plmmr package - RDocumentation

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plmmr

The plmmr (penalized linear mixed models in R) package contains functions that fit penalized linear mixed models to correct for unobserved confounding effects.

Three small datasets ship with plmmr, and tutorials walking through how to analyze these data sets are documented in the plmmr website.

Installation

To install the latest version of the package from GitHub, use this:

devtools::install_github("pbreheny/plmmr")

You can also install plmmr from CRAN:

install.packages('plmmr')

Minimal example

library(plmmr)
X <- rnorm(100*20) |> matrix(100, 20)
y <- rnorm(100)
fit <- plmm(X, y) 
plot(fit)

cvfit <- cv_plmm(X, y)
plot(cvfit)
summary(cvfit)

So how fast is `plmmr`? And how well does it scale?

These questions are addressed in our manuscript describing plmmr, along with its accompanying GitHub repository. However, using GWAS data from a study with 1,400 samples and 800,000 SNPs, a full plmmr analysis will run in about half an hour using a single core on a laptop.

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Version

Install

install.packages('plmmr')

Monthly Downloads

159

Version

4.2.2

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/pbreheny/plmmr/

Homepage

https://pbreheny.github.io/plmmr/

Maintainer

Patrick Breheny

Last Published

January 21st, 2026

Functions in plmmr (4.2.2)

create_design_in_memory

A function to create a design with an in-memory X matrix

helper function to implement lasso penalty

a function to return the computer's host name

A function to align genotype and phenotype data

Loss method for "plmm" class

Generate nicely formatted lambda vec

PLMM format: a function to format the output of a model constructed with plmm_fit

Evaluate the negative log-likelihood of an intercept-only Gaussian plmm model

Fit a linear mixed model via penalized maximum likelihood.

Predict method for plmm class

a function to create a design for PLMM modeling

Plot method for cv_plmm class

Plot method for plmm class

predict_within_cv

Predict method to use in cross-validation (within cvf)

A function to read in large data files as an FBM

print.summary.plmm

A function to print the summary of a plmm model

impute_snp_data

A function to impute SNP data

name_and_count_bigsnp

A helper function to label and summarize the contents of a bigSNP

rotate_filebacked

A function to rotate filebacked data

a function to format the time

print.summary.cv_plmm

Print method for summary.cv_plmm objects

Cross-validation for plmm

standardize_filebacked

A helper function to standardize a filebacked matrix

Compute sequence of lambda values

standardize_in_memory

A helper function to standardize matrices

read_plink_files

A function to read in PLINK files using bigsnpr methods

Read in processed data This function is intended to be called after either process_plink() or process_delim() has been called once.

PLMM fit: a function that fits a PLMM using the values returned by plmm_prep()

find_example_data

A function to help with accessing example PLINK files

untransform_in_memory

Untransform coefficient values back to the original scale In memory

untransform_delim

Untransform coefficient values back to the original scale for file-backed data

relatedness_mat

Calculate a relatedness matrix

unzip_example_data

For Linux/Unix and MacOS only, here is a companion function to unzip the .gz files that ship with the plmmr package

untransform_plink

Untransform coefficient values back to the original scale for file-backed data

subset_filebacked

A helper function to subset big.matrix objects

summary.cv_plmm

A summary function for cv_plmm objects

A summary method for the plmm objects

Untransform coefficient values back to the original scale

Preprocess PLINK files using the bigsnpr package

PLMM prep: a function to run checks, SVD, and rotation prior to fitting a PLMM model This is an internal function for cv_plmm

plmmr: Penalized Linear Mixed Models for Correlated Data

read_data_files

A function to read in a large file as a numeric file-backed matrix (FBM) Note: this function is a wrapper for bigstatsr::big_read()

Admix: Semi-simulated SNP data

Coef method for "plmm" class

Coef method for "cv_plmm" class

A helper function to support process_plink()

helper function to implement SCAD penalty

helper function to implement MCP penalty The helper functions to implement each penalty.

A helper function to add predictors to a filebacked matrix of data

Functions to convert between FBM and big.matrix type objects

a version of cbind() for file-backed matrices

count_constant_features

A helper function to count constant features

create_log_file

construct_variance

a function to create the estimated variance matrix from a PLMM fit

A helper function to count the number of cores available on the current machine

create_design_filebacked

A function to create a design matrix, outcome, and penalty factor to be passed to a model fitting function

Cross-validation internal function for cv_plmm

A function to take the eigendecomposition of K Note: This is faster than taking SVD of X when p >> n

Estimate eta (to be used in rotating the data) This function is called internally by plmm()