Hi everyone
I am stuck on specifying my own maximum likelihood function for a
special poisson model.
My poisson model is as follow: O ~ Pois(b*N + b*RR*E)
With
O = observed cases
b = constant (known)
N = number of unexposed persons (known)
E = number exposed persons (known)
RR = relative risk (value is assumed under a scenario, e.g. RR=2.0)
I used rpois to simulate the values of O for several years with known
values of b, N, E and RR.
Hence, an example simulated data set would look like:
O <- c(173, 184, 154, 162, 158, 196, 203, 230, 240, 252, 252, 268, 255,
297, 265, 270, 265, 283)
N <-
c(1345246.66,1325945.58,1311490.16,1301155.99,1212731.00,1052598.42,889413.52,592314.47,31694.02,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00)
E <-
c(1387.337,7294.417,13634.838,24372.007,120172.455,290369.579,471271.484,796911.525,1388343.975,1445355.000,1464523.000,1482467.000,1492197.000,1494862.000,1489437.000,1478092.000,1468076.000,1458794.000)
In this dataset, I now want to estimate RR under the above model with
known values for E and N (the ones above) and the constant b should be
treated as nuisance parameter. In addition, no intercept should be
estimated. The model formula would thus be: O = b1*N + b2*E
Then, I would use a nonlinear combination of the estimates to calculate the RR:
RR = (b2/b1)
Can anybody help me specifying my own likelihood function to be used
with optim?
That would help me alot. Thank you.
Denis
_____________________________________________
Denis Aydin, MSc
Swiss Tropical and Public Health Institute
Socinstrasse 57, P.O. Box
4002 Basel, Switzerland
T +41 (0)61 284 86 09
F +41 (0)61 284 81 01
denis.ay...@unibas.ch
_____________________________________________
Postal address:
Denis Aydin, MSc
Swiss Tropical and Public Health Institute
Socinstrasse 57, P.O. Box
4002 Basel, Switzerland
Visitors address:
Eulerstrasse 68
4051 Basel, Switzerland
_____________________________________________
www.swisstph.ch
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