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
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+ ,dim=c(6
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'Date')
+ ,1:156))
> y <- array(NA,dim=c(6,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','Date'),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 Date t
1 13 13 14 13 3 1 1
2 12 12 8 13 5 1 2
3 15 10 12 16 6 1 3
4 12 9 7 12 6 1 4
5 10 10 10 11 5 1 5
6 12 12 7 12 3 1 6
7 15 13 16 18 8 1 7
8 9 12 11 11 4 1 8
9 12 12 14 14 4 1 9
10 11 6 6 9 4 1 10
11 11 5 16 14 6 1 11
12 11 12 11 12 6 1 12
13 15 11 16 11 5 1 13
14 7 14 12 12 4 1 14
15 11 14 7 13 6 1 15
16 11 12 13 11 4 1 16
17 10 12 11 12 6 1 17
18 14 11 15 16 6 1 18
19 10 11 7 9 4 2 19
20 6 7 9 11 4 2 20
21 11 9 7 13 2 2 21
22 15 11 14 15 7 2 22
23 11 11 15 10 5 2 23
24 12 12 7 11 4 2 24
25 14 12 15 13 6 2 25
26 15 11 17 16 6 2 26
27 9 11 15 15 7 2 27
28 13 8 14 14 5 2 28
29 13 9 14 14 6 2 29
30 16 12 8 14 4 2 30
31 13 10 8 8 4 2 31
32 12 10 14 13 7 2 32
33 14 12 14 15 7 2 33
34 11 8 8 13 4 3 34
35 9 12 11 11 4 3 35
36 16 11 16 15 6 3 36
37 12 12 10 15 6 3 37
38 10 7 8 9 5 3 38
39 13 11 14 13 6 3 39
40 16 11 16 16 7 3 40
41 14 12 13 13 6 3 41
42 15 9 5 11 3 3 42
43 5 15 8 12 3 3 43
44 8 11 10 12 4 3 44
45 11 11 8 12 6 3 45
46 16 11 13 14 7 3 46
47 17 11 15 14 5 3 47
48 9 15 6 8 4 3 48
49 9 11 12 13 5 3 49
50 13 12 16 16 6 3 50
51 10 12 5 13 6 3 51
52 6 9 15 11 6 4 52
53 12 12 12 14 5 4 53
54 8 12 8 13 4 4 54
55 14 13 13 13 5 4 55
56 12 11 14 13 5 4 56
57 11 9 12 12 4 4 57
58 16 9 16 16 6 4 58
59 8 11 10 15 2 4 59
60 15 11 15 15 8 4 60
61 7 12 8 12 3 4 61
62 16 12 16 14 6 4 62
63 14 9 19 12 6 4 63
64 16 11 14 15 6 4 64
65 9 9 6 12 5 4 65
66 14 12 13 13 5 4 66
67 11 12 15 12 6 4 67
68 13 12 7 12 5 4 68
69 15 12 13 13 6 5 69
70 5 14 4 5 2 5 70
71 15 11 14 13 5 5 71
72 13 12 13 13 5 5 72
73 11 11 11 14 5 5 73
74 11 6 14 17 6 5 74
75 12 10 12 13 6 5 75
76 12 12 15 13 6 5 76
77 12 13 14 12 5 5 77
78 12 8 13 13 5 5 78
79 14 12 8 14 4 5 79
80 6 12 6 11 2 5 80
81 7 12 7 12 4 5 81
82 14 6 13 12 6 5 82
83 14 11 13 16 6 5 83
84 10 10 11 12 5 5 84
85 13 12 5 12 3 5 85
86 12 13 12 12 6 5 86
87 9 11 8 10 4 6 87
88 12 7 11 15 5 6 88
89 16 11 14 15 8 6 89
90 10 11 9 12 4 6 90
91 14 11 10 16 6 6 91
92 10 11 13 15 6 6 92
93 16 12 16 16 7 6 93
94 15 10 16 13 6 6 94
95 12 11 11 12 5 6 95
96 10 12 8 11 4 6 96
97 8 7 4 13 6 6 97
98 8 13 7 10 3 6 98
99 11 8 14 15 5 6 99
100 13 12 11 13 6 6 100
101 16 11 17 16 7 6 101
102 16 12 15 15 7 6 102
103 14 14 17 18 6 6 103
104 11 10 5 13 3 6 104
105 4 10 4 10 2 6 105
106 14 13 10 16 8 6 106
107 9 10 11 13 3 7 107
108 14 11 15 15 8 7 108
109 8 10 10 14 3 7 109
110 8 7 9 15 4 7 110
111 11 10 12 14 5 7 111
112 12 8 15 13 7 7 112
113 11 12 7 13 6 7 113
114 14 12 13 15 6 7 114
115 15 12 12 16 7 7 115
116 16 11 14 14 6 7 116
117 16 12 14 14 6 7 117
118 11 12 8 16 6 7 118
119 14 12 15 14 6 7 119
120 14 11 12 12 4 7 120
121 12 12 12 13 4 7 121
122 14 11 16 12 5 7 122
123 8 11 9 12 4 7 123
124 13 13 15 14 6 7 124
125 16 12 15 14 6 7 125
126 12 12 6 14 5 7 126
127 16 12 14 16 8 7 127
128 12 12 15 13 6 7 128
129 11 8 10 14 5 7 129
130 4 8 6 4 4 7 130
131 16 12 14 16 8 7 131
132 15 11 12 13 6 7 132
133 10 12 8 16 4 7 133
134 13 13 11 15 6 7 134
135 15 12 13 14 6 7 135
136 12 12 9 13 4 7 136
137 14 11 15 14 6 7 137
138 7 12 13 12 3 8 138
139 19 12 15 15 6 8 139
140 12 10 14 14 5 8 140
141 12 11 16 13 4 8 141
142 13 12 14 14 6 8 142
