R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(27.72
+ ,41837160
+ ,91.51
+ ,2747.48
+ ,0.016
+ ,62.7
+ ,26.90
+ ,35204750
+ ,91.09
+ ,2760.01
+ ,0.016
+ ,62.7
+ ,25.86
+ ,42367740
+ ,93.00
+ ,2778.11
+ ,0.016
+ ,62.7
+ ,26.81
+ ,61427940
+ ,93.08
+ ,2844.72
+ ,0.016
+ ,62.7
+ ,26.31
+ ,26132090
+ ,94.13
+ ,2831.02
+ ,0.016
+ ,62.7
+ ,27.10
+ ,3799718
+ ,96.26
+ ,2858.42
+ ,0.016
+ ,62.7
+ ,27.00
+ ,28202230
+ ,94.29
+ ,2809.73
+ ,0.016
+ ,62.7
+ ,27.40
+ ,15809640
+ ,94.46
+ ,2843.07
+ ,0.016
+ ,62.7
+ ,27.27
+ ,17110160
+ ,95.53
+ ,2818.61
+ ,0.016
+ ,62.7
+ ,28.29
+ ,16835510
+ ,98.29
+ ,2836.33
+ ,0.016
+ ,62.7
+ ,30.01
+ ,43517670
+ ,102.01
+ ,2872.80
+ ,0.016
+ ,62.7
+ ,31.41
+ ,42958450
+ ,105.16
+ ,2895.33
+ ,0.016
+ ,62.7
+ ,31.91
+ ,30826830
+ ,105.34
+ ,2929.76
+ ,0.016
+ ,62.7
+ ,31.60
+ ,15549740
+ ,105.27
+ ,2930.45
+ ,0.016
+ ,62.7
+ ,31.84
+ ,21843070
+ ,102.19
+ ,2859.09
+ ,0.016
+ ,62.7
+ ,33.05
+ ,73424890
+ ,106.85
+ ,2892.42
+ ,0.016
+ ,62.7
+ ,32.06
+ ,24330740
+ ,103.05
+ ,2836.16
+ ,0.016
+ ,62.7
+ ,33.10
+ ,24785970
+ ,106.42
+ ,2854.06
+ ,0.016
+ ,62.7
+ ,32.23
+ ,28553940
+ ,105.17
+ ,2875.32
+ ,0.016
+ ,62.7
+ ,31.36
+ ,17659080
+ ,102.74
+ ,2849.49
+ ,0.016
+ ,62.7
+ ,31.09
+ ,19508980
+ ,106.27
+ ,2935.05
+ ,0.016
+ ,62.7
+ ,30.77
+ ,14110230
+ ,107.63
+ ,2951.23
+ ,0.0141
+ ,65.4
+ ,31.20
+ ,8765498
+ ,108.54
+ ,2976.08
+ ,0.0141
+ ,65.4
+ ,31.47
+ ,10027250
+ ,108.24
+ ,2976.12
+ ,0.0141
+ ,65.4
+ ,31.73
+ ,10943350
+ ,108.86
+ ,2937.33
+ ,0.0141
+ ,65.4
+ ,32.17
+ ,17755740
+ ,102.98
+ ,2931.77
+ ,0.0141
+ ,65.4
+ ,31.47
+ ,14238190
+ ,99.53
+ ,2902.33
+ ,0.0141
+ ,65.4
+ ,30.97
+ ,12997760
+ ,101.08
+ ,2887.98
+ ,0.0141
+ ,65.4
+ ,30.81
+ ,11299240
+ ,104.64
+ ,2866.19
+ ,0.0141
+ ,65.4
+ ,30.72
+ ,8102653
+ ,105.59
+ ,2908.47
+ ,0.0141
+ ,65.4
+ ,28.24
+ ,24549800
+ ,103.21
+ ,2896.94
+ ,0.0141
+ ,65.4
+ ,28.09
+ ,30410530
+ ,103.84
+ ,2910.04
+ ,0.0141
+ ,65.4
+ ,29.11
+ ,16807730
+ ,104.61
+ ,2942.60
+ ,0.0141
+ ,65.4
+ ,29.00
+ ,13671200
+ ,108.65
+ ,2965.90
+ ,0.0141
+ ,65.4
+ ,28.76
+ ,11854290
+ ,106.26
+ ,2925.30
+ ,0.0141
+ ,65.4
+ ,28.75
+ ,12383610
+ ,104.20
+ ,2890.15
+ ,0.0141
+ ,65.4
+ ,28.45
+ ,11512350
+ ,102.99
+ ,2862.99
+ ,0.0141
+ ,65.4
+ ,29.34
+ ,16749990
+ ,102.19
+ ,2854.24
+ ,0.0141
+ ,65.4
+ ,26.84
+ ,61009290
+ ,100.82
+ ,2893.25
+ ,0.0141
+ ,65.4
+ ,23.70
+ ,123011300
+ ,103.42
+ ,2958.09
+ ,0.0141
+ ,65.4
+ ,23.15
+ ,29253590
+ ,104.18
+ ,2945.84
+ ,0.0141
+ ,65.4
+ ,21.71
+ ,55998620
+ ,102.65
+ ,2939.52
+ ,0.0141
+ ,65.4
+ ,20.88
+ ,44488370
+ ,95.64
+ ,2920.21
+ ,0.0169
+ ,61.3
+ ,20.04
+ ,56264460
+ ,93.51
+ ,2909.77
+ ,0.0169
+ ,61.3
+ ,21.09
+ ,80626220
+ ,108.51
+ ,2967.90
+ ,0.0169
+ ,61.3
+ ,21.92
+ ,27733830
+ ,111.55
+ ,2989.91
+ ,0.0169
+ ,61.3
+ ,20.72
+ ,36699380
+ ,106.70
+ ,3015.86
+ ,0.0169
+ ,61.3
+ ,20.72
+ ,29514550
+ ,104.93
+ ,3011.25
+ ,0.0169
+ ,61.3
+ ,21.01
+ ,15605960
+ ,105.23
+ ,3018.64
+ ,0.0169
+ ,61.3
+ ,21.80
+ ,25714310
+ ,104.92
+ ,3020.86
+ ,0.0169
+ ,61.3
+ ,21.60
+ ,24904700
+ ,104.60
+ ,3022.52
+ ,0.0169
+ ,61.3
+ ,20.38
+ ,38971320
+ ,101.76
+ ,3016.98
+ ,0.0169
+ ,61.3
+ ,21.20
+ ,47682050
+ ,102.23
+ ,3030.93
+ ,0.0169
+ ,61.3
+ ,19.87
+ ,157188200
+ ,103.99
+ ,3062.39
+ ,0.0169
+ ,61.3
+ ,19.05
+ ,129057400
+ ,101.36
+ ,3076.59
+ ,0.0169
+ ,61.3
+ ,20.01
+ ,100818300
+ ,102.92
+ ,3076.21
+ ,0.0169
+ ,61.3
+ ,19.15
+ ,70483330
+ ,105.25
+ ,3067.26
+ ,0.0169
+ ,61.3
+ ,19.43
+ ,49779450
+ ,105.71
+ ,3073.67
+ ,0.0169
+ ,61.3
+ ,19.44
+ ,32747000
+ ,105.42
+ ,3053.40
+ ,0.0169
+ ,61.3
+ ,19.40
+ ,29588690
+ ,105.11
+ ,3069.79
+ ,0.0169
+ ,61.3
+ ,19.15
+ ,20663220
+ ,104.67
+ ,3073.19
+ ,0.0169
+ ,61.3
+ ,19.34
+ ,25402980
+ ,107.51
+ ,3077.14
+ ,0.0169
+ ,61.3
+ ,19.10
+ ,16071190
+ ,109.00
+ ,3081.19
+ ,0.0169
+ ,61.3
+ ,19.08
+ ,30571430
+ ,107.37
+ ,3048.71
+ ,0.0169
+ ,61.3
+ ,18.05
+ ,58612440
+ ,107.30
+ ,3066.96
+ ,0.0169
+ ,61.3
+ ,17.72
+ ,46177000
+ ,107.37
+ ,3075.06
+ ,0.0199
+ ,70.3
+ ,18.58
+ ,60657900
+ ,113.28
+ ,3069.27
+ ,0.0199
+ ,70.3
+ ,18.96
+ ,46028860
+ ,119.10
+ ,3135.81
+ ,0.0199
+ ,70.3
+ ,18.98
+ ,36325880
+ ,119.04
+ ,3136.42
+ ,0.0199
+ ,70.3
+ ,18.81
+ ,24752340
+ ,117.80
+ ,3104.02
+ ,0.0199
+ ,70.3
+ ,19.43
+ ,47343020
+ ,117.90
+ ,3104.53
