R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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(8.3
+ ,98.6
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,96.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,95.9
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,103.7
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,103.1
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,103.7
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,112.1
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,86.9
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,95
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,111.8
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,108.8
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,109.3
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,101.4
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,100.5
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,100.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,113.5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,106.1
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,111.6
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,114.9
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,88.6
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,99.5
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,115.1
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,118
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,111.4
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,107.3
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,105.3
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,105.3
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,117.9
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,110.2
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,112.4
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,117.5
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,93
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,103.5
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,116.3
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,120
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,114.3
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,104.7
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,109.8
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,112.6
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,114.4
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,115.7
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,114.7
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,118.4
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,94.9
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,103.8
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,115.1
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,113.7
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,104
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,94.3
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,92.5
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.5
+ ,8
+ ,93.2
+ ,7.7
+ ,6.9
+ ,6.6
+ ,6.9
+ ,8
+ ,104.7
+ ,8
+ ,7.7
+ ,6.9
+ ,6.6
+ ,7.7
+ ,94
+ ,8
+ ,8
+ ,7.7
+ ,6.9
+ ,7.3
+ ,98.1
+ ,7.7
+ ,8
+ ,8
+ ,7.7
+ ,7.4
+ ,102.7
+ ,7.3
+ ,7.7
+ ,8
+ ,8
+ ,8.1
+ ,82.4
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.3 98.6 8.2 8.7 9.3 9.3 1 0 0 0 0 0 0 0 0 0 0 1
2 8.5 96.5 8.3 8.2 8.7 9.3 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 95.9 8.5 8.3 8.2 8.7 0 0 1 0 0 0 0 0 0 0 0 3
4 8.5 103.7 8.6 8.5 8.3 8.2 0 0 0 1 0 0 0 0 0 0 0 4
5 8.2 103.1 8.5 8.6 8.5 8.3 0 0 0 0 1 0 0 0 0 0 0 5
6 8.1 103.7 8.2 8.5 8.6 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 7.9 112.1 8.1 8.2 8.5 8.6 0 0 0 0 0 0 1 0 0 0 0 7
