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(2
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+ ,2)
+ ,dim=c(6
+ ,156)
+ ,dimnames=list(c('aantalVrienden'
+ ,'Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity
')
+ ,1:156))
> y <- array(NA,dim=c(6,156),dimnames=list(c('aantalVrienden','Popularity','FindingFriends','KnowingPeople','Liked','Celebrity
'),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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
aantalVrienden Popularity FindingFriends KnowingPeople Liked Celebrity\r
1 2 13 13 14 13 3
2 1 12 12 8 13 5
3 0 15 10 12 16 6
4 3 12 9 7 12 6
5 3 10 10 10 11 5
6 1 12 12 7 12 3
7 3 15 13 16 18 8
8 1 9 12 11 11 4
9 4 12 12 14 14 4
10 0 11 6 6 9 4
11 3 11 5 16 14 6
12 2 11 12 11 12 6
13 4 15 11 16 11 5
14 3 7 14 12 12 4
15 1 11 14 7 13 6
16 1 11 12 13 11 4
17 2 10 12 11 12 6
18 3 14 11 15 16 6
19 1 10 11 7 9 4
20 1 6 7 9 11 4
21 2 11 9 7 13 2
22 3 15 11 14 15 7
23 4 11 11 15 10 5
24 2 12 12 7 11 4
25 1 14 12 15 13 6
26 2 15 11 17 16 6
27 2 9 11 15 15 7
28 4 13 8 14 14 5
29 2 13 9 14 14 6
30 3 16 12 8 14 4
31 3 13 10 8 8 4
32 3 12 10 14 13 7
33 4 14 12 14 15 7
34 2 11 8 8 13 4
35 2 9 12 11 11 4
36 4 16 11 16 15 6
37 3 12 12 10 15 6
38 4 10 7 8 9 5
39 2 13 11 14 13 6
40 5 16 11 16 16 7
41 3 14 12 13 13 6
42 1 15 9 5 11 3
43 1 5 15 8 12 3
44 1 8 11 10 12 4
45 2 11 11 8 12 6
46 3 16 11 13 14 7
47 9 17 11 15 14 5
48 0 9 15 6 8 4
49 0 9 11 12 13 5
50 2 13 12 16 16 6
51 2 10 12 5 13 6
52 3 6 9 15 11 6
53 1 12 12 12 14 5
54 2 8 12 8 13 4
55 0 14 13 13 13 5
56 5 12 11 14 13 5
57 2 11 9 12 12 4
58 4 16 9 16 16 6
59 3 8 11 10 15 2
60 0 15 11 15 15 8
61 0 7 12 8 12 3
62 4 16 12 16 14 6
63 1 14 9 19 12 6
64 1 16 11 14 15 6
65 4 9 9 6 12 5
66 2 14 12 13 13 5
67 4 11 12 15 12 6
68 1 13 12 7 12 5
69 4 15 12 13 13 6
70 2 5 14 4 5 2
71 5 15 11 14 13 5
72 4 13 12 13 13 5
73 4 11 11 11 14 5
74 4 11 6 14 17 6
75 4 12 10 12 13 6
76 3 12 12 15 13 6
77 3 12 13 14 12 5
78 3 12 8 13 13 5
79 2 14 12 8 14 4
80 1 6 12 6 11 2
81 1 7 12 7 12 4
82 5 14 6 13 12 6
83 4 14 11 13 16 6
84 2 10 10 11 12 5
85 3 13 12 5 12 3
86 2 12 13 12 12 6
87 2 9 11 8 10 4
88 2 12 7 11 15 5
89 2 16 11 14 15 8
90 3 10 11 9 12 4
91 2 14 11 10 16 6
92 3 10 11 13 15 6
93 4 16 12 16 16 7
94 3 15 10 16 13 6
95 3 12 11 11 12 5
96 0 10 12 8 11 4
97 1 8 7 4 13 6
98 2 8 13 7 10 3
99 2 11 8 14 15 5
100 3 13 12 11 13 6
101 4 16 11 17 16 7
102 4 16 12 15 15 7
103 1 14 14 17 18 6
104 2 11 10 5 13 3
105 2 4 10 4 10 2
106 3 14 13 10 16 8
107 3 9 10 11 13 3
108 3 14 11 15 15 8
109 1 8 10 10 14 3
110 1 8 7 9 15 4
111 1 11 10 12 14 5
112 1 12 8 15 13 7
113 0 11 12 7 13 6
114 1 14 12 13 15 6
115 3 15 12 12 16 7
116 3 16 11 14 14 6
117 0 16 12 14 14 6
118 2 11 12 8 16 6
119 5 14 12 15 14 6
120 2 14 11 12 12 4
121 3 12 12 12 13 4
122 3 14 11 16 12 5
123 5 8 11 9 12 4
124 4 13 13 15 14 6
125 4 16 12 15 14 6
126 0 12 12 6 14 5
127 3 16 12 14 16 8
128 0 12 12 15 13 6
129 2 11 8 10 14 5
130 0 4 8 6 4 4
131 6 16 12 14 16 8
132 3 15 11 12 13 6
133 1 10 12 8 16 4
134 6 13 13 11 15 6
135 2 15 12 13 14 6
136 1 12 12 9 13 4
137 3 14 11 15 14 6
138 1 7 12 13 12 3
139 2 19 12 15 15 6
140 4 12 10 14 14 5
141 1 12 11 16 13 4
142 2 13 12 14 14 6
143 0 15 12 14 16 4
144 5 8 10 10 6 4
145 2 12 12 10 13 4
146 1 10 13 4 13 6
147 1 8 12 8 14 5
148 4 10 15 15 15 6
149 3 15 11 16 14 6
150 0 16 12 12 15 8
151 3 13 11 12 13 7
152 3 16 12 15 16 7
153 0 9 11 9 12 4
154 2 14 10 12 15 6
155 5 14 11 14 12 6
156 2 12 11 11 14 2
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
118 118
119 119
120 120
121 121
122 122
123 123
124 124
125 125
126 126
127 127
128 128
129 129
130 130
131 131
132 132
133 133
134 134
135 135
136 136
137 137
138 138
139 139
140 140
141 141
