predictions: Adjusted Predictions
In marginaleffects: Marginal Effects, Marginal Means, Predictions, and Contrasts

predictions

R Documentation

Adjusted Predictions

Description

Outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or factor levels (a.k.a. "reference grid"). The tidy() and summary() functions can be used to aggregate the output of predictions(). To learn more, read the predictions vignette, visit the package website, or scroll down this page for a full list of vignettes:

Usage

predictions(
  model,
  newdata = NULL,
  variables = NULL,
  vcov = TRUE,
  conf_level = 0.95,
  type = NULL,
  by = NULL,
  byfun = NULL,
  wts = NULL,
  transform_post = NULL,
  hypothesis = NULL,
  ...
)

Arguments

`model`	Model object
`newdata`	`NULL`, data frame, string, or `datagrid()` call. Determines the predictor values for which to compute marginal effects. `NULL` (default): Unit-level marginal effects for each observed value in the original dataset. data frame: Unit-level marginal effects for each row of the `newdata` data frame. string: "mean": Marginal Effects at the Mean. Marginal effects when each predictor is held at its mean or mode. "median": Marginal Effects at the Median. Marginal effects when each predictor is held at its median or mode. "marginalmeans": Marginal Effects at Marginal Means. See Details section below. "tukey": Marginal Effects at Tukey's 5 numbers. "grid": Marginal Effects on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). `datagrid()` call to specify a custom grid of regressors. For example: `newdata = datagrid(cyl = c(4, 6))`: `cyl` variable equal to 4 and 6 and other regressors fixed at their means or modes. See the Examples section and the `datagrid()` documentation.
`variables`	`NULL`, character vector, or named list. The subset of variables to use for creating a counterfactual grid of predictions. The entire dataset replicated for each unique combination of the variables in this list. See the Examples section below. Warning: This can use a lot of memory if there are many variables and values, and when the dataset is large. `NULL`: computes one prediction per row of `newdata` Named list: names identify the subset of variables of interest and their values. For numeric variables, the `variables` argument supports functions and string shortcuts: A function which returns a numeric value Numeric vector: Contrast between the 2nd element and the 1st element of the `x` vector. "iqr": Contrast across the interquartile range of the regressor. "sd": Contrast across one standard deviation around the regressor mean. "2sd": Contrast across two standard deviations around the regressor mean. "minmax": Contrast between the maximum and the minimum values of the regressor. "threenum": mean and 1 standard deviation on both sides "fivenum": Tukey's five numbers #' @param newdata `NULL`, data frame, string, or `datagrid()` call. Determines the grid of predictors on which we make predictions. `NULL` (default): Predictions for each observed value in the original dataset. data frame: Predictions for each row of the `newdata` data frame. string: "mean": Predictions at the Mean. Predictions when each predictor is held at its mean or mode. "median": Predictions at the Median. Predictions when each predictor is held at its median or mode. "marginalmeans": Predictions at Marginal Means. See Details section below. "tukey": Predictions at Tukey's 5 numbers. "grid": Predictions on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors). `datagrid()` call to specify a custom grid of regressors. For example: `newdata = datagrid(cyl = c(4, 6))`: `cyl` variable equal to 4 and 6 and other regressors fixed at their means or modes. See the Examples section and the `datagrid()` documentation.
