| ksplot.rocit | R Documentation | 
Generates cumulative density of diagnostic variable in positive and negative responses.
## S3 method for class 'rocit'
ksplot(
  object,
  col = c("#26484F", "#BEBEBE", "#FFA54F"),
  lty = c(1, 1, 1),
  legend = T,
  legendpos = "bottomright",
  values = T,
  ... = NULL
)
| object | An object of class  | 
| col | Colors to be used for plot. Minimum three colors need to be supplied for F(c), G(c) and KS Stat mark. | 
| lty | Line types of the plots. | 
| legend | A logical value indicating whether legends to appear in the plot. | 
| legendpos | Position of the legend. A single keyword from
 | 
| values | A logical value, indicating whether values to be returned. | 
| ... | 
 | 
This function plots the cumulative density functions $F(c)$ and $G(c) of the diagnostic variable in the negative and positive populations. If the positive population have higher value then negative curve ($F(c)$) ramps up quickly. The KS statistic is the maximum difference of $F(c)$ and $G(c)$.
If values = TRUE, then  Cutoff, F(c), G(c), KS stat,
KS Cutoff  are returned silently.
Customized plots can be made by using the returned values of the function.
data("Diabetes")
logistic.model <- glm(as.factor(dtest)~chol+age+bmi,
                      data = Diabetes,family = "binomial")
class <- logistic.model$y
score <- qlogis(logistic.model$fitted.values)
# -------------------------------------------------------------
roc_emp <- rocit(score = score, class = class) # default method empirical
# -------------------------------------------------------------
kplot1 <- ksplot(roc_emp)
message("KS Stat (empirical) : ", kplot1$`KS stat`)
message("KS Stat (empirical) cutoff : ", kplot1$`KS Cutoff`)
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