143 15 12 14 16 4 8 143
144 8 10 10 6 4 8 144
145 12 12 10 13 4 8 145
146 10 13 4 13 6 8 146
147 8 12 8 14 5 8 147
148 10 15 15 15 6 8 148
149 15 11 16 14 6 8 149
150 16 12 12 15 8 9 150
151 13 11 12 13 7 10 151
152 16 12 15 16 7 10 152
153 9 11 9 12 4 14 153
154 14 10 12 15 6 14 154
155 14 11 14 12 6 14 155
156 12 11 11 14 2 14 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
0.184728 0.102459 0.241835 0.349829 0.637259
Date t
0.098801 -0.006413
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.46780 -1.29639 -0.05226 1.27491 6.89700
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.184728 1.456114 0.127 0.899219
FindingFriends 0.102459 0.097707 1.049 0.296043
KnowingPeople 0.241835 0.061846 3.910 0.000140 ***
Liked 0.349829 0.097950 3.571 0.000478 ***
Celebrity 0.637259 0.158123 4.030 8.86e-05 ***
Date 0.098801 0.196706 0.502 0.616215
t -0.006413 0.011948 -0.537 0.592247
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.118 on 149 degrees of freedom
Multiple R-squared: 0.5002, Adjusted R-squared: 0.48
F-statistic: 24.85 on 6 and 149 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,] 0.07385393 0.147707868 0.926146066
[2,] 0.11141217 0.222824337 0.888587831
[3,] 0.07345787 0.146915748 0.926542126
[4,] 0.54882735 0.902345309 0.451172655
[5,] 0.71869921 0.562601583 0.281300791
[6,] 0.65209041 0.695819175 0.347909588
[7,] 0.57611249 0.847775016 0.423887508
[8,] 0.48920417 0.978408337 0.510795831
[9,] 0.45187949 0.903758985 0.548120507
[10,] 0.36600042 0.732000833 0.633999583
[11,] 0.51003874 0.979922519 0.489961259
[12,] 0.51642667 0.967146651 0.483573325
[13,] 0.54584659 0.908306828 0.454153414
[14,] 0.47236154 0.944723072 0.527638464
[15,] 0.47794543 0.955890863 0.522054568
[16,] 0.43473137 0.869462732 0.565268634
[17,] 0.37438615 0.748772302 0.625613849
[18,] 0.62007360 0.759852804 0.379926402
[19,] 0.57495464 0.850090714 0.425045357
[20,] 0.51976054 0.960478927 0.480239464
[21,] 0.71176397 0.576472060 0.288236030
[22,] 0.81257060 0.374858803 0.187429401
[23,] 0.77682572 0.446348566 0.223174283
[24,] 0.73078310 0.538433810 0.269216905
[25,] 0.69786199 0.604276028 0.302138014
[26,] 0.69575087 0.608498257 0.304249128
[27,] 0.70844533 0.583109343 0.291554671
[28,] 0.67539471 0.649210588 0.324605294
[29,] 0.62633492 0.747330166 0.373665083
[30,] 0.57289741 0.854205177 0.427102589
[31,] 0.54355096 0.912898070 0.456449035
[32,] 0.50209156 0.995816882 0.497908441
[33,] 0.75752882 0.484942369 0.242471184
[34,] 0.95630705 0.087385905 0.043692952
[35,] 0.96668342 0.066633158 0.033316579
[36,] 0.95629027 0.087419452 0.043709726
[37,] 0.96122545 0.077549106 0.038774553
[38,] 0.98065098 0.038698035 0.019349018
[39,] 0.97421653 0.051566949 0.025783475
[40,] 0.98213664 0.035726717 0.017863358
[41,] 0.97921015 0.041579698 0.020789849
[42,] 0.97446675 0.051066508 0.025533254
[43,] 0.99759746 0.004805077 0.002402539
[44,] 0.99653455 0.006930891 0.003465446
[45,] 0.99712135 0.005757308 0.002878654
[46,] 0.99710451 0.005790972 0.002895486
[47,] 0.99591307 0.008173859 0.004086930
[48,] 0.99422923 0.011541532 0.005770766
[49,] 0.99356498 0.012870039 0.006435019
[50,] 0.99514768 0.009704646 0.004852323
[51,] 0.99366228 0.012675447 0.006337723
[52,] 0.99448678 0.011026439 0.005513219
[53,] 0.99504169 0.009916624 0.004958312
[54,] 0.99335491 0.013290179 0.006645090
[55,] 0.99371541 0.012569188 0.006284594
[56,] 0.99207410 0.015851797 0.007925899
[57,] 0.99123301 0.017533973 0.008766987
