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)
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(27.72
+ ,91.51
+ ,2747.48
+ ,0.016
+ ,62.7
+ ,0.16
+ ,26.90
+ ,91.09
+ ,2760.01
+ ,0.016
+ ,62.7
+ ,0.17
+ ,25.86
+ ,93.00
+ ,2778.11
+ ,0.016
+ ,62.7
+ ,0.17
+ ,26.81
+ ,93.08
+ ,2844.72
+ ,0.016
+ ,62.7
+ ,0.16
+ ,26.31
+ ,94.13
+ ,2831.02
+ ,0.016
+ ,62.7
+ ,0.16
+ ,27.10
+ ,96.26
+ ,2858.42
+ ,0.016
+ ,62.7
+ ,0.17
+ ,27.00
+ ,94.29
+ ,2809.73
+ ,0.016
+ ,62.7
+ ,0.17
+ ,27.40
+ ,94.46
+ ,2843.07
+ ,0.016
+ ,62.7
+ ,0.16
+ ,27.27
+ ,95.53
+ ,2818.61
+ ,0.016
+ ,62.7
+ ,0.17
+ ,28.29
+ ,98.29
+ ,2836.33
+ ,0.016
+ ,62.7
+ ,0.17
+ ,30.01
+ ,102.01
+ ,2872.80
+ ,0.016
+ ,62.7
+ ,0.18
+ ,31.41
+ ,105.16
+ ,2895.33
+ ,0.016
+ ,62.7
+ ,0.17
+ ,31.91
+ ,105.34
+ ,2929.76
+ ,0.016
+ ,62.7
+ ,0.17
+ ,31.60
+ ,105.27
+ ,2930.45
+ ,0.016
+ ,62.7
+ ,0.16
+ ,31.84
+ ,102.19
+ ,2859.09
+ ,0.016
+ ,62.7
+ ,0.17
+ ,33.05
+ ,106.85
+ ,2892.42
+ ,0.016
+ ,62.7
+ ,0.17
+ ,32.06
+ ,103.05
+ ,2836.16
+ ,0.016
+ ,62.7
+ ,0.17
+ ,33.10
+ ,106.42
+ ,2854.06
+ ,0.016
+ ,62.7
+ ,0.16
+ ,32.23
+ ,105.17
+ ,2875.32
+ ,0.016
+ ,62.7
+ ,0.15
+ ,31.36
+ ,102.74
+ ,2849.49
+ ,0.016
+ ,62.7
+ ,0.15
+ ,31.09
+ ,106.27
+ ,2935.05
+ ,0.016
+ ,62.7
+ ,0.09
+ ,30.77
+ ,107.63
+ ,2951.23
+ ,0.0141
+ ,65.4
+ ,0.18
+ ,31.20
+ ,108.54
+ ,2976.08
+ ,0.0141
+ ,65.4
+ ,0.17
+ ,31.47
+ ,108.24
+ ,2976.12
+ ,0.0141
+ ,65.4
+ ,0.17
+ ,31.73
+ ,108.86
+ ,2937.33
+ ,0.0141
+ ,65.4
+ ,0.17
+ ,32.17
+ ,102.98
+ ,2931.77
+ ,0.0141
+ ,65.4
+ ,0.17
+ ,31.47
+ ,99.53
+ ,2902.33
+ ,0.0141
+ ,65.4
+ ,0.17
+ ,30.97
+ ,101.08
+ ,2887.98
+ ,0.0141
+ ,65.4
+ ,0.17
+ ,30.81
+ ,104.64
+ ,2866.19
+ ,0.0141
+ ,65.4
+ ,0.18
+ ,30.72
+ ,105.59
+ ,2908.47
+ ,0.0141
+ ,65.4
+ ,0.19
+ ,28.24
+ ,103.21
+ ,2896.94
+ ,0.0141
+ ,65.4
+ ,0.18
+ ,28.09
+ ,103.84
+ ,2910.04
+ ,0.0141
+ ,65.4
+ ,0.17
+ ,29.11
+ ,104.61
+ ,2942.60
+ ,0.0141
+ ,65.4
+ ,0.16
+ ,29.00
+ ,108.65
+ ,2965.90
+ ,0.0141
+ ,65.4
+ ,0.13
+ ,28.76
+ ,106.26
+ ,2925.30
+ ,0.0141
+ ,65.4
+ ,0.13
+ ,28.75
+ ,104.20
+ ,2890.15
+ ,0.0141
+ ,65.4
+ ,0.14
+ ,28.45
+ ,102.99
+ ,2862.99
+ ,0.0141
+ ,65.4
+ ,0.15
+ ,29.34
+ ,102.19
+ ,2854.24
+ ,0.0141
+ ,65.4
+ ,0.15
+ ,26.84
+ ,100.82
+ ,2893.25
+ ,0.0141
+ ,65.4
+ ,0.14
+ ,23.70
+ ,103.42
+ ,2958.09
+ ,0.0141
+ ,65.4
+ ,0.14
+ ,23.15
+ ,104.18
+ ,2945.84
+ ,0.0141
+ ,65.4
+ ,0.14
+ ,21.71
+ ,102.65
+ ,2939.52
+ ,0.0141
+ ,65.4
+ ,0.13
+ ,20.88
+ ,95.64
+ ,2920.21
+ ,0.0169
+ ,61.3
+ ,0.14
+ ,20.04
+ ,93.51
+ ,2909.77
+ ,0.0169
+ ,61.3
+ ,0.14
+ ,21.09
+ ,108.51
+ ,2967.90
+ ,0.0169
+ ,61.3
+ ,0.14
+ ,21.92
+ ,111.55
+ ,2989.91
+ ,0.0169
+ ,61.3
+ ,0.14
+ ,20.72
+ ,106.70
+ ,3015.86
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,20.72
+ ,104.93
+ ,3011.25
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,21.01
+ ,105.23
+ ,3018.64
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,21.80
+ ,104.92
+ ,3020.86
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,21.60
+ ,104.60
+ ,3022.52
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,20.38
+ ,101.76
+ ,3016.98
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,21.20
+ ,102.23
+ ,3030.93
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.87
+ ,103.99
+ ,3062.39
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.05
+ ,101.36
+ ,3076.59
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,20.01
+ ,102.92
+ ,3076.21
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.15
+ ,105.25
+ ,3067.26
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.43
+ ,105.71
+ ,3073.67
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.44
+ ,105.42
+ ,3053.40
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.40
+ ,105.11
+ ,3069.79
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.15
+ ,104.67
+ ,3073.19
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.34
+ ,107.51
+ ,3077.14
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.10
+ ,109.00
+ ,3081.19
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.08
+ ,107.37
+ ,3048.71
+ ,0.0169
+ ,61.3
+ ,0.14
+ ,18.05
+ ,107.30
+ ,3066.96
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,17.72
+ ,107.37
+ ,3075.06
+ ,0.0199
+ ,70.3
+ ,0.14
+ ,18.58
+ ,113.28
+ ,3069.27
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,18.96
+ ,119.10
+ ,3135.81
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,18.98
+ ,119.04
+ ,3136.42
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,18.81
+ ,117.80
+ ,3104.02
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,19.43
+ ,117.90
+ ,3104.53
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,20.93
+ ,119.55
+ ,3114.31
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,20.71
+ ,119.47
