Calculates the mean and a confidence limit on the mean based on
Result = BOOTSTRAP_MEAN(Values)
Values: A vector of values whose mean and error is to be calculated.
NBOOT: Number of bootstrap resamplings. Default: 1000.
CONFLIMIT: Confidence limit. Default: 0.68 (equivalent to 1sigma
for a normal distribution).
UNIQLIST: If independent points are associated with more than one
element of Values, then they should all be included or
excluded together in the bootstrap resampling. In this
case, set UNIQLIST to the result of running UNIQ on
a list with the same length as Values containing the
unique identifier associated with each. Note that for
this to work, Values must be sorted in order of the
Compares the expected error in the mean of normally-distributed values
to the bootstrap-determined error:
IDL> vals = RANDOMN(seed, 100)
IDL> vals = 2.5*RANDOMN(seed, 100)
IDL> PRINT, BOOTSTRAP_MEAN(vals)
-0.26419502 -0.014198994 0.22447498
IDL> PRINT, 2.5/SQRT(100)
Returns a 3-element vector containing the lower limit, mean, and
Written by: Jeremy Bailin
12 June 2008 Public release in JBIU