Automatic Differentiation
 
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◆ ordered_logistic_glm_lpmf() [2/3]

template<bool propto, typename T_y , typename T_x , typename T_beta , typename T_cuts , require_matrix_t< T_x > * = nullptr, require_all_col_vector_t< T_beta, T_cuts > * = nullptr>
return_type_t< T_x, T_beta, T_cuts > stan::math::ordered_logistic_glm_lpmf ( const T_y &  y,
const T_x &  x,
const T_beta &  beta,
const T_cuts &  cuts 
)

Returns the log PMF of the ordinal regression Generalized Linear Model (GLM).

This is equivalent to and faster than ordered_logistic_lpmf(y, x * beta, cuts).

Template Parameters
T_ytype of integer vector of classes. It can be either std::vector<int> or int.
T_xtype of the matrix of independent variables (features)
T_betatype of the vector of weights
T_cutstype of the vector of cutpoints
Parameters
ya scalar or vector of classes. If it is a scalar it will be broadcast - used for all instances. Values should be between 1 and number of classes, including endpoints.
xdesign matrix or row vector. If it is a row vector it will be broadcast - used for all instances.
betaweight vector
cutscutpoints vector
Returns
log probability
Exceptions
std::domain_errorIf any class is not between 1 and the number of cutpoints plus 2 or if the cutpoint vector is not sorted in ascending order or any input is not finite
std::invalid_argumentif container sizes mismatch.

Definition at line 49 of file ordered_logistic_glm_lpmf.hpp.