predict.cv_plmm() method.dfmax argument added to plmm().find_example_data() is now platform-independent.create_design_in_memory() was improperly handling the penalty factor when unpenalized covariates were not located in the first columns of X.K. This is now accounted for (allowed for in-memory model fits only).cv_plmm().warn option was not properly passed to biglasso when fitting a model on filebacked data.plot.cv_plmm() would throw an error if selected = FALSE.K was removed for a filebacked design matrix.cv_plmm() when the user provides a K matrix.K and requests BLUP prediction, cv_plmm() will use this K matrix to obtain Sigma_11 and Sigma_21 rather than estimate them empirically.relatedness_mat() redesigned; ns argument removed.diag_K is no longer an argument to plmm(); this is checked internally.plmm_prep() no longer scales a user-provided K matrix.eta_star argument to plmm() and cv_plmm() renamed to eta.xgboost imputation warning.plmmr's Briefings in Bioinformatics publication.admix dataset.fbm2bm() and get_hostname() functions removed.compute_blup().file.remove() to unlink() for backing file cleanup. This resolves persistent permissions errors.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.