pvcregr
Calculates covariance and correlation matrix of parameters for constant variation coeffients
Contents
Syntax
[cov, cor, sd, ss] = pvcregr (func, p, varargin)
Description
Calculates covariance and correlation matrix of parameters in regression models with sd proportional to mean, using the ML criterion. standard deviation and sum of squared deviations of model predictions with respect to observations
Input
- func: character string with name of user-defined function; see nrregr
- p: (np,2)-matrix with
p(:,1) parameter values p(:,2) binaries with yes or no conditional values all conditional parameters have zero (co)variance
- xywi: (ni,3) matrix with
xywi(:,1) independent variable xywi(:,2) dependent variable xywi(:,3) weight coefficients (optional) The number of data matrices xyw1, xyw2, ... is optional
Ouput
- cov: (np,np) matrix with covariances
- cor: (np,np) matrix with correlation coefficients
- sd: (np,1) matrix with standard deviations
- ss: scalar with weighted mean squared variation coefficient
Remarks
Similar to pregr, but sd propto mean
Example of use
See mydata_nrreg