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(1.79,194.9,1.95,195.5,2.26,196.0,2.04,196.2,2.16,196.2,2.75,196.2,2.79,196.2,2.88,197.0,3.36,197.7,2.97,198.0,3.10,198.2,2.49,198.5,2.2,198.6,2.25,199.5,2.09,200,2.79,201.3,3.14,202.2,2.93,202.9,2.65,203.5,2.67,203.5,2.26,204,2.35,204.1,2.13,204.3,2.18,204.5,2.9,204.8,2.63,205.1,2.67,205.7,1.81,206.5,1.33,206.9,0.88,207.1,1.28,207.8,1.26,208,1.26,208.5,1.29,208.6,1.1,209,1.37,209.1,1.21,209.7,1.74,209.8,1.76,209.9,1.48,210,1.04,210.8,1.62,211.4,1.49,211.7,1.79,212,1.8,212.2,1.58,212.4,1.86,212.9,1.74,213.4,1.59,213.7,1.26,214,1.13,214.3,1.92,214.8,2.61,215,2.26,215.9,2.41,216.4,2.26,216.9,2.03,217.2,2.86,217.5,2.55,217.9,2.27,218.1,2.26,218.6,2.57,218.9,3.07,219.3,2.76,220.4,2.51,220.9,2.87,221,3.14,221.8,3.11,222,3.16,222.2,2.47,222.5,2.57,222.9,2.89,223.1),dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.79 194.9 1 0 0 0 0 0 0 0 0 0 0 1
2 1.95 195.5 0 1 0 0 0 0 0 0 0 0 0 2
3 2.26 196.0 0 0 1 0 0 0 0 0 0 0 0 3
4 2.04 196.2 0 0 0 1 0 0 0 0 0 0 0 4
5 2.16 196.2 0 0 0 0 1 0 0 0 0 0 0 5
6 2.75 196.2 0 0 0 0 0 1 0 0 0 0 0 6
7 2.79 196.2 0 0 0 0 0 0 1 0 0 0 0 7
8 2.88 197.0 0 0 0 0 0 0 0 1 0 0 0 8
9 3.36 197.7 0 0 0 0 0 0 0 0 1 0 0 9
10 2.97 198.0 0 0 0 0 0 0 0 0 0 1 0 10
11 3.10 198.2 0 0 0 0 0 0 0 0 0 0 1 11
12 2.49 198.5 0 0 0 0 0 0 0 0 0 0 0 12
13 2.20 198.6 1 0 0 0 0 0 0 0 0 0 0 13
14 2.25 199.5 0 1 0 0 0 0 0 0 0 0 0 14
15 2.09 200.0 0 0 1 0 0 0 0 0 0 0 0 15
16 2.79 201.3 0 0 0 1 0 0 0 0 0 0 0 16
17 3.14 202.2 0 0 0 0 1 0 0 0 0 0 0 17
18 2.93 202.9 0 0 0 0 0 1 0 0 0 0 0 18
19 2.65 203.5 0 0 0 0 0 0 1 0 0 0 0 19
20 2.67 203.5 0 0 0 0 0 0 0 1 0 0 0 20
21 2.26 204.0 0 0 0 0 0 0 0 0 1 0 0 21
22 2.35 204.1 0 0 0 0 0 0 0 0 0 1 0 22
23 2.13 204.3 0 0 0 0 0 0 0 0 0 0 1 23
24 2.18 204.5 0 0 0 0 0 0 0 0 0 0 0 24
25 2.90 204.8 1 0 0 0 0 0 0 0 0 0 0 25
26 2.63 205.1 0 1 0 0 0 0 0 0 0 0 0 26
27 2.67 205.7 0 0 1 0 0 0 0 0 0 0 0 27
28 1.81 206.5 0 0 0 1 0 0 0 0 0 0 0 28
29 1.33 206.9 0 0 0 0 1 0 0 0 0 0 0 29
30 0.88 207.1 0 0 0 0 0 1 0 0 0 0 0 30
31 1.28 207.8 0 0 0 0 0 0 1 0 0 0 0 31
32 1.26 208.0 0 0 0 0 0 0 0 1 0 0 0 32
33 1.26 208.5 0 0 0 0 0 0 0 0 1 0 0 33
34 1.29 208.6 0 0 0 0 0 0 0 0 0 1 0 34
35 1.10 209.0 0 0 0 0 0 0 0 0 0 0 1 35
36 1.37 209.1 0 0 0 0 0 0 0 0 0 0 0 36
37 1.21 209.7 1 0 0 0 0 0 0 0 0 0 0 37
38 1.74 209.8 0 1 0 0 0 0 0 0 0 0 0 38
39 1.76 209.9 0 0 1 0 0 0 0 0 0 0 0 39
40 1.48 210.0 0 0 0 1 0 0 0 0 0 0 0 40
41 1.04 210.8 0 0 0 0 1 0 0 0 0 0 0 41
42 1.62 211.4 0 0 0 0 0 1 0 0 0 0 0 42
43 1.49 211.7 0 0 0 0 0 0 1 0 0 0 0 43
44 1.79 212.0 0 0 0 0 0 0 0 1 0 0 0 44
45 1.80 212.2 0 0 0 0 0 0 0 0 1 0 0 45
46 1.58 212.4 0 0 0 0 0 0 0 0 0 1 0 46
47 1.86 212.9 0 0 0 0 0 0 0 0 0 0 1 47
48 1.74 213.4 0 0 0 0 0 0 0 0 0 0 0 48
49 1.59 213.7 1 0 0 0 0 0 0 0 0 0 0 49
50 1.26 214.0 0 1 0 0 0 0 0 0 0 0 0 50
51 1.13 214.3 0 0 1 0 0 0 0 0 0 0 0 51
52 1.92 214.8 0 0 0 1 0 0 0 0 0 0 0 52
53 2.61 215.0 0 0 0 0 1 0 0 0 0 0 0 53
54 2.26 215.9 0 0 0 0 0 1 0 0 0 0 0 54
55 2.41 216.4 0 0 0 0 0 0 1 0 0 0 0 55
56 2.26 216.9 0 0 0 0 0 0 0 1 0 0 0 56
57 2.03 217.2 0 0 0 0 0 0 0 0 1 0 0 57
58 2.86 217.5 0 0 0 0 0 0 0 0 0 1 0 58
59 2.55 217.9 0 0 0 0 0 0 0 0 0 0 1 59
60 2.27 218.1 0 0 0 0 0 0 0 0 0 0 0 60
