# eval.monfd: Values of a Monotone Functional Data Object In fda: Functional Data Analysis

## Description

Evaluate a monotone functional data object at specified argument values, or evaluate a derivative of the functional object.

## Usage

 1 2 3 4 5 6 7 eval.monfd(evalarg, Wfdobj, Lfdobj=int2Lfd(0), returnMatrix=FALSE) ## S3 method for class 'monfd' predict(object, newdata=NULL, Lfdobj=0, returnMatrix=FALSE, ...) ## S3 method for class 'monfd' fitted(object, ...) ## S3 method for class 'monfd' residuals(object, ...)

## Arguments

 evalarg, newdata a vector of argument values at which the functional data object is to be evaluated. Wfdobj an object of class fd that defines the monotone function to be evaluated. Only univariate functions are permitted. Lfdobj a nonnegative integer specifying a derivative to be evaluated. At this time of writing, permissible derivative values are 0, 1, 2, or 3. A linear differential operator is not allowed. object an object of class monfd that defines the monotone function to be evaluated. Only univariate functions are permitted. returnMatrix logical: If TRUE, a two-dimensional is returned using a special class from the Matrix package. ... optional arguments required by predict; not currently used.

## Details

A monotone function data object \$h(t)\$ is defined by \$h(t) = [D^{-1} exp Wfdobj](t)\$. In this equation, the operator \$D^{-1}\$ means taking the indefinite integral of the function to which it applies. Note that this equation implies that the monotone function has a value of zero at the lower limit of the arguments. To actually fit monotone data, it will usually be necessary to estimate an intercept and a regression coefficient to be applied to \$h(t)\$, usually with the least squares regression function lsfit. The function Wfdobj that defines the monotone function is usually estimated by monotone smoothing function smooth.monotone.

eval.monfd only computes the standardized monotone form. predict.monfd computes the scaled version using with(object, beta[1] + beta[2]*eval.monfd(...)) if Lfdobj = 0 or beta[2]*eval.monfd(...) if Lfdobj > 0.

## Value

a matrix containing the monotone function values. The first dimension corresponds to the argument values in evalarg and the second to replications.