142 142
143 143
144 144
145 145
146 146
147 147
148 148
149 149
150 150
151 151
152 152
153 153
154 154
155 155
156 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Popularity FindingFriends KnowingPeople Liked
1.2390168 0.0963161 -0.0640953 0.1287303 -0.0742946
`Celebrity\r` t
0.0388674 -0.0001363
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.098005 -0.849892 0.002138 0.852716 5.749898
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2390168 0.9565071 1.295 0.19720
Popularity 0.0963161 0.0545922 1.764 0.07973 .
FindingFriends -0.0640953 0.0648554 -0.988 0.32462
KnowingPeople 0.1287303 0.0432867 2.974 0.00343 **
Liked -0.0742946 0.0680544 -1.092 0.27673
`Celebrity\r` 0.0388674 0.1101396 0.353 0.72467
t -0.0001363 0.0025570 -0.053 0.95755
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.412 on 149 degrees of freedom
Multiple R-squared: 0.1637, Adjusted R-squared: 0.13
F-statistic: 4.861 on 6 and 149 DF, p-value: 0.0001457
> 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,] 0.868498362 0.263003277 0.1315016
[2,] 0.776573187 0.446853626 0.2234268
[3,] 0.678445881 0.643108239 0.3215541
[4,] 0.588960543 0.822078914 0.4110395
[5,] 0.476213813 0.952427627 0.5237862
[6,] 0.374375535 0.748751070 0.6256245
[7,] 0.398478608 0.796957216 0.6015214
[8,] 0.307286126 0.614572252 0.6927139
[9,] 0.261794712 0.523589425 0.7382053
[10,] 0.193974424 0.387948849 0.8060256
[11,] 0.141129740 0.282259480 0.8588703
[12,] 0.161440119 0.322880238 0.8385599
[13,] 0.115930720 0.231861440 0.8840693
[14,] 0.091578210 0.183156419 0.9084218
[15,] 0.065648982 0.131297963 0.9343510
[16,] 0.122892556 0.245785112 0.8771074
[17,] 0.107360482 0.214720963 0.8926395
[18,] 0.082983833 0.165967667 0.9170162
[19,] 0.094131467 0.188262933 0.9058685
[20,] 0.073485939 0.146971878 0.9265141
[21,] 0.071672263 0.143344527 0.9283277
[22,] 0.056860667 0.113721333 0.9431393
[23,] 0.040464139 0.080928279 0.9595359
[24,] 0.036568295 0.073136591 0.9634317
[25,] 0.025289173 0.050578346 0.9747108
[26,] 0.018158142 0.036316284 0.9818419
[27,] 0.012737463 0.025474925 0.9872625
[28,] 0.009289592 0.018579184 0.9907104
[29,] 0.010933335 0.021866670 0.9890667
[30,] 0.011397856 0.022795712 0.9886021
[31,] 0.011853577 0.023707155 0.9881464
[32,] 0.008209906 0.016419813 0.9917901
[33,] 0.008558360 0.017116721 0.9914416
[34,] 0.006197255 0.012394509 0.9938027
[35,] 0.005759318 0.011518637 0.9942407
[36,] 0.003846400 0.007692801 0.9961536
[37,] 0.002609292 0.005218584 0.9973907
[38,] 0.172199165 0.344398331 0.8278008
[39,] 0.193277424 0.386554848 0.8067226
[40,] 0.281974697 0.563949394 0.7180253
[41,] 0.279931829 0.559863658 0.7200682
[42,] 0.251701882 0.503403765 0.7482981
[43,] 0.215085357 0.430170713 0.7849146
[44,] 0.232541746 0.465083491 0.7674583
[45,] 0.202055250 0.404110499 0.7979448
[46,] 0.364956260 0.729912519 0.6350437
[47,] 0.407188018 0.814376037 0.5928120
[48,] 0.375802032 0.751604065 0.6241980
[49,] 0.333321091 0.666642182 0.6666789
[50,] 0.324924870 0.649849741 0.6750751
[51,] 0.554528814 0.890942371 0.4454712
[52,] 0.566555645 0.866888709 0.4334444
[53,] 0.521553002 0.956893996 0.4784470
[54,] 0.691526613 0.616946775 0.3084734
[55,] 0.750998150 0.498003699 0.2490018
[56,] 0.806633905 0.386732189 0.1933661
[57,] 0.789440130 0.421119740 0.2105599
[58,] 0.775148510 0.449702979 0.2248515
[59,] 0.764579877 0.470840246 0.2354201
[60,] 0.742889678 0.514220644 0.2571103
[61,] 0.717476187 0.565047625 0.2825238
[62,] 0.730443093 0.539113814 0.2695569
[63,] 0.710231386 0.579537228 0.2897686
[64,] 0.712707275 0.574585450 0.2872927
[65,] 0.694348207 0.611303587 0.3056518
[66,] 0.677149661 0.645700678 0.3228503
[67,] 0.634066902 0.731866197 0.3659331
