toxboot is the main function that performs the bootstrap sampling,
fitting, and writing to the database
toxboot(dat, m4id, boot_method, replicates = 100, concvals = F, destination = "memory")
A data.table. Required columns are: logc: numeric, contains concentrations resp: numeric, normalized response values paired with concentrations m3id: numeric, value unique to each row corresponding to an individual concentration and response m4id: numeric, value unique to an aeid/spid pair. Multiple m3ids per m4id aeid: numeric, assay id spid: character, sample ID bmad: numeric, baseline mad. Unique to an aeid.
numeric length 1, m4id to bootstrap. Choice of m4id will determine which rows are selected, and therefore the values of logc, resp, m3id, aeid, spid, and bmad.
parameter passed to
number of bootstrap samples. Default 100
logical, default is FALSE. If TRUE, dose response samples written to the database as well.
string length 1, options are "mongo", "mysql", "file", "memory"
toxboot is the workhorse function of this package. This
function will typically be wrapped in a mclapply to perform in parallel
toxbootmc. The dose response data is passed to
toxbootReplicates. The returned matrix is passed to
There are multiple options for saving the results, based on the value of
mongo : If
destination is set to "mongo" a connection to the
mongo database will be created and the results will be written using the
rmongodb package. The connection will be established using the
parameters retrieved using
toxbootConfList by the function
toxbootConnectMongo. See the documentation on these functions
as well as
toxbootConf for how to properly setup the MongoDB
environment. For large scale screening of uncertainty parameters it is
recommended that MongoDB be used for performance and scaling.
file : A directory
toxboot/ will be created. A csv file for
each m4id will be created with name set to the value of m4id. The format of
the file will be tabular with one row for each bootstrap replicate.
Subsequent runs will be appended onto the file. This way further bootstrap
results can be created without loss of previous computational work. Note
that the file size can get quite large if many curves are run. With 1000
replicates the file size will typically range from 300 to 600 KB per m4id.
memory : Results will be returned as a single data.table. This is a reasonable option for checking a few curves or even an entire assay with 1000 replicates if a suitable amount of memory is available. Care must be taken as the resulting data.table can become multiple GB in memory
fitted results are assembled into a bson object using
written to the mongoDB.