View source: R/plot_predictions.R
plot_predictions  R Documentation 
Plot predictions on the yaxis against values of one or more predictors (xaxis, colors/shapes, and facets).
The by
argument is used to plot marginal predictions, that is, predictions made on the original data, but averaged by subgroups. This is analogous to using the by
argument in the predictions()
function.
The condition
argument is used to plot conditional predictions, that is, predictions made on a userspecified grid. This is analogous to using the newdata
argument and datagrid()
function in a predictions()
call. All variables whose values are not specified explicitly are treated as usual by datagrid()
, that is, they are held at their mean or mode (or rounded mean for integers). This includes grouping variables in mixedeffects models, so analysts who fit such models may want to specify the groups of interest using the condition
argument, or supply modelspecific arguments to compute populationlevel estimates. See details below.
See the "Plots" vignette and website for tutorials and information on how to customize plots:
https://marginaleffects.com/vignettes/plot.html
https://marginaleffects.com
plot_predictions(
model,
condition = NULL,
by = NULL,
newdata = NULL,
type = NULL,
vcov = NULL,
conf_level = 0.95,
wts = FALSE,
transform = NULL,
points = 0,
rug = FALSE,
gray = FALSE,
draw = TRUE,
...
)
model 
Model object 
condition 
Conditional predictions

by 
Marginal predictions

newdata 
When 
type 
string indicates the type (scale) of the predictions used to
compute contrasts or slopes. 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 modelspecific list of
acceptable values is returned in an error message. When 
vcov 
Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values:

conf_level 
numeric value between 0 and 1. Confidence level to use to build a confidence interval. 
wts 
logical, string or numeric: weights to use when computing average predictions, contrasts or slopes. These weights only affect the averaging in

transform 
A function applied to unitlevel 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. 
points 
Number between 0 and 1 which controls the transparency of raw data points. 0 (default) does not display any points. 
rug 
TRUE displays tick marks on the axes to mark the distribution of raw data. 
gray 
FALSE grayscale or color plot 
draw 

... 
Additional arguments are passed to the 
A ggplot2
object or data frame (if draw=FALSE
)
Some model types allow modelspecific arguments to modify the nature of
marginal effects, predictions, marginal means, and contrasts. Please report
other packagespecific predict()
arguments on Github so we can add them to
the table below.
https://github.com/vincentarelbundock/marginaleffects/issues
Package  Class  Argument  Documentation 
brms  brmsfit  ndraws  brms::posterior_predict 
re_formula  brms::posterior_predict  
lme4  merMod  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  
MCMCglmm  MCMCglmm  ndraws  
The type
argument determines the scale of the predictions used to compute quantities of interest with functions from the marginaleffects
package. Admissible values for type
depend on the model object. When users specify an incorrect value for type
, marginaleffects
will raise an informative error with a list of valid type
values for the specific model object. The first entry in the list in that error message is the default type.
The invlink(link)
is a special type defined by marginaleffects
. It is available for some (but not all) models and functions. With this link type, we first compute predictions on the link scale, then we use the inverse link function to backtransform the predictions to the response scale. This is useful for models with nonlinear link functions as it can ensure that confidence intervals stay within desirable bounds, ex: 0 to 1 for a logit model. Note that an average of estimates with type="invlink(link)"
will not always be equivalent to the average of estimates with type="response"
.
Some of the most common type
values are:
response, link, E, Ep, average, class, conditional, count, cum.prob, cumhaz, cumprob, density, detection, disp, ev, expected, expvalue, fitted, hazard, invlink(link), latent, latent_N, linear, linear.predictor, linpred, location, lp, mean, numeric, p, ppd, pr, precision, prediction, prob, probability, probs, quantile, risk, rmst, scale, survival, unconditional, utility, variance, xb, zero, zlink, zprob
mod < lm(mpg ~ hp + wt, data = mtcars)
plot_predictions(mod, condition = "wt")
mod < lm(mpg ~ hp * wt * am, data = mtcars)
plot_predictions(mod, condition = c("hp", "wt"))
plot_predictions(mod, condition = list("hp", wt = "threenum"))
plot_predictions(mod, condition = list("hp", wt = range))
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