R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(-1.2
+ ,23.6
+ ,0.2
+ ,-1.9
+ ,-1.6
+ ,-4.2
+ ,-2.2
+ ,-2.4
+ ,25.7
+ ,-1.2
+ ,0.2
+ ,-1.9
+ ,-1.6
+ ,-4.2
+ ,0.8
+ ,32.5
+ ,-2.4
+ ,-1.2
+ ,0.2
+ ,-1.9
+ ,-1.6
+ ,-0.1
+ ,33.5
+ ,0.8
+ ,-2.4
+ ,-1.2
+ ,0.2
+ ,-1.9
+ ,-1.5
+ ,34.5
+ ,-0.1
+ ,0.8
+ ,-2.4
+ ,-1.2
+ ,0.2
+ ,-4.4
+ ,27.9
+ ,-1.5
+ ,-0.1
+ ,0.8
+ ,-2.4
+ ,-1.2
+ ,-4.2
+ ,45.3
+ ,-4.4
+ ,-1.5
+ ,-0.1
+ ,0.8
+ ,-2.4
+ ,3.5
+ ,40.8
+ ,-4.2
+ ,-4.4
+ ,-1.5
+ ,-0.1
+ ,0.8
+ ,10
+ ,58.5
+ ,3.5
+ ,-4.2
+ ,-4.4
+ ,-1.5
+ ,-0.1
+ ,8.6
+ ,32.5
+ ,10
+ ,3.5
+ ,-4.2
+ ,-4.4
+ ,-1.5
+ ,9.5
+ ,35.5
+ ,8.6
+ ,10
+ ,3.5
+ ,-4.2
+ ,-4.4
+ ,9.9
+ ,46.7
+ ,9.5
+ ,8.6
+ ,10
+ ,3.5
+ ,-4.2
+ ,10.4
+ ,53.2
+ ,9.9
+ ,9.5
+ ,8.6
+ ,10
+ ,3.5
+ ,16
+ ,36.1
+ ,10.4
+ ,9.9
+ ,9.5
+ ,8.6
+ ,10
+ ,12.7
+ ,54
+ ,16
+ ,10.4
+ ,9.9
+ ,9.5
+ ,8.6
+ ,10.2
+ ,58.1
+ ,12.7
+ ,16
+ ,10.4
+ ,9.9
+ ,9.5
+ ,8.9
+ ,41.8
+ ,10.2
+ ,12.7
+ ,16
+ ,10.4
+ ,9.9
+ ,12.6
+ ,43.1
+ ,8.9
+ ,10.2
+ ,12.7
+ ,16
+ ,10.4
+ ,13.6
+ ,76
+ ,12.6
+ ,8.9
+ ,10.2
+ ,12.7
+ ,16
+ ,14.8
+ ,42.8
+ ,13.6
+ ,12.6
+ ,8.9
+ ,10.2
+ ,12.7
+ ,9.5
+ ,41
+ ,14.8
+ ,13.6
+ ,12.6
+ ,8.9
+ ,10.2
+ ,13.7
+ ,61.4
+ ,9.5
+ ,14.8
+ ,13.6
+ ,12.6
+ ,8.9
+ ,17
+ ,34.2
+ ,13.7
+ ,9.5
+ ,14.8
+ ,13.6
+ ,12.6
+ ,14.7
+ ,53.8
+ ,17
+ ,13.7
+ ,9.5
+ ,14.8
+ ,13.6
+ ,17.4
+ ,80.7
+ ,14.7
+ ,17
+ ,13.7
+ ,9.5
+ ,14.8
+ ,9
+ ,79.5
+ ,17.4
+ ,14.7
+ ,17
+ ,13.7
+ ,9.5
+ ,9.1
+ ,96.5
+ ,9
+ ,17.4
+ ,14.7
+ ,17
+ ,13.7
+ ,12.2
+ ,108.3
+ ,9.1
+ ,9
+ ,17.4
+ ,14.7
+ ,17
+ ,15.9
+ ,100.1
+ ,12.2
+ ,9.1
+ ,9
+ ,17.4
+ ,14.7
+ ,12.9
+ ,108.5
+ ,15.9
+ ,12.2
+ ,9.1
+ ,9
+ ,17.4
+ ,10.9
+ ,127.4
+ ,12.9
+ ,15.9
+ ,12.2
+ ,9.1
+ ,9
+ ,10.6
+ ,86.5
+ ,10.9
+ ,12.9
+ ,15.9
+ ,12.2
+ ,9.1
+ ,13.2
+ ,71.4
+ ,10.6
+ ,10.9
+ ,12.9
+ ,15.9
+ ,12.2
+ ,9.6
+ ,88.2
+ ,13.2
+ ,10.6
+ ,10.9
+ ,12.9
+ ,15.9
+ ,6.4
+ ,135.6
+ ,9.6
+ ,13.2
+ ,10.6
+ ,10.9
+ ,12.9
+ ,5.8
+ ,70.5
+ ,6.4
+ ,9.6
+ ,13.2
+ ,10.6
+ ,10.9
+ ,-1
+ ,87.5
+ ,5.8
+ ,6.4
+ ,9.6
+ ,13.2
+ ,10.6
+ ,-0.2
+ ,73.3
+ ,-1
+ ,5.8
+ ,6.4
+ ,9.6
+ ,13.2
+ ,2.7
+ ,92.2
+ ,-0.2
+ ,-1
+ ,5.8
+ ,6.4
+ ,9.6
+ ,3.6
+ ,61.1
+ ,2.7
+ ,-0.2
+ ,-1
+ ,5.8
+ ,6.4
+ ,-0.9
+ ,45.7
+ ,3.6
+ ,2.7
+ ,-0.2
+ ,-1
+ ,5.8
+ ,0.3
+ ,30.5
+ ,-0.9
+ ,3.6
+ ,2.7
+ ,-0.2
+ ,-1
+ ,-1.1
+ ,34.8
+ ,0.3
+ ,-0.9
+ ,3.6
+ ,2.7
+ ,-0.2
+ ,-2.5
+ ,29.2
+ ,-1.1
+ ,0.3
+ ,-0.9
+ ,3.6
+ ,2.7
+ ,-3.4
+ ,56.7
+ ,-2.5
+ ,-1.1
+ ,0.3
+ ,-0.9
+ ,3.6
+ ,-3.5
+ ,67.1
+ ,-3.4
+ ,-2.5
+ ,-1.1
+ ,0.3
+ ,-0.9
+ ,-3.9
+ ,41.8
+ ,-3.5
