eta_star argument in
plmm() and cv_plmm() was previously not being
passed to internal functions and hence ignored. The functionality of
this argument has been restored.Bug in BLUP: We caught a mathematical error in
our earlier implementation of best linear unbiased prediction. The issue
had to do with an inconsistency in the scaling among the terms used in
constructing this predictor. This issue impacted prediction within
cross-validation as well as the predict() method for our
plmm class.
Bug in processing delimited files: We noticed a
bug in the way that models were fit to data from delimited files. The
previous version was not correctly implementing the transformation of
model results from the standardized scale to the original scale due to
the inadvertent addition of two rows in the beta_vals
object (only one row should be added, for the intercept).
Clarification of parallelization option for
cross-validation: The cv_plmm() method offers
parallelization through the cluster option – we have now
clarified in our documentation that at this time, this option is only
available for analyzing data stored in-memory. We have added an example
that demonstrates this option at work in the article for analyzing
matrix data.
Change of default settings for prediction: The
default prediction method in both predict() and
cv_plmm() is now ‘blup’ (best linear unbiased
prediction).
Change in objects returned by default in
plmm(): By default, the main model fitting function
plmm() now returns the filepath for std_X when
the design matrix is stored file-backed; plmm() also
returns y (the outcome vector used to fit the model), and
std_Xbeta (the linear predictors on the standardized
scale). These components are used to construct the best linear unbiased
predictor.
Change in arguments passed to
predict(): In tandem with the change in what is returned by
plmm() by default, the predict() method no
longer needs a separate y argument to be supplied for
type = 'blup'.
Change in arguments supplied to
plmm() and cv_plmm(): the option
compact_save no longer exists; instead,
save_rds offers the option to save .rds/.log files, and
return_fit offers the option to return the output of
plmm() in the current R session. Note that .log files are
now only constructed when save_rds = TRUE.
Restore plmm(X,y) syntax: Where version 4.0.0
required that create_design() always be called prior to
plmm() or cv_plmm(); this update restores the
X,y syntax consistent with other packages (e.g., glmnet,
ncvreg). Note that this syntax is only available for the
case where the design matrix is stored in-memory as a
matrix or data.frame object. The
create_design() function is still required for cases where
the design matrix/dataset is stored in an external file.
Bug fix: The 4.0.0 version of
create_design() required X to have column
names, and errored out with an uninformative message if no names were
supplied (see issue 61). This is now fixed – column names are not
required unless the user wants to specify an argument to
unpen.
Argument name change: In
create_design(), the argument to specify an outcome in the
in-memory case has been renamed to y; this makes the syntax
consistent, e.g., create_design(X, y). Note again that this
change is relevant to in-memory data only.
Internal: Fixed LTO type mismatch bug.
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.
2024-09-02
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.
2024-07-13
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.
2024-06-27
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.
2024-03-16
plmmr; note that plmm(),
cv_plmm(), and other functions starting with
plmm_ have not changed names.
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