+ ,3155.83
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,22.00
+ ,123.23
+ ,3183.95
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,21.52
+ ,121.40
+ ,3178.67
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,21.87
+ ,121.43
+ ,3177.80
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,23.29
+ ,122.51
+ ,3182.62
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,22.59
+ ,122.78
+ ,3175.96
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,22.86
+ ,122.84
+ ,3179.96
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,20.79
+ ,122.70
+ ,3160.78
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,20.28
+ ,119.89
+ ,3117.73
+ ,0.0199
+ ,70.3
+ ,0.15
+ ,20.62
+ ,118.00
+ ,3093.70
+ ,0.0199
+ ,70.3
+ ,0.16
+ ,20.32
+ ,119.61
+ ,3136.60
+ ,0.0199
+ ,70.3
+ ,0.14
+ ,21.66
+ ,120.40
+ ,3116.23
+ ,0.0199
+ ,70.3
+ ,0.09
+ ,21.99
+ ,117.94
+ ,3113.53
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,22.27
+ ,118.77
+ ,3120.04
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,21.83
+ ,121.68
+ ,3135.23
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,21.94
+ ,121.98
+ ,3149.46
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,20.91
+ ,118.83
+ ,3136.19
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,20.40
+ ,117.97
+ ,3112.35
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,20.22
+ ,113.07
+ ,3065.02
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,19.64
+ ,111.98
+ ,3051.78
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,19.75
+ ,113.77
+ ,3049.41
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,19.51
+ ,110.41
+ ,3044.11
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,19.52
+ ,110.85
+ ,3064.18
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,19.48
+ ,111.18
+ ,3101.17
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,19.88
+ ,109.42
+ ,3104.12
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,18.97
+ ,108.87
+ ,3072.87
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,19.00
+ ,106.72
+ ,3005.62
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,19.32
+ ,107.28
+ ,3016.96
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,19.50
+ ,104.13
+ ,2990.46
+ ,0.0216
+ ,73.1
+ ,0.15
+ ,23.22
+ ,107.55
+ ,2981.70
+ ,0.0216
+ ,73.1
+ ,0.17
+ ,22.56
+ ,105.72
+ ,2986.12
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,21.94
+ ,104.55
+ ,2987.95
+ ,0.0216
+ ,73.1
+ ,0.16
+ ,21.11
+ ,106.93
+ ,2977.23
+ ,0.0216
+ ,73.1
+ ,0.18
+ ,21.21
+ ,106.85
+ ,3020.06
+ ,0.0176
+ ,73.1
+ ,0.17
+ ,21.18
+ ,106.78
+ ,2982.13
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,21.25
+ ,107.29
+ ,2999.66
+ ,0.0176
+ ,73.1
+ ,0.17
+ ,21.17
+ ,104.14
+ ,3011.93
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,20.47
+ ,101.21
+ ,2937.29
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,19.99
+ ,96.35
+ ,2895.58
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,19.21
+ ,95.62
+ ,2904.87
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,20.07
+ ,99.00
+ ,2904.26
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,19.86
+ ,99.26
+ ,2883.89
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,22.36
+ ,98.77
+ ,2846.81
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,22.17
+ ,100.65
+ ,2836.94
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,23.56
+ ,103.13
+ ,2853.13
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,22.92
+ ,105.53
+ ,2916.07
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,23.10
+ ,106.76
+ ,2916.68
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,24.32
+ ,107.59
+ ,2926.55
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,23.99
+ ,107.62
+ ,2966.85
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,25.94
+ ,108.82
+ ,2976.78
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,26.15
+ ,107.59
+ ,2967.79
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,26.36
+ ,107.85
+ ,2991.78
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,27.32
+ ,107.11
+ ,3012.03
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,28.00
+ ,108.14
+ ,3010.24
+ ,0.0176
+ ,73.1
+ ,0.16)
+ ,dim=c(6
+ ,126)
+ ,dimnames=list(c('FACEBOOK'
+ ,'LINKEDIN'
+ ,'NASDAQ'
+ ,'INFLATION'
+ ,'CONS.CONF'
+ ,'FED.FUNDS.RATE')
+ ,1:126))
> y <- array(NA,dim=c(6,126),dimnames=list(c('FACEBOOK','LINKEDIN','NASDAQ','INFLATION','CONS.CONF','FED.FUNDS.RATE'),1:126))
> 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 = 'Include Monthly 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
FACEBOOK LINKEDIN NASDAQ INFLATION CONS.CONF FED.FUNDS.RATE M1 M2 M3 M4 M5
1 27.72 91.51 2747.48 0.0160 62.7 0.16 1 0 0 0 0
2 26.90 91.09 2760.01 0.0160 62.7 0.17 0 1 0 0 0
3 25.86 93.00 2778.11 0.0160 62.7 0.17 0 0 1 0 0