+ ,-3.4
+ ,-2.5
+ ,-1.1
+ ,0.3
+ ,-4.6
+ ,46.8
+ ,-3.9
+ ,-3.5
+ ,-3.4
+ ,-2.5
+ ,-1.1
+ ,-0.1
+ ,50.1
+ ,-4.6
+ ,-3.9
+ ,-3.5
+ ,-3.4
+ ,-2.5
+ ,4.3
+ ,81.9
+ ,-0.1
+ ,-4.6
+ ,-3.9
+ ,-3.5
+ ,-3.4
+ ,10.2
+ ,115.8
+ ,4.3
+ ,-0.1
+ ,-4.6
+ ,-3.9
+ ,-3.5
+ ,8.7
+ ,102.5
+ ,10.2
+ ,4.3
+ ,-0.1
+ ,-4.6
+ ,-3.9
+ ,13.3
+ ,106.6
+ ,8.7
+ ,10.2
+ ,4.3
+ ,-0.1
+ ,-4.6
+ ,15
+ ,101.4
+ ,13.3
+ ,8.7
+ ,10.2
+ ,4.3
+ ,-0.1
+ ,20.7
+ ,136.1
+ ,15
+ ,13.3
+ ,8.7
+ ,10.2
+ ,4.3
+ ,20.7
+ ,143.4
+ ,20.7
+ ,15
+ ,13.3
+ ,8.7
+ ,10.2
+ ,26.4
+ ,127.5
+ ,20.7
+ ,20.7
+ ,15
+ ,13.3
+ ,8.7
+ ,31.2
+ ,113.8
+ ,26.4
+ ,20.7
+ ,20.7
+ ,15
+ ,13.3
+ ,31.4
+ ,75.3
+ ,31.2
+ ,26.4
+ ,20.7
+ ,20.7
+ ,15
+ ,26.6
+ ,98.5
+ ,31.4
+ ,31.2
+ ,26.4
+ ,20.7
+ ,20.7
+ ,26.6
+ ,113.7
+ ,26.6
+ ,31.4
+ ,31.2
+ ,26.4
+ ,20.7
+ ,19.2
+ ,103.7
+ ,26.6
+ ,26.6
+ ,31.4
+ ,31.2
+ ,26.4
+ ,6.5
+ ,73.9
+ ,19.2
+ ,26.6
+ ,26.6
+ ,31.4
+ ,31.2
+ ,3.1
+ ,52.5
+ ,6.5
+ ,19.2
+ ,26.6
+ ,26.6
+ ,31.4
+ ,-0.2
+ ,63.9
+ ,3.1
+ ,6.5
+ ,19.2
+ ,26.6
+ ,26.6
+ ,-4
+ ,44.9
+ ,-0.2
+ ,3.1
+ ,6.5
+ ,19.2
+ ,26.6
+ ,-12.6
+ ,31.3
+ ,-4
+ ,-0.2
+ ,3.1
+ ,6.5
+ ,19.2
+ ,-13
+ ,24.9
+ ,-12.6
+ ,-4
+ ,-0.2
+ ,3.1
+ ,6.5
+ ,-17.6
+ ,22.8
+ ,-13
+ ,-12.6
+ ,-4
+ ,-0.2
+ ,3.1
+ ,-21.7
+ ,24.8
+ ,-17.6
+ ,-13
+ ,-12.6
+ ,-4
+ ,-0.2
+ ,-23.2
+ ,22.8
+ ,-21.7
+ ,-17.6
+ ,-13
+ ,-12.6
+ ,-4
+ ,-16.8
+ ,20.9
+ ,-23.2
+ ,-21.7
+ ,-17.6
+ ,-13
+ ,-12.6
+ ,-19.8
+ ,21.5
+ ,-16.8
+ ,-23.2
+ ,-21.7
+ ,-17.6
+ ,-13)
+ ,dim=c(7
+ ,73)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'Y5')
+ ,1:73))
> y <- array(NA,dim=c(7,73),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4','Y5'),1:73))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 Y5 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 -1.2 23.6 0.2 -1.9 -1.6 -4.2 -2.2 1 0 0 0 0 0 0 0 0 0 0
2 -2.4 25.7 -1.2 0.2 -1.9 -1.6 -4.2 0 1 0 0 0 0 0 0 0 0 0
3 0.8 32.5 -2.4 -1.2 0.2 -1.9 -1.6 0 0 1 0 0 0 0 0 0 0 0
4 -0.1 33.5 0.8 -2.4 -1.2 0.2 -1.9 0 0 0 1 0 0 0 0 0 0 0
5 -1.5 34.5 -0.1 0.8 -2.4 -1.2 0.2 0 0 0 0 1 0 0 0 0 0 0
6 -4.4 27.9 -1.5 -0.1 0.8 -2.4 -1.2 0 0 0 0 0 1 0 0 0 0 0
7 -4.2 45.3 -4.4 -1.5 -0.1 0.8 -2.4 0 0 0 0 0 0 1 0 0 0 0
8 3.5 40.8 -4.2 -4.4 -1.5 -0.1 0.8 0 0 0 0 0 0 0 1 0 0 0
9 10.0 58.5 3.5 -4.2 -4.4 -1.5 -0.1 0 0 0 0 0 0 0 0 1 0 0
10 8.6 32.5 10.0 3.5 -4.2 -4.4 -1.5 0 0 0 0 0 0 0 0 0 1 0
11 9.5 35.5 8.6 10.0 3.5 -4.2 -4.4 0 0 0 0 0 0 0 0 0 0 1
12 9.9 46.7 9.5 8.6 10.0 3.5 -4.2 0 0 0 0 0 0 0 0 0 0 0
13 10.4 53.2 9.9 9.5 8.6 10.0 3.5 1 0 0 0 0 0 0 0 0 0 0
