Description Usage Arguments Details Value References
This method of the S3 em
class calculates the confidence intervals of
the parameters of the fitted finite mixture model.
1 2 |
t |
A numerical scalar indicating the value below which the E-M algorithm should stop. |
nb |
Number of Monte Carlo simulations. |
level |
The confidence level required. |
Confidence intervals of the parameters of probability distributions are
calculated by the confint
method of the S4 confint
class of the
bbmle
package with default values. See the help of this method for
technical details. The confidence interval of the lambda
parameter is
calculated thanks to the information-based method of Oakes (1999). The
possible non-independance between lambda and the parameters of the probability
distributions is accounted for by Monte Carlo simulations where each iteration
consists in (i) sampling values of theses parameters in a multinormal
distribution, and (ii) applying the method of Oakes (1999). Samplings in the
multinormal distribution is performed by the rmultinormal
function of
the mc2d
package.
A dataframe containing point estimates (first column) and confidence intervals (second and third columns) of each of the five parameters of the finite mixture model (five row, one per parameter).
David Oakes (1999) Direct calculation of the information matrix via the EM algorithm. J R Statist Soc B, 61: 479-482.
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