R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0
+ ,41
+ ,25
+ ,0
+ ,15
+ ,0
+ ,9
+ ,0
+ ,3
+ ,0
+ ,0
+ ,38
+ ,25
+ ,0
+ ,15
+ ,0
+ ,9
+ ,0
+ ,4
+ ,0
+ ,0
+ ,37
+ ,19
+ ,0
+ ,14
+ ,0
+ ,9
+ ,0
+ ,4
+ ,0
+ ,1
+ ,36
+ ,18
+ ,18
+ ,10
+ ,10
+ ,14
+ ,14
+ ,2
+ ,2
+ ,1
+ ,42
+ ,18
+ ,18
+ ,10
+ ,10
+ ,8
+ ,8
+ ,4
+ ,4
+ ,0
+ ,44
+ ,23
+ ,0
+ ,9
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,0
+ ,40
+ ,23
+ ,0
+ ,18
+ ,0
+ ,15
+ ,0
+ ,3
+ ,0
+ ,0
+ ,43
+ ,25
+ ,0
+ ,14
+ ,0
+ ,9
+ ,0
+ ,4
+ ,0
+ ,0
+ ,40
+ ,23
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,0
+ ,45
+ ,24
+ ,0
+ ,11
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,1
+ ,47
+ ,32
+ ,32
+ ,9
+ ,9
+ ,14
+ ,14
+ ,4
+ ,4
+ ,0
+ ,45
+ ,30
+ ,0
+ ,17
+ ,0
+ ,6
+ ,0
+ ,5
+ ,0
+ ,0
+ ,45
+ ,32
+ ,0
+ ,21
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,1
+ ,40
+ ,24
+ ,24
+ ,16
+ ,16
+ ,9
+ ,9
+ ,4
+ ,4
+ ,0
+ ,49
+ ,17
+ ,0
+ ,14
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,1
+ ,48
+ ,30
+ ,30
+ ,24
+ ,24
+ ,8
+ ,8
+ ,5
+ ,5
+ ,0
+ ,44
+ ,25
+ ,0
+ ,7
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,1
+ ,29
+ ,25
+ ,25
+ ,9
+ ,9
+ ,10
+ ,10
+ ,4
+ ,4
+ ,0
+ ,42
+ ,26
+ ,0
+ ,18
+ ,0
+ ,16
+ ,0
+ ,4
+ ,0
+ ,0
+ ,45
+ ,23
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,5
+ ,0
+ ,1
+ ,32
+ ,25
+ ,25
+ ,13
+ ,13
+ ,11
+ ,11
+ ,5
+ ,5
+ ,1
+ ,32
+ ,25
+ ,25
+ ,13
+ ,13
+ ,11
+ ,11
+ ,5
+ ,5
+ ,0
+ ,41
+ ,35
+ ,0
+ ,18
+ ,0
+ ,7
+ ,0
+ ,4
+ ,0
+ ,1
+ ,29
+ ,19
+ ,19
+ ,14
+ ,14
+ ,13
+ ,13
+ ,2
+ ,2
+ ,0
+ ,38
+ ,20
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,0
+ ,41
+ ,21
+ ,0
+ ,12
+ ,0
+ ,9
+ ,0
+ ,4
+ ,0
+ ,1
+ ,38
+ ,21
+ ,21
+ ,9
+ ,9
+ ,9
+ ,9
+ ,4
+ ,4
+ ,1
+ ,24
+ ,23
+ ,23
+ ,11
+ ,11
+ ,15
+ ,15
+ ,3
+ ,3
+ ,1
+ ,34
+ ,24
+ ,24
+ ,8
+ ,8
+ ,13
+ ,13
+ ,2
+ ,2
+ ,0
+ ,38
+ ,23
+ ,0
+ ,5
+ ,0
+ ,16
+ ,0
+ ,2
+ ,0
+ ,1
+ ,37
+ ,19
+ ,19
+ ,10
+ ,10
+ ,12
+ ,12
+ ,3
+ ,3
+ ,0
+ ,46
+ ,17
+ ,0
+ ,11
+ ,0
+ ,6
+ ,0
+ ,5
+ ,0
+ ,1
+ ,48
+ ,27
+ ,27
+ ,15
+ ,15
+ ,4
+ ,4
+ ,5
+ ,5
+ ,0
+ ,42
+ ,27
+ ,0
+ ,16
+ ,0
+ ,12
+ ,0
+ ,4
+ ,0
+ ,0
+ ,46
+ ,25
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,1
+ ,43
+ ,18
+ ,18
+ ,14
+ ,14
+ ,14
+ ,14
+ ,5
+ ,5
+ ,0
+ ,38
+ ,22
+ ,0
+ ,13
+ ,0
+ ,9
+ ,0
+ ,4
+ ,0
+ ,1
+ ,39
+ ,26
+ ,26
+ ,10
+ ,10
+ ,10
+ ,10
+ ,4
+ ,4
+ ,0
+ ,34
+ ,26
+ ,0
+ ,18
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,0
+ ,39
+ ,23
+ ,0
+ ,17
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,0
+ ,35
+ ,16
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,2
+ ,0
+ ,1
+ ,41
+ ,27
+ ,27
+ ,13
+ ,13
+ ,9
+ ,9
+ ,3
+ ,3
+ ,1
+ ,40
+ ,25
+ ,25
+ ,13
+ ,13
+ ,14
+ ,14
+ ,3
+ ,3
+ ,1
+ ,43
+ ,14
+ ,14
+ ,11
+ ,11
+ ,8
+ ,8
+ ,4
+ ,4
+ ,0
+ ,37
+ ,19
+ ,0
+ ,13
+ ,0
+ ,9
+ ,0
+ ,2
+ ,0
+ ,0
+ ,41
+ ,20
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,0
+ ,46
+ ,26
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,1
+ ,26
+ ,16
+ ,16
+ ,12
+ ,12
+ ,9
+ ,9
+ ,3
+ ,3
+ ,1
+ ,41
+ ,18
+ ,18
+ ,12
+ ,12
+ ,9
+ ,9
+ ,3
+ ,3
+ ,0
+ ,37
+ ,22
+ ,0
+ ,9
+ ,0
+ ,9
+ ,0
+ ,3
+ ,0
+ ,1
+ ,39
+ ,25
+ ,25
+ ,17
+ ,17
+ ,9
+ ,9
+ ,4
+ ,4
+ ,0
+ ,44
+ ,29
+ ,0
+ ,18
+ ,0
+ ,11
+ ,0
+ ,5
+ ,0
+ ,0
+ ,39
+ ,21
+ ,0
+ ,7
+ ,0
+ ,15
+ ,0
+ ,2
+ ,0
+ ,1
+ ,36
+ ,22
+ ,22
+ ,17
+ ,17
+ ,8
+ ,8
+ ,4
+ ,4
+ ,0
+ ,38
+ ,22
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,2
+ ,0
+ ,1
+ ,38
+ ,32
+ ,32
+ ,12
+ ,12
+ ,8
+ ,8
+ ,0
+ ,0
+ ,0
+ ,38
+ ,23
+ ,0
+ ,9
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,1
+ ,32
+ ,31
+ ,31
+ ,9
+ ,9
+ ,11
+ ,11
+ ,4
+ ,4
+ ,1
+ ,33
+ ,18
+ ,18
+ ,13
+ ,13
+ ,10
+ ,10
+ ,3
+ ,3
+ ,1
+ ,46
+ ,23
+ ,23
+ ,10
+ ,10
+ ,12
+ ,12
+ ,4
+ ,4
+ ,0
+ ,42
+ ,24
+ ,0
+ ,12
+ ,0
+ ,9
+ ,0
+ ,4
+ ,0
+ ,0
+ ,42
+ ,19
+ ,0
+ ,10
+ ,0
+ ,13
+ ,0
+ ,2
+ ,0
+ ,0
+ ,43
+ ,26
+ ,0
+ ,11
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,1
+ ,41
+ ,14
+ ,14
+ ,13
+ ,13
+ ,15
+ ,15
+ ,2
+ ,2
+ ,0
+ ,49
+ ,20
+ ,0
+ ,6
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,1
+ ,45
+ ,22
+ ,22
+ ,7
+ ,7
+ ,7
+ ,7
+ ,3
+ ,3
+ ,0
+ ,39
+ ,24
+ ,0
+ ,13
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,1
+ ,45
+ ,25
+ ,25
+ ,11
+ ,11
+ ,10
+ ,10
+ ,5
+ ,5
+ ,0
+ ,31
+ ,21
+ ,0
+ ,18
+ ,0
+ ,13
+ ,0
+ ,3
+ ,0
+ ,0
+ ,30
+ ,21
+ ,0
+ ,18
+ ,0
+ ,13
+ ,0
+ ,3
+ ,0
+ ,0
+ ,45
+ ,28
+ ,0
+ ,9
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,0
+ ,48
+ ,24
+ ,0
+ ,9
+ ,0
+ ,8
+ ,0
+ ,5
+ ,0
+ ,0
+ ,28
+ ,15
+ ,0
+ ,12
+ ,0
+ ,14
+ ,0
+ ,4
+ ,0
+ ,0
+ ,35
+ ,21
+ ,0
+ ,11
+ ,0
+ ,9
+ ,0
+ ,2
+ ,0
