18,] 0.29450097 0.5890019 0.7054990
[19,] 0.31428500 0.6285700 0.6857150
[20,] 0.23035725 0.4607145 0.7696427
[21,] 0.13876110 0.2775222 0.8612389
[22,] 0.15487397 0.3097479 0.8451260
> postscript(file="/var/www/html/rcomp/tmp/1bl8b1258746604.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2o0vs1258746604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cfzj1258746604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bx761258746604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56qra1258746604.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 = 59
Frequency = 1
1 2 3 4 5 6
-0.102569021 -0.009714692 -0.181461740 -0.267719122 -0.428487705 0.006522911
7 8 9 10 11 12
0.045294176 0.257368649 -0.062359252 -0.035944022 -0.274774094 -0.052685403
13 14 15 16 17 18
0.201176636 -0.033004877 0.065067909 -0.004546664 -0.057669527 0.149268954
19 20 21 22 23 24
-0.141875097 0.033574525 0.053101620 0.163575587 0.032875504 0.130952040
25 26 27 28 29 30
0.075620752 0.134215736 0.134101491 0.233222374 0.252629627 0.139486939
31 32 33 34 35 36
-0.022422815 -0.257210700 0.202379095 0.236422987 0.108511440 0.035586309
37 38 39 40 41 42
-0.070686005 0.062536584 0.124083813 0.148310914 0.377236254 -0.146182811
43 44 45 46 47 48
-0.214044626 -0.114356400 -0.049721030 -0.132329119 0.232546329 -0.113852946
49 50 51 52 53 54
-0.103542362 -0.154032751 -0.141791473 -0.109267502 -0.143708650 -0.149095993
55 56 57 58 59
0.333048362 0.080623926 -0.143400433 -0.231725433 -0.099159179
> postscript(file="/var/www/html/rcomp/tmp/69rh01258746604.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.102569021 NA
1 -0.009714692 -0.102569021
2 -0.181461740 -0.009714692
3 -0.267719122 -0.181461740
4 -0.428487705 -0.267719122
5 0.006522911 -0.428487705
6 0.045294176 0.006522911
7 0.257368649 0.045294176
8 -0.062359252 0.257368649
9 -0.035944022 -0.062359252
10 -0.274774094 -0.035944022
11 -0.052685403 -0.274774094
12 0.201176636 -0.052685403
13 -0.033004877 0.201176636
14 0.065067909 -0.033004877
15 -0.004546664 0.065067909
16 -0.057669527 -0.004546664
17 0.149268954 -0.057669527
18 -0.141875097 0.149268954
19 0.033574525 -0.141875097
20 0.053101620 0.033574525
21 0.163575587 0.053101620
22 0.032875504 0.163575587
23 0.130952040 0.032875504
24 0.075620752 0.130952040
25 0.134215736 0.075620752
26 0.134101491 0.134215736
27 0.233222374 0.134101491
28 0.252629627 0.233222374
29 0.139486939 0.252629627
30 -0.022422815 0.139486939
31 -0.257210700 -0.022422815
32 0.202379095 -0.257210700
33 0.236422987 0.202379095
34 0.108511440 0.236422987
35 0.035586309 0.108511440
36 -0.070686005 0.035586309
37 0.062536584 -0.070686005
38 0.124083813 0.062536584
39 0.148310914 0.124083813
40 0.377236254 0.148310914
41 -0.146182811 0.377236254
42 -0.214044626 -0.146182811
43 -0.114356400 -0.214044626
44 -0.049721030 -0.114356400
45 -0.132329119 -0.049721030
46 0.232546329 -0.132329119
47 -0.113852946 0.232546329
48 -0.103542362 -0.113852946
49 -0.154032751 -0.103542362
50 -0.141791473 -0.154032751
51 -0.109267502 -0.141791473
52 -0.143708650 -0.109267502
53 -0.149095993 -0.143708650
54 0.333048362 -0.149095993
55 0.080623926 0.333048362
56 -0.143400433 0.080623926
57 -0.231725433 -0.143400433
58 -0.099159179 -0.231725433
59 NA -0.099159179
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.009714692 -0.102569021
[2,] -0.181461740 -0.009714692
[3,] -0.267719122 -0.181461740
[4,] -0.428487705 -0.267719122
[5,] 0.006522911 -0.428487705
[6,] 0.045294176 0.006522911
[7,] 0.257368649 0.045294176
[8,] -0.062359252 0.257368649
[9,] -0.035944022 -0.062359252
[10,] -0.274774094 -0.035944022
[11,] -0.052685403 -0.274774094
[12,] 0.201176636 -0.052685403
[13,] -0.033004877 0.201176636
[14,] 0.065067909 -0.033004877
[15,] -0.004546664 0.065067909
[16,] -0.057669527 -0.004546664
[17,] 0.149268954 -0.057669527
[18,] -0.141875097 0.149268954
[19,] 0.033574525 -0.141875097
[20,] 0.053101620 0.033574525
[21,] 0.163575587 0.053101620
[22,] 0.032875504 0.163575587
[23,] 0.130952040 0.032875504
[24,] 0.075620752 0.130952040
[25,] 0.134215736 0.075620752
[26,] 0.134101491 0.134215736
