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Added custom distribution (skewed LaPlace) with 3 parameters to distribution fitter, fit errors

Asked by Malte Julian Deventer on 30 Jan 2019
Latest activity Edited by Malte Julian Deventer on 30 Jan 2019
I defined a custom distribution (assymetric laplace, see code below) by modyfing the template provided for the symmetric laplace distribution. I saved the new distribution into my Matlab folder/+prob/
When I try to create a fit using the fitt distribution app, I get error messages:
One argument must be a square matrix and the other must be a scalar. Use POWER (.^) for elementwise power.
is there a good way to debug the code?
here the distribution
classdef SkewedLaplaceDistribution < prob.ToolboxFittableParametricDistribution
% This is ad sample implementation of the Laplace distribution. You can use
% this template as a model to implement your own distribution. Create a
% directory called '+prob' somewhere on your path, and save this file in
% that directory using a name that matches your distribution name.
% An object of the LaplaceDistribution class represents a Laplace
% probability distribution with a specific location parameter MU and
% scale parameter SIGMA. This distribution object can be created directly
% using the MAKEDIST function or fit to data using the FITDIST function.
% SkewedLaplaceDistribution methods:
% cdf - Cumulative distribution function
% fit - Fit distribution to data
% icdf - Inverse cumulative distribution function
% iqr - Interquartile range
% mean - Mean
% median - Median
% paramci - Confidence intervals for parameters
% pdf - Probability density function
% proflik - Profile likelihood function
% random - Random number generation
% std - Standard deviation
% truncate - Truncation distribution to an interval
% var - Variance
% LaplaceDistribution properties:
% DistributionName - Name of the distribution
% mu - Value of the mu parameter
% sigma - Value of the sigma parameter
% kappa - Value of the kappa parameter
% NumParameters - Number of parameters
% ParameterNames - Names of parameters
% ParameterDescription - Descriptions of parameters
% ParameterValues - Vector of values of parameters
% Truncation - Two-element vector indicating truncation limits
% IsTruncated - Boolean flag indicating if distribution is truncated
% ParameterCovariance - Covariance matrix of estimated parameters
% ParameterIsFixed - Two-element boolean vector indicating fixed parameters
% InputData - Structure containing data used to fit the distribution
% NegativeLogLikelihood - Value of negative log likelihood function
% See also fitdist, makedist.
% All ProbabilityDistribution objects must specify a DistributionName
%DistributionName Name of distribution
% DistributionName is the name of this distribution.
DistributionName = 'SkewedLaplace';
% Optionally add your own properties here. For this distribution it's convenient
% to be able to refer to the mu and sigma parameters by name, and have them
% connected to the proper element of the ParameterValues property. These are
% dependent properties because they depend on ParameterValues.
%MU Location parameter
% MU is the location parameter for this distribution.
%SIGMA Scale parameter
% SIGMA is the scale parameter for this distribution.
%Kappa asymmetry parameter
% Kappa is the symmetry parameter for this distribution.
% All ParametricDistribution objects must specify values for the following
% constant properties (they are the same for all instances of this class).
%NumParameters Number of parameters
% NumParameters is the number of parameters in this distribution.
NumParameters = 3;
%ParameterName Name of parameter
% ParameterName is a two-element cell array containing names
% of the parameters of this distribution.
ParameterNames = {'mu' 'sigma' 'kappa'};
%ParameterDescription Description of parameter
% ParameterDescription is a two-element cell array containing
% descriptions of the parameters of this distribution.
ParameterDescription = {'location' 'scale' 'symmetry'};
% All ParametricDistribution objects must include a ParameterValues property
% whose value is a vector of the parameter values, in the same order as
% given in the ParameterNames property above.
%ParameterValues Values of the distribution parameters
% ParameterValues is a two-element vector containing the mu and sigma
% values of this distribution.
