Package 'visreg' reference manual

Title:	Visualization of Regression Models
Description:	Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.)
Authors:	Patrick Breheny [aut, cre] , Woodrow Burchett [ctb]
Maintainer:	Patrick Breheny <[email protected]>
License:	GPL-3
Version:	2.7.0.15
Built:	2024-10-29 04:48:09 UTC
Source:	https://github.com/pbreheny/visreg

Visualization of regression functions

Description

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 'plot.visreg()' function accepts a 'visreg' or 'visregList' object as calculated by [visreg()] and creates the plot.

Usage

## S3 method for class 'visreg'
plot(
  x,
  overlay = FALSE,
  print.cond = FALSE,
  whitespace = 0.2,
  partial = identical(x$meta$trans, I),
  band = TRUE,
  rug = ifelse(partial, 0, 2),
  strip.names = is.numeric(x$fit[, x$meta$by]),
  legend = TRUE,
  top = c("line", "points"),
  gg = FALSE,
  line.par = NULL,
  fill.par = NULL,
  points.par = NULL,
  ...
)
## S3 method for class 'visreg'
plot(
  x,
  overlay = FALSE,
  print.cond = FALSE,
  whitespace = 0.2,
  partial = identical(x$meta$trans, I),
  band = TRUE,
  rug = ifelse(partial, 0, 2),
  strip.names = is.numeric(x$fit[, x$meta$by]),
  legend = TRUE,
  top = c("line", "points"),
  gg = FALSE,
  line.par = NULL,
  fill.par = NULL,
  points.par = NULL,
  ...
)

Arguments

`x`	A 'visreg' or 'visregList' object; see [visreg()].
`overlay`	By default, when 'by' is specified, separate panels are used to display each cross-section. If 'overlay=TRUE', these cross-sections are overlaid on top of each other in a single plot.
`print.cond`	If 'print.cond=TRUE', the explanatory variable values conditioned on in a conditional plot are printed to the console (default: 'FALSE'). If 'print.cond=TRUE' and 'type="contrast"', the conditions will still be printed, but they have no bearing on the plot unless interactions are present.
`whitespace`	When 'xvar' is a factor, 'whitespace' determines the amount of space in between factors on the x-axis. Default is 0.2, meaning that 20 percent of the horizontal axis is whitespace.
`partial`	If 'partial=TRUE' (the default), partial residuals are shown on the plot.
`band`	If 'band=TRUE' (the default), confidence bands are shown on the plot.
`rug`	By default, partial residuals are plotted. Alternatively, a [rug()] may be plotted along the horizontal axis instead. Setting 'rug=TRUE' turns off partial residuals by default; if one wants both to be plotted, both 'rug=TRUE' and 'partial=TRUE' need to be specified. Two types of rug plots are available. If 'rug=1' or 'rug=TRUE', then a basic rug is drawn on the bottom. If `rug=2`, then separate rugs are drawn on the top for observations with positive residuals and on the bottom for observations with negative residuals. Such plots are particularly useful in logistic regression (see examples).
`strip.names`	When 'by=TRUE', 'strip.names=TRUE' adds the name of the 'by' variable to the strip at the top of each panel. Default is 'FALSE' for factors and 'TRUE' for numeric 'by' variables. 'strip.names' can also be a character vector, in which case it replaces the strip names altogether with values chosen by the user.
`legend`	For overlay plots, ('overlay=TRUE'), should visreg create a legend? If 'legend=TRUE' (the default), a legend is placed in the top margin.
`top`	By default, the fitted line is plotted on top of the partial residuals; usually this is preferable, but it does run the risk of obscuring certain residuals. To change this behavior and plot the partial residuals on top, specify ‘top=’points''.
`gg`	By default ('gg=FALSE'), 'visreg' will use the lattice package to render the plot if multiple panels are required. If 'gg=TRUE', it will use the ggplot2 package instead, provided that it is installed.
`line.par`	List of parameters (see [par()]) to pass to 'lines(...)' or [ggplot2::geom_line()] when lines are plotted.
`fill.par`	List of parameters (see [par()]) to pass to 'polygon(...)' or [ggplot2::geom_polygon()] when shaded confidence regions are plotted.
`points.par`	List of parameters ([par()]) to pass to 'points(...)' or [ggplot2::geom_point()] when partial residuals are plotted.
`...`	Graphical parameters can be passed to the function to customize the plots. If 'by=TRUE', lattice parameters can be passed, such as 'layout' (see examples below).

