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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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(13
+ ,14
+ ,13
+ ,3
+ ,12
+ ,8
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+ ,5
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+ ,2)
+ ,dim=c(4
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity')
+ ,1:156))
> y <- array(NA,dim=c(4,156),dimnames=list(c('Popularity','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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> #'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
Celebrity Popularity KnowingPeople Liked
1 3 13 14 13
2 5 12 8 13
3 6 15 12 16
4 6 12 7 12
5 5 10 10 11
6 3 12 7 12
7 8 15 16 18
8 4 9 11 11
9 4 12 14 14
10 4 11 6 9
11 6 11 16 14
12 6 11 11 12
13 5 15 16 11
14 4 7 12 12
15 6 11 7 13
16 4 11 13 11
17 6 10 11 12
18 6 14 15 16
19 4 10 7 9
20 4 6 9 11
21 2 11 7 13
22 7 15 14 15
23 5 11 15 10
24 4 12 7 11
25 6 14 15 13
26 6 15 17 16
27 7 9 15 15
28 5 13 14 14
29 6 13 14 14
30 4 16 8 14
31 4 13 8 8
32 7 12 14 13
33 7 14 14 15
34 4 11 8 13
35 4 9 11 11
36 6 16 16 15
37 6 12 10 15
38 5 10 8 9
39 6 13 14 13
40 7 16 16 16
41 6 14 13 13
42 3 15 5 11
43 3 5 8 12
44 4 8 10 12
45 6 11 8 12
46 7 16 13 14
47 5 17 15 14
48 4 9 6 8
49 5 9 12 13
50 6 13 16 16
51 6 10 5 13
52 6 6 15 11
53 5 12 12 14
54 4 8 8 13
55 5 14 13 13
56 5 12 14 13
57 4 11 12 12
58 6 16 16 16
59 2 8 10 15
60 8 15 15 15
61 3 7 8 12
62 6 16 16 14
63 6 14 19 12
64 6 16 14 15
65 5 9 6 12
66 5 14 13 13
67 6 11 15 12
68 5 13 7 12
69 6 15 13 13
70 2 5 4 5
71 5 15 14 13
72 5 13 13 13
73 5 11 11 14
74 6 11 14 17
75 6 12 12 13
76 6 12 15 13
77 5 12 14 12
78 5 12 13 13
79 4 14 8 14
80 2 6 6 11
81 4 7 7 12
82 6 14 13 12
83 6 14 13 16
84 5 10 11 12
85 3 13 5 12
86 6 12 12 12
87 4 9 8 10
88 5 12 11 15
89 8 16 14 15
90 4 10 9 12
91 6 14 10 16
92 6 10 13 15
93 7 16 16 16
94 6 15 16 13
95 5 12 11 12
96 4 10 8 11
97 6 8 4 13
98 3 8 7 10
99 5 11 14 15
100 6 13 11 13
101 7 16 17 16
102 7 16 15 15
103 6 14 17 18
104 3 11 5 13
105 2 4 4 10
106 8 14 10 16
107 3 9 11 13
108 8 14 15 15
109 3 8 10 14
110 4 8 9 15
111 5 11 12 14
112 7 12 15 13
113 6 11 7 13
114 6 14 13 15
115 7 15 12 16
116 6 16 14 14
117 6 16 14 14
118 6 11 8 16
119 6 14 15 14
120 4 14 12 12
121 4 12 12 13
122 5 14 16 12
123 4 8 9 12
124 6 13 15 14
125 6 16 15 14
126 5 12 6 14
127 8 16 14 16
128 6 12 15 13
129 5 11 10 14
130 4 4 6 4
131 8 16 14 16
132 6 15 12 13
133 4 10 8 16
134 6 13 11 15
135 6 15 13 14
136 4 12 9 13
137 6 14 15 14
138 3 7 13 12
139 6 19 15 15
140 5 12 14 14
141 4 12 16 13
142 6 13 14 14
143 4 15 14 16
144 4 8 10 6
145 4 12 10 13
146 6 10 4 13
147 5 8 8 14
148 6 10 15 15
149 6 15 16 14
150 8 16 12 15
151 7 13 12 13
152 7 16 15 16
153 4 9 9 12
154 6 14 12 15
155 6 14 14 12
156 2 12 11 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Popularity KnowingPeople Liked
0.2289 0.1529 0.1041 0.1466
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.26127 -0.59808 0.01644 0.59069 2.24380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.22887 0.51795 0.442 0.659207
Popularity 0.15295 0.03813 4.011 9.46e-05 ***
KnowingPeople 0.10410 0.03071 3.390 0.000891 ***
Liked 0.14657 0.04817 3.042 0.002766 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.041 on 152 degrees of freedom
Multiple R-squared: 0.4583, Adjusted R-squared: 0.4476
F-statistic: 42.87 on 3 and 152 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,] 0.822113740 0.35577252 0.17788626