4 26.81 93.08 2844.72 0.0160 62.7 0.16 0 0 0 1 0
5 26.31 94.13 2831.02 0.0160 62.7 0.16 0 0 0 0 1
6 27.10 96.26 2858.42 0.0160 62.7 0.17 0 0 0 0 0
7 27.00 94.29 2809.73 0.0160 62.7 0.17 0 0 0 0 0
8 27.40 94.46 2843.07 0.0160 62.7 0.16 0 0 0 0 0
9 27.27 95.53 2818.61 0.0160 62.7 0.17 0 0 0 0 0
10 28.29 98.29 2836.33 0.0160 62.7 0.17 0 0 0 0 0
11 30.01 102.01 2872.80 0.0160 62.7 0.18 0 0 0 0 0
12 31.41 105.16 2895.33 0.0160 62.7 0.17 0 0 0 0 0
13 31.91 105.34 2929.76 0.0160 62.7 0.17 1 0 0 0 0
14 31.60 105.27 2930.45 0.0160 62.7 0.16 0 1 0 0 0
15 31.84 102.19 2859.09 0.0160 62.7 0.17 0 0 1 0 0
16 33.05 106.85 2892.42 0.0160 62.7 0.17 0 0 0 1 0
17 32.06 103.05 2836.16 0.0160 62.7 0.17 0 0 0 0 1
18 33.10 106.42 2854.06 0.0160 62.7 0.16 0 0 0 0 0
19 32.23 105.17 2875.32 0.0160 62.7 0.15 0 0 0 0 0
20 31.36 102.74 2849.49 0.0160 62.7 0.15 0 0 0 0 0
21 31.09 106.27 2935.05 0.0160 62.7 0.09 0 0 0 0 0
22 30.77 107.63 2951.23 0.0141 65.4 0.18 0 0 0 0 0
23 31.20 108.54 2976.08 0.0141 65.4 0.17 0 0 0 0 0
24 31.47 108.24 2976.12 0.0141 65.4 0.17 0 0 0 0 0
25 31.73 108.86 2937.33 0.0141 65.4 0.17 1 0 0 0 0
26 32.17 102.98 2931.77 0.0141 65.4 0.17 0 1 0 0 0
27 31.47 99.53 2902.33 0.0141 65.4 0.17 0 0 1 0 0
28 30.97 101.08 2887.98 0.0141 65.4 0.17 0 0 0 1 0
29 30.81 104.64 2866.19 0.0141 65.4 0.18 0 0 0 0 1
30 30.72 105.59 2908.47 0.0141 65.4 0.19 0 0 0 0 0
31 28.24 103.21 2896.94 0.0141 65.4 0.18 0 0 0 0 0
32 28.09 103.84 2910.04 0.0141 65.4 0.17 0 0 0 0 0
33 29.11 104.61 2942.60 0.0141 65.4 0.16 0 0 0 0 0
34 29.00 108.65 2965.90 0.0141 65.4 0.13 0 0 0 0 0
35 28.76 106.26 2925.30 0.0141 65.4 0.13 0 0 0 0 0
36 28.75 104.20 2890.15 0.0141 65.4 0.14 0 0 0 0 0
37 28.45 102.99 2862.99 0.0141 65.4 0.15 1 0 0 0 0
38 29.34 102.19 2854.24 0.0141 65.4 0.15 0 1 0 0 0
39 26.84 100.82 2893.25 0.0141 65.4 0.14 0 0 1 0 0
40 23.70 103.42 2958.09 0.0141 65.4 0.14 0 0 0 1 0
41 23.15 104.18 2945.84 0.0141 65.4 0.14 0 0 0 0 1
42 21.71 102.65 2939.52 0.0141 65.4 0.13 0 0 0 0 0
43 20.88 95.64 2920.21 0.0169 61.3 0.14 0 0 0 0 0
44 20.04 93.51 2909.77 0.0169 61.3 0.14 0 0 0 0 0
45 21.09 108.51 2967.90 0.0169 61.3 0.14 0 0 0 0 0
46 21.92 111.55 2989.91 0.0169 61.3 0.14 0 0 0 0 0
47 20.72 106.70 3015.86 0.0169 61.3 0.13 0 0 0 0 0
48 20.72 104.93 3011.25 0.0169 61.3 0.13 0 0 0 0 0
49 21.01 105.23 3018.64 0.0169 61.3 0.13 1 0 0 0 0
50 21.80 104.92 3020.86 0.0169 61.3 0.13 0 1 0 0 0
51 21.60 104.60 3022.52 0.0169 61.3 0.13 0 0 1 0 0
52 20.38 101.76 3016.98 0.0169 61.3 0.13 0 0 0 1 0
53 21.20 102.23 3030.93 0.0169 61.3 0.13 0 0 0 0 1
54 19.87 103.99 3062.39 0.0169 61.3 0.13 0 0 0 0 0
55 19.05 101.36 3076.59 0.0169 61.3 0.13 0 0 0 0 0
56 20.01 102.92 3076.21 0.0169 61.3 0.13 0 0 0 0 0
57 19.15 105.25 3067.26 0.0169 61.3 0.13 0 0 0 0 0
58 19.43 105.71 3073.67 0.0169 61.3 0.13 0 0 0 0 0
59 19.44 105.42 3053.40 0.0169 61.3 0.13 0 0 0 0 0
60 19.40 105.11 3069.79 0.0169 61.3 0.13 0 0 0 0 0
61 19.15 104.67 3073.19 0.0169 61.3 0.13 1 0 0 0 0
62 19.34 107.51 3077.14 0.0169 61.3 0.13 0 1 0 0 0
63 19.10 109.00 3081.19 0.0169 61.3 0.13 0 0 1 0 0
64 19.08 107.37 3048.71 0.0169 61.3 0.14 0 0 0 1 0
65 18.05 107.30 3066.96 0.0169 61.3 0.13 0 0 0 0 1
66 17.72 107.37 3075.06 0.0199 70.3 0.14 0 0 0 0 0
67 18.58 113.28 3069.27 0.0199 70.3 0.16 0 0 0 0 0
68 18.96 119.10 3135.81 0.0199 70.3 0.16 0 0 0 0 0
69 18.98 119.04 3136.42 0.0199 70.3 0.15 0 0 0 0 0
70 18.81 117.80 3104.02 0.0199 70.3 0.15 0 0 0 0 0
71 19.43 117.90 3104.53 0.0199 70.3 0.15 0 0 0 0 0
72 20.93 119.55 3114.31 0.0199 70.3 0.15 0 0 0 0 0
73 20.71 119.47 3155.83 0.0199 70.3 0.15 1 0 0 0 0
74 22.00 123.23 3183.95 0.0199 70.3 0.16 0 1 0 0 0
75 21.52 121.40 3178.67 0.0199 70.3 0.16 0 0 1 0 0
76 21.87 121.43 3177.80 0.0199 70.3 0.16 0 0 0 1 0
77 23.29 122.51 3182.62 0.0199 70.3 0.15 0 0 0 0 1
78 22.59 122.78 3175.96 0.0199 70.3 0.16 0 0 0 0 0
79 22.86 122.84 3179.96 0.0199 70.3 0.15 0 0 0 0 0
80 20.79 122.70 3160.78 0.0199 70.3 0.16 0 0 0 0 0
81 20.28 119.89 3117.73 0.0199 70.3 0.15 0 0 0 0 0
82 20.62 118.00 3093.70 0.0199 70.3 0.16 0 0 0 0 0
83 20.32 119.61 3136.60 0.0199 70.3 0.14 0 0 0 0 0
84 21.66 120.40 3116.23 0.0199 70.3 0.09 0 0 0 0 0
85 21.99 117.94 3113.53 0.0216 73.1 0.15 1 0 0 0 0
86 22.27 118.77 3120.04 0.0216 73.1 0.16 0 1 0 0 0
87 21.83 121.68 3135.23 0.0216 73.1 0.16 0 0 1 0 0
88 21.94 121.98 3149.46 0.0216 73.1 0.15 0 0 0 1 0
89 20.91 118.83 3136.19 0.0216 73.1 0.15 0 0 0 0 1
90 20.40 117.97 3112.35 0.0216 73.1 0.15 0 0 0 0 0
91 20.22 113.07 3065.02 0.0216 73.1 0.16 0 0 0 0 0
92 19.64 111.98 3051.78 0.0216 73.1 0.16 0 0 0 0 0
93 19.75 113.77 3049.41 0.0216 73.1 0.16 0 0 0 0 0
94 19.51 110.41 3044.11 0.0216 73.1 0.16 0 0 0 0 0
95 19.52 110.85 3064.18 0.0216 73.1 0.16 0 0 0 0 0
96 19.48 111.18 3101.17 0.0216 73.1 0.16 0 0 0 0 0