Author(s)

Patrick Breheny and Woodrow Burchett

References

Breheny P and Burchett W. (2017) Visualization of regression models using visreg. *R Journal*, **9**: 56-71. doi:10.32614/RJ-2017-046

Examples

fit <- lm(Ozone ~ Solar.R + Wind + Temp,data=airquality)
visreg(fit, "Wind", line=list(col="red"), points=list(cex=1, pch=1))

## Changing appearance
visreg(fit, "Wind", line=list(col="red"), points=list(cex=1, pch=1))

## See ?visreg and https://pbreheny.github.io/visreg for more examples
fit <- lm(Ozone ~ Solar.R + Wind + Temp,data=airquality)
visreg(fit, "Wind", line=list(col="red"), points=list(cex=1, pch=1))

## Changing appearance
visreg(fit, "Wind", line=list(col="red"), points=list(cex=1, pch=1))

## See ?visreg and https://pbreheny.github.io/visreg for more examples

Visualization of regression functions for two variables

Description

Plot method for visualizing how two variables interact to affect the response in regression models.

Usage

## S3 method for class 'visreg2d'
plot(
  x,
  plot.type = c("image", "persp", "rgl", "gg"),
  xlab,
  ylab,
  zlab,
  color,
  print.cond = FALSE,
  whitespace = 0.2,
  ...
)
## S3 method for class 'visreg2d'
plot(
  x,
  plot.type = c("image", "persp", "rgl", "gg"),
  xlab,
  ylab,
  zlab,
  color,
  print.cond = FALSE,
  whitespace = 0.2,
  ...
)

Arguments

`x`	A [visreg2d()] object.
`plot.type`	The style of plot to be produced. The following options are supported: * 'image': a filled contour * 'gg': a filled contour plot using ggplot2 * 'persp': a 3 dimensional perspective plot * 'rgl': a version of the perspective plot that can be rotated (requires the rgl package to be installed)
`xlab`	Axis label for x variable
`ylab`	Axis label for y variable
`zlab`	Axis label for outcome
`color`	For ‘plot.type=’persp'‘ or 'plot.type=’rgl'', the color of the surface. For ‘plot.type=’image'‘ or 'plot.type=’gg'', a vector of colors used to establish a color palette.
`print.cond`	If 'print.cond==TRUE', the explanatory variable values conditioned on in a conditional plot are printed to the console (default: `FALSE`). If 'print.cond==TRUE' and 'type=="contrast"', the conditions will still be printed, but they have no bearing on the plot unless interactions are present.
`whitespace`	When 'xvar' or 'yvar' is a factor, 'whitespace' determines the amount of space in between the factors. Default is 0.2, meaning that 20 percent of the axis is whitespace.
`...`	Graphical parameters can be passed to the function to customize the plots.

Author(s)

Patrick Breheny and Woodrow Burchett

References

Breheny P and Burchett W. (2017) Visualization of regression models using visreg. *R Journal*, **9**: 56-71. doi:10.32614/RJ-2017-046

Examples

fit <- lm(Ozone ~ Solar.R + Wind + Temp + I(Wind^2) + I(Temp^2) +
I(Wind*Temp)+I(Wind*Temp^2) + I(Temp*Wind^2) + I(Temp^2*Wind^2),
data=airquality)

visreg2d(fit, x="Wind", y="Temp", plot.type="image")
visreg2d(fit, x="Wind", y="Temp", plot.type="image",
         color=c("purple", "green", "red"))
visreg2d(fit, x="Wind", y="Temp", plot.type="persp")

## Requires the rgl package

visreg2d(fit,x="Wind",y="Temp",plot.type="rgl")


