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
+ ,8.4
+ ,4.3
+ ,1.5
+ ,2.2
+ ,2.1
+ ,2.5
+ ,7.5
+ ,3.1
+ ,1.7
+ ,2.3
+ ,2.2
+ ,2.2
+ ,4.0
+ ,5.7
+ ,1.6
+ ,2.1
+ ,2.2
+ ,2.9
+ ,8.5
+ ,6.7
+ ,1.7
+ ,2.8
+ ,2.7
+ ,3.1
+ ,7.6
+ ,9.5
+ ,1.8
+ ,3.1
+ ,3.1
+ ,3.0
+ ,5.5
+ ,9.0
+ ,1.7
+ ,2.9
+ ,3.2
+ ,2.8
+ ,3.3
+ ,6.9
+ ,2.2
+ ,2.6
+ ,3.1
+ ,2.5
+ ,1.4
+ ,7.5
+ ,2.7
+ ,2.7
+ ,3.1
+ ,1.9
+ ,-4.4
+ ,7.0
+ ,3.0
+ ,2.3
+ ,2.8
+ ,1.9
+ ,-6.5
+ ,9.3
+ ,2.8
+ ,2.3
+ ,3.0
+ ,1.8
+ ,-8.5
+ ,7.2
+ ,2.7
+ ,2.1
+ ,2.8
+ ,2.0
+ ,-6.7
+ ,6.6
+ ,2.7
+ ,2.2
+ ,2.7
+ ,2.6
+ ,-3.3
+ ,10.4
+ ,2.5
+ ,2.9
+ ,3.2
+ ,2.5
+ ,-5.1
+ ,8.7
+ ,2.0
+ ,2.6
+ ,3.1
+ ,2.5
+ ,-3.5
+ ,7.9
+ ,1.8
+ ,2.7
+ ,3.0
+ ,1.6
+ ,-3.6
+ ,4.1
+ ,1.4
+ ,1.8
+ ,2.0
+ ,1.4
+ ,-6.3
+ ,2.2
+ ,1.5
+ ,1.3
+ ,1.7
+ ,0.8
+ ,-8.0
+ ,-0.5
+ ,1.6
+ ,0.9
+ ,1.2
+ ,1.1
+ ,-5.3
+ ,1.7
+ ,1.3
+ ,1.3
+ ,1.4
+ ,1.3
+ ,-4.0
+ ,0.4
+ ,1.1
+ ,1.3
+ ,1.3
+ ,1.2
+ ,-4.0
+ ,2.6
+ ,0.8
+ ,1.3
+ ,1.3
+ ,1.3
+ ,0.1
+ ,0.7
+ ,1.1
+ ,1.3
+ ,1.1
+ ,1.1
+ ,-0.9
+ ,0.7
+ ,1.3
+ ,1.1
+ ,0.9
+ ,1.3
+ ,1.1
+ ,0.5
+ ,1.5
+ ,1.4
+ ,1.2
+ ,1.2
+ ,3.1
+ ,-2.3
+ ,1.8
+ ,1.2
+ ,0.9
+ ,1.6
+ ,5.7
+ ,0.3
+ ,2.7
+ ,1.7
+ ,1.3
+ ,1.7
+ ,6.2
+ ,-0.2
+ ,3.0
+ ,1.8
+ ,1.4
+ ,1.5
+ ,-2.2
+ ,0.6
+ ,3.2
+ ,1.5
+ ,1.5
+ ,0.9
+ ,-4.2
+ ,-0.6
+ ,3.2
+ ,1.0
+ ,1.1
+ ,1.5
+ ,-1.6
+ ,2.7
+ ,3.3
+ ,1.6
+ ,1.6
+ ,1.4
+ ,-1.9
+ ,2.3
+ ,3.2
+ ,1.5
+ ,1.5
+ ,1.6
+ ,0.2
+ ,4.3
+ ,2.9
+ ,1.8
+ ,1.6
+ ,1.7
+ ,-1.2
+ ,5.4
+ ,2.7
+ ,1.8
+ ,1.7
+ ,1.4
+ ,-2.4
+ ,2.6
+ ,2.6
+ ,1.6
+ ,1.6
+ ,1.8
+ ,0.8
+ ,2.9
+ ,2.3
+ ,1.9
+ ,1.7
+ ,1.7
+ ,-0.1
+ ,2.9
+ ,2.2
+ ,1.7
+ ,1.6
+ ,1.4
+ ,-1.5
+ ,2.9
+ ,2.1
+ ,1.6
+ ,1.6
+ ,1.2
+ ,-4.4
+ ,1.4
+ ,2.4
+ ,1.3
+ ,1.3
+ ,1.0
+ ,-4.2
+ ,1.1
+ ,2.5
+ ,1.1
+ ,1.1
+ ,1.7
+ ,3.5
+ ,1.9
+ ,2.4
+ ,1.9
+ ,1.6
+ ,2.4
+ ,10.0
+ ,2.8
+ ,2.3
+ ,2.6
+ ,1.9
+ ,2.0
+ ,8.6
+ ,1.4
+ ,2.1
+ ,2.3
+ ,1.6
+ ,2.1
+ ,9.5
+ ,0.7
+ ,2.3
+ ,2.4
+ ,1.7
+ ,2.0
+ ,9.9
+ ,-0.8
+ ,2.2
+ ,2.2
+ ,1.6
+ ,1.8
+ ,10.4
+ ,-3.1
+ ,2.1
+ ,2.0
+ ,1.4
+ ,2.7
+ ,16.0
+ ,0.1
+ ,2.0
+ ,2.9
+ ,2.1
+ ,2.3
+ ,12.7
+ ,1.0
+ ,2.1
+ ,2.6
+ ,1.9
+ ,1.9
+ ,10.2
+ ,1.9
+ ,2.1
+ ,2.3
+ ,1.7
+ ,2.0
+ ,8.9
+ ,-0.5
+ ,2.5
+ ,2.3
+ ,1.8
+ ,2.3
+ ,12.6
+ ,1.5
+ ,2.2
+ ,2.6
+ ,2.0
+ ,2.8
+ ,13.6
+ ,3.9
+ ,2.3
+ ,3.1
+ ,2.5
+ ,2.4
+ ,14.8
+ ,1.9
+ ,2.3
+ ,2.8
+ ,2.1
+ ,2.3
+ ,9.5
+ ,2.6
+ ,2.2
+ ,2.5
+ ,2.1
+ ,2.7
+ ,13.7
+ ,1.7
+ ,2.2
+ ,2.9
+ ,2.3
+ ,2.7
+ ,17.0
+ ,1.4
+ ,1.6
+ ,3.1
+ ,2.4
+ ,2.9
+ ,14.7
+ ,2.8
+ ,1.8
+ ,3.1
+ ,2.4
+ ,3.0
+ ,17.4
+ ,0.5
+ ,1.7
+ ,3.2
+ ,2.3
+ ,2.2
+ ,9.0
+ ,1.0
+ ,1.9
+ ,2.5
+ ,1.7
+ ,2.3
+ ,9.1
+ ,1.5
+ ,1.8
+ ,2.6
+ ,2.0
+ ,2.8
+ ,12.2
+ ,1.8
+ ,1.9
+ ,2.9
+ ,2.3
+ ,2.8
+ ,15.9
+ ,2.7
+ ,1.5
+ ,2.6
+ ,2.0
+ ,2.8
+ ,12.9
+ ,3.0
+ ,1.0
+ ,2.4
+ ,2.0
+ ,2.2
+ ,10.9
+ ,-0.3
+ ,0.8
+ ,1.7
+ ,1.3
+ ,2.6
+ ,10.6
+ ,1.1
+ ,1.1
+ ,2.0
+ ,1.7
+ ,2.8
+ ,13.2
+ ,1.7
+ ,1.5
+ ,2.2
+ ,1.9
+ ,2.5
+ ,9.6
+ ,1.6
+ ,1.7
+ ,1.9
+ ,1.7
+ ,2.4
+ ,6.4
+ ,3.0
+ ,2.3
+ ,1.6
+ ,1.6
+ ,2.3
+ ,5.8
+ ,3.3
+ ,2.4
+ ,1.6
+ ,1.7
+ ,1.9
+ ,-1.0
+ ,6.7
+ ,3.0
+ ,1.2
+ ,1.8
+ ,1.7
+ ,-0.2
+ ,5.6
+ ,3.0
+ ,1.2
+ ,1.9
+ ,2.0
+ ,2.7
+ ,6.0
+ ,3.2
+ ,1.5
+ ,1.9
+ ,2.1
+ ,3.6
+ ,4.8
+ ,3.2
+ ,1.6
+ ,1.9
+ ,1.7
+ ,-0.9
+ ,5.9
+ ,3.2
+ ,1.7
+ ,2.0
+ ,1.8
+ ,0.3
+ ,4.3
+ ,3.5
+ ,1.8
+ ,2.1
+ ,1.8
+ ,-1.1
+ ,3.7
+ ,4.0
+ ,1.8
+ ,1.9
+ ,1.8
+ ,-2.5
+ ,5.6
+ ,4.3
+ ,1.8
+ ,1.9
+ ,1.3
+ ,-3.4
+ ,1.7
+ ,4.1
+ ,1.3
+ ,1.3
+ ,1.3
+ ,-3.5
+ ,3.2
+ ,4.0
+ ,1.3
+ ,1.3
+ ,1.3
+ ,-3.9
+ ,3.6
+ ,4.1
+ ,1.4
+ ,1.4
+ ,1.2
+ ,-4.6
+ ,1.7
+ ,4.2
+ ,1.1
+ ,1.2
+ ,1.4
+ ,-0.1
+ ,0.5
+ ,4.5
+ ,1.5
+ ,1.3
+ ,2.2
+ ,4.3
+ ,2.1
+ ,5.6
+ ,2.2
+ ,1.8
+ ,2.9
+ ,10.2
+ ,1.5
+ ,6.5
+ ,2.9
+ ,2.2
+ ,3.1
+ ,8.7
+ ,2.7
+ ,7.6
+ ,3.1
+ ,2.6
+ ,3.5
+ ,13.3
+ ,1.4
+ ,8.5
+ ,3.5
+ ,2.8
+ ,3.6
+ ,15.0
+ ,1.2
+ ,8.7
+ ,3.6
+ ,3.1
+ ,4.4
+ ,20.7
+ ,2.3
+ ,8.3
+ ,4.4
+ ,3.9
+ ,4.1
+ ,20.7
+ ,1.6
+ ,8.3
+ ,4.2
+ ,3.7
+ ,5.1
+ ,26.4
+ ,4.7
+ ,8.5
+ ,5.2
+ ,4.6
+ ,5.8
+ ,31.2
+ ,3.5
+ ,8.7
+ ,5.8
+ ,5.1
+ ,5.9
+ ,31.4
+ ,4.4
+ ,8.7
+ ,5.9
+ ,5.2
+ ,5.4
+ ,26.6
+ ,3.9
+ ,8.5
+ ,5.4
+ ,4.9
+ ,5.5
+ ,26.6
+ ,3.5
+ ,7.9
+ ,5.5
+ ,5.1
+ ,4.8
+ ,19.2
+ ,3.0
+ ,7.0
+ ,4.7
+ ,4.8
+ ,3.2
+ ,6.5
+ ,1.6
+ ,5.8
+ ,3.1
+ ,3.9
+ ,2.7
+ ,3.1
+ ,2.2
+ ,4.5
+ ,2.6
+ ,3.5
+ ,2.1
+ ,-0.2
+ ,4.1
+ ,3.7
+ ,2.3
+ ,3.3
+ ,1.9
+ ,-4.0
+ ,4.3
+ ,3.1
+ ,1.9
+ ,2.8
+ ,0.6
+ ,-12.6
+ ,3.5
+ ,2.7
+ ,0.6
+ ,1.6
+ ,0.7
+ ,-13.0
+ ,1.8
+ ,2.3
+ ,0.6
+ ,1.5
+ ,-0.2
+ ,-17.6
+ ,0.6
+ ,1.8