97 19.88 109.42 3104.12 0.0216 73.1 0.15 1 0 0 0 0
98 18.97 108.87 3072.87 0.0216 73.1 0.15 0 1 0 0 0
99 19.00 106.72 3005.62 0.0216 73.1 0.16 0 0 1 0 0
100 19.32 107.28 3016.96 0.0216 73.1 0.15 0 0 0 1 0
101 19.50 104.13 2990.46 0.0216 73.1 0.15 0 0 0 0 1
102 23.22 107.55 2981.70 0.0216 73.1 0.17 0 0 0 0 0
103 22.56 105.72 2986.12 0.0216 73.1 0.16 0 0 0 0 0
104 21.94 104.55 2987.95 0.0216 73.1 0.16 0 0 0 0 0
105 21.11 106.93 2977.23 0.0216 73.1 0.18 0 0 0 0 0
106 21.21 106.85 3020.06 0.0176 73.1 0.17 0 0 0 0 0
107 21.18 106.78 2982.13 0.0176 73.1 0.16 0 0 0 0 0
108 21.25 107.29 2999.66 0.0176 73.1 0.17 0 0 0 0 0
109 21.17 104.14 3011.93 0.0176 73.1 0.16 1 0 0 0 0
110 20.47 101.21 2937.29 0.0176 73.1 0.16 0 1 0 0 0
111 19.99 96.35 2895.58 0.0176 73.1 0.16 0 0 1 0 0
112 19.21 95.62 2904.87 0.0176 73.1 0.16 0 0 0 1 0
113 20.07 99.00 2904.26 0.0176 73.1 0.16 0 0 0 0 1
114 19.86 99.26 2883.89 0.0176 73.1 0.16 0 0 0 0 0
115 22.36 98.77 2846.81 0.0176 73.1 0.16 0 0 0 0 0
116 22.17 100.65 2836.94 0.0176 73.1 0.16 0 0 0 0 0
117 23.56 103.13 2853.13 0.0176 73.1 0.16 0 0 0 0 0
118 22.92 105.53 2916.07 0.0176 73.1 0.16 0 0 0 0 0
119 23.10 106.76 2916.68 0.0176 73.1 0.16 0 0 0 0 0
120 24.32 107.59 2926.55 0.0176 73.1 0.16 0 0 0 0 0
121 23.99 107.62 2966.85 0.0176 73.1 0.16 1 0 0 0 0
122 25.94 108.82 2976.78 0.0176 73.1 0.16 0 1 0 0 0
123 26.15 107.59 2967.79 0.0176 73.1 0.16 0 0 1 0 0
124 26.36 107.85 2991.78 0.0176 73.1 0.16 0 0 0 1 0
125 27.32 107.11 3012.03 0.0176 73.1 0.16 0 0 0 0 1
126 28.00 108.14 3010.24 0.0176 73.1 0.16 0 0 0 0 0
M6 M7 M8 M9 M10 M11 t
1 0 0 0 0 0 0 1
2 0 0 0 0 0 0 2
3 0 0 0 0 0 0 3
4 0 0 0 0 0 0 4
5 0 0 0 0 0 0 5
6 1 0 0 0 0 0 6
7 0 1 0 0 0 0 7
8 0 0 1 0 0 0 8
9 0 0 0 1 0 0 9
10 0 0 0 0 1 0 10
11 0 0 0 0 0 1 11
12 0 0 0 0 0 0 12
13 0 0 0 0 0 0 13
14 0 0 0 0 0 0 14
15 0 0 0 0 0 0 15
16 0 0 0 0 0 0 16
17 0 0 0 0 0 0 17
18 1 0 0 0 0 0 18
19 0 1 0 0 0 0 19
20 0 0 1 0 0 0 20
21 0 0 0 1 0 0 21
22 0 0 0 0 1 0 22
23 0 0 0 0 0 1 23
24 0 0 0 0 0 0 24
25 0 0 0 0 0 0 25
26 0 0 0 0 0 0 26
27 0 0 0 0 0 0 27
28 0 0 0 0 0 0 28
29 0 0 0 0 0 0 29
30 1 0 0 0 0 0 30
31 0 1 0 0 0 0 31
32 0 0 1 0 0 0 32
33 0 0 0 1 0 0 33
34 0 0 0 0 1 0 34
35 0 0 0 0 0 1 35
36 0 0 0 0 0 0 36
37 0 0 0 0 0 0 37
38 0 0 0 0 0 0 38
39 0 0 0 0 0 0 39
40 0 0 0 0 0 0 40
41 0 0 0 0 0 0 41
42 1 0 0 0 0 0 42
43 0 1 0 0 0 0 43
44 0 0 1 0 0 0 44
45 0 0 0 1 0 0 45
46 0 0 0 0 1 0 46
47 0 0 0 0 0 1 47
48 0 0 0 0 0 0 48
49 0 0 0 0 0 0 49
50 0 0 0 0 0 0 50
51 0 0 0 0 0 0 51
52 0 0 0 0 0 0 52
53 0 0 0 0 0 0 53
54 1 0 0 0 0 0 54
55 0 1 0 0 0 0 55
56 0 0 1 0 0 0 56
57 0 0 0 1 0 0 57
58 0 0 0 0 1 0 58
59 0 0 0 0 0 1 59
60 0 0 0 0 0 0 60
61 0 0 0 0 0 0 61
62 0 0 0 0 0 0 62
63 0 0 0 0 0 0 63
64 0 0 0 0 0 0 64
65 0 0 0 0 0 0 65
66 1 0 0 0 0 0 66
67 0 1 0 0 0 0 67
68 0 0 1 0 0 0 68
69 0 0 0 1 0 0 69
70 0 0 0 0 1 0 70
71 0 0 0 0 0 1 71
72 0 0 0 0 0 0 72
73 0 0 0 0 0 0 73
74 0 0 0 0 0 0 74
75 0 0 0 0 0 0 75
76 0 0 0 0 0 0 76
77 0 0 0 0 0 0 77
78 1 0 0 0 0 0 78
79 0 1 0 0 0 0 79
80 0 0 1 0 0 0 80
81 0 0 0 1 0 0 81
82 0 0 0 0 1 0 82
83 0 0 0 0 0 1 83
84 0 0 0 0 0 0 84
85 0 0 0 0 0 0 85
86 0 0 0 0 0 0 86
87 0 0 0 0 0 0 87
88 0 0 0 0 0 0 88
89 0 0 0 0 0 0 89
90 1 0 0 0 0 0 90
91 0 1 0 0 0 0 91
92 0 0 1 0 0 0 92
93 0 0 0 1 0 0 93
94 0 0 0 0 1 0 94
95 0 0 0 0 0 1 95
96 0 0 0 0 0 0 96
97 0 0 0 0 0 0 97
98 0 0 0 0 0 0 98
99 0 0 0 0 0 0 99
100 0 0 0 0 0 0 100
101 0 0 0 0 0 0 101
102 1 0 0 0 0 0 102
103 0 1 0 0 0 0 103
104 0 0 1 0 0 0 104
105 0 0 0 1 0 0 105
106 0 0 0 0 1 0 106
107 0 0 0 0 0 1 107
108 0 0 0 0 0 0 108
109 0 0 0 0 0 0 109
110 0 0 0 0 0 0 110
111 0 0 0 0 0 0 111
112 0 0 0 0 0 0 112
113 0 0 0 0 0 0 113
114 1 0 0 0 0 0 114
115 0 1 0 0 0 0 115
116 0 0 1 0 0 0 116
117 0 0 0 1 0 0 117
118 0 0 0 0 1 0 118
119 0 0 0 0 0 1 119
120 0 0 0 0 0 0 120
121 0 0 0 0 0 0 121
122 0 0 0 0 0 0 122
123 0 0 0 0 0 0 123
124 0 0 0 0 0 0 124
125 0 0 0 0 0 0 125
126 1 0 0 0 0 0 126
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) LINKEDIN NASDAQ INFLATION CONS.CONF
82.28292 0.43177 -0.03455 -761.52044 0.13420
FED.FUNDS.RATE M1 M2 M3 M4
38.43978 0.41475 0.58566 0.09830 0.33041
M5 M6 M7 M8 M9
0.15897 0.06942 -0.18145 -0.46578 -0.87669
M10 M11 t
-1.31408 -0.83127 -0.04513
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.530 -1.248 -0.115 1.163 6.478
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.228e+01 1.337e+01 6.157 1.29e-08 ***
LINKEDIN 4.318e-01 6.315e-02 6.837 5.03e-10 ***
NASDAQ -3.456e-02 5.153e-03 -6.706 9.48e-10 ***
INFLATION -7.615e+02 1.412e+02 -5.392 4.14e-07 ***
CONS.CONF 1.342e-01 1.170e-01 1.147 0.254089
FED.FUNDS.RATE 3.844e+01 1.606e+01 2.393 0.018433 *
M1 4.148e-01 9.442e-01 0.439 0.661349
M2 5.857e-01 9.458e-01 0.619 0.537072
