plot_lm_predict: Plot data and predictions from linear model

In grafify: Easy Graphs for Data Visualisation and Linear Models for ANOVA

Plot data and predictions from linear model

Description

This function takes a linear model, and up to three variables and plots observe data (circles) and model predictions (squares). If the X-variable is categorical, a box and whiskers plot is overlaid. A variable (ByFactor) can be used for faceting.

Usage

plot_lm_predict(
  Model,
  xcol,
  ycol,
  ByFactor,
  obs_size = 2,
  obs_alpha = 0.3,
  pred_size = 2,
  pred_alpha = 0.8,
  linethick,
  base_size = 15,
  ...
)

Arguments

Model	a linear model saved with simple_model, mixed_model or ga_model.
xcol	variable along the X axis (should match one of the dependent variables in model exactly).
ycol	independent variable along the Y axis (should match independent variable in model exactly).
ByFactor	optional faceting variable (should match one of the variables in model exactly).
obs_size	size of symbols for observed data (default = 2).
obs_alpha	opacity of symbols for observed data (default = 0.3).
pred_size	size of symbols for predicted data (default = 2).
pred_alpha	opacity of symbols for predicted data (default = 0.8).
linethick	thickness of border lines for boxes and symbols (default is base_size/20).
base_size	base fontsize for theme_grafify
...	any other parameters to be passed to theme_grafify

Value

This function returns a ggplot2 object of class "gg" and "ggplot".

Examples

#fit a model
deathm1 <- mixed_model(data_2w_Tdeath, 
"PI", c("Genotype", "Time"), 
"Experiment")
#plot model
plot_lm_predict(deathm1,
Genotype, PI, Time)
#fit zooplankton data
z1 <- ga_model(data = data_zooplankton,
Y_value = "log(density_adj)",
Fixed_Factor = "taxon",
Smooth_Factor = "day")

#plot fitted data
plot_lm_predict(Model = z1,
xcol = day, 
ycol = `log(density_adj)`,
ByFactor = taxon)

grafify documentation built on May 29, 2024, 3:49 a.m.