+ ,0
+ ,38
+ ,23
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,1
+ ,39
+ ,24
+ ,24
+ ,11
+ ,11
+ ,11
+ ,11
+ ,4
+ ,4
+ ,1
+ ,40
+ ,21
+ ,21
+ ,14
+ ,14
+ ,10
+ ,10
+ ,4
+ ,4
+ ,1
+ ,38
+ ,21
+ ,21
+ ,14
+ ,14
+ ,16
+ ,16
+ ,4
+ ,4
+ ,0
+ ,42
+ ,13
+ ,0
+ ,8
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,0
+ ,36
+ ,17
+ ,0
+ ,12
+ ,0
+ ,16
+ ,0
+ ,2
+ ,0
+ ,0
+ ,49
+ ,29
+ ,0
+ ,8
+ ,0
+ ,6
+ ,0
+ ,5
+ ,0
+ ,0
+ ,41
+ ,25
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,0
+ ,18
+ ,16
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,2
+ ,0
+ ,0
+ ,36
+ ,20
+ ,0
+ ,11
+ ,0
+ ,12
+ ,0
+ ,3
+ ,0
+ ,1
+ ,42
+ ,25
+ ,25
+ ,17
+ ,17
+ ,14
+ ,14
+ ,3
+ ,3
+ ,1
+ ,41
+ ,25
+ ,25
+ ,16
+ ,16
+ ,9
+ ,9
+ ,5
+ ,5
+ ,0
+ ,43
+ ,21
+ ,0
+ ,13
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,0
+ ,46
+ ,23
+ ,0
+ ,15
+ ,0
+ ,8
+ ,0
+ ,3
+ ,0
+ ,1
+ ,37
+ ,22
+ ,22
+ ,11
+ ,11
+ ,8
+ ,8
+ ,4
+ ,4
+ ,1
+ ,38
+ ,19
+ ,19
+ ,12
+ ,12
+ ,7
+ ,7
+ ,3
+ ,3
+ ,0
+ ,43
+ ,26
+ ,0
+ ,20
+ ,0
+ ,13
+ ,0
+ ,4
+ ,0
+ ,0
+ ,41
+ ,25
+ ,0
+ ,16
+ ,0
+ ,8
+ ,0
+ ,5
+ ,0
+ ,1
+ ,35
+ ,19
+ ,19
+ ,8
+ ,8
+ ,20
+ ,20
+ ,2
+ ,2
+ ,0
+ ,39
+ ,25
+ ,0
+ ,7
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,0
+ ,42
+ ,24
+ ,0
+ ,16
+ ,0
+ ,16
+ ,0
+ ,4
+ ,0
+ ,0
+ ,36
+ ,20
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,1
+ ,35
+ ,21
+ ,21
+ ,13
+ ,13
+ ,12
+ ,12
+ ,5
+ ,5
+ ,0
+ ,33
+ ,14
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,2
+ ,0
+ ,0
+ ,36
+ ,22
+ ,0
+ ,15
+ ,0
+ ,14
+ ,0
+ ,3
+ ,0
+ ,0
+ ,48
+ ,14
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,0
+ ,41
+ ,20
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,1
+ ,47
+ ,21
+ ,21
+ ,24
+ ,24
+ ,14
+ ,14
+ ,3
+ ,3
+ ,0
+ ,41
+ ,22
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,3
+ ,0
+ ,1
+ ,31
+ ,19
+ ,19
+ ,8
+ ,8
+ ,5
+ ,5
+ ,5
+ ,5
+ ,1
+ ,36
+ ,28
+ ,28
+ ,18
+ ,18
+ ,12
+ ,12
+ ,4
+ ,4
+ ,1
+ ,46
+ ,25
+ ,25
+ ,17
+ ,17
+ ,9
+ ,9
+ ,4
+ ,4
+ ,1
+ ,39
+ ,17
+ ,17
+ ,12
+ ,12
+ ,16
+ ,16
+ ,4
+ ,4
+ ,0
+ ,44
+ ,21
+ ,0
+ ,15
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,1
+ ,43
+ ,27
+ ,27
+ ,11
+ ,11
+ ,16
+ ,16
+ ,2
+ ,2
+ ,0
+ ,32
+ ,29
+ ,0
+ ,12
+ ,0
+ ,12
+ ,0
+ ,4
+ ,0
+ ,1
+ ,40
+ ,19
+ ,19
+ ,12
+ ,12
+ ,13
+ ,13
+ ,5
+ ,5
+ ,0
+ ,40
+ ,20
+ ,0
+ ,14
+ ,0
+ ,8
+ ,0
+ ,3
+ ,0
+ ,0
+ ,46
+ ,17
+ ,0
+ ,11
+ ,0
+ ,14
+ ,0
+ ,3
+ ,0
+ ,0
+ ,45
+ ,21
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,3
+ ,0
+ ,0
+ ,39
+ ,22
+ ,0
+ ,10
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,0
+ ,44
+ ,26
+ ,0
+ ,11
+ ,0
+ ,7
+ ,0
+ ,4
+ ,0
+ ,0
+ ,35
+ ,19
+ ,0
+ ,11
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,0
+ ,38
+ ,17
+ ,0
+ ,9
+ ,0
+ ,11
+ ,0
+ ,3
+ ,0
+ ,0
+ ,38
+ ,17
+ ,0
+ ,12
+ ,0
+ ,11
+ ,0
+ ,2
+ ,0
+ ,1
+ ,36
+ ,19
+ ,19
+ ,8
+ ,8
+ ,14
+ ,14
+ ,3
+ ,3
+ ,0
+ ,42
+ ,17
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,3
+ ,0
+ ,0
+ ,39
+ ,15
+ ,0
+ ,6
+ ,0
+ ,6
+ ,0
+ ,4
+ ,0
+ ,1
+ ,41
+ ,27
+ ,27
+ ,15
+ ,15
+ ,9
+ ,9
+ ,5
+ ,5
+ ,0
+ ,41
+ ,19
+ ,0
+ ,13
+ ,0
+ ,12
+ ,0
+ ,4
+ ,0
+ ,0
+ ,47
+ ,21
+ ,0
+ ,17
+ ,0
+ ,11
+ ,0
+ ,3
+ ,0
+ ,0
+ ,39
+ ,25
+ ,0
+ ,14
+ ,0
+ ,14
+ ,0
+ ,3
+ ,0
+ ,1
+ ,40
+ ,19
+ ,19
+ ,16
+ ,16
+ ,12
+ ,12
+ ,4
+ ,4
+ ,0
+ ,44
+ ,18
+ ,0
+ ,16
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,0
+ ,42
+ ,15
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,1
+ ,35
+ ,20
+ ,20
+ ,16
+ ,16
+ ,11
+ ,11
+ ,3
+ ,3
+ ,0
+ ,46
+ ,29
+ ,0
+ ,15
+ ,0
+ ,12
+ ,0
+ ,5
+ ,0
+ ,1
+ ,43
+ ,20
+ ,20
+ ,11
+ ,11
+ ,14
+ ,14
+ ,3
+ ,3
+ ,1
+ ,40
+ ,29
+ ,29
+ ,9
+ ,9
+ ,16
+ ,16
+ ,4
+ ,4
+ ,0
+ ,44
+ ,24
+ ,0
+ ,12
+ ,0
+ ,13
+ ,0
+ ,4
+ ,0
+ ,1
+ ,37
+ ,24
+ ,24
+ ,13
+ ,13
+ ,11
+ ,11
+ ,4
+ ,4
+ ,1
+ ,46
+ ,23
+ ,23
+ ,11
+ ,11
+ ,9
+ ,9
+ ,4
+ ,4
+ ,0
+ ,44
+ ,23
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,5
+ ,0
+ ,0
+ ,35
+ ,19
+ ,0
+ ,13
+ ,0
+ ,9
+ ,0
+ ,3
+ ,0
+ ,1
+ ,39
+ ,22
+ ,22
+ ,14
+ ,14
+ ,12
+ ,12
+ ,2
+ ,2
+ ,0
+ ,40
+ ,22
+ ,0
+ ,12
+ ,0
+ ,13
+ ,0
+ ,3
+ ,0
+ ,1
+ ,42
+ ,25
+ ,25
+ ,17
+ ,17
+ ,14
+ ,14
+ ,3
+ ,3
+ ,1
+ ,37
+ ,21
+ ,21
+ ,11
+ ,11
+ ,9
+ ,9
+ ,3
+ ,3
+ ,1
+ ,29
+ ,22
+ ,22
+ ,15
+ ,15
+ ,14
+ ,14
+ ,4
+ ,4
+ ,1
+ ,33
+ ,21
+ ,21
+ ,13
+ ,13
+ ,8
+ ,8
+ ,2
+ ,2
+ ,1
+ ,35
+ ,18
+ ,18
+ ,9
+ ,9
+ ,8
+ ,8
+ ,4
+ ,4
+ ,1
+ ,42
+ ,10
+ ,10
+ ,12
+ ,12
+ ,9
+ ,9
+ ,2
+ ,2)
+ ,dim=c(10
+ ,146)
+ ,dimnames=list(c('G'
+ ,'Career'
+ ,'PersonalStandards'
+ ,'PeG'
+ ,'ParentalExpectations'
+ ,'PaG'
+ ,'Doubts'
+ ,'DoG'
+ ,'LeadershipPreference'
+ ,'LeaderG')