## Requires the ggplot2 package

visreg2d(fit, x="Wind", y="Temp", plot.type="gg")

fit <- lm(Ozone ~ Solar.R + Wind + Temp + I(Wind^2) + I(Temp^2) +
I(Wind*Temp)+I(Wind*Temp^2) + I(Temp*Wind^2) + I(Temp^2*Wind^2),
data=airquality)

visreg2d(fit, x="Wind", y="Temp", plot.type="image")
visreg2d(fit, x="Wind", y="Temp", plot.type="image",
         color=c("purple", "green", "red"))
visreg2d(fit, x="Wind", y="Temp", plot.type="persp")

## Requires the rgl package

visreg2d(fit,x="Wind",y="Temp",plot.type="rgl")


## Requires the ggplot2 package

visreg2d(fit, x="Wind", y="Temp", plot.type="gg")

Subset a visreg object

Description

Subset a visreg object so that only a portion of the full model is plotted.

Usage

## S3 method for class 'visreg'
subset(x, sub, ...)
## S3 method for class 'visreg'
subset(x, sub, ...)

Arguments

`x`	A 'visreg' object.
`sub`	Logical expression indicating elements to keep, as in [subset()]
`...`	Not used.

Examples

# Fit a model and construct a visreg object
airquality$Heat <- cut(airquality$Temp,3,labels=c("Cool","Mild","Hot"))
fit <- lm(Ozone~ Solar.R + Wind*Heat,data=airquality)
v <- visreg(fit, "Wind", by="Heat", plot=FALSE)

# Plot only certain levels
vv <- subset(v, Heat %in% c("Cool", "Hot"))
plot(vv)

# Plot only up to wind 15 mph
vv <- subset(v, Wind < 15)
plot(vv)
# Fit a model and construct a visreg object
airquality$Heat <- cut(airquality$Temp,3,labels=c("Cool","Mild","Hot"))
fit <- lm(Ozone~ Solar.R + Wind*Heat,data=airquality)
v <- visreg(fit, "Wind", by="Heat", plot=FALSE)

# Plot only certain levels
vv <- subset(v, Heat %in% c("Cool", "Hot"))
plot(vv)

# Plot only up to wind 15 mph
vv <- subset(v, Wind < 15)
plot(vv)

Visualization of regression functions

Description

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

Author(s)

Patrick Breheny and Woodrow Burchett

References

https://pbreheny.github.io/visreg
Breheny, P. and Burchett, W. (2017), Visualizing regression models using visreg. https://journal.r-project.org/archive/2017/RJ-2017-046/index.html

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)
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)
data(ovarian)
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)
fit <- 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"))

###################
## 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)
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)
data(ovarian)
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)
fit <- 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"))

Visualization of regression functions for two variables

Description

A function used to visualize how two variables interact to affect the response in regression models.

Usage

visreg2d(
  fit,
  xvar,
  yvar,
  type = c("conditional", "contrast"),
  data = NULL,
  trans = I,
  scale = c("linear", "response"),
  nn = 99,
  cond = list(),
  plot = TRUE,
  ...
)
visreg2d(
  fit,
  xvar,
  yvar,
  type = c("conditional", "contrast"),
  data = NULL,
  trans = I,
  scale = c("linear", "response"),
  nn = 99,
  cond = list(),
  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.
`yvar`	Character string specifying the variable to be put on the y-axis of your plot. Both continuous variables and factors are supported.
`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.
`nn`	Resolution of the three dimensional plot. Higher values will results in a smoother looking plot.
`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).
`plot`	Send the calculations to `plot.visreg2d`, producing a plot? Default is TRUE.
`...`	Graphical parameters (e.g., `ylab`) can be passed to the function to customize the plots.

Value

A visreg2d object consisting of:

`x`	Values of `xvar` to be plotted
`y`	Values of `yvar` to be plotted
`z`	Values of outcome to be plotted
`meta`	Meta-information needed to construct plots, such as the name of the x and y variables.

