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(15
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+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'B'
+ ,'2B'
+ ,'3B'
+ ,'Month')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','B','2B','3B','Month'),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
Popularity FindingFriends KnowingPeople Liked Celebrity B 2B 3B Month t
1 15 10 12 16 6 2 0 0 9 1
2 12 9 7 12 6 1 1 2 9 2
3 9 12 11 11 4 1 2 1 9 3
4 10 12 11 12 6 0 0 0 9 4
5 13 9 14 14 6 0 0 0 9 5
6 16 11 16 16 7 1 0 0 9 6
7 14 12 13 13 6 0 0 0 9 7
8 16 11 13 14 7 1 1 0 9 8
9 10 12 5 13 6 0 0 0 9 9
10 8 12 8 13 4 2 0 1 10 10
11 12 11 14 13 5 1 0 0 10 11
12 15 11 15 15 8 0 0 0 10 12
13 14 12 8 14 4 0 1 0 10 13
14 14 6 13 12 6 1 1 2 10 14
15 12 13 12 12 6 1 2 1 10 15
16 12 11 11 12 5 0 0 0 10 16
17 10 12 8 11 4 0 0 0 10 17
18 4 10 4 10 2 0 0 0 10 18
19 14 11 15 15 8 0 1 0 10 19
20 15 12 12 16 7 0 0 0 10 20
21 16 12 14 14 6 0 0 0 10 21
22 12 12 9 13 4 0 1 0 10 22
23 12 11 16 13 4 0 0 0 10 23
24 12 12 10 13 4 0 0 1 10 24
25 12 12 8 13 5 1 0 1 9 25
26 12 12 14 14 4 0 0 0 9 26
27 11 6 6 9 4 3 2 1 9 27
28 11 5 16 14 6 1 0 0 9 28
29 11 12 11 12 6 1 1 0 9 29
30 11 14 7 13 6 1 1 0 9 30
31 11 12 13 11 4 3 1 1 9 31
32 11 9 7 13 2 0 0 0 9 32
33 15 11 14 15 7 0 0 0 9 33
34 15 11 17 16 6 0 0 0 9 34
35 9 11 15 15 7 0 0 0 9 35
36 16 12 8 14 4 0 0 0 9 36
37 13 10 8 8 4 0 2 1 9 37
38 9 12 11 11 4 1 0 0 9 38
39 16 11 16 15 6 0 0 0 9 39
40 12 12 10 15 6 0 0 0 9 40
41 15 9 5 11 3 0 0 2 9 41
42 5 15 8 12 3 0 0 0 9 42
43 11 11 8 12 6 2 2 0 9 43
44 17 11 15 14 5 2 2 0 9 44
45 9 15 6 8 4 0 1 1 9 45
46 13 12 16 16 6 0 0 0 9 46
47 16 9 16 16 6 0 0 0 10 47
48 16 12 16 14 6 0 0 0 10 48
49 14 9 19 12 6 2 0 2 10 49
50 16 11 14 15 6 1 0 0 10 50
51 11 12 15 12 6 0 0 0 10 51
52 11 11 11 14 5 0 0 0 10 52
53 11 6 14 17 6 0 0 0 10 53
54 12 10 12 13 6 0 0 0 10 54
55 12 12 15 13 6 1 1 1 10 55
56 12 13 14 12 5 0 0 0 10 56
57 14 11 13 16 6 0 0 0 10 57
58 10 10 11 12 5 2 0 0 10 58
59 9 11 8 10 4 0 2 0 10 59
60 12 7 11 15 5 0 0 1 10 60
61 10 11 9 12 4 0 0 0 10 61
62 14 11 10 16 6 0 0 0 10 62
63 8 7 4 13 6 0 0 0 10 63
64 16 12 15 15 7 1 0 0 10 64
65 14 14 17 18 6 1 0 0 10 65
66 14 11 12 12 4 0 0 0 10 66
67 12 12 12 13 4 0 0 0 10 67
68 14 11 15 14 6 1 0 0 10 68
69 7 12 13 12 3 1 1 1 10 69
70 19 12 15 15 6 0 0 0 10 70
71 15 12 14 16 4 0 0 0 10 71
72 8 12 8 14 5 0 0 0 10 72
73 10 15 15 15 6 0 0 0 10 73
74 13 11 12 13 7 0 0 0 10 74
75 13 13 14 13 3 0 0 0 9 75
76 10 10 10 11 5 0 0 0 9 76
77 12 12 7 12 3 0 0 0 9 77
78 15 13 16 18 8 0 1 1 9 78
79 7 14 12 12 4 1 0 0 9 79
80 14 11 15 16 6 0 0 0 9 80
81 10 11 7 9 4 0 0 0 9 81
82 6 7 9 11 4 0 3 0 9 82
83 11 11 15 10 5 2 0 0 9 83
84 12 12 7 11 4 0 0 0 9 84
85 14 12 15 13 6 0 0 2 9 85
86 12 10 14 13 7 0 0 0 9 86
87 14 12 14 15 7 0 0 0 9 87
88 11 8 8 13 4 2 2 0 9 88
89 10 7 8 9 5 1 0 1 9 89
90 13 11 14 13 6 0 0 1 9 90
91 8 11 10 12 4 0 0 0 9 91
92 9 11 12 13 5 0 0 0 9 92
93 6 9 15 11 6 0 0 0 10 93
94 12 12 12 14 5 1 0 2 10 94
95 14 13 13 13 5 0 0 0 10 95
96 11 9 12 12 4 0 0 0 10 96
97 8 11 10 15 2 1 0 1 10 97
98 7 12 8 12 3 0 0 0 10 98
99 9 9 6 12 5 0 2 1 10 99
100 14 12 13 13 5 2 1 0 10 100
101 13 12 7 12 5 0 0 0 10 101
102 15 12 13 13 6 0 0 0 10 102
103 5 14 4 5 2 0 0 0 10 103
104 15 11 14 13 5 3 1 0 10 104
105 13 12 13 13 5 0 1 0 10 105
106 12 8 13 13 5 0 0 0 10 106
107 6 12 6 11 2 1 0 0 10 107
108 7 12 7 12 4 0 0 0 10 108
109 13 12 5 12 3 0 0 0 10 109
110 16 11 14 15 8 1 1 0 10 110
111 10 11 13 15 6 0 0 0 10 111
112 16 12 16 16 7 0 0 0 10 112
113 15 10 16 13 6 0 0 0 10 113
114 8 13 7 10 3 0 0 0 10 114
