Major re-structuring of preprocessing pipeline:
Data from external files must now be processed with
process_plink() or process_delim(). All data
(including in-memory data) must be prepared for analysis via
create_design(). This change ensures that data are funneled
into a uniform format for analysis.
Documentation updated: The vignettes for the
package are now all revised to include examples of the complete pipeline
with the new create_design() syntax. There is an article
for each type of data input (matrix/data.frame, delimited file, and
PLINK).
CRAN: The package is on CRAN now.
bigsnpr now in Suggests, not Imports: The
essential filebacking support is now all done with
bigmemory and bigalgebra. The
bigsnpr package is used only for processing PLINK
files.
dev branch gwas_scale has a version of the pipeline that runs completely file-backed.
Enhancement: To make plmmr have
better functionality for writing scripts, the functions
process_plink(), plmmm(), and
cv_plmm() now (optionally) write ‘.log’ files, as in
PLINK.
Enhancement: In cases where users are working
with large datasets, it may not be practical or desirable for all the
results returned by plmmm() or cv_plmm() to be
saved in a single ‘.rds’ file. There is now an option in both of these
model fitting functions called ‘compact_save’, which gives users the
option to save the output in multiple, smaller ‘.rds’ files.
Argument removed: Argument
std_needed is no longer available in plmm()
and cv_plmm() functions.
Bug fix: Cross-validation implementation issues fixed. Previously, the full set of eigenvalues were used inside CV folds, which is not ideal as it involves information from outside the fold. Now, the entire modeling process is cross-validated: the standardization, the eigendecomposition of the relatedness matrix, the model fitting, and the backtransformation onto the original scale for prediction.
Computational speedup: The standardization and
rotation of filebacked data are now much faster; bigalgebra
and bigmemory are now used for these computations.
Internal: On the standardized scale, the intercept of the PLMM is the mean of the outcome. This derivation considerably simplifies the handling of the intercept internally during model fitting.
plmmr; note that plmm(),
cv_plmm(), and other functions starting with
plmm_ have not changed names.