[68,] 0.588478305 0.823043390 0.4115217
[69,] 0.544334705 0.911330590 0.4556653
[70,] 0.501190248 0.997619504 0.4988098
[71,] 0.459296723 0.918593447 0.5407033
[72,] 0.423641576 0.847283152 0.5763584
[73,] 0.447860417 0.895720835 0.5521396
[74,] 0.433142927 0.866285854 0.5668571
[75,] 0.394879640 0.789759281 0.6051204
[76,] 0.383092454 0.766184909 0.6169075
[77,] 0.355137230 0.710274459 0.6448628
[78,] 0.312276130 0.624552261 0.6877239
[79,] 0.287651665 0.575303330 0.7123483
[80,] 0.280387457 0.560774915 0.7196125
[81,] 0.255124939 0.510249878 0.7448751
[82,] 0.222080043 0.444160087 0.7779200
[83,] 0.190465882 0.380931763 0.8095341
[84,] 0.166269589 0.332539178 0.8337304
[85,] 0.142898047 0.285796095 0.8571020
[86,] 0.120223729 0.240447458 0.8797763
[87,] 0.149807032 0.299614063 0.8501930
[88,] 0.124723747 0.249447495 0.8752763
[89,] 0.101619799 0.203239598 0.8983802
[90,] 0.087842785 0.175685571 0.9121572
[91,] 0.070665881 0.141331762 0.9293341
[92,] 0.059061235 0.118122470 0.9409388
[93,] 0.050188122 0.100376245 0.9498119
[94,] 0.065980907 0.131961815 0.9340191
[95,] 0.058102926 0.116205853 0.9418971
[96,] 0.052736312 0.105472624 0.9472637
[97,] 0.042675744 0.085351488 0.9573243
[98,] 0.039904646 0.079809291 0.9600954
[99,] 0.030206545 0.060413090 0.9697935
[100,] 0.024495051 0.048990102 0.9755049
[101,] 0.020460137 0.040920274 0.9795399
[102,] 0.018318083 0.036636165 0.9816819
[103,] 0.025054377 0.050108753 0.9749456
[104,] 0.028572655 0.057145310 0.9714273
[105,] 0.032774552 0.065549104 0.9672254
[106,] 0.024332520 0.048665041 0.9756675
[107,] 0.017697606 0.035395211 0.9823024
[108,] 0.051026686 0.102053372 0.9489733
[109,] 0.038542751 0.077085502 0.9614572
[110,] 0.040736959 0.081473918 0.9592630
[111,] 0.031342277 0.062684553 0.9686577
[112,] 0.023620307 0.047240615 0.9763797
[113,] 0.017060030 0.034120061 0.9829400
[114,] 0.056976874 0.113953748 0.9430231
[115,] 0.047122943 0.094245886 0.9528771
[116,] 0.039230375 0.078460749 0.9607696
[117,] 0.034580026 0.069160053 0.9654200
[118,] 0.024493524 0.048987048 0.9755065
[119,] 0.078865988 0.157731975 0.9211340
[120,] 0.061485217 0.122970434 0.9385148
[121,] 0.111022944 0.222045889 0.8889771
[122,] 0.192886885 0.385773770 0.8071131
[123,] 0.148764351 0.297528702 0.8512356
[124,] 0.117043611 0.234087223 0.8829564
[125,] 0.651476310 0.697047380 0.3485237
[126,] 0.579635399 0.840729202 0.4203646
[127,] 0.509405025 0.981189951 0.4905950
[128,] 0.451558529 0.903117057 0.5484415
[129,] 0.434080707 0.868161414 0.5659193
[130,] 0.370926924 0.741853849 0.6290731
[131,] 0.669150610 0.661698781 0.3308494
[132,] 0.650156312 0.699687375 0.3498437
[133,] 0.541638265 0.916723470 0.4583617
[134,] 0.502817887 0.994364226 0.4971821
[135,] 0.396545904 0.793091808 0.6034541
[136,] 0.271590294 0.543180587 0.7284097
[137,] 0.394832238 0.789664477 0.6051678
> postscript(file="/var/www/html/rcomp/tmp/1qxg91291222065.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jofu1291222065.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jofu1291222065.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jofu1291222065.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jofu1291222065.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
-0.6107472528 -0.8837428724 -2.6316503332 0.9398121522 0.7750576982
6 7 8 9 10
-0.7510270640 0.1171140308 -1.0898895502 1.4579913642 -2.1717585112
11 12 13 14 15
-0.2292826110 -0.2854165604 0.5862811552 1.1773155019 -0.5676009338
16 17 18 19 20
-1.5388917860 -0.1884188397 0.1446149494 -0.8824692203 -0.8623212863
21 22 23 24 25
0.2682099807 0.0644125087 1.0273444841 0.1382648187 -2.0132193077
26 27 28 29 30
-1.2080712032 -0.4857396994 1.0690168308 -0.9056189094 0.8479719767
31 32 33 34 35
