sm (version 2.0-2)

sm.binomial.bootstrap: Bootstrap goodness-of-fit test for a logistic regression model.

Description

This function is associated with sm.logit 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.

Usage

sm.binomial.bootstrap(x, y, N=rep(1, length(x)), h, nboot=99, 
                      degree=1, ...)

Arguments

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.

nboot

number of bootstrap samples (default=100).

degree

specifies the degree of the fitted polynomial in x on the logit scale (default=1).

...

additional parameters passed to sm.binomial.

Value

• 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.

synopsis

sm.binomial.bootstrap(x, y, N = rep(1, length(x)), h, nboot = 99, degree = 1, fixed.disp = FALSE, family = binomial(logit), ...)

Side Effects

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.

Details

see Section 5.4 of the reference below.

References

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

See Also

sm.binomial, sm.poisson.bootstrap

Examples

sm.binomial.bootstrap(concentration, dead, N, 0.5, nboot=50)