# ggpoly: Get marginal effects for polynomial terms In strengejacke/ggtidyr: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

## Description

`ggpoly()` computes marginal effects for polynomial terms. The result is returned as tidy data frame.

## Usage

 `1` ```ggpoly(model, poly.term, ci.lvl = 0.95, ...) ```

## Arguments

 `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 `model`, as string. `ci.lvl` Numeric, the level of the confidence intervals. For `ggpredict()`, use `ci.lvl = NA`, if confidence intervals should not be calculated (for instance, due to computation time). `...` Further arguments passed down to `effect`.

## Value

A tibble (with `ggeffects` class attribute) with consistent data columns:

`x`

the values of the first term in `terms`, used as x-position in plots.

`predicted`

the predicted values, used as y-position in plots.

`conf.low`

the lower bound of the confidence interval for the predicted values.

`conf.high`

the upper bound of the confidence interval for the predicted values.

`group`

the grouping level from the second term in `terms`, used as grouping-aesthetics in plots.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```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) ```

strengejacke/ggtidyr documentation built on May 26, 2017, 9:58 p.m.