+ ,-0.4
+ ,0.7
+ ,-1.0
+ ,-21.7
+ ,-0.4
+ ,1.5
+ ,-1.1
+ ,-0.1
+ ,-1.7
+ ,-23.2
+ ,-2.5
+ ,1.2
+ ,-1.7
+ ,-0.7
+ ,-0.7
+ ,-16.8
+ ,-1.6
+ ,1.0
+ ,-0.8
+ ,-0.2
+ ,-1.0
+ ,-19.8
+ ,-1.9
+ ,0.9
+ ,-1.2
+ ,-0.6
+ ,-0.9
+ ,-17.2
+ ,-1.6
+ ,0.6
+ ,-1.0
+ ,-0.6
+ ,0.0
+ ,-10.4
+ ,-0.7
+ ,0.6
+ ,-0.1
+ ,-0.3
+ ,0.3
+ ,-6.8
+ ,-1.1
+ ,0.7
+ ,0.3
+ ,-0.3
+ ,0.8
+ ,-2.9
+ ,0.3
+ ,0.5
+ ,0.6
+ ,-0.1
+ ,0.8
+ ,-1.9
+ ,1.3
+ ,0.5
+ ,0.7
+ ,0.1
+ ,1.9
+ ,7.0
+ ,3.3
+ ,0.5
+ ,1.7
+ ,0.9
+ ,2.1
+ ,9.8
+ ,2.4
+ ,0.5
+ ,1.8
+ ,1.1
+ ,2.5
+ ,12.5
+ ,2.0
+ ,0.8
+ ,2.3
+ ,1.6
+ ,2.7
+ ,13.7
+ ,3.9
+ ,0.8
+ ,2.5
+ ,2.0
+ ,2.4
+ ,13.7
+ ,4.2
+ ,1.1
+ ,2.6
+ ,2.2
+ ,2.4
+ ,9.7
+ ,4.9
+ ,1.2
+ ,2.3
+ ,2.1
+ ,2.9
+ ,14.0
+ ,5.8
+ ,1.5
+ ,2.9
+ ,2.6
+ ,3.1
+ ,15.3
+ ,4.8
+ ,1.7
+ ,3.0
+ ,2.5
+ ,3.0
+ ,13.4
+ ,4.4
+ ,1.8
+ ,2.9
+ ,2.5
+ ,3.4
+ ,17.1
+ ,5.3
+ ,1.8
+ ,3.1
+ ,2.6
+ ,3.7
+ ,15.7
+ ,2.1
+ ,2.1
+ ,3.2
+ ,2.7
+ ,3.5
+ ,18.3
+ ,2.0
+ ,2.2
+ ,3.4
+ ,2.8
+ ,3.5
+ ,18.1
+ ,-0.9
+ ,2.5
+ ,3.5
+ ,2.9
+ ,3.3
+ ,16.3
+ ,0.1
+ ,2.7
+ ,3.4
+ ,2.9
+ ,3.1
+ ,15.8
+ ,-0.5
+ ,3.0
+ ,3.3
+ ,2.9
+ ,3.4
+ ,17.3
+ ,-0.1
+ ,3.4
+ ,3.7
+ ,3.3
+ ,4.0
+ ,18.0
+ ,0.7
+ ,3.4
+ ,3.8
+ ,3.3
+ ,3.4
+ ,17.6
+ ,-0.4
+ ,3.5
+ ,3.6
+ ,3.1
+ ,3.4
+ ,18.4
+ ,-1.5
+ ,3.5
+ ,3.6
+ ,3.0
+ ,3.4
+ ,17.4
+ ,-0.3
+ ,3.4
+ ,3.6
+ ,3.1
+ ,3.7
+ ,17.9
+ ,1.0
+ ,3.6
+ ,3.8
+ ,3.4
+ ,3.2
+ ,13.5
+ ,0.4
+ ,3.8
+ ,3.5
+ ,3.2
+ ,3.3
+ ,13.7
+ ,0.3
+ ,3.5
+ ,3.6
+ ,3.4
+ ,3.3
+ ,12.6
+ ,1.8
+ ,3.5
+ ,3.7
+ ,3.4
+ ,3.1
+ ,10.4
+ ,3.0
+ ,3.5
+ ,3.4
+ ,3.1
+ ,2.9
+ ,8.8
+ ,2.2
+ ,3.2
+ ,3.2
+ ,3.0
+ ,2.6
+ ,5.4
+ ,3.4
+ ,2.9
+ ,2.8
+ ,2.7
+ ,2.2
+ ,2.1
+ ,3.4
+ ,2.5
+ ,2.3
+ ,2.2
+ ,2.0
+ ,2.8
+ ,3.1
+ ,2.3
+ ,2.3
+ ,2.2
+ ,2.6
+ ,5.6
+ ,4.5
+ ,2.7
+ ,2.9
+ ,2.6
+ ,2.6
+ ,4.8
+ ,4.6
+ ,3.0
+ ,2.8
+ ,2.4
+ ,2.6
+ ,4.5
+ ,5.7
+ ,3.3
+ ,2.8
+ ,2.5
+ ,2.2
+ ,1.5
+ ,4.3
+ ,3.2
+ ,2.3
+ ,2.2)
+ ,dim=c(6
+ ,143)
+ ,dimnames=list(c('HICP'
+ ,'Energiedragers'
+ ,'Niet-bewerkte_levensmiddelen'
+ ,'Bewerkte_levensmiddelen'
+ ,'Algemene_index'
+ ,'Gezondheidsindex')
+ ,1:143))
> y <- array(NA,dim=c(6,143),dimnames=list(c('HICP','Energiedragers','Niet-bewerkte_levensmiddelen','Bewerkte_levensmiddelen','Algemene_index','Gezondheidsindex'),1:143))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
HICP Energiedragers Niet-bewerkte_levensmiddelen Bewerkte_levensmiddelen
1 2.7 8.4 4.3 1.5
2 2.5 7.5 3.1 1.7
3 2.2 4.0 5.7 1.6
4 2.9 8.5 6.7 1.7
5 3.1 7.6 9.5 1.8
6 3.0 5.5 9.0 1.7
7 2.8 3.3 6.9 2.2
8 2.5 1.4 7.5 2.7
9 1.9 -4.4 7.0 3.0
10 1.9 -6.5 9.3 2.8
11 1.8 -8.5 7.2 2.7
12 2.0 -6.7 6.6 2.7
13 2.6 -3.3 10.4 2.5
14 2.5 -5.1 8.7 2.0
15 2.5 -3.5 7.9 1.8
16 1.6 -3.6 4.1 1.4
17 1.4 -6.3 2.2 1.5
18 0.8 -8.0 -0.5 1.6
19 1.1 -5.3 1.7 1.3
20 1.3 -4.0 0.4 1.1
21 1.2 -4.0 2.6 0.8
22 1.3 0.1 0.7 1.1
23 1.1 -0.9 0.7 1.3
24 1.3 1.1 0.5 1.5
25 1.2 3.1 -2.3 1.8
26 1.6 5.7 0.3 2.7
27 1.7 6.2 -0.2 3.0
28 1.5 -2.2 0.6 3.2
29 0.9 -4.2 -0.6 3.2
30 1.5 -1.6 2.7 3.3
31 1.4 -1.9 2.3 3.2
32 1.6 0.2 4.3 2.9
33 1.7 -1.2 5.4 2.7
34 1.4 -2.4 2.6 2.6
35 1.8 0.8 2.9 2.3
36 1.7 -0.1 2.9 2.2
37 1.4 -1.5 2.9 2.1
38 1.2 -4.4 1.4 2.4
39 1.0 -4.2 1.1 2.5
40 1.7 3.5 1.9 2.4
41 2.4 10.0 2.8 2.3
42 2.0 8.6 1.4 2.1
43 2.1 9.5 0.7 2.3
44 2.0 9.9 -0.8 2.2
45 1.8 10.4 -3.1 2.1
46 2.7 16.0 0.1 2.0
47 2.3 12.7 1.0 2.1
48 1.9 10.2 1.9 2.1
49 2.0 8.9 -0.5 2.5
50 2.3 12.6 1.5 2.2
51 2.8 13.6 3.9 2.3
52 2.4 14.8 1.9 2.3
53 2.3 9.5 2.6 2.2
54 2.7 13.7 1.7 2.2
55 2.7 17.0 1.4 1.6
56 2.9 14.7 2.8 1.8
57 3.0 17.4 0.5 1.7
58 2.2 9.0 1.0 1.9
59 2.3 9.1 1.5 1.8
60 2.8 12.2 1.8 1.9
61 2.8 15.9 2.7 1.5
62 2.8 12.9 3.0 1.0
63 2.2 10.9 -0.3 0.8
64 2.6 10.6 1.1 1.1
65 2.8 13.2 1.7 1.5
66 2.5 9.6 1.6 1.7
67 2.4 6.4 3.0 2.3
68 2.3 5.8 3.3 2.4
69 1.9 -1.0 6.7 3.0
70 1.7 -0.2 5.6 3.0
71 2.0 2.7 6.0 3.2
72 2.1 3.6 4.8 3.2
73 1.7 -0.9 5.9 3.2
74 1.8 0.3 4.3 3.5
75 1.8 -1.1 3.7 4.0
76 1.8 -2.5 5.6 4.3
77 1.3 -3.4 1.7 4.1
78 1.3 -3.5 3.2 4.0
79 1.3 -3.9 3.6 4.1
80 1.2 -4.6 1.7 4.2
81 1.4 -0.1 0.5 4.5
82 2.2 4.3 2.1 5.6
83 2.9 10.2 1.5 6.5
84 3.1 8.7 2.7 7.6
85 3.5 13.3 1.4 8.5
86 3.6 15.0 1.2 8.7
87 4.4 20.7 2.3 8.3
88 4.1 20.7 1.6 8.3
89 5.1 26.4 4.7 8.5
90 5.8 31.2 3.5 8.7
91 5.9 31.4 4.4 8.7
92 5.4 26.6 3.9 8.5
93 5.5 26.6 3.5 7.9
94 4.8 19.2 3.0 7.0
95 3.2 6.5 1.6 5.8
96 2.7 3.1 2.2 4.5
97 2.1 -0.2 4.1 3.7
98 1.9 -4.0 4.3 3.1
99 0.6 -12.6 3.5 2.7
100 0.7 -13.0 1.8 2.3
101 -0.2 -17.6 0.6 1.8
102 -1.0 -21.7 -0.4 1.5