+ ,0.0199
+ ,70.3
+ ,20.93
+ ,121399400
+ ,119.55
+ ,3114.31
+ ,0.0199
+ ,70.3
+ ,20.71
+ ,64896660
+ ,119.47
+ ,3155.83
+ ,0.0199
+ ,70.3
+ ,22.00
+ ,72707430
+ ,123.23
+ ,3183.95
+ ,0.0199
+ ,70.3
+ ,21.52
+ ,50593510
+ ,121.40
+ ,3178.67
+ ,0.0199
+ ,70.3
+ ,21.87
+ ,36696330
+ ,121.43
+ ,3177.80
+ ,0.0199
+ ,70.3
+ ,23.29
+ ,78525460
+ ,122.51
+ ,3182.62
+ ,0.0199
+ ,70.3
+ ,22.59
+ ,57115160
+ ,122.78
+ ,3175.96
+ ,0.0199
+ ,70.3
+ ,22.86
+ ,51163120
+ ,122.84
+ ,3179.96
+ ,0.0199
+ ,70.3
+ ,20.79
+ ,78968380
+ ,122.70
+ ,3160.78
+ ,0.0199
+ ,70.3
+ ,20.28
+ ,46169460
+ ,119.89
+ ,3117.73
+ ,0.0199
+ ,70.3
+ ,20.62
+ ,38212360
+ ,118.00
+ ,3093.70
+ ,0.0199
+ ,70.3
+ ,20.32
+ ,30061050
+ ,119.61
+ ,3136.60
+ ,0.0199
+ ,70.3
+ ,21.66
+ ,65415370
+ ,120.40
+ ,3116.23
+ ,0.0199
+ ,70.3
+ ,21.99
+ ,51198150
+ ,117.94
+ ,3113.53
+ ,0.0216
+ ,73.1
+ ,22.27
+ ,29276680
+ ,118.77
+ ,3120.04
+ ,0.0216
+ ,73.1
+ ,21.83
+ ,31940720
+ ,121.68
+ ,3135.23
+ ,0.0216
+ ,73.1
+ ,21.94
+ ,46549400
+ ,121.98
+ ,3149.46
+ ,0.0216
+ ,73.1
+ ,20.91
+ ,40483780
+ ,118.83
+ ,3136.19
+ ,0.0216
+ ,73.1
+ ,20.40
+ ,32190200
+ ,117.97
+ ,3112.35
+ ,0.0216
+ ,73.1
+ ,20.22
+ ,27125670
+ ,113.07
+ ,3065.02
+ ,0.0216
+ ,73.1
+ ,19.64
+ ,39282420
+ ,111.98
+ ,3051.78
+ ,0.0216
+ ,73.1
+ ,19.75
+ ,21803710
+ ,113.77
+ ,3049.41
+ ,0.0216
+ ,73.1
+ ,19.51
+ ,18743920
+ ,110.41
+ ,3044.11
+ ,0.0216
+ ,73.1
+ ,19.52
+ ,20154860
+ ,110.85
+ ,3064.18
+ ,0.0216
+ ,73.1
+ ,19.48
+ ,21816100
+ ,111.18
+ ,3101.17
+ ,0.0216
+ ,73.1
+ ,19.88
+ ,44020450
+ ,109.42
+ ,3104.12
+ ,0.0216
+ ,73.1
+ ,18.97
+ ,52059860
+ ,108.87
+ ,3072.87
+ ,0.0216
+ ,73.1
+ ,19.00
+ ,34769600
+ ,106.72
+ ,3005.62
+ ,0.0216
+ ,73.1
+ ,19.32
+ ,32269470
+ ,107.28
+ ,3016.96
+ ,0.0216
+ ,73.1
+ ,19.50
+ ,72281000
+ ,104.13
+ ,2990.46
+ ,0.0216
+ ,73.1
+ ,23.22
+ ,228364700
+ ,107.55
+ ,2981.70
+ ,0.0216
+ ,73.1
+ ,22.56
+ ,76050080
+ ,105.72
+ ,2986.12
+ ,0.0216
+ ,73.1
+ ,21.94
+ ,9999999
+ ,104.55
+ ,2987.95
+ ,0.0216
+ ,73.1
+ ,21.11
+ ,99311480
+ ,106.93
+ ,2977.23
+ ,0.0216
+ ,73.1
+ ,21.21
+ ,37631000
+ ,106.85
+ ,3020.06
+ ,0.0176
+ ,73.1
+ ,21.18
+ ,38308550
+ ,106.78
+ ,2982.13
+ ,0.0176
+ ,73.1
+ ,21.25
+ ,31752420
+ ,107.29
+ ,2999.66
+ ,0.0176
+ ,73.1
+ ,21.17
+ ,29030780
+ ,104.14
+ ,3011.93
+ ,0.0176
+ ,73.1
+ ,20.47
+ ,33352920
+ ,101.21
+ ,2937.29
+ ,0.0176
+ ,73.1
+ ,19.99
+ ,34106840
+ ,96.35
+ ,2895.58
+ ,0.0176
+ ,73.1
+ ,19.21
+ ,42257790
+ ,95.62
+ ,2904.87
+ ,0.0176
+ ,73.1
+ ,20.07
+ ,67220540
+ ,99.00
+ ,2904.26
+ ,0.0176
+ ,73.1
+ ,19.86
+ ,71524510
+ ,99.26
+ ,2883.89
+ ,0.0176
+ ,73.1
+ ,22.36
+ ,229081600
+ ,98.77
+ ,2846.81
+ ,0.0176
+ ,73.1
+ ,22.17
+ ,78808770
+ ,100.65
+ ,2836.94
+ ,0.0176
+ ,73.1
+ ,23.56
+ ,107091400
+ ,103.13
+ ,2853.13
+ ,0.0176
+ ,73.1
+ ,22.92
+ ,84944370
+ ,105.53
+ ,2916.07
+ ,0.0176
+ ,73.1
+ ,23.10
+ ,46515660
+ ,106.76
+ ,2916.68
+ ,0.0176
+ ,73.1
+ ,24.32
+ ,89720920
+ ,107.59
+ ,2926.55
+ ,0.0176
+ ,73.1
+ ,23.99
+ ,29520310
+ ,107.62
+ ,2966.85
+ ,0.0176
+ ,73.1
+ ,25.94
+ ,123513900
+ ,108.82
+ ,2976.78
+ ,0.0176
+ ,73.1
+ ,26.15
+ ,85687430
+ ,107.59
+ ,2967.79
+ ,0.0176
+ ,73.1
+ ,26.36
+ ,49113040
+ ,107.85
+ ,2991.78
+ ,0.0176
+ ,73.1
+ ,27.32
+ ,88572990
+ ,107.11
+ ,3012.03
+ ,0.0176
+ ,73.1
+ ,28.00
+ ,126867400
+ ,108.14
+ ,3010.24
+ ,0.0176
+ ,73.1)
+ ,dim=c(6
+ ,126)
+ ,dimnames=list(c('FACEBOOK'
+ ,'VOLUME'
+ ,'LINKEDIN'
+ ,'NASDAQ'
+ ,'INF'
+ ,'CONS.CONF')
+ ,1:126))
> y <- array(NA,dim=c(6,126),dimnames=list(c('FACEBOOK','VOLUME','LINKEDIN','NASDAQ','INF','CONS.CONF'),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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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, 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 VOLUME LINKEDIN NASDAQ INF CONS.CONF t
1 27.72 41837160 91.51 2747.48 0.0160 62.7 1
2 26.90 35204750 91.09 2760.01 0.0160 62.7 2
3 25.86 42367740 93.00 2778.11 0.0160 62.7 3
4 26.81 61427940 93.08 2844.72 0.0160 62.7 4
5 26.31 26132090 94.13 2831.02 0.0160 62.7 5
6 27.10 3799718 96.26 2858.42 0.0160 62.7 6
7 27.00 28202230 94.29 2809.73 0.0160 62.7 7
8 27.40 15809640 94.46 2843.07 0.0160 62.7 8
9 27.27 17110160 95.53 2818.61 0.0160 62.7 9
10 28.29 16835510 98.29 2836.33 0.0160 62.7 10
