pcsstools: calculate_lm – R documentation

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calculate_lm

Calculate a linear model using PCSS

Description

calculate_lm describes the linear model of the last listed variable in means and covs as a function of all other variables in means and covs.

Usage

calculate_lm(
  means,
  covs,
  n,
  add_intercept = FALSE,
  keep_pcss = FALSE,
  terms = NULL
)

Arguments

`means`	a vector of means of all model predictors and the response with the last element the response mean.
`covs`	a matrix of the covariance of all model predictors and the response with the order of rows/columns corresponding to the order of `means`.
`n`	sample size
`add_intercept`	logical. If `TRUE` adds an intercept to the model.
`keep_pcss`	logical. If `TRUE`, returns `means` and `covs`.
`terms`	terms

Value

an object of class "pcsslm".

An object of class "pcsslm" is a list containing at least the following components:

`call`	the matched call
`terms`	the `terms` object used
`coefficients`	a p x 4 matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.
`sigma`	the square root of the estimated variance of the random error.
`df`	degrees of freedom, a 3-vector p, n-p, p*, the first being the number of non-aliased coefficients, the last being the total number of coefficients.
`fstatistic`	a 3-vector with the value of the F-statistic with its numerator and denominator degrees of freedom.
`r.squared`	R^2, the 'fraction of variance explained by the model'.
`adj.r.squared`	the above R^2 statistic 'adjusted', penalizing for higher p.
`cov.unscaled`	a p x p matrix of (unscaled) covariances of the coef[j], j=1,...p.
`Sum Sq`	a 3-vector with the model's Sum of Squares Regression (SSR), Sum of Squares Error (SSE), and Sum of Squares Total (SST).

References

Wolf JM, Westra J, Tintle N (2021). “Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates.” bioRxiv. doi: 10.1101/2021.03.08.433979, https://www.biorxiv.org/content/10.1101/2021.03.08.433979v1.

Wolf JM, Barnard M, Xia X, Ryder N, Westra J, Tintle N (2020). “Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks.” Pacific Symposium on Biocomputing, 25, 719–730. ISSN 2335-6928, doi: 10.1142/9789811215636_0063, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6907735/.

Gasdaska A, Friend D, Chen R, Westra J, Zawistowski M, Lindsey W, Tintle N (2019). “Leveraging summary statistics to make inferences about complex phenotypes in large biobanks.” Pacific Symposium on Biocomputing, 24, 391–402. ISSN 2335-6928, doi: 10.1142/9789813279827_0036, https://pubmed.ncbi.nlm.nih.gov/30963077/.

pcsstools

Tools for Regression Using Pre-Computed Summary Statistics

v0.1.1

GPL (>= 3)

Authors

Jack Wolf [aut, cre, cph] (<https://orcid.org/0000-0002-8919-8740>), R Core Team and contributors worldwide [cph, aut] (Author and copyright holder of modified 'stats' fragments)

Initial release