14 16.0 36.1 10.4 9.9 9.5 8.6 10.0 0 1 0 0 0 0 0 0 0 0 0
15 12.7 54.0 16.0 10.4 9.9 9.5 8.6 0 0 1 0 0 0 0 0 0 0 0
16 10.2 58.1 12.7 16.0 10.4 9.9 9.5 0 0 0 1 0 0 0 0 0 0 0
17 8.9 41.8 10.2 12.7 16.0 10.4 9.9 0 0 0 0 1 0 0 0 0 0 0
18 12.6 43.1 8.9 10.2 12.7 16.0 10.4 0 0 0 0 0 1 0 0 0 0 0
19 13.6 76.0 12.6 8.9 10.2 12.7 16.0 0 0 0 0 0 0 1 0 0 0 0
20 14.8 42.8 13.6 12.6 8.9 10.2 12.7 0 0 0 0 0 0 0 1 0 0 0
21 9.5 41.0 14.8 13.6 12.6 8.9 10.2 0 0 0 0 0 0 0 0 1 0 0
22 13.7 61.4 9.5 14.8 13.6 12.6 8.9 0 0 0 0 0 0 0 0 0 1 0
23 17.0 34.2 13.7 9.5 14.8 13.6 12.6 0 0 0 0 0 0 0 0 0 0 1
24 14.7 53.8 17.0 13.7 9.5 14.8 13.6 0 0 0 0 0 0 0 0 0 0 0
25 17.4 80.7 14.7 17.0 13.7 9.5 14.8 1 0 0 0 0 0 0 0 0 0 0
26 9.0 79.5 17.4 14.7 17.0 13.7 9.5 0 1 0 0 0 0 0 0 0 0 0
27 9.1 96.5 9.0 17.4 14.7 17.0 13.7 0 0 1 0 0 0 0 0 0 0 0
28 12.2 108.3 9.1 9.0 17.4 14.7 17.0 0 0 0 1 0 0 0 0 0 0 0
29 15.9 100.1 12.2 9.1 9.0 17.4 14.7 0 0 0 0 1 0 0 0 0 0 0
30 12.9 108.5 15.9 12.2 9.1 9.0 17.4 0 0 0 0 0 1 0 0 0 0 0
31 10.9 127.4 12.9 15.9 12.2 9.1 9.0 0 0 0 0 0 0 1 0 0 0 0
32 10.6 86.5 10.9 12.9 15.9 12.2 9.1 0 0 0 0 0 0 0 1 0 0 0
33 13.2 71.4 10.6 10.9 12.9 15.9 12.2 0 0 0 0 0 0 0 0 1 0 0
34 9.6 88.2 13.2 10.6 10.9 12.9 15.9 0 0 0 0 0 0 0 0 0 1 0
35 6.4 135.6 9.6 13.2 10.6 10.9 12.9 0 0 0 0 0 0 0 0 0 0 1
36 5.8 70.5 6.4 9.6 13.2 10.6 10.9 0 0 0 0 0 0 0 0 0 0 0
37 -1.0 87.5 5.8 6.4 9.6 13.2 10.6 1 0 0 0 0 0 0 0 0 0 0
38 -0.2 73.3 -1.0 5.8 6.4 9.6 13.2 0 1 0 0 0 0 0 0 0 0 0
39 2.7 92.2 -0.2 -1.0 5.8 6.4 9.6 0 0 1 0 0 0 0 0 0 0 0
40 3.6 61.1 2.7 -0.2 -1.0 5.8 6.4 0 0 0 1 0 0 0 0 0 0 0
41 -0.9 45.7 3.6 2.7 -0.2 -1.0 5.8 0 0 0 0 1 0 0 0 0 0 0
42 0.3 30.5 -0.9 3.6 2.7 -0.2 -1.0 0 0 0 0 0 1 0 0 0 0 0
43 -1.1 34.8 0.3 -0.9 3.6 2.7 -0.2 0 0 0 0 0 0 1 0 0 0 0
44 -2.5 29.2 -1.1 0.3 -0.9 3.6 2.7 0 0 0 0 0 0 0 1 0 0 0
45 -3.4 56.7 -2.5 -1.1 0.3 -0.9 3.6 0 0 0 0 0 0 0 0 1 0 0
46 -3.5 67.1 -3.4 -2.5 -1.1 0.3 -0.9 0 0 0 0 0 0 0 0 0 1 0
47 -3.9 41.8 -3.5 -3.4 -2.5 -1.1 0.3 0 0 0 0 0 0 0 0 0 0 1
48 -4.6 46.8 -3.9 -3.5 -3.4 -2.5 -1.1 0 0 0 0 0 0 0 0 0 0 0
49 -0.1 50.1 -4.6 -3.9 -3.5 -3.4 -2.5 1 0 0 0 0 0 0 0 0 0 0
50 4.3 81.9 -0.1 -4.6 -3.9 -3.5 -3.4 0 1 0 0 0 0 0 0 0 0 0
51 10.2 115.8 4.3 -0.1 -4.6 -3.9 -3.5 0 0 1 0 0 0 0 0 0 0 0
52 8.7 102.5 10.2 4.3 -0.1 -4.6 -3.9 0 0 0 1 0 0 0 0 0 0 0
53 13.3 106.6 8.7 10.2 4.3 -0.1 -4.6 0 0 0 0 1 0 0 0 0 0 0
54 15.0 101.4 13.3 8.7 10.2 4.3 -0.1 0 0 0 0 0 1 0 0 0 0 0
55 20.7 136.1 15.0 13.3 8.7 10.2 4.3 0 0 0 0 0 0 1 0 0 0 0
56 20.7 143.4 20.7 15.0 13.3 8.7 10.2 0 0 0 0 0 0 0 1 0 0 0
