Main Content

random

Simulate responses with random noise for generalized linear regression model

collapse all in page

Syntax

ysim = random(mdl,Xnew)
ysim = random(mdl,Xnew,Name,Value)

Description

ysim = random(mdl,Xnew) simulates responses to the predictor data in Xnew using the generalized linear regression model mdl, adding random noise.

example

ysim = random(mdl,Xnew,Name,Value) specifies additional options using one or more name-value pair arguments. For example, you can specify the number of trials for binomial distribution or the offset value used for fitting.

example

Examples

collapse all

Open Live Script

Create a generalized linear regression model, and simulate its response with random noise to new data.

Generate sample data using Poisson random numbers with one underlying predictor X. 

rng('default') % For reproducibility
X = rand(20,1);
mu = exp(1 + 2*X);
y = poissrnd(mu);

Create a generalized linear regression model of Poisson data. 

mdl = fitglm(X,y,'y ~ x1','Distribution','poisson');

Create data points for prediction. 

Xnew = (0:.05:1)';

Simulate responses with random noise at the data points. 

ysim = random(mdl,Xnew);

Plot the simulated values and the original values. 

plot(X,y,'rx',Xnew,ysim,'bo',Xnew,feval(mdl,Xnew),'g-')
legend('Data','Simulated Response with Noise','Predicted Response', ...
    'Location','best')

Figure contains an axes object. The axes object contains 3 objects of type line. One or more of the lines displays its values using only markers These objects represent Data, Simulated Response with Noise, Predicted Response.

This example uses:

Open Live Script

Fit a generalized linear regression model, and then save the model by using saveLearnerForCoder. Define an entry-point function that loads the model by using loadLearnerForCoder and calls the predict function of the fitted model. Then use codegen (MATLAB Coder) to generate C/C++ code. Note that generating C/C++ code requires MATLAB® Coder™.

This example briefly explains the code generation workflow for the prediction of linear regression models at the command line. For more details, see Code Generation for Prediction of Machine Learning Model at Command Line. You can also generate code using the MATLAB Coder app. For details, see Code Generation for Prediction of Machine Learning Model Using MATLAB Coder App.

Train Model

Generate sample data of the predictor x and response y with the following distributions:

  • x∼N(1,0.52).

  • β0=1 and β=-2.

  • y∼Binomial(10,exp(1+xβ)1+exp(1+xβ)).

rng('default') % For reproducibility
x = 1 + randn(100,1)*0.5;
beta = -2;
p = exp(1 + x*beta)./(1 + exp(1 + x*beta)); % Inverse logit
n = 10;
y = binornd(n,p,100,1);

Create a generalized linear regression model of binomial data. Specify a binomial sample size of 10.

mdl = fitglm(x,y,'y ~ x1','Distribution','Binomial','BinomialSize',n);

Save Model

Save the fitted generalized linear regression model to the file GLMMdl.mat by using saveLearnerForCoder.

saveLearnerForCoder(mdl,'GLMMdl');

Define Entry-Point Function

In your current folder, define an entry-point function named myrandomGLM.m that does the following:

  • Accept new predictor input and valid name-value pair arguments.

  • Load the fitted generalized linear regression model in GLMMdl.mat by using loadLearnerForCoder.

  • Simulate responses from the loaded GLM model.

function y = myrandomGLM(x,varargin) %#codegen
%MYRANDOMGLM Simulate responses using GLM model 
%   MYRANDOMGLM simulates responses for the n observations in the n-by-1
%   vector x using the GLM model stored in the MAT-file GLMMdl.mat, and
%   then returns the simulations in the n-by-1 vector y.
CompactMdl = loadLearnerForCoder('GLMMdl');
narginchk(1,Inf);
y = random(CompactMdl,x,varargin{:});
end

Add the %#codegen compiler directive (or pragma) to the entry-point function after the function signature to indicate that you intend to generate code for the MATLAB algorithm. Adding this directive instructs the MATLAB Code Analyzer to help you diagnose and fix violations that would result in errors during code generation.

Generate Code

Generate code for the entry-point function using codegen (MATLAB Coder). Because C and C++ are statically typed languages, you must determine the properties of all variables in the entry-point function at compile time. To specify the data type and exact input array size, pass a MATLAB® expression that represents the set of values with a certain data type and array size. Use coder.Constant (MATLAB Coder) for the names of name-value pair arguments.

