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(26
+ ,21
+ ,21
+ ,23
+ ,17
+ ,23
+ ,4
+ ,14
+ ,12
+ ,20
+ ,16
+ ,15
+ ,24
+ ,17
+ ,20
+ ,4
+ ,18
+ ,11
+ ,19
+ ,19
+ ,18
+ ,22
+ ,18
+ ,20
+ ,6
+ ,11
+ ,14
+ ,19
+ ,18
+ ,11
+ ,20
+ ,21
+ ,21
+ ,8
+ ,12
+ ,12
+ ,20
+ ,16
+ ,8
+ ,24
+ ,20
+ ,24
+ ,8
+ ,16
+ ,21
+ ,25
+ ,23
+ ,19
+ ,27
+ ,28
+ ,22
+ ,4
+ ,18
+ ,12
+ ,25
+ ,17
+ ,4
+ ,28
+ ,19
+ ,23
+ ,4
+ ,14
+ ,22
+ ,22
+ ,12
+ ,20
+ ,27
+ ,22
+ ,20
+ ,8
+ ,14
+ ,11
+ ,26
+ ,19
+ ,16
+ ,24
+ ,16
+ ,25
+ ,5
+ ,15
+ ,10
+ ,22
+ ,16
+ ,14
+ ,23
+ ,18
+ ,23
+ ,4
+ ,15
+ ,13
+ ,17
+ ,19
+ ,10
+ ,24
+ ,25
+ ,27
+ ,4
+ ,17
+ ,10
+ ,22
+ ,20
+ ,13
+ ,27
+ ,17
+ ,27
+ ,4
+ ,19
+ ,8
+ ,19
+ ,13
+ ,14
+ ,27
+ ,14
+ ,22
+ ,4
+ ,10
+ ,15
+ ,24
+ ,20
+ ,8
+ ,28
+ ,11
+ ,24
+ ,4
+ ,16
+ ,14
+ ,26
+ ,27
+ ,23
+ ,27
+ ,27
+ ,25
+ ,4
+ ,18
+ ,10
+ ,21
+ ,17
+ ,11
+ ,23
+ ,20
+ ,22
+ ,8
+ ,14
+ ,14
+ ,13
+ ,8
+ ,9
+ ,24
+ ,22
+ ,28
+ ,4
+ ,14
+ ,14
+ ,26
+ ,25
+ ,24
+ ,28
+ ,22
+ ,28
+ ,4
+ ,17
+ ,11
+ ,20
+ ,26
+ ,5
+ ,27
+ ,21
+ ,27
+ ,4
+ ,14
+ ,10
+ ,22
+ ,13
+ ,15
+ ,25
+ ,23
+ ,25
+ ,8
+ ,16
+ ,13
+ ,14
+ ,19
+ ,5
+ ,19
+ ,17
+ ,16
+ ,4
+ ,18
+ ,7
+ ,21
+ ,15
+ ,19
+ ,24
+ ,24
+ ,28
+ ,7
+ ,11
+ ,14
+ ,7
+ ,5
+ ,6
+ ,20
+ ,14
+ ,21
+ ,4
+ ,14
+ ,12
+ ,23
+ ,16
+ ,13
+ ,28
+ ,17
+ ,24
+ ,4
+ ,12
+ ,14
+ ,17
+ ,14
+ ,11
+ ,26
+ ,23
+ ,27
+ ,5
+ ,17
+ ,11
+ ,25
+ ,24
+ ,17
+ ,23
+ ,24
+ ,14
+ ,4
+ ,9
+ ,9
+ ,25
+ ,24
+ ,17
+ ,23
+ ,24
+ ,14
+ ,4
+ ,16
+ ,11
+ ,19
+ ,9
+ ,5
+ ,20
+ ,8
+ ,27
+ ,4
+ ,14
+ ,15
+ ,20
+ ,19
+ ,9
+ ,11
+ ,22
+ ,20
+ ,4
+ ,15
+ ,14
+ ,23
+ ,19
+ ,15
+ ,24
+ ,23
+ ,21
+ ,4
+ ,11
+ ,13
+ ,22
+ ,25
+ ,17
+ ,25
+ ,25
+ ,22
+ ,4
+ ,16
+ ,9
+ ,22
+ ,19
+ ,17
+ ,23
+ ,21
+ ,21
+ ,4
+ ,13
+ ,15
+ ,21
+ ,18
+ ,20
+ ,18
+ ,24
+ ,12
+ ,15
+ ,17
+ ,10
+ ,15
+ ,15
+ ,12
+ ,20
+ ,15
+ ,20
+ ,10
+ ,15
+ ,11
+ ,20
+ ,12
+ ,7
+ ,20
+ ,22
+ ,24
+ ,4
+ ,14
+ ,13
+ ,22
+ ,21
+ ,16
+ ,24
+ ,21
+ ,19
+ ,8
+ ,16
+ ,8
+ ,18
+ ,12
+ ,7
+ ,23
+ ,25
+ ,28
+ ,4
+ ,9
+ ,20
+ ,20
+ ,15
+ ,14
+ ,25
+ ,16
+ ,23
+ ,4
+ ,15
+ ,12
+ ,28
+ ,28
+ ,24
+ ,28
+ ,28
+ ,27
+ ,4
+ ,17
+ ,10
+ ,22
+ ,25
+ ,15
+ ,26
+ ,23
+ ,22
+ ,4
+ ,13
+ ,10
+ ,18
+ ,19
+ ,15
+ ,26
+ ,21
+ ,27
+ ,7
+ ,15
+ ,9
+ ,23
+ ,20
+ ,10
+ ,23
+ ,21
+ ,26
+ ,4
+ ,16
+ ,14
+ ,20
+ ,24
+ ,14
+ ,22
+ ,26
+ ,22
+ ,6
+ ,16
+ ,8
+ ,25
+ ,26
+ ,18
+ ,24
+ ,22
+ ,21
+ ,5
+ ,12
+ ,14
+ ,26
+ ,25
+ ,12
+ ,21
+ ,21
+ ,19
+ ,4
+ ,12
+ ,11
+ ,15
+ ,12
+ ,9
+ ,20
+ ,18
+ ,24
+ ,16
+ ,11
+ ,13
+ ,17
+ ,12
+ ,9
+ ,22
+ ,12
+ ,19
+ ,5
+ ,15
+ ,9
+ ,23
+ ,15
+ ,8
+ ,20
+ ,25
+ ,26
+ ,12
+ ,15
+ ,11
+ ,21
+ ,17
+ ,18
+ ,25
+ ,17
+ ,22
+ ,6
+ ,17
+ ,15
+ ,13
+ ,14
+ ,10
+ ,20
+ ,24
+ ,28
+ ,9
+ ,13
+ ,11
+ ,18
+ ,16
+ ,17
+ ,22
+ ,15
+ ,21
+ ,9
+ ,16
+ ,10
+ ,19
+ ,11
+ ,14
+ ,23
+ ,13
+ ,23
+ ,4
+ ,14
+ ,14
+ ,22
+ ,20
+ ,16
+ ,25
+ ,26
+ ,28
+ ,5
+ ,11
+ ,18
+ ,16
+ ,11
+ ,10
+ ,23
+ ,16
+ ,10
+ ,4
+ ,12
+ ,14
+ ,24
+ ,22
+ ,19
+ ,23
+ ,24
+ ,24
+ ,4
+ ,12
+ ,11
+ ,18
+ ,20
+ ,10
+ ,22
+ ,21
+ ,21
+ ,5
+ ,15
+ ,12
+ ,20
+ ,19
+ ,14
+ ,24
+ ,20
+ ,21
+ ,4
+ ,16
+ ,13
+ ,24
+ ,17
+ ,10
+ ,25
+ ,14
+ ,24
+ ,4
+ ,15
+ ,9
+ ,14
+ ,21
+ ,4
+ ,21
+ ,25
+ ,24
+ ,4
+ ,12
+ ,10
+ ,22
+ ,23
+ ,19
+ ,12
+ ,25
+ ,25
+ ,5
+ ,12
+ ,15
+ ,24
+ ,18
+ ,9
+ ,17
+ ,20
+ ,25
+ ,4
+ ,8
+ ,20
+ ,18
+ ,17
+ ,12
+ ,20
+ ,22
+ ,23
+ ,6
+ ,13
+ ,12
+ ,21
+ ,27
+ ,16
+ ,23
+ ,20
+ ,21
+ ,4
+ ,11
+ ,12
+ ,23
+ ,25
+ ,11
+ ,23
+ ,26
+ ,16
+ ,4
+ ,14
+ ,14
+ ,17
+ ,19
+ ,18
+ ,20
+ ,18
+ ,17
+ ,18
+ ,15
+ ,13
+ ,22
+ ,22
+ ,11
+ ,28
+ ,22
+ ,25
+ ,4
+ ,10
+ ,11
+ ,24
+ ,24
+ ,24
+ ,24
+ ,24
+ ,24
+ ,6
+ ,11
+ ,17
+ ,21
+ ,20
+ ,17
+ ,24
+ ,17
+ ,23
+ ,4
+ ,12
+ ,12
+ ,22
+ ,19
+ ,18
+ ,24
+ ,24
+ ,25
+ ,4
+ ,15
+ ,13
+ ,16
+ ,11
+ ,9
+ ,24
+ ,20
+ ,23
+ ,5
+ ,15
+ ,14
+ ,21
+ ,22
+ ,19
+ ,28
+ ,19
+ ,28
+ ,4
+ ,14
+ ,13
+ ,23
+ ,22
+ ,18
+ ,25
+ ,20
+ ,26
+ ,4
+ ,16
+ ,15
+ ,22
+ ,16
+ ,12
+ ,21
+ ,15
+ ,22
+ ,5
+ ,15
+ ,13
+ ,24
+ ,20
+ ,23
+ ,25
+ ,23
+ ,19
+ ,10
+ ,15
+ ,10
+ ,24
+ ,24
+ ,22
+ ,25
+ ,26
+ ,26
+ ,5
+ ,13
+ ,11
+ ,16
+ ,16
+ ,14
+ ,18
+ ,22
+ ,18
+ ,8
+ ,12
+ ,19
+ ,16
+ ,16
+ ,14
+ ,17
+ ,20
+ ,18
+ ,8
+ ,17
+ ,13
+ ,21
+ ,22
+ ,16
+ ,26
+ ,24
+ ,25
+ ,5
+ ,13
+ ,17
+ ,26
+ ,24
+ ,23
+ ,28
+ ,26
+ ,27
+ ,4
+ ,15
+ ,13
+ ,15
+ ,16
+ ,7
+ ,21
+ ,21
+ ,12
+ ,4
+ ,13
+ ,9
+ ,25
+ ,27
+ ,10
+ ,27
+ ,25
+ ,15
+ ,4
+ ,15
+ ,11
+ ,18
+ ,11
+ ,12
+ ,22
+ ,13
+ ,21
+ ,5
+ ,16
+ ,10
+ ,23
+ ,21
+ ,12
+ ,21
+ ,20
+ ,23
+ ,4
+ ,15
+ ,9
+ ,20
+ ,20
+ ,12
+ ,25
+ ,22
+ ,22
+ ,4
+ ,16
+ ,12
+ ,17
+ ,20
+ ,17
+ ,22
+ ,23
+ ,21
+ ,8
+ ,15
+ ,12
+ ,25
+ ,27
+ ,21
+ ,23
+ ,28
+ ,24
+ ,4
+ ,14
+ ,13
+ ,24
+ ,20
+ ,16
+ ,26
+ ,22
+ ,27
+ ,5
+ ,15
+ ,13
+ ,17
+ ,12
+ ,11
+ ,19
+ ,20
+ ,22
+ ,14
+ ,14
+ ,12
+ ,19
+ ,8
+ ,14
+ ,25
+ ,6
+ ,28
+ ,8
+ ,13
+ ,15
+ ,20
+ ,21
+ ,13
+ ,21
+ ,21
+ ,26
+ ,8
+ ,7
+ ,22
+ ,15
+ ,18
+ ,9
+ ,13
+ ,20
+ ,10
+ ,4
+ ,17
+ ,13
+ ,27
+ ,24
+ ,19
+ ,24
+ ,18
+ ,19
+ ,4
+ ,13