57 26.4 127.5 20.7 20.7 15.0 13.3 8.7 0 0 0 0 0 0 0 0 1 0 0
58 31.2 113.8 26.4 20.7 20.7 15.0 13.3 0 0 0 0 0 0 0 0 0 1 0
59 31.4 75.3 31.2 26.4 20.7 20.7 15.0 0 0 0 0 0 0 0 0 0 0 1
60 26.6 98.5 31.4 31.2 26.4 20.7 20.7 0 0 0 0 0 0 0 0 0 0 0
61 26.6 113.7 26.6 31.4 31.2 26.4 20.7 1 0 0 0 0 0 0 0 0 0 0
62 19.2 103.7 26.6 26.6 31.4 31.2 26.4 0 1 0 0 0 0 0 0 0 0 0
63 6.5 73.9 19.2 26.6 26.6 31.4 31.2 0 0 1 0 0 0 0 0 0 0 0
64 3.1 52.5 6.5 19.2 26.6 26.6 31.4 0 0 0 1 0 0 0 0 0 0 0
65 -0.2 63.9 3.1 6.5 19.2 26.6 26.6 0 0 0 0 1 0 0 0 0 0 0
66 -4.0 44.9 -0.2 3.1 6.5 19.2 26.6 0 0 0 0 0 1 0 0 0 0 0
67 -12.6 31.3 -4.0 -0.2 3.1 6.5 19.2 0 0 0 0 0 0 1 0 0 0 0
68 -13.0 24.9 -12.6 -4.0 -0.2 3.1 6.5 0 0 0 0 0 0 0 1 0 0 0
69 -17.6 22.8 -13.0 -12.6 -4.0 -0.2 3.1 0 0 0 0 0 0 0 0 1 0 0
70 -21.7 24.8 -17.6 -13.0 -12.6 -4.0 -0.2 0 0 0 0 0 0 0 0 0 1 0
71 -23.2 22.8 -21.7 -17.6 -13.0 -12.6 -4.0 0 0 0 0 0 0 0 0 0 0 1
72 -16.8 20.9 -23.2 -21.7 -17.6 -13.0 -12.6 0 0 0 0 0 0 0 0 0 0 0
73 -19.8 21.5 -16.8 -23.2 -21.7 -17.6 -13.0 1 0 0 0 0 0 0 0 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
-0.322809 0.051806 0.975171 -0.116024 0.008858 0.247107
Y5 M1 M2 M3 M4 M5
-0.332969 -0.688673 -1.091916 -0.908140 -0.371569 -0.164834
M6 M7 M8 M9 M10 M11
0.185268 -1.151771 1.479240 0.755289 0.176543 0.611098
t
-0.045575
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.1541 -2.0720 -0.6312 2.6039 8.3099
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.322809 1.850837 -0.174 0.8622
X 0.051806 0.019755 2.622 0.0113 *
Y1 0.975171 0.129620 7.523 5.83e-10 ***
Y2 -0.116024 0.188871 -0.614 0.5416
Y3 0.008858 0.191860 0.046 0.9633
Y4 0.247107 0.190309 1.298 0.1997
Y5 -0.332969 0.132367 -2.516 0.0149 *
M1 -0.688673 2.086495 -0.330 0.7426
M2 -1.091916 2.193078 -0.498 0.6206
M3 -0.908140 2.253233 -0.403 0.6885
M4 -0.371569 2.226363 -0.167 0.8681
M5 -0.164834 2.203134 -0.075 0.9406
M6 0.185268 2.192780 0.084 0.9330
M7 -1.151771 2.232651 -0.516 0.6080
M8 1.479240 2.182647 0.678 0.5008
M9 0.755289 2.181834 0.346 0.7306
M10 0.176543 2.183237 0.081 0.9358
M11 0.611098 2.165320 0.282 0.7789
t -0.045575 0.025781 -1.768 0.0828 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.718 on 54 degrees of freedom
Multiple R-squared: 0.9243, Adjusted R-squared: 0.899
F-statistic: 36.62 on 18 and 54 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,] 0.4128187 0.8256374 0.5871813
[2,] 0.5890035 0.8219930 0.4109965
[3,] 0.4727718 0.9455436 0.5272282
[4,] 0.4056864 0.8113729 0.5943136
