| ggpoly {ggeffects} | R Documentation |
ggpoly() computes marginal effects for polynomial terms.
The result is returned as tidy data frame.
ggpoly(model, poly.term, ci.lvl = 0.95, ...)
model |
A fitted model object, or a list of model objects. Any model that is supported by the effects-package should work. |
poly.term |
Name of the polynomial term in |
ci.lvl |
Numeric, the level of the confidence intervals. For |
... |
Further arguments passed down to |
ggpoly() is just an alternative call to ggpredict() for
polynomial model terms. It internally calls effect() from effects
(like ggeffect() does), hence it may work for certain models that are not
yet supported by ggpredict(). Otherwise, there should be no difference
in the results from ggpoly() and ggpredict(), or at least only
minor differences (as effect() holds categorical covariates constant at
other values, see 'Details' in ggeffect).
A tibble (with ggeffects class attribute) with consistent data columns:
xthe values of the first term in terms, used as x-position in plots.
predictedthe predicted values, used as y-position in plots.
conf.lowthe lower bound of the confidence interval for the predicted values.
conf.highthe upper bound of the confidence interval for the predicted values.
groupthe grouping level from the second term in terms, used as grouping-aesthetics in plots.
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)