+ ,15
+ ,22
+ ,16
+ ,13
+ ,25
+ ,23
+ ,22
+ ,6
+ ,15
+ ,13
+ ,23
+ ,18
+ ,19
+ ,26
+ ,20
+ ,21
+ ,4
+ ,14
+ ,15
+ ,16
+ ,20
+ ,13
+ ,25
+ ,24
+ ,24
+ ,7
+ ,13
+ ,10
+ ,19
+ ,20
+ ,13
+ ,25
+ ,22
+ ,25
+ ,7
+ ,16
+ ,11
+ ,25
+ ,19
+ ,13
+ ,22
+ ,21
+ ,21
+ ,4
+ ,12
+ ,16
+ ,19
+ ,17
+ ,14
+ ,21
+ ,18
+ ,20
+ ,6
+ ,14
+ ,11
+ ,19
+ ,16
+ ,12
+ ,23
+ ,21
+ ,21
+ ,4
+ ,17
+ ,11
+ ,26
+ ,26
+ ,22
+ ,25
+ ,23
+ ,24
+ ,7
+ ,15
+ ,10
+ ,21
+ ,15
+ ,11
+ ,24
+ ,23
+ ,23
+ ,4
+ ,17
+ ,10
+ ,20
+ ,22
+ ,5
+ ,21
+ ,15
+ ,18
+ ,4
+ ,12
+ ,16
+ ,24
+ ,17
+ ,18
+ ,21
+ ,21
+ ,24
+ ,8
+ ,16
+ ,12
+ ,22
+ ,23
+ ,19
+ ,25
+ ,24
+ ,24
+ ,4
+ ,11
+ ,11
+ ,20
+ ,21
+ ,14
+ ,22
+ ,23
+ ,19
+ ,4
+ ,15
+ ,16
+ ,18
+ ,19
+ ,15
+ ,20
+ ,21
+ ,20
+ ,10
+ ,9
+ ,19
+ ,18
+ ,14
+ ,12
+ ,20
+ ,21
+ ,18
+ ,8
+ ,16
+ ,11
+ ,24
+ ,17
+ ,19
+ ,23
+ ,20
+ ,20
+ ,6
+ ,15
+ ,16
+ ,24
+ ,12
+ ,15
+ ,28
+ ,11
+ ,27
+ ,4
+ ,10
+ ,15
+ ,22
+ ,24
+ ,17
+ ,23
+ ,22
+ ,23
+ ,4
+ ,10
+ ,24
+ ,23
+ ,18
+ ,8
+ ,28
+ ,27
+ ,26
+ ,4
+ ,15
+ ,14
+ ,22
+ ,20
+ ,10
+ ,24
+ ,25
+ ,23
+ ,5
+ ,11
+ ,15
+ ,20
+ ,16
+ ,12
+ ,18
+ ,18
+ ,17
+ ,4
+ ,13
+ ,11
+ ,18
+ ,20
+ ,12
+ ,20
+ ,20
+ ,21
+ ,6
+ ,14
+ ,15
+ ,25
+ ,22
+ ,20
+ ,28
+ ,24
+ ,25
+ ,4
+ ,18
+ ,12
+ ,18
+ ,12
+ ,12
+ ,21
+ ,10
+ ,23
+ ,5
+ ,16
+ ,10
+ ,16
+ ,16
+ ,12
+ ,21
+ ,27
+ ,27
+ ,7
+ ,14
+ ,14
+ ,20
+ ,17
+ ,14
+ ,25
+ ,21
+ ,24
+ ,8
+ ,14
+ ,13
+ ,19
+ ,22
+ ,6
+ ,19
+ ,21
+ ,20
+ ,5
+ ,14
+ ,9
+ ,15
+ ,12
+ ,10
+ ,18
+ ,18
+ ,27
+ ,8
+ ,14
+ ,15
+ ,19
+ ,14
+ ,18
+ ,21
+ ,15
+ ,21
+ ,10
+ ,12
+ ,15
+ ,19
+ ,23
+ ,18
+ ,22
+ ,24
+ ,24
+ ,8
+ ,14
+ ,14
+ ,16
+ ,15
+ ,7
+ ,24
+ ,22
+ ,21
+ ,5
+ ,15
+ ,11
+ ,17
+ ,17
+ ,18
+ ,15
+ ,14
+ ,15
+ ,12
+ ,15
+ ,8
+ ,28
+ ,28
+ ,9
+ ,28
+ ,28
+ ,25
+ ,4
+ ,15
+ ,11
+ ,23
+ ,20
+ ,17
+ ,26
+ ,18
+ ,25
+ ,5
+ ,13
+ ,11
+ ,25
+ ,23
+ ,22
+ ,23
+ ,26
+ ,22
+ ,4
+ ,17
+ ,8
+ ,20
+ ,13
+ ,11
+ ,26
+ ,17
+ ,24
+ ,6
+ ,17
+ ,10
+ ,17
+ ,18
+ ,15
+ ,20
+ ,19
+ ,21
+ ,4
+ ,19
+ ,11
+ ,23
+ ,23
+ ,17
+ ,22
+ ,22
+ ,22
+ ,4
+ ,15
+ ,13
+ ,16
+ ,19
+ ,15
+ ,20
+ ,18
+ ,23
+ ,7
+ ,13
+ ,11
+ ,23
+ ,23
+ ,22
+ ,23
+ ,24
+ ,22
+ ,7
+ ,9
+ ,20
+ ,11
+ ,12
+ ,9
+ ,22
+ ,15
+ ,20
+ ,10
+ ,15
+ ,10
+ ,18
+ ,16
+ ,13
+ ,24
+ ,18
+ ,23
+ ,4
+ ,15
+ ,15
+ ,24
+ ,23
+ ,20
+ ,23
+ ,26
+ ,25
+ ,5
+ ,15
+ ,12
+ ,23
+ ,13
+ ,14
+ ,22
+ ,11
+ ,23
+ ,8
+ ,16
+ ,14
+ ,21
+ ,22
+ ,14
+ ,26
+ ,26
+ ,22
+ ,11
+ ,11
+ ,23
+ ,16
+ ,18
+ ,12
+ ,23
+ ,21
+ ,25
+ ,7
+ ,14
+ ,14
+ ,24
+ ,23
+ ,20
+ ,27
+ ,23
+ ,26
+ ,4
+ ,11
+ ,16
+ ,23
+ ,20
+ ,20
+ ,23
+ ,23
+ ,22
+ ,8
+ ,15
+ ,11
+ ,18
+ ,10
+ ,8
+ ,21
+ ,15
+ ,24
+ ,6
+ ,13
+ ,12
+ ,20
+ ,17
+ ,17
+ ,26
+ ,22
+ ,24
+ ,7
+ ,15
+ ,10
+ ,9
+ ,18
+ ,9
+ ,23
+ ,26
+ ,25
+ ,5
+ ,16
+ ,14
+ ,24
+ ,15
+ ,18
+ ,21
+ ,16
+ ,20
+ ,4
+ ,14
+ ,12
+ ,25
+ ,23
+ ,22
+ ,27
+ ,20
+ ,26
+ ,8
+ ,15
+ ,12
+ ,20
+ ,17
+ ,10
+ ,19
+ ,18
+ ,21
+ ,4
+ ,16
+ ,11
+ ,21
+ ,17
+ ,13
+ ,23
+ ,22
+ ,26
+ ,8
+ ,16
+ ,12
+ ,25
+ ,22
+ ,15
+ ,25
+ ,16
+ ,21
+ ,6
+ ,11
+ ,13
+ ,22
+ ,20
+ ,18
+ ,23
+ ,19
+ ,22
+ ,4
+ ,12
+ ,11
+ ,21
+ ,20
+ ,18
+ ,22
+ ,20
+ ,16
+ ,9
+ ,9
+ ,19
+ ,21
+ ,19
+ ,12
+ ,22
+ ,19
+ ,26
+ ,5
+ ,16
+ ,12
+ ,22
+ ,18
+ ,12
+ ,25
+ ,23
+ ,28
+ ,6
+ ,13
+ ,17
+ ,27
+ ,22
+ ,20
+ ,25
+ ,24
+ ,18
+ ,4
+ ,16
+ ,9
+ ,24
+ ,20
+ ,12
+ ,28
+ ,25
+ ,25
+ ,4
+ ,12
+ ,12
+ ,24
+ ,22
+ ,16
+ ,28
+ ,21
+ ,23
+ ,4
+ ,9
+ ,19
+ ,21
+ ,18
+ ,16
+ ,20
+ ,21
+ ,21
+ ,5
+ ,13
+ ,18
+ ,18
+ ,16
+ ,18
+ ,25
+ ,23
+ ,20
+ ,6
+ ,13
+ ,15
+ ,16
+ ,16
+ ,16
+ ,19
+ ,27
+ ,25
+ ,16
+ ,14
+ ,14
+ ,22
+ ,16
+ ,13
+ ,25
+ ,23
+ ,22
+ ,6
+ ,19
+ ,11
+ ,20
+ ,16
+ ,17
+ ,22
+ ,18
+ ,21
+ ,6
+ ,13
+ ,9
+ ,18
+ ,17
+ ,13
+ ,18
+ ,16
+ ,16
+ ,4
+ ,12
+ ,18
+ ,20
+ ,18
+ ,17
+ ,20
+ ,16
+ ,18
+ ,4
+ ,13
+ ,16)
+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A'
+ ,'Happiness'
+ ,'Depression
')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('I1','I2','I3','E1','E2','E3','A','Happiness','Depression
'),1:162))
> 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 = '3'
> 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
I3 I1 I2 E1 E2 E3 A Happiness Depression\r
1 21 26 21 23 17 23 4 14 12
2 15 20 16 24 17 20 4 18 11
3 18 19 19 22 18 20 6 11 14
4 11 19 18 20 21 21 8 12 12
5 8 20 16 24 20 24 8 16 21
6 19 25 23 27 28 22 4 18 12
7 4 25 17 28 19 23 4 14 22
8 20 22 12 27 22 20 8 14 11
9 16 26 19 24 16 25 5 15 10
10 14 22 16 23 18 23 4 15 13
11 10 17 19 24 25 27 4 17 10
12 13 22 20 27 17 27 4 19 8
13 14 19 13 27 14 22 4 10 15
14 8 24 20 28 11 24 4 16 14
15 23 26 27 27 27 25 4 18 10
16 11 21 17 23 20 22 8 14 14
17 9 13 8 24 22 28 4 14 14
18 24 26 25 28 22 28 4 17 11
19 5 20 26 27 21 27 4 14 10
20 15 22 13 25 23 25 8 16 13
21 5 14 19 19 17 16 4 18 7
22 19 21 15 24 24 28 7 11 14
23 6 7 5 20 14 21 4 14 12
24 13 23 16 28 17 24 4 12 14
25 11 17 14 26 23 27 5 17 11
26 17 25 24 23 24 14 4 9 9
27 17 25 24 23 24 14 4 16 11
28 5 19 9 20 8 27 4 14 15
29 9 20 19 11 22 20 4 15 14