[58,] 0.99119366 0.017612689 0.008806345
[59,] 0.99240860 0.015182793 0.007591397
[60,] 0.99262299 0.014754022 0.007377011
[61,] 0.99003546 0.019929081 0.009964541
[62,] 0.99151234 0.016975311 0.008487655
[63,] 0.98874471 0.022510576 0.011255288
[64,] 0.98587554 0.028248929 0.014124464
[65,] 0.98968395 0.020632092 0.010316046
[66,] 0.98606498 0.027870031 0.013935015
[67,] 0.98381401 0.032371979 0.016185989
[68,] 0.97875723 0.042485539 0.021242769
[69,] 0.97189841 0.056203173 0.028101587
[70,] 0.98192884 0.036142321 0.018071160
[71,] 0.98117119 0.037657622 0.018828811
[72,] 0.98446562 0.031068762 0.015534381
[73,] 0.98553158 0.028936839 0.014468419
[74,] 0.98054811 0.038903774 0.019451887
[75,] 0.97602519 0.047949610 0.023974805
[76,] 0.99361474 0.012770514 0.006385257
[77,] 0.99118616 0.017627683 0.008813842
[78,] 0.98793741 0.024125173 0.012062587
[79,] 0.98416877 0.031662456 0.015831228
[80,] 0.98026700 0.039466009 0.019733005
[81,] 0.97393345 0.052133108 0.026066554
[82,] 0.96894738 0.062105247 0.031052623
[83,] 0.98148321 0.037033587 0.018516793
[84,] 0.97607710 0.047845802 0.023922901
[85,] 0.97282461 0.054350782 0.027175391
[86,] 0.96597904 0.068041929 0.034020964
[87,] 0.95754400 0.084912006 0.042456003
[88,] 0.95359737 0.092805260 0.046402630
[89,] 0.94102872 0.117942566 0.058971283
[90,] 0.93533203 0.129335932 0.064667966
[91,] 0.92149837 0.157003253 0.078501627
[92,] 0.90266029 0.194679421 0.097339710
[93,] 0.88868717 0.222625662 0.111312831
[94,] 0.89189046 0.216219075 0.108109538
[95,] 0.92892920 0.142141590 0.071070795
[96,] 0.92545692 0.149086167 0.074543084
[97,] 0.90562530 0.188749399 0.094374700
[98,] 0.88523091 0.229538181 0.114769090
[99,] 0.87954822 0.240903568 0.120451784
[100,] 0.87697145 0.246057103 0.123028552
[101,] 0.89671471 0.206570590 0.103285295
[102,] 0.88881883 0.222362336 0.111181168
[103,] 0.92652960 0.146940795 0.073470397
[104,] 0.90486601 0.190267983 0.095133991
[105,] 0.88276184 0.234476319 0.117238159
[106,] 0.85657865 0.286842696 0.143421348
[107,] 0.84736048 0.305279035 0.152639517
[108,] 0.84516007 0.309679864 0.154839932
[109,] 0.84621391 0.307572190 0.153786095
[110,] 0.81404065 0.371918694 0.185959347
[111,] 0.85132102 0.297357960 0.148678980
[112,] 0.81899052 0.362018968 0.181009484
[113,] 0.79045237 0.419095266 0.209547633
[114,] 0.78251078 0.434978438 0.217489219
[115,] 0.74670636 0.506587288 0.253293644
[116,] 0.73413091 0.531738181 0.265869091
[117,] 0.71539815 0.569203696 0.284601848
[118,] 0.65721142 0.685577167 0.342788584
[119,] 0.62927898 0.741442030 0.370721015
[120,] 0.63626513 0.727469742 0.363734871
[121,] 0.64008455 0.719830891 0.359915445
[122,] 0.59454615 0.810907707 0.405453853
[123,] 0.55310335 0.893793301 0.446896650
[124,] 0.54369990 0.912600197 0.456300098
[125,] 0.47134678 0.942693566 0.528653217
[126,] 0.41431560 0.828631191 0.585684404
[127,] 0.39370284 0.787405671 0.606297164
[128,] 0.32079045 0.641580902 0.679209549
[129,] 0.41161517 0.823230338 0.588384831
[130,] 0.77136457 0.457270857 0.228635429
[131,] 0.76377510 0.472449798 0.236224899
[132,] 0.70961141 0.580777182 0.290388591
[133,] 0.62334924 0.753301519 0.376650760
[134,] 0.55995156 0.880096890 0.440048445
[135,] 0.43466229 0.869324572 0.565337714
[136,] 0.69958066 0.600838689 0.300419344
[137,] 0.84924859 0.301502825 0.150751412
