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Can someone help me understand simulations of a one-sided Cusum? Consider the following: Q[i] = max(0, Q[i-1]+z[i]), z[i] ~ N(offset, 1), with Q[0] = FIR (fast initial response). With offset < 0, mean{Q[i] for fixed i averaged over many simulations} approaches an asymptote as i -> Inf. In simulations with abs(offset) small and FIR close to the asymptote, Q[i]

...omplementary information available for the patients. The purpose is not so much to understand whether the patient will be alive or dead (this info is not available for all the patients after its last therapy), but to try to guess the next therapy one is likely to undergo at some stage using a Markovian model. Let us assume that all patients are given the i-th therapy cycle at the same time. I did some experiments with the mcm package (http://cran.r-project.org/web/packages/msm/index.html), but I am banging a bit my head against the wall. In general, I do not have a death state and time does...

...solved by MCMC. I am fairly new to MCMC (in the learning process) - so apologize if the answer is totally obvious, and any hints, links etc are greatly appreciated. I'll illustrate the problem in a version cut-down to the essentials - the real problem is ways more complex. Suppose I have a Markovian series of poisson processes, which generate (or kill) some objects. At t0, say I have initially x = 3 objects. In the next time step, t1, the number of total objects is Poisson-distributed, subject to a function of x at t0, covariates, and parameters. So x_t1 ~ Pois, with E(x_t1) = f(x_t0, cova...

Dear All, I would like to apply two different "for loops" to each set of four columns of a matrix (the loops here are simplifications of the actual loops I will be running which involve multiple if/else statements). I don't know how to "alternate" between the loops depending on which column is "running through the loop" at the time. ## Set up matrix J <- 10 N