30 15 23 19 24 23 21 4 11 13
31 17 22 25 25 25 22 4 16 9
32 17 22 19 23 21 21 4 13 15
33 20 21 18 18 24 12 15 17 10
34 12 15 15 20 15 20 10 15 11
35 7 20 12 20 22 24 4 14 13
36 16 22 21 24 21 19 8 16 8
37 7 18 12 23 25 28 4 9 20
38 14 20 15 25 16 23 4 15 12
39 24 28 28 28 28 27 4 17 10
40 15 22 25 26 23 22 4 13 10
41 15 18 19 26 21 27 7 15 9
42 10 23 20 23 21 26 4 16 14
43 14 20 24 22 26 22 6 16 8
44 18 25 26 24 22 21 5 12 14
45 12 26 25 21 21 19 4 12 11
46 9 15 12 20 18 24 16 11 13
47 9 17 12 22 12 19 5 15 9
48 8 23 15 20 25 26 12 15 11
49 18 21 17 25 17 22 6 17 15
50 10 13 14 20 24 28 9 13 11
51 17 18 16 22 15 21 9 16 10
52 14 19 11 23 13 23 4 14 14
53 16 22 20 25 26 28 5 11 18
54 10 16 11 23 16 10 4 12 14
55 19 24 22 23 24 24 4 12 11
56 10 18 20 22 21 21 5 15 12
57 14 20 19 24 20 21 4 16 13
58 10 24 17 25 14 24 4 15 9
59 4 14 21 21 25 24 4 12 10
60 19 22 23 12 25 25 5 12 15
61 9 24 18 17 20 25 4 8 20
62 12 18 17 20 22 23 6 13 12
63 16 21 27 23 20 21 4 11 12
64 11 23 25 23 26 16 4 14 14
65 18 17 19 20 18 17 18 15 13
66 11 22 22 28 22 25 4 10 11
67 24 24 24 24 24 24 6 11 17
68 17 21 20 24 17 23 4 12 12
69 18 22 19 24 24 25 4 15 13
70 9 16 11 24 20 23 5 15 14
71 19 21 22 28 19 28 4 14 13
72 18 23 22 25 20 26 4 16 15
73 12 22 16 21 15 22 5 15 13
74 23 24 20 25 23 19 10 15 10
75 22 24 24 25 26 26 5 13 11
76 14 16 16 18 22 18 8 12 19
77 14 16 16 17 20 18 8 17 13
78 16 21 22 26 24 25 5 13 17
79 23 26 24 28 26 27 4 15 13
80 7 15 16 21 21 12 4 13 9
81 10 25 27 27 25 15 4 15 11
82 12 18 11 22 13 21 5 16 10
83 12 23 21 21 20 23 4 15 9
84 12 20 20 25 22 22 4 16 12
85 17 17 20 22 23 21 8 15 12
86 21 25 27 23 28 24 4 14 13
87 16 24 20 26 22 27 5 15 13
88 11 17 12 19 20 22 14 14 12
89 14 19 8 25 6 28 8 13 15
90 13 20 21 21 21 26 8 7 22
91 9 15 18 13 20 10 4 17 13
92 19 27 24 24 18 19 4 13 15
93 13 22 16 25 23 22 6 15 13
94 19 23 18 26 20 21 4 14 15
95 13 16 20 25 24 24 7 13 10
96 13 19 20 25 22 25 7 16 11
97 13 25 19 22 21 21 4 12 16
98 14 19 17 21 18 20 6 14 11
99 12 19 16 23 21 21 4 17 11
100 22 26 26 25 23 24 7 15 10
101 11 21 15 24 23 23 4 17 10
102 5 20 22 21 15 18 4 12 16
103 18 24 17 21 21 24 8 16 12
104 19 22 23 25 24 24 4 11 11
105 14 20 21 22 23 19 4 15 16
106 15 18 19 20 21 20 10 9 19
107 12 18 14 20 21 18 8 16 11
108 19 24 17 23 20 20 6 15 16
109 15 24 12 28 11 27 4 10 15
110 17 22 24 23 22 23 4 10 24
111 8 23 18 28 27 26 4 15 14
112 10 22 20 24 25 23 5 11 15
113 12 20 16 18 18 17 4 13 11
114 12 18 20 20 20 21 6 14 15
115 20 25 22 28 24 25 4 18 12
116 12 18 12 21 10 23 5 16 10
117 12 16 16 21 27 27 7 14 14
118 14 20 17 25 21 24 8 14 13
119 6 19 22 19 21 20 5 14 9
120 10 15 12 18 18 27 8 14 15
121 18 19 14 21 15 21 10 12 15
122 18 19 23 22 24 24 8 14 14
123 7 16 15 24 22 21 5 15 11
124 18 17 17 15 14 15 12 15 8
125 9 28 28 28 28 25 4 15 11
126 17 23 20 26 18 25 5 13 11
127 22 25 23 23 26 22 4 17 8
128 11 20 13 26 17 24 6 17 10
129 15 17 18 20 19 21 4 19 11
130 17 23 23 22 22 22 4 15 13
131 15 16 19 20 18 23 7 13 11
132 22 23 23 23 24 22 7 9 20
133 9 11 12 22 15 20 10 15 10
134 13 18 16 24 18 23 4 15 15
135 20 24 23 23 26 25 5 15 12
136 14 23 13 22 11 23 8 16 14
137 14 21 22 26 26 22 11 11 23
138 12 16 18 23 21 25 7 14 14
139 20 24 23 27 23 26 4 11 16
140 20 23 20 23 23 22 8 15 11
141 8 18 10 21 15 24 6 13 12
142 17 20 17 26 22 24 7 15 10
143 9 9 18 23 26 25 5 16 14
144 18 24 15 21 16 20 4 14 12
145 22 25 23 27 20 26 8 15 12
146 10 20 17 19 18 21 4 16 11
147 13 21 17 23 22 26 8 16 12
148 15 25 22 25 16 21 6 11 13
149 18 22 20 23 19 22 4 12 11
150 18 21 20 22 20 16 9 9 19
151 12 21 19 22 19 26 5 16 12
152 12 22 18 25 23 28 6 13 17
153 20 27 22 25 24 18 4 16 9
154 12 24 20 28 25 25 4 12 12
155 16 24 22 28 21 23 4 9 19
156 16 21 18 20 21 21 5 13 18
157 18 18 16 25 23 20 6 13 15
158 16 16 16 19 27 25 16 14 14
159 13 22 16 25 23 22 6 19 11
160 17 20 16 22 18 21 6 13 9
161 13 18 17 18 16 16 4 12 18
162 17 20 18 20 16 18 4 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) I1 I2 E1
-6.12669 0.55895 0.22704 0.10342
E2 E3 A Happiness
0.01915 -0.02636 0.51350 0.00609
`Depression\\r`
-0.05185
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.3585 -1.9593 0.3523 2.8636 6.6876
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.12669 4.28848 -1.429 0.1551
I1 0.55895 0.11187 4.996 1.58e-06 ***
I2 0.22704 0.10293 2.206 0.0289 *
E1 0.10342 0.11622 0.890 0.3749
E2 0.01915 0.08919 0.215 0.8303
E3 -0.02636 0.09196 -0.287 0.7748
A 0.51350 0.12098 4.244 3.78e-05 ***
Happiness 0.00609 0.15168 0.040 0.9680
`Depression\\r` -0.05185 0.11296 -0.459 0.6469
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.727 on 153 degrees of freedom
Multiple R-squared: 0.3763, Adjusted R-squared: 0.3436
F-statistic: 11.54 on 8 and 153 DF, p-value: 9.526e-13
> 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,] 0.8697264 0.26054715 0.130273576
[2,] 0.7998776 0.40024478 0.200122391
[3,] 0.8060972 0.38780550 0.193902750
