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 = '1'
> 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
I1 I2 I3 E1 E2 E3 A Happiness Depression
1 26 21 21 23 17 23 4 14 12
2 20 16 15 24 17 20 4 18 11
3 19 19 18 22 18 20 6 11 14
4 19 18 11 20 21 21 8 12 12
5 20 16 8 24 20 24 8 16 21
6 25 23 19 27 28 22 4 18 12
7 25 17 4 28 19 23 4 14 22
8 22 12 20 27 22 20 8 14 11
9 26 19 16 24 16 25 5 15 10
10 22 16 14 23 18 23 4 15 13
11 17 19 10 24 25 27 4 17 10
12 22 20 13 27 17 27 4 19 8
13 19 13 14 27 14 22 4 10 15
14 24 20 8 28 11 24 4 16 14
15 26 27 23 27 27 25 4 18 10
16 21 17 11 23 20 22 8 14 14
17 13 8 9 24 22 28 4 14 14
18 26 25 24 28 22 28 4 17 11
19 20 26 5 27 21 27 4 14 10
20 22 13 15 25 23 25 8 16 13
21 14 19 5 19 17 16 4 18 7
22 21 15 19 24 24 28 7 11 14
23 7 5 6 20 14 21 4 14 12
24 23 16 13 28 17 24 4 12 14
25 17 14 11 26 23 27 5 17 11
26 25 24 17 23 24 14 4 9 9
27 25 24 17 23 24 14 4 16 11
28 19 9 5 20 8 27 4 14 15
29 20 19 9 11 22 20 4 15 14
30 23 19 15 24 23 21 4 11 13
31 22 25 17 25 25 22 4 16 9
32 22 19 17 23 21 21 4 13 15
33 21 18 20 18 24 12 15 17 10
34 15 15 12 20 15 20 10 15 11
35 20 12 7 20 22 24 4 14 13
36 22 21 16 24 21 19 8 16 8
37 18 12 7 23 25 28 4 9 20
38 20 15 14 25 16 23 4 15 12
39 28 28 24 28 28 27 4 17 10
40 22 25 15 26 23 22 4 13 10
41 18 19 15 26 21 27 7 15 9
42 23 20 10 23 21 26 4 16 14
43 20 24 14 22 26 22 6 16 8
44 25 26 18 24 22 21 5 12 14
45 26 25 12 21 21 19 4 12 11
46 15 12 9 20 18 24 16 11 13
47 17 12 9 22 12 19 5 15 9
48 23 15 8 20 25 26 12 15 11
49 21 17 18 25 17 22 6 17 15
50 13 14 10 20 24 28 9 13 11
51 18 16 17 22 15 21 9 16 10
52 19 11 14 23 13 23 4 14 14
53 22 20 16 25 26 28 5 11 18
54 16 11 10 23 16 10 4 12 14
55 24 22 19 23 24 24 4 12 11
56 18 20 10 22 21 21 5 15 12
57 20 19 14 24 20 21 4 16 13
58 24 17 10 25 14 24 4 15 9
59 14 21 4 21 25 24 4 12 10
60 22 23 19 12 25 25 5 12 15
61 24 18 9 17 20 25 4 8 20
62 18 17 12 20 22 23 6 13 12
63 21 27 16 23 20 21 4 11 12
64 23 25 11 23 26 16 4 14 14
65 17 19 18 20 18 17 18 15 13
66 22 22 11 28 22 25 4 10 11
67 24 24 24 24 24 24 6 11 17
68 21 20 17 24 17 23 4 12 12
69 22 19 18 24 24 25 4 15 13
70 16 11 9 24 20 23 5 15 14
71 21 22 19 28 19 28 4 14 13
72 23 22 18 25 20 26 4 16 15
73 22 16 12 21 15 22 5 15 13
74 24 20 23 25 23 19 10 15 10
75 24 24 22 25 26 26 5 13 11
76 16 16 14 18 22 18 8 12 19
77 16 16 14 17 20 18 8 17 13
78 21 22 16 26 24 25 5 13 17
79 26 24 23 28 26 27 4 15 13
80 15 16 7 21 21 12 4 13 9
81 25 27 10 27 25 15 4 15 11
82 18 11 12 22 13 21 5 16 10
83 23 21 12 21 20 23 4 15 9
84 20 20 12 25 22 22 4 16 12
85 17 20 17 22 23 21 8 15 12
86 25 27 21 23 28 24 4 14 13
87 24 20 16 26 22 27 5 15 13
88 17 12 11 19 20 22 14 14 12
89 19 8 14 25 6 28 8 13 15
90 20 21 13 21 21 26 8 7 22
91 15 18 9 13 20 10 4 17 13
92 27 24 19 24 18 19 4 13 15
93 22 16 13 25 23 22 6 15 13
94 23 18 19 26 20 21 4 14 15
95 16 20 13 25 24 24 7 13 10
96 19 20 13 25 22 25 7 16 11
97 25 19 13 22 21 21 4 12 16
98 19 17 14 21 18 20 6 14 11
99 19 16 12 23 21 21 4 17 11
100 26 26 22 25 23 24 7 15 10
101 21 15 11 24 23 23 4 17 10
102 20 22 5 21 15 18 4 12 16
103 24 17 18 21 21 24 8 16 12
104 22 23 19 25 24 24 4 11 11
105 20 21 14 22 23 19 4 15 16
106 18 19 15 20 21 20 10 9 19
107 18 14 12 20 21 18 8 16 11
108 24 17 19 23 20 20 6 15 16
109 24 12 15 28 11 27 4 10 15
110 22 24 17 23 22 23 4 10 24
111 23 18 8 28 27 26 4 15 14
112 22 20 10 24 25 23 5 11 15
113 20 16 12 18 18 17 4 13 11
114 18 20 12 20 20 21 6 14 15
115 25 22 20 28 24 25 4 18 12
116 18 12 12 21 10 23 5 16 10
117 16 16 12 21 27 27 7 14 14
118 20 17 14 25 21 24 8 14 13
119 19 22 6 19 21 20 5 14 9
120 15 12 10 18 18 27 8 14 15
121 19 14 18 21 15 21 10 12 15
122 19 23 18 22 24 24 8 14 14
