Package 'modelbased'

Describe the smooth term (for GAMs) or non-linear predictors

Description

This function summarises the smooth term trend in terms of linear segments. Using the approximate derivative, it separates a non-linear vector into quasi-linear segments (in which the trend is either positive or negative). Each of this segment its characterized by its beginning, end, size (in proportion, relative to the total size) trend (the linear regression coefficient) and linearity (the R2 of the linear regression).

Usage

describe_nonlinear(data, ...)

## S3 method for class 'data.frame'
describe_nonlinear(data, x = NULL, y = NULL, ...)

estimate_smooth(data, ...)

Arguments

data	The data containing the link, as for instance obtained by estimate_relation().
...	Other arguments to be passed to or from.
x, y	The name of the responses variable (y) predicting variable (x).

Value

A data frame of linear description of non-linear terms.

Examples

# Create data
data <- data.frame(x = rnorm(200))
data$y <- data$x^2 + rnorm(200, 0, 0.5)

model <<- lm(y ~ poly(x, 2), data = data)
link_data <- estimate_relation(model, length = 100)

describe_nonlinear(link_data, x = "x")

---

Estimate Marginal Contrasts

Description

Run a contrast analysis by estimating the differences between each level of a factor. See also other related functions such as estimate_means() and estimate_slopes().

Usage

estimate_contrasts(
  model,
  contrast = NULL,
  by = NULL,
  predict = NULL,
  ci = 0.95,
  p_adjust = "none",
  comparison = "pairwise",
  marginalize = "average",
  backend = getOption("modelbased_backend", "emmeans"),
  transform = NULL,
  verbose = TRUE,
  ...
)

Arguments

model	A statistical model.
contrast	A character vector indicating the name of the variable(s) for which to compute the contrasts.
by	The (focal) 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). by can a character (vector) naming the focal predictors (and optionally, representative values or levels), or a list of named elements. See details in insight::get_datagrid().
predict	Is passed to the type argument in emmeans::emmeans() (when backend = "emmeans") or in marginaleffects::avg_predictions() (when backend = "marginaleffects"). For emmeans, see also this vignette. Valid options for 'predict“ are: backend = "emmeans": predict can be "response", "link", "mu", "unlink", or "log". If predict = NULL (default), the most appropriate transformation is selected (which usually is "response"). backend = "marginaleffects": predict can be "response", "link" or any valid type option supported by model's class predict() method (e.g., for zero-inflation models from package glmmTMB, you can choose predict = "zprob" or predict = "conditional" etc., see glmmTMB::predict.glmmTMB). By default, when predict = NULL, the most appropriate transformation is selected, which usually returns predictions or contrasts on the response-scale. "link" will leave the values on scale of the linear predictors. "response" (or NULL) will transform them on scale of the response variable. Thus for a logistic model, "link" will give estimations expressed in log-odds (probabilities on logit scale) and "response" in terms of probabilities. To predict distributional parameters (called "dpar" in other packages), for instance when using complex formulae in brms models, the predict argument can take the value of the parameter you want to estimate, for instance "sigma", "kappa", etc.
ci	Confidence Interval (CI) level. Default to 0.95 (⁠95%⁠).
p_adjust	The p-values adjustment method for frequentist multiple comparisons. Can be one of "none" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "tukey" or "holm". See the p-value adjustment section in the emmeans::test documentation or ?stats::p.adjust.
comparison	Specify the type of contrasts or tests that should be carried out. When backend = "emmeans", can be one of "pairwise", "poly", "consec", "eff", "del.eff", "mean_chg", "trt.vs.ctrl", "dunnett", "wtcon" and some more. See also method argument in emmeans::contrast and the ?emmeans::emmc-functions. For backend = "marginaleffects", can be a numeric value, vector, or matrix, a string equation specifying the hypothesis to test, a string naming the comparison method, a formula, or a function. Strings, string equations and formula are probably the most common options and described below. For other options and detailed descriptions of those options, see also marginaleffects::comparisons and this website. String: One of "pairwise", "reference", "sequential", "meandev" "meanotherdev" and the inverse options "revpairwise", "revreference", and "revsequential". String equation: To identify parameters from the output, either specify the term name, or "b1", "b2" etc. to indicate rows, e.g.:"hp = drat", "b1 = b2", or "b1 + b2 + b3 = 0". Formula: A formula like comparison ~ pairs | group, where the left-hand side indicates the type of comparison (difference or ratio), the right-hand side determines the pairs of estimates to compare (reference, sequential, or meandev). Optionally, comparisons can be carried out within subsets by indicating the grouping variable after a vertical bar ( |).
marginalize	Character string, indicating the type of marginalization. This dictates how the predictions are "averaged" over the non-focal predictors, i.e. those variables that are not specified in by or contrast. "average" (default): Takes the mean value for non-focal numeric predictors and marginalizes over the factor levels of non-focal terms, which computes a kind of "weighted average" for the values at which these terms are hold constant. These predictions are a good representation of the sample, because all possible values and levels of the non-focal predictors are taken into account. It answers the question, "What is the predicted value for an 'average' observation in my data?". It refers to randomly picking a subject of your sample and the result you get on average. This approach is the one taken by default in the emmeans package. "population": Non-focal predictors are marginalized over the observations in the sample, where the sample is replicated multiple times to produce "counterfactuals" and then takes the average of these predicted values (aggregated/grouped by the focal terms). It can be considered as extrapolation to the population. Counterfactual predictions are useful, insofar as the results can also be transferred to other contexts (Dickerman and Hernan, 2020). It answers the question, "What is the predicted for the 'average' observation in the general population?". It does not only refer to the actual data in your observed sample, but also "what would be if" we had more data, or if we had data from a different sample. In other words, the distinction between marginalization types resides in whether the prediction are made for: A specific "individual" (i.e., a specific combination of predictor values): this is what is obtained when using estimate_relation() and the other prediction functions. An average individual: obtained with estimate_means(..., marginalize = "average") The "general population": obtained with estimate_means(..., marginalize = "population")
backend	Whether to use "emmeans" or "marginaleffects" as a backend. Results are usually very similar. The major difference will be found for mixed models, where backend = "marginaleffects" will also average across random effects levels, producing "marginal predictions" (instead of "conditional predictions", see Heiss 2022). You can set a default backend via options(), e.g. use options(modelbased_backend = "emmeans") to use the emmeans package or options(modelbased_backend = "marginaleffects") to set marginaleffects as default backend.
transform	Deprecated, please use predict instead.
verbose	Use FALSE to silence messages and warnings.
...	Other arguments passed, for instance, to insight::get_datagrid(), to functions from the emmeans or marginaleffects package, or to process Bayesian models via bayestestR::describe_posterior(). Examples: insight::get_datagrid(): Argument such as length or range can be used to control the (number of) representative values. marginaleffects: Internally used functions are avg_predictions() for means and contrasts, and avg_slope() for slopes. Therefore, arguments for instance like vcov, transform, equivalence or slope can be passed to those functions. emmeans: Internally used functions are emmeans() and emtrends(). Additional arguments can be passed to these functions. Bayesian models: For Bayesian models, parameters are cleaned using describe_posterior(), thus, arguments like, for example, centrality, rope_range, or test are passed to that function.

