R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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(3
+ ,2
+ ,1
+ ,2
+ ,1
+ ,2
+ ,3
+ ,4
+ ,1
+ ,1
+ ,3
+ ,1
+ ,2
+ ,1
+ ,3
+ ,2
+ ,1
+ ,2
+ ,3
+ ,2
+ ,4
+ ,3
+ ,2
+ ,1
+ ,1
+ ,2
+ ,1
+ ,3
+ ,1
+ ,1
+ ,2
+ ,3
+ ,3
+ ,3
+ ,2
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,2
+ ,2
+ ,4
+ ,1
+ ,3
+ ,3
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,2
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,1
+ ,3
+ ,2
+ ,4
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,3
+ ,3
+ ,4
+ ,2
+ ,2
+ ,3
+ ,1
+ ,2
+ ,3
+ ,2
+ ,1
+ ,2
+ ,3
+ ,2
+ ,3
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
+ ,3
+ ,1
+ ,1
+ ,3
+ ,2
+ ,3
+ ,3
+ ,3
+ ,1
+ ,2
+ ,1
+ ,3
+ ,2
+ ,4
+ ,1
+ ,1
+ ,2
+ ,1
+ ,3
+ ,1
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,2
+ ,3
+ ,2
+ ,2
+ ,1
+ ,1
+ ,4
+ ,3
+ ,4
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,3
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,2
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,3
+ ,4
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,3
+ ,2
+ ,1
+ ,1
+ ,1
+ ,3
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,2
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,1
+ ,3
+ ,3
+ ,2
+ ,1
+ ,3
+ ,2
+ ,1
+ ,1
+ ,1
+ ,2
+ ,3
+ ,4
+ ,2
+ ,1
+ ,1
+ ,2
+ ,1
+ ,2
+ ,3
+ ,3
+ ,2
+ ,3
+ ,1
+ ,3
+ ,3
+ ,4
+ ,2
+ ,1
+ ,2
+ ,1
+ ,1
+ ,2
+ ,3
+ ,2
+ ,1
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,3
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,2
+ ,4
+ ,2
+ ,3
+ ,2
+ ,4
+ ,3
+ ,4
+ ,2
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
+ ,4
+ ,1
+ ,2
+ ,1
+ ,1
+ ,2
+ ,4
+ ,3
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,1
+ ,3
+ ,3
+ ,1
+ ,4
+ ,3
+ ,1
+ ,1
+ ,1
+ ,1
+ ,3
+ ,3
+ ,4
+ ,1
+ ,3
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,2
+ ,3
+ ,3
+ ,1
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,3
+ ,3
+ ,3
+ ,2
+ ,4
+ ,2
+ ,1
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,3
+ ,2
+ ,1
+ ,2
+ ,3
+ ,1
+ ,2
+ ,4
+ ,3
+ ,2
+ ,4
+ ,3
+ ,2
+ ,3
+ ,3
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,5
+ ,1
+ ,1
+ ,3
+ ,2
+ ,2
+ ,3
+ ,3
+ ,1
+ ,3
+ ,2
+ ,1
+ ,3
+ ,2
+ ,1
+ ,3
+ ,2
+ ,1
+ ,2
+ ,4
+ ,2
+ ,2
+ ,1
+ ,1
+ ,1
+ ,3
+ ,4
+ ,3
+ ,1
+ ,1
+ ,1
+ ,3
+ ,3
+ ,3
+ ,2
+ ,1
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,1
+ ,3
+ ,2
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,3
+ ,1
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,3
+ ,2
+ ,1
+ ,4
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,1
+ ,1
+ ,1
+ ,2
+ ,1
+ ,2
+ ,4
+ ,2
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,1
+ ,3
+ ,1
+ ,3
+ ,2
+ ,3
+ ,2
+ ,1
+ ,3
+ ,2
+ ,1
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,1
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,4
+ ,2
+ ,3
+ ,3
+ ,3
+ ,1
+ ,3
+ ,3
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,1
+ ,1
+ ,2
+ ,1
+ ,4
+ ,3
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,2
+ ,1
+ ,3
+ ,2
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,2
+ ,4
+ ,3
+ ,1
+ ,1
+ ,2
+ ,1
+ ,3
+ ,4
+ ,3
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,4
+ ,2
+ ,1
+ ,3
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,2
+ ,1
+ ,1
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,4
+ ,1
+ ,3
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,2
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,4
+ ,2
+ ,3
+ ,2
+ ,1
+ ,1
+ ,3
+ ,2
+ ,1
+ ,4
+ ,2
+ ,1
+ ,3
+ ,2
+ ,2
+ ,3
+ ,2
+ ,3
+ ,1
+ ,3
+ ,3
+ ,1
+ ,3
+ ,3
+ ,5
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,4
+ ,2
+ ,1
+ ,2
+ ,1
+ ,4
+ ,1
+ ,3
+ ,1
+ ,1
+ ,1
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,1
+ ,1
+ ,1
+ ,3
+ ,2
+ ,2
+ ,3
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,4
+ ,4
+ ,1
+ ,2
+ ,1
+ ,3
+ ,1
+ ,4
+ ,2
+ ,1
+ ,1
+ ,1
+ ,3
+ ,2
+ ,3
+ ,3
+ ,1
+ ,2
+ ,3
+ ,3
+ ,2
+ ,4
+ ,3
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,2
+ ,1
+ ,2
+ ,4
+ ,1
+ ,1
+ ,1
+ ,2
+ ,1
+ ,2
+ ,4
+ ,3
+ ,2
+ ,3
+ ,2
+ ,3
+ ,3
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,4
+ ,3
+ ,1
+ ,1
+ ,1
+ ,4
+ ,2
+ ,4
+ ,3
+ ,1
+ ,3
+ ,2
+ ,4
+ ,2
+ ,3
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,2
+ ,3
+ ,2
+ ,3
+ ,2
+ ,1
+ ,3
+ ,2
+ ,3
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
+ ,2
+ ,4
+ ,3
+ ,3
+ ,4
+ ,4
+ ,5
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,1
+ ,2
+ ,1
+ ,4
+ ,3
+ ,1
+ ,1
+ ,2
+ ,4
+ ,1