11 30.01 43517670 102.01 2872.80 0.0160 62.7 11
12 31.41 42958450 105.16 2895.33 0.0160 62.7 12
13 31.91 30826830 105.34 2929.76 0.0160 62.7 13
14 31.60 15549740 105.27 2930.45 0.0160 62.7 14
15 31.84 21843070 102.19 2859.09 0.0160 62.7 15
16 33.05 73424890 106.85 2892.42 0.0160 62.7 16
17 32.06 24330740 103.05 2836.16 0.0160 62.7 17
18 33.10 24785970 106.42 2854.06 0.0160 62.7 18
19 32.23 28553940 105.17 2875.32 0.0160 62.7 19
20 31.36 17659080 102.74 2849.49 0.0160 62.7 20
21 31.09 19508980 106.27 2935.05 0.0160 62.7 21
22 30.77 14110230 107.63 2951.23 0.0141 65.4 22
23 31.20 8765498 108.54 2976.08 0.0141 65.4 23
24 31.47 10027250 108.24 2976.12 0.0141 65.4 24
25 31.73 10943350 108.86 2937.33 0.0141 65.4 25
26 32.17 17755740 102.98 2931.77 0.0141 65.4 26
27 31.47 14238190 99.53 2902.33 0.0141 65.4 27
28 30.97 12997760 101.08 2887.98 0.0141 65.4 28
29 30.81 11299240 104.64 2866.19 0.0141 65.4 29
30 30.72 8102653 105.59 2908.47 0.0141 65.4 30
31 28.24 24549800 103.21 2896.94 0.0141 65.4 31
32 28.09 30410530 103.84 2910.04 0.0141 65.4 32
33 29.11 16807730 104.61 2942.60 0.0141 65.4 33
34 29.00 13671200 108.65 2965.90 0.0141 65.4 34
35 28.76 11854290 106.26 2925.30 0.0141 65.4 35
36 28.75 12383610 104.20 2890.15 0.0141 65.4 36
37 28.45 11512350 102.99 2862.99 0.0141 65.4 37
38 29.34 16749990 102.19 2854.24 0.0141 65.4 38
39 26.84 61009290 100.82 2893.25 0.0141 65.4 39
40 23.70 123011300 103.42 2958.09 0.0141 65.4 40
41 23.15 29253590 104.18 2945.84 0.0141 65.4 41
42 21.71 55998620 102.65 2939.52 0.0141 65.4 42
43 20.88 44488370 95.64 2920.21 0.0169 61.3 43
44 20.04 56264460 93.51 2909.77 0.0169 61.3 44
45 21.09 80626220 108.51 2967.90 0.0169 61.3 45
46 21.92 27733830 111.55 2989.91 0.0169 61.3 46
47 20.72 36699380 106.70 3015.86 0.0169 61.3 47
48 20.72 29514550 104.93 3011.25 0.0169 61.3 48
49 21.01 15605960 105.23 3018.64 0.0169 61.3 49
50 21.80 25714310 104.92 3020.86 0.0169 61.3 50
51 21.60 24904700 104.60 3022.52 0.0169 61.3 51
52 20.38 38971320 101.76 3016.98 0.0169 61.3 52
53 21.20 47682050 102.23 3030.93 0.0169 61.3 53
54 19.87 157188200 103.99 3062.39 0.0169 61.3 54
55 19.05 129057400 101.36 3076.59 0.0169 61.3 55
56 20.01 100818300 102.92 3076.21 0.0169 61.3 56
57 19.15 70483330 105.25 3067.26 0.0169 61.3 57
58 19.43 49779450 105.71 3073.67 0.0169 61.3 58
59 19.44 32747000 105.42 3053.40 0.0169 61.3 59
60 19.40 29588690 105.11 3069.79 0.0169 61.3 60
61 19.15 20663220 104.67 3073.19 0.0169 61.3 61
62 19.34 25402980 107.51 3077.14 0.0169 61.3 62
63 19.10 16071190 109.00 3081.19 0.0169 61.3 63
64 19.08 30571430 107.37 3048.71 0.0169 61.3 64
65 18.05 58612440 107.30 3066.96 0.0169 61.3 65
66 17.72 46177000 107.37 3075.06 0.0199 70.3 66
67 18.58 60657900 113.28 3069.27 0.0199 70.3 67
68 18.96 46028860 119.10 3135.81 0.0199 70.3 68
69 18.98 36325880 119.04 3136.42 0.0199 70.3 69
70 18.81 24752340 117.80 3104.02 0.0199 70.3 70
71 19.43 47343020 117.90 3104.53 0.0199 70.3 71
72 20.93 121399400 119.55 3114.31 0.0199 70.3 72
73 20.71 64896660 119.47 3155.83 0.0199 70.3 73
74 22.00 72707430 123.23 3183.95 0.0199 70.3 74
75 21.52 50593510 121.40 3178.67 0.0199 70.3 75
76 21.87 36696330 121.43 3177.80 0.0199 70.3 76
77 23.29 78525460 122.51 3182.62 0.0199 70.3 77
78 22.59 57115160 122.78 3175.96 0.0199 70.3 78
79 22.86 51163120 122.84 3179.96 0.0199 70.3 79
80 20.79 78968380 122.70 3160.78 0.0199 70.3 80
81 20.28 46169460 119.89 3117.73 0.0199 70.3 81
82 20.62 38212360 118.00 3093.70 0.0199 70.3 82
83 20.32 30061050 119.61 3136.60 0.0199 70.3 83
84 21.66 65415370 120.40 3116.23 0.0199 70.3 84
85 21.99 51198150 117.94 3113.53 0.0216 73.1 85
86 22.27 29276680 118.77 3120.04 0.0216 73.1 86
87 21.83 31940720 121.68 3135.23 0.0216 73.1 87
88 21.94 46549400 121.98 3149.46 0.0216 73.1 88
89 20.91 40483780 118.83 3136.19 0.0216 73.1 89
90 20.40 32190200 117.97 3112.35 0.0216 73.1 90
91 20.22 27125670 113.07 3065.02 0.0216 73.1 91
92 19.64 39282420 111.98 3051.78 0.0216 73.1 92
93 19.75 21803710 113.77 3049.41 0.0216 73.1 93
94 19.51 18743920 110.41 3044.11 0.0216 73.1 94
95 19.52 20154860 110.85 3064.18 0.0216 73.1 95
96 19.48 21816100 111.18 3101.17 0.0216 73.1 96
97 19.88 44020450 109.42 3104.12 0.0216 73.1 97
98 18.97 52059860 108.87 3072.87 0.0216 73.1 98
99 19.00 34769600 106.72 3005.62 0.0216 73.1 99