123 16 15 7 24 22 21 5 15 11
124 17 17 18 15 14 15 12 15 8
125 28 28 9 28 28 25 4 15 11
126 23 20 17 26 18 25 5 13 11
127 25 23 22 23 26 22 4 17 8
128 20 13 11 26 17 24 6 17 10
129 17 18 15 20 19 21 4 19 11
130 23 23 17 22 22 22 4 15 13
131 16 19 15 20 18 23 7 13 11
132 23 23 22 23 24 22 7 9 20
133 11 12 9 22 15 20 10 15 10
134 18 16 13 24 18 23 4 15 15
135 24 23 20 23 26 25 5 15 12
136 23 13 14 22 11 23 8 16 14
137 21 22 14 26 26 22 11 11 23
138 16 18 12 23 21 25 7 14 14
139 24 23 20 27 23 26 4 11 16
140 23 20 20 23 23 22 8 15 11
141 18 10 8 21 15 24 6 13 12
142 20 17 17 26 22 24 7 15 10
143 9 18 9 23 26 25 5 16 14
144 24 15 18 21 16 20 4 14 12
145 25 23 22 27 20 26 8 15 12
146 20 17 10 19 18 21 4 16 11
147 21 17 13 23 22 26 8 16 12
148 25 22 15 25 16 21 6 11 13
149 22 20 18 23 19 22 4 12 11
150 21 20 18 22 20 16 9 9 19
151 21 19 12 22 19 26 5 16 12
152 22 18 12 25 23 28 6 13 17
153 27 22 20 25 24 18 4 16 9
154 24 20 12 28 25 25 4 12 12
155 24 22 16 28 21 23 4 9 19
156 21 18 16 20 21 21 5 13 18
157 18 16 18 25 23 20 6 13 15
158 16 16 16 19 27 25 16 14 14
159 22 16 13 25 23 22 6 19 11
160 20 16 17 22 18 21 6 13 9
161 18 17 13 18 16 16 4 12 18
162 20 18 17 20 16 18 4 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) I2 I3 E1 E2 E3
5.09224 0.36689 0.25096 0.26871 -0.12012 0.02717
A Happiness Depression
-0.21228 0.04720 0.11027
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.9290 -1.6145 -0.1073 1.7307 7.9455
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.09224 2.86322 1.779 0.077307 .
I2 0.36689 0.06347 5.780 4.05e-08 ***
I3 0.25096 0.05023 4.996 1.58e-06 ***
E1 0.26871 0.07499 3.583 0.000455 ***
E2 -0.12012 0.05898 -2.037 0.043416 *
E3 0.02717 0.06160 0.441 0.659813
A -0.21228 0.08397 -2.528 0.012483 *
Happiness 0.04720 0.10157 0.465 0.642816
Depression 0.11027 0.07522 1.466 0.144684
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.497 on 153 degrees of freedom
Multiple R-squared: 0.5568, Adjusted R-squared: 0.5336
F-statistic: 24.03 on 8 and 153 DF, p-value: < 2.2e-16
> 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,] 0.87595201 0.248095975 0.124047988
[2,] 0.90685898 0.186282042 0.093141021
[3,] 0.84943174 0.301136515 0.150568258
[4,] 0.78915089 0.421698230 0.210849115
[5,] 0.71151100 0.576978010 0.288489005
[6,] 0.64056679 0.718866419 0.359433210
[7,] 0.56695792 0.866084152 0.433042076
[8,] 0.49769916 0.995398315 0.502300843
[9,] 0.47987316 0.959746311 0.520126844
[10,] 0.45137248 0.902744962 0.548627519
[11,] 0.36958159 0.739163182 0.630418409
[12,] 0.50414623 0.991707536 0.495853768
[13,] 0.45029122 0.900582450 0.549708775
[14,] 0.38599953 0.771999063 0.614000468
[15,] 0.42935473 0.858709463 0.570645268
[16,] 0.37178435 0.743568697 0.628215651
[17,] 0.57613600 0.847728009 0.423864005
[18,] 0.62556255 0.748874890 0.374437445
[19,] 0.58361204 0.832775927 0.416387963
[20,] 0.54643780 0.907124409 0.453562204
[21,] 0.50361071 0.992778590 0.496389295
[22,] 0.47108879 0.942177587 0.528911206
[23,] 0.53102213 0.937955749 0.468977874
[24,] 0.69106550 0.617868990 0.308934495
[25,] 0.64448883 0.711022336 0.355511168
[26,] 0.59243233 0.815135335 0.407567668
[27,] 0.53575609 0.928487822 0.464243911
[28,] 0.47805705 0.956114096 0.521942952
[29,] 0.44962134 0.899242685 0.550378657
[30,] 0.45324814 0.906496285 0.546751858
[31,] 0.42490474 0.849809482 0.575095259
[32,] 0.37732983 0.754659660 0.622670170
[33,] 0.34749577 0.694991531 0.652504235
[34,] 0.40850319 0.817006389 0.591496806
[35,] 0.35776818 0.715536356 0.642231822
[36,] 0.31739333 0.634786656 0.682606672
[37,] 0.74107789 0.517844230 0.258922115
[38,] 0.72837798 0.543244041 0.271622020
[39,] 0.76008897 0.479822062 0.239911031