Specify the predictor data x and binomial parameter n.

codegen -config:mex myrandomGLM -args {x,coder.Constant('BinomialSize'),coder.Constant(n)}
Code generation successful.

codegen generates the MEX function myrandomGLM_mex with a platform-dependent extension.

If the number of observations is unknown at compile time, you can also specify the input as variable-size by using coder.typeof (MATLAB Coder). For details, see Specify Variable-Size Arguments for Code Generation and Specify Types of Entry-Point Function Inputs (MATLAB Coder).

Verify Generated Code

Simulate responses using the MEX function. Specify the predictor data x and binomial parameter n.

ysim = myrandomGLM_mex(x,'BinomialSize',n);

Plot the simulated values and the data in the same figure.

figure
plot(x,y,'bo',x,ysim,'r*')
legend('Observed responses','Simulated responses')
xlabel('x')
ylabel('y')

Figure contains an axes object. The axes object with xlabel x, ylabel y contains 2 objects of type line. One or more of the lines displays its values using only markers These objects represent Observed responses, Simulated responses.

The observed and simulated responses appear to be similarly distributed.

Input Arguments

collapse all

Generalized linear regression model, specified as a GeneralizedLinearModel object created using fitglm or stepwiseglm, or a CompactGeneralizedLinearModel object created using compact.

New predictor input values, specified as a table, dataset array, or matrix. Each row of Xnew corresponds to one observation, and each column corresponds to one variable.

  • If Xnew is a table or dataset array, it must contain predictors that have the same predictor names as in the PredictorNames property of mdl.

  • If Xnew is a matrix, it must have the same number of variables (columns) in the same order as the predictor input used to create mdl. Note that Xnew must also contain any predictor variables that are not used as predictors in the fitted model. Also, all variables used in creating mdl must be numeric. To treat numerical predictors as categorical, identify the predictors using the 'CategoricalVars' name-value pair argument when you create mdl.

Data Types: single | double | table

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: ysim = random(Mdl,Xnew,'BinomialSize',50) returns the numbers of success, perturbed by random noise, using the number of trials specified by 'BinomialSize'.

Number of trials for the binomial distribution, specified as the comma-separated pair consisting of 'BinomialSize' and a scalar or vector of the same length as the response. random expands the scalar input into a constant array of the same size as the response. The scalar input means that all observations have the same number of trials.

The meaning of the output values in ysim depends on the value of 'BinomialSize'.

  • If 'BinomialSize' is 1 (default), then each value in the output ysim is the probability of success.

  • If 'BinomialSize' is not 1, then each value in the output ysim is the predicted number of successes in the trials.

Data Types: single | double

Offset value for each row in Xnew, specified as the comma-separated pair consisting of 'Offset' and a scalar or vector with the same length as the response. random expands the scalar input into a constant array of the same size as the response.

Note that the default value of this argument is a vector of zeros even if you specify the 'Offset' name-value pair argument when fitting a model. If you specify 'Offset' for fitting, the software treats the offset as an additional predictor with a coefficient value fixed at 1. In other words, the formula for fitting is

f(μ) = Offset + X*b,

where f is the link function, μ is the mean response, and X*b is the linear combination of predictors X. The Offset predictor has coefficient 1.

Data Types: single | double

Output Arguments

collapse all

Simulated response values, returned as a numeric vector. The simulated values are the predicted response values at Xnew perturbed by random noise with the distribution given by the fitted model. The values in ysim are independent, conditional on the predictors. For binomial and Poisson fits, random generates ysim with the specified distribution and no adjustment for any estimated dispersion.

  • If 'BinomialSize' is 1 (default), then each value in the output ysim is the probability of success.

  • If 'BinomialSize' is not 1, then each value in the output ysim is the predicted number of successes in the trials.

Alternative Functionality

For predictions without random noise, use predict or feval.

  • predict accepts a single input argument containing all predictor variables, and gives confidence intervals on its predictions.

  • feval accepts multiple input arguments with one input for each predictor variable, which is simpler to use with a model created from a table or dataset array. The feval function does not support the name-value pair arguments 'Offset' and 'BinomialSize'. The function uses 0 as the offset value, and the output values are predicted probabilities.

Extended Capabilities

Version History

Introduced in R2012a

See Also

| | |

Topics