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.

mlnorm(x, na.rm = FALSE)

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?

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.

See also

Normal for the normal density.

Examples

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