[2,] 0.787696930 0.42460614 0.21230307
[3,] 0.796973040 0.40605392 0.20302696
[4,] 0.844814093 0.31037181 0.15518591
[5,] 0.821251657 0.35749669 0.17874834
[6,] 0.852567642 0.29486472 0.14743236
[7,] 0.882458444 0.23508311 0.11754156
[8,] 0.840396504 0.31920699 0.15960350
[9,] 0.824241714 0.35151657 0.17575829
[10,] 0.770684269 0.45863146 0.22931573
[11,] 0.795431699 0.40913660 0.20456830
[12,] 0.736844629 0.52631074 0.26315537
[13,] 0.672178436 0.65564313 0.32782156
[14,] 0.606695197 0.78660961 0.39330480
[15,] 0.915701753 0.16859649 0.08429825
[16,] 0.911421069 0.17715786 0.08857893
[17,] 0.888156056 0.22368789 0.11184394
[18,] 0.856257379 0.28748524 0.14374262
[19,] 0.822278305 0.35544339 0.17772170
[20,] 0.787012199 0.42597560 0.21298780
[21,] 0.810851813 0.37829637 0.18914819
[22,] 0.782174522 0.43565096 0.21782548
[23,] 0.739420724 0.52115855 0.26057928
[24,] 0.751686823 0.49662635 0.24831318
[25,] 0.715607228 0.56878554 0.28439277
[26,] 0.773495271 0.45300946 0.22650473
[27,] 0.769889329 0.46022134 0.23011067
[28,] 0.743356358 0.51328728 0.25664364
[29,] 0.705747108 0.58850578 0.29425289
[30,] 0.661174618 0.67765076 0.33882538
[31,] 0.625757141 0.74848572 0.37424286
[32,] 0.640219857 0.71956029 0.35978014
[33,] 0.598682897 0.80263421 0.40131710
[34,] 0.554785719 0.89042856 0.44521428
[35,] 0.513201008 0.97359798 0.48679899
[36,] 0.544172284 0.91165543 0.45582772
[37,] 0.545316497 0.90936701 0.45468350
[38,] 0.501442532 0.99711494 0.49855747
[39,] 0.563519266 0.87296147 0.43648073
[40,] 0.563329614 0.87334077 0.43667039
[41,] 0.592084011 0.81583198 0.40791599
[42,] 0.561446884 0.87710623 0.43855312
[43,] 0.513517704 0.97296459 0.48648230
[44,] 0.468267970 0.93653594 0.53173203
[45,] 0.549035378 0.90192924 0.45096462
[46,] 0.604472084 0.79105583 0.39552792
[47,] 0.564648415 0.87070317 0.43535158
[48,] 0.528604646 0.94279071 0.47139535
[49,] 0.494142840 0.98828568 0.50585716
[50,] 0.453997220 0.90799444 0.54600278
[51,] 0.446058488 0.89211698 0.55394151
[52,] 0.413117393 0.82623479 0.58688261
[53,] 0.709056436 0.58188713 0.29094356
[54,] 0.781154934 0.43769013 0.21884507
[55,] 0.772871382 0.45425724 0.22712862
[56,] 0.740114431 0.51977114 0.25988557
[57,] 0.700923786 0.59815243 0.29907621
[58,] 0.662551147 0.67489771 0.33744885
[59,] 0.658407961 0.68318408 0.34159204
[60,] 0.629279568 0.74144086 0.37072043
[61,] 0.609065293 0.78186941 0.39093471
[62,] 0.569083877 0.86183225 0.43091612
[63,] 0.525978221 0.94804356 0.47402178
[64,] 0.482123169 0.96424634 0.51787683
[65,] 0.468412554 0.93682511 0.53158745
[66,] 0.431276220 0.86255244 0.56872378
[67,] 0.386174103 0.77234821 0.61382590
[68,] 0.345128443 0.69025689 0.65487156
[69,] 0.326765897 0.65353179 0.67323410
[70,] 0.295034434 0.59006887 0.70496557
[71,] 0.258551835 0.51710367 0.74144817
[72,] 0.225235833 0.45047167 0.77476417
[73,] 0.246927188 0.49385438 0.75307281
[74,] 0.277457693 0.55491539 0.72254231
[75,] 0.241644397 0.48328879 0.75835560
[76,] 0.214689325 0.42937865 0.78531067
[77,] 0.182388349 0.36477670 0.81761165
[78,] 0.156314335 0.31262867 0.84368566
[79,] 0.213725850 0.42745170 0.78627415
[80,] 0.205826371 0.41165274 0.79417363
[81,] 0.174236332 0.34847266 0.82576367
[82,] 0.149335201 0.29867040 0.85066480
[83,] 0.194498986 0.38899797 0.80550101
[84,] 0.170412247 0.34082449 0.82958775
[85,] 0.145064061 0.29012812 0.85493594
[86,] 0.138757344 0.27751469 0.86124266
