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
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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(7.6
+ ,4634
+ ,7.5
+ ,7.7
+ ,8.1
+ ,8
+ ,7.8
+ ,3996
+ ,7.6
+ ,7.5
+ ,7.7
+ ,8.1
+ ,7.8
+ ,4308
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.7
+ ,7.8
+ ,4143
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.5
+ ,4429
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,5219
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.1
+ ,4929
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.5
+ ,5755
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.5
+ ,5592
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.6
+ ,4163
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.7
+ ,4962
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.7
+ ,5208
+ ,7.7
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.9
+ ,4755
+ ,7.7
+ ,7.7
+ ,7.6
+ ,7.5
+ ,8.1
+ ,4491
+ ,7.9
+ ,7.7
+ ,7.7
+ ,7.6
+ ,8.2
+ ,5732
+ ,8.1
+ ,7.9
+ ,7.7
+ ,7.7
+ ,8.2
+ ,5731
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.7
+ ,8.2
+ ,5040
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.9
+ ,6102
+ ,8.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.3
+ ,4904
+ ,7.9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,6.9
+ ,5369
+ ,7.3
+ ,7.9
+ ,8.2
+ ,8.2
+ ,6.7
+ ,5578
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,6.7
+ ,4619
+ ,6.7
+ ,6.9
+ ,7.3
+ ,7.9
+ ,6.9
+ ,4731
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.3
+ ,7
+ ,5011
+ ,6.9
+ ,6.7
+ ,6.7
+ ,6.9
+ ,7.1
+ ,5299
+ ,7
+ ,6.9
+ ,6.7
+ ,6.7
+ ,7.2
+ ,4146
+ ,7.1
+ ,7
+ ,6.9
+ ,6.7
+ ,7.1
+ ,4625
+ ,7.2
+ ,7.1
+ ,7
+ ,6.9
+ ,6.9
+ ,4736
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,7
+ ,4219
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.8
+ ,5116
+ ,7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.4
+ ,4205
+ ,6.8
+ ,7
+ ,6.9
+ ,7.1
+ ,6.7
+ ,4121
+ ,6.4
+ ,6.8
+ ,7
+ ,6.9
+ ,6.6
+ ,5103
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7
+ ,6.4
+ ,4300
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,6.3
+ ,4578
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.2
+ ,3809
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.5
+ ,5526
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.8
+ ,4247
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.8
+ ,3830
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,4394
+ ,6.8
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.1
+ ,4826
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.5
+ ,5.8
+ ,4409
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.1
+ ,4569
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.2
+ ,4106
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,7.3
+ ,4794
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.9
+ ,3914
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,3793
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,4405
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.2
+ ,4022
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.1
+ ,4100
+ ,6.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.7
+ ,4788
+ ,7.1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,7.9
+ ,3163
+ ,7.7
+ ,7.1
+ ,6.2
+ ,5.8
+ ,7.7
+ ,3585
+ ,7.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,7.4
+ ,3903
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,7.5
+ ,4178
+ ,7.4
+ ,7.7
+ ,7.9
+ ,7.7
+ ,8
+ ,3863
+ ,7.5
+ ,7.4
+ ,7.7
+ ,7.9
+ ,8.1
+ ,4187
+ ,8
+ ,7.5
+ ,7.4
+ ,7.7)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),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 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.6 4634 7.5 7.7 8.1 8.0 1 0 0 0 0 0 0 0 0 0 0 1
2 7.8 3996 7.6 7.5 7.7 8.1 0 1 0 0 0 0 0 0 0 0 0 2
3 7.8 4308 7.8 7.6 7.5 7.7 0 0 1 0 0 0 0 0 0 0 0 3
