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(20
+ ,0
+ ,21
+ ,20
+ ,22
+ ,22
+ ,21
+ ,0
+ ,20
+ ,21
+ ,20
+ ,22
+ ,21
+ ,0
+ ,21
+ ,20
+ ,21
+ ,20
+ ,21
+ ,0
+ ,21
+ ,21
+ ,20
+ ,21
+ ,19
+ ,0
+ ,21
+ ,21
+ ,21
+ ,20
+ ,21
+ ,0
+ ,19
+ ,21
+ ,21
+ ,21
+ ,21
+ ,0
+ ,21
+ ,19
+ ,21
+ ,21
+ ,22
+ ,0
+ ,21
+ ,21
+ ,19
+ ,21
+ ,19
+ ,0
+ ,22
+ ,21
+ ,21
+ ,19
+ ,24
+ ,0
+ ,19
+ ,22
+ ,21
+ ,21
+ ,22
+ ,0
+ ,24
+ ,19
+ ,22
+ ,21
+ ,22
+ ,0
+ ,22
+ ,24
+ ,19
+ ,22
+ ,22
+ ,0
+ ,22
+ ,22
+ ,24
+ ,19
+ ,24
+ ,0
+ ,22
+ ,22
+ ,22
+ ,24
+ ,22
+ ,0
+ ,24
+ ,22
+ ,22
+ ,22
+ ,23
+ ,0
+ ,22
+ ,24
+ ,22
+ ,22
+ ,24
+ ,0
+ ,23
+ ,22
+ ,24
+ ,22
+ ,21
+ ,0
+ ,24
+ ,23
+ ,22
+ ,24
+ ,20
+ ,0
+ ,21
+ ,24
+ ,23
+ ,22
+ ,22
+ ,0
+ ,20
+ ,21
+ ,24
+ ,23
+ ,23
+ ,0
+ ,22
+ ,20
+ ,21
+ ,24
+ ,23
+ ,0
+ ,23
+ ,22
+ ,20
+ ,21
+ ,22
+ ,0
+ ,23
+ ,23
+ ,22
+ ,20
+ ,20
+ ,0
+ ,22
+ ,23
+ ,23
+ ,22
+ ,21
+ ,1
+ ,20
+ ,22
+ ,23
+ ,23
+ ,21
+ ,1
+ ,21
+ ,20
+ ,22
+ ,23
+ ,20
+ ,1
+ ,21
+ ,21
+ ,20
+ ,22
+ ,20
+ ,1
+ ,20
+ ,21
+ ,21
+ ,20
+ ,17
+ ,1
+ ,20
+ ,20
+ ,21
+ ,21
+ ,18
+ ,1
+ ,17
+ ,20
+ ,20
+ ,21
+ ,19
+ ,1
+ ,18
+ ,17
+ ,20
+ ,20
+ ,19
+ ,1
+ ,19
+ ,18
+ ,17
+ ,20
+ ,20
+ ,1
+ ,19
+ ,19
+ ,18
+ ,17
+ ,21
+ ,1
+ ,20
+ ,19
+ ,19
+ ,18
+ ,20
+ ,1
+ ,21
+ ,20
+ ,19
+ ,19
+ ,21
+ ,1
+ ,20
+ ,21
+ ,20
+ ,19
+ ,19
+ ,1
+ ,21
+ ,20
+ ,21
+ ,20
+ ,22
+ ,1
+ ,19
+ ,21
+ ,20
+ ,21
+ ,20
+ ,1
+ ,22
+ ,19
+ ,21
+ ,20
+ ,18
+ ,1
+ ,20
+ ,22
+ ,19
+ ,21
+ ,16
+ ,1
+ ,18
+ ,20
+ ,22
+ ,19
+ ,17
+ ,1
+ ,16
+ ,18
+ ,20
+ ,22
+ ,18
+ ,1
+ ,17
+ ,16
+ ,18
+ ,20
+ ,19
+ ,1
+ ,18
+ ,17
+ ,16
+ ,18
+ ,18
+ ,1
+ ,19
+ ,18
+ ,17
+ ,16
+ ,20
+ ,1
+ ,18
+ ,19
+ ,18
+ ,17
+ ,21
+ ,1
+ ,20
+ ,18
+ ,19
+ ,18
+ ,18
+ ,1
+ ,21
+ ,20
+ ,18
+ ,19
+ ,19
+ ,1
+ ,18
+ ,21
+ ,20
+ ,18
+ ,19
+ ,1
+ ,19
+ ,18
+ ,21
+ ,20
+ ,19
+ ,1
+ ,19
+ ,19
+ ,18
+ ,21
+ ,21
+ ,1
+ ,19
+ ,19
+ ,19
+ ,18
+ ,19
+ ,1
+ ,21
+ ,19
+ ,19
+ ,19
+ ,19
+ ,1
+ ,19
+ ,21
+ ,19
+ ,19
+ ,17
+ ,1
+ ,19
+ ,19
+ ,21
+ ,19
+ ,16
+ ,1
+ ,17
+ ,19
+ ,19
+ ,21
+ ,16
+ ,1
+ ,16
+ ,17
+ ,19
+ ,19
+ ,17
+ ,1
+ ,16
+ ,16
+ ,17
+ ,19
+ ,16
+ ,1
+ ,17
+ ,16
+ ,16
+ ,17
