mxComputeLoadData: Load columns into an MxData object

In OpenMx: Extended Structural Equation Modelling

Load columns into an MxData object

Description

\lifecycle

experimental

Usage

mxComputeLoadData(
  dest,
  column,
  method = c("csv", "data.frame"),
  ...,
  path = c(),
  originalDataIsIndexOne = FALSE,
  byrow = TRUE,
  row.names = c(),
  col.names = c(),
  skip.rows = 0,
  skip.cols = 0,
  verbose = 0L,
  cacheSize = 100L,
  checkpointMetadata = TRUE,
  na.strings = c("NA"),
  observed = NULL,
  rowFilter = c()
)

Arguments

dest	the name of the model where the columns will be loaded
column	a character vector. The column names to replace.
method	name of the conduit used to load the columns.
...	Not used. Forces remaining arguments to be specified by name.
path	the path to the file containing the data
originalDataIsIndexOne	logical. Whether to use the initial data for index 1
byrow	logical. Whether the data columns are stored in rows.
row.names	optional integer. Column containing the row names.
col.names	optional integer. Row containing the column names.
skip.rows	integer. Number of rows to skip before reading data.
skip.cols	integer. Number of columns to skip before reading data.
verbose	integer. Level of run-time diagnostic output. Set to zero to disable
cacheSize	integer. How many columns to cache per scan through the data. Only used when byrow=FALSE.
checkpointMetadata	logical. Whether to add per record metadata to the checkpoint
na.strings	character vector. A vector of strings that denote a missing value.
observed	data frame. The reservoir of data for method='data.frame'.
rowFilter	logical vector. Whether to skip the source row.

Details

The purpose of this compute step is to help quickly perform many similar analyses. For example, if we are given a sample of people with a few million SNPs (single-nucleotide polymorphism) per person then we could fit a separate model for each SNP by iterating over the SNP data.

The column names given in the column parameter must already exist in the model's MxData object. Pre-existing data is assumed to be a placeholder and is not used unless originalDataIsIndexOne is set to TRUE.

For method='csv', the highest performance arrangement is byrow=TRUE because entire columns are stored in single chunks (rows) on the disk and can be easily loaded. For byrow=FALSE, the data requires transposition. To load a single column of observed data, it is necessary to read through the whole file. This can be slow for large files. To amortize the cost of transposition, cacheSize columns are loaded on every pass through the file.

After mxRun returns, the dest mxData object will contain the most recently loaded data. Hence, any single analysis of a series can be reproduced by issuing mxComputeLoadData with the single index associated with a particular dataset, replacing the compute plan with something like omxDefaultComputePlan, and then passing the model back through mxRun. This can be a helpful approach when investigating unexpected results.

See Also

mxComputeLoadMatrix, mxComputeCheckpoint, mxRun, omxDefaultComputePlan

OpenMx documentation built on Oct. 19, 2024, 9:06 a.m.