Details

The estimate_slopes(), estimate_means() and estimate_contrasts() functions are forming a group, as they are all based on marginal estimations (estimations based on a model). All three are built on the emmeans or marginaleffects package (depending on the backend argument), so reading its documentation (for instance emmeans::emmeans(), emmeans::emtrends() or this website) 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 (see 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 estimate_contrasts(), are 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 estimate_slopes(), are 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_expectation()). 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 condition (using [estimate_slopes]).

Value

A data frame of estimated contrasts.

Examples

## Not run: 
# Basic usage
model <- lm(Sepal.Width ~ Species, data = iris)
estimate_contrasts(model)

# Dealing with interactions
model <- lm(Sepal.Width ~ Species * Petal.Width, data = iris)

# By default: selects first factor
estimate_contrasts(model)

# Can also run contrasts between points of numeric
estimate_contrasts(model, contrast = "Petal.Width", length = 4)

# Or both
estimate_contrasts(model, contrast = c("Species", "Petal.Width"), length = 2)

# Or with custom specifications
estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)"))

# Or modulate it
estimate_contrasts(model, by = "Petal.Width", length = 4)

# Standardized differences
estimated <- estimate_contrasts(lm(Sepal.Width ~ Species, data = iris))
standardize(estimated)

# Other models (mixed, Bayesian, ...)
data <- iris
data$Petal.Length_factor <- ifelse(data$Petal.Length < 4.2, "A", "B")

model <- lme4::lmer(Sepal.Width ~ Species + (1 | Petal.Length_factor), data = data)
estimate_contrasts(model)

data <- mtcars
data$cyl <- as.factor(data$cyl)
data$am <- as.factor(data$am)

model <- rstanarm::stan_glm(mpg ~ cyl * am, data = data, refresh = 0)
estimate_contrasts(model)
# fix `am` at value 1
estimate_contrasts(model, by = c("cyl", "am='1'"))

model <- rstanarm::stan_glm(mpg ~ cyl * wt, data = data, refresh = 0)
estimate_contrasts(model)
estimate_contrasts(model, by = "wt", length = 4)

model <- rstanarm::stan_glm(
  Sepal.Width ~ Species + Petal.Width + Petal.Length,
  data = iris,
  refresh = 0
)
estimate_contrasts(model, by = "Petal.Length", test = "bf")

## End(Not run)

---

Model-based predictions

Description

After fitting a model, it is useful generate model-based estimates of the response variables for different combinations of predictor values. Such estimates can be used to make inferences about relationships between variables, to make predictions about individual cases, or to compare the predicted values against the observed data.

The modelbased package includes 4 "related" functions, that mostly differ in their default arguments (in particular, data and predict):

• estimate_prediction(data = NULL, predict = "prediction", ...)

• estimate_expectation(data = NULL, predict = "expectation", ...)

• estimate_relation(data = "grid", predict = "expectation", ...)

• estimate_link(data = "grid", predict = "link", ...)

While they are all based on model-based predictions (using insight::get_predicted()), they differ in terms of the type of predictions they make by default. For instance, estimate_prediction/estimate_expectation return predictions for the original data used to fit the model, while estimate_relation/estimate_link return predictions on a insight::get_datagrid(). Similarly, estimate_link returns predictions on the link scale, while the others return predictions on the response scale. Note that the relevance of these differences depends on the model family (for instance, for linear models, estimate_relation is equivalent to estimate_link, since there is no difference between the link-scale and the response scale).

Note that you can run plot() on the output of these functions to get some visual insights (see the plotting examples).

See the details section below for details about the different possibilities.

Usage

estimate_expectation(
  model,
  data = NULL,
  by = NULL,
  predict = "expectation",
  ci = 0.95,
  keep_iterations = FALSE,
  ...
)

estimate_link(
  model,
  data = "grid",
  by = NULL,
  predict = "link",
  ci = 0.95,
  keep_iterations = FALSE,
  ...
)

estimate_prediction(
  model,
  data = NULL,
  by = NULL,
  predict = "prediction",
  ci = 0.95,
  keep_iterations = FALSE,
  ...
)

estimate_relation(
  model,
  data = "grid",
  by = NULL,
  predict = "expectation",
  ci = 0.95,
  keep_iterations = FALSE,
  ...
)

Arguments

model	A statistical model.
data	A data frame with model's predictors to estimate the response. If NULL, the model's data is used. If "grid", the model matrix is obtained (through insight::get_datagrid()).
by	The predictor variable(s) at which to estimate the response. Other predictors of the model that are not included here will be set to their mean value (for numeric predictors), reference level (for factors) or mode (other types). The by argument will be used to create a data grid via insight::get_datagrid(), which will then be used as data argument. Thus, you cannot specify both data and by but only of these two arguments.
predict	This parameter controls what is predicted (and gets internally passed to insight::get_predicted()). In most cases, you don't need to care about it: it is changed automatically according to the different predicting functions (i.e., estimate_expectation(), estimate_prediction(), estimate_link() or estimate_relation()). The only time you might be interested in manually changing it is to estimate other distributional parameters (called "dpar" in other packages) - for instance when using complex formulae in brms models. The predict argument can then be set to the parameter you want to estimate, for instance "sigma", "kappa", etc. Note that the distinction between "expectation", "link" and "prediction" does not then apply (as you are directly predicting the value of some distributional parameter), and the corresponding functions will then only differ in the default value of their data argument.
ci	Confidence Interval (CI) level. Default to 0.95 (⁠95%⁠).
keep_iterations	If TRUE, will keep all iterations (draws) of bootstrapped or Bayesian models. They will be added as additional columns named ⁠iter_1, iter_2, ...⁠. You can reshape them to a long format by running reshape_iterations().
...	You can add all the additional control arguments from insight::get_datagrid() (used when data = "grid") and insight::get_predicted().

