scoring  R Documentation 
Scoring algorithm for maximumlikelihood estimation of a penalized Poisson
model while treating the smoothing parameters as fixed. Since the model
matrix Z
when fitting a point process model on a geometric network is
very large with usually several millions of entries, scoring
builds
an sparse representations of matrices in R.
scoring(theta, rho, data, Z, K, ind, eps_theta = 1e05) score(theta, rho, data, Z, K, ind) fisher(theta, rho, data, Z, K, ind)
theta 
An initial vector of model coefficients. 
rho 
The current vector of smoothing parameters. For each smooth term, including the baseline intensity of the network, one smoothing parameter must be supplied. 
data 
A data frame containing the data. 
Z 
The (sparse) model matrix where the number of column must
correspond to the length of the vector of model coefficients 
K 
A (sparse) square penalty matrix of with the same dimension as

ind 
A list which contains the indices belonging to each smooth term and the linear terms. 
eps_theta 
The termination condition. If the relative change of the
norm of the model parameters is less than 
scoring
performs the scoring algorithm for maximumlikelihood
estimation according to Fahrmeir et al. (2013). This algorithm is based
on the scorefunction and the Fisherinformation of the loglikelihood.
score
returns the scorefunction (the gradient of the loglikelihood)
and fisher
returns the Fisherinformation (negative Hessian of the
loglikelihood).
The maximum likelihood estimate for fixed smoothing parameters.
Fahrmeir, L., Kneib, T., Lang, S. and Marx, B. (2013). Regression. Springer.
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