0.5630981708 0.1420394652 1.2263235152 -0.0005782048 -0.0862087080
36 37 38 39 40
0.7514117096 0.9732897588 1.6961419848 -0.8503596160 1.7873842545
41 42 43 44 45
0.2464226214 -1.0441874211 -0.0082147463 -0.8497360302 0.0411779598
46 47 48 49 50
0.0258040064 5.7498983386 -1.4713826349 -2.1674039304 -0.8193415115
51 52 53 54 55
0.6628929864 0.4201149901 -1.3174169255 0.5474778785 -2.6487060841
56 57 58 59 60
2.2871414231 -0.5225633537 0.7005148959 1.4529275298 -3.0980047800
61 62 63 64 65
-1.3906789808 0.7447569218 -2.7895408282 -1.9873104313 2.4046741119
66 67 68 69 70
-0.7113018027 1.2071600729 -0.9166256201 1.1539237079 0.9650971778
71 72 73 74 75
2.0002380859 1.3858322454 1.8462607311 1.3237459016 1.4442296252
76 77 78 79 80
0.1863655606 0.3439003189 0.2265850096 0.0472841900 -0.0697392209
81 82 83 84 85
-0.2980894750 1.7931454991 1.4109369255 -0.2686081513 1.4208874138
86 87 88 89 90
-0.4362794330 0.1686814110 -0.4300971002 -1.0616370213 1.0926332126
91 92 93 94 95
-0.2017814206 0.7231335822 0.8587049324 -0.3570497807 0.6043546057
96 97 98 99 100
-1.7880177803 -0.3299500430 0.5622854718 -0.6543771276 0.6082427158
101 102 103 104 105
0.6669698867 0.9143676010 -1.7603826934 0.5622137840 1.1812765566
106 107 108 109 110
0.8507193565 0.9828728649 0.0048550227 -0.7175134288 -0.7455054847
111 112 113 114 115
-1.3413844899 -2.1039753223 -1.6824314818 -1.5950362248 0.4729415990
116 117 118 119 120
-0.0545160254 -2.9902843769 0.4124036815 2.0738900998 -0.6747323295
121 122 123 124 125
0.6564261089 -0.2282484417 3.2897641861 1.2349831414 0.8820758983
126 127 128 129 130
-1.5350829509 0.0819333677 -2.8065454099 -0.2096605478 -1.7244691056
131 132 133 134 135
3.0824786777 0.2271473635 -0.4115005458 3.8255624184 -0.7627840543
136 137 138 139 140
-0.9553379448 0.0122486736 -1.0238334874 -1.3306691429 1.3087922348
141 142 143 144 145
-1.9198640410 -0.6979279401 -2.6640997548 2.6540336568 -0.0828413421
146 147 148 149 150
-0.1313302365 -0.4044164301 1.7296885162 -0.2111618244 -2.7317650342
151 152 153 154 155
0.3835023635 -0.0045214011 -1.8024620724 -0.5890434226 1.9948436661
156
-0.1221380046
> postscript(file="/var/www/html/rcomp/tmp/6cfff1291222065.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 -0.6107472528 NA
1 -0.8837428724 -0.6107472528
2 -2.6316503332 -0.8837428724
3 0.9398121522 -2.6316503332
4 0.7750576982 0.9398121522
5 -0.7510270640 0.7750576982
6 0.1171140308 -0.7510270640
7 -1.0898895502 0.1171140308
8 1.4579913642 -1.0898895502
9 -2.1717585112 1.4579913642
10 -0.2292826110 -2.1717585112
11 -0.2854165604 -0.2292826110
12 0.5862811552 -0.2854165604
13 1.1773155019 0.5862811552
14 -0.5676009338 1.1773155019
15 -1.5388917860 -0.5676009338
16 -0.1884188397 -1.5388917860
17 0.1446149494 -0.1884188397
18 -0.8824692203 0.1446149494
19 -0.8623212863 -0.8824692203
20 0.2682099807 -0.8623212863
21 0.0644125087 0.2682099807
22 1.0273444841 0.0644125087
23 0.1382648187 1.0273444841
24 -2.0132193077 0.1382648187
25 -1.2080712032 -2.0132193077
26 -0.4857396994 -1.2080712032
27 1.0690168308 -0.4857396994
28 -0.9056189094 1.0690168308
29 0.8479719767 -0.9056189094
30 0.5630981708 0.8479719767
31 0.1420394652 0.5630981708
32 1.2263235152 0.1420394652
33 -0.0005782048 1.2263235152
34 -0.0862087080 -0.0005782048
35 0.7514117096 -0.0862087080
36 0.9732897588 0.7514117096
37 1.6961419848 0.9732897588
38 -0.8503596160 1.6961419848
39 1.7873842545 -0.8503596160
40 0.2464226214 1.7873842545
41 -1.0441874211 0.2464226214
42 -0.0082147463 -1.0441874211
43 -0.8497360302 -0.0082147463
44 0.0411779598 -0.8497360302
45 0.0258040064 0.0411779598
46 5.7498983386 0.0258040064
47 -1.4713826349 5.7498983386
48 -2.1674039304 -1.4713826349
49 -0.8193415115 -2.1674039304
