s with known parameters. The following code can be used,

rm(x,mean=mu1,sd=sigma1)+

rm(x,mean=mu2,sd=sigma2)

bove approach requires the component parameters to be known

ce. The component parameters of a mixture model can be

d using an R package mclust, for which the R function is named

st,

Mclust(x,G=NULL,···)

rst input is a vector or a matrix. The second input is used to define

onent number or a set of the component numbers. The second

n be a scalar number to specify the component number. For

using G=3 will employ three components. It can also be a vector

G=2:5, which means that a density function estimation process

four models using two, three, four and five components. The

finds the best model to fit to a data set among these four models.

main output of the Mclust function includes a component

f a Gaussian mixture ($G) and the component parameters, which

parameter. The $parameter object includes the mixing

nts ($pro), the mean values ($mean) and the variances

ance) for an estimated density function. Here a toy data was used

strate how it works,

.24,-1.24,0.74,0.38,1.49,0.19,0.30,

0.99,0.58,-0.46,1.17,-2.89,-2.04,

0.82,0.92,0.91,-0.88,1.06,0.95,0.44,

1.16,-1.12,10.75,4.05,3.23,

.85,-1.56,4.21,9.34,8.05)

the Mclust function was called to estimate a density function

ata set, G was set two for employing two Gaussian densities for

,

model=Mclust(x,G=2)