R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9.4
+ ,0.5
+ ,5.1
+ ,-1.0
+ ,2504.7
+ ,9.4
+ ,0.8
+ ,5.0
+ ,3.0
+ ,2661.4
+ ,9.5
+ ,1.0
+ ,5.0
+ ,2.0
+ ,2880.4
+ ,9.5
+ ,1.3
+ ,5.1
+ ,3.0
+ ,3064.4
+ ,9.4
+ ,1.3
+ ,5.0
+ ,5.0
+ ,3141.1
+ ,9.4
+ ,1.2
+ ,4.9
+ ,5.0
+ ,3327.7
+ ,9.3
+ ,1.2
+ ,4.8
+ ,3.0
+ ,3565.0
+ ,9.4
+ ,1.0
+ ,4.5
+ ,2.0
+ ,3403.1
+ ,9.4
+ ,0.8
+ ,4.3
+ ,1.0
+ ,3149.9
+ ,9.2
+ ,0.7
+ ,4.3
+ ,-4.0
+ ,3006.8
+ ,9.1
+ ,0.6
+ ,4.2
+ ,1.0
+ ,3230.7
+ ,9.1
+ ,0.7
+ ,4.0
+ ,1.0
+ ,3361.1
+ ,9.1
+ ,1.0
+ ,3.8
+ ,6.0
+ ,3484.7
+ ,9.0
+ ,1.0
+ ,4.1
+ ,3.0
+ ,3411.1
+ ,9.0
+ ,1.3
+ ,4.2
+ ,2.0
+ ,3288.2
+ ,8.9
+ ,1.1
+ ,4.0
+ ,2.0
+ ,3280.4
+ ,8.8
+ ,0.8
+ ,4.3
+ ,2.0
+ ,3174.0
+ ,8.7
+ ,0.7
+ ,4.7
+ ,-8.0
+ ,3165.3
+ ,8.5
+ ,0.7
+ ,5.0
+ ,0.0
+ ,3092.7
+ ,8.3
+ ,0.9
+ ,5.1
+ ,-2.0
+ ,3053.1
+ ,8.1
+ ,1.3
+ ,5.4
+ ,3.0
+ ,3182.0
+ ,7.9
+ ,1.4
+ ,5.4
+ ,5.0
+ ,2999.9
+ ,7.8
+ ,1.6
+ ,5.4
+ ,8.0
+ ,3249.6
+ ,7.6
+ ,2.1
+ ,5.5
+ ,8.0
+ ,3210.5
+ ,7.4
+ ,0.3
+ ,5.8
+ ,9.0
+ ,3030.3
+ ,7.2
+ ,2.1
+ ,5.7
+ ,11.0
+ ,2803.5
+ ,7.0
+ ,2.5
+ ,5.5
+ ,13.0
+ ,2767.6
+ ,7.0
+ ,2.3
+ ,5.6
+ ,12.0
+ ,2882.6
+ ,6.8
+ ,2.4
+ ,5.6
+ ,13.0
+ ,2863.4
+ ,6.8
+ ,3.0
+ ,5.5
+ ,15.0
+ ,2897.1
+ ,6.7
+ ,1.7
+ ,5.5
+ ,13.0
+ ,3012.6
+ ,6.8
+ ,3.5
+ ,5.7
+ ,16.0
+ ,3143.0
+ ,6.7
+ ,4.0
+ ,5.6
+ ,10.0
+ ,3032.9
+ ,6.7
+ ,3.7
+ ,5.6
+ ,14.0
+ ,3045.8
+ ,6.7
+ ,3.7
+ ,5.4
+ ,14.0
+ ,3110.5
+ ,6.5
+ ,3.0
+ ,5.2
+ ,15.0
+ ,3013.2
+ ,6.3
+ ,2.7
+ ,5.1
+ ,13.0
+ ,2987.1
+ ,6.3
+ ,2.5
+ ,5.1
+ ,8.0
+ ,2995.6
+ ,6.3
+ ,2.2
+ ,5.0
+ ,7.0
+ ,2833.2
+ ,6.5
+ ,2.9
+ ,5.3
+ ,3.0
+ ,2849.0
+ ,6.6
+ ,3.1
+ ,5.4
+ ,3.0
+ ,2794.8
+ ,6.5
+ ,3.0
+ ,5.3
+ ,4.0
+ ,2845.3
+ ,6.3
+ ,2.8
+ ,5.1
+ ,4.0
+ ,2915.0
+ ,6.3
+ ,2.5
+ ,5.0
+ ,0.0
+ ,2892.6
+ ,6.5
+ ,1.9
+ ,5.0
+ ,-4.0
+ ,2604.4
+ ,7.0
+ ,1.9
+ ,4.6
+ ,-14.0
+ ,2641.7
+ ,7.1
+ ,1.8
+ ,4.8
+ ,-18.0
+ ,2659.8
+ ,7.3
+ ,2.0
+ ,5.1
+ ,-8.0
+ ,2638.5
+ ,7.3
+ ,2.6
+ ,5.1
+ ,-1.0
+ ,2720.3
+ ,7.4
+ ,2.5
+ ,5.1
+ ,1.0
+ ,2745.9
+ ,7.4
+ ,2.5
+ ,5.4
+ ,2.0
+ ,2735.7
+ ,7.3
+ ,1.6
+ ,5.3
+ ,0.0
+ ,2811.7
+ ,7.4
+ ,1.4
+ ,5.3
+ ,1.0
+ ,2799.4
+ ,7.5
+ ,0.8
+ ,5.1
+ ,0.0
+ ,2555.3
+ ,7.7
+ ,1.1
+ ,4.9
+ ,-1.0
+ ,2305.0
+ ,7.7
+ ,1.3
+ ,4.7
+ ,-3.0
+ ,2215.0
+ ,7.7
+ ,1.2
+ ,4.4
+ ,-3.0
+ ,2065.8
+ ,7.7
+ ,1.3
+ ,4.6
+ ,-3.0
+ ,1940.5
+ ,7.7
+ ,1.1
+ ,4.5
+ ,-4.0
+ ,2042.0
+ ,7.8
+ ,1.3
+ ,4.2
+ ,-8.0
+ ,1995.4
+ ,8.0
+ ,1.2
+ ,4.0
+ ,-9.0
+ ,1946.8
+ ,8.1
+ ,1.6
+ ,3.9
+ ,-13.0
+ ,1765.9
+ ,8.1
+ ,1.7
+ ,4.1
+ ,-18.0
+ ,1635.3
+ ,8.2
+ ,1.5
+ ,4.1
+ ,-11.0
+ ,1833.4
+ ,8.2
+ ,0.9
+ ,3.7
+ ,-9.0
+ ,1910.4
+ ,8.2
+ ,1.5
+ ,3.8
+ ,-10.0
+ ,1959.7
+ ,8.1
+ ,1.4
+ ,4.1
+ ,-13.0
+ ,1969.6
+ ,8.1
+ ,1.6
+ ,4.1
+ ,-11.0
+ ,2061.4
+ ,8.2
+ ,1.7
+ ,4.0
+ ,-5.0
+ ,2093.5
+ ,8.3
+ ,1.4
+ ,4.3
+ ,-15.0
+ ,2120.9
+ ,8.3
+ ,1.8
+ ,4.4
+ ,-6.0
+ ,2174.6
+ ,8.4
+ ,1.7
+ ,4.2
+ ,-6.0
+ ,2196.7
+ ,8.5
+ ,1.4
+ ,4.2
+ ,-3.0
+ ,2350.4
+ ,8.5
+ ,1.2
+ ,4.0
+ ,-1.0
+ ,2440.3
+ ,8.4
+ ,1.0
+ ,4.0
+ ,-3.0
+ ,2408.6
+ ,8.0
+ ,1.7
+ ,4.3
+ ,-4.0
+ ,2472.8
+ ,7.9
+ ,2.4
+ ,4.4
+ ,-6.0
+ ,2407.6
+ ,8.1
+ ,2.0
+ ,4.4
+ ,0.0
+ ,2454.6
+ ,8.5
+ ,2.1
+ ,4.3
+ ,-4.0
+ ,2448.1
+ ,8.8
+ ,2.0
+ ,4.1
+ ,-2.0
+ ,2497.8
+ ,8.8
+ ,1.8
+ ,4.1
+ ,-2.0
+ ,2645.6
+ ,8.6
+ ,2.7
+ ,3.9
+ ,-6.0
+ ,2756.8
+ ,8.3
+ ,2.3
+ ,3.8
+ ,-7.0
+ ,2849.3
+ ,8.3
+ ,1.9
+ ,3.7
+ ,-6.0
+ ,2921.4
+ ,8.3
+ ,2.0
+ ,3.5
+ ,-6.0
+ ,2981.9
+ ,8.4
+ ,2.3
+ ,3.7
+ ,-3.0
+ ,3080.6
+ ,8.4
+ ,2.8
+ ,3.7
+ ,-2.0
+ ,3106.2
+ ,8.5
+ ,2.4
+ ,3.5
+ ,-5.0
+ ,3119.3
+ ,8.6
+ ,2.3
+ ,3.3
+ ,-11.0
+ ,3061.3
+ ,8.6
+ ,2.7
+ ,3.2
+ ,-11.0
+ ,3097.3
+ ,8.6
+ ,2.7
+ ,3.3
+ ,-11.0
+ ,3161.7
+ ,8.6
+ ,2.9
+ ,3.1
+ ,-10.0
+ ,3257.2
+ ,8.6
+ ,3.0
+ ,3.2
+ ,-14.0
+ ,3277.0
+ ,8.5
+ ,2.2
+ ,3.4
+ ,-8.0
+ ,3295.3
+ ,8.4
+ ,2.3
+ ,3.5
+ ,-9.0
+ ,3364.0
+ ,8.4
+ ,2.8
+ ,3.3
+ ,-5.0
+ ,3494.2
+ ,8.4
+ ,2.8
+ ,3.5
+ ,-1.0
+ ,3667.0
+ ,8.5
+ ,2.8
+ ,3.5
+ ,-2.0
+ ,3813.1
+ ,8.5
+ ,2.2
+ ,3.8
+ ,-5.0
+ ,3918.0
+ ,8.6
+ ,2.6
+ ,4.0
+ ,-4.0
+ ,3895.5
+ ,8.6
+ ,2.8
+ ,4.0
+ ,-6.0
+ ,3801.1
+ ,8.4
+ ,2.5
+ ,4.1
+ ,-2.0
+ ,3570.1
+ ,8.2
+ ,2.4
+ ,4.0
+ ,-2.0
+ ,3701.6
+ ,8.0
+ ,2.3
+ ,3.8
+ ,-2.0
+ ,3862.3
+ ,8.0
+ ,1.9
+ ,3.7
+ ,-2.0
+ ,3970.1
+ ,8.0
+ ,1.7
+ ,3.8
+ ,2.0
+ ,4138.5
+ ,8.0
+ ,2.0
+ ,3.7
+ ,1.0
+ ,4199.8
+ ,7.9
+ ,2.1
+ ,4.0
+ ,-8.0
+ ,4290.9
+ ,7.9
+ ,1.7
+ ,4.2
+ ,-1.0
+ ,4443.9
+ ,7.8
+ ,1.8
+ ,4.0
+ ,1.0
+ ,4502.6
+ ,7.8
+ ,1.8
+ ,4.1
+ ,-1.0
+ ,4357.0
+ ,8.0
+ ,1.8
+ ,4.2
+ ,2.0
+ ,4591.3
+ ,7.8
+ ,1.3
+ ,4.5
+ ,2.0
+ ,4697.0
+ ,7.4
+ ,1.3
+ ,4.6
+ ,1.0
+ ,4621.4
+ ,7.2
+ ,1.3
+ ,4.5
+ ,-1.0