115 11 8 14 15 5 0 0 0 10 115
116 13 12 11 13 6 0 3 1 10 116
117 16 11 17 16 7 1 0 0 10 117
118 11 10 5 13 3 0 0 0 10 118
119 14 13 10 16 8 0 0 0 10 119
120 9 10 11 13 3 2 1 0 10 120
121 8 10 10 14 3 0 0 0 10 121
122 8 7 9 15 4 1 0 1 10 122
123 11 10 12 14 5 2 0 0 10 123
124 12 8 15 13 7 0 0 0 10 124
125 11 12 7 13 6 4 0 0 10 125
126 14 12 13 15 6 0 1 2 10 126
127 11 12 8 16 6 2 1 0 10 127
128 14 11 16 12 5 0 0 0 10 128
129 13 13 15 14 6 2 1 2 10 129
130 12 12 6 14 5 0 0 0 10 130
131 4 8 6 4 4 0 0 0 10 131
132 15 11 12 13 6 2 1 1 10 132
133 10 12 8 16 4 0 0 0 10 133
134 13 13 11 15 6 1 2 1 10 134
135 15 12 13 14 6 1 1 2 10 135
136 12 10 14 14 5 1 2 1 10 136
137 13 12 14 14 6 0 0 0 10 137
138 8 10 10 6 4 0 0 0 10 138
139 10 13 4 13 6 2 0 0 10 139
140 15 11 16 14 6 0 0 0 10 140
141 16 12 12 15 8 0 0 0 10 141
142 16 12 15 16 7 0 0 0 10 142
143 14 10 12 15 6 0 0 0 10 143
144 14 11 14 12 6 1 1 1 10 144
145 12 11 11 14 2 1 1 1 10 145
146 15 11 16 11 5 0 1 2 9 146
147 13 8 14 14 5 1 1 1 9 147
148 16 11 14 14 6 0 0 0 10 148
149 14 12 15 14 6 0 0 0 10 149
150 8 11 9 12 4 0 0 0 10 150
151 16 12 15 14 6 0 1 0 10 151
152 16 12 14 16 8 1 1 1 10 152
153 12 12 15 13 6 0 0 0 10 153
154 11 8 10 14 5 0 3 1 10 154
155 16 12 14 16 8 1 1 1 10 155
156 9 11 9 12 4 0 0 0 10 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
1.044996 0.119499 0.241564 0.378019 0.607492
B `2B` `3B` Month t
-0.048983 0.174147 0.508544 -0.136999 -0.001881
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0022 -1.2083 -0.1294 1.2691 5.9840
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.044996 3.928053 0.266 0.790588
FindingFriends 0.119499 0.096875 1.234 0.219357
KnowingPeople 0.241564 0.061926 3.901 0.000146 ***
Liked 0.378019 0.098076 3.854 0.000173 ***
Celebrity 0.607492 0.157196 3.865 0.000167 ***
B -0.048983 0.224347 -0.218 0.827473
`2B` 0.174147 0.270802 0.643 0.521181
`3B` 0.508544 0.318646 1.596 0.112661
Month -0.136999 0.402640 -0.340 0.734156
t -0.001881 0.004160 -0.452 0.651825
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.108 on 146 degrees of freedom
Multiple R-squared: 0.5148, Adjusted R-squared: 0.4849
F-statistic: 17.21 on 9 and 146 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.56523502 0.8695299642 0.4347649821
[2,] 0.39584431 0.7916886176 0.6041556912
[3,] 0.26950997 0.5390199363 0.7304900319
[4,] 0.18663697 0.3732739349 0.8133630326
[5,] 0.12023914 0.2404782788 0.8797608606
[6,] 0.21701702 0.4340340483 0.7829829758
[7,] 0.36424329 0.7284865854 0.6357567073
[8,] 0.27500502 0.5500100374 0.7249949813
[9,] 0.28308810 0.5661762084 0.7169118958
[10,] 0.21088701 0.4217740211 0.7891129894
[11,] 0.17669448 0.3533889574 0.8233055213
[12,] 0.12565498 0.2513099658 0.8743450171
[13,] 0.08664178 0.1732835658 0.9133582171
[14,] 0.07658124 0.1531624731 0.9234187634
[15,] 0.06710154 0.1342030777 0.9328984611
[16,] 0.17241569 0.3448313737 0.8275843131
[17,] 0.14449392 0.2889878444 0.8555060778
[18,] 0.11483698 0.2296739696 0.8851630152
[19,] 0.08478095 0.1695619071 0.9152190464
[20,] 0.07287113 0.1457422520 0.9271288740
[21,] 0.05180764 0.1036152764 0.9481923618
[22,] 0.03792182 0.0758436460 0.9620781770
[23,] 0.22515741 0.4503148271 0.7748425864
[24,] 0.44613092 0.8922618326 0.5538690837
[25,] 0.57786169 0.8442766245 0.4221383122
[26,] 0.52475988 0.9504802400 0.4752401200
[27,] 0.49588021 0.9917604218 0.5041197891
[28,] 0.46781126 0.9356225264 0.5321887368
[29,] 0.69902307 0.6019538576 0.3009769288
[30,] 0.85881549 0.2823690255 0.1411845128
[31,] 0.82715501 0.3456899712 0.1728449856
[32,] 0.86357999 0.2728400121 0.1364200060