4 7.8 4143 7.8 7.8 7.6 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 7.5 4429 7.8 7.8 7.8 7.6 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 5219 7.5 7.8 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.1 4929 7.5 7.5 7.8 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 5755 7.1 7.5 7.5 7.8 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 5592 7.5 7.1 7.5 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.6 4163 7.5 7.5 7.1 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.7 4962 7.6 7.5 7.5 7.1 0 0 0 0 0 0 0 0 0 0 1 11
12 7.7 5208 7.7 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 7.9 4755 7.7 7.7 7.6 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.1 4491 7.9 7.7 7.7 7.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.2 5732 8.1 7.9 7.7 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 5731 8.2 8.1 7.9 7.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 5040 8.2 8.2 8.1 7.9 0 0 0 0 1 0 0 0 0 0 0 17
18 7.9 6102 8.2 8.2 8.2 8.1 0 0 0 0 0 1 0 0 0 0 0 18
19 7.3 4904 7.9 8.2 8.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 6.9 5369 7.3 7.9 8.2 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 6.7 5578 6.9 7.3 7.9 8.2 0 0 0 0 0 0 0 0 1 0 0 21
22 6.7 4619 6.7 6.9 7.3 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 6.9 4731 6.7 6.7 6.9 7.3 0 0 0 0 0 0 0 0 0 0 1 23
24 7.0 5011 6.9 6.7 6.7 6.9 0 0 0 0 0 0 0 0 0 0 0 24
25 7.1 5299 7.0 6.9 6.7 6.7 1 0 0 0 0 0 0 0 0 0 0 25
26 7.2 4146 7.1 7.0 6.9 6.7 0 1 0 0 0 0 0 0 0 0 0 26
27 7.1 4625 7.2 7.1 7.0 6.9 0 0 1 0 0 0 0 0 0 0 0 27
28 6.9 4736 7.1 7.2 7.1 7.0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.0 4219 6.9 7.1 7.2 7.1 0 0 0 0 1 0 0 0 0 0 0 29
30 6.8 5116 7.0 6.9 7.1 7.2 0 0 0 0 0 1 0 0 0 0 0 30
31 6.4 4205 6.8 7.0 6.9 7.1 0 0 0 0 0 0 1 0 0 0 0 31
32 6.7 4121 6.4 6.8 7.0 6.9 0 0 0 0 0 0 0 1 0 0 0 32
33 6.6 5103 6.7 6.4 6.8 7.0 0 0 0 0 0 0 0 0 1 0 0 33
34 6.4 4300 6.6 6.7 6.4 6.8 0 0 0 0 0 0 0 0 0 1 0 34
35 6.3 4578 6.4 6.6 6.7 6.4 0 0 0 0 0 0 0 0 0 0 1 35
36 6.2 3809 6.3 6.4 6.6 6.7 0 0 0 0 0 0 0 0 0 0 0 36
37 6.5 5526 6.2 6.3 6.4 6.6 1 0 0 0 0 0 0 0 0 0 0 37
38 6.8 4247 6.5 6.2 6.3 6.4 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 3830 6.8 6.5 6.2 6.3 0 0 1 0 0 0 0 0 0 0 0 39
40 6.4 4394 6.8 6.8 6.5 6.2 0 0 0 1 0 0 0 0 0 0 0 40
41 6.1 4826 6.4 6.8 6.8 6.5 0 0 0 0 1 0 0 0 0 0 0 41
42 5.8 4409 6.1 6.4 6.8 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.1 4569 5.8 6.1 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 4106 6.1 5.8 6.1 6.4 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 4794 7.2 6.1 5.8 6.1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 3914 7.3 7.2 6.1 5.8 0 0 0 0 0 0 0 0 0 1 0 46
47 6.1 3793 6.9 7.3 7.2 6.1 0 0 0 0 0 0 0 0 0 0 1 47
48 5.8 4405 6.1 6.9 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.2 4022 5.8 6.1 6.9 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 4100 6.2 5.8 6.1 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 4788 7.1 6.2 5.8 6.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 3163 7.7 7.1 6.2 5.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 3585 7.9 7.7 7.1 6.2 0 0 0 0 1 0 0 0 0 0 0 53
54 7.4 3903 7.7 7.9 7.7 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.5 4178 7.4 7.7 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.0 3863 7.5 7.4 7.7 7.9 0 0 0 0 0 0 0 1 0 0 0 56
57 8.1 4187 8.0 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 Y4
-1.539e-01 -1.332e-05 1.506e+00 -6.559e-01 -3.180e-01 4.792e-01
M1 M2 M3 M4 M5 M6
2.611e-01 1.671e-01 -1.367e-02 6.044e-02 1.192e-01 -6.315e-02
M7 M8 M9 M10 M11 t
-1.148e-01 4.142e-01 -3.100e-01 -1.327e-01 1.164e-01 3.033e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.48679 -0.11200 -0.01506 0.12492 0.38249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.539e-01 6.203e-01 -0.248 0.80532
X -1.332e-05 6.093e-05 -0.219 0.82808
Y1 1.506e+00 1.440e-01 10.460 7.05e-13 ***
Y2 -6.559e-01 2.715e-01 -2.416 0.02047 *
Y3 -3.180e-01 2.714e-01 -1.172 0.24840
Y4 4.792e-01 1.529e-01 3.134 0.00327 **
M1 2.611e-01 1.395e-01 1.872 0.06876 .
