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(2
+ ,7
+ ,41
+ ,38
+ ,13
+ ,12
+ ,14
+ ,12
+ ,2
+ ,5
+ ,39
+ ,32
+ ,16
+ ,11
+ ,18
+ ,11
+ ,2
+ ,5
+ ,30
+ ,35
+ ,19
+ ,15
+ ,11
+ ,14
+ ,1
+ ,5
+ ,31
+ ,33
+ ,15
+ ,6
+ ,12
+ ,12
+ ,2
+ ,8
+ ,34
+ ,37
+ ,14
+ ,13
+ ,16
+ ,21
+ ,2
+ ,6
+ ,35
+ ,29
+ ,13
+ ,10
+ ,18
+ ,12
+ ,2
+ ,5
+ ,39
+ ,31
+ ,19
+ ,12
+ ,14
+ ,22
+ ,2
+ ,6
+ ,34
+ ,36
+ ,15
+ ,14
+ ,14
+ ,11
+ ,2
+ ,5
+ ,36
+ ,35
+ ,14
+ ,12
+ ,15
+ ,10
+ ,2
+ ,4
+ ,37
+ ,38
+ ,15
+ ,6
+ ,15
+ ,13
+ ,1
+ ,6
+ ,38
+ ,31
+ ,16
+ ,10
+ ,17
+ ,10
+ ,2
+ ,5
+ ,36
+ ,34
+ ,16
+ ,12
+ ,19
+ ,8
+ ,1
+ ,5
+ ,38
+ ,35
+ ,16
+ ,12
+ ,10
+ ,15
+ ,2
+ ,6
+ ,39
+ ,38
+ ,16
+ ,11
+ ,16
+ ,14
+ ,2
+ ,7
+ ,33
+ ,37
+ ,17
+ ,15
+ ,18
+ ,10
+ ,1
+ ,6
+ ,32
+ ,33
+ ,15
+ ,12
+ ,14
+ ,14
+ ,1
+ ,7
+ ,36
+ ,32
+ ,15
+ ,10
+ ,14
+ ,14
+ ,2
+ ,6
+ ,38
+ ,38
+ ,20
+ ,12
+ ,17
+ ,11
+ ,1
+ ,8
+ ,39
+ ,38
+ ,18
+ ,11
+ ,14
+ ,10
+ ,2
+ ,7
+ ,32
+ ,32
+ ,16
+ ,12
+ ,16
+ ,13
+ ,1
+ ,5
+ ,32
+ ,33
+ ,16
+ ,11
+ ,18
+ ,7
+ ,2
+ ,5
+ ,31
+ ,31
+ ,16
+ ,12
+ ,11
+ ,14
+ ,2
+ ,7
+ ,39
+ ,38
+ ,19
+ ,13
+ ,14
+ ,12
+ ,2
+ ,7
+ ,37
+ ,39
+ ,16
+ ,11
+ ,12
+ ,14
+ ,1
+ ,5
+ ,39
+ ,32
+ ,17
+ ,9
+ ,17
+ ,11
+ ,2
+ ,4
+ ,41
+ ,32
+ ,17
+ ,13
+ ,9
+ ,9
+ ,1
+ ,10
+ ,36
+ ,35
+ ,16
+ ,10
+ ,16
+ ,11
+ ,2
+ ,6
+ ,33
+ ,37
+ ,15
+ ,14
+ ,14
+ ,15
+ ,2
+ ,5
+ ,33
+ ,33
+ ,16
+ ,12
+ ,15
+ ,14
+ ,1
+ ,5
+ ,34
+ ,33
+ ,14
+ ,10
+ ,11
+ ,13
+ ,2
+ ,5
+ ,31
+ ,28
+ ,15
+ ,12
+ ,16
+ ,9
+ ,1
+ ,5
+ ,27
+ ,32
+ ,12
+ ,8
+ ,13
+ ,15
+ ,2
+ ,6
+ ,37
+ ,31
+ ,14
+ ,10
+ ,17
+ ,10
+ ,2
+ ,5
+ ,34
+ ,37
+ ,16
+ ,12
+ ,15
+ ,11
+ ,1
+ ,5
+ ,34
+ ,30
+ ,14
+ ,12
+ ,14
+ ,13
+ ,1
+ ,5
+ ,32
+ ,33
+ ,7
+ ,7
+ ,16
+ ,8
+ ,1
+ ,5
+ ,29
+ ,31
+ ,10
+ ,6
+ ,9
+ ,20
+ ,1
+ ,5
+ ,36
+ ,33
+ ,14
+ ,12
+ ,15
+ ,12
+ ,2
+ ,5
+ ,29
+ ,31
+ ,16
+ ,10
+ ,17
+ ,10
+ ,1
+ ,5
+ ,35
+ ,33
+ ,16
+ ,10
+ ,13
+ ,10
+ ,1
+ ,5
+ ,37
+ ,32
+ ,16
+ ,10
+ ,15
+ ,9
+ ,2
+ ,7
+ ,34
+ ,33
+ ,14
+ ,12
+ ,16
+ ,14
+ ,1
+ ,5
+ ,38
+ ,32
+ ,20
+ ,15
+ ,16
+ ,8
+ ,1
+ ,6
+ ,35
+ ,33
+ ,14
+ ,10
+ ,12
+ ,14
+ ,2
+ ,7
+ ,38
+ ,28
+ ,14
+ ,10
+ ,12
+ ,11
+ ,2
+ ,7
+ ,37
+ ,35
+ ,11
+ ,12
+ ,11
+ ,13
+ ,2
+ ,5
+ ,38
+ ,39
+ ,14
+ ,13
+ ,15
+ ,9
+ ,2
+ ,5
+ ,33
+ ,34
+ ,15
+ ,11
+ ,15
+ ,11
+ ,2
+ ,4
+ ,36
+ ,38
+ ,16
+ ,11
+ ,17
+ ,15
+ ,1
+ ,5
+ ,38
+ ,32
+ ,14
+ ,12
+ ,13
+ ,11
+ ,2
+ ,4
+ ,32
+ ,38
+ ,16
+ ,14
+ ,16
+ ,10
+ ,1
+ ,5
+ ,32
+ ,30
+ ,14
+ ,10
+ ,14
+ ,14
+ ,1
+ ,5
+ ,32
+ ,33
+ ,12
+ ,12
+ ,11
+ ,18
+ ,2
+ ,7
+ ,34
+ ,38
+ ,16
+ ,13
+ ,12
+ ,14
+ ,1
+ ,5
+ ,32
+ ,32
+ ,9
+ ,5
+ ,12
+ ,11
+ ,2
+ ,5
+ ,37
+ ,32
+ ,14
+ ,6
+ ,15
+ ,12
+ ,2
+ ,6
+ ,39
+ ,34
+ ,16
+ ,12
+ ,16
+ ,13
+ ,2
+ ,4
+ ,29
+ ,34
+ ,16
+ ,12
+ ,15
+ ,9
+ ,1
+ ,6
+ ,37
+ ,36
+ ,15
+ ,11
+ ,12
+ ,10
+ ,2
+ ,6
+ ,35
+ ,34
+ ,16
+ ,10
+ ,12
+ ,15
+ ,1
+ ,5
+ ,30
+ ,28
+ ,12
+ ,7
+ ,8
+ ,20
+ ,1
+ ,7
+ ,38
+ ,34
+ ,16
+ ,12
+ ,13
+ ,12
+ ,2
+ ,6
+ ,34
+ ,35
+ ,16
+ ,14
+ ,11
+ ,12
+ ,2
+ ,8
+ ,31
+ ,35
+ ,14
+ ,11
+ ,14
+ ,14
+ ,2
+ ,7
+ ,34
+ ,31
+ ,16
+ ,12
+ ,15
+ ,13
+ ,1
+ ,5
+ ,35
+ ,37
+ ,17
+ ,13
+ ,10
+ ,11
+ ,2
+ ,6
+ ,36
+ ,35
+ ,18
+ ,14
+ ,11
+ ,17
+ ,1
+ ,6
+ ,30
+ ,27
+ ,18
+ ,11
+ ,12
+ ,12
+ ,2
+ ,5
+ ,39
+ ,40
+ ,12
+ ,12
+ ,15
+ ,13
+ ,1
+ ,5
+ ,35
+ ,37
+ ,16
+ ,12
+ ,15
+ ,14
+ ,1
+ ,5
+ ,38
+ ,36
+ ,10
+ ,8
+ ,14
+ ,13
+ ,2
+ ,5
+ ,31
+ ,38
+ ,14
+ ,11
+ ,16
+ ,15
+ ,2
+ ,4
+ ,34
+ ,39
+ ,18
+ ,14
+ ,15
+ ,13
+ ,1
+ ,6
+ ,38
+ ,41
+ ,18
+ ,14
+ ,15
+ ,10
+ ,1
+ ,6
+ ,34
+ ,27
+ ,16
+ ,12
+ ,13
+ ,11
+ ,2
+ ,6
+ ,39
+ ,30
+ ,17
+ ,9
+ ,12
+ ,19
+ ,2
+ ,6
+ ,37
+ ,37
+ ,16
+ ,13
+ ,17
+ ,13
+ ,2
+ ,7
+ ,34
+ ,31
+ ,16
+ ,11
+ ,13
+ ,17
+ ,1
+ ,5
+ ,28
+ ,31
+ ,13
+ ,12
+ ,15
+ ,13
+ ,1
+ ,7
+ ,37
+ ,27
+ ,16
+ ,12
+ ,13
+ ,9
+ ,1
+ ,6
+ ,33
+ ,36
+ ,16
+ ,12
+ ,15
+ ,11
+ ,1
+ ,5
+ ,37
+ ,38
+ ,20
+ ,12
+ ,16
+ ,10
+ ,2
+ ,5
+ ,35
+ ,37
+ ,16
+ ,12
+ ,15
+ ,9
+ ,1
+ ,4
+ ,37
+ ,33
+ ,15
+ ,12
+ ,16
+ ,12
+ ,2
+ ,8
+ ,32
+ ,34
+ ,15
+ ,11
+ ,15
+ ,12
+ ,2
+ ,8
+ ,33
+ ,31
+ ,16
+ ,10
+ ,14
+ ,13
+ ,1
+ ,5
+ ,38
+ ,39
+ ,14
+ ,9
+ ,15
+ ,13
+ ,2
+ ,5
+ ,33
+ ,34
+ ,16
+ ,12
+ ,14
+ ,12
+ ,2
+ ,6
+ ,29
+ ,32
+ ,16
+ ,12
+ ,13
+ ,15
+ ,2
+ ,4
+ ,33
+ ,33
+ ,15
+ ,12
+ ,7
+ ,22
+ ,2
+ ,5
+ ,31
+ ,36
+ ,12
+ ,9
+ ,17
+ ,13
+ ,2
+ ,5
+ ,36
+ ,32
+ ,17
+ ,15
+ ,13
+ ,15
+ ,2
+ ,5
+ ,35
+ ,41
+ ,16
+ ,12
+ ,15
+ ,13
+ ,2
+ ,5
+ ,32
+ ,28
+ ,15
+ ,12
+ ,14
+ ,15
+ ,2
+ ,6
+ ,29
+ ,30
+ ,13
+ ,12
+ ,13
+ ,10
+ ,2
+ ,6
+ ,39
+ ,36
+ ,16
+ ,10
+ ,16
+ ,11
+ ,2
+ ,5
+ ,37
+ ,35
+ ,16
+ ,13
+ ,12
+ ,16