[33,] 0.83718634 0.3256273283 0.1628136642
[34,] 0.83622840 0.3275431921 0.1637715961
[35,] 0.81430041 0.3713991704 0.1856995852
[36,] 0.82440990 0.3511801965 0.1755900982
[37,] 0.78935215 0.4212957030 0.2106478515
[38,] 0.79580307 0.4083938678 0.2041969339
[39,] 0.77224823 0.4555035334 0.2277517667
[40,] 0.75928906 0.4814218871 0.2407109436
[41,] 0.86594446 0.2681110733 0.1340555367
[42,] 0.83617098 0.3276580490 0.1638290245
[43,] 0.82719876 0.3456024764 0.1728012382
[44,] 0.79610782 0.4077843639 0.2038921820
[45,] 0.75948583 0.4810283441 0.2405141721
[46,] 0.71911743 0.5617651424 0.2808825712
[47,] 0.68538039 0.6292392238 0.3146196119
[48,] 0.66438645 0.6712270925 0.3356135462
[49,] 0.61852153 0.7629569426 0.3814784713
[50,] 0.58533496 0.8293300848 0.4146650424
[51,] 0.55854941 0.8829011776 0.4414505888
[52,] 0.55957112 0.8808577528 0.4404288764
[53,] 0.54755314 0.9048937205 0.4524468603
[54,] 0.63298177 0.7340364529 0.3670182265
[55,] 0.59598295 0.8080340909 0.4040170454
[56,] 0.56140676 0.8771864823 0.4385932411
[57,] 0.69713942 0.6057211601 0.3028605801
[58,] 0.88130880 0.2373824074 0.1186912037
[59,] 0.89281824 0.2143635108 0.1071817554
[60,] 0.90767337 0.1846532693 0.0923266346
[61,] 0.94842617 0.1031476662 0.0515738331
[62,] 0.93615660 0.1276868090 0.0638434045
[63,] 0.92902415 0.1419516932 0.0709758466
[64,] 0.91149969 0.1770006157 0.0885003079
[65,] 0.93284476 0.1343104858 0.0671552429
[66,] 0.93491259 0.1301748101 0.0650874051
[67,] 0.97112368 0.0577526374 0.0288763187
[68,] 0.96214295 0.0757141079 0.0378570539
[69,] 0.95943136 0.0811372853 0.0405686426
[70,] 0.97748062 0.0450387682 0.0225193841
[71,] 0.97121752 0.0575649580 0.0287824790
[72,] 0.97714227 0.0457154614 0.0228577307
[73,] 0.96979499 0.0604100238 0.0302050119
[74,] 0.96318478 0.0736304489 0.0368152244
[75,] 0.95307445 0.0938511002 0.0469255501
[76,] 0.94597000 0.1080599918 0.0540299959
[77,] 0.95046826 0.0990634802 0.0495317401
[78,] 0.93736290 0.1252741901 0.0626370950
[79,] 0.93486049 0.1302790176 0.0651395088
[80,] 0.95405605 0.0918879026 0.0459439513
[81,] 0.99606361 0.0078727705 0.0039363852
[82,] 0.99440253 0.0111949416 0.0055974708
[83,] 0.99358057 0.0128388670 0.0064194335
[84,] 0.99140582 0.0171883596 0.0085941798
[85,] 0.99143056 0.0171388726 0.0085694363
[86,] 0.99248705 0.0150258944 0.0075129472
[87,] 0.98946973 0.0210605415 0.0105302707
[88,] 0.98830581 0.0233883860 0.0116941930
[89,] 0.99232552 0.0153489596 0.0076744798
[90,] 0.99272701 0.0145459895 0.0072729948
[91,] 0.98989697 0.0202060569 0.0101030284
[92,] 0.99282516 0.0143496703 0.0071748351
[93,] 0.98994941 0.0201011835 0.0100505917
[94,] 0.98726662 0.0254667645 0.0127333823
[95,] 0.98528167 0.0294366677 0.0147183339
[96,] 0.98971774 0.0205645206 0.0102822603
[97,] 0.99898399 0.0020320123 0.0010160062
[98,] 0.99867157 0.0026568644 0.0013284322
[99,] 0.99957756 0.0008448893 0.0004224446
[100,] 0.99930483 0.0013903417 0.0006951709
[101,] 0.99929346 0.0014130834 0.0007065417
[102,] 0.99884336 0.0023132762 0.0011566381
[103,] 0.99826630 0.0034674011 0.0017337005
[104,] 0.99713469 0.0057306131 0.0028653066
[105,] 0.99557116 0.0088576887 0.0044288443
[106,] 0.99928575 0.0014284923 0.0007142462
[107,] 0.99875690 0.0024862032 0.0012431016
[108,] 0.99790663 0.0041867319 0.0020933660
[109,] 0.99737516 0.0052496836 0.0026248418
[110,] 0.99662832 0.0067433539 0.0033716770
[111,] 0.99472490 0.0105501983 0.0052750992
[112,] 0.99462270 0.0107545906 0.0053772953
[113,] 0.99111186 0.0177762827 0.0088881413
[114,] 0.98706737 0.0258652532 0.0129326266
[115,] 0.98365094 0.0326981156 0.0163490578
[116,] 0.97589200 0.0482160095 0.0241080047
