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(1.4
+ ,8.2
+ ,1.2
+ ,1.4
+ ,1.2
+ ,8.0
+ ,1
+ ,1.2
+ ,1.0
+ ,7.5
+ ,1.7
+ ,1
+ ,1.7
+ ,6.8
+ ,7.5
+ ,1.7
+ ,2.4
+ ,6.5
+ ,6.8
+ ,7.5
+ ,2.0
+ ,6.6
+ ,6.5
+ ,6.8
+ ,2.1
+ ,7.6
+ ,6.6
+ ,6.5
+ ,2.0
+ ,8.0
+ ,7.6
+ ,6.6
+ ,1.8
+ ,8.1
+ ,8.0
+ ,7.6
+ ,2.7
+ ,7.7
+ ,8.1
+ ,8.0
+ ,2.3
+ ,7.5
+ ,7.7
+ ,8.1
+ ,1.9
+ ,7.6
+ ,7.5
+ ,7.7
+ ,2.0
+ ,7.8
+ ,7.6
+ ,7.5
+ ,2.3
+ ,7.8
+ ,7.8
+ ,7.6
+ ,2.8
+ ,7.8
+ ,7.8
+ ,7.8
+ ,2.4
+ ,7.5
+ ,7.8
+ ,7.8
+ ,2.3
+ ,7.5
+ ,7.5
+ ,7.8
+ ,2.7
+ ,7.1
+ ,7.5
+ ,7.5
+ ,2.7
+ ,7.5
+ ,7.1
+ ,7.5
+ ,2.9
+ ,7.5
+ ,7.5
+ ,7.1
+ ,3.0
+ ,7.6
+ ,7.5
+ ,7.5
+ ,2.2
+ ,7.7
+ ,7.6
+ ,7.5
+ ,2.3
+ ,7.7
+ ,7.7
+ ,7.6
+ ,2.8
+ ,7.9
+ ,7.7
+ ,7.7
+ ,2.8
+ ,8.1
+ ,7.9
+ ,7.7
+ ,2.8
+ ,8.2
+ ,8.1
+ ,7.9
+ ,2.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,2.6
+ ,8.2
+ ,8.2
+ ,8.2
+ ,2.8
+ ,7.9
+ ,8.2
+ ,8.2
+ ,2.5
+ ,7.3
+ ,7.9
+ ,8.2
+ ,2.4
+ ,6.9
+ ,7.3
+ ,7.9
+ ,2.3
+ ,6.6
+ ,6.9
+ ,7.3
+ ,1.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,1.7
+ ,6.9
+ ,6.7
+ ,6.6
+ ,2.0
+ ,7.0
+ ,6.9
+ ,6.7
+ ,2.1
+ ,7.1
+ ,7.0
+ ,6.9
+ ,1.7
+ ,7.2
+ ,7.1
+ ,7.0
+ ,1.8
+ ,7.1
+ ,7.2
+ ,7.1
+ ,1.8
+ ,6.9
+ ,7.1
+ ,7.2
+ ,1.8
+ ,7.0
+ ,6.9
+ ,7.1
+ ,1.3
+ ,6.8
+ ,7.0
+ ,6.9
+ ,1.3
+ ,6.4
+ ,6.8
+ ,7.0
+ ,1.3
+ ,6.7
+ ,6.4
+ ,6.8
+ ,1.2
+ ,6.6
+ ,6.7
+ ,6.4
+ ,1.4
+ ,6.4
+ ,6.6
+ ,6.7
+ ,2.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,2.9
+ ,6.2
+ ,6.3
+ ,6.4
+ ,3.1
+ ,6.5
+ ,6.2
+ ,6.3
+ ,3.5
+ ,6.8
+ ,6.5
+ ,6.2
+ ,3.6
+ ,6.8
+ ,6.8
+ ,6.5
+ ,4.4
+ ,6.4
+ ,6.8
+ ,6.8
+ ,4.1
+ ,6.1
+ ,6.4
+ ,6.8
+ ,5.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,5.8
+ ,6.1
+ ,5.8
+ ,6.1
+ ,5.9
+ ,7.2
+ ,6.1
+ ,5.8
+ ,5.4
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.5
+ ,6.9
+ ,7.3
+ ,7.2
+ ,4.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,3.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,2.7
+ ,6.2
+ ,5.8
+ ,6.1
+ ,2.1
+ ,7.1
+ ,6.2
+ ,5.8
+ ,1.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,0.6
+ ,7.9
+ ,7.7
+ ,7.1
+ ,0.7
+ ,7.7
+ ,7.9
+ ,7.7)
+ ,dim=c(4
+ ,64)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:64))
> y <- array(NA,dim=c(4,64),dimnames=list(c('Y','X','Y1','Y2'),1:64))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.4 8.2 1.2 1.4 1 0 0 0 0 0 0 0 0 0 0 1
2 1.2 8.0 1.0 1.2 0 1 0 0 0 0 0 0 0 0 0 2
3 1.0 7.5 1.7 1.0 0 0 1 0 0 0 0 0 0 0 0 3
4 1.7 6.8 7.5 1.7 0 0 0 1 0 0 0 0 0 0 0 4
5 2.4 6.5 6.8 7.5 0 0 0 0 1 0 0 0 0 0 0 5
6 2.0 6.6 6.5 6.8 0 0 0 0 0 1 0 0 0 0 0 6
7 2.1 7.6 6.6 6.5 0 0 0 0 0 0 1 0 0 0 0 7
8 2.0 8.0 7.6 6.6 0 0 0 0 0 0 0 1 0 0 0 8
9 1.8 8.1 8.0 7.6 0 0 0 0 0 0 0 0 1 0 0 9
10 2.7 7.7 8.1 8.0 0 0 0 0 0 0 0 0 0 1 0 10
