| parameters_type {parameters} | R Documentation |
Type of model parameters
Description
In a regression model, the parameters do not all have the meaning. For
instance, the intercept has to be interpreted as theoretical outcome value
under some conditions (when predictors are set to 0), whereas other
coefficients are to be interpreted as amounts of change. Others, such as
interactions, represent changes in another of the parameter. The
parameters_type function attempts to retrieve information and meaning
of parameters. It outputs a dataframe of information for each parameters,
such as the Type (whether the parameter corresponds to a factor or a
numeric predictor, or whether it is a (regular) interaction or a nested
one), the Link (whether the parameter can be interpreted as a mean
value, the slope of an association or a difference between two levels) and,
in the case of interactions, which other parameters is impacted by which
parameter.
Usage
parameters_type(model, ...)
Arguments
model |
A statistical model. |
... |
Arguments passed to or from other methods. |
Value
A data frame.
Examples
library(parameters)
model <- lm(Sepal.Length ~ Petal.Length + Species, data = iris)
parameters_type(model)
model <- lm(Sepal.Length ~ Species + poly(Sepal.Width, 2), data = iris)
parameters_type(model)
model <- lm(Sepal.Length ~ Species + poly(Sepal.Width, 2, raw = TRUE), data = iris)
parameters_type(model)
# Interactions
model <- lm(Sepal.Length ~ Sepal.Width * Species, data = iris)
parameters_type(model)
model <- lm(Sepal.Length ~ Sepal.Width * Species * Petal.Length, data = iris)
parameters_type(model)
model <- lm(Sepal.Length ~ Species * Sepal.Width, data = iris)
parameters_type(model)
model <- lm(Sepal.Length ~ Species / Sepal.Width, data = iris)
parameters_type(model)
# Complex interactions
data <- iris
data$fac2 <- ifelse(data$Sepal.Width > mean(data$Sepal.Width), "A", "B")
model <- lm(Sepal.Length ~ Species / fac2 / Petal.Length, data = data)
parameters_type(model)
model <- lm(Sepal.Length ~ Species / fac2 * Petal.Length, data = data)
parameters_type(model)