M2 1.671e-01 1.483e-01 1.127 0.26680
M3 -1.367e-02 1.493e-01 -0.092 0.92752
M4 6.044e-02 1.478e-01 0.409 0.68491
M5 1.192e-01 1.456e-01 0.818 0.41810
M6 -6.315e-02 1.441e-01 -0.438 0.66361
M7 -1.148e-01 1.418e-01 -0.809 0.42337
M8 4.142e-01 1.406e-01 2.946 0.00541 **
M9 -3.100e-01 1.626e-01 -1.907 0.06396 .
M10 -1.327e-01 1.745e-01 -0.761 0.45152
M11 1.164e-01 1.552e-01 0.750 0.45746
t 3.033e-03 2.601e-03 1.166 0.25066
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2058 on 39 degrees of freedom
Multiple R-squared: 0.932, Adjusted R-squared: 0.9024
F-statistic: 31.45 on 17 and 39 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,] 0.5932757 0.8134486 0.4067243
[2,] 0.4740802 0.9481605 0.5259198
[3,] 0.3457232 0.6914464 0.6542768
[4,] 0.2207637 0.4415275 0.7792363
[5,] 0.3168819 0.6337639 0.6831181
[6,] 0.2283998 0.4567996 0.7716002
[7,] 0.1607879 0.3215758 0.8392121
[8,] 0.1002695 0.2005391 0.8997305
[9,] 0.2378196 0.4756393 0.7621804
[10,] 0.1531861 0.3063722 0.8468139
[11,] 0.2157919 0.4315838 0.7842081
[12,] 0.2973718 0.5947435 0.7026282
[13,] 0.3256203 0.6512406 0.6743797
[14,] 0.2861593 0.5723185 0.7138407
[15,] 0.4180920 0.8361841 0.5819080
[16,] 0.5540933 0.8918134 0.4459067
> postscript(file="/var/www/html/rcomp/tmp/1upoa1259258883.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2r2z91259258883.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34nxp1259258883.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4djwz1259258883.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5weq21259258883.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
0.04567715 -0.12882870 -0.05456721 0.12491790 -0.21733626 0.32856101
7 8 9 10 11 12
-0.22348690 0.16273894 0.16047219 0.19627536 0.22300373 0.06294797
13 14 15 16 17 18
0.09018786 0.06021781 0.13643935 0.10342049 0.06581891 -0.10480012
19 20 21 22 23 24
-0.26815406 -0.48679334 0.15071924 -0.05047502 -0.07202087 -0.02808958
25 26 27 28 29 30
-0.11199804 0.04215479 -0.02283242 -0.09837974 0.15257227 -0.21775774
31 32 33 34 35 36
-0.23011221 0.13587865 -0.05578849 -0.13074261 0.04359631 -0.10933262
37 38 39 40 41 42
0.01879027 -0.06077866 -0.12766061 -0.25718904 -0.05894659 -0.13940554
43 44 45 46 47 48
0.33926560 0.34870856 -0.23305038 -0.01505773 -0.19457917 0.07447423
49 50 51 52 53 54
-0.04265724 0.08723475 0.06862089 0.12723039 0.05789167 0.13340239
55 56 57
0.38248757 -0.16053281 -0.02235256
> postscript(file="/var/www/html/rcomp/tmp/6dytl1259258883.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 0.04567715 NA
1 -0.12882870 0.04567715
2 -0.05456721 -0.12882870
3 0.12491790 -0.05456721
4 -0.21733626 0.12491790
5 0.32856101 -0.21733626
6 -0.22348690 0.32856101
7 0.16273894 -0.22348690
8 0.16047219 0.16273894
9 0.19627536 0.16047219
10 0.22300373 0.19627536
11 0.06294797 0.22300373
12 0.09018786 0.06294797
13 0.06021781 0.09018786
