condgaussmixt {condmixt} R Documentation

## The conditional Gaussian mixture distribution

### Description

Distribution function and density function for the conditional Gaussian mixture without discrete component.

### Usage

pcondgaussmixt(params, m, y, trunc = TRUE)
dcondgaussmixt(params,m,y,log=FALSE,trunc=TRUE)


### Arguments

 params m x 3 x n matrix of mixture parameters where n is the number of examples m Number of mixture components. y Vector of n dependent variables. log logical, if TRUE, probabilities p are given as log(p). trunc logical, if TRUE (default), the hybrid Pareto density is truncated below zero.

### Details

params can be computed by applying condgaussmixt.fwd on the explanatory variables x of dimension d x n associated with y.

### Value

Distribution function evaluated at n points for pcondgaussmixt and density function for dcondgaussmixt.

Julie Carreau

### References

Bishop, C. (1995), Neural Networks for Pattern Recognition, Oxford

Carreau, J. and Bengio, Y. (2009), A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distributions, 20, IEEE Transactions on Neural Networks

condmixt.nll, condmixt.fwd

### Examples

# generate train data
ntrain <- 200
xtrain <- runif(ntrain)
ytrain <- rfrechet(ntrain,loc = 3*xtrain+1,scale =
0.5*xtrain+0.001,shape=xtrain+1)
plot(xtrain,ytrain,pch=22) # plot train data
qgen <- qfrechet(0.99,loc = 3*xtrain+1,scale = 0.5*xtrain+0.001,shape=xtrain+1)
points(xtrain,qgen,pch="*",col="orange")

h <- 4 # number of hidden units
m <- 2 # number of components

# initialize a conditional mixture with Gaussian components and a dirac at zero
thetainit <- condgaussmixt.init(1,h,m,ytrain)

# compute mixture parameters
params.mixt <- condgaussmixt.fwd(thetainit,h,m,t(xtrain))

cdf <- pcondgaussmixt(params.mixt,m,ytrain) # compute CDF



[Package condmixt version 1.1 Index]