| Type: | Package |
| Title: | Combined Graphs for Logistic Regression |
| Version: | 1.1 |
| Date: | 2025-04-30 |
| Depends: | ggplot2 |
| Suggests: | popbio |
| Description: | Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2025-04-30 19:09:55 UTC; marcelino |
| Author: | Marcelino de la Cruz
 [aut, cre] |
| Maintainer: | Marcelino de la Cruz <marcelino.delacruz@urjc.es> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-30 19:30:02 UTC |
Plot logistic regression
Description
Plot combined graphs for logistic regressions
Usage
logi.hist.plot2(independ, depend, logi.mod = 1, type = "dit",
boxp = TRUE, rug = FALSE, ylabel = "Probability", ylabel2 = "Frequency",
xlabel = "", mainlabel = "", las.h = 1, counts = FALSE, cex.p = 1,
pch.dit = 1, incre = 0.02, ...)
Arguments
independ |
explanatory variable |
depend |
dependent variable, typically a logical vector |
logi.mod |
type of fitting, 1 = logistic; 2 = "gaussian" logistic |
type |
type of representation, "dit" = dot plot; "hist" = histogram |
boxp |
TRUE = with box plots, FALSE = without |
rug |
TRUE = with rug plots, FALSE = without |
ylabel |
y-axis label |
ylabel2 |
2nd y-axis label |
xlabel |
x-axix label |
mainlabel |
overall title for plot |
las.h |
orientation of axes labels (0 = vertical, 1 = horizontal |
counts |
add counts above histogram bars |
cex.p |
size of points in dotplots |
pch.dit |
Either an integer specifying a symbol or a single character to be used as the default in plotting points. See |
incre |
increment, i.e., separation between neighbour points in the dotplot. |
... |
additional options passed to logi.hist |
Value
A combined logistic regression plot
Note
This is a new version of function logi.hist.plot already available in the package popbio. In this new version, control of points in the dot plot is provided by the arguments cex.p, pch.dit and incre.
Author(s)
M. de la Cruz Rot
References
Examples
data(aq.trans, package="popbio")
aq.trans$survived<-aq.trans$fate!="dead"
a<-subset(aq.trans, leaf<50 & stage!="recruit", c(leaf,survived))
logi.hist.plot2(a$leaf, a$survived,
type="hist", boxp=FALSE, counts=TRUE, int=10,
ylabel="Survival probability", ylabel2="Number of plants",
xlab="Number of leaves" )
b<-glm(survived ~ leaf, binomial, data=a)
summary(b)
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 |
scale.hist |
Value to scale the maximum vertical size of histograms. |
breaks |
How to compute the breakpoints for the histograms. See |
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 |
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 |
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
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, linewidth=1, colour="black") + theme_light()
# The same from 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 a 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)