+ ,4562.8
+ ,7.0
+ ,1.2
+ ,4.5
+ ,-2.0
+ ,4202.5
+ ,7.0
+ ,1.4
+ ,4.5
+ ,-2.0
+ ,4296.5
+ ,7.2
+ ,2.2
+ ,4.4
+ ,-1.0
+ ,4435.2
+ ,7.2
+ ,2.9
+ ,4.3
+ ,-8.0
+ ,4105.2
+ ,7.2
+ ,3.1
+ ,4.5
+ ,-4.0
+ ,4116.7
+ ,7.0
+ ,3.5
+ ,4.1
+ ,-6.0
+ ,3844.5
+ ,6.9
+ ,3.6
+ ,4.1
+ ,-3.0
+ ,3721.0
+ ,6.8
+ ,4.4
+ ,4.3
+ ,-3.0
+ ,3674.4
+ ,6.8
+ ,4.1
+ ,4.4
+ ,-7.0
+ ,3857.6
+ ,6.8
+ ,5.1
+ ,4.7
+ ,-9.0
+ ,3801.1
+ ,6.9
+ ,5.8
+ ,5.0
+ ,-11.0
+ ,3504.4
+ ,7.2
+ ,5.9
+ ,4.7
+ ,-13.0
+ ,3032.6
+ ,7.2
+ ,5.4
+ ,4.5
+ ,-11.0
+ ,3047.0
+ ,7.2
+ ,5.5
+ ,4.5
+ ,-9.0
+ ,2962.3
+ ,7.1
+ ,4.8
+ ,4.5
+ ,-17.0
+ ,2197.8
+ ,7.2
+ ,3.2
+ ,5.5
+ ,-22.0
+ ,2014.5
+ ,7.3
+ ,2.7
+ ,4.5
+ ,-25.0
+ ,1862.8
+ ,7.5
+ ,2.1
+ ,4.4
+ ,-20.0
+ ,1905.4
+ ,7.6
+ ,1.9
+ ,4.2
+ ,-24.0
+ ,1811.0
+ ,7.7
+ ,0.6
+ ,3.9
+ ,-24.0
+ ,1670.1
+ ,7.7
+ ,0.7
+ ,3.9
+ ,-22.0
+ ,1864.4
+ ,7.7
+ ,-0.2
+ ,4.2
+ ,-19.0
+ ,2052.0
+ ,7.8
+ ,-1.0
+ ,4.0
+ ,-18.0
+ ,2029.6
+ ,8.0
+ ,-1.7
+ ,3.8
+ ,-17.0
+ ,2070.8
+ ,8.1
+ ,-0.7
+ ,3.7
+ ,-11.0
+ ,2293.4
+ ,8.1
+ ,-1.0
+ ,3.7
+ ,-11.0
+ ,2443.3
+ ,8.0
+ ,-0.9
+ ,3.7
+ ,-12.0
+ ,2513.2
+ ,8.1
+ ,0.0
+ ,3.7
+ ,-10.0
+ ,2466.9
+ ,8.2
+ ,0.3
+ ,3.7
+ ,-15.0
+ ,2502.7
+ ,8.3
+ ,0.8
+ ,3.8
+ ,-15.0
+ ,2539.9
+ ,8.4
+ ,0.8
+ ,3.7
+ ,-15.0
+ ,2482.6
+ ,8.4
+ ,1.9
+ ,3.5
+ ,-13.0
+ ,2626.2
+ ,8.4
+ ,2.1
+ ,3.5
+ ,-8.0
+ ,2656.3
+ ,8.5
+ ,2.5
+ ,3.1
+ ,-13.0
+ ,2446.7
+ ,8.5
+ ,2.7
+ ,3.4
+ ,-9.0
+ ,2467.4
+ ,8.6
+ ,2.4
+ ,3.3
+ ,-7.0
+ ,2462.3
+ ,8.6
+ ,2.4
+ ,2.8
+ ,-4.0
+ ,2504.6
+ ,8.5
+ ,2.9
+ ,3.2
+ ,-4.0
+ ,2579.4
+ ,8.5
+ ,3.1
+ ,3.4
+ ,-2.0
+ ,2649.2)
+ ,dim=c(5
+ ,154)
+ ,dimnames=list(c('werkloosheid'
+ ,'hicp'
+ ,'rente'
+ ,'consumer'
+ ,'bel20')
+ ,1:154))
> y <- array(NA,dim=c(5,154),dimnames=list(c('werkloosheid','hicp','rente','consumer','bel20'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = '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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
werkloosheid hicp rente consumer bel20 t
1 9.4 0.5 5.1 -1 2504.7 1
2 9.4 0.8 5.0 3 2661.4 2
3 9.5 1.0 5.0 2 2880.4 3
4 9.5 1.3 5.1 3 3064.4 4
5 9.4 1.3 5.0 5 3141.1 5
6 9.4 1.2 4.9 5 3327.7 6
7 9.3 1.2 4.8 3 3565.0 7
8 9.4 1.0 4.5 2 3403.1 8
9 9.4 0.8 4.3 1 3149.9 9
10 9.2 0.7 4.3 -4 3006.8 10
11 9.1 0.6 4.2 1 3230.7 11
12 9.1 0.7 4.0 1 3361.1 12
13 9.1 1.0 3.8 6 3484.7 13
14 9.0 1.0 4.1 3 3411.1 14
15 9.0 1.3 4.2 2 3288.2 15
16 8.9 1.1 4.0 2 3280.4 16
17 8.8 0.8 4.3 2 3174.0 17
18 8.7 0.7 4.7 -8 3165.3 18
19 8.5 0.7 5.0 0 3092.7 19
20 8.3 0.9 5.1 -2 3053.1 20
21 8.1 1.3 5.4 3 3182.0 21
22 7.9 1.4 5.4 5 2999.9 22
23 7.8 1.6 5.4 8 3249.6 23
24 7.6 2.1 5.5 8 3210.5 24
25 7.4 0.3 5.8 9 3030.3 25
26 7.2 2.1 5.7 11 2803.5 26
27 7.0 2.5 5.5 13 2767.6 27
28 7.0 2.3 5.6 12 2882.6 28
29 6.8 2.4 5.6 13 2863.4 29
30 6.8 3.0 5.5 15 2897.1 30
31 6.7 1.7 5.5 13 3012.6 31
32 6.8 3.5 5.7 16 3143.0 32
33 6.7 4.0 5.6 10 3032.9 33
34 6.7 3.7 5.6 14 3045.8 34
35 6.7 3.7 5.4 14 3110.5 35
36 6.5 3.0 5.2 15 3013.2 36
37 6.3 2.7 5.1 13 2987.1 37
38 6.3 2.5 5.1 8 2995.6 38
39 6.3 2.2 5.0 7 2833.2 39
40 6.5 2.9 5.3 3 2849.0 40
41 6.6 3.1 5.4 3 2794.8 41
42 6.5 3.0 5.3 4 2845.3 42
43 6.3 2.8 5.1 4 2915.0 43
44 6.3 2.5 5.0 0 2892.6 44
45 6.5 1.9 5.0 -4 2604.4 45
46 7.0 1.9 4.6 -14 2641.7 46
47 7.1 1.8 4.8 -18 2659.8 47
48 7.3 2.0 5.1 -8 2638.5 48
49 7.3 2.6 5.1 -1 2720.3 49
50 7.4 2.5 5.1 1 2745.9 50
51 7.4 2.5 5.4 2 2735.7 51
52 7.3 1.6 5.3 0 2811.7 52
53 7.4 1.4 5.3 1 2799.4 53
54 7.5 0.8 5.1 0 2555.3 54
55 7.7 1.1 4.9 -1 2305.0 55
56 7.7 1.3 4.7 -3 2215.0 56
57 7.7 1.2 4.4 -3 2065.8 57
58 7.7 1.3 4.6 -3 1940.5 58
59 7.7 1.1 4.5 -4 2042.0 59
60 7.8 1.3 4.2 -8 1995.4 60
61 8.0 1.2 4.0 -9 1946.8 61
62 8.1 1.6 3.9 -13 1765.9 62
63 8.1 1.7 4.1 -18 1635.3 63
64 8.2 1.5 4.1 -11 1833.4 64
65 8.2 0.9 3.7 -9 1910.4 65
66 8.2 1.5 3.8 -10 1959.7 66
67 8.1 1.4 4.1 -13 1969.6 67
68 8.1 1.6 4.1 -11 2061.4 68
69 8.2 1.7 4.0 -5 2093.5 69
70 8.3 1.4 4.3 -15 2120.9 70
71 8.3 1.8 4.4 -6 2174.6 71
72 8.4 1.7 4.2 -6 2196.7 72
73 8.5 1.4 4.2 -3 2350.4 73
74 8.5 1.2 4.0 -1 2440.3 74
75 8.4 1.0 4.0 -3 2408.6 75
76 8.0 1.7 4.3 -4 2472.8 76
77 7.9 2.4 4.4 -6 2407.6 77
78 8.1 2.0 4.4 0 2454.6 78
79 8.5 2.1 4.3 -4 2448.1 79
80 8.8 2.0 4.1 -2 2497.8 80
81 8.8 1.8 4.1 -2 2645.6 81
82 8.6 2.7 3.9 -6 2756.8 82
83 8.3 2.3 3.8 -7 2849.3 83
84 8.3 1.9 3.7 -6 2921.4 84
85 8.3 2.0 3.5 -6 2981.9 85
86 8.4 2.3 3.7 -3 3080.6 86
87 8.4 2.8 3.7 -2 3106.2 87
88 8.5 2.4 3.5 -5 3119.3 88
89 8.6 2.3 3.3 -11 3061.3 89
90 8.6 2.7 3.2 -11 3097.3 90
91 8.6 2.7 3.3 -11 3161.7 91
92 8.6 2.9 3.1 -10 3257.2 92
