R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(13
+ ,14
+ ,13
+ ,3
+ ,25
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+ ,63
+ ,11
+ ,11
+ ,14
+ ,2
+ ,142
+ ,39
+ ,66)
+ ,dim=c(7
+ ,156)
+ ,dimnames=list(c('findingfriends'
+ ,'knowingpeople'
+ ,'liked'
+ ,'celebrity'
+ ,'selectfbf'
+ ,'selectsbf'
+ ,'selecttbf
')
+ ,1:156))
> y <- array(NA,dim=c(7,156),dimnames=list(c('findingfriends','knowingpeople','liked','celebrity','selectfbf','selectsbf','selecttbf
'),1:156))
> 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 = '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
liked findingfriends knowingpeople celebrity selectfbf selectsbf
1 13 13 14 3 25 55
2 13 12 8 5 158 7
3 16 10 12 6 0 0
4 12 9 7 6 143 10
5 11 10 10 5 67 74
6 12 12 7 3 0 0
7 18 13 16 8 148 138
8 11 12 11 4 28 0
9 14 12 14 4 114 113
10 9 6 6 4 0 0
11 14 5 16 6 123 115
12 12 12 11 6 145 9
13 11 11 16 5 113 114
14 12 14 12 4 152 59
15 13 14 7 6 0 0
16 11 12 13 4 36 114
17 12 12 11 6 0 0
18 16 11 15 6 8 102
19 9 11 7 4 108 0
20 11 7 9 4 112 86
21 13 9 7 2 51 17
22 15 11 14 7 43 45
23 10 11 15 5 120 123
24 11 12 7 4 13 24
25 13 12 15 6 55 5
26 16 11 17 6 103 123
27 15 11 15 7 127 136
28 14 8 14 5 14 4
29 14 9 14 6 135 76
30 14 12 8 4 38 99
31 8 10 8 4 11 98
32 13 10 14 7 43 67
33 15 12 14 7 141 92
34 13 8 8 4 62 13
35 11 12 11 4 62 24
36 15 11 16 6 135 129
37 15 12 10 6 117 117
38 9 7 8 5 82 11
39 13 11 14 6 145 20
40 16 11 16 7 87 91
41 13 12 13 6 76 111
42 11 9 5 3 124 0
43 12 15 8 3 151 58
44 12 11 10 4 131 0
45 12 11 8 6 127 146
46 14 11 13 7 76 129
47 14 11 15 5 25 48
48 8 15 6 4 0 0
49 13 11 12 5 58 111
50 16 12 16 6 115 32
51 13 12 5 6 130 112
52 11 9 15 6 17 51
53 14 12 12 5 102 53
54 13 12 8 4 21 131
55 13 13 13 5 0 0
56 13 11 14 5 14 76
57 12 9 12 4 110 106
58 16 9 16 6 133 26
59 15 11 10 2 83 44
60 15 11 15 8 56 63
61 12 12 8 3 0 0
62 14 12 16 6 44 116
63 12 9 19 6 70 119
64 15 11 14 6 36 18
65 12 9 6 5 5 134
66 13 12 13 5 118 138
67 12 12 15 6 17 41
68 12 12 7 5 79 0
69 13 12 13 6 122 57
70 5 14 4 2 119 101
71 13 11 14 5 36 114
72 13 12 13 5 36 113
73 14 11 11 5 141 122
74 17 6 14 6 0 14
75 13 10 12 6 37 10
76 13 12 15 6 110 27
77 12 13 14 5 10 39
78 13 8 13 5 14 133
79 14 12 8 4 157 42
80 11 12 6 2 59 0
81 12 12 7 4 77 58
82 12 6 13 6 129 133
83 16 11 13 6 125 151
84 12 10 11 5 87 111
85 12 12 5 3 61 139
86 12 13 12 6 146 126
87 10 11 8 4 96 139
88 15 7 11 5 133 138
89 15 11 14 8 47 52
90 12 11 9 4 74 67
91 16 11 10 6 109 97
92 15 11 13 6 30 137
93 16 12 16 7 116 56
94 13 10 16 6 149 3
95 12 11 11 5 19 78
96 11 12 8 4 96 0
97 13 7 4 6 0 0
98 10 13 7 3 21 0
99 15 8 14 5 26 118
100 13 12 11 6 156 39
101 16 11 17 7 53 63
102 15 12 15 7 72 78
103 18 14 17 6 27 26
104 13 10 5 3 66 50
105 10 10 4 2 71 104
106 16 13 10 8 66 54
107 13 10 11 3 40 104
108 15 11 15 8 57 148
109 14 10 10 3 3 30
110 15 7 9 4 12 38
111 14 10 12 5 107 132
112 13 8 15 7 80 132
113 13 12 7 6 98 84
114 15 12 13 6 155 71
115 16 12 12 7 111 125
116 14 11 14 6 81 25
117 14 12 14 6 50 66
118 16 12 8 6 49 86
119 14 12 15 6 96 61
120 12 11 12 4 2 60
121 13 12 12 4 1 144
122 12 11 16 5 22 120
123 12 11 9 4 64 139
124 14 13 15 6 56 131
125 14 12 15 6 144 159
126 14 12 6 5 0 0
127 16 12 14 8 94 18
128 13 12 15 6 25 123
129 14 8 10 5 93 18
130 4 8 6 4 0 0
131 16 12 14 8 48 123
132 13 11 12 6 30 105
133 16 12 8 4 19 0
134 15 13 11 6 0 0
135 14 12 13 6 10 68
136 13 12 9 4 78 157
137 14 11 15 6 93 94
138 12 12 13 3 0 0
139 15 12 15 6 95 87
140 14 10 14 5 50 156
141 13 11 16 4 86 139
142 14 12 14 6 33 145
143 16 12 14 4 152 55
144 6 10 10 4 51 41
145 13 12 10 4 48 25
146 13 13 4 6 97 47
147 14 12 8 5 77 0
148 15 15 15 6 130 143
149 14 11 16 6 8 102
150 15 12 12 8 84 148
151 13 11 12 7 51 153
152 16 12 15 7 33 32
153 12 11 9 4 6 106
154 15 10 12 6 116 63
155 12 11 14 6 88 56
156 14 11 11 2 142 39
selecttbf\r t
1 147 1
2 71 2
3 0 3