[4,] 0.8016006 0.39679878 0.198399390
[5,] 0.7577213 0.48455733 0.242278666
[6,] 0.7194288 0.56114250 0.280571248
[7,] 0.8530636 0.29387283 0.146936413
[8,] 0.9732083 0.05358341 0.026791706
[9,] 0.9579562 0.08408762 0.042043812
[10,] 0.9600898 0.07982048 0.039910242
[11,] 0.9495380 0.10092392 0.050461960
[12,] 0.9532175 0.09356501 0.046782504
[13,] 0.9416505 0.11669891 0.058349455
[14,] 0.9185334 0.16293316 0.081466582
[15,] 0.9394177 0.12116458 0.060582291
[16,] 0.9158397 0.16832069 0.084160346
[17,] 0.9256075 0.14878504 0.074392522
[18,] 0.9075421 0.18491587 0.092457933
[19,] 0.8835456 0.23290884 0.116454420
[20,] 0.8520143 0.29597131 0.147985655
[21,] 0.8590460 0.28190809 0.140954045
[22,] 0.8300714 0.33985719 0.169928593
[23,] 0.8069802 0.38603952 0.193019759
[24,] 0.8672472 0.26550552 0.132752761
[25,] 0.8430447 0.31391057 0.156955285
[26,] 0.8155089 0.36898226 0.184491130
[27,] 0.7853267 0.42934662 0.214673308
[28,] 0.7753113 0.44937734 0.224688670
[29,] 0.7326874 0.53462528 0.267312639
[30,] 0.6858403 0.62831939 0.314159697
[31,] 0.6881093 0.62378143 0.311890717
[32,] 0.6508558 0.69828835 0.349144176
[33,] 0.6222086 0.75558271 0.377791355
[34,] 0.6695235 0.66095303 0.330476514
[35,] 0.6772834 0.64543311 0.322716554
[36,] 0.6393353 0.72132950 0.360664748
[37,] 0.8650548 0.26989037 0.134945185
[38,] 0.8933446 0.21331078 0.106655392
[39,] 0.8703051 0.25938983 0.129694914
[40,] 0.8780674 0.24386514 0.121932569
[41,] 0.8790651 0.24186978 0.120934891
[42,] 0.8713515 0.25729699 0.128648493
[43,] 0.8526559 0.29468819 0.147344094
[44,] 0.8431377 0.31372451 0.156862253
[45,] 0.8214738 0.35705236 0.178526180
[46,] 0.7911224 0.41775528 0.208877642
[47,] 0.8297217 0.34055664 0.170278318
[48,] 0.8676595 0.26468093 0.132340464
[49,] 0.9220064 0.15598715 0.077993576
[50,] 0.9292425 0.14151499 0.070757496
[51,] 0.9117192 0.17656164 0.088280818
[52,] 0.8972975 0.20540496 0.102702478
[53,] 0.9109241 0.17815190 0.089075950
[54,] 0.9036240 0.19275204 0.096376018
[55,] 0.9083386 0.18332289 0.091661443
[56,] 0.9543932 0.09121366 0.045606832
[57,] 0.9523499 0.09530011 0.047650057
[58,] 0.9520781 0.09584390 0.047921948
[59,] 0.9391068 0.12178641 0.060893205
[60,] 0.9458647 0.10827061 0.054135305
[61,] 0.9401431 0.11971373 0.059856866
[62,] 0.9293778 0.14124436 0.070622179
[63,] 0.9321863 0.13562749 0.067813743
[64,] 0.9404466 0.11910680 0.059553399
[65,] 0.9359853 0.12802949 0.064014747
[66,] 0.9272238 0.14555237 0.072776184
[67,] 0.9112800 0.17743992 0.088719958
[68,] 0.9249915 0.15001699 0.075008496
[69,] 0.9159927 0.16801452 0.084007258
[70,] 0.9679756 0.06404884 0.032024418
[71,] 0.9599148 0.08017049 0.040085246
[72,] 0.9575673 0.08486542 0.042432710
[73,] 0.9481548 0.10369050 0.051845249
[74,] 0.9452507 0.10949864 0.054749322
[75,] 0.9426328 0.11473443 0.057367216
[76,] 0.9276089 0.14478223 0.072391117
[77,] 0.9278691 0.14426182 0.072130912
[78,] 0.9174508 0.16509836 0.082549182
[79,] 0.9033528 0.19329445 0.096647225
[80,] 0.8875991 0.22480179 0.112400897
[81,] 0.8654350 0.26912995 0.134564976
[82,] 0.8439907 0.31201860 0.156009302
[83,] 0.8537026 0.29259484 0.146297419
[84,] 0.8243724 0.35125528 0.175627638
[85,] 0.7960408 0.40791842 0.203959208
[86,] 0.7815551 0.43688974 0.218444871
[87,] 0.7465085 0.50698290 0.253491451
[88,] 0.7055829 0.58883429 0.294417146
[89,] 0.6764883 0.64702337 0.323511683
[90,] 0.6438964 0.71220713 0.356103564
[91,] 0.8549038 0.29019245 0.145096225
[92,] 0.8283775 0.34324500 0.171622500
[93,] 0.8351791 0.32964170 0.164820852
[94,] 0.8045438 0.39091230 0.195456151
[95,] 0.7731366 0.45372688 0.226863440
[96,] 0.7391693 0.52166135 0.260830673
[97,] 0.7188132 0.56237364 0.281186821
[98,] 0.6847945 0.63041090 0.315205451
[99,] 0.6482853 0.70342948 0.351714738
[100,] 0.7286554 0.54268923 0.271344614
[101,] 0.7625470 0.47490604 0.237453022
[102,] 0.7334340 0.53313203 0.266566015
[103,] 0.7043280 0.59134397 0.295671983
[104,] 0.7050317 0.58993654 0.294968268
[105,] 0.6610129 0.67797411 0.338987054
[106,] 0.6102353 0.77952935 0.389764677
[107,] 0.5573134 0.88537325 0.442686624
[108,] 0.8436694 0.31266111 0.156330554
[109,] 0.8099850 0.38003008 0.190015040
[110,] 0.8040338 0.39193237 0.195966185
[111,] 0.7727174 0.45456528 0.227282639
[112,] 0.8068960 0.38620808 0.193104039
[113,] 0.7780201 0.44395974 0.221979868
[114,] 0.9944407 0.01111855 0.005559276
[115,] 0.9915264 0.01694723 0.008473614
[116,] 0.9890902 0.02181956 0.010909778
[117,] 0.9835305 0.03293893 0.016469463
[118,] 0.9793759 0.04124812 0.020624058
[119,] 0.9707135 0.05857308 0.029286542
[120,] 0.9582844 0.08343122 0.041715608
[121,] 0.9620186 0.07596273 0.037981364
[122,] 0.9506261 0.09874789 0.049373946
[123,] 0.9342029 0.13159429 0.065797144
[124,] 0.9225459 0.15490829 0.077454143
[125,] 0.8927491 0.21450181 0.107250905
[126,] 0.9030110 0.19397791 0.096988957
[127,] 0.8648026 0.27039484 0.135197418
[128,] 0.8903141 0.21937172 0.109685862
