[R] Poisson-Gamma computation (parameters and likelihood)
Good afternoon/morning readers. This is the first time I am trying to run some Bayesian computation in R, and am experiencing a few problems. I am working on a Poisson model for cancer rates which has a conjugate Gamma prior. 1) The first question is precisely how I work out the parameters. #Suppose I assign values to theta with seq() theta<-seq(0,1,len=500) #Then I try out the parameters that seem to fit with a certain prior idea on theta (see next) a=182 b=3530 gaprior<-dgamma(theta,a,b) It should work by trial-and-error (according to "Bayesian Computation with R") , but how can I check the parameters turned out well: should I just look at the plot, or evaluate it through the 1 - pgamma(x,a,b) function, having knowledge of the 5th percentile (data from US Cancer Statistics)? 2) Then, the next problem I have regards the likelihood distribution. #Having the react table, I name the columns (with y=deaths, and x=exposures) react x y 1 6 15 2 5 16 3 3 12 4 4 6 5 27 77 6 7 17 7 4 11 8 5 10 9 23 63 10 11 29 yr <- react[,2] xr <- react[,1] #I then compute the likelihood poislike=dpois(yr, theta*xr) #And this is what I come up with, which I really don't understand. poislike [1] 0 0 0 0 0 0 0 0 0 0 The values shouldn't be all null, otherwise my posterior cannot be computed properly. Does anyone have any idea on where I could possibly have messed it up? Thank you very much for your attention. Regards, Sebastiano Putoto (University of Pavia, Italy) [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Poisson-Gamma computation (parameters and likelihood)
Good afternoon/morning readers. This is the first time I am trying to run some Bayesian computation in R, and am experiencing a few problems. I am working on a Poisson model for cancer rates which has a conjugate Gamma prior. 1) The first question is precisely how I work out the parameters. #Suppose I assign values to theta with *seq()* *theta<-seq(0,1,len=500)* #Then I try out the parameters that seem to fit with a certain prior idea on theta (see next) *a=182* *b=3530* *gaprior<-dgamma(theta,a,b)* * * It should work by trial-and-error (according to "Bayesian Computation with R") , but how can I check the parameters turned out well: should I just look at the plot, or evaluate it through the *1 - pgamma(x,a,b) *function, having knowledge of the 5th percentile (data from US Cancer Statistics)? 2) Then, the next problem I have regards the likelihood distribution. #Having the *react* table, I name the columns (with y=deaths, and x=exposures) * react x y 1 6 15 2 5 16 3 3 12 4 4 6 5 27 77 6 7 17 7 4 11 8 5 10 9 23 63 10 11 29 * *yr <- react[,2]* *xr <- react[,1]* #I then compute the likelihood *poislike=dpois(yr, theta*xr)* #And this is what I come up with, which I really don't understand. *poislike* *[1] 0 0 0 0 0 0 0 0 0 0* The values shouldn't be all null, otherwise my posterior cannot be computed properly. Does anyone have any idea on where I could possibly have messed it up? Thank you very much for your attention. Regards, Sebastiano Putoto (University of Pavia, Italy) [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Poisson-Gamma computation (parameters and likelihood)
Good afternoon/morning readers. This is the first time I am trying to run some Bayesian computation in R, and am experiencing a few problems. I am working on a Poisson model for cancer rates which has a conjugate Gamma prior. 1) The first question is precisely how I work out the parameters. #Suppose I assign values to theta with *seq()* *theta<-seq(0,1,len=500)* #Then I try out the parameters that seem to fit with a certain prior idea on theta (see next) *a=182* *b=3530* *gaprior<-dgamma(theta,a,b)* * * It should work by trial-and-error (according to "Bayesian Computation with R") , but how can I check the parameters turned out well: should I just look at the plot, or evaluate it through the *1 - pgamma(x,a,b) *function, having knowledge of the 5th percentile (data from US Cancer Statistics)? 2) Then, the next problem I have regards the likelihood distribution. #Having the *react* table, I name the columns (with y=deaths, and x=exposures) * react x y 1 6 15 2 5 16 3 3 12 4 4 6 5 27 77 6 7 17 7 4 11 8 5 10 9 23 63 10 11 29 * *yr <- react[,2]* *xr <- react[,1]* #I then compute the likelihood *poislike=dpois(yr, theta*xr)* #And this is what I come up with, which I really don't understand. *poislike* *[1] 0 0 0 0 0 0 0 0 0 0* The values shouldn't be all null, otherwise my posterior cannot be computed properly. Does anyone have any idea on where I could possibly have messed it up? Thank you very much for your attention. Regards, Sebastiano Putoto (University of Pavia, Italy) [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
