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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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
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+ ,91.51
+ ,2747.48
+ ,0.016
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+ ,26.90
+ ,35204750
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+ ,62.7
+ ,0.17
+ ,25.86
+ ,42367740
+ ,93.00
+ ,2778.11
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+ ,0.17
+ ,26.81
+ ,61427940
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+ ,27.40
+ ,15809640
+ ,94.46
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+ ,10943350
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+ ,17755740
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+ ,101.08
+ ,2887.98
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+ ,65.4
+ ,0.17
+ ,30.81
+ ,11299240
+ ,104.64
+ ,2866.19
+ ,0.0141
+ ,65.4
+ ,0.18
+ ,30.72
+ ,8102653
+ ,105.59
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+ ,24549800
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+ ,0.18
+ ,28.09
+ ,30410530
+ ,103.84
+ ,2910.04
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+ ,65.4
+ ,0.17
+ ,29.11
+ ,16807730
+ ,104.61
+ ,2942.60
+ ,0.0141
+ ,65.4
+ ,0.16
+ ,29.00
+ ,13671200
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+ ,2965.90
+ ,0.0141
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+ ,0.13
+ ,28.76
+ ,11854290
+ ,106.26
+ ,2925.30
+ ,0.0141
+ ,65.4
+ ,0.13
+ ,28.75
+ ,12383610
+ ,104.20
+ ,2890.15
+ ,0.0141
+ ,65.4
+ ,0.14
+ ,28.45
+ ,11512350
+ ,102.99
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+ ,16749990
+ ,102.19
+ ,2854.24
+ ,0.0141
+ ,65.4
+ ,0.15
+ ,26.84
+ ,61009290
+ ,100.82
+ ,2893.25
+ ,0.0141
+ ,65.4
+ ,0.14
+ ,23.70
+ ,123011300
+ ,103.42
+ ,2958.09
+ ,0.0141
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+ ,0.14
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+ ,29253590
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+ ,21.20
+ ,47682050
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+ ,0.0169
+ ,61.3
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+ ,157188200
+ ,103.99
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+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.05
+ ,129057400
+ ,101.36
+ ,3076.59
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+ ,61.3
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+ ,100818300
+ ,102.92
+ ,3076.21
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+ ,70483330
+ ,105.25
+ ,3067.26
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+ ,49779450
+ ,105.71
+ ,3073.67
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+ ,61.3
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+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.40
+ ,29588690
+ ,105.11
+ ,3069.79
+ ,0.0169
+ ,61.3
+ ,0.13
+ ,19.15
+ ,20663220
+ ,104.67
+ ,3073.19
+ ,0.0169
+ ,61.3
+ ,0.13
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+ ,25402980
+ ,107.51
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+ ,3066.96
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+ ,29276680
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+ ,39282420
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+ ,21803710
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+ ,38308550
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+ ,19.86
+ ,71524510
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+ ,229081600
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+ ,2916.07
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+ ,73.1
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+ ,23.99
+ ,29520310
+ ,107.62
+ ,2966.85
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,25.94
+ ,123513900
+ ,108.82
+ ,2976.78
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,26.15
+ ,85687430
+ ,107.59
+ ,2967.79