103 -1.7 -23.2 -2.5 1.2
104 -0.7 -16.8 -1.6 1.0
105 -1.0 -19.8 -1.9 0.9
106 -0.9 -17.2 -1.6 0.6
107 0.0 -10.4 -0.7 0.6
108 0.3 -6.8 -1.1 0.7
109 0.8 -2.9 0.3 0.5
110 0.8 -1.9 1.3 0.5
111 1.9 7.0 3.3 0.5
112 2.1 9.8 2.4 0.5
113 2.5 12.5 2.0 0.8
114 2.7 13.7 3.9 0.8
115 2.4 13.7 4.2 1.1
116 2.4 9.7 4.9 1.2
117 2.9 14.0 5.8 1.5
118 3.1 15.3 4.8 1.7
119 3.0 13.4 4.4 1.8
120 3.4 17.1 5.3 1.8
121 3.7 15.7 2.1 2.1
122 3.5 18.3 2.0 2.2
123 3.5 18.1 -0.9 2.5
124 3.3 16.3 0.1 2.7
125 3.1 15.8 -0.5 3.0
126 3.4 17.3 -0.1 3.4
127 4.0 18.0 0.7 3.4
128 3.4 17.6 -0.4 3.5
129 3.4 18.4 -1.5 3.5
130 3.4 17.4 -0.3 3.4
131 3.7 17.9 1.0 3.6
132 3.2 13.5 0.4 3.8
133 3.3 13.7 0.3 3.5
134 3.3 12.6 1.8 3.5
135 3.1 10.4 3.0 3.5
136 2.9 8.8 2.2 3.2
137 2.6 5.4 3.4 2.9
138 2.2 2.1 3.4 2.5
139 2.0 2.8 3.1 2.3
140 2.6 5.6 4.5 2.7
141 2.6 4.8 4.6 3.0
142 2.6 4.5 5.7 3.3
143 2.2 1.5 4.3 3.2
Algemene_index Gezondheidsindex
1 2.2 2.1
2 2.3 2.2
3 2.1 2.2
4 2.8 2.7
5 3.1 3.1
6 2.9 3.2
7 2.6 3.1
8 2.7 3.1
9 2.3 2.8
10 2.3 3.0
11 2.1 2.8
12 2.2 2.7
13 2.9 3.2
14 2.6 3.1
15 2.7 3.0
16 1.8 2.0
17 1.3 1.7
18 0.9 1.2
19 1.3 1.4
20 1.3 1.3
21 1.3 1.3
22 1.3 1.1
23 1.1 0.9
24 1.4 1.2
25 1.2 0.9
26 1.7 1.3
27 1.8 1.4
28 1.5 1.5
29 1.0 1.1
30 1.6 1.6
31 1.5 1.5
32 1.8 1.6
33 1.8 1.7
34 1.6 1.6
35 1.9 1.7
36 1.7 1.6
37 1.6 1.6
38 1.3 1.3
39 1.1 1.1
40 1.9 1.6
41 2.6 1.9
42 2.3 1.6
43 2.4 1.7
44 2.2 1.6
45 2.0 1.4
46 2.9 2.1
47 2.6 1.9
48 2.3 1.7
49 2.3 1.8
50 2.6 2.0
51 3.1 2.5
52 2.8 2.1
53 2.5 2.1
54 2.9 2.3
55 3.1 2.4
56 3.1 2.4
57 3.2 2.3
58 2.5 1.7
59 2.6 2.0
60 2.9 2.3
61 2.6 2.0
62 2.4 2.0
63 1.7 1.3
64 2.0 1.7
65 2.2 1.9
66 1.9 1.7
67 1.6 1.6
68 1.6 1.7
69 1.2 1.8
70 1.2 1.9
71 1.5 1.9
72 1.6 1.9
73 1.7 2.0
74 1.8 2.1
75 1.8 1.9
76 1.8 1.9
77 1.3 1.3
78 1.3 1.3
79 1.4 1.4
80 1.1 1.2
81 1.5 1.3
82 2.2 1.8
83 2.9 2.2
84 3.1 2.6
85 3.5 2.8
86 3.6 3.1
87 4.4 3.9
88 4.2 3.7
89 5.2 4.6
90 5.8 5.1
91 5.9 5.2
92 5.4 4.9
93 5.5 5.1
94 4.7 4.8
95 3.1 3.9
96 2.6 3.5
97 2.3 3.3
98 1.9 2.8
99 0.6 1.6
100 0.6 1.5
101 -0.4 0.7
102 -1.1 -0.1
103 -1.7 -0.7
104 -0.8 -0.2
105 -1.2 -0.6
106 -1.0 -0.6
107 -0.1 -0.3
108 0.3 -0.3
109 0.6 -0.1
110 0.7 0.1
111 1.7 0.9
112 1.8 1.1
113 2.3 1.6
114 2.5 2.0
115 2.6 2.2
116 2.3 2.1
117 2.9 2.6
118 3.0 2.5
119 2.9 2.5
120 3.1 2.6
121 3.2 2.7
122 3.4 2.8
123 3.5 2.9
124 3.4 2.9
125 3.3 2.9
126 3.7 3.3
127 3.8 3.3
128 3.6 3.1
129 3.6 3.0
130 3.6 3.1
131 3.8 3.4
132 3.5 3.2
133 3.6 3.4
134 3.7 3.4
135 3.4 3.1
136 3.2 3.0
137 2.8 2.7
138 2.3 2.2
139 2.3 2.2
140 2.9 2.6
141 2.8 2.4
142 2.8 2.5
143 2.3 2.2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Energiedragers
0.55390 0.05922
`Niet-bewerkte_levensmiddelen` Bewerkte_levensmiddelen
0.04375 0.04194
Algemene_index Gezondheidsindex
0.17513 0.34990
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.43502 -0.08843 -0.00549 0.09634 0.53133
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.553905 0.050367 10.997 < 2e-16 ***
Energiedragers 0.059215 0.004754 12.456 < 2e-16 ***
`Niet-bewerkte_levensmiddelen` 0.043754 0.007371 5.936 2.28e-08 ***
Bewerkte_levensmiddelen 0.041936 0.010629 3.945 0.000127 ***
Algemene_index 0.175129 0.069894 2.506 0.013394 *
Gezondheidsindex 0.349897 0.052636 6.648 6.50e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1674 on 137 degrees of freedom
Multiple R-squared: 0.9821, Adjusted R-squared: 0.9814
F-statistic: 1500 on 5 and 137 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.1420175710 0.2840351421 0.8579824290
[2,] 0.0749237630 0.1498475259 0.9250762370
[3,] 0.1100000969 0.2200001938 0.8899999031
[4,] 0.1331796974 0.2663593948 0.8668203026
[5,] 0.1123683352 0.2247366705 0.8876316648
[6,] 0.0667418174 0.1334836349 0.9332581826
[7,] 0.0642611187 0.1285222374 0.9357388813
[8,] 0.1486014787 0.2972029574 0.8513985213
[9,] 0.1149884979 0.2299769958 0.8850115021
[10,] 0.0872841478 0.1745682956 0.9127158522
[11,] 0.0794945632 0.1589891265 0.9205054368
[12,] 0.0558601247 0.1117202493 0.9441398753
[13,] 0.0384117446 0.0768234892 0.9615882554
[14,] 0.0238503280 0.0477006560 0.9761496720
[15,] 0.0157693080 0.0315386161 0.9842306920
[16,] 0.0122770702 0.0245541403 0.9877229298
[17,] 0.0074241785 0.0148483569 0.9925758215
[18,] 0.0050520976 0.0101041951 0.9949479024
[19,] 0.0032118680 0.0064237360 0.9967881320
[20,] 0.0158399766 0.0316799532 0.9841600234
[21,] 0.0127851874 0.0255703749 0.9872148126
[22,] 0.0126976287 0.0253952574 0.9873023713