M3 9.829e-02 9.498e-01 0.103 0.917765
M4 3.304e-01 9.492e-01 0.348 0.728442
M5 1.590e-01 9.452e-01 0.168 0.866752
M6 6.942e-02 9.477e-01 0.073 0.941741
M7 -1.815e-01 9.827e-01 -0.185 0.853851
M8 -4.658e-01 9.794e-01 -0.476 0.635351
M9 -8.767e-01 9.682e-01 -0.905 0.367237
M10 -1.314e+00 9.640e-01 -1.363 0.175680
M11 -8.313e-01 9.590e-01 -0.867 0.387984
t -4.513e-02 1.313e-02 -3.437 0.000838 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.141 on 108 degrees of freedom
Multiple R-squared: 0.8027, Adjusted R-squared: 0.7717
F-statistic: 25.85 on 17 and 108 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,] 0.073255582 1.465112e-01 9.267444e-01
[2,] 0.023075680 4.615136e-02 9.769243e-01
[3,] 0.007756577 1.551315e-02 9.922434e-01
[4,] 0.003097935 6.195871e-03 9.969021e-01
[5,] 0.005766021 1.153204e-02 9.942340e-01
[6,] 0.003414949 6.829899e-03 9.965851e-01
[7,] 0.001735974 3.471948e-03 9.982640e-01
[8,] 0.001756683 3.513367e-03 9.982433e-01
[9,] 0.001444870 2.889741e-03 9.985551e-01
[10,] 0.001760917 3.521834e-03 9.982391e-01
[11,] 0.009625343 1.925069e-02 9.903747e-01
[12,] 0.023615818 4.723164e-02 9.763842e-01
[13,] 0.020996928 4.199386e-02 9.790031e-01
[14,] 0.066089616 1.321792e-01 9.339104e-01
[15,] 0.093360496 1.867210e-01 9.066395e-01
[16,] 0.095141671 1.902833e-01 9.048583e-01
[17,] 0.096125830 1.922517e-01 9.038742e-01
[18,] 0.114279825 2.285596e-01 8.857202e-01
[19,] 0.198792596 3.975852e-01 8.012074e-01
[20,] 0.573168853 8.536623e-01 4.268311e-01
[21,] 0.719275769 5.614485e-01 2.807242e-01
[22,] 0.767871831 4.642563e-01 2.321282e-01
[23,] 0.750572295 4.988554e-01 2.494277e-01
[24,] 0.762248253 4.755035e-01 2.377517e-01
[25,] 0.979973177 4.005365e-02 2.002682e-02
[26,] 0.985093394 2.981321e-02 1.490661e-02
[27,] 0.983350983 3.329803e-02 1.664902e-02
[28,] 0.981386014 3.722797e-02 1.861399e-02
[29,] 0.978636252 4.272750e-02 2.136375e-02
[30,] 0.983572201 3.285560e-02 1.642780e-02
[31,] 0.990258523 1.948295e-02 9.741477e-03
[32,] 0.995167106 9.665788e-03 4.832894e-03
[33,] 0.999437008 1.125985e-03 5.629925e-04
[34,] 0.999325622 1.348755e-03 6.743777e-04
[35,] 0.998943744 2.112513e-03 1.056256e-03
[36,] 0.999230248 1.539504e-03 7.697519e-04
[37,] 0.999071686 1.856628e-03 9.283142e-04
[38,] 0.999116945 1.766110e-03 8.830549e-04
[39,] 0.999313118 1.373764e-03 6.868818e-04
[40,] 0.999385208 1.229583e-03 6.147917e-04
[41,] 0.999280289 1.439422e-03 7.197109e-04
[42,] 0.999293392 1.413216e-03 7.066078e-04
[43,] 0.999307870 1.384260e-03 6.921299e-04
[44,] 0.999008517 1.982967e-03 9.914834e-04
[45,] 0.999314159 1.371681e-03 6.858406e-04
[46,] 0.999734567 5.308652e-04 2.654326e-04
[47,] 0.999705299 5.894026e-04 2.947013e-04
[48,] 0.999711397 5.772057e-04 2.886028e-04
[49,] 0.999563684 8.726325e-04 4.363163e-04
[50,] 0.999307653 1.384693e-03 6.923467e-04
[51,] 0.999194688 1.610625e-03 8.053123e-04
[52,] 0.999305173 1.389654e-03 6.948272e-04
[53,] 0.999015297 1.969406e-03 9.847028e-04
[54,] 0.998441087 3.117826e-03 1.558913e-03
[55,] 0.997686565 4.626871e-03 2.313435e-03
[56,] 0.997342507 5.314986e-03 2.657493e-03
[57,] 0.998812102 2.375795e-03 1.187898e-03
[58,] 0.998606019 2.787962e-03 1.393981e-03
[59,] 0.997904289 4.191421e-03 2.095711e-03
[60,] 0.997719866 4.560268e-03 2.280134e-03
[61,] 0.996930391 6.139218e-03 3.069609e-03
[62,] 0.996554249 6.891502e-03 3.445751e-03
[63,] 0.997364887 5.270227e-03 2.635113e-03
[64,] 0.996103237 7.793525e-03 3.896763e-03
[65,] 0.999575248 8.495032e-04 4.247516e-04
[66,] 0.999919393 1.612146e-04 8.060729e-05
[67,] 0.999861056 2.778876e-04 1.389438e-04
[68,] 0.999874496 2.510079e-04 1.255040e-04
[69,] 0.999838120 3.237590e-04 1.618795e-04
[70,] 0.999685897 6.282058e-04 3.141029e-04
[71,] 0.999538101 9.237977e-04 4.618988e-04
[72,] 0.999303460 1.393079e-03 6.965397e-04
[73,] 0.998947958 2.104083e-03 1.052042e-03
[74,] 0.998365920 3.268159e-03 1.634080e-03
[75,] 0.996899738 6.200523e-03 3.100262e-03
[76,] 0.994380454 1.123909e-02 5.619546e-03
[77,] 0.990953718 1.809256e-02 9.046282e-03
[78,] 0.987533839 2.493232e-02 1.246616e-02
[79,] 0.984810048 3.037990e-02 1.518995e-02
[80,] 0.982943324 3.411335e-02 1.705668e-02
[81,] 0.978758032 4.248394e-02 2.124197e-02
[82,] 0.999993301 1.339717e-05 6.698583e-06
[83,] 0.999971094 5.781215e-05 2.890608e-05
[84,] 0.999838625 3.227508e-04 1.613754e-04
[85,] 0.998183083 3.633834e-03 1.816917e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1q1nd1356076907.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/2sw3j1356076907.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3fyrl1356076907.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4jq5l1356076907.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5h51q1356076907.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 126