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,26.36
+ ,49113040
+ ,107.85
+ ,2991.78
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,27.32
+ ,88572990
+ ,107.11
+ ,3012.03
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,28.00
+ ,126867400
+ ,108.14
+ ,3010.24
+ ,0.0176
+ ,73.1
+ ,0.16)
+ ,dim=c(7
+ ,126)
+ ,dimnames=list(c('FACEBOOK'
+ ,'VOLUME'
+ ,'LINKEDIN'
+ ,'NASDAQ'
+ ,'INF.CONS.CONF'
+ ,'FED'
+ ,'FUNDS.RATE')
+ ,1:126))
> y <- array(NA,dim=c(7,126),dimnames=list(c('FACEBOOK','VOLUME','LINKEDIN','NASDAQ','INF.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 = '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 FED FUNDS.RATE t
1 27.72 41837160 91.51 2747.48 0.0160 62.7 0.16 1
2 26.90 35204750 91.09 2760.01 0.0160 62.7 0.17 2
3 25.86 42367740 93.00 2778.11 0.0160 62.7 0.17 3
4 26.81 61427940 93.08 2844.72 0.0160 62.7 0.16 4
5 26.31 26132090 94.13 2831.02 0.0160 62.7 0.16 5
6 27.10 3799718 96.26 2858.42 0.0160 62.7 0.17 6
7 27.00 28202230 94.29 2809.73 0.0160 62.7 0.17 7
8 27.40 15809640 94.46 2843.07 0.0160 62.7 0.16 8
9 27.27 17110160 95.53 2818.61 0.0160 62.7 0.17 9
10 28.29 16835510 98.29 2836.33 0.0160 62.7 0.17 10
11 30.01 43517670 102.01 2872.80 0.0160 62.7 0.18 11
12 31.41 42958450 105.16 2895.33 0.0160 62.7 0.17 12
13 31.91 30826830 105.34 2929.76 0.0160 62.7 0.17 13
14 31.60 15549740 105.27 2930.45 0.0160 62.7 0.16 14
15 31.84 21843070 102.19 2859.09 0.0160 62.7 0.17 15
16 33.05 73424890 106.85 2892.42 0.0160 62.7 0.17 16
17 32.06 24330740 103.05 2836.16 0.0160 62.7 0.17 17
18 33.10 24785970 106.42 2854.06 0.0160 62.7 0.16 18
19 32.23 28553940 105.17 2875.32 0.0160 62.7 0.15 19
20 31.36 17659080 102.74 2849.49 0.0160 62.7 0.15 20
21 31.09 19508980 106.27 2935.05 0.0160 62.7 0.09 21
22 30.77 14110230 107.63 2951.23 0.0141 65.4 0.18 22
23 31.20 8765498 108.54 2976.08 0.0141 65.4 0.17 23
24 31.47 10027250 108.24 2976.12 0.0141 65.4 0.17 24
25 31.73 10943350 108.86 2937.33 0.0141 65.4 0.17 25
26 32.17 17755740 102.98 2931.77 0.0141 65.4 0.17 26
27 31.47 14238190 99.53 2902.33 0.0141 65.4 0.17 27
28 30.97 12997760 101.08 2887.98 0.0141 65.4 0.17 28
29 30.81 11299240 104.64 2866.19 0.0141 65.4 0.18 29
30 30.72 8102653 105.59 2908.47 0.0141 65.4 0.19 30
31 28.24 24549800 103.21 2896.94 0.0141 65.4 0.18 31
32 28.09 30410530 103.84 2910.04 0.0141 65.4 0.17 32
33 29.11 16807730 104.61 2942.60 0.0141 65.4 0.16 33
34 29.00 13671200 108.65 2965.90 0.0141 65.4 0.13 34
35 28.76 11854290 106.26 2925.30 0.0141 65.4 0.13 35
36 28.75 12383610 104.20 2890.15 0.0141 65.4 0.14 36
37 28.45 11512350 102.99 2862.99 0.0141 65.4 0.15 37
38 29.34 16749990 102.19 2854.24 0.0141 65.4 0.15 38
39 26.84 61009290 100.82 2893.25 0.0141 65.4 0.14 39
40 23.70 123011300 103.42 2958.09 0.0141 65.4 0.14 40
41 23.15 29253590 104.18 2945.84 0.0141 65.4 0.14 41
42 21.71 55998620 102.65 2939.52 0.0141 65.4 0.13 42
43 20.88 44488370 95.64 2920.21 0.0169 61.3 0.14 43
44 20.04 56264460 93.51 2909.77 0.0169 61.3 0.14 44
45 21.09 80626220 108.51 2967.90 0.0169 61.3 0.14 45
46 21.92 27733830 111.55 2989.91 0.0169 61.3 0.14 46
47 20.72 36699380 106.70 3015.86 0.0169 61.3 0.13 47
48 20.72 29514550 104.93 3011.25 0.0169 61.3 0.13 48
49 21.01 15605960 105.23 3018.64 0.0169 61.3 0.13 49
50 21.80 25714310 104.92 3020.86 0.0169 61.3 0.13 50
51 21.60 24904700 104.60 3022.52 0.0169 61.3 0.13 51
52 20.38 38971320 101.76 3016.98 0.0169 61.3 0.13 52
53 21.20 47682050 102.23 3030.93 0.0169 61.3 0.13 53
54 19.87 157188200 103.99 3062.39 0.0169 61.3 0.13 54
55 19.05 129057400 101.36 3076.59 0.0169 61.3 0.13 55
56 20.01 100818300 102.92 3076.21 0.0169 61.3 0.13 56
57 19.15 70483330 105.25 3067.26 0.0169 61.3 0.13 57
58 19.43 49779450 105.71 3073.67 0.0169 61.3 0.13 58
59 19.44 32747000 105.42 3053.40 0.0169 61.3 0.13 59
60 19.40 29588690 105.11 3069.79 0.0169 61.3 0.13 60
61 19.15 20663220 104.67 3073.19 0.0169 61.3 0.13 61
62 19.34 25402980 107.51 3077.14 0.0169 61.3 0.13 62