[23,] 0.0099531072 0.0199062143 0.9900468928
[24,] 0.0065771103 0.0131542206 0.9934228897
[25,] 0.0055744552 0.0111489103 0.9944255448
[26,] 0.0035101848 0.0070203695 0.9964898152
[27,] 0.0022325340 0.0044650680 0.9977674660
[28,] 0.0016889864 0.0033779727 0.9983110136
[29,] 0.0013803317 0.0027606633 0.9986196683
[30,] 0.0009809701 0.0019619402 0.9990190299
[31,] 0.0005911139 0.0011822278 0.9994088861
[32,] 0.0005704495 0.0011408991 0.9994295505
[33,] 0.0004123970 0.0008247939 0.9995876030
[34,] 0.0005120600 0.0010241201 0.9994879400
[35,] 0.0004524323 0.0009048647 0.9995475677
[36,] 0.0003816113 0.0007632225 0.9996183887
[37,] 0.0003796191 0.0007592382 0.9996203809
[38,] 0.0002556196 0.0005112391 0.9997443804
[39,] 0.0003547984 0.0007095969 0.9996452016
[40,] 0.0033878098 0.0067756196 0.9966121902
[41,] 0.0030232679 0.0060465359 0.9969767321
[42,] 0.0052194485 0.0104388969 0.9947805515
[43,] 0.0048493670 0.0096987339 0.9951506330
[44,] 0.0200222318 0.0400444636 0.9799777682
[45,] 0.0187969016 0.0375938033 0.9812030984
[46,] 0.0166919696 0.0333839393 0.9833080304
[47,] 0.0496226870 0.0992453739 0.9503773130
[48,] 0.0464726977 0.0929453954 0.9535273023
[49,] 0.0524728787 0.1049457574 0.9475271213
[50,] 0.0484091765 0.0968183530 0.9515908235
[51,] 0.0418334552 0.0836669103 0.9581665448
[52,] 0.0448128465 0.0896256931 0.9551871535
[53,] 0.0438340754 0.0876681508 0.9561659246
[54,] 0.0527815746 0.1055631491 0.9472184254
[55,] 0.0549510704 0.1099021408 0.9450489296
[56,] 0.1317054696 0.2634109393 0.8682945304
[57,] 0.1759549375 0.3519098751 0.8240450625
[58,] 0.2491793521 0.4983587041 0.7508206479
[59,] 0.4845821678 0.9691643356 0.5154178322
[60,] 0.5781756903 0.8436486195 0.4218243097
[61,] 0.6333278891 0.7333442219 0.3666721109
[62,] 0.6988133419 0.6023733163 0.3011866581
[63,] 0.6782073271 0.6435853458 0.3217926729
[64,] 0.6771321811 0.6457356379 0.3228678189
[65,] 0.6658868761 0.6682262478 0.3341131239
[66,] 0.6330992977 0.7338014047 0.3669007023
[67,] 0.6445645861 0.7108708279 0.3554354139
[68,] 0.6561176327 0.6877647347 0.3438823673
[69,] 0.6531409821 0.6937180359 0.3468590179
[70,] 0.6231204755 0.7537590490 0.3768795245
[71,] 0.5990539373 0.8018921253 0.4009460627
[72,] 0.5835150591 0.8329698818 0.4164849409
[73,] 0.5836204065 0.8327591871 0.4163795935
[74,] 0.6735527501 0.6528944997 0.3264472499
[75,] 0.7917394500 0.4165210999 0.2082605500
[76,] 0.8265872741 0.3468254518 0.1734127259
[77,] 0.8287195441 0.3425609118 0.1712804559
[78,] 0.7976300134 0.4047399732 0.2023699866
[79,] 0.7599102610 0.4801794780 0.2400897390
[80,] 0.7808375182 0.4383249636 0.2191624818
[81,] 0.7878933459 0.4242133082 0.2121066541
[82,] 0.7588785779 0.4822428443 0.2411214221
[83,] 0.7298602956 0.5402794088 0.2701397044
[84,] 0.7039054862 0.5921890277 0.2960945138
[85,] 0.6629930059 0.6740139882 0.3370069941
[86,] 0.6400420882 0.7199158237 0.3599579118
[87,] 0.6824465647 0.6351068707 0.3175534353
[88,] 0.7407905309 0.5184189382 0.2592094691
[89,] 0.8105042786 0.3789914428 0.1894957214
[90,] 0.7946316666 0.4107366669 0.2053683334
[91,] 0.7728718358 0.4542563283 0.2271281642
[92,] 0.7995420541 0.4009158918 0.2004579459
[93,] 0.8291876703 0.3416246594 0.1708123297
[94,] 0.8116743386 0.3766513228 0.1883256614
[95,] 0.8155482496 0.3689035009 0.1844517504
[96,] 0.7758109481 0.4483781038 0.2241890519
[97,] 0.7751200003 0.4497599994 0.2248799997
[98,] 0.7565376629 0.4869246741 0.2434623371
[99,] 0.7737497334 0.4525005333 0.2262502666
[100,] 0.7568401995 0.4863196010 0.2431598005
[101,] 0.7608979969 0.4782040062 0.2391020031
[102,] 0.7165141507 0.5669716986 0.2834858493
[103,] 0.6823413962 0.6353172076 0.3176586038
[104,] 0.6306518695 0.7386962611 0.3693481305
[105,] 0.5760416934 0.8479166132 0.4239583066
[106,] 0.5112575501 0.9774848999 0.4887424499
[107,] 0.8003565883 0.3992868235 0.1996434117
[108,] 0.7568670373 0.4862659255 0.2431329627
[109,] 0.7882159355 0.4235681291 0.2117840645
[110,] 0.7975221862 0.4049556277 0.2024778138
[111,] 0.7841469640 0.4317060720 0.2158530360
[112,] 0.8505502755 0.2988994491 0.1494497245
[113,] 0.9758834600 0.0482330800 0.0241165400
[114,] 0.9681763822 0.0636472356 0.0318236178
[115,] 0.9638647631 0.0722704738 0.0361352369
[116,] 0.9419024866 0.1161950268 0.0580975134
[117,] 0.9229437906 0.1541124188 0.0770562094
[118,] 0.9218213962 0.1563572077 0.0781786038