4 0 4
5 43 5
6 0 6
7 8 7
8 0 8
9 34 9
10 0 10
11 103 11
12 0 12
13 73 13
14 159 14
15 0 15
16 113 16
17 0 17
18 44 18
19 0 19
20 0 20
21 41 21
22 74 22
23 0 23
24 0 24
25 0 25
26 32 26
27 126 27
28 154 28
29 129 29
30 98 30
31 82 31
32 45 32
33 8 33
34 0 34
35 129 35
36 31 36
37 117 37
38 99 38
39 55 39
40 132 40
41 58 41
42 0 42
43 0 43
44 0 44
45 101 45
46 31 46
47 147 47
48 0 48
49 132 49
50 123 50
51 39 51
52 136 52
53 141 53
54 0 54
55 0 55
56 135 56
57 118 57
58 154 58
59 0 59
60 116 60
61 0 61
62 88 62
63 25 63
64 113 64
65 157 65
66 26 66
67 38 67
68 0 68
69 53 69
70 0 70
71 106 71
72 106 72
73 102 73
74 138 74
75 142 75
76 73 76
77 130 77
78 86 78
79 78 79
80 0 80
81 0 81
82 4 82
83 91 83
84 132 84
85 0 85
86 0 86
87 0 87
88 14 88
89 97 89
90 45 90
91 0 91
92 149 92
93 57 93
94 105 94
95 0 95
96 0 96
97 0 97
98 0 98
99 128 99
100 29 100
101 148 101
102 93 102
103 4 103
104 0 104
105 158 105
106 144 106
107 0 107
108 122 108
109 149 109
110 17 110
111 91 111
112 111 112
113 99 113
114 40 114
115 132 115
116 123 116
117 54 117
118 90 118
119 86 119
120 152 120
121 152 121
122 123 122
123 100 123
124 116 124
125 59 125
126 0 126
127 5 127
128 147 128
129 139 129
130 0 130
131 81 131
132 3 132
133 0 133
134 0 134
135 37 135
136 5 136
137 69 137
138 0 138
139 0 139
140 142 140
141 17 141
142 100 142
143 70 143
144 0 144
145 123 145
146 109 146
147 0 147
148 37 148
149 44 149
150 98 150
151 11 151
152 9 152
153 0 153
154 57 154
155 63 155
156 66 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) findingfriends knowingpeople celebrity selectfbf
6.684876 0.080478 0.171089 0.554953 0.003307
selectsbf `selecttbf\r` t
-0.002360 0.003342 0.005902
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.34233 -0.90025 -0.02911 0.92487 4.09016
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.684876 1.062645 6.291 3.38e-09 ***
findingfriends 0.080478 0.081404 0.989 0.32446
knowingpeople 0.171089 0.051901 3.296 0.00123 **
celebrity 0.554953 0.122991 4.512 1.30e-05 ***
selectfbf 0.003307 0.003029 1.092 0.27659
selectsbf -0.002360 0.003057 -0.772 0.44139
`selecttbf\r` 0.003342 0.002762 1.210 0.22821
t 0.005902 0.003247 1.818 0.07111 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.763 on 148 degrees of freedom
Multiple R-squared: 0.3733, Adjusted R-squared: 0.3436
F-statistic: 12.59 on 7 and 148 DF, p-value: 1.304e-12
> 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,] 0.61418325 0.77163350 0.3858167
[2,] 0.45700436 0.91400871 0.5429956
[3,] 0.42348730 0.84697461 0.5765127
[4,] 0.51883933 0.96232134 0.4811607
[5,] 0.40899765 0.81799530 0.5910023
[6,] 0.31238041 0.62476082 0.6876196
[7,] 0.23121244 0.46242488 0.7687876
[8,] 0.31235724 0.62471449 0.6876428
[9,] 0.25248337 0.50496675 0.7475166
[10,] 0.27682493 0.55364987 0.7231751
[11,] 0.73702828 0.52594344 0.2629717
[12,] 0.68939373 0.62121254 0.3106063
[13,] 0.75943099 0.48113802 0.2405690
[14,] 0.69815581 0.60368838 0.3018442
[15,] 0.63225791 0.73548419 0.3677421
[16,] 0.67556684 0.64886632 0.3244332
[17,] 0.61427658 0.77144685 0.3857234
[18,] 0.56379989 0.87240022 0.4362001
[19,] 0.50113856 0.99772288 0.4988614
[20,] 0.52119195 0.95761610 0.4788080
[21,] 0.70183979 0.59632041 0.2981602
[22,] 0.66501206 0.66997588 0.3349879
[23,] 0.63266554 0.73466893 0.3673345
[24,] 0.67749996 0.64500008 0.3225000
[25,] 0.64187920 0.71624160 0.3581208
[26,] 0.60764965 0.78470070 0.3923504
[27,] 0.60212975 0.79574050 0.3978703
[28,] 0.66126764 0.67746473 0.3387324
[29,] 0.61315877 0.77368246 0.3868412
[30,] 0.57719304 0.84561392 0.4228070
[31,] 0.52837373 0.94325254 0.4716263
[32,] 0.51867837 0.96264326 0.4813216
[33,] 0.47552590 0.95105179 0.5244741
[34,] 0.42420505 0.84841010 0.5757949
[35,] 0.38188430 0.76376860 0.6181157
[36,] 0.33147130 0.66294260 0.6685287
[37,] 0.28749677 0.57499355 0.7125032
[38,] 0.39087790 0.78175580 0.6091221