[129,] 0.8611388 0.27772239 0.138861197
[130,] 0.8588470 0.28230592 0.141152962
[131,] 0.8071133 0.38577341 0.192886707
[132,] 0.7339274 0.53214517 0.266072585
[133,] 0.7863815 0.42723701 0.213618507
[134,] 0.8613285 0.27734304 0.138671521
[135,] 0.9049431 0.19011383 0.095056913
[136,] 0.8363097 0.32738059 0.163690297
[137,] 0.7518855 0.49622905 0.248114524
[138,] 0.6590147 0.68197068 0.340985339
[139,] 0.5518855 0.89622907 0.448114534
> postscript(file="/var/fisher/rcomp/tmp/13o871353157613.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/fisher/rcomp/tmp/2vsz01353157613.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/30yzh1353157613.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/47vpw1353157613.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5cop01353157613.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5
4.211346016 2.441491339 4.678203737 -3.055776266 -6.033848511
6 7 8 9 10
1.641212019 -11.358489994 4.796120397 -0.989423583 0.608918816
11 12 13 14 15
-1.577190502 -1.871928686 2.737545635 -7.728089069 4.168649967
16 17 18 19 20
-4.119877028 2.465442504 5.752042227 -10.058690345 -0.019909523
21 22 23 24 25
-4.681661735 4.844113133 3.778866515 -1.351512023 -0.072213409
26 27 28 29 30
-0.407155918 -0.346092895 -4.408052621 -2.817050609 0.141359487
31 32 33 34 35
0.984894088 2.933531744 1.009876802 -0.147487165 -4.098914838
36 37 38 39 40
-1.111874782 -2.849907499 1.733445901 3.759964128 -1.010118305
41 42 43 44 45
1.153441664 -4.790773529 -1.457903832 -0.014378693 -5.711639193
46 47 48 49 50
-5.371313946 -1.296238852 -9.679348390 3.791289442 -0.238321139
51 52 53 54 55
3.223717141 3.574600098 1.242661027 0.863515033 2.954873714
56 57 58 59 60
-2.635522334 0.845180759 -5.047299501 -6.092787069 4.684470591
61 62 63 64 65
-4.922524161 -0.215316682 0.551972860 -5.273089117 -0.314325819
66 67 68 69 70
-4.367513333 6.687558198 3.141867370 3.762233562 -0.505592916
71 72 73 74 75
4.407278856 2.619304159 -1.666658364 3.938346536 4.788796588
76 77 78 79 80
2.546684470 2.346861651 1.139299414 4.992012478 -2.814213722
81 82 83 84 85
-8.427583831 1.451181113 -3.124043916 -1.549056502 3.344637241
86 87 88 89 90
3.275680453 -0.212025704 -3.519913381 2.318622680 -1.718532002
91 92 93 94 95
-0.291456451 0.904922606 -1.747007380 4.304412931 0.075232593
96 97 98 99 100
-1.503376723 -2.581944892 1.061884481 0.059725135 2.130528671
101 102 103 104 105
-1.971971859 -8.329108069 1.327827176 3.644975316 0.649430048
106 107 108 109 110
0.603980175 -0.743971800 3.275170365 1.255664369 2.316820976
111 112 113 114 115
-6.962592586 -4.922108110 -0.005749799 -0.761399388 2.920485095
116 117 118 119 120
1.437721013 0.620002197 -0.786050221 -7.514085538 0.108015443
121 122 123 124 125
3.992365164 2.715354583 -3.660287849 3.502471119 -11.228725657
126 127 128 129 130
1.279269846 4.891890278 -2.051562077 4.059919123 1.461202052
131 132 133 134 135
2.959738478 5.178467876 -0.489288570 1.844973132 3.235983870
136 137 138 139 140
-1.039713713 -3.735792640 0.021242573 3.651341621 2.862055334
141 142 143 144 145
-2.569108571 2.443248443 1.852947802 3.838364931 2.864089623
146 147 148 149 150
-2.249042841 -2.168608054 -2.654038327 3.569846385 1.920473503
151 152 153 154 155
-1.921327202 -2.823356399 1.784985884 -4.049101651 -0.098106742
156 157 158 159 160
2.671821807 5.551931381 0.152643885 -1.875063133 3.555307908
161 162
2.266058944 4.657221785
> postscript(file="/var/fisher/rcomp/tmp/6ogdf1353157613.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 4.211346016 NA
1 2.441491339 4.211346016
2 4.678203737 2.441491339
3 -3.055776266 4.678203737
4 -6.033848511 -3.055776266
5 1.641212019 -6.033848511
6 -11.358489994 1.641212019
7 4.796120397 -11.358489994
8 -0.989423583 4.796120397
9 0.608918816 -0.989423583
10 -1.577190502 0.608918816
11 -1.871928686 -1.577190502
12 2.737545635 -1.871928686
13 -7.728089069 2.737545635
14 4.168649967 -7.728089069
15 -4.119877028 4.168649967
16 2.465442504 -4.119877028
17 5.752042227 2.465442504
18 -10.058690345 5.752042227
19 -0.019909523 -10.058690345
20 -4.681661735 -0.019909523
21 4.844113133 -4.681661735
22 3.778866515 4.844113133
23 -1.351512023 3.778866515
24 -0.072213409 -1.351512023
25 -0.407155918 -0.072213409
26 -0.346092895 -0.407155918
27 -4.408052621 -0.346092895
28 -2.817050609 -4.408052621
29 0.141359487 -2.817050609
30 0.984894088 0.141359487
31 2.933531744 0.984894088
32 1.009876802 2.933531744
33 -0.147487165 1.009876802
34 -4.098914838 -0.147487165
35 -1.111874782 -4.098914838
36 -2.849907499 -1.111874782
37 1.733445901 -2.849907499
38 3.759964128 1.733445901
39 -1.010118305 3.759964128
40 1.153441664 -1.010118305
41 -4.790773529 1.153441664
42 -1.457903832 -4.790773529
43 -0.014378693 -1.457903832
44 -5.711639193 -0.014378693
45 -5.371313946 -5.711639193
46 -1.296238852 -5.371313946
47 -9.679348390 -1.296238852
48 3.791289442 -9.679348390