11 2.3 7.5 7.7 8.1 0 0 0 0 0 0 0 0 0 0 1 11
12 1.9 7.6 7.5 7.7 0 0 0 0 0 0 0 0 0 0 0 12
13 2.0 7.8 7.6 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 2.3 7.8 7.8 7.6 0 1 0 0 0 0 0 0 0 0 0 14
15 2.8 7.8 7.8 7.8 0 0 1 0 0 0 0 0 0 0 0 15
16 2.4 7.5 7.8 7.8 0 0 0 1 0 0 0 0 0 0 0 16
17 2.3 7.5 7.5 7.8 0 0 0 0 1 0 0 0 0 0 0 17
18 2.7 7.1 7.5 7.5 0 0 0 0 0 1 0 0 0 0 0 18
19 2.7 7.5 7.1 7.5 0 0 0 0 0 0 1 0 0 0 0 19
20 2.9 7.5 7.5 7.1 0 0 0 0 0 0 0 1 0 0 0 20
21 3.0 7.6 7.5 7.5 0 0 0 0 0 0 0 0 1 0 0 21
22 2.2 7.7 7.6 7.5 0 0 0 0 0 0 0 0 0 1 0 22
23 2.3 7.7 7.7 7.6 0 0 0 0 0 0 0 0 0 0 1 23
24 2.8 7.9 7.7 7.7 0 0 0 0 0 0 0 0 0 0 0 24
25 2.8 8.1 7.9 7.7 1 0 0 0 0 0 0 0 0 0 0 25
26 2.8 8.2 8.1 7.9 0 1 0 0 0 0 0 0 0 0 0 26
27 2.2 8.2 8.2 8.1 0 0 1 0 0 0 0 0 0 0 0 27
28 2.6 8.2 8.2 8.2 0 0 0 1 0 0 0 0 0 0 0 28
29 2.8 7.9 8.2 8.2 0 0 0 0 1 0 0 0 0 0 0 29
30 2.5 7.3 7.9 8.2 0 0 0 0 0 1 0 0 0 0 0 30
31 2.4 6.9 7.3 7.9 0 0 0 0 0 0 1 0 0 0 0 31
32 2.3 6.6 6.9 7.3 0 0 0 0 0 0 0 1 0 0 0 32
33 1.9 6.7 6.6 6.9 0 0 0 0 0 0 0 0 1 0 0 33
34 1.7 6.9 6.7 6.6 0 0 0 0 0 0 0 0 0 1 0 34
35 2.0 7.0 6.9 6.7 0 0 0 0 0 0 0 0 0 0 1 35
36 2.1 7.1 7.0 6.9 0 0 0 0 0 0 0 0 0 0 0 36
37 1.7 7.2 7.1 7.0 1 0 0 0 0 0 0 0 0 0 0 37
38 1.8 7.1 7.2 7.1 0 1 0 0 0 0 0 0 0 0 0 38
39 1.8 6.9 7.1 7.2 0 0 1 0 0 0 0 0 0 0 0 39
40 1.8 7.0 6.9 7.1 0 0 0 1 0 0 0 0 0 0 0 40
41 1.3 6.8 7.0 6.9 0 0 0 0 1 0 0 0 0 0 0 41
42 1.3 6.4 6.8 7.0 0 0 0 0 0 1 0 0 0 0 0 42
43 1.3 6.7 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 43
44 1.2 6.6 6.7 6.4 0 0 0 0 0 0 0 1 0 0 0 44
45 1.4 6.4 6.6 6.7 0 0 0 0 0 0 0 0 1 0 0 45
46 2.2 6.3 6.4 6.6 0 0 0 0 0 0 0 0 0 1 0 46
47 2.9 6.2 6.3 6.4 0 0 0 0 0 0 0 0 0 0 1 47
48 3.1 6.5 6.2 6.3 0 0 0 0 0 0 0 0 0 0 0 48
49 3.5 6.8 6.5 6.2 1 0 0 0 0 0 0 0 0 0 0 49
50 3.6 6.8 6.8 6.5 0 1 0 0 0 0 0 0 0 0 0 50
51 4.4 6.4 6.8 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 4.1 6.1 6.4 6.8 0 0 0 1 0 0 0 0 0 0 0 52
53 5.1 5.8 6.1 6.4 0 0 0 0 1 0 0 0 0 0 0 53
54 5.8 6.1 5.8 6.1 0 0 0 0 0 1 0 0 0 0 0 54
55 5.9 7.2 6.1 5.8 0 0 0 0 0 0 1 0 0 0 0 55
56 5.4 7.3 7.2 6.1 0 0 0 0 0 0 0 1 0 0 0 56
57 5.5 6.9 7.3 7.2 0 0 0 0 0 0 0 0 1 0 0 57
58 4.8 6.1 6.9 7.3 0 0 0 0 0 0 0 0 0 1 0 58
59 3.2 5.8 6.1 6.9 0 0 0 0 0 0 0 0 0 0 1 59
60 2.7 6.2 5.8 6.1 0 0 0 0 0 0 0 0 0 0 0 60
61 2.1 7.1 6.2 5.8 1 0 0 0 0 0 0 0 0 0 0 61
62 1.9 7.7 7.1 6.2 0 1 0 0 0 0 0 0 0 0 0 62
63 0.6 7.9 7.7 7.1 0 0 1 0 0 0 0 0 0 0 0 63
64 0.7 7.7 7.9 7.7 0 0 0 1 0 0 0 0 0 0 0 64
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
3.308214 -0.272803 0.070240 -0.001572 0.003504 0.002471
M3 M4 M5 M6 M7 M8
-0.205171 -0.266930 0.327279 0.349232 0.495318 0.328182
M9 M10 M11 t