[39,] 0.34198631 0.68397262 0.6580137
[40,] 0.32863637 0.65727274 0.6713636
[41,] 0.29899095 0.59798189 0.7010091
[42,] 0.36446153 0.72892305 0.6355385
[43,] 0.33127801 0.66255602 0.6687220
[44,] 0.33206715 0.66413430 0.6679329
[45,] 0.28746144 0.57492288 0.7125386
[46,] 0.24623866 0.49247733 0.7537613
[47,] 0.20973274 0.41946549 0.7902673
[48,] 0.20632493 0.41264986 0.7936751
[49,] 0.38104920 0.76209840 0.6189508
[50,] 0.33517062 0.67034124 0.6648294
[51,] 0.30211227 0.60422454 0.6978877
[52,] 0.26396981 0.52793962 0.7360302
[53,] 0.31430239 0.62860478 0.6856976
[54,] 0.28703686 0.57407372 0.7129631
[55,] 0.24982713 0.49965426 0.7501729
[56,] 0.21441774 0.42883549 0.7855823
[57,] 0.22030735 0.44061469 0.7796927
[58,] 0.18646707 0.37293415 0.8135329
[59,] 0.16403122 0.32806244 0.8359688
[60,] 0.45493284 0.90986568 0.5450672
[61,] 0.40981278 0.81962555 0.5901872
[62,] 0.36639895 0.73279790 0.6336011
[63,] 0.33758458 0.67516916 0.6624154
[64,] 0.45799132 0.91598264 0.5420087
[65,] 0.41997731 0.83995462 0.5800227
[66,] 0.40790375 0.81580751 0.5920962
[67,] 0.41378580 0.82757161 0.5862142
[68,] 0.36842479 0.73684958 0.6315752
[69,] 0.36703524 0.73407048 0.6329648
[70,] 0.33186100 0.66372200 0.6681390
[71,] 0.29533612 0.59067224 0.7046639
[72,] 0.27209788 0.54419577 0.7279021
[73,] 0.28139880 0.56279760 0.7186012
[74,] 0.25939294 0.51878589 0.7406071
[75,] 0.24221949 0.48443898 0.7577805
[76,] 0.25769752 0.51539504 0.7423025
[77,] 0.27592291 0.55184581 0.7240771
[78,] 0.30555174 0.61110347 0.6944483
[79,] 0.26735754 0.53471507 0.7326425
[80,] 0.23457113 0.46914227 0.7654289
[81,] 0.27014625 0.54029250 0.7298537
[82,] 0.24063353 0.48126706 0.7593665
[83,] 0.20806029 0.41612058 0.7919397
[84,] 0.21890281 0.43780562 0.7810972
[85,] 0.19433944 0.38867887 0.8056606
[86,] 0.19907452 0.39814903 0.8009255
[87,] 0.18326810 0.36653619 0.8167319
[88,] 0.21751539 0.43503078 0.7824846
[89,] 0.22841216 0.45682431 0.7715878
[90,] 0.24877057 0.49754115 0.7512294
[91,] 0.21293228 0.42586457 0.7870677
[92,] 0.18509811 0.37019622 0.8149019
[93,] 0.21012269 0.42024538 0.7898773
[94,] 0.20425165 0.40850330 0.7957484
[95,] 0.18247620 0.36495241 0.8175238
[96,] 0.15454761 0.30909522 0.8454524
[97,] 0.13454653 0.26909305 0.8654535
[98,] 0.11011674 0.22023348 0.8898833
[99,] 0.11195845 0.22391690 0.8880416
[100,] 0.34013195 0.68026391 0.6598680
[101,] 0.31815952 0.63631903 0.6818405
[102,] 0.31053526 0.62107053 0.6894647
[103,] 0.27765947 0.55531894 0.7223405
[104,] 0.23543833 0.47087665 0.7645617
[105,] 0.20414662 0.40829323 0.7958534
[106,] 0.17097407 0.34194815 0.8290259
[107,] 0.13927378 0.27854756 0.8607262
[108,] 0.17138084 0.34276168 0.8286192
[109,] 0.15001975 0.30003950 0.8499802
[110,] 0.12421747 0.24843494 0.8757825
[111,] 0.09790993 0.19581985 0.9020901
[112,] 0.09192492 0.18384984 0.9080751
[113,] 0.07057266 0.14114531 0.9294273
[114,] 0.06336891 0.12673782 0.9366311
[115,] 0.05305020 0.10610039 0.9469498
[116,] 0.05266212 0.10532423 0.9473379
[117,] 0.03778446 0.07556892 0.9622155
[118,] 0.04626000 0.09251999 0.9537400
[119,] 0.06020302 0.12040603 0.9397970
[120,] 0.32234828 0.64469655 0.6776517
[121,] 0.26427057 0.52854114 0.7357294
[122,] 0.20955380 0.41910761 0.7904462
[123,] 0.38849437 0.77698874 0.6115056
[124,] 0.36298750 0.72597500 0.6370125
[125,] 0.30362454 0.60724908 0.6963755
[126,] 0.28280599 0.56561198 0.7171940
[127,] 0.21564539 0.43129078 0.7843546
[128,] 0.15805155 0.31610309 0.8419485
[129,] 0.13021124 0.26042247 0.8697888
[130,] 0.10050710 0.20101420 0.8994929
[131,] 0.07270701 0.14541403 0.9272930
[132,] 0.04863361 0.09726723 0.9513664
[133,] 0.14701801 0.29403602 0.8529820
[134,] 0.59189882 0.81620236 0.4081012
[135,] 0.42824726 0.85649452 0.5717527
