approx_and: Approximate a linear model for a series of logical AND...

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

Approximate a linear model for a series of logical AND statements

Description

approx_and approximates the linear model for the a conjunction of m phenotypes as a function of a set of predictors.

Usage

approx_and(
  means,
  covs,
  n,
  predictors,
  add_intercept = TRUE,
  verbose = FALSE,
  response_assumption = "binary",
  ...
)

Arguments

means	vector of predictor and response means with the last m means being the means of m binary responses to combine in a logical and statement.
covs	a matrix of the covariance of all model predictors and the responses with the order of rows/columns corresponding to the order of means.
n	sample size.
predictors	list of objects of class predictor corresponding to the order of the predictors in means.
add_intercept	logical. Should the linear model add an intercept term?
verbose	should output be printed to console?
response_assumption	character. Either "binary" or "continuous". If "binary", specific calculations will be done to estimate product means and variances.
...	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_using_2021pcsstools

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