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(), poisson(), and Gamma() with canonical links are implemented. |

`coefprior` |
function specifying prior distribution on coefficients with
optional hyperparameters leading to marginal likelihood calculations;
options include |

`control` |
a list of parameters that control convergence in the fitting
process. See the documentation for |

`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

### See Also

### 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)
```

*BAS*version 1.7.1 Index]