> postscript(file="/var/www/html/rcomp/tmp/1z2dn1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2z2dn1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3z2dn1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4auup1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5auup1291121911.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 = 156
Frequency = 1
1 2 3 4 5
0.7587235490 0.4507489547 3.1098483458 -0.4095002420 -1.1954898338
6 7 8 9 10
1.4514901438 2.8599927275 -0.8922340919 1.4572121680 -1.4731134123
11 12 13 14 15
0.3010160889 -1.3914843781 -2.5077253284 -1.0619744150 0.5725562875
16 17 18 19 20
-1.4166973188 -0.9626491506 2.4947880954 -2.4569226874 -0.2933506640
21 22 23 24 25
2.8937564798 0.7367626382 -3.1535936044 -0.1960564531 -0.8643356966
26 27 28 29 30
1.8808477761 0.2993127029 0.7844709630 -0.0035872018 2.3642337789
31 32 33 34 35
-3.3402957712 -1.0929366237 0.5987321179 1.7077159661 -1.5385402893
36 37 38 39 40
0.9045781272 1.5885238199 -3.1921086961 -1.1414892414 1.0575366165
41 42 43 44 45
-0.6297203365 0.4124944913 0.4580404263 -0.1938177720 -0.9471943005
46 47 48 49 50
-0.0009860747 0.3506636154 -3.4216993576 -0.0582006172 1.2712113737
51 52 53 54 55
0.5672280081 -3.0025399922 0.5252163641 1.6818616402 -0.0546363292
56 57 58 59 60
-0.3887952815 -0.5265099395 1.2881263340 4.0901600127 -0.3544473217
61 62 63 64 65
0.9557805164 -0.2495576324 -2.3956537173 0.8729094534 0.1808833254
66 67 68 69 70
-0.1905657650 -2.0285792972 -0.2856431791 -1.0578624192 -5.1742054615
71 72 73 74 75
-0.3634865891 -0.2811376780 0.8229391468 3.2423541701 -0.8884682146
76 77 78 79 80
-1.5393126769 -1.7310812472 0.1921716096 1.6137500025 0.5456278347
81 82 83 84 85
0.3360830980 -1.3317410554 2.0249129474 -1.1091272321 1.4536954608
86 87 88 89 90
-1.8069869235 -1.6616138613 2.4146483015 -0.2872147832 -0.0979694999
91 92 93 94 95
2.7205708532 1.0591202064 0.7363808214 -1.9482687823 -0.6663420065
96 97 98 99 100
-1.1232685519 1.1651914765 -1.2414466685 1.6816749839 -0.9733775621
101 102 103 104 105
0.5193112788 -0.0685137263 3.3009628473 2.2759217587 -0.4210863328
106 107 108 109 110
0.9206466323 1.4451208559 -0.4605022774 2.0541564657 3.3361001950
111 112 113 114 115
0.6808644183 -1.7647950427 -0.3016576607 0.6438811223 1.2196158791
116 117 118 119 120
-0.5997422004 -0.2562178903 2.6946089462 -0.7100053530 -0.9242922225
121 122 123 124 125
0.1908852174 -2.0030273252 -0.2735552408 -0.6227499101 -0.5826488948
126 127 128 129 130
1.8044116226 0.4798036414 -1.5858352056 0.6945885868 -7.3423321021
131 132 133 134 135
0.6021505101 -0.5934505145 3.9130278808 1.2663168132 0.0024684766
136 137 138 139 140
0.8829180429 -0.5911438736 -0.3541344821 0.5240449635 0.2422439181
141 142 143 144 145
-0.3727877506 -0.3148335969 2.2834302655 -6.3421935818 0.0520262838
146 147 148 149 150
-0.1810783374 1.0836096886 0.1222364667 -0.4494877871 -0.2846955848
151 152 153 154 155
-1.2434534268 0.9375428751 -0.0024567157 0.7931343200 -2.5793882745
156
1.9190347432
> postscript(file="/var/www/html/rcomp/tmp/6auup1291121911.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 0.7587235490 NA
1 0.4507489547 0.7587235490
2 3.1098483458 0.4507489547
3 -0.4095002420 3.1098483458
4 -1.1954898338 -0.4095002420
5 1.4514901438 -1.1954898338
6 2.8599927275 1.4514901438
7 -0.8922340919 2.8599927275
8 1.4572121680 -0.8922340919
9 -1.4731134123 1.4572121680
10 0.3010160889 -1.4731134123
11 -1.3914843781 0.3010160889
12 -2.5077253284 -1.3914843781
13 -1.0619744150 -2.5077253284
14 0.5725562875 -1.0619744150
15 -1.4166973188 0.5725562875
16 -0.9626491506 -1.4166973188
17 2.4947880954 -0.9626491506
18 -2.4569226874 2.4947880954
19 -0.2933506640 -2.4569226874
20 2.8937564798 -0.2933506640
21 0.7367626382 2.8937564798
22 -3.1535936044 0.7367626382
23 -0.1960564531 -3.1535936044
24 -0.8643356966 -0.1960564531
25 1.8808477761 -0.8643356966
26 0.2993127029 1.8808477761
27 0.7844709630 0.2993127029
28 -0.0035872018 0.7844709630
29 2.3642337789 -0.0035872018
