Package: pcsstools 0.1.1.9000

pcsstools: Tools for Regression Using Pre-Computed Summary Statistics

Defines functions to describe regression models using only pre-computed summary statistics (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) <doi:10.3389/fgene.2021.745901> Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.

Authors:Jack Wolf [aut, cre, cph], R Core Team and contributors worldwide [cph, aut]

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pcsstools.pdf |pcsstools.html✨
pcsstools/json (API)
NEWS

# Install 'pcsstools' in R:
install.packages('pcsstools', repos = c('https://jackmwolf.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jackmwolf/pcsstools/issues

Datasets:

On CRAN:

gwasstatistical-genetics

3.40 score 5 stars 5 scripts 135 downloads 11 exports 3 dependencies

Last updated 4 months agofrom:a703697c2b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-winOKNov 01 2024
R-4.5-linuxOKNov 01 2024
R-4.4-winOKNov 01 2024
R-4.4-macOKNov 01 2024
R-4.3-winOKNov 01 2024
R-4.3-macOKNov 01 2024

Exports:model_andmodel_combomodel_ormodel_prcompmodel_productmodel_singularnew_predictornew_predictor_binarynew_predictor_normalnew_predictor_snppcsslm

Dependencies:gtoolsrbibutilsRdpack

Readme and manuals

Help Manual

Help pageTopics
ANOVA for linear models fit using PCSSanova.pcsslm anova.pcsslmlist
Approximate a linear model for a series of logical AND statementsapprox_and
Approximate the mean of Y conditional on Xapprox_conditional
Approximate the covariance of a set of predictors and a product of responsesapprox_mult_prod
Approximate a linear model for a series of logical OR statementsapprox_or
Approximate summary statistics for a product of phenotypes and a set of predictorsapprox_prod_stats
Approximate the covariance of one response with an arbitrary product of responses.approx_response_cov_recursive
Calculate a linear model using PCSScalculate_lm
Calculate a linear model for a linear combination of responsescalculate_lm_combo
Check that independent and dependent variables are accounted for through PCSScheck_terms
Extract independent variables from a formulaextract_predictors
Extract dependent variables from a formula as a stringextract_response
Approximate the partial correlation of Y and Z given Xget_pcor
Guess the function that is applied to a set of responsesguess_response
List all permutations of a sequence of integersmake_permutations
Approximate a linear model for a series of logical AND statements using PCSSmodel_and
Model a linear combination of a set of phenotypes using PCSSmodel_combo
Approximate a linear model for a series of logical OR statements using PCSSmodel_or
Model the principal component score of a set of phenotypes using PCSSmodel_prcomp
Approximate a linear model for a product using PCSSmodel_product
Model an individual phenotype using PCSSmodel_singular
Create an object of class "predictor"new_predictor
Shortcut to create a predictor object for a binary variablenew_predictor_binary
Shortcut to create a predictor object for a continuous variablenew_predictor_normal
Shortcut to create a predictor object for a SNP's minor allele countsnew_predictor_snp
Approximate a linear model using PCSSpcsslm
Simulated example datapcsstools_example
Print an object of class pcsslmprint.pcsslm