[40,] 0.74289379 0.514212412 0.257106206
[41,] 0.70166639 0.596667217 0.298333609
[42,] 0.68422877 0.631542468 0.315771234
[43,] 0.65696612 0.686067764 0.343033882
[44,] 0.61643386 0.767132270 0.383566135
[45,] 0.60838876 0.783222479 0.391611239
[46,] 0.58383753 0.832324936 0.416162468
[47,] 0.68651308 0.626973844 0.313486922
[48,] 0.74028772 0.519424557 0.259712279
[49,] 0.71681755 0.566364894 0.283182447
[50,] 0.81943714 0.361125718 0.180562859
[51,] 0.78790161 0.424196779 0.212098389
[52,] 0.83767503 0.324649943 0.162324971
[53,] 0.81082266 0.378354673 0.189177336
[54,] 0.83545933 0.329081332 0.164540666
[55,] 0.80586041 0.388279176 0.194139588
[56,] 0.80185727 0.396285462 0.198142731
[57,] 0.78658150 0.426836991 0.213418495
[58,] 0.75052684 0.498946324 0.249473162
[59,] 0.72157626 0.556847472 0.278423736
[60,] 0.79850228 0.402995432 0.201497716
[61,] 0.77805290 0.443894196 0.221947098
[62,] 0.77658298 0.446834041 0.223417020
[63,] 0.75260738 0.494785239 0.247392619
[64,] 0.71562505 0.568749897 0.284374948
[65,] 0.73958926 0.520821485 0.260410743
[66,] 0.72261905 0.554761895 0.277380947
[67,] 0.72866003 0.542679946 0.271339973
[68,] 0.69031058 0.619378846 0.309689423
[69,] 0.67537266 0.649254679 0.324627339
[70,] 0.64470120 0.710597598 0.355298799
[71,] 0.60522877 0.789542466 0.394771233
[72,] 0.60804989 0.783900219 0.391950110
[73,] 0.57878916 0.842421671 0.421210836
[74,] 0.63967086 0.720658270 0.360329135
[75,] 0.59538965 0.809220708 0.404610354
[76,] 0.56242069 0.875158611 0.437579306
[77,] 0.57639987 0.847200253 0.423600126
[78,] 0.53102123 0.937957539 0.468978769
[79,] 0.51320373 0.973592538 0.486796269
[80,] 0.48677225 0.973544492 0.513227754
[81,] 0.46305838 0.926116766 0.536941617
[82,] 0.45207208 0.904144158 0.547927921
[83,] 0.40883166 0.817663325 0.591168337
[84,] 0.51668045 0.966639103 0.483319551
[85,] 0.50147847 0.997043056 0.498521528
[86,] 0.60284858 0.794302847 0.397151424
[87,] 0.55757678 0.884846449 0.442423225
[88,] 0.51249369 0.975012613 0.487506306
[89,] 0.46714554 0.934291089 0.532854456
[90,] 0.45490548 0.909810968 0.545094516
[91,] 0.40990082 0.819801646 0.590099177
[92,] 0.50338757 0.993224862 0.496612431
[93,] 0.49205472 0.984109439 0.507945281
[94,] 0.46029241 0.920584818 0.539707591
[95,] 0.43035872 0.860717439 0.569641281
[96,] 0.39640764 0.792815276 0.603592362
[97,] 0.40439339 0.808786783 0.595606609
[98,] 0.38461491 0.769229820 0.615385090
[99,] 0.37153323 0.743066456 0.628466772
[100,] 0.38900584 0.778011677 0.610994161
[101,] 0.37774581 0.755491626 0.622254187
[102,] 0.37217455 0.744349101 0.627825450
[103,] 0.34127499 0.682549974 0.658725013
[104,] 0.29720148 0.594402968 0.702798516
[105,] 0.25589837 0.511796746 0.744101627
[106,] 0.22536869 0.450737380 0.774631310
[107,] 0.18691557 0.373831141 0.813084429
[108,] 0.15920225 0.318404502 0.840797749
[109,] 0.13777501 0.275550025 0.862224988
[110,] 0.11012596 0.220251924 0.889874038
[111,] 0.11834081 0.236681612 0.881659194
[112,] 0.09643260 0.192865194 0.903567403
[113,] 0.07572527 0.151450535 0.924274732
[114,] 0.13803458 0.276069162 0.861965419
[115,] 0.11260970 0.225219397 0.887390302
[116,] 0.09127363 0.182547254 0.908726373
[117,] 0.07053841 0.141076810 0.929461595
[118,] 0.08313928 0.166278565 0.916860717
[119,] 0.06216699 0.124333976 0.937833012
[120,] 0.08986013 0.179720256 0.910139872
[121,] 0.06945589 0.138911773 0.930544113
[122,] 0.19955933 0.399118666 0.800440667
[123,] 0.20233818 0.404676356 0.797661822
[124,] 0.18265607 0.365312136 0.817343932
[125,] 0.15474939 0.309498774 0.845250613
[126,] 0.12975244 0.259504883 0.870247559
[127,] 0.15496310 0.309926204 0.845036898
[128,] 0.11521314 0.230426270 0.884786865