> postscript(file="/var/www/html/rcomp/tmp/1ea041290276880.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ea041290276880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/371zp1290276880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/471zp1290276880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/571zp1290276880.ps",horizontal=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
1.54567249 0.83103819 1.38828198 1.10564515 -0.72881948 2.72287163
7 8 9 10 11 12
-1.83496131 -1.51907532 -0.28765467 3.01733797 -2.31580620 -1.11777154
13 14 15 16 17 18
2.76901080 -4.27718083 -0.68593807 0.04855561 -2.08570793 -0.34349239
19 20 21 22 23 24
1.22212161 -3.54495806 2.31506904 0.53776241 -0.67399881 2.45206912
25 26 27 28 29 30
0.54962268 0.12533768 -5.67200941 0.50796261 -0.22534284 5.19922425
31 32 33 34 35 36
4.50952636 -1.59599426 -0.49415655 0.88573854 -1.54353351 1.68232807
37 38 39 40 41 42
-0.96270553 0.77590345 -0.11510571 0.72089111 1.03709625 6.89700415
43 44 45 46 47 48
-4.78667151 -2.49135325 -0.27578819 2.18453093 3.98179131 0.49111808
49 50 51 52 53 54
-2.93004835 -1.68018132 -0.96409306 -6.46780158 -0.45548583 -2.49464353
55 56 57 58 59 60
1.56287386 -0.46763102 0.21445826 1.57969636 -2.26893052 -0.29525053
61 62 63 64 65 66
-2.46266661 1.99762769 0.28556805 2.24675432 -0.92048663 1.73587273
67 68 69 70 71 72
-2.02881608 2.54953936 2.01905078 -0.65526965 2.52975897 0.67554821
73 74 75 76 77 78
-1.08173788 -2.97528192 -0.49571962 -1.41973102 -0.28685393 0.12386021
79 80 81 82 83 84
3.21704510 -1.96886701 -2.82863684 2.06699827 0.16180204 -1.20908183
85 86 87 88 89 90
4.31794418 -0.38272782 -0.32868069 -0.02434083 0.93495215 -0.25093514
91 92 93 94 95 96
0.83980926 -3.52945572 0.66190393 1.55997953 0.66019835 0.27674631
97 98 99 100 101 102
-2.21138037 -0.58396388 -1.78176608 0.60271519 0.57382920 1.31128247
103 104 105 106 107 108
-1.78312002 2.19607429 -2.86893251 -0.54343625 -1.33450099 -1.28384240
109 110 111 112 113 114
-2.42966870 -2.86113165 -1.17503264 -1.61389831 -0.44537854 0.41036441
115 116 117 118 119 120
0.67152471 2.63364193 2.53759574 -1.70463613 0.30858575 3.11713940
121 122 123 124 125 126
0.67126461 1.52536385 -2.13811614 -0.76180955 2.34706209 1.16725296
127 128 129 130 131 132
0.62754726 -1.28387115 -0.37101493 -2.26171529 0.65319815 2.56974496
133 134 135 136 137 138
-1.33392677 -0.08016918 1.89486018 1.49296175 0.52647368 -3.57326753
139 140 141 142 143 144
4.98821076 -0.57153540 -0.16416460 -0.40088705 2.18038701 -0.14265477
145 146 147 148 149 150
1.21003997 -0.70951216 -3.28055158 -4.26145148 1.26279007 1.41093763
151 152 153 154 155 156
-0.24207515 0.88688659 -1.63734046 0.42202058 0.89178930 1.47308818
> postscript(file="/var/www/html/rcomp/tmp/60sys1290276880.ps",horizontal=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 1.54567249 NA
1 0.83103819 1.54567249
2 1.38828198 0.83103819
3 1.10564515 1.38828198
4 -0.72881948 1.10564515
5 2.72287163 -0.72881948
6 -1.83496131 2.72287163
7 -1.51907532 -1.83496131
8 -0.28765467 -1.51907532
9 3.01733797 -0.28765467
10 -2.31580620 3.01733797
11 -1.11777154 -2.31580620
12 2.76901080 -1.11777154
13 -4.27718083 2.76901080
14 -0.68593807 -4.27718083
15 0.04855561 -0.68593807
16 -2.08570793 0.04855561
17 -0.34349239 -2.08570793
18 1.22212161 -0.34349239
19 -3.54495806 1.22212161
20 2.31506904 -3.54495806
21 0.53776241 2.31506904
22 -0.67399881 0.53776241
23 2.45206912 -0.67399881
24 0.54962268 2.45206912
25 0.12533768 0.54962268
26 -5.67200941 0.12533768
27 0.50796261 -5.67200941
28 -0.22534284 0.50796261
