Tools for Regression Using Pre-Computed Summary Statistics
ANOVA for linear models fit using PCSS
Approximate a linear model for a series of logical AND statements
Approximate the mean of Y conditional on X
Approximate the covariance of a set of predictors and a product of res...
Approximate a linear model for a series of logical OR statements
Approximate summary statistics for a product of phenotypes and a set o...
Approximate the covariance of one response with an arbitrary product o...
Calculate a linear model using PCSS
Calculate a linear model for a linear combination of responses
Check that independent and dependent variables are accounted for throu...
Extract independent variables from a formula
Extract dependent variables from a formula as a string
Approximate the partial correlation of Y and Z given X
Guess the function that is applied to a set of responses
List all permutations of a sequence of integers
Approximate a linear model for a series of logical AND statements usin...
Model a linear combination of a set of phenotypes using PCSS
Approximate a linear model for a series of logical OR statements using...
Model the principal component score of a set of phenotypes using PCSS
Approximate a linear model for a product using PCSS
Model an individual phenotype using PCSS
Create an object of class "predictor"
Shortcut to create a predictor object for a binary variable
Shortcut to create a predictor object for a continuous variable
Shortcut to create a predictor object for a SNP's minor allele counts
Approximate a linear model using PCSS
Print an object of class pcsslm
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>.