Is there a function in R that fits a curve to a histogram?
Let's say you had the following histogram
hist(c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)))
It looks normal, but it's skewed. I want to fit a normal curve that is skewed to wrap around this histogram.
This question is rather basic, but I can't seem to find the answer for R on the internet.
In order to add a density curve over a histogram you can use the lines function for plotting the curve and density for calculating the underlying non-parametric (kernel) density of the distribution. The bandwidth selection for adjusting non-parametric densities is an area of intense research.
In this method to overlay the histogram with the fitted density curve, the user first needs to install and import the ggplot2 package in the R console, and call the ggplot() function which will create the plot of the given data with the required parameters and add the geom_histogram() to create the histogram of the ...
If I understand your question correctly, then you probably want a density estimate along with the histogram:
X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)) hist(X, prob=TRUE) # prob=TRUE for probabilities not counts lines(density(X)) # add a density estimate with defaults lines(density(X, adjust=2), lty="dotted") # add another "smoother" density
Edit a long while later:
Here is a slightly more dressed-up version:
X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)) hist(X, prob=TRUE, col="grey")# prob=TRUE for probabilities not counts lines(density(X), col="blue", lwd=2) # add a density estimate with defaults lines(density(X, adjust=2), lty="dotted", col="darkgreen", lwd=2)
along with the graph it produces:
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