predict.gMAP: Predictions from gMAP analyses

In RBesT: R Bayesian Evidence Synthesis Tools

Predictions from gMAP analyses

Description

Produces a sample of the predictive distribution.

Usage

## S3 method for class 'gMAP'
predict(
  object,
  newdata,
  type = c("response", "link"),
  probs = c(0.025, 0.5, 0.975),
  na.action = na.pass,
  thin,
  ...
)

## S3 method for class 'gMAPpred'
print(x, digits = 3, ...)

## S3 method for class 'gMAPpred'
summary(object, ...)

## S3 method for class '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)

RBesT documentation built on May 29, 2024, 10:40 a.m.