29 5.19922425 -0.22534284
30 4.50952636 5.19922425
31 -1.59599426 4.50952636
32 -0.49415655 -1.59599426
33 0.88573854 -0.49415655
34 -1.54353351 0.88573854
35 1.68232807 -1.54353351
36 -0.96270553 1.68232807
37 0.77590345 -0.96270553
38 -0.11510571 0.77590345
39 0.72089111 -0.11510571
40 1.03709625 0.72089111
41 6.89700415 1.03709625
42 -4.78667151 6.89700415
43 -2.49135325 -4.78667151
44 -0.27578819 -2.49135325
45 2.18453093 -0.27578819
46 3.98179131 2.18453093
47 0.49111808 3.98179131
48 -2.93004835 0.49111808
49 -1.68018132 -2.93004835
50 -0.96409306 -1.68018132
51 -6.46780158 -0.96409306
52 -0.45548583 -6.46780158
53 -2.49464353 -0.45548583
54 1.56287386 -2.49464353
55 -0.46763102 1.56287386
56 0.21445826 -0.46763102
57 1.57969636 0.21445826
58 -2.26893052 1.57969636
59 -0.29525053 -2.26893052
60 -2.46266661 -0.29525053
61 1.99762769 -2.46266661
62 0.28556805 1.99762769
63 2.24675432 0.28556805
64 -0.92048663 2.24675432
65 1.73587273 -0.92048663
66 -2.02881608 1.73587273
67 2.54953936 -2.02881608
68 2.01905078 2.54953936
69 -0.65526965 2.01905078
70 2.52975897 -0.65526965
71 0.67554821 2.52975897
72 -1.08173788 0.67554821
73 -2.97528192 -1.08173788
74 -0.49571962 -2.97528192
75 -1.41973102 -0.49571962
76 -0.28685393 -1.41973102
77 0.12386021 -0.28685393
78 3.21704510 0.12386021
79 -1.96886701 3.21704510
80 -2.82863684 -1.96886701
81 2.06699827 -2.82863684
82 0.16180204 2.06699827
83 -1.20908183 0.16180204
84 4.31794418 -1.20908183
85 -0.38272782 4.31794418
86 -0.32868069 -0.38272782
87 -0.02434083 -0.32868069
88 0.93495215 -0.02434083
89 -0.25093514 0.93495215
90 0.83980926 -0.25093514
91 -3.52945572 0.83980926
92 0.66190393 -3.52945572
93 1.55997953 0.66190393
94 0.66019835 1.55997953
95 0.27674631 0.66019835
96 -2.21138037 0.27674631
97 -0.58396388 -2.21138037
98 -1.78176608 -0.58396388
99 0.60271519 -1.78176608
100 0.57382920 0.60271519
101 1.31128247 0.57382920
102 -1.78312002 1.31128247
103 2.19607429 -1.78312002
104 -2.86893251 2.19607429
105 -0.54343625 -2.86893251
106 -1.33450099 -0.54343625
107 -1.28384240 -1.33450099
108 -2.42966870 -1.28384240
109 -2.86113165 -2.42966870
110 -1.17503264 -2.86113165
111 -1.61389831 -1.17503264
112 -0.44537854 -1.61389831
113 0.41036441 -0.44537854
114 0.67152471 0.41036441
115 2.63364193 0.67152471
116 2.53759574 2.63364193
117 -1.70463613 2.53759574
118 0.30858575 -1.70463613
119 3.11713940 0.30858575
120 0.67126461 3.11713940
121 1.52536385 0.67126461
122 -2.13811614 1.52536385
123 -0.76180955 -2.13811614
124 2.34706209 -0.76180955
125 1.16725296 2.34706209
126 0.62754726 1.16725296
127 -1.28387115 0.62754726
128 -0.37101493 -1.28387115
129 -2.26171529 -0.37101493
130 0.65319815 -2.26171529
131 2.56974496 0.65319815
132 -1.33392677 2.56974496
133 -0.08016918 -1.33392677
134 1.89486018 -0.08016918
135 1.49296175 1.89486018
136 0.52647368 1.49296175
137 -3.57326753 0.52647368
138 4.98821076 -3.57326753
139 -0.57153540 4.98821076
140 -0.16416460 -0.57153540
141 -0.40088705 -0.16416460
142 2.18038701 -0.40088705
143 -0.14265477 2.18038701
144 1.21003997 -0.14265477
145 -0.70951216 1.21003997
146 -3.28055158 -0.70951216
147 -4.26145148 -3.28055158
148 1.26279007 -4.26145148
149 1.41093763 1.26279007
150 -0.24207515 1.41093763
151 0.88688659 -0.24207515
152 -1.63734046 0.88688659
153 0.42202058 -1.63734046
154 0.89178930 0.42202058
155 1.47308818 0.89178930
156 NA 1.47308818
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.83103819 1.54567249