30 -3.3402957712 2.3642337789
31 -1.0929366237 -3.3402957712
32 0.5987321179 -1.0929366237
33 1.7077159661 0.5987321179
34 -1.5385402893 1.7077159661
35 0.9045781272 -1.5385402893
36 1.5885238199 0.9045781272
37 -3.1921086961 1.5885238199
38 -1.1414892414 -3.1921086961
39 1.0575366165 -1.1414892414
40 -0.6297203365 1.0575366165
41 0.4124944913 -0.6297203365
42 0.4580404263 0.4124944913
43 -0.1938177720 0.4580404263
44 -0.9471943005 -0.1938177720
45 -0.0009860747 -0.9471943005
46 0.3506636154 -0.0009860747
47 -3.4216993576 0.3506636154
48 -0.0582006172 -3.4216993576
49 1.2712113737 -0.0582006172
50 0.5672280081 1.2712113737
51 -3.0025399922 0.5672280081
52 0.5252163641 -3.0025399922
53 1.6818616402 0.5252163641
54 -0.0546363292 1.6818616402
55 -0.3887952815 -0.0546363292
56 -0.5265099395 -0.3887952815
57 1.2881263340 -0.5265099395
58 4.0901600127 1.2881263340
59 -0.3544473217 4.0901600127
60 0.9557805164 -0.3544473217
61 -0.2495576324 0.9557805164
62 -2.3956537173 -0.2495576324
63 0.8729094534 -2.3956537173
64 0.1808833254 0.8729094534
65 -0.1905657650 0.1808833254
66 -2.0285792972 -0.1905657650
67 -0.2856431791 -2.0285792972
68 -1.0578624192 -0.2856431791
69 -5.1742054615 -1.0578624192
70 -0.3634865891 -5.1742054615
71 -0.2811376780 -0.3634865891
72 0.8229391468 -0.2811376780
73 3.2423541701 0.8229391468
74 -0.8884682146 3.2423541701
75 -1.5393126769 -0.8884682146
76 -1.7310812472 -1.5393126769
77 0.1921716096 -1.7310812472
78 1.6137500025 0.1921716096
79 0.5456278347 1.6137500025
80 0.3360830980 0.5456278347
81 -1.3317410554 0.3360830980
82 2.0249129474 -1.3317410554
83 -1.1091272321 2.0249129474
84 1.4536954608 -1.1091272321
85 -1.8069869235 1.4536954608
86 -1.6616138613 -1.8069869235
87 2.4146483015 -1.6616138613
88 -0.2872147832 2.4146483015
89 -0.0979694999 -0.2872147832
90 2.7205708532 -0.0979694999
91 1.0591202064 2.7205708532
92 0.7363808214 1.0591202064
93 -1.9482687823 0.7363808214
94 -0.6663420065 -1.9482687823
95 -1.1232685519 -0.6663420065
96 1.1651914765 -1.1232685519
97 -1.2414466685 1.1651914765
98 1.6816749839 -1.2414466685
99 -0.9733775621 1.6816749839
100 0.5193112788 -0.9733775621
101 -0.0685137263 0.5193112788
102 3.3009628473 -0.0685137263
103 2.2759217587 3.3009628473
104 -0.4210863328 2.2759217587
105 0.9206466323 -0.4210863328
106 1.4451208559 0.9206466323
107 -0.4605022774 1.4451208559
108 2.0541564657 -0.4605022774
109 3.3361001950 2.0541564657
110 0.6808644183 3.3361001950
111 -1.7647950427 0.6808644183
112 -0.3016576607 -1.7647950427
113 0.6438811223 -0.3016576607
114 1.2196158791 0.6438811223
115 -0.5997422004 1.2196158791
116 -0.2562178903 -0.5997422004
117 2.6946089462 -0.2562178903
118 -0.7100053530 2.6946089462
119 -0.9242922225 -0.7100053530
120 0.1908852174 -0.9242922225
121 -2.0030273252 0.1908852174
122 -0.2735552408 -2.0030273252
123 -0.6227499101 -0.2735552408
124 -0.5826488948 -0.6227499101
125 1.8044116226 -0.5826488948
126 0.4798036414 1.8044116226
127 -1.5858352056 0.4798036414
128 0.6945885868 -1.5858352056
129 -7.3423321021 0.6945885868
130 0.6021505101 -7.3423321021
131 -0.5934505145 0.6021505101
132 3.9130278808 -0.5934505145
133 1.2663168132 3.9130278808
134 0.0024684766 1.2663168132
135 0.8829180429 0.0024684766
136 -0.5911438736 0.8829180429
137 -0.3541344821 -0.5911438736
138 0.5240449635 -0.3541344821
139 0.2422439181 0.5240449635
140 -0.3727877506 0.2422439181
141 -0.3148335969 -0.3727877506
142 2.2834302655 -0.3148335969
143 -6.3421935818 2.2834302655
144 0.0520262838 -6.3421935818
145 -0.1810783374 0.0520262838
146 1.0836096886 -0.1810783374
147 0.1222364667 1.0836096886
148 -0.4494877871 0.1222364667
149 -0.2846955848 -0.4494877871
150 -1.2434534268 -0.2846955848
151 0.9375428751 -1.2434534268
152 -0.0024567157 0.9375428751
153 0.7931343200 -0.0024567157
154 -2.5793882745 0.7931343200
155 1.9190347432 -2.5793882745