+ ,1:146))
> y <- array(NA,dim=c(10,146),dimnames=list(c('G','Career','PersonalStandards','PeG','ParentalExpectations','PaG','Doubts','DoG','LeadershipPreference','LeaderG'),1:146))
> 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 = '2'
> #'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
Career G PersonalStandards PeG ParentalExpectations PaG Doubts DoG
1 41 0 25 0 15 0 9 0
2 38 0 25 0 15 0 9 0
3 37 0 19 0 14 0 9 0
4 36 1 18 18 10 10 14 14
5 42 1 18 18 10 10 8 8
6 44 0 23 0 9 0 14 0
7 40 0 23 0 18 0 15 0
8 43 0 25 0 14 0 9 0
9 40 0 23 0 11 0 11 0
10 45 0 24 0 11 0 14 0
11 47 1 32 32 9 9 14 14
12 45 0 30 0 17 0 6 0
13 45 0 32 0 21 0 10 0
14 40 1 24 24 16 16 9 9
15 49 0 17 0 14 0 14 0
16 48 1 30 30 24 24 8 8
17 44 0 25 0 7 0 11 0
18 29 1 25 25 9 9 10 10
19 42 0 26 0 18 0 16 0
20 45 0 23 0 11 0 11 0
21 32 1 25 25 13 13 11 11
22 32 1 25 25 13 13 11 11
23 41 0 35 0 18 0 7 0
24 29 1 19 19 14 14 13 13
25 38 0 20 0 12 0 10 0
26 41 0 21 0 12 0 9 0
27 38 1 21 21 9 9 9 9
28 24 1 23 23 11 11 15 15
29 34 1 24 24 8 8 13 13
30 38 0 23 0 5 0 16 0
31 37 1 19 19 10 10 12 12
32 46 0 17 0 11 0 6 0
33 48 1 27 27 15 15 4 4
34 42 0 27 0 16 0 12 0
35 46 0 25 0 12 0 10 0
36 43 1 18 18 14 14 14 14
37 38 0 22 0 13 0 9 0
38 39 1 26 26 10 10 10 10
39 34 0 26 0 18 0 14 0
40 39 0 23 0 17 0 14 0
41 35 0 16 0 12 0 10 0
42 41 1 27 27 13 13 9 9
43 40 1 25 25 13 13 14 14
44 43 1 14 14 11 11 8 8
45 37 0 19 0 13 0 9 0
46 41 0 20 0 12 0 8 0
47 46 0 26 0 12 0 10 0
48 26 1 16 16 12 12 9 9
49 41 1 18 18 12 12 9 9
50 37 0 22 0 9 0 9 0
51 39 1 25 25 17 17 9 9
52 44 0 29 0 18 0 11 0
53 39 0 21 0 7 0 15 0
54 36 1 22 22 17 17 8 8
55 38 0 22 0 12 0 10 0
56 38 1 32 32 12 12 8 8
57 38 0 23 0 9 0 14 0
58 32 1 31 31 9 9 11 11
59 33 1 18 18 13 13 10 10
60 46 1 23 23 10 10 12 12
61 42 0 24 0 12 0 9 0
62 42 0 19 0 10 0 13 0
63 43 0 26 0 11 0 14 0
64 41 1 14 14 13 13 15 15
65 49 0 20 0 6 0 8 0
66 45 1 22 22 7 7 7 7
67 39 0 24 0 13 0 10 0
68 45 1 25 25 11 11 10 10
69 31 0 21 0 18 0 13 0
70 30 0 21 0 18 0 13 0
71 45 0 28 0 9 0 11 0
72 48 0 24 0 9 0 8 0
73 28 0 15 0 12 0 14 0
74 35 0 21 0 11 0 9 0
75 38 0 23 0 15 0 10 0
76 39 1 24 24 11 11 11 11
77 40 1 21 21 14 14 10 10
78 38 1 21 21 14 14 16 16
79 42 0 13 0 8 0 11 0
80 36 0 17 0 12 0 16 0
81 49 0 29 0 8 0 6 0
82 41 0 25 0 11 0 11 0
83 18 0 16 0 10 0 12 0
84 36 0 20 0 11 0 12 0
85 42 1 25 25 17 17 14 14
86 41 1 25 25 16 16 9 9
87 43 0 21 0 13 0 11 0
88 46 0 23 0 15 0 8 0
89 37 1 22 22 11 11 8 8
90 38 1 19 19 12 12 7 7
91 43 0 26 0 20 0 13 0
92 41 0 25 0 16 0 8 0
93 35 1 19 19 8 8 20 20
94 39 0 25 0 7 0 11 0
95 42 0 24 0 16 0 16 0
96 36 0 20 0 11 0 11 0
97 35 1 21 21 13 13 12 12
98 33 0 14 0 15 0 10 0
99 36 0 22 0 15 0 14 0
100 48 0 14 0 12 0 8 0
101 41 0 20 0 12 0 10 0
102 47 1 21 21 24 24 14 14
103 41 0 22 0 15 0 10 0
104 31 1 19 19 8 8 5 5
105 36 1 28 28 18 18 12 12
106 46 1 25 25 17 17 9 9
107 39 1 17 17 12 12 16 16
108 44 0 21 0 15 0 8 0
109 43 1 27 27 11 11 16 16
110 32 0 29 0 12 0 12 0
111 40 1 19 19 12 12 13 13
112 40 0 20 0 14 0 8 0
113 46 0 17 0 11 0 14 0
114 45 0 21 0 12 0 8 0
115 39 0 22 0 10 0 8 0
116 44 0 26 0 11 0 7 0
117 35 0 19 0 11 0 10 0
118 38 0 17 0 9 0 11 0
119 38 0 17 0 12 0 11 0
120 36 1 19 19 8 8 14 14
121 42 0 17 0 12 0 10 0
122 39 0 15 0 6 0 6 0
123 41 1 27 27 15 15 9 9
124 41 0 19 0 13 0 12 0
125 47 0 21 0 17 0 11 0
126 39 0 25 0 14 0 14 0
127 40 1 19 19 16 16 12 12
128 44 0 18 0 16 0 8 0
129 42 0 15 0 11 0 8 0
130 35 1 20 20 16 16 11 11
131 46 0 29 0 15 0 12 0
132 43 1 20 20 11 11 14 14
133 40 1 29 29 9 9 16 16
134 44 0 24 0 12 0 13 0
135 37 1 24 24 13 13 11 11
136 46 1 23 23 11 11 9 9
137 44 0 23 0 11 0 11 0
138 35 0 19 0 13 0 9 0
139 39 1 22 22 14 14 12 12
140 40 0 22 0 12 0 13 0
141 42 1 25 25 17 17 14 14
142 37 1 21 21 11 11 9 9
143 29 1 22 22 15 15 14 14
144 33 1 21 21 13 13 8 8
145 35 1 18 18 9 9 8 8
146 42 1 10 10 12 12 9 9
LeadershipPreference LeaderG
1 3 0
2 4 0
3 4 0
4 2 2
5 4 4
6 4 0
7 3 0
8 4 0
9 4 0
10 4 0
11 4 4
12 5 0
13 4 0
14 4 4
15 4 0
16 5 5
17 4 0
18 4 4
19 4 0
20 5 0
21 5 5
22 5 5
23 4 0
24 2 2
25 4 0
26 4 0
27 4 4
28 3 3
29 2 2
30 2 0
31 3 3
32 5 0
33 5 5
34 4 0
35 4 0
36 5 5
37 4 0
38 4 4
39 4 0
40 4 0
41 2 0
42 3 3
43 3 3
44 4 4
45 2 0
46 4 0
47 4 0
48 3 3
49 3 3
50 3 0
51 4 4
52 5 0
53 2 0
54 4 4
55 2 0
56 0 0
57 4 0
58 4 4
59 3 3
60 4 4
61 4 0
62 2 0
63 4 0
64 2 2
65 4 0
66 3 3
67 4 0
68 5 5
69 3 0
70 3 0
71 4 0
72 5 0
73 4 0
74 2 0
75 4 0
76 4 4
77 4 4
78 4 4
79 4 0
80 2 0
81 5 0
82 4 0
83 2 0
84 3 0
85 3 3
86 5 5
87 4 0
88 3 0
89 4 4
90 3 3
91 4 0
92 5 0
93 2 2
94 4 0
