Description Usage Arguments Details Value Data Processing Steps Author(s) References
dspDat
is used to create an object of class
"dspDat"
;
the resultant object may then be used as input to the dsp
function to
sample an MCMC chain for the methodology proposed by Dunson and Stanford in
Bayesian Inferences on Predictors of Conception Probabilities (2005).
The dspDat
function is essentially a convenience function provided to
(if necessary) merge multiple datasets of varying time-specificities, as is
common for the type of fertility data for which the aformentioned methodology
is designed.
1 2 |
formula |
An object of class |
baseline |
Either |
cycle |
Either |
daily |
A |
idName |
A string specifying the name of the column in each of the
non- |
cycName |
A string specifying the name of the column in each of the
non- |
sexName |
A string specifying the name of the column in the |
fwName |
If non- As a convenience, if the name specified by |
fwLen |
A value specifying the number of days belonging to a cycle's fertile window. The length of the fertile window is assumed to be same across all cycles. |
useNA |
One of either |
The class
"dspDat"
is equipped with a summary
function.
It is natural to record fertility study data in up to three datasets
of varying time-specificities. First, a dataset of variables that do not
change throughout the study which we denote as the baseline
data,
second a dataset of cycle-specific variables which we denote as the
cycle
data, and third a dataset of day-specific variables which we
denote as the daily
data. dspDat
is provided as a
convenience function which merges all of the provided datasets into one
day-specific dataset and creates some internal objects for use by the MCMC
sampler function dsp
.
At a minimum the daily
data must be provided so that daily
intercourse data is available. baseline
and cycle
data are
optional, so long as pregnancy information is included in one of either the
cycle
data or daily
data. For example, if the data was
collected only in a daily format or has already been combined, then only a
day-specific dataset would need to be passed to dspDat
.
The usual model.matrix
is used to construct the design
matrix for the specified model, so any of the usual
formula
commands are available. In particular, a
formula has an implied intercept term which may not be desireable for these
types of models. To remove this use either y ~ x - 1
or y ~ 0
+ x
.
dspDat
returns an object of class
"dspDat"
. An object of class "dspDat"
is a list containing
the following components:
cleanDat
A list containing objects bas
, cyc
,
and day
, which are the datasets after removing missing and reducing
the daily
data to fertile window days as described in Data
Processing Steps. If NULL
was supplied for baseline
or
cycle
, then the value of bas
or cyc
is also
NULL
.
redDat
A list containing objects bas
, cyc
, and
day
, which are the datasets after reducing the cleaned data to the
set of IDs and cycles that are common to every non-NULL
dataset. If
NULL
was supplied for baseline
or cycle
, then the
value of bas
or cyc
is also NULL
.
combDat
*******
modelObj
A list containing objects Y
, X
,
U
, and id
. Y
, X
, U
are as in the Dunson
and Stanford paper, and id
is a vector of subject IDs such that each
observation specifies the subject ID for the corresponding observation.
samplerObj
A list containing objects for use by the
dsp
function when executing the MCMC algorithm
datInfo
A list containing objects for use by the
summary
function
If either a
baseline
or cycle
dataset is provided, then all observations
that contain missing data among the model variables are removed. All
non-fertile window days are removed from the daily
dataset, and any
cycles that either contain missing in the fertile window or have too many
or too few fertile window days are also removed.
Each non-NULL
dataset is reduced to the set of
IDs and cycles that are common to every non-NULL
dataset.
David A. Pritchard and Sam Berchuck, 2015
Dunson, David B., and Joseph B. Stanford. "Bayesian inferences on predictors of conception probabilities." Biometrics 61.1 (2005): 126-133.
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