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BUGS  September 2009

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Subject:

Re: censoring distributions using the I(,) mechanism

From:

Dave Lunn <[log in to unmask]>

Reply-To:

Dave Lunn <[log in to unmask]>

Date:

Fri, 18 Sep 2009 21:55:28 +0100

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text/plain

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Hi all,

I thought I'd try and clarify matters on the I(,) construct/notation. The main 
reason for the confusion appears to be the subtle difference between 
truncation and censoring. Put simply, truncation is a modelling assumption, i.e. 
it represents a belief about the system being modelled, such as measured drug 
concentrations should be positive. Censoring, on the other hand, represents an 
observation, e.g. some drug concentration has been observed to be less that 1 
mg/L, say. A simple way to determine which assumption is appropriate in a 
given setting is to think “would I want to make the same assumption if 
predicting a new observation?” If yes, then you probably want to truncate; if 
no, you probably want to use I(,). If truncation is the appropriate assumption, 
i.e. you wish to actually apply some kind of constraint, then, in general, you 
should *not* use I(,). However, as discussed below, you can get away with it 
in certain circumstances. First, though, I’ll illustrate what the problem is… 
Imagine the following situation

for (i in 1:n) {theta[i] ~ dnorm(mu, tau)}

where mu and tau are unknown parameters, and the thetas should all be 
positive, not because they have been observed to be positive (when negative 
values are permissible) but because negative values simply don’t make sense in 
the given setting. Hence truncation is appropriate (we would also want to 
apply the same constraint if predicting a new theta). If one applies I(0, ) to 
the distribution above, this simply has the effect of ensuring that no negative 
values will be sampled for any of the thetas, which is what we want in either 
case! But, now consider what happens when sampling mu and tau… First note 
that BUGS does not modify the sampling distributions for mu and tau in any way 
if we use I(,) above. If the appropriate assumption is truncation, then the 
correct contribution from each theta[i] to the likelihoods for mu and tau is not 
dnorm(mu, tau) but dnorm(mu, tau)/const, where const is the integral of 
dnorm(mu, tau) between theta = 0 and theta = infinity (so that the density for 
each theta integrates to 1). This normalizing constant (const) is a complex 
function of both mu and tau, but BUGS is ignoring this (potentially) important 
contribution to the full conditional distributions of mu and tau. Hence the 
sampling for mu and tau will be wrong!

So, back to when we can get away with using I(,) for truncation… Basically, 
this is ok when the parameters of the distribution to be truncated are known, 
because it is only they that would otherwise have been sampled incorrectly – if 
they are known then they are not sampled anyway.

Best wishes,
Dave Lunn

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