8 8.6 86.9 7.9 8.1 8.2 8.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.7 95.0 8.6 7.9 8.1 8.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 111.8 8.7 8.6 7.9 8.1 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 108.8 8.7 8.7 8.6 7.9 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 109.3 8.5 8.7 8.7 8.6 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 101.4 8.4 8.5 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 100.5 8.5 8.4 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.7 100.7 8.7 8.5 8.4 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.6 113.5 8.7 8.7 8.5 8.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 106.1 8.6 8.7 8.7 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.3 111.6 8.5 8.6 8.7 8.7 0 0 0 0 0 1 0 0 0 0 0 18
19 8.0 114.9 8.3 8.5 8.6 8.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 88.6 8.0 8.3 8.5 8.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 99.5 8.2 8.0 8.3 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.1 115.1 8.1 8.2 8.0 8.3 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 118.0 8.1 8.1 8.2 8.0 0 0 0 0 0 0 0 0 0 0 1 23
24 7.9 111.4 8.0 8.1 8.1 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 107.3 7.9 8.0 8.1 8.1 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 105.3 7.9 7.9 8.0 8.1 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 105.3 8.0 7.9 7.9 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 117.9 8.0 8.0 7.9 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 110.2 7.9 8.0 8.0 7.9 0 0 0 0 1 0 0 0 0 0 0 29
30 7.7 112.4 8.0 7.9 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.2 117.5 7.7 8.0 7.9 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.5 93.0 7.2 7.7 8.0 7.9 0 0 0 0 0 0 0 1 0 0 0 32
33 7.3 103.5 7.5 7.2 7.7 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.0 116.3 7.3 7.5 7.2 7.7 0 0 0 0 0 0 0 0 0 1 0 34
35 7.0 120.0 7.0 7.3 7.5 7.2 0 0 0 0 0 0 0 0 0 0 1 35
36 7.0 114.3 7.0 7.0 7.3 7.5 0 0 0 0 0 0 0 0 0 0 0 36
37 7.2 104.7 7.0 7.0 7.0 7.3 1 0 0 0 0 0 0 0 0 0 0 37
38 7.3 109.8 7.2 7.0 7.0 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.1 112.6 7.3 7.2 7.0 7.0 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 114.4 7.1 7.3 7.2 7.0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 115.7 6.8 7.1 7.3 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 114.7 6.4 6.8 7.1 7.3 0 0 0 0 0 1 0 0 0 0 0 42
43 6.5 118.4 6.1 6.4 6.8 7.1 0 0 0 0 0 0 1 0 0 0 0 43
44 7.7 94.9 6.5 6.1 6.4 6.8 0 0 0 0 0 0 0 1 0 0 0 44
45 7.9 103.8 7.7 6.5 6.1 6.4 0 0 0 0 0 0 0 0 1 0 0 45
46 7.5 115.1 7.9 7.7 6.5 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 113.7 7.5 7.9 7.7 6.5 0 0 0 0 0 0 0 0 0 0 1 47
48 6.6 104.0 6.9 7.5 7.9 7.7 0 0 0 0 0 0 0 0 0 0 0 48
49 6.9 94.3 6.6 6.9 7.5 7.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.7 92.5 6.9 6.6 6.9 7.5 0 1 0 0 0 0 0 0 0 0 0 50
51 8.0 93.2 7.7 6.9 6.6 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 8.0 104.7 8.0 7.7 6.9 6.6 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 94.0 8.0 8.0 7.7 6.9 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 98.1 7.7 8.0 8.0 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 7.4 102.7 7.3 7.7 8.0 8.0 0 0 0 0 0 0 1 0 0 0 0 55
56 8.1 82.4 7.4 7.3 7.7 8.0 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
2.424287 -0.011785 1.466175 -0.818010 -0.040460 0.251900
M1 M2 M3 M4 M5 M6
0.075991 0.009227 -0.169016 0.050003 -0.021451 -0.125440
M7 M8 M9 M10 M11 t
0.039372 0.336063 -0.489194 0.046660 0.149764 -0.002981
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.24256 -0.10182 -0.01407 0.10301 0.33807
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.424287 1.067172 2.272 0.02885 *
X -0.011785 0.004771 -2.470 0.01811 *
Y1 1.466175 0.153913 9.526 1.29e-11 ***
Y2 -0.818010 0.289833 -2.822 0.00754 **
Y3 -0.040460 0.288756 -0.140 0.88931
Y4 0.251900 0.157692 1.597 0.11846
M1 0.075991 0.116485 0.652 0.51809
M2 0.009227 0.123248 0.075 0.94072
M3 -0.169016 0.126806 -1.333 0.19051
M4 0.050003 0.117720 0.425 0.67340
M5 -0.021451 0.113407 -0.189 0.85098
M6 -0.125440 0.107946 -1.162 0.25246
M7 0.039372 0.110079 0.358 0.72257
M8 0.336063 0.148666 2.261 0.02960 *
M9 -0.489194 0.156086 -3.134 0.00332 **
M10 0.046660 0.166212 0.281 0.78045
M11 0.149764 0.141060 1.062 0.29507
t -0.002981 0.002959 -1.008 0.32003
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1603 on 38 degrees of freedom
Multiple R-squared: 0.9599, Adjusted R-squared: 0.9419
