I'm trying to run a zero-inflated regression for a continuous response
variable in R. I'm aware of a gamlss implementation, but I'd really like to
try out this algorithm by Dale McLerran that is conceptually a bit more
straightforward. Unfortunately, the code is in SAS and I'm not sure how to
re-write it for something like nlme (if at all possible - with conditions
etc). Does anyone know both languages enough to try translating it? Would
very much appreciate your help!
The code is as follows:
proc nlmixed data=mydata;
parms b0_f=0 b1_f=0
b0_h=0 b1_h=0
log_theta=0;
eta_f = b0_f + b1_f*x1 ;
p_yEQ0 = 1 / (1 + exp(-eta_f));
eta_h = b0_h + b1_h*x1;
mu = exp(eta_h);
theta = exp(log_theta);
r = mu/theta;
if y=0 then
ll = log(p_yEQ0);
else
ll = log(1 - p_yEQ0)
- lgamma(theta) + (theta-1)*log(y) - theta*log(r) - y/r;
model y ~ general(ll);
predict (1 - p_yEQ0)*mu out=expect_zig;
predict r out=shape;
estimate "scale" theta;
run;
From: http://listserv.uga.edu/cgi-bin/wa?A2=ind0805A&L=sas-l&P=R20779
Best,
Mikhail
PS. I cross-posted this to stackoverflow under R and SAS - apologies if
you're reading this twice...
==
Mikhail Spivakov, PhD
European Bioinformatics Institute
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