% The constructor for this class can be called with a set of parameter
% values or it can supply default values. These values should be
% checked to make sure they are valid. They should be stored in the
% ParameterValues property.
function pd = SkewedLaplaceDistribution(mu,sigma,kappa)
if nargin==0
mu = 0;
sigma = 1;
kappa = 1;
pd.ParameterValues = [mu sigma kappa];
% All FittableParametricDistribution objects must assign values
% to the following two properties. When an object is created by
% the constructor, all parameters are fixed and the covariance
% matrix is entirely zero.
pd.ParameterIsFixed = [true true true]; %MEMO
pd.ParameterCovariance = zeros(pd.NumParameters);
% Implement methods to compute the mean, variance, and standard
% deviation.
function m = mean(this)
m =;
function s = std(this)
s = sqrt((1+this.kappa^4)/(this.sigma^(2)*this.kappa^(2))); %sqrt(2)*this.sigma;
function v = var(this) %MEMO
v = (1+this.kappa^4)/(this.sigma^2*this.kappa^2); % 2*this.sigma^2;
% If this class defines dependent properties to represent parameter
% values, their get and set methods must be defined. The set method
% should mark the distribution as no longer fitted, because any
% old results such as the covariance matrix are not valid when the
% parameters are changed from their estimated values.
function this =,mu)
this.ParameterValues(1) = mu;
this = invalidateFit(this);
function this = set.sigma(this,sigma)
this.ParameterValues(2) = sigma;
this = invalidateFit(this);
function this = set.kappa(this,kappa)
this.ParameterValues(3) = kappa;
this = invalidateFit(this);
function mu =
mu = this.ParameterValues(1);
function sigma = get.sigma(this)
sigma = this.ParameterValues(2);
function kappa = get.kappa(this)
kappa = this.ParameterValues(3);
% All FittableDistribution classes must implement a fit method to fit
% the distribution from data. This method is called by the FITDIST
% function, and is not intended to be called directly
function pd = fit(x,varargin)
%FIT Fit from data
% P =
% P =, NAME1,VAL1, NAME2,VAL2, ...)
% with the following optional parameter name/value pairs:
% 'censoring' Boolean vector indicating censored x values
% 'frequency' Vector indicating frequencies of corresponding
% x values
% 'options' Options structure for fitting, as create by
% the STATSET function
% Get the optional arguments. The fourth output would be the
% options structure, but this function doesn't use that.
[x,cens,freq] = prob.ToolboxFittableParametricDistribution.processFitArgs(x,varargin{:});%MEMO
% This distribution was not written to support censoring or to process
% a frequency vector. The following utility expands x by the frequency
% vector, and displays an error message if there is censoring.
x = prob.ToolboxFittableParametricDistribution.removeCensoring(x,cens,freq,'SkewedLaplace');%MEMO
freq = ones(size(x));
% Estimate the parameters from the data. If this is an iterative procedure,
% use the values in the opt argument.
m = median(x);
s = mean(abs(x-m));
v = s^2; %MEMO
% Create the distribution by calling the constructor.
pd = prob.SkewedLaplaceDistribution(m,s,v); %MEMO
% Fill in remaining properties defined above
pd.ParameterIsFixed = [false false false]; %MEMO added 3rd false
[nll,acov] = prob.SkewedLaplaceDistribution.likefunc([m s v],x); %MEMo
pd.ParameterCovariance = acov;
% Assign properties required for the FittableDistribution class
pd.NegativeLogLikelihood = nll;
pd.InputData = struct('data',x,'cens',[],'freq',freq);
% The following static methods are required for the
% ToolboxParametricDistribution class and are used by various
% Statistics and Machine Learning Toolbox functions. These functions operate on
% parameter values supplied as input arguments, not on the
% parameter values stored in a LaplaceDistribution object. For
% example, the cdf method implemented in a parent class invokes the
% cdffunc static method and provides it with the parameter values.