[87,] 0.116965449 0.23393090 0.88303455
[88,] 0.095459342 0.19091868 0.90454066
[89,] 0.077041312 0.15408262 0.92295869
[90,] 0.063124940 0.12624988 0.93687506
[91,] 0.124019797 0.24803959 0.87598020
[92,] 0.111749644 0.22349929 0.88825036
[93,] 0.094817626 0.18963525 0.90518237
[94,] 0.082869731 0.16573946 0.91713027
[95,] 0.067184111 0.13436822 0.93281589
[96,] 0.056327557 0.11265511 0.94367244
[97,] 0.047696236 0.09539247 0.95230376
[98,] 0.068743749 0.13748750 0.93125625
[99,] 0.062674025 0.12534805 0.93732597
[100,] 0.122649137 0.24529827 0.87735086
[101,] 0.153270963 0.30654193 0.84672904
[102,] 0.249083425 0.49816685 0.75091658
[103,] 0.279940156 0.55988031 0.72005984
[104,] 0.247537900 0.49507580 0.75246210
[105,] 0.209429375 0.41885875 0.79057063
[106,] 0.275192620 0.55038524 0.72480738
[107,] 0.281740537 0.56348107 0.71825946
[108,] 0.239939353 0.47987871 0.76006065
[109,] 0.226448820 0.45289764 0.77355118
[110,] 0.189528980 0.37905796 0.81047102
[111,] 0.156442047 0.31288409 0.84355795
[112,] 0.145820325 0.29164065 0.85417967
[113,] 0.118534928 0.23706986 0.88146507
[114,] 0.156100701 0.31220140 0.84389930
[115,] 0.166042000 0.33208400 0.83395800
[116,] 0.149392213 0.29878443 0.85060779
[117,] 0.119155445 0.23831089 0.88084456
[118,] 0.096877213 0.19375443 0.90312279
[119,] 0.076480602 0.15296120 0.92351940
[120,] 0.059570158 0.11914032 0.94042984
[121,] 0.082864960 0.16572992 0.91713504
[122,] 0.073152174 0.14630435 0.92684783
[123,] 0.054164431 0.10832886 0.94583557
[124,] 0.062862448 0.12572490 0.93713755
[125,] 0.090838912 0.18167782 0.90916109
[126,] 0.067555936 0.13511187 0.93244406
[127,] 0.058966060 0.11793212 0.94103394
[128,] 0.044387494 0.08877499 0.95561251
[129,] 0.030729524 0.06145905 0.96927048
[130,] 0.032222358 0.06444472 0.96777764
[131,] 0.022881384 0.04576277 0.97711862
[132,] 0.019119415 0.03823883 0.98088058
[133,] 0.015912318 0.03182464 0.98408768
[134,] 0.009942433 0.01988487 0.99005757
[135,] 0.009399832 0.01879966 0.99060017
[136,] 0.005794679 0.01158936 0.99420532
[137,] 0.021166284 0.04233257 0.97883372
[138,] 0.014393926 0.02878785 0.98560607
[139,] 0.015857027 0.03171405 0.98414297
[140,] 0.015334203 0.03066841 0.98466580
[141,] 0.013892184 0.02778437 0.98610782
[142,] 0.056576469 0.11315294 0.94342353
[143,] 0.026547587 0.05309517 0.97345241
> postscript(file="/var/www/html/rcomp/tmp/148js1290425749.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/248js1290425749.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3x0id1290425749.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4x0id1290425749.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x0id1290425749.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
-2.579951007 0.197597472 -0.117344323 1.448262650 0.588425563 -1.551737350
7 8 9 10 11 12
1.173125418 -0.362725619 -1.573567463 0.145007051 0.371181404 1.184811615
13 14 15 16 17 18
-0.800917997 -0.307493256 1.454646194 -0.876823062 1.337760385 -0.276695408
19 20 21 22 23 24
0.193855870 0.304320595 -2.545353806 0.821021000 0.061542262 -0.405172123
25 26 27 28 29 30
0.163000272 -0.637844081 1.634613670 -0.726516233 0.273483767 -1.560762835
31 32 33 34 35 36
-0.222525166 1.572997764 0.973969770 -0.649453758 -0.362725619 -0.540127673
37 38 39 40 41 42
0.696267116 1.089755918 0.420048993 0.313307101 0.371200175 -1.655818531
43 44 45 46 47 48
-0.585195910 -0.252242124 1.497111469 0.918737408 -1.442411265 0.597469818
49 50 51 52 53 54
