eval.stage.gauss {delt} R Documentation

## Returns a 1D Gaussian mixture density estimate

### Description

Estimates a 1D density with a mixture of Gaussians. The mixture is found by minimizing the L2 empirical risk in a stagewise manner.

### Usage

```eval.stage.gauss(dendat, M, mugrid, siggrid = 1, sigeka = TRUE, src = "c",
sampstart=FALSE, boost=FALSE, N=60)
```

### Arguments

 `dendat` n-vector of 1D observations `M` integer >= 1; the number of mixture components in the estimate `mugrid` a vector of real numbers; the range for the means of mixture components `siggrid` a vector of real numbers; the range of possible standard deviations in the mixture components `sigeka` TRUE or FALSE; if TRUE, then the standard deviation of the first mixture component is equal to 1, otherwise the standard deviation of the first mixture component is found by minimization `src` "R" or "c"; if "R", then the R-code is used, otherwise the c-code is used `sampstart` internal `boost` internal `N` postive integer; the number of evaluation points

### Value

A piecewise constant function with the additional components:

 `muut ` vector of real numbers; the means of the mixture components `sigit ` vector of positive real numbers; the standard deviations of the mixture components `curmix ` a probability vector; the weights of the mixture components

Jussi Klemela

### References

Jussi Klemela (2005). Density Estimation with Stagewise Optimization of the Empirical Risk

`eval.stage`

### Examples

```dendat<-sim.data(n=100,type="1d2modal",seed=1)

mugrid<-seq(-1,5,0.3)    # grid of mu-values
siggrid<-seq(0.2,2,0.2)  # grid of sigma-values
M<-17                     # number of mixture components
pcf<-eval.stage.gauss(dendat,M,mugrid,siggrid)

dp<-draw.pcf(pcf)
plot(dp\$x,dp\$y,type="l")

# draw the estimate with the help of package "denpro"
#N<-100
#pcf2<-pcf.func("mixt",N,sig=pcf\$sigit,M=pcf\$muut,p=pcf\$curmix)
#pnum<-100
#dm<-draw.pcf(pcf2,pnum=pnum)
#plot(dm\$x,dm\$y,type="l")

```

[Package delt version 0.8.0 Index]