0.252601 0.183724 -0.028164 0.018562
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.04701 -0.59849 -0.02734 0.58101 2.62038
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.308214 2.519453 1.313 0.1954
X -0.272803 0.340819 -0.800 0.4274
Y1 0.070240 0.232244 0.302 0.7636
Y2 -0.001572 0.211320 -0.007 0.9941
M1 0.003504 0.758324 0.005 0.9963
M2 0.002471 0.761527 0.003 0.9974
M3 -0.205171 0.747499 -0.274 0.7849
M4 -0.266930 0.762942 -0.350 0.7280
M5 0.327279 0.777999 0.421 0.6759
M6 0.349232 0.785483 0.445 0.6586
M7 0.495318 0.766220 0.646 0.5211
M8 0.328182 0.774618 0.424 0.6737
M9 0.252601 0.766335 0.330 0.7431
M10 0.183724 0.768576 0.239 0.8121
M11 -0.028164 0.770074 -0.037 0.9710
t 0.018562 0.010941 1.697 0.0963 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.209 on 48 degrees of freedom
Multiple R-squared: 0.2069, Adjusted R-squared: -0.04097
F-statistic: 0.8347 on 15 and 48 DF, p-value: 0.636
> 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,] 2.665771e-02 5.331541e-02 0.9733423
[2,] 7.460719e-03 1.492144e-02 0.9925393
[3,] 2.299849e-03 4.599697e-03 0.9977002
[4,] 2.087341e-03 4.174681e-03 0.9979127
[5,] 5.531626e-04 1.106325e-03 0.9994468
[6,] 2.243776e-04 4.487552e-04 0.9997756
[7,] 5.872278e-05 1.174456e-04 0.9999413
[8,] 1.584711e-05 3.169422e-05 0.9999842
[9,] 8.424003e-06 1.684801e-05 0.9999916
[10,] 3.232454e-06 6.464908e-06 0.9999968
[11,] 1.519741e-06 3.039481e-06 0.9999985
[12,] 7.679215e-07 1.535843e-06 0.9999992
[13,] 6.816586e-07 1.363317e-06 0.9999993
[14,] 5.426889e-07 1.085378e-06 0.9999995
[15,] 2.414769e-07 4.829538e-07 0.9999998
[16,] 1.110685e-07 2.221370e-07 0.9999999
[17,] 2.688332e-08 5.376665e-08 1.0000000
[18,] 8.201777e-09 1.640355e-08 1.0000000
[19,] 7.448225e-09 1.489645e-08 1.0000000
[20,] 6.593886e-09 1.318777e-08 1.0000000
[21,] 4.899320e-09 9.798640e-09 1.0000000
[22,] 7.049519e-08 1.409904e-07 0.9999999
[23,] 1.828377e-07 3.656754e-07 0.9999998
[24,] 2.231982e-06 4.463964e-06 0.9999978
[25,] 2.381404e-05 4.762807e-05 0.9999762
[26,] 3.031078e-05 6.062156e-05 0.9999697
[27,] 2.338797e-01 4.677594e-01 0.7661203
> postscript(file="/var/www/html/rcomp/tmp/1gyi31258718651.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2umi31258718651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36mxq1258718651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/499cn1258718651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xyy81258718651.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 = 64
Frequency = 1
1 2 3 4 5 6
0.224614616 -0.033741338 -0.230545827 -0.084605913 -0.020933408 -0.414196313
7 8 9 10 11 12
-0.213536743 -0.125925225 -0.268150878 0.566647331 0.333666623 -0.072360004