+ ,15
+ ,1
+ ,16
+ ,17
+ ,16
+ ,16
+ ,16
+ ,1
+ ,15
+ ,16
+ ,17
+ ,16
+ ,16
+ ,1
+ ,16
+ ,15
+ ,16
+ ,17
+ ,16
+ ,1
+ ,16
+ ,16
+ ,15
+ ,16
+ ,18
+ ,1
+ ,16
+ ,16
+ ,16
+ ,15
+ ,19
+ ,1
+ ,18
+ ,16
+ ,16
+ ,16
+ ,16
+ ,1
+ ,19
+ ,18
+ ,16
+ ,16
+ ,16
+ ,1
+ ,16
+ ,19
+ ,18
+ ,16
+ ,16
+ ,1
+ ,16
+ ,16
+ ,19
+ ,18)
+ ,dim=c(6
+ ,68)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:68))
> y <- array(NA,dim=c(6,68),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:68))
> 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 20 0 21 20 22 22 1 0 0 0 0 0 0 0 0 0 0 1
2 21 0 20 21 20 22 0 1 0 0 0 0 0 0 0 0 0 2
3 21 0 21 20 21 20 0 0 1 0 0 0 0 0 0 0 0 3
4 21 0 21 21 20 21 0 0 0 1 0 0 0 0 0 0 0 4
5 19 0 21 21 21 20 0 0 0 0 1 0 0 0 0 0 0 5
6 21 0 19 21 21 21 0 0 0 0 0 1 0 0 0 0 0 6
7 21 0 21 19 21 21 0 0 0 0 0 0 1 0 0 0 0 7
8 22 0 21 21 19 21 0 0 0 0 0 0 0 1 0 0 0 8
9 19 0 22 21 21 19 0 0 0 0 0 0 0 0 1 0 0 9
10 24 0 19 22 21 21 0 0 0 0 0 0 0 0 0 1 0 10
11 22 0 24 19 22 21 0 0 0 0 0 0 0 0 0 0 1 11
12 22 0 22 24 19 22 0 0 0 0 0 0 0 0 0 0 0 12
13 22 0 22 22 24 19 1 0 0 0 0 0 0 0 0 0 0 13
14 24 0 22 22 22 24 0 1 0 0 0 0 0 0 0 0 0 14
15 22 0 24 22 22 22 0 0 1 0 0 0 0 0 0 0 0 15
16 23 0 22 24 22 22 0 0 0 1 0 0 0 0 0 0 0 16
17 24 0 23 22 24 22 0 0 0 0 1 0 0 0 0 0 0 17
18 21 0 24 23 22 24 0 0 0 0 0 1 0 0 0 0 0 18
19 20 0 21 24 23 22 0 0 0 0 0 0 1 0 0 0 0 19
20 22 0 20 21 24 23 0 0 0 0 0 0 0 1 0 0 0 20
21 23 0 22 20 21 24 0 0 0 0 0 0 0 0 1 0 0 21
22 23 0 23 22 20 21 0 0 0 0 0 0 0 0 0 1 0 22
23 22 0 23 23 22 20 0 0 0 0 0 0 0 0 0 0 1 23
24 20 0 22 23 23 22 0 0 0 0 0 0 0 0 0 0 0 24
25 21 1 20 22 23 23 1 0 0 0 0 0 0 0 0 0 0 25
26 21 1 21 20 22 23 0 1 0 0 0 0 0 0 0 0 0 26
27 20 1 21 21 20 22 0 0 1 0 0 0 0 0 0 0 0 27
28 20 1 20 21 21 20 0 0 0 1 0 0 0 0 0 0 0 28
29 17 1 20 20 21 21 0 0 0 0 1 0 0 0 0 0 0 29
30 18 1 17 20 20 21 0 0 0 0 0 1 0 0 0 0 0 30
31 19 1 18 17 20 20 0 0 0 0 0 0 1 0 0 0 0 31
32 19 1 19 18 17 20 0 0 0 0 0 0 0 1 0 0 0 32
33 20 1 19 19 18 17 0 0 0 0 0 0 0 0 1 0 0 33
34 21 1 20 19 19 18 0 0 0 0 0 0 0 0 0 1 0 34
35 20 1 21 20 19 19 0 0 0 0 0 0 0 0 0 0 1 35