0.240043977 -0.227846589 1.815794867 1.679720886 -0.365367560 -0.190607447
55 56 57 58 59 60
-0.628799825 -0.427002236 -0.919288337 -0.686692899 -2.691937803 1.716921049
61 62 63 64 65 66
-0.891093451 -0.393562446 -0.106834307 -0.331927770 1.011208912 -0.628799825
67 68 69 70 71 72
0.768411809 0.295313880 0.218251405 -0.142839519 -0.885848547 -0.475851055
73 74 75 76 77 78
-0.108318838 0.139685628 0.781197666 0.468897812 -0.280437010 -0.322902285
79 80 81 82 83 84
-1.254865295 -1.383379551 0.213006501 0.517765401 -0.068495505 0.337760385
85 86 87 88 89 90
-1.496486217 0.927762893 0.096139462 -0.407832835 1.668072230 -0.454039712
91 92 93 94 95 96
0.243804350 0.689864802 0.313307101 -0.094048450 0.031862844 -0.203374535
97 98 99 100 101 102
2.225792358 -0.646811816 -0.567183919 0.732348848 0.209207149 0.563972279
103 104 105 106 107 108
-0.778025763 -1.337153903 -0.722716881 2.243804350 -1.655856072 1.869869819
109 110 111 112 113 114
-1.545372577 -0.587837852 -0.212418790 1.468897812 1.454646194 0.078069722
115 116 117 118 119 120
0.882655677 -0.185362543 -0.185362543 0.910850563 0.016435045 -1.378134647
121 122 123 124 125 126
-1.218802334 -0.794534453 -0.148142172 0.169383816 -0.289462495 0.259232149
127 128 129 130 131 132
1.521507004 0.468897812 -0.004218887 1.948474575 1.521507004 0.322351356
133 134 135 136 137 138
-0.936200667 0.439218395 0.071686178 -0.906502479 0.016435045 -1.411593208
139 140 141 142 143 144
-0.894874032 -0.573567463 -1.635202139 0.273483767 -2.325544226 0.627149235
145 146 147 148 149 150
-1.010602431 1.919894818 0.662827326 0.481664900 -0.240613676 1.876272133
151 152 153 154 155 156
1.628248896 0.417407052 -0.301090942 0.182169673 0.413665450 -3.261267609
> postscript(file="/var/www/html/rcomp/tmp/6qrhg1290425749.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 -2.579951007 NA
1 0.197597472 -2.579951007
2 -0.117344323 0.197597472
3 1.448262650 -0.117344323
4 0.588425563 1.448262650
5 -1.551737350 0.588425563
6 1.173125418 -1.551737350
7 -0.362725619 1.173125418
8 -1.573567463 -0.362725619
9 0.145007051 -1.573567463
10 0.371181404 0.145007051
11 1.184811615 0.371181404
12 -0.800917997 1.184811615
13 -0.307493256 -0.800917997
14 1.454646194 -0.307493256
15 -0.876823062 1.454646194
16 1.337760385 -0.876823062
17 -0.276695408 1.337760385
18 0.193855870 -0.276695408
19 0.304320595 0.193855870
20 -2.545353806 0.304320595
21 0.821021000 -2.545353806
22 0.061542262 0.821021000
23 -0.405172123 0.061542262
24 0.163000272 -0.405172123
25 -0.637844081 0.163000272
26 1.634613670 -0.637844081
27 -0.726516233 1.634613670
28 0.273483767 -0.726516233
29 -1.560762835 0.273483767
30 -0.222525166 -1.560762835
31 1.572997764 -0.222525166
32 0.973969770 1.572997764
33 -0.649453758 0.973969770
34 -0.362725619 -0.649453758
35 -0.540127673 -0.362725619
36 0.696267116 -0.540127673
37 1.089755918 0.696267116
38 0.420048993 1.089755918
39 0.313307101 0.420048993
40 0.371200175 0.313307101
41 -1.655818531 0.371200175
42 -0.585195910 -1.655818531
43 -0.252242124 -0.585195910
44 1.497111469 -0.252242124
45 0.918737408 1.497111469
46 -1.442411265 0.918737408
47 0.597469818 -1.442411265
48 0.240043977 0.597469818
49 -0.227846589 0.240043977
50 1.815794867 -0.227846589
51 1.679720886 1.815794867
52 -0.365367560 1.679720886
53 -0.190607447 -0.365367560
54 -0.628799825 -0.190607447
55 -0.427002236 -0.628799825
56 -0.919288337 -0.427002236
57 -0.686692899 -0.919288337