[129,] 0.08478463 0.169569262 0.915215369
[130,] 0.06079062 0.121581248 0.939209376
[131,] 0.05420736 0.108414713 0.945792644
[132,] 0.89898603 0.202027930 0.101013965
[133,] 0.99526286 0.009474281 0.004737140
[134,] 0.99139906 0.017201881 0.008600941
[135,] 0.97997489 0.040050213 0.020025107
[136,] 0.95828255 0.083434902 0.041717451
[137,] 0.92056004 0.158879912 0.079439956
[138,] 0.85124426 0.297511470 0.148755735
[139,] 0.76592179 0.468156415 0.234078208
> postscript(file="/var/fisher/rcomp/tmp/1amvq1353168590.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/26nth1353168590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/33lxc1353168590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4q1in1353168590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/57fgq1353168590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6
2.03479175 -0.89073320 -2.66261019 0.92951006 0.95847190 0.88774508
7 8 9 10 11 12
4.56261350 2.15438675 3.96582660 1.58860537 -2.80837078 -0.56907043
13 14 15 16 17 18
-1.82343935 1.25777266 -0.56472814 2.02804012 -3.20863009 -1.09578152
19 20 21 22 23 24
-2.26682917 3.24906714 -3.58849668 0.73221463 -6.83045778 1.38003066
25 26 27 28 29 30
-1.90053105 2.59007484 2.03915659 2.73842292 3.41899371 1.81196931
31 32 33 34 35 36
-1.74183706 0.02359588 3.28368003 -2.52107472 4.11951950 0.80581888
37 38 39 40 41 42
1.02915240 -0.71189610 0.66168943 -1.71754835 -3.23955774 2.24626419
43 44 45 46 47 48
-1.16098043 0.42555614 4.15708893 0.82608444 -0.37899985 7.94549429
49 50 51 52 53 54
-1.30287290 -2.90643128 -1.85656005 -0.24060409 -0.24348197 -1.42885452
55 56 57 58 59 60
1.18813824 -1.86916988 -1.53340759 3.62156910 -3.91277273 1.64124790
61 62 63 64 65 66
5.46628580 -0.36053181 -3.35547962 1.12750234 -1.57483634 -0.32076783
67 68 69 70 71 72
-1.25901712 -1.76880281 -0.11824401 -1.24861826 -4.17961955 -1.26300298
73 74 75 76 77 78
2.50704549 1.63873387 -0.48496188 -2.12243494 -1.66831584 -2.38883352
79 80 81 82 83 84
-0.09644377 -1.92259200 1.76049005 0.14333026 2.47481653 -1.34375530
85 86 87 88 89 90
-3.74880670 0.01730398 1.39706767 2.43156898 0.13203021 -0.94434891
91 92 93 94 95 96
-1.70423316 2.11249434 2.35445081 -0.08488706 -4.40984107 -1.92910344
97 98 99 100 101 102
4.23307128 -0.46705863 -0.36864329 0.91567650 2.27666389 -0.23041873
103 104 105 106 107 108
4.00068714 -1.66898290 -1.59868992 -1.61979273 1.14909647 2.38240440
109 110 111 112 113 114
2.52759207 -2.59591713 2.90629468 1.87751355 1.91202754 -2.02511242
115 116 117 118 119 120
0.17299271 -0.36953203 -1.82589332 -0.08620967 0.61220179 -1.02932008
121 122 123 124 125 126
-0.25531060 -3.23516396 -1.58888139 -0.64595081 4.46452901 0.03490748
127 128 129 130 131 132
1.45776798 1.14970606 -3.38378736 0.04384692 -4.00510780 -1.09129993
133 134 135 136 137 138
-5.09468419 -2.64969077 0.74377648 3.80882160 -0.85872977 -3.76347223
139 140 141 142 143 144
-1.18317343 1.31270669 2.07483332 -0.91634774 -9.92896240 3.48782066
145 146 147 148 149 150
0.05597293 1.52808386 1.78383774 2.02632326 -0.37337569 -0.50071758
151 152 153 154 155 156
0.57253712 1.36193928 3.59450697 2.31774200 -0.47631422 0.32905473
157 158 159 160 161 162
-2.97215776 -0.32744606 2.38620517 0.11881238 -1.64358721 -1.43272966
> postscript(file="/var/fisher/rcomp/tmp/6diii1353168590.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 2.03479175 NA
1 -0.89073320 2.03479175
2 -2.66261019 -0.89073320
3 0.92951006 -2.66261019
4 0.95847190 0.92951006
5 0.88774508 0.95847190
6 4.56261350 0.88774508
7 2.15438675 4.56261350
8 3.96582660 2.15438675
9 1.58860537 3.96582660
10 -2.80837078 1.58860537
11 -0.56907043 -2.80837078