[117,] 0.98769067 0.0246186569 0.0123093285
[118,] 0.98978922 0.0204215615 0.0102107808
[119,] 0.98489977 0.0302004683 0.0151002341
[120,] 0.98199611 0.0360077861 0.0180038931
[121,] 0.97488405 0.0502318909 0.0251159454
[122,] 0.96221397 0.0755720586 0.0377860293
[123,] 0.93787863 0.1242427430 0.0621213715
[124,] 0.95383948 0.0923210454 0.0461605227
[125,] 0.97214418 0.0557116402 0.0278558201
[126,] 0.94758417 0.1048316516 0.0524158258
[127,] 0.90425799 0.1914840149 0.0957420075
[128,] 0.85385804 0.2922839108 0.1461419554
[129,] 0.77312045 0.4537591045 0.2268795523
[130,] 0.70995135 0.5800973007 0.2900486503
[131,] 0.55442965 0.8911407001 0.4455703501
> postscript(file="/var/www/html/rcomp/tmp/1w5g91293204076.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2w5g91293204076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37wfc1293204076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47wfc1293204076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57wfc1293204076.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 6
1.50083206 0.10188999 -2.29358477 -2.07685053 -0.19720170 0.76800609
7 8 9 10 11 12
1.06764512 2.07835009 -0.99608036 -2.77748695 -0.25342245 -0.12060130
13 14 15 16 17 18
3.08656747 1.17057261 -1.08808087 0.80971002 0.40229445 -2.79756754
19 20 21 22 23 24
-1.28158275 0.72911169 2.61139354 1.23994976 -0.15547143 0.66775117
25 26 27 28 29 30
0.45725197 -0.30121789 2.53037466 -2.11009053 -1.15499381 -0.80387372
31 32 33 34 35 36
-0.45193602 2.35251593 0.63095324 0.13761486 -5.60684916 5.16697525
37 38 39 40 41 42
3.81912833 -1.37091641 1.76660194 -0.90163183 5.98403018 -4.81670812
43 44 45 46 47 48
-0.40963494 3.75275158 -0.10604537 -1.71775002 1.77962781 2.17904839
49 50 51 52 53 54
-0.34834958 2.45640163 -1.81770753 -0.87861667 -2.74547987 -0.22639291
55 56 57 58 59 60
-1.82191108 -0.07874661 0.28413019 -0.89382927 -0.36948662 -0.27213525
61 62 63 64 65 66
-0.01503158 1.01822676 -1.91845472 1.51417846 -1.73263169 3.26968039
67 68 69 70 71 72
0.77404330 0.62671160 -4.11195684 5.08397256 2.16438238 -3.23580652
73 74 75 76 77 78
-4.26888258 0.08423314 1.65695520 -0.47535601 2.84918356 -2.43077400
79 80 81 82 83 84
-4.15238269 -0.29273767 1.50277054 -3.77895959 -0.31352544 2.63287645
85 86 87 88 89 90
-0.28586377 -1.39382487 -0.38697990 0.87046604 0.68719759 -0.40685253
91 92 93 94 95 96
-2.33716935 -2.80392720 -6.00219528 -1.12878860 1.85815208 0.56510446
97 98 99 100 101 102
-2.56751831 -2.21588333 -1.44419865 1.91087377 2.81633975 2.38332549
103 104 105 106 107 108
-0.22559837 2.84531513 0.82231241 0.47633763 -1.68133414 -2.56300249
109 110 111 112 113 114
4.52949836 1.18012184 -3.23628501 0.93589381 1.91832104 -0.30768739
115 116 117 118 119 120
-1.50433621 -0.13820027 0.87221602 2.40740580 -0.32854663 -1.11439891
121 122 123 124 125 126
-2.17279086 -3.01591952 -0.76917531 -1.18791879 0.07190099 -0.51880613
127 128 129 130 131 132
-1.57207059 1.81254511 -1.63980667 1.35641119 -1.77603215 2.21608900
133 134 135 136 137 138
-1.26962008 -0.75675132 0.92512307 -1.13367372 -0.17042744 0.27584153
139 140 141 142 143 144
-0.39453961 1.47158615 1.72722189 1.23388352 1.18496564 1.08456694
145 146 147 148 149 150
1.48506984 1.89618521 0.16316228 2.96976118 0.61057870 -1.84763561
151 152 153 154 155 156
2.44019298 0.25305612 -1.00387918 -1.11769373 0.25869868 -0.83635049
> postscript(file="/var/www/html/rcomp/tmp/60owy1293204076.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 1.50083206 NA
1 0.10188999 1.50083206
2 -2.29358477 0.10188999
3 -2.07685053 -2.29358477
4 -0.19720170 -2.07685053