`vcov`	Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values: FALSE: Do not compute standard errors. This can speed up computation considerably. TRUE: Unit-level standard errors using the default `vcov(model)` variance-covariance matrix. String which indicates the kind of uncertainty estimates to return. Heteroskedasticity-consistent: `"HC"`, `"HC0"`, `"HC1"`, `"HC2"`, `"HC3"`, `"HC4"`, `"HC4m"`, `"HC5"`. See `?sandwich::vcovHC` Heteroskedasticity and autocorrelation consistent: `"HAC"` Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger" Other: `"NeweyWest"`, `"KernHAC"`, `"OPG"`. See the `sandwich` package documentation. One-sided formula which indicates the name of cluster variables (e.g., `~unit_id`). This formula is passed to the `cluster` argument of the `sandwich::vcovCL` function. Square covariance matrix Function which returns a covariance matrix (e.g., `stats::vcov(model)`)
`conf_level`	numeric value between 0 and 1. Confidence level to use to build a confidence interval.
`type`	string indicates the type (scale) of the predictions used to compute marginal effects or contrasts. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When `type` is `NULL`, the default value is used. This default is the first model-related row in the `marginaleffects:::type_dictionary` dataframe.
`by`	Character vector of variable names over which to compute group-wise estimates.
`byfun`	A function such as `mean()` or `sum()` used to aggregate estimates within the subgroups defined by the `by` argument. `NULL` uses the `mean()` function. Must accept a numeric vector and return a single numeric value. This is sometimes used to take the sum or mean of predicted probabilities across outcome or predictor levels. See examples section.
`wts`	string or numeric: weights to use when computing average contrasts or marginaleffects. These weights only affect the averaging in `tidy()` or `summary()`, and not the unit-level estimates themselves. string: column name of the weights variable in `newdata`. When supplying a column name to `wts`, it is recommended to supply the original data (including the weights variable) explicitly to `newdata`. numeric: vector of length equal to the number of rows in the original data or in `newdata` (if supplied).
`transform_post`	(experimental) A function applied to unit-level adjusted predictions and confidence intervals just before the function returns results. For bayesian models, this function is applied to individual draws from the posterior distribution, before computing summaries.
`hypothesis`	specify a hypothesis test or custom contrast using a vector, matrix, string, or string formula. String: "pairwise": pairwise differences between estimates in each row. "reference": differences between the estimates in each row and the estimate in the first row. "sequential": difference between an estimate and the estimate in the next row. "revpairwise", "revreference", "revsequential": inverse of the corresponding hypotheses, as described above. String formula to specify linear or non-linear hypothesis tests. If the `term` column uniquely identifies rows, terms can be used in the formula. Otherwise, use `b1`, `b2`, etc. to identify the position of each parameter. Examples: `hp = drat` `hp + drat = 12` `b1 + b2 + b3 = 0` Numeric vector: Weights to compute a linear combination of (custom contrast between) estimates. Length equal to the number of rows generated by the same function call, but without the `hypothesis` argument. Numeric matrix: Each column is a vector of weights, as describe above, used to compute a distinct linear combination of (contrast between) estimates. The column names of the matrix are used as labels in the output. See the Examples section below and the vignette: https://vincentarelbundock.github.io/marginaleffects/articles/hypothesis.html
`...`	Additional arguments are passed to the `predict()` method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the `marginaleffects` website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the `?marginaleffects` documentation for a non-exhaustive list of available arguments.