50 0.6628929864 -0.8193415115
51 0.4201149901 0.6628929864
52 -1.3174169255 0.4201149901
53 0.5474778785 -1.3174169255
54 -2.6487060841 0.5474778785
55 2.2871414231 -2.6487060841
56 -0.5225633537 2.2871414231
57 0.7005148959 -0.5225633537
58 1.4529275298 0.7005148959
59 -3.0980047800 1.4529275298
60 -1.3906789808 -3.0980047800
61 0.7447569218 -1.3906789808
62 -2.7895408282 0.7447569218
63 -1.9873104313 -2.7895408282
64 2.4046741119 -1.9873104313
65 -0.7113018027 2.4046741119
66 1.2071600729 -0.7113018027
67 -0.9166256201 1.2071600729
68 1.1539237079 -0.9166256201
69 0.9650971778 1.1539237079
70 2.0002380859 0.9650971778
71 1.3858322454 2.0002380859
72 1.8462607311 1.3858322454
73 1.3237459016 1.8462607311
74 1.4442296252 1.3237459016
75 0.1863655606 1.4442296252
76 0.3439003189 0.1863655606
77 0.2265850096 0.3439003189
78 0.0472841900 0.2265850096
79 -0.0697392209 0.0472841900
80 -0.2980894750 -0.0697392209
81 1.7931454991 -0.2980894750
82 1.4109369255 1.7931454991
83 -0.2686081513 1.4109369255
84 1.4208874138 -0.2686081513
85 -0.4362794330 1.4208874138
86 0.1686814110 -0.4362794330
87 -0.4300971002 0.1686814110
88 -1.0616370213 -0.4300971002
89 1.0926332126 -1.0616370213
90 -0.2017814206 1.0926332126
91 0.7231335822 -0.2017814206
92 0.8587049324 0.7231335822
93 -0.3570497807 0.8587049324
94 0.6043546057 -0.3570497807
95 -1.7880177803 0.6043546057
96 -0.3299500430 -1.7880177803
97 0.5622854718 -0.3299500430
98 -0.6543771276 0.5622854718
99 0.6082427158 -0.6543771276
100 0.6669698867 0.6082427158
101 0.9143676010 0.6669698867
102 -1.7603826934 0.9143676010
103 0.5622137840 -1.7603826934
104 1.1812765566 0.5622137840
105 0.8507193565 1.1812765566
106 0.9828728649 0.8507193565
107 0.0048550227 0.9828728649
108 -0.7175134288 0.0048550227
109 -0.7455054847 -0.7175134288
110 -1.3413844899 -0.7455054847
111 -2.1039753223 -1.3413844899
112 -1.6824314818 -2.1039753223
113 -1.5950362248 -1.6824314818
114 0.4729415990 -1.5950362248
115 -0.0545160254 0.4729415990
116 -2.9902843769 -0.0545160254
117 0.4124036815 -2.9902843769
118 2.0738900998 0.4124036815
119 -0.6747323295 2.0738900998
120 0.6564261089 -0.6747323295
121 -0.2282484417 0.6564261089
122 3.2897641861 -0.2282484417
123 1.2349831414 3.2897641861
124 0.8820758983 1.2349831414
125 -1.5350829509 0.8820758983
126 0.0819333677 -1.5350829509
127 -2.8065454099 0.0819333677
128 -0.2096605478 -2.8065454099
129 -1.7244691056 -0.2096605478
130 3.0824786777 -1.7244691056
131 0.2271473635 3.0824786777
132 -0.4115005458 0.2271473635
133 3.8255624184 -0.4115005458
134 -0.7627840543 3.8255624184
135 -0.9553379448 -0.7627840543
136 0.0122486736 -0.9553379448
137 -1.0238334874 0.0122486736
138 -1.3306691429 -1.0238334874
139 1.3087922348 -1.3306691429
140 -1.9198640410 1.3087922348
141 -0.6979279401 -1.9198640410
142 -2.6640997548 -0.6979279401
143 2.6540336568 -2.6640997548
144 -0.0828413421 2.6540336568
145 -0.1313302365 -0.0828413421
146 -0.4044164301 -0.1313302365
147 1.7296885162 -0.4044164301
148 -0.2111618244 1.7296885162
149 -2.7317650342 -0.2111618244
150 0.3835023635 -2.7317650342
151 -0.0045214011 0.3835023635
152 -1.8024620724 -0.0045214011
153 -0.5890434226 -1.8024620724
154 1.9948436661 -0.5890434226
155 -0.1221380046 1.9948436661
156 NA -0.1221380046
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.8837428724 -0.6107472528
[2,] -2.6316503332 -0.8837428724
[3,] 0.9398121522 -2.6316503332
[4,] 0.7750576982 0.9398121522
[5,] -0.7510270640 0.7750576982
[6,] 0.1171140308 -0.7510270640
[7,] -1.0898895502 0.1171140308
[8,] 1.4579913642 -1.0898895502
[9,] -2.1717585112 1.4579913642
[10,] -0.2292826110 -2.1717585112
[11,] -0.2854165604 -0.2292826110
[12,] 0.5862811552 -0.2854165604