5 0.76800609 -0.19720170
6 1.06764512 0.76800609
7 2.07835009 1.06764512
8 -0.99608036 2.07835009
9 -2.77748695 -0.99608036
10 -0.25342245 -2.77748695
11 -0.12060130 -0.25342245
12 3.08656747 -0.12060130
13 1.17057261 3.08656747
14 -1.08808087 1.17057261
15 0.80971002 -1.08808087
16 0.40229445 0.80971002
17 -2.79756754 0.40229445
18 -1.28158275 -2.79756754
19 0.72911169 -1.28158275
20 2.61139354 0.72911169
21 1.23994976 2.61139354
22 -0.15547143 1.23994976
23 0.66775117 -0.15547143
24 0.45725197 0.66775117
25 -0.30121789 0.45725197
26 2.53037466 -0.30121789
27 -2.11009053 2.53037466
28 -1.15499381 -2.11009053
29 -0.80387372 -1.15499381
30 -0.45193602 -0.80387372
31 2.35251593 -0.45193602
32 0.63095324 2.35251593
33 0.13761486 0.63095324
34 -5.60684916 0.13761486
35 5.16697525 -5.60684916
36 3.81912833 5.16697525
37 -1.37091641 3.81912833
38 1.76660194 -1.37091641
39 -0.90163183 1.76660194
40 5.98403018 -0.90163183
41 -4.81670812 5.98403018
42 -0.40963494 -4.81670812
43 3.75275158 -0.40963494
44 -0.10604537 3.75275158
45 -1.71775002 -0.10604537
46 1.77962781 -1.71775002
47 2.17904839 1.77962781
48 -0.34834958 2.17904839
49 2.45640163 -0.34834958
50 -1.81770753 2.45640163
51 -0.87861667 -1.81770753
52 -2.74547987 -0.87861667
53 -0.22639291 -2.74547987
54 -1.82191108 -0.22639291
55 -0.07874661 -1.82191108
56 0.28413019 -0.07874661
57 -0.89382927 0.28413019
58 -0.36948662 -0.89382927
59 -0.27213525 -0.36948662
60 -0.01503158 -0.27213525
61 1.01822676 -0.01503158
62 -1.91845472 1.01822676
63 1.51417846 -1.91845472
64 -1.73263169 1.51417846
65 3.26968039 -1.73263169
66 0.77404330 3.26968039
67 0.62671160 0.77404330
68 -4.11195684 0.62671160
69 5.08397256 -4.11195684
70 2.16438238 5.08397256
71 -3.23580652 2.16438238
72 -4.26888258 -3.23580652
73 0.08423314 -4.26888258
74 1.65695520 0.08423314
75 -0.47535601 1.65695520
76 2.84918356 -0.47535601
77 -2.43077400 2.84918356
78 -4.15238269 -2.43077400
79 -0.29273767 -4.15238269
80 1.50277054 -0.29273767
81 -3.77895959 1.50277054
82 -0.31352544 -3.77895959
83 2.63287645 -0.31352544
84 -0.28586377 2.63287645
85 -1.39382487 -0.28586377
86 -0.38697990 -1.39382487
87 0.87046604 -0.38697990
88 0.68719759 0.87046604
89 -0.40685253 0.68719759
90 -2.33716935 -0.40685253
91 -2.80392720 -2.33716935
92 -6.00219528 -2.80392720
93 -1.12878860 -6.00219528
94 1.85815208 -1.12878860
95 0.56510446 1.85815208
96 -2.56751831 0.56510446
97 -2.21588333 -2.56751831
98 -1.44419865 -2.21588333
99 1.91087377 -1.44419865
100 2.81633975 1.91087377
101 2.38332549 2.81633975
102 -0.22559837 2.38332549
103 2.84531513 -0.22559837
104 0.82231241 2.84531513
105 0.47633763 0.82231241
106 -1.68133414 0.47633763
107 -2.56300249 -1.68133414
108 4.52949836 -2.56300249
109 1.18012184 4.52949836
110 -3.23628501 1.18012184
111 0.93589381 -3.23628501
112 1.91832104 0.93589381
113 -0.30768739 1.91832104
114 -1.50433621 -0.30768739
115 -0.13820027 -1.50433621
116 0.87221602 -0.13820027
117 2.40740580 0.87221602
118 -0.32854663 2.40740580
119 -1.11439891 -0.32854663
120 -2.17279086 -1.11439891
121 -3.01591952 -2.17279086
122 -0.76917531 -3.01591952
123 -1.18791879 -0.76917531
124 0.07190099 -1.18791879
125 -0.51880613 0.07190099
126 -1.57207059 -0.51880613
127 1.81254511 -1.57207059
128 -1.63980667 1.81254511
129 1.35641119 -1.63980667
130 -1.77603215 1.35641119
131 2.21608900 -1.77603215
132 -1.26962008 2.21608900
133 -0.75675132 -1.26962008
134 0.92512307 -0.75675132
135 -1.13367372 0.92512307
136 -0.17042744 -1.13367372
137 0.27584153 -0.17042744
138 -0.39453961 0.27584153
139 1.47158615 -0.39453961
140 1.72722189 1.47158615