+ ,2
+ ,6
+ ,35
+ ,31
+ ,16
+ ,9
+ ,14
+ ,11
+ ,1
+ ,5
+ ,37
+ ,34
+ ,16
+ ,12
+ ,17
+ ,11
+ ,1
+ ,7
+ ,32
+ ,36
+ ,14
+ ,10
+ ,15
+ ,10
+ ,2
+ ,5
+ ,38
+ ,36
+ ,16
+ ,14
+ ,17
+ ,10
+ ,1
+ ,6
+ ,37
+ ,35
+ ,16
+ ,11
+ ,12
+ ,16
+ ,2
+ ,6
+ ,36
+ ,37
+ ,20
+ ,15
+ ,16
+ ,12
+ ,1
+ ,6
+ ,32
+ ,28
+ ,15
+ ,11
+ ,11
+ ,11
+ ,2
+ ,4
+ ,33
+ ,39
+ ,16
+ ,11
+ ,15
+ ,16
+ ,1
+ ,5
+ ,40
+ ,32
+ ,13
+ ,12
+ ,9
+ ,19
+ ,2
+ ,5
+ ,38
+ ,35
+ ,17
+ ,12
+ ,16
+ ,11
+ ,1
+ ,7
+ ,41
+ ,39
+ ,16
+ ,12
+ ,15
+ ,16
+ ,1
+ ,6
+ ,36
+ ,35
+ ,16
+ ,11
+ ,10
+ ,15
+ ,2
+ ,9
+ ,43
+ ,42
+ ,12
+ ,7
+ ,10
+ ,24
+ ,2
+ ,6
+ ,30
+ ,34
+ ,16
+ ,12
+ ,15
+ ,14
+ ,2
+ ,6
+ ,31
+ ,33
+ ,16
+ ,14
+ ,11
+ ,15
+ ,2
+ ,5
+ ,32
+ ,41
+ ,17
+ ,11
+ ,13
+ ,11
+ ,1
+ ,6
+ ,32
+ ,33
+ ,13
+ ,11
+ ,14
+ ,15
+ ,2
+ ,5
+ ,37
+ ,34
+ ,12
+ ,10
+ ,18
+ ,12
+ ,1
+ ,8
+ ,37
+ ,32
+ ,18
+ ,13
+ ,16
+ ,10
+ ,2
+ ,7
+ ,33
+ ,40
+ ,14
+ ,13
+ ,14
+ ,14
+ ,2
+ ,5
+ ,34
+ ,40
+ ,14
+ ,8
+ ,14
+ ,13
+ ,2
+ ,7
+ ,33
+ ,35
+ ,13
+ ,11
+ ,14
+ ,9
+ ,2
+ ,6
+ ,38
+ ,36
+ ,16
+ ,12
+ ,14
+ ,15
+ ,2
+ ,6
+ ,33
+ ,37
+ ,13
+ ,11
+ ,12
+ ,15
+ ,2
+ ,9
+ ,31
+ ,27
+ ,16
+ ,13
+ ,14
+ ,14
+ ,2
+ ,7
+ ,38
+ ,39
+ ,13
+ ,12
+ ,15
+ ,11
+ ,2
+ ,6
+ ,37
+ ,38
+ ,16
+ ,14
+ ,15
+ ,8
+ ,2
+ ,5
+ ,33
+ ,31
+ ,15
+ ,13
+ ,15
+ ,11
+ ,2
+ ,5
+ ,31
+ ,33
+ ,16
+ ,15
+ ,13
+ ,11
+ ,1
+ ,6
+ ,39
+ ,32
+ ,15
+ ,10
+ ,17
+ ,8
+ ,2
+ ,6
+ ,44
+ ,39
+ ,17
+ ,11
+ ,17
+ ,10
+ ,2
+ ,7
+ ,33
+ ,36
+ ,15
+ ,9
+ ,19
+ ,11
+ ,2
+ ,5
+ ,35
+ ,33
+ ,12
+ ,11
+ ,15
+ ,13
+ ,1
+ ,5
+ ,32
+ ,33
+ ,16
+ ,10
+ ,13
+ ,11
+ ,1
+ ,5
+ ,28
+ ,32
+ ,10
+ ,11
+ ,9
+ ,20
+ ,2
+ ,6
+ ,40
+ ,37
+ ,16
+ ,8
+ ,15
+ ,10
+ ,1
+ ,4
+ ,27
+ ,30
+ ,12
+ ,11
+ ,15
+ ,15
+ ,1
+ ,5
+ ,37
+ ,38
+ ,14
+ ,12
+ ,15
+ ,12
+ ,2
+ ,7
+ ,32
+ ,29
+ ,15
+ ,12
+ ,16
+ ,14
+ ,1
+ ,5
+ ,28
+ ,22
+ ,13
+ ,9
+ ,11
+ ,23
+ ,1
+ ,7
+ ,34
+ ,35
+ ,15
+ ,11
+ ,14
+ ,14
+ ,2
+ ,7
+ ,30
+ ,35
+ ,11
+ ,10
+ ,11
+ ,16
+ ,2
+ ,6
+ ,35
+ ,34
+ ,12
+ ,8
+ ,15
+ ,11
+ ,1
+ ,5
+ ,31
+ ,35
+ ,8
+ ,9
+ ,13
+ ,12
+ ,2
+ ,8
+ ,32
+ ,34
+ ,16
+ ,8
+ ,15
+ ,10
+ ,1
+ ,5
+ ,30
+ ,34
+ ,15
+ ,9
+ ,16
+ ,14
+ ,2
+ ,5
+ ,30
+ ,35
+ ,17
+ ,15
+ ,14
+ ,12
+ ,1
+ ,5
+ ,31
+ ,23
+ ,16
+ ,11
+ ,15
+ ,12
+ ,2
+ ,6
+ ,40
+ ,31
+ ,10
+ ,8
+ ,16
+ ,11
+ ,2
+ ,4
+ ,32
+ ,27
+ ,18
+ ,13
+ ,16
+ ,12
+ ,1
+ ,5
+ ,36
+ ,36
+ ,13
+ ,12
+ ,11
+ ,13
+ ,1
+ ,5
+ ,32
+ ,31
+ ,16
+ ,12
+ ,12
+ ,11
+ ,1
+ ,7
+ ,35
+ ,32
+ ,13
+ ,9
+ ,9
+ ,19
+ ,2
+ ,6
+ ,38
+ ,39
+ ,10
+ ,7
+ ,16
+ ,12
+ ,2
+ ,7
+ ,42
+ ,37
+ ,15
+ ,13
+ ,13
+ ,17
+ ,1
+ ,10
+ ,34
+ ,38
+ ,16
+ ,9
+ ,16
+ ,9
+ ,2
+ ,6
+ ,35
+ ,39
+ ,16
+ ,6
+ ,12
+ ,12
+ ,2
+ ,8
+ ,35
+ ,34
+ ,14
+ ,8
+ ,9
+ ,19
+ ,2
+ ,4
+ ,33
+ ,31
+ ,10
+ ,8
+ ,13
+ ,18
+ ,2
+ ,5
+ ,36
+ ,32
+ ,17
+ ,15
+ ,13
+ ,15
+ ,2
+ ,6
+ ,32
+ ,37
+ ,13
+ ,6
+ ,14
+ ,14
+ ,2
+ ,7
+ ,33
+ ,36
+ ,15
+ ,9
+ ,19
+ ,11
+ ,2
+ ,7
+ ,34
+ ,32
+ ,16
+ ,11
+ ,13
+ ,9
+ ,2
+ ,6
+ ,32
+ ,35
+ ,12
+ ,8
+ ,12
+ ,18
+ ,2
+ ,6
+ ,34
+ ,36
+ ,13
+ ,8
+ ,13
+ ,16)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Gender'
+ ,'Age'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Gender','Age','Connected','Separate','Learning','Software','Happiness','Depression'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Connected Gender Age Separate Learning Software Happiness Depression
1 41 2 7 38 13 12 14 12
2 39 2 5 32 16 11 18 11
3 30 2 5 35 19 15 11 14
4 31 1 5 33 15 6 12 12
5 34 2 8 37 14 13 16 21
6 35 2 6 29 13 10 18 12
7 39 2 5 31 19 12 14 22
8 34 2 6 36 15 14 14 11
9 36 2 5 35 14 12 15 10
10 37 2 4 38 15 6 15 13
11 38 1 6 31 16 10 17 10
12 36 2 5 34 16 12 19 8
13 38 1 5 35 16 12 10 15
14 39 2 6 38 16 11 16 14
15 33 2 7 37 17 15 18 10
16 32 1 6 33 15 12 14 14
17 36 1 7 32 15 10 14 14
18 38 2 6 38 20 12 17 11
19 39 1 8 38 18 11 14 10
20 32 2 7 32 16 12 16 13
21 32 1 5 33 16 11 18 7
22 31 2 5 31 16 12 11 14
23 39 2 7 38 19 13 14 12
24 37 2 7 39 16 11 12 14
25 39 1 5 32 17 9 17 11
26 41 2 4 32 17 13 9 9
27 36 1 10 35 16 10 16 11
28 33 2 6 37 15 14 14 15
29 33 2 5 33 16 12 15 14
30 34 1 5 33 14 10 11 13
31 31 2 5 28 15 12 16 9
32 27 1 5 32 12 8 13 15
33 37 2 6 31 14 10 17 10
34 34 2 5 37 16 12 15 11
35 34 1 5 30 14 12 14 13
36 32 1 5 33 7 7 16 8
37 29 1 5 31 10 6 9 20
38 36 1 5 33 14 12 15 12
39 29 2 5 31 16 10 17 10
40 35 1 5 33 16 10 13 10
41 37 1 5 32 16 10 15 9
42 34 2 7 33 14 12 16 14
43 38 1 5 32 20 15 16 8
44 35 1 6 33 14 10 12 14
45 38 2 7 28 14 10 12 11
46 37 2 7 35 11 12 11 13
47 38 2 5 39 14 13 15 9
48 33 2 5 34 15 11 15 11
49 36 2 4 38 16 11 17 15
