Documentación

Esta página aún no se ha traducido para esta versión. Puede ver la versión más reciente de esta página en inglés.

# random

Clase: CompactLinearModel

Simulate responses for linear regression model

## Sintaxis

```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.

expandir todo

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

expandir todo

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.

## Ejemplos

expandir todo

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`.