Description Usage Arguments Value
Given a particular mean parametrized COM-Poisson distribution i.e. mu and nu, this function is used to find a lambda that can satisfy the mean constraint with a combination of bisection and Newton-Raphson updates. The function is also vectorized but will only update those that have not converged.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | comp_lambdas(
mu,
nu,
lambdalb = 1e-10,
lambdaub = 1000,
maxlambdaiter = 1000,
tol = 1e-06,
lambdaint = 1,
summax = 100
)
comp_lambdas_fixed_ub(
mu,
nu,
lambdalb = 1e-10,
lambdaub = 1000,
maxlambdaiter = 1000,
tol = 1e-06,
lambdaint = 1,
summax = 100
)
|
mu, nu |
mean and dispersion parameters. Must be straightly positive. |
lambdalb, lambdaub |
numeric; the lower and upper end points for the interval to be searched for lambda(s). |
maxlambdaiter |
numeric; the maximum number of iterations allowed to solve for lambda(s). |
tol |
numeric; the convergence threshold. A lambda is said to satisfy the mean constraint if the absolute difference between the calculated mean and the corresponding mu values is less than tol. |
lambdaint |
numeric vector; initial gauss for lambda(s). |
summax |
maximum number of terms to be considered in the truncated sum |
Both comp_lambdas
and comp_lambdas_fixed_ub
returns the lambda value(s)
that satisfies the mean constraint(s) as well as the current lambdaub value.
lambda value(s) returns by comp_lambdas_fixed_ub
is bounded by the lambdaub
value.
comp_lambdas
has the extra ability to scale up/down lambdaub to find the most
appropriate lambda values.
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