Normal distribution maximum likelihood estimation

The maximum likelihood estimate of mean is the empirical mean and the maximum likelihood estimate of sd is the square root of the biased sample variance.

Usage

mlnorm(x, na.rm = FALSE, ...)

Arguments

x: a (non-empty) numeric vector of data values.
na.rm: logical. Should missing values be removed?
...: currently affects nothing.

Value

mlnorm returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for mean and sd and the following attributes:

model: The name of the model.
density: The density associated with the estimates.
logLik: The loglikelihood at the maximum.
support: The support of the density.
n: The number of observations.
call: The call as captured my match.call

Details

For the density function of the normal distribution see Normal.

References

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 13. Wiley, New York.

Examples

mlnorm(precip)
#> Maximum likelihood estimates for the Normal model 
#>  mean     sd  
#> 34.89  13.61