93 8.6 3.0 3.2 -14 3277.0 93
94 8.5 2.2 3.4 -8 3295.3 94
95 8.4 2.3 3.5 -9 3364.0 95
96 8.4 2.8 3.3 -5 3494.2 96
97 8.4 2.8 3.5 -1 3667.0 97
98 8.5 2.8 3.5 -2 3813.1 98
99 8.5 2.2 3.8 -5 3918.0 99
100 8.6 2.6 4.0 -4 3895.5 100
101 8.6 2.8 4.0 -6 3801.1 101
102 8.4 2.5 4.1 -2 3570.1 102
103 8.2 2.4 4.0 -2 3701.6 103
104 8.0 2.3 3.8 -2 3862.3 104
105 8.0 1.9 3.7 -2 3970.1 105
106 8.0 1.7 3.8 2 4138.5 106
107 8.0 2.0 3.7 1 4199.8 107
108 7.9 2.1 4.0 -8 4290.9 108
109 7.9 1.7 4.2 -1 4443.9 109
110 7.8 1.8 4.0 1 4502.6 110
111 7.8 1.8 4.1 -1 4357.0 111
112 8.0 1.8 4.2 2 4591.3 112
113 7.8 1.3 4.5 2 4697.0 113
114 7.4 1.3 4.6 1 4621.4 114
115 7.2 1.3 4.5 -1 4562.8 115
116 7.0 1.2 4.5 -2 4202.5 116
117 7.0 1.4 4.5 -2 4296.5 117
118 7.2 2.2 4.4 -1 4435.2 118
119 7.2 2.9 4.3 -8 4105.2 119
120 7.2 3.1 4.5 -4 4116.7 120
121 7.0 3.5 4.1 -6 3844.5 121
122 6.9 3.6 4.1 -3 3721.0 122
123 6.8 4.4 4.3 -3 3674.4 123
124 6.8 4.1 4.4 -7 3857.6 124
125 6.8 5.1 4.7 -9 3801.1 125
126 6.9 5.8 5.0 -11 3504.4 126
127 7.2 5.9 4.7 -13 3032.6 127
128 7.2 5.4 4.5 -11 3047.0 128
129 7.2 5.5 4.5 -9 2962.3 129
130 7.1 4.8 4.5 -17 2197.8 130
131 7.2 3.2 5.5 -22 2014.5 131
132 7.3 2.7 4.5 -25 1862.8 132
133 7.5 2.1 4.4 -20 1905.4 133
134 7.6 1.9 4.2 -24 1811.0 134
135 7.7 0.6 3.9 -24 1670.1 135
136 7.7 0.7 3.9 -22 1864.4 136
137 7.7 -0.2 4.2 -19 2052.0 137
138 7.8 -1.0 4.0 -18 2029.6 138
139 8.0 -1.7 3.8 -17 2070.8 139
140 8.1 -0.7 3.7 -11 2293.4 140
141 8.1 -1.0 3.7 -11 2443.3 141
142 8.0 -0.9 3.7 -12 2513.2 142
143 8.1 0.0 3.7 -10 2466.9 143
144 8.2 0.3 3.7 -15 2502.7 144
145 8.3 0.8 3.8 -15 2539.9 145
146 8.4 0.8 3.7 -15 2482.6 146
147 8.4 1.9 3.5 -13 2626.2 147
148 8.4 2.1 3.5 -8 2656.3 148
149 8.5 2.5 3.1 -13 2446.7 149
150 8.5 2.7 3.4 -9 2467.4 150
151 8.6 2.4 3.3 -7 2462.3 151
152 8.6 2.4 2.8 -4 2504.6 152
153 8.5 2.9 3.2 -4 2579.4 153
154 8.5 3.1 3.4 -2 2649.2 154
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) hicp rente consumer bel20 t
12.8816811 -0.1493339 -0.9484259 -0.0146404 0.0001130 -0.0119659
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.2667 -0.2787 -0.0405 0.2760 1.3948
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.288e+01 4.710e-01 27.351 < 2e-16 ***
hicp -1.493e-01 3.662e-02 -4.078 7.39e-05 ***
rente -9.484e-01 8.160e-02 -11.623 < 2e-16 ***
consumer -1.464e-02 7.864e-03 -1.862 0.0646 .
bel20 1.130e-04 7.064e-05 1.600 0.1117
t -1.197e-02 1.441e-03 -8.304 5.84e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5045 on 148 degrees of freedom
Multiple R-squared: 0.6179, Adjusted R-squared: 0.605
F-statistic: 47.87 on 5 and 148 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,] 2.591795e-03 5.183590e-03 9.974082e-01
[2,] 1.254277e-03 2.508555e-03 9.987457e-01
[3,] 1.801105e-04 3.602211e-04 9.998199e-01
[4,] 8.313087e-05 1.662617e-04 9.999169e-01
[5,] 4.153611e-05 8.307222e-05 9.999585e-01
[6,] 7.070311e-06 1.414062e-05 9.999929e-01
[7,] 1.171894e-06 2.343788e-06 9.999988e-01
[8,] 1.852192e-07 3.704384e-07 9.999998e-01
[9,] 1.043473e-07 2.086946e-07 9.999999e-01
[10,] 1.920312e-08 3.840624e-08 1.000000e+00
[11,] 3.712501e-09 7.425002e-09 1.000000e+00
[12,] 4.906457e-09 9.812914e-09 1.000000e+00
[13,] 7.845809e-09 1.569162e-08 1.000000e+00
[14,] 8.056988e-09 1.611398e-08 1.000000e+00
[15,] 6.227910e-09 1.245582e-08 1.000000e+00
[16,] 1.288039e-08 2.576078e-08 1.000000e+00
[17,] 4.382805e-09 8.765610e-09 1.000000e+00
[18,] 2.003025e-09 4.006050e-09 1.000000e+00
[19,] 1.333213e-09 2.666427e-09 1.000000e+00
[20,] 4.372148e-10 8.744295e-10 1.000000e+00
[21,] 2.480377e-10 4.960754e-10 1.000000e+00
[22,] 6.039971e-11 1.207994e-10 1.000000e+00
[23,] 4.992615e-11 9.985230e-11 1.000000e+00
[24,] 2.568609e-11 5.137218e-11 1.000000e+00
[25,] 8.229045e-12 1.645809e-11 1.000000e+00
[26,] 4.636337e-12 9.272674e-12 1.000000e+00
[27,] 1.959609e-12 3.919218e-12 1.000000e+00
[28,] 5.940964e-13 1.188193e-12 1.000000e+00
[29,] 9.990201e-13 1.998040e-12 1.000000e+00
[30,] 3.721064e-12 7.442128e-12 1.000000e+00
[31,] 3.982822e-12 7.965644e-12 1.000000e+00
[32,] 3.451623e-12 6.903245e-12 1.000000e+00
[33,] 3.347169e-11 6.694338e-11 1.000000e+00
[34,] 7.661855e-11 1.532371e-10 1.000000e+00
[35,] 1.131213e-10 2.262426e-10 1.000000e+00
[36,] 3.027007e-10 6.054013e-10 1.000000e+00
[37,] 2.177317e-09 4.354633e-09 1.000000e+00
[38,] 5.335841e-09 1.067168e-08 1.000000e+00
[39,] 5.356944e-09 1.071389e-08 1.000000e+00
[40,] 2.624193e-04 5.248386e-04 9.997376e-01
[41,] 8.719257e-02 1.743851e-01 9.128074e-01
