get_predicted_ci {insight} | R Documentation |
Confidence intervals around predicted values
get_predicted_ci(x, ...) ## Default S3 method: get_predicted_ci( x, predictions = NULL, data = NULL, se = NULL, ci = 0.95, ci_type = "confidence", ci_method = NULL, dispersion_method = "sd", vcov = NULL, vcov_args = NULL, verbose = TRUE, ... )
x |
A statistical model (can also be a data.frame, in which case the second argument has to be a model). |
... |
Other argument to be passed, for instance to |
predictions |
A vector of predicted values (as obtained by
|
data |
An optional data frame in which to look for variables with which
to predict. If omitted, the data used to fit the model is used. Visualization
matrices can be generated using |
se |
Numeric vector of standard error of predicted values. If |
ci |
The interval level. Default is |
ci_type |
Can be |
ci_method |
The method for computing p values and confidence intervals. Possible values depend on model type.
See |
dispersion_method |
Bootstrap dispersion and Bayesian posterior summary:
|
vcov |
Variance-covariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.
|
vcov_args |
List of arguments to be passed to the function identified by
the |
verbose |
Toggle warnings. |
# Confidence Intervals for Model Predictions # ------------------------------------------ data(mtcars) # Linear model # ------------ x <- lm(mpg ~ cyl + hp, data = mtcars) predictions <- predict(x) ci_vals <- get_predicted_ci(x, predictions, ci_type = "prediction") head(ci_vals) ci_vals <- get_predicted_ci(x, predictions, ci_type = "confidence") head(ci_vals) ci_vals <- get_predicted_ci(x, predictions, ci = c(0.8, 0.9, 0.95)) head(ci_vals) # Bootstrapped # ------------ if (require("boot")) { predictions <- get_predicted(x, iterations = 500) get_predicted_ci(x, predictions) } if (require("datawizard") && require("bayestestR")) { ci_vals <- get_predicted_ci(x, predictions, ci = c(0.80, 0.95)) head(ci_vals) datawizard::reshape_ci(ci_vals) ci_vals <- get_predicted_ci(x, predictions, dispersion_method = "MAD", ci_method = "HDI" ) head(ci_vals) } # Logistic model # -------------- x <- glm(vs ~ wt, data = mtcars, family = "binomial") predictions <- predict(x, type = "link") ci_vals <- get_predicted_ci(x, predictions, ci_type = "prediction") head(ci_vals) ci_vals <- get_predicted_ci(x, predictions, ci_type = "confidence") head(ci_vals)