50 38 1 5 32 14 12 13 11
51 32 2 4 38 16 14 16 10
52 32 1 5 30 14 10 14 14
53 32 1 5 33 12 12 11 18
54 34 2 7 38 16 13 12 14
55 32 1 5 32 9 5 12 11
56 37 2 5 32 14 6 15 12
57 39 2 6 34 16 12 16 13
58 29 2 4 34 16 12 15 9
59 37 1 6 36 15 11 12 10
60 35 2 6 34 16 10 12 15
61 30 1 5 28 12 7 8 20
62 38 1 7 34 16 12 13 12
63 34 2 6 35 16 14 11 12
64 31 2 8 35 14 11 14 14
65 34 2 7 31 16 12 15 13
66 35 1 5 37 17 13 10 11
67 36 2 6 35 18 14 11 17
68 30 1 6 27 18 11 12 12
69 39 2 5 40 12 12 15 13
70 35 1 5 37 16 12 15 14
71 38 1 5 36 10 8 14 13
72 31 2 5 38 14 11 16 15
73 34 2 4 39 18 14 15 13
74 38 1 6 41 18 14 15 10
75 34 1 6 27 16 12 13 11
76 39 2 6 30 17 9 12 19
77 37 2 6 37 16 13 17 13
78 34 2 7 31 16 11 13 17
79 28 1 5 31 13 12 15 13
80 37 1 7 27 16 12 13 9
81 33 1 6 36 16 12 15 11
82 37 1 5 38 20 12 16 10
83 35 2 5 37 16 12 15 9
84 37 1 4 33 15 12 16 12
85 32 2 8 34 15 11 15 12
86 33 2 8 31 16 10 14 13
87 38 1 5 39 14 9 15 13
88 33 2 5 34 16 12 14 12
89 29 2 6 32 16 12 13 15
90 33 2 4 33 15 12 7 22
91 31 2 5 36 12 9 17 13
92 36 2 5 32 17 15 13 15
93 35 2 5 41 16 12 15 13
94 32 2 5 28 15 12 14 15
95 29 2 6 30 13 12 13 10
96 39 2 6 36 16 10 16 11
97 37 2 5 35 16 13 12 16
98 35 2 6 31 16 9 14 11
99 37 1 5 34 16 12 17 11
100 32 1 7 36 14 10 15 10
101 38 2 5 36 16 14 17 10
102 37 1 6 35 16 11 12 16
103 36 2 6 37 20 15 16 12
104 32 1 6 28 15 11 11 11
105 33 2 4 39 16 11 15 16
106 40 1 5 32 13 12 9 19
107 38 2 5 35 17 12 16 11
108 41 1 7 39 16 12 15 16
109 36 1 6 35 16 11 10 15
110 43 2 9 42 12 7 10 24
111 30 2 6 34 16 12 15 14
112 31 2 6 33 16 14 11 15
113 32 2 5 41 17 11 13 11
114 32 1 6 33 13 11 14 15
115 37 2 5 34 12 10 18 12
116 37 1 8 32 18 13 16 10
117 33 2 7 40 14 13 14 14
118 34 2 5 40 14 8 14 13
119 33 2 7 35 13 11 14 9
120 38 2 6 36 16 12 14 15
121 33 2 6 37 13 11 12 15
122 31 2 9 27 16 13 14 14
123 38 2 7 39 13 12 15 11
124 37 2 6 38 16 14 15 8
125 33 2 5 31 15 13 15 11
126 31 2 5 33 16 15 13 11
127 39 1 6 32 15 10 17 8
128 44 2 6 39 17 11 17 10
129 33 2 7 36 15 9 19 11
130 35 2 5 33 12 11 15 13
131 32 1 5 33 16 10 13 11
132 28 1 5 32 10 11 9 20
133 40 2 6 37 16 8 15 10
134 27 1 4 30 12 11 15 15
135 37 1 5 38 14 12 15 12
136 32 2 7 29 15 12 16 14
137 28 1 5 22 13 9 11 23
138 34 1 7 35 15 11 14 14
139 30 2 7 35 11 10 11 16
140 35 2 6 34 12 8 15 11
141 31 1 5 35 8 9 13 12
142 32 2 8 34 16 8 15 10
143 30 1 5 34 15 9 16 14
144 30 2 5 35 17 15 14 12
145 31 1 5 23 16 11 15 12
146 40 2 6 31 10 8 16 11
147 32 2 4 27 18 13 16 12
148 36 1 5 36 13 12 11 13
149 32 1 5 31 16 12 12 11
150 35 1 7 32 13 9 9 19
151 38 2 6 39 10 7 16 12
152 42 2 7 37 15 13 13 17
153 34 1 10 38 16 9 16 9
154 35 2 6 39 16 6 12 12
155 35 2 8 34 14 8 9 19
156 33 2 4 31 10 8 13 18
157 36 2 5 32 17 15 13 15
158 32 2 6 37 13 6 14 14
159 33 2 7 36 15 9 19 11
160 34 2 7 32 16 11 13 9
161 32 2 6 35 12 8 12 18
162 34 2 6 36 13 8 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Age Separate Learning Software
18.18215 -0.33115 0.29146 0.33016 0.31233 -0.10855
Happiness Depression
0.06861 -0.03075
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1837 -2.2167 -0.1848 2.2657 7.5099
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.18215 3.91823 4.640 7.39e-06 ***
Gender -0.33115 0.54193 -0.611 0.5421
Age 0.29146 0.21418 1.361 0.1756
Separate 0.33016 0.07181 4.598 8.86e-06 ***
Learning 0.31233 0.13307 2.347 0.0202 *
Software -0.10855 0.13863 -0.783 0.4348
Happiness 0.06861 0.12958 0.529 0.5973
Depression -0.03075 0.09539 -0.322 0.7476
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.105 on 154 degrees of freedom
Multiple R-squared: 0.1902, Adjusted R-squared: 0.1534
F-statistic: 5.169 on 7 and 154 DF, p-value: 2.632e-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,] 0.94872152 0.1025570 0.05127848
[2,] 0.93318759 0.1336248 0.06681241
[3,] 0.92268618 0.1546276 0.07731382
[4,] 0.89210578 0.2157884 0.10789422
[5,] 0.86700598 0.2659880 0.13299402
[6,] 0.89312738 0.2137452 0.10687262
[7,] 0.85834083 0.2833183 0.14165917
[8,] 0.82137686 0.3572463 0.17862314
[9,] 0.80393901 0.3921220 0.19606099
[10,] 0.78885941 0.4222812 0.21114059
[11,] 0.80233722 0.3953256 0.19766278
[12,] 0.76163126 0.4767375 0.23836874
[13,] 0.73026218 0.5394756 0.26973782
[14,] 0.66446109 0.6710778 0.33553891
[15,] 0.65490679 0.6901864 0.34509321
[16,] 0.87129826 0.2574035 0.12870174
[17,] 0.83322255 0.3335549 0.16677745
[18,] 0.80782180 0.3843564 0.19217820
[19,] 0.78070087 0.4385983 0.21929913
[20,] 0.73056022 0.5388796 0.26943978
[21,] 0.71065455 0.5786909 0.28934545