[5,] 0.6826569 0.6346862 0.3173431
[6,] 0.5846655 0.8306690 0.4153345
[7,] 0.4886194 0.9772387 0.5113806
[8,] 0.4916989 0.9833978 0.5083011
[9,] 0.4109001 0.8218002 0.5890999
[10,] 0.3744093 0.7488186 0.6255907
[11,] 0.2854089 0.5708179 0.7145911
[12,] 0.2651553 0.5303107 0.7348447
[13,] 0.2631747 0.5263495 0.7368253
[14,] 0.4857648 0.9715295 0.5142352
[15,] 0.4057919 0.8115838 0.5942081
[16,] 0.5926170 0.8147659 0.4073830
[17,] 0.5478725 0.9042550 0.4521275
[18,] 0.5556259 0.8887482 0.4443741
[19,] 0.5071973 0.9856055 0.4928027
[20,] 0.4077720 0.8155441 0.5922280
[21,] 0.3665903 0.7331807 0.6334097
[22,] 0.2745262 0.5490523 0.7254738
[23,] 0.2080078 0.4160156 0.7919922
[24,] 0.1410025 0.2820050 0.8589975
[25,] 0.1191228 0.2382455 0.8808772
[26,] 0.0922579 0.1845158 0.9077421
[27,] 0.1246828 0.2493655 0.8753172
[28,] 0.1447455 0.2894909 0.8552545
[29,] 0.1073833 0.2147665 0.8926167
[30,] 0.2378730 0.4757460 0.7621270
> postscript(file="/var/www/html/rcomp/tmp/1odto1261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2t43g1261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3a4c31261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a0qq1261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50edm1261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 73
Frequency = 1
1 2 3 4 5 6
-1.46155813 -2.01840280 2.62013639 -2.68885396 -1.99707458 -3.79683898
7 8 9 10 11 12
-1.63242235 4.48406528 3.42298079 -1.20228117 0.18947528 -2.26787385
13 14 15 16 17 18
-0.68593939 8.30986496 -2.15070393 -1.28990762 0.10844759 3.22615439
19 20 21 22 23 24
2.84761078 2.16674142 -3.86862198 3.85047284 4.43422704 -0.87187168
25 26 27 28 29 30
5.46658709 -8.15406149 0.03512474 2.60392317 2.19753977 -1.81678727
31 32 33 34 35 36
-2.90762167 -2.83742645 1.51936084 -2.90588926 -5.64024041 -0.12296662
37 38 39 40 41 42
-7.56607528 2.76358508 2.57451580 1.00251361 -1.92855818 1.75949198
43 44 45 46 47 48
-0.63117884 -2.03895507 -0.99027025 -2.07200211 -0.79927632 -0.83540349
49 50 51 52 53 54
4.62121951 3.08169906 3.39037562 -3.15467460 1.83502328 -0.80110279
55 56 57 58 59 60
3.38018288 -2.65023756 3.65314003 5.28981835 2.23343447 -0.90248067
61 62 63 64 65 66
2.29731028 -3.98268481 -6.46944861 3.52699940 -0.21537788 1.42908267
67 68 69 70 71 72
-1.05657081 0.87581238 -3.73658942 -2.96011865 -0.41762005 5.00059630
73
-2.67154408
> postscript(file="/var/www/html/rcomp/tmp/6u1i81261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.46155813 NA
1 -2.01840280 -1.46155813
2 2.62013639 -2.01840280
