Estimate average value of response variable at each factor levels. For
plotting, check the examples in
visualisation_recipe(). See also
other related functions such as
estimate_means( model, at = "auto", fixed = NULL, transform = "response", ci = 0.95, backend = "emmeans", ... )
A statistical model.
The predictor variable(s) at which to evaluate the desired effect / mean / contrasts. Other predictors of the model that are not included here will be collapsed and "averaged" over (the effect will be estimated across them).
A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.
Is passed to the
Confidence Interval (CI) level. Default to
Whether to use 'emmeans' or 'marginaleffects' as a backend. The latter is experimental and some features might not work.
Other arguments passed for instance to
functions are forming a group, as they are all based on marginal
estimations (estimations based on a model). All three are also built on the
emmeans package, so reading its documentation (for instance for
emmeans::emtrends()) is recommended to understand
the idea behind these types of procedures.
Model-based predictions is the basis for all that follows. Indeed,
the first thing to understand is how models can be used to make predictions
estimate_link()). This corresponds to the predicted response (or
"outcome variable") given specific predictor values of the predictors (i.e.,
given a specific data configuration). This is why the concept of
reference grid() is so important for direct predictions.
Marginal "means", obtained via
estimate_means(), are an extension
of such predictions, allowing to "average" (collapse) some of the predictors,
to obtain the average response value at a specific predictors configuration.
This is typically used when some of the predictors of interest are factors.
Indeed, the parameters of the model will usually give you the intercept value
and then the "effect" of each factor level (how different it is from the
intercept). Marginal means can be used to directly give you the mean value of
the response variable at all the levels of a factor. Moreover, it can also be
used to control, or average over predictors, which is useful in the case of
multiple predictors with or without interactions.
Marginal contrasts, obtained via
themselves at extension of marginal means, in that they allow to investigate
the difference (i.e., the contrast) between the marginal means. This is,
again, often used to get all pairwise differences between all levels of a
factor. It works also for continuous predictors, for instance one could also
be interested in whether the difference at two extremes of a continuous
predictor is significant.
Finally, marginal effects, obtained via
different in that their focus is not values on the response variable, but the
model's parameters. The idea is to assess the effect of a predictor at a
specific configuration of the other predictors. This is relevant in the case
of interactions or non-linear relationships, when the effect of a predictor
variable changes depending on the other predictors. Moreover, these effects
can also be "averaged" over other predictors, to get for instance the
"general trend" of a predictor over different factor levels.
Example: Let's imagine the following model
lm(y ~ condition * x) where
condition is a factor with 3 levels A, B and C and
x a continuous
variable (like age for example). One idea is to see how this model performs,
and compare the actual response y to the one predicted by the model (using
estimate_response()). Another idea is evaluate the average mean at each of
the condition's levels (using
estimate_means()), which can be useful to
visualize them. Another possibility is to evaluate the difference between
these levels (using
estimate_contrasts()). Finally, one could also estimate
the effect of x averaged over all conditions, or instead within each
A data frame of estimated marginal means.
library(modelbased) # Frequentist models # ------------------- model <- lm(Petal.Length ~ Sepal.Width * Species, data = iris) estimate_means(model) estimate_means(model, fixed = "Sepal.Width") estimate_means(model, at = c("Species", "Sepal.Width"), length = 2) estimate_means(model, at = "Species=c('versicolor', 'setosa')") estimate_means(model, at = "Sepal.Width=c(2, 4)") estimate_means(model, at = c("Species", "Sepal.Width=0")) estimate_means(model, at = "Sepal.Width", length = 5) estimate_means(model, at = "Sepal.Width=c(2, 4)") # Methods that can be applied to it: means <- estimate_means(model, fixed = "Sepal.Width") plot(means) # which runs visualisation_recipe() standardize(means) data <- iris data$Petal.Length_factor <- ifelse(data$Petal.Length < 4.2, "A", "B") model <- lmer(Petal.Length ~ Sepal.Width + Species + (1 | Petal.Length_factor), data = data) estimate_means(model) estimate_means(model, at = "Sepal.Width", length = 3)
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