bmaPredict | R Documentation |
Make a Bayesian model averaged prediction for new data points, from
those models saved in a BayesMfp
object.
bmaPredict(BayesMfpObject, postProbs = posteriors(BayesMfpObject), newdata)
BayesMfpObject |
|
postProbs |
vector of posterior probabilities, which are then normalized to the weights of the model average (defaults to the normalized posterior probability estimates) |
newdata |
new covariate data as data.frame |
The predicted values as a vector.
Note that this function is not an S3 predict method for
BmaSamples
objects, but a function working on
BayesMfp
objects (because we do not need BMA samples to
do BMA point predictions).
Daniel Saban\'es Bov\'e
BmaSamples Methods
## generate a BayesMfp object set.seed(19) x1 <- rnorm(n=15) x2 <- rbinom(n=15, size=20, prob=0.5) x3 <- rexp(n=15) y <- rt(n=15, df=2) test <- BayesMfp(y ~ bfp (x2, max = 4) + uc (x1 + x3), nModels = 100, method="exhaustive") ## predict new responses at (again random) covariates bmaPredict(test, newdata = list(x1 = rnorm(n=15), x2 = rbinom(n=15, size=5, prob=0.2) + 1, x3 = rexp(n=15)))
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