[42,] 6.301436e-01 7.397129e-01 3.698564e-01
[43,] 9.475156e-01 1.049689e-01 5.248443e-02
[44,] 9.806863e-01 3.862730e-02 1.931365e-02
[45,] 9.925326e-01 1.493477e-02 7.467383e-03
[46,] 9.951932e-01 9.613532e-03 4.806766e-03
[47,] 9.973449e-01 5.310148e-03 2.655074e-03
[48,] 9.978078e-01 4.384340e-03 2.192170e-03
[49,] 9.980131e-01 3.973797e-03 1.986899e-03
[50,] 9.979823e-01 4.035428e-03 2.017714e-03
[51,] 9.979312e-01 4.137670e-03 2.068835e-03
[52,] 9.980854e-01 3.829150e-03 1.914575e-03
[53,] 9.981946e-01 3.610814e-03 1.805407e-03
[54,] 9.981860e-01 3.628052e-03 1.814026e-03
[55,] 9.976903e-01 4.619390e-03 2.309695e-03
[56,] 9.976844e-01 4.631280e-03 2.315640e-03
[57,] 9.982123e-01 3.575418e-03 1.787709e-03
[58,] 9.986413e-01 2.717441e-03 1.358721e-03
[59,] 9.986644e-01 2.671143e-03 1.335571e-03
[60,] 9.989597e-01 2.080569e-03 1.040284e-03
[61,] 9.994890e-01 1.022049e-03 5.110246e-04
[62,] 9.996473e-01 7.053391e-04 3.526696e-04
[63,] 9.998888e-01 2.224149e-04 1.112075e-04
[64,] 9.999498e-01 1.003348e-04 5.016742e-05
[65,] 9.999770e-01 4.603403e-05 2.301702e-05
[66,] 9.999806e-01 3.888627e-05 1.944313e-05
[67,] 9.999771e-01 4.579550e-05 2.289775e-05
[68,] 9.999803e-01 3.940655e-05 1.970327e-05
[69,] 9.999882e-01 2.357074e-05 1.178537e-05
[70,] 9.999926e-01 1.471011e-05 7.355055e-06
[71,] 9.999966e-01 6.838794e-06 3.419397e-06
[72,] 9.999986e-01 2.777320e-06 1.388660e-06
[73,] 9.999993e-01 1.329922e-06 6.649611e-07
[74,] 9.999995e-01 9.545584e-07 4.772792e-07
[75,] 9.999991e-01 1.704582e-06 8.522908e-07
[76,] 9.999986e-01 2.865620e-06 1.432810e-06
[77,] 9.999982e-01 3.559834e-06 1.779917e-06
[78,] 9.999970e-01 6.019579e-06 3.009790e-06
[79,] 9.999954e-01 9.148380e-06 4.574190e-06
[80,] 9.999924e-01 1.522786e-05 7.613931e-06
[81,] 9.999868e-01 2.635169e-05 1.317585e-05
[82,] 9.999782e-01 4.357774e-05 2.178887e-05
[83,] 9.999631e-01 7.384822e-05 3.692411e-05
[84,] 9.999426e-01 1.147380e-04 5.736898e-05
[85,] 9.999047e-01 1.905257e-04 9.526283e-05
[86,] 9.998492e-01 3.015171e-04 1.507585e-04
[87,] 9.997601e-01 4.798359e-04 2.399180e-04
[88,] 9.996936e-01 6.128115e-04 3.064058e-04
[89,] 9.995585e-01 8.830923e-04 4.415462e-04
[90,] 9.993080e-01 1.384055e-03 6.920277e-04
[91,] 9.991409e-01 1.718114e-03 8.590571e-04
[92,] 9.995769e-01 8.461528e-04 4.230764e-04
[93,] 9.998999e-01 2.001041e-04 1.000520e-04
[94,] 9.999511e-01 9.787468e-05 4.893734e-05
[95,] 9.999605e-01 7.900828e-05 3.950414e-05
[96,] 9.999471e-01 1.058284e-04 5.291418e-05
[97,] 9.999355e-01 1.289576e-04 6.447882e-05
[98,] 9.999257e-01 1.486945e-04 7.434725e-05
[99,] 9.999141e-01 1.717167e-04 8.585837e-05
[100,] 9.999424e-01 1.151851e-04 5.759256e-05
[101,] 9.999757e-01 4.864475e-05 2.432237e-05
[102,] 9.999842e-01 3.169624e-05 1.584812e-05
[103,] 9.999956e-01 8.770448e-06 4.385224e-06
[104,] 1.000000e+00 4.925699e-08 2.462850e-08
[105,] 1.000000e+00 5.425771e-11 2.712886e-11
[106,] 1.000000e+00 2.161522e-12 1.080761e-12
[107,] 1.000000e+00 1.164409e-12 5.822043e-13
[108,] 1.000000e+00 2.488500e-12 1.244250e-12
[109,] 1.000000e+00 6.911031e-12 3.455516e-12
[110,] 1.000000e+00 3.401533e-12 1.700766e-12
[111,] 1.000000e+00 1.247098e-12 6.235492e-13
[112,] 1.000000e+00 8.172591e-15 4.086295e-15
[113,] 1.000000e+00 1.078718e-14 5.393592e-15
[114,] 1.000000e+00 4.137530e-14 2.068765e-14
[115,] 1.000000e+00 2.091350e-13 1.045675e-13
[116,] 1.000000e+00 3.529283e-13 1.764642e-13
[117,] 1.000000e+00 7.410898e-14 3.705449e-14
[118,] 1.000000e+00 2.689381e-14 1.344691e-14
[119,] 1.000000e+00 6.506180e-14 3.253090e-14
[120,] 1.000000e+00 3.405531e-13 1.702766e-13
[121,] 1.000000e+00 1.174362e-12 5.871811e-13
[122,] 1.000000e+00 5.374561e-12 2.687280e-12
[123,] 1.000000e+00 1.800791e-11 9.003954e-12
[124,] 1.000000e+00 4.094970e-11 2.047485e-11
[125,] 1.000000e+00 2.407278e-10 1.203639e-10
[126,] 1.000000e+00 1.458412e-09 7.292060e-10
[127,] 1.000000e+00 9.221339e-09 4.610669e-09
[128,] 1.000000e+00 5.056253e-08 2.528126e-08
[129,] 9.999999e-01 1.533258e-07 7.666290e-08
[130,] 1.000000e+00 7.054251e-08 3.527125e-08
[131,] 9.999999e-01 2.790357e-07 1.395179e-07
[132,] 9.999989e-01 2.208463e-06 1.104232e-06
[133,] 9.999955e-01 8.956378e-06 4.478189e-06
[134,] 9.999765e-01 4.690940e-05 2.345470e-05
[135,] 9.998852e-01 2.296652e-04 1.148326e-04
[136,] 9.998728e-01 2.544885e-04 1.272443e-04
[137,] 9.992957e-01 1.408507e-03 7.042535e-04