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,4
+ ,1
+ ,1
+ ,1
+ ,3
+ ,1
+ ,1
+ ,2
+ ,3
+ ,1
+ ,2
+ ,2
+ ,2
+ ,3
+ ,4
+ ,3
+ ,1
+ ,3
+ ,4
+ ,1
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,2
+ ,1
+ ,2
+ ,4
+ ,4
+ ,1
+ ,3
+ ,4
+ ,3
+ ,1
+ ,3
+ ,3
+ ,2
+ ,3
+ ,1
+ ,1
+ ,3
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,2
+ ,3
+ ,1
+ ,4
+ ,2
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,3
+ ,1
+ ,1
+ ,1
+ ,2
+ ,3
+ ,3
+ ,2
+ ,1
+ ,1
+ ,1
+ ,2
+ ,1
+ ,4
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,1
+ ,1
+ ,3
+ ,1
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,3
+ ,2
+ ,3
+ ,2
+ ,2
+ ,1
+ ,1
+ ,2
+ ,2
+ ,3
+ ,4
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,3
+ ,1
+ ,2
+ ,2
+ ,1
+ ,2
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,2
+ ,4
+ ,3
+ ,1
+ ,1
+ ,3
+ ,1
+ ,3
+ ,4
+ ,3
+ ,1
+ ,2
+ ,3
+ ,3
+ ,1
+ ,2
+ ,3
+ ,4
+ ,3
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,1
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,3
+ ,4
+ ,4
+ ,2
+ ,1
+ ,1
+ ,2
+ ,3
+ ,3
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,3
+ ,2
+ ,3
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,3
+ ,4
+ ,5
+ ,1
+ ,4
+ ,1
+ ,4
+ ,1
+ ,3
+ ,2
+ ,1
+ ,1
+ ,3
+ ,2
+ ,1
+ ,3
+ ,2
+ ,1
+ ,2
+ ,2
+ ,1
+ ,2
+ ,4
+ ,3
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,4
+ ,2
+ ,1
+ ,1
+ ,3
+ ,2
+ ,1
+ ,3
+ ,3
+ ,1
+ ,1
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,1
+ ,2
+ ,1
+ ,2
+ ,3
+ ,4
+ ,4
+ ,2
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,2
+ ,3
+ ,2
+ ,2
+ ,4
+ ,2
+ ,1
+ ,1
+ ,4
+ ,1
+ ,4
+ ,2
+ ,1
+ ,1
+ ,1
+ ,2
+ ,2
+ ,4
+ ,3
+ ,1
+ ,2
+ ,2
+ ,3
+ ,2
+ ,1
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,1
+ ,1
+ ,3
+ ,2
+ ,1
+ ,3
+ ,1
+ ,1
+ ,1
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,1
+ ,2
+ ,1
+ ,4
+ ,3
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,2
+ ,1
+ ,1
+ ,3
+ ,3
+ ,2
+ ,3
+ ,3
+ ,3
+ ,1
+ ,4
+ ,4
+ ,1
+ ,3
+ ,4
+ ,1
+ ,4)
+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('Life'
+ ,'Stress'
+ ,'Depression'
+ ,'Effort'
+ ,'Focus'
+ ,'Sleep'
+ ,'Belong')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('Life','Stress','Depression','Effort','Focus','Sleep','Belong'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'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
> 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
Life Stress Depression Effort Focus Sleep Belong
1 3 2 1 2 1 2 3
2 4 1 1 3 1 2 1
3 3 2 1 2 3 2 4
4 3 2 1 1 2 1 3
5 1 1 2 3 3 3 2
6 4 1 1 2 2 2 1
7 2 2 4 1 3 3 2
8 3 3 1 1 2 1 1
9 4 2 1 2 2 1 2
10 3 2 2 2 2 2 2
11 4 2 1 1 1 3 2
12 4 1 1 1 1 1 3
13 3 4 2 2 3 1 2
14 3 2 1 2 3 2 3
15 4 2 1 2 2 1 1
16 3 3 1 1 3 2 3
17 3 3 1 2 1 3 2
18 4 1 1 2 1 3 1
19 4 1 1 2 2 1 2
20 3 2 2 1 1 4 3
21 4 1 1 1 1 2 3
22 3 2 1 2 2 2 3
23 2 1 2 2 1 3 4
24 3 2 2 2 2 4 3
25 2 1 1 1 3 2 4
26 4 1 1 2 1 1 4
27 2 1 2 2 2 2 3
28 2 2 2 3 3 4 4
29 2 1 3 3 2 1 3
30 2 1 1 1 2 3 4
31 2 1 1 2 1 2 3
32 3 2 3 1 3 3 4
33 2 1 2 1 1 2 3
34 2 1 2 1 2 2 3
35 3 1 1 2 3 2 4
36 2 1 1 1 1 2 2
37 4 2 3 2 4 3 4
38 2 1 1 2 2 2 4
39 2 1 2 2 1 1 3
40 4 1 2 1 1 2 4
41 3 1 1 1 1 1 4
42 2 1 1 3 3 1 4
43 3 1 1 1 1 3 3
44 4 1 3 2 2 3 3
45 2 1 2 2 1 2 3
46 3 1 2 1 2 2 3
47 3 1 1 1 1 4 3
48 2 1 2 2 1 1 4
49 1 1 3 3 3 2 4
50 2 1 2 1 2 2 3
51 1 1 1 1 1 2 3
52 2 1 2 3 1 2 4
53 3 2 4 3 2 3 3
54 4 2 2 2 2 4 4
55 5 1 1 3 2 2 3
56 3 1 3 2 1 3 2
57 1 3 2 1 2 4 2
58 2 1 1 1 3 4 3
59 1 1 1 3 3 3 2
60 1 3 3 2 2 2 4
61 2 2 3 3 4 4 3
62 1 3 2 1 1 4 2
63 1 3 1 1 1 2 2
64 1 3 2 1 4 3 2
65 1 2 2 2 3 3 3
66 1 1 1 2 1 2 4
67 2 3 2 2 4 4 3
68 1 3 1 3 2 3 2
69 1 3 2 1 3 3 2
70 2 2 2 1 2 3 3
71 1 1 2 2 3 3 1
72 1 2 4 2 3 3 3
73 1 3 3 1 1 4 2
74 1 1 1 2 1 4 3
75 1 2 2 2 1 4 4
76 3 3 3 2 1 3 2
77 2 1 2 2 3 2 2
78 2 2 2 2 2 4 2
79 1 2 2 3 2 4 3
80 1 1 2 1 3 4 3
81 1 1 1 1 2 4 2
82 1 3 1 1 4 2 1
83 2 1 1 2 4 2 1
84 1 4 1 3 4 2 1
85 2 2 1 2 4 2 1
86 2 2 4 2 3 2 1
87 1 3 2 1 4 2 1
88 3 2 2 3 2 3 1
89 3 3 1 3 3 5 3
90 3 4 4 2 4 2 1
91 2 1 4 1 3 1 1
92 1 3 3 3 3 2 1
93 1 1 3 2 2 3 1
94 2 2 2 1 4 4 1
95 2 1 3 1 4 2 1
96 1 1 3 2 3 3 1
97 2 3 3 2 4 3 1
98 2 2 2 1 4 1 1
99 2 2 1 2 4 2 1
100 1 2 1 2 4 1 1
101 1 2 1 2 4 3 2
102 3 2 3 3 3 2 1
103 2 2 2 1 4 3 1
104 1 1 4 2 4 3 1
105 3 2 4 2 3 4 2
106 2 3 3 3 4 2 1
107 2 2 2 1 2 3 2
108 3 2 1 3 2 3 2
109 2 1 1 3 2 4 3
110 3 4 4 5 3 2 2
111 2 2 1 1 3 4 2
112 2 2 2 2 3 3 1
113 1 1 2 1 4 3 1
114 1 2 4 1 4 2 1
115 2 2 2 1 4 1 1
116 1 3 1 1 2 3 1
117 2 2 2 3 4 3 1
118 3 4 1 2 4 2 1
119 1 2 1 2 4 4 1