58 -2.691937803 -0.686692899
59 1.716921049 -2.691937803
60 -0.891093451 1.716921049
61 -0.393562446 -0.891093451
62 -0.106834307 -0.393562446
63 -0.331927770 -0.106834307
64 1.011208912 -0.331927770
65 -0.628799825 1.011208912
66 0.768411809 -0.628799825
67 0.295313880 0.768411809
68 0.218251405 0.295313880
69 -0.142839519 0.218251405
70 -0.885848547 -0.142839519
71 -0.475851055 -0.885848547
72 -0.108318838 -0.475851055
73 0.139685628 -0.108318838
74 0.781197666 0.139685628
75 0.468897812 0.781197666
76 -0.280437010 0.468897812
77 -0.322902285 -0.280437010
78 -1.254865295 -0.322902285
79 -1.383379551 -1.254865295
80 0.213006501 -1.383379551
81 0.517765401 0.213006501
82 -0.068495505 0.517765401
83 0.337760385 -0.068495505
84 -1.496486217 0.337760385
85 0.927762893 -1.496486217
86 0.096139462 0.927762893
87 -0.407832835 0.096139462
88 1.668072230 -0.407832835
89 -0.454039712 1.668072230
90 0.243804350 -0.454039712
91 0.689864802 0.243804350
92 0.313307101 0.689864802
93 -0.094048450 0.313307101
94 0.031862844 -0.094048450
95 -0.203374535 0.031862844
96 2.225792358 -0.203374535
97 -0.646811816 2.225792358
98 -0.567183919 -0.646811816
99 0.732348848 -0.567183919
100 0.209207149 0.732348848
101 0.563972279 0.209207149
102 -0.778025763 0.563972279
103 -1.337153903 -0.778025763
104 -0.722716881 -1.337153903
105 2.243804350 -0.722716881
106 -1.655856072 2.243804350
107 1.869869819 -1.655856072
108 -1.545372577 1.869869819
109 -0.587837852 -1.545372577
110 -0.212418790 -0.587837852
111 1.468897812 -0.212418790
112 1.454646194 1.468897812
113 0.078069722 1.454646194
114 0.882655677 0.078069722
115 -0.185362543 0.882655677
116 -0.185362543 -0.185362543
117 0.910850563 -0.185362543
118 0.016435045 0.910850563
119 -1.378134647 0.016435045
120 -1.218802334 -1.378134647
121 -0.794534453 -1.218802334
122 -0.148142172 -0.794534453
123 0.169383816 -0.148142172
124 -0.289462495 0.169383816
125 0.259232149 -0.289462495
126 1.521507004 0.259232149
127 0.468897812 1.521507004
128 -0.004218887 0.468897812
129 1.948474575 -0.004218887
130 1.521507004 1.948474575
131 0.322351356 1.521507004
132 -0.936200667 0.322351356
133 0.439218395 -0.936200667
134 0.071686178 0.439218395
135 -0.906502479 0.071686178
136 0.016435045 -0.906502479
137 -1.411593208 0.016435045
138 -0.894874032 -1.411593208
139 -0.573567463 -0.894874032
140 -1.635202139 -0.573567463
141 0.273483767 -1.635202139
142 -2.325544226 0.273483767
143 0.627149235 -2.325544226
144 -1.010602431 0.627149235
145 1.919894818 -1.010602431
146 0.662827326 1.919894818
147 0.481664900 0.662827326
148 -0.240613676 0.481664900
149 1.876272133 -0.240613676
150 1.628248896 1.876272133
151 0.417407052 1.628248896
152 -0.301090942 0.417407052
153 0.182169673 -0.301090942
154 0.413665450 0.182169673
155 -3.261267609 0.413665450
156 NA -3.261267609
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.197597472 -2.579951007
[2,] -0.117344323 0.197597472
[3,] 1.448262650 -0.117344323
[4,] 0.588425563 1.448262650
[5,] -1.551737350 0.588425563
[6,] 1.173125418 -1.551737350
[7,] -0.362725619 1.173125418
[8,] -1.573567463 -0.362725619
[9,] 0.145007051 -1.573567463
[10,] 0.371181404 0.145007051
[11,] 1.184811615 0.371181404
[12,] -0.800917997 1.184811615
[13,] -0.307493256 -0.800917997
[14,] 1.454646194 -0.307493256
[15,] -0.876823062 1.454646194
[16,] 1.337760385 -0.876823062
[17,] -0.276695408 1.337760385
[18,] 0.193855870 -0.276695408
[19,] 0.304320595 0.193855870
