Simulate responses for linear regression model
ysim = random(mdl)
ysim = random(mdl,Xnew)
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.
Linear model, as constructed by fitlm or stepwiselm.
Points at which mdl predicts responses.
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
Vector of predicted mean values at Xnew,
perturbed by random noise. The noise is independent, normally distributed,
with mean zero, and variance equal to the estimated error variance
of the model.
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.
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.
For predictions without random noise, use predict or feval.
feval | LinearModel | predict
Was this topic helpful?