3 -2.68885396 2.62013639
4 -1.99707458 -2.68885396
5 -3.79683898 -1.99707458
6 -1.63242235 -3.79683898
7 4.48406528 -1.63242235
8 3.42298079 4.48406528
9 -1.20228117 3.42298079
10 0.18947528 -1.20228117
11 -2.26787385 0.18947528
12 -0.68593939 -2.26787385
13 8.30986496 -0.68593939
14 -2.15070393 8.30986496
15 -1.28990762 -2.15070393
16 0.10844759 -1.28990762
17 3.22615439 0.10844759
18 2.84761078 3.22615439
19 2.16674142 2.84761078
20 -3.86862198 2.16674142
21 3.85047284 -3.86862198
22 4.43422704 3.85047284
23 -0.87187168 4.43422704
24 5.46658709 -0.87187168
25 -8.15406149 5.46658709
26 0.03512474 -8.15406149
27 2.60392317 0.03512474
28 2.19753977 2.60392317
29 -1.81678727 2.19753977
30 -2.90762167 -1.81678727
31 -2.83742645 -2.90762167
32 1.51936084 -2.83742645
33 -2.90588926 1.51936084
34 -5.64024041 -2.90588926
35 -0.12296662 -5.64024041
36 -7.56607528 -0.12296662
37 2.76358508 -7.56607528
38 2.57451580 2.76358508
39 1.00251361 2.57451580
40 -1.92855818 1.00251361
41 1.75949198 -1.92855818
42 -0.63117884 1.75949198
43 -2.03895507 -0.63117884
44 -0.99027025 -2.03895507
45 -2.07200211 -0.99027025
46 -0.79927632 -2.07200211
47 -0.83540349 -0.79927632
48 4.62121951 -0.83540349
49 3.08169906 4.62121951
50 3.39037562 3.08169906
51 -3.15467460 3.39037562
52 1.83502328 -3.15467460
53 -0.80110279 1.83502328
54 3.38018288 -0.80110279
55 -2.65023756 3.38018288
56 3.65314003 -2.65023756
57 5.28981835 3.65314003
58 2.23343447 5.28981835
59 -0.90248067 2.23343447
60 2.29731028 -0.90248067
61 -3.98268481 2.29731028
62 -6.46944861 -3.98268481
63 3.52699940 -6.46944861
64 -0.21537788 3.52699940
65 1.42908267 -0.21537788
66 -1.05657081 1.42908267
67 0.87581238 -1.05657081
68 -3.73658942 0.87581238
69 -2.96011865 -3.73658942
70 -0.41762005 -2.96011865
71 5.00059630 -0.41762005
72 -2.67154408 5.00059630
73 NA -2.67154408
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.01840280 -1.46155813
[2,] 2.62013639 -2.01840280
[3,] -2.68885396 2.62013639
[4,] -1.99707458 -2.68885396
[5,] -3.79683898 -1.99707458
[6,] -1.63242235 -3.79683898
[7,] 4.48406528 -1.63242235
[8,] 3.42298079 4.48406528
[9,] -1.20228117 3.42298079
[10,] 0.18947528 -1.20228117
[11,] -2.26787385 0.18947528
[12,] -0.68593939 -2.26787385
[13,] 8.30986496 -0.68593939
[14,] -2.15070393 8.30986496
[15,] -1.28990762 -2.15070393
[16,] 0.10844759 -1.28990762
[17,] 3.22615439 0.10844759
[18,] 2.84761078 3.22615439
[19,] 2.16674142 2.84761078
[20,] -3.86862198 2.16674142
[21,] 3.85047284 -3.86862198