156 NA 1.9190347432
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4507489547 0.7587235490
[2,] 3.1098483458 0.4507489547
[3,] -0.4095002420 3.1098483458
[4,] -1.1954898338 -0.4095002420
[5,] 1.4514901438 -1.1954898338
[6,] 2.8599927275 1.4514901438
[7,] -0.8922340919 2.8599927275
[8,] 1.4572121680 -0.8922340919
[9,] -1.4731134123 1.4572121680
[10,] 0.3010160889 -1.4731134123
[11,] -1.3914843781 0.3010160889
[12,] -2.5077253284 -1.3914843781
[13,] -1.0619744150 -2.5077253284
[14,] 0.5725562875 -1.0619744150
[15,] -1.4166973188 0.5725562875
[16,] -0.9626491506 -1.4166973188
[17,] 2.4947880954 -0.9626491506
[18,] -2.4569226874 2.4947880954
[19,] -0.2933506640 -2.4569226874
[20,] 2.8937564798 -0.2933506640
[21,] 0.7367626382 2.8937564798
[22,] -3.1535936044 0.7367626382
[23,] -0.1960564531 -3.1535936044
[24,] -0.8643356966 -0.1960564531
[25,] 1.8808477761 -0.8643356966
[26,] 0.2993127029 1.8808477761
[27,] 0.7844709630 0.2993127029
[28,] -0.0035872018 0.7844709630
[29,] 2.3642337789 -0.0035872018
[30,] -3.3402957712 2.3642337789
[31,] -1.0929366237 -3.3402957712
[32,] 0.5987321179 -1.0929366237
[33,] 1.7077159661 0.5987321179
[34,] -1.5385402893 1.7077159661
[35,] 0.9045781272 -1.5385402893
[36,] 1.5885238199 0.9045781272
[37,] -3.1921086961 1.5885238199
[38,] -1.1414892414 -3.1921086961
[39,] 1.0575366165 -1.1414892414
[40,] -0.6297203365 1.0575366165
[41,] 0.4124944913 -0.6297203365
[42,] 0.4580404263 0.4124944913
[43,] -0.1938177720 0.4580404263
[44,] -0.9471943005 -0.1938177720
[45,] -0.0009860747 -0.9471943005
[46,] 0.3506636154 -0.0009860747
[47,] -3.4216993576 0.3506636154
[48,] -0.0582006172 -3.4216993576
[49,] 1.2712113737 -0.0582006172
[50,] 0.5672280081 1.2712113737
[51,] -3.0025399922 0.5672280081
[52,] 0.5252163641 -3.0025399922
[53,] 1.6818616402 0.5252163641
[54,] -0.0546363292 1.6818616402
[55,] -0.3887952815 -0.0546363292
[56,] -0.5265099395 -0.3887952815
[57,] 1.2881263340 -0.5265099395
[58,] 4.0901600127 1.2881263340
[59,] -0.3544473217 4.0901600127
[60,] 0.9557805164 -0.3544473217
[61,] -0.2495576324 0.9557805164
[62,] -2.3956537173 -0.2495576324
[63,] 0.8729094534 -2.3956537173
[64,] 0.1808833254 0.8729094534
[65,] -0.1905657650 0.1808833254
[66,] -2.0285792972 -0.1905657650
[67,] -0.2856431791 -2.0285792972
[68,] -1.0578624192 -0.2856431791
[69,] -5.1742054615 -1.0578624192
[70,] -0.3634865891 -5.1742054615
[71,] -0.2811376780 -0.3634865891
[72,] 0.8229391468 -0.2811376780
[73,] 3.2423541701 0.8229391468
[74,] -0.8884682146 3.2423541701
[75,] -1.5393126769 -0.8884682146
[76,] -1.7310812472 -1.5393126769
[77,] 0.1921716096 -1.7310812472
[78,] 1.6137500025 0.1921716096
[79,] 0.5456278347 1.6137500025
[80,] 0.3360830980 0.5456278347
[81,] -1.3317410554 0.3360830980
[82,] 2.0249129474 -1.3317410554
[83,] -1.1091272321 2.0249129474
[84,] 1.4536954608 -1.1091272321
[85,] -1.8069869235 1.4536954608
[86,] -1.6616138613 -1.8069869235
[87,] 2.4146483015 -1.6616138613
[88,] -0.2872147832 2.4146483015
[89,] -0.0979694999 -0.2872147832
[90,] 2.7205708532 -0.0979694999
[91,] 1.0591202064 2.7205708532
[92,] 0.7363808214 1.0591202064
[93,] -1.9482687823 0.7363808214
[94,] -0.6663420065 -1.9482687823
[95,] -1.1232685519 -0.6663420065
[96,] 1.1651914765 -1.1232685519
[97,] -1.2414466685 1.1651914765
[98,] 1.6816749839 -1.2414466685
[99,] -0.9733775621 1.6816749839
[100,] 0.5193112788 -0.9733775621
[101,] -0.0685137263 0.5193112788
[102,] 3.3009628473 -0.0685137263
[103,] 2.2759217587 3.3009628473
[104,] -0.4210863328 2.2759217587
[105,] 0.9206466323 -0.4210863328
[106,] 1.4451208559 0.9206466323
[107,] -0.4605022774 1.4451208559
[108,] 2.0541564657 -0.4605022774
[109,] 3.3361001950 2.0541564657
[110,] 0.6808644183 3.3361001950
[111,] -1.7647950427 0.6808644183
[112,] -0.3016576607 -1.7647950427
[113,] 0.6438811223 -0.3016576607
[114,] 1.2196158791 0.6438811223
