Plots a binary response variable versus a quantitative explanatory variable.
A function to plot a binary response variable versus a quantitative explanatory variable.
plotBinResp(x, ...) ## Default S3 method: plotBinResp( x, y, xlab = paste(deparse(substitute(x))), ylab = paste(deparse(substitute(y))), plot.pts = TRUE, col.pt = "black", transparency = NULL, plot.p = TRUE, breaks = 25, p.col = "blue", p.pch = 3, p.cex = 1.25, yaxis1.ticks = seq(0, 1, 0.1), yaxis1.lbls = c(0, 0.5, 1), yaxis2.show = TRUE, ... ) ## S3 method for class 'formula' plotBinResp(x, data = NULL, xlab = names(mf)[2], ylab = names(mf)[1], ...)
x |
A quantitative explanatory variable or a formula of the form |
... |
Other arguments to be passed to the plot functions. |
y |
A binary response variable. |
xlab |
A string for labeling the x-axis. |
ylab |
A string for labeling the y-axis. |
plot.pts |
A logical that indicates ( |
col.pt |
A string used to indicate the color of the plotted points. Will be transparent unless |
transparency |
A numeric that indicates how many points would be plotted on top of each other before the ‘point’ would have the full |
plot.p |
A logical that indicates if the proportion for categorized values of X are plotted ( |
breaks |
A number that indicates how many intervals over which to compute proportions or a numeric vector that contains the endpoints of the intervals over which to compute proportions if |
p.col |
A color to plot the proportions. |
p.pch |
A plotting character for plotting the proportions. |
p.cex |
A character expansion factor for plotting the proportions. |
yaxis1.ticks |
A numeric vector that indicates where tick marks should be placed on the left y-axis (for the proportion of ‘successes’). |
yaxis1.lbls |
A numeric vector that indicates labels for the tick marks on the left y-axis (for the proportion of ‘successes’). |
yaxis2.show |
A logical that indicates whether the right y-axis should be created ( |
data |
The data frame from which the formula should be evaluated. |
This function produces a plot that can be used to visualize the density of points for a binary response variable as a function of a quantitative explanatory variable. In addition, the proportion of “1”s for the response variable at various “levels” of the explanatory variable are shown.
None. However, a plot is produced.
This function is meant to allow newbie students the ability to visualize the data corresponding to a binary logistic regression without getting “bogged-down” in the gritty details of how to produce this plot.
Derek H. Ogle, derek@derekogle.com
## NASA space shuttle o-ring failures -- from graphics package
d <- data.frame(temperature=c(53,57,58,63,66,67,67,67,68,69,70,70,70,70,
72,73,75,75,76,76,78,79,81),
fail=factor(c(2,2,2,2,1,1,1,1,1,1,2,1,2,1,1,1,1,2,1,1,1,1,1),
levels=1:2,labels=c("no","yes")))
## Default plot (using formula notation)
plotBinResp(fail~temperature,data=d)
## Controlling where proportions are computed
plotBinResp(fail~temperature,data=d,breaks=seq(50,85,5))
plotBinResp(fail~temperature,data=d,breaks=10)
## Don't plot points, just plot proportions
plotBinResp(fail~temperature,data=d,plot.pts=FALSE)
## Change left y-axis ticks
plotBinResp(fail~temperature,data=d,yaxis1.ticks=c(0,1),yaxis1.lbls=c(0,1))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.