[22,] 0.82755302 0.3448940 0.17244698
[23,] 0.81139182 0.3772164 0.18860818
[24,] 0.78448349 0.4310330 0.21551651
[25,] 0.75618049 0.4876390 0.24381951
[26,] 0.70948456 0.5810309 0.29051544
[27,] 0.68616952 0.6276610 0.31383048
[28,] 0.67060158 0.6587968 0.32939842
[29,] 0.78040153 0.4391969 0.21959847
[30,] 0.73669123 0.5266175 0.26330877
[31,] 0.71045438 0.5790912 0.28954562
[32,] 0.66134731 0.6773054 0.33865269
[33,] 0.62913341 0.7417332 0.37086659
[34,] 0.58220190 0.8355962 0.41779810
[35,] 0.64134357 0.7173129 0.35865643
[36,] 0.64056213 0.7188757 0.35943787
[37,] 0.61760048 0.7647990 0.38239952
[38,] 0.58544406 0.8291119 0.41455594
[39,] 0.53754834 0.9249033 0.46245166
[40,] 0.58028619 0.8394276 0.41971381
[41,] 0.59303708 0.8139258 0.40696292
[42,] 0.54848351 0.9030330 0.45151649
[43,] 0.49963413 0.9992683 0.50036587
[44,] 0.47854467 0.9570893 0.52145533
[45,] 0.43058675 0.8611735 0.56941325
[46,] 0.42073938 0.8414788 0.57926062
[47,] 0.44645666 0.8929133 0.55354334
[48,] 0.55681557 0.8863689 0.44318443
[49,] 0.51838957 0.9632209 0.48161043
[50,] 0.47128923 0.9425785 0.52871077
[51,] 0.43465428 0.8693086 0.56534572
[52,] 0.41392048 0.8278410 0.58607952
[53,] 0.37507220 0.7501444 0.62492780
[54,] 0.42289384 0.8457877 0.57710616
[55,] 0.37981330 0.7596266 0.62018670
[56,] 0.33815046 0.6763009 0.66184954
[57,] 0.29783002 0.5956600 0.70216998
[58,] 0.32681875 0.6536375 0.67318125
[59,] 0.35009435 0.7001887 0.64990565
[60,] 0.30896779 0.6179356 0.69103221
[61,] 0.33678883 0.6735777 0.66321117
[62,] 0.37597961 0.7519592 0.62402039
[63,] 0.35259748 0.7051950 0.64740252
[64,] 0.30966327 0.6193265 0.69033673
[65,] 0.27682514 0.5536503 0.72317486
[66,] 0.34846684 0.6969337 0.65153316
[67,] 0.31066520 0.6213304 0.68933480
[68,] 0.27101582 0.5420316 0.72898418
[69,] 0.32397852 0.6479570 0.67602148
[70,] 0.35425262 0.7085052 0.64574738
[71,] 0.34459429 0.6891886 0.65540571
[72,] 0.30301551 0.6060310 0.69698449
[73,] 0.26581832 0.5316366 0.73418168
[74,] 0.26851099 0.5370220 0.73148901
[75,] 0.27730795 0.5546159 0.72269205
[76,] 0.25171412 0.5034282 0.74828588
[77,] 0.22919007 0.4583801 0.77080993
[78,] 0.20211356 0.4042271 0.79788644
[79,] 0.25315609 0.5063122 0.74684391
[80,] 0.21785486 0.4357097 0.78214514
[81,] 0.22558536 0.4511707 0.77441464
[82,] 0.20924959 0.4184992 0.79075041
[83,] 0.18896642 0.3779328 0.81103358
[84,] 0.15872566 0.3174513 0.84127434
[85,] 0.16220800 0.3244160 0.83779200
[86,] 0.16305509 0.3261102 0.83694491
[87,] 0.15330545 0.3066109 0.84669455
[88,] 0.13327495 0.2665499 0.86672505
[89,] 0.11864778 0.2372956 0.88135222
[90,] 0.12613215 0.2522643 0.87386785
[91,] 0.11992625 0.2398525 0.88007375
[92,] 0.10545440 0.2109088 0.89454560
[93,] 0.08601938 0.1720388 0.91398062
[94,] 0.07037071 0.1407414 0.92962929
[95,] 0.06825579 0.1365116 0.93174421
[96,] 0.18357626 0.3671525 0.81642374
[97,] 0.17832499 0.3566500 0.82167501
[98,] 0.19648423 0.3929685 0.80351577
[99,] 0.18106673 0.3621335 0.81893327
[100,] 0.29440034 0.5888007 0.70559966
[101,] 0.34256618 0.6851324 0.65743382
[102,] 0.32341214 0.6468243 0.67658786
[103,] 0.38129308 0.7625862 0.61870692
[104,] 0.34248003 0.6849601 0.65751997
[105,] 0.32879074 0.6575815 0.67120926
[106,] 0.30727370 0.6145474 0.69272630
[107,] 0.31474441 0.6294888 0.68525559
[108,] 0.29495117 0.5899023 0.70504883
[109,] 0.26638199 0.5327640 0.73361801
[110,] 0.25440536 0.5088107 0.74559464
[111,] 0.22686151 0.4537230 0.77313849
[112,] 0.19927505 0.3985501 0.80072495
[113,] 0.17065569 0.3413114 0.82934431
[114,] 0.13857312 0.2771462 0.86142688
[115,] 0.11070633 0.2214127 0.88929367
[116,] 0.11855989 0.2371198 0.88144011
[117,] 0.17826841 0.3565368 0.82173159
[118,] 0.37649989 0.7529998 0.62350011
[119,] 0.35111797 0.7022359 0.64888203
[120,] 0.30280304 0.6056061 0.69719696
[121,] 0.25915941 0.5183188 0.74084059
[122,] 0.27871629 0.5574326 0.72128371
[123,] 0.37334605 0.7466921 0.62665395
[124,] 0.45928390 0.9185678 0.54071610
[125,] 0.43662466 0.8732493 0.56337534
[126,] 0.39611108 0.7922222 0.60388892
[127,] 0.36143350 0.7228670 0.63856650
[128,] 0.29810213 0.5962043 0.70189787
[129,] 0.55697410 0.8860518 0.44302590
[130,] 0.47841843 0.9568369 0.52158157
[131,] 0.61040497 0.7791901 0.38959503
[132,] 0.57189490 0.8562102 0.42810510
[133,] 0.50202107 0.9959579 0.49797893
[134,] 0.76998670 0.4600266 0.23001330
[135,] 0.72954079 0.5409184 0.27045921
[136,] 0.92573349 0.1485330 0.07426651
[137,] 0.91042599 0.1791480 0.08957401
[138,] 0.90656324 0.1868735 0.09343676