63 19.10 16071190 109.00 3081.19 0.0169 61.3 0.13 63
64 19.08 30571430 107.37 3048.71 0.0169 61.3 0.14 64
65 18.05 58612440 107.30 3066.96 0.0169 61.3 0.13 65
66 17.72 46177000 107.37 3075.06 0.0199 70.3 0.14 66
67 18.58 60657900 113.28 3069.27 0.0199 70.3 0.16 67
68 18.96 46028860 119.10 3135.81 0.0199 70.3 0.16 68
69 18.98 36325880 119.04 3136.42 0.0199 70.3 0.15 69
70 18.81 24752340 117.80 3104.02 0.0199 70.3 0.15 70
71 19.43 47343020 117.90 3104.53 0.0199 70.3 0.15 71
72 20.93 121399400 119.55 3114.31 0.0199 70.3 0.15 72
73 20.71 64896660 119.47 3155.83 0.0199 70.3 0.15 73
74 22.00 72707430 123.23 3183.95 0.0199 70.3 0.16 74
75 21.52 50593510 121.40 3178.67 0.0199 70.3 0.16 75
76 21.87 36696330 121.43 3177.80 0.0199 70.3 0.16 76
77 23.29 78525460 122.51 3182.62 0.0199 70.3 0.15 77
78 22.59 57115160 122.78 3175.96 0.0199 70.3 0.16 78
79 22.86 51163120 122.84 3179.96 0.0199 70.3 0.15 79
80 20.79 78968380 122.70 3160.78 0.0199 70.3 0.16 80
81 20.28 46169460 119.89 3117.73 0.0199 70.3 0.15 81
82 20.62 38212360 118.00 3093.70 0.0199 70.3 0.16 82
83 20.32 30061050 119.61 3136.60 0.0199 70.3 0.14 83
84 21.66 65415370 120.40 3116.23 0.0199 70.3 0.09 84
85 21.99 51198150 117.94 3113.53 0.0216 73.1 0.15 85
86 22.27 29276680 118.77 3120.04 0.0216 73.1 0.16 86
87 21.83 31940720 121.68 3135.23 0.0216 73.1 0.16 87
88 21.94 46549400 121.98 3149.46 0.0216 73.1 0.15 88
89 20.91 40483780 118.83 3136.19 0.0216 73.1 0.15 89
90 20.40 32190200 117.97 3112.35 0.0216 73.1 0.15 90
91 20.22 27125670 113.07 3065.02 0.0216 73.1 0.16 91
92 19.64 39282420 111.98 3051.78 0.0216 73.1 0.16 92
93 19.75 21803710 113.77 3049.41 0.0216 73.1 0.16 93
94 19.51 18743920 110.41 3044.11 0.0216 73.1 0.16 94
95 19.52 20154860 110.85 3064.18 0.0216 73.1 0.16 95
96 19.48 21816100 111.18 3101.17 0.0216 73.1 0.16 96
97 19.88 44020450 109.42 3104.12 0.0216 73.1 0.15 97
98 18.97 52059860 108.87 3072.87 0.0216 73.1 0.15 98
99 19.00 34769600 106.72 3005.62 0.0216 73.1 0.16 99
100 19.32 32269470 107.28 3016.96 0.0216 73.1 0.15 100
101 19.50 72281000 104.13 2990.46 0.0216 73.1 0.15 101
102 23.22 228364700 107.55 2981.70 0.0216 73.1 0.17 102
103 22.56 76050080 105.72 2986.12 0.0216 73.1 0.16 103
104 21.94 9999999 104.55 2987.95 0.0216 73.1 0.16 104
105 21.11 99311480 106.93 2977.23 0.0216 73.1 0.18 105
106 21.21 37631000 106.85 3020.06 0.0176 73.1 0.17 106
107 21.18 38308550 106.78 2982.13 0.0176 73.1 0.16 107
108 21.25 31752420 107.29 2999.66 0.0176 73.1 0.17 108
109 21.17 29030780 104.14 3011.93 0.0176 73.1 0.16 109
110 20.47 33352920 101.21 2937.29 0.0176 73.1 0.16 110
111 19.99 34106840 96.35 2895.58 0.0176 73.1 0.16 111
112 19.21 42257790 95.62 2904.87 0.0176 73.1 0.16 112
113 20.07 67220540 99.00 2904.26 0.0176 73.1 0.16 113
114 19.86 71524510 99.26 2883.89 0.0176 73.1 0.16 114
115 22.36 229081600 98.77 2846.81 0.0176 73.1 0.16 115
116 22.17 78808770 100.65 2836.94 0.0176 73.1 0.16 116
117 23.56 107091400 103.13 2853.13 0.0176 73.1 0.16 117
118 22.92 84944370 105.53 2916.07 0.0176 73.1 0.16 118
119 23.10 46515660 106.76 2916.68 0.0176 73.1 0.16 119
120 24.32 89720920 107.59 2926.55 0.0176 73.1 0.16 120
121 23.99 29520310 107.62 2966.85 0.0176 73.1 0.16 121
122 25.94 123513900 108.82 2976.78 0.0176 73.1 0.16 122
123 26.15 85687430 107.59 2967.79 0.0176 73.1 0.16 123
124 26.36 49113040 107.85 2991.78 0.0176 73.1 0.16 124
125 27.32 88572990 107.11 3012.03 0.0176 73.1 0.16 125
126 28.00 126867400 108.14 3010.24 0.0176 73.1 0.16 126
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) VOLUME LINKEDIN NASDAQ INF.CONS.CONF
7.547e+01 2.908e-09 3.960e-01 -3.184e-02 -7.938e+02
FED FUNDS.RATE t
1.790e-01 3.944e+01 -5.068e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.4823 -1.3666 -0.1849 1.1519 6.1534
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.547e+01 1.317e+01 5.730 7.82e-08 ***
VOLUME 2.908e-09 5.647e-09 0.515 0.607590
