dLR_nb {RCM} | R Documentation

## A function that returns the value of the partial derivative of the log-likelihood ratio to alpha, keeping the response functions fixed

### Description

A function that returns the value of the partial derivative of the log-likelihood ratio to alpha, keeping the response functions fixed

### Usage

    dLR_nb(
      Alpha,
      X,
      CC,
      responseFun = c("linear", "quadratic", "nonparametric", "dynamic"),
      psi,
      NB_params,
      NB_params_noLab,
      d,
      alphaK,
      k,
      centMat,
      nLambda,
      nLambda1s,
      thetaMat,
      muMarg,
      ncols,
      envGradEst,
      allowMissingness,
      naId,
      ...
    )

### Arguments

`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
`ncols` | a scalar, the number of columns of X
`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 methods

### Value

: The value of the lagrangian and the constraining equations

[Package RCM version 1.6.0 Index]