[139,] 0.84695913 0.3060817 0.15304087
[140,] 0.80212991 0.3957402 0.19787009
[141,] 0.65359267 0.6928147 0.34640733
> postscript(file="/var/wessaorg/rcomp/tmp/1ja4n1354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2m2j11354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3a5p51354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4b2gh1354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/54p8s1354802079.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
5.54455467 4.75771288 -5.16308271 -3.69161629 -1.48100748 2.31593310
7 8 9 10 11 12
4.87208422 -0.94198352 1.67552285 1.10513028 3.39456541 1.04508752
13 14 15 16 17 18
3.21648072 2.71475092 -3.38490523 -2.40750550 1.41409301 0.41310553
19 20 21 22 23 24
1.19021712 -2.51795579 -3.02659550 -2.23108425 1.77909377 0.36755370
25 26 27 28 29 30
3.96573600 7.50990161 -0.99257548 -2.14914659 -1.16583073 0.15426810
31 32 33 34 35 36
-1.42504968 -6.18371433 3.35038712 -1.57871724 1.15602392 -0.48181248
37 38 39 40 41 42
-3.01780878 2.06618875 -4.98281820 0.30013734 2.46233443 -0.19269661
43 44 45 46 47 48
2.65638098 0.82494608 5.42318482 3.39627309 2.43268308 -1.38445151
49 50 51 52 53 54
0.25982224 4.50281282 -3.49967166 -1.03032371 -0.85021949 -2.08518975
55 56 57 58 59 60
-0.62674481 3.07621005 4.11318915 -5.35827188 1.50768366 0.23201767
61 62 63 64 65 66
-1.47484415 2.66564383 -0.68758924 -4.11581547 -0.11918930 -0.77062195
67 68 69 70 71 72
0.84148684 -3.39638489 3.74164137 -0.81762168 3.99021135 -4.33836570
73 74 75 76 77 78
-2.29364693 0.03970522 1.23747736 5.25477007 1.16265155 0.03247247
79 80 81 82 83 84
-4.93040769 3.88451458 -2.87117389 -0.58872439 -0.64021558 2.97671157
85 86 87 88 89 90
-3.22809569 -1.55914380 1.79033422 -1.48888208 -4.95917277 0.23281591
91 92 93 94 95 96
-3.40057831 2.34560199 -1.83785791 -0.10334118 -3.51559447 3.17427451
97 98 99 100 101 102
2.54971674 0.85373836 1.94339657 -3.78581454 2.80057664 1.71000355
103 104 105 106 107 108
-0.83173361 -0.75168241 -2.90237485 7.33556804 2.70066063 4.00062826
109 110 111 112 113 114
0.81646787 6.05399667 -4.78745493 -2.93502223 -5.18302604 -1.86063496
115 116 117 118 119 120
3.26893368 1.25105894 -3.25805298 -2.24861524 -1.66576198 2.65158148
121 122 123 124 125 126
-1.71290853 -2.17357488 2.11503900 0.92450785 -0.17687546 -2.79521955
127 128 129 130 131 132
4.31524221 6.88065286 -3.11922369 1.94421123 -2.66911349 -3.80522654
133 134 135 136 137 138
3.66487554 -5.04349867 1.41539000 -1.18439249 -1.70269646 -1.46783785
139 140 141 142 143 144
-3.72857620 0.93544350 -1.90855316 -3.92757410 -4.90905949 -4.80573151
145 146 147 148 149 150
-0.36543190 7.48198577 -0.53963423 1.69322053 -1.72309045 1.42699395
151 152 153 154 155 156
2.76290853 6.58094564 -4.15310147 -1.94522218 0.38547866 1.48597909
157 158 159 160 161 162
2.34560199 -3.42361319 -3.11922369 -0.54368063 -1.97365184 -0.74625155
> postscript(file="/var/wessaorg/rcomp/tmp/68avi1354802079.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 5.54455467 NA
1 4.75771288 5.54455467
2 -5.16308271 4.75771288
3 -3.69161629 -5.16308271
4 -1.48100748 -3.69161629
5 2.31593310 -1.48100748
6 4.87208422 2.31593310
7 -0.94198352 4.87208422
8 1.67552285 -0.94198352
9 1.10513028 1.67552285
10 3.39456541 1.10513028
11 1.04508752 3.39456541
12 3.21648072 1.04508752
13 2.71475092 3.21648072
14 -3.38490523 2.71475092
15 -2.40750550 -3.38490523
16 1.41409301 -2.40750550
17 0.41310553 1.41409301
18 1.19021712 0.41310553
19 -2.51795579 1.19021712
20 -3.02659550 -2.51795579
21 -2.23108425 -3.02659550
22 1.77909377 -2.23108425
23 0.36755370 1.77909377
24 3.96573600 0.36755370
25 7.50990161 3.96573600
26 -0.99257548 7.50990161
27 -2.14914659 -0.99257548
28 -1.16583073 -2.14914659
29 0.15426810 -1.16583073
30 -1.42504968 0.15426810
31 -6.18371433 -1.42504968
32 3.35038712 -6.18371433
33 -1.57871724 3.35038712
34 1.15602392 -1.57871724
35 -0.48181248 1.15602392
36 -3.01780878 -0.48181248
37 2.06618875 -3.01780878
38 -4.98281820 2.06618875
39 0.30013734 -4.98281820
40 2.46233443 0.30013734
41 -0.19269661 2.46233443
42 2.65638098 -0.19269661
43 0.82494608 2.65638098
44 5.42318482 0.82494608
45 3.39627309 5.42318482
46 2.43268308 3.39627309
47 -1.38445151 2.43268308
48 0.25982224 -1.38445151
49 4.50281282 0.25982224
50 -3.49967166 4.50281282
51 -1.03032371 -3.49967166
52 -0.85021949 -1.03032371
53 -2.08518975 -0.85021949
54 -0.62674481 -2.08518975
55 3.07621005 -0.62674481
56 4.11318915 3.07621005
57 -5.35827188 4.11318915