100 19.32 32269470 107.28 3016.96 0.0216 73.1 100
101 19.50 72281000 104.13 2990.46 0.0216 73.1 101
102 23.22 228364700 107.55 2981.70 0.0216 73.1 102
103 22.56 76050080 105.72 2986.12 0.0216 73.1 103
104 21.94 9999999 104.55 2987.95 0.0216 73.1 104
105 21.11 99311480 106.93 2977.23 0.0216 73.1 105
106 21.21 37631000 106.85 3020.06 0.0176 73.1 106
107 21.18 38308550 106.78 2982.13 0.0176 73.1 107
108 21.25 31752420 107.29 2999.66 0.0176 73.1 108
109 21.17 29030780 104.14 3011.93 0.0176 73.1 109
110 20.47 33352920 101.21 2937.29 0.0176 73.1 110
111 19.99 34106840 96.35 2895.58 0.0176 73.1 111
112 19.21 42257790 95.62 2904.87 0.0176 73.1 112
113 20.07 67220540 99.00 2904.26 0.0176 73.1 113
114 19.86 71524510 99.26 2883.89 0.0176 73.1 114
115 22.36 229081600 98.77 2846.81 0.0176 73.1 115
116 22.17 78808770 100.65 2836.94 0.0176 73.1 116
117 23.56 107091400 103.13 2853.13 0.0176 73.1 117
118 22.92 84944370 105.53 2916.07 0.0176 73.1 118
119 23.10 46515660 106.76 2916.68 0.0176 73.1 119
120 24.32 89720920 107.59 2926.55 0.0176 73.1 120
121 23.99 29520310 107.62 2966.85 0.0176 73.1 121
122 25.94 123513900 108.82 2976.78 0.0176 73.1 122
123 26.15 85687430 107.59 2967.79 0.0176 73.1 123
124 26.36 49113040 107.85 2991.78 0.0176 73.1 124
125 27.32 88572990 107.11 3012.03 0.0176 73.1 125
126 28.00 126867400 108.14 3010.24 0.0176 73.1 126
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) VOLUME LINKEDIN NASDAQ INF CONS.CONF
7.970e+01 3.050e-09 4.000e-01 -3.422e-02 -8.036e+02 3.193e-01
t
-6.337e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.5114 -1.2799 -0.1206 1.1860 6.4633
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.970e+01 1.335e+01 5.969 2.53e-08 ***
VOLUME 3.050e-09 5.772e-09 0.528 0.59819
LINKEDIN 4.000e-01 6.186e-02 6.466 2.32e-09 ***
NASDAQ -3.422e-02 5.021e-03 -6.817 4.09e-10 ***
INF -8.036e+02 1.394e+02 -5.763 6.62e-08 ***
CONS.CONF 3.193e-01 1.046e-01 3.054 0.00279 **
t -6.337e-02 1.299e-02 -4.879 3.34e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.169 on 119 degrees of freedom
Multiple R-squared: 0.7769, Adjusted R-squared: 0.7656
F-statistic: 69.06 on 6 and 119 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,] 1.012077e-01 2.024155e-01 8.987923e-01
[2,] 4.874872e-02 9.749744e-02 9.512513e-01
[3,] 1.950160e-02 3.900320e-02 9.804984e-01
[4,] 1.281798e-02 2.563596e-02 9.871820e-01
[5,] 6.038812e-03 1.207762e-02 9.939612e-01
[6,] 5.436260e-03 1.087252e-02 9.945637e-01
[7,] 2.300922e-03 4.601844e-03 9.976991e-01
[8,] 8.915315e-04 1.783063e-03 9.991085e-01
[9,] 3.525021e-04 7.050042e-04 9.996475e-01
[10,] 1.498060e-04 2.996119e-04 9.998502e-01
[11,] 6.237810e-05 1.247562e-04 9.999376e-01
[12,] 5.442921e-05 1.088584e-04 9.999456e-01
[13,] 1.887681e-05 3.775362e-05 9.999811e-01
[14,] 7.268663e-06 1.453733e-05 9.999927e-01
[15,] 3.418720e-06 6.837440e-06 9.999966e-01
[16,] 1.355330e-06 2.710660e-06 9.999986e-01
[17,] 2.777014e-05 5.554028e-05 9.999722e-01
[18,] 3.856782e-05 7.713565e-05 9.999614e-01
[19,] 2.649157e-05 5.298313e-05 9.999735e-01
[20,] 5.783881e-05 1.156776e-04 9.999422e-01
[21,] 1.048562e-04 2.097124e-04 9.998951e-01
[22,] 2.844154e-03 5.688308e-03 9.971558e-01
[23,] 1.228616e-02 2.457233e-02 9.877138e-01
[24,] 1.576336e-02 3.152671e-02 9.842366e-01
[25,] 4.236051e-02 8.472103e-02 9.576395e-01
[26,] 6.428983e-02 1.285797e-01 9.357102e-01
[27,] 7.559806e-02 1.511961e-01 9.244019e-01
[28,] 8.908304e-02 1.781661e-01 9.109170e-01
[29,] 1.644302e-01 3.288605e-01 8.355698e-01
[30,] 2.584055e-01 5.168110e-01 7.415945e-01
[31,] 3.519236e-01 7.038472e-01 6.480764e-01
[32,] 7.090537e-01 5.818926e-01 2.909463e-01
[33,] 8.443151e-01 3.113699e-01 1.556849e-01
[34,] 8.663531e-01 2.672939e-01 1.336469e-01
[35,] 8.887776e-01 2.224448e-01 1.112224e-01
[36,] 9.689158e-01 6.216845e-02 3.108423e-02
[37,] 9.900686e-01 1.986286e-02 9.931432e-03
[38,] 9.896556e-01 2.068887e-02 1.034443e-02
[39,] 9.899204e-01 2.015918e-02 1.007959e-02
[40,] 9.923983e-01 1.520331e-02 7.601654e-03
[41,] 9.974752e-01 5.049590e-03 2.524795e-03
[42,] 9.995240e-01 9.520926e-04 4.760463e-04
[43,] 9.998941e-01 2.118480e-04 1.059240e-04
[44,] 9.999975e-01 5.015465e-06 2.507732e-06
[45,] 9.999981e-01 3.731019e-06 1.865510e-06
[46,] 9.999975e-01 4.975723e-06 2.487862e-06
