MINI MINI MANI MO

%
% Copyright (c) 2012, 2014, Oracle and/or its affiliates. All rights reserved. 
%
\name{ore.predict-glm}
\alias{ore.predict-glm}
\alias{ore.predict,glm-method}
\title{Oracle R Enterprise Predictions Using \code{\link[stats]{glm}} Models}
\description{
  Oracle R Enterprise method for generating predictions using
  \code{\link[stats]{glm}} models.
}
\usage{
  \S4method{ore.predict}{glm}(object, newdata, type = c("link", "response"),
            se.fit = FALSE, dispersion = NULL, na.action = na.pass,
            ...)
}
\arguments{
  \item{object}{A \code{\link[stats]{glm}} model object.}
  \item{newdata}{An \code{\link[OREbase:ore.frame-class]{ore.frame}}
    object.}
  \item{type}{A character string specifying the type of prediction to
    make; either \code{"link"} (scale of the link function) or
    \code{"response"} (scale of the response variable).}
  \item{se.fit}{A logical indicating whether to return the standard
    errors for the predictions.}
  \item{dispersion}{The dispersion parameter to use when calculating
    the standard errors for the predictions.}
  \item{na.action}{The manner in which \code{NA} values are handled,
    either \code{na.omit} or \code{na.pass}.}
  \item{\dots}{Optional arguments.}
}
\value{
  When argument \code{se.fit} is \code{FALSE}, returns an
  \code{\link[OREbase:ore.numeric-class]{ore.numeric}} object containing
  the predictions in the specified type.

  When argument \code{se.fit} is \code{TRUE}, returns an
  \code{\link[OREbase:ore.frame-class]{ore.frame}} object with two
  columns: \code{"PRED"} and \code{"SE.PRED"}.
}
\references{
  \href{http://www.oracle.com/technetwork/database/database-technologies/r/r-enterprise/documentation/index.html}{Oracle R Enterprise}
}
\note{
  Use of date/time terms in this method will result in an error.  
}
\author{
  Oracle \email{oracle-r-enterprise@oracle.com}
}
\seealso{
  \code{\link{ore.predict}},
  \code{\link{ore.predict-lm}},
  \code{\link[stats]{predict.glm}}.
}
\examples{
\dontshow{
if (!interactive())
    ore.connect(user     = Sys.getenv("ORE_USERNAME", "rquser"),
                sid      = Sys.getenv("ORACLE_SID"),
                host     = Sys.getenv("HOST"),
                password = Sys.getenv("ORE_PASSWORD", "rquser"),
                port  = if (.Platform$OS.type == "windows")
                           Sys.getenv("ORACLE_PORT")
                        else
                           Sys.getenv("TCPPORT"),
                all = TRUE)
}

infertModel <-
  glm(case ~ age + parity + education + spontaneous + induced,
      data = infert, family = binomial())
INFERT <- ore.push(infert)
INFERTpred <- ore.predict(infertModel, INFERT, type = "response",
                          se.fit = TRUE)
INFERT <- cbind(INFERT, INFERTpred)
head(INFERT)
}
\keyword{models}
\keyword{regression}