[115,] -0.5997422004 1.2196158791
[116,] -0.2562178903 -0.5997422004
[117,] 2.6946089462 -0.2562178903
[118,] -0.7100053530 2.6946089462
[119,] -0.9242922225 -0.7100053530
[120,] 0.1908852174 -0.9242922225
[121,] -2.0030273252 0.1908852174
[122,] -0.2735552408 -2.0030273252
[123,] -0.6227499101 -0.2735552408
[124,] -0.5826488948 -0.6227499101
[125,] 1.8044116226 -0.5826488948
[126,] 0.4798036414 1.8044116226
[127,] -1.5858352056 0.4798036414
[128,] 0.6945885868 -1.5858352056
[129,] -7.3423321021 0.6945885868
[130,] 0.6021505101 -7.3423321021
[131,] -0.5934505145 0.6021505101
[132,] 3.9130278808 -0.5934505145
[133,] 1.2663168132 3.9130278808
[134,] 0.0024684766 1.2663168132
[135,] 0.8829180429 0.0024684766
[136,] -0.5911438736 0.8829180429
[137,] -0.3541344821 -0.5911438736
[138,] 0.5240449635 -0.3541344821
[139,] 0.2422439181 0.5240449635
[140,] -0.3727877506 0.2422439181
[141,] -0.3148335969 -0.3727877506
[142,] 2.2834302655 -0.3148335969
[143,] -6.3421935818 2.2834302655
[144,] 0.0520262838 -6.3421935818
[145,] -0.1810783374 0.0520262838
[146,] 1.0836096886 -0.1810783374
[147,] 0.1222364667 1.0836096886
[148,] -0.4494877871 0.1222364667
[149,] -0.2846955848 -0.4494877871
[150,] -1.2434534268 -0.2846955848
[151,] 0.9375428751 -1.2434534268
[152,] -0.0024567157 0.9375428751
[153,] 0.7931343200 -0.0024567157
[154,] -2.5793882745 0.7931343200
[155,] 1.9190347432 -2.5793882745
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4507489547 0.7587235490
2 3.1098483458 0.4507489547
3 -0.4095002420 3.1098483458
4 -1.1954898338 -0.4095002420
5 1.4514901438 -1.1954898338
6 2.8599927275 1.4514901438
7 -0.8922340919 2.8599927275
8 1.4572121680 -0.8922340919
9 -1.4731134123 1.4572121680
10 0.3010160889 -1.4731134123
11 -1.3914843781 0.3010160889
12 -2.5077253284 -1.3914843781
13 -1.0619744150 -2.5077253284
14 0.5725562875 -1.0619744150
15 -1.4166973188 0.5725562875
16 -0.9626491506 -1.4166973188
17 2.4947880954 -0.9626491506
18 -2.4569226874 2.4947880954
19 -0.2933506640 -2.4569226874
20 2.8937564798 -0.2933506640
21 0.7367626382 2.8937564798
22 -3.1535936044 0.7367626382
23 -0.1960564531 -3.1535936044
24 -0.8643356966 -0.1960564531
25 1.8808477761 -0.8643356966
26 0.2993127029 1.8808477761
27 0.7844709630 0.2993127029
28 -0.0035872018 0.7844709630
29 2.3642337789 -0.0035872018
30 -3.3402957712 2.3642337789
31 -1.0929366237 -3.3402957712
32 0.5987321179 -1.0929366237
33 1.7077159661 0.5987321179
34 -1.5385402893 1.7077159661
35 0.9045781272 -1.5385402893
36 1.5885238199 0.9045781272
37 -3.1921086961 1.5885238199
38 -1.1414892414 -3.1921086961
39 1.0575366165 -1.1414892414
40 -0.6297203365 1.0575366165
41 0.4124944913 -0.6297203365
42 0.4580404263 0.4124944913
43 -0.1938177720 0.4580404263
44 -0.9471943005 -0.1938177720
45 -0.0009860747 -0.9471943005
46 0.3506636154 -0.0009860747
47 -3.4216993576 0.3506636154
48 -0.0582006172 -3.4216993576
49 1.2712113737 -0.0582006172
50 0.5672280081 1.2712113737
51 -3.0025399922 0.5672280081
52 0.5252163641 -3.0025399922
53 1.6818616402 0.5252163641
54 -0.0546363292 1.6818616402
55 -0.3887952815 -0.0546363292
56 -0.5265099395 -0.3887952815
57 1.2881263340 -0.5265099395
58 4.0901600127 1.2881263340
59 -0.3544473217 4.0901600127
60 0.9557805164 -0.3544473217
61 -0.2495576324 0.9557805164
62 -2.3956537173 -0.2495576324
63 0.8729094534 -2.3956537173
64 0.1808833254 0.8729094534
65 -0.1905657650 0.1808833254
66 -2.0285792972 -0.1905657650
67 -0.2856431791 -2.0285792972
68 -1.0578624192 -0.2856431791
69 -5.1742054615 -1.0578624192
70 -0.3634865891 -5.1742054615
71 -0.2811376780 -0.3634865891
72 0.8229391468 -0.2811376780
73 3.2423541701 0.8229391468
74 -0.8884682146 3.2423541701
75 -1.5393126769 -0.8884682146
76 -1.7310812472 -1.5393126769
77 0.1921716096 -1.7310812472
78 1.6137500025 0.1921716096
79 0.5456278347 1.6137500025
80 0.3360830980 0.5456278347
81 -1.3317410554 0.3360830980