14 0.13643935 0.06021781
15 0.10342049 0.13643935
16 0.06581891 0.10342049
17 -0.10480012 0.06581891
18 -0.26815406 -0.10480012
19 -0.48679334 -0.26815406
20 0.15071924 -0.48679334
21 -0.05047502 0.15071924
22 -0.07202087 -0.05047502
23 -0.02808958 -0.07202087
24 -0.11199804 -0.02808958
25 0.04215479 -0.11199804
26 -0.02283242 0.04215479
27 -0.09837974 -0.02283242
28 0.15257227 -0.09837974
29 -0.21775774 0.15257227
30 -0.23011221 -0.21775774
31 0.13587865 -0.23011221
32 -0.05578849 0.13587865
33 -0.13074261 -0.05578849
34 0.04359631 -0.13074261
35 -0.10933262 0.04359631
36 0.01879027 -0.10933262
37 -0.06077866 0.01879027
38 -0.12766061 -0.06077866
39 -0.25718904 -0.12766061
40 -0.05894659 -0.25718904
41 -0.13940554 -0.05894659
42 0.33926560 -0.13940554
43 0.34870856 0.33926560
44 -0.23305038 0.34870856
45 -0.01505773 -0.23305038
46 -0.19457917 -0.01505773
47 0.07447423 -0.19457917
48 -0.04265724 0.07447423
49 0.08723475 -0.04265724
50 0.06862089 0.08723475
51 0.12723039 0.06862089
52 0.05789167 0.12723039
53 0.13340239 0.05789167
54 0.38248757 0.13340239
55 -0.16053281 0.38248757
56 -0.02235256 -0.16053281
57 NA -0.02235256
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.12882870 0.04567715
[2,] -0.05456721 -0.12882870
[3,] 0.12491790 -0.05456721
[4,] -0.21733626 0.12491790
[5,] 0.32856101 -0.21733626
[6,] -0.22348690 0.32856101
[7,] 0.16273894 -0.22348690
[8,] 0.16047219 0.16273894
[9,] 0.19627536 0.16047219
[10,] 0.22300373 0.19627536
[11,] 0.06294797 0.22300373
[12,] 0.09018786 0.06294797
[13,] 0.06021781 0.09018786
[14,] 0.13643935 0.06021781
[15,] 0.10342049 0.13643935
[16,] 0.06581891 0.10342049
[17,] -0.10480012 0.06581891
[18,] -0.26815406 -0.10480012
[19,] -0.48679334 -0.26815406
[20,] 0.15071924 -0.48679334
[21,] -0.05047502 0.15071924
[22,] -0.07202087 -0.05047502
[23,] -0.02808958 -0.07202087
[24,] -0.11199804 -0.02808958
[25,] 0.04215479 -0.11199804
[26,] -0.02283242 0.04215479
[27,] -0.09837974 -0.02283242
[28,] 0.15257227 -0.09837974
[29,] -0.21775774 0.15257227
[30,] -0.23011221 -0.21775774
[31,] 0.13587865 -0.23011221
[32,] -0.05578849 0.13587865
[33,] -0.13074261 -0.05578849
[34,] 0.04359631 -0.13074261
[35,] -0.10933262 0.04359631
[36,] 0.01879027 -0.10933262
[37,] -0.06077866 0.01879027
[38,] -0.12766061 -0.06077866
[39,] -0.25718904 -0.12766061
[40,] -0.05894659 -0.25718904
[41,] -0.13940554 -0.05894659
[42,] 0.33926560 -0.13940554
[43,] 0.34870856 0.33926560
[44,] -0.23305038 0.34870856
[45,] -0.01505773 -0.23305038
[46,] -0.19457917 -0.01505773
[47,] 0.07447423 -0.19457917
[48,] -0.04265724 0.07447423
[49,] 0.08723475 -0.04265724
[50,] 0.06862089 0.08723475
[51,] 0.12723039 0.06862089
[52,] 0.05789167 0.12723039
[53,] 0.13340239 0.05789167
[54,] 0.38248757 0.13340239
[55,] -0.16053281 0.38248757
[56,] -0.02235256 -0.16053281
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.12882870 0.04567715