[2,] 1.38828198 0.83103819
[3,] 1.10564515 1.38828198
[4,] -0.72881948 1.10564515
[5,] 2.72287163 -0.72881948
[6,] -1.83496131 2.72287163
[7,] -1.51907532 -1.83496131
[8,] -0.28765467 -1.51907532
[9,] 3.01733797 -0.28765467
[10,] -2.31580620 3.01733797
[11,] -1.11777154 -2.31580620
[12,] 2.76901080 -1.11777154
[13,] -4.27718083 2.76901080
[14,] -0.68593807 -4.27718083
[15,] 0.04855561 -0.68593807
[16,] -2.08570793 0.04855561
[17,] -0.34349239 -2.08570793
[18,] 1.22212161 -0.34349239
[19,] -3.54495806 1.22212161
[20,] 2.31506904 -3.54495806
[21,] 0.53776241 2.31506904
[22,] -0.67399881 0.53776241
[23,] 2.45206912 -0.67399881
[24,] 0.54962268 2.45206912
[25,] 0.12533768 0.54962268
[26,] -5.67200941 0.12533768
[27,] 0.50796261 -5.67200941
[28,] -0.22534284 0.50796261
[29,] 5.19922425 -0.22534284
[30,] 4.50952636 5.19922425
[31,] -1.59599426 4.50952636
[32,] -0.49415655 -1.59599426
[33,] 0.88573854 -0.49415655
[34,] -1.54353351 0.88573854
[35,] 1.68232807 -1.54353351
[36,] -0.96270553 1.68232807
[37,] 0.77590345 -0.96270553
[38,] -0.11510571 0.77590345
[39,] 0.72089111 -0.11510571
[40,] 1.03709625 0.72089111
[41,] 6.89700415 1.03709625
[42,] -4.78667151 6.89700415
[43,] -2.49135325 -4.78667151
[44,] -0.27578819 -2.49135325
[45,] 2.18453093 -0.27578819
[46,] 3.98179131 2.18453093
[47,] 0.49111808 3.98179131
[48,] -2.93004835 0.49111808
[49,] -1.68018132 -2.93004835
[50,] -0.96409306 -1.68018132
[51,] -6.46780158 -0.96409306
[52,] -0.45548583 -6.46780158
[53,] -2.49464353 -0.45548583
[54,] 1.56287386 -2.49464353
[55,] -0.46763102 1.56287386
[56,] 0.21445826 -0.46763102
[57,] 1.57969636 0.21445826
[58,] -2.26893052 1.57969636
[59,] -0.29525053 -2.26893052
[60,] -2.46266661 -0.29525053
[61,] 1.99762769 -2.46266661
[62,] 0.28556805 1.99762769
[63,] 2.24675432 0.28556805
[64,] -0.92048663 2.24675432
[65,] 1.73587273 -0.92048663
[66,] -2.02881608 1.73587273
[67,] 2.54953936 -2.02881608
[68,] 2.01905078 2.54953936
[69,] -0.65526965 2.01905078
[70,] 2.52975897 -0.65526965
[71,] 0.67554821 2.52975897
[72,] -1.08173788 0.67554821
[73,] -2.97528192 -1.08173788
[74,] -0.49571962 -2.97528192
[75,] -1.41973102 -0.49571962
[76,] -0.28685393 -1.41973102
[77,] 0.12386021 -0.28685393
[78,] 3.21704510 0.12386021
[79,] -1.96886701 3.21704510
[80,] -2.82863684 -1.96886701
[81,] 2.06699827 -2.82863684
[82,] 0.16180204 2.06699827
[83,] -1.20908183 0.16180204
[84,] 4.31794418 -1.20908183
[85,] -0.38272782 4.31794418
[86,] -0.32868069 -0.38272782
[87,] -0.02434083 -0.32868069
[88,] 0.93495215 -0.02434083
[89,] -0.25093514 0.93495215
[90,] 0.83980926 -0.25093514
[91,] -3.52945572 0.83980926
[92,] 0.66190393 -3.52945572
[93,] 1.55997953 0.66190393
[94,] 0.66019835 1.55997953
[95,] 0.27674631 0.66019835
[96,] -2.21138037 0.27674631
[97,] -0.58396388 -2.21138037
[98,] -1.78176608 -0.58396388
[99,] 0.60271519 -1.78176608
[100,] 0.57382920 0.60271519
[101,] 1.31128247 0.57382920
[102,] -1.78312002 1.31128247
[103,] 2.19607429 -1.78312002
[104,] -2.86893251 2.19607429
[105,] -0.54343625 -2.86893251
[106,] -1.33450099 -0.54343625
[107,] -1.28384240 -1.33450099
[108,] -2.42966870 -1.28384240
[109,] -2.86113165 -2.42966870
[110,] -1.17503264 -2.86113165
[111,] -1.61389831 -1.17503264
[112,] -0.44537854 -1.61389831
[113,] 0.41036441 -0.44537854
[114,] 0.67152471 0.41036441
[115,] 2.63364193 0.67152471
[116,] 2.53759574 2.63364193
[117,] -1.70463613 2.53759574
[118,] 0.30858575 -1.70463613