[20,] -2.545353806 0.304320595
[21,] 0.821021000 -2.545353806
[22,] 0.061542262 0.821021000
[23,] -0.405172123 0.061542262
[24,] 0.163000272 -0.405172123
[25,] -0.637844081 0.163000272
[26,] 1.634613670 -0.637844081
[27,] -0.726516233 1.634613670
[28,] 0.273483767 -0.726516233
[29,] -1.560762835 0.273483767
[30,] -0.222525166 -1.560762835
[31,] 1.572997764 -0.222525166
[32,] 0.973969770 1.572997764
[33,] -0.649453758 0.973969770
[34,] -0.362725619 -0.649453758
[35,] -0.540127673 -0.362725619
[36,] 0.696267116 -0.540127673
[37,] 1.089755918 0.696267116
[38,] 0.420048993 1.089755918
[39,] 0.313307101 0.420048993
[40,] 0.371200175 0.313307101
[41,] -1.655818531 0.371200175
[42,] -0.585195910 -1.655818531
[43,] -0.252242124 -0.585195910
[44,] 1.497111469 -0.252242124
[45,] 0.918737408 1.497111469
[46,] -1.442411265 0.918737408
[47,] 0.597469818 -1.442411265
[48,] 0.240043977 0.597469818
[49,] -0.227846589 0.240043977
[50,] 1.815794867 -0.227846589
[51,] 1.679720886 1.815794867
[52,] -0.365367560 1.679720886
[53,] -0.190607447 -0.365367560
[54,] -0.628799825 -0.190607447
[55,] -0.427002236 -0.628799825
[56,] -0.919288337 -0.427002236
[57,] -0.686692899 -0.919288337
[58,] -2.691937803 -0.686692899
[59,] 1.716921049 -2.691937803
[60,] -0.891093451 1.716921049
[61,] -0.393562446 -0.891093451
[62,] -0.106834307 -0.393562446
[63,] -0.331927770 -0.106834307
[64,] 1.011208912 -0.331927770
[65,] -0.628799825 1.011208912
[66,] 0.768411809 -0.628799825
[67,] 0.295313880 0.768411809
[68,] 0.218251405 0.295313880
[69,] -0.142839519 0.218251405
[70,] -0.885848547 -0.142839519
[71,] -0.475851055 -0.885848547
[72,] -0.108318838 -0.475851055
[73,] 0.139685628 -0.108318838
[74,] 0.781197666 0.139685628
[75,] 0.468897812 0.781197666
[76,] -0.280437010 0.468897812
[77,] -0.322902285 -0.280437010
[78,] -1.254865295 -0.322902285
[79,] -1.383379551 -1.254865295
[80,] 0.213006501 -1.383379551
[81,] 0.517765401 0.213006501
[82,] -0.068495505 0.517765401
[83,] 0.337760385 -0.068495505
[84,] -1.496486217 0.337760385
[85,] 0.927762893 -1.496486217
[86,] 0.096139462 0.927762893
[87,] -0.407832835 0.096139462
[88,] 1.668072230 -0.407832835
[89,] -0.454039712 1.668072230
[90,] 0.243804350 -0.454039712
[91,] 0.689864802 0.243804350
[92,] 0.313307101 0.689864802
[93,] -0.094048450 0.313307101
[94,] 0.031862844 -0.094048450
[95,] -0.203374535 0.031862844
[96,] 2.225792358 -0.203374535
[97,] -0.646811816 2.225792358
[98,] -0.567183919 -0.646811816
[99,] 0.732348848 -0.567183919
[100,] 0.209207149 0.732348848
[101,] 0.563972279 0.209207149
[102,] -0.778025763 0.563972279
[103,] -1.337153903 -0.778025763
[104,] -0.722716881 -1.337153903
[105,] 2.243804350 -0.722716881
[106,] -1.655856072 2.243804350
[107,] 1.869869819 -1.655856072
[108,] -1.545372577 1.869869819
[109,] -0.587837852 -1.545372577
[110,] -0.212418790 -0.587837852
[111,] 1.468897812 -0.212418790
[112,] 1.454646194 1.468897812
[113,] 0.078069722 1.454646194
[114,] 0.882655677 0.078069722
[115,] -0.185362543 0.882655677
[116,] -0.185362543 -0.185362543
[117,] 0.910850563 -0.185362543
[118,] 0.016435045 0.910850563
[119,] -1.378134647 0.016435045
[120,] -1.218802334 -1.378134647
[121,] -0.794534453 -1.218802334
[122,] -0.148142172 -0.794534453
[123,] 0.169383816 -0.148142172
[124,] -0.289462495 0.169383816
[125,] 0.259232149 -0.289462495
[126,] 1.521507004 0.259232149
[127,] 0.468897812 1.521507004
[128,] -0.004218887 0.468897812
[129,] 1.948474575 -0.004218887