141 1.23388352 1.72722189
142 1.18496564 1.23388352
143 1.08456694 1.18496564
144 1.48506984 1.08456694
145 1.89618521 1.48506984
146 0.16316228 1.89618521
147 2.96976118 0.16316228
148 0.61057870 2.96976118
149 -1.84763561 0.61057870
150 2.44019298 -1.84763561
151 0.25305612 2.44019298
152 -1.00387918 0.25305612
153 -1.11769373 -1.00387918
154 0.25869868 -1.11769373
155 -0.83635049 0.25869868
156 NA -0.83635049
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.10188999 1.50083206
[2,] -2.29358477 0.10188999
[3,] -2.07685053 -2.29358477
[4,] -0.19720170 -2.07685053
[5,] 0.76800609 -0.19720170
[6,] 1.06764512 0.76800609
[7,] 2.07835009 1.06764512
[8,] -0.99608036 2.07835009
[9,] -2.77748695 -0.99608036
[10,] -0.25342245 -2.77748695
[11,] -0.12060130 -0.25342245
[12,] 3.08656747 -0.12060130
[13,] 1.17057261 3.08656747
[14,] -1.08808087 1.17057261
[15,] 0.80971002 -1.08808087
[16,] 0.40229445 0.80971002
[17,] -2.79756754 0.40229445
[18,] -1.28158275 -2.79756754
[19,] 0.72911169 -1.28158275
[20,] 2.61139354 0.72911169
[21,] 1.23994976 2.61139354
[22,] -0.15547143 1.23994976
[23,] 0.66775117 -0.15547143
[24,] 0.45725197 0.66775117
[25,] -0.30121789 0.45725197
[26,] 2.53037466 -0.30121789
[27,] -2.11009053 2.53037466
[28,] -1.15499381 -2.11009053
[29,] -0.80387372 -1.15499381
[30,] -0.45193602 -0.80387372
[31,] 2.35251593 -0.45193602
[32,] 0.63095324 2.35251593
[33,] 0.13761486 0.63095324
[34,] -5.60684916 0.13761486
[35,] 5.16697525 -5.60684916
[36,] 3.81912833 5.16697525
[37,] -1.37091641 3.81912833
[38,] 1.76660194 -1.37091641
[39,] -0.90163183 1.76660194
[40,] 5.98403018 -0.90163183
[41,] -4.81670812 5.98403018
[42,] -0.40963494 -4.81670812
[43,] 3.75275158 -0.40963494
[44,] -0.10604537 3.75275158
[45,] -1.71775002 -0.10604537
[46,] 1.77962781 -1.71775002
[47,] 2.17904839 1.77962781
[48,] -0.34834958 2.17904839
[49,] 2.45640163 -0.34834958
[50,] -1.81770753 2.45640163
[51,] -0.87861667 -1.81770753
[52,] -2.74547987 -0.87861667
[53,] -0.22639291 -2.74547987
[54,] -1.82191108 -0.22639291
[55,] -0.07874661 -1.82191108
[56,] 0.28413019 -0.07874661
[57,] -0.89382927 0.28413019
[58,] -0.36948662 -0.89382927
[59,] -0.27213525 -0.36948662
[60,] -0.01503158 -0.27213525
[61,] 1.01822676 -0.01503158
[62,] -1.91845472 1.01822676
[63,] 1.51417846 -1.91845472
[64,] -1.73263169 1.51417846
[65,] 3.26968039 -1.73263169
[66,] 0.77404330 3.26968039
[67,] 0.62671160 0.77404330
[68,] -4.11195684 0.62671160
[69,] 5.08397256 -4.11195684
[70,] 2.16438238 5.08397256
[71,] -3.23580652 2.16438238
[72,] -4.26888258 -3.23580652
[73,] 0.08423314 -4.26888258
[74,] 1.65695520 0.08423314
[75,] -0.47535601 1.65695520
[76,] 2.84918356 -0.47535601
[77,] -2.43077400 2.84918356
[78,] -4.15238269 -2.43077400
[79,] -0.29273767 -4.15238269
[80,] 1.50277054 -0.29273767
[81,] -3.77895959 1.50277054
[82,] -0.31352544 -3.77895959
[83,] 2.63287645 -0.31352544
[84,] -0.28586377 2.63287645
[85,] -1.39382487 -0.28586377
[86,] -0.38697990 -1.39382487
[87,] 0.87046604 -0.38697990
[88,] 0.68719759 0.87046604
[89,] -0.40685253 0.68719759
[90,] -2.33716935 -0.40685253
[91,] -2.80392720 -2.33716935
[92,] -6.00219528 -2.80392720
[93,] -1.12878860 -6.00219528
[94,] 1.85815208 -1.12878860
[95,] 0.56510446 1.85815208
[96,] -2.56751831 0.56510446
[97,] -2.21588333 -2.56751831
[98,] -1.44419865 -2.21588333
[99,] 1.91087377 -1.44419865
[100,] 2.81633975 1.91087377
[101,] 2.38332549 2.81633975
[102,] -0.22559837 2.38332549
[103,] 2.84531513 -0.22559837
[104,] 0.82231241 2.84531513
[105,] 0.47633763 0.82231241
[106,] -1.68133414 0.47633763
[107,] -2.56300249 -1.68133414
