R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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(0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,1,0,0,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,1,0),dim=c(3,68),dimnames=list(c('T20','Used','Useful'),1:68))
> y <- array(NA,dim=c(3,68),dimnames=list(c('T20','Used','Useful'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal 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, 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
T20 Used Useful
1 0 0 0
2 1 1 0
3 0 0 0
4 0 0 0
5 0 0 1
6 1 0 0
7 0 0 1
8 0 0 0
9 1 0 0
10 0 0 0
11 1 0 0
12 0 0 0
13 0 0 0
14 0 0 0
15 0 0 0
16 0 0 0
17 0 0 0
18 0 0 0
19 1 1 0
20 0 0 0
21 0 0 0
22 1 1 0
23 0 0 0
24 0 0 0
25 1 1 1
26 1 0 0
27 0 1 0
28 1 1 0
29 0 0 0
30 0 0 0
31 0 0 0
32 0 0 0
33 0 0 0
34 0 0 0
35 0 0 0
36 0 0 0
37 1 1 0
38 0 1 1
39 0 0 0
40 1 0 0
41 0 0 1
42 0 0 0
43 0 0 0
44 0 0 0
45 0 0 0
46 0 0 0
47 0 1 0
48 0 0 0
49 0 0 0
50 0 0 0
51 0 1 1
52 1 1 1
53 1 0 0
54 0 0 0
55 0 1 0
56 1 1 0
57 0 0 0
58 0 0 1
59 0 0 1
60 1 0 0
61 1 1 0
62 1 0 0
63 0 0 0
64 0 0 1
65 0 0 0
66 0 1 0
67 0 1 1
68 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Used Useful
0.1780 0.4043 -0.1800
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.5824 -0.1780 -0.1780 0.1059 0.8220
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.17804 0.05903 3.016 0.003650 **
Used 0.40431 0.11613 3.482 0.000896 ***
Useful -0.18000 0.13655 -1.318 0.192075
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4056 on 65 degrees of freedom
Multiple R-squared: 0.1612, Adjusted R-squared: 0.1354
F-statistic: 6.247 on 2 and 65 DF, p-value: 0.003301
> 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,] 0.78011082 0.43977837 0.2198892
[2,] 0.64280424 0.71439152 0.3571958
[3,] 0.53762909 0.92474182 0.4623709
[4,] 0.76210003 0.47579994 0.2379000
[5,] 0.70776154 0.58447693 0.2922385
[6,] 0.83255830 0.33488341 0.1674417
[7,] 0.80100174 0.39799652 0.1989983
[8,] 0.76006714 0.47986571 0.2399329
[9,] 0.71079833 0.57840333 0.2892017
[10,] 0.65441497 0.69117005 0.3455850
[11,] 0.59249976 0.81500049 0.4075002
[12,] 0.52698876 0.94602248 0.4730112
[13,] 0.46004873 0.92009746 0.5399513
[14,] 0.40173246 0.80346492 0.5982675
[15,] 0.33959921 0.67919843 0.6604008
[16,] 0.28132631 0.56265262 0.7186737
[17,] 0.24159229 0.48318457 0.7584077
[18,] 0.19393783 0.38787566 0.8060622
[19,] 0.15249608 0.30499215 0.8475039
[20,] 0.14879486 0.29758972 0.8512051
[21,] 0.35932932 0.71865865 0.6406707
[22,] 0.56396597 0.87206807 0.4360340
[23,] 0.55132293 0.89735414 0.4486771
[24,] 0.49163889 0.98327779 0.5083611
[25,] 0.43206288 0.86412577 0.5679371
[26,] 0.37403392 0.74806783 0.6259661
[27,] 0.31886973 0.63773946 0.6811303
[28,] 0.26767654 0.53535308 0.7323235
[29,] 0.22128487 0.44256974 0.7787151
[30,] 0.18021734 0.36043468 0.8197827
[31,] 0.14468902 0.28937804 0.8553110
[32,] 0.14789233 0.29578465 0.8521077
[33,] 0.17990258 0.35980517 0.8200974
[34,] 0.14474631 0.28949263 0.8552537
[35,] 0.30773251 0.61546502 0.6922675
[36,] 0.24736905 0.49473810 0.7526310
[37,] 0.20263956 0.40527913 0.7973604
[38,] 0.16344550 0.32689100 0.8365545
[39,] 0.12996291 0.25992582 0.8700371
[40,] 0.10208336 0.20416672 0.8979166
[41,] 0.07946981 0.15893962 0.9205302
[42,] 0.10811429 0.21622859 0.8918857
