logihist {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)

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.

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.

Value

A combined graph for logistic regression.

Note

logidot would try to find the optimal size of dots to avoid overlaping. The default computed sizes and separation could be fine-tunned by the arguments sizedot and incre.

Author(s)

Marcelino de la Cruz

References

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

See Also

The original implementation of the logi.hist.plot function for the R graphic sytem in the package popbio.

Examples

# 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)