[130,] 1.521507004 1.948474575
[131,] 0.322351356 1.521507004
[132,] -0.936200667 0.322351356
[133,] 0.439218395 -0.936200667
[134,] 0.071686178 0.439218395
[135,] -0.906502479 0.071686178
[136,] 0.016435045 -0.906502479
[137,] -1.411593208 0.016435045
[138,] -0.894874032 -1.411593208
[139,] -0.573567463 -0.894874032
[140,] -1.635202139 -0.573567463
[141,] 0.273483767 -1.635202139
[142,] -2.325544226 0.273483767
[143,] 0.627149235 -2.325544226
[144,] -1.010602431 0.627149235
[145,] 1.919894818 -1.010602431
[146,] 0.662827326 1.919894818
[147,] 0.481664900 0.662827326
[148,] -0.240613676 0.481664900
[149,] 1.876272133 -0.240613676
[150,] 1.628248896 1.876272133
[151,] 0.417407052 1.628248896
[152,] -0.301090942 0.417407052
[153,] 0.182169673 -0.301090942
[154,] 0.413665450 0.182169673
[155,] -3.261267609 0.413665450
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.197597472 -2.579951007
2 -0.117344323 0.197597472
3 1.448262650 -0.117344323
4 0.588425563 1.448262650
5 -1.551737350 0.588425563
6 1.173125418 -1.551737350
7 -0.362725619 1.173125418
8 -1.573567463 -0.362725619
9 0.145007051 -1.573567463
10 0.371181404 0.145007051
11 1.184811615 0.371181404
12 -0.800917997 1.184811615
13 -0.307493256 -0.800917997
14 1.454646194 -0.307493256
15 -0.876823062 1.454646194
16 1.337760385 -0.876823062
17 -0.276695408 1.337760385
18 0.193855870 -0.276695408
19 0.304320595 0.193855870
20 -2.545353806 0.304320595
21 0.821021000 -2.545353806
22 0.061542262 0.821021000
23 -0.405172123 0.061542262
24 0.163000272 -0.405172123
25 -0.637844081 0.163000272
26 1.634613670 -0.637844081
27 -0.726516233 1.634613670
28 0.273483767 -0.726516233
29 -1.560762835 0.273483767
30 -0.222525166 -1.560762835
31 1.572997764 -0.222525166
32 0.973969770 1.572997764
33 -0.649453758 0.973969770
34 -0.362725619 -0.649453758
35 -0.540127673 -0.362725619
36 0.696267116 -0.540127673
37 1.089755918 0.696267116
38 0.420048993 1.089755918
39 0.313307101 0.420048993
40 0.371200175 0.313307101
41 -1.655818531 0.371200175
42 -0.585195910 -1.655818531
43 -0.252242124 -0.585195910
44 1.497111469 -0.252242124
45 0.918737408 1.497111469
46 -1.442411265 0.918737408
47 0.597469818 -1.442411265
48 0.240043977 0.597469818
49 -0.227846589 0.240043977
50 1.815794867 -0.227846589
51 1.679720886 1.815794867
52 -0.365367560 1.679720886
53 -0.190607447 -0.365367560
54 -0.628799825 -0.190607447
55 -0.427002236 -0.628799825
56 -0.919288337 -0.427002236
57 -0.686692899 -0.919288337
58 -2.691937803 -0.686692899
59 1.716921049 -2.691937803
60 -0.891093451 1.716921049
61 -0.393562446 -0.891093451
62 -0.106834307 -0.393562446
63 -0.331927770 -0.106834307
64 1.011208912 -0.331927770
65 -0.628799825 1.011208912
66 0.768411809 -0.628799825
67 0.295313880 0.768411809
68 0.218251405 0.295313880
69 -0.142839519 0.218251405
70 -0.885848547 -0.142839519
71 -0.475851055 -0.885848547
72 -0.108318838 -0.475851055
73 0.139685628 -0.108318838
74 0.781197666 0.139685628
75 0.468897812 0.781197666
76 -0.280437010 0.468897812
77 -0.322902285 -0.280437010
78 -1.254865295 -0.322902285
79 -1.383379551 -1.254865295
80 0.213006501 -1.383379551
81 0.517765401 0.213006501
82 -0.068495505 0.517765401
83 0.337760385 -0.068495505
84 -1.496486217 0.337760385
85 0.927762893 -1.496486217
86 0.096139462 0.927762893
87 -0.407832835 0.096139462
88 1.668072230 -0.407832835
89 -0.454039712 1.668072230
90 0.243804350 -0.454039712
91 0.689864802 0.243804350
92 0.313307101 0.689864802