120 3 4 3 1 3 3 2
121 3 1 1 3 3 2 1
122 2 2 3 2 4 2 1
123 1 2 3 1 4 2 1
124 1 1 1 1 4 3 1
125 1 1 2 3 3 2 1
126 1 1 2 1 4 1 1
127 1 1 1 1 4 2 1
128 1 1 3 1 4 1 1
129 2 2 2 2 3 3 1
130 1 2 3 2 3 2 2
131 1 1 2 2 3 4 2
132 3 3 4 4 4 3 1
133 2 2 1 2 3 2 1
134 2 2 3 2 4 3 1
135 1 3 1 3 4 3 1
136 2 3 3 1 2 3 4
137 3 2 2 3 3 3 1
138 2 2 2 3 3 3 2
139 2 2 3 4 4 2 1
140 1 2 3 3 4 2 1
141 2 2 3 2 3 1 1
142 1 1 2 3 4 5 1
143 4 1 4 1 3 2 1
144 1 3 2 1 3 2 1
145 2 2 1 2 4 3 1
146 2 2 1 1 4 2 1
147 1 3 2 1 3 3 1
148 1 3 3 3 4 4 1
149 2 1 2 3 4 4 2
150 4 3 4 2 4 2 1
151 1 2 3 2 2 4 2
152 1 1 4 1 4 2 1
153 1 1 2 2 4 3 1
154 2 2 3 2 1 3 3
155 2 2 2 2 2 4 1
156 1 3 2 1 3 1 1
157 1 3 3 3 3 3 1
158 2 1 4 3 4 1 1
159 2 2 2 2 4 4 1
160 1 2 1 1 3 3 2
161 3 3 3 1 4 4 1
162 3 4 1 4 3 2 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Stress Depression Effort Focus Sleep
2.77263 0.08002 -0.02855 0.15769 -0.25668 -0.21164
Belong
0.08241
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.78916 -0.76168 -0.05268 0.60870 2.39224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.77263 0.44130 6.283 3.21e-09 ***
Stress 0.08002 0.09053 0.884 0.37816
Depression -0.02855 0.07955 -0.359 0.72019
Effort 0.15769 0.09022 1.748 0.08247 .
Focus -0.25668 0.08373 -3.066 0.00256 **
Sleep -0.21164 0.07631 -2.773 0.00623 **
Belong 0.08241 0.08322 0.990 0.32357
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9345 on 155 degrees of freedom
Multiple R-squared: 0.1562, Adjusted R-squared: 0.1235
F-statistic: 4.782 on 6 and 155 DF, p-value: 0.0001680
> 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,] 0.634610516 0.730778967 0.365389484
[2,] 0.539609624 0.920780751 0.460390376
[3,] 0.433009645 0.866019290 0.566990355
[4,] 0.361183496 0.722366992 0.638816504
[5,] 0.269668717 0.539337434 0.730331283
[6,] 0.204615654 0.409231309 0.795384346
[7,] 0.137950993 0.275901987 0.862049007
[8,] 0.094919610 0.189839221 0.905080390
[9,] 0.074360982 0.148721964 0.925639018
[10,] 0.055436165 0.110872330 0.944563835
[11,] 0.036650726 0.073301452 0.963349274
[12,] 0.025976633 0.051953266 0.974023367
[13,] 0.015858263 0.031716527 0.984141737
[14,] 0.013478595 0.026957189 0.986521405
[15,] 0.016730210 0.033460419 0.983269790
[16,] 0.019589391 0.039178781 0.980410609
[17,] 0.018693593 0.037387186 0.981306407
[18,] 0.018556984 0.037113968 0.981443016
[19,] 0.014892331 0.029784662 0.985107669
[20,] 0.009775155 0.019550310 0.990224845
[21,] 0.010745847 0.021491693 0.989254153
[22,] 0.029539944 0.059079887 0.970460056
[23,] 0.053628527 0.107257054 0.946371473
[24,] 0.067770199 0.135540399 0.932229801
[25,] 0.061110583 0.122221166 0.938889417
[26,] 0.053205348 0.106410696 0.946794652
[27,] 0.094857072 0.189714144 0.905142928
[28,] 0.318403006 0.636806012 0.681596994
[29,] 0.301076066 0.602152132 0.698923934
[30,] 0.281654530 0.563309059 0.718345470
[31,] 0.405522168 0.811044335 0.594477832
[32,] 0.370925564 0.741851128 0.629074436
[33,] 0.338444831 0.676889662 0.661555169
[34,] 0.316077975 0.632155950 0.683922025
[35,] 0.483559723 0.967119445 0.516440277
[36,] 0.466081009 0.932162019 0.533918991
[37,] 0.451824981 0.903649963 0.548175019
[38,] 0.448272671 0.896545342 0.551727329
[39,] 0.415079372 0.830158743 0.584920628
[40,] 0.436981774 0.873963548 0.563018226
[41,] 0.422277910 0.844555820 0.577722090
[42,] 0.584483345 0.831033311 0.415516655
[43,] 0.537857478 0.924285044 0.462142522
[44,] 0.524534342 0.950931315 0.475465658
[45,] 0.669804241 0.660391517 0.330195759
[46,] 0.942285475 0.115429050 0.057714525
[47,] 0.943995120 0.112009760 0.056004880
[48,] 0.975963300 0.048073399 0.024036700
[49,] 0.975741447 0.048517106 0.024258553
[50,] 0.984971426 0.030057148 0.015028574
[51,] 0.990011949 0.019976102 0.009988051
[52,] 0.986415908 0.027168185 0.013584092
[53,] 0.991843253 0.016313493 0.008156747
[54,] 0.995616973 0.008766054 0.004383027
[55,] 0.996089218 0.007821563 0.003910782
[56,] 0.996496833 0.007006335 0.003503167
[57,] 0.997492216 0.005015567 0.002507784
[58,] 0.996469704 0.007060591 0.003530296
[59,] 0.997611976 0.004776048 0.002388024
[60,] 0.997770363 0.004459274 0.002229637
[61,] 0.997040555 0.005918890 0.002959445
[62,] 0.997543280 0.004913440 0.002456720
[63,] 0.997515147 0.004969707 0.002484853
[64,] 0.997701642 0.004596717 0.002298358
[65,] 0.997899111 0.004201778 0.002100889
[66,] 0.997989001 0.004021999 0.002010999
[67,] 0.997782623 0.004434754 0.002217377
[68,] 0.997127008 0.005745984 0.002872992
