Approximate a linear model for a series of logical AND statements
approx_and approximates the linear model for the a conjunction
of m phenotypes as a function of a set of predictors.
approx_and( means, covs, n, predictors, add_intercept = TRUE, verbose = FALSE, response_assumption = "binary", ... )
means |
vector of predictor and response means with the last |
covs |
a matrix of the covariance of all model predictors and the
responses with the order of rows/columns corresponding to the order of
|
n |
sample size. |
predictors |
list of objects of class |
add_intercept |
logical. Should the linear model add an intercept term? |
verbose |
should output be printed to console? |
response_assumption |
character. Either |
... |
additional arguments |
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 |
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). |
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.
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