[47,] 9.999975e-01 5.057272e-06 2.528636e-06
[48,] 9.999959e-01 8.194589e-06 4.097294e-06
[49,] 9.999942e-01 1.152327e-05 5.761636e-06
[50,] 9.999940e-01 1.194744e-05 5.973720e-06
[51,] 9.999934e-01 1.329980e-05 6.649900e-06
[52,] 9.999929e-01 1.419782e-05 7.098908e-06
[53,] 9.999911e-01 1.770607e-05 8.853034e-06
[54,] 9.999890e-01 2.203090e-05 1.101545e-05
[55,] 9.999871e-01 2.588998e-05 1.294499e-05
[56,] 9.999825e-01 3.505075e-05 1.752537e-05
[57,] 9.999954e-01 9.206891e-06 4.603446e-06
[58,] 9.999965e-01 6.922391e-06 3.461196e-06
[59,] 9.999950e-01 9.997805e-06 4.998902e-06
[60,] 9.999914e-01 1.714419e-05 8.572094e-06
[61,] 9.999860e-01 2.807837e-05 1.403919e-05
[62,] 9.999776e-01 4.476513e-05 2.238257e-05
[63,] 9.999688e-01 6.233923e-05 3.116961e-05
[64,] 9.999584e-01 8.316835e-05 4.158417e-05
[65,] 9.999409e-01 1.181551e-04 5.907756e-05
[66,] 9.999260e-01 1.480010e-04 7.400048e-05
[67,] 9.999346e-01 1.308599e-04 6.542997e-05
[68,] 9.999773e-01 4.547236e-05 2.273618e-05
[69,] 9.999827e-01 3.455113e-05 1.727556e-05
[70,] 9.999926e-01 1.487647e-05 7.438235e-06
[71,] 9.999884e-01 2.329525e-05 1.164762e-05
[72,] 9.999783e-01 4.338707e-05 2.169353e-05
[73,] 9.999694e-01 6.116527e-05 3.058264e-05
[74,] 9.999476e-01 1.047373e-04 5.236866e-05
[75,] 9.999198e-01 1.603568e-04 8.017842e-05
[76,] 9.999861e-01 2.788935e-05 1.394467e-05
[77,] 9.999988e-01 2.363208e-06 1.181604e-06
[78,] 9.999987e-01 2.614970e-06 1.307485e-06
[79,] 9.999983e-01 3.465549e-06 1.732774e-06
[80,] 9.999973e-01 5.490918e-06 2.745459e-06
[81,] 9.999945e-01 1.093684e-05 5.468419e-06
[82,] 9.999957e-01 8.508459e-06 4.254229e-06
[83,] 9.999936e-01 1.287753e-05 6.438767e-06
[84,] 9.999874e-01 2.511285e-05 1.255643e-05
[85,] 9.999818e-01 3.639486e-05 1.819743e-05
[86,] 9.999666e-01 6.686249e-05 3.343125e-05
[87,] 9.999455e-01 1.089476e-04 5.447380e-05
[88,] 9.999280e-01 1.439859e-04 7.199295e-05
[89,] 9.999644e-01 7.129806e-05 3.564903e-05
[90,] 9.999489e-01 1.022507e-04 5.112536e-05
[91,] 9.999573e-01 8.534597e-05 4.267299e-05
[92,] 9.999708e-01 5.839018e-05 2.919509e-05
[93,] 9.999553e-01 8.941040e-05 4.470520e-05
[94,] 9.999565e-01 8.708615e-05 4.354308e-05
[95,] 9.999905e-01 1.890493e-05 9.452465e-06
[96,] 9.999708e-01 5.835368e-05 2.917684e-05
[97,] 9.999163e-01 1.674020e-04 8.370098e-05
[98,] 9.997995e-01 4.010385e-04 2.005193e-04
[99,] 9.994583e-01 1.083489e-03 5.417443e-04
[100,] 9.989212e-01 2.157625e-03 1.078812e-03
[101,] 9.995583e-01 8.833033e-04 4.416517e-04
[102,] 9.999393e-01 1.214795e-04 6.073977e-05
[103,] 9.998053e-01 3.894414e-04 1.947207e-04
[104,] 9.999232e-01 1.535650e-04 7.678249e-05
[105,] 9.995307e-01 9.386196e-04 4.693098e-04
[106,] 9.983579e-01 3.284225e-03 1.642112e-03
[107,] 9.925095e-01 1.498098e-02 7.490492e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1x3xh1356079314.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2gru31356079314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38f1i1356079314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ikzf1356079314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5m8vg1356079314.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
-1.78362226 -1.92318399 -3.06622880 0.13669950 -1.08116358 -0.07395307
7 8 9 10 11 12
-1.06338900 0.51082521 -0.82492483 -0.23830045 1.22379524 2.19990235
13 14 15 16 17 18
3.90662520 3.75820767 2.83215425 3.22481952 2.04251661 2.40906613
19 20 21 22 23 24
2.81857325 2.13318630 3.43711708 0.81776437 1.81390266 2.26479413
25 26 27 28 29 30
1.00978448 3.65416878 3.40074292 1.85674740 -0.40451143 0.64560891
31 32 33 34 35 36
-1.26376793 -1.17194480 0.75925691 -0.09643451 -0.70101012 -1.02822308
37 38 39 40 41 42
-1.70772160 -0.74978484 -1.43829950 -3.52497094 -4.44887810 -5.51136871
43 44 45 46 47 48
-0.54049625 -0.85832381 -3.83001027 -3.23806670 -1.57384751 -0.93831440
49 50 51 52 53 54
-0.40960673 0.61290982 0.66356374 0.41046043 1.55668349 0.32870488
55 56 57 58 59 60
1.19590709 1.66838330 -0.27406375 0.16782909 -0.28457888 0.43336777
61 62 63 64 65 66
0.56633006 -0.19561517 -0.80119577 -1.26165074 -1.66121697 -2.10389988
67 68 69 70 71 72
-3.78695126 -3.34974066 -3.19189899 -3.87608896 -3.28417732 -2.27201178
73 74 75 76 77 78
-0.80328752 -0.01540467 0.18673697 0.60071843 1.68944086 0.78217550
79 80 81 82 83 84