61 2.26 218.6 1 0 0 0 0 0 0 0 0 0 0 61
62 2.57 218.9 0 1 0 0 0 0 0 0 0 0 0 62
63 3.07 219.3 0 0 1 0 0 0 0 0 0 0 0 63
64 2.76 220.4 0 0 0 1 0 0 0 0 0 0 0 64
65 2.51 220.9 0 0 0 0 1 0 0 0 0 0 0 65
66 2.87 221.0 0 0 0 0 0 1 0 0 0 0 0 66
67 3.14 221.8 0 0 0 0 0 0 1 0 0 0 0 67
68 3.11 222.0 0 0 0 0 0 0 0 1 0 0 0 68
69 3.16 222.2 0 0 0 0 0 0 0 0 1 0 0 69
70 2.47 222.5 0 0 0 0 0 0 0 0 0 1 0 70
71 2.57 222.9 0 0 0 0 0 0 0 0 0 0 1 71
72 2.89 223.1 0 0 0 0 0 0 0 0 0 0 0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
34.265574 -0.165158 -0.169195 -0.091061 0.005987 0.020411
M5 M6 M7 M8 M9 M10
0.030136 0.119937 0.209081 0.233452 0.217166 0.128936
M11 t
0.086059 0.065682
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.2718 -0.4716 0.0737 0.4914 1.1226
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.265574 30.273129 1.132 0.262
X -0.165158 0.155704 -1.061 0.293
M1 -0.169195 0.400678 -0.422 0.674
M2 -0.091061 0.400246 -0.228 0.821
M3 0.005987 0.399872 0.015 0.988
M4 0.020411 0.401650 0.051 0.960
M5 0.030136 0.402600 0.075 0.941
M6 0.119937 0.402740 0.298 0.767
M7 0.209081 0.404560 0.517 0.607
M8 0.233452 0.402817 0.580 0.564
M9 0.217166 0.402734 0.539 0.592
M10 0.128936 0.399511 0.323 0.748
M11 0.086059 0.398957 0.216 0.830
t 0.065682 0.062038 1.059 0.294
Residual standard error: 0.6898 on 58 degrees of freedom
Multiple R-squared: 0.04215, Adjusted R-squared: -0.1725
F-statistic: 0.1963 on 13 and 58 DF, p-value: 0.9987
> 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,] 0.04756686 0.095133729 0.952433135
[2,] 0.09175438 0.183508769 0.908245615
[3,] 0.09130759 0.182615190 0.908692405
[4,] 0.07387856 0.147757120 0.926121440
[5,] 0.22268300 0.445366009 0.777316996
[6,] 0.21191556 0.423831122 0.788084439
[7,] 0.25934563 0.518691256 0.740654372
[8,] 0.21542184 0.430843678 0.784578161
[9,] 0.49161697 0.983233947 0.508383026
[10,] 0.65753751 0.684924977 0.342462488
[11,] 0.85474084 0.290518330 0.145259165
[12,] 0.92884087 0.142318261 0.071159131
[13,] 0.98436780 0.031264408 0.015632204
[14,] 0.99848697 0.003026051 0.001513026
[15,] 0.99875812 0.002483762 0.001241881
[16,] 0.99884620 0.002307607 0.001153804
[17,] 0.99874646 0.002507082 0.001253541
[18,] 0.99817034 0.003659319 0.001829660
[19,] 0.99759471 0.004810585 0.002405292
[20,] 0.99558251 0.008834990 0.004417495
[21,] 0.99181280 0.016374393 0.008187196
[22,] 0.99287316 0.014253683 0.007126842
[23,] 0.99470857 0.010582860 0.005291430
[24,] 0.99011434 0.019771325 0.009885663
[25,] 0.98628505 0.027429898 0.013714949
[26,] 0.97712044 0.045759129 0.022879564
[27,] 0.96396723 0.072065534 0.036032767
[28,] 0.94196130 0.116077404 0.058038702
[29,] 0.91168074 0.176638521 0.088319260
[30,] 0.86581666 0.268366685 0.134183343
[31,] 0.82341227 0.353175457 0.176587728
[32,] 0.78167017 0.436659666 0.218329833
[33,] 0.70442126 0.591157474 0.295578737
[34,] 0.63682100 0.726358001 0.363179001
[35,] 0.83733069 0.325338619 0.162669310
[36,] 0.80636465 0.387270697 0.193635349
[37,] 0.81467698 0.370646037 0.185323018
[38,] 0.70604276 0.587914485 0.293957242
[39,] 0.59636256 0.807274883 0.403637441