95 4 0
96 4 0
97 5 5
98 2 0
99 3 0
100 4 0
101 4 0
102 3 3
103 3 0
104 5 5
105 4 4
106 4 4
107 4 4
108 4 0
109 2 2
110 4 0
111 5 5
112 3 0
113 3 0
114 3 0
115 4 0
116 4 0
117 4 0
118 3 0
119 2 0
120 3 3
121 3 0
122 4 0
123 5 5
124 4 0
125 3 0
126 3 0
127 4 4
128 4 0
129 4 0
130 3 3
131 5 0
132 3 3
133 4 4
134 4 0
135 4 4
136 4 4
137 5 0
138 3 0
139 2 2
140 3 0
141 3 3
142 3 3
143 4 4
144 2 2
145 4 4
146 2 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G PersonalStandards
32.47111 -1.86340 0.19103
PeG ParentalExpectations PaG
-0.05401 -0.17367 0.53424
Doubts DoG LeadershipPreference
-0.24865 0.14097 2.38392
LeaderG
-1.98395
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.575 -2.757 0.633 2.576 9.658
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.47111 4.19331 7.744 1.98e-12 ***
G -1.86340 6.39787 -0.291 0.77130
PersonalStandards 0.19103 0.14446 1.322 0.18825
PeG -0.05401 0.20421 -0.265 0.79179
ParentalExpectations -0.17367 0.16881 -1.029 0.30542
PaG 0.53424 0.24981 2.139 0.03425 *
Doubts -0.24865 0.21530 -1.155 0.25014
DoG 0.14097 0.30031 0.469 0.63954
LeadershipPreference 2.38392 0.71880 3.317 0.00117 **
LeaderG -1.98395 0.94964 -2.089 0.03856 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.806 on 136 degrees of freedom
Multiple R-squared: 0.2238, Adjusted R-squared: 0.1725
F-statistic: 4.358 on 9 and 136 DF, p-value: 5.223e-05
> 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,] 0.13859716 0.27719432 0.86140284
[2,] 0.05457686 0.10915372 0.94542314
[3,] 0.32434299 0.64868598 0.67565701
[4,] 0.21161658 0.42323317 0.78838342
[5,] 0.14440391 0.28880782 0.85559609
[6,] 0.16186738 0.32373475 0.83813262
[7,] 0.16360822 0.32721644 0.83639178
[8,] 0.10671675 0.21343349 0.89328325
[9,] 0.65911069 0.68177862 0.34088931
[10,] 0.61028817 0.77942367 0.38971183
[11,] 0.53392206 0.93215589 0.46607794
[12,] 0.67433363 0.65133273 0.32566637
[13,] 0.63230622 0.73538756 0.36769378
[14,] 0.55792129 0.88415741 0.44207871
[15,] 0.48905618 0.97811236 0.51094382
[16,] 0.61519850 0.76960299 0.38480150
[17,] 0.60562880 0.78874240 0.39437120
[18,] 0.54123038 0.91753924 0.45876962
[19,] 0.54762644 0.90474712 0.45237356
[20,] 0.51059907 0.97880185 0.48940093
[21,] 0.48654189 0.97308378 0.51345811
[22,] 0.42417750 0.84835501 0.57582250
[23,] 0.40295774 0.80591548 0.59704226
[24,] 0.66282233 0.67435534 0.33717767
[25,] 0.63637976 0.72724048 0.36362024
[26,] 0.57869266 0.84261468 0.42130734
[27,] 0.67941382 0.64117235 0.32058618
[28,] 0.63593137 0.72813726 0.36406863
[29,] 0.57974966 0.84050069 0.42025034
[30,] 0.53003444 0.93993111 0.46996556
[31,] 0.51133159 0.97733681 0.48866841
[32,] 0.52532762 0.94934476 0.47467238
[33,] 0.47321008 0.94642016 0.52678992
[34,] 0.41833590 0.83667179 0.58166410
[35,] 0.39280455 0.78560911 0.60719545
[36,] 0.61383699 0.77232602 0.38616301
[37,] 0.60028589 0.79942822 0.39971411
[38,] 0.56677368 0.86645264 0.43322632
[39,] 0.51893712 0.96212576 0.48106288
[40,] 0.46537574 0.93075148 0.53462426
[41,] 0.42753049 0.85506098 0.57246951
[42,] 0.41384151 0.82768302 0.58615849
[43,] 0.36682917 0.73365835 0.63317083
[44,] 0.32175764 0.64351528 0.67824236
[45,] 0.31066679 0.62133359 0.68933321
[46,] 0.36723977 0.73447954 0.63276023
[47,] 0.35667222 0.71334444 0.64332778
[48,] 0.47796990 0.95593981 0.52203010
[49,] 0.42709977 0.85419955 0.57290023
[50,] 0.45665452 0.91330903 0.54334548
[51,] 0.41550529 0.83101057 0.58449471
[52,] 0.46491344 0.92982688 0.53508656
[53,] 0.50471059 0.99057883 0.49528941
[54,] 0.57166053 0.85667893 0.42833947
[55,] 0.54050836 0.91898329 0.45949164
[56,] 0.57116019 0.85767962 0.42883981
[57,] 0.61564405 0.76871190 0.38435595
[58,] 0.68864983 0.62270034 0.31135017
[59,] 0.66243287 0.67513426 0.33756713
[60,] 0.63416231 0.73167538 0.36583769
[61,] 0.81769589 0.36460822 0.18230411
[62,] 0.78607286 0.42785428 0.21392714
[63,] 0.77637460 0.44725081 0.22362540
[64,] 0.73830960 0.52338079 0.26169040
[65,] 0.69835916 0.60328167 0.30164084
[66,] 0.65827705 0.68344590 0.34172295
[67,] 0.62342543 0.75314913 0.37657457
[68,] 0.59253643 0.81492713 0.40746357
[69,] 0.57996497 0.84007007 0.42003503
[70,] 0.53519053 0.92961893 0.46480947
[71,] 0.95091047 0.09817907 0.04908953
[72,] 0.94066641 0.11866718 0.05933359
[73,] 0.93017164 0.13965672 0.06982836
[74,] 0.91314497 0.17371006 0.08685503
[75,] 0.89490783 0.21018434 0.10509217
[76,] 0.91715684 0.16568633 0.08284316
[77,] 0.89690192 0.20619616 0.10309808
[78,] 0.87101359 0.25797282 0.12898641
[79,] 0.84811939 0.30376122 0.15188061
[80,] 0.83702022 0.32595956 0.16297978
