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random

Class: CompactLinearModel

Simulate responses for linear regression model

Syntax

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

Description

ysim = random(mdl) simulates responses from the fitted linear model mdl at the original design points.

ysim = random(mdl,Xnew) simulates responses from the mdl linear model to the data in Xnew, adding random noise.

Input Arguments

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Linear model object, specified as a full LinearModel object constructed using fitlm or stepwiselm, or a compacted CompactLinearModel object constructed using compact.

New predictor input values, specified as a table, dataset array, or numeric matrix.

  • If Xnew is a table or dataset array, it must contain the predictor names in mdl.

  • If Xnew is a numeric matrix, it must have the same number of variables (columns) as was used to create mdl. Furthermore, all variables used in creating mdl must be numeric.

Output Arguments

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Predicted mean values at Xnew, perturbed by random noise, returned as a numeric vector. The noise is independent and normally distributed, with mean equal to zero, and variance equal to the estimated error variance of the model.

Examples

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Create a model of car mileage as a function of weight, and simulate the response.

Create a quadratic model of car mileage as a function of weight from the carsmall data.

load carsmall
X = Weight;
y = MPG;
mdl = fitlm(X,y,'quadratic');

Create simulated responses to the data.

Xnew = X;
ysim = random(mdl,Xnew);

Plot the original responses and the simulated responses to see how they differ.

plot(X,y,'o',X,ysim,'x')
legend('Data','Simulated')

Alternatives

For predictions without random noise, use predict or feval.

Extended Capabilities

Introduced in R2012a