[43,] 0.08543291 0.17086583 0.9145671
[44,] 0.06783315 0.13566630 0.9321669
[45,] 0.05484163 0.10968327 0.9451584
[46,] 0.04742875 0.09485751 0.9525712
[47,] 0.11159831 0.22319661 0.8884017
[48,] 0.19425206 0.38850413 0.8057479
[49,] 0.16939927 0.33879853 0.8306007
[50,] 0.19029703 0.38059407 0.8097030
[51,] 0.23041268 0.46082535 0.7695873
[52,] 0.21894588 0.43789175 0.7810541
[53,] 0.14775195 0.29550391 0.8522480
[54,] 0.09221074 0.18442148 0.9077893
[55,] 0.14454771 0.28909543 0.8554523
[56,] 0.34434215 0.68868431 0.6556578
[57,] 1.00000000 0.00000000 0.0000000
> postscript(file="/var/wessaorg/rcomp/tmp/1f0ms1355851252.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xww11355851252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3utkp1355851252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4y0gb1355851252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5vxm71355851252.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 = 68
Frequency = 1
1 2 3 4 5 6
-0.178039216 0.417647059 -0.178039216 -0.178039216 0.001960784 0.821960784
7 8 9 10 11 12
0.001960784 -0.178039216 0.821960784 -0.178039216 0.821960784 -0.178039216
13 14 15 16 17 18
-0.178039216 -0.178039216 -0.178039216 -0.178039216 -0.178039216 -0.178039216
19 20 21 22 23 24
0.417647059 -0.178039216 -0.178039216 0.417647059 -0.178039216 -0.178039216
25 26 27 28 29 30
0.597647059 0.821960784 -0.582352941 0.417647059 -0.178039216 -0.178039216
31 32 33 34 35 36
-0.178039216 -0.178039216 -0.178039216 -0.178039216 -0.178039216 -0.178039216
37 38 39 40 41 42
0.417647059 -0.402352941 -0.178039216 0.821960784 0.001960784 -0.178039216
43 44 45 46 47 48
-0.178039216 -0.178039216 -0.178039216 -0.178039216 -0.582352941 -0.178039216
49 50 51 52 53 54
-0.178039216 -0.178039216 -0.402352941 0.597647059 0.821960784 -0.178039216
55 56 57 58 59 60
-0.582352941 0.417647059 -0.178039216 0.001960784 0.001960784 0.821960784
61 62 63 64 65 66
0.417647059 0.821960784 -0.178039216 0.001960784 -0.178039216 -0.582352941
67 68
-0.402352941 -0.582352941
> postscript(file="/var/wessaorg/rcomp/tmp/6u0fz1355851252.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.178039216 NA
1 0.417647059 -0.178039216
2 -0.178039216 0.417647059
3 -0.178039216 -0.178039216
4 0.001960784 -0.178039216
5 0.821960784 0.001960784
6 0.001960784 0.821960784
7 -0.178039216 0.001960784
8 0.821960784 -0.178039216
9 -0.178039216 0.821960784
10 0.821960784 -0.178039216
11 -0.178039216 0.821960784
12 -0.178039216 -0.178039216
13 -0.178039216 -0.178039216
14 -0.178039216 -0.178039216
15 -0.178039216 -0.178039216
16 -0.178039216 -0.178039216
17 -0.178039216 -0.178039216
18 0.417647059 -0.178039216
19 -0.178039216 0.417647059
20 -0.178039216 -0.178039216
21 0.417647059 -0.178039216
22 -0.178039216 0.417647059
23 -0.178039216 -0.178039216
24 0.597647059 -0.178039216
25 0.821960784 0.597647059
26 -0.582352941 0.821960784
27 0.417647059 -0.582352941
28 -0.178039216 0.417647059
29 -0.178039216 -0.178039216
30 -0.178039216 -0.178039216
31 -0.178039216 -0.178039216
32 -0.178039216 -0.178039216
33 -0.178039216 -0.178039216
34 -0.178039216 -0.178039216
35 -0.178039216 -0.178039216
36 0.417647059 -0.178039216
37 -0.402352941 0.417647059
38 -0.178039216 -0.402352941
39 0.821960784 -0.178039216
40 0.001960784 0.821960784
41 -0.178039216 0.001960784
