We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials. Plotly is a free and open-source graphing library for R. eq.label.Visualize regression in Tidymodels with Plotly p.label., sep = "~`,`~")), # adds R^2 and p-valueĪlso works well with facet_wrap() when you have multiple groups df$group <- rep(1:2,50) eq.label.), # adds equation to linear regressionĬould also add p-value to the figure above ggplot(data = df, aes(x = x, y = y)) + Stat_cor(aes(label = paste(.rr.label.)), # adds R^2 value Stat_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) + I prefer using ggpubr because it does not require custom functions such as the top answer to this question. Similar to and answers except using ggplot2 and ggpubr. Y = 11.3382963933174 + 2.5893419 * x + 0.1002227 * zĪnd if we ask for a latex equation, rounding the parameters to 3 digits: print_equation(model = lm_mod, latex = TRUE, digits= 3) Print_equation(model = lm_mod, latex = FALSE) Print_equation(model = lm_mod, latex = TRUE) I also added an option to output it as latex so you can use this function in a rmarkdown like this: argument pass its values to the format function. The model argument expects an lm object, the latex argument is a boolean to ask for a simple character or a latex-formated equation, and the. Inspired by the equation style provided in this answer, a more generic approach (more than one predictor + latex output as option) can be: print_equation= function(model, latex= FALSE. Unfortunately, this doesn't work with facet_wrap or facet_grid. Ggrepel::geom_text_repel(label=rownames(mtcars)) + To allow use to customize the regression formula (instead of fixed as y and x as literal variable names), and added the p-value into the printout as well (as T commented), here is the mod: lm_eqn |t|)'], digits=1) # a single fit with grouped data as asked by = dfg, aes(x = x, y = y)) + # as shown here, aes() has to be used instead of use_label(). # flexibility is needed like different separators at different positions, # use_label() makes it easier to create the mappings, but when more Ggplot(data = dfg, aes(x = x, y = y, linetype = group, grp.label = group)) + # A group label is available, for grouped data Ggplot(data = dfg, aes(x = x, y = y, colour = group)) + Labs(x = expression(italic(z)), y = expression(italic(h))) + # variable substitution as asked by same labels in equation and axes # adding a hat as asked by and = df, aes(x = x, y = y)) + Stat_poly_eq(formula = y ~ poly(x, 2, raw = TRUE), use_label("eq")) + Stat_poly_line(formula = y ~ poly(x, 2, raw = TRUE)) + # adding separate labels with equation and R2 # assembling a single label with R2, its confidence interval, and n # assembling a single label with equation, adjusted R2, F-value, n, P-value # assembling a single label with equation and R2 # using default formula, label and methods I have omitted in all code examples the additional arguments to stat_poly_line() as they are irrelevant to the question of adding labels. In the examples I use stat_poly_line() instead of stat_smooth() as it has the same defaults as stat_poly_eq() for method and formula. Although use of aes() and after_stat() remains unchanged, use_label() makes coding of mappings and assembly of labels simpler. The main change is the assembly of labels and their mapping using function use_label() added in 'ggpmisc' (=0.5.0). I have updated this answer for 'ggpmisc' (>= 0.5.0) and 'ggplot2' (>= 3.4.0) on. Each eq stat has a matching line drawing stat.) (Statistics stat_ma_eq() and stat_quant_eq() work similarly and support major axis regression and quantile regression, respectively. Statistic stat_poly_eq() in my package ggpmisc makes it possible to add text labels to plots based on a linear model fit.
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