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Visualization of regression functions

Source: R/visreg.R
visreg.Rd

A function for visualizing regression models quickly and easily. Default plots contain a confidence band, prediction line, and partial residuals. Factors, transformations, conditioning, interactions, and a variety of other options are supported. The visreg function performs the calculations and, if plot=TRUE (the default), these calculations are passed to plot.visreg for plotting.

Usage

visreg(
  fit,
  xvar,
  by,
  breaks = 3,
  type = c("conditional", "contrast"),
  data = NULL,
  trans = I,
  scale = c("linear", "response"),
  alpha = 0.05,
  nn = 101,
  cond = list(),
  collapse = FALSE,
  plot = TRUE,
  predict = list(),
  ...
)

Arguments

fit

The fitted model object you wish to visualize. Any object with 'predict' and 'model.frame' methods are supported, including lm, glm, gam, rlm, coxph, and many more.

xvar

Character string specifying the variable to be put on the x-axis of your plot. Both continuous variables and factors are supported.

by

(Optional) A variable allowing you to divide your plot into cross-sections based on levels of the by variable; particularly useful for visualizing models with interactions. Supplied as a character string. Cross-sections are displayed as facets of a ggplot2 plot. Both continuous variables and factors are supported.

breaks

If a continuous variable is used for the by option, the breaks argument controls the values at which the cross-sections are taken. By default, cross-sections are taken at the 10th, 50th, and 90th quantiles. If breaks is a single number, it specifies the number of breaks. If breaks is a vector of numbers, it specifies the values at which the cross-sections are to be taken. Each partial residuals appears exactly once in the plot, in the panel it is closest to.

type

The type of plot to be produced. The following options are supported:

  • If "conditional" is selected, the plot returned shows the value of the variable on the x-axis and the change in response on the y-axis, holding all other variables constant (by default, median for numeric variables and most common category for factors).

  • If "contrast" is selected, the plot returned shows the effect on the expected value of the response by moving the x variable away from a reference point on the x-axis (for numeric variables, this is taken to be the mean). For more details, see references.

data

The data frame used to fit the model. Typically, visreg() can figure out where the data is, so it is not necessary to provide this. In some cases, however, the data set cannot be located and must be supplied explicitly.

trans

(Optional) A function specifying a transformation for the vertical axis.

scale

By default, the model is plotted on the scale of the linear predictor. If scale='response' for a glm, the inverse link function will be applied so that the model is plotted on the scale of the original response.

alpha

Alpha level (1-coverage) for the confidence band displayed in the plot (default: 0.05).

nn

Controls the smoothness of the line and confidence band. Increasing this number will add to the computational burden, but produce a smoother plot (default: 101).

cond

Named list specifying conditional values of other explanatory variables. By default, conditional plots in visreg are constructed by filling in other explanatory variables with the median (for numeric variables) or most common category (for factors), but this can be overridden by specifying their values using cond (see examples).

collapse

If the predict method for fit returns a matrix, should this be returns as multiple visreg objects bound together as a list (collapse=FALSE) or collapsed down to a single visreg object (collapse=TRUE).

plot

Send the calculations to plot.visreg()? Default is TRUE.

predict

Named list of additional arguments to pass to predict() (e.g., list(re.form = NA) for a mixed model). Most models never need this; it exists as an escape hatch since predict() methods vary by model class and aren't otherwise accessible from visreg().

...

Graphical parameters (e.g., partial) to pass to plot.visreg() (see examples below).

Value

A visreg or visreg_list object (which is simply a list of visreg objects). A visreg` object has three components:

fit

A data frame with nn rows containing the fit of the model as xvar varies, along with lower and upper confidence bounds (named visreg_fit, visreg_lwr, and visreg_upr, respectively). The fitted matrix of coefficients.

res

A data frame with n rows, where n is the number of observations in the original data set used to model. This frame contains information about the residuals, named visregReg and visreg_pos; the latter records whether the residual was positive or negative.

meta

Contains meta-information needed to construct plots, such as the name of the x and y variables, whether there were any by variables, etc.

Note that if plot = TRUE (the default), then the visreg object is passed to plot.visreg() and a ggplot object is returned instead.

Details

See plot.visreg() for plotting options, such as changing the appearance of points, lines, confidence bands, etc.

References

See also

plot.visreg() for plotting options, visreg2d() for creating two-dimensional visreg objects, and the package website for detailed explanations and examples.

Examples

library(ggplot2)

# --- Linear models ----------------------------------------

## Basic
fit <- lm(Ozone ~ Solar.R + Wind + Temp, data = airquality)
visreg(fit, "Wind", type = "contrast")

visreg(fit, "Wind", type = "conditional")


## Factors
airquality$Heat <- cut(airquality$Temp, 3, labels = c("Cool", "Mild", "Hot"))
fit.heat <- lm(Ozone ~ Solar.R + Wind + Heat, data = airquality)
visreg(fit.heat, "Heat", type = "contrast")

visreg(fit.heat, "Heat", type = "conditional")


## Transformations
fit1 <- lm(Ozone ~ Solar.R + Wind + Temp + I(Wind^2), data = airquality)
fit2 <- lm(log(Ozone) ~ Solar.R + Wind + Temp, data = airquality)
fit3 <- lm(log(Ozone) ~ Solar.R + Wind + Temp + I(Wind^2), data = airquality)
visreg(fit1, "Wind")

visreg(fit2, "Wind", trans = exp) + ylab("Ozone")

visreg(fit3, "Wind", trans = exp) + ylab("Ozone")


## Conditioning
visreg(fit, "Wind", cond = list(Temp = 50))

visreg(fit, "Wind", print_cond = TRUE)
#> Conditions used in construction of plot
#> Solar.R: 207
#> Temp: 79

visreg(fit, "Wind", cond = list(Temp = 100))


## Interactions
fit_int <- lm(Ozone ~ Solar.R + Wind * Heat, data = airquality)
visreg(fit_int, "Wind", by = "Heat")

visreg(fit_int, "Heat", by = "Wind")

visreg(fit_int, "Wind", by = "Heat", type = "contrast")

visreg(fit_int, "Heat", by = "Wind", breaks = 6)

visreg(fit_int, "Heat", by = "Wind", breaks = c(0, 10, 20))


## Overlay
visreg(fit_int, "Wind", by = "Heat", overlay = TRUE)



# --- Nonlinear models -------------------------------------

## Logistic regression
data("birthwt", package = "MASS")
birthwt$race <- factor(birthwt$race, labels = c("White", "Black", "Other"))
birthwt$smoke <- factor(birthwt$smoke, labels = c("Nonsmoker", "Smoker"))
fit <- glm(low ~ age + race + smoke + lwt, data = birthwt, family = "binomial")
visreg(fit, "lwt") +
  xlab("Mother's Weight") +
  ylab("Log odds (low birthweight)")

visreg(fit, "lwt", scale = "response", partial = FALSE) +
  xlab("Mother's Weight") +
  ylab("P(low birthweight)")


## Proportional hazards
data(cancer, package="survival")
ovarian$rx <- factor(ovarian$rx)
fit <- survival::coxph(
  survival::Surv(futime, fustat) ~ age + rx,
  data = ovarian
)
visreg(fit, "age") + ylab("log(Hazard ratio)")


## Robust regression
fit <- MASS::rlm(Ozone ~ Solar.R + Wind * Heat, data = airquality)
visreg(fit, "Wind", cond = list(Heat = "Mild"))


## And more...; anything with a 'predict' method should work

## Return raw components of plot
v <- visreg(fit, "Wind", cond = list(Heat = "Mild"))