[69,] 0.995940246 0.008119507 0.004059754
[70,] 0.996396365 0.007207269 0.003603635
[71,] 0.995804127 0.008391746 0.004195873
[72,] 0.995362733 0.009274533 0.004637267
[73,] 0.995424299 0.009151401 0.004575701
[74,] 0.994408456 0.011183088 0.005591544
[75,] 0.995653576 0.008692848 0.004346424
[76,] 0.994191733 0.011616534 0.005808267
[77,] 0.991927037 0.016145925 0.008072963
[78,] 0.991570136 0.016859728 0.008429864
[79,] 0.992074679 0.015850642 0.007925321
[80,] 0.993092339 0.013815321 0.006907661
[81,] 0.992813390 0.014373219 0.007186610
[82,] 0.990494378 0.019011243 0.009505622
[83,] 0.993362539 0.013274921 0.006637461
[84,] 0.993175721 0.013648557 0.006824279
[85,] 0.991678436 0.016643128 0.008321564
[86,] 0.989694975 0.020610050 0.010305025
[87,] 0.988536475 0.022927050 0.011463525
[88,] 0.984594495 0.030811009 0.015405505
[89,] 0.980422637 0.039154726 0.019577363
[90,] 0.975371153 0.049257694 0.024628847
[91,] 0.974514511 0.050970978 0.025485489
[92,] 0.970528299 0.058943401 0.029471701
[93,] 0.970160811 0.059678379 0.029839189
[94,] 0.963902597 0.072194805 0.036097403
[95,] 0.958288998 0.083422004 0.041711002
[96,] 0.964920851 0.070158297 0.035079149
[97,] 0.954634520 0.090730959 0.045365480
[98,] 0.942553025 0.114893951 0.057446975
[99,] 0.945693277 0.108613446 0.054306723
[100,] 0.934282984 0.131434032 0.065717016
[101,] 0.918309588 0.163380823 0.081690412
[102,] 0.908978776 0.182042449 0.091021224
[103,] 0.888057710 0.223884579 0.111942290
[104,] 0.864107928 0.271784144 0.135892072
[105,] 0.860801165 0.278397670 0.139198835
[106,] 0.832922961 0.334154078 0.167077039
[107,] 0.825962418 0.348075163 0.174037582
[108,] 0.790572441 0.418855119 0.209427559
[109,] 0.798742677 0.402514646 0.201257323
[110,] 0.765892871 0.468214257 0.234107129
[111,] 0.774876731 0.450246539 0.225123269
[112,] 0.833454551 0.333090898 0.166545449
[113,] 0.796253882 0.407492236 0.203746118
[114,] 0.783774519 0.432450962 0.216225481
[115,] 0.741424654 0.517150691 0.258575346
[116,] 0.723111585 0.553776831 0.276888415
[117,] 0.685029897 0.629940206 0.314970103
[118,] 0.635079394 0.729841213 0.364920606
[119,] 0.612121067 0.775757865 0.387878933
[120,] 0.556851456 0.886297087 0.443148544
[121,] 0.561657280 0.876685440 0.438342720
[122,] 0.513148338 0.973703325 0.486851662
[123,] 0.482868152 0.965736304 0.517131848
[124,] 0.430073804 0.860147608 0.569926196
[125,] 0.368842520 0.737685039 0.631157480
[126,] 0.337827709 0.675655418 0.662172291
[127,] 0.279317763 0.558635527 0.720682237
[128,] 0.319495467 0.638990933 0.680504533
[129,] 0.264782085 0.529564170 0.735217915
[130,] 0.208740032 0.417480065 0.791259968
[131,] 0.218177956 0.436355911 0.781822044
[132,] 0.166928583 0.333857166 0.833071417
[133,] 0.129799257 0.259598513 0.870200743
[134,] 0.449583425 0.899166850 0.550416575
[135,] 0.408051691 0.816103381 0.591948309
[136,] 0.334732548 0.669465095 0.665267452
[137,] 0.282119841 0.564239681 0.717880159
[138,] 0.233941589 0.467883178 0.766058411
[139,] 0.446267424 0.892534848 0.553732576
[140,] 0.335587770 0.671175541 0.664412230
[141,] 0.449416010 0.898832020 0.550583990
[142,] 0.418729042 0.837458083 0.581270958
[143,] 0.275686176 0.551372352 0.724313824
> postscript(file="/var/www/rcomp/tmp/1mmvb1322145884.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2hihv1322145884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3mfxq1322145884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4vu9h1322145884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5iod81322145884.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
0.213235140 1.300374603 0.644195439 0.415974589 -1.028479432 1.714752628
7 8 9 10 11 12
0.263985184 0.500773329 1.340688739 0.580874405 1.664975601 1.239307725
13 14 15 16 17 18
0.465885397 0.726603457 1.423096757 0.804280029 0.427264440 1.669707046
19 20 21 22 23 24
1.420706034 0.822753249 1.450946301 0.469919299 -0.548969917 0.921743541
25 26 27 28 29 30
-0.118093400 0.999205841 -0.421516318 -0.061674185 -0.762301673 -0.163138981
31 32 33 34 35 36
-0.706747565 1.070622060 -0.520506610 -0.263822452 0.724212734 -0.466645681
37 38 39 40 41 42
2.169612352 -0.532471424 -0.889839053 1.397085373 0.156899707 -0.645119709
43 44 45 46 47 48
0.662584878 1.818669347 -0.678200476 0.736177548 0.874223455 -0.972247071
49 50 51 52 53 54
-1.376386955 -0.263822452 -1.549053699 -0.918302360 0.609505275 1.839335523
55 56 57 58 59 60
2.392242727 0.644393206 -0.918171870 0.387591771 -1.057026521 -1.635411836