> postscript(file="/var/www/html/rcomp/tmp/1bg4v1291470694.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bg4v1291470694.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3eznb1291470695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4eznb1291470695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5eznb1291470695.ps",horizontal=F,onefile=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 = 72
Frequency = 1
1 2 3 4 5 6
-0.182827592 -0.067548682 0.162300421 -0.104773168 -0.060180091 0.374337538
7 8 9 10 11 12
0.259511321 0.391584910 0.937798884 0.619895358 0.760121705 0.220046256
13 14 15 16 17 18
0.050074883 0.104901100 -0.135249798 0.699350073 1.122585072 0.872713084
19 20 21 22 23 24
0.536981481 0.466928918 0.090111354 0.219176290 0.009402636 0.112811418
25 26 27 28 29 30
0.985871583 0.621603186 0.597968058 -0.210010917 -0.699354763 -1.271805596
31 32 33 34 35 36
-0.911021430 -0.988042455 -0.954860019 -0.885795083 -1.032537199 -0.725644186
37 38 39 40 41 42
-0.683036714 -0.280336649 -0.406550623 -0.750139981 -1.133420750 -0.609808507
43 44 45 46 47 48
-0.845087417 -0.585592673 -0.591957545 -0.756376840 -0.416603186 -0.433647097
49 50 51 52 53 54
-0.430586932 -0.854855329 -1.098037765 -0.305564047 0.342060569 -0.014779880
55 56 57 58 59 60
0.062972748 -0.094500970 -0.324350073 0.577746401 0.311004286 0.084413068
61 62 63 64 65 66
0.260504771 0.476236374 0.879569707 0.671138040 0.428309963 0.649343361
67 68 69 70 71 72
0.896643296 0.809622271 0.843257399 0.225353874 0.368611758 0.742020540
> postscript(file="/var/www/html/rcomp/tmp/67rne1291470695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.182827592 NA
1 -0.067548682 -0.182827592
2 0.162300421 -0.067548682
3 -0.104773168 0.162300421
4 -0.060180091 -0.104773168
5 0.374337538 -0.060180091
6 0.259511321 0.374337538
7 0.391584910 0.259511321
8 0.937798884 0.391584910
9 0.619895358 0.937798884
10 0.760121705 0.619895358
11 0.220046256 0.760121705
12 0.050074883 0.220046256
13 0.104901100 0.050074883
14 -0.135249798 0.104901100
15 0.699350073 -0.135249798
16 1.122585072 0.699350073
17 0.872713084 1.122585072
18 0.536981481 0.872713084
19 0.466928918 0.536981481
20 0.090111354 0.466928918
21 0.219176290 0.090111354
22 0.009402636 0.219176290
23 0.112811418 0.009402636
24 0.985871583 0.112811418
25 0.621603186 0.985871583
26 0.597968058 0.621603186
27 -0.210010917 0.597968058
28 -0.699354763 -0.210010917
29 -1.271805596 -0.699354763
30 -0.911021430 -1.271805596
31 -0.988042455 -0.911021430
32 -0.954860019 -0.988042455
33 -0.885795083 -0.954860019
34 -1.032537199 -0.885795083
35 -0.725644186 -1.032537199
36 -0.683036714 -0.725644186
37 -0.280336649 -0.683036714
38 -0.406550623 -0.280336649
39 -0.750139981 -0.406550623
40 -1.133420750 -0.750139981
41 -0.609808507 -1.133420750
42 -0.845087417 -0.609808507
43 -0.585592673 -0.845087417
44 -0.591957545 -0.585592673
45 -0.756376840 -0.591957545
46 -0.416603186 -0.756376840
47 -0.433647097 -0.416603186
48 -0.430586932 -0.433647097
49 -0.854855329 -0.430586932
50 -1.098037765 -0.854855329
51 -0.305564047 -1.098037765
52 0.342060569 -0.305564047
53 -0.014779880 0.342060569
54 0.062972748 -0.014779880
55 -0.094500970 0.062972748
56 -0.324350073 -0.094500970
57 0.577746401 -0.324350073
