Compute Parameters for Proportional Odds Markov Model
Given a vector intercepts
of initial guesses at the intercepts in a Markov proportional odds model, and a vector extra
if there are other parameters, solves for the intercepts
and extra
vectors that yields a set of occupancy probabilities at time t
that equal, as closely as possible, a vector of target values.
intMarkovOrd( y, times, initial, absorb = NULL, intercepts, extra = NULL, g, target, t, ftarget = NULL, onlycrit = FALSE, constraints = NULL, printsop = FALSE, ... )
y |
vector of possible y values in order (numeric, character, factor) |
times |
vector of measurement times |
initial |
initial value of |
absorb |
vector of absorbing states, a subset of |
intercepts |
vector of initial guesses for the intercepts |
extra |
an optional vector of intial guesses for other parameters passed to |
g |
a user-specified function of three or more arguments which in order are |
target |
vector of target state occupancy probabilities at time |
t |
target times. Can have more than one element only if |
ftarget |
an optional function defining constraints that relate to transition probabilities. The function returns a penalty which is a sum of absolute differences in probabilities from target probabilities over possibly multiple targets. The |
onlycrit |
set to |
constraints |
a function of two arguments: the vector of current intercept values and the vector of |
printsop |
set to |
... |
optional arguments to pass to |
list containing two vectors named intercepts
and extra
unless oncrit=TRUE
in which case the best achieved sum of absolute errors is returned
Frank Harrell
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