Value

A data frame of predicted values and uncertainty intervals, with class "estimate_predicted". Methods for visualisation_recipe() and plot() are available.

Expected (average) values

The most important way that various types of response estimates differ is in terms of what quantity is being estimated and the meaning of the uncertainty intervals. The major choices are expected values for uncertainty in the regression line and predicted values for uncertainty in the individual case predictions.

Expected values refer to the fitted regression line - the estimated average response value (i.e., the "expectation") for individuals with specific predictor values. For example, in a linear model y = 2 + 3x + 4z + e, the estimated average y for individuals with x = 1 and z = 2 is 11.

For expected values, uncertainty intervals refer to uncertainty in the estimated conditional average (where might the true regression line actually fall)? Uncertainty intervals for expected values are also called "confidence intervals".

Expected values and their uncertainty intervals are useful for describing the relationship between variables and for describing how precisely a model has been estimated.

For generalized linear models, expected values are reported on one of two scales:

• The link scale refers to scale of the fitted regression line, after transformation by the link function. For example, for a logistic regression (logit binomial) model, the link scale gives expected log-odds. For a log-link Poisson model, the link scale gives the expected log-count.

• The response scale refers to the original scale of the response variable (i.e., without any link function transformation). Expected values on the link scale are back-transformed to the original response variable metric (e.g., expected probabilities for binomial models, expected counts for Poisson models).

Individual case predictions

In contrast to expected values, predicted values refer to predictions for individual cases. Predicted values are also called "posterior predictions" or "posterior predictive draws".

For predicted values, uncertainty intervals refer to uncertainty in the individual response values for each case (where might any single case actually fall)? Uncertainty intervals for predicted values are also called "prediction intervals" or "posterior predictive intervals".

Predicted values and their uncertainty intervals are useful for forecasting the range of values that might be observed in new data, for making decisions about individual cases, and for checking if model predictions are reasonable ("posterior predictive checks").

Predicted values and intervals are always on the scale of the original response variable (not the link scale).

Functions for estimating predicted values and uncertainty

modelbased provides 4 functions for generating model-based response estimates and their uncertainty:

• estimate_expectation():

  • Generates expected values (conditional average) on the response scale.

  • The uncertainty interval is a confidence interval.

  • By default, values are computed using the data used to fit the model.

• estimate_link():

  • Generates expected values (conditional average) on the link scale.

  • The uncertainty interval is a confidence interval.

  • By default, values are computed using a reference grid spanning the observed range of predictor values (see insight::get_datagrid()).

• estimate_prediction():

  • Generates predicted values (for individual cases) on the response scale.

  • The uncertainty interval is a prediction interval.

  • By default, values are computed using the data used to fit the model.

• estimate_relation():

  • Like estimate_expectation().

  • Useful for visualizing a model.

  • Generates expected values (conditional average) on the response scale.

  • The uncertainty interval is a confidence interval.

  • By default, values are computed using a reference grid spanning the observed range of predictor values (see insight::get_datagrid()).

Data for predictions

If the data = NULL, values are estimated using the data used to fit the model. If data = "grid", values are computed using a reference grid spanning the observed range of predictor values with insight::get_datagrid(). This can be useful for model visualization. The number of predictor values used for each variable can be controlled with the length argument. data can also be a data frame containing columns with names matching the model frame (see insight::get_data()). This can be used to generate model predictions for specific combinations of predictor values.

Note

These functions are built on top of insight::get_predicted() and correspond to different specifications of its parameters. It may be useful to read its documentation, in particular the description of the predict argument for additional details on the difference between expected vs. predicted values and link vs. response scales.

Additional control parameters can be used to control results from insight::get_datagrid() (when data = "grid") and from insight::get_predicted() (the function used internally to compute predictions).

For plotting, check the examples in visualisation_recipe(). Also check out the Vignettes and README examples for various examples, tutorials and usecases.

Examples

library(modelbased)

# Linear Models
model <- lm(mpg ~ wt, data = mtcars)

# Get predicted and prediction interval (see insight::get_predicted)
estimate_expectation(model)

# Get expected values with confidence interval
pred <- estimate_relation(model)
pred

# Visualisation (see visualisation_recipe())
plot(pred)

# Standardize predictions
pred <- estimate_relation(lm(mpg ~ wt + am, data = mtcars))
z <- standardize(pred, include_response = FALSE)
z
unstandardize(z, include_response = FALSE)

# Logistic Models
model <- glm(vs ~ wt, data = mtcars, family = "binomial")
estimate_expectation(model)
estimate_relation(model)

# Mixed models
model <- lme4::lmer(mpg ~ wt + (1 | gear), data = mtcars)
estimate_expectation(model)
estimate_relation(model)

# Bayesian models

model <- suppressWarnings(rstanarm::stan_glm(
  mpg ~ wt,
  data = mtcars, refresh = 0, iter = 200
))
estimate_expectation(model)
estimate_relation(model)

---

Group-specific parameters of mixed models random effects

Description

Extract random parameters of each individual group in the context of mixed models. Can be reshaped to be of the same dimensions as the original data, which can be useful to add the random effects to the original data.

Usage

estimate_grouplevel(model, type = "random", ...)

reshape_grouplevel(x, indices = "all", group = "all", ...)