[13,] 1.1773155019 0.5862811552
[14,] -0.5676009338 1.1773155019
[15,] -1.5388917860 -0.5676009338
[16,] -0.1884188397 -1.5388917860
[17,] 0.1446149494 -0.1884188397
[18,] -0.8824692203 0.1446149494
[19,] -0.8623212863 -0.8824692203
[20,] 0.2682099807 -0.8623212863
[21,] 0.0644125087 0.2682099807
[22,] 1.0273444841 0.0644125087
[23,] 0.1382648187 1.0273444841
[24,] -2.0132193077 0.1382648187
[25,] -1.2080712032 -2.0132193077
[26,] -0.4857396994 -1.2080712032
[27,] 1.0690168308 -0.4857396994
[28,] -0.9056189094 1.0690168308
[29,] 0.8479719767 -0.9056189094
[30,] 0.5630981708 0.8479719767
[31,] 0.1420394652 0.5630981708
[32,] 1.2263235152 0.1420394652
[33,] -0.0005782048 1.2263235152
[34,] -0.0862087080 -0.0005782048
[35,] 0.7514117096 -0.0862087080
[36,] 0.9732897588 0.7514117096
[37,] 1.6961419848 0.9732897588
[38,] -0.8503596160 1.6961419848
[39,] 1.7873842545 -0.8503596160
[40,] 0.2464226214 1.7873842545
[41,] -1.0441874211 0.2464226214
[42,] -0.0082147463 -1.0441874211
[43,] -0.8497360302 -0.0082147463
[44,] 0.0411779598 -0.8497360302
[45,] 0.0258040064 0.0411779598
[46,] 5.7498983386 0.0258040064
[47,] -1.4713826349 5.7498983386
[48,] -2.1674039304 -1.4713826349
[49,] -0.8193415115 -2.1674039304
[50,] 0.6628929864 -0.8193415115
[51,] 0.4201149901 0.6628929864
[52,] -1.3174169255 0.4201149901
[53,] 0.5474778785 -1.3174169255
[54,] -2.6487060841 0.5474778785
[55,] 2.2871414231 -2.6487060841
[56,] -0.5225633537 2.2871414231
[57,] 0.7005148959 -0.5225633537
[58,] 1.4529275298 0.7005148959
[59,] -3.0980047800 1.4529275298
[60,] -1.3906789808 -3.0980047800
[61,] 0.7447569218 -1.3906789808
[62,] -2.7895408282 0.7447569218
[63,] -1.9873104313 -2.7895408282
[64,] 2.4046741119 -1.9873104313
[65,] -0.7113018027 2.4046741119
[66,] 1.2071600729 -0.7113018027
[67,] -0.9166256201 1.2071600729
[68,] 1.1539237079 -0.9166256201
[69,] 0.9650971778 1.1539237079
[70,] 2.0002380859 0.9650971778
[71,] 1.3858322454 2.0002380859
[72,] 1.8462607311 1.3858322454
[73,] 1.3237459016 1.8462607311
[74,] 1.4442296252 1.3237459016
[75,] 0.1863655606 1.4442296252
[76,] 0.3439003189 0.1863655606
[77,] 0.2265850096 0.3439003189
[78,] 0.0472841900 0.2265850096
[79,] -0.0697392209 0.0472841900
[80,] -0.2980894750 -0.0697392209
[81,] 1.7931454991 -0.2980894750
[82,] 1.4109369255 1.7931454991
[83,] -0.2686081513 1.4109369255
[84,] 1.4208874138 -0.2686081513
[85,] -0.4362794330 1.4208874138
[86,] 0.1686814110 -0.4362794330
[87,] -0.4300971002 0.1686814110
[88,] -1.0616370213 -0.4300971002
[89,] 1.0926332126 -1.0616370213
[90,] -0.2017814206 1.0926332126
[91,] 0.7231335822 -0.2017814206
[92,] 0.8587049324 0.7231335822
[93,] -0.3570497807 0.8587049324
[94,] 0.6043546057 -0.3570497807
[95,] -1.7880177803 0.6043546057
[96,] -0.3299500430 -1.7880177803
[97,] 0.5622854718 -0.3299500430
[98,] -0.6543771276 0.5622854718
[99,] 0.6082427158 -0.6543771276
[100,] 0.6669698867 0.6082427158
[101,] 0.9143676010 0.6669698867
[102,] -1.7603826934 0.9143676010
[103,] 0.5622137840 -1.7603826934
[104,] 1.1812765566 0.5622137840
[105,] 0.8507193565 1.1812765566
[106,] 0.9828728649 0.8507193565
[107,] 0.0048550227 0.9828728649
[108,] -0.7175134288 0.0048550227
[109,] -0.7455054847 -0.7175134288
[110,] -1.3413844899 -0.7455054847
[111,] -2.1039753223 -1.3413844899
[112,] -1.6824314818 -2.1039753223
[113,] -1.5950362248 -1.6824314818
[114,] 0.4729415990 -1.5950362248
[115,] -0.0545160254 0.4729415990
[116,] -2.9902843769 -0.0545160254
[117,] 0.4124036815 -2.9902843769
[118,] 2.0738900998 0.4124036815
[119,] -0.6747323295 2.0738900998
[120,] 0.6564261089 -0.6747323295
[121,] -0.2282484417 0.6564261089
[122,] 3.2897641861 -0.2282484417
[123,] 1.2349831414 3.2897641861
[124,] 0.8820758983 1.2349831414