Frequency = 1
1 2 3 4 5 6
-1.88542999 -2.60128759 -3.30802547 0.10654189 -1.10364458 -0.53621878
7 8 9 10 11 12
-1.17210073 1.02041394 -0.34514330 0.57801177 1.12996932 1.54667307
13 14 15 16 17 18
2.78905752 2.79174754 2.04386795 2.20655074 1.12979276 1.85234979
19 20 21 22 23 24
2.93710002 2.55320939 6.47800364 1.34361489 2.18611287 1.80088518
25 26 27 28 29 30
0.08318880 2.74409268 3.04890270 1.19681497 -1.42105841 -0.70996952
31 32 33 34 35 36
-1.88037193 -1.13586023 1.51722358 2.10372332 1.05505031 -0.45064474
37 38 39 40 41 42
-1.92072896 -1.11344255 -0.75703870 -2.96608960 -4.05095854 -4.52964954
43 44 45 46 47 48
-0.40611973 -0.35774277 -3.31956663 -2.55906686 -0.82156788 -1.00277463
49 50 51 52 53 54
-0.95656424 -0.08177860 0.44624799 0.07405493 1.38973479 0.52160551
55 56 57 58 59 60
1.62384255 2.22661236 0.50736865 1.29277816 0.28988885 0.16394981
61 62 63 64 65 66
-0.14820423 -1.17371184 -1.38460157 -2.39453950 -2.16272080 -1.41598674
67 68 69 70 71 72
-3.78060496 -3.28476491 -2.37733846 -2.64899302 -2.49222361 -2.15283754
73 74 75 76 77 78
-1.27319957 -1.14514321 -0.48495539 -0.36495522 1.35625793 0.05983804
79 80 81 82 83 84
1.12255475 -1.60469581 -1.54856652 -1.12474524 -0.30637378 1.12449580
85 86 87 88 89 90
0.66617217 0.30258315 -0.33647842 0.33311936 0.42122219 -0.40655210
91 92 93 94 95 96
-0.19475352 -0.43216999 -0.72088773 0.78924351 0.86510325 1.17466077
97 98 99 100 101 102
2.45129027 0.57315141 -0.64425333 0.02322108 0.86416392 2.17070806
103 104 105 106 107 108
3.13398162 3.41184751 0.87105769 0.30642441 -1.05729405 -1.77228921
109 110 111 112 113 114
-0.05344975 -2.19331059 -1.48369334 -1.81447180 -2.21835825 -3.10980891
115 116 117 118 119 120
-1.38352809 -2.39684948 -1.06215092 -0.08099094 -0.84866527 -0.43211851
121 122 123 124 125 126
0.24786799 1.89709961 2.86002759 3.59975314 5.79556899 6.10368419
> postscript(file="/var/wessaorg/rcomp/tmp/6d9lo1356076907.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 = 126
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.88542999 NA
1 -2.60128759 -1.88542999
2 -3.30802547 -2.60128759
3 0.10654189 -3.30802547
4 -1.10364458 0.10654189
5 -0.53621878 -1.10364458
6 -1.17210073 -0.53621878
7 1.02041394 -1.17210073
8 -0.34514330 1.02041394
9 0.57801177 -0.34514330
10 1.12996932 0.57801177
11 1.54667307 1.12996932
12 2.78905752 1.54667307
13 2.79174754 2.78905752
14 2.04386795 2.79174754
15 2.20655074 2.04386795
16 1.12979276 2.20655074
17 1.85234979 1.12979276
18 2.93710002 1.85234979
19 2.55320939 2.93710002
20 6.47800364 2.55320939
21 1.34361489 6.47800364
22 2.18611287 1.34361489
23 1.80088518 2.18611287
24 0.08318880 1.80088518
25 2.74409268 0.08318880
26 3.04890270 2.74409268
27 1.19681497 3.04890270
28 -1.42105841 1.19681497
29 -0.70996952 -1.42105841
30 -1.88037193 -0.70996952
31 -1.13586023 -1.88037193
32 1.51722358 -1.13586023
33 2.10372332 1.51722358
34 1.05505031 2.10372332
35 -0.45064474 1.05505031
36 -1.92072896 -0.45064474
37 -1.11344255 -1.92072896
38 -0.75703870 -1.11344255
39 -2.96608960 -0.75703870
40 -4.05095854 -2.96608960
41 -4.52964954 -4.05095854
42 -0.40611973 -4.52964954
43 -0.35774277 -0.40611973
44 -3.31956663 -0.35774277
45 -2.55906686 -3.31956663
46 -0.82156788 -2.55906686
47 -1.00277463 -0.82156788
48 -0.95656424 -1.00277463
49 -0.08177860 -0.95656424
50 0.44624799 -0.08177860
51 0.07405493 0.44624799
52 1.38973479 0.07405493
53 0.52160551 1.38973479
54 1.62384255 0.52160551
55 2.22661236 1.62384255
56 0.50736865 2.22661236
57 1.29277816 0.50736865
58 0.28988885 1.29277816
59 0.16394981 0.28988885
60 -0.14820423 0.16394981
61 -1.17371184 -0.14820423
62 -1.38460157 -1.17371184
63 -2.39453950 -1.38460157
64 -2.16272080 -2.39453950
65 -1.41598674 -2.16272080
66 -3.78060496 -1.41598674
67 -3.28476491 -3.78060496
68 -2.37733846 -3.28476491
69 -2.64899302 -2.37733846
70 -2.49222361 -2.64899302
71 -2.15283754 -2.49222361
72 -1.27319957 -2.15283754
73 -1.14514321 -1.27319957
74 -0.48495539 -1.14514321
75 -0.36495522 -0.48495539
76 1.35625793 -0.36495522
77 0.05983804 1.35625793
78 1.12255475 0.05983804
79 -1.60469581 1.12255475
80 -1.54856652 -1.60469581
81 -1.12474524 -1.54856652
82 -0.30637378 -1.12474524
83 1.12449580 -0.30637378
84 0.66617217 1.12449580
85 0.30258315 0.66617217
86 -0.33647842 0.30258315
87 0.33311936 -0.33647842
88 0.42122219 0.33311936
89 -0.40655210 0.42122219
90 -0.19475352 -0.40655210
91 -0.43216999 -0.19475352
92 -0.72088773 -0.43216999
93 0.78924351 -0.72088773
94 0.86510325 0.78924351
95 1.17466077 0.86510325
96 2.45129027 1.17466077
97 0.57315141 2.45129027
98 -0.64425333 0.57315141
99 0.02322108 -0.64425333
100 0.86416392 0.02322108
