How do you make a density curve in R?
To create a density plot in R you can plot the object created with the R density function, that will plot a density curve in a new R window. You can also overlay the density curve over an R histogram with the lines function. The result is the empirical density function.
How do you overlay a density plot in R?
1 Answer
- To overlay density plots, you can do the following:
- In base R graphics, you can use the lines() function. But make sure the limits of the first plot are suitable to plot the second one.
- For example: plot(density(mtcars$drat)) lines(density(mtcars$wt))
- Output:
- In ggplot2, you can do the following:
- Output:
Does R stand for density?
The ideal gas law is given by: PV = gRT/M, where P = pressure, V= volume, T = temperature, g = weight of the gas R = the ideal gas constant. This equation may be rearranged to give the density of the gas : r = g/V = PM/RT.
Can a density curve be greater than 1?
There is nothing wrong with the density being greater than 1 at some points. The area under the curve must be 1, but at specific points the density can be greater than 1. For example, dnorm(0,0, 0.1) [1] 3.989423.
What should the AUC of a ROC curve be?
Unlike accuracy, ROC curves are insensitive to class imbalance; the bogus screening test would have an AUC of 0.5, which is like not having a test at all. In this post I’ll work through the geometry exercise of computing the area, and develop a concise vectorized function that uses this approach.
Is the path of a ROC curve always at coordinates?
The step sizes are inversely proportional to the number of actual positives (in the y-direction) or negatives (in the x-direction), so the path always ends at coordinates (1, 1). The result is a plot of true positive rate (TPR, or specificity) against false positive rate (FPR, or 1 – sensitivity), which is all an ROC curve is.
How is a classifier used to calculate AUC?
In an earlier post, I described a simple “turtle’s eye view” of these plots: a classifier is used to sort cases in order from most to least likely to be positive, and a Logo-like turtle marches along this string of cases. The turtle considers all the cases it has passed as having tested positive.