[81,] 0.82325549 0.35348901 0.17674451
[82,] 0.80373522 0.39252956 0.19626478
[83,] 0.76697400 0.46605201 0.23302600
[84,] 0.77399848 0.45200305 0.22600152
[85,] 0.75239343 0.49521314 0.24760657
[86,] 0.79316669 0.41366662 0.20683331
[87,] 0.79470538 0.41058924 0.20529462
[88,] 0.81851248 0.36297504 0.18148752
[89,] 0.77909798 0.44180404 0.22090202
[90,] 0.78919732 0.42160536 0.21080268
[91,] 0.75031962 0.49936077 0.24968038
[92,] 0.78694020 0.42611960 0.21305980
[93,] 0.77998342 0.44003317 0.22001658
[94,] 0.82376751 0.35246497 0.17623249
[95,] 0.78377105 0.43245790 0.21622895
[96,] 0.74032437 0.51935125 0.25967563
[97,] 0.73112473 0.53775053 0.26887527
[98,] 0.86596118 0.26807765 0.13403882
[99,] 0.83075926 0.33848147 0.16924074
[100,] 0.79081141 0.41837717 0.20918859
[101,] 0.85868076 0.28263848 0.14131924
[102,] 0.85497997 0.29004006 0.14502003
[103,] 0.82260641 0.35478718 0.17739359
[104,] 0.78269538 0.43460923 0.21730462
[105,] 0.82490543 0.35018914 0.17509457
[106,] 0.77226111 0.45547779 0.22773889
[107,] 0.71396023 0.57207953 0.28603977
[108,] 0.67486994 0.65026012 0.32513006
[109,] 0.62356945 0.75286110 0.37643055
[110,] 0.57922464 0.84155073 0.42077536
[111,] 0.55839232 0.88321535 0.44160768
[112,] 0.56926206 0.86147589 0.43073794
[113,] 0.54725185 0.90549631 0.45274815
[114,] 0.46439740 0.92879479 0.53560260
[115,] 0.42278536 0.84557073 0.57721464
[116,] 0.33532630 0.67065260 0.66467370
[117,] 0.24435654 0.48871308 0.75564346
[118,] 0.17076427 0.34152855 0.82923573
[119,] 0.10603437 0.21206875 0.89396563
[120,] 0.07183820 0.14367639 0.92816180
[121,] 0.03520122 0.07040243 0.96479878
> postscript(file="/var/www/html/freestat/rcomp/tmp/1p6o21290464993.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2p6o21290464993.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3p6o21290464993.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/40fnn1290464993.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/50fnn1290464993.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 = 146
Frequency = 1
1 2 3 4 5 6
1.44419130 -3.93973033 -3.96720610 0.02788353 4.58182374 2.64360784
7 8 9 10 11 12
2.83916751 0.88660467 -1.75502122 3.79990631 8.67028169 -0.67743872
13 14 15 16 17 18
3.01369192 -0.29605484 9.65812209 3.48956914 1.16825570 -8.80134543
19 20 21 22 23 24
2.13080428 0.86105715 -7.53593697 -7.53593697 -2.82635676 -8.65912873
25 26 27 28 29 30
-3.25691462 -0.69659918 0.63903627 -13.31006216 -2.18075153 1.21409714
31 32 33 34 35 36
0.27552324 1.76398128 6.71506767 0.59783065 3.78792768 4.38566571
37 38 39 40 41 42
-3.71396572 0.70106036 -6.36650176 -0.96707214 -0.72494520 1.77459499
43 44 45 46 47 48
1.58706271 5.76931502 0.62697216 -0.75422066 3.59689615 -11.35763980
49 50 51 52 53 54
3.36832600 -3.02470409 -1.79364906 -0.06947707 2.69483720 -4.49028447
55 56 57 58 59 60
1.12886556 -0.45769073 -3.35639216 -6.51576132 -4.88456441 8.32748506
61 62 63 64 65 66
-0.26969380 6.10058924 1.41784323 4.60190728 6.20378933 8.40776978
67 68 69 70 71 72
-2.84737577 6.07753056 -6.27607546 -7.27607546 1.94249108 2.57673655
73 74 75 76 77 78
-11.30714484 -2.10242092 -3.30901423 0.72220414 0.94383739 -0.41004239
79 80 81 82 83 84
1.63429917 1.57594139 1.95060781 -1.13708430 -17.57496916 -2.54935195
85 86 87 88 89 90
2.14475424 0.16695827 1.97437186 6.57760135 -1.32682177 0.01593549
91 92 93 94 95 96
2.73217522 -3.39863998 0.25814089 -3.83174430 2.16553736 -5.18192660
97 98 99 100 101 102
-3.88018187 -1.82188712 -1.73944898 7.39196857 -0.25691462 5.16878282
103 104 105 106 107 108
2.26593894 -6.55706902 -5.24221736 5.20635094 1.85918024 2.57574280
109 110 111 112 113 114
5.64952593 -10.47889243 1.86211614 0.97703097 8.52104871 5.43866943
115 116 117 118 119 120
-3.48361374 0.67727208 -6.23954808 -0.57224035 2.33267628 0.21205088
121 122 123 124 125 126
3.70010163 -3.33835902 0.25350119 0.60508796 9.05295350 0.51379140
127 128 129 130 131 132
0.71209076 3.32250242 1.02727203 -4.13264326 1.65818095 5.99330243
133 134 135 136 137 138
2.29670640 2.72491829 -1.99895009 7.64384784 -0.13894285 -3.75694947
139 140 141 142 143 144
0.82213327 1.49090300 2.14475424 -0.68214818 -10.12301001 -5.11101933
145 146
-2.05759914 5.86443258
> postscript(file="/var/www/html/freestat/rcomp/tmp/60fnn1290464993.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 = 146
Frequency = 1
lag(myerror, k = 1) myerror
0 1.44419130 NA
1 -3.93973033 1.44419130
2 -3.96720610 -3.93973033
3 0.02788353 -3.96720610
4 4.58182374 0.02788353
5 2.64360784 4.58182374