> postscript(file="/var/www/html/rcomp/tmp/1s8n21292864413.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2s8n21292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/33hnn1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43hnn1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53hnn1292864413.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 = 154
Frequency = 1
1 2 3 4 5 6
1.144144490 1.146915682 1.249351581 1.394800723 1.232534432 1.113630563
7 8 9 10 11 12
0.874648035 0.675880709 0.482276794 0.222283872 0.072365415 -0.105161252
13 14 15 16 17 18
-0.178850540 -0.017958039 0.132903231 -0.173801051 -0.010079748 0.120902782
19 20 21 22 23 24
0.342726452 0.254597473 0.469455374 0.346220581 0.303747593 0.289643015
25 26 27 28 29 30
0.152346488 0.193189688 -0.091457006 -0.042155592 -0.198445472 -0.166250601
31 32 33 34 35 36
-0.490755920 0.108876464 -0.074729532 -0.050460485 -0.235493613 -0.692107093
37 38 39 40 41 42
-1.046114254 -1.138177879 -1.262136897 -0.721457124 -0.478654909 -0.667533243
43 44 45 46 47 48
-1.082998357 -1.266704543 -1.170321472 -1.188346225 -0.962236122 -0.287064020
49 50 51 52 53 54
-0.092262015 0.031157391 0.343444498 -0.011704667 0.086425300 -0.067940848
55 56 57 58 59 60
0.012794341 -0.154165031 -0.424794234 -0.194045462 -0.332903141 -0.528891930
61 62 63 64 65 66
-0.530691056 -0.491946227 -0.333800243 -0.171612224 -0.608040501 -0.431845077
67 68 69 70 71 72
-0.295325052 -0.234588935 -0.118318592 0.083873728 0.376108822 0.280957940
73 74 75 76 77 78
0.374670150 0.186202334 0.042604185 0.021733827 0.111165677 0.345927338
79 80 81 82 83 84
0.620157311 0.751167208 0.716558609 0.402107921 -0.065599142 -0.201719361
85 86 87 88 89 90
-0.371344323 0.007870739 0.106250076 -0.076604725 -0.250543162 -0.277755827
91 92 93 94 95 96
-0.178227283 -0.322234981 -0.261292866 -0.193335241 -0.193999779 -0.253208763
97 98 99 100 101 102
-0.012529961 0.068280001 0.219394013 0.597962505 0.621185695 0.567868514
103 104 105 106 107 108
0.255193315 -0.155625326 -0.310421563 -0.193954730 -0.253601152 -0.184235745
109 110 111 112 113 114
0.042868915 -0.197271804 -0.103284997 0.220958691 0.230836804 -0.068449017
115 116 117 118 119 120
-0.373982123 -0.550860575 -0.519653918 -0.284102173 -0.327623626 -0.038844218
121 122 123 124 125 126
-0.545025558 -0.560244287 -0.333858299 -0.351120952 0.071812753 0.577099229
127 128 129 130 131 132
0.643523700 0.418790473 0.484545292 0.261275823 1.030252307 0.092352915
133 134 135 136 137 138
0.188262225 0.032785934 -0.317982155 -0.283766330 -0.098958833 -0.278972619
139 140 141 142 143 144
-0.351242553 -0.122106428 -0.171885804 -0.267528580 0.013352474 0.092869703
145 146 147 148 149 150
0.370139945 0.393740649 0.393336437 0.504968456 0.247789476 0.630371475
151 152 153 154
0.632551943 0.209444369 0.566991953 0.819900188
> postscript(file="/var/www/html/rcomp/tmp/6w84q1292864413.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 1.144144490 NA
1 1.146915682 1.144144490
2 1.249351581 1.146915682
3 1.394800723 1.249351581
4 1.232534432 1.394800723
5 1.113630563 1.232534432
6 0.874648035 1.113630563
7 0.675880709 0.874648035
8 0.482276794 0.675880709
9 0.222283872 0.482276794
10 0.072365415 0.222283872
11 -0.105161252 0.072365415
12 -0.178850540 -0.105161252
13 -0.017958039 -0.178850540
14 0.132903231 -0.017958039
15 -0.173801051 0.132903231
16 -0.010079748 -0.173801051
17 0.120902782 -0.010079748
18 0.342726452 0.120902782
19 0.254597473 0.342726452
20 0.469455374 0.254597473
21 0.346220581 0.469455374
22 0.303747593 0.346220581
23 0.289643015 0.303747593
24 0.152346488 0.289643015
25 0.193189688 0.152346488
26 -0.091457006 0.193189688
27 -0.042155592 -0.091457006
28 -0.198445472 -0.042155592
29 -0.166250601 -0.198445472
30 -0.490755920 -0.166250601
31 0.108876464 -0.490755920
32 -0.074729532 0.108876464
33 -0.050460485 -0.074729532
34 -0.235493613 -0.050460485
35 -0.692107093 -0.235493613
36 -1.046114254 -0.692107093
37 -1.138177879 -1.046114254
38 -1.262136897 -1.138177879
39 -0.721457124 -1.262136897
40 -0.478654909 -0.721457124
41 -0.667533243 -0.478654909
42 -1.082998357 -0.667533243
43 -1.266704543 -1.082998357
44 -1.170321472 -1.266704543
45 -1.188346225 -1.170321472
46 -0.962236122 -1.188346225
47 -0.287064020 -0.962236122
48 -0.092262015 -0.287064020
49 0.031157391 -0.092262015
50 0.343444498 0.031157391
51 -0.011704667 0.343444498
52 0.086425300 -0.011704667
53 -0.067940848 0.086425300
54 0.012794341 -0.067940848
55 -0.154165031 0.012794341
56 -0.424794234 -0.154165031
57 -0.194045462 -0.424794234
58 -0.332903141 -0.194045462
59 -0.528891930 -0.332903141
60 -0.530691056 -0.528891930
61 -0.491946227 -0.530691056
