Description Usage Arguments Details Value References See Also Examples
em
returns points estimations of the parameters of a finite mixture
model using the Expectation-Maximization (E-M) algorithm.
1 |
data |
A vector of real numbers, the data to model with a finite mixture model. |
D1, D2 |
Probability distributions constituting the finite mixture model. See Details. |
t |
A numerical scalar indicating the value below which the E-M algorithm should stop. |
The finite mixture model considered in this function is a mixture of two probability distributions that are one of the following: normal, log-normal, gamma or Weibull. Each of these distributions is defined by two parameters: a location and a scale parameter:
location | scale | |
normal | mean | sd |
log-normal | meanlog | sdlog |
gamma | shape | rate |
Weibull | shape | scale |
These parameters, together with the mixture parameter, are estimated by the Expection-Maximization algorithm.
A list with class em
containing the following components:
lambda |
a numerical vector of length |
param |
the location (mu) and scale (sigma) parameters of the
probability distributions |
mu2
and lambda2
.
Chuong B. Do and Serafim Batzoglou (2008) What is the expectation
maximization algorithm? Nature Biotechnology 26(8): 897-899.
Peter Schlattmann (2009) Medical Applications of Finite Mixture Models.
Springer-Verlag, Berlin.
confint.em
method for calculating the confidence
intervals of the parameters and cutoff
for deriving a
cut-off value.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # Measles IgG concentration data:
length(measles)
range(measles)
# Plotting the data:
hist(measles,100,F,xlab="concentration",ylab="density",ylim=c(0,.55),
main=NULL,col="grey")
# The kernel density:
lines(density(measles),lwd=1.5,col="blue")
# Estimating the parameters of the finite mixture model:
(measles_out <- em(measles,"normal","normal"))
# The confidence interval of the parameter estimates:
confint(measles_out,t=1e-64,nb=100,level=.95)
# Adding the E-M estimated finite mixture model:
lines(measles_out,lwd=1.5,col="red")
# The legend:
legend("topleft",leg=c("non-parametric","E-M"),col=c("blue","red"),
lty=1,lwd=1.5,bty="n")
|
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