12 -1.82343935 -0.56907043
13 1.25777266 -1.82343935
14 -0.56472814 1.25777266
15 2.02804012 -0.56472814
16 -3.20863009 2.02804012
17 -1.09578152 -3.20863009
18 -2.26682917 -1.09578152
19 3.24906714 -2.26682917
20 -3.58849668 3.24906714
21 0.73221463 -3.58849668
22 -6.83045778 0.73221463
23 1.38003066 -6.83045778
24 -1.90053105 1.38003066
25 2.59007484 -1.90053105
26 2.03915659 2.59007484
27 2.73842292 2.03915659
28 3.41899371 2.73842292
29 1.81196931 3.41899371
30 -1.74183706 1.81196931
31 0.02359588 -1.74183706
32 3.28368003 0.02359588
33 -2.52107472 3.28368003
34 4.11951950 -2.52107472
35 0.80581888 4.11951950
36 1.02915240 0.80581888
37 -0.71189610 1.02915240
38 0.66168943 -0.71189610
39 -1.71754835 0.66168943
40 -3.23955774 -1.71754835
41 2.24626419 -3.23955774
42 -1.16098043 2.24626419
43 0.42555614 -1.16098043
44 4.15708893 0.42555614
45 0.82608444 4.15708893
46 -0.37899985 0.82608444
47 7.94549429 -0.37899985
48 -1.30287290 7.94549429
49 -2.90643128 -1.30287290
50 -1.85656005 -2.90643128
51 -0.24060409 -1.85656005
52 -0.24348197 -0.24060409
53 -1.42885452 -0.24348197
54 1.18813824 -1.42885452
55 -1.86916988 1.18813824
56 -1.53340759 -1.86916988
57 3.62156910 -1.53340759
58 -3.91277273 3.62156910
59 1.64124790 -3.91277273
60 5.46628580 1.64124790
61 -0.36053181 5.46628580
62 -3.35547962 -0.36053181
63 1.12750234 -3.35547962
64 -1.57483634 1.12750234
65 -0.32076783 -1.57483634
66 -1.25901712 -0.32076783
67 -1.76880281 -1.25901712
68 -0.11824401 -1.76880281
69 -1.24861826 -0.11824401
70 -4.17961955 -1.24861826
71 -1.26300298 -4.17961955
72 2.50704549 -1.26300298
73 1.63873387 2.50704549
74 -0.48496188 1.63873387
75 -2.12243494 -0.48496188
76 -1.66831584 -2.12243494
77 -2.38883352 -1.66831584
78 -0.09644377 -2.38883352
79 -1.92259200 -0.09644377
80 1.76049005 -1.92259200
81 0.14333026 1.76049005
82 2.47481653 0.14333026
83 -1.34375530 2.47481653
84 -3.74880670 -1.34375530
85 0.01730398 -3.74880670
86 1.39706767 0.01730398
87 2.43156898 1.39706767
88 0.13203021 2.43156898
89 -0.94434891 0.13203021
90 -1.70423316 -0.94434891
91 2.11249434 -1.70423316
92 2.35445081 2.11249434
93 -0.08488706 2.35445081
94 -4.40984107 -0.08488706
95 -1.92910344 -4.40984107
96 4.23307128 -1.92910344
97 -0.46705863 4.23307128
98 -0.36864329 -0.46705863
99 0.91567650 -0.36864329
100 2.27666389 0.91567650
101 -0.23041873 2.27666389
102 4.00068714 -0.23041873
103 -1.66898290 4.00068714
104 -1.59868992 -1.66898290
105 -1.61979273 -1.59868992
106 1.14909647 -1.61979273
107 2.38240440 1.14909647
108 2.52759207 2.38240440
109 -2.59591713 2.52759207
110 2.90629468 -2.59591713
111 1.87751355 2.90629468
112 1.91202754 1.87751355
113 -2.02511242 1.91202754
114 0.17299271 -2.02511242
115 -0.36953203 0.17299271
116 -1.82589332 -0.36953203
117 -0.08620967 -1.82589332
118 0.61220179 -0.08620967
119 -1.02932008 0.61220179
120 -0.25531060 -1.02932008
121 -3.23516396 -0.25531060
122 -1.58888139 -3.23516396
123 -0.64595081 -1.58888139
124 4.46452901 -0.64595081
125 0.03490748 4.46452901
126 1.45776798 0.03490748
127 1.14970606 1.45776798
128 -3.38378736 1.14970606
129 0.04384692 -3.38378736
130 -4.00510780 0.04384692
131 -1.09129993 -4.00510780
132 -5.09468419 -1.09129993
133 -2.64969077 -5.09468419
134 0.74377648 -2.64969077
135 3.80882160 0.74377648
136 -0.85872977 3.80882160
137 -3.76347223 -0.85872977
138 -1.18317343 -3.76347223
139 1.31270669 -1.18317343
140 2.07483332 1.31270669
141 -0.91634774 2.07483332
142 -9.92896240 -0.91634774
143 3.48782066 -9.92896240
144 0.05597293 3.48782066
145 1.52808386 0.05597293
146 1.78383774 1.52808386
147 2.02632326 1.78383774
148 -0.37337569 2.02632326
149 -0.50071758 -0.37337569
150 0.57253712 -0.50071758