[108,] 4.52949836 -2.56300249
[109,] 1.18012184 4.52949836
[110,] -3.23628501 1.18012184
[111,] 0.93589381 -3.23628501
[112,] 1.91832104 0.93589381
[113,] -0.30768739 1.91832104
[114,] -1.50433621 -0.30768739
[115,] -0.13820027 -1.50433621
[116,] 0.87221602 -0.13820027
[117,] 2.40740580 0.87221602
[118,] -0.32854663 2.40740580
[119,] -1.11439891 -0.32854663
[120,] -2.17279086 -1.11439891
[121,] -3.01591952 -2.17279086
[122,] -0.76917531 -3.01591952
[123,] -1.18791879 -0.76917531
[124,] 0.07190099 -1.18791879
[125,] -0.51880613 0.07190099
[126,] -1.57207059 -0.51880613
[127,] 1.81254511 -1.57207059
[128,] -1.63980667 1.81254511
[129,] 1.35641119 -1.63980667
[130,] -1.77603215 1.35641119
[131,] 2.21608900 -1.77603215
[132,] -1.26962008 2.21608900
[133,] -0.75675132 -1.26962008
[134,] 0.92512307 -0.75675132
[135,] -1.13367372 0.92512307
[136,] -0.17042744 -1.13367372
[137,] 0.27584153 -0.17042744
[138,] -0.39453961 0.27584153
[139,] 1.47158615 -0.39453961
[140,] 1.72722189 1.47158615
[141,] 1.23388352 1.72722189
[142,] 1.18496564 1.23388352
[143,] 1.08456694 1.18496564
[144,] 1.48506984 1.08456694
[145,] 1.89618521 1.48506984
[146,] 0.16316228 1.89618521
[147,] 2.96976118 0.16316228
[148,] 0.61057870 2.96976118
[149,] -1.84763561 0.61057870
[150,] 2.44019298 -1.84763561
[151,] 0.25305612 2.44019298
[152,] -1.00387918 0.25305612
[153,] -1.11769373 -1.00387918
[154,] 0.25869868 -1.11769373
[155,] -0.83635049 0.25869868
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.10188999 1.50083206
2 -2.29358477 0.10188999
3 -2.07685053 -2.29358477
4 -0.19720170 -2.07685053
5 0.76800609 -0.19720170
6 1.06764512 0.76800609
7 2.07835009 1.06764512
8 -0.99608036 2.07835009
9 -2.77748695 -0.99608036
10 -0.25342245 -2.77748695
11 -0.12060130 -0.25342245
12 3.08656747 -0.12060130
13 1.17057261 3.08656747
14 -1.08808087 1.17057261
15 0.80971002 -1.08808087
16 0.40229445 0.80971002
17 -2.79756754 0.40229445
18 -1.28158275 -2.79756754
19 0.72911169 -1.28158275
20 2.61139354 0.72911169
21 1.23994976 2.61139354
22 -0.15547143 1.23994976
23 0.66775117 -0.15547143
24 0.45725197 0.66775117
25 -0.30121789 0.45725197
26 2.53037466 -0.30121789
27 -2.11009053 2.53037466
28 -1.15499381 -2.11009053
29 -0.80387372 -1.15499381
30 -0.45193602 -0.80387372
31 2.35251593 -0.45193602
32 0.63095324 2.35251593
33 0.13761486 0.63095324
34 -5.60684916 0.13761486
35 5.16697525 -5.60684916
36 3.81912833 5.16697525
37 -1.37091641 3.81912833
38 1.76660194 -1.37091641
39 -0.90163183 1.76660194
40 5.98403018 -0.90163183
41 -4.81670812 5.98403018
42 -0.40963494 -4.81670812
43 3.75275158 -0.40963494
44 -0.10604537 3.75275158
45 -1.71775002 -0.10604537
46 1.77962781 -1.71775002
47 2.17904839 1.77962781
48 -0.34834958 2.17904839
49 2.45640163 -0.34834958
50 -1.81770753 2.45640163
51 -0.87861667 -1.81770753
52 -2.74547987 -0.87861667
53 -0.22639291 -2.74547987
54 -1.82191108 -0.22639291
55 -0.07874661 -1.82191108
56 0.28413019 -0.07874661
57 -0.89382927 0.28413019
58 -0.36948662 -0.89382927
59 -0.27213525 -0.36948662
60 -0.01503158 -0.27213525
61 1.01822676 -0.01503158
62 -1.91845472 1.01822676
63 1.51417846 -1.91845472
64 -1.73263169 1.51417846
65 3.26968039 -1.73263169
66 0.77404330 3.26968039
67 0.62671160 0.77404330
68 -4.11195684 0.62671160
69 5.08397256 -4.11195684
70 2.16438238 5.08397256
71 -3.23580652 2.16438238
72 -4.26888258 -3.23580652
73 0.08423314 -4.26888258
74 1.65695520 0.08423314
75 -0.47535601 1.65695520
76 2.84918356 -0.47535601
77 -2.43077400 2.84918356
78 -4.15238269 -2.43077400
79 -0.29273767 -4.15238269
80 1.50277054 -0.29273767
81 -3.77895959 1.50277054
82 -0.31352544 -3.77895959