61 62 63 64 65 66
0.305965079 -1.174856028 -1.626680270 -0.616442130 -1.033210878 -1.789155583
67 68 69 70 71 72
0.355094563 -1.473745268 -0.873126288 -0.132201170 -0.788377548 -0.976116700
73 74 75 76 77 78
-1.146308939 -1.283470412 -1.417348635 0.484358617 -0.082424143 0.004151558
79 80 81 82 83 84
-1.235950326 -0.583861140 -0.786684370 -0.774219778 0.228120944 -1.169624805
85 86 87 88 89 90
0.148103650 -0.022939242 -0.745672689 0.717227132 1.123808026 1.073710327
91 92 93 94 95 96
0.003133342 -1.289197492 -1.016514618 0.757621758 0.442908988 -0.759830460
97 98 99 100 101 102
0.336819110 0.122706029 0.148103650 -1.063534927 -0.722665791 0.790819803
103 104 105 106 107 108
0.545983182 -0.474599213 1.317929894 -0.032513334 -0.049793152 0.606272026
109 110 111 112 113 114
-0.184480120 0.261536552 0.389982494 0.131605157 -0.373999524 -0.608561217
115 116 117 118 119 120
0.122706029 -1.075949518 0.230595449 0.988069061 -0.428619197 1.075403506
121 122 123 124 125 126
0.813742920 0.205197827 -0.637108306 -0.402546612 -1.157709992 -0.797276677
127 128 129 130 131 132
-0.614185189 -0.768729588 0.131605157 -1.133894348 -0.659146989 1.049978465
133 134 135 136 137 138
-0.108580508 0.416836404 -0.877968934 -0.266079393 0.973911291 -0.108496727
139 140 141 142 143 144
-0.110189906 -0.952496039 -0.263124907 -0.266110104 2.214771919 -1.002356847
145 146 147 148 149 150
0.359742227 0.305797516 -0.790718271 -0.609236180 0.439843302 2.153727622
151 152 153 154 155 156
-0.967301353 -0.528543923 -0.531693390 -0.518032106 0.086559575 -1.213995424
157 158 159 160 161 162
-1.077558915 -0.055570233 0.599927892 -0.821656083 1.706151553 0.415997170
> postscript(file="/var/www/rcomp/tmp/6wiq71322145884.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 0.213235140 NA
1 1.300374603 0.213235140
2 0.644195439 1.300374603
3 0.415974589 0.644195439
4 -1.028479432 0.415974589
5 1.714752628 -1.028479432
6 0.263985184 1.714752628
7 0.500773329 0.263985184
8 1.340688739 0.500773329
9 0.580874405 1.340688739
10 1.664975601 0.580874405
11 1.239307725 1.664975601
12 0.465885397 1.239307725
13 0.726603457 0.465885397
14 1.423096757 0.726603457
15 0.804280029 1.423096757
16 0.427264440 0.804280029
17 1.669707046 0.427264440
18 1.420706034 1.669707046
19 0.822753249 1.420706034
20 1.450946301 0.822753249
21 0.469919299 1.450946301
22 -0.548969917 0.469919299
23 0.921743541 -0.548969917
24 -0.118093400 0.921743541
25 0.999205841 -0.118093400
26 -0.421516318 0.999205841
27 -0.061674185 -0.421516318
28 -0.762301673 -0.061674185
29 -0.163138981 -0.762301673
30 -0.706747565 -0.163138981
31 1.070622060 -0.706747565
32 -0.520506610 1.070622060
33 -0.263822452 -0.520506610
34 0.724212734 -0.263822452
35 -0.466645681 0.724212734
36 2.169612352 -0.466645681
37 -0.532471424 2.169612352
38 -0.889839053 -0.532471424
39 1.397085373 -0.889839053
40 0.156899707 1.397085373
41 -0.645119709 0.156899707
42 0.662584878 -0.645119709
43 1.818669347 0.662584878
44 -0.678200476 1.818669347
45 0.736177548 -0.678200476
46 0.874223455 0.736177548
47 -0.972247071 0.874223455
48 -1.376386955 -0.972247071
49 -0.263822452 -1.376386955
50 -1.549053699 -0.263822452
51 -0.918302360 -1.549053699
52 0.609505275 -0.918302360
53 1.839335523 0.609505275
54 2.392242727 1.839335523
55 0.644393206 2.392242727
56 -0.918171870 0.644393206
57 0.387591771 -0.918171870
58 -1.057026521 0.387591771
59 -1.635411836 -1.057026521
60 0.305965079 -1.635411836
61 -1.174856028 0.305965079
62 -1.626680270 -1.174856028
63 -0.616442130 -1.626680270
64 -1.033210878 -0.616442130
65 -1.789155583 -1.033210878
66 0.355094563 -1.789155583
67 -1.473745268 0.355094563
68 -0.873126288 -1.473745268
69 -0.132201170 -0.873126288
70 -0.788377548 -0.132201170
71 -0.976116700 -0.788377548
72 -1.146308939 -0.976116700
73 -1.283470412 -1.146308939
74 -1.417348635 -1.283470412
75 0.484358617 -1.417348635
76 -0.082424143 0.484358617
77 0.004151558 -0.082424143
78 -1.235950326 0.004151558
79 -0.583861140 -1.235950326
80 -0.786684370 -0.583861140
81 -0.774219778 -0.786684370
82 0.228120944 -0.774219778
83 -1.169624805 0.228120944
84 0.148103650 -1.169624805
85 -0.022939242 0.148103650
86 -0.745672689 -0.022939242
87 0.717227132 -0.745672689
88 1.123808026 0.717227132
89 1.073710327 1.123808026
90 0.003133342 1.073710327
91 -1.289197492 0.003133342
92 -1.016514618 -1.289197492
93 0.757621758 -1.016514618
94 0.442908988 0.757621758
95 -0.759830460 0.442908988