58 1.50768366 -5.35827188
59 0.23201767 1.50768366
60 -1.47484415 0.23201767
61 2.66564383 -1.47484415
62 -0.68758924 2.66564383
63 -4.11581547 -0.68758924
64 -0.11918930 -4.11581547
65 -0.77062195 -0.11918930
66 0.84148684 -0.77062195
67 -3.39638489 0.84148684
68 3.74164137 -3.39638489
69 -0.81762168 3.74164137
70 3.99021135 -0.81762168
71 -4.33836570 3.99021135
72 -2.29364693 -4.33836570
73 0.03970522 -2.29364693
74 1.23747736 0.03970522
75 5.25477007 1.23747736
76 1.16265155 5.25477007
77 0.03247247 1.16265155
78 -4.93040769 0.03247247
79 3.88451458 -4.93040769
80 -2.87117389 3.88451458
81 -0.58872439 -2.87117389
82 -0.64021558 -0.58872439
83 2.97671157 -0.64021558
84 -3.22809569 2.97671157
85 -1.55914380 -3.22809569
86 1.79033422 -1.55914380
87 -1.48888208 1.79033422
88 -4.95917277 -1.48888208
89 0.23281591 -4.95917277
90 -3.40057831 0.23281591
91 2.34560199 -3.40057831
92 -1.83785791 2.34560199
93 -0.10334118 -1.83785791
94 -3.51559447 -0.10334118
95 3.17427451 -3.51559447
96 2.54971674 3.17427451
97 0.85373836 2.54971674
98 1.94339657 0.85373836
99 -3.78581454 1.94339657
100 2.80057664 -3.78581454
101 1.71000355 2.80057664
102 -0.83173361 1.71000355
103 -0.75168241 -0.83173361
104 -2.90237485 -0.75168241
105 7.33556804 -2.90237485
106 2.70066063 7.33556804
107 4.00062826 2.70066063
108 0.81646787 4.00062826
109 6.05399667 0.81646787
110 -4.78745493 6.05399667
111 -2.93502223 -4.78745493
112 -5.18302604 -2.93502223
113 -1.86063496 -5.18302604
114 3.26893368 -1.86063496
115 1.25105894 3.26893368
116 -3.25805298 1.25105894
117 -2.24861524 -3.25805298
118 -1.66576198 -2.24861524
119 2.65158148 -1.66576198
120 -1.71290853 2.65158148
121 -2.17357488 -1.71290853
122 2.11503900 -2.17357488
123 0.92450785 2.11503900
124 -0.17687546 0.92450785
125 -2.79521955 -0.17687546
126 4.31524221 -2.79521955
127 6.88065286 4.31524221
128 -3.11922369 6.88065286
129 1.94421123 -3.11922369
130 -2.66911349 1.94421123
131 -3.80522654 -2.66911349
132 3.66487554 -3.80522654
133 -5.04349867 3.66487554
134 1.41539000 -5.04349867
135 -1.18439249 1.41539000
136 -1.70269646 -1.18439249
137 -1.46783785 -1.70269646
138 -3.72857620 -1.46783785
139 0.93544350 -3.72857620
140 -1.90855316 0.93544350
141 -3.92757410 -1.90855316
142 -4.90905949 -3.92757410
143 -4.80573151 -4.90905949
144 -0.36543190 -4.80573151
145 7.48198577 -0.36543190
146 -0.53963423 7.48198577
147 1.69322053 -0.53963423
148 -1.72309045 1.69322053
149 1.42699395 -1.72309045
150 2.76290853 1.42699395
151 6.58094564 2.76290853
152 -4.15310147 6.58094564
153 -1.94522218 -4.15310147
154 0.38547866 -1.94522218
155 1.48597909 0.38547866
156 2.34560199 1.48597909
157 -3.42361319 2.34560199
158 -3.11922369 -3.42361319
159 -0.54368063 -3.11922369
160 -1.97365184 -0.54368063
161 -0.74625155 -1.97365184
162 NA -0.74625155
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.75771288 5.54455467
[2,] -5.16308271 4.75771288
[3,] -3.69161629 -5.16308271
[4,] -1.48100748 -3.69161629
[5,] 2.31593310 -1.48100748
[6,] 4.87208422 2.31593310
[7,] -0.94198352 4.87208422
[8,] 1.67552285 -0.94198352
[9,] 1.10513028 1.67552285
[10,] 3.39456541 1.10513028
[11,] 1.04508752 3.39456541
[12,] 3.21648072 1.04508752
[13,] 2.71475092 3.21648072
[14,] -3.38490523 2.71475092
[15,] -2.40750550 -3.38490523
[16,] 1.41409301 -2.40750550
[17,] 0.41310553 1.41409301
[18,] 1.19021712 0.41310553
[19,] -2.51795579 1.19021712
[20,] -3.02659550 -2.51795579
[21,] -2.23108425 -3.02659550
[22,] 1.77909377 -2.23108425
[23,] 0.36755370 1.77909377
[24,] 3.96573600 0.36755370
[25,] 7.50990161 3.96573600
[26,] -0.99257548 7.50990161
[27,] -2.14914659 -0.99257548
[28,] -1.16583073 -2.14914659
[29,] 0.15426810 -1.16583073
[30,] -1.42504968 0.15426810
[31,] -6.18371433 -1.42504968
[32,] 3.35038712 -6.18371433
[33,] -1.57871724 3.35038712
[34,] 1.15602392 -1.57871724
[35,] -0.48181248 1.15602392
[36,] -3.01780878 -0.48181248
[37,] 2.06618875 -3.01780878
[38,] -4.98281820 2.06618875
[39,] 0.30013734 -4.98281820
[40,] 2.46233443 0.30013734
[41,] -0.19269661 2.46233443
[42,] 2.65638098 -0.19269661
[43,] 0.82494608 2.65638098
[44,] 5.42318482 0.82494608
[45,] 3.39627309 5.42318482
[46,] 2.43268308 3.39627309
[47,] -1.38445151 2.43268308
[48,] 0.25982224 -1.38445151
[49,] 4.50281282 0.25982224
[50,] -3.49967166 4.50281282
[51,] -1.03032371 -3.49967166
[52,] -0.85021949 -1.03032371
[53,] -2.08518975 -0.85021949
[54,] -0.62674481 -2.08518975
[55,] 3.07621005 -0.62674481
[56,] 4.11318915 3.07621005
[57,] -5.35827188 4.11318915