[22,] 4.43422704 3.85047284
[23,] -0.87187168 4.43422704
[24,] 5.46658709 -0.87187168
[25,] -8.15406149 5.46658709
[26,] 0.03512474 -8.15406149
[27,] 2.60392317 0.03512474
[28,] 2.19753977 2.60392317
[29,] -1.81678727 2.19753977
[30,] -2.90762167 -1.81678727
[31,] -2.83742645 -2.90762167
[32,] 1.51936084 -2.83742645
[33,] -2.90588926 1.51936084
[34,] -5.64024041 -2.90588926
[35,] -0.12296662 -5.64024041
[36,] -7.56607528 -0.12296662
[37,] 2.76358508 -7.56607528
[38,] 2.57451580 2.76358508
[39,] 1.00251361 2.57451580
[40,] -1.92855818 1.00251361
[41,] 1.75949198 -1.92855818
[42,] -0.63117884 1.75949198
[43,] -2.03895507 -0.63117884
[44,] -0.99027025 -2.03895507
[45,] -2.07200211 -0.99027025
[46,] -0.79927632 -2.07200211
[47,] -0.83540349 -0.79927632
[48,] 4.62121951 -0.83540349
[49,] 3.08169906 4.62121951
[50,] 3.39037562 3.08169906
[51,] -3.15467460 3.39037562
[52,] 1.83502328 -3.15467460
[53,] -0.80110279 1.83502328
[54,] 3.38018288 -0.80110279
[55,] -2.65023756 3.38018288
[56,] 3.65314003 -2.65023756
[57,] 5.28981835 3.65314003
[58,] 2.23343447 5.28981835
[59,] -0.90248067 2.23343447
[60,] 2.29731028 -0.90248067
[61,] -3.98268481 2.29731028
[62,] -6.46944861 -3.98268481
[63,] 3.52699940 -6.46944861
[64,] -0.21537788 3.52699940
[65,] 1.42908267 -0.21537788
[66,] -1.05657081 1.42908267
[67,] 0.87581238 -1.05657081
[68,] -3.73658942 0.87581238
[69,] -2.96011865 -3.73658942
[70,] -0.41762005 -2.96011865
[71,] 5.00059630 -0.41762005
[72,] -2.67154408 5.00059630
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.01840280 -1.46155813
2 2.62013639 -2.01840280
3 -2.68885396 2.62013639
4 -1.99707458 -2.68885396
5 -3.79683898 -1.99707458
6 -1.63242235 -3.79683898
7 4.48406528 -1.63242235
8 3.42298079 4.48406528
9 -1.20228117 3.42298079
10 0.18947528 -1.20228117
11 -2.26787385 0.18947528
12 -0.68593939 -2.26787385
13 8.30986496 -0.68593939
14 -2.15070393 8.30986496
15 -1.28990762 -2.15070393
16 0.10844759 -1.28990762
17 3.22615439 0.10844759
18 2.84761078 3.22615439
19 2.16674142 2.84761078
20 -3.86862198 2.16674142
21 3.85047284 -3.86862198
22 4.43422704 3.85047284
23 -0.87187168 4.43422704
24 5.46658709 -0.87187168
25 -8.15406149 5.46658709
26 0.03512474 -8.15406149
27 2.60392317 0.03512474
28 2.19753977 2.60392317
29 -1.81678727 2.19753977
30 -2.90762167 -1.81678727
31 -2.83742645 -2.90762167
32 1.51936084 -2.83742645
33 -2.90588926 1.51936084
34 -5.64024041 -2.90588926
35 -0.12296662 -5.64024041
36 -7.56607528 -0.12296662
37 2.76358508 -7.56607528
38 2.57451580 2.76358508