36 21 1 20 21 20 19 0 0 0 0 0 0 0 0 0 0 0 36
37 19 1 21 20 21 20 1 0 0 0 0 0 0 0 0 0 0 37
38 22 1 19 21 20 21 0 1 0 0 0 0 0 0 0 0 0 38
39 20 1 22 19 21 20 0 0 1 0 0 0 0 0 0 0 0 39
40 18 1 20 22 19 21 0 0 0 1 0 0 0 0 0 0 0 40
41 16 1 18 20 22 19 0 0 0 0 1 0 0 0 0 0 0 41
42 17 1 16 18 20 22 0 0 0 0 0 1 0 0 0 0 0 42
43 18 1 17 16 18 20 0 0 0 0 0 0 1 0 0 0 0 43
44 19 1 18 17 16 18 0 0 0 0 0 0 0 1 0 0 0 44
45 18 1 19 18 17 16 0 0 0 0 0 0 0 0 1 0 0 45
46 20 1 18 19 18 17 0 0 0 0 0 0 0 0 0 1 0 46
47 21 1 20 18 19 18 0 0 0 0 0 0 0 0 0 0 1 47
48 18 1 21 20 18 19 0 0 0 0 0 0 0 0 0 0 0 48
49 19 1 18 21 20 18 1 0 0 0 0 0 0 0 0 0 0 49
50 19 1 19 18 21 20 0 1 0 0 0 0 0 0 0 0 0 50
51 19 1 19 19 18 21 0 0 1 0 0 0 0 0 0 0 0 51
52 21 1 19 19 19 18 0 0 0 1 0 0 0 0 0 0 0 52
53 19 1 21 19 19 19 0 0 0 0 1 0 0 0 0 0 0 53
54 19 1 19 21 19 19 0 0 0 0 0 1 0 0 0 0 0 54
55 17 1 19 19 21 19 0 0 0 0 0 0 1 0 0 0 0 55
56 16 1 17 19 19 21 0 0 0 0 0 0 0 1 0 0 0 56
57 16 1 16 17 19 19 0 0 0 0 0 0 0 0 1 0 0 57
58 17 1 16 16 17 19 0 0 0 0 0 0 0 0 0 1 0 58
59 16 1 17 16 16 17 0 0 0 0 0 0 0 0 0 0 1 59
60 15 1 16 17 16 16 0 0 0 0 0 0 0 0 0 0 0 60
61 16 1 15 16 17 16 1 0 0 0 0 0 0 0 0 0 0 61
62 16 1 16 15 16 17 0 1 0 0 0 0 0 0 0 0 0 62
63 16 1 16 16 15 16 0 0 1 0 0 0 0 0 0 0 0 63
64 18 1 16 16 16 15 0 0 0 1 0 0 0 0 0 0 0 64
65 19 1 18 16 16 16 0 0 0 0 1 0 0 0 0 0 0 65
66 16 1 19 18 16 16 0 0 0 0 0 1 0 0 0 0 0 66
67 16 1 16 19 18 16 0 0 0 0 0 0 1 0 0 0 0 67
68 16 1 16 16 19 18 0 0 0 0 0 0 0 1 0 0 0 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
8.01600 -0.39679 0.42904 0.11702 0.03219 0.03387
M1 M2 M3 M4 M5 M6
0.52208 1.60882 0.42910 1.21952 -0.06901 0.05998
M7 M8 M9 M10 M11 t
0.21231 0.87078 0.42980 2.35820 0.84752 -0.02733
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.41567 -0.90492 -0.09324 0.86943 2.57422
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.01600 3.51932 2.278 0.02705 *
X -0.39679 0.64090 -0.619 0.53865
Y1 0.42904 0.14242 3.012 0.00406 **
Y2 0.11702 0.15074 0.776 0.44123
Y3 0.03219 0.15259 0.211 0.83378
Y4 0.03387 0.14306 0.237 0.81384
M1 0.52208 0.90711 0.576 0.56750
M2 1.60882 0.89196 1.804 0.07730 .