[119,] 3.11713940 0.30858575
[120,] 0.67126461 3.11713940
[121,] 1.52536385 0.67126461
[122,] -2.13811614 1.52536385
[123,] -0.76180955 -2.13811614
[124,] 2.34706209 -0.76180955
[125,] 1.16725296 2.34706209
[126,] 0.62754726 1.16725296
[127,] -1.28387115 0.62754726
[128,] -0.37101493 -1.28387115
[129,] -2.26171529 -0.37101493
[130,] 0.65319815 -2.26171529
[131,] 2.56974496 0.65319815
[132,] -1.33392677 2.56974496
[133,] -0.08016918 -1.33392677
[134,] 1.89486018 -0.08016918
[135,] 1.49296175 1.89486018
[136,] 0.52647368 1.49296175
[137,] -3.57326753 0.52647368
[138,] 4.98821076 -3.57326753
[139,] -0.57153540 4.98821076
[140,] -0.16416460 -0.57153540
[141,] -0.40088705 -0.16416460
[142,] 2.18038701 -0.40088705
[143,] -0.14265477 2.18038701
[144,] 1.21003997 -0.14265477
[145,] -0.70951216 1.21003997
[146,] -3.28055158 -0.70951216
[147,] -4.26145148 -3.28055158
[148,] 1.26279007 -4.26145148
[149,] 1.41093763 1.26279007
[150,] -0.24207515 1.41093763
[151,] 0.88688659 -0.24207515
[152,] -1.63734046 0.88688659
[153,] 0.42202058 -1.63734046
[154,] 0.89178930 0.42202058
[155,] 1.47308818 0.89178930
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.83103819 1.54567249
2 1.38828198 0.83103819
3 1.10564515 1.38828198
4 -0.72881948 1.10564515
5 2.72287163 -0.72881948
6 -1.83496131 2.72287163
7 -1.51907532 -1.83496131
8 -0.28765467 -1.51907532
9 3.01733797 -0.28765467
10 -2.31580620 3.01733797
11 -1.11777154 -2.31580620
12 2.76901080 -1.11777154
13 -4.27718083 2.76901080
14 -0.68593807 -4.27718083
15 0.04855561 -0.68593807
16 -2.08570793 0.04855561
17 -0.34349239 -2.08570793
18 1.22212161 -0.34349239
19 -3.54495806 1.22212161
20 2.31506904 -3.54495806
21 0.53776241 2.31506904
22 -0.67399881 0.53776241
23 2.45206912 -0.67399881
24 0.54962268 2.45206912
25 0.12533768 0.54962268
26 -5.67200941 0.12533768
27 0.50796261 -5.67200941
28 -0.22534284 0.50796261
29 5.19922425 -0.22534284
30 4.50952636 5.19922425
31 -1.59599426 4.50952636
32 -0.49415655 -1.59599426
33 0.88573854 -0.49415655
34 -1.54353351 0.88573854
35 1.68232807 -1.54353351
36 -0.96270553 1.68232807
37 0.77590345 -0.96270553
38 -0.11510571 0.77590345
39 0.72089111 -0.11510571
40 1.03709625 0.72089111
41 6.89700415 1.03709625
42 -4.78667151 6.89700415
43 -2.49135325 -4.78667151
44 -0.27578819 -2.49135325
45 2.18453093 -0.27578819
46 3.98179131 2.18453093
47 0.49111808 3.98179131
48 -2.93004835 0.49111808
49 -1.68018132 -2.93004835
50 -0.96409306 -1.68018132
51 -6.46780158 -0.96409306
52 -0.45548583 -6.46780158
53 -2.49464353 -0.45548583
54 1.56287386 -2.49464353
55 -0.46763102 1.56287386
56 0.21445826 -0.46763102
57 1.57969636 0.21445826
58 -2.26893052 1.57969636
59 -0.29525053 -2.26893052
60 -2.46266661 -0.29525053
61 1.99762769 -2.46266661
62 0.28556805 1.99762769
63 2.24675432 0.28556805
64 -0.92048663 2.24675432
65 1.73587273 -0.92048663
66 -2.02881608 1.73587273
67 2.54953936 -2.02881608
68 2.01905078 2.54953936
69 -0.65526965 2.01905078
70 2.52975897 -0.65526965
71 0.67554821 2.52975897
72 -1.08173788 0.67554821
73 -2.97528192 -1.08173788
74 -0.49571962 -2.97528192
75 -1.41973102 -0.49571962
76 -0.28685393 -1.41973102
77 0.12386021 -0.28685393
78 3.21704510 0.12386021
79 -1.96886701 3.21704510
80 -2.82863684 -1.96886701
81 2.06699827 -2.82863684
82 0.16180204 2.06699827
83 -1.20908183 0.16180204
84 4.31794418 -1.20908183
85 -0.38272782 4.31794418
86 -0.32868069 -0.38272782
87 -0.02434083 -0.32868069
88 0.93495215 -0.02434083