101 2.17070806 0.86416392
102 3.13398162 2.17070806
103 3.41184751 3.13398162
104 0.87105769 3.41184751
105 0.30642441 0.87105769
106 -1.05729405 0.30642441
107 -1.77228921 -1.05729405
108 -0.05344975 -1.77228921
109 -2.19331059 -0.05344975
110 -1.48369334 -2.19331059
111 -1.81447180 -1.48369334
112 -2.21835825 -1.81447180
113 -3.10980891 -2.21835825
114 -1.38352809 -3.10980891
115 -2.39684948 -1.38352809
116 -1.06215092 -2.39684948
117 -0.08099094 -1.06215092
118 -0.84866527 -0.08099094
119 -0.43211851 -0.84866527
120 0.24786799 -0.43211851
121 1.89709961 0.24786799
122 2.86002759 1.89709961
123 3.59975314 2.86002759
124 5.79556899 3.59975314
125 6.10368419 5.79556899
126 NA 6.10368419
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.60128759 -1.88542999
[2,] -3.30802547 -2.60128759
[3,] 0.10654189 -3.30802547
[4,] -1.10364458 0.10654189
[5,] -0.53621878 -1.10364458
[6,] -1.17210073 -0.53621878
[7,] 1.02041394 -1.17210073
[8,] -0.34514330 1.02041394
[9,] 0.57801177 -0.34514330
[10,] 1.12996932 0.57801177
[11,] 1.54667307 1.12996932
[12,] 2.78905752 1.54667307
[13,] 2.79174754 2.78905752
[14,] 2.04386795 2.79174754
[15,] 2.20655074 2.04386795
[16,] 1.12979276 2.20655074
[17,] 1.85234979 1.12979276
[18,] 2.93710002 1.85234979
[19,] 2.55320939 2.93710002
[20,] 6.47800364 2.55320939
[21,] 1.34361489 6.47800364
[22,] 2.18611287 1.34361489
[23,] 1.80088518 2.18611287
[24,] 0.08318880 1.80088518
[25,] 2.74409268 0.08318880
[26,] 3.04890270 2.74409268
[27,] 1.19681497 3.04890270
[28,] -1.42105841 1.19681497
[29,] -0.70996952 -1.42105841
[30,] -1.88037193 -0.70996952
[31,] -1.13586023 -1.88037193
[32,] 1.51722358 -1.13586023
[33,] 2.10372332 1.51722358
[34,] 1.05505031 2.10372332
[35,] -0.45064474 1.05505031
[36,] -1.92072896 -0.45064474
[37,] -1.11344255 -1.92072896
[38,] -0.75703870 -1.11344255
[39,] -2.96608960 -0.75703870
[40,] -4.05095854 -2.96608960
[41,] -4.52964954 -4.05095854
[42,] -0.40611973 -4.52964954
[43,] -0.35774277 -0.40611973
[44,] -3.31956663 -0.35774277
[45,] -2.55906686 -3.31956663
[46,] -0.82156788 -2.55906686
[47,] -1.00277463 -0.82156788
[48,] -0.95656424 -1.00277463
[49,] -0.08177860 -0.95656424
[50,] 0.44624799 -0.08177860
[51,] 0.07405493 0.44624799
[52,] 1.38973479 0.07405493
[53,] 0.52160551 1.38973479
[54,] 1.62384255 0.52160551
[55,] 2.22661236 1.62384255
[56,] 0.50736865 2.22661236
[57,] 1.29277816 0.50736865
[58,] 0.28988885 1.29277816
[59,] 0.16394981 0.28988885
[60,] -0.14820423 0.16394981
[61,] -1.17371184 -0.14820423
[62,] -1.38460157 -1.17371184
[63,] -2.39453950 -1.38460157
[64,] -2.16272080 -2.39453950
[65,] -1.41598674 -2.16272080
[66,] -3.78060496 -1.41598674
[67,] -3.28476491 -3.78060496
[68,] -2.37733846 -3.28476491
[69,] -2.64899302 -2.37733846
[70,] -2.49222361 -2.64899302
[71,] -2.15283754 -2.49222361
[72,] -1.27319957 -2.15283754
[73,] -1.14514321 -1.27319957
[74,] -0.48495539 -1.14514321
[75,] -0.36495522 -0.48495539
[76,] 1.35625793 -0.36495522
[77,] 0.05983804 1.35625793
[78,] 1.12255475 0.05983804
[79,] -1.60469581 1.12255475
[80,] -1.54856652 -1.60469581
[81,] -1.12474524 -1.54856652
[82,] -0.30637378 -1.12474524
[83,] 1.12449580 -0.30637378
[84,] 0.66617217 1.12449580
[85,] 0.30258315 0.66617217
[86,] -0.33647842 0.30258315
[87,] 0.33311936 -0.33647842
[88,] 0.42122219 0.33311936
[89,] -0.40655210 0.42122219
[90,] -0.19475352 -0.40655210
[91,] -0.43216999 -0.19475352
[92,] -0.72088773 -0.43216999
[93,] 0.78924351 -0.72088773
[94,] 0.86510325 0.78924351
[95,] 1.17466077 0.86510325
[96,] 2.45129027 1.17466077
[97,] 0.57315141 2.45129027
[98,] -0.64425333 0.57315141
[99,] 0.02322108 -0.64425333
[100,] 0.86416392 0.02322108
[101,] 2.17070806 0.86416392
[102,] 3.13398162 2.17070806
[103,] 3.41184751 3.13398162
[104,] 0.87105769 3.41184751
[105,] 0.30642441 0.87105769
[106,] -1.05729405 0.30642441
[107,] -1.77228921 -1.05729405
[108,] -0.05344975 -1.77228921
[109,] -2.19331059 -0.05344975
[110,] -1.48369334 -2.19331059
[111,] -1.81447180 -1.48369334
[112,] -2.21835825 -1.81447180
[113,] -3.10980891 -2.21835825
[114,] -1.38352809 -3.10980891
[115,] -2.39684948 -1.38352809
[116,] -1.06215092 -2.39684948
[117,] -0.08099094 -1.06215092
[118,] -0.84866527 -0.08099094
[119,] -0.43211851 -0.84866527
[120,] 0.24786799 -0.43211851
[121,] 1.89709961 0.24786799
[122,] 2.86002759 1.89709961
[123,] 3.59975314 2.86002759
[124,] 5.79556899 3.59975314
[125,] 6.10368419 5.79556899
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.60128759 -1.88542999
2 -3.30802547 -2.60128759
3 0.10654189 -3.30802547
4 -1.10364458 0.10654189
5 -0.53621878 -1.10364458
6 -1.17210073 -0.53621878
7 1.02041394 -1.17210073
8 -0.34514330 1.02041394
9 0.57801177 -0.34514330
10 1.12996932 0.57801177