42 -0.178039216 -0.178039216
43 -0.178039216 -0.178039216
44 -0.178039216 -0.178039216
45 -0.178039216 -0.178039216
46 -0.582352941 -0.178039216
47 -0.178039216 -0.582352941
48 -0.178039216 -0.178039216
49 -0.178039216 -0.178039216
50 -0.402352941 -0.178039216
51 0.597647059 -0.402352941
52 0.821960784 0.597647059
53 -0.178039216 0.821960784
54 -0.582352941 -0.178039216
55 0.417647059 -0.582352941
56 -0.178039216 0.417647059
57 0.001960784 -0.178039216
58 0.001960784 0.001960784
59 0.821960784 0.001960784
60 0.417647059 0.821960784
61 0.821960784 0.417647059
62 -0.178039216 0.821960784
63 0.001960784 -0.178039216
64 -0.178039216 0.001960784
65 -0.582352941 -0.178039216
66 -0.402352941 -0.582352941
67 -0.582352941 -0.402352941
68 NA -0.582352941
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.417647059 -0.178039216
[2,] -0.178039216 0.417647059
[3,] -0.178039216 -0.178039216
[4,] 0.001960784 -0.178039216
[5,] 0.821960784 0.001960784
[6,] 0.001960784 0.821960784
[7,] -0.178039216 0.001960784
[8,] 0.821960784 -0.178039216
[9,] -0.178039216 0.821960784
[10,] 0.821960784 -0.178039216
[11,] -0.178039216 0.821960784
[12,] -0.178039216 -0.178039216
[13,] -0.178039216 -0.178039216
[14,] -0.178039216 -0.178039216
[15,] -0.178039216 -0.178039216
[16,] -0.178039216 -0.178039216
[17,] -0.178039216 -0.178039216
[18,] 0.417647059 -0.178039216
[19,] -0.178039216 0.417647059
[20,] -0.178039216 -0.178039216
[21,] 0.417647059 -0.178039216
[22,] -0.178039216 0.417647059
[23,] -0.178039216 -0.178039216
[24,] 0.597647059 -0.178039216
[25,] 0.821960784 0.597647059
[26,] -0.582352941 0.821960784
[27,] 0.417647059 -0.582352941
[28,] -0.178039216 0.417647059
[29,] -0.178039216 -0.178039216
[30,] -0.178039216 -0.178039216
[31,] -0.178039216 -0.178039216
[32,] -0.178039216 -0.178039216
[33,] -0.178039216 -0.178039216
[34,] -0.178039216 -0.178039216
[35,] -0.178039216 -0.178039216
[36,] 0.417647059 -0.178039216
[37,] -0.402352941 0.417647059
[38,] -0.178039216 -0.402352941
[39,] 0.821960784 -0.178039216
[40,] 0.001960784 0.821960784
[41,] -0.178039216 0.001960784
[42,] -0.178039216 -0.178039216
[43,] -0.178039216 -0.178039216
[44,] -0.178039216 -0.178039216
[45,] -0.178039216 -0.178039216
[46,] -0.582352941 -0.178039216
[47,] -0.178039216 -0.582352941
[48,] -0.178039216 -0.178039216
[49,] -0.178039216 -0.178039216
[50,] -0.402352941 -0.178039216
[51,] 0.597647059 -0.402352941
[52,] 0.821960784 0.597647059
[53,] -0.178039216 0.821960784
[54,] -0.582352941 -0.178039216
[55,] 0.417647059 -0.582352941
[56,] -0.178039216 0.417647059
[57,] 0.001960784 -0.178039216
[58,] 0.001960784 0.001960784
[59,] 0.821960784 0.001960784
[60,] 0.417647059 0.821960784
[61,] 0.821960784 0.417647059
[62,] -0.178039216 0.821960784
[63,] 0.001960784 -0.178039216
[64,] -0.178039216 0.001960784
[65,] -0.582352941 -0.178039216
[66,] -0.402352941 -0.582352941
[67,] -0.582352941 -0.402352941
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.417647059 -0.178039216
2 -0.178039216 0.417647059
3 -0.178039216 -0.178039216
4 0.001960784 -0.178039216
5 0.821960784 0.001960784
6 0.001960784 0.821960784
7 -0.178039216 0.001960784
8 0.821960784 -0.178039216
9 -0.178039216 0.821960784
10 0.821960784 -0.178039216
11 -0.178039216 0.821960784
12 -0.178039216 -0.178039216
13 -0.178039216 -0.178039216
14 -0.178039216 -0.178039216
