logihist: Combined Graphs for Logistic Regression

Description

Plot combined graphs for logistic regressions in the ggplot2 system.

Usage

logihist(x, y, scale.hist = 5, breaks = "Sturges", counts = TRUE, intervalo = 0,
   ylab2 = "Frequency", fillb = 1, colob = 1, sizeb = 1, pglm = FALSE, se = FALSE,
   sizeglm = 1, colglm = 1)
logibox(x, y, boxwd = 0.1, wiswd = 0.05, displac = 0, sizepat = 1, fillb = NA, colob = 1,
   colpat = 1, colbig = 1, colmed = 1, sizeb = 1, sizebig = 1, sizemed = 2, colout = 1,
   sizeout = 1, shapeout = 1, pglm = FALSE, se = FALSE, sizeglm = 1, colglm = 1)
logidot(x, y, incre = NULL, sizedot = NULL, coldot = 1, shapedot = 1, pglm = FALSE,
   se = FALSE, sizeglm = 1, colglm = 1)

Value

A combined graph for logistic regression.

Arguments

x: Either the predictor variable or a glm object.
y: If x is a predictor variable, y is the response variable, i.e., either a logic (FALSE and TRUE) vector or a vector of 1's and 0's.
scale.hist: Value to scale the maximum vertical size of histograms.
breaks: How to compute the breakpoints for the histograms. See hist in package graphics.
counts: Add counts above histogram bars (not implemented in this version).
intervalo: Width of histogram bins.
ylab2: Label for the y-axis of the histograms.
fillb: Color to fill the histogram bars or the boxes of the boxplots. Either a single value or a vector witth different values for the 0 and 1 cases.
colob: Color to delineate the histogram bars or the boxes of the boxplots. Either a single or double value.
colpat: Color of the line representing the whiskers in boxplots.
colbig: Color of the line representing the end of whiskers in boxplots.
colmed: Color of the line representing the median in boxplots.
colout: Color of the line representing the outliers in boxplots.
sizeb: Width of the lines that delineate the histogram bars or the boxes of the boxplots.
boxwd: Width of the boxes in boxplots.
wiswd: Size of the end of whiskers in boxplots.
sizebig: Width of the line representing the end of whiskers in boxplots.
sizepat: Width of the line representing the whiskers in boxplots.
sizemed: Width of the line representing the median in boxplots.
sizeout: Size of the points representing the outliers in boxplots.
shapeout: Shape of the points representing the outliers. See the entry for pch in points.
displac: Vertical displacement of boxplots along the y-axis.
incre: Separation among points in the dotplot.
sizedot: Size of points in the dotplot.
coldot: Color of points in the dotplot.
shapedot: Shape of the points in the dotplot. See the entry for pch in points.
pglm: Draw the fitted logistic curve? (TRUE or FALSE).
se: Draw the standard error around the fitted logistic curve?(TRUE or FALSE).
sizeglm: Width of the line for the fitted fitted logistic curve.
colglm: Color of the line for the fitted fitted logistic curve.

Author

Marcelino de la Cruz

Details

The use of functions logihist, logibox or logidot will render a combined graph for logistic regression. Either a double histogram, a double boxplot or a double dotplot, which could be modified or integrated with other graphical elements of ggplot2.

References

De la Cruz Rot, M. 2005. Improving the Presentation of Results of Logistic Regression with R. ESA Bulletin 86:41-48.

Examples

Run this code


# Get some data from package popbio
data(aq.trans, package="popbio")
aq.trans$survived<-aq.trans$fate!="dead"
a<-subset(aq.trans, leaf<50 & stage!="recruit", c(leaf,survived))


# Transform the "survived" variable in numeric
# This is necesary to correctly plot the combined graph.
 
a$survived <-a$survived*1

# Fit a logistic regression
glm.a<- glm(survived~leaf, data=a, family=binomial)


# Histograms
# From individual variables
# Modifying the appearence of histogram bins
logihist(a$leaf,a$survived)
logihist(a$leaf,a$survived, fillb="blue")
logihist(a$leaf,a$survived, fillb="blue", colob="blue")
logihist(a$leaf,a$survived, fillb=c("orange","blue"), colob=c("orange","blue"))
logihist(a$leaf,a$survived, fillb=c("orange","blue"), colob=c("orange","blue"),
scale.hist=2)
logihist(a$leaf,a$survived, fillb=c("orange","blue"), colob=c("orange","blue"),
  breaks=seq(0,50, by=2))

# Modifying the combined plot using functions from ggpplot2

logihist(a$leaf,a$survived, fillb=NA, sizeb=0.5, ylab2="Number of plants", )+
   ylab("survived")+xlab("leaf")+ stat_smooth(method = "glm", method.args =
   list(family = "binomial"), se=TRUE, size=1, colour="black")+theme_light()


# The same fromm a glm object

logihist(glm.a, fillb=NA, sizeb=0.5, ylab2="Number of plants", se=TRUE)+ 
           theme_light()



# Dotplots
# Double dotplot from the individual variables, with different symbols for
#  0 and 1 categories (black and void, redand solid respectively).

logidot(a$leaf,a$survived,coldot=c(1,2),shapedot=c(1,19))

# Try with a larger dot.
logidot(a$leaf,a$survived,coldot=c(1,2),shapedot=c(1,19), sizedot=1)

# Add the fitted logistic curve and change the presentation using
# respectively an stat and a theme from ggplot2 

logidot(a$leaf,a$survived, coldot=c(1,2),shapedot=c(1,19),  sizedot=1)+
    stat_smooth(method = "glm", method.args = list(family = "binomial"),
           se=FALSE, size=2)+theme_light()+ylab("leaf")+xlab("survived")

# Get the same graphic directly form the fitted glm object
logidot(glm.a, coldot=c(1,2),shapedot=c(1,19),  sizedot=1, sizeglm=2,
           colglm="blue")+theme_light()



# Boxplots
logibox(a$leaf,a$survived)+theme_light()+ stat_smooth(method = "glm",
           method.args = list(family = "binomial"),se=FALSE )+
	   geom_jitter(height=0.02, size=0.5, colour="pink", alpha=0.5)

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