coefplot: Plotting Model Coefficients

In coefplot: Plots Coefficients from Fitted Models

Plotting Model Coefficients

Description

Provides an S3 generic method for plotting coefficients from a model so it can be extended to other model types.

A graphical display of the coefficients and standard errors from a fitted model

coefplot is the S3 generic method for plotting the coefficients from a fitted model.

This can be extended with new methods for other types of models not currently available.

Coefplot method for workflow objects

Coefplot method for parsnip objects

Usage

coefplot(model, ...)

## Default S3 method:
coefplot(
  model,
  title = "Coefficient Plot",
  xlab = "Value",
  ylab = "Coefficient",
  innerCI = 1,
  outerCI = 2,
  lwdInner = 1 + interactive * 2,
  lwdOuter = if (interactive) 1 else unname((Sys.info()["sysname"] != "Windows") * 0.5),
  pointSize = 3 + interactive * 5,
  color = "blue",
  shape = 16,
  cex = 0.8,
  textAngle = 0,
  numberAngle = 0,
  zeroColor = "grey",
  zeroLWD = 1,
  zeroType = 2,
  facet = FALSE,
  scales = "free",
  sort = c("natural", "magnitude", "alphabetical"),
  decreasing = FALSE,
  numeric = FALSE,
  fillColor = "grey",
  alpha = 1/2,
  horizontal = FALSE,
  factors = NULL,
  only = NULL,
  shorten = TRUE,
  intercept = TRUE,
  interceptName = "(Intercept)",
  coefficients = NULL,
  predictors = NULL,
  strict = FALSE,
  trans = identity,
  interactive = FALSE,
  newNames = NULL,
  plot = TRUE,
  ...
)

## S3 method for class 'lm'
coefplot(...)

## S3 method for class 'glm'
coefplot(...)

## S3 method for class 'workflow'
coefplot(model, ...)

## S3 method for class 'model_fit'
coefplot(model, ...)

## S3 method for class 'rxGlm'
coefplot(...)

## S3 method for class 'rxLinMod'
coefplot(...)

## S3 method for class 'rxLogit'
coefplot(...)

Arguments

model	A parsnip object
...	All arguments are passed on to coefplot.lm. Please see that function for argument information.
title	The name of the plot, if NULL then no name is given
xlab	The x label
ylab	The y label
innerCI	How wide the inner confidence interval should be, normally 1 standard deviation. If 0, then there will be no inner confidence interval.
outerCI	How wide the outer confidence interval should be, normally 2 standard deviations. If 0, then there will be no outer confidence interval.
lwdInner	The thickness of the inner confidence interval
lwdOuter	The thickness of the outer confidence interval
pointSize	Size of coefficient point
color	The color of the points and lines
shape	The shape of the points
cex	The text size multiplier, currently not used
textAngle	The angle for the coefficient labels, 0 is horizontal
numberAngle	The angle for the value labels, 0 is horizontal
zeroColor	The color of the line indicating 0
zeroLWD	The thickness of the 0 line
zeroType	The type of 0 line, 0 will mean no line
facet	logical; If the coefficients should be faceted by the variables, numeric coefficients (including the intercept) will be one facet. Currently not available.
scales	The way the axes should be treated in a faceted plot. Can be c("fixed", "free", "free_x", "free_y"). Currently not available.
sort	Determines the sort order of the coefficients. Possible values are c("natural", "magnitude", "alphabetical")
decreasing	logical; Whether the coefficients should be ascending or descending
numeric	logical; If true and factors has exactly one value, then it is displayed in a horizontal graph with continuous confidence bounds. Currently not available.
fillColor	The color of the confidence bounds for a numeric factor. Currently not available.
alpha	The transparency level of the numeric factor's confidence bound. Currently not available.
horizontal	logical; If the plot should be displayed horizontally. Currently not available.
factors	Vector of factor variables that will be the only ones shown
only	logical; If factors has a value this determines how interactions are treated. True means just that variable will be shown and not its interactions. False means interactions will be included.
shorten	logical or character; If FALSE then coefficients for factor levels will include their variable name. If TRUE coefficients for factor levels will be stripped of their variable names. If a character vector of variables only coefficients for factor levels associated with those variables will the variable names stripped. Currently not available.
intercept	logical; Whether the Intercept coefficient should be plotted
interceptName	Specifies name of intercept it case it is not the default of "(Intercept").
coefficients	A character vector specifying which factor coefficients to keep. It will keep all levels and any interactions, even if those are not listed.
predictors	A character vector specifying which coefficients to keep. Each individual coefficient can be specified. Use predictors to specify entire factors.
strict	If TRUE then predictors will only be matched to its own coefficients, not its interactions
trans	A transformation function to apply to the values and confidence intervals. identity by default. Use invlogit for binary regression.
interactive	If TRUE an interactive plot is generated instead of ggplot2
newNames	Named character vector of new names for coefficients
plot	logical; If the plot should be drawn, if false then a data.frame of the values will be returned