96 0.336819110 -0.759830460
97 0.122706029 0.336819110
98 0.148103650 0.122706029
99 -1.063534927 0.148103650
100 -0.722665791 -1.063534927
101 0.790819803 -0.722665791
102 0.545983182 0.790819803
103 -0.474599213 0.545983182
104 1.317929894 -0.474599213
105 -0.032513334 1.317929894
106 -0.049793152 -0.032513334
107 0.606272026 -0.049793152
108 -0.184480120 0.606272026
109 0.261536552 -0.184480120
110 0.389982494 0.261536552
111 0.131605157 0.389982494
112 -0.373999524 0.131605157
113 -0.608561217 -0.373999524
114 0.122706029 -0.608561217
115 -1.075949518 0.122706029
116 0.230595449 -1.075949518
117 0.988069061 0.230595449
118 -0.428619197 0.988069061
119 1.075403506 -0.428619197
120 0.813742920 1.075403506
121 0.205197827 0.813742920
122 -0.637108306 0.205197827
123 -0.402546612 -0.637108306
124 -1.157709992 -0.402546612
125 -0.797276677 -1.157709992
126 -0.614185189 -0.797276677
127 -0.768729588 -0.614185189
128 0.131605157 -0.768729588
129 -1.133894348 0.131605157
130 -0.659146989 -1.133894348
131 1.049978465 -0.659146989
132 -0.108580508 1.049978465
133 0.416836404 -0.108580508
134 -0.877968934 0.416836404
135 -0.266079393 -0.877968934
136 0.973911291 -0.266079393
137 -0.108496727 0.973911291
138 -0.110189906 -0.108496727
139 -0.952496039 -0.110189906
140 -0.263124907 -0.952496039
141 -0.266110104 -0.263124907
142 2.214771919 -0.266110104
143 -1.002356847 2.214771919
144 0.359742227 -1.002356847
145 0.305797516 0.359742227
146 -0.790718271 0.305797516
147 -0.609236180 -0.790718271
148 0.439843302 -0.609236180
149 2.153727622 0.439843302
150 -0.967301353 2.153727622
151 -0.528543923 -0.967301353
152 -0.531693390 -0.528543923
153 -0.518032106 -0.531693390
154 0.086559575 -0.518032106
155 -1.213995424 0.086559575
156 -1.077558915 -1.213995424
157 -0.055570233 -1.077558915
158 0.599927892 -0.055570233
159 -0.821656083 0.599927892
160 1.706151553 -0.821656083
161 0.415997170 1.706151553
162 NA 0.415997170
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.300374603 0.213235140
[2,] 0.644195439 1.300374603
[3,] 0.415974589 0.644195439
[4,] -1.028479432 0.415974589
[5,] 1.714752628 -1.028479432
[6,] 0.263985184 1.714752628
[7,] 0.500773329 0.263985184
[8,] 1.340688739 0.500773329
[9,] 0.580874405 1.340688739
[10,] 1.664975601 0.580874405
[11,] 1.239307725 1.664975601
[12,] 0.465885397 1.239307725
[13,] 0.726603457 0.465885397
[14,] 1.423096757 0.726603457
[15,] 0.804280029 1.423096757
[16,] 0.427264440 0.804280029
[17,] 1.669707046 0.427264440
[18,] 1.420706034 1.669707046
[19,] 0.822753249 1.420706034
[20,] 1.450946301 0.822753249
[21,] 0.469919299 1.450946301
[22,] -0.548969917 0.469919299
[23,] 0.921743541 -0.548969917
[24,] -0.118093400 0.921743541
[25,] 0.999205841 -0.118093400
[26,] -0.421516318 0.999205841
[27,] -0.061674185 -0.421516318
[28,] -0.762301673 -0.061674185
[29,] -0.163138981 -0.762301673
[30,] -0.706747565 -0.163138981
[31,] 1.070622060 -0.706747565
[32,] -0.520506610 1.070622060
[33,] -0.263822452 -0.520506610
[34,] 0.724212734 -0.263822452
[35,] -0.466645681 0.724212734
[36,] 2.169612352 -0.466645681
[37,] -0.532471424 2.169612352
[38,] -0.889839053 -0.532471424
[39,] 1.397085373 -0.889839053
[40,] 0.156899707 1.397085373
[41,] -0.645119709 0.156899707
[42,] 0.662584878 -0.645119709
[43,] 1.818669347 0.662584878
[44,] -0.678200476 1.818669347
[45,] 0.736177548 -0.678200476
[46,] 0.874223455 0.736177548
[47,] -0.972247071 0.874223455
[48,] -1.376386955 -0.972247071
[49,] -0.263822452 -1.376386955
[50,] -1.549053699 -0.263822452
[51,] -0.918302360 -1.549053699
[52,] 0.609505275 -0.918302360
[53,] 1.839335523 0.609505275
[54,] 2.392242727 1.839335523
[55,] 0.644393206 2.392242727
[56,] -0.918171870 0.644393206
[57,] 0.387591771 -0.918171870
[58,] -1.057026521 0.387591771
[59,] -1.635411836 -1.057026521
[60,] 0.305965079 -1.635411836
[61,] -1.174856028 0.305965079
[62,] -1.626680270 -1.174856028
[63,] -0.616442130 -1.626680270
[64,] -1.033210878 -0.616442130
[65,] -1.789155583 -1.033210878
[66,] 0.355094563 -1.789155583
[67,] -1.473745268 0.355094563
[68,] -0.873126288 -1.473745268
[69,] -0.132201170 -0.873126288
[70,] -0.788377548 -0.132201170
[71,] -0.976116700 -0.788377548
[72,] -1.146308939 -0.976116700
[73,] -1.283470412 -1.146308939
[74,] -1.417348635 -1.283470412
[75,] 0.484358617 -1.417348635
[76,] -0.082424143 0.484358617
[77,] 0.004151558 -0.082424143
[78,] -1.235950326 0.004151558
[79,] -0.583861140 -1.235950326