LINKEDIN 3.960e-01 6.054e-02 6.542 1.63e-09 ***
NASDAQ -3.184e-02 5.002e-03 -6.366 3.85e-09 ***
INF.CONS.CONF -7.938e+02 1.365e+02 -5.817 5.23e-08 ***
FED 1.790e-01 1.165e-01 1.537 0.126990
FUNDS.RATE 3.944e+01 1.566e+01 2.518 0.013129 *
t -5.068e-02 1.367e-02 -3.709 0.000319 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.122 on 118 degrees of freedom
Multiple R-squared: 0.7883, Adjusted R-squared: 0.7757
F-statistic: 62.76 on 7 and 118 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,] 1.154577e-01 2.309153e-01 8.845423e-01
[2,] 4.264674e-02 8.529348e-02 9.573533e-01
[3,] 2.397784e-02 4.795567e-02 9.760222e-01
[4,] 8.747198e-03 1.749440e-02 9.912528e-01
[5,] 5.965125e-03 1.193025e-02 9.940349e-01
[6,] 2.557010e-03 5.114020e-03 9.974430e-01
[7,] 8.776529e-04 1.755306e-03 9.991223e-01
[8,] 5.211282e-04 1.042256e-03 9.994789e-01
[9,] 3.203277e-04 6.406555e-04 9.996797e-01
[10,] 1.445090e-04 2.890180e-04 9.998555e-01
[11,] 2.140795e-04 4.281589e-04 9.997859e-01
[12,] 7.627676e-05 1.525535e-04 9.999237e-01
[13,] 3.002692e-05 6.005384e-05 9.999700e-01
[14,] 1.355581e-05 2.711161e-05 9.999864e-01
[15,] 5.190154e-06 1.038031e-05 9.999948e-01
[16,] 4.438382e-05 8.876765e-05 9.999556e-01
[17,] 3.960042e-05 7.920083e-05 9.999604e-01
[18,] 2.625631e-05 5.251262e-05 9.999737e-01
[19,] 6.786845e-05 1.357369e-04 9.999321e-01
[20,] 1.629623e-04 3.259246e-04 9.998370e-01
[21,] 4.375369e-03 8.750738e-03 9.956246e-01
[22,] 1.687624e-02 3.375248e-02 9.831238e-01
[23,] 2.074845e-02 4.149689e-02 9.792516e-01
[24,] 4.365201e-02 8.730403e-02 9.563480e-01
[25,] 5.342156e-02 1.068431e-01 9.465784e-01
[26,] 5.822647e-02 1.164529e-01 9.417735e-01
[27,] 6.669380e-02 1.333876e-01 9.333062e-01
[28,] 1.243659e-01 2.487318e-01 8.756341e-01
[29,] 2.017816e-01 4.035632e-01 7.982184e-01
[30,] 2.956140e-01 5.912280e-01 7.043860e-01
[31,] 6.901967e-01 6.196066e-01 3.098033e-01
[32,] 8.357073e-01 3.285854e-01 1.642927e-01
[33,] 8.560499e-01 2.879001e-01 1.439501e-01
[34,] 8.769980e-01 2.460040e-01 1.230020e-01
[35,] 9.556265e-01 8.874704e-02 4.437352e-02
[36,] 9.836033e-01 3.279333e-02 1.639666e-02
[37,] 9.829253e-01 3.414939e-02 1.707470e-02
[38,] 9.833702e-01 3.325951e-02 1.662975e-02
[39,] 9.872689e-01 2.546227e-02 1.273113e-02
[40,] 9.952767e-01 9.446560e-03 4.723280e-03
[41,] 9.989670e-01 2.066064e-03 1.033032e-03
[42,] 9.997268e-01 5.463222e-04 2.731611e-04
[43,] 9.999906e-01 1.879228e-05 9.396138e-06
[44,] 9.999917e-01 1.658527e-05 8.292633e-06
[45,] 9.999873e-01 2.544924e-05 1.272462e-05
[46,] 9.999858e-01 2.843050e-05 1.421525e-05
[47,] 9.999808e-01 3.842563e-05 1.921281e-05
[48,] 9.999774e-01 4.524336e-05 2.262168e-05
[49,] 9.999799e-01 4.017370e-05 2.008685e-05
[50,] 9.999799e-01 4.010252e-05 2.005126e-05
[51,] 9.999806e-01 3.884781e-05 1.942391e-05
[52,] 9.999789e-01 4.222980e-05 2.111490e-05
[53,] 9.999776e-01 4.479332e-05 2.239666e-05
[54,] 9.999757e-01 4.851339e-05 2.425669e-05
[55,] 9.999708e-01 5.836407e-05 2.918204e-05
[56,] 9.999957e-01 8.646953e-06 4.323477e-06
[57,] 9.999966e-01 6.793780e-06 3.396890e-06
[58,] 9.999951e-01 9.718330e-06 4.859165e-06
[59,] 9.999916e-01 1.687742e-05 8.438709e-06
[60,] 9.999861e-01 2.789983e-05 1.394991e-05
[61,] 9.999777e-01 4.457433e-05 2.228717e-05
[62,] 9.999690e-01 6.209254e-05 3.104627e-05
[63,] 9.999587e-01 8.262573e-05 4.131286e-05
[64,] 9.999370e-01 1.260978e-04 6.304888e-05
[65,] 9.999131e-01 1.738086e-04 8.690429e-05
[66,] 9.999110e-01 1.779615e-04 8.898077e-05
[67,] 9.999670e-01 6.599174e-05 3.299587e-05
[68,] 9.999710e-01 5.803857e-05 2.901928e-05
[69,] 9.999867e-01 2.655031e-05 1.327516e-05
[70,] 9.999800e-01 4.001747e-05 2.000874e-05
[71,] 9.999640e-01 7.206684e-05 3.603342e-05
[72,] 9.999443e-01 1.114022e-04 5.570109e-05
[73,] 9.999131e-01 1.738220e-04 8.691098e-05
[74,] 9.998863e-01 2.274307e-04 1.137153e-04
[75,] 9.999829e-01 3.423723e-05 1.711861e-05
[76,] 9.999982e-01 3.551640e-06 1.775820e-06