11 1.54667307 1.12996932
12 2.78905752 1.54667307
13 2.79174754 2.78905752
14 2.04386795 2.79174754
15 2.20655074 2.04386795
16 1.12979276 2.20655074
17 1.85234979 1.12979276
18 2.93710002 1.85234979
19 2.55320939 2.93710002
20 6.47800364 2.55320939
21 1.34361489 6.47800364
22 2.18611287 1.34361489
23 1.80088518 2.18611287
24 0.08318880 1.80088518
25 2.74409268 0.08318880
26 3.04890270 2.74409268
27 1.19681497 3.04890270
28 -1.42105841 1.19681497
29 -0.70996952 -1.42105841
30 -1.88037193 -0.70996952
31 -1.13586023 -1.88037193
32 1.51722358 -1.13586023
33 2.10372332 1.51722358
34 1.05505031 2.10372332
35 -0.45064474 1.05505031
36 -1.92072896 -0.45064474
37 -1.11344255 -1.92072896
38 -0.75703870 -1.11344255
39 -2.96608960 -0.75703870
40 -4.05095854 -2.96608960
41 -4.52964954 -4.05095854
42 -0.40611973 -4.52964954
43 -0.35774277 -0.40611973
44 -3.31956663 -0.35774277
45 -2.55906686 -3.31956663
46 -0.82156788 -2.55906686
47 -1.00277463 -0.82156788
48 -0.95656424 -1.00277463
49 -0.08177860 -0.95656424
50 0.44624799 -0.08177860
51 0.07405493 0.44624799
52 1.38973479 0.07405493
53 0.52160551 1.38973479
54 1.62384255 0.52160551
55 2.22661236 1.62384255
56 0.50736865 2.22661236
57 1.29277816 0.50736865
58 0.28988885 1.29277816
59 0.16394981 0.28988885
60 -0.14820423 0.16394981
61 -1.17371184 -0.14820423
62 -1.38460157 -1.17371184
63 -2.39453950 -1.38460157
64 -2.16272080 -2.39453950
65 -1.41598674 -2.16272080
66 -3.78060496 -1.41598674
67 -3.28476491 -3.78060496
68 -2.37733846 -3.28476491
69 -2.64899302 -2.37733846
70 -2.49222361 -2.64899302
71 -2.15283754 -2.49222361
72 -1.27319957 -2.15283754
73 -1.14514321 -1.27319957
74 -0.48495539 -1.14514321
75 -0.36495522 -0.48495539
76 1.35625793 -0.36495522
77 0.05983804 1.35625793
78 1.12255475 0.05983804
79 -1.60469581 1.12255475
80 -1.54856652 -1.60469581
81 -1.12474524 -1.54856652
82 -0.30637378 -1.12474524
83 1.12449580 -0.30637378
84 0.66617217 1.12449580
85 0.30258315 0.66617217
86 -0.33647842 0.30258315
87 0.33311936 -0.33647842
88 0.42122219 0.33311936
89 -0.40655210 0.42122219
90 -0.19475352 -0.40655210
91 -0.43216999 -0.19475352
92 -0.72088773 -0.43216999
93 0.78924351 -0.72088773
94 0.86510325 0.78924351
95 1.17466077 0.86510325
96 2.45129027 1.17466077
97 0.57315141 2.45129027
98 -0.64425333 0.57315141
99 0.02322108 -0.64425333
100 0.86416392 0.02322108
101 2.17070806 0.86416392
102 3.13398162 2.17070806
103 3.41184751 3.13398162
104 0.87105769 3.41184751
105 0.30642441 0.87105769
106 -1.05729405 0.30642441
107 -1.77228921 -1.05729405
108 -0.05344975 -1.77228921
109 -2.19331059 -0.05344975
110 -1.48369334 -2.19331059
111 -1.81447180 -1.48369334
112 -2.21835825 -1.81447180
113 -3.10980891 -2.21835825
114 -1.38352809 -3.10980891
115 -2.39684948 -1.38352809
116 -1.06215092 -2.39684948
117 -0.08099094 -1.06215092
118 -0.84866527 -0.08099094
119 -0.43211851 -0.84866527
120 0.24786799 -0.43211851
121 1.89709961 0.24786799
122 2.86002759 1.89709961
123 3.59975314 2.86002759
124 5.79556899 3.59975314
125 6.10368419 5.79556899
> 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/7ciwa1356076908.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/80ma71356076908.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/9npc61356076908.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/10xuco1356076908.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11gm451356076908.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12nb981356076908.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/136l031356076908.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14i3571356076908.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/15xj9x1356076908.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/16ftkr1356076908.tab")
+ }
>
> try(system("convert tmp/1q1nd1356076907.ps tmp/1q1nd1356076907.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sw3j1356076907.ps tmp/2sw3j1356076907.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fyrl1356076907.ps tmp/3fyrl1356076907.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jq5l1356076907.ps tmp/4jq5l1356076907.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h51q1356076907.ps tmp/5h51q1356076907.png",intern=TRUE))
character(0)
> try(system("convert tmp/6d9lo1356076907.ps tmp/6d9lo1356076907.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ciwa1356076908.ps tmp/7ciwa1356076908.png",intern=TRUE))
character(0)
> try(system("convert tmp/80ma71356076908.ps tmp/80ma71356076908.png",intern=TRUE))
character(0)
> try(system("convert tmp/9npc61356076908.ps tmp/9npc61356076908.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xuco1356076908.ps tmp/10xuco1356076908.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.257 1.189 8.644