Linear models can be used to carry out regression, single stratum analysis of variance and analysis of covariance.
Usage
linear_model(formula, na_action = "na.omit", contrasts = list(), ...)Arguments
- formula
(formula) A symbolic description of the model to be fitted.
- na_action
(character) NA action. Allowed values are limited to the following:
"na.omit": Incomplete cases are removed."na.fail": An error is thrown if NA are present."na.exclude": Incomplete cases are removed, and the output result is padded to the correct size using NA."na.pass": Does not apply a linear model if NA are present.
The default is
"na.omit".- contrasts
(list) The contrasts associated with a factor. The default is
list().- ...
Additional slots and values passed to
struct_class.
Value
A linear_model object with the following output slots:
lm | (lm) The lm object for this model_. |
coefficients | (numeric) The coefficients for the fitted model_. |
residuals | (numeric) The residuals for the fitted model_. |
fitted_values | (numeric) The fitted values for the data used to train the model_. |
predicted_values | (numeric) The predicted values for new data using the fitted model_. |
r_squared | (numeric) The value of R Squared for the fitted model_. |
adj_r_squared | (numeric) The value ofAdjusted R Squared for the fitted model_. |
Inheritance
A linear_model object inherits the following struct classes: [linear_model] >> [model] >> [struct_class]
References
R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Examples
M = linear_model(
formula = y ~ x,
na_action = "na.omit",
contrasts = list())
D = iris_DatasetExperiment()
M = linear_model(formula = y~Species)