13 14 15 16 17 18
0.052796369 0.321376289 1.010770231 0.572125956 -0.119572800 0.130319428
19 20 21 22 23 24
0.102889079 0.422737921 0.607665442 -0.121763631 0.164695973 0.672687379
25 26 27 28 29 30
0.691134045 0.687151415 0.269521308 0.712875043 0.218263298 -0.264861373
31 32 33 34 35 36
-0.596957375 -0.603071317 -0.898329012 -1.000949318 -0.494233483 -0.420389236
37 38 39 40 41 42
-0.822041630 -0.773717941 -0.632017680 -0.547649906 -1.722319763 -1.857750752
43 44 45 46 47 48
-1.912775723 -1.913183111 -1.703229551 -0.866304206 0.006451705 0.248433100
49 50 51 52 53 54
0.686978825 0.748849036 1.629279029 1.318730953 1.644562674 2.406489011
55 56 57 58 59 60
2.620380763 2.219441732 2.262043998 1.422369823 -0.010580819 -0.428371239
61 62 63 64
-0.833482225 -0.949917461 -2.047007062 -1.971476133
> postscript(file="/var/www/html/rcomp/tmp/69i7o1258718651.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 = 64
Frequency = 1
lag(myerror, k = 1) myerror
0 0.224614616 NA
1 -0.033741338 0.224614616
2 -0.230545827 -0.033741338
3 -0.084605913 -0.230545827
4 -0.020933408 -0.084605913
5 -0.414196313 -0.020933408
6 -0.213536743 -0.414196313
7 -0.125925225 -0.213536743
8 -0.268150878 -0.125925225
9 0.566647331 -0.268150878
10 0.333666623 0.566647331
11 -0.072360004 0.333666623
12 0.052796369 -0.072360004
13 0.321376289 0.052796369
14 1.010770231 0.321376289
15 0.572125956 1.010770231
16 -0.119572800 0.572125956
17 0.130319428 -0.119572800
18 0.102889079 0.130319428
19 0.422737921 0.102889079
20 0.607665442 0.422737921
21 -0.121763631 0.607665442
22 0.164695973 -0.121763631
23 0.672687379 0.164695973
24 0.691134045 0.672687379
25 0.687151415 0.691134045
26 0.269521308 0.687151415
27 0.712875043 0.269521308
28 0.218263298 0.712875043
29 -0.264861373 0.218263298
30 -0.596957375 -0.264861373
31 -0.603071317 -0.596957375
32 -0.898329012 -0.603071317
33 -1.000949318 -0.898329012
34 -0.494233483 -1.000949318
35 -0.420389236 -0.494233483
36 -0.822041630 -0.420389236
37 -0.773717941 -0.822041630
38 -0.632017680 -0.773717941
39 -0.547649906 -0.632017680
40 -1.722319763 -0.547649906
41 -1.857750752 -1.722319763
42 -1.912775723 -1.857750752
43 -1.913183111 -1.912775723
44 -1.703229551 -1.913183111
45 -0.866304206 -1.703229551
46 0.006451705 -0.866304206
47 0.248433100 0.006451705
48 0.686978825 0.248433100
49 0.748849036 0.686978825
50 1.629279029 0.748849036
51 1.318730953 1.629279029
52 1.644562674 1.318730953
53 2.406489011 1.644562674
54 2.620380763 2.406489011
55 2.219441732 2.620380763
56 2.262043998 2.219441732
57 1.422369823 2.262043998
58 -0.010580819 1.422369823
59 -0.428371239 -0.010580819
60 -0.833482225 -0.428371239
