Documentation Center

  • Trial Software
  • Product Updates

Contents

ecmmvnrfish

Fisher information matrix for multivariate normal regression model

Syntax

Fisher = ecmmvnrfish(Data, Design, Covariance, Method,
MatrixFormat, CovarFormat)

Arguments

Data

NUMSAMPLES-by-NUMSERIES matrix with NUMSAMPLES samples of a NUMSERIES-dimensional random vector. Missing values are represented as NaNs. Only samples that are entirely NaNs are ignored. (To ignore samples with at least one NaN, use mvnrfish.)

Design

A matrix or a cell array that handles two model structures:

  • If NUMSERIES = 1, Design is a NUMSAMPLES-by-NUMPARAMS matrix with known values. This structure is the standard form for regression on a single series.

  • If NUMSERIES 1, Design is a cell array. The cell array contains either one or NUMSAMPLES cells. Each cell contains a NUMSERIES-by-NUMPARAMS matrix of known values.

    If Design has a single cell, it is assumed to have the same Design matrix for each sample. If Design has more than one cell, each cell contains a Design matrix for each sample.

Covariance

NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the residuals of the regression.

Method

(Optional) String that identifies method of calculation for the information matrix:

  • hessian - Default method. Use the expected Hessian matrix of the observed log-likelihood function. This method is recommended since the resultant standard errors incorporate the increased uncertainties due to missing data.

  • fisher - Use the Fisher information matrix.

MatrixFormat

(Optional) String that identifies parameters to be included in the Fisher information matrix:

  • full - Default format. Compute the full Fisher information matrix for both model and covariance parameter estimates.

  • paramonly - Compute only components of the Fisher information matrix associated with the model parameter estimates.

CovarFormat

(Optional) String that specifies the format for the covariance matrix. The choices are:

  • 'full' - Default method. The covariance matrix is a full matrix.

  • 'diagonal' - The covariance matrix is a diagonal matrix.

Description

Fisher = ecmmvnrfish(Data, Design, Covariance, Method, MatrixFormat, CovarFormat) computes a Fisher information matrix based on current maximum likelihood or least-squares parameter estimates that account for missing data.

Fisher is a NUMPARAMS-by-NUMPARAMS Fisher information matrix or Hessian matrix. The size of NUMPARAMS depends on MatrixFormat and on current parameter estimates. If MatrixFormat = 'full',

NUMPARAMS = NUMSERIES * (NUMSERIES + 3)/2

If MatrixFormat = 'paramonly',

NUMPARAMS = NUMSERIES

    Note   ecmmvnrfish operates slowly if you calculate the full Fisher information matrix.

See Also

|

Was this topic helpful?