62 -0.333800243 -0.491946227
63 -0.171612224 -0.333800243
64 -0.608040501 -0.171612224
65 -0.431845077 -0.608040501
66 -0.295325052 -0.431845077
67 -0.234588935 -0.295325052
68 -0.118318592 -0.234588935
69 0.083873728 -0.118318592
70 0.376108822 0.083873728
71 0.280957940 0.376108822
72 0.374670150 0.280957940
73 0.186202334 0.374670150
74 0.042604185 0.186202334
75 0.021733827 0.042604185
76 0.111165677 0.021733827
77 0.345927338 0.111165677
78 0.620157311 0.345927338
79 0.751167208 0.620157311
80 0.716558609 0.751167208
81 0.402107921 0.716558609
82 -0.065599142 0.402107921
83 -0.201719361 -0.065599142
84 -0.371344323 -0.201719361
85 0.007870739 -0.371344323
86 0.106250076 0.007870739
87 -0.076604725 0.106250076
88 -0.250543162 -0.076604725
89 -0.277755827 -0.250543162
90 -0.178227283 -0.277755827
91 -0.322234981 -0.178227283
92 -0.261292866 -0.322234981
93 -0.193335241 -0.261292866
94 -0.193999779 -0.193335241
95 -0.253208763 -0.193999779
96 -0.012529961 -0.253208763
97 0.068280001 -0.012529961
98 0.219394013 0.068280001
99 0.597962505 0.219394013
100 0.621185695 0.597962505
101 0.567868514 0.621185695
102 0.255193315 0.567868514
103 -0.155625326 0.255193315
104 -0.310421563 -0.155625326
105 -0.193954730 -0.310421563
106 -0.253601152 -0.193954730
107 -0.184235745 -0.253601152
108 0.042868915 -0.184235745
109 -0.197271804 0.042868915
110 -0.103284997 -0.197271804
111 0.220958691 -0.103284997
112 0.230836804 0.220958691
113 -0.068449017 0.230836804
114 -0.373982123 -0.068449017
115 -0.550860575 -0.373982123
116 -0.519653918 -0.550860575
117 -0.284102173 -0.519653918
118 -0.327623626 -0.284102173
119 -0.038844218 -0.327623626
120 -0.545025558 -0.038844218
121 -0.560244287 -0.545025558
122 -0.333858299 -0.560244287
123 -0.351120952 -0.333858299
124 0.071812753 -0.351120952
125 0.577099229 0.071812753
126 0.643523700 0.577099229
127 0.418790473 0.643523700
128 0.484545292 0.418790473
129 0.261275823 0.484545292
130 1.030252307 0.261275823
131 0.092352915 1.030252307
132 0.188262225 0.092352915
133 0.032785934 0.188262225
134 -0.317982155 0.032785934
135 -0.283766330 -0.317982155
136 -0.098958833 -0.283766330
137 -0.278972619 -0.098958833
138 -0.351242553 -0.278972619
139 -0.122106428 -0.351242553
140 -0.171885804 -0.122106428
141 -0.267528580 -0.171885804
142 0.013352474 -0.267528580
143 0.092869703 0.013352474
144 0.370139945 0.092869703
145 0.393740649 0.370139945
146 0.393336437 0.393740649
147 0.504968456 0.393336437
148 0.247789476 0.504968456
149 0.630371475 0.247789476
150 0.632551943 0.630371475
151 0.209444369 0.632551943
152 0.566991953 0.209444369
153 0.819900188 0.566991953
154 NA 0.819900188
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.146915682 1.144144490
[2,] 1.249351581 1.146915682
[3,] 1.394800723 1.249351581
[4,] 1.232534432 1.394800723
[5,] 1.113630563 1.232534432
[6,] 0.874648035 1.113630563
[7,] 0.675880709 0.874648035
[8,] 0.482276794 0.675880709
[9,] 0.222283872 0.482276794
[10,] 0.072365415 0.222283872
[11,] -0.105161252 0.072365415
[12,] -0.178850540 -0.105161252
[13,] -0.017958039 -0.178850540
[14,] 0.132903231 -0.017958039
[15,] -0.173801051 0.132903231
[16,] -0.010079748 -0.173801051
[17,] 0.120902782 -0.010079748
[18,] 0.342726452 0.120902782
[19,] 0.254597473 0.342726452
[20,] 0.469455374 0.254597473
[21,] 0.346220581 0.469455374
[22,] 0.303747593 0.346220581
[23,] 0.289643015 0.303747593
[24,] 0.152346488 0.289643015
[25,] 0.193189688 0.152346488
[26,] -0.091457006 0.193189688
[27,] -0.042155592 -0.091457006
[28,] -0.198445472 -0.042155592
[29,] -0.166250601 -0.198445472
[30,] -0.490755920 -0.166250601
[31,] 0.108876464 -0.490755920
[32,] -0.074729532 0.108876464
[33,] -0.050460485 -0.074729532
[34,] -0.235493613 -0.050460485
[35,] -0.692107093 -0.235493613
[36,] -1.046114254 -0.692107093
[37,] -1.138177879 -1.046114254
[38,] -1.262136897 -1.138177879
[39,] -0.721457124 -1.262136897
[40,] -0.478654909 -0.721457124
[41,] -0.667533243 -0.478654909
[42,] -1.082998357 -0.667533243
[43,] -1.266704543 -1.082998357
[44,] -1.170321472 -1.266704543
[45,] -1.188346225 -1.170321472
[46,] -0.962236122 -1.188346225
[47,] -0.287064020 -0.962236122
[48,] -0.092262015 -0.287064020
[49,] 0.031157391 -0.092262015
[50,] 0.343444498 0.031157391
[51,] -0.011704667 0.343444498
[52,] 0.086425300 -0.011704667
[53,] -0.067940848 0.086425300
[54,] 0.012794341 -0.067940848
[55,] -0.154165031 0.012794341
[56,] -0.424794234 -0.154165031
[57,] -0.194045462 -0.424794234
[58,] -0.332903141 -0.194045462