93 -0.094048450 0.313307101
94 0.031862844 -0.094048450
95 -0.203374535 0.031862844
96 2.225792358 -0.203374535
97 -0.646811816 2.225792358
98 -0.567183919 -0.646811816
99 0.732348848 -0.567183919
100 0.209207149 0.732348848
101 0.563972279 0.209207149
102 -0.778025763 0.563972279
103 -1.337153903 -0.778025763
104 -0.722716881 -1.337153903
105 2.243804350 -0.722716881
106 -1.655856072 2.243804350
107 1.869869819 -1.655856072
108 -1.545372577 1.869869819
109 -0.587837852 -1.545372577
110 -0.212418790 -0.587837852
111 1.468897812 -0.212418790
112 1.454646194 1.468897812
113 0.078069722 1.454646194
114 0.882655677 0.078069722
115 -0.185362543 0.882655677
116 -0.185362543 -0.185362543
117 0.910850563 -0.185362543
118 0.016435045 0.910850563
119 -1.378134647 0.016435045
120 -1.218802334 -1.378134647
121 -0.794534453 -1.218802334
122 -0.148142172 -0.794534453
123 0.169383816 -0.148142172
124 -0.289462495 0.169383816
125 0.259232149 -0.289462495
126 1.521507004 0.259232149
127 0.468897812 1.521507004
128 -0.004218887 0.468897812
129 1.948474575 -0.004218887
130 1.521507004 1.948474575
131 0.322351356 1.521507004
132 -0.936200667 0.322351356
133 0.439218395 -0.936200667
134 0.071686178 0.439218395
135 -0.906502479 0.071686178
136 0.016435045 -0.906502479
137 -1.411593208 0.016435045
138 -0.894874032 -1.411593208
139 -0.573567463 -0.894874032
140 -1.635202139 -0.573567463
141 0.273483767 -1.635202139
142 -2.325544226 0.273483767
143 0.627149235 -2.325544226
144 -1.010602431 0.627149235
145 1.919894818 -1.010602431
146 0.662827326 1.919894818
147 0.481664900 0.662827326
148 -0.240613676 0.481664900
149 1.876272133 -0.240613676
150 1.628248896 1.876272133
151 0.417407052 1.628248896
152 -0.301090942 0.417407052
153 0.182169673 -0.301090942
154 0.413665450 0.182169673
155 -3.261267609 0.413665450
> 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/7qrhg1290425749.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jig11290425749.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jig11290425749.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bag41290425749.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xswa1290425749.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/120tdg1290425749.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/13wlap1290425749.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/14i39v1290425749.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/15l48i1290425749.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/1664o61290425749.tab")
+ }
>
> try(system("convert tmp/148js1290425749.ps tmp/148js1290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/248js1290425749.ps tmp/248js1290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/3x0id1290425749.ps tmp/3x0id1290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/4x0id1290425749.ps tmp/4x0id1290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x0id1290425749.ps tmp/5x0id1290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qrhg1290425749.ps tmp/6qrhg1290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qrhg1290425749.ps tmp/7qrhg1290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jig11290425749.ps tmp/8jig11290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jig11290425749.ps tmp/9jig11290425749.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bag41290425749.ps tmp/10bag41290425749.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.966 1.771 8.673