search for: univarmixingdistribution

Hi, Thanks for your mail, and time It is not working for some arguments, when mean value is like >6. case mc0 <- c(0.08844446,0.1744455,0.1379778,0.1209769,0.1573065,0. 1134463,0.2074027) rv <-UnivarMixingDistribution(Norm(486.4255, 53.24133), Norm(664.0713, 3.674773), Norm(669.0484, 4.101381), Norm(677.1753, 4.869985), Norm(683.2635, 7.288175), Norm(727.6229,...

...gmail.com> wrote: > > Hi, > > Thanks for your mail, and time > > It is not working for some arguments, when mean value is like >6. > > > case > > mc0 <- c(0.08844446,0.1744455,0.1379778,0.1209769,0.1573065,0. > 1134463,0.2074027) > > rv <-UnivarMixingDistribution(Norm(486.4255, 53.24133), > > Norm(664.0713, 3.674773), > > Norm(669.0484, 4.101381), > > Norm(677.1753, 4.869985), > > Norm(683.2635, 7.288175), > &gt...

Hello all, Can someone tell me why the following mixture of two log-normal distributions does not get truncated? What puzzles me is that the function works almost always, but for certain combinations (like the one below), it does not. # R code example library(distr) mix<-UnivarMixingDistribution(Lnorm(3.2,0.5),Lnorm(5.4,0.6),mixCoeff=c(0.3,0.7)) mix.trunc<-Truncate(mix,lower=0.001,upper=3000) distr.sample<-r(mix.trunc)(1000000) range(distr.sample) Why do I get values over 3000 (which was the defoned upper limit)? Some help would be greatly appreciated. Duarte Viana

...gmail.com> wrote: > Hi > > Thanks for you mail, > I really appreciate your time on my problem > > I have posted this problem on > > > https://stats.stackexchange.com/questions/299590/to-find-maxima-for-gaussian-mixture-model > > > The plot I am getting using UnivarMixingDistribution from distr package in > R > > code is > > mc0= c(0.1241933, 0.6329082 <06329%20082>, 0.2428986 <02428%20986>) > rv > <-UnivarMixingDistribution(Norm(506.8644,61.02859),Norm(672.8448,9.149168),Norm( > 829.902,74.84682), mixCoeff=mc0/sum(mc0)) > plot(rv, to.d...

...for(n in 1 :l) { mu=meanval[seg,n] sg=sigmaval[seg,n] cval=coeffval[seg,n] val=cval*(dnorm(xval,mu,sg)) #print(paste0("The dnorm value for x is.: ", val)) sum_prob=sum_prob+val } sum_prob } The output is not correct. Since I check my data using* UnivarMixingDistribution* from distr package and according to this the max should lie somewhere between 600-800 Code I used to check mc0=c( 0.6329082,0.6329082,0.2428986) rv <-UnivarMixingDistribution(Norm(506.8644,61.02859),Norm(672.8448,9.149168),Norm( 829.902,74.84682), mixCoeff=mc0/sum(mc0)) plot(rv, to.draw.arg...

...lts of *,/, exp() now is correct at 0 by extrapolation COMFORT METHODS + getLow/getUp methods (for "reasonable" endpoints for distributions with possibly unbounded support) for *DiscreteDistribution, *AbscontDistribution, *UnivarLebDecDistribution, *UnivarMixingDistribution + q.r, p.l (methods for right continuous quantile function and left continuous cdf) for * DiscreteDistribution * AbscontDistribution (takes care of constancy regions in p) * UnivarLebDecDistribution * UnivarMixingDistribution + prob methods (returning v...

...lts of *,/, exp() now is correct at 0 by extrapolation COMFORT METHODS + getLow/getUp methods (for "reasonable" endpoints for distributions with possibly unbounded support) for *DiscreteDistribution, *AbscontDistribution, *UnivarLebDecDistribution, *UnivarMixingDistribution + q.r, p.l (methods for right continuous quantile function and left continuous cdf) for * DiscreteDistribution * AbscontDistribution (takes care of constancy regions in p) * UnivarLebDecDistribution * UnivarMixingDistribution + prob methods (returning v...

...t;> > >> It is not working for some arguments, when mean value is like >6. > >> > >> > >> case > >> > >> mc0 <- c(0.08844446,0.1744455,0.1379778,0.1209769,0.1573065,0. > >> 1134463,0.2074027) > >> > >> rv <-UnivarMixingDistribution(Norm(486.4255, 53.24133), > >> > >> Norm(664.0713, 3.674773), > >> > >> Norm(669.0484, 4.101381), > >> > >> Norm(677.1753, 4.869985), > >> > >>...