Sampling a random normal to create a vector of truncated normals

Is there a way in matlab as there is in Fortran to draw samples from normrnd with specified mean and sd and reject any that are less than a specified lower bound or greater than an upper bound. In Fortran you can simply set a zero flag for each random that is outside the limits, draw another random and if it passes the limits test add it to a vector. How can I do this in Matlab?

Answer 1

[the cyclist]

Less elegant than truncate, but can be used in core MATLAB ...

% Desired number of samples
N = 1000;

% Draw some extra, because of the rejection. Need to adjust this to be sure to get enough extra. Will depend on how severe the truncation is.
N_extra = 100;

% Draw from normal
x = randn(N+N_extra,1);

% Reject values that lie outside truncated region
x(x<-2 | x>2) = [];

% Trim to the desired number of samples
x = x(1:N);

[Nick]

Thank you. I will give this a try. I have programmed something similar. But this may be more efficient.

Answer 2

[the cyclist]

If you have the Statistics and Machine Learning Toolbox, check out the truncate function.

[Nick]

I appreciate the suggestion. Unfortunately I only have Matlab 2010 and the Statistics toolbox, which does not have the truncate function. But it seems that it should be easy to simply reject random samples that fail a criterion and draw another candidate random to evaluate and accept or reject as appropriate. But it's not obvious to me how to do this in Matlab.

[the cyclist]

See the other answer I just posted.