ggpoly() computes marginal effects for polynomial terms.
The result is returned as tidy data frame.
A fitted model object, or a list of model objects. Any model that is supported by the effects-package should work.
Name of the polynomial term in
Numeric, the level of the confidence intervals. For
Further arguments passed down to
A tibble (with
ggeffects class attribute) with consistent data columns:
the values of the first term in
terms, used as x-position in plots.
the predicted values, used as y-position in plots.
the lower bound of the confidence interval for the predicted values.
the upper bound of the confidence interval for the predicted values.
the grouping level from the second term in
terms, used as grouping-aesthetics in plots.
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data(efc) fit <- lm( tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3), data = efc ) dat <- ggpoly(fit, "e17age") # this would give the same result ggpredict(fit, "e17age") library(ggplot2) ggplot(dat, aes(x, predicted)) + stat_smooth(se = FALSE) + geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .15) + labs(x = get_x_title(dat), y = get_y_title(dat)) ## Not run: # or: plot(dat) ## End(Not run)
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