logMeanExpLogs | R Documentation |

Given a vector of numeric values of real values represented in log form,
`logMeanExpLogs`

computes the logarithm of the mean of the
(exponentiated) values. `logCumMeanExpLogs`

computes the logarithm of
the cumulative mean.

logMeanExpLogs(v)

`v` |
A vector of (log) values |

Given a vector of values of log values `v`, one could compute
`log(mean(exp(v)))`

in R. However, exponentiating and summing will cause
a loss of precision, and possibly an overflow. These functions use the
identity

*log(e^a + e^b) = a +
log[ 1 + e^(b-a) ]*

and the method of computing *log(1+e^x)*
that avoids overflow (see the references). The code is written in C for very
fast computations.

`logMeanExpLogs`

returns a single value,
`logCumMeanExpLogs`

returns a vector of values of the same length as
`v`, and `logSummaryStats`

returns a list of the
log mean, log variance, and cumulative log means.

Richard D. Morey (richarddmorey@gmail.com)

For details of the approximation of *log(1+e^x)*
used to prevent loss of precision, see
https://www.codeproject.com/Articles/25294/Avoiding-Overflow-Underflow-and-Loss-of-Precision and
https://www.johndcook.com/blog/standard_deviation/.

# Sample 100 values y = log(rexp(100,1)) # These will give the same value, # since e^y is "small" logMeanExpLogs(y) log(mean(exp(y))) # We can make e^x overflow by multiplying # e^y by e^1000 largeVals = y + 1000 # This will return 1000 + log(mean(exp(y))) logMeanExpLogs(largeVals) # This will overflow log(mean(exp(largeVals)))

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