M3 0.42910 0.86574 0.496 0.62232
M4 1.21952 0.82923 1.471 0.14764
M5 -0.06901 0.88428 -0.078 0.93811
M6 0.05998 0.85095 0.070 0.94408
M7 0.21231 0.89983 0.236 0.81444
M8 0.87078 0.88683 0.982 0.33087
M9 0.42980 0.90518 0.475 0.63698
M10 2.35820 0.87782 2.686 0.00978 **
M11 0.84752 0.93023 0.911 0.36662
t -0.02733 0.01878 -1.455 0.15195
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.351 on 50 degrees of freedom
Multiple R-squared: 0.7472, Adjusted R-squared: 0.6613
F-statistic: 8.694 on 17 and 50 DF, p-value: 1.008e-09
> 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,] 0.8360132 0.3279737 0.16398683
[2,] 0.9018512 0.1962976 0.09814881
[3,] 0.8262189 0.3475622 0.17378109
[4,] 0.8806994 0.2386012 0.11930060
[5,] 0.8219181 0.3561638 0.17808190
[6,] 0.7363751 0.5272499 0.26362493
[7,] 0.6439590 0.7120819 0.35604095
[8,] 0.5472785 0.9054431 0.45272155
[9,] 0.6556198 0.6887605 0.34438023
[10,] 0.5559326 0.8881348 0.44406741
[11,] 0.4888564 0.9777128 0.51114358
[12,] 0.3921392 0.7842783 0.60786083
[13,] 0.3942776 0.7885552 0.60572240
[14,] 0.3012690 0.6025380 0.69873101
[15,] 0.2280811 0.4561623 0.77191886
[16,] 0.2905001 0.5810001 0.70949995
[17,] 0.2575957 0.5151914 0.74240429
[18,] 0.3350718 0.6701435 0.66492824
[19,] 0.2910725 0.5821450 0.70892748
[20,] 0.4764222 0.9528445 0.52357776
[21,] 0.8264606 0.3470788 0.17353942
[22,] 0.8253329 0.3493342 0.17466711
[23,] 0.7325282 0.5349435 0.26747175
[24,] 0.6695706 0.6608588 0.33042942
[25,] 0.5334836 0.9330327 0.46651637
[26,] 0.3877857 0.7755713 0.61221435
[27,] 0.5084062 0.9831877 0.49159383
> postscript(file="/var/www/html/rcomp/tmp/18bkh1258728336.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2sxwu1258728336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/305fm1258728336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4f7w01258728336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53w4e1258728336.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 = 68
Frequency = 1
1 2 3 4 5 6
-1.314325758 -0.997329253 -0.066758722 -0.948547111 -1.631010887 1.091541606
7 8 9 10 11 12
0.342503637 0.541705633 -2.415673324 1.785625460 -0.502699079 0.707830384
13 14 15 16 17 18
0.387765273 1.223406830 -0.359894295 0.501053734 2.557534239 -1.093543003
19 20 21 22 23 24
-1.012897886 1.070013364 1.859968502 -0.570391570 -0.179921815 -0.975955958
25 26 27 28 29 30
0.867319353 -0.354892022 -0.166618122 -0.465124417 -2.066109840 0.151541693
31 32 33 34 35 36
0.982433467 -0.098192872 1.322505550 -0.073661639 -0.115585185 2.039093274
37 38 39 40 41 42
-0.833739150 1.846240219 0.001883375 -2.223675475 -1.844528807 0.108714656
43 44 45 46 47 48
0.920833814 0.875750781 -0.166458880 0.178434175 1.909322040 -0.880588284
49 50 51 52 53 54
0.764243086 -0.473062803 0.679667790 1.985987674 0.409898687 0.932274991
55 56 57 58 59 60
-1.023062986 -1.799470028 -0.600341848 -1.320006425 -1.111115962 -0.890379415
61 62 63 64 65 66
0.128737196 -1.244362970 -0.088280026 1.150305595 2.574216608 -1.190529944
67 68
-0.209810046 -0.589806879
> postscript(file="/var/www/html/rcomp/tmp/63jxj1258728337.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.314325758 NA
1 -0.997329253 -1.314325758
2 -0.066758722 -0.997329253
3 -0.948547111 -0.066758722
4 -1.631010887 -0.948547111
5 1.091541606 -1.631010887
6 0.342503637 1.091541606
7 0.541705633 0.342503637
8 -2.415673324 0.541705633
9 1.785625460 -2.415673324
10 -0.502699079 1.785625460
11 0.707830384 -0.502699079
12 0.387765273 0.707830384
13 1.223406830 0.387765273