58 0.311004286 0.577746401
59 0.084413068 0.311004286
60 0.260504771 0.084413068
61 0.476236374 0.260504771
62 0.879569707 0.476236374
63 0.671138040 0.879569707
64 0.428309963 0.671138040
65 0.649343361 0.428309963
66 0.896643296 0.649343361
67 0.809622271 0.896643296
68 0.843257399 0.809622271
69 0.225353874 0.843257399
70 0.368611758 0.225353874
71 0.742020540 0.368611758
72 NA 0.742020540
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.067548682 -0.182827592
[2,] 0.162300421 -0.067548682
[3,] -0.104773168 0.162300421
[4,] -0.060180091 -0.104773168
[5,] 0.374337538 -0.060180091
[6,] 0.259511321 0.374337538
[7,] 0.391584910 0.259511321
[8,] 0.937798884 0.391584910
[9,] 0.619895358 0.937798884
[10,] 0.760121705 0.619895358
[11,] 0.220046256 0.760121705
[12,] 0.050074883 0.220046256
[13,] 0.104901100 0.050074883
[14,] -0.135249798 0.104901100
[15,] 0.699350073 -0.135249798
[16,] 1.122585072 0.699350073
[17,] 0.872713084 1.122585072
[18,] 0.536981481 0.872713084
[19,] 0.466928918 0.536981481
[20,] 0.090111354 0.466928918
[21,] 0.219176290 0.090111354
[22,] 0.009402636 0.219176290
[23,] 0.112811418 0.009402636
[24,] 0.985871583 0.112811418
[25,] 0.621603186 0.985871583
[26,] 0.597968058 0.621603186
[27,] -0.210010917 0.597968058
[28,] -0.699354763 -0.210010917
[29,] -1.271805596 -0.699354763
[30,] -0.911021430 -1.271805596
[31,] -0.988042455 -0.911021430
[32,] -0.954860019 -0.988042455
[33,] -0.885795083 -0.954860019
[34,] -1.032537199 -0.885795083
[35,] -0.725644186 -1.032537199
[36,] -0.683036714 -0.725644186
[37,] -0.280336649 -0.683036714
[38,] -0.406550623 -0.280336649
[39,] -0.750139981 -0.406550623
[40,] -1.133420750 -0.750139981
[41,] -0.609808507 -1.133420750
[42,] -0.845087417 -0.609808507
[43,] -0.585592673 -0.845087417
[44,] -0.591957545 -0.585592673
[45,] -0.756376840 -0.591957545
[46,] -0.416603186 -0.756376840
[47,] -0.433647097 -0.416603186
[48,] -0.430586932 -0.433647097
[49,] -0.854855329 -0.430586932
[50,] -1.098037765 -0.854855329
[51,] -0.305564047 -1.098037765
[52,] 0.342060569 -0.305564047
[53,] -0.014779880 0.342060569
[54,] 0.062972748 -0.014779880
[55,] -0.094500970 0.062972748
[56,] -0.324350073 -0.094500970
[57,] 0.577746401 -0.324350073
[58,] 0.311004286 0.577746401
[59,] 0.084413068 0.311004286
[60,] 0.260504771 0.084413068
[61,] 0.476236374 0.260504771
[62,] 0.879569707 0.476236374
[63,] 0.671138040 0.879569707
[64,] 0.428309963 0.671138040
[65,] 0.649343361 0.428309963
[66,] 0.896643296 0.649343361
[67,] 0.809622271 0.896643296
[68,] 0.843257399 0.809622271
[69,] 0.225353874 0.843257399
[70,] 0.368611758 0.225353874
[71,] 0.742020540 0.368611758
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.067548682 -0.182827592
2 0.162300421 -0.067548682
3 -0.104773168 0.162300421
4 -0.060180091 -0.104773168
5 0.374337538 -0.060180091
6 0.259511321 0.374337538
7 0.391584910 0.259511321
8 0.937798884 0.391584910
9 0.619895358 0.937798884
10 0.760121705 0.619895358
11 0.220046256 0.760121705
12 0.050074883 0.220046256
13 0.104901100 0.050074883
14 -0.135249798 0.104901100
15 0.699350073 -0.135249798
16 1.122585072 0.699350073
17 0.872713084 1.122585072
18 0.536981481 0.872713084
19 0.466928918 0.536981481
