fit_model: Fit (regularized) generalized linear model

In quadbiolab/Pando: Inferring regulomes from multi-modal single-cell genomics data

Fit (regularized) generalized linear model

Description

Fit (regularized) generalized linear model

Usage

fit_model(
  formula,
  data,
  method = c("glm", "glmnet", "cv.glmnet", "brms", "xgb", "bagging_ridge",
    "bayesian_ridge"),
  family = gaussian,
  alpha = 1,
  ...
)

Arguments

formula	An object of class formula with a symbolic description of the model to be fitted.
data	A data.frame containing the variables in the model.
method	A character string indicating the method to fit the model. 'glm' - Generalized Liner Model with stats. 'glmnet', 'cv.glmnet' - Regularized Generalized Liner Model with glmnet. 'brms' - Bayesian Regression Models using brms. 'xgb' - Gradient Boosting Regression using xgboost. 'bagging_ridge' - Bagging Ridge Regression using scikit-learn via reticulate. 'bayesian_ridge' - Bayesian Ridge Regression using scikit-learn via reticulate.
family	A description of the error distribution and link function to be used in the model. See stats for mode details.
alpha	The elasticnet mixing parameter. See glmnet for details.
...	Other parameters for the model fitting function.

Value

A list with two data frames: gof contains goodness of fit measures of the fit and coefs contains the fitted coefficients.

quadbiolab/Pando documentation built on April 22, 2024, 8:14 a.m.