6 2.83916751 2.64360784
7 0.88660467 2.83916751
8 -1.75502122 0.88660467
9 3.79990631 -1.75502122
10 8.67028169 3.79990631
11 -0.67743872 8.67028169
12 3.01369192 -0.67743872
13 -0.29605484 3.01369192
14 9.65812209 -0.29605484
15 3.48956914 9.65812209
16 1.16825570 3.48956914
17 -8.80134543 1.16825570
18 2.13080428 -8.80134543
19 0.86105715 2.13080428
20 -7.53593697 0.86105715
21 -7.53593697 -7.53593697
22 -2.82635676 -7.53593697
23 -8.65912873 -2.82635676
24 -3.25691462 -8.65912873
25 -0.69659918 -3.25691462
26 0.63903627 -0.69659918
27 -13.31006216 0.63903627
28 -2.18075153 -13.31006216
29 1.21409714 -2.18075153
30 0.27552324 1.21409714
31 1.76398128 0.27552324
32 6.71506767 1.76398128
33 0.59783065 6.71506767
34 3.78792768 0.59783065
35 4.38566571 3.78792768
36 -3.71396572 4.38566571
37 0.70106036 -3.71396572
38 -6.36650176 0.70106036
39 -0.96707214 -6.36650176
40 -0.72494520 -0.96707214
41 1.77459499 -0.72494520
42 1.58706271 1.77459499
43 5.76931502 1.58706271
44 0.62697216 5.76931502
45 -0.75422066 0.62697216
46 3.59689615 -0.75422066
47 -11.35763980 3.59689615
48 3.36832600 -11.35763980
49 -3.02470409 3.36832600
50 -1.79364906 -3.02470409
51 -0.06947707 -1.79364906
52 2.69483720 -0.06947707
53 -4.49028447 2.69483720
54 1.12886556 -4.49028447
55 -0.45769073 1.12886556
56 -3.35639216 -0.45769073
57 -6.51576132 -3.35639216
58 -4.88456441 -6.51576132
59 8.32748506 -4.88456441
60 -0.26969380 8.32748506
61 6.10058924 -0.26969380
62 1.41784323 6.10058924
63 4.60190728 1.41784323
64 6.20378933 4.60190728
65 8.40776978 6.20378933
66 -2.84737577 8.40776978
67 6.07753056 -2.84737577
68 -6.27607546 6.07753056
69 -7.27607546 -6.27607546
70 1.94249108 -7.27607546
71 2.57673655 1.94249108
72 -11.30714484 2.57673655
73 -2.10242092 -11.30714484
74 -3.30901423 -2.10242092
75 0.72220414 -3.30901423
76 0.94383739 0.72220414
77 -0.41004239 0.94383739
78 1.63429917 -0.41004239
79 1.57594139 1.63429917
80 1.95060781 1.57594139
81 -1.13708430 1.95060781
82 -17.57496916 -1.13708430
83 -2.54935195 -17.57496916
84 2.14475424 -2.54935195
85 0.16695827 2.14475424
86 1.97437186 0.16695827
87 6.57760135 1.97437186
88 -1.32682177 6.57760135
89 0.01593549 -1.32682177
90 2.73217522 0.01593549
91 -3.39863998 2.73217522
92 0.25814089 -3.39863998
93 -3.83174430 0.25814089
94 2.16553736 -3.83174430
95 -5.18192660 2.16553736
96 -3.88018187 -5.18192660
97 -1.82188712 -3.88018187
98 -1.73944898 -1.82188712
99 7.39196857 -1.73944898
100 -0.25691462 7.39196857
101 5.16878282 -0.25691462
102 2.26593894 5.16878282
103 -6.55706902 2.26593894
104 -5.24221736 -6.55706902
105 5.20635094 -5.24221736
106 1.85918024 5.20635094
107 2.57574280 1.85918024
108 5.64952593 2.57574280
109 -10.47889243 5.64952593
110 1.86211614 -10.47889243
111 0.97703097 1.86211614
112 8.52104871 0.97703097
113 5.43866943 8.52104871
114 -3.48361374 5.43866943
115 0.67727208 -3.48361374
116 -6.23954808 0.67727208
117 -0.57224035 -6.23954808
118 2.33267628 -0.57224035
119 0.21205088 2.33267628
120 3.70010163 0.21205088
121 -3.33835902 3.70010163
122 0.25350119 -3.33835902
123 0.60508796 0.25350119
124 9.05295350 0.60508796
125 0.51379140 9.05295350
126 0.71209076 0.51379140
127 3.32250242 0.71209076
128 1.02727203 3.32250242
129 -4.13264326 1.02727203
130 1.65818095 -4.13264326
131 5.99330243 1.65818095
132 2.29670640 5.99330243
133 2.72491829 2.29670640
134 -1.99895009 2.72491829
135 7.64384784 -1.99895009
136 -0.13894285 7.64384784
137 -3.75694947 -0.13894285
138 0.82213327 -3.75694947
139 1.49090300 0.82213327
140 2.14475424 1.49090300
141 -0.68214818 2.14475424
142 -10.12301001 -0.68214818
143 -5.11101933 -10.12301001
144 -2.05759914 -5.11101933
145 5.86443258 -2.05759914
146 NA 5.86443258
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.93973033 1.44419130
[2,] -3.96720610 -3.93973033
[3,] 0.02788353 -3.96720610
[4,] 4.58182374 0.02788353
[5,] 2.64360784 4.58182374
[6,] 2.83916751 2.64360784
[7,] 0.88660467 2.83916751
[8,] -1.75502122 0.88660467
[9,] 3.79990631 -1.75502122
[10,] 8.67028169 3.79990631
[11,] -0.67743872 8.67028169
[12,] 3.01369192 -0.67743872
[13,] -0.29605484 3.01369192
[14,] 9.65812209 -0.29605484
[15,] 3.48956914 9.65812209
[16,] 1.16825570 3.48956914
[17,] -8.80134543 1.16825570
[18,] 2.13080428 -8.80134543
[19,] 0.86105715 2.13080428
[20,] -7.53593697 0.86105715
[21,] -7.53593697 -7.53593697
[22,] -2.82635676 -7.53593697
[23,] -8.65912873 -2.82635676
[24,] -3.25691462 -8.65912873
[25,] -0.69659918 -3.25691462
[26,] 0.63903627 -0.69659918
[27,] -13.31006216 0.63903627
