Parametric and non-parametric bootstrap for linear regression model | R Documentation |

Parametric and non-parametric bootstrap for linear regression model.

```
lm.parboot(x, y, R = 1000)
lm.nonparboot(x, y, R = 1000)
```

`x` |
The predictor variables, a vector or a matrix or a data frame. |

`y` |
The response variable, a numerical vector with data. |

`R` |
The number of parametric bootstrap replications to perform. |

An efficient implementation of parametric or non-parametric bootstrapping the residuals for linear models is provided.

A matrix with R columns and rows equal to the number of the regression parameters. Each column contains the set of a bootstrap beta regression coefficients.

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Efron Bradley and Robert J. Tibshirani (1993). An introduction to the bootstrap. New York: Chapman & Hall/CRC.

```
lm.drop1, leverage, pc.sel, mmpc
```

```
y <- rnorm(50)
x <- matrix( rnorm( 50 * 2), ncol = 2 )
a <- lm.parboot(x, y, 500)
```

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