151 1.36193928 0.57253712
152 3.59450697 1.36193928
153 2.31774200 3.59450697
154 -0.47631422 2.31774200
155 0.32905473 -0.47631422
156 -2.97215776 0.32905473
157 -0.32744606 -2.97215776
158 2.38620517 -0.32744606
159 0.11881238 2.38620517
160 -1.64358721 0.11881238
161 -1.43272966 -1.64358721
162 NA -1.43272966
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.89073320 2.03479175
[2,] -2.66261019 -0.89073320
[3,] 0.92951006 -2.66261019
[4,] 0.95847190 0.92951006
[5,] 0.88774508 0.95847190
[6,] 4.56261350 0.88774508
[7,] 2.15438675 4.56261350
[8,] 3.96582660 2.15438675
[9,] 1.58860537 3.96582660
[10,] -2.80837078 1.58860537
[11,] -0.56907043 -2.80837078
[12,] -1.82343935 -0.56907043
[13,] 1.25777266 -1.82343935
[14,] -0.56472814 1.25777266
[15,] 2.02804012 -0.56472814
[16,] -3.20863009 2.02804012
[17,] -1.09578152 -3.20863009
[18,] -2.26682917 -1.09578152
[19,] 3.24906714 -2.26682917
[20,] -3.58849668 3.24906714
[21,] 0.73221463 -3.58849668
[22,] -6.83045778 0.73221463
[23,] 1.38003066 -6.83045778
[24,] -1.90053105 1.38003066
[25,] 2.59007484 -1.90053105
[26,] 2.03915659 2.59007484
[27,] 2.73842292 2.03915659
[28,] 3.41899371 2.73842292
[29,] 1.81196931 3.41899371
[30,] -1.74183706 1.81196931
[31,] 0.02359588 -1.74183706
[32,] 3.28368003 0.02359588
[33,] -2.52107472 3.28368003
[34,] 4.11951950 -2.52107472
[35,] 0.80581888 4.11951950
[36,] 1.02915240 0.80581888
[37,] -0.71189610 1.02915240
[38,] 0.66168943 -0.71189610
[39,] -1.71754835 0.66168943
[40,] -3.23955774 -1.71754835
[41,] 2.24626419 -3.23955774
[42,] -1.16098043 2.24626419
[43,] 0.42555614 -1.16098043
[44,] 4.15708893 0.42555614
[45,] 0.82608444 4.15708893
[46,] -0.37899985 0.82608444
[47,] 7.94549429 -0.37899985
[48,] -1.30287290 7.94549429
[49,] -2.90643128 -1.30287290
[50,] -1.85656005 -2.90643128
[51,] -0.24060409 -1.85656005
[52,] -0.24348197 -0.24060409
[53,] -1.42885452 -0.24348197
[54,] 1.18813824 -1.42885452
[55,] -1.86916988 1.18813824
[56,] -1.53340759 -1.86916988
[57,] 3.62156910 -1.53340759
[58,] -3.91277273 3.62156910
[59,] 1.64124790 -3.91277273
[60,] 5.46628580 1.64124790
[61,] -0.36053181 5.46628580
[62,] -3.35547962 -0.36053181
[63,] 1.12750234 -3.35547962
[64,] -1.57483634 1.12750234
[65,] -0.32076783 -1.57483634
[66,] -1.25901712 -0.32076783
[67,] -1.76880281 -1.25901712
[68,] -0.11824401 -1.76880281
[69,] -1.24861826 -0.11824401
[70,] -4.17961955 -1.24861826
[71,] -1.26300298 -4.17961955
[72,] 2.50704549 -1.26300298
[73,] 1.63873387 2.50704549
[74,] -0.48496188 1.63873387
[75,] -2.12243494 -0.48496188
[76,] -1.66831584 -2.12243494
[77,] -2.38883352 -1.66831584
[78,] -0.09644377 -2.38883352
[79,] -1.92259200 -0.09644377
[80,] 1.76049005 -1.92259200
[81,] 0.14333026 1.76049005
[82,] 2.47481653 0.14333026
[83,] -1.34375530 2.47481653
[84,] -3.74880670 -1.34375530
[85,] 0.01730398 -3.74880670
[86,] 1.39706767 0.01730398
[87,] 2.43156898 1.39706767
[88,] 0.13203021 2.43156898
[89,] -0.94434891 0.13203021
[90,] -1.70423316 -0.94434891
[91,] 2.11249434 -1.70423316
[92,] 2.35445081 2.11249434
[93,] -0.08488706 2.35445081
[94,] -4.40984107 -0.08488706
[95,] -1.92910344 -4.40984107
[96,] 4.23307128 -1.92910344
[97,] -0.46705863 4.23307128
[98,] -0.36864329 -0.46705863
[99,] 0.91567650 -0.36864329
[100,] 2.27666389 0.91567650
[101,] -0.23041873 2.27666389
[102,] 4.00068714 -0.23041873
[103,] -1.66898290 4.00068714
[104,] -1.59868992 -1.66898290
[105,] -1.61979273 -1.59868992
[106,] 1.14909647 -1.61979273
[107,] 2.38240440 1.14909647
[108,] 2.52759207 2.38240440
[109,] -2.59591713 2.52759207
[110,] 2.90629468 -2.59591713
[111,] 1.87751355 2.90629468
[112,] 1.91202754 1.87751355
[113,] -2.02511242 1.91202754
[114,] 0.17299271 -2.02511242