2 -0.05456721 -0.12882870
3 0.12491790 -0.05456721
4 -0.21733626 0.12491790
5 0.32856101 -0.21733626
6 -0.22348690 0.32856101
7 0.16273894 -0.22348690
8 0.16047219 0.16273894
9 0.19627536 0.16047219
10 0.22300373 0.19627536
11 0.06294797 0.22300373
12 0.09018786 0.06294797
13 0.06021781 0.09018786
14 0.13643935 0.06021781
15 0.10342049 0.13643935
16 0.06581891 0.10342049
17 -0.10480012 0.06581891
18 -0.26815406 -0.10480012
19 -0.48679334 -0.26815406
20 0.15071924 -0.48679334
21 -0.05047502 0.15071924
22 -0.07202087 -0.05047502
23 -0.02808958 -0.07202087
24 -0.11199804 -0.02808958
25 0.04215479 -0.11199804
26 -0.02283242 0.04215479
27 -0.09837974 -0.02283242
28 0.15257227 -0.09837974
29 -0.21775774 0.15257227
30 -0.23011221 -0.21775774
31 0.13587865 -0.23011221
32 -0.05578849 0.13587865
33 -0.13074261 -0.05578849
34 0.04359631 -0.13074261
35 -0.10933262 0.04359631
36 0.01879027 -0.10933262
37 -0.06077866 0.01879027
38 -0.12766061 -0.06077866
39 -0.25718904 -0.12766061
40 -0.05894659 -0.25718904
41 -0.13940554 -0.05894659
42 0.33926560 -0.13940554
43 0.34870856 0.33926560
44 -0.23305038 0.34870856
45 -0.01505773 -0.23305038
46 -0.19457917 -0.01505773
47 0.07447423 -0.19457917
48 -0.04265724 0.07447423
49 0.08723475 -0.04265724
50 0.06862089 0.08723475
51 0.12723039 0.06862089
52 0.05789167 0.12723039
53 0.13340239 0.05789167
54 0.38248757 0.13340239
55 -0.16053281 0.38248757
56 -0.02235256 -0.16053281
> 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/7u8kt1259258884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v32o1259258884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xe3g1259258884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xa771259258884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mzhh1259258884.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/127p5k1259258884.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/13tt781259258884.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/14ywac1259258884.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/157iw51259258884.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/169sb81259258884.tab")
+ }
>
> system("convert tmp/1upoa1259258883.ps tmp/1upoa1259258883.png")
> system("convert tmp/2r2z91259258883.ps tmp/2r2z91259258883.png")
> system("convert tmp/34nxp1259258883.ps tmp/34nxp1259258883.png")
> system("convert tmp/4djwz1259258883.ps tmp/4djwz1259258883.png")
> system("convert tmp/5weq21259258883.ps tmp/5weq21259258883.png")
> system("convert tmp/6dytl1259258883.ps tmp/6dytl1259258883.png")
> system("convert tmp/7u8kt1259258884.ps tmp/7u8kt1259258884.png")
> system("convert tmp/8v32o1259258884.ps tmp/8v32o1259258884.png")
> system("convert tmp/9xe3g1259258884.ps tmp/9xe3g1259258884.png")
> system("convert tmp/10xa771259258884.ps tmp/10xa771259258884.png")
>
>
> proc.time()
user system elapsed
2.406 1.613 4.273