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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/grants.R
\docType{data}
\name{grants}
\alias{grants}
\alias{grants_other}
\alias{grants_test}
\alias{grants_2008}
\title{Grant acceptance data}
\source{
Kuhn and Johnson (2013). \emph{Applied Predictive Modeling}. Springer.
}
\value{
\item{grants_other,grants_test,grants_2008}{two tibbles and an integer
vector of data points used for training}
}
\description{
A data set related to the success or failure of academic grants.
}
\details{
The data are discussed in Kuhn and Johnson (2013):
"These data are from a 2011 Kaggle competition sponsored by the University
of Melbourne where there was interest in predicting whether or not a grant
application would be accepted. Since public funding of grants had decreased
over time, triaging grant applications based on their likelihood of success
could be important for estimating the amount of potential funding to the
university. In addition to predicting grant success, the university sought
to understand factors that were important in predicting success."
The data ranged from 2005 and 2008 and the data spending strategy was
driven by the date of the grant. Kuhn and Johnson (2013) describe:
"The compromise taken here is to build models on the pre-2008 data and
tune them by evaluating a random sample of 2,075 grants from 2008. Once the
optimal parameters are determined, final model is built using these
parameters and the entire training set (i.e., the data prior to 2008 and the
additional 2,075 grants). A small holdout set of 518 grants from 2008 will
be used to ensure that no gross methodology errors occur from repeatedly
evaluating the 2008 data during model tuning. In the text, this set of
samples is called the 2 0 0 8 holdout set. This small set of year 2008
grants will be referred to as the test set and will not be evaluated until
set of candidate models are identified."
To emulate this, \code{grants_other} contains the training (pre-2008, n = 6,633)
and holdout/validation data (2008, n = 1,557). \code{grants_test} has 518 grant
samples from 2008. The object \code{grants_2008} is an integer vector that can
be used to separate the modeling with the holdout/validation sets.
}
\examples{
data(grants)
str(grants_other)
str(grants_test)
str(grants_2008)
}
\keyword{datasets}
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