multiplot: Plot multiple coefplots

Description

Plot the coefficients from multiple models

Usage

multiplot(
  ...,
  title = "Coefficient Plot",
  xlab = "Value",
  ylab = "Coefficient",
  innerCI = 1,
  outerCI = 2,
  lwdInner = 1,
  lwdOuter = (Sys.info()["sysname"] != "Windows") * 0.5,
  pointSize = 3,
  dodgeHeight = 1,
  color = "blue",
  shape = 16,
  linetype = 1,
  cex = 0.8,
  textAngle = 0,
  numberAngle = 90,
  zeroColor = "grey",
  zeroLWD = 1,
  zeroType = 2,
  single = TRUE,
  scales = "fixed",
  ncol = length(unique(modelCI$Model)),
  sort = c("natural", "normal", "magnitude", "size", "alphabetical"),
  decreasing = FALSE,
  names = NULL,
  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,
  newNames = NULL,
  plot = TRUE,
  drop = FALSE,
  by = c("Coefficient", "Model"),
  plot.shapes = FALSE,
  plot.linetypes = FALSE,
  legend.position = c("bottom", "right", "left", "top", "none"),
  secret.weapon = FALSE,
  legend.reverse = FALSE,
  trans = identity
)

Arguments

…

Models to be plotted

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

dodgeHeight

Amount of vertical dodging

color

The color of the points and lines

shape

The shape of the points

linetype

The type of line drawn for the standard errors

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

single

logical; If TRUE there will be one plot with the points and bars stacked, otherwise the models will be displayed in separate facets

scales

The way the axes should be treated in a faceted plot. Can be c("fixed", "free", "free_x", "free_y")

ncol

The number of columns that the models should be plotted in

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

names

Names for models, if NULL then they will be named after their inputs

numeric

logical; If true and factors has exactly one value, then it is displayed in a horizontal graph with continuous confidence bounds.

fillColor

The color of the confidence bounds for a numeric factor

alpha

The transparency level of the numeric factor's confidence bound

horizontal

logical; If the plot should be displayed horizontally

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.

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

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

drop

logical; if TRUE then models without valid coefficients to show will not be plotted

If "Coefficient" then a normal multiplot is plotted, if "Model" then the coefficients are plotted along the axis with one for each model. If plotting by model only one coefficient at a time can be selected. This is called the secret weapon by Andy Gelman.

plot.shapes

If TRUE points will have different shapes for different models

plot.linetypes

If TRUE lines will have different shapes for different models

legend.position

position of legend, one of "left", "right", "bottom", "top", "none"

secret.weapon

If this is TRUE and exactly one coefficient is listed in coefficients then Andy Gelman's secret weapon is plotted.

legend.reverse

Setting to reverse the legend in a multiplot so that it matches the order they are drawn in the plot

trans

A transformation function to apply to the values and confidence intervals. identity by default. Use invlogit for binary regression.

Value

A ggplot object

Details

Plots a graph similar to coefplot but for multiple plots at once.

For now, if names is provided the plots will appear in alphabetical order of the names. This will be adjusted in future iterations. When setting by to "Model" and specifying exactly one variable in variables that one coefficient will be plotted repeatedly with the axis labeled by model. This is Andy Gelman's secret weapon.

Examples

Run this code

# NOT RUN {
data(diamonds)
model1 <- lm(price ~ carat + cut, data=diamonds)
model2 <- lm(price ~ carat + cut + color, data=diamonds)
model3 <- lm(price ~ carat + color, data=diamonds)
multiplot(model1, model2, model3)
multiplot(model1, model2, model3, single=FALSE)
multiplot(model1, model2, model3, plot=FALSE)
require(reshape2)
data(tips, package="reshape2")
mod1 <- lm(tip ~ total_bill + sex, data=tips)
mod2 <- lm(tip ~ total_bill * sex, data=tips)
mod3 <- lm(tip ~ total_bill * sex * day, data=tips)
mod7 <- lm(tip ~ total_bill + day + time, data=tips)
multiplot(mod1, mod2, mod3, mod7, single=FALSE, scales="free_x")
multiplot(mod1, mod2, mod3, mod7, single=FALSE, scales="free_x")
multiplot(mod1, mod2, mod3, mod7, single=FALSE, scales="free_x", plot.shapes=TRUE)
multiplot(mod1, mod2, mod3, mod7, single=TRUE, scales="free_x", 
plot.shapes=TRUE, plot.linetypes=TRUE)
multiplot(mod1, mod2, mod3, mod7, single=TRUE, scales="free_x", 
plot.shapes=FALSE, plot.linetypes=TRUE, legend.position="bottom")
# the secret weapon
multiplot(mod1, mod2, mod3, mod7, coefficients="total_bill", secret.weapon=TRUE)
# horizontal secret weapon
multiplot(mod1, mod2, mod3, mod7, coefficients="total_bill", by="Model", horizontal=FALSE)


# }