Arguments

model	A mixed model with random effects.
type	If "random" (default), the coefficients are the ones estimated natively by the model (as they are returned by, for instance, lme4::ranef()). They correspond to the deviation of each individual group from their fixed effect. As such, a coefficient close to 0 means that the participants' effect is the same as the population-level effect (in other words, it is "in the norm"). If "total", it will return the sum of the random effect and its corresponding fixed effects. These are known as BLUPs (Best Linear Unbiased Predictions). This argument can be used to reproduce the results given by lme4::ranef() and coef() (see ?coef.merMod). Note that BLUPs currently don't have uncertainty indices (such as SE and CI), as these are not computable.
...	Other arguments passed to or from other methods.
x	The output of estimate_grouplevel().
indices	A list containing the indices to extract (e.g., "Coefficient").
group	A list containing the random factors to select.

Examples

# lme4 model
data(mtcars)
model <- lme4::lmer(mpg ~ hp + (1 | carb), data = mtcars)
random <- estimate_grouplevel(model)
random

# Visualize random effects
plot(random)

# Show group-specific effects
estimate_grouplevel(model, deviation = FALSE)

# Reshape to wide data so that it matches the original dataframe...
reshaped <- reshape_grouplevel(random, indices = c("Coefficient", "SE"))

# ... and can be easily combined
alldata <- cbind(mtcars, reshaped)

# Use summary() to remove duplicated rows
summary(reshaped)

# Compute BLUPs
estimate_grouplevel(model, type = "total")

---

Estimate Marginal Means (Model-based average at each factor level)

Description

Estimate average value of response variable at each factor level. For plotting, check the examples in visualisation_recipe(). See also other related functions such as estimate_contrasts() and estimate_slopes().

Usage

estimate_means(
  model,
  by = "auto",
  predict = NULL,
  ci = 0.95,
  marginalize = "average",
  backend = getOption("modelbased_backend", "emmeans"),
  transform = NULL,
  verbose = TRUE,
  ...
)

Arguments

model	A statistical model.
by	The (focal) 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). by can a character (vector) naming the focal predictors (and optionally, representative values or levels), or a list of named elements. See details in insight::get_datagrid().
predict	Is passed to the type argument in emmeans::emmeans() (when backend = "emmeans") or in marginaleffects::avg_predictions() (when backend = "marginaleffects"). For emmeans, see also this vignette. Valid options for 'predict“ are: backend = "emmeans": predict can be "response", "link", "mu", "unlink", or "log". If predict = NULL (default), the most appropriate transformation is selected (which usually is "response"). backend = "marginaleffects": predict can be "response", "link" or any valid type option supported by model's class predict() method (e.g., for zero-inflation models from package glmmTMB, you can choose predict = "zprob" or predict = "conditional" etc., see glmmTMB::predict.glmmTMB). By default, when predict = NULL, the most appropriate transformation is selected, which usually returns predictions or contrasts on the response-scale. "link" will leave the values on scale of the linear predictors. "response" (or NULL) will transform them on scale of the response variable. Thus for a logistic model, "link" will give estimations expressed in log-odds (probabilities on logit scale) and "response" in terms of probabilities. To predict distributional parameters (called "dpar" in other packages), for instance when using complex formulae in brms models, the predict argument can take the value of the parameter you want to estimate, for instance "sigma", "kappa", etc.
ci	Confidence Interval (CI) level. Default to 0.95 (⁠95%⁠).
marginalize	Character string, indicating the type of marginalization. This dictates how the predictions are "averaged" over the non-focal predictors, i.e. those variables that are not specified in by or contrast. "average" (default): Takes the mean value for non-focal numeric predictors and marginalizes over the factor levels of non-focal terms, which computes a kind of "weighted average" for the values at which these terms are hold constant. These predictions are a good representation of the sample, because all possible values and levels of the non-focal predictors are taken into account. It answers the question, "What is the predicted value for an 'average' observation in my data?". It refers to randomly picking a subject of your sample and the result you get on average. This approach is the one taken by default in the emmeans package. "population": Non-focal predictors are marginalized over the observations in the sample, where the sample is replicated multiple times to produce "counterfactuals" and then takes the average of these predicted values (aggregated/grouped by the focal terms). It can be considered as extrapolation to the population. Counterfactual predictions are useful, insofar as the results can also be transferred to other contexts (Dickerman and Hernan, 2020). It answers the question, "What is the predicted for the 'average' observation in the general population?". It does not only refer to the actual data in your observed sample, but also "what would be if" we had more data, or if we had data from a different sample. In other words, the distinction between marginalization types resides in whether the prediction are made for: A specific "individual" (i.e., a specific combination of predictor values): this is what is obtained when using estimate_relation() and the other prediction functions. An average individual: obtained with estimate_means(..., marginalize = "average") The "general population": obtained with estimate_means(..., marginalize = "population")
backend	Whether to use "emmeans" or "marginaleffects" as a backend. Results are usually very similar. The major difference will be found for mixed models, where backend = "marginaleffects" will also average across random effects levels, producing "marginal predictions" (instead of "conditional predictions", see Heiss 2022). You can set a default backend via options(), e.g. use options(modelbased_backend = "emmeans") to use the emmeans package or options(modelbased_backend = "marginaleffects") to set marginaleffects as default backend.
transform	Deprecated, please use predict instead.
verbose	Use FALSE to silence messages and warnings.
...	Other arguments passed, for instance, to insight::get_datagrid(), to functions from the emmeans or marginaleffects package, or to process Bayesian models via bayestestR::describe_posterior(). Examples: insight::get_datagrid(): Argument such as length or range can be used to control the (number of) representative values. marginaleffects: Internally used functions are avg_predictions() for means and contrasts, and avg_slope() for slopes. Therefore, arguments for instance like vcov, transform, equivalence or slope can be passed to those functions. emmeans: Internally used functions are emmeans() and emtrends(). Additional arguments can be passed to these functions. Bayesian models: For Bayesian models, parameters are cleaned using describe_posterior(), thus, arguments like, for example, centrality, rope_range, or test are passed to that function.

Details

The estimate_slopes(), estimate_means() and estimate_contrasts() functions are forming a group, as they are all based on marginal estimations (estimations based on a model). All three are built on the emmeans or marginaleffects package (depending on the backend argument), so reading its documentation (for instance emmeans::emmeans(), emmeans::emtrends() or this website) 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 (see 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 estimate_contrasts(), are 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 estimate_slopes(), are 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_expectation()). 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 condition (using [estimate_slopes]).

Value

A data frame of estimated marginal means.

