LR_nb_Jac {RCM} | R Documentation |
A function that returns the Jacobian of the likelihood ratio
LR_nb_Jac( Alpha, X, CC, responseFun = c("linear", "quadratic", "nonparametric", "dynamic"), psi, NB_params, NB_params_noLab, d, alphaK, k, centMat, nLambda, nLambda1s, thetaMat, muMarg, n, ncols, preFabMat, envGradEst, allowMissingness, naId, ... )
Alpha |
a vector of length d + k*(2+(k-1)/2), the environmental gradient plus the lagrangian multipliers |
X |
the n-by-p count matrix |
CC |
a n-by-d covariate vector |
responseFun |
a character string indicating the type of response function |
psi |
a scalar, an importance parameter |
NB_params |
Starting values for the NB_params |
NB_params_noLab |
Starting values for the NB_params without label |
d |
an integer, the number of covariate parameters |
alphaK |
a matrix of environmental gradients of lower dimensions |
k |
an integer, the current dimension |
centMat |
a nLambda1s-by-d centering matrix |
nLambda |
an integer, number of lagrangian multipliers |
nLambda1s |
an integer, number of centering restrictions |
thetaMat |
a matrix of size n-by-p with estimated dispersion parameters |
muMarg |
an n-by-p offset matrix |
n |
an integer, the number of rows of X |
ncols |
a scalar, the number of columns of X |
preFabMat |
a prefabricated matrix |
envGradEst |
a character string, indicating how the environmental gradient should be fitted. 'LR' using the likelihood-ratio criterion, or 'ML' a full maximum likelihood solution |
allowMissingness |
A boolean, are missing values present |
naId |
The numeric index of the missing values in X |
... |
Further arguments passed on to other functions |
A symmetric matrix, the evaluated Jacobian