61 -0.949917461 -0.833482225
62 -2.047007062 -0.949917461
63 -1.971476133 -2.047007062
64 NA -1.971476133
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.033741338 0.224614616
[2,] -0.230545827 -0.033741338
[3,] -0.084605913 -0.230545827
[4,] -0.020933408 -0.084605913
[5,] -0.414196313 -0.020933408
[6,] -0.213536743 -0.414196313
[7,] -0.125925225 -0.213536743
[8,] -0.268150878 -0.125925225
[9,] 0.566647331 -0.268150878
[10,] 0.333666623 0.566647331
[11,] -0.072360004 0.333666623
[12,] 0.052796369 -0.072360004
[13,] 0.321376289 0.052796369
[14,] 1.010770231 0.321376289
[15,] 0.572125956 1.010770231
[16,] -0.119572800 0.572125956
[17,] 0.130319428 -0.119572800
[18,] 0.102889079 0.130319428
[19,] 0.422737921 0.102889079
[20,] 0.607665442 0.422737921
[21,] -0.121763631 0.607665442
[22,] 0.164695973 -0.121763631
[23,] 0.672687379 0.164695973
[24,] 0.691134045 0.672687379
[25,] 0.687151415 0.691134045
[26,] 0.269521308 0.687151415
[27,] 0.712875043 0.269521308
[28,] 0.218263298 0.712875043
[29,] -0.264861373 0.218263298
[30,] -0.596957375 -0.264861373
[31,] -0.603071317 -0.596957375
[32,] -0.898329012 -0.603071317
[33,] -1.000949318 -0.898329012
[34,] -0.494233483 -1.000949318
[35,] -0.420389236 -0.494233483
[36,] -0.822041630 -0.420389236
[37,] -0.773717941 -0.822041630
[38,] -0.632017680 -0.773717941
[39,] -0.547649906 -0.632017680
[40,] -1.722319763 -0.547649906
[41,] -1.857750752 -1.722319763
[42,] -1.912775723 -1.857750752
[43,] -1.913183111 -1.912775723
[44,] -1.703229551 -1.913183111
[45,] -0.866304206 -1.703229551
[46,] 0.006451705 -0.866304206
[47,] 0.248433100 0.006451705
[48,] 0.686978825 0.248433100
[49,] 0.748849036 0.686978825
[50,] 1.629279029 0.748849036
[51,] 1.318730953 1.629279029
[52,] 1.644562674 1.318730953
[53,] 2.406489011 1.644562674
[54,] 2.620380763 2.406489011
[55,] 2.219441732 2.620380763
[56,] 2.262043998 2.219441732
[57,] 1.422369823 2.262043998
[58,] -0.010580819 1.422369823
[59,] -0.428371239 -0.010580819
[60,] -0.833482225 -0.428371239
[61,] -0.949917461 -0.833482225
[62,] -2.047007062 -0.949917461
[63,] -1.971476133 -2.047007062
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.033741338 0.224614616
2 -0.230545827 -0.033741338
3 -0.084605913 -0.230545827
4 -0.020933408 -0.084605913
5 -0.414196313 -0.020933408
6 -0.213536743 -0.414196313
7 -0.125925225 -0.213536743
8 -0.268150878 -0.125925225
9 0.566647331 -0.268150878
10 0.333666623 0.566647331
11 -0.072360004 0.333666623
12 0.052796369 -0.072360004
13 0.321376289 0.052796369
14 1.010770231 0.321376289
15 0.572125956 1.010770231
16 -0.119572800 0.572125956
17 0.130319428 -0.119572800
18 0.102889079 0.130319428
19 0.422737921 0.102889079