Details

The newdata argument, the tidy() function, and datagrid() function can be used to control the kind of predictions to report:

Average Predictions
Predictions at the Mean
Predictions at User-Specified values (aka Predictions at Representative values).

When possible, predictions() delegates the computation of confidence intervals to the insight::get_predicted() function, which uses back transformation to produce adequate confidence intervals on the scale specified by the type argument. When this is not possible, predictions() uses the Delta Method to compute standard errors around adjusted predictions, and builds symmetric confidence intervals. These naive symmetric intervals may not always be appropriate. For instance, they may stretch beyond the bounds of a binary response variables.

Value

A data.frame with one row per observation and several columns:

rowid: row number of the newdata data frame
type: prediction type, as defined by the type argument
group: (optional) value of the grouped outcome (e.g., categorical outcome models)
predicted: predicted outcome
std.error: standard errors computed by the insight::get_predicted function or, if unavailable, via marginaleffects delta method functionality.
conf.low: lower bound of the confidence interval (or equal-tailed interval for bayesian models)
conf.high: upper bound of the confidence interval (or equal-tailed interval for bayesian models)

Vignettes and documentation

Vignettes:

Case studies:

Tips and technical notes:

Model-Specific Arguments

Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts.

Package	Class	Argument	Documentation
`brms`	`brmsfit`	`ndraws`	brms::posterior_predict
		`re_formula`
`lme4`	`merMod`	`include_random`	insight::get_predicted
		`re.form`	lme4::predict.merMod
		`allow.new.levels`	lme4::predict.merMod
`glmmTMB`	`glmmTMB`	`re.form`	glmmTMB::predict.glmmTMB
		`allow.new.levels`	glmmTMB::predict.glmmTMB
		`zitype`	glmmTMB::predict.glmmTMB
`mgcv`	`bam`	`exclude`	mgcv::predict.bam
`robustlmm`	`rlmerMod`	`re.form`	robustlmm::predict.rlmerMod
		`allow.new.levels`	robustlmm::predict.rlmerMod

Examples

# Adjusted Prediction for every row of the original dataset
mod <- lm(mpg ~ hp + factor(cyl), data = mtcars)
pred <- predictions(mod)
head(pred)

# Adjusted Predictions at User-Specified Values of the Regressors
predictions(mod, newdata = datagrid(hp = c(100, 120), cyl = 4))

m <- lm(mpg ~ hp + drat + factor(cyl) + factor(am), data = mtcars)
predictions(m, newdata = datagrid(FUN_factor = unique, FUN_numeric = median))

# Average Adjusted Predictions (AAP)
library(dplyr)
mod <- lm(mpg ~ hp * am * vs, mtcars)

pred <- predictions(mod)
summary(pred)

predictions(mod, by = "am")

# Conditional Adjusted Predictions
plot_cap(mod, condition = "hp")

# Counterfactual predictions with the `variables` argument
# the `mtcars` dataset has 32 rows

mod <- lm(mpg ~ hp + am, data = mtcars)
p <- predictions(mod)
head(p)
nrow(p)

# counterfactual predictions obtained by replicating the entire for different
# values of the predictors
p <- predictions(mod, variables = list(hp = c(90, 110)))
nrow(p)


# hypothesis test: is the prediction in the 1st row equal to the prediction in the 2nd row
mod <- lm(mpg ~ wt + drat, data = mtcars)

predictions(
    mod,
    newdata = datagrid(wt = 2:3),
    hypothesis = "b1 = b2")

# same hypothesis test using row indices
predictions(
    mod,
    newdata = datagrid(wt = 2:3),
    hypothesis = "b1 - b2 = 0")

# same hypothesis test using numeric vector of weights
predictions(
    mod,
    newdata = datagrid(wt = 2:3),
    hypothesis = c(1, -1))

# two custom contrasts using a matrix of weights
lc <- matrix(c(
    1, -1,
    2, 3),
    ncol = 2)
predictions(
    mod,
    newdata = datagrid(wt = 2:3),
    hypothesis = lc)


# `by` argument
mod <- lm(mpg ~ hp * am * vs, data = mtcars)
predictions(mod, by = c("am", "vs")) 

library(nnet)
nom <- multinom(factor(gear) ~ mpg + am * vs, data = mtcars, trace = FALSE)

# first 5 raw predictions
predictions(nom, type = "probs") |> head()

# average predictions
predictions(nom, type = "probs", by = "group") |> summary()

by <- data.frame(
    group = c("3", "4", "5"),
    by = c("3,4", "3,4", "5"))

predictions(nom, type = "probs", by = by)

# sum of predicted probabilities for combined response levels
mod <- multinom(factor(cyl) ~ mpg + am, data = mtcars, trace = FALSE)
by <- data.frame(
    by = c("4,6", "4,6", "8"),
    group = as.character(c(4, 6, 8)))
predictions(mod, newdata = "mean", byfun = sum, by = by)

marginaleffects documentation built on Nov. 24, 2022, 1:06 a.m.