[77,] 9.999978e-01 4.304008e-06 2.152004e-06
[78,] 9.999974e-01 5.244088e-06 2.622044e-06
[79,] 9.999962e-01 7.564580e-06 3.782290e-06
[80,] 9.999930e-01 1.404006e-05 7.020029e-06
[81,] 9.999946e-01 1.073494e-05 5.367470e-06
[82,] 9.999919e-01 1.618244e-05 8.091221e-06
[83,] 9.999845e-01 3.092667e-05 1.546334e-05
[84,] 9.999774e-01 4.526200e-05 2.263100e-05
[85,] 9.999573e-01 8.549041e-05 4.274521e-05
[86,] 9.999294e-01 1.412175e-04 7.060874e-05
[87,] 9.998979e-01 2.042381e-04 1.021190e-04
[88,] 9.999234e-01 1.532311e-04 7.661557e-05
[89,] 9.998869e-01 2.262321e-04 1.131160e-04
[90,] 9.999092e-01 1.815522e-04 9.077611e-05
[91,] 9.999909e-01 1.823683e-05 9.118415e-06
[92,] 9.999829e-01 3.425927e-05 1.712964e-05
[93,] 9.999627e-01 7.453646e-05 3.726823e-05
[94,] 9.999677e-01 6.458821e-05 3.229410e-05
[95,] 9.999028e-01 1.943185e-04 9.715927e-05
[96,] 9.997309e-01 5.381897e-04 2.690949e-04
[97,] 9.994125e-01 1.174997e-03 5.874984e-04
[98,] 9.984027e-01 3.194542e-03 1.597271e-03
[99,] 9.968670e-01 6.265972e-03 3.132986e-03
[100,] 9.984727e-01 3.054619e-03 1.527310e-03
[101,] 9.997005e-01 5.990558e-04 2.995279e-04
[102,] 9.990571e-01 1.885814e-03 9.429070e-04
[103,] 9.994785e-01 1.043023e-03 5.215116e-04
[104,] 9.970624e-01 5.875257e-03 2.937628e-03
[105,] 9.894190e-01 2.116194e-02 1.058097e-02
> postscript(file="/var/wessaorg/rcomp/tmp/15blg1356078939.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xa5b1356078939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39kca1356078939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ptlb1356078939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5oq0y1356078939.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
-1.40411622 -1.98319547 -3.17342129 0.25565981 -0.94313295 -0.40296825
7 8 9 10 11 12
-1.29349665 0.58195727 -1.09820230 -0.55554318 0.43120183 1.74778990
13 14 15 16 17 18
3.35884199 3.58803708 2.41349117 2.73994969 1.65684754 2.37591849
19 20 21 22 23 24
3.11209784 2.46433766 5.93249371 0.11437359 1.43590414 1.87300821
25 26 27 28 29 30
0.70025986 3.32285206 3.11265416 1.59611206 -1.00646604 -0.46077177
31 32 33 34 35 36
-1.96808188 -1.52240484 0.71410063 0.98900345 0.45867237 -0.20002103
37 38 39 40 41 42
-1.22685194 -0.26319097 -0.66200012 -2.89658430 -3.81435391 -4.48234397
43 44 45 46 47 48
-0.50455220 -0.81697454 -3.87678702 -3.34540998 -1.37922073 -0.75343709
49 50 51 52 53 54
-0.25579855 0.74896121 0.78159622 0.51974257 1.62317522 0.33019546
55 56 57 58 59 60
1.13646871 1.59932997 -0.32957295 0.08325024 -0.33715663 0.32740575
61 62 63 64 65 66
0.43657407 -0.33551995 -0.95884620 -1.75344254 -1.81103182 -1.44816547
67 68 69 70 71 72
-3.89339750 -3.60629707 -3.06981837 -3.69611674 -3.11448600 -2.12119176
73 74 75 76 77 78
-0.77237238 -0.44249029 -0.25086818 0.15064110 1.61984094 0.31937770
79 80 81 82 83 84
1.15537381 -1.89450210 -2.12202805 -2.11927054 -0.82764160 1.47066476
85 86 87 88 89 90
1.26283824 1.14145604 0.07559802 0.92251733 0.78582732 -0.06792471
91 92 93 94 95 96
-0.14342999 -0.69801262 -1.27090075 -0.28936623 0.23205792 1.28511574
97 98 99 100 101 102
2.85661182 1.19662939 -0.35678786 0.55487935 1.07291340 1.96751445
103 104 105 106 107 108
3.06101091 3.20540737 0.09364601 -0.96157093 -1.72857404 -1.62698193
109 110 111 112 113 114
0.38428573 -1.49399098 -1.32890313 -1.49697583 -2.01694820 -2.94040847
115 116 117 118 119 120
-1.83457069 -2.59579225 -1.70399926 -1.17518800 -1.30047590 -0.16984662
121 122 123 124 125 126
0.99731069 2.56563006 3.13717151 4.16516576 5.99902058 6.15342183
> postscript(file="/var/wessaorg/rcomp/tmp/6bw8g1356078939.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 -1.40411622 NA
1 -1.98319547 -1.40411622
2 -3.17342129 -1.98319547
3 0.25565981 -3.17342129
4 -0.94313295 0.25565981
5 -0.40296825 -0.94313295
6 -1.29349665 -0.40296825
7 0.58195727 -1.29349665
8 -1.09820230 0.58195727
9 -0.55554318 -1.09820230
