RBesT (version 1.8-1)

predict.gMAP: Predictions from gMAP analyses

Description

Produces a sample of the predictive distribution.

Usage

# S3 method for gMAP
predict(
  object,
  newdata,
  type = c("response", "link"),
  probs = c(0.025, 0.5, 0.975),
  na.action = na.pass,
  thin,
  ...
)
# S3 method for gMAPpred
print(x, digits = 3, ...)
# S3 method for gMAPpred
summary(object, ...)
# S3 method for gMAPpred
as.matrix(x, ...)

Arguments

newdata

data.frame which must contain the same columns as input into the gMAP analysis. If left out, then a posterior prediction for the fitted data entries from the gMAP object is performed (shrinkage estimates).

type

sets reported scale (response (default) or link).

probs

defines quantiles to be reported.

na.action

how to handle missings.

thin

thinning applied is derived from the gMAP object.

...

ignored.

x, object

gMAP analysis object for which predictions are performed

digits

number of displayed significant digits.

Details

Predictions are made using the \(\tau\) prediction stratum of the gMAP object. For details on the syntax, please refer to predict.glm and the example below.

See Also

gMAP, predict.glm

Examples

## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 20x more warmup & iter in practice
.user_mc_options <- options(RBesT.MC.warmup=50, RBesT.MC.iter=100,
                            RBesT.MC.chains=2, RBesT.MC.thin=1)

# create a fake data set with a covariate
trans_cov <- transform(transplant, country = cut(1:11, c(0, 5, 8, Inf), c("CH", "US", "DE")))
set.seed(34246)
map <- gMAP(cbind(r, n - r) ~ 1 + country | study,
  data = trans_cov,
  tau.dist = "HalfNormal",
  tau.prior = 1,
  # Note on priors: we make the overall intercept weakly-informative
  # and the regression coefficients must have tighter sd as these are
  # deviations in the default contrast parametrization
  beta.prior = rbind(c(0, 2), c(0, 1), c(0, 1)),
  family = binomial,
  ## ensure fast example runtime
  thin = 1, chains = 1
)

# posterior predictive distribution for each input data item (shrinkage estimates)
pred_cov <- predict(map)
pred_cov

# extract sample as matrix
samp <- as.matrix(pred_cov)

# predictive distribution for each input data item (if the input studies were new ones)
pred_cov_pred <- predict(map, trans_cov)
pred_cov_pred


# a summary function returns the results as matrix
summary(pred_cov)

# obtain a prediction for new data with specific covariates
pred_new <- predict(map, data.frame(country = "CH", study = 12))
pred_new
## Recover user set sampling defaults
options(.user_mc_options)