# cond.mode: Conditional mode In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 cond.mode R Documentation

## Conditional mode

### Description

Computes the mode for conditional distribution function.

### Usage

```cond.mode(Fc, method = "monoH.FC", draw = TRUE)
```

### Arguments

 `Fc` Object estimated by `cond.F` function. `method` Specifies the type of spline to be used. Possible values are "diff", "fmm", "natural", "periodic" and "monoH.FC". `draw` =TRUE, plots the conditional distribution and density function.

### Details

The conditional mode is calculated as the maximum argument of the derivative of the conditional distribution function (density function `f`).

### Value

Return the mode for conditional distribution function.

• `mode.cond` Conditional mode.

• `x` Grid of length `n` where the the conditional density function is evaluated.

• `f` The conditional density function evaluated in `x`.

### Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

### References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

See Also as: `cond.F`, `cond.quantile` and splinefun .

### Examples

```## Not run:
n= 500
t= seq(0,1,len=101)
beta = t*sin(2*pi*t)^2
x = matrix(NA, ncol=101, nrow=n)
y=numeric(n)
x0<-rproc2fdata(n,seq(0,1,len=101),sigma="wiener")
x1<-rproc2fdata(n,seq(0,1,len=101),sigma=0.1)
x<-x0*3+x1
fbeta = fdata(beta,t)
y<-inprod.fdata(x,fbeta)+rnorm(n,sd=0.1)
prx=x[1:100];pry=y[1:100]
ind=101;ind2=101:110
pr0=x[ind];pr10=x[ind2]
ndist=161
gridy=seq(-1.598069,1.598069, len=ndist)
# Conditional Function
I=5
# Time consuming
res = cond.F(pr10[I], gridy, prx, pry, h=1)
mcond=cond.mode(res)
mcond2=cond.mode(res,method="diff")

## End(Not run)

```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.