15 -0.178039216 -0.178039216
16 -0.178039216 -0.178039216
17 -0.178039216 -0.178039216
18 0.417647059 -0.178039216
19 -0.178039216 0.417647059
20 -0.178039216 -0.178039216
21 0.417647059 -0.178039216
22 -0.178039216 0.417647059
23 -0.178039216 -0.178039216
24 0.597647059 -0.178039216
25 0.821960784 0.597647059
26 -0.582352941 0.821960784
27 0.417647059 -0.582352941
28 -0.178039216 0.417647059
29 -0.178039216 -0.178039216
30 -0.178039216 -0.178039216
31 -0.178039216 -0.178039216
32 -0.178039216 -0.178039216
33 -0.178039216 -0.178039216
34 -0.178039216 -0.178039216
35 -0.178039216 -0.178039216
36 0.417647059 -0.178039216
37 -0.402352941 0.417647059
38 -0.178039216 -0.402352941
39 0.821960784 -0.178039216
40 0.001960784 0.821960784
41 -0.178039216 0.001960784
42 -0.178039216 -0.178039216
43 -0.178039216 -0.178039216
44 -0.178039216 -0.178039216
45 -0.178039216 -0.178039216
46 -0.582352941 -0.178039216
47 -0.178039216 -0.582352941
48 -0.178039216 -0.178039216
49 -0.178039216 -0.178039216
50 -0.402352941 -0.178039216
51 0.597647059 -0.402352941
52 0.821960784 0.597647059
53 -0.178039216 0.821960784
54 -0.582352941 -0.178039216
55 0.417647059 -0.582352941
56 -0.178039216 0.417647059
57 0.001960784 -0.178039216
58 0.001960784 0.001960784
59 0.821960784 0.001960784
60 0.417647059 0.821960784
61 0.821960784 0.417647059
62 -0.178039216 0.821960784
63 0.001960784 -0.178039216
64 -0.178039216 0.001960784
65 -0.582352941 -0.178039216
66 -0.402352941 -0.582352941
67 -0.582352941 -0.402352941
> 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/wessaorg/rcomp/tmp/7ahd61355851252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8lkg61355851253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9gi4c1355851253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10yd5t1355851253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/116oie1355851253.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/wessaorg/rcomp/tmp/121hrl1355851253.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/wessaorg/rcomp/tmp/13qkn51355851253.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/wessaorg/rcomp/tmp/14l2yl1355851253.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/wessaorg/rcomp/tmp/159loy1355851253.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/wessaorg/rcomp/tmp/16h2pb1355851253.tab")
+ }
>
> try(system("convert tmp/1f0ms1355851252.ps tmp/1f0ms1355851252.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xww11355851252.ps tmp/2xww11355851252.png",intern=TRUE))
character(0)
> try(system("convert tmp/3utkp1355851252.ps tmp/3utkp1355851252.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y0gb1355851252.ps tmp/4y0gb1355851252.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vxm71355851252.ps tmp/5vxm71355851252.png",intern=TRUE))
character(0)
> try(system("convert tmp/6u0fz1355851252.ps tmp/6u0fz1355851252.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ahd61355851252.ps tmp/7ahd61355851252.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lkg61355851253.ps tmp/8lkg61355851253.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gi4c1355851253.ps tmp/9gi4c1355851253.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yd5t1355851253.ps tmp/10yd5t1355851253.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.834 1.143 8.968