[115,] -0.36953203 0.17299271
[116,] -1.82589332 -0.36953203
[117,] -0.08620967 -1.82589332
[118,] 0.61220179 -0.08620967
[119,] -1.02932008 0.61220179
[120,] -0.25531060 -1.02932008
[121,] -3.23516396 -0.25531060
[122,] -1.58888139 -3.23516396
[123,] -0.64595081 -1.58888139
[124,] 4.46452901 -0.64595081
[125,] 0.03490748 4.46452901
[126,] 1.45776798 0.03490748
[127,] 1.14970606 1.45776798
[128,] -3.38378736 1.14970606
[129,] 0.04384692 -3.38378736
[130,] -4.00510780 0.04384692
[131,] -1.09129993 -4.00510780
[132,] -5.09468419 -1.09129993
[133,] -2.64969077 -5.09468419
[134,] 0.74377648 -2.64969077
[135,] 3.80882160 0.74377648
[136,] -0.85872977 3.80882160
[137,] -3.76347223 -0.85872977
[138,] -1.18317343 -3.76347223
[139,] 1.31270669 -1.18317343
[140,] 2.07483332 1.31270669
[141,] -0.91634774 2.07483332
[142,] -9.92896240 -0.91634774
[143,] 3.48782066 -9.92896240
[144,] 0.05597293 3.48782066
[145,] 1.52808386 0.05597293
[146,] 1.78383774 1.52808386
[147,] 2.02632326 1.78383774
[148,] -0.37337569 2.02632326
[149,] -0.50071758 -0.37337569
[150,] 0.57253712 -0.50071758
[151,] 1.36193928 0.57253712
[152,] 3.59450697 1.36193928
[153,] 2.31774200 3.59450697
[154,] -0.47631422 2.31774200
[155,] 0.32905473 -0.47631422
[156,] -2.97215776 0.32905473
[157,] -0.32744606 -2.97215776
[158,] 2.38620517 -0.32744606
[159,] 0.11881238 2.38620517
[160,] -1.64358721 0.11881238
[161,] -1.43272966 -1.64358721
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.89073320 2.03479175
2 -2.66261019 -0.89073320
3 0.92951006 -2.66261019
4 0.95847190 0.92951006
5 0.88774508 0.95847190
6 4.56261350 0.88774508
7 2.15438675 4.56261350
8 3.96582660 2.15438675
9 1.58860537 3.96582660
10 -2.80837078 1.58860537
11 -0.56907043 -2.80837078
12 -1.82343935 -0.56907043
13 1.25777266 -1.82343935
14 -0.56472814 1.25777266
15 2.02804012 -0.56472814
16 -3.20863009 2.02804012
17 -1.09578152 -3.20863009
18 -2.26682917 -1.09578152
19 3.24906714 -2.26682917
20 -3.58849668 3.24906714
21 0.73221463 -3.58849668
22 -6.83045778 0.73221463
23 1.38003066 -6.83045778
24 -1.90053105 1.38003066
25 2.59007484 -1.90053105
26 2.03915659 2.59007484
27 2.73842292 2.03915659
28 3.41899371 2.73842292
29 1.81196931 3.41899371
30 -1.74183706 1.81196931
31 0.02359588 -1.74183706
32 3.28368003 0.02359588
33 -2.52107472 3.28368003
34 4.11951950 -2.52107472
35 0.80581888 4.11951950
36 1.02915240 0.80581888
37 -0.71189610 1.02915240
38 0.66168943 -0.71189610
39 -1.71754835 0.66168943
40 -3.23955774 -1.71754835
41 2.24626419 -3.23955774
42 -1.16098043 2.24626419
43 0.42555614 -1.16098043
44 4.15708893 0.42555614
45 0.82608444 4.15708893
46 -0.37899985 0.82608444
47 7.94549429 -0.37899985
48 -1.30287290 7.94549429
49 -2.90643128 -1.30287290
50 -1.85656005 -2.90643128
51 -0.24060409 -1.85656005
52 -0.24348197 -0.24060409
53 -1.42885452 -0.24348197
54 1.18813824 -1.42885452
55 -1.86916988 1.18813824
56 -1.53340759 -1.86916988
57 3.62156910 -1.53340759
58 -3.91277273 3.62156910
59 1.64124790 -3.91277273
60 5.46628580 1.64124790
61 -0.36053181 5.46628580
62 -3.35547962 -0.36053181
63 1.12750234 -3.35547962
64 -1.57483634 1.12750234
65 -0.32076783 -1.57483634
66 -1.25901712 -0.32076783
67 -1.76880281 -1.25901712
68 -0.11824401 -1.76880281
69 -1.24861826 -0.11824401
70 -4.17961955 -1.24861826
71 -1.26300298 -4.17961955
72 2.50704549 -1.26300298
73 1.63873387 2.50704549
74 -0.48496188 1.63873387
75 -2.12243494 -0.48496188
76 -1.66831584 -2.12243494
77 -2.38883352 -1.66831584
78 -0.09644377 -2.38883352
79 -1.92259200 -0.09644377
80 1.76049005 -1.92259200
81 0.14333026 1.76049005
82 2.47481653 0.14333026
83 -1.34375530 2.47481653
84 -3.74880670 -1.34375530
85 0.01730398 -3.74880670