[119,] 0.9996363578 0.0007272845 0.0003636422
[120,] 0.9989912879 0.0020174242 0.0010087121
[121,] 0.9970460948 0.0059078103 0.0029539052
[122,] 0.9920866277 0.0158267445 0.0079133723
[123,] 0.9953112312 0.0093775377 0.0046887688
[124,] 0.9851917085 0.0296165830 0.0148082915
[125,] 0.9785469046 0.0429061908 0.0214530954
[126,] 0.9404088216 0.1191823569 0.0595911784
> postscript(file="/var/wessaorg/rcomp/tmp/138qo1354884942.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/2878g1354884942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3z8951354884942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42yjj1354884942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/55x4x1354884942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 143
Frequency = 1
1 2 3 4 5 6
0.277573406 0.122482120 -0.044805607 0.043239324 -0.022669368 0.027789345
7 8 9 10 11 12
0.116506869 -0.135717453 -0.207952123 -0.245826631 -0.026313963 0.110827811
13 14 15 16 17 18
0.054078974 0.243544549 0.209667313 0.006141739 0.237495626 0.097103943
19 20 21 22 23 24
0.013513635 0.236790909 0.053112815 0.050861733 0.006695059 -0.068879601
25 26 27 28 29 30
-0.037384564 -0.170370160 -0.143184114 0.128382422 -0.073158878 -0.055726323
31 32 33 34 35 36
-0.063763951 -0.150571701 -0.042402359 -0.074623741 0.047813592 0.075316430
37 38 39 40 41 42
-0.120075761 0.062206529 -0.035698618 -0.137517347 -0.085160906 -0.175108960
43 44 45 46 47 48
-0.158664622 -0.142510545 -0.162285094 -0.132253824 -0.257897608 -0.426719984
49 50 51 52 53 54
-0.196494581 -0.313036352 -0.243967914 -0.435020563 -0.195075360 -0.144431781
55 56 57 58 59 60
-0.371569900 -0.105017679 -0.042594207 -0.042921870 -0.089008831 0.052596351
61 62 63 64 65 66
-0.031596330 0.188916848 0.227641063 0.379071708 0.277080203 0.308761415
67 68 69 70 71 72
0.399361550 0.282581211 0.146381648 -0.087850799 -0.038002484 0.043695692
73 74 75 76 77 78
-0.190467914 -0.156603107 0.001562097 -0.011249945 0.018574368 -0.036941547
79 80 81 82 83 84
-0.087453274 0.055454621 -0.076131193 0.049647120 0.126238271 0.141442322
85 86 87 88 89 90
0.148159257 0.025375048 0.036472188 -0.127894742 -0.099482634 0.080375920
91 92 93 94 95 96
0.076651640 0.083682575 0.138853297 0.181737621 0.040463558 -0.002417207
97 98 99 100 101 102
-0.334072834 -0.047644184 -0.139071358 0.110760519 0.011670103 -0.086704699
103 104 105 106 107 108
-0.278401955 -0.020935685 0.084040246 -0.005490664 0.189881753 0.219963256
109 110 111 112 113 114
0.313637274 0.123175739 0.153605425 0.139689128 0.122215783 -0.006959721
115 116 117 118 119 120
-0.420158966 -0.130590991 -0.217201820 -0.041337987 0.001991881 0.073501430
121 122 123 124 125 126
0.531332187 0.107538897 0.181185203 0.053144368 -0.086063401 -0.119172642
127 128 129 130 131 132
0.386860563 -0.040512254 -0.004765324 -0.028851040 0.036279046 -0.062790573
133 134 135 136 137 138
-0.045169815 -0.063177040 -0.027900492 -0.015556657 0.020871806 0.095569451
139 140 141 142 143
-0.124367845 -0.013236751 0.104671615 0.026736361 0.062364954
> postscript(file="/var/wessaorg/rcomp/tmp/6qdcw1354884942.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 = 143
Frequency = 1
lag(myerror, k = 1) myerror
0 0.277573406 NA
1 0.122482120 0.277573406
2 -0.044805607 0.122482120
3 0.043239324 -0.044805607
4 -0.022669368 0.043239324
5 0.027789345 -0.022669368
6 0.116506869 0.027789345
7 -0.135717453 0.116506869
8 -0.207952123 -0.135717453
9 -0.245826631 -0.207952123
10 -0.026313963 -0.245826631
11 0.110827811 -0.026313963
12 0.054078974 0.110827811
13 0.243544549 0.054078974
14 0.209667313 0.243544549
15 0.006141739 0.209667313
16 0.237495626 0.006141739
17 0.097103943 0.237495626
18 0.013513635 0.097103943
19 0.236790909 0.013513635
20 0.053112815 0.236790909
21 0.050861733 0.053112815
22 0.006695059 0.050861733
23 -0.068879601 0.006695059
24 -0.037384564 -0.068879601
25 -0.170370160 -0.037384564
26 -0.143184114 -0.170370160
27 0.128382422 -0.143184114
28 -0.073158878 0.128382422
29 -0.055726323 -0.073158878
30 -0.063763951 -0.055726323
31 -0.150571701 -0.063763951
32 -0.042402359 -0.150571701
33 -0.074623741 -0.042402359
34 0.047813592 -0.074623741
35 0.075316430 0.047813592
36 -0.120075761 0.075316430
37 0.062206529 -0.120075761
38 -0.035698618 0.062206529
39 -0.137517347 -0.035698618
40 -0.085160906 -0.137517347
41 -0.175108960 -0.085160906