10 0.43120183 -0.55554318
11 1.74778990 0.43120183
12 3.35884199 1.74778990
13 3.58803708 3.35884199
14 2.41349117 3.58803708
15 2.73994969 2.41349117
16 1.65684754 2.73994969
17 2.37591849 1.65684754
18 3.11209784 2.37591849
19 2.46433766 3.11209784
20 5.93249371 2.46433766
21 0.11437359 5.93249371
22 1.43590414 0.11437359
23 1.87300821 1.43590414
24 0.70025986 1.87300821
25 3.32285206 0.70025986
26 3.11265416 3.32285206
27 1.59611206 3.11265416
28 -1.00646604 1.59611206
29 -0.46077177 -1.00646604
30 -1.96808188 -0.46077177
31 -1.52240484 -1.96808188
32 0.71410063 -1.52240484
33 0.98900345 0.71410063
34 0.45867237 0.98900345
35 -0.20002103 0.45867237
36 -1.22685194 -0.20002103
37 -0.26319097 -1.22685194
38 -0.66200012 -0.26319097
39 -2.89658430 -0.66200012
40 -3.81435391 -2.89658430
41 -4.48234397 -3.81435391
42 -0.50455220 -4.48234397
43 -0.81697454 -0.50455220
44 -3.87678702 -0.81697454
45 -3.34540998 -3.87678702
46 -1.37922073 -3.34540998
47 -0.75343709 -1.37922073
48 -0.25579855 -0.75343709
49 0.74896121 -0.25579855
50 0.78159622 0.74896121
51 0.51974257 0.78159622
52 1.62317522 0.51974257
53 0.33019546 1.62317522
54 1.13646871 0.33019546
55 1.59932997 1.13646871
56 -0.32957295 1.59932997
57 0.08325024 -0.32957295
58 -0.33715663 0.08325024
59 0.32740575 -0.33715663
60 0.43657407 0.32740575
61 -0.33551995 0.43657407
62 -0.95884620 -0.33551995
63 -1.75344254 -0.95884620
64 -1.81103182 -1.75344254
65 -1.44816547 -1.81103182
66 -3.89339750 -1.44816547
67 -3.60629707 -3.89339750
68 -3.06981837 -3.60629707
69 -3.69611674 -3.06981837
70 -3.11448600 -3.69611674
71 -2.12119176 -3.11448600
72 -0.77237238 -2.12119176
73 -0.44249029 -0.77237238
74 -0.25086818 -0.44249029
75 0.15064110 -0.25086818
76 1.61984094 0.15064110
77 0.31937770 1.61984094
78 1.15537381 0.31937770
79 -1.89450210 1.15537381
80 -2.12202805 -1.89450210
81 -2.11927054 -2.12202805
82 -0.82764160 -2.11927054
83 1.47066476 -0.82764160
84 1.26283824 1.47066476
85 1.14145604 1.26283824
86 0.07559802 1.14145604
87 0.92251733 0.07559802
88 0.78582732 0.92251733
89 -0.06792471 0.78582732
90 -0.14342999 -0.06792471
91 -0.69801262 -0.14342999
92 -1.27090075 -0.69801262
93 -0.28936623 -1.27090075
94 0.23205792 -0.28936623
95 1.28511574 0.23205792
96 2.85661182 1.28511574
97 1.19662939 2.85661182
98 -0.35678786 1.19662939
99 0.55487935 -0.35678786
100 1.07291340 0.55487935
101 1.96751445 1.07291340
102 3.06101091 1.96751445
103 3.20540737 3.06101091
104 0.09364601 3.20540737
105 -0.96157093 0.09364601
106 -1.72857404 -0.96157093
107 -1.62698193 -1.72857404
108 0.38428573 -1.62698193
109 -1.49399098 0.38428573
110 -1.32890313 -1.49399098
111 -1.49697583 -1.32890313
112 -2.01694820 -1.49697583
113 -2.94040847 -2.01694820
114 -1.83457069 -2.94040847
115 -2.59579225 -1.83457069
116 -1.70399926 -2.59579225
117 -1.17518800 -1.70399926
118 -1.30047590 -1.17518800
119 -0.16984662 -1.30047590
120 0.99731069 -0.16984662
121 2.56563006 0.99731069
122 3.13717151 2.56563006
123 4.16516576 3.13717151
124 5.99902058 4.16516576
125 6.15342183 5.99902058
126 NA 6.15342183
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.98319547 -1.40411622
[2,] -3.17342129 -1.98319547
[3,] 0.25565981 -3.17342129
[4,] -0.94313295 0.25565981
[5,] -0.40296825 -0.94313295
[6,] -1.29349665 -0.40296825
[7,] 0.58195727 -1.29349665
[8,] -1.09820230 0.58195727
[9,] -0.55554318 -1.09820230
[10,] 0.43120183 -0.55554318
[11,] 1.74778990 0.43120183
[12,] 3.35884199 1.74778990
[13,] 3.58803708 3.35884199
[14,] 2.41349117 3.58803708
[15,] 2.73994969 2.41349117
[16,] 1.65684754 2.73994969
[17,] 2.37591849 1.65684754
[18,] 3.11209784 2.37591849
[19,] 2.46433766 3.11209784
[20,] 5.93249371 2.46433766
[21,] 0.11437359 5.93249371
[22,] 1.43590414 0.11437359
[23,] 1.87300821 1.43590414
[24,] 0.70025986 1.87300821
[25,] 3.32285206 0.70025986
[26,] 3.11265416 3.32285206
[27,] 1.59611206 3.11265416
[28,] -1.00646604 1.59611206