[28,] -2.18075153 -13.31006216
[29,] 1.21409714 -2.18075153
[30,] 0.27552324 1.21409714
[31,] 1.76398128 0.27552324
[32,] 6.71506767 1.76398128
[33,] 0.59783065 6.71506767
[34,] 3.78792768 0.59783065
[35,] 4.38566571 3.78792768
[36,] -3.71396572 4.38566571
[37,] 0.70106036 -3.71396572
[38,] -6.36650176 0.70106036
[39,] -0.96707214 -6.36650176
[40,] -0.72494520 -0.96707214
[41,] 1.77459499 -0.72494520
[42,] 1.58706271 1.77459499
[43,] 5.76931502 1.58706271
[44,] 0.62697216 5.76931502
[45,] -0.75422066 0.62697216
[46,] 3.59689615 -0.75422066
[47,] -11.35763980 3.59689615
[48,] 3.36832600 -11.35763980
[49,] -3.02470409 3.36832600
[50,] -1.79364906 -3.02470409
[51,] -0.06947707 -1.79364906
[52,] 2.69483720 -0.06947707
[53,] -4.49028447 2.69483720
[54,] 1.12886556 -4.49028447
[55,] -0.45769073 1.12886556
[56,] -3.35639216 -0.45769073
[57,] -6.51576132 -3.35639216
[58,] -4.88456441 -6.51576132
[59,] 8.32748506 -4.88456441
[60,] -0.26969380 8.32748506
[61,] 6.10058924 -0.26969380
[62,] 1.41784323 6.10058924
[63,] 4.60190728 1.41784323
[64,] 6.20378933 4.60190728
[65,] 8.40776978 6.20378933
[66,] -2.84737577 8.40776978
[67,] 6.07753056 -2.84737577
[68,] -6.27607546 6.07753056
[69,] -7.27607546 -6.27607546
[70,] 1.94249108 -7.27607546
[71,] 2.57673655 1.94249108
[72,] -11.30714484 2.57673655
[73,] -2.10242092 -11.30714484
[74,] -3.30901423 -2.10242092
[75,] 0.72220414 -3.30901423
[76,] 0.94383739 0.72220414
[77,] -0.41004239 0.94383739
[78,] 1.63429917 -0.41004239
[79,] 1.57594139 1.63429917
[80,] 1.95060781 1.57594139
[81,] -1.13708430 1.95060781
[82,] -17.57496916 -1.13708430
[83,] -2.54935195 -17.57496916
[84,] 2.14475424 -2.54935195
[85,] 0.16695827 2.14475424
[86,] 1.97437186 0.16695827
[87,] 6.57760135 1.97437186
[88,] -1.32682177 6.57760135
[89,] 0.01593549 -1.32682177
[90,] 2.73217522 0.01593549
[91,] -3.39863998 2.73217522
[92,] 0.25814089 -3.39863998
[93,] -3.83174430 0.25814089
[94,] 2.16553736 -3.83174430
[95,] -5.18192660 2.16553736
[96,] -3.88018187 -5.18192660
[97,] -1.82188712 -3.88018187
[98,] -1.73944898 -1.82188712
[99,] 7.39196857 -1.73944898
[100,] -0.25691462 7.39196857
[101,] 5.16878282 -0.25691462
[102,] 2.26593894 5.16878282
[103,] -6.55706902 2.26593894
[104,] -5.24221736 -6.55706902
[105,] 5.20635094 -5.24221736
[106,] 1.85918024 5.20635094
[107,] 2.57574280 1.85918024
[108,] 5.64952593 2.57574280
[109,] -10.47889243 5.64952593
[110,] 1.86211614 -10.47889243
[111,] 0.97703097 1.86211614
[112,] 8.52104871 0.97703097
[113,] 5.43866943 8.52104871
[114,] -3.48361374 5.43866943
[115,] 0.67727208 -3.48361374
[116,] -6.23954808 0.67727208
[117,] -0.57224035 -6.23954808
[118,] 2.33267628 -0.57224035
[119,] 0.21205088 2.33267628
[120,] 3.70010163 0.21205088
[121,] -3.33835902 3.70010163
[122,] 0.25350119 -3.33835902
[123,] 0.60508796 0.25350119
[124,] 9.05295350 0.60508796
[125,] 0.51379140 9.05295350
[126,] 0.71209076 0.51379140
[127,] 3.32250242 0.71209076
[128,] 1.02727203 3.32250242
[129,] -4.13264326 1.02727203
[130,] 1.65818095 -4.13264326
[131,] 5.99330243 1.65818095
[132,] 2.29670640 5.99330243
[133,] 2.72491829 2.29670640
[134,] -1.99895009 2.72491829
[135,] 7.64384784 -1.99895009
[136,] -0.13894285 7.64384784
[137,] -3.75694947 -0.13894285
[138,] 0.82213327 -3.75694947
[139,] 1.49090300 0.82213327
[140,] 2.14475424 1.49090300
[141,] -0.68214818 2.14475424
[142,] -10.12301001 -0.68214818
[143,] -5.11101933 -10.12301001
[144,] -2.05759914 -5.11101933
[145,] 5.86443258 -2.05759914
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.93973033 1.44419130
2 -3.96720610 -3.93973033
3 0.02788353 -3.96720610
4 4.58182374 0.02788353
5 2.64360784 4.58182374
6 2.83916751 2.64360784
7 0.88660467 2.83916751
8 -1.75502122 0.88660467
9 3.79990631 -1.75502122
10 8.67028169 3.79990631
11 -0.67743872 8.67028169
12 3.01369192 -0.67743872
13 -0.29605484 3.01369192
14 9.65812209 -0.29605484
15 3.48956914 9.65812209
16 1.16825570 3.48956914
17 -8.80134543 1.16825570
18 2.13080428 -8.80134543
19 0.86105715 2.13080428
20 -7.53593697 0.86105715
21 -7.53593697 -7.53593697
22 -2.82635676 -7.53593697
23 -8.65912873 -2.82635676
24 -3.25691462 -8.65912873
25 -0.69659918 -3.25691462
26 0.63903627 -0.69659918
27 -13.31006216 0.63903627
28 -2.18075153 -13.31006216
29 1.21409714 -2.18075153
30 0.27552324 1.21409714
31 1.76398128 0.27552324
32 6.71506767 1.76398128
33 0.59783065 6.71506767
34 3.78792768 0.59783065
35 4.38566571 3.78792768
36 -3.71396572 4.38566571
37 0.70106036 -3.71396572
38 -6.36650176 0.70106036
39 -0.96707214 -6.36650176
40 -0.72494520 -0.96707214