42 -0.158664622 -0.175108960
43 -0.142510545 -0.158664622
44 -0.162285094 -0.142510545
45 -0.132253824 -0.162285094
46 -0.257897608 -0.132253824
47 -0.426719984 -0.257897608
48 -0.196494581 -0.426719984
49 -0.313036352 -0.196494581
50 -0.243967914 -0.313036352
51 -0.435020563 -0.243967914
52 -0.195075360 -0.435020563
53 -0.144431781 -0.195075360
54 -0.371569900 -0.144431781
55 -0.105017679 -0.371569900
56 -0.042594207 -0.105017679
57 -0.042921870 -0.042594207
58 -0.089008831 -0.042921870
59 0.052596351 -0.089008831
60 -0.031596330 0.052596351
61 0.188916848 -0.031596330
62 0.227641063 0.188916848
63 0.379071708 0.227641063
64 0.277080203 0.379071708
65 0.308761415 0.277080203
66 0.399361550 0.308761415
67 0.282581211 0.399361550
68 0.146381648 0.282581211
69 -0.087850799 0.146381648
70 -0.038002484 -0.087850799
71 0.043695692 -0.038002484
72 -0.190467914 0.043695692
73 -0.156603107 -0.190467914
74 0.001562097 -0.156603107
75 -0.011249945 0.001562097
76 0.018574368 -0.011249945
77 -0.036941547 0.018574368
78 -0.087453274 -0.036941547
79 0.055454621 -0.087453274
80 -0.076131193 0.055454621
81 0.049647120 -0.076131193
82 0.126238271 0.049647120
83 0.141442322 0.126238271
84 0.148159257 0.141442322
85 0.025375048 0.148159257
86 0.036472188 0.025375048
87 -0.127894742 0.036472188
88 -0.099482634 -0.127894742
89 0.080375920 -0.099482634
90 0.076651640 0.080375920
91 0.083682575 0.076651640
92 0.138853297 0.083682575
93 0.181737621 0.138853297
94 0.040463558 0.181737621
95 -0.002417207 0.040463558
96 -0.334072834 -0.002417207
97 -0.047644184 -0.334072834
98 -0.139071358 -0.047644184
99 0.110760519 -0.139071358
100 0.011670103 0.110760519
101 -0.086704699 0.011670103
102 -0.278401955 -0.086704699
103 -0.020935685 -0.278401955
104 0.084040246 -0.020935685
105 -0.005490664 0.084040246
106 0.189881753 -0.005490664
107 0.219963256 0.189881753
108 0.313637274 0.219963256
109 0.123175739 0.313637274
110 0.153605425 0.123175739
111 0.139689128 0.153605425
112 0.122215783 0.139689128
113 -0.006959721 0.122215783
114 -0.420158966 -0.006959721
115 -0.130590991 -0.420158966
116 -0.217201820 -0.130590991
117 -0.041337987 -0.217201820
118 0.001991881 -0.041337987
119 0.073501430 0.001991881
120 0.531332187 0.073501430
121 0.107538897 0.531332187
122 0.181185203 0.107538897
123 0.053144368 0.181185203
124 -0.086063401 0.053144368
125 -0.119172642 -0.086063401
126 0.386860563 -0.119172642
127 -0.040512254 0.386860563
128 -0.004765324 -0.040512254
129 -0.028851040 -0.004765324
130 0.036279046 -0.028851040
131 -0.062790573 0.036279046
132 -0.045169815 -0.062790573
133 -0.063177040 -0.045169815
134 -0.027900492 -0.063177040
135 -0.015556657 -0.027900492
136 0.020871806 -0.015556657
137 0.095569451 0.020871806
138 -0.124367845 0.095569451
139 -0.013236751 -0.124367845
140 0.104671615 -0.013236751
141 0.026736361 0.104671615
142 0.062364954 0.026736361
143 NA 0.062364954
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.122482120 0.277573406
[2,] -0.044805607 0.122482120
[3,] 0.043239324 -0.044805607
[4,] -0.022669368 0.043239324
[5,] 0.027789345 -0.022669368
[6,] 0.116506869 0.027789345
[7,] -0.135717453 0.116506869
[8,] -0.207952123 -0.135717453
[9,] -0.245826631 -0.207952123
[10,] -0.026313963 -0.245826631
[11,] 0.110827811 -0.026313963
[12,] 0.054078974 0.110827811
[13,] 0.243544549 0.054078974
[14,] 0.209667313 0.243544549
[15,] 0.006141739 0.209667313
[16,] 0.237495626 0.006141739
[17,] 0.097103943 0.237495626
[18,] 0.013513635 0.097103943
[19,] 0.236790909 0.013513635
[20,] 0.053112815 0.236790909
[21,] 0.050861733 0.053112815
[22,] 0.006695059 0.050861733
[23,] -0.068879601 0.006695059
[24,] -0.037384564 -0.068879601
[25,] -0.170370160 -0.037384564
[26,] -0.143184114 -0.170370160
[27,] 0.128382422 -0.143184114
[28,] -0.073158878 0.128382422
[29,] -0.055726323 -0.073158878
[30,] -0.063763951 -0.055726323
[31,] -0.150571701 -0.063763951
[32,] -0.042402359 -0.150571701
[33,] -0.074623741 -0.042402359
[34,] 0.047813592 -0.074623741
[35,] 0.075316430 0.047813592
[36,] -0.120075761 0.075316430
[37,] 0.062206529 -0.120075761
[38,] -0.035698618 0.062206529
[39,] -0.137517347 -0.035698618
[40,] -0.085160906 -0.137517347
[41,] -0.175108960 -0.085160906
[42,] -0.158664622 -0.175108960
[43,] -0.142510545 -0.158664622
[44,] -0.162285094 -0.142510545
[45,] -0.132253824 -0.162285094
[46,] -0.257897608 -0.132253824