function [nll,acov] = likefunc(params,x) % likelihood function
n = length(x);
mu = params(1);
sigma = params(2);
kappa = params(3);
nll = -sum(log(prob.SkewedLaplaceDistribution.pdffunc(x,mu,sigma,kappa)));
acov = (sigma^2/n) * eye(2);
function y = cdffunc(x,mu,sigma,kappa) % cumulative distribution function
if x <= mu
y = (kappa^(2)/(1+kappa^(2))).*exp((sigma/kappa).*(x-mu));
y = 1-(1/(1+kappa^2)).*exp(-sigma*kappa.*(x-mu));
y(isnan(x)) = NaN;
% if sigma==0
% y = double(x>=mu);
% else
% z = (x-mu) ./ sigma;
% y = 0.5 + sign(z).*(1-exp(-abs(z)))/2;
% end
% y(isnan(x)) = NaN;
function y = pdffunc(x,mu,sigma,kappa) % probability density function
stemp = sign(x-mu);
y = (sigma/(kappa+1/kappa)).*exp(-(x-mu).*sigma.*stemp.*kappa^stemp);
clear stemp
% y = exp(-abs(x - mu)/sigma) / (2*sigma);
function y = invfunc(p,mu,sigma) % inverse cdf
if nargin<2, mu = 0; end if nargin<2, sigma = 1; end
if sigma==0
y = mu + zeros(size(p));
u = p-0.5;
y = mu - sigma.*sign(u).*log(1-2*abs(u));
y(p < 0 | 1 < p) = NaN;
function y = randfunc(mu,sigma,varargin) % random number generator %MEMO
y = prob.SkewedLaplaceDistribution.invfunc(rand(varargin{:}),mu,sigma); %MEMO
% All ToolboxDistributions must implement a getInfo static method
% so that Statistics and Machine Learning Toolbox functions can get information about
% the distribution.
function info = getInfo
% First get default info from parent class
info = getInfo@prob.ToolboxFittableParametricDistribution('prob.SkewedLaplaceDistribution');
% Then override fields as necessary = 'SkewedLaplace'; %'SkewedLaplace'
info.code = 'SkewedLaplace'; %MEMO
% info.pnames is obtained from the ParameterNames property
% info.pdescription is obtained from the ParameterDescription property
% info.prequired = [false false] % Change if any parameter must
% be specified before fitting.
% An example would be the N
% parameter of the binomial
% distribution.
% info.hasconfbounds = false % Set to true if the cdf and
% icdf methods can return
% lower and upper bounds as
% their 2nd and 3rd outputs.
% censoring = false % Set to true if the fit
% method supports censoring.
% = [-Inf, Inf] % Set to other lower and upper
% bounds if the distribution
% doesn't cover the whole real
% line. For example, for a
% distribution on positive
% values use [0, Inf].
% info.closedbound = [false false] % Set the Jth value to
% true if the distribution
% allows x to be equal to the
% Jth element of the support
% vector.
% info.iscontinuous = true % Set to false if x can take
% only integer values.
info.islocscale = false; % Set to true if this is a
% location/scale distribution
% (no other parameters).
% info.uselogpp = false % Set to true if a probability
% plot should be drawn on the
% log scale.
% info.optimopts = false % Set to true if the fit
% method can be called with an
% options structure.
info.logci = [false false false]; % Set to true for a parameter %MEMO
% that should have its Wald
% confidence interval computed
% using a normal approximation
% on the log scale.
end % classdef
% The following utilities check for valid parameter values
function checkargs(mu,sigma,kappa)
if ~(isscalar(mu) && isnumeric(mu) && isreal(mu) && isfinite(mu))
error('MU must be a real finite numeric scalar.')
if ~(isscalar(sigma) && isnumeric(sigma) && isreal(sigma) && sigma>=0 && isfinite(sigma))
error('SIGMA must be a positive finite numeric scalar.')
if ~(isscalar(kappa) && isnumeric(kappa) && isreal(kappa) && kappa>=0 && isfinite(kappa))
error('KAPPA must be a positive finite numeric scalar.')


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