References

Heiss, A. (2022). Marginal and conditional effects for GLMMs with marginaleffects. Andrew Heiss. doi:10.59350/xwnfm-x1827

Examples

library(modelbased)

# Frequentist models
# -------------------
model <- lm(Petal.Length ~ Sepal.Width * Species, data = iris)

estimate_means(model)
estimate_means(model, by = c("Species", "Sepal.Width"), length = 2)
estimate_means(model, by = "Species=c('versicolor', 'setosa')")
estimate_means(model, by = "Sepal.Width=c(2, 4)")
estimate_means(model, by = c("Species", "Sepal.Width=0"))
estimate_means(model, by = "Sepal.Width", length = 5)
estimate_means(model, by = "Sepal.Width=c(2, 4)")

# Methods that can be applied to it:
means <- estimate_means(model, by = c("Species", "Sepal.Width=0"))

plot(means) # which runs visualisation_recipe()
standardize(means)


data <- iris
data$Petal.Length_factor <- ifelse(data$Petal.Length < 4.2, "A", "B")

model <- lme4::lmer(
  Petal.Length ~ Sepal.Width + Species + (1 | Petal.Length_factor),
  data = data
)
estimate_means(model)
estimate_means(model, by = "Sepal.Width", length = 3)

---

Estimate Marginal Effects

Description

Estimate the slopes (i.e., the coefficient) of a predictor over or within different factor levels, or alongside a numeric variable. In other words, to assess the effect of a predictor at specific configurations data. It corresponds to the derivative and can be useful to understand where a predictor has a significant role when interactions or non-linear relationships are present.

Other related functions based on marginal estimations includes estimate_contrasts() and estimate_means().

See the Details section below, and don't forget to also check out the Vignettes and README examples for various examples, tutorials and use cases.

Usage

estimate_slopes(
  model,
  trend = NULL,
  by = NULL,
  ci = 0.95,
  backend = getOption("modelbased_backend", "emmeans"),
  verbose = TRUE,
  ...
)

Arguments

model	A statistical model.
trend	A character indicating the name of the variable for which to compute the slopes.
by	The (focal) 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). by can a character (vector) naming the focal predictors (and optionally, representative values or levels), or a list of named elements. See details in insight::get_datagrid().
ci	Confidence Interval (CI) level. Default to 0.95 (⁠95%⁠).
backend	Whether to use "emmeans" or "marginaleffects" as a backend. Results are usually very similar. The major difference will be found for mixed models, where backend = "marginaleffects" will also average across random effects levels, producing "marginal predictions" (instead of "conditional predictions", see Heiss 2022). You can set a default backend via options(), e.g. use options(modelbased_backend = "emmeans") to use the emmeans package or options(modelbased_backend = "marginaleffects") to set marginaleffects as default backend.
verbose	Use FALSE to silence messages and warnings.
...	Other arguments passed, for instance, to insight::get_datagrid(), to functions from the emmeans or marginaleffects package, or to process Bayesian models via bayestestR::describe_posterior(). Examples: insight::get_datagrid(): Argument such as length or range can be used to control the (number of) representative values. marginaleffects: Internally used functions are avg_predictions() for means and contrasts, and avg_slope() for slopes. Therefore, arguments for instance like vcov, transform, equivalence or slope can be passed to those functions. emmeans: Internally used functions are emmeans() and emtrends(). Additional arguments can be passed to these functions. Bayesian models: For Bayesian models, parameters are cleaned using describe_posterior(), thus, arguments like, for example, centrality, rope_range, or test are passed to that function.

Details

The estimate_slopes(), estimate_means() and estimate_contrasts() functions are forming a group, as they are all based on marginal estimations (estimations based on a model). All three are built on the emmeans or marginaleffects package (depending on the backend argument), so reading its documentation (for instance emmeans::emmeans(), emmeans::emtrends() or this website) 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 (see 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 estimate_contrasts(), are 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 estimate_slopes(), are 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_expectation()). 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 condition (using [estimate_slopes]).

Value

A data.frame of class estimate_slopes.

Examples

library(ggplot2)
# Get an idea of the data
ggplot(iris, aes(x = Petal.Length, y = Sepal.Width)) +
  geom_point(aes(color = Species)) +
  geom_smooth(color = "black", se = FALSE) +
  geom_smooth(aes(color = Species), linetype = "dotted", se = FALSE) +
  geom_smooth(aes(color = Species), method = "lm", se = FALSE)

# Model it
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
# Compute the marginal effect of Petal.Length at each level of Species
slopes <- estimate_slopes(model, trend = "Petal.Length", by = "Species")
slopes

# Plot it
plot(slopes)
standardize(slopes)

model <- mgcv::gam(Sepal.Width ~ s(Petal.Length), data = iris)
slopes <- estimate_slopes(model, by = "Petal.Length", length = 50)
summary(slopes)
plot(slopes)

model <- mgcv::gam(Sepal.Width ~ s(Petal.Length, by = Species), data = iris)
slopes <- estimate_slopes(model,
  trend = "Petal.Length",
  by = c("Petal.Length", "Species"), length = 20
)
summary(slopes)
plot(slopes)

---

Consistent API for 'emmeans' and 'marginaleffects'

Description

These functions are convenient wrappers around the emmeans and the marginaleffects packages. They are mostly available for developers who want to leverage a unified API for getting model-based estimates, and regular users should use the ⁠estimate_*⁠ set of functions.

The get_emmeans(), get_emcontrasts() and get_emtrends() functions are wrappers around emmeans::emmeans() and emmeans::emtrends().

Usage

get_emcontrasts(
  model,
  contrast = NULL,
  by = NULL,
  predict = NULL,
  comparison = "pairwise",
  transform = NULL,
  verbose = TRUE,
  ...
)

get_emmeans(
  model,
  by = "auto",
  predict = NULL,
  transform = NULL,
  verbose = TRUE,
  ...
)

get_emtrends(model, trend = NULL, by = NULL, verbose = TRUE, ...)

get_marginalcontrasts(
  model,
  contrast = NULL,
  by = NULL,
  predict = NULL,
  comparison = "pairwise",
  marginalize = "average",
  ci = 0.95,
  p_adjust = "none",
  verbose = TRUE,
  ...
)

get_marginalmeans(
  model,
  by = "auto",
  predict = NULL,
  ci = 0.95,
  marginalize = "average",
  transform = NULL,
  verbose = TRUE,
  ...
)

get_marginaltrends(model, trend = NULL, by = NULL, verbose = TRUE, ...)