1.24659539 -1.44527822 -2.14119369 -1.77994528 -1.16750198 -0.88514339
85 86 87 88 89 90
0.91517406 1.21619795 0.18726797 0.68308593 0.54084014 -0.35239750
91 92 93 94 95 96
-0.11336181 -0.68419467 -1.25465121 -0.25929237 0.32065232 1.47291570
97 98 99 100 101 102
2.67354109 0.95287402 -0.34259291 0.21249901 0.68691256 2.32632011
103 104 105 106 107 108
3.07759122 3.25307853 0.89509609 -0.46978284 -1.70862155 -1.15930650
109 110 111 112 113 114
0.51234437 -1.51995529 -1.42232582 -1.55386440 -2.07956792 -3.04048880
115 116 117 118 119 120
-2.03075945 -2.78884059 -1.85968393 -1.17469974 -1.28525500 -0.12789219
121 122 123 124 125 126
1.15635603 2.74284746 3.31593489 4.41790923 6.30997091 6.46325127
> postscript(file="/var/wessaorg/rcomp/tmp/6qam91356079314.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 -1.78362226 NA
1 -1.92318399 -1.78362226
2 -3.06622880 -1.92318399
3 0.13669950 -3.06622880
4 -1.08116358 0.13669950
5 -0.07395307 -1.08116358
6 -1.06338900 -0.07395307
7 0.51082521 -1.06338900
8 -0.82492483 0.51082521
9 -0.23830045 -0.82492483
10 1.22379524 -0.23830045
11 2.19990235 1.22379524
12 3.90662520 2.19990235
13 3.75820767 3.90662520
14 2.83215425 3.75820767
15 3.22481952 2.83215425
16 2.04251661 3.22481952
17 2.40906613 2.04251661
18 2.81857325 2.40906613
19 2.13318630 2.81857325
20 3.43711708 2.13318630
21 0.81776437 3.43711708
22 1.81390266 0.81776437
23 2.26479413 1.81390266
24 1.00978448 2.26479413
25 3.65416878 1.00978448
26 3.40074292 3.65416878
27 1.85674740 3.40074292
28 -0.40451143 1.85674740
29 0.64560891 -0.40451143
30 -1.26376793 0.64560891
31 -1.17194480 -1.26376793
32 0.75925691 -1.17194480
33 -0.09643451 0.75925691
34 -0.70101012 -0.09643451
35 -1.02822308 -0.70101012
36 -1.70772160 -1.02822308
37 -0.74978484 -1.70772160
38 -1.43829950 -0.74978484
39 -3.52497094 -1.43829950
40 -4.44887810 -3.52497094
41 -5.51136871 -4.44887810
42 -0.54049625 -5.51136871
43 -0.85832381 -0.54049625
44 -3.83001027 -0.85832381
45 -3.23806670 -3.83001027
46 -1.57384751 -3.23806670
47 -0.93831440 -1.57384751
48 -0.40960673 -0.93831440
49 0.61290982 -0.40960673
50 0.66356374 0.61290982
51 0.41046043 0.66356374
52 1.55668349 0.41046043
53 0.32870488 1.55668349
54 1.19590709 0.32870488
55 1.66838330 1.19590709
56 -0.27406375 1.66838330
57 0.16782909 -0.27406375
58 -0.28457888 0.16782909
59 0.43336777 -0.28457888
60 0.56633006 0.43336777
61 -0.19561517 0.56633006
62 -0.80119577 -0.19561517
63 -1.26165074 -0.80119577
64 -1.66121697 -1.26165074
65 -2.10389988 -1.66121697
66 -3.78695126 -2.10389988
67 -3.34974066 -3.78695126
68 -3.19189899 -3.34974066
69 -3.87608896 -3.19189899
70 -3.28417732 -3.87608896
71 -2.27201178 -3.28417732
72 -0.80328752 -2.27201178
73 -0.01540467 -0.80328752
74 0.18673697 -0.01540467
75 0.60071843 0.18673697
76 1.68944086 0.60071843
77 0.78217550 1.68944086
78 1.24659539 0.78217550
79 -1.44527822 1.24659539
80 -2.14119369 -1.44527822
81 -1.77994528 -2.14119369
82 -1.16750198 -1.77994528
83 -0.88514339 -1.16750198
84 0.91517406 -0.88514339
85 1.21619795 0.91517406
86 0.18726797 1.21619795
87 0.68308593 0.18726797
88 0.54084014 0.68308593
89 -0.35239750 0.54084014
90 -0.11336181 -0.35239750
91 -0.68419467 -0.11336181
92 -1.25465121 -0.68419467
93 -0.25929237 -1.25465121
94 0.32065232 -0.25929237
95 1.47291570 0.32065232
96 2.67354109 1.47291570
97 0.95287402 2.67354109
98 -0.34259291 0.95287402
99 0.21249901 -0.34259291
100 0.68691256 0.21249901
101 2.32632011 0.68691256
102 3.07759122 2.32632011
103 3.25307853 3.07759122
104 0.89509609 3.25307853
105 -0.46978284 0.89509609
106 -1.70862155 -0.46978284
107 -1.15930650 -1.70862155
108 0.51234437 -1.15930650
109 -1.51995529 0.51234437
110 -1.42232582 -1.51995529
111 -1.55386440 -1.42232582
112 -2.07956792 -1.55386440
113 -3.04048880 -2.07956792
114 -2.03075945 -3.04048880
115 -2.78884059 -2.03075945
116 -1.85968393 -2.78884059
117 -1.17469974 -1.85968393
118 -1.28525500 -1.17469974
119 -0.12789219 -1.28525500
120 1.15635603 -0.12789219
121 2.74284746 1.15635603
122 3.31593489 2.74284746
123 4.41790923 3.31593489
124 6.30997091 4.41790923
125 6.46325127 6.30997091
126 NA 6.46325127
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.92318399 -1.78362226
[2,] -3.06622880 -1.92318399
[3,] 0.13669950 -3.06622880