89 -0.25093514 0.93495215
90 0.83980926 -0.25093514
91 -3.52945572 0.83980926
92 0.66190393 -3.52945572
93 1.55997953 0.66190393
94 0.66019835 1.55997953
95 0.27674631 0.66019835
96 -2.21138037 0.27674631
97 -0.58396388 -2.21138037
98 -1.78176608 -0.58396388
99 0.60271519 -1.78176608
100 0.57382920 0.60271519
101 1.31128247 0.57382920
102 -1.78312002 1.31128247
103 2.19607429 -1.78312002
104 -2.86893251 2.19607429
105 -0.54343625 -2.86893251
106 -1.33450099 -0.54343625
107 -1.28384240 -1.33450099
108 -2.42966870 -1.28384240
109 -2.86113165 -2.42966870
110 -1.17503264 -2.86113165
111 -1.61389831 -1.17503264
112 -0.44537854 -1.61389831
113 0.41036441 -0.44537854
114 0.67152471 0.41036441
115 2.63364193 0.67152471
116 2.53759574 2.63364193
117 -1.70463613 2.53759574
118 0.30858575 -1.70463613
119 3.11713940 0.30858575
120 0.67126461 3.11713940
121 1.52536385 0.67126461
122 -2.13811614 1.52536385
123 -0.76180955 -2.13811614
124 2.34706209 -0.76180955
125 1.16725296 2.34706209
126 0.62754726 1.16725296
127 -1.28387115 0.62754726
128 -0.37101493 -1.28387115
129 -2.26171529 -0.37101493
130 0.65319815 -2.26171529
131 2.56974496 0.65319815
132 -1.33392677 2.56974496
133 -0.08016918 -1.33392677
134 1.89486018 -0.08016918
135 1.49296175 1.89486018
136 0.52647368 1.49296175
137 -3.57326753 0.52647368
138 4.98821076 -3.57326753
139 -0.57153540 4.98821076
140 -0.16416460 -0.57153540
141 -0.40088705 -0.16416460
142 2.18038701 -0.40088705
143 -0.14265477 2.18038701
144 1.21003997 -0.14265477
145 -0.70951216 1.21003997
146 -3.28055158 -0.70951216
147 -4.26145148 -3.28055158
148 1.26279007 -4.26145148
149 1.41093763 1.26279007
150 -0.24207515 1.41093763
151 0.88688659 -0.24207515
152 -1.63734046 0.88688659
153 0.42202058 -1.63734046
154 0.89178930 0.42202058
155 1.47308818 0.89178930
> 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/7akfd1290276880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8akfd1290276880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9akfd1290276880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103tfg1290276880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11obvl1290276880.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/12auc91290276880.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/13gd931290276880.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/14rm8o1290276880.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/15cn6u1290276880.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/16rxml1290276880.tab")
+ }
>
> try(system("convert tmp/1ea041290276880.ps tmp/1ea041290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ea041290276880.ps tmp/2ea041290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/371zp1290276880.ps tmp/371zp1290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/471zp1290276880.ps tmp/471zp1290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/571zp1290276880.ps tmp/571zp1290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/60sys1290276880.ps tmp/60sys1290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/7akfd1290276880.ps tmp/7akfd1290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/8akfd1290276880.ps tmp/8akfd1290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/9akfd1290276880.ps tmp/9akfd1290276880.png",intern=TRUE))
character(0)
> try(system("convert tmp/103tfg1290276880.ps tmp/103tfg1290276880.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.973 1.776 8.979