Hi, I have a dataset with the following properties: Y_i ~ N(mu_i, theta * (mu_i)^2) ln(mu_i) = B'Xi theta and beta's are the parameters here. I want to come up with a model to fit the data with the above property and test that model on the built in R dataset quine. Does nls() make sense in this case? Or is there any existing R package which can fit this model? -Shelly -- View this message in context: http://r.789695.n4.nabble.com/Should-I-use-nls-for-this-tp4190496p4190496.html Sent from the R help mailing list archive at Nabble.com.
Should I use nls for this?
2 messages · xfei, Ben Bolker
xfei <xiaoyifei <at> gmail.com> writes:
I have a dataset with the following properties: Y_i ~ N(mu_i, theta * (mu_i)^2) ln(mu_i) = B'Xi theta and beta's are the parameters here. I want to come up with a model to fit the data with the above property and test that model on the built in R dataset quine. Does nls() make sense in this case? Or is there any existing R package which can fit this model?
You don't need nls(): see ?gls, ?varCorr, ?varPower in the nlme package.