[58,] 1.50768366 -5.35827188
[59,] 0.23201767 1.50768366
[60,] -1.47484415 0.23201767
[61,] 2.66564383 -1.47484415
[62,] -0.68758924 2.66564383
[63,] -4.11581547 -0.68758924
[64,] -0.11918930 -4.11581547
[65,] -0.77062195 -0.11918930
[66,] 0.84148684 -0.77062195
[67,] -3.39638489 0.84148684
[68,] 3.74164137 -3.39638489
[69,] -0.81762168 3.74164137
[70,] 3.99021135 -0.81762168
[71,] -4.33836570 3.99021135
[72,] -2.29364693 -4.33836570
[73,] 0.03970522 -2.29364693
[74,] 1.23747736 0.03970522
[75,] 5.25477007 1.23747736
[76,] 1.16265155 5.25477007
[77,] 0.03247247 1.16265155
[78,] -4.93040769 0.03247247
[79,] 3.88451458 -4.93040769
[80,] -2.87117389 3.88451458
[81,] -0.58872439 -2.87117389
[82,] -0.64021558 -0.58872439
[83,] 2.97671157 -0.64021558
[84,] -3.22809569 2.97671157
[85,] -1.55914380 -3.22809569
[86,] 1.79033422 -1.55914380
[87,] -1.48888208 1.79033422
[88,] -4.95917277 -1.48888208
[89,] 0.23281591 -4.95917277
[90,] -3.40057831 0.23281591
[91,] 2.34560199 -3.40057831
[92,] -1.83785791 2.34560199
[93,] -0.10334118 -1.83785791
[94,] -3.51559447 -0.10334118
[95,] 3.17427451 -3.51559447
[96,] 2.54971674 3.17427451
[97,] 0.85373836 2.54971674
[98,] 1.94339657 0.85373836
[99,] -3.78581454 1.94339657
[100,] 2.80057664 -3.78581454
[101,] 1.71000355 2.80057664
[102,] -0.83173361 1.71000355
[103,] -0.75168241 -0.83173361
[104,] -2.90237485 -0.75168241
[105,] 7.33556804 -2.90237485
[106,] 2.70066063 7.33556804
[107,] 4.00062826 2.70066063
[108,] 0.81646787 4.00062826
[109,] 6.05399667 0.81646787
[110,] -4.78745493 6.05399667
[111,] -2.93502223 -4.78745493
[112,] -5.18302604 -2.93502223
[113,] -1.86063496 -5.18302604
[114,] 3.26893368 -1.86063496
[115,] 1.25105894 3.26893368
[116,] -3.25805298 1.25105894
[117,] -2.24861524 -3.25805298
[118,] -1.66576198 -2.24861524
[119,] 2.65158148 -1.66576198
[120,] -1.71290853 2.65158148
[121,] -2.17357488 -1.71290853
[122,] 2.11503900 -2.17357488
[123,] 0.92450785 2.11503900
[124,] -0.17687546 0.92450785
[125,] -2.79521955 -0.17687546
[126,] 4.31524221 -2.79521955
[127,] 6.88065286 4.31524221
[128,] -3.11922369 6.88065286
[129,] 1.94421123 -3.11922369
[130,] -2.66911349 1.94421123
[131,] -3.80522654 -2.66911349
[132,] 3.66487554 -3.80522654
[133,] -5.04349867 3.66487554
[134,] 1.41539000 -5.04349867
[135,] -1.18439249 1.41539000
[136,] -1.70269646 -1.18439249
[137,] -1.46783785 -1.70269646
[138,] -3.72857620 -1.46783785
[139,] 0.93544350 -3.72857620
[140,] -1.90855316 0.93544350
[141,] -3.92757410 -1.90855316
[142,] -4.90905949 -3.92757410
[143,] -4.80573151 -4.90905949
[144,] -0.36543190 -4.80573151
[145,] 7.48198577 -0.36543190
[146,] -0.53963423 7.48198577
[147,] 1.69322053 -0.53963423
[148,] -1.72309045 1.69322053
[149,] 1.42699395 -1.72309045
[150,] 2.76290853 1.42699395
[151,] 6.58094564 2.76290853
[152,] -4.15310147 6.58094564
[153,] -1.94522218 -4.15310147
[154,] 0.38547866 -1.94522218
[155,] 1.48597909 0.38547866
[156,] 2.34560199 1.48597909
[157,] -3.42361319 2.34560199
[158,] -3.11922369 -3.42361319
[159,] -0.54368063 -3.11922369
[160,] -1.97365184 -0.54368063
[161,] -0.74625155 -1.97365184
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.75771288 5.54455467
2 -5.16308271 4.75771288
3 -3.69161629 -5.16308271
4 -1.48100748 -3.69161629
5 2.31593310 -1.48100748
6 4.87208422 2.31593310
7 -0.94198352 4.87208422
8 1.67552285 -0.94198352
9 1.10513028 1.67552285
10 3.39456541 1.10513028
11 1.04508752 3.39456541
12 3.21648072 1.04508752
13 2.71475092 3.21648072
14 -3.38490523 2.71475092
15 -2.40750550 -3.38490523
16 1.41409301 -2.40750550
17 0.41310553 1.41409301
18 1.19021712 0.41310553
19 -2.51795579 1.19021712
20 -3.02659550 -2.51795579
21 -2.23108425 -3.02659550
22 1.77909377 -2.23108425
23 0.36755370 1.77909377
24 3.96573600 0.36755370
25 7.50990161 3.96573600
26 -0.99257548 7.50990161
27 -2.14914659 -0.99257548
28 -1.16583073 -2.14914659
29 0.15426810 -1.16583073
30 -1.42504968 0.15426810
31 -6.18371433 -1.42504968
32 3.35038712 -6.18371433
33 -1.57871724 3.35038712
34 1.15602392 -1.57871724
35 -0.48181248 1.15602392
36 -3.01780878 -0.48181248
37 2.06618875 -3.01780878
38 -4.98281820 2.06618875
39 0.30013734 -4.98281820
40 2.46233443 0.30013734
41 -0.19269661 2.46233443
42 2.65638098 -0.19269661
43 0.82494608 2.65638098
44 5.42318482 0.82494608
45 3.39627309 5.42318482
46 2.43268308 3.39627309
47 -1.38445151 2.43268308
48 0.25982224 -1.38445151
49 4.50281282 0.25982224
50 -3.49967166 4.50281282
51 -1.03032371 -3.49967166
52 -0.85021949 -1.03032371
53 -2.08518975 -0.85021949
