Description Usage Arguments Details Value See Also Examples
View source: R/plot_coefficients.merMod.R
Plot fixed or random effects coefficients for merMod objects.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 
| model | The model. For example, lm, glm, gam, lme4, brms. | 
| order | The order of the plots- "increasing", "decreasing", or a numeric vector giving the order. The default is NULL, i.e. the default ordering. Not applied to random effects. | 
| sd_multi | For non-brmsfit objects, the multiplier that determines the width of the interval. Default is 2. | 
| keep_intercept | Default is FALSE. Intercepts are typically on a very different scale than covariate effects. | 
| palette | A scico palette. Default is 'bilbao'. | 
| ref_line | A reference line. Default is zero. | 
| trans | A transformation function to be applied to the coefficients (e.g. exponentiation). | 
| plot | Default is TRUE, but sometimes you just want the data. | 
| ranef | If applicable, whether to plot random effects instead of fixed effects. | 
| which_ranef | If plotting random effects, which one to plot. | 
| ... | Other arguments applied for specific methods. | 
This plots the fixed or random effects of lme4 objects. For more
information on the fixed effects, see plot_coefficients.It
requires the lme4 package.  The plot for random effects is
basically the dotplot demonstrated at ?lme4::ranef, but instead uses
ggplot2 so you would have a little easier time working with
it to do with as you wish (for multiple random effects, a list of ggplot
objects can be returned). Many of the options for fixed effects are
removed, as they either don't make much sense or for practical reasons.
A ggplot of the coefficients and their interval estimates. Or the data that would be used to create the plot.
Other model visualization: 
plot_coefficients.brmsfit(),
plot_coefficients.lm(),
plot_coefficients(),
plot_gam_2d(),
plot_gam_3d(),
plot_gam_check(),
plot_gam()
| 1 2 3 | library(lme4)
fit_mer = lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
plot_coefficients(fit_mer, ranef = TRUE, which_ranef = 'Subject')
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