41 1.77459499 -0.72494520
42 1.58706271 1.77459499
43 5.76931502 1.58706271
44 0.62697216 5.76931502
45 -0.75422066 0.62697216
46 3.59689615 -0.75422066
47 -11.35763980 3.59689615
48 3.36832600 -11.35763980
49 -3.02470409 3.36832600
50 -1.79364906 -3.02470409
51 -0.06947707 -1.79364906
52 2.69483720 -0.06947707
53 -4.49028447 2.69483720
54 1.12886556 -4.49028447
55 -0.45769073 1.12886556
56 -3.35639216 -0.45769073
57 -6.51576132 -3.35639216
58 -4.88456441 -6.51576132
59 8.32748506 -4.88456441
60 -0.26969380 8.32748506
61 6.10058924 -0.26969380
62 1.41784323 6.10058924
63 4.60190728 1.41784323
64 6.20378933 4.60190728
65 8.40776978 6.20378933
66 -2.84737577 8.40776978
67 6.07753056 -2.84737577
68 -6.27607546 6.07753056
69 -7.27607546 -6.27607546
70 1.94249108 -7.27607546
71 2.57673655 1.94249108
72 -11.30714484 2.57673655
73 -2.10242092 -11.30714484
74 -3.30901423 -2.10242092
75 0.72220414 -3.30901423
76 0.94383739 0.72220414
77 -0.41004239 0.94383739
78 1.63429917 -0.41004239
79 1.57594139 1.63429917
80 1.95060781 1.57594139
81 -1.13708430 1.95060781
82 -17.57496916 -1.13708430
83 -2.54935195 -17.57496916
84 2.14475424 -2.54935195
85 0.16695827 2.14475424
86 1.97437186 0.16695827
87 6.57760135 1.97437186
88 -1.32682177 6.57760135
89 0.01593549 -1.32682177
90 2.73217522 0.01593549
91 -3.39863998 2.73217522
92 0.25814089 -3.39863998
93 -3.83174430 0.25814089
94 2.16553736 -3.83174430
95 -5.18192660 2.16553736
96 -3.88018187 -5.18192660
97 -1.82188712 -3.88018187
98 -1.73944898 -1.82188712
99 7.39196857 -1.73944898
100 -0.25691462 7.39196857
101 5.16878282 -0.25691462
102 2.26593894 5.16878282
103 -6.55706902 2.26593894
104 -5.24221736 -6.55706902
105 5.20635094 -5.24221736
106 1.85918024 5.20635094
107 2.57574280 1.85918024
108 5.64952593 2.57574280
109 -10.47889243 5.64952593
110 1.86211614 -10.47889243
111 0.97703097 1.86211614
112 8.52104871 0.97703097
113 5.43866943 8.52104871
114 -3.48361374 5.43866943
115 0.67727208 -3.48361374
116 -6.23954808 0.67727208
117 -0.57224035 -6.23954808
118 2.33267628 -0.57224035
119 0.21205088 2.33267628
120 3.70010163 0.21205088
121 -3.33835902 3.70010163
122 0.25350119 -3.33835902
123 0.60508796 0.25350119
124 9.05295350 0.60508796
125 0.51379140 9.05295350
126 0.71209076 0.51379140
127 3.32250242 0.71209076
128 1.02727203 3.32250242
129 -4.13264326 1.02727203
130 1.65818095 -4.13264326
131 5.99330243 1.65818095
132 2.29670640 5.99330243
133 2.72491829 2.29670640
134 -1.99895009 2.72491829
135 7.64384784 -1.99895009
136 -0.13894285 7.64384784
137 -3.75694947 -0.13894285
138 0.82213327 -3.75694947
139 1.49090300 0.82213327
140 2.14475424 1.49090300
141 -0.68214818 2.14475424
142 -10.12301001 -0.68214818
143 -5.11101933 -10.12301001
144 -2.05759914 -5.11101933
145 5.86443258 -2.05759914
> 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/freestat/rcomp/tmp/7a6n81290464993.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8lx4b1290464993.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9lx4b1290464993.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10lx4b1290464993.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11msr81290464993.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/freestat/rcomp/tmp/12l8i81290464993.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/freestat/rcomp/tmp/13h0yh1290464993.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/freestat/rcomp/tmp/1499x21290464993.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/freestat/rcomp/tmp/15d9w71290464993.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/freestat/rcomp/tmp/1691cy1290464993.tab")
+ }
>
> try(system("convert tmp/1p6o21290464993.ps tmp/1p6o21290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/2p6o21290464993.ps tmp/2p6o21290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/3p6o21290464993.ps tmp/3p6o21290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/40fnn1290464993.ps tmp/40fnn1290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/50fnn1290464993.ps tmp/50fnn1290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/60fnn1290464993.ps tmp/60fnn1290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a6n81290464993.ps tmp/7a6n81290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lx4b1290464993.ps tmp/8lx4b1290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lx4b1290464993.ps tmp/9lx4b1290464993.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lx4b1290464993.ps tmp/10lx4b1290464993.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.965 2.701 6.428