Author(s)

Patrick Breheny and Woodrow Burchett

References

https://pbreheny.github.io/visreg
Breheny, P. and Burchett, W. (2017), Visualizing regression models using visreg. https://journal.r-project.org/archive/2017/RJ-2017-046/index.html

Examples


fit <- lm(Ozone ~ Solar.R + Wind + Temp + I(Wind^2) + I(Temp^2) +
I(Wind*Temp)+I(Wind*Temp^2) + I(Temp*Wind^2) + I(Temp^2*Wind^2),
data=airquality)

visreg2d(fit, x="Wind", y="Temp", plot.type="image")
visreg2d(fit, x="Wind", y="Temp", plot.type="persp")

## Requires the rgl package
## Not run: 
visreg2d(fit, x="Wind", y="Temp", plot.type="rgl")

## End(Not run)

## Requires the ggplot2 package
## Not run: 
visreg2d(fit, x="Wind", y="Temp", plot.type="gg")

## End(Not run)


fit <- lm(Ozone ~ Solar.R + Wind + Temp + I(Wind^2) + I(Temp^2) +
I(Wind*Temp)+I(Wind*Temp^2) + I(Temp*Wind^2) + I(Temp^2*Wind^2),
data=airquality)

visreg2d(fit, x="Wind", y="Temp", plot.type="image")
visreg2d(fit, x="Wind", y="Temp", plot.type="persp")

## Requires the rgl package
## Not run: 
visreg2d(fit, x="Wind", y="Temp", plot.type="rgl")

## End(Not run)

## Requires the ggplot2 package
## Not run: 
visreg2d(fit, x="Wind", y="Temp", plot.type="gg")

## End(Not run)

Join multiple visreg objects together in a list

Description

This function takes multiple visreg objects, from separate calls to [visreg()], and joins them together in a single object. The single object will be of type 'visregList' unless 'collapse=TRUE' is specified, in which case the list will be collapsed back down into a single 'visreg' object.

Usage

visregList(..., labels, collapse = FALSE)
visregList(..., labels, collapse = FALSE)

Arguments

`...`	'visreg' objects, as produced by calls to [visreg()].
`labels`	A character vector with length corresponding to the number of 'visreg' objects passed to the function that provides labels for the different objects in subsequent plots. Only has an effect if 'collapse=TRUE'.
`collapse`	If 'TRUE', the resulting object will be collapsed down into a single 'visreg' object. If 'FALSE', the resulting object will be a 'visregList'.

Value

A 'visreg' or 'visregList' object, depending on the value of 'collapse'.

Author(s)

Patrick Breheny

Examples

fit <- lm(Ozone ~ Solar.R + Wind + Temp, data=airquality)
v1 <- visreg(fit, "Wind", plot=FALSE, alpha=0.2)
v2 <- visreg(fit, "Wind", plot=FALSE, alpha=0.01)
vv1 <- visregList(v1, v2, collapse=FALSE)
vv2 <- visregList(v1, v2, collapse=TRUE,
                  labels=c("Confidence: 0.80", "Confidence: 0.99"))
op <- par(mfrow=c(1,2))
plot(vv1)
par(op)
plot(vv2)

fit <- lm(Ozone ~ Solar.R + Wind + Temp, data=airquality)
v1 <- visreg(fit, "Wind", plot=FALSE, alpha=0.2)
v2 <- visreg(fit, "Wind", plot=FALSE, alpha=0.01)
vv1 <- visregList(v1, v2, collapse=FALSE)
vv2 <- visregList(v1, v2, collapse=TRUE,
                  labels=c("Confidence: 0.80", "Confidence: 0.99"))
op <- par(mfrow=c(1,2))
plot(vv1)
par(op)
plot(vv2)

Package 'visreg'

Help Index

Visualization of regression functions

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Visualization of regression functions for two variables

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Subset a visreg object

Description

Usage

Arguments

Examples

Visualization of regression functions

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Visualization of regression functions for two variables

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Join multiple visreg objects together in a list

Description

Usage

Arguments

Value

Author(s)

See Also

Examples