# approx_or: Approximate a linear model for a series of logical OR... In jackmwolf/pcsstools: Tools for Regression Using Pre-Computed Summary Statistics

 approx_or R Documentation

## Approximate a linear model for a series of logical OR statements

### Description

`approx_or` approximates the linear model for a disjunction of m phenotypes as a function of a set of predictors.

### Usage

``````approx_or(
means,
covs,
n,
predictors,
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 OR 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.