Description Usage Arguments Details Value Side Effects References See Also Examples

This function is associated with `sm.binomial`

for the underlying fitting
procedure. It performs a Pseudo-Likelihood Ratio Test for the
goodness-of-fit of a standard parametric logistic regression of specified
`degree`

in the covariate `x`

.

1 2 | ```
sm.binomial.bootstrap(x, y, N = rep(1, length(x)), h, degree = 1,
fixed.disp=FALSE, ...)
``` |

`x` |
vector of the covariate values |

`y` |
vector of the response values; they must be nonnegative integers. |

`h` |
the smoothing parameter; it must be positive. |

`N` |
a vector containing the binomial denominators. If missing, it is assumed to contain all 1's. |

`degree` |
specifies the degree of the fitted polynomial in |

`fixed.disp` |
if |

`...` |
additional parameters passed to |

see Section 5.4 of the reference below.

a list containing the observed value of the Pseudo-Likelihood Ratio Test statistic, its observed p-value as estimated via the bootstrap method, and the vector of estimated dispersion parameters when this value is not forced to be 1.

Graphical output representing the bootstrap samples is produced on the current graphical device. The estimated dispersion parameter, the value of the test statistic and the observed significance level are printed.

Bowman, A.W. and Azzalini, A. (1997).
*Applied Smoothing Techniques for Data Analysis: *
*the Kernel Approach with S-Plus Illustrations.*
Oxford University Press, Oxford.

`sm.binomial`

, `sm.poisson.bootstrap`

1 | ```
## Not run: sm.binomial.bootstrap(concentration, dead, N, 0.5, nboot=50)
``` |

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