49 -0.238321139 3.791289442
50 3.223717141 -0.238321139
51 3.574600098 3.223717141
52 1.242661027 3.574600098
53 0.863515033 1.242661027
54 2.954873714 0.863515033
55 -2.635522334 2.954873714
56 0.845180759 -2.635522334
57 -5.047299501 0.845180759
58 -6.092787069 -5.047299501
59 4.684470591 -6.092787069
60 -4.922524161 4.684470591
61 -0.215316682 -4.922524161
62 0.551972860 -0.215316682
63 -5.273089117 0.551972860
64 -0.314325819 -5.273089117
65 -4.367513333 -0.314325819
66 6.687558198 -4.367513333
67 3.141867370 6.687558198
68 3.762233562 3.141867370
69 -0.505592916 3.762233562
70 4.407278856 -0.505592916
71 2.619304159 4.407278856
72 -1.666658364 2.619304159
73 3.938346536 -1.666658364
74 4.788796588 3.938346536
75 2.546684470 4.788796588
76 2.346861651 2.546684470
77 1.139299414 2.346861651
78 4.992012478 1.139299414
79 -2.814213722 4.992012478
80 -8.427583831 -2.814213722
81 1.451181113 -8.427583831
82 -3.124043916 1.451181113
83 -1.549056502 -3.124043916
84 3.344637241 -1.549056502
85 3.275680453 3.344637241
86 -0.212025704 3.275680453
87 -3.519913381 -0.212025704
88 2.318622680 -3.519913381
89 -1.718532002 2.318622680
90 -0.291456451 -1.718532002
91 0.904922606 -0.291456451
92 -1.747007380 0.904922606
93 4.304412931 -1.747007380
94 0.075232593 4.304412931
95 -1.503376723 0.075232593
96 -2.581944892 -1.503376723
97 1.061884481 -2.581944892
98 0.059725135 1.061884481
99 2.130528671 0.059725135
100 -1.971971859 2.130528671
101 -8.329108069 -1.971971859
102 1.327827176 -8.329108069
103 3.644975316 1.327827176
104 0.649430048 3.644975316
105 0.603980175 0.649430048
106 -0.743971800 0.603980175
107 3.275170365 -0.743971800
108 1.255664369 3.275170365
109 2.316820976 1.255664369
110 -6.962592586 2.316820976
111 -4.922108110 -6.962592586
112 -0.005749799 -4.922108110
113 -0.761399388 -0.005749799
114 2.920485095 -0.761399388
115 1.437721013 2.920485095
116 0.620002197 1.437721013
117 -0.786050221 0.620002197
118 -7.514085538 -0.786050221
119 0.108015443 -7.514085538
120 3.992365164 0.108015443
121 2.715354583 3.992365164
122 -3.660287849 2.715354583
123 3.502471119 -3.660287849
124 -11.228725657 3.502471119
125 1.279269846 -11.228725657
126 4.891890278 1.279269846
127 -2.051562077 4.891890278
128 4.059919123 -2.051562077
129 1.461202052 4.059919123
130 2.959738478 1.461202052
131 5.178467876 2.959738478
132 -0.489288570 5.178467876
133 1.844973132 -0.489288570
134 3.235983870 1.844973132
135 -1.039713713 3.235983870
136 -3.735792640 -1.039713713
137 0.021242573 -3.735792640
138 3.651341621 0.021242573
139 2.862055334 3.651341621
140 -2.569108571 2.862055334
141 2.443248443 -2.569108571
142 1.852947802 2.443248443
143 3.838364931 1.852947802
144 2.864089623 3.838364931
145 -2.249042841 2.864089623
146 -2.168608054 -2.249042841
147 -2.654038327 -2.168608054
148 3.569846385 -2.654038327
149 1.920473503 3.569846385
150 -1.921327202 1.920473503
151 -2.823356399 -1.921327202
152 1.784985884 -2.823356399
153 -4.049101651 1.784985884
154 -0.098106742 -4.049101651
155 2.671821807 -0.098106742
156 5.551931381 2.671821807
157 0.152643885 5.551931381
158 -1.875063133 0.152643885
159 3.555307908 -1.875063133
160 2.266058944 3.555307908
161 4.657221785 2.266058944
162 NA 4.657221785
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.441491339 4.211346016
[2,] 4.678203737 2.441491339
[3,] -3.055776266 4.678203737
[4,] -6.033848511 -3.055776266
[5,] 1.641212019 -6.033848511
[6,] -11.358489994 1.641212019
[7,] 4.796120397 -11.358489994
[8,] -0.989423583 4.796120397
[9,] 0.608918816 -0.989423583
[10,] -1.577190502 0.608918816
[11,] -1.871928686 -1.577190502
[12,] 2.737545635 -1.871928686
[13,] -7.728089069 2.737545635
[14,] 4.168649967 -7.728089069
[15,] -4.119877028 4.168649967
[16,] 2.465442504 -4.119877028
[17,] 5.752042227 2.465442504
[18,] -10.058690345 5.752042227
[19,] -0.019909523 -10.058690345
[20,] -4.681661735 -0.019909523
[21,] 4.844113133 -4.681661735
[22,] 3.778866515 4.844113133
[23,] -1.351512023 3.778866515
[24,] -0.072213409 -1.351512023
[25,] -0.407155918 -0.072213409
[26,] -0.346092895 -0.407155918
[27,] -4.408052621 -0.346092895
[28,] -2.817050609 -4.408052621
[29,] 0.141359487 -2.817050609
[30,] 0.984894088 0.141359487
[31,] 2.933531744 0.984894088
[32,] 1.009876802 2.933531744
[33,] -0.147487165 1.009876802
[34,] -4.098914838 -0.147487165
[35,] -1.111874782 -4.098914838
[36,] -2.849907499 -1.111874782
[37,] 1.733445901 -2.849907499
[38,] 3.759964128 1.733445901
[39,] -1.010118305 3.759964128
[40,] 1.153441664 -1.010118305
[41,] -4.790773529 1.153441664
[42,] -1.457903832 -4.790773529
[43,] -0.014378693 -1.457903832
[44,] -5.711639193 -0.014378693
[45,] -5.371313946 -5.711639193
[46,] -1.296238852 -5.371313946
[47,] -9.679348390 -1.296238852
[48,] 3.791289442 -9.679348390
[49,] -0.238321139 3.791289442
[50,] 3.223717141 -0.238321139
[51,] 3.574600098 3.223717141
[52,] 1.242661027 3.574600098