[4,] -1.08116358 0.13669950
[5,] -0.07395307 -1.08116358
[6,] -1.06338900 -0.07395307
[7,] 0.51082521 -1.06338900
[8,] -0.82492483 0.51082521
[9,] -0.23830045 -0.82492483
[10,] 1.22379524 -0.23830045
[11,] 2.19990235 1.22379524
[12,] 3.90662520 2.19990235
[13,] 3.75820767 3.90662520
[14,] 2.83215425 3.75820767
[15,] 3.22481952 2.83215425
[16,] 2.04251661 3.22481952
[17,] 2.40906613 2.04251661
[18,] 2.81857325 2.40906613
[19,] 2.13318630 2.81857325
[20,] 3.43711708 2.13318630
[21,] 0.81776437 3.43711708
[22,] 1.81390266 0.81776437
[23,] 2.26479413 1.81390266
[24,] 1.00978448 2.26479413
[25,] 3.65416878 1.00978448
[26,] 3.40074292 3.65416878
[27,] 1.85674740 3.40074292
[28,] -0.40451143 1.85674740
[29,] 0.64560891 -0.40451143
[30,] -1.26376793 0.64560891
[31,] -1.17194480 -1.26376793
[32,] 0.75925691 -1.17194480
[33,] -0.09643451 0.75925691
[34,] -0.70101012 -0.09643451
[35,] -1.02822308 -0.70101012
[36,] -1.70772160 -1.02822308
[37,] -0.74978484 -1.70772160
[38,] -1.43829950 -0.74978484
[39,] -3.52497094 -1.43829950
[40,] -4.44887810 -3.52497094
[41,] -5.51136871 -4.44887810
[42,] -0.54049625 -5.51136871
[43,] -0.85832381 -0.54049625
[44,] -3.83001027 -0.85832381
[45,] -3.23806670 -3.83001027
[46,] -1.57384751 -3.23806670
[47,] -0.93831440 -1.57384751
[48,] -0.40960673 -0.93831440
[49,] 0.61290982 -0.40960673
[50,] 0.66356374 0.61290982
[51,] 0.41046043 0.66356374
[52,] 1.55668349 0.41046043
[53,] 0.32870488 1.55668349
[54,] 1.19590709 0.32870488
[55,] 1.66838330 1.19590709
[56,] -0.27406375 1.66838330
[57,] 0.16782909 -0.27406375
[58,] -0.28457888 0.16782909
[59,] 0.43336777 -0.28457888
[60,] 0.56633006 0.43336777
[61,] -0.19561517 0.56633006
[62,] -0.80119577 -0.19561517
[63,] -1.26165074 -0.80119577
[64,] -1.66121697 -1.26165074
[65,] -2.10389988 -1.66121697
[66,] -3.78695126 -2.10389988
[67,] -3.34974066 -3.78695126
[68,] -3.19189899 -3.34974066
[69,] -3.87608896 -3.19189899
[70,] -3.28417732 -3.87608896
[71,] -2.27201178 -3.28417732
[72,] -0.80328752 -2.27201178
[73,] -0.01540467 -0.80328752
[74,] 0.18673697 -0.01540467
[75,] 0.60071843 0.18673697
[76,] 1.68944086 0.60071843
[77,] 0.78217550 1.68944086
[78,] 1.24659539 0.78217550
[79,] -1.44527822 1.24659539
[80,] -2.14119369 -1.44527822
[81,] -1.77994528 -2.14119369
[82,] -1.16750198 -1.77994528
[83,] -0.88514339 -1.16750198
[84,] 0.91517406 -0.88514339
[85,] 1.21619795 0.91517406
[86,] 0.18726797 1.21619795
[87,] 0.68308593 0.18726797
[88,] 0.54084014 0.68308593
[89,] -0.35239750 0.54084014
[90,] -0.11336181 -0.35239750
[91,] -0.68419467 -0.11336181
[92,] -1.25465121 -0.68419467
[93,] -0.25929237 -1.25465121
[94,] 0.32065232 -0.25929237
[95,] 1.47291570 0.32065232
[96,] 2.67354109 1.47291570
[97,] 0.95287402 2.67354109
[98,] -0.34259291 0.95287402
[99,] 0.21249901 -0.34259291
[100,] 0.68691256 0.21249901
[101,] 2.32632011 0.68691256
[102,] 3.07759122 2.32632011
[103,] 3.25307853 3.07759122
[104,] 0.89509609 3.25307853
[105,] -0.46978284 0.89509609
[106,] -1.70862155 -0.46978284
[107,] -1.15930650 -1.70862155
[108,] 0.51234437 -1.15930650
[109,] -1.51995529 0.51234437
[110,] -1.42232582 -1.51995529
[111,] -1.55386440 -1.42232582
[112,] -2.07956792 -1.55386440
[113,] -3.04048880 -2.07956792
[114,] -2.03075945 -3.04048880
[115,] -2.78884059 -2.03075945
[116,] -1.85968393 -2.78884059
[117,] -1.17469974 -1.85968393
[118,] -1.28525500 -1.17469974
[119,] -0.12789219 -1.28525500
[120,] 1.15635603 -0.12789219
[121,] 2.74284746 1.15635603
[122,] 3.31593489 2.74284746
[123,] 4.41790923 3.31593489
[124,] 6.30997091 4.41790923
[125,] 6.46325127 6.30997091
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.92318399 -1.78362226
2 -3.06622880 -1.92318399
3 0.13669950 -3.06622880
4 -1.08116358 0.13669950
5 -0.07395307 -1.08116358
6 -1.06338900 -0.07395307
7 0.51082521 -1.06338900
8 -0.82492483 0.51082521
9 -0.23830045 -0.82492483
10 1.22379524 -0.23830045
11 2.19990235 1.22379524
12 3.90662520 2.19990235
13 3.75820767 3.90662520
14 2.83215425 3.75820767
15 3.22481952 2.83215425
16 2.04251661 3.22481952
17 2.40906613 2.04251661
18 2.81857325 2.40906613
19 2.13318630 2.81857325
20 3.43711708 2.13318630
21 0.81776437 3.43711708
22 1.81390266 0.81776437
23 2.26479413 1.81390266
24 1.00978448 2.26479413
25 3.65416878 1.00978448