[47,] -0.426719984 -0.257897608
[48,] -0.196494581 -0.426719984
[49,] -0.313036352 -0.196494581
[50,] -0.243967914 -0.313036352
[51,] -0.435020563 -0.243967914
[52,] -0.195075360 -0.435020563
[53,] -0.144431781 -0.195075360
[54,] -0.371569900 -0.144431781
[55,] -0.105017679 -0.371569900
[56,] -0.042594207 -0.105017679
[57,] -0.042921870 -0.042594207
[58,] -0.089008831 -0.042921870
[59,] 0.052596351 -0.089008831
[60,] -0.031596330 0.052596351
[61,] 0.188916848 -0.031596330
[62,] 0.227641063 0.188916848
[63,] 0.379071708 0.227641063
[64,] 0.277080203 0.379071708
[65,] 0.308761415 0.277080203
[66,] 0.399361550 0.308761415
[67,] 0.282581211 0.399361550
[68,] 0.146381648 0.282581211
[69,] -0.087850799 0.146381648
[70,] -0.038002484 -0.087850799
[71,] 0.043695692 -0.038002484
[72,] -0.190467914 0.043695692
[73,] -0.156603107 -0.190467914
[74,] 0.001562097 -0.156603107
[75,] -0.011249945 0.001562097
[76,] 0.018574368 -0.011249945
[77,] -0.036941547 0.018574368
[78,] -0.087453274 -0.036941547
[79,] 0.055454621 -0.087453274
[80,] -0.076131193 0.055454621
[81,] 0.049647120 -0.076131193
[82,] 0.126238271 0.049647120
[83,] 0.141442322 0.126238271
[84,] 0.148159257 0.141442322
[85,] 0.025375048 0.148159257
[86,] 0.036472188 0.025375048
[87,] -0.127894742 0.036472188
[88,] -0.099482634 -0.127894742
[89,] 0.080375920 -0.099482634
[90,] 0.076651640 0.080375920
[91,] 0.083682575 0.076651640
[92,] 0.138853297 0.083682575
[93,] 0.181737621 0.138853297
[94,] 0.040463558 0.181737621
[95,] -0.002417207 0.040463558
[96,] -0.334072834 -0.002417207
[97,] -0.047644184 -0.334072834
[98,] -0.139071358 -0.047644184
[99,] 0.110760519 -0.139071358
[100,] 0.011670103 0.110760519
[101,] -0.086704699 0.011670103
[102,] -0.278401955 -0.086704699
[103,] -0.020935685 -0.278401955
[104,] 0.084040246 -0.020935685
[105,] -0.005490664 0.084040246
[106,] 0.189881753 -0.005490664
[107,] 0.219963256 0.189881753
[108,] 0.313637274 0.219963256
[109,] 0.123175739 0.313637274
[110,] 0.153605425 0.123175739
[111,] 0.139689128 0.153605425
[112,] 0.122215783 0.139689128
[113,] -0.006959721 0.122215783
[114,] -0.420158966 -0.006959721
[115,] -0.130590991 -0.420158966
[116,] -0.217201820 -0.130590991
[117,] -0.041337987 -0.217201820
[118,] 0.001991881 -0.041337987
[119,] 0.073501430 0.001991881
[120,] 0.531332187 0.073501430
[121,] 0.107538897 0.531332187
[122,] 0.181185203 0.107538897
[123,] 0.053144368 0.181185203
[124,] -0.086063401 0.053144368
[125,] -0.119172642 -0.086063401
[126,] 0.386860563 -0.119172642
[127,] -0.040512254 0.386860563
[128,] -0.004765324 -0.040512254
[129,] -0.028851040 -0.004765324
[130,] 0.036279046 -0.028851040
[131,] -0.062790573 0.036279046
[132,] -0.045169815 -0.062790573
[133,] -0.063177040 -0.045169815
[134,] -0.027900492 -0.063177040
[135,] -0.015556657 -0.027900492
[136,] 0.020871806 -0.015556657
[137,] 0.095569451 0.020871806
[138,] -0.124367845 0.095569451
[139,] -0.013236751 -0.124367845
[140,] 0.104671615 -0.013236751
[141,] 0.026736361 0.104671615
[142,] 0.062364954 0.026736361
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.122482120 0.277573406
2 -0.044805607 0.122482120
3 0.043239324 -0.044805607
4 -0.022669368 0.043239324
5 0.027789345 -0.022669368
6 0.116506869 0.027789345
7 -0.135717453 0.116506869
8 -0.207952123 -0.135717453
9 -0.245826631 -0.207952123
10 -0.026313963 -0.245826631
11 0.110827811 -0.026313963
12 0.054078974 0.110827811
13 0.243544549 0.054078974
14 0.209667313 0.243544549
15 0.006141739 0.209667313
16 0.237495626 0.006141739
17 0.097103943 0.237495626
18 0.013513635 0.097103943
19 0.236790909 0.013513635
20 0.053112815 0.236790909
21 0.050861733 0.053112815
22 0.006695059 0.050861733
23 -0.068879601 0.006695059
24 -0.037384564 -0.068879601
25 -0.170370160 -0.037384564
26 -0.143184114 -0.170370160
27 0.128382422 -0.143184114
28 -0.073158878 0.128382422
29 -0.055726323 -0.073158878
30 -0.063763951 -0.055726323
31 -0.150571701 -0.063763951
32 -0.042402359 -0.150571701
33 -0.074623741 -0.042402359
34 0.047813592 -0.074623741
35 0.075316430 0.047813592
36 -0.120075761 0.075316430
37 0.062206529 -0.120075761
38 -0.035698618 0.062206529
39 -0.137517347 -0.035698618
40 -0.085160906 -0.137517347
41 -0.175108960 -0.085160906
42 -0.158664622 -0.175108960
43 -0.142510545 -0.158664622
44 -0.162285094 -0.142510545
45 -0.132253824 -0.162285094
46 -0.257897608 -0.132253824