F-statistic: 53.46 on 17 and 38 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,] 0.72437432 0.5512514 0.2756257
[2,] 0.59786230 0.8042754 0.4021377
[3,] 0.46209466 0.9241893 0.5379053
[4,] 0.32355893 0.6471179 0.6764411
[5,] 0.24209501 0.4841900 0.7579050
[6,] 0.14746465 0.2949293 0.8525354
[7,] 0.09929768 0.1985954 0.9007023
[8,] 0.05933908 0.1186782 0.9406609
[9,] 0.39815628 0.7963126 0.6018437
[10,] 0.40606926 0.8121385 0.5939307
[11,] 0.64650289 0.7069942 0.3534971
[12,] 0.58280479 0.8343904 0.4171952
[13,] 0.49635573 0.9927115 0.5036443
[14,] 0.43216778 0.8643356 0.5678322
[15,] 0.44494941 0.8898988 0.5550506
> postscript(file="/var/www/html/rcomp/tmp/16krx1258751636.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2pdzu1258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31o511258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4f9os1258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53i0a1258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 56
Frequency = 1
1 2 3 4 5 6
0.092344062 -0.242557086 -0.048929035 -0.126065893 -0.147381399 0.178377622
7 8 9 10 11 12
-0.212483977 0.121318160 0.026611702 0.134808792 -0.040165281 0.139423132
13 14 15 16 17 18
0.031139752 0.053768462 0.072248535 0.099892641 0.116640398 0.102863300
19 20 21 22 23 24
-0.112690571 -0.218943085 0.116207873 0.115637026 -0.048449800 0.018707760
25 26 27 28 29 30
-0.012612962 0.047716113 0.103465727 0.042905835 0.277262121 -0.143449341
31 32 33 34 35 36
-0.227571273 0.006914546 -0.127293838 -0.215345327 0.142472865 -0.101019946
37 38 39 40 41 42
-0.048920855 -0.036738697 -0.005533876 -0.117231078 -0.197559892 -0.094589068
43 44 45 46 47 48
0.338073124 0.194936181 -0.015525736 -0.035100491 -0.053857784 -0.057110947
49 50 51 52 53 54
-0.061949998 0.177811208 -0.121251351 0.100498494 -0.048961228 -0.043202514
55 56
0.214672696 -0.104225802
> postscript(file="/var/www/html/rcomp/tmp/68xia1258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 0.092344062 NA
1 -0.242557086 0.092344062
2 -0.048929035 -0.242557086
3 -0.126065893 -0.048929035
4 -0.147381399 -0.126065893
5 0.178377622 -0.147381399
6 -0.212483977 0.178377622
7 0.121318160 -0.212483977
8 0.026611702 0.121318160
9 0.134808792 0.026611702
10 -0.040165281 0.134808792
11 0.139423132 -0.040165281
12 0.031139752 0.139423132
13 0.053768462 0.031139752
14 0.072248535 0.053768462
15 0.099892641 0.072248535
16 0.116640398 0.099892641
17 0.102863300 0.116640398
18 -0.112690571 0.102863300
19 -0.218943085 -0.112690571
20 0.116207873 -0.218943085
21 0.115637026 0.116207873
22 -0.048449800 0.115637026
23 0.018707760 -0.048449800
24 -0.012612962 0.018707760
25 0.047716113 -0.012612962
26 0.103465727 0.047716113
27 0.042905835 0.103465727
28 0.277262121 0.042905835
29 -0.143449341 0.277262121
30 -0.227571273 -0.143449341
31 0.006914546 -0.227571273
32 -0.127293838 0.006914546
33 -0.215345327 -0.127293838
34 0.142472865 -0.215345327
35 -0.101019946 0.142472865
36 -0.048920855 -0.101019946
37 -0.036738697 -0.048920855
38 -0.005533876 -0.036738697
39 -0.117231078 -0.005533876
40 -0.197559892 -0.117231078
41 -0.094589068 -0.197559892
42 0.338073124 -0.094589068
43 0.194936181 0.338073124
44 -0.015525736 0.194936181
45 -0.035100491 -0.015525736
46 -0.053857784 -0.035100491
47 -0.057110947 -0.053857784
48 -0.061949998 -0.057110947
49 0.177811208 -0.061949998
50 -0.121251351 0.177811208
51 0.100498494 -0.121251351
52 -0.048961228 0.100498494
53 -0.043202514 -0.048961228
54 0.214672696 -0.043202514
55 -0.104225802 0.214672696
56 NA -0.104225802
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.242557086 0.092344062
[2,] -0.048929035 -0.242557086
[3,] -0.126065893 -0.048929035
[4,] -0.147381399 -0.126065893
[5,] 0.178377622 -0.147381399
[6,] -0.212483977 0.178377622
[7,] 0.121318160 -0.212483977
[8,] 0.026611702 0.121318160
[9,] 0.134808792 0.026611702
[10,] -0.040165281 0.134808792
[11,] 0.139423132 -0.040165281
[12,] 0.031139752 0.139423132
[13,] 0.053768462 0.031139752
[14,] 0.072248535 0.053768462
[15,] 0.099892641 0.072248535
[16,] 0.116640398 0.099892641
[17,] 0.102863300 0.116640398
[18,] -0.112690571 0.102863300
[19,] -0.218943085 -0.112690571