83 2.63287645 -0.31352544
84 -0.28586377 2.63287645
85 -1.39382487 -0.28586377
86 -0.38697990 -1.39382487
87 0.87046604 -0.38697990
88 0.68719759 0.87046604
89 -0.40685253 0.68719759
90 -2.33716935 -0.40685253
91 -2.80392720 -2.33716935
92 -6.00219528 -2.80392720
93 -1.12878860 -6.00219528
94 1.85815208 -1.12878860
95 0.56510446 1.85815208
96 -2.56751831 0.56510446
97 -2.21588333 -2.56751831
98 -1.44419865 -2.21588333
99 1.91087377 -1.44419865
100 2.81633975 1.91087377
101 2.38332549 2.81633975
102 -0.22559837 2.38332549
103 2.84531513 -0.22559837
104 0.82231241 2.84531513
105 0.47633763 0.82231241
106 -1.68133414 0.47633763
107 -2.56300249 -1.68133414
108 4.52949836 -2.56300249
109 1.18012184 4.52949836
110 -3.23628501 1.18012184
111 0.93589381 -3.23628501
112 1.91832104 0.93589381
113 -0.30768739 1.91832104
114 -1.50433621 -0.30768739
115 -0.13820027 -1.50433621
116 0.87221602 -0.13820027
117 2.40740580 0.87221602
118 -0.32854663 2.40740580
119 -1.11439891 -0.32854663
120 -2.17279086 -1.11439891
121 -3.01591952 -2.17279086
122 -0.76917531 -3.01591952
123 -1.18791879 -0.76917531
124 0.07190099 -1.18791879
125 -0.51880613 0.07190099
126 -1.57207059 -0.51880613
127 1.81254511 -1.57207059
128 -1.63980667 1.81254511
129 1.35641119 -1.63980667
130 -1.77603215 1.35641119
131 2.21608900 -1.77603215
132 -1.26962008 2.21608900
133 -0.75675132 -1.26962008
134 0.92512307 -0.75675132
135 -1.13367372 0.92512307
136 -0.17042744 -1.13367372
137 0.27584153 -0.17042744
138 -0.39453961 0.27584153
139 1.47158615 -0.39453961
140 1.72722189 1.47158615
141 1.23388352 1.72722189
142 1.18496564 1.23388352
143 1.08456694 1.18496564
144 1.48506984 1.08456694
145 1.89618521 1.48506984
146 0.16316228 1.89618521
147 2.96976118 0.16316228
148 0.61057870 2.96976118
149 -1.84763561 0.61057870
150 2.44019298 -1.84763561
151 0.25305612 2.44019298
152 -1.00387918 0.25305612
153 -1.11769373 -1.00387918
154 0.25869868 -1.11769373
155 -0.83635049 0.25869868
> 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/7sfe01293204076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sfe01293204076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sfe01293204076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10l6vl1293204076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o7t91293204076.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/12hgbu1293204076.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/13oh861293204076.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/14g8791293204076.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/15uj8a1293204077.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/168a601293204077.tab")
+ }
>
> try(system("convert tmp/1w5g91293204076.ps tmp/1w5g91293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w5g91293204076.ps tmp/2w5g91293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/37wfc1293204076.ps tmp/37wfc1293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/47wfc1293204076.ps tmp/47wfc1293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/57wfc1293204076.ps tmp/57wfc1293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/60owy1293204076.ps tmp/60owy1293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sfe01293204076.ps tmp/7sfe01293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sfe01293204076.ps tmp/8sfe01293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sfe01293204076.ps tmp/9sfe01293204076.png",intern=TRUE))
character(0)
> try(system("convert tmp/10l6vl1293204076.ps tmp/10l6vl1293204076.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.355 1.853 10.794