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Data:
-0.0192508842078761 -0.0243384070148590 0.0158952358252125 -0.0260722519603377 0.00884040098416066 -0.0212744425298722 0.0374645687523337 0.000287491611075654 -0.00339379103432857 0.0171818334669544 -0.033426965424278 0.00217934347065340 -0.0274115854113506 -0.0569170628639589 -0.000209124152662078 -0.00400646079770227 -0.00748529583959452 -0.00585685997080524 0.0752980051262616 0.00262906294324769 -0.00639835830921532 0.0257097451976159 0.0327139661508631 0.0363171189336884 -0.00129610688362125 -0.0219713395125436 -0.0176410998712386 0.0411130767250018 0.0124816435524092 0.020793906880278 -0.0484360434735618 0.0263730615102121 -0.0200050532987077 -0.0134631900868672 -0.013113310509618 0.00219519903716346 0.00349361484962575 0.0314382751789365 0.0140064877187480 -0.0167139079425391 0.0122645988561186 0.00114204009422627 -0.0409497518101819 -0.00376200146549827 0.000868011842158073 -0.0322586305360591 0.0682477804173895 -0.0118187390548836 -0.0359119197592944 -0.00236630329867407 0.0668542802302875 0.000748549879677433 -0.0097707458515968 -0.0168718298288839 -0.00438484896246825 0.00552586381473103 0.0219829902429555 -0.00573500441714712 0.0238920416800163 -0.0221259220432080 -0.0310115591186089 0.00225903855869600 -0.00289136404543944 0.00486404360736588 -0.0379857416078679 -0.0192308004977295 -0.0604836676986188 -0.0523951106616251 -0.0264459462048099 0.0190059857944224 0.051579422018824 0.0105776720459108 -0.00355602540245227 -0.0163280881061046 -0.0107869871318581 -0.00460914546299842 -0.0694566072240325 0.0275743671281808 0.103706651222640 -0.0682976491656814 0.00956417905378418 0.00777995668698742 -0.000471725526274496 -0.0170756133168365 -0.0219398274893341 0.0266415497232234 -0.00718744004864717 0.0255235333060244 -0.0689794396176606 -0.00269863966055686 -0.00134991577133056 -0.05643065977133 -0.0254379905104139 0.165898547230958 0.0537206816137991 0.0642377469045096 -0.00592693533019978 0.0194027109676582 -0.0100689167955129 0.0582650246376592 0.0165272380190866 0.0724558629310153 0.0306429617300824 -0.0387863822572402 0.0310497861315557 -0.0857565364396057 0.00579522547072407 -0.0384897916145452 0.0360585980557015 -0.00320594887898824 0.0137113324902744 -0.0240598741976394 0.0184468748372574 0.00325157937184180 0.0254779586910709 -0.0283919147746814 -0.0227883523855816 -0.0430964564458684 -0.0216370051000690 -0.0140707505733256 -0.0154464375494437 -0.0406067597697643 -0.0137719609823467 -0.0143160597857540 -0.0155991144608775 -0.0225547231065048 0.0228793700809542 -0.0265463902544472 0.0287954700277272 -0.0300888958159422 0.00711730170513459 -0.0388479619658106 -0.0210517741393705 0.0209016660704892 -0.00736398444160426 -0.00695634652202435 -0.0215118032211888 0.0130815818198251 -0.0294445694816839 -0.0117484040652358 0.0237400494796378 -0.000850108661828754 -0.0633816370082633 0.0161387564701593 0.0039191450328204 -0.0300291383913910 -0.00743127215616451 -0.0087841632926546 0.0442649141203364 -0.0331697190770205 -0.0340606317626467 -0.0469800100804107 0.00632880283139771 -0.0186025106792085 0.0183753561468092 0.0199570007781874 -0.0224484565035172 0.0120439983018481 0.0276616630681987 0.0595111245787349 -0.0756624215189736 0.0273649927990675 0.0215180661735496 0.0274133738668157 -0.00463098036463181 0.0393431417763206 -0.00890522610843237 0.0664244228906462 -0.0242474922714065 0.0613863253342411 0.0261613141046088 -0.0436635790987991 0.0076741977290358 0.0201218132900302
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R Code
gp <- function(lambda, p) { (p^lambda-(1-p)^lambda)/lambda } sortx <- sort(x) c <- array(NA,dim=c(201)) for (i in 1:201) { if (i != 101) c[i] <- cor(gp(ppoints(x), lambda=(i-101)/100),sortx) } bitmap(file='test1.png') plot((-100:100)/100,c[1:201],xlab='lambda',ylab='correlation',main='PPCC Plot - Tukey lambda') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tukey Lambda - Key Values',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Distribution (lambda)',1,TRUE) a<-table.element(a,'Correlation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Cauchy (lambda=-1)',header=TRUE) a<-table.element(a,c[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exact Logistic (lambda=0)',header=TRUE) a<-table.element(a,(c[100]+c[102])/2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Normal (lambda=0.14)',header=TRUE) a<-table.element(a,c[115]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'U-shaped (lambda=0.5)',header=TRUE) a<-table.element(a,c[151]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exactly Uniform (lambda=1)',header=TRUE) a<-table.element(a,c[201]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Big Analytics Cloud Computing Center
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