Arguments

model	A statistical model.
contrast	A character vector indicating the name of the variable(s) for which to compute the contrasts.
by	The (focal) 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). by can a character (vector) naming the focal predictors (and optionally, representative values or levels), or a list of named elements. See details in insight::get_datagrid().
predict	Is passed to the type argument in emmeans::emmeans() (when backend = "emmeans") or in marginaleffects::avg_predictions() (when backend = "marginaleffects"). For emmeans, see also this vignette. Valid options for 'predict“ are: backend = "emmeans": predict can be "response", "link", "mu", "unlink", or "log". If predict = NULL (default), the most appropriate transformation is selected (which usually is "response"). backend = "marginaleffects": predict can be "response", "link" or any valid type option supported by model's class predict() method (e.g., for zero-inflation models from package glmmTMB, you can choose predict = "zprob" or predict = "conditional" etc., see glmmTMB::predict.glmmTMB). By default, when predict = NULL, the most appropriate transformation is selected, which usually returns predictions or contrasts on the response-scale. "link" will leave the values on scale of the linear predictors. "response" (or NULL) will transform them on scale of the response variable. Thus for a logistic model, "link" will give estimations expressed in log-odds (probabilities on logit scale) and "response" in terms of probabilities. To predict distributional parameters (called "dpar" in other packages), for instance when using complex formulae in brms models, the predict argument can take the value of the parameter you want to estimate, for instance "sigma", "kappa", etc.
comparison	Specify the type of contrasts or tests that should be carried out. When backend = "emmeans", can be one of "pairwise", "poly", "consec", "eff", "del.eff", "mean_chg", "trt.vs.ctrl", "dunnett", "wtcon" and some more. See also method argument in emmeans::contrast and the ?emmeans::emmc-functions. For backend = "marginaleffects", can be a numeric value, vector, or matrix, a string equation specifying the hypothesis to test, a string naming the comparison method, a formula, or a function. Strings, string equations and formula are probably the most common options and described below. For other options and detailed descriptions of those options, see also marginaleffects::comparisons and this website. String: One of "pairwise", "reference", "sequential", "meandev" "meanotherdev" and the inverse options "revpairwise", "revreference", and "revsequential". String equation: To identify parameters from the output, either specify the term name, or "b1", "b2" etc. to indicate rows, e.g.:"hp = drat", "b1 = b2", or "b1 + b2 + b3 = 0". Formula: A formula like comparison ~ pairs | group, where the left-hand side indicates the type of comparison (difference or ratio), the right-hand side determines the pairs of estimates to compare (reference, sequential, or meandev). Optionally, comparisons can be carried out within subsets by indicating the grouping variable after a vertical bar ( |).
transform	Deprecated, please use predict instead.
verbose	Use FALSE to silence messages and warnings.
...	Other arguments passed, for instance, to insight::get_datagrid(), to functions from the emmeans or marginaleffects package, or to process Bayesian models via bayestestR::describe_posterior(). Examples: insight::get_datagrid(): Argument such as length or range can be used to control the (number of) representative values. marginaleffects: Internally used functions are avg_predictions() for means and contrasts, and avg_slope() for slopes. Therefore, arguments for instance like vcov, transform, equivalence or slope can be passed to those functions. emmeans: Internally used functions are emmeans() and emtrends(). Additional arguments can be passed to these functions. Bayesian models: For Bayesian models, parameters are cleaned using describe_posterior(), thus, arguments like, for example, centrality, rope_range, or test are passed to that function.
trend	A character indicating the name of the variable for which to compute the slopes.
marginalize	Character string, indicating the type of marginalization. This dictates how the predictions are "averaged" over the non-focal predictors, i.e. those variables that are not specified in by or contrast. "average" (default): Takes the mean value for non-focal numeric predictors and marginalizes over the factor levels of non-focal terms, which computes a kind of "weighted average" for the values at which these terms are hold constant. These predictions are a good representation of the sample, because all possible values and levels of the non-focal predictors are taken into account. It answers the question, "What is the predicted value for an 'average' observation in my data?". It refers to randomly picking a subject of your sample and the result you get on average. This approach is the one taken by default in the emmeans package. "population": Non-focal predictors are marginalized over the observations in the sample, where the sample is replicated multiple times to produce "counterfactuals" and then takes the average of these predicted values (aggregated/grouped by the focal terms). It can be considered as extrapolation to the population. Counterfactual predictions are useful, insofar as the results can also be transferred to other contexts (Dickerman and Hernan, 2020). It answers the question, "What is the predicted for the 'average' observation in the general population?". It does not only refer to the actual data in your observed sample, but also "what would be if" we had more data, or if we had data from a different sample. In other words, the distinction between marginalization types resides in whether the prediction are made for: A specific "individual" (i.e., a specific combination of predictor values): this is what is obtained when using estimate_relation() and the other prediction functions. An average individual: obtained with estimate_means(..., marginalize = "average") The "general population": obtained with estimate_means(..., marginalize = "population")
ci	Confidence Interval (CI) level. Default to 0.95 (⁠95%⁠).
p_adjust	The p-values adjustment method for frequentist multiple comparisons. Can be one of "none" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "tukey" or "holm". See the p-value adjustment section in the emmeans::test documentation or ?stats::p.adjust.

