Empirical logit plot for one quantitative variable
This function produces an empirical logit plot for a binary response variable and a single quantitative predictor variable.
emplogitplot1(formula, data = NULL, ngroups = 3, breaks = NULL, yes = NULL, padj = TRUE, out = FALSE, showplot = TRUE, showline = TRUE, ylab = "Log(Odds)", xlab = NULL, dotcol = "black", linecol = "blue", pch = 16, main = "", ylim = NULL, xlim = NULL, lty = 1, lwd = 1, cex = 1)
formula |
A formula of the form (binary) Response~Predictor |
data |
A dataframe |
ngroups |
Number of groups to use (not needed if breaks is used), ngroups="all" uses all unique values |
breaks |
A vector of endpoints for the bins (not needed if ngroups is used) |
yes |
Set a value for the response to be counted for proportions (optional) |
padj |
Should proportions be adjusted to avoid zero and one? (default is TRUE) |
out |
Should the function return a dataframe with group information? (default is FALSE) |
showplot |
Show the plot? default is TRUE |
showline |
Show the regression line? default is TRUE |
ylab |
Text label for the vertical axis (default is "Log(Odds)") |
xlab |
Text label for the horizontal axis (default is NULL) |
dotcol |
Color for the dots (default is "black") |
linecol |
Color for the line (default is "black") |
pch |
Plot character for the dots (default is 16) |
main |
Title for plot |
ylim |
Limits for the vertical axis |
xlim |
Limits for the horizontal axis |
lty |
Line type (default is 1) |
lwd |
Line width (default is 1) |
cex |
Multiplier for plot symbols |
Values of the quantitative explanatory variable will be grouped into ngroups
roughly equal sized groups, unless breaks is used to determine the boundaries of the groups.
Using ngroups="all" will make each distinct value of the explanatory variable its own group
We find an adjusted proportion for the binary response variable within each of the groups with
(Number yes +0.5)/(Number of cases+1). This is converted to an adjusted log odds
log(adjp/(1-adjp)). The adjustment avoids problems if there are no "successes" or
all "successes" in a group. What constitutes a "success" can be specified with yes=
and the proportion adjustment can be turned off (if no group proportions are likely to be zero or one)
with padj=FALSE.
The function plots the log odds versus the mean of the explanatory variable within each group.
A least square line is fit to these points. The plot can be suppressed with showplot=FALSE.
The out=TRUE option will return a dataframe with the boundaries of each group, proportion,
adjusted proportion, mean explanatory variable, and (adjusted or unadjusted) log odds.
A dataframe with group information (if out=TRUE)
data(MedGPA) emplogitplot1(Acceptance~GPA,data=MedGPA) GroupTable=emplogitplot1(Acceptance~MCAT,ngroups=5,out=TRUE,data=MedGPA) emplogitplot1(Acceptance~MCAT,data=MedGPA,breaks=c(0,34.5,39.5,50.5),dotcol="red",linecol="black") data(Putts1) emplogitplot1(Made~Length,data=Putts1,ngroups="all")
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