R: How to plot gumbel distribution using ggplot2's stat_function

Question

Please bear with me if this is rather tenuous, and feel free to ask questions if I have left anything out...

I'm attempting to do some 50 year extreme wind calculations based on the following link

http://www.wasp.dk/Products/weng/ExtremeWinds.htm

They seem to use the gumbel distribution, so I have used function gumbel in package "evir" to fit the distribution to the data, and function dgumbel in package "evd" as the plotting function.

package("evd")
package("evir")

speeds2 <- data.frame(speed=sample(10:50,1000,rep=TRUE))
gumbel(speeds2$speed)

I have then tried to plot this using ggplot2's stat_function, like so (except for now I have put in dummy values for loc and scale.

library(ggplot2)
ggplot(data=speeds2, aes(x=speed)) + 
  stat_function(fun=dgumbel, args=list(loc=1, scale=0.5))

I get the following error:

Error in dgev(x, loc = loc, scale = scale, shape = 0, log = log) : 
  unused argument(s) (loc = loc, scale = scale, shape = 0, log = log)

I am unsure if I am doing this the right way. Any pointers would be much appreciated.

Answer 1

Here is a generic function that I wrote to simplify plotting of data with fitted and empirical densities.

# FUNCTION TO DRAW HISTOGRAM OF DATA WITH EMPIRICAL AND FITTED DENSITITES
# data  = values to be fitted
# func  = name of function to fit (e.g., 'norm', 'gumbel' etc.)
# start = named list of parameters to pass to fitting function 
hist_with_density = function(data, func, start = NULL){
    # load libraries
    library(VGAM); library(fitdistrplus); library(ggplot2)

    # fit density to data
    fit   = fitdist(data, func, start = start)
    args  = as.list(fit$estimate)
    dfunc = match.fun(paste('d', func, sep = ''))

    # plot histogram, empirical and fitted densities
    p0 = qplot(data, geom = 'blank') +
       geom_line(aes(y = ..density..,colour = 'Empirical'),stat = 'density') +
       stat_function(fun = dfunc, args = args, aes(colour = func))  +
       geom_histogram(aes(y = ..density..), alpha = 0.4) +
       scale_colour_manual(name = '', values = c('red', 'blue')) + 
       opts(legend.position = 'top', legend.direction = 'horizontal')
    return(p0)  
}

Here are two examples of how you would use it Example 1: Fit a Gumbel

data1 = sample(10:50,1000,rep=TRUE)
(hist_with_density(data1, 'gumbel', start = list(location = 0, scale = 1)))

Example 2: Fit a Normal Distribution

data2 = rnorm(1000, 2, 1)
(hist_with_density(data2, 'norm'))