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Visualization of regression functions

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"), xtrans,  alpha=.05,
nn=101, cond=list(), jitter=FALSE, collapse=FALSE, plot=TRUE, ...)

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. Uses the lattice package. 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.

xtrans

(Optional) A function specifying a transformation for the horizontal axis. Note that, for model terms such as log(x), visreg automatically plots on the original axis (see examples).

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).

jitter

Adds a small amount of noise to xvar. Potentially useful if many observations have exactly the same value. Default is FALSE.

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.

...

Graphical parameters (e.g., ylab) can be passed to the function to customize the plots. If by=TRUE, lattice parameters can be passed, such as layout (see examples below).

Details

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

Value

A visreg or visregList 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 visregFit, visregLwr, and visregUpr, 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 visregPos; 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.

References

Author

Patrick Breheny and Woodrow Burchett

See also

https://pbreheny.github.io/visreg plot.visreg visreg2d visreg-faq

Examples

###################
## Linear models ##
###################

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



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.in1 <- lm(Ozone~ Solar.R + Wind*Heat, data=airquality)
visreg(fit.in1, "Wind", by="Heat")

visreg(fit.in1, "Heat", by="Wind")

visreg(fit.in1, "Wind", by="Heat", type="contrast")

visreg(fit.in1, "Heat", by="Wind", breaks=6)

visreg(fit.in1, "Heat", by="Wind", breaks=c(0,10,20))


## Overlay
visreg(fit.in1, "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)")
visreg(fit, "lwt", scale="response", partial=FALSE,
       xlab="Mother's Weight", ylab="P(low birthweight)", rug=2)


## Proportional hazards
require(survival)
#> Loading required package: survival
data(ovarian)
#> Warning: data set ‘ovarian’ not found
ovarian$rx <- factor(ovarian$rx)
fit <- coxph(Surv(futime, fustat) ~ age + rx, data=ovarian)
visreg(fit, "age", ylab="log(Hazard ratio)")


## Robust regression
require(MASS)
#> Loading required package: MASS
fit <- rlm(Ozone ~ Solar.R + Wind*Heat, data=airquality)
visreg(fit, "Wind", cond=list(Heat="Mild"))
#> Warning:   Note that you are attempting to plot a 'main effect' in a model that contains an
#>   interaction.  This is potentially misleading; you may wish to consider using the 'by'
#>   argument.
#> Conditions used in construction of plot
#> Solar.R: 207
#> Heat: Mild

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

## Return raw components of plot
v <- visreg(fit, "Wind", cond=list(Heat="Mild"))
#> Warning:   Note that you are attempting to plot a 'main effect' in a model that contains an
#>   interaction.  This is potentially misleading; you may wish to consider using the 'by'
#>   argument.
#> Conditions used in construction of plot
#> Solar.R: 207
#> Heat: Mild