20 0.090111354 0.466928918
21 0.219176290 0.090111354
22 0.009402636 0.219176290
23 0.112811418 0.009402636
24 0.985871583 0.112811418
25 0.621603186 0.985871583
26 0.597968058 0.621603186
27 -0.210010917 0.597968058
28 -0.699354763 -0.210010917
29 -1.271805596 -0.699354763
30 -0.911021430 -1.271805596
31 -0.988042455 -0.911021430
32 -0.954860019 -0.988042455
33 -0.885795083 -0.954860019
34 -1.032537199 -0.885795083
35 -0.725644186 -1.032537199
36 -0.683036714 -0.725644186
37 -0.280336649 -0.683036714
38 -0.406550623 -0.280336649
39 -0.750139981 -0.406550623
40 -1.133420750 -0.750139981
41 -0.609808507 -1.133420750
42 -0.845087417 -0.609808507
43 -0.585592673 -0.845087417
44 -0.591957545 -0.585592673
45 -0.756376840 -0.591957545
46 -0.416603186 -0.756376840
47 -0.433647097 -0.416603186
48 -0.430586932 -0.433647097
49 -0.854855329 -0.430586932
50 -1.098037765 -0.854855329
51 -0.305564047 -1.098037765
52 0.342060569 -0.305564047
53 -0.014779880 0.342060569
54 0.062972748 -0.014779880
55 -0.094500970 0.062972748
56 -0.324350073 -0.094500970
57 0.577746401 -0.324350073
58 0.311004286 0.577746401
59 0.084413068 0.311004286
60 0.260504771 0.084413068
61 0.476236374 0.260504771
62 0.879569707 0.476236374
63 0.671138040 0.879569707
64 0.428309963 0.671138040
65 0.649343361 0.428309963
66 0.896643296 0.649343361
67 0.809622271 0.896643296
68 0.843257399 0.809622271
69 0.225353874 0.843257399
70 0.368611758 0.225353874
71 0.742020540 0.368611758
> 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/70i4z1291470695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80i4z1291470695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90i4z1291470695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10brlk1291470695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wa271291470695.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/12zsiv1291470695.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/13v2gm1291470695.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/14zlea1291470695.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/15klvg1291470695.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/16omc41291470695.tab")
+ }
>
> try(system("convert tmp/1bg4v1291470694.ps tmp/1bg4v1291470694.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bg4v1291470694.ps tmp/2bg4v1291470694.png",intern=TRUE))
character(0)
> try(system("convert tmp/3eznb1291470695.ps tmp/3eznb1291470695.png",intern=TRUE))
character(0)
> try(system("convert tmp/4eznb1291470695.ps tmp/4eznb1291470695.png",intern=TRUE))
character(0)
> try(system("convert tmp/5eznb1291470695.ps tmp/5eznb1291470695.png",intern=TRUE))
character(0)
> try(system("convert tmp/67rne1291470695.ps tmp/67rne1291470695.png",intern=TRUE))
character(0)
> try(system("convert tmp/70i4z1291470695.ps tmp/70i4z1291470695.png",intern=TRUE))
character(0)
> try(system("convert tmp/80i4z1291470695.ps tmp/80i4z1291470695.png",intern=TRUE))
character(0)
> try(system("convert tmp/90i4z1291470695.ps tmp/90i4z1291470695.png",intern=TRUE))
character(0)
> try(system("convert tmp/10brlk1291470695.ps tmp/10brlk1291470695.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.700 1.715 107.727