39 1.00251361 2.57451580
40 -1.92855818 1.00251361
41 1.75949198 -1.92855818
42 -0.63117884 1.75949198
43 -2.03895507 -0.63117884
44 -0.99027025 -2.03895507
45 -2.07200211 -0.99027025
46 -0.79927632 -2.07200211
47 -0.83540349 -0.79927632
48 4.62121951 -0.83540349
49 3.08169906 4.62121951
50 3.39037562 3.08169906
51 -3.15467460 3.39037562
52 1.83502328 -3.15467460
53 -0.80110279 1.83502328
54 3.38018288 -0.80110279
55 -2.65023756 3.38018288
56 3.65314003 -2.65023756
57 5.28981835 3.65314003
58 2.23343447 5.28981835
59 -0.90248067 2.23343447
60 2.29731028 -0.90248067
61 -3.98268481 2.29731028
62 -6.46944861 -3.98268481
63 3.52699940 -6.46944861
64 -0.21537788 3.52699940
65 1.42908267 -0.21537788
66 -1.05657081 1.42908267
67 0.87581238 -1.05657081
68 -3.73658942 0.87581238
69 -2.96011865 -3.73658942
70 -0.41762005 -2.96011865
71 5.00059630 -0.41762005
72 -2.67154408 5.00059630
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7rejt1261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85yxx1261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9pvuh1261320555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10329b1261320556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/113x9a1261320556.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12j36x1261320556.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13aiap1261320556.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14ql8j1261320556.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15e66x1261320556.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/165vll1261320556.tab")
+ }
>
> try(system("convert tmp/1odto1261320555.ps tmp/1odto1261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t43g1261320555.ps tmp/2t43g1261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a4c31261320555.ps tmp/3a4c31261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a0qq1261320555.ps tmp/4a0qq1261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/50edm1261320555.ps tmp/50edm1261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/6u1i81261320555.ps tmp/6u1i81261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rejt1261320555.ps tmp/7rejt1261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/85yxx1261320555.ps tmp/85yxx1261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pvuh1261320555.ps tmp/9pvuh1261320555.png",intern=TRUE))
character(0)
> try(system("convert tmp/10329b1261320556.ps tmp/10329b1261320556.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.459 1.565 3.240