[27,] 0.233222374 0.134101491
[28,] 0.252629627 0.233222374
[29,] 0.139486939 0.252629627
[30,] -0.022422815 0.139486939
[31,] -0.257210700 -0.022422815
[32,] 0.202379095 -0.257210700
[33,] 0.236422987 0.202379095
[34,] 0.108511440 0.236422987
[35,] 0.035586309 0.108511440
[36,] -0.070686005 0.035586309
[37,] 0.062536584 -0.070686005
[38,] 0.124083813 0.062536584
[39,] 0.148310914 0.124083813
[40,] 0.377236254 0.148310914
[41,] -0.146182811 0.377236254
[42,] -0.214044626 -0.146182811
[43,] -0.114356400 -0.214044626
[44,] -0.049721030 -0.114356400
[45,] -0.132329119 -0.049721030
[46,] 0.232546329 -0.132329119
[47,] -0.113852946 0.232546329
[48,] -0.103542362 -0.113852946
[49,] -0.154032751 -0.103542362
[50,] -0.141791473 -0.154032751
[51,] -0.109267502 -0.141791473
[52,] -0.143708650 -0.109267502
[53,] -0.149095993 -0.143708650
[54,] 0.333048362 -0.149095993
[55,] 0.080623926 0.333048362
[56,] -0.143400433 0.080623926
[57,] -0.231725433 -0.143400433
[58,] -0.099159179 -0.231725433
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.009714692 -0.102569021
2 -0.181461740 -0.009714692
3 -0.267719122 -0.181461740
4 -0.428487705 -0.267719122
5 0.006522911 -0.428487705
6 0.045294176 0.006522911
7 0.257368649 0.045294176
8 -0.062359252 0.257368649
9 -0.035944022 -0.062359252
10 -0.274774094 -0.035944022
11 -0.052685403 -0.274774094
12 0.201176636 -0.052685403
13 -0.033004877 0.201176636
14 0.065067909 -0.033004877
15 -0.004546664 0.065067909
16 -0.057669527 -0.004546664
17 0.149268954 -0.057669527
18 -0.141875097 0.149268954
19 0.033574525 -0.141875097
20 0.053101620 0.033574525
21 0.163575587 0.053101620
22 0.032875504 0.163575587
23 0.130952040 0.032875504
24 0.075620752 0.130952040
25 0.134215736 0.075620752
26 0.134101491 0.134215736
27 0.233222374 0.134101491
28 0.252629627 0.233222374
29 0.139486939 0.252629627
30 -0.022422815 0.139486939
31 -0.257210700 -0.022422815
32 0.202379095 -0.257210700
33 0.236422987 0.202379095
34 0.108511440 0.236422987
35 0.035586309 0.108511440
36 -0.070686005 0.035586309
37 0.062536584 -0.070686005
38 0.124083813 0.062536584
39 0.148310914 0.124083813
40 0.377236254 0.148310914
41 -0.146182811 0.377236254
42 -0.214044626 -0.146182811
43 -0.114356400 -0.214044626
44 -0.049721030 -0.114356400
45 -0.132329119 -0.049721030
46 0.232546329 -0.132329119
47 -0.113852946 0.232546329
48 -0.103542362 -0.113852946
49 -0.154032751 -0.103542362
50 -0.141791473 -0.154032751
51 -0.109267502 -0.141791473
52 -0.143708650 -0.109267502
53 -0.149095993 -0.143708650
54 0.333048362 -0.149095993
55 0.080623926 0.333048362
56 -0.143400433 0.080623926
57 -0.231725433 -0.143400433
58 -0.099159179 -0.231725433
> 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/73wln1258746604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nckj1258746604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9a4ck1258746604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gdh91258746604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11977v1258746604.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/12wdax1258746604.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/136fcx1258746604.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/14xhi51258746604.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/15934a1258746604.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/162f7a1258746604.tab")
+ }
> system("convert tmp/1bl8b1258746604.ps tmp/1bl8b1258746604.png")
> system("convert tmp/2o0vs1258746604.ps tmp/2o0vs1258746604.png")
> system("convert tmp/3cfzj1258746604.ps tmp/3cfzj1258746604.png")
> system("convert tmp/4bx761258746604.ps tmp/4bx761258746604.png")
> system("convert tmp/56qra1258746604.ps tmp/56qra1258746604.png")
> system("convert tmp/69rh01258746604.ps tmp/69rh01258746604.png")
> system("convert tmp/73wln1258746604.ps tmp/73wln1258746604.png")
> system("convert tmp/8nckj1258746604.ps tmp/8nckj1258746604.png")
> system("convert tmp/9a4ck1258746604.ps tmp/9a4ck1258746604.png")
> system("convert tmp/10gdh91258746604.ps tmp/10gdh91258746604.png")
>
>
> proc.time()
user system elapsed
2.403 1.578 2.857