14 -0.359894295 1.223406830
15 0.501053734 -0.359894295
16 2.557534239 0.501053734
17 -1.093543003 2.557534239
18 -1.012897886 -1.093543003
19 1.070013364 -1.012897886
20 1.859968502 1.070013364
21 -0.570391570 1.859968502
22 -0.179921815 -0.570391570
23 -0.975955958 -0.179921815
24 0.867319353 -0.975955958
25 -0.354892022 0.867319353
26 -0.166618122 -0.354892022
27 -0.465124417 -0.166618122
28 -2.066109840 -0.465124417
29 0.151541693 -2.066109840
30 0.982433467 0.151541693
31 -0.098192872 0.982433467
32 1.322505550 -0.098192872
33 -0.073661639 1.322505550
34 -0.115585185 -0.073661639
35 2.039093274 -0.115585185
36 -0.833739150 2.039093274
37 1.846240219 -0.833739150
38 0.001883375 1.846240219
39 -2.223675475 0.001883375
40 -1.844528807 -2.223675475
41 0.108714656 -1.844528807
42 0.920833814 0.108714656
43 0.875750781 0.920833814
44 -0.166458880 0.875750781
45 0.178434175 -0.166458880
46 1.909322040 0.178434175
47 -0.880588284 1.909322040
48 0.764243086 -0.880588284
49 -0.473062803 0.764243086
50 0.679667790 -0.473062803
51 1.985987674 0.679667790
52 0.409898687 1.985987674
53 0.932274991 0.409898687
54 -1.023062986 0.932274991
55 -1.799470028 -1.023062986
56 -0.600341848 -1.799470028
57 -1.320006425 -0.600341848
58 -1.111115962 -1.320006425
59 -0.890379415 -1.111115962
60 0.128737196 -0.890379415
61 -1.244362970 0.128737196
62 -0.088280026 -1.244362970
63 1.150305595 -0.088280026
64 2.574216608 1.150305595
65 -1.190529944 2.574216608
66 -0.209810046 -1.190529944
67 -0.589806879 -0.209810046
68 NA -0.589806879
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.997329253 -1.314325758
[2,] -0.066758722 -0.997329253
[3,] -0.948547111 -0.066758722
[4,] -1.631010887 -0.948547111
[5,] 1.091541606 -1.631010887
[6,] 0.342503637 1.091541606
[7,] 0.541705633 0.342503637
[8,] -2.415673324 0.541705633
[9,] 1.785625460 -2.415673324
[10,] -0.502699079 1.785625460
[11,] 0.707830384 -0.502699079
[12,] 0.387765273 0.707830384
[13,] 1.223406830 0.387765273
[14,] -0.359894295 1.223406830
[15,] 0.501053734 -0.359894295
[16,] 2.557534239 0.501053734
[17,] -1.093543003 2.557534239
[18,] -1.012897886 -1.093543003
[19,] 1.070013364 -1.012897886
[20,] 1.859968502 1.070013364
[21,] -0.570391570 1.859968502
[22,] -0.179921815 -0.570391570
[23,] -0.975955958 -0.179921815
[24,] 0.867319353 -0.975955958
[25,] -0.354892022 0.867319353
[26,] -0.166618122 -0.354892022
[27,] -0.465124417 -0.166618122
[28,] -2.066109840 -0.465124417
[29,] 0.151541693 -2.066109840
[30,] 0.982433467 0.151541693
[31,] -0.098192872 0.982433467
[32,] 1.322505550 -0.098192872
[33,] -0.073661639 1.322505550
[34,] -0.115585185 -0.073661639
[35,] 2.039093274 -0.115585185
[36,] -0.833739150 2.039093274
[37,] 1.846240219 -0.833739150
[38,] 0.001883375 1.846240219
[39,] -2.223675475 0.001883375
[40,] -1.844528807 -2.223675475
[41,] 0.108714656 -1.844528807
[42,] 0.920833814 0.108714656
[43,] 0.875750781 0.920833814
[44,] -0.166458880 0.875750781
[45,] 0.178434175 -0.166458880
[46,] 1.909322040 0.178434175
[47,] -0.880588284 1.909322040
[48,] 0.764243086 -0.880588284
[49,] -0.473062803 0.764243086
[50,] 0.679667790 -0.473062803
[51,] 1.985987674 0.679667790
[52,] 0.409898687 1.985987674
[53,] 0.932274991 0.409898687
[54,] -1.023062986 0.932274991
[55,] -1.799470028 -1.023062986
[56,] -0.600341848 -1.799470028
[57,] -1.320006425 -0.600341848
[58,] -1.111115962 -1.320006425
[59,] -0.890379415 -1.111115962
[60,] 0.128737196 -0.890379415
[61,] -1.244362970 0.128737196
[62,] -0.088280026 -1.244362970
