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2D Slicer

Source: R/slice_2d.R
slice_2d.Rd

Take a linear model and display a 2D slice in base R. This function creates a new plot comparing the response y variable with one predictor x variable and displaying a linear model line with all other predictor variables held at constant values. For unspecified variables, the x_axis is assumed to be the first predictor in the model and all other variable are imputed (mean for numeric, first level for factors).

Usage

slice_2d(
  model,
  x_axis = NULL,
  n = 100,
  caption = TRUE,
  back_transform = TRUE,
  interval = "none",
  ...,
  xaxis = NULL
)

Arguments

model

A linear model

x_axis

A non-required specification of the x-axis variable; default is the first x var in the lm. May also be an expression of the model's predictors, such as x_axis = "x * x_pos", in which case predictions are made at the model's own data points and placed at the expression's value.

n

Number of displayed points; default is 100

caption

Boolean to include caption with values of predictor variables not shown in the chart, TRUE by default

back_transform

How to map predictions onto the y-axis when the model's response is transformed. Default TRUE auto-detects from the model formula; FALSE turns back-transformation off; a one-argument function (e.g. exp) or a name ("log", "log10", "log2", "sqrt", "exp", "inverse") applies that transformation.

interval

Draw a shaded band around the line: "none" (default), or "confidence" / "prediction" for the corresponding predict.lm() interval.

...

Remaining vars (color, linetype, etc.)

xaxis

Deprecated; use x_axis instead.

Value

A 2D graph of a sliced model

Examples

model <- lm(log(mpg) ~ disp + hp, data = mtcars)
slice_2d(model)
#> X-axis not specified - Used first x variable as x-axis
#>     To specify, use 'x_axis = "var_name"' such as 'x_axis = "disp"'
#> Value for `hp` not specified - Used mean: 146.7
#>     To specify, use 'var_name = value', such as 'hp = 146.7'


# Choose the x-axis, hold disp at a value, and style the line
slice_2d(model, x_axis = "hp", disp = 250, n = 150, col = "blue", lwd = 2)


# With a 95% confidence band around the line
slice_2d(model, interval = "confidence")
#> X-axis not specified - Used first x variable as x-axis
#>     To specify, use 'x_axis = "var_name"' such as 'x_axis = "disp"'
#> Value for `hp` not specified - Used mean: 146.7
#>     To specify, use 'var_name = value', such as 'hp = 146.7'