Examples

# Basic usage
model <- lm(Sepal.Width ~ Species, data = iris)
get_emcontrasts(model)

# Dealing with interactions
model <- lm(Sepal.Width ~ Species * Petal.Width, data = iris)
# By default: selects first factor
get_emcontrasts(model)
# Can also run contrasts between points of numeric
get_emcontrasts(model, contrast = "Petal.Width", length = 3)
# Or both
get_emcontrasts(model, contrast = c("Species", "Petal.Width"), length = 2)
# Or with custom specifications
estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)"))
# Or modulate it
get_emcontrasts(model, by = "Petal.Width", length = 4)


model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris)

# By default, 'by' is set to "Species"
get_emmeans(model)

# Overall mean (close to 'mean(iris$Sepal.Length)')
get_emmeans(model, by = NULL)

# One can estimate marginal means at several values of a 'modulate' variable
get_emmeans(model, by = "Petal.Width", length = 3)

# Interactions
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)

get_emmeans(model)
get_emmeans(model, by = c("Species", "Petal.Length"), length = 2)
get_emmeans(model, by = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2)


model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)

get_emtrends(model)
get_emtrends(model, by = "Species")
get_emtrends(model, by = "Petal.Length")
get_emtrends(model, by = c("Species", "Petal.Length"))

model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris)
get_emtrends(model)
get_emtrends(model, by = "Sepal.Width")


model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris)

# By default, 'by' is set to "Species"
get_marginalmeans(model)

# Overall mean (close to 'mean(iris$Sepal.Length)')
get_marginalmeans(model, by = NULL)

# One can estimate marginal means at several values of a 'modulate' variable
get_marginalmeans(model, by = "Petal.Width", length = 3)

# Interactions
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)

get_marginalmeans(model)
get_marginalmeans(model, by = c("Species", "Petal.Length"), length = 2)
get_marginalmeans(model, by = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2)


model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)

get_marginaltrends(model, trend = "Petal.Length", by = "Species")
get_marginaltrends(model, trend = "Petal.Length", by = "Petal.Length")
get_marginaltrends(model, trend = "Petal.Length", by = c("Species", "Petal.Length"))

---

Smoothing a vector or a time series

Description

Smoothing a vector or a time series. For data.frames, the function will smooth all numeric variables stratified by factor levels (i.e., will smooth within each factor level combination).

Usage

smoothing(x, method = "loess", strength = 0.25, ...)

Arguments

x	A numeric vector.
method	Can be "loess" (default) or "smooth". A loess smoothing can be slow.
strength	This argument only applies when method = "loess". Degree of smoothing passed to span (see loess()).
...	Arguments passed to or from other methods.

Value

A smoothed vector or data frame.

Examples

x <- sin(seq(0, 4 * pi, length.out = 100)) + rnorm(100, 0, 0.2)
plot(x, type = "l")
lines(smoothing(x, method = "smooth"), type = "l", col = "blue")
lines(smoothing(x, method = "loess"), type = "l", col = "red")

x <- sin(seq(0, 4 * pi, length.out = 10000)) + rnorm(10000, 0, 0.2)
plot(x, type = "l")
lines(smoothing(x, method = "smooth"), type = "l", col = "blue")
lines(smoothing(x, method = "loess"), type = "l", col = "red")

---

Create a reference grid

Description

This function is an alias (another name) for the insight::get_datagrid() function. Same arguments apply.

Usage

visualisation_matrix(x, ...)

## S3 method for class 'data.frame'
visualisation_matrix(
  x,
  by = "all",
  factors = "reference",
  numerics = "mean",
  preserve_range = FALSE,
  reference = x,
  ...
)

## S3 method for class 'numeric'
visualisation_matrix(x, ...)

## S3 method for class 'factor'
visualisation_matrix(x, ...)

Arguments

x	An object from which to construct the reference grid.
...	Arguments passed to or from other methods (for instance, length or range to control the spread of numeric variables.).
by	Indicates the focal predictors (variables) for the reference grid and at which values focal predictors should be represented. If not specified otherwise, representative values for numeric variables or predictors are evenly distributed from the minimum to the maximum, with a total number of length values covering that range (see 'Examples'). Possible options for by are: "all", which will include all variables or predictors. a character vector of one or more variable or predictor names, like c("Species", "Sepal.Width"), which will create a grid of all combinations of unique values. For factors, will use all levels, for numeric variables, will use a range of length length (evenly spread from minimum to maximum) and for character vectors, will use all unique values. a list of named elements, indicating focal predictors and their representative values, e.g. by = list(Sepal.Length = c(2, 4), Species = "setosa"). a string with assignments, e.g. by = "Sepal.Length = 2" or by = c("Sepal.Length = 2", "Species = 'setosa'") - note the usage of single and double quotes to assign strings within strings. There is a special handling of assignments with brackets, i.e. values defined inside [ and ⁠]⁠.For numeric variables, the value(s) inside the brackets should either be two values, indicating minimum and maximum (e.g. by = "Sepal.Length = [0, 5]"), for which a range of length length (evenly spread from given minimum to maximum) is created. more than two numeric values by = "Sepal.Length = [2,3,4,5]", in which case these values are used as representative values. a "token" that creates pre-defined representative values: for mean and -/+ 1 SD around the mean: "x = [sd]" for median and -/+ 1 MAD around the median: "x = [mad]" for Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): "x = [fivenum]" for terciles, including minimum and maximum: "x = [terciles]" for terciles, excluding minimum and maximum: "x = [terciles2]" for quartiles, including minimum and maximum: "x = [quartiles]" (same as "x = [fivenum]") for quartiles, excluding minimum and maximum: "x = [quartiles2]" for a pretty value range: "x = [pretty]" for minimum and maximum value: "x = [minmax]" for 0 and the maximum value: "x = [zeromax]" For factor variables, the value(s) inside the brackets should indicate one or more factor levels, like by = "Species = [setosa, versicolor]". Note: the length argument will be ignored when using brackets-tokens. The remaining variables not specified in by will be fixed (see also arguments factors and numerics).
factors	Type of summary for factors. Can be "reference" (set at the reference level), "mode" (set at the most common level) or "all" to keep all levels.
numerics	Type of summary for numeric values. Can be "all" (will duplicate the grid for all unique values), any function ("mean", "median", ...) or a value (e.g., numerics = 0).
preserve_range	In the case of combinations between numeric variables and factors, setting preserve_range = TRUE will drop the observations where the value of the numeric variable is originally not present in the range of its factor level. This leads to an unbalanced grid. Also, if you want the minimum and the maximum to closely match the actual ranges, you should increase the length argument.
reference	The reference vector from which to compute the mean and SD. Used when standardizing or unstandardizing the grid using effectsize::standardize.

Value

Reference grid data frame.

Examples

# See `?insight::get_datagrid`

---

Automated plotting for 'modelbased' objects

Description

Most 'modelbased' objects can be visualized using the plot() function, which internally calls the visualisation_recipe() function. See the examples below for more information and examples on how to create and customize plots.

