Comments (1)
The intercepts are all equal in this model formulation as the explanatory variables have all been centered about their respective means. When the X's are equal to their mean, then the centered predictors are all 0 and the fitted value is just the intercept, which is bas.lm
.
The estimates are MLEs/OLS if there are no weights, and GLS/MLEs if there are weights used.
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Related Issues (20)
- Remove intercept in bas.lm? HOT 1
- Multithreading Problem HOT 1
- dropping the null model from Jeffrey's prior conflicts with include.always HOT 1
- Using pivot = FALSE in bas.lm does not create a warning on M1mac in non-full rank case HOT 1
- Add Firth logistic regression to BAS
- coef throws warnings with bas.glm objects if prior is AIC or BIC HOT 1
- `bayesglm.fit` does not check arguments `x` or `y` for correct type before calling C HOT 1
- `bas.predict` error with a single covariate HOT 2
- segmentation fault with method "MCMC+BAS" if initial (best model) arg is not the null model HOT 1
- `bas.predict` error when n<p when using `estimator= BMA` and `se.fit =T` HOT 2
- Release BAS 1.6.6 HOT 1
- valgrind identifies uninitialized variables in `glm_fit.c` HOT 1
- Release BAS 1.7.0 HOT 1
- `bas.lm` and `bas.glm` ignoring prior model probabilities that are 0 HOT 1
- uninitialized values in `hyp1f1.c` HOT 1
- Release BAS 1.7.1 HOT 1
- issue with hypergeometric1F1 HOT 1
- error in hypergeometric1F1 HOT 1
- Linux vs Windows: different results HOT 4
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