Details

Currently, methods are available for lm, glm and rxLinMod objects.

For more information on this function and it's arguments see coefplot.default

Pulls model element out of workflow object then calls coefplot.

Pulls model element out of parsnip object then calls coefplot.

Value

A ggplot2 object or data.frame. See details in coefplot.lm for more information

If plot is TRUE then a ggplot object is returned. Otherwise a data.frame listing coefficients and confidence bands is returned.

A ggplot object. See coefplot.lm for more information.

A ggplot object. See coefplot.lm for more information.

A ggplot object. See coefplot.lm for more information.

Methods (by class)

• default: Default method

• lm: lm

• glm: glm

• workflow: tidymodels workflows

• model_fit: parsnip

• rxGlm: rxGlm

• rxLinMod: rxLinMod

• rxLogit: rxLogit

Author(s)

Jared P. Lander

See Also

coefplot.lm coefplot.data.frame

lm glm ggplot coefplot plotcoef

Examples

data(diamonds)
head(diamonds)
model1 <- lm(price ~ carat + cut*color, data=diamonds)
model2 <- lm(price ~ carat*color, data=diamonds)
model3 <- glm(price > 10000 ~ carat*color, data=diamonds)
coefplot(model1)
coefplot(model2)
coefplot(model3)
coefplot(model1, predictors="color")
coefplot(model1, predictors="color", strict=TRUE)
coefplot(model1, coefficients=c("(Intercept)", "color.Q"))
coefplot(model1, predictors="cut", coefficients=c("(Intercept)", "color.Q"), strict=TRUE)
coefplot(model1, predictors="cut", coefficients=c("(Intercept)", "color.Q"), strict=FALSE)
coefplot(model1, predictors="cut", coefficients=c("(Intercept)", "color.Q"), 
strict=TRUE, newNames=c(color.Q="Color", "cut^4"="Fourth"))
coefplot(model1, predictors=c("(Intercept)", "carat"), newNames=c(carat="Size"))
coefplot(model1, predictors=c("(Intercept)", "carat"), 
newNames=c(carat="Size", "(Intercept)"="Constant"))


data(diamonds)
head(diamonds)
model1 <- lm(price ~ carat + cut*color, data=diamonds)
model2 <- lm(price ~ carat*color, data=diamonds)
coefplot(model1)
coefplot(model2)
coefplot(model1, predictors="color")
coefplot(model1, predictors="color", strict=TRUE)
coefplot(model1, coefficients=c("(Intercept)", "color.Q"))


model1 <- lm(price ~ carat + cut*color, data=diamonds)
coefplot(model1)

model2 <- glm(price > 10000 ~ carat + cut*color, data=diamonds, family=binomial(link="logit"))
coefplot(model2)
coefplot(model2, trans=invlogit)

## Not run: 
mod4 <- rxGlm(price ~ carat + cut + x, data=diamonds)
mod5 <- rxGlm(price > 10000 ~ carat + cut + x, data=diamonds, family="binomial")
coefplot(mod4)
coefplot(mod5)

## End(Not run)


## Not run: 
data(diamonds)
mod3 <- rxLinMod(price ~ carat + cut + x, data=diamonds)
coefplot(mod3)

## End(Not run)

## Not run: 
data(diamonds)
mod6 <- rxLogit(price > 10000 ~ carat + cut + x, data=diamonds)
coefplot(mod6)

## End(Not run)

coefplot documentation built on March 18, 2022, 7:58 p.m.