calculate_lm: Calculate a linear model using PCSS
In jackmwolf/pcsstools: Tools for Regression Using Pre-Computed Summary Statistics

calculate_lm

R Documentation

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).