model_product: Approximate a linear model for a product using PCSS

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

Approximate a linear model for a product using PCSS

Description

model_product approximates the linear model for the product of m phenotypes as a function of a set of predictors.

Usage

model_product(
  formula,
  n,
  means,
  covs,
  predictors,
  responses = NULL,
  response = "continuous",
  ...
)

Arguments

formula	an object of class formula whose dependent variable is a combination of variables and * operators. All model terms must be accounted for in means and covs.
n	sample size.
means	named vector of predictor and response means.
covs	named matrix of the covariance of all model predictors and the responses.
predictors	named list of objects of class predictor
responses	named list of objects of class predictor corresponding to all terms being multiplied in the response. Can be left NULL if only multiplying two terms
response	character. Describe distribution of all product terms. Either "continuous" or "binary". If "binary" different approximations of product means and variances are used.
...	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

Examples

ex_data <- pcsstools_example[c("g1", "g2", "g3", "x1", "y4", "y5", "y6")]
head(ex_data)
means <- colMeans(ex_data)
covs <- cov(ex_data)
n <- nrow(ex_data)
predictors <- list(
  g1 = new_predictor_snp(maf = mean(ex_data$g1) / 2),
  g2 = new_predictor_snp(maf = mean(ex_data$g2) / 2),
  g3 = new_predictor_snp(maf = mean(ex_data$g3) / 2),
  x1 = new_predictor_normal(mean = mean(ex_data$x1), sd = sd(ex_data$x1))
)
responses <- lapply(means[c("y4", "y5", "y6")], new_predictor_binary)

model_product(
  y4 * y5 * y6 ~ g1 + g2 + g3 + x1,
  means = means, covs = covs, n = n,
  predictors = predictors, responses = responses, response = "binary"
)

summary(lm(y4 * y5 * y6 ~ g1 + g2 + g3 + x1, data = ex_data))

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