[63,] 1.150305595 -0.088280026
[64,] 2.574216608 1.150305595
[65,] -1.190529944 2.574216608
[66,] -0.209810046 -1.190529944
[67,] -0.589806879 -0.209810046
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.997329253 -1.314325758
2 -0.066758722 -0.997329253
3 -0.948547111 -0.066758722
4 -1.631010887 -0.948547111
5 1.091541606 -1.631010887
6 0.342503637 1.091541606
7 0.541705633 0.342503637
8 -2.415673324 0.541705633
9 1.785625460 -2.415673324
10 -0.502699079 1.785625460
11 0.707830384 -0.502699079
12 0.387765273 0.707830384
13 1.223406830 0.387765273
14 -0.359894295 1.223406830
15 0.501053734 -0.359894295
16 2.557534239 0.501053734
17 -1.093543003 2.557534239
18 -1.012897886 -1.093543003
19 1.070013364 -1.012897886
20 1.859968502 1.070013364
21 -0.570391570 1.859968502
22 -0.179921815 -0.570391570
23 -0.975955958 -0.179921815
24 0.867319353 -0.975955958
25 -0.354892022 0.867319353
26 -0.166618122 -0.354892022
27 -0.465124417 -0.166618122
28 -2.066109840 -0.465124417
29 0.151541693 -2.066109840
30 0.982433467 0.151541693
31 -0.098192872 0.982433467
32 1.322505550 -0.098192872
33 -0.073661639 1.322505550
34 -0.115585185 -0.073661639
35 2.039093274 -0.115585185
36 -0.833739150 2.039093274
37 1.846240219 -0.833739150
38 0.001883375 1.846240219
39 -2.223675475 0.001883375
40 -1.844528807 -2.223675475
41 0.108714656 -1.844528807
42 0.920833814 0.108714656
43 0.875750781 0.920833814
44 -0.166458880 0.875750781
45 0.178434175 -0.166458880
46 1.909322040 0.178434175
47 -0.880588284 1.909322040
48 0.764243086 -0.880588284
49 -0.473062803 0.764243086
50 0.679667790 -0.473062803
51 1.985987674 0.679667790
52 0.409898687 1.985987674
53 0.932274991 0.409898687
54 -1.023062986 0.932274991
55 -1.799470028 -1.023062986
56 -0.600341848 -1.799470028
57 -1.320006425 -0.600341848
58 -1.111115962 -1.320006425
59 -0.890379415 -1.111115962
60 0.128737196 -0.890379415
61 -1.244362970 0.128737196
62 -0.088280026 -1.244362970
63 1.150305595 -0.088280026
64 2.574216608 1.150305595
65 -1.190529944 2.574216608
66 -0.209810046 -1.190529944
67 -0.589806879 -0.209810046
> 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/7zq6n1258728337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83hkr1258728337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rpvq1258728337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fde01258728337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jw6y1258728337.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/12u03a1258728337.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/13oz731258728337.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/14x43a1258728337.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/15rv8z1258728337.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/161olh1258728337.tab")
+ }
> system("convert tmp/18bkh1258728336.ps tmp/18bkh1258728336.png")
> system("convert tmp/2sxwu1258728336.ps tmp/2sxwu1258728336.png")
> system("convert tmp/305fm1258728336.ps tmp/305fm1258728336.png")
> system("convert tmp/4f7w01258728336.ps tmp/4f7w01258728336.png")
> system("convert tmp/53w4e1258728336.ps tmp/53w4e1258728336.png")
> system("convert tmp/63jxj1258728337.ps tmp/63jxj1258728337.png")
> system("convert tmp/7zq6n1258728337.ps tmp/7zq6n1258728337.png")
> system("convert tmp/83hkr1258728337.ps tmp/83hkr1258728337.png")
> system("convert tmp/9rpvq1258728337.ps tmp/9rpvq1258728337.png")
> system("convert tmp/10fde01258728337.ps tmp/10fde01258728337.png")
>
>
> proc.time()
user system elapsed
2.536 1.640 5.605