54 -0.62674481 -2.08518975
55 3.07621005 -0.62674481
56 4.11318915 3.07621005
57 -5.35827188 4.11318915
58 1.50768366 -5.35827188
59 0.23201767 1.50768366
60 -1.47484415 0.23201767
61 2.66564383 -1.47484415
62 -0.68758924 2.66564383
63 -4.11581547 -0.68758924
64 -0.11918930 -4.11581547
65 -0.77062195 -0.11918930
66 0.84148684 -0.77062195
67 -3.39638489 0.84148684
68 3.74164137 -3.39638489
69 -0.81762168 3.74164137
70 3.99021135 -0.81762168
71 -4.33836570 3.99021135
72 -2.29364693 -4.33836570
73 0.03970522 -2.29364693
74 1.23747736 0.03970522
75 5.25477007 1.23747736
76 1.16265155 5.25477007
77 0.03247247 1.16265155
78 -4.93040769 0.03247247
79 3.88451458 -4.93040769
80 -2.87117389 3.88451458
81 -0.58872439 -2.87117389
82 -0.64021558 -0.58872439
83 2.97671157 -0.64021558
84 -3.22809569 2.97671157
85 -1.55914380 -3.22809569
86 1.79033422 -1.55914380
87 -1.48888208 1.79033422
88 -4.95917277 -1.48888208
89 0.23281591 -4.95917277
90 -3.40057831 0.23281591
91 2.34560199 -3.40057831
92 -1.83785791 2.34560199
93 -0.10334118 -1.83785791
94 -3.51559447 -0.10334118
95 3.17427451 -3.51559447
96 2.54971674 3.17427451
97 0.85373836 2.54971674
98 1.94339657 0.85373836
99 -3.78581454 1.94339657
100 2.80057664 -3.78581454
101 1.71000355 2.80057664
102 -0.83173361 1.71000355
103 -0.75168241 -0.83173361
104 -2.90237485 -0.75168241
105 7.33556804 -2.90237485
106 2.70066063 7.33556804
107 4.00062826 2.70066063
108 0.81646787 4.00062826
109 6.05399667 0.81646787
110 -4.78745493 6.05399667
111 -2.93502223 -4.78745493
112 -5.18302604 -2.93502223
113 -1.86063496 -5.18302604
114 3.26893368 -1.86063496
115 1.25105894 3.26893368
116 -3.25805298 1.25105894
117 -2.24861524 -3.25805298
118 -1.66576198 -2.24861524
119 2.65158148 -1.66576198
120 -1.71290853 2.65158148
121 -2.17357488 -1.71290853
122 2.11503900 -2.17357488
123 0.92450785 2.11503900
124 -0.17687546 0.92450785
125 -2.79521955 -0.17687546
126 4.31524221 -2.79521955
127 6.88065286 4.31524221
128 -3.11922369 6.88065286
129 1.94421123 -3.11922369
130 -2.66911349 1.94421123
131 -3.80522654 -2.66911349
132 3.66487554 -3.80522654
133 -5.04349867 3.66487554
134 1.41539000 -5.04349867
135 -1.18439249 1.41539000
136 -1.70269646 -1.18439249
137 -1.46783785 -1.70269646
138 -3.72857620 -1.46783785
139 0.93544350 -3.72857620
140 -1.90855316 0.93544350
141 -3.92757410 -1.90855316
142 -4.90905949 -3.92757410
143 -4.80573151 -4.90905949
144 -0.36543190 -4.80573151
145 7.48198577 -0.36543190
146 -0.53963423 7.48198577
147 1.69322053 -0.53963423
148 -1.72309045 1.69322053
149 1.42699395 -1.72309045
150 2.76290853 1.42699395
151 6.58094564 2.76290853
152 -4.15310147 6.58094564
153 -1.94522218 -4.15310147
154 0.38547866 -1.94522218
155 1.48597909 0.38547866
156 2.34560199 1.48597909
157 -3.42361319 2.34560199
158 -3.11922369 -3.42361319
159 -0.54368063 -3.11922369
160 -1.97365184 -0.54368063
161 -0.74625155 -1.97365184
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7ljgn1354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8tw8a1354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9deo51354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10c8451354802079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/111rfq1354802079.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12qspv1354802079.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13eec61354802079.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14wcyb1354802079.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/153hkd1354802079.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/167mao1354802079.tab")
+ }
>
> try(system("convert tmp/1ja4n1354802079.ps tmp/1ja4n1354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m2j11354802079.ps tmp/2m2j11354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a5p51354802079.ps tmp/3a5p51354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b2gh1354802079.ps tmp/4b2gh1354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/54p8s1354802079.ps tmp/54p8s1354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/68avi1354802079.ps tmp/68avi1354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ljgn1354802079.ps tmp/7ljgn1354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tw8a1354802079.ps tmp/8tw8a1354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/9deo51354802079.ps tmp/9deo51354802079.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c8451354802079.ps tmp/10c8451354802079.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.042 1.015 9.059