[59,] -0.528891930 -0.332903141
[60,] -0.530691056 -0.528891930
[61,] -0.491946227 -0.530691056
[62,] -0.333800243 -0.491946227
[63,] -0.171612224 -0.333800243
[64,] -0.608040501 -0.171612224
[65,] -0.431845077 -0.608040501
[66,] -0.295325052 -0.431845077
[67,] -0.234588935 -0.295325052
[68,] -0.118318592 -0.234588935
[69,] 0.083873728 -0.118318592
[70,] 0.376108822 0.083873728
[71,] 0.280957940 0.376108822
[72,] 0.374670150 0.280957940
[73,] 0.186202334 0.374670150
[74,] 0.042604185 0.186202334
[75,] 0.021733827 0.042604185
[76,] 0.111165677 0.021733827
[77,] 0.345927338 0.111165677
[78,] 0.620157311 0.345927338
[79,] 0.751167208 0.620157311
[80,] 0.716558609 0.751167208
[81,] 0.402107921 0.716558609
[82,] -0.065599142 0.402107921
[83,] -0.201719361 -0.065599142
[84,] -0.371344323 -0.201719361
[85,] 0.007870739 -0.371344323
[86,] 0.106250076 0.007870739
[87,] -0.076604725 0.106250076
[88,] -0.250543162 -0.076604725
[89,] -0.277755827 -0.250543162
[90,] -0.178227283 -0.277755827
[91,] -0.322234981 -0.178227283
[92,] -0.261292866 -0.322234981
[93,] -0.193335241 -0.261292866
[94,] -0.193999779 -0.193335241
[95,] -0.253208763 -0.193999779
[96,] -0.012529961 -0.253208763
[97,] 0.068280001 -0.012529961
[98,] 0.219394013 0.068280001
[99,] 0.597962505 0.219394013
[100,] 0.621185695 0.597962505
[101,] 0.567868514 0.621185695
[102,] 0.255193315 0.567868514
[103,] -0.155625326 0.255193315
[104,] -0.310421563 -0.155625326
[105,] -0.193954730 -0.310421563
[106,] -0.253601152 -0.193954730
[107,] -0.184235745 -0.253601152
[108,] 0.042868915 -0.184235745
[109,] -0.197271804 0.042868915
[110,] -0.103284997 -0.197271804
[111,] 0.220958691 -0.103284997
[112,] 0.230836804 0.220958691
[113,] -0.068449017 0.230836804
[114,] -0.373982123 -0.068449017
[115,] -0.550860575 -0.373982123
[116,] -0.519653918 -0.550860575
[117,] -0.284102173 -0.519653918
[118,] -0.327623626 -0.284102173
[119,] -0.038844218 -0.327623626
[120,] -0.545025558 -0.038844218
[121,] -0.560244287 -0.545025558
[122,] -0.333858299 -0.560244287
[123,] -0.351120952 -0.333858299
[124,] 0.071812753 -0.351120952
[125,] 0.577099229 0.071812753
[126,] 0.643523700 0.577099229
[127,] 0.418790473 0.643523700
[128,] 0.484545292 0.418790473
[129,] 0.261275823 0.484545292
[130,] 1.030252307 0.261275823
[131,] 0.092352915 1.030252307
[132,] 0.188262225 0.092352915
[133,] 0.032785934 0.188262225
[134,] -0.317982155 0.032785934
[135,] -0.283766330 -0.317982155
[136,] -0.098958833 -0.283766330
[137,] -0.278972619 -0.098958833
[138,] -0.351242553 -0.278972619
[139,] -0.122106428 -0.351242553
[140,] -0.171885804 -0.122106428
[141,] -0.267528580 -0.171885804
[142,] 0.013352474 -0.267528580
[143,] 0.092869703 0.013352474
[144,] 0.370139945 0.092869703
[145,] 0.393740649 0.370139945
[146,] 0.393336437 0.393740649
[147,] 0.504968456 0.393336437
[148,] 0.247789476 0.504968456
[149,] 0.630371475 0.247789476
[150,] 0.632551943 0.630371475
[151,] 0.209444369 0.632551943
[152,] 0.566991953 0.209444369
[153,] 0.819900188 0.566991953
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.146915682 1.144144490
2 1.249351581 1.146915682
3 1.394800723 1.249351581
4 1.232534432 1.394800723
5 1.113630563 1.232534432
6 0.874648035 1.113630563
7 0.675880709 0.874648035
8 0.482276794 0.675880709
9 0.222283872 0.482276794
10 0.072365415 0.222283872
11 -0.105161252 0.072365415
12 -0.178850540 -0.105161252
13 -0.017958039 -0.178850540
14 0.132903231 -0.017958039
15 -0.173801051 0.132903231
16 -0.010079748 -0.173801051
17 0.120902782 -0.010079748
18 0.342726452 0.120902782
19 0.254597473 0.342726452
20 0.469455374 0.254597473
21 0.346220581 0.469455374
22 0.303747593 0.346220581
23 0.289643015 0.303747593
24 0.152346488 0.289643015
25 0.193189688 0.152346488
26 -0.091457006 0.193189688
27 -0.042155592 -0.091457006
28 -0.198445472 -0.042155592
29 -0.166250601 -0.198445472
30 -0.490755920 -0.166250601
31 0.108876464 -0.490755920
32 -0.074729532 0.108876464
33 -0.050460485 -0.074729532
34 -0.235493613 -0.050460485
35 -0.692107093 -0.235493613
36 -1.046114254 -0.692107093
37 -1.138177879 -1.046114254
38 -1.262136897 -1.138177879
39 -0.721457124 -1.262136897
40 -0.478654909 -0.721457124
41 -0.667533243 -0.478654909
42 -1.082998357 -0.667533243
43 -1.266704543 -1.082998357
44 -1.170321472 -1.266704543
45 -1.188346225 -1.170321472
46 -0.962236122 -1.188346225
47 -0.287064020 -0.962236122
48 -0.092262015 -0.287064020
49 0.031157391 -0.092262015
50 0.343444498 0.031157391
51 -0.011704667 0.343444498
52 0.086425300 -0.011704667
