I'm trying to generate 10000 random numbers taken from a log normal distribution who's associated normal distribution has mean = 0.3 and std. dev. = 0.05 in MATLAB.
I'm using the built in lognrnd
function.
My attempt is to do:
R = lognrnd(0.3,0.05,10000,1)
However, when I plot the histogram of R
using hist(R)
, the associated plot is normal, not log normal.
Where am I messing up? If the mean = 0.3 and std. dev. = 0.05 of the normal distribution, shouldn't the generated log normal numbers have a mean = 0.3 and std. dev = 0.05?
Thanks guys.
The method is simple: you use the RAND function to generate X ~ N(μ, σ), then compute Y = exp(X). The random variable Y is lognormally distributed with parameters μ and σ. This is the standard definition, but notice that the parameters are specified as the mean and standard deviation of X = log(Y).
X = rand( sz ) returns an array of random numbers where size vector sz defines size(X) . For example, rand([3 4]) returns a 3-by-4 matrix. X = rand(___, typename ) returns an array of random numbers of data type typename . The typename input can be either "single" or "double" .
The numbers you generate are actually from log-normal distribution. Plot just looks similar for your parameters. Compare hist(R)
with hist(log(R))
- the shape is pretty much the same.
As for mean and deviation, take a look at lognrnd documentation:
mu and sigma are the mean and standard deviation, respectively,
of the associated normal distribution.
hence generated numbers are expected to have different mean and deviation.
EDIT: I'm not sure if Matlab lets you specify lognormal distribution parameters directly, but you can derive one set of the parameters from the other. Assuming M and V are desired parameters of lognormal variable, you can calculate mu
and sigma
using following formulas:
x = 1 + V / M^2
sigma = sqrt(log(x))
mi = log(M / sqrt(x))
See wikipedia for opposite conversion.
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