20 0.607665442 0.422737921
21 -0.121763631 0.607665442
22 0.164695973 -0.121763631
23 0.672687379 0.164695973
24 0.691134045 0.672687379
25 0.687151415 0.691134045
26 0.269521308 0.687151415
27 0.712875043 0.269521308
28 0.218263298 0.712875043
29 -0.264861373 0.218263298
30 -0.596957375 -0.264861373
31 -0.603071317 -0.596957375
32 -0.898329012 -0.603071317
33 -1.000949318 -0.898329012
34 -0.494233483 -1.000949318
35 -0.420389236 -0.494233483
36 -0.822041630 -0.420389236
37 -0.773717941 -0.822041630
38 -0.632017680 -0.773717941
39 -0.547649906 -0.632017680
40 -1.722319763 -0.547649906
41 -1.857750752 -1.722319763
42 -1.912775723 -1.857750752
43 -1.913183111 -1.912775723
44 -1.703229551 -1.913183111
45 -0.866304206 -1.703229551
46 0.006451705 -0.866304206
47 0.248433100 0.006451705
48 0.686978825 0.248433100
49 0.748849036 0.686978825
50 1.629279029 0.748849036
51 1.318730953 1.629279029
52 1.644562674 1.318730953
53 2.406489011 1.644562674
54 2.620380763 2.406489011
55 2.219441732 2.620380763
56 2.262043998 2.219441732
57 1.422369823 2.262043998
58 -0.010580819 1.422369823
59 -0.428371239 -0.010580819
60 -0.833482225 -0.428371239
61 -0.949917461 -0.833482225
62 -2.047007062 -0.949917461
63 -1.971476133 -2.047007062
> 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/7x6h71258718651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8na481258718651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/913fh1258718651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109j1y1258718651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11arq71258718651.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/12ce9j1258718651.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/132thu1258718652.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/1429j61258718652.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/158m4l1258718652.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/16v4c41258718652.tab")
+ }
>
> system("convert tmp/1gyi31258718651.ps tmp/1gyi31258718651.png")
> system("convert tmp/2umi31258718651.ps tmp/2umi31258718651.png")
> system("convert tmp/36mxq1258718651.ps tmp/36mxq1258718651.png")
> system("convert tmp/499cn1258718651.ps tmp/499cn1258718651.png")
> system("convert tmp/5xyy81258718651.ps tmp/5xyy81258718651.png")
> system("convert tmp/69i7o1258718651.ps tmp/69i7o1258718651.png")
> system("convert tmp/7x6h71258718651.ps tmp/7x6h71258718651.png")
> system("convert tmp/8na481258718651.ps tmp/8na481258718651.png")
> system("convert tmp/913fh1258718651.ps tmp/913fh1258718651.png")
> system("convert tmp/109j1y1258718651.ps tmp/109j1y1258718651.png")
>
>
> proc.time()
user system elapsed
2.415 1.538 2.822