model_combo: Model a linear combination of a set of phenotypes using PCSS

In jackmwolf/pcsstools: Tools for Regression Using Pre-Computed Summary Statistics

Model a linear combination of a set of phenotypes using PCSS

Description

model_combo calculates the linear model for a linear combination of phenotypes as a function of a set of predictors.

Usage

model_combo(formula, phi, n, means, covs, ...)

Arguments

formula	an object of class formula whose dependent variable is a series of variables joined by + operators. model_combo will treat a principal component score of those variables as the actual dependent variable. All model terms must be accounted for in means and covs.
phi	named vector of linear weights for each variable in the dependent variable in formula.
n	sample size.
means	named vector of predictor and response means.
covs	named matrix of the covariance of all model predictors and the responses.
...	additional arguments

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

\insertRef

wolf_computationally_2020pcsstools

\insertRef

gasdaska_leveraging_2019pcsstools

Examples

ex_data <- pcsstools_example[c("g1", "x1", "x2", "x3", "y1", "y2", "y3")]
head(ex_data)
means <- colMeans(ex_data)
covs <- cov(ex_data)
n <- nrow(ex_data)
phi <- c("y1" = 1, "y2" = -1, "y3" = 0.5)

model_combo(
  y1 + y2 + y3 ~ g1 + x1 + x2 + x3, 
  phi = phi, n = n, means = means, covs = covs
)

summary(lm(y1 - y2 + 0.5 * y3 ~ g1 + x1 + x2 + x3, data = ex_data))

jackmwolf/pcsstools documentation built on July 7, 2024, 7:46 p.m.