86 1.39706767 0.01730398
87 2.43156898 1.39706767
88 0.13203021 2.43156898
89 -0.94434891 0.13203021
90 -1.70423316 -0.94434891
91 2.11249434 -1.70423316
92 2.35445081 2.11249434
93 -0.08488706 2.35445081
94 -4.40984107 -0.08488706
95 -1.92910344 -4.40984107
96 4.23307128 -1.92910344
97 -0.46705863 4.23307128
98 -0.36864329 -0.46705863
99 0.91567650 -0.36864329
100 2.27666389 0.91567650
101 -0.23041873 2.27666389
102 4.00068714 -0.23041873
103 -1.66898290 4.00068714
104 -1.59868992 -1.66898290
105 -1.61979273 -1.59868992
106 1.14909647 -1.61979273
107 2.38240440 1.14909647
108 2.52759207 2.38240440
109 -2.59591713 2.52759207
110 2.90629468 -2.59591713
111 1.87751355 2.90629468
112 1.91202754 1.87751355
113 -2.02511242 1.91202754
114 0.17299271 -2.02511242
115 -0.36953203 0.17299271
116 -1.82589332 -0.36953203
117 -0.08620967 -1.82589332
118 0.61220179 -0.08620967
119 -1.02932008 0.61220179
120 -0.25531060 -1.02932008
121 -3.23516396 -0.25531060
122 -1.58888139 -3.23516396
123 -0.64595081 -1.58888139
124 4.46452901 -0.64595081
125 0.03490748 4.46452901
126 1.45776798 0.03490748
127 1.14970606 1.45776798
128 -3.38378736 1.14970606
129 0.04384692 -3.38378736
130 -4.00510780 0.04384692
131 -1.09129993 -4.00510780
132 -5.09468419 -1.09129993
133 -2.64969077 -5.09468419
134 0.74377648 -2.64969077
135 3.80882160 0.74377648
136 -0.85872977 3.80882160
137 -3.76347223 -0.85872977
138 -1.18317343 -3.76347223
139 1.31270669 -1.18317343
140 2.07483332 1.31270669
141 -0.91634774 2.07483332
142 -9.92896240 -0.91634774
143 3.48782066 -9.92896240
144 0.05597293 3.48782066
145 1.52808386 0.05597293
146 1.78383774 1.52808386
147 2.02632326 1.78383774
148 -0.37337569 2.02632326
149 -0.50071758 -0.37337569
150 0.57253712 -0.50071758
151 1.36193928 0.57253712
152 3.59450697 1.36193928
153 2.31774200 3.59450697
154 -0.47631422 2.31774200
155 0.32905473 -0.47631422
156 -2.97215776 0.32905473
157 -0.32744606 -2.97215776
158 2.38620517 -0.32744606
159 0.11881238 2.38620517
160 -1.64358721 0.11881238
161 -1.43272966 -1.64358721
> 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/73cru1353168590.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/8h7j81353168590.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/901e01353168590.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/10cfeh1353168590.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1181zp1353168590.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12fsb51353168590.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13y4kx1353168590.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14eam51353168590.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/15ksl61353168590.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/16n8hk1353168590.tab")
+ }
>
> try(system("convert tmp/1amvq1353168590.ps tmp/1amvq1353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/26nth1353168590.ps tmp/26nth1353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/33lxc1353168590.ps tmp/33lxc1353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q1in1353168590.ps tmp/4q1in1353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/57fgq1353168590.ps tmp/57fgq1353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/6diii1353168590.ps tmp/6diii1353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/73cru1353168590.ps tmp/73cru1353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h7j81353168590.ps tmp/8h7j81353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/901e01353168590.ps tmp/901e01353168590.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cfeh1353168590.ps tmp/10cfeh1353168590.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.189 1.274 9.464