R: Fitting Generalized Linear Models and Bayesian marginal...

bayesglm.fit {BAS} | R Documentation

## Fitting Generalized Linear Models and Bayesian marginal likelihood evaluation

### Description

A version of glm.fit rewritten in C; also returns marginal likelihoods for Bayesian model comparison

### Usage

    bayesglm.fit(
      x,
      y,
      weights = rep(1, nobs),
      start = NULL,
      etastart = NULL,
      mustart = NULL,
      offset = rep(0, nobs),
      family = binomial(),
      coefprior = bic.prior(nobs),
      control = glm.control(),
      intercept = TRUE
    )

### Arguments

`x` | design matrix
`y` | response
`weights` | optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector.
`start` | starting value for coefficients in the linear predictor
`etastart` | starting values for the linear predictor
`mustart` | starting values for the vectors of means
`offset` | a priori known component to be included in the linear predictor
`family` | a description of the error distribution and link function for exponential family; currently only binomial() and poisson() with canonical links are implemented.
`coefprior` | function specifying prior distribution on coefficients with optional hyperparameters leading to marginal likelihood calculations; options include `bic.prior()`,`aic.prior()`, and `ic.prior()`
`control` | a list of parameters that control convergence in the fitting process. See the documentation for `glm.control()`
`intercept` | should an intercept be included in the null model?

### Details

C version of glm-fit. For different prior choices returns, marginal likelihood of model using a Laplace approximation.

### Value

`coefficients` | MLEs
`se` | Standard errors of coefficients based on the sqrt of the diagonal of the inverse information matrix
`mu` | fitted mean
`rank` | numeric rank of the fitted linear model
`deviance` | minus twice the log likelihood evaluated at the MLEs
`g` | value of g in g-priors
`shrinkage` | shrinkage factor for coefficients in linear predictor
`RegSS` | quadratic form beta'I(beta)beta used in shrinkage
`logmarglik` | the log marginal or integrated log likelihood (up to a constant)

### Author(s)

Merlise Clyde translated the `glm.fit` from R base into C using the .Call interface

### References

`glm`

### See Also

`bic.prior`

### Examples

    data(Pima.tr, package="MASS")
    Y <- as.numeric(Pima.tr$type) - 1
    X <- cbind(1, as.matrix(Pima.tr[,1:7]))
    out <- bayesglm.fit(X, Y, family=binomial(),coefprior=bic.prior(n=length(Y)))
    out$coef
    out$se
    # using built in function
    glm(type ~ ., family=binomial(), data=Pima.tr)

[Package BAS version 1.5.5 Index]