[80,] -0.786684370 -0.583861140
[81,] -0.774219778 -0.786684370
[82,] 0.228120944 -0.774219778
[83,] -1.169624805 0.228120944
[84,] 0.148103650 -1.169624805
[85,] -0.022939242 0.148103650
[86,] -0.745672689 -0.022939242
[87,] 0.717227132 -0.745672689
[88,] 1.123808026 0.717227132
[89,] 1.073710327 1.123808026
[90,] 0.003133342 1.073710327
[91,] -1.289197492 0.003133342
[92,] -1.016514618 -1.289197492
[93,] 0.757621758 -1.016514618
[94,] 0.442908988 0.757621758
[95,] -0.759830460 0.442908988
[96,] 0.336819110 -0.759830460
[97,] 0.122706029 0.336819110
[98,] 0.148103650 0.122706029
[99,] -1.063534927 0.148103650
[100,] -0.722665791 -1.063534927
[101,] 0.790819803 -0.722665791
[102,] 0.545983182 0.790819803
[103,] -0.474599213 0.545983182
[104,] 1.317929894 -0.474599213
[105,] -0.032513334 1.317929894
[106,] -0.049793152 -0.032513334
[107,] 0.606272026 -0.049793152
[108,] -0.184480120 0.606272026
[109,] 0.261536552 -0.184480120
[110,] 0.389982494 0.261536552
[111,] 0.131605157 0.389982494
[112,] -0.373999524 0.131605157
[113,] -0.608561217 -0.373999524
[114,] 0.122706029 -0.608561217
[115,] -1.075949518 0.122706029
[116,] 0.230595449 -1.075949518
[117,] 0.988069061 0.230595449
[118,] -0.428619197 0.988069061
[119,] 1.075403506 -0.428619197
[120,] 0.813742920 1.075403506
[121,] 0.205197827 0.813742920
[122,] -0.637108306 0.205197827
[123,] -0.402546612 -0.637108306
[124,] -1.157709992 -0.402546612
[125,] -0.797276677 -1.157709992
[126,] -0.614185189 -0.797276677
[127,] -0.768729588 -0.614185189
[128,] 0.131605157 -0.768729588
[129,] -1.133894348 0.131605157
[130,] -0.659146989 -1.133894348
[131,] 1.049978465 -0.659146989
[132,] -0.108580508 1.049978465
[133,] 0.416836404 -0.108580508
[134,] -0.877968934 0.416836404
[135,] -0.266079393 -0.877968934
[136,] 0.973911291 -0.266079393
[137,] -0.108496727 0.973911291
[138,] -0.110189906 -0.108496727
[139,] -0.952496039 -0.110189906
[140,] -0.263124907 -0.952496039
[141,] -0.266110104 -0.263124907
[142,] 2.214771919 -0.266110104
[143,] -1.002356847 2.214771919
[144,] 0.359742227 -1.002356847
[145,] 0.305797516 0.359742227
[146,] -0.790718271 0.305797516
[147,] -0.609236180 -0.790718271
[148,] 0.439843302 -0.609236180
[149,] 2.153727622 0.439843302
[150,] -0.967301353 2.153727622
[151,] -0.528543923 -0.967301353
[152,] -0.531693390 -0.528543923
[153,] -0.518032106 -0.531693390
[154,] 0.086559575 -0.518032106
[155,] -1.213995424 0.086559575
[156,] -1.077558915 -1.213995424
[157,] -0.055570233 -1.077558915
[158,] 0.599927892 -0.055570233
[159,] -0.821656083 0.599927892
[160,] 1.706151553 -0.821656083
[161,] 0.415997170 1.706151553
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.300374603 0.213235140
2 0.644195439 1.300374603
3 0.415974589 0.644195439
4 -1.028479432 0.415974589
5 1.714752628 -1.028479432
6 0.263985184 1.714752628
7 0.500773329 0.263985184
8 1.340688739 0.500773329
9 0.580874405 1.340688739
10 1.664975601 0.580874405
11 1.239307725 1.664975601
12 0.465885397 1.239307725
13 0.726603457 0.465885397
14 1.423096757 0.726603457
15 0.804280029 1.423096757
16 0.427264440 0.804280029
17 1.669707046 0.427264440
18 1.420706034 1.669707046
19 0.822753249 1.420706034
20 1.450946301 0.822753249
21 0.469919299 1.450946301
22 -0.548969917 0.469919299
23 0.921743541 -0.548969917
24 -0.118093400 0.921743541
25 0.999205841 -0.118093400
26 -0.421516318 0.999205841
27 -0.061674185 -0.421516318
28 -0.762301673 -0.061674185
29 -0.163138981 -0.762301673
30 -0.706747565 -0.163138981
31 1.070622060 -0.706747565
32 -0.520506610 1.070622060
33 -0.263822452 -0.520506610
34 0.724212734 -0.263822452
35 -0.466645681 0.724212734
36 2.169612352 -0.466645681
37 -0.532471424 2.169612352
38 -0.889839053 -0.532471424
39 1.397085373 -0.889839053
40 0.156899707 1.397085373
41 -0.645119709 0.156899707
42 0.662584878 -0.645119709
43 1.818669347 0.662584878
44 -0.678200476 1.818669347
45 0.736177548 -0.678200476
46 0.874223455 0.736177548
47 -0.972247071 0.874223455
48 -1.376386955 -0.972247071
49 -0.263822452 -1.376386955
50 -1.549053699 -0.263822452
51 -0.918302360 -1.549053699
52 0.609505275 -0.918302360
53 1.839335523 0.609505275
54 2.392242727 1.839335523
55 0.644393206 2.392242727
56 -0.918171870 0.644393206
57 0.387591771 -0.918171870
58 -1.057026521 0.387591771
59 -1.635411836 -1.057026521
60 0.305965079 -1.635411836
61 -1.174856028 0.305965079
62 -1.626680270 -1.174856028
63 -0.616442130 -1.626680270
64 -1.033210878 -0.616442130
65 -1.789155583 -1.033210878