53 -0.067940848 0.086425300
54 0.012794341 -0.067940848
55 -0.154165031 0.012794341
56 -0.424794234 -0.154165031
57 -0.194045462 -0.424794234
58 -0.332903141 -0.194045462
59 -0.528891930 -0.332903141
60 -0.530691056 -0.528891930
61 -0.491946227 -0.530691056
62 -0.333800243 -0.491946227
63 -0.171612224 -0.333800243
64 -0.608040501 -0.171612224
65 -0.431845077 -0.608040501
66 -0.295325052 -0.431845077
67 -0.234588935 -0.295325052
68 -0.118318592 -0.234588935
69 0.083873728 -0.118318592
70 0.376108822 0.083873728
71 0.280957940 0.376108822
72 0.374670150 0.280957940
73 0.186202334 0.374670150
74 0.042604185 0.186202334
75 0.021733827 0.042604185
76 0.111165677 0.021733827
77 0.345927338 0.111165677
78 0.620157311 0.345927338
79 0.751167208 0.620157311
80 0.716558609 0.751167208
81 0.402107921 0.716558609
82 -0.065599142 0.402107921
83 -0.201719361 -0.065599142
84 -0.371344323 -0.201719361
85 0.007870739 -0.371344323
86 0.106250076 0.007870739
87 -0.076604725 0.106250076
88 -0.250543162 -0.076604725
89 -0.277755827 -0.250543162
90 -0.178227283 -0.277755827
91 -0.322234981 -0.178227283
92 -0.261292866 -0.322234981
93 -0.193335241 -0.261292866
94 -0.193999779 -0.193335241
95 -0.253208763 -0.193999779
96 -0.012529961 -0.253208763
97 0.068280001 -0.012529961
98 0.219394013 0.068280001
99 0.597962505 0.219394013
100 0.621185695 0.597962505
101 0.567868514 0.621185695
102 0.255193315 0.567868514
103 -0.155625326 0.255193315
104 -0.310421563 -0.155625326
105 -0.193954730 -0.310421563
106 -0.253601152 -0.193954730
107 -0.184235745 -0.253601152
108 0.042868915 -0.184235745
109 -0.197271804 0.042868915
110 -0.103284997 -0.197271804
111 0.220958691 -0.103284997
112 0.230836804 0.220958691
113 -0.068449017 0.230836804
114 -0.373982123 -0.068449017
115 -0.550860575 -0.373982123
116 -0.519653918 -0.550860575
117 -0.284102173 -0.519653918
118 -0.327623626 -0.284102173
119 -0.038844218 -0.327623626
120 -0.545025558 -0.038844218
121 -0.560244287 -0.545025558
122 -0.333858299 -0.560244287
123 -0.351120952 -0.333858299
124 0.071812753 -0.351120952
125 0.577099229 0.071812753
126 0.643523700 0.577099229
127 0.418790473 0.643523700
128 0.484545292 0.418790473
129 0.261275823 0.484545292
130 1.030252307 0.261275823
131 0.092352915 1.030252307
132 0.188262225 0.092352915
133 0.032785934 0.188262225
134 -0.317982155 0.032785934
135 -0.283766330 -0.317982155
136 -0.098958833 -0.283766330
137 -0.278972619 -0.098958833
138 -0.351242553 -0.278972619
139 -0.122106428 -0.351242553
140 -0.171885804 -0.122106428
141 -0.267528580 -0.171885804
142 0.013352474 -0.267528580
143 0.092869703 0.013352474
144 0.370139945 0.092869703
145 0.393740649 0.370139945
146 0.393336437 0.393740649
147 0.504968456 0.393336437
148 0.247789476 0.504968456
149 0.630371475 0.247789476
150 0.632551943 0.630371475
151 0.209444369 0.632551943
152 0.566991953 0.209444369
153 0.819900188 0.566991953
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/770lt1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/870lt1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/970lt1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10z92e1292864413.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11l9j11292864413.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/126ah71292864413.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/132kfy1292864413.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1462e41292864413.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/159lca1292864413.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16c3ty1292864413.tab")
+ }
>
> try(system("convert tmp/1s8n21292864413.ps tmp/1s8n21292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/2s8n21292864413.ps tmp/2s8n21292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/33hnn1292864413.ps tmp/33hnn1292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/43hnn1292864413.ps tmp/43hnn1292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/53hnn1292864413.ps tmp/53hnn1292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/6w84q1292864413.ps tmp/6w84q1292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/770lt1292864413.ps tmp/770lt1292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/870lt1292864413.ps tmp/870lt1292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/970lt1292864413.ps tmp/970lt1292864413.png",intern=TRUE))
character(0)
> try(system("convert tmp/10z92e1292864413.ps tmp/10z92e1292864413.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.964 1.778 9.738