26 3.40074292 3.65416878
27 1.85674740 3.40074292
28 -0.40451143 1.85674740
29 0.64560891 -0.40451143
30 -1.26376793 0.64560891
31 -1.17194480 -1.26376793
32 0.75925691 -1.17194480
33 -0.09643451 0.75925691
34 -0.70101012 -0.09643451
35 -1.02822308 -0.70101012
36 -1.70772160 -1.02822308
37 -0.74978484 -1.70772160
38 -1.43829950 -0.74978484
39 -3.52497094 -1.43829950
40 -4.44887810 -3.52497094
41 -5.51136871 -4.44887810
42 -0.54049625 -5.51136871
43 -0.85832381 -0.54049625
44 -3.83001027 -0.85832381
45 -3.23806670 -3.83001027
46 -1.57384751 -3.23806670
47 -0.93831440 -1.57384751
48 -0.40960673 -0.93831440
49 0.61290982 -0.40960673
50 0.66356374 0.61290982
51 0.41046043 0.66356374
52 1.55668349 0.41046043
53 0.32870488 1.55668349
54 1.19590709 0.32870488
55 1.66838330 1.19590709
56 -0.27406375 1.66838330
57 0.16782909 -0.27406375
58 -0.28457888 0.16782909
59 0.43336777 -0.28457888
60 0.56633006 0.43336777
61 -0.19561517 0.56633006
62 -0.80119577 -0.19561517
63 -1.26165074 -0.80119577
64 -1.66121697 -1.26165074
65 -2.10389988 -1.66121697
66 -3.78695126 -2.10389988
67 -3.34974066 -3.78695126
68 -3.19189899 -3.34974066
69 -3.87608896 -3.19189899
70 -3.28417732 -3.87608896
71 -2.27201178 -3.28417732
72 -0.80328752 -2.27201178
73 -0.01540467 -0.80328752
74 0.18673697 -0.01540467
75 0.60071843 0.18673697
76 1.68944086 0.60071843
77 0.78217550 1.68944086
78 1.24659539 0.78217550
79 -1.44527822 1.24659539
80 -2.14119369 -1.44527822
81 -1.77994528 -2.14119369
82 -1.16750198 -1.77994528
83 -0.88514339 -1.16750198
84 0.91517406 -0.88514339
85 1.21619795 0.91517406
86 0.18726797 1.21619795
87 0.68308593 0.18726797
88 0.54084014 0.68308593
89 -0.35239750 0.54084014
90 -0.11336181 -0.35239750
91 -0.68419467 -0.11336181
92 -1.25465121 -0.68419467
93 -0.25929237 -1.25465121
94 0.32065232 -0.25929237
95 1.47291570 0.32065232
96 2.67354109 1.47291570
97 0.95287402 2.67354109
98 -0.34259291 0.95287402
99 0.21249901 -0.34259291
100 0.68691256 0.21249901
101 2.32632011 0.68691256
102 3.07759122 2.32632011
103 3.25307853 3.07759122
104 0.89509609 3.25307853
105 -0.46978284 0.89509609
106 -1.70862155 -0.46978284
107 -1.15930650 -1.70862155
108 0.51234437 -1.15930650
109 -1.51995529 0.51234437
110 -1.42232582 -1.51995529
111 -1.55386440 -1.42232582
112 -2.07956792 -1.55386440
113 -3.04048880 -2.07956792
114 -2.03075945 -3.04048880
115 -2.78884059 -2.03075945
116 -1.85968393 -2.78884059
117 -1.17469974 -1.85968393
118 -1.28525500 -1.17469974
119 -0.12789219 -1.28525500
120 1.15635603 -0.12789219
121 2.74284746 1.15635603
122 3.31593489 2.74284746
123 4.41790923 3.31593489
124 6.30997091 4.41790923
125 6.46325127 6.30997091
> 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/7a4a51356079314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/84x581356079314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9mk8z1356079314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10q5k11356079314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jf9k1356079315.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/123lh21356079315.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/13v7vr1356079315.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/146pjc1356079315.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/158lph1356079315.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/16kd9i1356079315.tab")
+ }
>
> try(system("convert tmp/1x3xh1356079314.ps tmp/1x3xh1356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gru31356079314.ps tmp/2gru31356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/38f1i1356079314.ps tmp/38f1i1356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ikzf1356079314.ps tmp/4ikzf1356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m8vg1356079314.ps tmp/5m8vg1356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qam91356079314.ps tmp/6qam91356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a4a51356079314.ps tmp/7a4a51356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/84x581356079314.ps tmp/84x581356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mk8z1356079314.ps tmp/9mk8z1356079314.png",intern=TRUE))
character(0)
> try(system("convert tmp/10q5k11356079314.ps tmp/10q5k11356079314.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.830 1.186 8.093