66 0.355094563 -1.789155583
67 -1.473745268 0.355094563
68 -0.873126288 -1.473745268
69 -0.132201170 -0.873126288
70 -0.788377548 -0.132201170
71 -0.976116700 -0.788377548
72 -1.146308939 -0.976116700
73 -1.283470412 -1.146308939
74 -1.417348635 -1.283470412
75 0.484358617 -1.417348635
76 -0.082424143 0.484358617
77 0.004151558 -0.082424143
78 -1.235950326 0.004151558
79 -0.583861140 -1.235950326
80 -0.786684370 -0.583861140
81 -0.774219778 -0.786684370
82 0.228120944 -0.774219778
83 -1.169624805 0.228120944
84 0.148103650 -1.169624805
85 -0.022939242 0.148103650
86 -0.745672689 -0.022939242
87 0.717227132 -0.745672689
88 1.123808026 0.717227132
89 1.073710327 1.123808026
90 0.003133342 1.073710327
91 -1.289197492 0.003133342
92 -1.016514618 -1.289197492
93 0.757621758 -1.016514618
94 0.442908988 0.757621758
95 -0.759830460 0.442908988
96 0.336819110 -0.759830460
97 0.122706029 0.336819110
98 0.148103650 0.122706029
99 -1.063534927 0.148103650
100 -0.722665791 -1.063534927
101 0.790819803 -0.722665791
102 0.545983182 0.790819803
103 -0.474599213 0.545983182
104 1.317929894 -0.474599213
105 -0.032513334 1.317929894
106 -0.049793152 -0.032513334
107 0.606272026 -0.049793152
108 -0.184480120 0.606272026
109 0.261536552 -0.184480120
110 0.389982494 0.261536552
111 0.131605157 0.389982494
112 -0.373999524 0.131605157
113 -0.608561217 -0.373999524
114 0.122706029 -0.608561217
115 -1.075949518 0.122706029
116 0.230595449 -1.075949518
117 0.988069061 0.230595449
118 -0.428619197 0.988069061
119 1.075403506 -0.428619197
120 0.813742920 1.075403506
121 0.205197827 0.813742920
122 -0.637108306 0.205197827
123 -0.402546612 -0.637108306
124 -1.157709992 -0.402546612
125 -0.797276677 -1.157709992
126 -0.614185189 -0.797276677
127 -0.768729588 -0.614185189
128 0.131605157 -0.768729588
129 -1.133894348 0.131605157
130 -0.659146989 -1.133894348
131 1.049978465 -0.659146989
132 -0.108580508 1.049978465
133 0.416836404 -0.108580508
134 -0.877968934 0.416836404
135 -0.266079393 -0.877968934
136 0.973911291 -0.266079393
137 -0.108496727 0.973911291
138 -0.110189906 -0.108496727
139 -0.952496039 -0.110189906
140 -0.263124907 -0.952496039
141 -0.266110104 -0.263124907
142 2.214771919 -0.266110104
143 -1.002356847 2.214771919
144 0.359742227 -1.002356847
145 0.305797516 0.359742227
146 -0.790718271 0.305797516
147 -0.609236180 -0.790718271
148 0.439843302 -0.609236180
149 2.153727622 0.439843302
150 -0.967301353 2.153727622
151 -0.528543923 -0.967301353
152 -0.531693390 -0.528543923
153 -0.518032106 -0.531693390
154 0.086559575 -0.518032106
155 -1.213995424 0.086559575
156 -1.077558915 -1.213995424
157 -0.055570233 -1.077558915
158 0.599927892 -0.055570233
159 -0.821656083 0.599927892
160 1.706151553 -0.821656083
161 0.415997170 1.706151553
> 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/rcomp/tmp/7tok51322145884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8pnxb1322145884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/99snq1322145884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10b4my1322145884.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11kjog1322145884.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/rcomp/tmp/12dxxw1322145884.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/rcomp/tmp/13ma5j1322145884.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/rcomp/tmp/14tvrm1322145884.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/rcomp/tmp/15kbsa1322145884.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/rcomp/tmp/16ypl81322145884.tab")
+ }
>
> try(system("convert tmp/1mmvb1322145884.ps tmp/1mmvb1322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hihv1322145884.ps tmp/2hihv1322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mfxq1322145884.ps tmp/3mfxq1322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vu9h1322145884.ps tmp/4vu9h1322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/5iod81322145884.ps tmp/5iod81322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wiq71322145884.ps tmp/6wiq71322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tok51322145884.ps tmp/7tok51322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pnxb1322145884.ps tmp/8pnxb1322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/99snq1322145884.ps tmp/99snq1322145884.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b4my1322145884.ps tmp/10b4my1322145884.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.86 0.32 6.17