By the scale of log-likelihood, I did not mean the scale parameter of the gamma density...
Generally, as you get more and more data, the log-likelihood will get more and more negative. Hence, what I mean by scale is how negative of the values of loglik. So the 10 values returned from your dgamma are the log-densities evaluated for your data points, respectively. The loglik for your samples is just the sum of those from all data points, under the independency assumption. X Edward Wijaya 写道: > Dear Xiaohui, > > Thanks. > > >> The scale of log-likelihood depends on the number of your data samples >> > Can you explain what do you mean by this? > > For example if I have 10 data points. Should I use "scale=10" ? > And how about "shape" parameters. What's the rule to choose its value? > > Hope to hear from you again. > > Regards, > Edward > > > > >> Edward Wijaya 写道: >> >>> Dear all, >>> >>> How can I compute the log likelihood of a gamma >>> distributions of a vector. >>> >>> I tried the following. But it doesn't seem to work: >>> >>> samples<-c(6.1, 2.2, 14.9, 9.9, 24.6, 13.2) >>> llgm <- dgamma(samples, scale=1, shape=2, log = TRUE) >>> >>> It gives >>> >>> [1] -4.291711 -1.411543 -12.198639 -7.607465 -21.397254 -10.619783 >>> >>> I expect it only returns "one" value instead of vector. >>> What's wrong with my command above? >>> >>> - Edward >>> >>> ______________________________________________ >>> 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-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.