[20,] 0.116207873 -0.218943085
[21,] 0.115637026 0.116207873
[22,] -0.048449800 0.115637026
[23,] 0.018707760 -0.048449800
[24,] -0.012612962 0.018707760
[25,] 0.047716113 -0.012612962
[26,] 0.103465727 0.047716113
[27,] 0.042905835 0.103465727
[28,] 0.277262121 0.042905835
[29,] -0.143449341 0.277262121
[30,] -0.227571273 -0.143449341
[31,] 0.006914546 -0.227571273
[32,] -0.127293838 0.006914546
[33,] -0.215345327 -0.127293838
[34,] 0.142472865 -0.215345327
[35,] -0.101019946 0.142472865
[36,] -0.048920855 -0.101019946
[37,] -0.036738697 -0.048920855
[38,] -0.005533876 -0.036738697
[39,] -0.117231078 -0.005533876
[40,] -0.197559892 -0.117231078
[41,] -0.094589068 -0.197559892
[42,] 0.338073124 -0.094589068
[43,] 0.194936181 0.338073124
[44,] -0.015525736 0.194936181
[45,] -0.035100491 -0.015525736
[46,] -0.053857784 -0.035100491
[47,] -0.057110947 -0.053857784
[48,] -0.061949998 -0.057110947
[49,] 0.177811208 -0.061949998
[50,] -0.121251351 0.177811208
[51,] 0.100498494 -0.121251351
[52,] -0.048961228 0.100498494
[53,] -0.043202514 -0.048961228
[54,] 0.214672696 -0.043202514
[55,] -0.104225802 0.214672696
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.242557086 0.092344062
2 -0.048929035 -0.242557086
3 -0.126065893 -0.048929035
4 -0.147381399 -0.126065893
5 0.178377622 -0.147381399
6 -0.212483977 0.178377622
7 0.121318160 -0.212483977
8 0.026611702 0.121318160
9 0.134808792 0.026611702
10 -0.040165281 0.134808792
11 0.139423132 -0.040165281
12 0.031139752 0.139423132
13 0.053768462 0.031139752
14 0.072248535 0.053768462
15 0.099892641 0.072248535
16 0.116640398 0.099892641
17 0.102863300 0.116640398
18 -0.112690571 0.102863300
19 -0.218943085 -0.112690571
20 0.116207873 -0.218943085
21 0.115637026 0.116207873
22 -0.048449800 0.115637026
23 0.018707760 -0.048449800
24 -0.012612962 0.018707760
25 0.047716113 -0.012612962
26 0.103465727 0.047716113
27 0.042905835 0.103465727
28 0.277262121 0.042905835
29 -0.143449341 0.277262121
30 -0.227571273 -0.143449341
31 0.006914546 -0.227571273
32 -0.127293838 0.006914546
33 -0.215345327 -0.127293838
34 0.142472865 -0.215345327
35 -0.101019946 0.142472865
36 -0.048920855 -0.101019946
37 -0.036738697 -0.048920855
38 -0.005533876 -0.036738697
39 -0.117231078 -0.005533876
40 -0.197559892 -0.117231078
41 -0.094589068 -0.197559892
42 0.338073124 -0.094589068
43 0.194936181 0.338073124
44 -0.015525736 0.194936181
45 -0.035100491 -0.015525736
46 -0.053857784 -0.035100491
47 -0.057110947 -0.053857784
48 -0.061949998 -0.057110947
49 0.177811208 -0.061949998
50 -0.121251351 0.177811208
51 0.100498494 -0.121251351
52 -0.048961228 0.100498494
53 -0.043202514 -0.048961228
54 0.214672696 -0.043202514
55 -0.104225802 0.214672696
> 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/7j7gl1258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8eev21258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91lvv1258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zc4y1258751636.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bntb1258751636.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/129pvs1258751636.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/13vwuf1258751636.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/147gxm1258751636.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/15ac451258751636.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/16txh81258751636.tab")
+ }
>
> system("convert tmp/16krx1258751636.ps tmp/16krx1258751636.png")
> system("convert tmp/2pdzu1258751636.ps tmp/2pdzu1258751636.png")
> system("convert tmp/31o511258751636.ps tmp/31o511258751636.png")
> system("convert tmp/4f9os1258751636.ps tmp/4f9os1258751636.png")
> system("convert tmp/53i0a1258751636.ps tmp/53i0a1258751636.png")
> system("convert tmp/68xia1258751636.ps tmp/68xia1258751636.png")
> system("convert tmp/7j7gl1258751636.ps tmp/7j7gl1258751636.png")
> system("convert tmp/8eev21258751636.ps tmp/8eev21258751636.png")
> system("convert tmp/91lvv1258751636.ps tmp/91lvv1258751636.png")
> system("convert tmp/10zc4y1258751636.ps tmp/10zc4y1258751636.png")
>
>
> proc.time()
user system elapsed
2.295 1.521 2.757