[125,] -1.5350829509 0.8820758983
[126,] 0.0819333677 -1.5350829509
[127,] -2.8065454099 0.0819333677
[128,] -0.2096605478 -2.8065454099
[129,] -1.7244691056 -0.2096605478
[130,] 3.0824786777 -1.7244691056
[131,] 0.2271473635 3.0824786777
[132,] -0.4115005458 0.2271473635
[133,] 3.8255624184 -0.4115005458
[134,] -0.7627840543 3.8255624184
[135,] -0.9553379448 -0.7627840543
[136,] 0.0122486736 -0.9553379448
[137,] -1.0238334874 0.0122486736
[138,] -1.3306691429 -1.0238334874
[139,] 1.3087922348 -1.3306691429
[140,] -1.9198640410 1.3087922348
[141,] -0.6979279401 -1.9198640410
[142,] -2.6640997548 -0.6979279401
[143,] 2.6540336568 -2.6640997548
[144,] -0.0828413421 2.6540336568
[145,] -0.1313302365 -0.0828413421
[146,] -0.4044164301 -0.1313302365
[147,] 1.7296885162 -0.4044164301
[148,] -0.2111618244 1.7296885162
[149,] -2.7317650342 -0.2111618244
[150,] 0.3835023635 -2.7317650342
[151,] -0.0045214011 0.3835023635
[152,] -1.8024620724 -0.0045214011
[153,] -0.5890434226 -1.8024620724
[154,] 1.9948436661 -0.5890434226
[155,] -0.1221380046 1.9948436661
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.8837428724 -0.6107472528
2 -2.6316503332 -0.8837428724
3 0.9398121522 -2.6316503332
4 0.7750576982 0.9398121522
5 -0.7510270640 0.7750576982
6 0.1171140308 -0.7510270640
7 -1.0898895502 0.1171140308
8 1.4579913642 -1.0898895502
9 -2.1717585112 1.4579913642
10 -0.2292826110 -2.1717585112
11 -0.2854165604 -0.2292826110
12 0.5862811552 -0.2854165604
13 1.1773155019 0.5862811552
14 -0.5676009338 1.1773155019
15 -1.5388917860 -0.5676009338
16 -0.1884188397 -1.5388917860
17 0.1446149494 -0.1884188397
18 -0.8824692203 0.1446149494
19 -0.8623212863 -0.8824692203
20 0.2682099807 -0.8623212863
21 0.0644125087 0.2682099807
22 1.0273444841 0.0644125087
23 0.1382648187 1.0273444841
24 -2.0132193077 0.1382648187
25 -1.2080712032 -2.0132193077
26 -0.4857396994 -1.2080712032
27 1.0690168308 -0.4857396994
28 -0.9056189094 1.0690168308
29 0.8479719767 -0.9056189094
30 0.5630981708 0.8479719767
31 0.1420394652 0.5630981708
32 1.2263235152 0.1420394652
33 -0.0005782048 1.2263235152
34 -0.0862087080 -0.0005782048
35 0.7514117096 -0.0862087080
36 0.9732897588 0.7514117096
37 1.6961419848 0.9732897588
38 -0.8503596160 1.6961419848
39 1.7873842545 -0.8503596160
40 0.2464226214 1.7873842545
41 -1.0441874211 0.2464226214
42 -0.0082147463 -1.0441874211
43 -0.8497360302 -0.0082147463
44 0.0411779598 -0.8497360302
45 0.0258040064 0.0411779598
46 5.7498983386 0.0258040064
47 -1.4713826349 5.7498983386
48 -2.1674039304 -1.4713826349
49 -0.8193415115 -2.1674039304
50 0.6628929864 -0.8193415115
51 0.4201149901 0.6628929864
52 -1.3174169255 0.4201149901
53 0.5474778785 -1.3174169255
54 -2.6487060841 0.5474778785
55 2.2871414231 -2.6487060841
56 -0.5225633537 2.2871414231
57 0.7005148959 -0.5225633537
58 1.4529275298 0.7005148959
59 -3.0980047800 1.4529275298
60 -1.3906789808 -3.0980047800
61 0.7447569218 -1.3906789808
62 -2.7895408282 0.7447569218
63 -1.9873104313 -2.7895408282
64 2.4046741119 -1.9873104313
65 -0.7113018027 2.4046741119
66 1.2071600729 -0.7113018027
67 -0.9166256201 1.2071600729
68 1.1539237079 -0.9166256201
69 0.9650971778 1.1539237079
70 2.0002380859 0.9650971778
71 1.3858322454 2.0002380859
72 1.8462607311 1.3858322454
73 1.3237459016 1.8462607311
74 1.4442296252 1.3237459016
75 0.1863655606 1.4442296252
76 0.3439003189 0.1863655606
77 0.2265850096 0.3439003189
78 0.0472841900 0.2265850096
79 -0.0697392209 0.0472841900
80 -0.2980894750 -0.0697392209
81 1.7931454991 -0.2980894750
82 1.4109369255 1.7931454991
83 -0.2686081513 1.4109369255
84 1.4208874138 -0.2686081513
85 -0.4362794330 1.4208874138
86 0.1686814110 -0.4362794330
87 -0.4300971002 0.1686814110