[29,] -0.46077177 -1.00646604
[30,] -1.96808188 -0.46077177
[31,] -1.52240484 -1.96808188
[32,] 0.71410063 -1.52240484
[33,] 0.98900345 0.71410063
[34,] 0.45867237 0.98900345
[35,] -0.20002103 0.45867237
[36,] -1.22685194 -0.20002103
[37,] -0.26319097 -1.22685194
[38,] -0.66200012 -0.26319097
[39,] -2.89658430 -0.66200012
[40,] -3.81435391 -2.89658430
[41,] -4.48234397 -3.81435391
[42,] -0.50455220 -4.48234397
[43,] -0.81697454 -0.50455220
[44,] -3.87678702 -0.81697454
[45,] -3.34540998 -3.87678702
[46,] -1.37922073 -3.34540998
[47,] -0.75343709 -1.37922073
[48,] -0.25579855 -0.75343709
[49,] 0.74896121 -0.25579855
[50,] 0.78159622 0.74896121
[51,] 0.51974257 0.78159622
[52,] 1.62317522 0.51974257
[53,] 0.33019546 1.62317522
[54,] 1.13646871 0.33019546
[55,] 1.59932997 1.13646871
[56,] -0.32957295 1.59932997
[57,] 0.08325024 -0.32957295
[58,] -0.33715663 0.08325024
[59,] 0.32740575 -0.33715663
[60,] 0.43657407 0.32740575
[61,] -0.33551995 0.43657407
[62,] -0.95884620 -0.33551995
[63,] -1.75344254 -0.95884620
[64,] -1.81103182 -1.75344254
[65,] -1.44816547 -1.81103182
[66,] -3.89339750 -1.44816547
[67,] -3.60629707 -3.89339750
[68,] -3.06981837 -3.60629707
[69,] -3.69611674 -3.06981837
[70,] -3.11448600 -3.69611674
[71,] -2.12119176 -3.11448600
[72,] -0.77237238 -2.12119176
[73,] -0.44249029 -0.77237238
[74,] -0.25086818 -0.44249029
[75,] 0.15064110 -0.25086818
[76,] 1.61984094 0.15064110
[77,] 0.31937770 1.61984094
[78,] 1.15537381 0.31937770
[79,] -1.89450210 1.15537381
[80,] -2.12202805 -1.89450210
[81,] -2.11927054 -2.12202805
[82,] -0.82764160 -2.11927054
[83,] 1.47066476 -0.82764160
[84,] 1.26283824 1.47066476
[85,] 1.14145604 1.26283824
[86,] 0.07559802 1.14145604
[87,] 0.92251733 0.07559802
[88,] 0.78582732 0.92251733
[89,] -0.06792471 0.78582732
[90,] -0.14342999 -0.06792471
[91,] -0.69801262 -0.14342999
[92,] -1.27090075 -0.69801262
[93,] -0.28936623 -1.27090075
[94,] 0.23205792 -0.28936623
[95,] 1.28511574 0.23205792
[96,] 2.85661182 1.28511574
[97,] 1.19662939 2.85661182
[98,] -0.35678786 1.19662939
[99,] 0.55487935 -0.35678786
[100,] 1.07291340 0.55487935
[101,] 1.96751445 1.07291340
[102,] 3.06101091 1.96751445
[103,] 3.20540737 3.06101091
[104,] 0.09364601 3.20540737
[105,] -0.96157093 0.09364601
[106,] -1.72857404 -0.96157093
[107,] -1.62698193 -1.72857404
[108,] 0.38428573 -1.62698193
[109,] -1.49399098 0.38428573
[110,] -1.32890313 -1.49399098
[111,] -1.49697583 -1.32890313
[112,] -2.01694820 -1.49697583
[113,] -2.94040847 -2.01694820
[114,] -1.83457069 -2.94040847
[115,] -2.59579225 -1.83457069
[116,] -1.70399926 -2.59579225
[117,] -1.17518800 -1.70399926
[118,] -1.30047590 -1.17518800
[119,] -0.16984662 -1.30047590
[120,] 0.99731069 -0.16984662
[121,] 2.56563006 0.99731069
[122,] 3.13717151 2.56563006
[123,] 4.16516576 3.13717151
[124,] 5.99902058 4.16516576
[125,] 6.15342183 5.99902058
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.98319547 -1.40411622
2 -3.17342129 -1.98319547
3 0.25565981 -3.17342129
4 -0.94313295 0.25565981
5 -0.40296825 -0.94313295
6 -1.29349665 -0.40296825
7 0.58195727 -1.29349665
8 -1.09820230 0.58195727
9 -0.55554318 -1.09820230
10 0.43120183 -0.55554318
11 1.74778990 0.43120183
12 3.35884199 1.74778990
13 3.58803708 3.35884199
14 2.41349117 3.58803708
15 2.73994969 2.41349117
16 1.65684754 2.73994969
17 2.37591849 1.65684754
18 3.11209784 2.37591849
19 2.46433766 3.11209784
20 5.93249371 2.46433766
21 0.11437359 5.93249371
22 1.43590414 0.11437359
23 1.87300821 1.43590414
24 0.70025986 1.87300821
25 3.32285206 0.70025986
26 3.11265416 3.32285206
27 1.59611206 3.11265416
28 -1.00646604 1.59611206
29 -0.46077177 -1.00646604
30 -1.96808188 -0.46077177
31 -1.52240484 -1.96808188
32 0.71410063 -1.52240484
33 0.98900345 0.71410063
34 0.45867237 0.98900345
35 -0.20002103 0.45867237
36 -1.22685194 -0.20002103
37 -0.26319097 -1.22685194
38 -0.66200012 -0.26319097
