emplogitplot1: Empirical logit plot for one quantitative variable
In statmanrobin/Stat2Data: Datasets for Stat2

Description Usage Arguments Details Value Examples

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")