nfw: The Standard Distribution Functions for the 3D NFW Profile

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Description

Density, distribution function, quantile function and random generation for the 3D NFW profile

Usage

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dnfw(x, con = 5, log = FALSE)
pnfw(q, con = 5, log.p = FALSE)
qnfw(p, con = 5, log.p = FALSE)
rnfw(n, con = 5)

Arguments

x,q

Vector of quantiles. This is scaled such that x and q are equal to R/Rvir for NFW. This means the PDF is only defined between 0 and 1.

p

Vector of probabilities.

n

Number of observations. If length(n) > 1, the length is taken to be the number required.

con

The NFW profile concentration parameter, where c=Rvir/Rs.

log, log.p

Logical; if TRUE, probabilities/densities p are returned as log(p).

Details

The novel part of this package is the general solution for the CDF inversion (i.e. qnfw). As far as I can see this has not been published anywhere, and it is a useful function for populating halos in something like an HOD.

One of lamW (fastest) or gsl (easier to install) must be installed to use the qnfw and rnfw functions!. Try to install lamW first (since it is about four times faster), but if that is tricky due to Rcpp dependencies then use gsl instead.

Value

dnfw gives the density, pnfw gives the distribution function, qnfw gives the quantile function, and rnfw generates random deviates.

Note

This seems to work at least as efficiently as accept reject, but it is ultimately much more elegant code in any case.

Author(s)

Aaron Robotham

References

Robotham & Howlett, 2018, arXiv 1805.09550

See Also

lambert_W0 (gsl) or lambertW0 (lamW).

Examples

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#Both the PDF (dnfw) integrated up to x, and CDF at q (pnfw) should be the same:
#0.373, 0.562, 0.644, 0.712

for(con in c(1,5,10,20)){
  print(integrate(dnfw, lower=0, upper=0.5, con=con)$value)
  print(pnfw(0.5, con=con))
}

#The qnfw should invert the pnfw, returning the input vector (1:9)/10:
for(con in c(1,5,10,20)){
  print(qnfw(p=pnfw(q=(1:9)/10,con=con), con=con))
}

#The sampling from rnfw should recreate the expected PDF from dnfw:

for(con in c(1,5,10,20)){
  plot(density(rnfw(1e6,con=con), bw=0.01))
  lines(seq(0,1,len=1e3), dnfw(seq(0,1,len=1e3),con=con),col='red')
  legend('topright',legend=paste('con =',con))
}

NFWdist documentation built on May 29, 2018, 9:04 a.m.