82 2.0249129474 -1.3317410554
83 -1.1091272321 2.0249129474
84 1.4536954608 -1.1091272321
85 -1.8069869235 1.4536954608
86 -1.6616138613 -1.8069869235
87 2.4146483015 -1.6616138613
88 -0.2872147832 2.4146483015
89 -0.0979694999 -0.2872147832
90 2.7205708532 -0.0979694999
91 1.0591202064 2.7205708532
92 0.7363808214 1.0591202064
93 -1.9482687823 0.7363808214
94 -0.6663420065 -1.9482687823
95 -1.1232685519 -0.6663420065
96 1.1651914765 -1.1232685519
97 -1.2414466685 1.1651914765
98 1.6816749839 -1.2414466685
99 -0.9733775621 1.6816749839
100 0.5193112788 -0.9733775621
101 -0.0685137263 0.5193112788
102 3.3009628473 -0.0685137263
103 2.2759217587 3.3009628473
104 -0.4210863328 2.2759217587
105 0.9206466323 -0.4210863328
106 1.4451208559 0.9206466323
107 -0.4605022774 1.4451208559
108 2.0541564657 -0.4605022774
109 3.3361001950 2.0541564657
110 0.6808644183 3.3361001950
111 -1.7647950427 0.6808644183
112 -0.3016576607 -1.7647950427
113 0.6438811223 -0.3016576607
114 1.2196158791 0.6438811223
115 -0.5997422004 1.2196158791
116 -0.2562178903 -0.5997422004
117 2.6946089462 -0.2562178903
118 -0.7100053530 2.6946089462
119 -0.9242922225 -0.7100053530
120 0.1908852174 -0.9242922225
121 -2.0030273252 0.1908852174
122 -0.2735552408 -2.0030273252
123 -0.6227499101 -0.2735552408
124 -0.5826488948 -0.6227499101
125 1.8044116226 -0.5826488948
126 0.4798036414 1.8044116226
127 -1.5858352056 0.4798036414
128 0.6945885868 -1.5858352056
129 -7.3423321021 0.6945885868
130 0.6021505101 -7.3423321021
131 -0.5934505145 0.6021505101
132 3.9130278808 -0.5934505145
133 1.2663168132 3.9130278808
134 0.0024684766 1.2663168132
135 0.8829180429 0.0024684766
136 -0.5911438736 0.8829180429
137 -0.3541344821 -0.5911438736
138 0.5240449635 -0.3541344821
139 0.2422439181 0.5240449635
140 -0.3727877506 0.2422439181
141 -0.3148335969 -0.3727877506
142 2.2834302655 -0.3148335969
143 -6.3421935818 2.2834302655
144 0.0520262838 -6.3421935818
145 -0.1810783374 0.0520262838
146 1.0836096886 -0.1810783374
147 0.1222364667 1.0836096886
148 -0.4494877871 0.1222364667
149 -0.2846955848 -0.4494877871
150 -1.2434534268 -0.2846955848
151 0.9375428751 -1.2434534268
152 -0.0024567157 0.9375428751
153 0.7931343200 -0.0024567157
154 -2.5793882745 0.7931343200
155 1.9190347432 -2.5793882745
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7llca1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dcbd1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dcbd1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dcbd1291121911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11s4941291121911.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12v47a1291121911.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13re511291121911.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14dx4p1291121911.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15yxku1291121911.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/161gi01291121911.tab")
+ }
>
> try(system("convert tmp/1z2dn1291121911.ps tmp/1z2dn1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z2dn1291121911.ps tmp/2z2dn1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z2dn1291121911.ps tmp/3z2dn1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/4auup1291121911.ps tmp/4auup1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/5auup1291121911.ps tmp/5auup1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/6auup1291121911.ps tmp/6auup1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/7llca1291121911.ps tmp/7llca1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dcbd1291121911.ps tmp/8dcbd1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dcbd1291121911.ps tmp/9dcbd1291121911.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dcbd1291121911.ps tmp/10dcbd1291121911.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.148 1.816 9.889