47 -0.426719984 -0.257897608
48 -0.196494581 -0.426719984
49 -0.313036352 -0.196494581
50 -0.243967914 -0.313036352
51 -0.435020563 -0.243967914
52 -0.195075360 -0.435020563
53 -0.144431781 -0.195075360
54 -0.371569900 -0.144431781
55 -0.105017679 -0.371569900
56 -0.042594207 -0.105017679
57 -0.042921870 -0.042594207
58 -0.089008831 -0.042921870
59 0.052596351 -0.089008831
60 -0.031596330 0.052596351
61 0.188916848 -0.031596330
62 0.227641063 0.188916848
63 0.379071708 0.227641063
64 0.277080203 0.379071708
65 0.308761415 0.277080203
66 0.399361550 0.308761415
67 0.282581211 0.399361550
68 0.146381648 0.282581211
69 -0.087850799 0.146381648
70 -0.038002484 -0.087850799
71 0.043695692 -0.038002484
72 -0.190467914 0.043695692
73 -0.156603107 -0.190467914
74 0.001562097 -0.156603107
75 -0.011249945 0.001562097
76 0.018574368 -0.011249945
77 -0.036941547 0.018574368
78 -0.087453274 -0.036941547
79 0.055454621 -0.087453274
80 -0.076131193 0.055454621
81 0.049647120 -0.076131193
82 0.126238271 0.049647120
83 0.141442322 0.126238271
84 0.148159257 0.141442322
85 0.025375048 0.148159257
86 0.036472188 0.025375048
87 -0.127894742 0.036472188
88 -0.099482634 -0.127894742
89 0.080375920 -0.099482634
90 0.076651640 0.080375920
91 0.083682575 0.076651640
92 0.138853297 0.083682575
93 0.181737621 0.138853297
94 0.040463558 0.181737621
95 -0.002417207 0.040463558
96 -0.334072834 -0.002417207
97 -0.047644184 -0.334072834
98 -0.139071358 -0.047644184
99 0.110760519 -0.139071358
100 0.011670103 0.110760519
101 -0.086704699 0.011670103
102 -0.278401955 -0.086704699
103 -0.020935685 -0.278401955
104 0.084040246 -0.020935685
105 -0.005490664 0.084040246
106 0.189881753 -0.005490664
107 0.219963256 0.189881753
108 0.313637274 0.219963256
109 0.123175739 0.313637274
110 0.153605425 0.123175739
111 0.139689128 0.153605425
112 0.122215783 0.139689128
113 -0.006959721 0.122215783
114 -0.420158966 -0.006959721
115 -0.130590991 -0.420158966
116 -0.217201820 -0.130590991
117 -0.041337987 -0.217201820
118 0.001991881 -0.041337987
119 0.073501430 0.001991881
120 0.531332187 0.073501430
121 0.107538897 0.531332187
122 0.181185203 0.107538897
123 0.053144368 0.181185203
124 -0.086063401 0.053144368
125 -0.119172642 -0.086063401
126 0.386860563 -0.119172642
127 -0.040512254 0.386860563
128 -0.004765324 -0.040512254
129 -0.028851040 -0.004765324
130 0.036279046 -0.028851040
131 -0.062790573 0.036279046
132 -0.045169815 -0.062790573
133 -0.063177040 -0.045169815
134 -0.027900492 -0.063177040
135 -0.015556657 -0.027900492
136 0.020871806 -0.015556657
137 0.095569451 0.020871806
138 -0.124367845 0.095569451
139 -0.013236751 -0.124367845
140 0.104671615 -0.013236751
141 0.026736361 0.104671615
142 0.062364954 0.026736361
> 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/7mzkj1354884942.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/82oj61354884942.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/9x2al1354884942.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/10gwhe1354884942.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11rrqi1354884942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12pexf1354884942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13h1701354884942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/144uii1354884942.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/15oqe01354884942.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/16rhex1354884942.tab")
+ }
>
> try(system("convert tmp/138qo1354884942.ps tmp/138qo1354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/2878g1354884942.ps tmp/2878g1354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z8951354884942.ps tmp/3z8951354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/42yjj1354884942.ps tmp/42yjj1354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/55x4x1354884942.ps tmp/55x4x1354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qdcw1354884942.ps tmp/6qdcw1354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mzkj1354884942.ps tmp/7mzkj1354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/82oj61354884942.ps tmp/82oj61354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/9x2al1354884942.ps tmp/9x2al1354884942.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gwhe1354884942.ps tmp/10gwhe1354884942.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.148 1.041 8.192