88 -1.0616370213 -0.4300971002
89 1.0926332126 -1.0616370213
90 -0.2017814206 1.0926332126
91 0.7231335822 -0.2017814206
92 0.8587049324 0.7231335822
93 -0.3570497807 0.8587049324
94 0.6043546057 -0.3570497807
95 -1.7880177803 0.6043546057
96 -0.3299500430 -1.7880177803
97 0.5622854718 -0.3299500430
98 -0.6543771276 0.5622854718
99 0.6082427158 -0.6543771276
100 0.6669698867 0.6082427158
101 0.9143676010 0.6669698867
102 -1.7603826934 0.9143676010
103 0.5622137840 -1.7603826934
104 1.1812765566 0.5622137840
105 0.8507193565 1.1812765566
106 0.9828728649 0.8507193565
107 0.0048550227 0.9828728649
108 -0.7175134288 0.0048550227
109 -0.7455054847 -0.7175134288
110 -1.3413844899 -0.7455054847
111 -2.1039753223 -1.3413844899
112 -1.6824314818 -2.1039753223
113 -1.5950362248 -1.6824314818
114 0.4729415990 -1.5950362248
115 -0.0545160254 0.4729415990
116 -2.9902843769 -0.0545160254
117 0.4124036815 -2.9902843769
118 2.0738900998 0.4124036815
119 -0.6747323295 2.0738900998
120 0.6564261089 -0.6747323295
121 -0.2282484417 0.6564261089
122 3.2897641861 -0.2282484417
123 1.2349831414 3.2897641861
124 0.8820758983 1.2349831414
125 -1.5350829509 0.8820758983
126 0.0819333677 -1.5350829509
127 -2.8065454099 0.0819333677
128 -0.2096605478 -2.8065454099
129 -1.7244691056 -0.2096605478
130 3.0824786777 -1.7244691056
131 0.2271473635 3.0824786777
132 -0.4115005458 0.2271473635
133 3.8255624184 -0.4115005458
134 -0.7627840543 3.8255624184
135 -0.9553379448 -0.7627840543
136 0.0122486736 -0.9553379448
137 -1.0238334874 0.0122486736
138 -1.3306691429 -1.0238334874
139 1.3087922348 -1.3306691429
140 -1.9198640410 1.3087922348
141 -0.6979279401 -1.9198640410
142 -2.6640997548 -0.6979279401
143 2.6540336568 -2.6640997548
144 -0.0828413421 2.6540336568
145 -0.1313302365 -0.0828413421
146 -0.4044164301 -0.1313302365
147 1.7296885162 -0.4044164301
148 -0.2111618244 1.7296885162
149 -2.7317650342 -0.2111618244
150 0.3835023635 -2.7317650342
151 -0.0045214011 0.3835023635
152 -1.8024620724 -0.0045214011
153 -0.5890434226 -1.8024620724
154 1.9948436661 -0.5890434226
155 -0.1221380046 1.9948436661
> 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/7m6ez1291222065.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8m6ez1291222065.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m6ez1291222065.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xgd31291222065.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111guq1291222065.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/124zse1291222065.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/130q8n1291222065.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/14mrpt1291222065.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/157s5z1291222065.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/16ssl51291222065.tab")
+ }
>
> try(system("convert tmp/1qxg91291222065.ps tmp/1qxg91291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jofu1291222065.ps tmp/2jofu1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jofu1291222065.ps tmp/3jofu1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jofu1291222065.ps tmp/4jofu1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jofu1291222065.ps tmp/5jofu1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cfff1291222065.ps tmp/6cfff1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m6ez1291222065.ps tmp/7m6ez1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m6ez1291222065.ps tmp/8m6ez1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m6ez1291222065.ps tmp/9m6ez1291222065.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xgd31291222065.ps tmp/10xgd31291222065.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.065 1.849 9.306