[53,] 0.863515033 1.242661027
[54,] 2.954873714 0.863515033
[55,] -2.635522334 2.954873714
[56,] 0.845180759 -2.635522334
[57,] -5.047299501 0.845180759
[58,] -6.092787069 -5.047299501
[59,] 4.684470591 -6.092787069
[60,] -4.922524161 4.684470591
[61,] -0.215316682 -4.922524161
[62,] 0.551972860 -0.215316682
[63,] -5.273089117 0.551972860
[64,] -0.314325819 -5.273089117
[65,] -4.367513333 -0.314325819
[66,] 6.687558198 -4.367513333
[67,] 3.141867370 6.687558198
[68,] 3.762233562 3.141867370
[69,] -0.505592916 3.762233562
[70,] 4.407278856 -0.505592916
[71,] 2.619304159 4.407278856
[72,] -1.666658364 2.619304159
[73,] 3.938346536 -1.666658364
[74,] 4.788796588 3.938346536
[75,] 2.546684470 4.788796588
[76,] 2.346861651 2.546684470
[77,] 1.139299414 2.346861651
[78,] 4.992012478 1.139299414
[79,] -2.814213722 4.992012478
[80,] -8.427583831 -2.814213722
[81,] 1.451181113 -8.427583831
[82,] -3.124043916 1.451181113
[83,] -1.549056502 -3.124043916
[84,] 3.344637241 -1.549056502
[85,] 3.275680453 3.344637241
[86,] -0.212025704 3.275680453
[87,] -3.519913381 -0.212025704
[88,] 2.318622680 -3.519913381
[89,] -1.718532002 2.318622680
[90,] -0.291456451 -1.718532002
[91,] 0.904922606 -0.291456451
[92,] -1.747007380 0.904922606
[93,] 4.304412931 -1.747007380
[94,] 0.075232593 4.304412931
[95,] -1.503376723 0.075232593
[96,] -2.581944892 -1.503376723
[97,] 1.061884481 -2.581944892
[98,] 0.059725135 1.061884481
[99,] 2.130528671 0.059725135
[100,] -1.971971859 2.130528671
[101,] -8.329108069 -1.971971859
[102,] 1.327827176 -8.329108069
[103,] 3.644975316 1.327827176
[104,] 0.649430048 3.644975316
[105,] 0.603980175 0.649430048
[106,] -0.743971800 0.603980175
[107,] 3.275170365 -0.743971800
[108,] 1.255664369 3.275170365
[109,] 2.316820976 1.255664369
[110,] -6.962592586 2.316820976
[111,] -4.922108110 -6.962592586
[112,] -0.005749799 -4.922108110
[113,] -0.761399388 -0.005749799
[114,] 2.920485095 -0.761399388
[115,] 1.437721013 2.920485095
[116,] 0.620002197 1.437721013
[117,] -0.786050221 0.620002197
[118,] -7.514085538 -0.786050221
[119,] 0.108015443 -7.514085538
[120,] 3.992365164 0.108015443
[121,] 2.715354583 3.992365164
[122,] -3.660287849 2.715354583
[123,] 3.502471119 -3.660287849
[124,] -11.228725657 3.502471119
[125,] 1.279269846 -11.228725657
[126,] 4.891890278 1.279269846
[127,] -2.051562077 4.891890278
[128,] 4.059919123 -2.051562077
[129,] 1.461202052 4.059919123
[130,] 2.959738478 1.461202052
[131,] 5.178467876 2.959738478
[132,] -0.489288570 5.178467876
[133,] 1.844973132 -0.489288570
[134,] 3.235983870 1.844973132
[135,] -1.039713713 3.235983870
[136,] -3.735792640 -1.039713713
[137,] 0.021242573 -3.735792640
[138,] 3.651341621 0.021242573
[139,] 2.862055334 3.651341621
[140,] -2.569108571 2.862055334
[141,] 2.443248443 -2.569108571
[142,] 1.852947802 2.443248443
[143,] 3.838364931 1.852947802
[144,] 2.864089623 3.838364931
[145,] -2.249042841 2.864089623
[146,] -2.168608054 -2.249042841
[147,] -2.654038327 -2.168608054
[148,] 3.569846385 -2.654038327
[149,] 1.920473503 3.569846385
[150,] -1.921327202 1.920473503
[151,] -2.823356399 -1.921327202
[152,] 1.784985884 -2.823356399
[153,] -4.049101651 1.784985884
[154,] -0.098106742 -4.049101651
[155,] 2.671821807 -0.098106742
[156,] 5.551931381 2.671821807
[157,] 0.152643885 5.551931381
[158,] -1.875063133 0.152643885
[159,] 3.555307908 -1.875063133
[160,] 2.266058944 3.555307908
[161,] 4.657221785 2.266058944
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.441491339 4.211346016
2 4.678203737 2.441491339
3 -3.055776266 4.678203737
4 -6.033848511 -3.055776266
5 1.641212019 -6.033848511
6 -11.358489994 1.641212019
7 4.796120397 -11.358489994
8 -0.989423583 4.796120397
9 0.608918816 -0.989423583
10 -1.577190502 0.608918816
11 -1.871928686 -1.577190502
12 2.737545635 -1.871928686
13 -7.728089069 2.737545635
14 4.168649967 -7.728089069
15 -4.119877028 4.168649967
16 2.465442504 -4.119877028
17 5.752042227 2.465442504
18 -10.058690345 5.752042227
19 -0.019909523 -10.058690345
20 -4.681661735 -0.019909523
21 4.844113133 -4.681661735
22 3.778866515 4.844113133
23 -1.351512023 3.778866515
24 -0.072213409 -1.351512023
25 -0.407155918 -0.072213409
26 -0.346092895 -0.407155918
27 -4.408052621 -0.346092895
28 -2.817050609 -4.408052621
29 0.141359487 -2.817050609
30 0.984894088 0.141359487
31 2.933531744 0.984894088
32 1.009876802 2.933531744
33 -0.147487165 1.009876802
34 -4.098914838 -0.147487165
35 -1.111874782 -4.098914838
36 -2.849907499 -1.111874782
37 1.733445901 -2.849907499
38 3.759964128 1.733445901
39 -1.010118305 3.759964128
40 1.153441664 -1.010118305
41 -4.790773529 1.153441664
42 -1.457903832 -4.790773529
43 -0.014378693 -1.457903832
44 -5.711639193 -0.014378693
45 -5.371313946 -5.711639193
46 -1.296238852 -5.371313946
47 -9.679348390 -1.296238852
48 3.791289442 -9.679348390
49 -0.238321139 3.791289442
50 3.223717141 -0.238321139