39 -2.89658430 -0.66200012
40 -3.81435391 -2.89658430
41 -4.48234397 -3.81435391
42 -0.50455220 -4.48234397
43 -0.81697454 -0.50455220
44 -3.87678702 -0.81697454
45 -3.34540998 -3.87678702
46 -1.37922073 -3.34540998
47 -0.75343709 -1.37922073
48 -0.25579855 -0.75343709
49 0.74896121 -0.25579855
50 0.78159622 0.74896121
51 0.51974257 0.78159622
52 1.62317522 0.51974257
53 0.33019546 1.62317522
54 1.13646871 0.33019546
55 1.59932997 1.13646871
56 -0.32957295 1.59932997
57 0.08325024 -0.32957295
58 -0.33715663 0.08325024
59 0.32740575 -0.33715663
60 0.43657407 0.32740575
61 -0.33551995 0.43657407
62 -0.95884620 -0.33551995
63 -1.75344254 -0.95884620
64 -1.81103182 -1.75344254
65 -1.44816547 -1.81103182
66 -3.89339750 -1.44816547
67 -3.60629707 -3.89339750
68 -3.06981837 -3.60629707
69 -3.69611674 -3.06981837
70 -3.11448600 -3.69611674
71 -2.12119176 -3.11448600
72 -0.77237238 -2.12119176
73 -0.44249029 -0.77237238
74 -0.25086818 -0.44249029
75 0.15064110 -0.25086818
76 1.61984094 0.15064110
77 0.31937770 1.61984094
78 1.15537381 0.31937770
79 -1.89450210 1.15537381
80 -2.12202805 -1.89450210
81 -2.11927054 -2.12202805
82 -0.82764160 -2.11927054
83 1.47066476 -0.82764160
84 1.26283824 1.47066476
85 1.14145604 1.26283824
86 0.07559802 1.14145604
87 0.92251733 0.07559802
88 0.78582732 0.92251733
89 -0.06792471 0.78582732
90 -0.14342999 -0.06792471
91 -0.69801262 -0.14342999
92 -1.27090075 -0.69801262
93 -0.28936623 -1.27090075
94 0.23205792 -0.28936623
95 1.28511574 0.23205792
96 2.85661182 1.28511574
97 1.19662939 2.85661182
98 -0.35678786 1.19662939
99 0.55487935 -0.35678786
100 1.07291340 0.55487935
101 1.96751445 1.07291340
102 3.06101091 1.96751445
103 3.20540737 3.06101091
104 0.09364601 3.20540737
105 -0.96157093 0.09364601
106 -1.72857404 -0.96157093
107 -1.62698193 -1.72857404
108 0.38428573 -1.62698193
109 -1.49399098 0.38428573
110 -1.32890313 -1.49399098
111 -1.49697583 -1.32890313
112 -2.01694820 -1.49697583
113 -2.94040847 -2.01694820
114 -1.83457069 -2.94040847
115 -2.59579225 -1.83457069
116 -1.70399926 -2.59579225
117 -1.17518800 -1.70399926
118 -1.30047590 -1.17518800
119 -0.16984662 -1.30047590
120 0.99731069 -0.16984662
121 2.56563006 0.99731069
122 3.13717151 2.56563006
123 4.16516576 3.13717151
124 5.99902058 4.16516576
125 6.15342183 5.99902058
> 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/79b8v1356078939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/832kn1356078939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/90hnn1356078939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10d71x1356078939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117z5i1356078939.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/1230sx1356078939.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/13638v1356078939.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/14guy11356078939.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/15lot91356078939.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/1618v91356078939.tab")
+ }
>
> try(system("convert tmp/15blg1356078939.ps tmp/15blg1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xa5b1356078939.ps tmp/2xa5b1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/39kca1356078939.ps tmp/39kca1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ptlb1356078939.ps tmp/4ptlb1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oq0y1356078939.ps tmp/5oq0y1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bw8g1356078939.ps tmp/6bw8g1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/79b8v1356078939.ps tmp/79b8v1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/832kn1356078939.ps tmp/832kn1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/90hnn1356078939.ps tmp/90hnn1356078939.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d71x1356078939.ps tmp/10d71x1356078939.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.248 1.159 8.514