Plot Observed vs. Fitted Values to Check Linearity

Generates a scatter plot of observed values versus fitted values from a linear model, with optional prediction intervals and identification of outlier points. The plot includes a reference line y = x for assessing linearity.

Usage

lm_plot.fit(
  mdl,
  opt = list(),
  parm = list(),
  df = lm_plot.df(mdl),
  plts = list()
)

Arguments

mdl: A fitted model object (typically from lm).
opt: List of options, where pred_intvl_pts (numeric, default = 100) is used for prediction interval bounds of fitted values (0 to skip).
parm: List of plotting parameters, usually from lm_plot.parms().
df: Data frame with augmented model data. Defaults to lm_plot.df(mdl).
plts: List of ggplot objects to which this plot will be added.

Value

A list containing:

mdl Fitted model object,
opt Options used, including pred_intvl_pts,
parm Parameter list with autocorrelation test results added,
df Data frame used for plotting,
plts List of ggplot objects, including the $fit element.

Details

The plot visualizes observed versus fitted values, includes a diagonal reference line, marks outliers, and can optionally display loess-smoothed prediction intervals. Outlier and regular points can be labeled. This plot is useful for visually assessing linearity and model fit quality.

Examples

mdl <- lm(Sepal.Length ~ Sepal.Width, data = iris)
result <- lm_plot.fit(mdl)
print(result$plts$fit)