Usage

## S3 method for class 'estimate_predicted'
visualisation_recipe(
  x,
  show_data = FALSE,
  point = NULL,
  line = NULL,
  pointrange = NULL,
  ribbon = NULL,
  facet = NULL,
  grid = NULL,
  join_dots = TRUE,
  ...
)

## S3 method for class 'estimate_slopes'
visualisation_recipe(
  x,
  line = NULL,
  pointrange = NULL,
  ribbon = NULL,
  facet = NULL,
  grid = NULL,
  ...
)

## S3 method for class 'estimate_grouplevel'
visualisation_recipe(
  x,
  line = NULL,
  pointrange = NULL,
  ribbon = NULL,
  facet = NULL,
  grid = NULL,
  ...
)

Arguments

x	A modelbased object.
show_data	Display the "raw" data as a background to the model-based estimation.
point, line, pointrange, ribbon, facet, grid	Additional aesthetics and parameters for the geoms (see customization example).
join_dots	Logical, if TRUE and for categorical focal terms in by, dots (estimates) are connected by lines, i.e. plots will be a combination of dots with error bars and connecting lines. If FALSE, only dots and error bars are shown.
...	Not used.

Details

The plotting works by mapping any predictors from the by argument to the x-axis, colors, alpha (transparency) and facets. Thus, the appearance of the plot depends on the order of the variables that you specify in the by argument. For instance, the plots corresponding to estimate_relation(model, by=c("Species", "Sepal.Length")) and estimate_relation(model, by=c("Sepal.Length", "Species")) will look different.

The automated plotting is primarily meant for convenient visual checks, but for publication-ready figures, we recommend re-creating the figures using the ggplot2 package directly.

Examples

# ==============================================
# estimate_relation, estimate_expectation, ...
# ==============================================
# Simple Model ---------------
x <- estimate_relation(lm(mpg ~ wt, data = mtcars))
layers <- visualisation_recipe(x)
layers
plot(layers)

# visualization_recipe() is called implicitly when you call plot()
plot(estimate_relation(lm(mpg ~ qsec, data = mtcars)))

# And can be used in a pipe workflow
lm(mpg ~ qsec, data = mtcars) |>
  estimate_relation(ci = c(0.5, 0.8, 0.9)) |>
  plot()

# Customize aesthetics ----------

plot(x,
  point = list(color = "red", alpha = 0.6, size = 3),
  line = list(color = "blue", size = 3),
  ribbon = list(fill = "green", alpha = 0.7)
) +
  theme_minimal() +
  labs(title = "Relationship between MPG and WT")


# Customize raw data -------------

plot(x, point = list(geom = "density_2d_filled"), line = list(color = "white")) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(legend.position = "none")

# Single predictors examples -----------

plot(estimate_relation(lm(Sepal.Length ~ Species, data = iris)))

# 2-ways interaction ------------

# Numeric * numeric
x <- estimate_relation(lm(mpg ~ wt * qsec, data = mtcars))
plot(x)

# Numeric * factor
x <- estimate_relation(lm(Sepal.Width ~ Sepal.Length * Species, data = iris))
plot(x)

# ==============================================
# estimate_means
# ==============================================
# Simple Model ---------------
x <- estimate_means(lm(Sepal.Width ~ Species, data = iris), by = "Species")
layers <- visualisation_recipe(x)
layers
plot(layers)

# Customize aesthetics
layers <- visualisation_recipe(x,
  point = list(width = 0.03, color = "red"),
  pointrange = list(size = 2, linewidth = 2),
  line = list(linetype = "dashed", color = "blue")
)
plot(layers)

# Two levels ---------------
data <- mtcars
data$cyl <- as.factor(data$cyl)

model <- lm(mpg ~ cyl * wt, data = data)

x <- estimate_means(model, by = c("cyl", "wt"))
plot(x)


# GLMs ---------------------
data <- data.frame(vs = mtcars$vs, cyl = as.factor(mtcars$cyl))
x <- estimate_means(glm(vs ~ cyl, data = data, family = "binomial"), by = c("cyl"))
plot(x)


# ==============================================
# estimate_slopes
# ==============================================
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
x <- estimate_slopes(model, trend = "Petal.Length", by = "Species")

layers <- visualisation_recipe(x)
layers
plot(layers)

# Customize aesthetics and add horizontal line and theme
layers <- visualisation_recipe(x, pointrange = list(size = 2, linewidth = 2))
plot(layers) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
  theme_minimal() +
  labs(y = "Effect of Petal.Length", title = "Marginal Effects")

model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris)
x <- estimate_slopes(model, trend = "Sepal.Width", by = "Sepal.Width", length = 20)
plot(visualisation_recipe(x))

model <- lm(Petal.Length ~ Species * poly(Sepal.Width, 3), data = iris)
x <- estimate_slopes(model, trend = "Sepal.Width", by = c("Sepal.Width", "Species"))
plot(visualisation_recipe(x))


# ==============================================
# estimate_grouplevel
# ==============================================
data <- lme4::sleepstudy
data <- rbind(data, data)
data$Newfactor <- rep(c("A", "B", "C", "D"))

# 1 random intercept
model <- lme4::lmer(Reaction ~ Days + (1 | Subject), data = data)
x <- estimate_grouplevel(model)
layers <- visualisation_recipe(x)
layers
plot(layers)

# 2 random intercepts
model <- lme4::lmer(Reaction ~ Days + (1 | Subject) + (1 | Newfactor), data = data)
x <- estimate_grouplevel(model)
plot(x) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_minimal()
# Note: we need to use hline instead of vline because the axes is flipped

model <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Newfactor), data = data)
x <- estimate_grouplevel(model)
plot(x)

---

Find zero-crossings and inversion points

Description

Find zero crossings of a vector, i.e., indices when the numeric variable crosses 0. It is useful for finding the points where a function changes by looking at the zero crossings of its derivative.

Usage

zero_crossings(x)

find_inversions(x)

Arguments

x	A numeric vector.

Value

Vector of zero crossings or points of inversion.

See Also

Based on the uniroot.all function from the rootSolve package.

Examples

x <- sin(seq(0, 4 * pi, length.out = 100))
plot(x, type = "b")

zero_crossings(x)
find_inversions(x)

---