CODEWITHSUNDEEP
rglm
user1165199
user1165199

Reputation: 6649

Using glm in R to solve simple equation

I have some data from a poisson distribution and have a simple equation I want to solve using glm.

The mathematical equation is observed = y * expected.

I have the observed and expected data and want to use glm to find the optimal value of y which I need to multiply expected by to get observed. I also want to get confidence intervals for y.

Should I be doing something like this

glm(observed ~ expected + offset(log(expected)) + 0, family = 'poisson', data = dataDF)

Then taking the exponential of the coefficient? I tried this but the value given is pretty different to what I get when I divide the sum of the observed by the sum of the expected, and I thought these should be similar.

Am I doing something wrong?

Thanks

Upvotes: 0

Views: 377

Answers (1)

IRTFM
IRTFM

Reputation: 263352

Try this:

 logFac <- coef( glm(observed ~ offset(expected) , family = 'poisson', data = dataDF))
 Fac <- exp( logFac[1] )  # That's the intercept term

That model is really : observed ~ 1 + offset(expected) and since it's being estimated on a log scale, the intercept becomes that conversion factor to convert between 'expected' and 'observed'. The negative comments are evidence that you should have posted on CrossValidated.com where general statistics methods questions are more welcomed.

Upvotes: 1

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