51 3.574600098 3.223717141
52 1.242661027 3.574600098
53 0.863515033 1.242661027
54 2.954873714 0.863515033
55 -2.635522334 2.954873714
56 0.845180759 -2.635522334
57 -5.047299501 0.845180759
58 -6.092787069 -5.047299501
59 4.684470591 -6.092787069
60 -4.922524161 4.684470591
61 -0.215316682 -4.922524161
62 0.551972860 -0.215316682
63 -5.273089117 0.551972860
64 -0.314325819 -5.273089117
65 -4.367513333 -0.314325819
66 6.687558198 -4.367513333
67 3.141867370 6.687558198
68 3.762233562 3.141867370
69 -0.505592916 3.762233562
70 4.407278856 -0.505592916
71 2.619304159 4.407278856
72 -1.666658364 2.619304159
73 3.938346536 -1.666658364
74 4.788796588 3.938346536
75 2.546684470 4.788796588
76 2.346861651 2.546684470
77 1.139299414 2.346861651
78 4.992012478 1.139299414
79 -2.814213722 4.992012478
80 -8.427583831 -2.814213722
81 1.451181113 -8.427583831
82 -3.124043916 1.451181113
83 -1.549056502 -3.124043916
84 3.344637241 -1.549056502
85 3.275680453 3.344637241
86 -0.212025704 3.275680453
87 -3.519913381 -0.212025704
88 2.318622680 -3.519913381
89 -1.718532002 2.318622680
90 -0.291456451 -1.718532002
91 0.904922606 -0.291456451
92 -1.747007380 0.904922606
93 4.304412931 -1.747007380
94 0.075232593 4.304412931
95 -1.503376723 0.075232593
96 -2.581944892 -1.503376723
97 1.061884481 -2.581944892
98 0.059725135 1.061884481
99 2.130528671 0.059725135
100 -1.971971859 2.130528671
101 -8.329108069 -1.971971859
102 1.327827176 -8.329108069
103 3.644975316 1.327827176
104 0.649430048 3.644975316
105 0.603980175 0.649430048
106 -0.743971800 0.603980175
107 3.275170365 -0.743971800
108 1.255664369 3.275170365
109 2.316820976 1.255664369
110 -6.962592586 2.316820976
111 -4.922108110 -6.962592586
112 -0.005749799 -4.922108110
113 -0.761399388 -0.005749799
114 2.920485095 -0.761399388
115 1.437721013 2.920485095
116 0.620002197 1.437721013
117 -0.786050221 0.620002197
118 -7.514085538 -0.786050221
119 0.108015443 -7.514085538
120 3.992365164 0.108015443
121 2.715354583 3.992365164
122 -3.660287849 2.715354583
123 3.502471119 -3.660287849
124 -11.228725657 3.502471119
125 1.279269846 -11.228725657
126 4.891890278 1.279269846
127 -2.051562077 4.891890278
128 4.059919123 -2.051562077
129 1.461202052 4.059919123
130 2.959738478 1.461202052
131 5.178467876 2.959738478
132 -0.489288570 5.178467876
133 1.844973132 -0.489288570
134 3.235983870 1.844973132
135 -1.039713713 3.235983870
136 -3.735792640 -1.039713713
137 0.021242573 -3.735792640
138 3.651341621 0.021242573
139 2.862055334 3.651341621
140 -2.569108571 2.862055334
141 2.443248443 -2.569108571
142 1.852947802 2.443248443
143 3.838364931 1.852947802
144 2.864089623 3.838364931
145 -2.249042841 2.864089623
146 -2.168608054 -2.249042841
147 -2.654038327 -2.168608054
148 3.569846385 -2.654038327
149 1.920473503 3.569846385
150 -1.921327202 1.920473503
151 -2.823356399 -1.921327202
152 1.784985884 -2.823356399
153 -4.049101651 1.784985884
154 -0.098106742 -4.049101651
155 2.671821807 -0.098106742
156 5.551931381 2.671821807
157 0.152643885 5.551931381
158 -1.875063133 0.152643885
159 3.555307908 -1.875063133
160 2.266058944 3.555307908
161 4.657221785 2.266058944
> 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/fisher/rcomp/tmp/7mjov1353157613.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/fisher/rcomp/tmp/89fkq1353157613.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/fisher/rcomp/tmp/94jpa1353157613.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/fisher/rcomp/tmp/10tcsm1353157613.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11o6n21353157613.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/121fci1353157613.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/137mf01353157613.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14i3oq1353157613.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/fisher/rcomp/tmp/15d7t91353157613.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/fisher/rcomp/tmp/160h1g1353157614.tab")
+ }
>
> try(system("convert tmp/13o871353157613.ps tmp/13o871353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vsz01353157613.ps tmp/2vsz01353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/30yzh1353157613.ps tmp/30yzh1353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/47vpw1353157613.ps tmp/47vpw1353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/5cop01353157613.ps tmp/5cop01353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ogdf1353157613.ps tmp/6ogdf1353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mjov1353157613.ps tmp/7mjov1353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/89fkq1353157613.ps tmp/89fkq1353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/94jpa1353157613.ps tmp/94jpa1353157613.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tcsm1353157613.ps tmp/10tcsm1353157613.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.307 1.328 9.637