dppi {scorematchingad}

Improper Log-Density of the PPI Model

Description

Compute the natural logarithm of the improper density for the PPI model for the given matrix of measurements Y. Rows with negative values or with a sum that differs from 1 by more than 1E-15 are assigned a value of -Inf.

Usage

dppi(Y, ..., paramvec = NULL)

Arguments

Y

A matrix of measurements in the simplex. Each row is a multivariate measurement.

...

Arguments passed on to ppi_paramvec

AL

Either NULL, a p-1 x p-1 symmetric matrix, a number, or "diag". If NULL then all A_L elements will be set to NA. If a single number, then A_L will be fixed as a matrix of the given value. If "diag" then the non-diagonal elements of A_L will be fixed to 0, and the diagonal will be NA.

bL

Either NULL, a number, or a vector of length p-1. If NULL, then all elements of b_L will be set to NA. If a single number, then b_L will be fixed at the supplied value.

beta

Either NULL, a number, or a vector of length p. If NULL then all elements of \beta will be set to NA. If a single number then the \beta elements will be fixed at the given number.

betaL

Either NULL, a number, or a vector of length p-1. If NULL then the 1...(p-1)th \beta elements will be set to NA. If a single number then the 1...(p-1)th \beta elements will be fixed at the given number.

betap

Either NULL or a number. If NULL then the pth element of \beta will be set to NA, and ppi() will estimate it. If a number, then the pth element of \beta will be fixed at the given value.

p

The number of components. If NULL then p will be inferred from other inputs.

Astar

The A^* matrix (a p by p symmetric matrix)

paramvec

The PPI parameter vector, created easily using ppi_paramvec() and also returned by ppi(). Use paramvec instead of ....

Details

The value calculated by dppi is

z_L^TA_Lz_L + b_L^Tz_L + \beta^T \log(z),

where z is the multivariate observation (i.e. a row of Y), and z_L omits the final element of z.

See Also

Other PPI model tools: ppi_param_tools, ppi_robust(), ppi(), rppi()

Examples

m <- rppi_egmodel(10)
dppi(m$sample, paramvec = m$theta)